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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 22 Dec 2010 17:19:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t12930382988wb82in3b78d6iu.htm/, Retrieved Mon, 06 May 2024 07:47:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114423, Retrieved Mon, 06 May 2024 07:47:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple regression] [2010-12-22 17:19:07] [fba9c6aa004af59d8497d682e70ddad5] [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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.4893520503043 + 0.0216155702168623Connected[t] + 0.0649005494285598Separate[t] + 0.0731669569957836Learning[t] -0.0475755057203567Software[t] -0.385554294834322Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  15.4893520503043 +  0.0216155702168623Connected[t] +  0.0649005494285598Separate[t] +  0.0731669569957836Learning[t] -0.0475755057203567Software[t] -0.385554294834322Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  15.4893520503043 +  0.0216155702168623Connected[t] +  0.0649005494285598Separate[t] +  0.0731669569957836Learning[t] -0.0475755057203567Software[t] -0.385554294834322Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.4893520503043 + 0.0216155702168623Connected[t] + 0.0649005494285598Separate[t] + 0.0731669569957836Learning[t] -0.0475755057203567Software[t] -0.385554294834322Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.48935205030432.2778046.800100
Connected0.02161557021686230.0506780.42650.6703120.335156
Separate0.06490054942855980.047061.37910.169840.08492
Learning0.07316695699578360.0853030.85770.3923580.196179
Software-0.04757550572035670.086735-0.54850.5841210.29206
Depression-0.3855542948343220.050539-7.628800

\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) & 15.4893520503043 & 2.277804 & 6.8001 & 0 & 0 \tabularnewline
Connected & 0.0216155702168623 & 0.050678 & 0.4265 & 0.670312 & 0.335156 \tabularnewline
Separate & 0.0649005494285598 & 0.04706 & 1.3791 & 0.16984 & 0.08492 \tabularnewline
Learning & 0.0731669569957836 & 0.085303 & 0.8577 & 0.392358 & 0.196179 \tabularnewline
Software & -0.0475755057203567 & 0.086735 & -0.5485 & 0.584121 & 0.29206 \tabularnewline
Depression & -0.385554294834322 & 0.050539 & -7.6288 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&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]15.4893520503043[/C][C]2.277804[/C][C]6.8001[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.0216155702168623[/C][C]0.050678[/C][C]0.4265[/C][C]0.670312[/C][C]0.335156[/C][/ROW]
[ROW][C]Separate[/C][C]0.0649005494285598[/C][C]0.04706[/C][C]1.3791[/C][C]0.16984[/C][C]0.08492[/C][/ROW]
[ROW][C]Learning[/C][C]0.0731669569957836[/C][C]0.085303[/C][C]0.8577[/C][C]0.392358[/C][C]0.196179[/C][/ROW]
[ROW][C]Software[/C][C]-0.0475755057203567[/C][C]0.086735[/C][C]-0.5485[/C][C]0.584121[/C][C]0.29206[/C][/ROW]
[ROW][C]Depression[/C][C]-0.385554294834322[/C][C]0.050539[/C][C]-7.6288[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&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)15.48935205030432.2778046.800100
Connected0.02161557021686230.0506780.42650.6703120.335156
Separate0.06490054942855980.047061.37910.169840.08492
Learning0.07316695699578360.0853030.85770.3923580.196179
Software-0.04757550572035670.086735-0.54850.5841210.29206
Depression-0.3855542948343220.050539-7.628800






Multiple Linear Regression - Regression Statistics
Multiple R0.559914901167637
R-squared0.313504696549565
Adjusted R-squared0.291501641951794
F-TEST (value)14.2482351782799
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.75743863906064e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96762793629027
Sum Squared Residuals603.963312524505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.559914901167637 \tabularnewline
R-squared & 0.313504696549565 \tabularnewline
Adjusted R-squared & 0.291501641951794 \tabularnewline
F-TEST (value) & 14.2482351782799 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 1.75743863906064e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.96762793629027 \tabularnewline
Sum Squared Residuals & 603.963312524505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.559914901167637[/C][/ROW]
[ROW][C]R-squared[/C][C]0.313504696549565[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.291501641951794[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.2482351782799[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]1.75743863906064e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.96762793629027[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]603.963312524505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&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 R0.559914901167637
R-squared0.313504696549565
Adjusted R-squared0.291501641951794
F-TEST (value)14.2482351782799
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.75743863906064e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.96762793629027
Sum Squared Residuals603.963312524505






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.5954241417700-0.595424141769973
21814.81542037630693.18457962369312
31113.6881178562438-2.68811785624375
41214.4865526407722-2.48655264077222
51610.93481739858995.06518260141014
61813.97677678705244.02322321294757
71410.68134794896783.31865205103223
81414.7510512487800-0.751051248779952
91515.1369201890644-0.136920189064369
101514.55519451438190.444805485618133
111715.16203405721611.83796594278386
121915.98946214329603.01053785670398
131013.3987137693181-3.39871376931805
141614.04816078837531.95183921162473
151815.27864893109722.72135106890282
161413.45160658699830.548393413001706
171413.56831932987790.431680670122103
181715.42830042492421.57169957507585
191415.7367118817041-1.73671188170413
201613.84542728939982.15457271060016
211816.27122911355471.72877088644531
221113.3733568749201-2.37335687492010
231414.9436192375906-0.943619237590552
241214.0698301973701-2.06983019737010
251714.98373834474342.01626165525662
26915.6077760519643-6.60777605196432
271614.99285081966231.00714918033767
281413.25211904865440.74788095134564
291513.54638911421091.45361088578906
301113.9023760767113-2.90237607671127
311615.03325974381020.966740256189755
321312.86387504354520.136124956454824
331714.99408457300772.00591542699229
341514.9842697666450.015730233354991
351413.61252341698490.387476583015122
361615.41747422863970.582525771360257
37910.8632512578279-1.86325125782788
381514.23601050053860.763989499461395
391714.96749392526442.03250607473563
401315.2269884454227-2.22698844542267
411515.5908733312622-0.590873331262156
421613.42167077043622.57832922956377
431616.0528334956947-0.0528334956946907
441213.5384373520938-1.53843735209381
451214.4354442001046-2.43544420010457
461113.7823720037909-2.78237200379091
471515.7777323163263-0.777732316326299
481514.74236109686700.257638903132960
491713.59775978289043.40224021710964
501314.5998953863781-1.59989538637809
511615.29634245903350.703657540966547
521413.27888899315750.721111006842455
531111.6898885366737-0.689888536673653
541213.8449319258502-1.84493192585024
551214.4373957201405-2.43739572014050
561514.47817855564900.521821444350952
571614.1265373797751.87346262022500
581515.4525988569437-0.452598856943663
591215.3441787714259-3.34417877142593
601213.3641175206796-1.36411752067962
61810.7889235880303-2.78892358803027
621314.4904761043925-1.49047610439246
631114.3737633615129-3.37376336151285
641413.53420066436310.465799335636875
651513.8237578804051.17624211959499
661015.0314767881373-5.0314767881373
671112.6355569417665-1.63555694176653
681214.0571571163696-2.05715711636956
691514.22327284836320.77672715163678
701513.84922245235891.15077754764110
711413.98602318932200.0139768106780217
721613.34334801781452.65665198218552
731514.39414517838430.605854821615662
741515.7670714426119-0.767071442611874
751314.3352642723594-1.33526427235941
761211.76950288721170.230497112788324
771714.23043238190662.76956761809341
781312.32911620678810.670883793211928
791513.47456358811651.52543641188352
801315.1712195726786-2.17121957267864
811514.89775364699960.102246353000413
821615.79223914954160.207760850458387
831515.7769939265305-0.776993926530516
841614.33079302775131.66920697224875
851514.33519123181590.664808768184144
861413.89729332162890.102706678371143
871514.42581715987040.574182840129564
881414.3823982533081-0.382398253308145
891313.0094719890806-0.00947198908060875
90710.3887877985406-3.38878779854058
911713.93347260607523.06652739392485
921313.0912214204334-0.0912214204333582
931514.49437894490750.505621055092534
941412.74154954502121.25845045497883
951314.5879414934078-1.58794149340775
961615.12259807974150.877401920258526
971212.9439683985465-0.943968398546507
981414.7592085574516-0.759208557451583
991714.85441482900992.14558517099008
1001515.2105094690662-0.210509469066193
1011715.29623478147751.70376521852249
1021213.0391194099872-1.03911940998722
1031614.79188792306651.20811207693352
1041114.3313422300788-3.33134223007882
1051513.2122593268341.78774067316599
106911.4855252111415-2.48552521114145
1071615.01409790565110.985902094348878
1081513.33760838284861.66239161715145
1091013.4030581346047-3.40305813460468
1101010.4363165135120-0.436316513512027
1111513.54644295298891.45355704701109
1121113.0224526675022-2.02245266750218
1131315.3213832866417-2.32138328664166
1141412.96729388389281.03270611610724
1151814.27134371763323.72865628236683
1161615.20892643325830.79107356674167
1171413.80678354049890.193216459501065
1181414.4518309341519-0.451830934151903
1191415.4320363219727-1.43203632197268
1201413.46361431874660.536385681253391
1211213.2485116518239-1.24851165182386
1221413.06617917148550.9338208285145
1231514.98103227538220.0189677246177731
1241516.1755288997864-1.17552889978641
1251514.45250843714060.547491562859352
1261314.5170943411191-1.51709434111911
1271715.94649180953441.05350819046558
1281715.83652332522121.16347667477879
1291914.96731320716494.03268679283513
1301513.73008222721621.26991777278379
1311314.7765874399378-1.77658743993776
132910.6686587084378-1.66865870843779
1331515.6898195056619-0.689819505661932
1341512.59134742752702.40865257247301
1351514.58212881789830.417871182101734
1361613.19200438928412.80799561071594
137119.177642212077291.82235778792271
1381413.67221433200950.327785667990505
1391112.5695511392106-1.56955113921062
1401514.70881788347450.291182116525516
1411313.9614585234978-0.96145852349778
1421515.3221932956414-0.322193295641354
1431613.61600251315422.3839974868458
1441414.3128925319208-0.312892531920831
1451513.67283657488061.32716342511938
1461614.47586017228151.52413982771845
1471614.00523724536261.99476275463743
