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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 11:08:59 +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/t1290510479ovo0541jws8oizd.htm/, Retrieved Sat, 20 Apr 2024 10:59:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98930, Retrieved Sat, 20 Apr 2024 10:59:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
F RMPD    [Multiple Regression] [ws 7 Model 2: tijd] [2010-11-23 11:08:59] [350231caf55a86a218fd48dc4d2e2f8b] [Current]
-    D      [Multiple Regression] [Paper: Multiple L...] [2010-12-05 12:22:18] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
-    D      [Multiple Regression] [Paper: Multiple L...] [2010-12-05 12:58:27] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
-    D      [Multiple Regression] [Paper: Multiple L...] [2010-12-05 13:52:33] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
Feedback Forum
2010-11-27 16:26:09 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student heeft de software hier op een correcte manier gebruikt en ook de interpretatie is correct. Echter had men dit resultaat - de afwezigheid van invloed van de tijd - reeds kunnen voorspellen op basis van de hiervoor opgestelde 'run sequence plot'. De hier uitgevoerde berekeningen zijn naar mijn mening dan ook een klein beetje overbodig.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	13	13	14	13	3
9	12	12	8	13	5
9	15	10	12	16	6
9	12	9	7	12	6
9	10	10	10	11	5
9	12	12	7	12	3
9	15	13	16	18	8
9	9	12	11	11	4
9	12	12	14	14	4
9	11	6	6	9	4
9	11	5	16	14	6
9	11	12	11	12	6
9	15	11	16	11	5
9	7	14	12	12	4
9	11	14	7	13	6
9	11	12	13	11	4
9	10	12	11	12	6
9	14	11	15	16	6
9	10	11	7	9	4
9	6	7	9	11	4
9	11	9	7	13	2
9	15	11	14	15	7
9	11	11	15	10	5
9	12	12	7	11	4
9	14	12	15	13	6
9	15	11	17	16	6
9	9	11	15	15	7
9	13	8	14	14	5
9	13	9	14	14	6
9	16	12	8	14	4
9	13	10	8	8	4
9	12	10	14	13	7
9	14	12	14	15	7
9	11	8	8	13	4
9	9	12	11	11	4
9	16	11	16	15	6
9	12	12	10	15	6
9	10	7	8	9	5
9	13	11	14	13	6
9	16	11	16	16	7
9	14	12	13	13	6
9	15	9	5	11	3
9	5	15	8	12	3
9	8	11	10	12	4
9	11	11	8	12	6
9	16	11	13	14	7
9	17	11	15	14	5
9	9	15	6	8	4
9	9	11	12	13	5
9	13	12	16	16	6
9	10	12	5	13	6
10	6	9	15	11	6
10	12	12	12	14	5
10	8	12	8	13	4
10	14	13	13	13	5
10	12	11	14	13	5
10	11	9	12	12	4
10	16	9	16	16	6
10	8	11	10	15	2
10	15	11	15	15	8
10	7	12	8	12	3
10	16	12	16	14	6
10	14	9	19	12	6
10	16	11	14	15	6
10	9	9	6	12	5
10	14	12	13	13	5
10	11	12	15	12	6
10	13	12	7	12	5
10	15	12	13	13	6
10	5	14	4	5	2
10	15	11	14	13	5
10	13	12	13	13	5
10	11	11	11	14	5
10	11	6	14	17	6
10	12	10	12	13	6
10	12	12	15	13	6
10	12	13	14	12	5
10	12	8	13	13	5
10	14	12	8	14	4
10	6	12	6	11	2
10	7	12	7	12	4
10	14	6	13	12	6
10	14	11	13	16	6
10	10	10	11	12	5
10	13	12	5	12	3
10	12	13	12	12	6
10	9	11	8	10	4
10	12	7	11	15	5
10	16	11	14	15	8
10	10	11	9	12	4
10	14	11	10	16	6
10	10	11	13	15	6
10	16	12	16	16	7
10	15	10	16	13	6
10	12	11	11	12	5
10	10	12	8	11	4
10	8	7	4	13	6
10	8	13	7	10	3
10	11	8	14	15	5
10	13	12	11	13	6
10	16	11	17	16	7
10	16	12	15	15	7
10	14	14	17	18	6
10	11	10	5	13	3
10	4	10	4	10	2
10	14	13	10	16	8
10	9	10	11	13	3
10	14	11	15	15	8
10	8	10	10	14	3
10	8	7	9	15	4
10	11	10	12	14	5
10	12	8	15	13	7
10	11	12	7	13	6
10	14	12	13	15	6
10	15	12	12	16	7
10	16	11	14	14	6
10	16	12	14	14	6
10	11	12	8	16	6
10	14	12	15	14	6
10	14	11	12	12	4
10	12	12	12	13	4
10	14	11	16	12	5
10	8	11	9	12	4
10	13	13	15	14	6
10	16	12	15	14	6
10	12	12	6	14	5
10	16	12	14	16	8
10	12	12	15	13	6
10	11	8	10	14	5
10	4	8	6	4	4
10	16	12	14	16	8
10	15	11	12	13	6
10	10	12	8	16	4
10	13	13	11	15	6
10	15	12	13	14	6
10	12	12	9	13	4
10	14	11	15	14	6
10	7	12	13	12	3
10	19	12	15	15	6
10	12	10	14	14	5
10	12	11	16	13	4
10	13	12	14	14	6
10	15	12	14	16	4
10	8	10	10	6	4
10	12	12	10	13	4
10	10	13	4	13	6
10	8	12	8	14	5
10	10	15	15	15	6
10	15	11	16	14	6
10	16	12	12	15	8
10	13	11	12	13	7
10	16	12	15	16	7
10	9	11	9	12	4
10	14	10	12	15	6
10	14	11	14	12	6
10	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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 2.6093942753828 -0.24986593522325Tijd[t] + 0.0962642440224116FindingFriends[t] + 0.243936452579795KnowingPeople[t] + 0.357484705624815Liked[t] + 0.622809523303693Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  2.6093942753828 -0.24986593522325Tijd[t] +  0.0962642440224116FindingFriends[t] +  0.243936452579795KnowingPeople[t] +  0.357484705624815Liked[t] +  0.622809523303693Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  2.6093942753828 -0.24986593522325Tijd[t] +  0.0962642440224116FindingFriends[t] +  0.243936452579795KnowingPeople[t] +  0.357484705624815Liked[t] +  0.622809523303693Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&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] = + 2.6093942753828 -0.24986593522325Tijd[t] + 0.0962642440224116FindingFriends[t] + 0.243936452579795KnowingPeople[t] + 0.357484705624815Liked[t] + 0.622809523303693Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.60939427538283.6481510.71530.4755570.237779
Tijd-0.249865935223250.363799-0.68680.4932540.246627
FindingFriends0.09626424402241160.0961591.00110.3183910.159196
KnowingPeople0.2439364525797950.0614813.96760.0001125.6e-05
Liked0.3574847056248150.0974533.66830.0003390.000169
Celebrity0.6228095233036930.156433.98140.0001065.3e-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) & 2.6093942753828 & 3.648151 & 0.7153 & 0.475557 & 0.237779 \tabularnewline
Tijd & -0.24986593522325 & 0.363799 & -0.6868 & 0.493254 & 0.246627 \tabularnewline
FindingFriends & 0.0962642440224116 & 0.096159 & 1.0011 & 0.318391 & 0.159196 \tabularnewline
KnowingPeople & 0.243936452579795 & 0.061481 & 3.9676 & 0.000112 & 5.6e-05 \tabularnewline
Liked & 0.357484705624815 & 0.097453 & 3.6683 & 0.000339 & 0.000169 \tabularnewline
Celebrity & 0.622809523303693 & 0.15643 & 3.9814 & 0.000106 & 5.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&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]2.6093942753828[/C][C]3.648151[/C][C]0.7153[/C][C]0.475557[/C][C]0.237779[/C][/ROW]
[ROW][C]Tijd[/C][C]-0.24986593522325[/C][C]0.363799[/C][C]-0.6868[/C][C]0.493254[/C][C]0.246627[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0962642440224116[/C][C]0.096159[/C][C]1.0011[/C][C]0.318391[/C][C]0.159196[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.243936452579795[/C][C]0.061481[/C][C]3.9676[/C][C]0.000112[/C][C]5.6e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.357484705624815[/C][C]0.097453[/C][C]3.6683[/C][C]0.000339[/C][C]0.000169[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.622809523303693[/C][C]0.15643[/C][C]3.9814[/C][C]0.000106[/C][C]5.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&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)2.60939427538283.6481510.71530.4755570.237779
Tijd-0.249865935223250.363799-0.68680.4932540.246627
FindingFriends0.09626424402241160.0961591.00110.3183910.159196
KnowingPeople0.2439364525797950.0614813.96760.0001125.6e-05
Liked0.3574847056248150.0974533.66830.0003390.000169
Celebrity0.6228095233036930.156433.98140.0001065.3e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.707651177863378
R-squared0.500770189531427
Adjusted R-squared0.484129195849141
F-TEST (value)30.0925653294667
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10920750873077
Sum Squared Residuals667.313447232938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.707651177863378 \tabularnewline
R-squared & 0.500770189531427 \tabularnewline
Adjusted R-squared & 0.484129195849141 \tabularnewline
F-TEST (value) & 30.0925653294667 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10920750873077 \tabularnewline
Sum Squared Residuals & 667.313447232938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.707651177863378[/C][/ROW]
[ROW][C]R-squared[/C][C]0.500770189531427[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.484129195849141[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.0925653294667[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/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]2.10920750873077[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]667.313447232938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&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 R0.707651177863378
R-squared0.500770189531427
Adjusted R-squared0.484129195849141
F-TEST (value)30.0925653294667
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10920750873077
Sum Squared Residuals667.313447232938






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.54287610981571.45712389018429
21211.22861219692190.771387803078095
31513.70709315937441.29290684062560
41210.96120782995371.03879217004625
51010.8089872027870-0.808987202787039
6129.38157199210992.61842800789009
71516.9322201596178-1.93222015961783
8910.6226426201080-1.62264262010796
91212.4269060947218-0.426906094721793
10118.110405481824892.88959451817511
111113.4865483383319-2.48654833833189
121112.2257463723402-1.22574637234017
131512.36887016228822.63112983771178
14711.4165922663574-4.4165922663574
151111.8000137556906-0.800013755690625
161111.1105155252676-0.110515525267554
171012.2257463723402-2.22574637234016
181414.5351667611362-0.535166761136192
19108.835663154516741.16433684548326
2069.65344849483632-3.65344849483632
21118.82745444236382.17254555763621
221514.55655512623530.443444873764725
231111.7674490040836-0.76744900408361
24129.646896809788792.35310319021121
251413.55897688828420.441023111715841
261515.0230396662958-0.0230396662957811
27914.8004915788151-5.80049157881507
281312.66465864193580.33534135806416
291313.3837324092619-0.383732409261944
301610.96328737924305.03671262075697
31138.625850657449314.37414934255069
321213.7453214709632-1.74532147096323
331414.6528193702577-0.652819370257688
341110.22074569752860.779254302471436
35910.6226426201080-1.62264262010796
361614.42161850809121.57838149190883
371213.0542640366348-1.05426403663482
38109.317352154310590.682647845689415
391313.2187761916820-0.218776191681953
401615.40191273701970.59808726298032
411413.07110398312460.92889601687543
42158.247421649258276.75257835074173
4359.91430117675694-4.91430117675694
44810.6399266291306-2.63992662913057
451111.3976727705784-0.39767277057837
461613.95513396803072.04486603196933
471713.19738782658293.80261217341713
4898.619298972401780.38070102759822
49912.1080937632187-3.10809376321867
501314.8753674577384-1.87536745773840
511011.1196123624862-1.11961236248621
52612.3053488097440-6.30534880974405
531212.3119767776426-0.311976777642647
54810.3559367383950-2.35593673839496
551412.29469276862001.70530723137996
561212.346100733155-0.346100733155009
571110.68540511102210.314594888977911
581614.33670879044791.66329120955209
