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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 10:06:28 +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/Nov/23/t1290506720kvll7phnhcg4i2s.htm/, Retrieved Thu, 25 Apr 2024 23:08:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98888, Retrieved Thu, 25 Apr 2024 23:08:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
F   PD    [Multiple Regression] [ws 7 Popularity] [2010-11-23 10:06:28] [350231caf55a86a218fd48dc4d2e2f8b] [Current]
-    D      [Multiple Regression] [Social Visible Te...] [2010-11-24 07:12:43] [d59201e34006b7e3f71c33fa566f42b3]
-   PD        [Multiple Regression] [Extra parameter m...] [2010-11-24 08:14:05] [d59201e34006b7e3f71c33fa566f42b3]
F               [Multiple Regression] [Liniear Trend on ...] [2010-11-24 08:24:56] [d59201e34006b7e3f71c33fa566f42b3]
-   P             [Multiple Regression] [] [2010-12-02 15:26:11] [8e0d27d3447b6ae48398467ddbde7cca]
-               [Multiple Regression] [] [2010-12-02 15:23:25] [8e0d27d3447b6ae48398467ddbde7cca]
-             [Multiple Regression] [] [2010-12-02 15:07:12] [8e0d27d3447b6ae48398467ddbde7cca]
Feedback Forum
2010-11-27 16:20:35 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student geeft hier op een correcte manier de software gebruikt. Ook de interpretatie van de verkregen resultaten is zeer goed en gebeurde op een duidelijke en logische manier.

Het enige waaraan de student geen aandacht heeft besteed zijn de voorwaarden die moeten voldaan zijn om dit model te mogen gebruiken om voorspellingen te maken. Zo moeten de residu's (verschil tussen de werkelijke waarden en datgene wat voorspeld wordt op basis van het model) normaal verdeeld zijn en moet het gemiddelde van deze residu's doorheen de tijd nul zijn. Dit kan men zien door op de grafiek 'residuals' een denkbeeldige lijn te trekken door nul. Nu kan men zien of de overschattingen en onderschattingen die gemaakt worden al dan niet systematisch zijn. Ten slotte mag er ook geen autocorrelatie zijn. Wanneer we naar dit model kijken zien we dat aan deze voorwaarden vrij goed voldaan is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3
12	12	8	13	5
15	10	12	16	6
12	9	7	12	6
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5	15	8	12	3
8	11	10	12	4
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6	9	15	11	6
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13	12	7	12	5
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5	14	4	5	2
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13	12	13	13	5
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11	6	14	17	6
12	10	12	13	6
12	12	15	13	6
12	13	14	12	5
12	8	13	13	5
14	12	8	14	4
6	12	6	11	2
7	12	7	12	4
14	6	13	12	6
14	11	13	16	6
10	10	11	12	5
13	12	5	12	3
12	13	12	12	6
9	11	8	10	4
12	7	11	15	5
16	11	14	15	8
10	11	9	12	4
14	11	10	16	6
10	11	13	15	6
16	12	16	16	7
15	10	16	13	6
12	11	11	12	5
10	12	8	11	4
8	7	4	13	6
8	13	7	10	3
11	8	14	15	5
13	12	11	13	6
16	11	17	16	7
16	12	15	15	7
14	14	17	18	6
11	10	5	13	3
4	10	4	10	2
14	13	10	16	8
9	10	11	13	3
14	11	15	15	8
8	10	10	14	3
8	7	9	15	4
11	10	12	14	5
12	8	15	13	7
11	12	7	13	6
14	12	13	15	6
15	12	12	16	7
16	11	14	14	6
16	12	14	14	6
11	12	8	16	6
14	12	15	14	6
14	11	12	12	4
12	12	12	13	4
14	11	16	12	5
8	11	9	12	4
13	13	15	14	6
16	12	15	14	6
12	12	6	14	5
16	12	14	16	8
12	12	15	13	6
11	8	10	14	5
4	8	6	4	4
16	12	14	16	8
15	11	12	13	6
10	12	8	16	4
13	13	11	15	6
15	12	13	14	6
12	12	9	13	4
14	11	15	14	6
7	12	13	12	3
19	12	15	15	6
12	10	14	14	5
12	11	16	13	4
13	12	14	14	6
15	12	14	16	4
8	10	10	6	4
12	12	10	13	4
10	13	4	13	6
8	12	8	14	5
10	15	15	15	6
15	11	16	14	6
16	12	12	15	8
13	11	12	13	7
16	12	15	16	7
9	11	9	12	4
14	10	12	15	6
14	11	14	12	6
12	11	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time28 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 28 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&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]28 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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 time28 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.303577528406638 + 0.0945470254137573FindingFriends[t] + 0.243821570238501KnowingPeople[t] + 0.348903064267891Liked[t] + 0.627086448309146Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.303577528406638 +  0.0945470254137573FindingFriends[t] +  0.243821570238501KnowingPeople[t] +  0.348903064267891Liked[t] +  0.627086448309146Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.303577528406638 +  0.0945470254137573FindingFriends[t] +  0.243821570238501KnowingPeople[t] +  0.348903064267891Liked[t] +  0.627086448309146Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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
Popularity[t] = + 0.303577528406638 + 0.0945470254137573FindingFriends[t] + 0.243821570238501KnowingPeople[t] + 0.348903064267891Liked[t] + 0.627086448309146Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3035775284066381.4251180.2130.8315990.4158
FindingFriends0.09454702541375730.0959580.98530.3260540.163027
KnowingPeople0.2438215702385010.0613743.97270.000115.5e-05
Liked0.3489030642678910.0964793.61640.0004070.000203
Celebrity0.6270864483091460.1560334.01899.2e-054.6e-05

\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) & 0.303577528406638 & 1.425118 & 0.213 & 0.831599 & 0.4158 \tabularnewline
FindingFriends & 0.0945470254137573 & 0.095958 & 0.9853 & 0.326054 & 0.163027 \tabularnewline
KnowingPeople & 0.243821570238501 & 0.061374 & 3.9727 & 0.00011 & 5.5e-05 \tabularnewline
Liked & 0.348903064267891 & 0.096479 & 3.6164 & 0.000407 & 0.000203 \tabularnewline
Celebrity & 0.627086448309146 & 0.156033 & 4.0189 & 9.2e-05 & 4.6e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&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]0.303577528406638[/C][C]1.425118[/C][C]0.213[/C][C]0.831599[/C][C]0.4158[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0945470254137573[/C][C]0.095958[/C][C]0.9853[/C][C]0.326054[/C][C]0.163027[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.243821570238501[/C][C]0.061374[/C][C]3.9727[/C][C]0.00011[/C][C]5.5e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.348903064267891[/C][C]0.096479[/C][C]3.6164[/C][C]0.000407[/C][C]0.000203[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.627086448309146[/C][C]0.156033[/C][C]4.0189[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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)0.3035775284066381.4251180.2130.8315990.4158
FindingFriends0.09454702541375730.0959580.98530.3260540.163027
KnowingPeople0.2438215702385010.0613743.97270.000115.5e-05
Liked0.3489030642678910.0964793.61640.0004070.000203
Celebrity0.6270864483091460.1560334.01899.2e-054.6e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.706541003093969
R-squared0.499200189053031
Adjusted R-squared0.485933968895496
F-TEST (value)37.6294214271328
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10551474685275
Sum Squared Residuals669.412044731377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.706541003093969 \tabularnewline
R-squared & 0.499200189053031 \tabularnewline
Adjusted R-squared & 0.485933968895496 \tabularnewline
F-TEST (value) & 37.6294214271328 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10551474685275 \tabularnewline
Sum Squared Residuals & 669.412044731377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.706541003093969[/C][/ROW]
[ROW][C]R-squared[/C][C]0.499200189053031[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.485933968895496[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.6294214271328[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.10551474685275[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.412044731377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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 R0.706541003093969
R-squared0.499200189053031
Adjusted R-squared0.485933968895496
F-TEST (value)37.6294214271328
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10551474685275
Sum Squared Residuals669.412044731377






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.36319002253451.63680997746553
21211.05988647230800.940113527691957
31513.51987434354731.48012565645266
41210.81060720986951.18939279013048
51010.6606294334218-0.66062943342175
6129.212988941183372.78701105881663
71516.7307807258967-1.73078072589670
8910.4664586061786-1.46645860617862
91212.2446325096978-0.244632509697798
10117.98226247396783.01773752603220
111113.3246193688968-2.32461936889679
121112.0695345670648-1.06953456706481
131512.21810588026652.78189411973348
14711.2482772915125-4.24827729151253
151111.6322454012062-0.632245401206208
161110.95410174665560.045898253344375
171012.0695345670648-2.06953456706481
181414.3458860796766-0.345886079676615
19108.698819171275081.30118082872492
2069.50608033863284-3.50608033863284
21118.651164480900842.34883551909916
221514.38024789347940.619752106520631
231111.6253812457601-0.625381245760124
24129.491172325224622.50882767477538
251413.39372391228670.6062760877133
261514.83352922015360.166470779846383
27914.6240694637179-5.62406946371787
281312.49353085635190.506469143648084
291313.2151643300748-0.215164330074818
301610.78170308826685.21829691173321
31138.499190651831934.50080934816807
321213.5878947395298-1.58789473952983
331414.4747949188931-0.474794918893127
341110.05461192234390.945388077656125
35910.4664586061786-1.46645860617862
361614.24080458564721.75919541435277
371212.8724221896300-0.872422189629978
38109.19153908816770.8084609118323
391313.0553553166344-0.0553553166344419
401615.21679409822430.783205901775738
411412.90608077180971.09391922819030
42158.09280166019726.9071983398028
4359.74045158766314-4.74045158766314
44810.4769930747943-2.47699307479426
451111.2435228309355-0.243522830935547
461613.78752325897302.21247674102702
471713.02099350283173.97900649716831
4898.484282638423720.515717361576282
49911.9406257278483-2.94062572784829
501314.6842546753289-1.68425467532887
511010.9555082099017-0.955508209901692
52612.4122767075096-6.41227670750965
531212.3840758175299-0.384075817529942
54810.4328000239989-2.43280002399890
551412.37354134891431.62645865108569
561212.4282688683253-0.428268868325296
571110.77554216444370.224457835556256
581614.40061359908761.5993864009124
59810.2695293709796-2.26952937097964
601515.251155912027-0.251155912027016
6179.45681051142187-2.45681051142187
621613.98644854679312.01355145320691
631413.73646605273150.263533947268459
641613.75316144517022.24683855482978
6599.93969919132189-0.939699191321885
661412.27899432350061.72100567649945
671113.0448208480188-2.04482084801881
681310.46716183780172.53283816219834
691512.90608077180972.09391922819030