1481113.9719908969942-2.97199089699418
1491214.5516353296399-2.55163532963993
150911.5201738772182-2.52017387721821
1511614.61385463816231.38614536183766
1521312.72312609665780.276873903342167
1531615.96300542290330.0369945770967159
1541215.0355851752068-3.03558517520681
155911.7707174387915-2.77071743879147
1561311.62567111692331.37432888307674
1571313.0912214204334-0.0912214204333582
1581413.85032790504310.149672094956893
1591914.96731320716494.03268679283513
1601315.4784511148912-2.47845111489121
1611212.0099916584122-0.0099916584121995
1621312.96239889493890.0376011050610872

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.5954241417700 & -0.595424141769973 \tabularnewline
2 & 18 & 14.8154203763069 & 3.18457962369312 \tabularnewline
3 & 11 & 13.6881178562438 & -2.68811785624375 \tabularnewline
4 & 12 & 14.4865526407722 & -2.48655264077222 \tabularnewline
5 & 16 & 10.9348173985899 & 5.06518260141014 \tabularnewline
6 & 18 & 13.9767767870524 & 4.02322321294757 \tabularnewline
7 & 14 & 10.6813479489678 & 3.31865205103223 \tabularnewline
8 & 14 & 14.7510512487800 & -0.751051248779952 \tabularnewline
9 & 15 & 15.1369201890644 & -0.136920189064369 \tabularnewline
10 & 15 & 14.5551945143819 & 0.444805485618133 \tabularnewline
11 & 17 & 15.1620340572161 & 1.83796594278386 \tabularnewline
12 & 19 & 15.9894621432960 & 3.01053785670398 \tabularnewline
13 & 10 & 13.3987137693181 & -3.39871376931805 \tabularnewline
14 & 16 & 14.0481607883753 & 1.95183921162473 \tabularnewline
15 & 18 & 15.2786489310972 & 2.72135106890282 \tabularnewline
16 & 14 & 13.4516065869983 & 0.548393413001706 \tabularnewline
17 & 14 & 13.5683193298779 & 0.431680670122103 \tabularnewline
18 & 17 & 15.4283004249242 & 1.57169957507585 \tabularnewline
19 & 14 & 15.7367118817041 & -1.73671188170413 \tabularnewline
20 & 16 & 13.8454272893998 & 2.15457271060016 \tabularnewline
21 & 18 & 16.2712291135547 & 1.72877088644531 \tabularnewline
22 & 11 & 13.3733568749201 & -2.37335687492010 \tabularnewline
23 & 14 & 14.9436192375906 & -0.943619237590552 \tabularnewline
24 & 12 & 14.0698301973701 & -2.06983019737010 \tabularnewline
25 & 17 & 14.9837383447434 & 2.01626165525662 \tabularnewline
26 & 9 & 15.6077760519643 & -6.60777605196432 \tabularnewline
27 & 16 & 14.9928508196623 & 1.00714918033767 \tabularnewline
28 & 14 & 13.2521190486544 & 0.74788095134564 \tabularnewline
29 & 15 & 13.5463891142109 & 1.45361088578906 \tabularnewline
30 & 11 & 13.9023760767113 & -2.90237607671127 \tabularnewline
31 & 16 & 15.0332597438102 & 0.966740256189755 \tabularnewline
32 & 13 & 12.8638750435452 & 0.136124956454824 \tabularnewline
33 & 17 & 14.9940845730077 & 2.00591542699229 \tabularnewline
34 & 15 & 14.984269766645 & 0.015730233354991 \tabularnewline
35 & 14 & 13.6125234169849 & 0.387476583015122 \tabularnewline
36 & 16 & 15.4174742286397 & 0.582525771360257 \tabularnewline
37 & 9 & 10.8632512578279 & -1.86325125782788 \tabularnewline
38 & 15 & 14.2360105005386 & 0.763989499461395 \tabularnewline
39 & 17 & 14.9674939252644 & 2.03250607473563 \tabularnewline
40 & 13 & 15.2269884454227 & -2.22698844542267 \tabularnewline
41 & 15 & 15.5908733312622 & -0.590873331262156 \tabularnewline
42 & 16 & 13.4216707704362 & 2.57832922956377 \tabularnewline
43 & 16 & 16.0528334956947 & -0.0528334956946907 \tabularnewline
44 & 12 & 13.5384373520938 & -1.53843735209381 \tabularnewline
45 & 12 & 14.4354442001046 & -2.43544420010457 \tabularnewline
46 & 11 & 13.7823720037909 & -2.78237200379091 \tabularnewline
47 & 15 & 15.7777323163263 & -0.777732316326299 \tabularnewline
48 & 15 & 14.7423610968670 & 0.257638903132960 \tabularnewline
49 & 17 & 13.5977597828904 & 3.40224021710964 \tabularnewline
50 & 13 & 14.5998953863781 & -1.59989538637809 \tabularnewline
51 & 16 & 15.2963424590335 & 0.703657540966547 \tabularnewline
52 & 14 & 13.2788889931575 & 0.721111006842455 \tabularnewline
53 & 11 & 11.6898885366737 & -0.689888536673653 \tabularnewline
54 & 12 & 13.8449319258502 & -1.84493192585024 \tabularnewline
55 & 12 & 14.4373957201405 & -2.43739572014050 \tabularnewline
56 & 15 & 14.4781785556490 & 0.521821444350952 \tabularnewline
57 & 16 & 14.126537379775 & 1.87346262022500 \tabularnewline
58 & 15 & 15.4525988569437 & -0.452598856943663 \tabularnewline
59 & 12 & 15.3441787714259 & -3.34417877142593 \tabularnewline
60 & 12 & 13.3641175206796 & -1.36411752067962 \tabularnewline
61 & 8 & 10.7889235880303 & -2.78892358803027 \tabularnewline
62 & 13 & 14.4904761043925 & -1.49047610439246 \tabularnewline
63 & 11 & 14.3737633615129 & -3.37376336151285 \tabularnewline
64 & 14 & 13.5342006643631 & 0.465799335636875 \tabularnewline
65 & 15 & 13.823757880405 & 1.17624211959499 \tabularnewline
66 & 10 & 15.0314767881373 & -5.0314767881373 \tabularnewline
67 & 11 & 12.6355569417665 & -1.63555694176653 \tabularnewline
68 & 12 & 14.0571571163696 & -2.05715711636956 \tabularnewline
69 & 15 & 14.2232728483632 & 0.77672715163678 \tabularnewline
70 & 15 & 13.8492224523589 & 1.15077754764110 \tabularnewline
71 & 14 & 13.9860231893220 & 0.0139768106780217 \tabularnewline
72 & 16 & 13.3433480178145 & 2.65665198218552 \tabularnewline
73 & 15 & 14.3941451783843 & 0.605854821615662 \tabularnewline
74 & 15 & 15.7670714426119 & -0.767071442611874 \tabularnewline
75 & 13 & 14.3352642723594 & -1.33526427235941 \tabularnewline
76 & 12 & 11.7695028872117 & 0.230497112788324 \tabularnewline
77 & 17 & 14.2304323819066 & 2.76956761809341 \tabularnewline
78 & 13 & 12.3291162067881 & 0.670883793211928 \tabularnewline
79 & 15 & 13.4745635881165 & 1.52543641188352 \tabularnewline
80 & 13 & 15.1712195726786 & -2.17121957267864 \tabularnewline
81 & 15 & 14.8977536469996 & 0.102246353000413 \tabularnewline
82 & 16 & 15.7922391495416 & 0.207760850458387 \tabularnewline
83 & 15 & 15.7769939265305 & -0.776993926530516 \tabularnewline
84 & 16 & 14.3307930277513 & 1.66920697224875 \tabularnewline
85 & 15 & 14.3351912318159 & 0.664808768184144 \tabularnewline
86 & 14 & 13.8972933216289 & 0.102706678371143 \tabularnewline
87 & 15 & 14.4258171598704 & 0.574182840129564 \tabularnewline
88 & 14 & 14.3823982533081 & -0.382398253308145 \tabularnewline
89 & 13 & 13.0094719890806 & -0.00947198908060875 \tabularnewline
90 & 7 & 10.3887877985406 & -3.38878779854058 \tabularnewline
91 & 17 & 13.9334726060752 & 3.06652739392485 \tabularnewline
92 & 13 & 13.0912214204334 & -0.0912214204333582 \tabularnewline
93 & 15 & 14.4943789449075 & 0.505621055092534 \tabularnewline
94 & 14 & 12.7415495450212 & 1.25845045497883 \tabularnewline
95 & 13 & 14.5879414934078 & -1.58794149340775 \tabularnewline
96 & 16 & 15.1225980797415 & 0.877401920258526 \tabularnewline
97 & 12 & 12.9439683985465 & -0.943968398546507 \tabularnewline
98 & 14 & 14.7592085574516 & -0.759208557451583 \tabularnewline
99 & 17 & 14.8544148290099 & 2.14558517099008 \tabularnewline
100 & 15 & 15.2105094690662 & -0.210509469066193 \tabularnewline
101 & 17 & 15.2962347814775 & 1.70376521852249 \tabularnewline
102 & 12 & 13.0391194099872 & -1.03911940998722 \tabularnewline
103 & 16 & 14.7918879230665 & 1.20811207693352 \tabularnewline
104 & 11 & 14.3313422300788 & -3.33134223007882 \tabularnewline
105 & 15 & 13.212259326834 & 1.78774067316599 \tabularnewline
106 & 9 & 11.4855252111415 & -2.48552521114145 \tabularnewline
107 & 16 & 15.0140979056511 & 0.985902094348878 \tabularnewline
108 & 15 & 13.3376083828486 & 1.66239161715145 \tabularnewline
109 & 10 & 13.4030581346047 & -3.40305813460468 \tabularnewline
110 & 10 & 10.4363165135120 & -0.436316513512027 \tabularnewline
111 & 15 & 13.5464429529889 & 1.45355704701109 \tabularnewline
112 & 11 & 13.0224526675022 & -2.02245266750218 \tabularnewline
113 & 13 & 15.3213832866417 & -2.32138328664166 \tabularnewline
114 & 14 & 12.9672938838928 & 1.03270611610724 \tabularnewline
115 & 18 & 14.2713437176332 & 3.72865628236683 \tabularnewline
116 & 16 & 15.2089264332583 & 0.79107356674167 \tabularnewline
117 & 14 & 13.8067835404989 & 0.193216459501065 \tabularnewline
118 & 14 & 14.4518309341519 & -0.451830934151903 \tabularnewline
119 & 14 & 15.4320363219727 & -1.43203632197268 \tabularnewline
120 & 14 & 13.4636143187466 & 0.536385681253391 \tabularnewline
121 & 12 & 13.2485116518239 & -1.24851165182386 \tabularnewline
122 & 14 & 13.0661791714855 & 0.9338208285145 \tabularnewline
123 & 15 & 14.9810322753822 & 0.0189677246177731 \tabularnewline
124 & 15 & 16.1755288997864 & -1.17552889978641 \tabularnewline
125 & 15 & 14.4525084371406 & 0.547491562859352 \tabularnewline
126 & 13 & 14.5170943411191 & -1.51709434111911 \tabularnewline
127 & 17 & 15.9464918095344 & 1.05350819046558 \tabularnewline
128 & 17 & 15.8365233252212 & 1.16347667477879 \tabularnewline
129 & 19 & 14.9673132071649 & 4.03268679283513 \tabularnewline
130 & 15 & 13.7300822272162 & 1.26991777278379 \tabularnewline
131 & 13 & 14.7765874399378 & -1.77658743993776 \tabularnewline
132 & 9 & 10.6686587084378 & -1.66865870843779 \tabularnewline
133 & 15 & 15.6898195056619 & -0.689819505661932 \tabularnewline
134 & 15 & 12.5913474275270 & 2.40865257247301 \tabularnewline
135 & 15 & 14.5821288178983 & 0.417871182101734 \tabularnewline
136 & 16 & 13.1920043892841 & 2.80799561071594 \tabularnewline
137 & 11 & 9.17764221207729 & 1.82235778792271 \tabularnewline
138 & 14 & 13.6722143320095 & 0.327785667990505 \tabularnewline
139 & 11 & 12.5695511392106 & -1.56955113921062 \tabularnewline
140 & 15 & 14.7088178834745 & 0.291182116525516 \tabularnewline
141 & 13 & 13.9614585234978 & -0.96145852349778 \tabularnewline
142 & 15 & 15.3221932956414 & -0.322193295641354 \tabularnewline
143 & 16 & 13.6160025131542 & 2.3839974868458 \tabularnewline
144 & 14 & 14.3128925319208 & -0.312892531920831 \tabularnewline
145 & 15 & 13.6728365748806 & 1.32716342511938 \tabularnewline
146 & 16 & 14.4758601722815 & 1.52413982771845 \tabularnewline
147 & 16 & 14.0052372453626 & 1.99476275463743 \tabularnewline
148 & 11 & 13.9719908969942 & -2.97199089699418 \tabularnewline
149 & 12 & 14.5516353296399 & -2.55163532963993 \tabularnewline
150 & 9 & 11.5201738772182 & -2.52017387721821 \tabularnewline
151 & 16 & 14.6138546381623 & 1.38614536183766 \tabularnewline
152 & 13 & 12.7231260966578 & 0.276873903342167 \tabularnewline
153 & 16 & 15.9630054229033 & 0.0369945770967159 \tabularnewline
154 & 12 & 15.0355851752068 & -3.03558517520681 \tabularnewline
155 & 9 & 11.7707174387915 & -2.77071743879147 \tabularnewline
156 & 13 & 11.6256711169233 & 1.37432888307674 \tabularnewline
157 & 13 & 13.0912214204334 & -0.0912214204333582 \tabularnewline
158 & 14 & 13.8503279050431 & 0.149672094956893 \tabularnewline
159 & 19 & 14.9673132071649 & 4.03268679283513 \tabularnewline