59810.2168957641744-2.21689576417438
601515.1734351668955-0.173435166895513
6179.37564250946645-2.37564250946645
621613.91053211126552.08946788873448
631413.63857932568800.361420674311962
641613.68387966770832.31612033229167
6599.84459591884701-0.844595918847013
661412.19842852459761.80157147540237
671112.9516262474361-1.95162624743609
681310.37732510349402.62267489650596
691512.82123804790132.17876195209868
7055.4672227245147-0.467222724514699
711512.3461007331552.65389926684499
721312.19842852459760.801571475402373
731111.9717760810404-0.97177608104044
741113.9175278588459-2.91752785884590
751212.3847731072767-0.384773107276702
761213.3091109530609-1.30911095306091
771212.1811445155750-0.181144515575018
781211.81337154850800.18662845149202
791410.71342144401983.28657855598023
8067.90747537537835-1.90747537537835
8179.75451558019035-2.75451558019035
821411.88616787814202.11383212185797
831413.79742792075340.202572079246647
841011.1605424257684-1.1605424257684
85138.643833151727074.35616684827293
861212.3160811337191-0.316081133719121
8799.1872183774981-0.187218377498103
881211.94420381057560.0557961894243915
891614.92949871431571.07050128568428
901010.1461242413275-0.146124241327528
911413.06561856301400.934381436986031
921013.4399432151285-3.43994321512854
931615.24831104581880.751688954181159
941513.36051891759591.63948108240412
951211.25680666979080.74319333020919
96109.640967327145330.35903267285467
97810.1444887545711-2.14448875457111
9888.51300088965944-0.513000889659439
991112.7722774123374-1.77227741233740
1001312.33336514274170.66663485725827
1011615.39598325437620.604016745623776
1021614.64688988761421.35311011238577
1031415.7769358743894-1.77693587438940
104118.808789369307062.19121063069294
10546.86958927654913-2.86958927654913
1061414.5037660976662-0.503766097666178
107910.2724080847858-1.27240808478583
1081415.1734351668955-1.17343516689551
109810.3859563378308-2.38595633783085
110810.8335213821123-2.83352138211233
1111112.1194482895978-1.11944828959782
1121213.5468635002750-1.54686350027496
1131111.3576193324226-0.357619332422551
1141413.53620745915090.463792540849051
1151514.27256523549970.727434764500338
1161613.32639496208352.67360503791648
1171613.42265920610592.57734079389407
1181112.6740099018768-1.67400990187679
1191413.66659565868570.333404341314276
1201410.87793359906693.12206640093309
1211211.33168254871410.668317451285861
1221412.47648893268981.52351106731022
123810.1461242413275-2.14612424132753
1241313.7628599027081-0.762859902708136
1251613.66659565868572.33340434131428
1261210.84835806216391.15164193783612
1271615.38324766396290.616752336037055
1281213.3091109530609-1.30911095306091
1291111.4390468963934-0.439046896393411
13046.26564450652239-2.26564450652239
1311615.38324766396290.616752336037055
1321512.48103735129912.51896264870089
1331011.4283908552694-1.42839085526940
1341313.1445987980138-0.144598798013771
1351513.17872275352611.82127724647387
1361210.59987319097481.40012680902525
1371413.57033141466330.429668585336687
138710.5953247723654-3.59532477236542
1391914.02408036431054.97591963568946
1401212.6073211947574-0.607321194757413
1411212.2111641150109-0.211164115010906
1421313.4226592061059-0.422659206105929
1431512.89200957074822.10799042925183
14488.14888821613602-0.148888216136022
1451210.84380964355451.15619035644545
1461010.7220742187056-0.722074218705579
147811.3362309673235-3.33623096732347
1481014.3128730963778-4.31287309637777
1491513.81426786724311.18573213275689
1501614.53789005317851.46210994682146
1511313.1038468746028-0.103846874602806
1521615.00437459323900.995625406760954
153910.1461242413275-1.14612424132753
1541413.09974251852630.900257481473668
1551412.61142555083391.38857444916611
1561210.10334751112941.89665248887064

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.5428761098157 & 1.45712389018429 \tabularnewline
2 & 12 & 11.2286121969219 & 0.771387803078095 \tabularnewline
3 & 15 & 13.7070931593744 & 1.29290684062560 \tabularnewline
4 & 12 & 10.9612078299537 & 1.03879217004625 \tabularnewline
5 & 10 & 10.8089872027870 & -0.808987202787039 \tabularnewline
6 & 12 & 9.3815719921099 & 2.61842800789009 \tabularnewline
7 & 15 & 16.9322201596178 & -1.93222015961783 \tabularnewline
8 & 9 & 10.6226426201080 & -1.62264262010796 \tabularnewline
9 & 12 & 12.4269060947218 & -0.426906094721793 \tabularnewline
10 & 11 & 8.11040548182489 & 2.88959451817511 \tabularnewline
11 & 11 & 13.4865483383319 & -2.48654833833189 \tabularnewline
12 & 11 & 12.2257463723402 & -1.22574637234017 \tabularnewline
13 & 15 & 12.3688701622882 & 2.63112983771178 \tabularnewline
14 & 7 & 11.4165922663574 & -4.4165922663574 \tabularnewline
15 & 11 & 11.8000137556906 & -0.800013755690625 \tabularnewline
16 & 11 & 11.1105155252676 & -0.110515525267554 \tabularnewline
17 & 10 & 12.2257463723402 & -2.22574637234016 \tabularnewline
18 & 14 & 14.5351667611362 & -0.535166761136192 \tabularnewline
19 & 10 & 8.83566315451674 & 1.16433684548326 \tabularnewline
20 & 6 & 9.65344849483632 & -3.65344849483632 \tabularnewline
21 & 11 & 8.8274544423638 & 2.17254555763621 \tabularnewline
22 & 15 & 14.5565551262353 & 0.443444873764725 \tabularnewline
23 & 11 & 11.7674490040836 & -0.76744900408361 \tabularnewline
24 & 12 & 9.64689680978879 & 2.35310319021121 \tabularnewline
25 & 14 & 13.5589768882842 & 0.441023111715841 \tabularnewline
26 & 15 & 15.0230396662958 & -0.0230396662957811 \tabularnewline
27 & 9 & 14.8004915788151 & -5.80049157881507 \tabularnewline
28 & 13 & 12.6646586419358 & 0.33534135806416 \tabularnewline
29 & 13 & 13.3837324092619 & -0.383732409261944 \tabularnewline
30 & 16 & 10.9632873792430 & 5.03671262075697 \tabularnewline
31 & 13 & 8.62585065744931 & 4.37414934255069 \tabularnewline
32 & 12 & 13.7453214709632 & -1.74532147096323 \tabularnewline
33 & 14 & 14.6528193702577 & -0.652819370257688 \tabularnewline
34 & 11 & 10.2207456975286 & 0.779254302471436 \tabularnewline
35 & 9 & 10.6226426201080 & -1.62264262010796 \tabularnewline
36 & 16 & 14.4216185080912 & 1.57838149190883 \tabularnewline
37 & 12 & 13.0542640366348 & -1.05426403663482 \tabularnewline
38 & 10 & 9.31735215431059 & 0.682647845689415 \tabularnewline
39 & 13 & 13.2187761916820 & -0.218776191681953 \tabularnewline
40 & 16 & 15.4019127370197 & 0.59808726298032 \tabularnewline
41 & 14 & 13.0711039831246 & 0.92889601687543 \tabularnewline
42 & 15 & 8.24742164925827 & 6.75257835074173 \tabularnewline
43 & 5 & 9.91430117675694 & -4.91430117675694 \tabularnewline
44 & 8 & 10.6399266291306 & -2.63992662913057 \tabularnewline
45 & 11 & 11.3976727705784 & -0.39767277057837 \tabularnewline
46 & 16 & 13.9551339680307 & 2.04486603196933 \tabularnewline
47 & 17 & 13.1973878265829 & 3.80261217341713 \tabularnewline
48 & 9 & 8.61929897240178 & 0.38070102759822 \tabularnewline
49 & 9 & 12.1080937632187 & -3.10809376321867 \tabularnewline
50 & 13 & 14.8753674577384 & -1.87536745773840 \tabularnewline
51 & 10 & 11.1196123624862 & -1.11961236248621 \tabularnewline
52 & 6 & 12.3053488097440 & -6.30534880974405 \tabularnewline
53 & 12 & 12.3119767776426 & -0.311976777642647 \tabularnewline
54 & 8 & 10.3559367383950 & -2.35593673839496 \tabularnewline
55 & 14 & 12.2946927686200 & 1.70530723137996 \tabularnewline
56 & 12 & 12.346100733155 & -0.346100733155009 \tabularnewline
57 & 11 & 10.6854051110221 & 0.314594888977911 \tabularnewline
58 & 16 & 14.3367087904479 & 1.66329120955209 \tabularnewline
59 & 8 & 10.2168957641744 & -2.21689576417438 \tabularnewline
60 & 15 & 15.1734351668955 & -0.173435166895513 \tabularnewline
61 & 7 & 9.37564250946645 & -2.37564250946645 \tabularnewline
62 & 16 & 13.9105321112655 & 2.08946788873448 \tabularnewline
63 & 14 & 13.6385793256880 & 0.361420674311962 \tabularnewline
64 & 16 & 13.6838796677083 & 2.31612033229167 \tabularnewline
65 & 9 & 9.84459591884701 & -0.844595918847013 \tabularnewline
66 & 14 & 12.1984285245976 & 1.80157147540237 \tabularnewline
67 & 11 & 12.9516262474361 & -1.95162624743609 \tabularnewline
68 & 13 & 10.3773251034940 & 2.62267489650596 \tabularnewline
69 & 15 & 12.8212380479013 & 2.17876195209868 \tabularnewline
70 & 5 & 5.4672227245147 & -0.467222724514699 \tabularnewline
71 & 15 & 12.346100733155 & 2.65389926684499 \tabularnewline
72 & 13 & 12.1984285245976 & 0.801571475402373 \tabularnewline
73 & 11 & 11.9717760810404 & -0.97177608104044 \tabularnewline
74 & 11 & 13.9175278588459 & -2.91752785884590 \tabularnewline
75 & 12 & 12.3847731072767 & -0.384773107276702 \tabularnewline
76 & 12 & 13.3091109530609 & -1.30911095306091 \tabularnewline
77 & 12 & 12.1811445155750 & -0.181144515575018 \tabularnewline
78 & 12 & 11.8133715485080 & 0.18662845149202 \tabularnewline
79 & 14 & 10.7134214440198 & 3.28657855598023 \tabularnewline
80 & 6 & 7.90747537537835 & -1.90747537537835 \tabularnewline
81 & 7 & 9.75451558019035 & -2.75451558019035 \tabularnewline
82 & 14 & 11.8861678781420 & 2.11383212185797 \tabularnewline
83 & 14 & 13.7974279207534 & 0.202572079246647 \tabularnewline
84 & 10 & 11.1605424257684 & -1.1605424257684 \tabularnewline
85 & 13 & 8.64383315172707 & 4.35616684827293 \tabularnewline
86 & 12 & 12.3160811337191 & -0.316081133719121 \tabularnewline
87 & 9 & 9.1872183774981 & -0.187218377498103 \tabularnewline
88 & 12 & 11.9442038105756 & 0.0557961894243915 \tabularnewline
89 & 16 & 14.9294987143157 & 1.07050128568428 \tabularnewline
90 & 10 & 10.1461242413275 & -0.146124241327528 \tabularnewline
91 & 14 & 13.0656185630140 & 0.934381436986031 \tabularnewline
92 & 10 & 13.4399432151285 & -3.43994321512854 \tabularnewline
93 & 16 & 15.2483110458188 & 0.751688954181159 \tabularnewline
94 & 15 & 13.3605189175959 & 1.63948108240412 \tabularnewline
95 & 12 & 11.2568066697908 & 0.74319333020919 \tabularnewline
96 & 10 & 9.64096732714533 & 0.35903267285467 \tabularnewline
97 & 8 & 10.1444887545711 & -2.14448875457111 \tabularnewline
98 & 8 & 8.51300088965944 & -0.513000889659439 \tabularnewline
99 & 11 & 12.7722774123374 & -1.77227741233740 \tabularnewline
100 & 13 & 12.3333651427417 & 0.66663485725827 \tabularnewline
101 & 16 & 15.3959832543762 & 0.604016745623776 \tabularnewline
102 & 16 & 14.6468898876142 & 1.35311011238577 \tabularnewline
103 & 14 & 15.7769358743894 & -1.77693587438940 \tabularnewline
104 & 11 & 8.80878936930706 & 2.19121063069294 \tabularnewline
105 & 4 & 6.86958927654913 & -2.86958927654913 \tabularnewline
106 & 14 & 14.5037660976662 & -0.503766097666178 \tabularnewline
107 & 9 & 10.2724080847858 & -1.27240808478583 \tabularnewline
108 & 14 & 15.1734351668955 & -1.17343516689551 \tabularnewline
109 & 8 & 10.3859563378308 & -2.38595633783085 \tabularnewline