7055.60121038311100-0.601210383110995
711512.42826886832532.57173113167470
721312.27899432350060.721005676499447
731112.0457072218777-1.04570722187768
741113.9782324466372-2.97823244663722
751212.4731651507437-0.473165150743683
761213.3937239122867-1.3937239122867
771212.2684598548849-0.268459854884919
781211.90080622184550.099193778154476
791410.78170308826683.21829691173321
8067.99317785836783-1.99317785836783
8179.84007538949251-2.84007538949251
821411.98989555505932.01010444494074
831413.85824293919960.141757060800387
841011.2533540679281-1.25335406792815
85138.725345800706364.27465419929364
861212.4079031627171-0.407903162717064
8799.29154380578147-0.291543805781473
881212.0164221844905-0.0164221844905469
891615.00733434178850.992665658211485
901010.2331715045558-0.233171504555755
911413.12677822848410.873221771515889
921013.5093398749317-3.50933987493172
931615.31134112363800.688658876361981
941513.44845143169771.55154856830231
951211.34790109334190.652098906658097
96109.734993895463120.265006104536879
97810.2389515125944-2.23895151259441
9888.60972983806134-0.609729838061341
991112.8424339206198-1.84243392061981
1001312.41843763133270.581562368667303
1011615.46061566846280.539384331537237
1021614.71861648913161.28138351086837
1031415.8149764249307-1.81497642493067
104118.885154814146742.11484518585326
10546.96753760279542-2.96753760279542
1061414.5700451759299-0.570045175929917
107910.3480842355777-1.34808423557775
1081415.251155912027-1.25115591202702
109810.4531657296071-2.45316572960713
110810.9016925957044-2.9016925957044
1111112.1949817667024-1.19498176670243
1121213.6426222589408-1.64262225894082
1131111.4431513503787-0.443151350378693
1141413.60388690034550.39611309965452
1151514.33605484268400.663945157315984
1161613.40425838090232.59574161909767
1171613.49880540631612.50119459368391
1181112.7336821134209-1.73368211342087
1191413.74262697655460.257373023445409
1201410.96463621527133.03536378472874
1211211.40808630495290.591913695047094
1221412.56700894453441.43299105546559
123810.2331715045558-2.23317150455576
1241313.8371740019683-0.837174001968348
1251613.74262697655462.25737302344541
1261210.92114639609891.07885360390106
1271615.45078443147020.549215568529837
1281213.3937239122867-1.3937239122867
1291111.5182445753979-0.518244575397912
13046.42684120345586-2.42684120345586
1311615.45078443147020.549215568529837
1321512.56771217615742.43228782384256
1331011.4795092168026-1.47950921680258
1341313.2107907852822-0.210790785282235
1351513.25498383607761.74501616392241
1361210.67662159423741.32337840576260
1371413.64807995114080.351920048859167
138710.6759183626144-3.67591836261437
1391914.09153004082254.90846995917752
1401212.6826249071794-0.68262490717943
1411212.2888255604932-0.288825560493152
1421313.4988054063161-0.49880540631609
1431512.94243863823362.05756136176642
14488.28902766377315-0.289027663773155
1451210.92044316447591.07955683552410
1461010.8062336650769-0.806233665076948
147811.4087895365759-3.40878953657594
1481014.3751711170638-4.37517111706375
1491513.89190152137931.10809847862067
1501614.61423822672531.38576177327473
1511313.1947986244666-0.194798624466586
1521615.06751955339950.932480446600482
153910.2331715045558-1.23317150455576
1541413.17097127927950.829028720720535
1551412.70645225236661.29354774763345
1561210.16444787695021.83555212304975

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3631900225345 & 1.63680997746553 \tabularnewline
2 & 12 & 11.0598864723080 & 0.940113527691957 \tabularnewline
3 & 15 & 13.5198743435473 & 1.48012565645266 \tabularnewline
4 & 12 & 10.8106072098695 & 1.18939279013048 \tabularnewline
5 & 10 & 10.6606294334218 & -0.66062943342175 \tabularnewline
6 & 12 & 9.21298894118337 & 2.78701105881663 \tabularnewline
7 & 15 & 16.7307807258967 & -1.73078072589670 \tabularnewline
8 & 9 & 10.4664586061786 & -1.46645860617862 \tabularnewline
9 & 12 & 12.2446325096978 & -0.244632509697798 \tabularnewline
10 & 11 & 7.9822624739678 & 3.01773752603220 \tabularnewline
11 & 11 & 13.3246193688968 & -2.32461936889679 \tabularnewline
12 & 11 & 12.0695345670648 & -1.06953456706481 \tabularnewline
13 & 15 & 12.2181058802665 & 2.78189411973348 \tabularnewline
14 & 7 & 11.2482772915125 & -4.24827729151253 \tabularnewline
15 & 11 & 11.6322454012062 & -0.632245401206208 \tabularnewline
16 & 11 & 10.9541017466556 & 0.045898253344375 \tabularnewline
17 & 10 & 12.0695345670648 & -2.06953456706481 \tabularnewline
18 & 14 & 14.3458860796766 & -0.345886079676615 \tabularnewline
19 & 10 & 8.69881917127508 & 1.30118082872492 \tabularnewline
20 & 6 & 9.50608033863284 & -3.50608033863284 \tabularnewline
21 & 11 & 8.65116448090084 & 2.34883551909916 \tabularnewline
22 & 15 & 14.3802478934794 & 0.619752106520631 \tabularnewline
23 & 11 & 11.6253812457601 & -0.625381245760124 \tabularnewline
24 & 12 & 9.49117232522462 & 2.50882767477538 \tabularnewline
25 & 14 & 13.3937239122867 & 0.6062760877133 \tabularnewline
26 & 15 & 14.8335292201536 & 0.166470779846383 \tabularnewline
27 & 9 & 14.6240694637179 & -5.62406946371787 \tabularnewline
28 & 13 & 12.4935308563519 & 0.506469143648084 \tabularnewline
29 & 13 & 13.2151643300748 & -0.215164330074818 \tabularnewline
30 & 16 & 10.7817030882668 & 5.21829691173321 \tabularnewline
31 & 13 & 8.49919065183193 & 4.50080934816807 \tabularnewline
32 & 12 & 13.5878947395298 & -1.58789473952983 \tabularnewline
33 & 14 & 14.4747949188931 & -0.474794918893127 \tabularnewline
34 & 11 & 10.0546119223439 & 0.945388077656125 \tabularnewline
35 & 9 & 10.4664586061786 & -1.46645860617862 \tabularnewline
36 & 16 & 14.2408045856472 & 1.75919541435277 \tabularnewline
37 & 12 & 12.8724221896300 & -0.872422189629978 \tabularnewline
38 & 10 & 9.1915390881677 & 0.8084609118323 \tabularnewline
39 & 13 & 13.0553553166344 & -0.0553553166344419 \tabularnewline
40 & 16 & 15.2167940982243 & 0.783205901775738 \tabularnewline
41 & 14 & 12.9060807718097 & 1.09391922819030 \tabularnewline
42 & 15 & 8.0928016601972 & 6.9071983398028 \tabularnewline
43 & 5 & 9.74045158766314 & -4.74045158766314 \tabularnewline
44 & 8 & 10.4769930747943 & -2.47699307479426 \tabularnewline
45 & 11 & 11.2435228309355 & -0.243522830935547 \tabularnewline
46 & 16 & 13.7875232589730 & 2.21247674102702 \tabularnewline
47 & 17 & 13.0209935028317 & 3.97900649716831 \tabularnewline
48 & 9 & 8.48428263842372 & 0.515717361576282 \tabularnewline
49 & 9 & 11.9406257278483 & -2.94062572784829 \tabularnewline
50 & 13 & 14.6842546753289 & -1.68425467532887 \tabularnewline
51 & 10 & 10.9555082099017 & -0.955508209901692 \tabularnewline
52 & 6 & 12.4122767075096 & -6.41227670750965 \tabularnewline
53 & 12 & 12.3840758175299 & -0.384075817529942 \tabularnewline
54 & 8 & 10.4328000239989 & -2.43280002399890 \tabularnewline
55 & 14 & 12.3735413489143 & 1.62645865108569 \tabularnewline
56 & 12 & 12.4282688683253 & -0.428268868325296 \tabularnewline
57 & 11 & 10.7755421644437 & 0.224457835556256 \tabularnewline
58 & 16 & 14.4006135990876 & 1.5993864009124 \tabularnewline
59 & 8 & 10.2695293709796 & -2.26952937097964 \tabularnewline
60 & 15 & 15.251155912027 & -0.251155912027016 \tabularnewline
61 & 7 & 9.45681051142187 & -2.45681051142187 \tabularnewline
62 & 16 & 13.9864485467931 & 2.01355145320691 \tabularnewline
63 & 14 & 13.7364660527315 & 0.263533947268459 \tabularnewline
64 & 16 & 13.7531614451702 & 2.24683855482978 \tabularnewline
65 & 9 & 9.93969919132189 & -0.939699191321885 \tabularnewline
66 & 14 & 12.2789943235006 & 1.72100567649945 \tabularnewline
67 & 11 & 13.0448208480188 & -2.04482084801881 \tabularnewline
68 & 13 & 10.4671618378017 & 2.53283816219834 \tabularnewline
69 & 15 & 12.9060807718097 & 2.09391922819030 \tabularnewline
70 & 5 & 5.60121038311100 & -0.601210383110995 \tabularnewline
71 & 15 & 12.4282688683253 & 2.57173113167470 \tabularnewline
72 & 13 & 12.2789943235006 & 0.721005676499447 \tabularnewline
73 & 11 & 12.0457072218777 & -1.04570722187768 \tabularnewline
74 & 11 & 13.9782324466372 & -2.97823244663722 \tabularnewline
75 & 12 & 12.4731651507437 & -0.473165150743683 \tabularnewline
76 & 12 & 13.3937239122867 & -1.3937239122867 \tabularnewline
77 & 12 & 12.2684598548849 & -0.268459854884919 \tabularnewline
78 & 12 & 11.9008062218455 & 0.099193778154476 \tabularnewline
79 & 14 & 10.7817030882668 & 3.21829691173321 \tabularnewline
80 & 6 & 7.99317785836783 & -1.99317785836783 \tabularnewline
81 & 7 & 9.84007538949251 & -2.84007538949251 \tabularnewline
82 & 14 & 11.9898955550593 & 2.01010444494074 \tabularnewline
83 & 14 & 13.8582429391996 & 0.141757060800387 \tabularnewline
84 & 10 & 11.2533540679281 & -1.25335406792815 \tabularnewline
85 & 13 & 8.72534580070636 & 4.27465419929364 \tabularnewline
86 & 12 & 12.4079031627171 & -0.407903162717064 \tabularnewline
87 & 9 & 9.29154380578147 & -0.291543805781473 \tabularnewline
88 & 12 & 12.0164221844905 & -0.0164221844905469 \tabularnewline
89 & 16 & 15.0073343417885 & 0.992665658211485 \tabularnewline
90 & 10 & 10.2331715045558 & -0.233171504555755 \tabularnewline
91 & 14 & 13.1267782284841 & 0.873221771515889 \tabularnewline
92 & 10 & 13.5093398749317 & -3.50933987493172 \tabularnewline
93 & 16 & 15.3113411236380 & 0.688658876361981 \tabularnewline
94 & 15 & 13.4484514316977 & 1.55154856830231 \tabularnewline
95 & 12 & 11.3479010933419 & 0.652098906658097 \tabularnewline
96 & 10 & 9.73499389546312 & 0.265006104536879 \tabularnewline
97 & 8 & 10.2389515125944 & -2.23895151259441 \tabularnewline
98 & 8 & 8.60972983806134 & -0.609729838061341 \tabularnewline
99 & 11 & 12.8424339206198 & -1.84243392061981 \tabularnewline
100 & 13 & 12.4184376313327 & 0.581562368667303 \tabularnewline
101 & 16 & 15.4606156684628 & 0.539384331537237 \tabularnewline
102 & 16 & 14.7186164891316 & 1.28138351086837 \tabularnewline
103 & 14 & 15.8149764249307 & -1.81497642493067 \tabularnewline
104 & 11 & 8.88515481414674 & 2.11484518585326 \tabularnewline
105 & 4 & 6.96753760279542 & -2.96753760279542 \tabularnewline
106 & 14 & 14.5700451759299 & -0.570045175929917 \tabularnewline
107 & 9 & 10.3480842355777 & -1.34808423557775 \tabularnewline
108 & 14 & 15.251155912027 & -1.25115591202702 \tabularnewline
109 & 8 & 10.4531657296071 & -2.45316572960713 \tabularnewline
110 & 8 & 10.9016925957044 & -2.9016925957044 \tabularnewline
111 & 11 & 12.1949817667024 & -1.19498176670243 \tabularnewline
112 & 12 & 13.6426222589408 & -1.64262225894082 \tabularnewline
113 & 11 & 11.4431513503787 & -0.443151350378693 \tabularnewline
114 & 14 & 13.6038869003455 & 0.39611309965452 \tabularnewline
115 & 15 & 14.3360548426840 & 0.663945157315984 \tabularnewline
116 & 16 & 13.4042583809023 & 2.59574161909767 \tabularnewline