160 & 13 & 15.4784511148912 & -2.47845111489121 \tabularnewline
161 & 12 & 12.0099916584122 & -0.0099916584121995 \tabularnewline
162 & 13 & 12.9623988949389 & 0.0376011050610872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&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]14[/C][C]14.5954241417700[/C][C]-0.595424141769973[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.8154203763069[/C][C]3.18457962369312[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.6881178562438[/C][C]-2.68811785624375[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.4865526407722[/C][C]-2.48655264077222[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]10.9348173985899[/C][C]5.06518260141014[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.9767767870524[/C][C]4.02322321294757[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.6813479489678[/C][C]3.31865205103223[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.7510512487800[/C][C]-0.751051248779952[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1369201890644[/C][C]-0.136920189064369[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.5551945143819[/C][C]0.444805485618133[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.1620340572161[/C][C]1.83796594278386[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.9894621432960[/C][C]3.01053785670398[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3987137693181[/C][C]-3.39871376931805[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.0481607883753[/C][C]1.95183921162473[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.2786489310972[/C][C]2.72135106890282[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4516065869983[/C][C]0.548393413001706[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.5683193298779[/C][C]0.431680670122103[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.4283004249242[/C][C]1.57169957507585[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.7367118817041[/C][C]-1.73671188170413[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.8454272893998[/C][C]2.15457271060016[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.2712291135547[/C][C]1.72877088644531[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.3733568749201[/C][C]-2.37335687492010[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.9436192375906[/C][C]-0.943619237590552[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.0698301973701[/C][C]-2.06983019737010[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9837383447434[/C][C]2.01626165525662[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.6077760519643[/C][C]-6.60777605196432[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9928508196623[/C][C]1.00714918033767[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2521190486544[/C][C]0.74788095134564[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.5463891142109[/C][C]1.45361088578906[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.9023760767113[/C][C]-2.90237607671127[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.0332597438102[/C][C]0.966740256189755[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.8638750435452[/C][C]0.136124956454824[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.9940845730077[/C][C]2.00591542699229[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.984269766645[/C][C]0.015730233354991[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.6125234169849[/C][C]0.387476583015122[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.4174742286397[/C][C]0.582525771360257[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.8632512578279[/C][C]-1.86325125782788[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2360105005386[/C][C]0.763989499461395[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]14.9674939252644[/C][C]2.03250607473563[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.2269884454227[/C][C]-2.22698844542267[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.5908733312622[/C][C]-0.590873331262156[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.4216707704362[/C][C]2.57832922956377[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.0528334956947[/C][C]-0.0528334956946907[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.5384373520938[/C][C]-1.53843735209381[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.4354442001046[/C][C]-2.43544420010457[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.7823720037909[/C][C]-2.78237200379091[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.7777323163263[/C][C]-0.777732316326299[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7423610968670[/C][C]0.257638903132960[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.5977597828904[/C][C]3.40224021710964[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.5998953863781[/C][C]-1.59989538637809[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2963424590335[/C][C]0.703657540966547[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.2788889931575[/C][C]0.721111006842455[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.6898885366737[/C][C]-0.689888536673653[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.8449319258502[/C][C]-1.84493192585024[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.4373957201405[/C][C]-2.43739572014050[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.4781785556490[/C][C]0.521821444350952[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.126537379775[/C][C]1.87346262022500[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.4525988569437[/C][C]-0.452598856943663[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.3441787714259[/C][C]-3.34417877142593[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3641175206796[/C][C]-1.36411752067962[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.7889235880303[/C][C]-2.78892358803027[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.4904761043925[/C][C]-1.49047610439246[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.3737633615129[/C][C]-3.37376336151285[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.5342006643631[/C][C]0.465799335636875[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.823757880405[/C][C]1.17624211959499[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.0314767881373[/C][C]-5.0314767881373[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.6355569417665[/C][C]-1.63555694176653[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0571571163696[/C][C]-2.05715711636956[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.2232728483632[/C][C]0.77672715163678[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.8492224523589[/C][C]1.15077754764110[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.9860231893220[/C][C]0.0139768106780217[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.3433480178145[/C][C]2.65665198218552[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.3941451783843[/C][C]0.605854821615662[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.7670714426119[/C][C]-0.767071442611874[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.3352642723594[/C][C]-1.33526427235941[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.7695028872117[/C][C]0.230497112788324[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.2304323819066[/C][C]2.76956761809341[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.3291162067881[/C][C]0.670883793211928[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.4745635881165[/C][C]1.52543641188352[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]15.1712195726786[/C][C]-2.17121957267864[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.8977536469996[/C][C]0.102246353000413[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7922391495416[/C][C]0.207760850458387[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7769939265305[/C][C]-0.776993926530516[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.3307930277513[/C][C]1.66920697224875[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.3351912318159[/C][C]0.664808768184144[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.8972933216289[/C][C]0.102706678371143[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.4258171598704[/C][C]0.574182840129564[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.3823982533081[/C][C]-0.382398253308145[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.0094719890806[/C][C]-0.00947198908060875[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.3887877985406[/C][C]-3.38878779854058[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.9334726060752[/C][C]3.06652739392485[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.0912214204334[/C][C]-0.0912214204333582[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.4943789449075[/C][C]0.505621055092534[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]12.7415495450212[/C][C]1.25845045497883[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.5879414934078[/C][C]-1.58794149340775[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1225980797415[/C][C]0.877401920258526[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.9439683985465[/C][C]-0.943968398546507[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.7592085574516[/C][C]-0.759208557451583[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.8544148290099[/C][C]2.14558517099008[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.2105094690662[/C][C]-0.210509469066193[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.2962347814775[/C][C]1.70376521852249[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.0391194099872[/C][C]-1.03911940998722[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.7918879230665[/C][C]1.20811207693352[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.3313422300788[/C][C]-3.33134223007882[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.212259326834[/C][C]1.78774067316599[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.4855252111415[/C][C]-2.48552521114145[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.0140979056511[/C][C]0.985902094348878[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.3376083828486[/C][C]1.66239161715145[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.4030581346047[/C][C]-3.40305813460468[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.4363165135120[/C][C]-0.436316513512027[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.5464429529889[/C][C]1.45355704701109[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.0224526675022[/C][C]-2.02245266750218[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.3213832866417[/C][C]-2.32138328664166[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.9672