110 & 8 & 10.8335213821123 & -2.83352138211233 \tabularnewline
111 & 11 & 12.1194482895978 & -1.11944828959782 \tabularnewline
112 & 12 & 13.5468635002750 & -1.54686350027496 \tabularnewline
113 & 11 & 11.3576193324226 & -0.357619332422551 \tabularnewline
114 & 14 & 13.5362074591509 & 0.463792540849051 \tabularnewline
115 & 15 & 14.2725652354997 & 0.727434764500338 \tabularnewline
116 & 16 & 13.3263949620835 & 2.67360503791648 \tabularnewline
117 & 16 & 13.4226592061059 & 2.57734079389407 \tabularnewline
118 & 11 & 12.6740099018768 & -1.67400990187679 \tabularnewline
119 & 14 & 13.6665956586857 & 0.333404341314276 \tabularnewline
120 & 14 & 10.8779335990669 & 3.12206640093309 \tabularnewline
121 & 12 & 11.3316825487141 & 0.668317451285861 \tabularnewline
122 & 14 & 12.4764889326898 & 1.52351106731022 \tabularnewline
123 & 8 & 10.1461242413275 & -2.14612424132753 \tabularnewline
124 & 13 & 13.7628599027081 & -0.762859902708136 \tabularnewline
125 & 16 & 13.6665956586857 & 2.33340434131428 \tabularnewline
126 & 12 & 10.8483580621639 & 1.15164193783612 \tabularnewline
127 & 16 & 15.3832476639629 & 0.616752336037055 \tabularnewline
128 & 12 & 13.3091109530609 & -1.30911095306091 \tabularnewline
129 & 11 & 11.4390468963934 & -0.439046896393411 \tabularnewline
130 & 4 & 6.26564450652239 & -2.26564450652239 \tabularnewline
131 & 16 & 15.3832476639629 & 0.616752336037055 \tabularnewline
132 & 15 & 12.4810373512991 & 2.51896264870089 \tabularnewline
133 & 10 & 11.4283908552694 & -1.42839085526940 \tabularnewline
134 & 13 & 13.1445987980138 & -0.144598798013771 \tabularnewline
135 & 15 & 13.1787227535261 & 1.82127724647387 \tabularnewline
136 & 12 & 10.5998731909748 & 1.40012680902525 \tabularnewline
137 & 14 & 13.5703314146633 & 0.429668585336687 \tabularnewline
138 & 7 & 10.5953247723654 & -3.59532477236542 \tabularnewline
139 & 19 & 14.0240803643105 & 4.97591963568946 \tabularnewline
140 & 12 & 12.6073211947574 & -0.607321194757413 \tabularnewline
141 & 12 & 12.2111641150109 & -0.211164115010906 \tabularnewline
142 & 13 & 13.4226592061059 & -0.422659206105929 \tabularnewline
143 & 15 & 12.8920095707482 & 2.10799042925183 \tabularnewline
144 & 8 & 8.14888821613602 & -0.148888216136022 \tabularnewline
145 & 12 & 10.8438096435545 & 1.15619035644545 \tabularnewline
146 & 10 & 10.7220742187056 & -0.722074218705579 \tabularnewline
147 & 8 & 11.3362309673235 & -3.33623096732347 \tabularnewline
148 & 10 & 14.3128730963778 & -4.31287309637777 \tabularnewline
149 & 15 & 13.8142678672431 & 1.18573213275689 \tabularnewline
150 & 16 & 14.5378900531785 & 1.46210994682146 \tabularnewline
151 & 13 & 13.1038468746028 & -0.103846874602806 \tabularnewline
152 & 16 & 15.0043745932390 & 0.995625406760954 \tabularnewline
153 & 9 & 10.1461242413275 & -1.14612424132753 \tabularnewline
154 & 14 & 13.0997425185263 & 0.900257481473668 \tabularnewline
155 & 14 & 12.6114255508339 & 1.38857444916611 \tabularnewline
156 & 12 & 10.1033475111294 & 1.89665248887064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&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]11.5428761098157[/C][C]1.45712389018429[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.2286121969219[/C][C]0.771387803078095[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.7070931593744[/C][C]1.29290684062560[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.9612078299537[/C][C]1.03879217004625[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.8089872027870[/C][C]-0.808987202787039[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.3815719921099[/C][C]2.61842800789009[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.9322201596178[/C][C]-1.93222015961783[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.6226426201080[/C][C]-1.62264262010796[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.4269060947218[/C][C]-0.426906094721793[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]8.11040548182489[/C][C]2.88959451817511[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.4865483383319[/C][C]-2.48654833833189[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.2257463723402[/C][C]-1.22574637234017[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.3688701622882[/C][C]2.63112983771178[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4165922663574[/C][C]-4.4165922663574[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.8000137556906[/C][C]-0.800013755690625[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]11.1105155252676[/C][C]-0.110515525267554[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.2257463723402[/C][C]-2.22574637234016[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.5351667611362[/C][C]-0.535166761136192[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.83566315451674[/C][C]1.16433684548326[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.65344849483632[/C][C]-3.65344849483632[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.8274544423638[/C][C]2.17254555763621[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.5565551262353[/C][C]0.443444873764725[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.7674490040836[/C][C]-0.76744900408361[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.64689680978879[/C][C]2.35310319021121[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.5589768882842[/C][C]0.441023111715841[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]15.0230396662958[/C][C]-0.0230396662957811[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.8004915788151[/C][C]-5.80049157881507[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.6646586419358[/C][C]0.33534135806416[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.3837324092619[/C][C]-0.383732409261944[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.9632873792430[/C][C]5.03671262075697[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.62585065744931[/C][C]4.37414934255069[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.7453214709632[/C][C]-1.74532147096323[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.6528193702577[/C][C]-0.652819370257688[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.2207456975286[/C][C]0.779254302471436[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.6226426201080[/C][C]-1.62264262010796[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.4216185080912[/C][C]1.57838149190883[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.0542640366348[/C][C]-1.05426403663482[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.31735215431059[/C][C]0.682647845689415[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.2187761916820[/C][C]-0.218776191681953[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.4019127370197[/C][C]0.59808726298032[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.0711039831246[/C][C]0.92889601687543[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.24742164925827[/C][C]6.75257835074173[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.91430117675694[/C][C]-4.91430117675694[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.6399266291306[/C][C]-2.63992662913057[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.3976727705784[/C][C]-0.39767277057837[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.9551339680307[/C][C]2.04486603196933[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.1973878265829[/C][C]3.80261217341713[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.61929897240178[/C][C]0.38070102759822[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]12.1080937632187[/C][C]-3.10809376321867[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8753674577384[/C][C]-1.87536745773840[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.1196123624862[/C][C]-1.11961236248621[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.3053488097440[/C][C]-6.30534880974405[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.3119767776426[/C][C]-0.311976777642647[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.3559367383950[/C][C]-2.35593673839496[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.2946927686200[/C][C]1.70530723137996[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.346100733155[/C][C]-0.346100733155009[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.6854051110221[/C][C]0.314594888977911[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.3367087904479[/C][C]1.66329120955209[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2168957641744[/C][C]-2.21689576417438[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.1734351668955[/C][C]-0.173435166895513[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.37564250946645[/C][C]-2.37564250946645[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.9105321112655[/C][C]2.08946788873448[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.6385793256880[/C][C]0.361420674311962[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.6838796677083[/C][C]2.31612033229167[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.84459591884701[/C][C]-0.844595918847013[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1984285245976[/C][C]1.80157147540237[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.9516262474361[/C][C]-1.95162624743609[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3773251034940[/C][C]2.62267489650596[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.8212380479013[/C][C]2.17876195209868[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.4672227245147[/C][C]-0.467222724514699[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.346100733155[/C][C]2.65389926684499[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.1984285245976[/C][C]0.801571475402373[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]11.9717760810404[/C][C]-0.97177608104044[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9175278588459[/C][C]-2.91752785884590[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.3847731072767[/C][C]-0.384773107276702[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.3091109530609[/C][C]-1.30911095306091[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.1811445155750[/C][C]-0.181144515575018[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.8133715485080[/C][C]0.18662845149202[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7134214440198[/C][C]3.28657855598023[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.90747537537835[/C][C]-1.90747537537835[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.75451558019035[/C][C]-2.75451558019035[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.8861678781420[/C][C]2.11383212185797[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.7974279207534[/