117 & 16 & 13.4988054063161 & 2.50119459368391 \tabularnewline
118 & 11 & 12.7336821134209 & -1.73368211342087 \tabularnewline
119 & 14 & 13.7426269765546 & 0.257373023445409 \tabularnewline
120 & 14 & 10.9646362152713 & 3.03536378472874 \tabularnewline
121 & 12 & 11.4080863049529 & 0.591913695047094 \tabularnewline
122 & 14 & 12.5670089445344 & 1.43299105546559 \tabularnewline
123 & 8 & 10.2331715045558 & -2.23317150455576 \tabularnewline
124 & 13 & 13.8371740019683 & -0.837174001968348 \tabularnewline
125 & 16 & 13.7426269765546 & 2.25737302344541 \tabularnewline
126 & 12 & 10.9211463960989 & 1.07885360390106 \tabularnewline
127 & 16 & 15.4507844314702 & 0.549215568529837 \tabularnewline
128 & 12 & 13.3937239122867 & -1.3937239122867 \tabularnewline
129 & 11 & 11.5182445753979 & -0.518244575397912 \tabularnewline
130 & 4 & 6.42684120345586 & -2.42684120345586 \tabularnewline
131 & 16 & 15.4507844314702 & 0.549215568529837 \tabularnewline
132 & 15 & 12.5677121761574 & 2.43228782384256 \tabularnewline
133 & 10 & 11.4795092168026 & -1.47950921680258 \tabularnewline
134 & 13 & 13.2107907852822 & -0.210790785282235 \tabularnewline
135 & 15 & 13.2549838360776 & 1.74501616392241 \tabularnewline
136 & 12 & 10.6766215942374 & 1.32337840576260 \tabularnewline
137 & 14 & 13.6480799511408 & 0.351920048859167 \tabularnewline
138 & 7 & 10.6759183626144 & -3.67591836261437 \tabularnewline
139 & 19 & 14.0915300408225 & 4.90846995917752 \tabularnewline
140 & 12 & 12.6826249071794 & -0.68262490717943 \tabularnewline
141 & 12 & 12.2888255604932 & -0.288825560493152 \tabularnewline
142 & 13 & 13.4988054063161 & -0.49880540631609 \tabularnewline
143 & 15 & 12.9424386382336 & 2.05756136176642 \tabularnewline
144 & 8 & 8.28902766377315 & -0.289027663773155 \tabularnewline
145 & 12 & 10.9204431644759 & 1.07955683552410 \tabularnewline
146 & 10 & 10.8062336650769 & -0.806233665076948 \tabularnewline
147 & 8 & 11.4087895365759 & -3.40878953657594 \tabularnewline
148 & 10 & 14.3751711170638 & -4.37517111706375 \tabularnewline
149 & 15 & 13.8919015213793 & 1.10809847862067 \tabularnewline
150 & 16 & 14.6142382267253 & 1.38576177327473 \tabularnewline
151 & 13 & 13.1947986244666 & -0.194798624466586 \tabularnewline
152 & 16 & 15.0675195533995 & 0.932480446600482 \tabularnewline
153 & 9 & 10.2331715045558 & -1.23317150455576 \tabularnewline
154 & 14 & 13.1709712792795 & 0.829028720720535 \tabularnewline
155 & 14 & 12.7064522523666 & 1.29354774763345 \tabularnewline
156 & 12 & 10.1644478769502 & 1.83555212304975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.3631900225345[/C][C]1.63680997746553[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0598864723080[/C][C]0.940113527691957[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5198743435473[/C][C]1.48012565645266[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8106072098695[/C][C]1.18939279013048[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.6606294334218[/C][C]-0.66062943342175[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.21298894118337[/C][C]2.78701105881663[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7307807258967[/C][C]-1.73078072589670[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.4664586061786[/C][C]-1.46645860617862[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.2446325096978[/C][C]-0.244632509697798[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.9822624739678[/C][C]3.01773752603220[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3246193688968[/C][C]-2.32461936889679[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.0695345670648[/C][C]-1.06953456706481[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2181058802665[/C][C]2.78189411973348[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.2482772915125[/C][C]-4.24827729151253[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.6322454012062[/C][C]-0.632245401206208[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9541017466556[/C][C]0.045898253344375[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.0695345670648[/C][C]-2.06953456706481[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3458860796766[/C][C]-0.345886079676615[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.69881917127508[/C][C]1.30118082872492[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.50608033863284[/C][C]-3.50608033863284[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.65116448090084[/C][C]2.34883551909916[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3802478934794[/C][C]0.619752106520631[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6253812457601[/C][C]-0.625381245760124[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.49117232522462[/C][C]2.50882767477538[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.3937239122867[/C][C]0.6062760877133[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.8335292201536[/C][C]0.166470779846383[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.6240694637179[/C][C]-5.62406946371787[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4935308563519[/C][C]0.506469143648084[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2151643300748[/C][C]-0.215164330074818[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.7817030882668[/C][C]5.21829691173321[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.49919065183193[/C][C]4.50080934816807[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5878947395298[/C][C]-1.58789473952983[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4747949188931[/C][C]-0.474794918893127[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0546119223439[/C][C]0.945388077656125[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4664586061786[/C][C]-1.46645860617862[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2408045856472[/C][C]1.75919541435277[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.8724221896300[/C][C]-0.872422189629978[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.1915390881677[/C][C]0.8084609118323[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.0553553166344[/C][C]-0.0553553166344419[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2167940982243[/C][C]0.783205901775738[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9060807718097[/C][C]1.09391922819030[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.0928016601972[/C][C]6.9071983398028[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.74045158766314[/C][C]-4.74045158766314[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.4769930747943[/C][C]-2.47699307479426[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2435228309355[/C][C]-0.243522830935547[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7875232589730[/C][C]2.21247674102702[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.0209935028317[/C][C]3.97900649716831[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.48428263842372[/C][C]0.515717361576282[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.9406257278483[/C][C]-2.94062572784829[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6842546753289[/C][C]-1.68425467532887[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9555082099017[/C][C]-0.955508209901692[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4122767075096[/C][C]-6.41227670750965[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.3840758175299[/C][C]-0.384075817529942[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4328000239989[/C][C]-2.43280002399890[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3735413489143[/C][C]1.62645865108569[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4282688683253[/C][C]-0.428268868325296[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7755421644437[/C][C]0.224457835556256[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4006135990876[/C][C]1.5993864009124[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2695293709796[/C][C]-2.26952937097964[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.251155912027[/C][C]-0.251155912027016[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.45681051142187[/C][C]-2.45681051142187[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.9864485467931[/C][C]2.01355145320691[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.7364660527315[/C][C]0.263533947268459[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7531614451702[/C][C]2.24683855482978[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.93969919132189[/C][C]-0.939699191321885[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2789943235006[/C][C]1.72100567649945[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.0448208480188[/C][C]-2.04482084801881[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4671618378017[/C][C]2.53283816219834[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9060807718097[/C][C]2.09391922819030[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.60121038311100[/C][C]-0.601210383110995[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4282688683253[/C][C]2.57173113167470[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.2789943235006[/C][C]0.721005676499447[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.0457072218777[/C][C]-1.04570722187768[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9782324466372[/C][C]-2.97823244663722[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4731651507437[/C][C]-0.473165150743683[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.3937239122867[/C][C]-1.3937239122867[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.2684598548849[/C][C]-0.268459854884919[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9008062218455[/C][C]0.099193778154476[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7817030882668[/C][C]3.21829691173321[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.99317785836783[/C][C]-1.99317785836783[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.84007538949251[/C][C]-2.84007538949251[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.9898955550593[/C][C]2.01010444494074[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8582429391996[/C][C]0.141757060800387[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2533540679281[/C][C]-1.25335406792815[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.72534580070636[/C][C]4.27465419929364[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4079031627171[/C][C]-0.407903162717064[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.29154380578147[/C][C]-0.291543805781473[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0164221844905[/C][C]-0.0164221844905469[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0073343417885[/C][C]0.992665658211485[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