938838928[/C][C]1.03270611610724[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.2713437176332[/C][C]3.72865628236683[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.2089264332583[/C][C]0.79107356674167[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.8067835404989[/C][C]0.193216459501065[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.4518309341519[/C][C]-0.451830934151903[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.4320363219727[/C][C]-1.43203632197268[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.4636143187466[/C][C]0.536385681253391[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.2485116518239[/C][C]-1.24851165182386[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.0661791714855[/C][C]0.9338208285145[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]14.9810322753822[/C][C]0.0189677246177731[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1755288997864[/C][C]-1.17552889978641[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.4525084371406[/C][C]0.547491562859352[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.5170943411191[/C][C]-1.51709434111911[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.9464918095344[/C][C]1.05350819046558[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.8365233252212[/C][C]1.16347667477879[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.9673132071649[/C][C]4.03268679283513[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.7300822272162[/C][C]1.26991777278379[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.7765874399378[/C][C]-1.77658743993776[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.6686587084378[/C][C]-1.66865870843779[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.6898195056619[/C][C]-0.689819505661932[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.5913474275270[/C][C]2.40865257247301[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.5821288178983[/C][C]0.417871182101734[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.1920043892841[/C][C]2.80799561071594[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]9.17764221207729[/C][C]1.82235778792271[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.6722143320095[/C][C]0.327785667990505[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.5695511392106[/C][C]-1.56955113921062[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.7088178834745[/C][C]0.291182116525516[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.9614585234978[/C][C]-0.96145852349778[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.3221932956414[/C][C]-0.322193295641354[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.6160025131542[/C][C]2.3839974868458[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.3128925319208[/C][C]-0.312892531920831[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.6728365748806[/C][C]1.32716342511938[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.4758601722815[/C][C]1.52413982771845[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.0052372453626[/C][C]1.99476275463743[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.9719908969942[/C][C]-2.97199089699418[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.5516353296399[/C][C]-2.55163532963993[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.5201738772182[/C][C]-2.52017387721821[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.6138546381623[/C][C]1.38614536183766[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.7231260966578[/C][C]0.276873903342167[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.9630054229033[/C][C]0.0369945770967159[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.0355851752068[/C][C]-3.03558517520681[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.7707174387915[/C][C]-2.77071743879147[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]11.6256711169233[/C][C]1.37432888307674[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.0912214204334[/C][C]-0.0912214204333582[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8503279050431[/C][C]0.149672094956893[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.9673132071649[/C][C]4.03268679283513[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.4784511148912[/C][C]-2.47845111489121[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.0099916584122[/C][C]-0.0099916584121995[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.9623988949389[/C][C]0.0376011050610872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&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
11414.5954241417700-0.595424141769973
21814.81542037630693.18457962369312
31113.6881178562438-2.68811785624375
41214.4865526407722-2.48655264077222
51610.93481739858995.06518260141014
61813.97677678705244.02322321294757
71410.68134794896783.31865205103223
81414.7510512487800-0.751051248779952
91515.1369201890644-0.136920189064369
101514.55519451438190.444805485618133
111715.16203405721611.83796594278386
121915.98946214329603.01053785670398
131013.3987137693181-3.39871376931805
141614.04816078837531.95183921162473
151815.27864893109722.72135106890282
161413.45160658699830.548393413001706
171413.56831932987790.431680670122103
181715.42830042492421.57169957507585
191415.7367118817041-1.73671188170413
201613.84542728939982.15457271060016
211816.27122911355471.72877088644531
221113.3733568749201-2.37335687492010
231414.9436192375906-0.943619237590552
241214.0698301973701-2.06983019737010
251714.98373834474342.01626165525662
26915.6077760519643-6.60777605196432
271614.99285081966231.00714918033767
281413.25211904865440.74788095134564
291513.54638911421091.45361088578906
301113.9023760767113-2.90237607671127
311615.03325974381020.966740256189755
321312.86387504354520.136124956454824
331714.99408457300772.00591542699229
341514.9842697666450.015730233354991
351413.61252341698490.387476583015122
361615.41747422863970.582525771360257
37910.8632512578279-1.86325125782788
381514.23601050053860.763989499461395
391714.96749392526442.03250607473563
401315.2269884454227-2.22698844542267
411515.5908733312622-0.590873331262156
421613.42167077043622.57832922956377
431616.0528334956947-0.0528334956946907
441213.5384373520938-1.53843735209381
451214.4354442001046-2.43544420010457
461113.7823720037909-2.78237200379091
471515.7777323163263-0.777732316326299
481514.74236109686700.257638903132960
491713.59775978289043.40224021710964
501314.5998953863781-1.59989538637809
511615.29634245903350.703657540966547
521413.27888899315750.721111006842455
531111.6898885366737-0.689888536673653
541213.8449319258502-1.84493192585024
551214.4373957201405-2.43739572014050
561514.47817855564900.521821444350952
571614.1265373797751.87346262022500
581515.4525988569437-0.452598856943663
591215.3441787714259-3.34417877142593
601213.3641175206796-1.36411752067962
61810.7889235880303-2.78892358803027
621314.4904761043925-1.49047610439246
631114.3737633615129-3.37376336151285
641413.53420066436310.465799335636875
651513.8237578804051.17624211959499
661015.0314767881373-5.0314767881373
671112.6355569417665-1.63555694176653
681214.0571571163696-2.05715711636956
691514.22327284836320.77672715163678
701513.84922245235891.15077754764110
711413.98602318932200.0139768106780217
721613.34334801781452.65665198218552
731514.39414517838430.605854821615662
741515.7670714426119-0.767071442611874
751314.3352642723594-1.33526427235941
761211.76950288721170.230497112788324
771714.23043238190662.76956761809341
781312.32911620678810.670883793211928
791513.47456358811651.52543641188352
801315.1712195726786-2.17121957267864
811514.89775364699960.102246353000413
821615.79223914954160.207760850458387
831515.7769939265305-0.776993926530516
841614.33079302775131.66920697224875
851514.33519123181590.664808768184144
861413.89729332162890.102706678371143
871514.42581715987040.574182840129564
881414.3823982533081-0.382398253308145
891313.0094719890806-0.00947198908060875
90710.3887877985406-3.38878779854058
911713.93347260607523.06652739392485
921313.0912214204334-0.0912214204333582
931514.49437894490750.505621055092534
941412.74154954502121.25845045497883
951314.5879414934078-1.58794149340775
961615.12259807974150.877401920258526
971212.9439683985465-0.943968398546507
981414.7592085574516-0.759208557451583
991714.85441482900992.14558517099008
1001515.2105094690662-0.210509469066193
1011715.29623478147751.70376521852249
1021213.0391194099872-1.03911940998722
1031614.79188792306651.20811207693352
1041114.3313422300788-3.33134223007882
1051513.2122593268341.78774067316599
106911.4855252111415-2.48552521114145
1071615.01409790565110.985902094348878
1081513.33760838284861.66239161715145
1091013.4030581346047-3.40305813460468
1101010.4363165135120-0.436316513512027
1111513.54644295298891.45355704701109
1121113.0224526675022-2.02245266750218
1131315.3213832866417-2.32138328664166
1141412.96729388389281.03270611610724
1151814.27134371763323.72865628236683
1161615.20892643325830.79107356674167
1171413.80678354049890.193216459501065
1181414.4518309341519-0.451830934151903
1191415.4320363219727-1.43203632197268
1201413.46361431874660.536385681253391
1211213.2485116518239-1.24851165182386
1221413.06617917148550.9338208285145
1231514.98103227538220.0189677246177731
1241516.1755288997864-1.17552889978641
1251514.45250843714060.547491562859352
1261314.5170943411191-1.51709434111911
1271715.94649180953441.05350819046558
1281715.83652332522121.16347667477879
1291914.96731320716494.03268679283513
1301513.73008222721621.26991777278379
1311314.7765874399378-1.77658743993776
132910.6686587084378-1.66865870843779
1331515.6898195056619-0.689819505661932
1341512.59134742752702.40865257247301
1351514.58212881789830.417871182101734
1361613.19200438928412.80799561071594
137119.177642212077291.82235778792271
1381413.67221433200950.327785667990505
1391112.5695511392106-1.56955113921062
1401514.70881788347450.291182116525516
1411313.9614585234978-0.96145852349778
1421515.3221932956414-0.322193295641354
1431613.61600251315422.3839974868458
1441414.3128925319208-0.312892531920831
1451513.67283657488061.32716342511938
1461614.47586017228151.52413982771845
1471614.00523724536261.99476275463743
1481113.9719908969942-2.97199089699418
1491214.5516353296399-2.55163532963993
150911.5201738772182-2.52017387721821
1511614.61385463816231.38614536183766
1521312.72312609665780.276873903342167
1531615.96300542290330.0369945770967159
1541215.0355851752068-3.03558517520681
155911.7707174387915-2.77071743879147
1561311.62567111692331.37432888307674
1571313.0912214204334-0.0912214204333582
1581413.85032790504310.149672094956893
1591914.96731320716494.03268679283513
1601315.4784511148912-2.47845111489121