C][C]0.202572079246647[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1605424257684[/C][C]-1.1605424257684[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.64383315172707[/C][C]4.35616684827293[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.3160811337191[/C][C]-0.316081133719121[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.1872183774981[/C][C]-0.187218377498103[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9442038105756[/C][C]0.0557961894243915[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.9294987143157[/C][C]1.07050128568428[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.1461242413275[/C][C]-0.146124241327528[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.0656185630140[/C][C]0.934381436986031[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.4399432151285[/C][C]-3.43994321512854[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2483110458188[/C][C]0.751688954181159[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.3605189175959[/C][C]1.63948108240412[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.2568066697908[/C][C]0.74319333020919[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.64096732714533[/C][C]0.35903267285467[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.1444887545711[/C][C]-2.14448875457111[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.51300088965944[/C][C]-0.513000889659439[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.7722774123374[/C][C]-1.77227741233740[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.3333651427417[/C][C]0.66663485725827[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.3959832543762[/C][C]0.604016745623776[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.6468898876142[/C][C]1.35311011238577[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.7769358743894[/C][C]-1.77693587438940[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.80878936930706[/C][C]2.19121063069294[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.86958927654913[/C][C]-2.86958927654913[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5037660976662[/C][C]-0.503766097666178[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.2724080847858[/C][C]-1.27240808478583[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.1734351668955[/C][C]-1.17343516689551[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.3859563378308[/C][C]-2.38595633783085[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.8335213821123[/C][C]-2.83352138211233[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1194482895978[/C][C]-1.11944828959782[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.5468635002750[/C][C]-1.54686350027496[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.3576193324226[/C][C]-0.357619332422551[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.5362074591509[/C][C]0.463792540849051[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.2725652354997[/C][C]0.727434764500338[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3263949620835[/C][C]2.67360503791648[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4226592061059[/C][C]2.57734079389407[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.6740099018768[/C][C]-1.67400990187679[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.6665956586857[/C][C]0.333404341314276[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8779335990669[/C][C]3.12206640093309[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.3316825487141[/C][C]0.668317451285861[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4764889326898[/C][C]1.52351106731022[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.1461242413275[/C][C]-2.14612424132753[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.7628599027081[/C][C]-0.762859902708136[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.6665956586857[/C][C]2.33340434131428[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.8483580621639[/C][C]1.15164193783612[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3832476639629[/C][C]0.616752336037055[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.3091109530609[/C][C]-1.30911095306091[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4390468963934[/C][C]-0.439046896393411[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.26564450652239[/C][C]-2.26564450652239[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.3832476639629[/C][C]0.616752336037055[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.4810373512991[/C][C]2.51896264870089[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4283908552694[/C][C]-1.42839085526940[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.1445987980138[/C][C]-0.144598798013771[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.1787227535261[/C][C]1.82127724647387[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.5998731909748[/C][C]1.40012680902525[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.5703314146633[/C][C]0.429668585336687[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.5953247723654[/C][C]-3.59532477236542[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0240803643105[/C][C]4.97591963568946[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.6073211947574[/C][C]-0.607321194757413[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2111641150109[/C][C]-0.211164115010906[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.4226592061059[/C][C]-0.422659206105929[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.8920095707482[/C][C]2.10799042925183[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.14888821613602[/C][C]-0.148888216136022[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.8438096435545[/C][C]1.15619035644545[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.7220742187056[/C][C]-0.722074218705579[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.3362309673235[/C][C]-3.33623096732347[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3128730963778[/C][C]-4.31287309637777[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8142678672431[/C][C]1.18573213275689[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.5378900531785[/C][C]1.46210994682146[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1038468746028[/C][C]-0.103846874602806[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0043745932390[/C][C]0.995625406760954[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.1461242413275[/C][C]-1.14612424132753[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.0997425185263[/C][C]0.900257481473668[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6114255508339[/C][C]1.38857444916611[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1033475111294[/C][C]1.89665248887064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&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
11311.54287610981571.45712389018429
21211.22861219692190.771387803078095
31513.70709315937441.29290684062560
41210.96120782995371.03879217004625
51010.8089872027870-0.808987202787039
6129.38157199210992.61842800789009
71516.9322201596178-1.93222015961783
8910.6226426201080-1.62264262010796
91212.4269060947218-0.426906094721793
10118.110405481824892.88959451817511
111113.4865483383319-2.48654833833189
121112.2257463723402-1.22574637234017
131512.36887016228822.63112983771178
14711.4165922663574-4.4165922663574
151111.8000137556906-0.800013755690625
161111.1105155252676-0.110515525267554
171012.2257463723402-2.22574637234016
181414.5351667611362-0.535166761136192
19108.835663154516741.16433684548326
2069.65344849483632-3.65344849483632
21118.82745444236382.17254555763621
221514.55655512623530.443444873764725
231111.7674490040836-0.76744900408361
24129.646896809788792.35310319021121
251413.55897688828420.441023111715841
261515.0230396662958-0.0230396662957811
27914.8004915788151-5.80049157881507
281312.66465864193580.33534135806416
291313.3837324092619-0.383732409261944
301610.96328737924305.03671262075697
31138.625850657449314.37414934255069
321213.7453214709632-1.74532147096323
331414.6528193702577-0.652819370257688
341110.22074569752860.779254302471436
35910.6226426201080-1.62264262010796
361614.42161850809121.57838149190883
371213.0542640366348-1.05426403663482
38109.317352154310590.682647845689415
391313.2187761916820-0.218776191681953
401615.40191273701970.59808726298032
411413.07110398312460.92889601687543
42158.247421649258276.75257835074173
4359.91430117675694-4.91430117675694
44810.6399266291306-2.63992662913057
451111.3976727705784-0.39767277057837
461613.95513396803072.04486603196933
471713.19738782658293.80261217341713
4898.619298972401780.38070102759822
49912.1080937632187-3.10809376321867
501314.8753674577384-1.87536745773840
511011.1196123624862-1.11961236248621
52612.3053488097440-6.30534880974405
531212.3119767776426-0.311976777642647
54810.3559367383950-2.35593673839496
551412.29469276862001.70530723137996
561212.346100733155-0.346100733155009
571110.68540511102210.314594888977911
581614.33670879044791.66329120955209
59810.2168957641744-2.21689576417438
601515.1734351668955-0.173435166895513
6179.37564250946645-2.37564250946645
621613.91053211126552.08946788873448
631413.63857932568800.361420674311962
641613.68387966770832.31612033229167
6599.84459591884701-0.844595918847013
661412.19842852459761.80157147540237
671112.9516262474361-1.95162624743609
681310.37732510349402.62267489650596
691512.82123804790132.17876195209868
7055.4672227245147-0.467222724514699
711512.3461007331552.65389926684499
721312.19842852459760.801571475402373
731111.9717760810404-0.97177608104044
741113.9175278588459-2.91752785884590
751212.3847731072767-0.384773107276702
761213.3091109530609-1.30911095306091
771212.1811445155750-0.181144515575018
781211.81337154850800.18662845149202
791410.71342144401983.28657855598023
8067.90747537537835-1.90747537537835
8179.75451558019035-2.75451558019035
821411.88616787814202.11383212185797
831413.79742792075340.202572079246647
841011.1605424257684-1.1605424257684
85138.643833151727074.35616684827293
861212.3160811337191-0.316081133719121
8799.1872183774981-0.187218377498103
881211.94420381057560.0557961894243915
891614.92949871431571.07050128568428
901010.1461242413275-0.146124241327528
911413.06561856301400.934381436986031
921013.4399432151285-3.43994321512854
931615.24831104581880.751688954181159
941513.36051891759591.63948108240412
951211.25680666979080.74319333020919
96109.640967327145330.35903267285467
97810.1444887545711-2.14448875457111
9888.51300088965944-0.513000889659439
991112.7722774123374-1.77227741233740
1001312.33336514274170.66663485725827
1011615.39598325437620.604016745623776