2331715045558[/C][C]-0.233171504555755[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1267782284841[/C][C]0.873221771515889[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5093398749317[/C][C]-3.50933987493172[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3113411236380[/C][C]0.688658876361981[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4484514316977[/C][C]1.55154856830231[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.3479010933419[/C][C]0.652098906658097[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.73499389546312[/C][C]0.265006104536879[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2389515125944[/C][C]-2.23895151259441[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.60972983806134[/C][C]-0.609729838061341[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.8424339206198[/C][C]-1.84243392061981[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.4184376313327[/C][C]0.581562368667303[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4606156684628[/C][C]0.539384331537237[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7186164891316[/C][C]1.28138351086837[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.8149764249307[/C][C]-1.81497642493067[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.88515481414674[/C][C]2.11484518585326[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.96753760279542[/C][C]-2.96753760279542[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5700451759299[/C][C]-0.570045175929917[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3480842355777[/C][C]-1.34808423557775[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.251155912027[/C][C]-1.25115591202702[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4531657296071[/C][C]-2.45316572960713[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.9016925957044[/C][C]-2.9016925957044[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1949817667024[/C][C]-1.19498176670243[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6426222589408[/C][C]-1.64262225894082[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4431513503787[/C][C]-0.443151350378693[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.6038869003455[/C][C]0.39611309965452[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.3360548426840[/C][C]0.663945157315984[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4042583809023[/C][C]2.59574161909767[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4988054063161[/C][C]2.50119459368391[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7336821134209[/C][C]-1.73368211342087[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7426269765546[/C][C]0.257373023445409[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9646362152713[/C][C]3.03536378472874[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.4080863049529[/C][C]0.591913695047094[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5670089445344[/C][C]1.43299105546559[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.2331715045558[/C][C]-2.23317150455576[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.8371740019683[/C][C]-0.837174001968348[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.7426269765546[/C][C]2.25737302344541[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.9211463960989[/C][C]1.07885360390106[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.4507844314702[/C][C]0.549215568529837[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.3937239122867[/C][C]-1.3937239122867[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.5182445753979[/C][C]-0.518244575397912[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.42684120345586[/C][C]-2.42684120345586[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.4507844314702[/C][C]0.549215568529837[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5677121761574[/C][C]2.43228782384256[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4795092168026[/C][C]-1.47950921680258[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.2107907852822[/C][C]-0.210790785282235[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.2549838360776[/C][C]1.74501616392241[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.6766215942374[/C][C]1.32337840576260[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6480799511408[/C][C]0.351920048859167[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.6759183626144[/C][C]-3.67591836261437[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0915300408225[/C][C]4.90846995917752[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.6826249071794[/C][C]-0.68262490717943[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2888255604932[/C][C]-0.288825560493152[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.4988054063161[/C][C]-0.49880540631609[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.9424386382336[/C][C]2.05756136176642[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.28902766377315[/C][C]-0.289027663773155[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9204431644759[/C][C]1.07955683552410[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.8062336650769[/C][C]-0.806233665076948[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4087895365759[/C][C]-3.40878953657594[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3751711170638[/C][C]-4.37517111706375[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8919015213793[/C][C]1.10809847862067[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.6142382267253[/C][C]1.38576177327473[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1947986244666[/C][C]-0.194798624466586[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0675195533995[/C][C]0.932480446600482[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.2331715045558[/C][C]-1.23317150455576[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.1709712792795[/C][C]0.829028720720535[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.7064522523666[/C][C]1.29354774763345[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1644478769502[/C][C]1.83555212304975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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
11311.36319002253451.63680997746553
21211.05988647230800.940113527691957
31513.51987434354731.48012565645266
41210.81060720986951.18939279013048
51010.6606294334218-0.66062943342175
6129.212988941183372.78701105881663
71516.7307807258967-1.73078072589670
8910.4664586061786-1.46645860617862
91212.2446325096978-0.244632509697798
10117.98226247396783.01773752603220
111113.3246193688968-2.32461936889679
121112.0695345670648-1.06953456706481
131512.21810588026652.78189411973348
14711.2482772915125-4.24827729151253
151111.6322454012062-0.632245401206208
161110.95410174665560.045898253344375
171012.0695345670648-2.06953456706481
181414.3458860796766-0.345886079676615
19108.698819171275081.30118082872492
2069.50608033863284-3.50608033863284
21118.651164480900842.34883551909916
221514.38024789347940.619752106520631
231111.6253812457601-0.625381245760124
24129.491172325224622.50882767477538
251413.39372391228670.6062760877133
261514.83352922015360.166470779846383
27914.6240694637179-5.62406946371787
281312.49353085635190.506469143648084
291313.2151643300748-0.215164330074818
301610.78170308826685.21829691173321
31138.499190651831934.50080934816807
321213.5878947395298-1.58789473952983
331414.4747949188931-0.474794918893127
341110.05461192234390.945388077656125
35910.4664586061786-1.46645860617862
361614.24080458564721.75919541435277
371212.8724221896300-0.872422189629978
38109.19153908816770.8084609118323
391313.0553553166344-0.0553553166344419
401615.21679409822430.783205901775738
411412.90608077180971.09391922819030
42158.09280166019726.9071983398028
4359.74045158766314-4.74045158766314
44810.4769930747943-2.47699307479426
451111.2435228309355-0.243522830935547
461613.78752325897302.21247674102702
471713.02099350283173.97900649716831
4898.484282638423720.515717361576282
49911.9406257278483-2.94062572784829
501314.6842546753289-1.68425467532887
511010.9555082099017-0.955508209901692
52612.4122767075096-6.41227670750965
531212.3840758175299-0.384075817529942
54810.4328000239989-2.43280002399890
551412.37354134891431.62645865108569
561212.4282688683253-0.428268868325296
571110.77554216444370.224457835556256
581614.40061359908761.5993864009124
59810.2695293709796-2.26952937097964
601515.251155912027-0.251155912027016
6179.45681051142187-2.45681051142187
621613.98644854679312.01355145320691
631413.73646605273150.263533947268459
641613.75316144517022.24683855482978
6599.93969919132189-0.939699191321885
661412.27899432350061.72100567649945
671113.0448208480188-2.04482084801881
681310.46716183780172.53283816219834
691512.90608077180972.09391922819030
7055.60121038311100-0.601210383110995
711512.42826886832532.57173113167470
721312.27899432350060.721005676499447
731112.0457072218777-1.04570722187768
741113.9782324466372-2.97823244663722
751212.4731651507437-0.473165150743683
761213.3937239122867-1.3937239122867
771212.2684598548849-0.268459854884919
781211.90080622184550.099193778154476
791410.78170308826683.21829691173321
8067.99317785836783-1.99317785836783
8179.84007538949251-2.84007538949251
821411.98989555505932.01010444494074
831413.85824293919960.141757060800387
841011.2533540679281-1.25335406792815
85138.725345800706364.27465419929364
861212.4079031627171-0.407903162717064
8799.29154380578147-0.291543805781473
881212.0164221844905-0.0164221844905469
891615.00733434178850.992665658211485
901010.2331715045558-0.233171504555755
911413.12677822848410.873221771515889
921013.5093398749317-3.50933987493172
931615.31134112363800.688658876361981
941513.44845143169771.55154856830231
951211.34790109334190.652098906658097
96109.734993895463120.265006104536879
97810.2389515125944-2.23895151259441
9888.60972983806134-0.609729838061341
991112.8424339206198-1.84243392061981
1001312.41843763133270.581562368667303
1011615.46061566846280.539384331537237
1021614.71861648913161.28138351086837
1031415.8149764249307-1.81497642493067
104118.885154814146742.11484518585326
10546.96753760279542-2.96753760279542
1061414.5700451759299-0.570045175929917
107910.3480842355777-1.34808423557775
1081415.251155912027-1.25115591202702
109810.4531657296071-2.45316572960713
110810.9016925957044-2.9016925957044
1111112.1949817667024-1.19498176670243
1121213.6426222589408-1.64262225894082
1131111.4431513503787-0.443151350378693
1141413.60388690034550.39611309965452
1151514.33605484268400.663945157315984
1161613.40425838090232.59574161909767
1171613.49880540631612.50119459368391
1181112.7336821134209-1.73368211342087
1191413.74262697655460.257373023445409