1611212.0099916584122-0.0099916584121995
1621312.96239889493890.0376011050610872






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6103845363248880.7792309273502240.389615463675112
100.6976947530059090.6046104939881820.302305246994091
110.601337361065530.7973252778689410.398662638934470
120.8375101184354550.3249797631290890.162489881564545
130.9711782856582120.05764342868357570.0288217143417878
140.9690039833386670.06199203332266570.0309960166613329
150.9864617025077440.0270765949845120.013538297492256
160.977965894665620.04406821066876140.0220341053343807
170.9687899729291660.0624200541416680.031210027070834
180.9648634915888690.0702730168222630.0351365084111315
190.958996473316510.08200705336697870.0410035266834893
200.9482935021065520.1034129957868960.0517064978934478
210.940227199590510.1195456008189810.0597728004094907
220.964303971693940.07139205661211960.0356960283060598
230.9537125246351040.09257495072979230.0462874753648962
240.9500672735826670.0998654528346670.0499327264173335
250.9369948857265810.1260102285468370.0630051142734187
260.9993042023582730.001391595283453850.000695797641726925
270.998924920816250.002150158367499260.00107507918374963
280.9982708737084410.003458252583117530.00172912629155877
290.9974765534974880.005046893005023350.00252344650251167
300.9988586715576680.002282656884663370.00114132844233168
310.9982392728137280.003521454372543620.00176072718627181
320.9974309003168270.005138199366346630.00256909968317332
330.996885435692270.006229128615459850.00311456430772993
340.9952863373756970.009427325248606320.00471366262430316
350.993312036045720.01337592790855930.00668796395427964
360.9903361649972950.01932767000541010.00966383500270503
370.9924218228218260.01515635435634880.00757817717817441
380.9893380647580380.02132387048392410.0106619352419621
390.9887725326237970.02245493475240590.0112274673762030
400.9895472219681810.02090555606363780.0104527780318189
410.985828095506720.02834380898656120.0141719044932806
420.986303297122920.02739340575416030.0136967028770801
430.9812549673273160.0374900653453670.0187450326726835
440.9799555544015840.04008889119683120.0200444455984156
450.9841915912917250.03161681741655100.0158084087082755
460.9884281393447990.02314372131040270.0115718606552013
470.9844540415149290.03109191697014250.0155459584850712
480.9788973907785680.04220521844286370.0211026092214319
490.9870543193107230.02589136137855440.0129456806892772
500.9853413940417540.02931721191649260.0146586059582463
510.9806140056297160.03877198874056730.0193859943702837
520.9745818927203780.05083621455924310.0254181072796216
530.9687947383799960.06241052324000880.0312052616200044
540.968365212207020.06326957558596080.0316347877929804
550.9708415794645370.05831684107092640.0291584205354632
560.962727464570920.07454507085816050.0372725354290802
570.960766846377250.07846630724549910.0392331536227495
580.9503200460622050.09935990787558930.0496799539377946
590.9669423573168180.06611528536636350.0330576426831817
600.9628833254956610.07423334900867790.0371166745043390
610.9729973055822110.05400538883557730.0270026944177886
620.9696178055973080.06076438880538350.0303821944026918
630.981939547745560.03612090450887970.0180604522544398
640.9764022689433610.04719546211327770.0235977310566388
650.9713709290024280.05725814199514310.0286290709975716
660.9940034626383680.01199307472326370.00599653736163187
670.9933981609443230.01320367811135400.00660183905567702
680.9938436918064390.01231261638712290.00615630819356147
690.9919863617430550.01602727651389100.00801363825694548
700.9901665925376280.01966681492474440.00983340746237221
710.9867701920197290.02645961596054210.0132298079802710
720.989744056333960.02051188733208130.0102559436660407
730.9866278349963080.02674433000738360.0133721650036918
740.9827508263003670.03449834739926650.0172491736996333
750.980313428322970.0393731433540580.019686571677029
760.974149369728990.05170126054202040.0258506302710102
770.9802953615608430.03940927687831340.0197046384391567
780.9749101546253750.05017969074925090.0250898453746255
790.9719045864792460.0561908270415070.0280954135207535
800.9760448978349060.04791020433018870.0239551021650943
810.968703505398820.06259298920235980.0312964946011799
820.9597144678024540.08057106439509190.0402855321975460
830.9507625339416350.09847493211673070.0492374660583653
840.9462698230934540.1074603538130910.0537301769065455
850.9338782688174730.1322434623650530.0661217311825267
860.9175720791561320.1648558416877360.0824279208438682
870.899748079995740.2005038400085200.100251920004260
880.8785038550460420.2429922899079160.121496144953958
890.85365289995440.29269420009120.1463471000456
900.8951665175773660.2096669648452680.104833482422634
910.9230181110354490.1539637779291030.0769818889645513
920.9045262800049470.1909474399901070.0954737199950534
930.886388239952360.2272235200952810.113611760047641
940.8712942344443820.2574115311112350.128705765555618
950.8647647938025350.2704704123949300.135235206197465
960.8413243632159820.3173512735680370.158675636784018
970.8171745966030260.3656508067939490.182825403396974
980.7937333463640880.4125333072718240.206266653635912
990.7956677934839210.4086644130321580.204332206516079
1000.760314451430320.479371097139360.23968554856968
1010.7501390081275130.4997219837449740.249860991872487
1020.7208492101856430.5583015796287150.279150789814357
1030.7026445689397770.5947108621204460.297355431060223
1040.801761522134140.396476955731720.19823847786586
1050.818609730895840.3627805382083210.181390269104161
1060.8481273872336130.3037452255327750.151872612766387
1070.8238400168531260.3523199662937490.176159983146874
1080.8297468012804260.3405063974391480.170253198719574
1090.884273387446420.2314532251071610.115726612553580
1100.8601015520259110.2797968959481780.139898447974089
1110.8574856635587650.2850286728824700.142514336441235
1120.8506532185824090.2986935628351820.149346781417591
1130.8448104597929050.310379080414190.155189540207095
1140.8209381696316350.3581236607367290.179061830368365
1150.8868806055502860.2262387888994270.113119394449714
1160.8615960804806040.2768078390387920.138403919519396
1170.8423517119706070.3152965760587860.157648288029393
1180.8088264383879240.3823471232241510.191173561612076
1190.7953905778317380.4092188443365240.204609422168262
1200.7644766588204380.4710466823591240.235523341179562
1210.7278867117584260.5442265764831480.272113288241574
1220.684500828628250.63099834274350.31549917137175
1230.6356511775984620.7286976448030760.364348822401538
1240.5930581677040780.8138836645918440.406941832295922
1250.5380031628643350.923993674271330.461996837135665
1260.5153760744840440.9692478510319110.484623925515956
1270.4612490306407610.9224980612815230.538750969359239
1280.4373039749972410.8746079499944820.562696025002759
1290.6349632874825150.7300734250349710.365036712517486
1300.5934604856616810.8130790286766370.406539514338319
1310.5914386945137710.8171226109724590.408561305486229
1320.580292714188350.83941457162330.41970728581165
1330.5184405100651160.9631189798697690.481559489934884
1340.5078383178852370.9843233642295260.492161682114763
1350.4590782763272370.9181565526544740.540921723672763
1360.4959887237106520.9919774474213030.504011276289348
1370.4541800686332830.9083601372665650.545819931366717
1380.395372665744390.790745331488780.60462733425561
1390.3530365923147130.7060731846294260.646963407685287
1400.2873698368633650.5747396737267290.712630163136635
1410.2838933704268120.5677867408536230.716106629573188
1420.2236700796697770.4473401593395540.776329920330223
1430.2527240794767390.5054481589534780.747275920523261
1440.1920409936441470.3840819872882940.807959006355853
1450.1478488009935970.2956976019871930.852151199006403
1460.1084841522672550.2169683045345090.891515847732745
1470.1978109559697290.3956219119394590.80218904403027
1480.6319139124585670.7361721750828660.368086087541433
1490.6762650624951840.6474698750096320.323734937504816
1500.5708998135538490.8582003728923010.429100186446151
1510.5047228317494530.9905543365010950.495277168250547
1520.3630772990538510.7261545981077020.636922700946149
1530.2661378213806960.5322756427613920.733862178619304

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.610384536324888 & 0.779230927350224 & 0.389615463675112 \tabularnewline
10 & 0.697694753005909 & 0.604610493988182 & 0.302305246994091 \tabularnewline
11 & 0.60133736106553 & 0.797325277868941 & 0.398662638934470 \tabularnewline
12 & 0.837510118435455 & 0.324979763129089 & 0.162489881564545 \tabularnewline
13 & 0.971178285658212 & 0.0576434286835757 & 0.0288217143417878 \tabularnewline
14 & 0.969003983338667 & 0.0619920333226657 & 0.0309960166613329 \tabularnewline
15 & 0.986461702507744 & 0.027076594984512 & 0.013538297492256 \tabularnewline
16 & 0.97796589466562 & 0.0440682106687614 & 0.0220341053343807 \tabularnewline
17 & 0.968789972929166 & 0.062420054141668 & 0.031210027070834 \tabularnewline
18 & 0.964863491588869 & 0.070273016822263 & 0.0351365084111315 \tabularnewline
19 & 0.95899647331651 & 0.0820070533669787 & 0.0410035266834893 \tabularnewline
20 & 0.948293502106552 & 0.103412995786896 & 0.0517064978934478 \tabularnewline
21 & 0.94022719959051 & 0.119545600818981 & 0.0597728004094907 \tabularnewline
22 & 0.96430397169394 & 0.0713920566121196 & 0.0356960283060598 \tabularnewline
23 & 0.953712524635104 & 0.0925749507297923 & 0.0462874753648962 \tabularnewline
24 & 0.950067273582667 & 0.099865452834667 & 0.0499327264173335 \tabularnewline
25 & 0.936994885726581 & 0.126010228546837 & 0.0630051142734187 \tabularnewline
26 & 0.999304202358273 & 0.00139159528345385 & 0.000695797641726925 \tabularnewline
27 & 0.99892492081625 & 0.00215015836749926 & 0.00107507918374963 \tabularnewline
28 & 0.998270873708441 & 0.00345825258311753 & 0.00172912629155877 \tabularnewline
29 & 0.997476553497488 & 0.00504689300502335 & 0.00252344650251167 \tabularnewline
30 & 0.998858671557668 & 0.00228265688466337 & 0.00114132844233168 \tabularnewline
31 & 0.998239272813728 & 0.00352145437254362 & 0.00176072718627181 \tabularnewline
32 & 0.997430900316827 & 0.00513819936634663 & 0.00256909968317332 \tabularnewline
33 & 0.99688543569227 & 0.00622912861545985 & 0.00311456430772993 \tabularnewline
34 & 0.995286337375697 & 0.00942732524860632 & 0.00471366262430316 \tabularnewline
35 & 0.99331203604572 & 0.0133759279085593 & 0.00668796395427964 \tabularnewline
36 & 0.990336164997295 & 0.0193276700054101 & 0.00966383500270503 \tabularnewline
37 & 0.992421822821826 & 0.0151563543563488 & 0.00757817717817441 \tabularnewline
38 & 0.989338064758038 & 0.0213238704839241 & 0.0106619352419621 \tabularnewline
39 & 0.988772532623797 & 0.0224549347524059 & 0.0112274673762030 \tabularnewline