1021614.64688988761421.35311011238577
1031415.7769358743894-1.77693587438940
104118.808789369307062.19121063069294
10546.86958927654913-2.86958927654913
1061414.5037660976662-0.503766097666178
107910.2724080847858-1.27240808478583
1081415.1734351668955-1.17343516689551
109810.3859563378308-2.38595633783085
110810.8335213821123-2.83352138211233
1111112.1194482895978-1.11944828959782
1121213.5468635002750-1.54686350027496
1131111.3576193324226-0.357619332422551
1141413.53620745915090.463792540849051
1151514.27256523549970.727434764500338
1161613.32639496208352.67360503791648
1171613.42265920610592.57734079389407
1181112.6740099018768-1.67400990187679
1191413.66659565868570.333404341314276
1201410.87793359906693.12206640093309
1211211.33168254871410.668317451285861
1221412.47648893268981.52351106731022
123810.1461242413275-2.14612424132753
1241313.7628599027081-0.762859902708136
1251613.66659565868572.33340434131428
1261210.84835806216391.15164193783612
1271615.38324766396290.616752336037055
1281213.3091109530609-1.30911095306091
1291111.4390468963934-0.439046896393411
13046.26564450652239-2.26564450652239
1311615.38324766396290.616752336037055
1321512.48103735129912.51896264870089
1331011.4283908552694-1.42839085526940
1341313.1445987980138-0.144598798013771
1351513.17872275352611.82127724647387
1361210.59987319097481.40012680902525
1371413.57033141466330.429668585336687
138710.5953247723654-3.59532477236542
1391914.02408036431054.97591963568946
1401212.6073211947574-0.607321194757413
1411212.2111641150109-0.211164115010906
1421313.4226592061059-0.422659206105929
1431512.89200957074822.10799042925183
14488.14888821613602-0.148888216136022
1451210.84380964355451.15619035644545
1461010.7220742187056-0.722074218705579
147811.3362309673235-3.33623096732347
1481014.3128730963778-4.31287309637777
1491513.81426786724311.18573213275689
1501614.53789005317851.46210994682146
1511313.1038468746028-0.103846874602806
1521615.00437459323900.995625406760954
153910.1461242413275-1.14612424132753
1541413.09974251852630.900257481473668
1551412.61142555083391.38857444916611
1561210.10334751112941.89665248887064






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1099066623509470.2198133247018940.890093337649053
100.0478060903171790.0956121806343580.952193909682821
110.07272965627741940.1454593125548390.92727034372258
120.03807835479920440.07615670959840870.961921645200796
130.4402804947073120.8805609894146240.559719505292688
140.7624254754877040.4751490490245920.237574524512296
150.6820338784022580.6359322431954840.317966121597742
160.592928156773980.814143686452040.40707184322602
170.5347173537571110.9305652924857780.465282646242889
180.447716309952050.89543261990410.55228369004795
190.3704081811435480.7408163622870960.629591818856452
200.6651752712083690.6696494575832620.334824728791631
210.6005542563600740.7988914872798530.399445743639926
220.5668068985383640.8663862029232720.433193101461636
230.4960293306347260.9920586612694520.503970669365274
240.4768823910699050.953764782139810.523117608930095
250.4388901476241530.8777802952483060.561109852375847
260.3808077455838490.7616154911676980.619192254416151
270.6484716296833410.7030567406333180.351528370316659
280.5907580991174080.8184838017651840.409241900882592
290.5306221849362960.9387556301274080.469377815063704
300.6885644145274270.6228711709451470.311435585472573
310.8056357689663110.3887284620673780.194364231033689
320.7697890445274520.4604219109450950.230210955472548
330.7292910483586630.5414179032826730.270708951641337
340.6847424958502050.630515008299590.315257504149795
350.6785749979456770.6428500041086460.321425002054323
360.6960467251368910.6079065497262180.303953274863109
370.6574614965524840.6850770068950310.342538503447516
380.606977509602480.786044980795040.39302249039752
390.5567306883698310.8865386232603390.443269311630169
400.5386054479975950.922789104004810.461394552002405
410.5083116329504590.9833767340990810.491688367049541
420.8102366364284850.379526727143030.189763363571515
430.951972892758120.09605421448376150.0480271072418808
440.9618133467347730.07637330653045340.0381866532652267
450.950286168996270.09942766200746180.0497138310037309
460.9565562163797780.08688756724044440.0434437836202222
470.9829906472953390.03401870540932230.0170093527046611
480.9795070278899090.04098594422018280.0204929721100914
490.9819640752683030.0360718494633950.0180359247316975
500.9771810494243310.0456379011513380.022818950575669
510.9719506318467680.05609873630646370.0280493681532319
520.9892137702466850.02157245950662940.0107862297533147
530.9920212114723850.01595757705522910.00797878852761455
540.9905176915695320.01896461686093600.00948230843046798
550.9946171284903560.01076574301928820.0053828715096441
560.9931225613106920.01375487737861510.00687743868930756
570.9908312326145180.01833753477096330.00916876738548164
580.9912809637711850.01743807245763040.00871903622881522
590.9923122652969820.01537546940603610.00768773470301807
600.9908551757936650.01828964841266920.00914482420633459
610.9909179120658960.01816417586820760.00908208793410379
620.9930652395366390.01386952092672290.00693476046336143
630.9911493175366920.01770136492661600.00885068246330802
640.9924600908068720.01507981838625640.00753990919312822
650.9900936903760820.01981261924783510.00990630962391757
660.9897506989631020.0204986020737960.010249301036898
670.9893141572535140.02137168549297230.0106858427464862
680.991537703948250.01692459210350050.00846229605175024
690.9919567187235160.01608656255296810.00804328127648406
700.9891203551131850.02175928977363030.0108796448868151
710.9909247665167610.01815046696647790.00907523348323894
720.9880729446947450.02385411061051070.0119270553052554
730.9849736849417860.03005263011642850.0150263150582142
740.988942857971290.02211428405741870.0110571420287093
750.9851146151012980.02977076979740290.0148853848987015
760.9825681226349240.03486375473015240.0174318773650762
770.9770907852489850.0458184295020310.0229092147510155
780.9698799788324630.06024004233507360.0301200211675368
790.9811415111250370.03771697774992690.0188584888749634
800.9801820372753510.0396359254492980.019817962724649
810.983345826020420.03330834795916120.0166541739795806
820.9844042362590040.03119152748199180.0155957637409959
830.9791748897514770.04165022049704680.0208251102485234
840.9743694222538040.05126115549239180.0256305777461959
850.9930124347279430.01397513054411370.00698756527205687
860.9905124542516320.01897509149673560.00948754574836781
870.9870793503403380.02584129931932400.0129206496596620
880.9830038099664860.03399238006702840.0169961900335142
890.9787683241927180.04246335161456310.0212316758072815
900.9719509099069260.05609818018614820.0280490900930741
910.9660621139042020.06787577219159550.0339378860957977
920.9806406500348980.03871869993020350.0193593499651018
930.9750596757098820.04988064858023550.0249403242901177
940.9714017513117680.05719649737646410.0285982486882320
950.9638365157515850.07232696849683070.0361634842484154
960.9540942427849840.09181151443003160.0459057572150158
970.9501294057082440.0997411885835120.049870594291756
980.9364481276853280.1271037446293440.063551872314672
990.9329778885908070.1340442228183860.067022111409193
1000.9173365462304340.1653269075391310.0826634537695656
1010.89829316333170.2034136733366010.101706836668301
1020.8813895629904970.2372208740190060.118610437009503
1030.892570926890470.2148581462190590.107429073109529
1040.923850564182780.1522988716344380.0761494358172192
1050.9223812776734840.1552374446530320.0776187223265161
1060.9033704685831870.1932590628336270.0966295314168133
1070.8848548682414920.2302902635170160.115145131758508
1080.8801693977777620.2396612044444760.119830602222238
1090.8796348675985160.2407302648029670.120365132401484
1100.9032255540609570.1935488918780860.096774445939043
1110.8933949157284470.2132101685431060.106605084271553
1120.923330193122960.1533396137540810.0766698068770406
1130.9021429525452480.1957140949095030.0978570474547515
1140.8765923549612360.2468152900775290.123407645038764
1150.8469762029181960.3060475941636070.153023797081804
1160.8466477473406690.3067045053186620.153352252659331
1170.8549623914266050.290075217146790.145037608573395
1180.8446589688113860.3106820623772290.155341031188614
1190.8077191387550450.384561722489910.192280861244955
1200.861421152136840.2771576957263200.138578847863160
1210.8342800578682960.3314398842634080.165719942131704
1220.8097727134829670.3804545730340660.190227286517033
1230.7996999638079390.4006000723841230.200300036192061
1240.7586675696704620.4826648606590750.241332430329538
1250.7585078826617580.4829842346764840.241492117338242
1260.7399295199602950.520140960079410.260070480039705
1270.6869399817418380.6261200365163240.313060018258162
1280.6583975916486630.6832048167026730.341602408351337
1290.675586953089330.648826093821340.32441304691067
1300.6673301440094470.6653397119811060.332669855990553
1310.6066842399992140.7866315200015730.393315760000786
1320.594035835994940.8119283280101190.405964164005059
1330.5644061612960730.8711876774078550.435593838703927
1340.4899443981336520.9798887962673040.510055601866348
1350.4634216768889270.9268433537778540.536578323111073
1360.4569622371714050.913924474342810.543037762828595
1370.3833521541245690.7667043082491380.616647845875431
1380.4461766697664550.892353339532910.553823330233545
1390.7943971110752750.411205777849450.205602888924725
1400.8245348828376440.3509302343247130.175465117162356
1410.7790238156470740.4419523687058520.220976184352926
1420.6942859943340670.6114280113318650.305714005665933
1430.6553363809922170.6893272380155660.344663619007783
1440.5406140261899650.918771947620070.459385973810035
1450.5186404750468310.9627190499063370.481359524953169
1460.6332466981542050.733506603691590.366753301845795
1470.5705968111263920.8588063777472160.429403188873608

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.109906662350947 & 0.219813324701894 & 0.890093337649053 \tabularnewline
10 & 0.047806090317179 & 0.095612180634358 & 0.952193909682821 \tabularnewline
11 & 0.0727296562774194 & 0.145459312554839 & 0.92727034372258 \tabularnewline
12 & 0.0380783547992044 & 0.0761567095984087 & 0.961921645200796 \tabularnewline
13 & 0.440280494707312 & 0.880560989414624 & 0.559719505292688 \tabularnewline
14 & 0.762425475487704 & 0.475149049024592 & 0.237574524512296 \tabularnewline
15 & 0.682033878402258 & 0.635932243195484 & 0.317966121597742 \tabularnewline
16 & 0.59292815677398 & 0.81414368645204 & 0.40707184322602 \tabularnewline
17 & 0.534717353757111 & 0.930565292485778 & 0.465282646242889 \tabularnewline
18 & 0.44771630995205 & 0.8954326199041 & 0.55228369004795 \tabularnewline