1201410.96463621527133.03536378472874
1211211.40808630495290.591913695047094
1221412.56700894453441.43299105546559
123810.2331715045558-2.23317150455576
1241313.8371740019683-0.837174001968348
1251613.74262697655462.25737302344541
1261210.92114639609891.07885360390106
1271615.45078443147020.549215568529837
1281213.3937239122867-1.3937239122867
1291111.5182445753979-0.518244575397912
13046.42684120345586-2.42684120345586
1311615.45078443147020.549215568529837
1321512.56771217615742.43228782384256
1331011.4795092168026-1.47950921680258
1341313.2107907852822-0.210790785282235
1351513.25498383607761.74501616392241
1361210.67662159423741.32337840576260
1371413.64807995114080.351920048859167
138710.6759183626144-3.67591836261437
1391914.09153004082254.90846995917752
1401212.6826249071794-0.68262490717943
1411212.2888255604932-0.288825560493152
1421313.4988054063161-0.49880540631609
1431512.94243863823362.05756136176642
14488.28902766377315-0.289027663773155
1451210.92044316447591.07955683552410
1461010.8062336650769-0.806233665076948
147811.4087895365759-3.40878953657594
1481014.3751711170638-4.37517111706375
1491513.89190152137931.10809847862067
1501614.61423822672531.38576177327473
1511313.1947986244666-0.194798624466586
1521615.06751955339950.932480446600482
153910.2331715045558-1.23317150455576
1541413.17097127927950.829028720720535
1551412.70645225236661.29354774763345
1561210.16444787695021.83555212304975






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.06025482791299410.1205096558259880.939745172087006
90.0406954098125640.0813908196251280.959304590187436
100.01701945403569110.03403890807138230.98298054596431
110.03324462410852530.06648924821705050.966755375891475
120.01685239051616030.03370478103232070.98314760948384
130.3400622488499660.6801244976999320.659937751150034
140.6873835015880530.6252329968238950.312616498411947
150.6012092341853730.7975815316292550.398790765814627
160.5105821672358610.9788356655282780.489417832764139
170.4535987466287950.907197493257590.546401253371205
180.3707235097170030.7414470194340060.629276490282997
190.3013179996571590.6026359993143190.698682000342841
200.5975462860960890.8049074278078230.402453713903911
210.5348627666316890.9302744667366220.465137233368311
220.5029403951166360.9941192097667280.497059604883364
230.4328018044117470.8656036088234950.567198195588253
240.4185839827894390.8371679655788780.581416017210561
250.3835480252706630.7670960505413250.616451974729338
260.3290064505902180.6580129011804350.670993549409782
270.589952522480590.8200949550388190.410047477519409
280.5317834451220430.9364331097559150.468216554877957
290.4704994501200950.940998900240190.529500549879905
300.6459148446975520.7081703106048950.354085155302448
310.7776194645504560.4447610708990870.222380535449544
320.7381322898998420.5237354202003160.261867710100158
330.6945479331478840.6109041337042330.305452066852116
340.6502482964694070.6995034070611860.349751703530593
350.6428795252159050.7142409495681890.357120474784095
360.6645268017999460.6709463964001070.335473198200054
370.6236498757545080.7527002484909840.376350124245492
380.5742704823215970.8514590353568060.425729517678403
390.5234171683315450.953165663336910.476582831668455
400.5065545935911010.9868908128177980.493445406408899
410.4782864758028270.9565729516056530.521713524197173
420.7847179161315720.4305641677368560.215282083868428
430.9463041867797540.1073916264404930.0536958132202463
440.9574280168490020.08514396630199580.0425719831509979
450.9450185032535570.1099629934928860.0549814967464428
460.9519147494393270.0961705011213460.048085250560673
470.9774773956164270.04504520876714680.0225226043835734
480.9704046328422150.05919073431557030.0295953671577851
490.9780464314618140.04390713707637240.0219535685381862
500.9749737380156960.05005252396860710.0250262619843035
510.969720419682740.0605591606345180.030279580317259
520.9971950944675510.005609811064897310.00280490553244866
530.9960151860001930.00796962799961490.00398481399980745
540.9970664581600680.005867083679864090.00293354183993205
550.9967770542126450.006445891574709370.00322294578735469
560.9954521634667750.009095673066450140.00454783653322507
570.9936983097251510.0126033805496980.006301690274849
580.9928244923345930.01435101533081340.00717550766540671
590.9948755413886040.01024891722279220.00512445861139612
600.9931078366989610.01378432660207710.00689216330103856
610.9943035599736130.01139288005277440.00569644002638722
620.994618210572370.01076357885525850.00538178942762927
630.9927116967501250.01457660649975080.00728830324987541
640.993145262741360.01370947451727910.00685473725863956
650.9915210335169250.01695793296615010.00847896648307504
660.9906400312074340.01871993758513180.00935996879256588
670.9905792676869370.01884146462612560.00942073231306278
680.9920527826438850.01589443471222920.0079472173561146
690.9921794112651230.01564117746975430.00782058873487714
700.9895339729612780.02093205407744400.0104660270387220
710.9911188159670930.01776236806581430.00888118403290715
720.988299003951780.02340199209643880.0117009960482194
730.9853847296478660.02923054070426820.0146152703521341
740.9896747351764060.02065052964718710.0103252648235935
750.9861518732867260.02769625342654830.0138481267132742
760.9839573921778190.03208521564436280.0160426078221814
770.978848235528330.04230352894333920.0211517644716696
780.9721454368926180.0557091262147640.027854563107382
790.9822727781780780.03545444364384430.0177272218219222
800.981837554459240.03632489108151890.0181624455407594
810.9854206847977940.02915863040441250.0145793152022063
820.9860931986057190.02781360278856260.0139068013942813
830.9813073644520550.03738527109588990.0186926355479449
840.9772777699119120.04544446017617690.0227222300880884
850.9937818370323720.01243632593525510.00621816296762756
860.991538283792010.01692343241598010.00846171620799004
870.98850468802550.02299062394899830.0114953119744991
880.9849102225631930.03017955487361350.0150897774368067
890.9808971934403340.03820561311933250.0191028065596663
900.9747753662833420.05044926743331540.0252246337166577
910.969338911513750.0613221769724980.030661088486249
920.9831730816758790.03365383664824280.0168269183241214
930.9780670412970310.04386591740593750.0219329587029687
940.9745862171231950.050827565753610.025413782876805
950.9676912335440750.06461753291184910.0323087664559245
960.9588710609814570.08225787803708510.0411289390185425
970.9560434051392860.08791318972142860.0439565948607143
980.9440023909730540.1119952180538920.0559976090269459
990.9415310416885330.1169379166229340.058468958311467
1000.927235535914430.1455289281711390.0727644640855697
1010.9096423520111910.1807152959776170.0903576479888087
1020.893626100634780.2127477987304410.106373899365220
1030.9046921340887070.1906157318225870.0953078659112934
1040.9332530481912390.1334939036175230.0667469518087613
1050.9330786262369960.1338427475260080.0669213737630038
1060.9163290922377540.1673418155244920.083670907762246
1070.9003312966006640.1993374067986720.0996687033993362
1080.8968412946739830.2063174106520340.103158705326017
1090.897519008070580.2049619838588400.102480991929420
1100.9195621391629540.1608757216740920.0804378608370458
1110.91161627005250.1767674598950010.0883837299475005
1120.9383769188566260.1232461622867480.0616230811433741
1130.9207273068043120.1585453863913770.0792726931956884
1140.8986139378332830.2027721243334330.101386062166717
1150.8725093718426540.2549812563146910.127490628157346
1160.8711970883278740.2576058233442520.128802911672126
1170.8779524793149630.2440950413700740.122047520685037
1180.8697217026380880.2605565947238240.130278297361912
1190.8368168781945850.3263662436108310.163183121805415
1200.8844673073692350.2310653852615310.115532692630765
1210.860662772722780.2786744545544390.139337227277219
1220.8387255467957450.3225489064085110.161274453204255
1230.83116045772160.3376790845567980.168839542278399
1240.7948497716061770.4103004567876470.205150228393823
1250.79485241066910.41029517866180.2051475893309
1260.7785030718169870.4429938563660260.221496928183013
1270.7300541982644710.5398916034710580.269945801735529
1280.7050093286279470.5899813427441060.294990671372053
1290.7229483003899980.5541033992200050.277051699610002
1300.7186493105007460.5627013789985080.281350689499254
1310.6625361731803120.6749276536393760.337463826819688
1320.6512050199096170.6975899601807660.348794980090383
1330.6250240742971170.7499518514057660.374975925702883
1340.5538353589992710.8923292820014580.446164641000729
1350.5290316728198140.9419366543603730.470968327180186
1360.5249689801089280.9500620397821440.475031019891072
1370.4518375920058130.9036751840116270.548162407994187
1380.5218511957828370.9562976084343260.478148804217163
1390.8500554446455770.2998891107088460.149944555354423
1400.877750977115010.2444980457699800.122249022884990
1410.8442131212104780.3115737575790440.155786878789522
1420.7772333154367740.4455333691264520.222766684563226
1430.7493126862805940.5013746274388110.250687313719406
1440.6522203170475290.6955593659049420.347779682952471
1450.6428770048645410.7142459902709180.357122995135459
1460.7639182410131730.4721635179736540.236081758986827
1470.7316741929776480.5366516140447050.268325807022352
1480.8524624487800220.2950751024399550.147537551219978

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0602548279129941 & 0.120509655825988 & 0.939745172087006 \tabularnewline
9 & 0.040695409812564 & 0.081390819625128 & 0.959304590187436 \tabularnewline
10 & 0.0170194540356911 & 0.0340389080713823 & 0.98298054596431 \tabularnewline
11 & 0.0332446241085253 & 0.0664892482170505 & 0.966755375891475 \tabularnewline
12 & 0.0168523905161603 & 0.0337047810323207 & 0.98314760948384 \tabularnewline
13 & 0.340062248849966 & 0.680124497699932 & 0.659937751150034 \tabularnewline
14 & 0.687383501588053 & 0.625232996823895 & 0.312616498411947 \tabularnewline
15 & 0.601209234185373 & 0.797581531629255 & 0.398790765814627 \tabularnewline
16 & 0.510582167235861 & 0.978835665528278 & 0.489417832764139 \tabularnewline
17 & 0.453598746628795 & 0.90719749325759 & 0.546401253371205 \tabularnewline
18 & 0.370723509717003 & 0.741447019434006 & 0.629276490282997 \tabularnewline
19 & 0.301317999657159 & 0.602635999314319 & 0.698682000342841 \tabularnewline
20 & 0.597546286096089 & 0.804907427807823 & 0.402453713903911 \tabularnewline
21 & 0.534862766631689 & 0.930274466736622 & 0.465137233368311 \tabularnewline
22 & 0.502940395116636 & 0.994119209766728 & 0.497059604883364 \tabularnewline
23 & 0.432801804411747 & 0.865603608823495 & 0.567198195588253 \tabularnewline
24 & 0.418583982789439 & 0.837167965578878 & 0.581416017210561 \tabularnewline
25 & 0.383548025270663 & 0.767096050541325 & 0.616451974729338 \tabularnewline