40 & 0.989547221968181 & 0.0209055560636378 & 0.0104527780318189 \tabularnewline
41 & 0.98582809550672 & 0.0283438089865612 & 0.0141719044932806 \tabularnewline
42 & 0.98630329712292 & 0.0273934057541603 & 0.0136967028770801 \tabularnewline
43 & 0.981254967327316 & 0.037490065345367 & 0.0187450326726835 \tabularnewline
44 & 0.979955554401584 & 0.0400888911968312 & 0.0200444455984156 \tabularnewline
45 & 0.984191591291725 & 0.0316168174165510 & 0.0158084087082755 \tabularnewline
46 & 0.988428139344799 & 0.0231437213104027 & 0.0115718606552013 \tabularnewline
47 & 0.984454041514929 & 0.0310919169701425 & 0.0155459584850712 \tabularnewline
48 & 0.978897390778568 & 0.0422052184428637 & 0.0211026092214319 \tabularnewline
49 & 0.987054319310723 & 0.0258913613785544 & 0.0129456806892772 \tabularnewline
50 & 0.985341394041754 & 0.0293172119164926 & 0.0146586059582463 \tabularnewline
51 & 0.980614005629716 & 0.0387719887405673 & 0.0193859943702837 \tabularnewline
52 & 0.974581892720378 & 0.0508362145592431 & 0.0254181072796216 \tabularnewline
53 & 0.968794738379996 & 0.0624105232400088 & 0.0312052616200044 \tabularnewline
54 & 0.96836521220702 & 0.0632695755859608 & 0.0316347877929804 \tabularnewline
55 & 0.970841579464537 & 0.0583168410709264 & 0.0291584205354632 \tabularnewline
56 & 0.96272746457092 & 0.0745450708581605 & 0.0372725354290802 \tabularnewline
57 & 0.96076684637725 & 0.0784663072454991 & 0.0392331536227495 \tabularnewline
58 & 0.950320046062205 & 0.0993599078755893 & 0.0496799539377946 \tabularnewline
59 & 0.966942357316818 & 0.0661152853663635 & 0.0330576426831817 \tabularnewline
60 & 0.962883325495661 & 0.0742333490086779 & 0.0371166745043390 \tabularnewline
61 & 0.972997305582211 & 0.0540053888355773 & 0.0270026944177886 \tabularnewline
62 & 0.969617805597308 & 0.0607643888053835 & 0.0303821944026918 \tabularnewline
63 & 0.98193954774556 & 0.0361209045088797 & 0.0180604522544398 \tabularnewline
64 & 0.976402268943361 & 0.0471954621132777 & 0.0235977310566388 \tabularnewline
65 & 0.971370929002428 & 0.0572581419951431 & 0.0286290709975716 \tabularnewline
66 & 0.994003462638368 & 0.0119930747232637 & 0.00599653736163187 \tabularnewline
67 & 0.993398160944323 & 0.0132036781113540 & 0.00660183905567702 \tabularnewline
68 & 0.993843691806439 & 0.0123126163871229 & 0.00615630819356147 \tabularnewline
69 & 0.991986361743055 & 0.0160272765138910 & 0.00801363825694548 \tabularnewline
70 & 0.990166592537628 & 0.0196668149247444 & 0.00983340746237221 \tabularnewline
71 & 0.986770192019729 & 0.0264596159605421 & 0.0132298079802710 \tabularnewline
72 & 0.98974405633396 & 0.0205118873320813 & 0.0102559436660407 \tabularnewline
73 & 0.986627834996308 & 0.0267443300073836 & 0.0133721650036918 \tabularnewline
74 & 0.982750826300367 & 0.0344983473992665 & 0.0172491736996333 \tabularnewline
75 & 0.98031342832297 & 0.039373143354058 & 0.019686571677029 \tabularnewline
76 & 0.97414936972899 & 0.0517012605420204 & 0.0258506302710102 \tabularnewline
77 & 0.980295361560843 & 0.0394092768783134 & 0.0197046384391567 \tabularnewline
78 & 0.974910154625375 & 0.0501796907492509 & 0.0250898453746255 \tabularnewline
79 & 0.971904586479246 & 0.056190827041507 & 0.0280954135207535 \tabularnewline
80 & 0.976044897834906 & 0.0479102043301887 & 0.0239551021650943 \tabularnewline
81 & 0.96870350539882 & 0.0625929892023598 & 0.0312964946011799 \tabularnewline
82 & 0.959714467802454 & 0.0805710643950919 & 0.0402855321975460 \tabularnewline
83 & 0.950762533941635 & 0.0984749321167307 & 0.0492374660583653 \tabularnewline
84 & 0.946269823093454 & 0.107460353813091 & 0.0537301769065455 \tabularnewline
85 & 0.933878268817473 & 0.132243462365053 & 0.0661217311825267 \tabularnewline
86 & 0.917572079156132 & 0.164855841687736 & 0.0824279208438682 \tabularnewline
87 & 0.89974807999574 & 0.200503840008520 & 0.100251920004260 \tabularnewline
88 & 0.878503855046042 & 0.242992289907916 & 0.121496144953958 \tabularnewline
89 & 0.8536528999544 & 0.2926942000912 & 0.1463471000456 \tabularnewline
90 & 0.895166517577366 & 0.209666964845268 & 0.104833482422634 \tabularnewline
91 & 0.923018111035449 & 0.153963777929103 & 0.0769818889645513 \tabularnewline
92 & 0.904526280004947 & 0.190947439990107 & 0.0954737199950534 \tabularnewline
93 & 0.88638823995236 & 0.227223520095281 & 0.113611760047641 \tabularnewline
94 & 0.871294234444382 & 0.257411531111235 & 0.128705765555618 \tabularnewline
95 & 0.864764793802535 & 0.270470412394930 & 0.135235206197465 \tabularnewline
96 & 0.841324363215982 & 0.317351273568037 & 0.158675636784018 \tabularnewline
97 & 0.817174596603026 & 0.365650806793949 & 0.182825403396974 \tabularnewline
98 & 0.793733346364088 & 0.412533307271824 & 0.206266653635912 \tabularnewline
99 & 0.795667793483921 & 0.408664413032158 & 0.204332206516079 \tabularnewline
100 & 0.76031445143032 & 0.47937109713936 & 0.23968554856968 \tabularnewline
101 & 0.750139008127513 & 0.499721983744974 & 0.249860991872487 \tabularnewline
102 & 0.720849210185643 & 0.558301579628715 & 0.279150789814357 \tabularnewline
103 & 0.702644568939777 & 0.594710862120446 & 0.297355431060223 \tabularnewline
104 & 0.80176152213414 & 0.39647695573172 & 0.19823847786586 \tabularnewline
105 & 0.81860973089584 & 0.362780538208321 & 0.181390269104161 \tabularnewline
106 & 0.848127387233613 & 0.303745225532775 & 0.151872612766387 \tabularnewline
107 & 0.823840016853126 & 0.352319966293749 & 0.176159983146874 \tabularnewline
108 & 0.829746801280426 & 0.340506397439148 & 0.170253198719574 \tabularnewline
109 & 0.88427338744642 & 0.231453225107161 & 0.115726612553580 \tabularnewline
110 & 0.860101552025911 & 0.279796895948178 & 0.139898447974089 \tabularnewline
111 & 0.857485663558765 & 0.285028672882470 & 0.142514336441235 \tabularnewline
112 & 0.850653218582409 & 0.298693562835182 & 0.149346781417591 \tabularnewline
113 & 0.844810459792905 & 0.31037908041419 & 0.155189540207095 \tabularnewline
114 & 0.820938169631635 & 0.358123660736729 & 0.179061830368365 \tabularnewline
115 & 0.886880605550286 & 0.226238788899427 & 0.113119394449714 \tabularnewline
116 & 0.861596080480604 & 0.276807839038792 & 0.138403919519396 \tabularnewline
117 & 0.842351711970607 & 0.315296576058786 & 0.157648288029393 \tabularnewline
118 & 0.808826438387924 & 0.382347123224151 & 0.191173561612076 \tabularnewline
119 & 0.795390577831738 & 0.409218844336524 & 0.204609422168262 \tabularnewline
120 & 0.764476658820438 & 0.471046682359124 & 0.235523341179562 \tabularnewline
121 & 0.727886711758426 & 0.544226576483148 & 0.272113288241574 \tabularnewline
122 & 0.68450082862825 & 0.6309983427435 & 0.31549917137175 \tabularnewline
123 & 0.635651177598462 & 0.728697644803076 & 0.364348822401538 \tabularnewline
124 & 0.593058167704078 & 0.813883664591844 & 0.406941832295922 \tabularnewline
125 & 0.538003162864335 & 0.92399367427133 & 0.461996837135665 \tabularnewline
126 & 0.515376074484044 & 0.969247851031911 & 0.484623925515956 \tabularnewline
127 & 0.461249030640761 & 0.922498061281523 & 0.538750969359239 \tabularnewline
128 & 0.437303974997241 & 0.874607949994482 & 0.562696025002759 \tabularnewline
129 & 0.634963287482515 & 0.730073425034971 & 0.365036712517486 \tabularnewline
130 & 0.593460485661681 & 0.813079028676637 & 0.406539514338319 \tabularnewline
131 & 0.591438694513771 & 0.817122610972459 & 0.408561305486229 \tabularnewline
132 & 0.58029271418835 & 0.8394145716233 & 0.41970728581165 \tabularnewline
133 & 0.518440510065116 & 0.963118979869769 & 0.481559489934884 \tabularnewline
134 & 0.507838317885237 & 0.984323364229526 & 0.492161682114763 \tabularnewline
135 & 0.459078276327237 & 0.918156552654474 & 0.540921723672763 \tabularnewline
136 & 0.495988723710652 & 0.991977447421303 & 0.504011276289348 \tabularnewline
137 & 0.454180068633283 & 0.908360137266565 & 0.545819931366717 \tabularnewline
138 & 0.39537266574439 & 0.79074533148878 & 0.60462733425561 \tabularnewline
139 & 0.353036592314713 & 0.706073184629426 & 0.646963407685287 \tabularnewline
140 & 0.287369836863365 & 0.574739673726729 & 0.712630163136635 \tabularnewline
141 & 0.283893370426812 & 0.567786740853623 & 0.716106629573188 \tabularnewline
142 & 0.223670079669777 & 0.447340159339554 & 0.776329920330223 \tabularnewline
143 & 0.252724079476739 & 0.505448158953478 & 0.747275920523261 \tabularnewline
144 & 0.192040993644147 & 0.384081987288294 & 0.807959006355853 \tabularnewline
145 & 0.147848800993597 & 0.295697601987193 & 0.852151199006403 \tabularnewline
146 & 0.108484152267255 & 0.216968304534509 & 0.891515847732745 \tabularnewline
147 & 0.197810955969729 & 0.395621911939459 & 0.80218904403027 \tabularnewline
148 & 0.631913912458567 & 0.736172175082866 & 0.368086087541433 \tabularnewline
149 & 0.676265062495184 & 0.647469875009632 & 0.323734937504816 \tabularnewline
150 & 0.570899813553849 & 0.858200372892301 & 0.429100186446151 \tabularnewline
151 & 0.504722831749453 & 0.990554336501095 & 0.495277168250547 \tabularnewline
152 & 0.363077299053851 & 0.726154598107702 & 0.636922700946149 \tabularnewline
153 & 0.266137821380696 & 0.532275642761392 & 0.733862178619304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.610384536324888[/C][C]0.779230927350224[/C][C]0.389615463675112[/C][/ROW]
[ROW][C]10[/C][C]0.697694753005909[/C][C]0.604610493988182[/C][C]0.302305246994091[/C][/ROW]
[ROW][C]11[/C][C]0.60133736106553[/C][C]0.797325277868941[/C][C]0.398662638934470[/C][/ROW]
[ROW][C]12[/C][C]0.837510118435455[/C][C]0.324979763129089[/C][C]0.162489881564545[/C][/ROW]
[ROW][C]13[/C][C]0.971178285658212[/C][C]0.0576434286835757[/C][C]0.0288217143417878[/C][/ROW]
[ROW][C]14[/C][C]0.969003983338667[/C][C]0.0619920333226657[/C][C]0.0309960166613329[/C][/ROW]
[ROW][C]15[/C][C]0.986461702507744[/C][C]0.027076594984512[/C][C]0.013538297492256[/C][/ROW]
[ROW][C]16[/C][C]0.97796589466562[/C][C]0.0440682106687614[/C][C]0.0220341053343807[/C][/ROW]
[ROW][C]17[/C][C]0.968789972929166[/C][C]0.062420054141668[/C][C]0.031210027070834[/C][/ROW]
[ROW][C]18[/C][C]0.964863491588869[/C][C]0.070273016822263[/C][C]0.0351365084111315[/C][/ROW]
[ROW][C]19[/C][C]0.95899647331651[/C][C]0.0820070533669787[/C][C]0.0410035266834893[/C][/ROW]
[ROW][C]20[/C][C]0.948293502106552[/C][C]0.103412995786896[/C][C]0.0517064978934478[/C][/ROW]
[ROW][C]21[/C][C]0.94022719959051[/C][C]0.119545600818981[/C][C]0.0597728004094907[/C][/ROW]
[ROW][C]22[/C][C]0.96430397169394[/C][C]0.0713920566121196[/C][C]0.0356960283060598[/C][/ROW]
[ROW][C]23[/C][C]0.953712524635104[/C][C]0.0925749507297923[/C][C]0.0462874753648962[/C][/ROW]
[ROW][C]24[/C][C]0.950067273582667[/C][C]0.099865452834667[/C][C]0.0499327264173335[/C][/ROW]