19 & 0.370408181143548 & 0.740816362287096 & 0.629591818856452 \tabularnewline
20 & 0.665175271208369 & 0.669649457583262 & 0.334824728791631 \tabularnewline
21 & 0.600554256360074 & 0.798891487279853 & 0.399445743639926 \tabularnewline
22 & 0.566806898538364 & 0.866386202923272 & 0.433193101461636 \tabularnewline
23 & 0.496029330634726 & 0.992058661269452 & 0.503970669365274 \tabularnewline
24 & 0.476882391069905 & 0.95376478213981 & 0.523117608930095 \tabularnewline
25 & 0.438890147624153 & 0.877780295248306 & 0.561109852375847 \tabularnewline
26 & 0.380807745583849 & 0.761615491167698 & 0.619192254416151 \tabularnewline
27 & 0.648471629683341 & 0.703056740633318 & 0.351528370316659 \tabularnewline
28 & 0.590758099117408 & 0.818483801765184 & 0.409241900882592 \tabularnewline
29 & 0.530622184936296 & 0.938755630127408 & 0.469377815063704 \tabularnewline
30 & 0.688564414527427 & 0.622871170945147 & 0.311435585472573 \tabularnewline
31 & 0.805635768966311 & 0.388728462067378 & 0.194364231033689 \tabularnewline
32 & 0.769789044527452 & 0.460421910945095 & 0.230210955472548 \tabularnewline
33 & 0.729291048358663 & 0.541417903282673 & 0.270708951641337 \tabularnewline
34 & 0.684742495850205 & 0.63051500829959 & 0.315257504149795 \tabularnewline
35 & 0.678574997945677 & 0.642850004108646 & 0.321425002054323 \tabularnewline
36 & 0.696046725136891 & 0.607906549726218 & 0.303953274863109 \tabularnewline
37 & 0.657461496552484 & 0.685077006895031 & 0.342538503447516 \tabularnewline
38 & 0.60697750960248 & 0.78604498079504 & 0.39302249039752 \tabularnewline
39 & 0.556730688369831 & 0.886538623260339 & 0.443269311630169 \tabularnewline
40 & 0.538605447997595 & 0.92278910400481 & 0.461394552002405 \tabularnewline
41 & 0.508311632950459 & 0.983376734099081 & 0.491688367049541 \tabularnewline
42 & 0.810236636428485 & 0.37952672714303 & 0.189763363571515 \tabularnewline
43 & 0.95197289275812 & 0.0960542144837615 & 0.0480271072418808 \tabularnewline
44 & 0.961813346734773 & 0.0763733065304534 & 0.0381866532652267 \tabularnewline
45 & 0.95028616899627 & 0.0994276620074618 & 0.0497138310037309 \tabularnewline
46 & 0.956556216379778 & 0.0868875672404444 & 0.0434437836202222 \tabularnewline
47 & 0.982990647295339 & 0.0340187054093223 & 0.0170093527046611 \tabularnewline
48 & 0.979507027889909 & 0.0409859442201828 & 0.0204929721100914 \tabularnewline
49 & 0.981964075268303 & 0.036071849463395 & 0.0180359247316975 \tabularnewline
50 & 0.977181049424331 & 0.045637901151338 & 0.022818950575669 \tabularnewline
51 & 0.971950631846768 & 0.0560987363064637 & 0.0280493681532319 \tabularnewline
52 & 0.989213770246685 & 0.0215724595066294 & 0.0107862297533147 \tabularnewline
53 & 0.992021211472385 & 0.0159575770552291 & 0.00797878852761455 \tabularnewline
54 & 0.990517691569532 & 0.0189646168609360 & 0.00948230843046798 \tabularnewline
55 & 0.994617128490356 & 0.0107657430192882 & 0.0053828715096441 \tabularnewline
56 & 0.993122561310692 & 0.0137548773786151 & 0.00687743868930756 \tabularnewline
57 & 0.990831232614518 & 0.0183375347709633 & 0.00916876738548164 \tabularnewline
58 & 0.991280963771185 & 0.0174380724576304 & 0.00871903622881522 \tabularnewline
59 & 0.992312265296982 & 0.0153754694060361 & 0.00768773470301807 \tabularnewline
60 & 0.990855175793665 & 0.0182896484126692 & 0.00914482420633459 \tabularnewline
61 & 0.990917912065896 & 0.0181641758682076 & 0.00908208793410379 \tabularnewline
62 & 0.993065239536639 & 0.0138695209267229 & 0.00693476046336143 \tabularnewline
63 & 0.991149317536692 & 0.0177013649266160 & 0.00885068246330802 \tabularnewline
64 & 0.992460090806872 & 0.0150798183862564 & 0.00753990919312822 \tabularnewline
65 & 0.990093690376082 & 0.0198126192478351 & 0.00990630962391757 \tabularnewline
66 & 0.989750698963102 & 0.020498602073796 & 0.010249301036898 \tabularnewline
67 & 0.989314157253514 & 0.0213716854929723 & 0.0106858427464862 \tabularnewline
68 & 0.99153770394825 & 0.0169245921035005 & 0.00846229605175024 \tabularnewline
69 & 0.991956718723516 & 0.0160865625529681 & 0.00804328127648406 \tabularnewline
70 & 0.989120355113185 & 0.0217592897736303 & 0.0108796448868151 \tabularnewline
71 & 0.990924766516761 & 0.0181504669664779 & 0.00907523348323894 \tabularnewline
72 & 0.988072944694745 & 0.0238541106105107 & 0.0119270553052554 \tabularnewline
73 & 0.984973684941786 & 0.0300526301164285 & 0.0150263150582142 \tabularnewline
74 & 0.98894285797129 & 0.0221142840574187 & 0.0110571420287093 \tabularnewline
75 & 0.985114615101298 & 0.0297707697974029 & 0.0148853848987015 \tabularnewline
76 & 0.982568122634924 & 0.0348637547301524 & 0.0174318773650762 \tabularnewline
77 & 0.977090785248985 & 0.045818429502031 & 0.0229092147510155 \tabularnewline
78 & 0.969879978832463 & 0.0602400423350736 & 0.0301200211675368 \tabularnewline
79 & 0.981141511125037 & 0.0377169777499269 & 0.0188584888749634 \tabularnewline
80 & 0.980182037275351 & 0.039635925449298 & 0.019817962724649 \tabularnewline
81 & 0.98334582602042 & 0.0333083479591612 & 0.0166541739795806 \tabularnewline
82 & 0.984404236259004 & 0.0311915274819918 & 0.0155957637409959 \tabularnewline
83 & 0.979174889751477 & 0.0416502204970468 & 0.0208251102485234 \tabularnewline
84 & 0.974369422253804 & 0.0512611554923918 & 0.0256305777461959 \tabularnewline
85 & 0.993012434727943 & 0.0139751305441137 & 0.00698756527205687 \tabularnewline
86 & 0.990512454251632 & 0.0189750914967356 & 0.00948754574836781 \tabularnewline
87 & 0.987079350340338 & 0.0258412993193240 & 0.0129206496596620 \tabularnewline
88 & 0.983003809966486 & 0.0339923800670284 & 0.0169961900335142 \tabularnewline
89 & 0.978768324192718 & 0.0424633516145631 & 0.0212316758072815 \tabularnewline
90 & 0.971950909906926 & 0.0560981801861482 & 0.0280490900930741 \tabularnewline
91 & 0.966062113904202 & 0.0678757721915955 & 0.0339378860957977 \tabularnewline
92 & 0.980640650034898 & 0.0387186999302035 & 0.0193593499651018 \tabularnewline
93 & 0.975059675709882 & 0.0498806485802355 & 0.0249403242901177 \tabularnewline
94 & 0.971401751311768 & 0.0571964973764641 & 0.0285982486882320 \tabularnewline
95 & 0.963836515751585 & 0.0723269684968307 & 0.0361634842484154 \tabularnewline
96 & 0.954094242784984 & 0.0918115144300316 & 0.0459057572150158 \tabularnewline
97 & 0.950129405708244 & 0.099741188583512 & 0.049870594291756 \tabularnewline
98 & 0.936448127685328 & 0.127103744629344 & 0.063551872314672 \tabularnewline
99 & 0.932977888590807 & 0.134044222818386 & 0.067022111409193 \tabularnewline
100 & 0.917336546230434 & 0.165326907539131 & 0.0826634537695656 \tabularnewline
101 & 0.8982931633317 & 0.203413673336601 & 0.101706836668301 \tabularnewline
102 & 0.881389562990497 & 0.237220874019006 & 0.118610437009503 \tabularnewline
103 & 0.89257092689047 & 0.214858146219059 & 0.107429073109529 \tabularnewline
104 & 0.92385056418278 & 0.152298871634438 & 0.0761494358172192 \tabularnewline
105 & 0.922381277673484 & 0.155237444653032 & 0.0776187223265161 \tabularnewline
106 & 0.903370468583187 & 0.193259062833627 & 0.0966295314168133 \tabularnewline
107 & 0.884854868241492 & 0.230290263517016 & 0.115145131758508 \tabularnewline
108 & 0.880169397777762 & 0.239661204444476 & 0.119830602222238 \tabularnewline
109 & 0.879634867598516 & 0.240730264802967 & 0.120365132401484 \tabularnewline
110 & 0.903225554060957 & 0.193548891878086 & 0.096774445939043 \tabularnewline
111 & 0.893394915728447 & 0.213210168543106 & 0.106605084271553 \tabularnewline
112 & 0.92333019312296 & 0.153339613754081 & 0.0766698068770406 \tabularnewline
113 & 0.902142952545248 & 0.195714094909503 & 0.0978570474547515 \tabularnewline
114 & 0.876592354961236 & 0.246815290077529 & 0.123407645038764 \tabularnewline
115 & 0.846976202918196 & 0.306047594163607 & 0.153023797081804 \tabularnewline
116 & 0.846647747340669 & 0.306704505318662 & 0.153352252659331 \tabularnewline
117 & 0.854962391426605 & 0.29007521714679 & 0.145037608573395 \tabularnewline
118 & 0.844658968811386 & 0.310682062377229 & 0.155341031188614 \tabularnewline
119 & 0.807719138755045 & 0.38456172248991 & 0.192280861244955 \tabularnewline
120 & 0.86142115213684 & 0.277157695726320 & 0.138578847863160 \tabularnewline
121 & 0.834280057868296 & 0.331439884263408 & 0.165719942131704 \tabularnewline
122 & 0.809772713482967 & 0.380454573034066 & 0.190227286517033 \tabularnewline
123 & 0.799699963807939 & 0.400600072384123 & 0.200300036192061 \tabularnewline
124 & 0.758667569670462 & 0.482664860659075 & 0.241332430329538 \tabularnewline
125 & 0.758507882661758 & 0.482984234676484 & 0.241492117338242 \tabularnewline
126 & 0.739929519960295 & 0.52014096007941 & 0.260070480039705 \tabularnewline
127 & 0.686939981741838 & 0.626120036516324 & 0.313060018258162 \tabularnewline
128 & 0.658397591648663 & 0.683204816702673 & 0.341602408351337 \tabularnewline
129 & 0.67558695308933 & 0.64882609382134 & 0.32441304691067 \tabularnewline
130 & 0.667330144009447 & 0.665339711981106 & 0.332669855990553 \tabularnewline
131 & 0.606684239999214 & 0.786631520001573 & 0.393315760000786 \tabularnewline
132 & 0.59403583599494 & 0.811928328010119 & 0.405964164005059 \tabularnewline
133 & 0.564406161296073 & 0.871187677407855 & 0.435593838703927 \tabularnewline
134 & 0.489944398133652 & 0.979888796267304 & 0.510055601866348 \tabularnewline
135 & 0.463421676888927 & 0.926843353777854 & 0.536578323111073 \tabularnewline
136 & 0.456962237171405 & 0.91392447434281 & 0.543037762828595 \tabularnewline
137 & 0.383352154124569 & 0.766704308249138 & 0.616647845875431 \tabularnewline
138 & 0.446176669766455 & 0.89235333953291 & 0.553823330233545 \tabularnewline
139 & 0.794397111075275 & 0.41120577784945 & 0.205602888924725 \tabularnewline
140 & 0.824534882837644 & 0.350930234324713 & 0.175465117162356 \tabularnewline
141 & 0.779023815647074 & 0.441952368705852 & 0.220976184352926 \tabularnewline
142 & 0.694285994334067 & 0.611428011331865 & 0.305714005665933 \tabularnewline
143 & 0.655336380992217 & 0.689327238015566 & 0.344663619007783 \tabularnewline
144 & 0.540614026189965 & 0.91877194762007 & 0.459385973810035 \tabularnewline
145 & 0.518640475046831 & 0.962719049906337 & 0.481359524953169 \tabularnewline
146 & 0.633246698154205 & 0.73350660369159 & 0.366753301845795 \tabularnewline
147 & 0.570596811126392 & 0.858806377747216 & 0.429403188873608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.109906662350947[/C][C]0.219813324701894[/C][C]0.890093337649053[/C][/ROW]
[ROW][C]10[/C][C]0.047806090317179[/C][C]0.095612180634358[/C][C]0.952193909682821[/C][/ROW]
[ROW][C]11[/C][C]0.0727296562774194[/C][C]0.145459312554839[/C][C]0.92727034372258[/C][/ROW]