26 & 0.329006450590218 & 0.658012901180435 & 0.670993549409782 \tabularnewline
27 & 0.58995252248059 & 0.820094955038819 & 0.410047477519409 \tabularnewline
28 & 0.531783445122043 & 0.936433109755915 & 0.468216554877957 \tabularnewline
29 & 0.470499450120095 & 0.94099890024019 & 0.529500549879905 \tabularnewline
30 & 0.645914844697552 & 0.708170310604895 & 0.354085155302448 \tabularnewline
31 & 0.777619464550456 & 0.444761070899087 & 0.222380535449544 \tabularnewline
32 & 0.738132289899842 & 0.523735420200316 & 0.261867710100158 \tabularnewline
33 & 0.694547933147884 & 0.610904133704233 & 0.305452066852116 \tabularnewline
34 & 0.650248296469407 & 0.699503407061186 & 0.349751703530593 \tabularnewline
35 & 0.642879525215905 & 0.714240949568189 & 0.357120474784095 \tabularnewline
36 & 0.664526801799946 & 0.670946396400107 & 0.335473198200054 \tabularnewline
37 & 0.623649875754508 & 0.752700248490984 & 0.376350124245492 \tabularnewline
38 & 0.574270482321597 & 0.851459035356806 & 0.425729517678403 \tabularnewline
39 & 0.523417168331545 & 0.95316566333691 & 0.476582831668455 \tabularnewline
40 & 0.506554593591101 & 0.986890812817798 & 0.493445406408899 \tabularnewline
41 & 0.478286475802827 & 0.956572951605653 & 0.521713524197173 \tabularnewline
42 & 0.784717916131572 & 0.430564167736856 & 0.215282083868428 \tabularnewline
43 & 0.946304186779754 & 0.107391626440493 & 0.0536958132202463 \tabularnewline
44 & 0.957428016849002 & 0.0851439663019958 & 0.0425719831509979 \tabularnewline
45 & 0.945018503253557 & 0.109962993492886 & 0.0549814967464428 \tabularnewline
46 & 0.951914749439327 & 0.096170501121346 & 0.048085250560673 \tabularnewline
47 & 0.977477395616427 & 0.0450452087671468 & 0.0225226043835734 \tabularnewline
48 & 0.970404632842215 & 0.0591907343155703 & 0.0295953671577851 \tabularnewline
49 & 0.978046431461814 & 0.0439071370763724 & 0.0219535685381862 \tabularnewline
50 & 0.974973738015696 & 0.0500525239686071 & 0.0250262619843035 \tabularnewline
51 & 0.96972041968274 & 0.060559160634518 & 0.030279580317259 \tabularnewline
52 & 0.997195094467551 & 0.00560981106489731 & 0.00280490553244866 \tabularnewline
53 & 0.996015186000193 & 0.0079696279996149 & 0.00398481399980745 \tabularnewline
54 & 0.997066458160068 & 0.00586708367986409 & 0.00293354183993205 \tabularnewline
55 & 0.996777054212645 & 0.00644589157470937 & 0.00322294578735469 \tabularnewline
56 & 0.995452163466775 & 0.00909567306645014 & 0.00454783653322507 \tabularnewline
57 & 0.993698309725151 & 0.012603380549698 & 0.006301690274849 \tabularnewline
58 & 0.992824492334593 & 0.0143510153308134 & 0.00717550766540671 \tabularnewline
59 & 0.994875541388604 & 0.0102489172227922 & 0.00512445861139612 \tabularnewline
60 & 0.993107836698961 & 0.0137843266020771 & 0.00689216330103856 \tabularnewline
61 & 0.994303559973613 & 0.0113928800527744 & 0.00569644002638722 \tabularnewline
62 & 0.99461821057237 & 0.0107635788552585 & 0.00538178942762927 \tabularnewline
63 & 0.992711696750125 & 0.0145766064997508 & 0.00728830324987541 \tabularnewline
64 & 0.99314526274136 & 0.0137094745172791 & 0.00685473725863956 \tabularnewline
65 & 0.991521033516925 & 0.0169579329661501 & 0.00847896648307504 \tabularnewline
66 & 0.990640031207434 & 0.0187199375851318 & 0.00935996879256588 \tabularnewline
67 & 0.990579267686937 & 0.0188414646261256 & 0.00942073231306278 \tabularnewline
68 & 0.992052782643885 & 0.0158944347122292 & 0.0079472173561146 \tabularnewline
69 & 0.992179411265123 & 0.0156411774697543 & 0.00782058873487714 \tabularnewline
70 & 0.989533972961278 & 0.0209320540774440 & 0.0104660270387220 \tabularnewline
71 & 0.991118815967093 & 0.0177623680658143 & 0.00888118403290715 \tabularnewline
72 & 0.98829900395178 & 0.0234019920964388 & 0.0117009960482194 \tabularnewline
73 & 0.985384729647866 & 0.0292305407042682 & 0.0146152703521341 \tabularnewline
74 & 0.989674735176406 & 0.0206505296471871 & 0.0103252648235935 \tabularnewline
75 & 0.986151873286726 & 0.0276962534265483 & 0.0138481267132742 \tabularnewline
76 & 0.983957392177819 & 0.0320852156443628 & 0.0160426078221814 \tabularnewline
77 & 0.97884823552833 & 0.0423035289433392 & 0.0211517644716696 \tabularnewline
78 & 0.972145436892618 & 0.055709126214764 & 0.027854563107382 \tabularnewline
79 & 0.982272778178078 & 0.0354544436438443 & 0.0177272218219222 \tabularnewline
80 & 0.98183755445924 & 0.0363248910815189 & 0.0181624455407594 \tabularnewline
81 & 0.985420684797794 & 0.0291586304044125 & 0.0145793152022063 \tabularnewline
82 & 0.986093198605719 & 0.0278136027885626 & 0.0139068013942813 \tabularnewline
83 & 0.981307364452055 & 0.0373852710958899 & 0.0186926355479449 \tabularnewline
84 & 0.977277769911912 & 0.0454444601761769 & 0.0227222300880884 \tabularnewline
85 & 0.993781837032372 & 0.0124363259352551 & 0.00621816296762756 \tabularnewline
86 & 0.99153828379201 & 0.0169234324159801 & 0.00846171620799004 \tabularnewline
87 & 0.9885046880255 & 0.0229906239489983 & 0.0114953119744991 \tabularnewline
88 & 0.984910222563193 & 0.0301795548736135 & 0.0150897774368067 \tabularnewline
89 & 0.980897193440334 & 0.0382056131193325 & 0.0191028065596663 \tabularnewline
90 & 0.974775366283342 & 0.0504492674333154 & 0.0252246337166577 \tabularnewline
91 & 0.96933891151375 & 0.061322176972498 & 0.030661088486249 \tabularnewline
92 & 0.983173081675879 & 0.0336538366482428 & 0.0168269183241214 \tabularnewline
93 & 0.978067041297031 & 0.0438659174059375 & 0.0219329587029687 \tabularnewline
94 & 0.974586217123195 & 0.05082756575361 & 0.025413782876805 \tabularnewline
95 & 0.967691233544075 & 0.0646175329118491 & 0.0323087664559245 \tabularnewline
96 & 0.958871060981457 & 0.0822578780370851 & 0.0411289390185425 \tabularnewline
97 & 0.956043405139286 & 0.0879131897214286 & 0.0439565948607143 \tabularnewline
98 & 0.944002390973054 & 0.111995218053892 & 0.0559976090269459 \tabularnewline
99 & 0.941531041688533 & 0.116937916622934 & 0.058468958311467 \tabularnewline
100 & 0.92723553591443 & 0.145528928171139 & 0.0727644640855697 \tabularnewline
101 & 0.909642352011191 & 0.180715295977617 & 0.0903576479888087 \tabularnewline
102 & 0.89362610063478 & 0.212747798730441 & 0.106373899365220 \tabularnewline
103 & 0.904692134088707 & 0.190615731822587 & 0.0953078659112934 \tabularnewline
104 & 0.933253048191239 & 0.133493903617523 & 0.0667469518087613 \tabularnewline
105 & 0.933078626236996 & 0.133842747526008 & 0.0669213737630038 \tabularnewline
106 & 0.916329092237754 & 0.167341815524492 & 0.083670907762246 \tabularnewline
107 & 0.900331296600664 & 0.199337406798672 & 0.0996687033993362 \tabularnewline
108 & 0.896841294673983 & 0.206317410652034 & 0.103158705326017 \tabularnewline
109 & 0.89751900807058 & 0.204961983858840 & 0.102480991929420 \tabularnewline
110 & 0.919562139162954 & 0.160875721674092 & 0.0804378608370458 \tabularnewline
111 & 0.9116162700525 & 0.176767459895001 & 0.0883837299475005 \tabularnewline
112 & 0.938376918856626 & 0.123246162286748 & 0.0616230811433741 \tabularnewline
113 & 0.920727306804312 & 0.158545386391377 & 0.0792726931956884 \tabularnewline
114 & 0.898613937833283 & 0.202772124333433 & 0.101386062166717 \tabularnewline
115 & 0.872509371842654 & 0.254981256314691 & 0.127490628157346 \tabularnewline
116 & 0.871197088327874 & 0.257605823344252 & 0.128802911672126 \tabularnewline
117 & 0.877952479314963 & 0.244095041370074 & 0.122047520685037 \tabularnewline
118 & 0.869721702638088 & 0.260556594723824 & 0.130278297361912 \tabularnewline
119 & 0.836816878194585 & 0.326366243610831 & 0.163183121805415 \tabularnewline
120 & 0.884467307369235 & 0.231065385261531 & 0.115532692630765 \tabularnewline
121 & 0.86066277272278 & 0.278674454554439 & 0.139337227277219 \tabularnewline
122 & 0.838725546795745 & 0.322548906408511 & 0.161274453204255 \tabularnewline
123 & 0.8311604577216 & 0.337679084556798 & 0.168839542278399 \tabularnewline
124 & 0.794849771606177 & 0.410300456787647 & 0.205150228393823 \tabularnewline
125 & 0.7948524106691 & 0.4102951786618 & 0.2051475893309 \tabularnewline
126 & 0.778503071816987 & 0.442993856366026 & 0.221496928183013 \tabularnewline
127 & 0.730054198264471 & 0.539891603471058 & 0.269945801735529 \tabularnewline
128 & 0.705009328627947 & 0.589981342744106 & 0.294990671372053 \tabularnewline
129 & 0.722948300389998 & 0.554103399220005 & 0.277051699610002 \tabularnewline
130 & 0.718649310500746 & 0.562701378998508 & 0.281350689499254 \tabularnewline
131 & 0.662536173180312 & 0.674927653639376 & 0.337463826819688 \tabularnewline
132 & 0.651205019909617 & 0.697589960180766 & 0.348794980090383 \tabularnewline
133 & 0.625024074297117 & 0.749951851405766 & 0.374975925702883 \tabularnewline
134 & 0.553835358999271 & 0.892329282001458 & 0.446164641000729 \tabularnewline
135 & 0.529031672819814 & 0.941936654360373 & 0.470968327180186 \tabularnewline
136 & 0.524968980108928 & 0.950062039782144 & 0.475031019891072 \tabularnewline
137 & 0.451837592005813 & 0.903675184011627 & 0.548162407994187 \tabularnewline
138 & 0.521851195782837 & 0.956297608434326 & 0.478148804217163 \tabularnewline
139 & 0.850055444645577 & 0.299889110708846 & 0.149944555354423 \tabularnewline
140 & 0.87775097711501 & 0.244498045769980 & 0.122249022884990 \tabularnewline
141 & 0.844213121210478 & 0.311573757579044 & 0.155786878789522 \tabularnewline
142 & 0.777233315436774 & 0.445533369126452 & 0.222766684563226 \tabularnewline
143 & 0.749312686280594 & 0.501374627438811 & 0.250687313719406 \tabularnewline
144 & 0.652220317047529 & 0.695559365904942 & 0.347779682952471 \tabularnewline
145 & 0.642877004864541 & 0.714245990270918 & 0.357122995135459 \tabularnewline
146 & 0.763918241013173 & 0.472163517973654 & 0.236081758986827 \tabularnewline
147 & 0.731674192977648 & 0.536651614044705 & 0.268325807022352 \tabularnewline
148 & 0.852462448780022 & 0.295075102439955 & 0.147537551219978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0602548279129941[/C][C]0.120509655825988[/C][C]0.939745172087006[/C][/ROW]
[ROW][C]9[/C][C]0.040695409812564[/C][C]0.081390819625128[/C][C]0.959304590187436[/C][/ROW]
[ROW][C]10[/C][C]0.0170194540356911[/C][C]0.0340389080713823[/C][C]0.98298054596431[/C][/ROW]
[ROW][C]11[/C][C]0.0332446241085253[/C][C]0.0664892482170505[/C][C]0.966755375891475[/C][/ROW]
[ROW][C]12[/C][C]0.0168523905161603[/C][C]0.0337047810323207[/C][C]0.98314760948384[/C][/ROW]
[ROW][C]13[/C][C]0.340062248849966[/C][C]0.680124497699932[/C][C]0.659937751150034[/C][/ROW]
[ROW][C]14[/C][C]0.687383501588053[/C][C]0.625232996823895[/C][C]0.312616498411947[/C][/ROW]
[ROW][C]15[/C][C]0.601209234185373[/C][C]0.797581531629255[/C][C]0.398790765814627[/C][/ROW]