[ROW][C]25[/C][C]0.936994885726581[/C][C]0.126010228546837[/C][C]0.0630051142734187[/C][/ROW]
[ROW][C]26[/C][C]0.999304202358273[/C][C]0.00139159528345385[/C][C]0.000695797641726925[/C][/ROW]
[ROW][C]27[/C][C]0.99892492081625[/C][C]0.00215015836749926[/C][C]0.00107507918374963[/C][/ROW]
[ROW][C]28[/C][C]0.998270873708441[/C][C]0.00345825258311753[/C][C]0.00172912629155877[/C][/ROW]
[ROW][C]29[/C][C]0.997476553497488[/C][C]0.00504689300502335[/C][C]0.00252344650251167[/C][/ROW]
[ROW][C]30[/C][C]0.998858671557668[/C][C]0.00228265688466337[/C][C]0.00114132844233168[/C][/ROW]
[ROW][C]31[/C][C]0.998239272813728[/C][C]0.00352145437254362[/C][C]0.00176072718627181[/C][/ROW]
[ROW][C]32[/C][C]0.997430900316827[/C][C]0.00513819936634663[/C][C]0.00256909968317332[/C][/ROW]
[ROW][C]33[/C][C]0.99688543569227[/C][C]0.00622912861545985[/C][C]0.00311456430772993[/C][/ROW]
[ROW][C]34[/C][C]0.995286337375697[/C][C]0.00942732524860632[/C][C]0.00471366262430316[/C][/ROW]
[ROW][C]35[/C][C]0.99331203604572[/C][C]0.0133759279085593[/C][C]0.00668796395427964[/C][/ROW]
[ROW][C]36[/C][C]0.990336164997295[/C][C]0.0193276700054101[/C][C]0.00966383500270503[/C][/ROW]
[ROW][C]37[/C][C]0.992421822821826[/C][C]0.0151563543563488[/C][C]0.00757817717817441[/C][/ROW]
[ROW][C]38[/C][C]0.989338064758038[/C][C]0.0213238704839241[/C][C]0.0106619352419621[/C][/ROW]
[ROW][C]39[/C][C]0.988772532623797[/C][C]0.0224549347524059[/C][C]0.0112274673762030[/C][/ROW]
[ROW][C]40[/C][C]0.989547221968181[/C][C]0.0209055560636378[/C][C]0.0104527780318189[/C][/ROW]
[ROW][C]41[/C][C]0.98582809550672[/C][C]0.0283438089865612[/C][C]0.0141719044932806[/C][/ROW]
[ROW][C]42[/C][C]0.98630329712292[/C][C]0.0273934057541603[/C][C]0.0136967028770801[/C][/ROW]
[ROW][C]43[/C][C]0.981254967327316[/C][C]0.037490065345367[/C][C]0.0187450326726835[/C][/ROW]
[ROW][C]44[/C][C]0.979955554401584[/C][C]0.0400888911968312[/C][C]0.0200444455984156[/C][/ROW]
[ROW][C]45[/C][C]0.984191591291725[/C][C]0.0316168174165510[/C][C]0.0158084087082755[/C][/ROW]
[ROW][C]46[/C][C]0.988428139344799[/C][C]0.0231437213104027[/C][C]0.0115718606552013[/C][/ROW]
[ROW][C]47[/C][C]0.984454041514929[/C][C]0.0310919169701425[/C][C]0.0155459584850712[/C][/ROW]
[ROW][C]48[/C][C]0.978897390778568[/C][C]0.0422052184428637[/C][C]0.0211026092214319[/C][/ROW]
[ROW][C]49[/C][C]0.987054319310723[/C][C]0.0258913613785544[/C][C]0.0129456806892772[/C][/ROW]
[ROW][C]50[/C][C]0.985341394041754[/C][C]0.0293172119164926[/C][C]0.0146586059582463[/C][/ROW]
[ROW][C]51[/C][C]0.980614005629716[/C][C]0.0387719887405673[/C][C]0.0193859943702837[/C][/ROW]
[ROW][C]52[/C][C]0.974581892720378[/C][C]0.0508362145592431[/C][C]0.0254181072796216[/C][/ROW]
[ROW][C]53[/C][C]0.968794738379996[/C][C]0.0624105232400088[/C][C]0.0312052616200044[/C][/ROW]
[ROW][C]54[/C][C]0.96836521220702[/C][C]0.0632695755859608[/C][C]0.0316347877929804[/C][/ROW]
[ROW][C]55[/C][C]0.970841579464537[/C][C]0.0583168410709264[/C][C]0.0291584205354632[/C][/ROW]
[ROW][C]56[/C][C]0.96272746457092[/C][C]0.0745450708581605[/C][C]0.0372725354290802[/C][/ROW]
[ROW][C]57[/C][C]0.96076684637725[/C][C]0.0784663072454991[/C][C]0.0392331536227495[/C][/ROW]
[ROW][C]58[/C][C]0.950320046062205[/C][C]0.0993599078755893[/C][C]0.0496799539377946[/C][/ROW]
[ROW][C]59[/C][C]0.966942357316818[/C][C]0.0661152853663635[/C][C]0.0330576426831817[/C][/ROW]
[ROW][C]60[/C][C]0.962883325495661[/C][C]0.0742333490086779[/C][C]0.0371166745043390[/C][/ROW]
[ROW][C]61[/C][C]0.972997305582211[/C][C]0.0540053888355773[/C][C]0.0270026944177886[/C][/ROW]
[ROW][C]62[/C][C]0.969617805597308[/C][C]0.0607643888053835[/C][C]0.0303821944026918[/C][/ROW]
[ROW][C]63[/C][C]0.98193954774556[/C][C]0.0361209045088797[/C][C]0.0180604522544398[/C][/ROW]
[ROW][C]64[/C][C]0.976402268943361[/C][C]0.0471954621132777[/C][C]0.0235977310566388[/C][/ROW]
[ROW][C]65[/C][C]0.971370929002428[/C][C]0.0572581419951431[/C][C]0.0286290709975716[/C][/ROW]
[ROW][C]66[/C][C]0.994003462638368[/C][C]0.0119930747232637[/C][C]0.00599653736163187[/C][/ROW]
[ROW][C]67[/C][C]0.993398160944323[/C][C]0.0132036781113540[/C][C]0.00660183905567702[/C][/ROW]
[ROW][C]68[/C][C]0.993843691806439[/C][C]0.0123126163871229[/C][C]0.00615630819356147[/C][/ROW]
[ROW][C]69[/C][C]0.991986361743055[/C][C]0.0160272765138910[/C][C]0.00801363825694548[/C][/ROW]
[ROW][C]70[/C][C]0.990166592537628[/C][C]0.0196668149247444[/C][C]0.00983340746237221[/C][/ROW]
[ROW][C]71[/C][C]0.986770192019729[/C][C]0.0264596159605421[/C][C]0.0132298079802710[/C][/ROW]
[ROW][C]72[/C][C]0.98974405633396[/C][C]0.0205118873320813[/C][C]0.0102559436660407[/C][/ROW]
[ROW][C]73[/C][C]0.986627834996308[/C][C]0.0267443300073836[/C][C]0.0133721650036918[/C][/ROW]
[ROW][C]74[/C][C]0.982750826300367[/C][C]0.0344983473992665[/C][C]0.0172491736996333[/C][/ROW]
[ROW][C]75[/C][C]0.98031342832297[/C][C]0.039373143354058[/C][C]0.019686571677029[/C][/ROW]
[ROW][C]76[/C][C]0.97414936972899[/C][C]0.0517012605420204[/C][C]0.0258506302710102[/C][/ROW]
[ROW][C]77[/C][C]0.980295361560843[/C][C]0.0394092768783134[/C][C]0.0197046384391567[/C][/ROW]
[ROW][C]78[/C][C]0.974910154625375[/C][C]0.0501796907492509[/C][C]0.0250898453746255[/C][/ROW]
[ROW][C]79[/C][C]0.971904586479246[/C][C]0.056190827041507[/C][C]0.0280954135207535[/C][/ROW]
[ROW][C]80[/C][C]0.976044897834906[/C][C]0.0479102043301887[/C][C]0.0239551021650943[/C][/ROW]
[ROW][C]81[/C][C]0.96870350539882[/C][C]0.0625929892023598[/C][C]0.0312964946011799[/C][/ROW]
[ROW][C]82[/C][C]0.959714467802454[/C][C]0.0805710643950919[/C][C]0.0402855321975460[/C][/ROW]
[ROW][C]83[/C][C]0.950762533941635[/C][C]0.0984749321167307[/C][C]0.0492374660583653[/C][/ROW]
[ROW][C]84[/C][C]0.946269823093454[/C][C]0.107460353813091[/C][C]0.0537301769065455[/C][/ROW]
[ROW][C]85[/C][C]0.933878268817473[/C][C]0.132243462365053[/C][C]0.0661217311825267[/C][/ROW]
[ROW][C]86[/C][C]0.917572079156132[/C][C]0.164855841687736[/C][C]0.0824279208438682[/C][/ROW]
[ROW][C]87[/C][C]0.89974807999574[/C][C]0.200503840008520[/C][C]0.100251920004260[/C][/ROW]
[ROW][C]88[/C][C]0.878503855046042[/C][C]0.242992289907916[/C][C]0.121496144953958[/C][/ROW]
[ROW][C]89[/C][C]0.8536528999544[/C][C]0.2926942000912[/C][C]0.1463471000456[/C][/ROW]
[ROW][C]90[/C][C]0.895166517577366[/C][C]0.209666964845268[/C][C]0.104833482422634[/C][/ROW]
[ROW][C]91[/C][C]0.923018111035449[/C][C]0.153963777929103[/C][C]0.0769818889645513[/C][/ROW]
[ROW][C]92[/C][C]0.904526280004947[/C][C]0.190947439990107[/C][C]0.0954737199950534[/C][/ROW]
[ROW][C]93[/C][C]0.88638823995236[/C][C]0.227223520095281[/C][C]0.113611760047641[/C][/ROW]
[ROW][C]94[/C][C]0.871294234444382[/C][C]0.257411531111235[/C][C]0.128705765555618[/C][/ROW]
[ROW][C]95[/C][C]0.864764793802535[/C][C]0.270470412394930[/C][C]0.135235206197465[/C][/ROW]
[ROW][C]96[/C][C]0.841324363215982[/C][C]0.317351273568037[/C][C]0.158675636784018[/C][/ROW]
[ROW][C]97[/C][C]0.817174596603026[/C][C]0.365650806793949[/C][C]0.182825403396974[/C][/ROW]
[ROW][C]98[/C][C]0.793733346364088[/C][C]0.412533307271824[/C][C]0.206266653635912[/C][/ROW]
[ROW][C]99[/C][C]0.795667793483921[/C][C]0.408664413032158[/C][C]0.204332206516079[/C][/ROW]
[ROW][C]100[/C][C]0.76031445143032[/C][C]0.47937109713936[/C][C]0.23968554856968[/C][/ROW]
[ROW][C]101[/C][C]0.750139008127513[/C][C]0.499721983744974[/C][C]0.249860991872487[/C][/ROW]
[ROW][C]102[/C][C]0.720849210185643[/C][C]0.558301579628715[/C][C]0.279150789814357[/C][/ROW]
[ROW][C]103[/C][C]0.702644568939777[/C][C]0.594710862120446[/C][C]0.297355431060223[/C][/ROW]
[ROW][C]104[/C][C]0.80176152213414[/C][C]0.39647695573172[/C][C]0.19823847786586[/C][/ROW]
[ROW][C]105[/C][C]0.81860973089584[/C][C]0.362780538208321[/C][C]0.181390269104161[/C][/ROW]
[ROW][C]106[/C][C]0.848127387233613[/C][C]0.303745225532775[/C][C]0.151872612766387[/C][/ROW]
[ROW][C]107[/C][C]0.823840016853126[/C][C]0.352319966293749[/C][C]0.176159983146874[/C][/ROW]
[ROW][C]108[/C][C]0.829746801280426[/C][C]0.340506397439148[/C][C]0.170253198719574[/C][/ROW]
[ROW][C]109[/C][C]0.88427338744642[/C][C]0.231453225107161[/C][C]0.115726612553580[/C][/ROW]
[ROW][C]110[/C][C]0.860101552025911[/C][C]0.279796895948178[/C][C]0.139898447974089[/C][/ROW]
[ROW][C]111[/C][C]0.857485663558765[/C][C]0.285028672882470[/C][C]0.142514336441235[/C][/ROW]
[ROW][C]112[/C][C]0.850653218582409[/C][C]0.298693562835182[/C][C]0.149346781417591[/C][/ROW]
[ROW][C]113[/C][C]0.844810459792905[/C][C]0.31037908041419[/C][C]0.155189540207095[/C][/ROW]
[ROW][C]114[/C][C]0.820938169631635[/C][C]0.358123660736729[/C][C]0.179061830368365[/C][/ROW]
[ROW][C]115[/C][C]0.886880605550286[/C][C]0.226238788899427[/C][C]0.113119394449714[/C][/ROW]
[ROW][C]116[/C][C]0.861596080480604[/C][C]0.276807839038792[/C][C]0.138403919519396[/C][/ROW]
[ROW][C]117[/C][C]0.842351711970607[/C][C]0.315296576058786[/C][C]0.157648288029393[/C][/ROW]
[ROW][C]118[/C][C]0.808826438387924[/C][C]0.382347123224151[/C][C]0.191173561612076[/C][/ROW]
[ROW][C]119[/C][C]0.795390577831738[/C][C]0.409218844336524[/C][C]0.204609422168262[/C][/ROW]
[ROW][C]120[/C][C]0.764476658820438[/C][C]0.471046682359124[/C][C]0.235523341179562[/C][/ROW]
[ROW][C]121[/C][C]0.727886711758426[/C][C]0.544226576483148[/C][C]0.272113288241574[/C][/ROW]
[ROW][C]122[/C][C]0.68450082862825[/C][C]0.6309983427435[/C][C]0.31549917137175[/C][/ROW]
[ROW][C]123[/C][C]0.635651177598462[/C][C]0.728697644803076[/C][C]0.364348822401538[/C][/ROW]
[ROW][C]124[/C][C]0.593058167704078[/C][C]0.813883664591844[/C][C]0.406941832295922[/C][/ROW]
[ROW][C]125[/C][C]0.538003162864335[/C][C]0.92399367427133[/C][C]0.461996837135665[/C][/ROW]
[ROW][C]126[/C][C]0.515376074484044[/C][C]0.969247851031911[/C][C]0.484623925515956[/C][/ROW]
[ROW][C]127[/C][C]0.461249030640761[/C][C]0.922498061281523[/C][C]0.538750969359239[/C][/ROW]
[ROW][C]128[/C][C]0.437303974997241[/C][C]0.874607949994482[/C][C]0.562696025002759[/C][/ROW]
[ROW][C]129[/C][C]0.634963287482515[/C][C]0.730073425034971[/C][C]0.365036712517486[/C][/ROW]
[ROW][C]130[/C][C]0.593460485661681[/C][C]0.813079028676637[/C][C]0.406539514338319[/C][/ROW]
[ROW][C]131[/C][C]0.591438694513771[/C][C]0.817122610972459[/C][C]0.408561305486229[/C][/ROW]
[ROW][C]132[/C][C]0.58029271418835[/C][C]0.8394145716233[/C][C]0.41970728581165[/C][/ROW]
[ROW][C]133[/C][C]0.518440510065116[/C][C]0.963118979869769[/C][C]0.481559489934884[/C][/ROW]
[ROW][C]134[/C][C]0.507838317885237[/C][C]0.984323364229526[/C][C]0.492161682114763[/C][/ROW]
[ROW][C]135[/C][C]0.459078276327237[/C][C]0.918156552654474[/C][C]0.540921723672763[/C][/ROW]
[ROW][C]136[/C][C]0.495988723710652[/C][C]0.991977447421303[/C][C]0.504011276289348[/C][/ROW]
[ROW][C]137[/C][C]0.454180068633283[/C][C]0.908360137266565[/C][C]0.545819931366717[/C][/ROW]