[ROW][C]12[/C][C]0.0380783547992044[/C][C]0.0761567095984087[/C][C]0.961921645200796[/C][/ROW]
[ROW][C]13[/C][C]0.440280494707312[/C][C]0.880560989414624[/C][C]0.559719505292688[/C][/ROW]
[ROW][C]14[/C][C]0.762425475487704[/C][C]0.475149049024592[/C][C]0.237574524512296[/C][/ROW]
[ROW][C]15[/C][C]0.682033878402258[/C][C]0.635932243195484[/C][C]0.317966121597742[/C][/ROW]
[ROW][C]16[/C][C]0.59292815677398[/C][C]0.81414368645204[/C][C]0.40707184322602[/C][/ROW]
[ROW][C]17[/C][C]0.534717353757111[/C][C]0.930565292485778[/C][C]0.465282646242889[/C][/ROW]
[ROW][C]18[/C][C]0.44771630995205[/C][C]0.8954326199041[/C][C]0.55228369004795[/C][/ROW]
[ROW][C]19[/C][C]0.370408181143548[/C][C]0.740816362287096[/C][C]0.629591818856452[/C][/ROW]
[ROW][C]20[/C][C]0.665175271208369[/C][C]0.669649457583262[/C][C]0.334824728791631[/C][/ROW]
[ROW][C]21[/C][C]0.600554256360074[/C][C]0.798891487279853[/C][C]0.399445743639926[/C][/ROW]
[ROW][C]22[/C][C]0.566806898538364[/C][C]0.866386202923272[/C][C]0.433193101461636[/C][/ROW]
[ROW][C]23[/C][C]0.496029330634726[/C][C]0.992058661269452[/C][C]0.503970669365274[/C][/ROW]
[ROW][C]24[/C][C]0.476882391069905[/C][C]0.95376478213981[/C][C]0.523117608930095[/C][/ROW]
[ROW][C]25[/C][C]0.438890147624153[/C][C]0.877780295248306[/C][C]0.561109852375847[/C][/ROW]
[ROW][C]26[/C][C]0.380807745583849[/C][C]0.761615491167698[/C][C]0.619192254416151[/C][/ROW]
[ROW][C]27[/C][C]0.648471629683341[/C][C]0.703056740633318[/C][C]0.351528370316659[/C][/ROW]
[ROW][C]28[/C][C]0.590758099117408[/C][C]0.818483801765184[/C][C]0.409241900882592[/C][/ROW]
[ROW][C]29[/C][C]0.530622184936296[/C][C]0.938755630127408[/C][C]0.469377815063704[/C][/ROW]
[ROW][C]30[/C][C]0.688564414527427[/C][C]0.622871170945147[/C][C]0.311435585472573[/C][/ROW]
[ROW][C]31[/C][C]0.805635768966311[/C][C]0.388728462067378[/C][C]0.194364231033689[/C][/ROW]
[ROW][C]32[/C][C]0.769789044527452[/C][C]0.460421910945095[/C][C]0.230210955472548[/C][/ROW]
[ROW][C]33[/C][C]0.729291048358663[/C][C]0.541417903282673[/C][C]0.270708951641337[/C][/ROW]
[ROW][C]34[/C][C]0.684742495850205[/C][C]0.63051500829959[/C][C]0.315257504149795[/C][/ROW]
[ROW][C]35[/C][C]0.678574997945677[/C][C]0.642850004108646[/C][C]0.321425002054323[/C][/ROW]
[ROW][C]36[/C][C]0.696046725136891[/C][C]0.607906549726218[/C][C]0.303953274863109[/C][/ROW]
[ROW][C]37[/C][C]0.657461496552484[/C][C]0.685077006895031[/C][C]0.342538503447516[/C][/ROW]
[ROW][C]38[/C][C]0.60697750960248[/C][C]0.78604498079504[/C][C]0.39302249039752[/C][/ROW]
[ROW][C]39[/C][C]0.556730688369831[/C][C]0.886538623260339[/C][C]0.443269311630169[/C][/ROW]
[ROW][C]40[/C][C]0.538605447997595[/C][C]0.92278910400481[/C][C]0.461394552002405[/C][/ROW]
[ROW][C]41[/C][C]0.508311632950459[/C][C]0.983376734099081[/C][C]0.491688367049541[/C][/ROW]
[ROW][C]42[/C][C]0.810236636428485[/C][C]0.37952672714303[/C][C]0.189763363571515[/C][/ROW]
[ROW][C]43[/C][C]0.95197289275812[/C][C]0.0960542144837615[/C][C]0.0480271072418808[/C][/ROW]
[ROW][C]44[/C][C]0.961813346734773[/C][C]0.0763733065304534[/C][C]0.0381866532652267[/C][/ROW]
[ROW][C]45[/C][C]0.95028616899627[/C][C]0.0994276620074618[/C][C]0.0497138310037309[/C][/ROW]
[ROW][C]46[/C][C]0.956556216379778[/C][C]0.0868875672404444[/C][C]0.0434437836202222[/C][/ROW]
[ROW][C]47[/C][C]0.982990647295339[/C][C]0.0340187054093223[/C][C]0.0170093527046611[/C][/ROW]
[ROW][C]48[/C][C]0.979507027889909[/C][C]0.0409859442201828[/C][C]0.0204929721100914[/C][/ROW]
[ROW][C]49[/C][C]0.981964075268303[/C][C]0.036071849463395[/C][C]0.0180359247316975[/C][/ROW]
[ROW][C]50[/C][C]0.977181049424331[/C][C]0.045637901151338[/C][C]0.022818950575669[/C][/ROW]
[ROW][C]51[/C][C]0.971950631846768[/C][C]0.0560987363064637[/C][C]0.0280493681532319[/C][/ROW]
[ROW][C]52[/C][C]0.989213770246685[/C][C]0.0215724595066294[/C][C]0.0107862297533147[/C][/ROW]
[ROW][C]53[/C][C]0.992021211472385[/C][C]0.0159575770552291[/C][C]0.00797878852761455[/C][/ROW]
[ROW][C]54[/C][C]0.990517691569532[/C][C]0.0189646168609360[/C][C]0.00948230843046798[/C][/ROW]
[ROW][C]55[/C][C]0.994617128490356[/C][C]0.0107657430192882[/C][C]0.0053828715096441[/C][/ROW]
[ROW][C]56[/C][C]0.993122561310692[/C][C]0.0137548773786151[/C][C]0.00687743868930756[/C][/ROW]
[ROW][C]57[/C][C]0.990831232614518[/C][C]0.0183375347709633[/C][C]0.00916876738548164[/C][/ROW]
[ROW][C]58[/C][C]0.991280963771185[/C][C]0.0174380724576304[/C][C]0.00871903622881522[/C][/ROW]
[ROW][C]59[/C][C]0.992312265296982[/C][C]0.0153754694060361[/C][C]0.00768773470301807[/C][/ROW]
[ROW][C]60[/C][C]0.990855175793665[/C][C]0.0182896484126692[/C][C]0.00914482420633459[/C][/ROW]
[ROW][C]61[/C][C]0.990917912065896[/C][C]0.0181641758682076[/C][C]0.00908208793410379[/C][/ROW]
[ROW][C]62[/C][C]0.993065239536639[/C][C]0.0138695209267229[/C][C]0.00693476046336143[/C][/ROW]
[ROW][C]63[/C][C]0.991149317536692[/C][C]0.0177013649266160[/C][C]0.00885068246330802[/C][/ROW]
[ROW][C]64[/C][C]0.992460090806872[/C][C]0.0150798183862564[/C][C]0.00753990919312822[/C][/ROW]
[ROW][C]65[/C][C]0.990093690376082[/C][C]0.0198126192478351[/C][C]0.00990630962391757[/C][/ROW]
[ROW][C]66[/C][C]0.989750698963102[/C][C]0.020498602073796[/C][C]0.010249301036898[/C][/ROW]
[ROW][C]67[/C][C]0.989314157253514[/C][C]0.0213716854929723[/C][C]0.0106858427464862[/C][/ROW]
[ROW][C]68[/C][C]0.99153770394825[/C][C]0.0169245921035005[/C][C]0.00846229605175024[/C][/ROW]
[ROW][C]69[/C][C]0.991956718723516[/C][C]0.0160865625529681[/C][C]0.00804328127648406[/C][/ROW]
[ROW][C]70[/C][C]0.989120355113185[/C][C]0.0217592897736303[/C][C]0.0108796448868151[/C][/ROW]
[ROW][C]71[/C][C]0.990924766516761[/C][C]0.0181504669664779[/C][C]0.00907523348323894[/C][/ROW]
[ROW][C]72[/C][C]0.988072944694745[/C][C]0.0238541106105107[/C][C]0.0119270553052554[/C][/ROW]
[ROW][C]73[/C][C]0.984973684941786[/C][C]0.0300526301164285[/C][C]0.0150263150582142[/C][/ROW]
[ROW][C]74[/C][C]0.98894285797129[/C][C]0.0221142840574187[/C][C]0.0110571420287093[/C][/ROW]
[ROW][C]75[/C][C]0.985114615101298[/C][C]0.0297707697974029[/C][C]0.0148853848987015[/C][/ROW]
[ROW][C]76[/C][C]0.982568122634924[/C][C]0.0348637547301524[/C][C]0.0174318773650762[/C][/ROW]
[ROW][C]77[/C][C]0.977090785248985[/C][C]0.045818429502031[/C][C]0.0229092147510155[/C][/ROW]
[ROW][C]78[/C][C]0.969879978832463[/C][C]0.0602400423350736[/C][C]0.0301200211675368[/C][/ROW]
[ROW][C]79[/C][C]0.981141511125037[/C][C]0.0377169777499269[/C][C]0.0188584888749634[/C][/ROW]
[ROW][C]80[/C][C]0.980182037275351[/C][C]0.039635925449298[/C][C]0.019817962724649[/C][/ROW]
[ROW][C]81[/C][C]0.98334582602042[/C][C]0.0333083479591612[/C][C]0.0166541739795806[/C][/ROW]
[ROW][C]82[/C][C]0.984404236259004[/C][C]0.0311915274819918[/C][C]0.0155957637409959[/C][/ROW]
[ROW][C]83[/C][C]0.979174889751477[/C][C]0.0416502204970468[/C][C]0.0208251102485234[/C][/ROW]
[ROW][C]84[/C][C]0.974369422253804[/C][C]0.0512611554923918[/C][C]0.0256305777461959[/C][/ROW]
[ROW][C]85[/C][C]0.993012434727943[/C][C]0.0139751305441137[/C][C]0.00698756527205687[/C][/ROW]
[ROW][C]86[/C][C]0.990512454251632[/C][C]0.0189750914967356[/C][C]0.00948754574836781[/C][/ROW]
[ROW][C]87[/C][C]0.987079350340338[/C][C]0.0258412993193240[/C][C]0.0129206496596620[/C][/ROW]
[ROW][C]88[/C][C]0.983003809966486[/C][C]0.0339923800670284[/C][C]0.0169961900335142[/C][/ROW]
[ROW][C]89[/C][C]0.978768324192718[/C][C]0.0424633516145631[/C][C]0.0212316758072815[/C][/ROW]
[ROW][C]90[/C][C]0.971950909906926[/C][C]0.0560981801861482[/C][C]0.0280490900930741[/C][/ROW]
[ROW][C]91[/C][C]0.966062113904202[/C][C]0.0678757721915955[/C][C]0.0339378860957977[/C][/ROW]
[ROW][C]92[/C][C]0.980640650034898[/C][C]0.0387186999302035[/C][C]0.0193593499651018[/C][/ROW]
[ROW][C]93[/C][C]0.975059675709882[/C][C]0.0498806485802355[/C][C]0.0249403242901177[/C][/ROW]
[ROW][C]94[/C][C]0.971401751311768[/C][C]0.0571964973764641[/C][C]0.0285982486882320[/C][/ROW]
[ROW][C]95[/C][C]0.963836515751585[/C][C]0.0723269684968307[/C][C]0.0361634842484154[/C][/ROW]
[ROW][C]96[/C][C]0.954094242784984[/C][C]0.0918115144300316[/C][C]0.0459057572150158[/C][/ROW]
[ROW][C]97[/C][C]0.950129405708244[/C][C]0.099741188583512[/C][C]0.049870594291756[/C][/ROW]
[ROW][C]98[/C][C]0.936448127685328[/C][C]0.127103744629344[/C][C]0.063551872314672[/C][/ROW]
[ROW][C]99[/C][C]0.932977888590807[/C][C]0.134044222818386[/C][C]0.067022111409193[/C][/ROW]
[ROW][C]100[/C][C]0.917336546230434[/C][C]0.165326907539131[/C][C]0.0826634537695656[/C][/ROW]
[ROW][C]101[/C][C]0.8982931633317[/C][C]0.203413673336601[/C][C]0.101706836668301[/C][/ROW]
[ROW][C]102[/C][C]0.881389562990497[/C][C]0.237220874019006[/C][C]0.118610437009503[/C][/ROW]
[ROW][C]103[/C][C]0.89257092689047[/C][C]0.214858146219059[/C][C]0.107429073109529[/C][/ROW]
[ROW][C]104[/C][C]0.92385056418278[/C][C]0.152298871634438[/C][C]0.0761494358172192[/C][/ROW]
[ROW][C]105[/C][C]0.922381277673484[/C][C]0.155237444653032[/C][C]0.0776187223265161[/C][/ROW]
[ROW][C]106[/C][C]0.903370468583187[/C][C]0.193259062833627[/C][C]0.0966295314168133[/C][/ROW]
[ROW][C]107[/C][C]0.884854868241492[/C][C]0.230290263517016[/C][C]0.115145131758508[/C][/ROW]
[ROW][C]108[/C][C]0.880169397777762[/C][C]0.239661204444476[/C][C]0.119830602222238[/C][/ROW]
[ROW][C]109[/C][C]0.879634867598516[/C][C]0.240730264802967[/C][C]0.120365132401484[/C][/ROW]
[ROW][C]110[/C][C]0.903225554060957[/C][C]0.193548891878086[/C][C]0.096774445939043[/C][/ROW]
[ROW][C]111[/C][C]0.893394915728447[/C][C]0.213210168543106[/C][C]0.106605084271553[/C][/ROW]
[ROW][C]112[/C][C]0.92333019312296[/C][C]0.153339613754081[/C][C]0.0766698068770406[/C][/ROW]
[ROW][C]113[/C][C]0.902142952545248[/C][C]0.195714094909503[/C][C]0.0978570474547515[/C][/ROW]
[ROW][C]114[/C][C]0.876592354961236[/C][C]0.246815290077529[/C][C]0.123407645038764[/C][/ROW]
[ROW][C]115[/C][C]0.846976202918196[/C][C]0.306047594163607[/C][C]0.153023797081804[/C][/ROW]
[ROW][C]116[/C][C]0.846647747340669[/C][C]0.306704505318662[/C][C]0.153352252659331[/C][/ROW]
[ROW][C]117[/C][C]0.854962391426605[/C][C]0.29007521714679[/C][C]0.145037608573395[/C][/ROW]
[ROW][C]118[/C][C]0.844658968811386[/C][C]0.310682062377229[/C][C]0.155341031188614[/C][/ROW]
[ROW][C]119[/C][C]0.807719138755045[/C][C]0.38456172248991[/C][C]0.192280861244955[/C][/ROW]
[ROW][C]120[/C][C]0.86142115213684[/C][C]0.277157695726320[/C][C]0.138578847863160[/C][/ROW]
[ROW][C]121[/C][C]0.834280057868296[/C][C]0.331439884263408[/C][C]0.165719942131704[/C][/ROW]
[ROW][C]122[/C][C]0.809772713482967[/C][C]0.380454573034066[/C][C]0.190227286517033[/C][/ROW]
[ROW][C]123[/C][C]0.799699963807939[/C][C]0.400600072384123[/C][C]0.200300036192061[/C][/ROW]
[ROW][C]124[/C][C]0.758667569670462[/C][C]0.482664860659075[/C][C]0.241332430329538[/C][/ROW]