[ROW][C]16[/C][C]0.510582167235861[/C][C]0.978835665528278[/C][C]0.489417832764139[/C][/ROW]
[ROW][C]17[/C][C]0.453598746628795[/C][C]0.90719749325759[/C][C]0.546401253371205[/C][/ROW]
[ROW][C]18[/C][C]0.370723509717003[/C][C]0.741447019434006[/C][C]0.629276490282997[/C][/ROW]
[ROW][C]19[/C][C]0.301317999657159[/C][C]0.602635999314319[/C][C]0.698682000342841[/C][/ROW]
[ROW][C]20[/C][C]0.597546286096089[/C][C]0.804907427807823[/C][C]0.402453713903911[/C][/ROW]
[ROW][C]21[/C][C]0.534862766631689[/C][C]0.930274466736622[/C][C]0.465137233368311[/C][/ROW]
[ROW][C]22[/C][C]0.502940395116636[/C][C]0.994119209766728[/C][C]0.497059604883364[/C][/ROW]
[ROW][C]23[/C][C]0.432801804411747[/C][C]0.865603608823495[/C][C]0.567198195588253[/C][/ROW]
[ROW][C]24[/C][C]0.418583982789439[/C][C]0.837167965578878[/C][C]0.581416017210561[/C][/ROW]
[ROW][C]25[/C][C]0.383548025270663[/C][C]0.767096050541325[/C][C]0.616451974729338[/C][/ROW]
[ROW][C]26[/C][C]0.329006450590218[/C][C]0.658012901180435[/C][C]0.670993549409782[/C][/ROW]
[ROW][C]27[/C][C]0.58995252248059[/C][C]0.820094955038819[/C][C]0.410047477519409[/C][/ROW]
[ROW][C]28[/C][C]0.531783445122043[/C][C]0.936433109755915[/C][C]0.468216554877957[/C][/ROW]
[ROW][C]29[/C][C]0.470499450120095[/C][C]0.94099890024019[/C][C]0.529500549879905[/C][/ROW]
[ROW][C]30[/C][C]0.645914844697552[/C][C]0.708170310604895[/C][C]0.354085155302448[/C][/ROW]
[ROW][C]31[/C][C]0.777619464550456[/C][C]0.444761070899087[/C][C]0.222380535449544[/C][/ROW]
[ROW][C]32[/C][C]0.738132289899842[/C][C]0.523735420200316[/C][C]0.261867710100158[/C][/ROW]
[ROW][C]33[/C][C]0.694547933147884[/C][C]0.610904133704233[/C][C]0.305452066852116[/C][/ROW]
[ROW][C]34[/C][C]0.650248296469407[/C][C]0.699503407061186[/C][C]0.349751703530593[/C][/ROW]
[ROW][C]35[/C][C]0.642879525215905[/C][C]0.714240949568189[/C][C]0.357120474784095[/C][/ROW]
[ROW][C]36[/C][C]0.664526801799946[/C][C]0.670946396400107[/C][C]0.335473198200054[/C][/ROW]
[ROW][C]37[/C][C]0.623649875754508[/C][C]0.752700248490984[/C][C]0.376350124245492[/C][/ROW]
[ROW][C]38[/C][C]0.574270482321597[/C][C]0.851459035356806[/C][C]0.425729517678403[/C][/ROW]
[ROW][C]39[/C][C]0.523417168331545[/C][C]0.95316566333691[/C][C]0.476582831668455[/C][/ROW]
[ROW][C]40[/C][C]0.506554593591101[/C][C]0.986890812817798[/C][C]0.493445406408899[/C][/ROW]
[ROW][C]41[/C][C]0.478286475802827[/C][C]0.956572951605653[/C][C]0.521713524197173[/C][/ROW]
[ROW][C]42[/C][C]0.784717916131572[/C][C]0.430564167736856[/C][C]0.215282083868428[/C][/ROW]
[ROW][C]43[/C][C]0.946304186779754[/C][C]0.107391626440493[/C][C]0.0536958132202463[/C][/ROW]
[ROW][C]44[/C][C]0.957428016849002[/C][C]0.0851439663019958[/C][C]0.0425719831509979[/C][/ROW]
[ROW][C]45[/C][C]0.945018503253557[/C][C]0.109962993492886[/C][C]0.0549814967464428[/C][/ROW]
[ROW][C]46[/C][C]0.951914749439327[/C][C]0.096170501121346[/C][C]0.048085250560673[/C][/ROW]
[ROW][C]47[/C][C]0.977477395616427[/C][C]0.0450452087671468[/C][C]0.0225226043835734[/C][/ROW]
[ROW][C]48[/C][C]0.970404632842215[/C][C]0.0591907343155703[/C][C]0.0295953671577851[/C][/ROW]
[ROW][C]49[/C][C]0.978046431461814[/C][C]0.0439071370763724[/C][C]0.0219535685381862[/C][/ROW]
[ROW][C]50[/C][C]0.974973738015696[/C][C]0.0500525239686071[/C][C]0.0250262619843035[/C][/ROW]
[ROW][C]51[/C][C]0.96972041968274[/C][C]0.060559160634518[/C][C]0.030279580317259[/C][/ROW]
[ROW][C]52[/C][C]0.997195094467551[/C][C]0.00560981106489731[/C][C]0.00280490553244866[/C][/ROW]
[ROW][C]53[/C][C]0.996015186000193[/C][C]0.0079696279996149[/C][C]0.00398481399980745[/C][/ROW]
[ROW][C]54[/C][C]0.997066458160068[/C][C]0.00586708367986409[/C][C]0.00293354183993205[/C][/ROW]
[ROW][C]55[/C][C]0.996777054212645[/C][C]0.00644589157470937[/C][C]0.00322294578735469[/C][/ROW]
[ROW][C]56[/C][C]0.995452163466775[/C][C]0.00909567306645014[/C][C]0.00454783653322507[/C][/ROW]
[ROW][C]57[/C][C]0.993698309725151[/C][C]0.012603380549698[/C][C]0.006301690274849[/C][/ROW]
[ROW][C]58[/C][C]0.992824492334593[/C][C]0.0143510153308134[/C][C]0.00717550766540671[/C][/ROW]
[ROW][C]59[/C][C]0.994875541388604[/C][C]0.0102489172227922[/C][C]0.00512445861139612[/C][/ROW]
[ROW][C]60[/C][C]0.993107836698961[/C][C]0.0137843266020771[/C][C]0.00689216330103856[/C][/ROW]
[ROW][C]61[/C][C]0.994303559973613[/C][C]0.0113928800527744[/C][C]0.00569644002638722[/C][/ROW]
[ROW][C]62[/C][C]0.99461821057237[/C][C]0.0107635788552585[/C][C]0.00538178942762927[/C][/ROW]
[ROW][C]63[/C][C]0.992711696750125[/C][C]0.0145766064997508[/C][C]0.00728830324987541[/C][/ROW]
[ROW][C]64[/C][C]0.99314526274136[/C][C]0.0137094745172791[/C][C]0.00685473725863956[/C][/ROW]
[ROW][C]65[/C][C]0.991521033516925[/C][C]0.0169579329661501[/C][C]0.00847896648307504[/C][/ROW]
[ROW][C]66[/C][C]0.990640031207434[/C][C]0.0187199375851318[/C][C]0.00935996879256588[/C][/ROW]
[ROW][C]67[/C][C]0.990579267686937[/C][C]0.0188414646261256[/C][C]0.00942073231306278[/C][/ROW]
[ROW][C]68[/C][C]0.992052782643885[/C][C]0.0158944347122292[/C][C]0.0079472173561146[/C][/ROW]
[ROW][C]69[/C][C]0.992179411265123[/C][C]0.0156411774697543[/C][C]0.00782058873487714[/C][/ROW]
[ROW][C]70[/C][C]0.989533972961278[/C][C]0.0209320540774440[/C][C]0.0104660270387220[/C][/ROW]
[ROW][C]71[/C][C]0.991118815967093[/C][C]0.0177623680658143[/C][C]0.00888118403290715[/C][/ROW]
[ROW][C]72[/C][C]0.98829900395178[/C][C]0.0234019920964388[/C][C]0.0117009960482194[/C][/ROW]
[ROW][C]73[/C][C]0.985384729647866[/C][C]0.0292305407042682[/C][C]0.0146152703521341[/C][/ROW]
[ROW][C]74[/C][C]0.989674735176406[/C][C]0.0206505296471871[/C][C]0.0103252648235935[/C][/ROW]
[ROW][C]75[/C][C]0.986151873286726[/C][C]0.0276962534265483[/C][C]0.0138481267132742[/C][/ROW]
[ROW][C]76[/C][C]0.983957392177819[/C][C]0.0320852156443628[/C][C]0.0160426078221814[/C][/ROW]
[ROW][C]77[/C][C]0.97884823552833[/C][C]0.0423035289433392[/C][C]0.0211517644716696[/C][/ROW]
[ROW][C]78[/C][C]0.972145436892618[/C][C]0.055709126214764[/C][C]0.027854563107382[/C][/ROW]
[ROW][C]79[/C][C]0.982272778178078[/C][C]0.0354544436438443[/C][C]0.0177272218219222[/C][/ROW]
[ROW][C]80[/C][C]0.98183755445924[/C][C]0.0363248910815189[/C][C]0.0181624455407594[/C][/ROW]
[ROW][C]81[/C][C]0.985420684797794[/C][C]0.0291586304044125[/C][C]0.0145793152022063[/C][/ROW]
[ROW][C]82[/C][C]0.986093198605719[/C][C]0.0278136027885626[/C][C]0.0139068013942813[/C][/ROW]
[ROW][C]83[/C][C]0.981307364452055[/C][C]0.0373852710958899[/C][C]0.0186926355479449[/C][/ROW]
[ROW][C]84[/C][C]0.977277769911912[/C][C]0.0454444601761769[/C][C]0.0227222300880884[/C][/ROW]
[ROW][C]85[/C][C]0.993781837032372[/C][C]0.0124363259352551[/C][C]0.00621816296762756[/C][/ROW]
[ROW][C]86[/C][C]0.99153828379201[/C][C]0.0169234324159801[/C][C]0.00846171620799004[/C][/ROW]
[ROW][C]87[/C][C]0.9885046880255[/C][C]0.0229906239489983[/C][C]0.0114953119744991[/C][/ROW]
[ROW][C]88[/C][C]0.984910222563193[/C][C]0.0301795548736135[/C][C]0.0150897774368067[/C][/ROW]
[ROW][C]89[/C][C]0.980897193440334[/C][C]0.0382056131193325[/C][C]0.0191028065596663[/C][/ROW]
[ROW][C]90[/C][C]0.974775366283342[/C][C]0.0504492674333154[/C][C]0.0252246337166577[/C][/ROW]
[ROW][C]91[/C][C]0.96933891151375[/C][C]0.061322176972498[/C][C]0.030661088486249[/C][/ROW]
[ROW][C]92[/C][C]0.983173081675879[/C][C]0.0336538366482428[/C][C]0.0168269183241214[/C][/ROW]
[ROW][C]93[/C][C]0.978067041297031[/C][C]0.0438659174059375[/C][C]0.0219329587029687[/C][/ROW]
[ROW][C]94[/C][C]0.974586217123195[/C][C]0.05082756575361[/C][C]0.025413782876805[/C][/ROW]
[ROW][C]95[/C][C]0.967691233544075[/C][C]0.0646175329118491[/C][C]0.0323087664559245[/C][/ROW]
[ROW][C]96[/C][C]0.958871060981457[/C][C]0.0822578780370851[/C][C]0.0411289390185425[/C][/ROW]
[ROW][C]97[/C][C]0.956043405139286[/C][C]0.0879131897214286[/C][C]0.0439565948607143[/C][/ROW]
[ROW][C]98[/C][C]0.944002390973054[/C][C]0.111995218053892[/C][C]0.0559976090269459[/C][/ROW]
[ROW][C]99[/C][C]0.941531041688533[/C][C]0.116937916622934[/C][C]0.058468958311467[/C][/ROW]
[ROW][C]100[/C][C]0.92723553591443[/C][C]0.145528928171139[/C][C]0.0727644640855697[/C][/ROW]
[ROW][C]101[/C][C]0.909642352011191[/C][C]0.180715295977617[/C][C]0.0903576479888087[/C][/ROW]
[ROW][C]102[/C][C]0.89362610063478[/C][C]0.212747798730441[/C][C]0.106373899365220[/C][/ROW]
[ROW][C]103[/C][C]0.904692134088707[/C][C]0.190615731822587[/C][C]0.0953078659112934[/C][/ROW]
[ROW][C]104[/C][C]0.933253048191239[/C][C]0.133493903617523[/C][C]0.0667469518087613[/C][/ROW]
[ROW][C]105[/C][C]0.933078626236996[/C][C]0.133842747526008[/C][C]0.0669213737630038[/C][/ROW]
[ROW][C]106[/C][C]0.916329092237754[/C][C]0.167341815524492[/C][C]0.083670907762246[/C][/ROW]
[ROW][C]107[/C][C]0.900331296600664[/C][C]0.199337406798672[/C][C]0.0996687033993362[/C][/ROW]
[ROW][C]108[/C][C]0.896841294673983[/C][C]0.206317410652034[/C][C]0.103158705326017[/C][/ROW]
[ROW][C]109[/C][C]0.89751900807058[/C][C]0.204961983858840[/C][C]0.102480991929420[/C][/ROW]
[ROW][C]110[/C][C]0.919562139162954[/C][C]0.160875721674092[/C][C]0.0804378608370458[/C][/ROW]
[ROW][C]111[/C][C]0.9116162700525[/C][C]0.176767459895001[/C][C]0.0883837299475005[/C][/ROW]
[ROW][C]112[/C][C]0.938376918856626[/C][C]0.123246162286748[/C][C]0.0616230811433741[/C][/ROW]
[ROW][C]113[/C][C]0.920727306804312[/C][C]0.158545386391377[/C][C]0.0792726931956884[/C][/ROW]
[ROW][C]114[/C][C]0.898613937833283[/C][C]0.202772124333433[/C][C]0.101386062166717[/C][/ROW]
[ROW][C]115[/C][C]0.872509371842654[/C][C]0.254981256314691[/C][C]0.127490628157346[/C][/ROW]
[ROW][C]116[/C][C]0.871197088327874[/C][C]0.257605823344252[/C][C]0.128802911672126[/C][/ROW]
[ROW][C]117[/C][C]0.877952479314963[/C][C]0.244095041370074[/C][C]0.122047520685037[/C][/ROW]
[ROW][C]118[/C][C]0.869721702638088[/C][C]0.260556594723824[/C][C]0.130278297361912[/C][/ROW]
[ROW][C]119[/C][C]0.836816878194585[/C][C]0.326366243610831[/C][C]0.163183121805415[/C][/ROW]
[ROW][C]120[/C][C]0.884467307369235[/C][C]0.231065385261531[/C][C]0.115532692630765[/C][/ROW]
[ROW][C]121[/C][C]0.86066277272278[/C][C]0.278674454554439[/C][C]0.139337227277219[/C][/ROW]
[ROW][C]122[/C][C]0.838725546795745[/C][C]0.322548906408511[/C][C]0.161274453204255[/C][/ROW]
[ROW][C]123[/C][C]0.8311604577216[/C][C]0.337679084556798[/C][C]0.168839542278399[/C][/ROW]
[ROW][C]124[/C][C]0.794849771606177[/C][C]0.410300456787647[/C][C]0.205150228393823[/C][/ROW]
[ROW][C]125[/C][C]0.7948524106691[/C][C]0.4102951786618[/C][C]0.2051475893309[/C][/ROW]
[ROW][C]126[/C][C]0.778503071816987[/C][C]0.442993856366026[/C][C]0.221496928183013[/C][/ROW]
[ROW][C]127[/C][C]0.730054198264471[/C][C]0.539891603471058[/C][C]0.269945801735529[/C][/ROW]
[ROW][C]128[/C][C]0.705009328627947[/C][C]0.589981342744106[/C][C]0.294990671372053[/C][/ROW]
[ROW][C]129[/C][C]0.722948300389998[/C][C]0.554103399220005[/C][C]0.277051699610002[/C][/ROW]