[ROW][C]138[/C][C]0.39537266574439[/C][C]0.79074533148878[/C][C]0.60462733425561[/C][/ROW]
[ROW][C]139[/C][C]0.353036592314713[/C][C]0.706073184629426[/C][C]0.646963407685287[/C][/ROW]
[ROW][C]140[/C][C]0.287369836863365[/C][C]0.574739673726729[/C][C]0.712630163136635[/C][/ROW]
[ROW][C]141[/C][C]0.283893370426812[/C][C]0.567786740853623[/C][C]0.716106629573188[/C][/ROW]
[ROW][C]142[/C][C]0.223670079669777[/C][C]0.447340159339554[/C][C]0.776329920330223[/C][/ROW]
[ROW][C]143[/C][C]0.252724079476739[/C][C]0.505448158953478[/C][C]0.747275920523261[/C][/ROW]
[ROW][C]144[/C][C]0.192040993644147[/C][C]0.384081987288294[/C][C]0.807959006355853[/C][/ROW]
[ROW][C]145[/C][C]0.147848800993597[/C][C]0.295697601987193[/C][C]0.852151199006403[/C][/ROW]
[ROW][C]146[/C][C]0.108484152267255[/C][C]0.216968304534509[/C][C]0.891515847732745[/C][/ROW]
[ROW][C]147[/C][C]0.197810955969729[/C][C]0.395621911939459[/C][C]0.80218904403027[/C][/ROW]
[ROW][C]148[/C][C]0.631913912458567[/C][C]0.736172175082866[/C][C]0.368086087541433[/C][/ROW]
[ROW][C]149[/C][C]0.676265062495184[/C][C]0.647469875009632[/C][C]0.323734937504816[/C][/ROW]
[ROW][C]150[/C][C]0.570899813553849[/C][C]0.858200372892301[/C][C]0.429100186446151[/C][/ROW]
[ROW][C]151[/C][C]0.504722831749453[/C][C]0.990554336501095[/C][C]0.495277168250547[/C][/ROW]
[ROW][C]152[/C][C]0.363077299053851[/C][C]0.726154598107702[/C][C]0.636922700946149[/C][/ROW]
[ROW][C]153[/C][C]0.266137821380696[/C][C]0.532275642761392[/C][C]0.733862178619304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&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
90.6103845363248880.7792309273502240.389615463675112
100.6976947530059090.6046104939881820.302305246994091
110.601337361065530.7973252778689410.398662638934470
120.8375101184354550.3249797631290890.162489881564545
130.9711782856582120.05764342868357570.0288217143417878
140.9690039833386670.06199203332266570.0309960166613329
150.9864617025077440.0270765949845120.013538297492256
160.977965894665620.04406821066876140.0220341053343807
170.9687899729291660.0624200541416680.031210027070834
180.9648634915888690.0702730168222630.0351365084111315
190.958996473316510.08200705336697870.0410035266834893
200.9482935021065520.1034129957868960.0517064978934478
210.940227199590510.1195456008189810.0597728004094907
220.964303971693940.07139205661211960.0356960283060598
230.9537125246351040.09257495072979230.0462874753648962
240.9500672735826670.0998654528346670.0499327264173335
250.9369948857265810.1260102285468370.0630051142734187
260.9993042023582730.001391595283453850.000695797641726925
270.998924920816250.002150158367499260.00107507918374963
280.9982708737084410.003458252583117530.00172912629155877
290.9974765534974880.005046893005023350.00252344650251167
300.9988586715576680.002282656884663370.00114132844233168
310.9982392728137280.003521454372543620.00176072718627181
320.9974309003168270.005138199366346630.00256909968317332
330.996885435692270.006229128615459850.00311456430772993
340.9952863373756970.009427325248606320.00471366262430316
350.993312036045720.01337592790855930.00668796395427964
360.9903361649972950.01932767000541010.00966383500270503
370.9924218228218260.01515635435634880.00757817717817441
380.9893380647580380.02132387048392410.0106619352419621
390.9887725326237970.02245493475240590.0112274673762030
400.9895472219681810.02090555606363780.0104527780318189
410.985828095506720.02834380898656120.0141719044932806
420.986303297122920.02739340575416030.0136967028770801
430.9812549673273160.0374900653453670.0187450326726835
440.9799555544015840.04008889119683120.0200444455984156
450.9841915912917250.03161681741655100.0158084087082755
460.9884281393447990.02314372131040270.0115718606552013
470.9844540415149290.03109191697014250.0155459584850712
480.9788973907785680.04220521844286370.0211026092214319
490.9870543193107230.02589136137855440.0129456806892772
500.9853413940417540.02931721191649260.0146586059582463
510.9806140056297160.03877198874056730.0193859943702837
520.9745818927203780.05083621455924310.0254181072796216
530.9687947383799960.06241052324000880.0312052616200044
540.968365212207020.06326957558596080.0316347877929804
550.9708415794645370.05831684107092640.0291584205354632
560.962727464570920.07454507085816050.0372725354290802
570.960766846377250.07846630724549910.0392331536227495
580.9503200460622050.09935990787558930.0496799539377946
590.9669423573168180.06611528536636350.0330576426831817
600.9628833254956610.07423334900867790.0371166745043390
610.9729973055822110.05400538883557730.0270026944177886
620.9696178055973080.06076438880538350.0303821944026918
630.981939547745560.03612090450887970.0180604522544398
640.9764022689433610.04719546211327770.0235977310566388
650.9713709290024280.05725814199514310.0286290709975716
660.9940034626383680.01199307472326370.00599653736163187
670.9933981609443230.01320367811135400.00660183905567702
680.9938436918064390.01231261638712290.00615630819356147
690.9919863617430550.01602727651389100.00801363825694548
700.9901665925376280.01966681492474440.00983340746237221
710.9867701920197290.02645961596054210.0132298079802710
720.989744056333960.02051188733208130.0102559436660407
730.9866278349963080.02674433000738360.0133721650036918
740.9827508263003670.03449834739926650.0172491736996333
750.980313428322970.0393731433540580.019686571677029
760.974149369728990.05170126054202040.0258506302710102
770.9802953615608430.03940927687831340.0197046384391567
780.9749101546253750.05017969074925090.0250898453746255
790.9719045864792460.0561908270415070.0280954135207535
800.9760448978349060.04791020433018870.0239551021650943
810.968703505398820.06259298920235980.0312964946011799
820.9597144678024540.08057106439509190.0402855321975460
830.9507625339416350.09847493211673070.0492374660583653
840.9462698230934540.1074603538130910.0537301769065455
850.9338782688174730.1322434623650530.0661217311825267
860.9175720791561320.1648558416877360.0824279208438682
870.899748079995740.2005038400085200.100251920004260
880.8785038550460420.2429922899079160.121496144953958
890.85365289995440.29269420009120.1463471000456
900.8951665175773660.2096669648452680.104833482422634
910.9230181110354490.1539637779291030.0769818889645513
920.9045262800049470.1909474399901070.0954737199950534
930.886388239952360.2272235200952810.113611760047641
940.8712942344443820.2574115311112350.128705765555618
950.8647647938025350.2704704123949300.135235206197465
960.8413243632159820.3173512735680370.158675636784018
970.8171745966030260.3656508067939490.182825403396974
980.7937333463640880.4125333072718240.206266653635912
990.7956677934839210.4086644130321580.204332206516079
1000.760314451430320.479371097139360.23968554856968
1010.7501390081275130.4997219837449740.249860991872487
1020.7208492101856430.5583015796287150.279150789814357
1030.7026445689397770.5947108621204460.297355431060223
1040.801761522134140.396476955731720.19823847786586
1050.818609730895840.3627805382083210.181390269104161
1060.8481273872336130.3037452255327750.151872612766387
1070.8238400168531260.3523199662937490.176159983146874
1080.8297468012804260.3405063974391480.170253198719574
1090.884273387446420.2314532251071610.115726612553580
1100.8601015520259110.2797968959481780.139898447974089
1110.8574856635587650.2850286728824700.142514336441235
1120.8506532185824090.2986935628351820.149346781417591
1130.8448104597929050.310379080414190.155189540207095
1140.8209381696316350.3581236607367290.179061830368365
1150.8868806055502860.2262387888994270.113119394449714
1160.8615960804806040.2768078390387920.138403919519396
1170.8423517119706070.3152965760587860.157648288029393
1180.8088264383879240.3823471232241510.191173561612076
1190.7953905778317380.4092188443365240.204609422168262
1200.7644766588204380.4710466823591240.235523341179562
1210.7278867117584260.5442265764831480.272113288241574
1220.684500828628250.63099834274350.31549917137175
1230.6356511775984620.7286976448030760.364348822401538
1240.5930581677040780.8138836645918440.406941832295922
1250.5380031628643350.923993674271330.461996837135665
1260.5153760744840440.9692478510319110.484623925515956
1270.4612490306407610.9224980612815230.538750969359239
1280.4373039749972410.8746079499944820.562696025002759
1290.6349632874825150.7300734250349710.365036712517486
1300.5934604856616810.8130790286766370.406539514338319
1310.5914386945137710.8171226109724590.408561305486229
1320.580292714188350.83941457162330.41970728581165
1330.5184405100651160.9631189798697690.481559489934884
1340.5078383178852370.9843233642295260.492161682114763
1350.4590782763272370.9181565526544740.540921723672763
1360.4959887237106520.9919774474213030.504011276289348
1370.4541800686332830.9083601372665650.545819931366717
1380.395372665744390.790745331488780.60462733425561
1390.3530365923147130.7060731846294260.646963407685287
1400.2873698368633650.5747396737267290.712630163136635
1410.2838933704268120.5677867408536230.716106629573188
1420.2236700796697770.4473401593395540.776329920330223
1430.2527240794767390.5054481589534780.747275920523261
1440.1920409936441470.3840819872882940.807959006355853
1450.1478488009935970.2956976019871930.852151199006403
1460.1084841522672550.2169683045345090.891515847732745
1470.1978109559697290.3956219119394590.80218904403027
1480.6319139124585670.7361721750828660.368086087541433
1490.6762650624951840.6474698750096320.323734937504816
1500.5708998135538490.8582003728923010.429100186446151
1510.5047228317494530.9905543365010950.495277168250547
1520.3630772990538510.7261545981077020.636922700946149
1530.2661378213806960.5322756427613920.733862178619304






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.0620689655172414NOK
5% type I error level420.289655172413793NOK
10% type I error level680.468965517241379NOK

\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 & 9 & 0.0620689655172414 & NOK \tabularnewline
5% type I error level & 42 & 0.289655172413793 & NOK \tabularnewline
10% type I error level & 68 & 0.468965517241379 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114423&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]9[/C][C]0.0620689655172414[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.289655172413793[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.468965517241379[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114423&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114423&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 level90.0620689655172414NOK
5% type I error level420.289655172413793NOK
10% type I error level680.468965517241379NOK


Figures (Output of Computation)

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

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