[ROW][C]125[/C][C]0.758507882661758[/C][C]0.482984234676484[/C][C]0.241492117338242[/C][/ROW]
[ROW][C]126[/C][C]0.739929519960295[/C][C]0.52014096007941[/C][C]0.260070480039705[/C][/ROW]
[ROW][C]127[/C][C]0.686939981741838[/C][C]0.626120036516324[/C][C]0.313060018258162[/C][/ROW]
[ROW][C]128[/C][C]0.658397591648663[/C][C]0.683204816702673[/C][C]0.341602408351337[/C][/ROW]
[ROW][C]129[/C][C]0.67558695308933[/C][C]0.64882609382134[/C][C]0.32441304691067[/C][/ROW]
[ROW][C]130[/C][C]0.667330144009447[/C][C]0.665339711981106[/C][C]0.332669855990553[/C][/ROW]
[ROW][C]131[/C][C]0.606684239999214[/C][C]0.786631520001573[/C][C]0.393315760000786[/C][/ROW]
[ROW][C]132[/C][C]0.59403583599494[/C][C]0.811928328010119[/C][C]0.405964164005059[/C][/ROW]
[ROW][C]133[/C][C]0.564406161296073[/C][C]0.871187677407855[/C][C]0.435593838703927[/C][/ROW]
[ROW][C]134[/C][C]0.489944398133652[/C][C]0.979888796267304[/C][C]0.510055601866348[/C][/ROW]
[ROW][C]135[/C][C]0.463421676888927[/C][C]0.926843353777854[/C][C]0.536578323111073[/C][/ROW]
[ROW][C]136[/C][C]0.456962237171405[/C][C]0.91392447434281[/C][C]0.543037762828595[/C][/ROW]
[ROW][C]137[/C][C]0.383352154124569[/C][C]0.766704308249138[/C][C]0.616647845875431[/C][/ROW]
[ROW][C]138[/C][C]0.446176669766455[/C][C]0.89235333953291[/C][C]0.553823330233545[/C][/ROW]
[ROW][C]139[/C][C]0.794397111075275[/C][C]0.41120577784945[/C][C]0.205602888924725[/C][/ROW]
[ROW][C]140[/C][C]0.824534882837644[/C][C]0.350930234324713[/C][C]0.175465117162356[/C][/ROW]
[ROW][C]141[/C][C]0.779023815647074[/C][C]0.441952368705852[/C][C]0.220976184352926[/C][/ROW]
[ROW][C]142[/C][C]0.694285994334067[/C][C]0.611428011331865[/C][C]0.305714005665933[/C][/ROW]
[ROW][C]143[/C][C]0.655336380992217[/C][C]0.689327238015566[/C][C]0.344663619007783[/C][/ROW]
[ROW][C]144[/C][C]0.540614026189965[/C][C]0.91877194762007[/C][C]0.459385973810035[/C][/ROW]
[ROW][C]145[/C][C]0.518640475046831[/C][C]0.962719049906337[/C][C]0.481359524953169[/C][/ROW]
[ROW][C]146[/C][C]0.633246698154205[/C][C]0.73350660369159[/C][C]0.366753301845795[/C][/ROW]
[ROW][C]147[/C][C]0.570596811126392[/C][C]0.858806377747216[/C][C]0.429403188873608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1099066623509470.2198133247018940.890093337649053
100.0478060903171790.0956121806343580.952193909682821
110.07272965627741940.1454593125548390.92727034372258
120.03807835479920440.07615670959840870.961921645200796
130.4402804947073120.8805609894146240.559719505292688
140.7624254754877040.4751490490245920.237574524512296
150.6820338784022580.6359322431954840.317966121597742
160.592928156773980.814143686452040.40707184322602
170.5347173537571110.9305652924857780.465282646242889
180.447716309952050.89543261990410.55228369004795
190.3704081811435480.7408163622870960.629591818856452
200.6651752712083690.6696494575832620.334824728791631
210.6005542563600740.7988914872798530.399445743639926
220.5668068985383640.8663862029232720.433193101461636
230.4960293306347260.9920586612694520.503970669365274
240.4768823910699050.953764782139810.523117608930095
250.4388901476241530.8777802952483060.561109852375847
260.3808077455838490.7616154911676980.619192254416151
270.6484716296833410.7030567406333180.351528370316659
280.5907580991174080.8184838017651840.409241900882592
290.5306221849362960.9387556301274080.469377815063704
300.6885644145274270.6228711709451470.311435585472573
310.8056357689663110.3887284620673780.194364231033689
320.7697890445274520.4604219109450950.230210955472548
330.7292910483586630.5414179032826730.270708951641337
340.6847424958502050.630515008299590.315257504149795
350.6785749979456770.6428500041086460.321425002054323
360.6960467251368910.6079065497262180.303953274863109
370.6574614965524840.6850770068950310.342538503447516
380.606977509602480.786044980795040.39302249039752
390.5567306883698310.8865386232603390.443269311630169
400.5386054479975950.922789104004810.461394552002405
410.5083116329504590.9833767340990810.491688367049541
420.8102366364284850.379526727143030.189763363571515
430.951972892758120.09605421448376150.0480271072418808
440.9618133467347730.07637330653045340.0381866532652267
450.950286168996270.09942766200746180.0497138310037309
460.9565562163797780.08688756724044440.0434437836202222
470.9829906472953390.03401870540932230.0170093527046611
480.9795070278899090.04098594422018280.0204929721100914
490.9819640752683030.0360718494633950.0180359247316975
500.9771810494243310.0456379011513380.022818950575669
510.9719506318467680.05609873630646370.0280493681532319
520.9892137702466850.02157245950662940.0107862297533147
530.9920212114723850.01595757705522910.00797878852761455
540.9905176915695320.01896461686093600.00948230843046798
550.9946171284903560.01076574301928820.0053828715096441
560.9931225613106920.01375487737861510.00687743868930756
570.9908312326145180.01833753477096330.00916876738548164
580.9912809637711850.01743807245763040.00871903622881522
590.9923122652969820.01537546940603610.00768773470301807
600.9908551757936650.01828964841266920.00914482420633459
610.9909179120658960.01816417586820760.00908208793410379
620.9930652395366390.01386952092672290.00693476046336143
630.9911493175366920.01770136492661600.00885068246330802
640.9924600908068720.01507981838625640.00753990919312822
650.9900936903760820.01981261924783510.00990630962391757
660.9897506989631020.0204986020737960.010249301036898
670.9893141572535140.02137168549297230.0106858427464862
680.991537703948250.01692459210350050.00846229605175024
690.9919567187235160.01608656255296810.00804328127648406
700.9891203551131850.02175928977363030.0108796448868151
710.9909247665167610.01815046696647790.00907523348323894
720.9880729446947450.02385411061051070.0119270553052554
730.9849736849417860.03005263011642850.0150263150582142
740.988942857971290.02211428405741870.0110571420287093
750.9851146151012980.02977076979740290.0148853848987015
760.9825681226349240.03486375473015240.0174318773650762
770.9770907852489850.0458184295020310.0229092147510155
780.9698799788324630.06024004233507360.0301200211675368
790.9811415111250370.03771697774992690.0188584888749634
800.9801820372753510.0396359254492980.019817962724649
810.983345826020420.03330834795916120.0166541739795806
820.9844042362590040.03119152748199180.0155957637409959
830.9791748897514770.04165022049704680.0208251102485234
840.9743694222538040.05126115549239180.0256305777461959
850.9930124347279430.01397513054411370.00698756527205687
860.9905124542516320.01897509149673560.00948754574836781
870.9870793503403380.02584129931932400.0129206496596620
880.9830038099664860.03399238006702840.0169961900335142
890.9787683241927180.04246335161456310.0212316758072815
900.9719509099069260.05609818018614820.0280490900930741
910.9660621139042020.06787577219159550.0339378860957977
920.9806406500348980.03871869993020350.0193593499651018
930.9750596757098820.04988064858023550.0249403242901177
940.9714017513117680.05719649737646410.0285982486882320
950.9638365157515850.07232696849683070.0361634842484154
960.9540942427849840.09181151443003160.0459057572150158
970.9501294057082440.0997411885835120.049870594291756
980.9364481276853280.1271037446293440.063551872314672
990.9329778885908070.1340442228183860.067022111409193
1000.9173365462304340.1653269075391310.0826634537695656
1010.89829316333170.2034136733366010.101706836668301
1020.8813895629904970.2372208740190060.118610437009503
1030.892570926890470.2148581462190590.107429073109529
1040.923850564182780.1522988716344380.0761494358172192
1050.9223812776734840.1552374446530320.0776187223265161
1060.9033704685831870.1932590628336270.0966295314168133
1070.8848548682414920.2302902635170160.115145131758508
1080.8801693977777620.2396612044444760.119830602222238
1090.8796348675985160.2407302648029670.120365132401484
1100.9032255540609570.1935488918780860.096774445939043
1110.8933949157284470.2132101685431060.106605084271553
1120.923330193122960.1533396137540810.0766698068770406
1130.9021429525452480.1957140949095030.0978570474547515
1140.8765923549612360.2468152900775290.123407645038764
1150.8469762029181960.3060475941636070.153023797081804
1160.8466477473406690.3067045053186620.153352252659331
1170.8549623914266050.290075217146790.145037608573395
1180.8446589688113860.3106820623772290.155341031188614
1190.8077191387550450.384561722489910.192280861244955
1200.861421152136840.2771576957263200.138578847863160
1210.8342800578682960.3314398842634080.165719942131704
1220.8097727134829670.3804545730340660.190227286517033
1230.7996999638079390.4006000723841230.200300036192061
1240.7586675696704620.4826648606590750.241332430329538
1250.7585078826617580.4829842346764840.241492117338242
1260.7399295199602950.520140960079410.260070480039705
1270.6869399817418380.6261200365163240.313060018258162
1280.6583975916486630.6832048167026730.341602408351337
1290.675586953089330.648826093821340.32441304691067
1300.6673301440094470.6653397119811060.332669855990553
1310.6066842399992140.7866315200015730.393315760000786
1320.594035835994940.8119283280101190.405964164005059
1330.5644061612960730.8711876774078550.435593838703927
1340.4899443981336520.9798887962673040.510055601866348
1350.4634216768889270.9268433537778540.536578323111073
1360.4569622371714050.913924474342810.543037762828595
1370.3833521541245690.7667043082491380.616647845875431
1380.4461766697664550.892353339532910.553823330233545
1390.7943971110752750.411205777849450.205602888924725
1400.8245348828376440.3509302343247130.175465117162356
1410.7790238156470740.4419523687058520.220976184352926
1420.6942859943340670.6114280113318650.305714005665933
1430.6553363809922170.6893272380155660.344663619007783
1440.5406140261899650.918771947620070.459385973810035
1450.5186404750468310.9627190499063370.481359524953169
1460.6332466981542050.733506603691590.366753301845795
1470.5705968111263920.8588063777472160.429403188873608






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level420.302158273381295NOK
10% type I error level570.410071942446043NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 42 & 0.302158273381295 & NOK \tabularnewline
10% type I error level & 57 & 0.410071942446043 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98930&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.302158273381295[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.410071942446043[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98930&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98930&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level420.302158273381295NOK
10% type I error level570.410071942446043NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}