[ROW][C]130[/C][C]0.718649310500746[/C][C]0.562701378998508[/C][C]0.281350689499254[/C][/ROW]
[ROW][C]131[/C][C]0.662536173180312[/C][C]0.674927653639376[/C][C]0.337463826819688[/C][/ROW]
[ROW][C]132[/C][C]0.651205019909617[/C][C]0.697589960180766[/C][C]0.348794980090383[/C][/ROW]
[ROW][C]133[/C][C]0.625024074297117[/C][C]0.749951851405766[/C][C]0.374975925702883[/C][/ROW]
[ROW][C]134[/C][C]0.553835358999271[/C][C]0.892329282001458[/C][C]0.446164641000729[/C][/ROW]
[ROW][C]135[/C][C]0.529031672819814[/C][C]0.941936654360373[/C][C]0.470968327180186[/C][/ROW]
[ROW][C]136[/C][C]0.524968980108928[/C][C]0.950062039782144[/C][C]0.475031019891072[/C][/ROW]
[ROW][C]137[/C][C]0.451837592005813[/C][C]0.903675184011627[/C][C]0.548162407994187[/C][/ROW]
[ROW][C]138[/C][C]0.521851195782837[/C][C]0.956297608434326[/C][C]0.478148804217163[/C][/ROW]
[ROW][C]139[/C][C]0.850055444645577[/C][C]0.299889110708846[/C][C]0.149944555354423[/C][/ROW]
[ROW][C]140[/C][C]0.87775097711501[/C][C]0.244498045769980[/C][C]0.122249022884990[/C][/ROW]
[ROW][C]141[/C][C]0.844213121210478[/C][C]0.311573757579044[/C][C]0.155786878789522[/C][/ROW]
[ROW][C]142[/C][C]0.777233315436774[/C][C]0.445533369126452[/C][C]0.222766684563226[/C][/ROW]
[ROW][C]143[/C][C]0.749312686280594[/C][C]0.501374627438811[/C][C]0.250687313719406[/C][/ROW]
[ROW][C]144[/C][C]0.652220317047529[/C][C]0.695559365904942[/C][C]0.347779682952471[/C][/ROW]
[ROW][C]145[/C][C]0.642877004864541[/C][C]0.714245990270918[/C][C]0.357122995135459[/C][/ROW]
[ROW][C]146[/C][C]0.763918241013173[/C][C]0.472163517973654[/C][C]0.236081758986827[/C][/ROW]
[ROW][C]147[/C][C]0.731674192977648[/C][C]0.536651614044705[/C][C]0.268325807022352[/C][/ROW]
[ROW][C]148[/C][C]0.852462448780022[/C][C]0.295075102439955[/C][C]0.147537551219978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.06025482791299410.1205096558259880.939745172087006
90.0406954098125640.0813908196251280.959304590187436
100.01701945403569110.03403890807138230.98298054596431
110.03324462410852530.06648924821705050.966755375891475
120.01685239051616030.03370478103232070.98314760948384
130.3400622488499660.6801244976999320.659937751150034
140.6873835015880530.6252329968238950.312616498411947
150.6012092341853730.7975815316292550.398790765814627
160.5105821672358610.9788356655282780.489417832764139
170.4535987466287950.907197493257590.546401253371205
180.3707235097170030.7414470194340060.629276490282997
190.3013179996571590.6026359993143190.698682000342841
200.5975462860960890.8049074278078230.402453713903911
210.5348627666316890.9302744667366220.465137233368311
220.5029403951166360.9941192097667280.497059604883364
230.4328018044117470.8656036088234950.567198195588253
240.4185839827894390.8371679655788780.581416017210561
250.3835480252706630.7670960505413250.616451974729338
260.3290064505902180.6580129011804350.670993549409782
270.589952522480590.8200949550388190.410047477519409
280.5317834451220430.9364331097559150.468216554877957
290.4704994501200950.940998900240190.529500549879905
300.6459148446975520.7081703106048950.354085155302448
310.7776194645504560.4447610708990870.222380535449544
320.7381322898998420.5237354202003160.261867710100158
330.6945479331478840.6109041337042330.305452066852116
340.6502482964694070.6995034070611860.349751703530593
350.6428795252159050.7142409495681890.357120474784095
360.6645268017999460.6709463964001070.335473198200054
370.6236498757545080.7527002484909840.376350124245492
380.5742704823215970.8514590353568060.425729517678403
390.5234171683315450.953165663336910.476582831668455
400.5065545935911010.9868908128177980.493445406408899
410.4782864758028270.9565729516056530.521713524197173
420.7847179161315720.4305641677368560.215282083868428
430.9463041867797540.1073916264404930.0536958132202463
440.9574280168490020.08514396630199580.0425719831509979
450.9450185032535570.1099629934928860.0549814967464428
460.9519147494393270.0961705011213460.048085250560673
470.9774773956164270.04504520876714680.0225226043835734
480.9704046328422150.05919073431557030.0295953671577851
490.9780464314618140.04390713707637240.0219535685381862
500.9749737380156960.05005252396860710.0250262619843035
510.969720419682740.0605591606345180.030279580317259
520.9971950944675510.005609811064897310.00280490553244866
530.9960151860001930.00796962799961490.00398481399980745
540.9970664581600680.005867083679864090.00293354183993205
550.9967770542126450.006445891574709370.00322294578735469
560.9954521634667750.009095673066450140.00454783653322507
570.9936983097251510.0126033805496980.006301690274849
580.9928244923345930.01435101533081340.00717550766540671
590.9948755413886040.01024891722279220.00512445861139612
600.9931078366989610.01378432660207710.00689216330103856
610.9943035599736130.01139288005277440.00569644002638722
620.994618210572370.01076357885525850.00538178942762927
630.9927116967501250.01457660649975080.00728830324987541
640.993145262741360.01370947451727910.00685473725863956
650.9915210335169250.01695793296615010.00847896648307504
660.9906400312074340.01871993758513180.00935996879256588
670.9905792676869370.01884146462612560.00942073231306278
680.9920527826438850.01589443471222920.0079472173561146
690.9921794112651230.01564117746975430.00782058873487714
700.9895339729612780.02093205407744400.0104660270387220
710.9911188159670930.01776236806581430.00888118403290715
720.988299003951780.02340199209643880.0117009960482194
730.9853847296478660.02923054070426820.0146152703521341
740.9896747351764060.02065052964718710.0103252648235935
750.9861518732867260.02769625342654830.0138481267132742
760.9839573921778190.03208521564436280.0160426078221814
770.978848235528330.04230352894333920.0211517644716696
780.9721454368926180.0557091262147640.027854563107382
790.9822727781780780.03545444364384430.0177272218219222
800.981837554459240.03632489108151890.0181624455407594
810.9854206847977940.02915863040441250.0145793152022063
820.9860931986057190.02781360278856260.0139068013942813
830.9813073644520550.03738527109588990.0186926355479449
840.9772777699119120.04544446017617690.0227222300880884
850.9937818370323720.01243632593525510.00621816296762756
860.991538283792010.01692343241598010.00846171620799004
870.98850468802550.02299062394899830.0114953119744991
880.9849102225631930.03017955487361350.0150897774368067
890.9808971934403340.03820561311933250.0191028065596663
900.9747753662833420.05044926743331540.0252246337166577
910.969338911513750.0613221769724980.030661088486249
920.9831730816758790.03365383664824280.0168269183241214
930.9780670412970310.04386591740593750.0219329587029687
940.9745862171231950.050827565753610.025413782876805
950.9676912335440750.06461753291184910.0323087664559245
960.9588710609814570.08225787803708510.0411289390185425
970.9560434051392860.08791318972142860.0439565948607143
980.9440023909730540.1119952180538920.0559976090269459
990.9415310416885330.1169379166229340.058468958311467
1000.927235535914430.1455289281711390.0727644640855697
1010.9096423520111910.1807152959776170.0903576479888087
1020.893626100634780.2127477987304410.106373899365220
1030.9046921340887070.1906157318225870.0953078659112934
1040.9332530481912390.1334939036175230.0667469518087613
1050.9330786262369960.1338427475260080.0669213737630038
1060.9163290922377540.1673418155244920.083670907762246
1070.9003312966006640.1993374067986720.0996687033993362
1080.8968412946739830.2063174106520340.103158705326017
1090.897519008070580.2049619838588400.102480991929420
1100.9195621391629540.1608757216740920.0804378608370458
1110.91161627005250.1767674598950010.0883837299475005
1120.9383769188566260.1232461622867480.0616230811433741
1130.9207273068043120.1585453863913770.0792726931956884
1140.8986139378332830.2027721243334330.101386062166717
1150.8725093718426540.2549812563146910.127490628157346
1160.8711970883278740.2576058233442520.128802911672126
1170.8779524793149630.2440950413700740.122047520685037
1180.8697217026380880.2605565947238240.130278297361912
1190.8368168781945850.3263662436108310.163183121805415
1200.8844673073692350.2310653852615310.115532692630765
1210.860662772722780.2786744545544390.139337227277219
1220.8387255467957450.3225489064085110.161274453204255
1230.83116045772160.3376790845567980.168839542278399
1240.7948497716061770.4103004567876470.205150228393823
1250.79485241066910.41029517866180.2051475893309
1260.7785030718169870.4429938563660260.221496928183013
1270.7300541982644710.5398916034710580.269945801735529
1280.7050093286279470.5899813427441060.294990671372053
1290.7229483003899980.5541033992200050.277051699610002
1300.7186493105007460.5627013789985080.281350689499254
1310.6625361731803120.6749276536393760.337463826819688
1320.6512050199096170.6975899601807660.348794980090383
1330.6250240742971170.7499518514057660.374975925702883
1340.5538353589992710.8923292820014580.446164641000729
1350.5290316728198140.9419366543603730.470968327180186
1360.5249689801089280.9500620397821440.475031019891072
1370.4518375920058130.9036751840116270.548162407994187
1380.5218511957828370.9562976084343260.478148804217163
1390.8500554446455770.2998891107088460.149944555354423
1400.877750977115010.2444980457699800.122249022884990
1410.8442131212104780.3115737575790440.155786878789522
1420.7772333154367740.4455333691264520.222766684563226
1430.7493126862805940.5013746274388110.250687313719406
1440.6522203170475290.6955593659049420.347779682952471
1450.6428770048645410.7142459902709180.357122995135459
1460.7639182410131730.4721635179736540.236081758986827
1470.7316741929776480.5366516140447050.268325807022352
1480.8524624487800220.2950751024399550.147537551219978






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0354609929078014NOK
5% type I error level430.304964539007092NOK
10% type I error level570.404255319148936NOK

\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 & 5 & 0.0354609929078014 & NOK \tabularnewline
5% type I error level & 43 & 0.304964539007092 & NOK \tabularnewline
10% type I error level & 57 & 0.404255319148936 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98888&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]5[/C][C]0.0354609929078014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.304964539007092[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.404255319148936[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98888&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98888&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 level50.0354609929078014NOK
5% type I error level430.304964539007092NOK
10% type I error level570.404255319148936NOK


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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}