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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, 22 Nov 2011 12:04:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321981537rlyz8tizxa1bogv.htm/, Retrieved Sat, 20 Apr 2024 02:55:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146311, Retrieved Sat, 20 Apr 2024 02:55:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R     [Multiple Regression] [] [2011-11-20 11:39:15] [90a803f646514fc2f7a5d6de952a552a]
-   PD      [Multiple Regression] [] [2011-11-22 17:04:59] [1ec9c877c5c85e817bc4a3ecfdd6e5aa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	41	38	13	12	14	12
5	39	32	16	11	18	11
5	30	35	19	15	11	14
5	31	33	15	6	12	12
8	34	37	14	13	16	21
6	35	29	13	10	18	12
5	39	31	19	12	14	22
6	34	36	15	14	14	11
5	36	35	14	12	15	10
4	37	38	15	6	15	13
6	38	31	16	10	17	10
5	36	34	16	12	19	8
5	38	35	16	12	10	15
6	39	38	16	11	16	14
7	33	37	17	15	18	10
6	32	33	15	12	14	14
7	36	32	15	10	14	14
6	38	38	20	12	17	11
8	39	38	18	11	14	10
7	32	32	16	12	16	13
5	32	33	16	11	18	7
5	31	31	16	12	11	14
7	39	38	19	13	14	12
7	37	39	16	11	12	14
5	39	32	17	9	17	11
4	41	32	17	13	9	9
10	36	35	16	10	16	11
6	33	37	15	14	14	15
5	33	33	16	12	15	14
5	34	33	14	10	11	13
5	31	28	15	12	16	9
5	27	32	12	8	13	15
6	37	31	14	10	17	10
5	34	37	16	12	15	11
5	34	30	14	12	14	13
5	32	33	7	7	16	8
5	29	31	10	6	9	20
5	36	33	14	12	15	12
5	29	31	16	10	17	10
5	35	33	16	10	13	10
5	37	32	16	10	15	9
7	34	33	14	12	16	14
5	38	32	20	15	16	8
6	35	33	14	10	12	14
7	38	28	14	10	12	11
7	37	35	11	12	11	13
5	38	39	14	13	15	9
5	33	34	15	11	15	11
4	36	38	16	11	17	15
5	38	32	14	12	13	11
4	32	38	16	14	16	10
5	32	30	14	10	14	14
5	32	33	12	12	11	18
7	34	38	16	13	12	14
5	32	32	9	5	12	11
5	37	32	14	6	15	12
6	39	34	16	12	16	13
4	29	34	16	12	15	9
6	37	36	15	11	12	10
6	35	34	16	10	12	15
5	30	28	12	7	8	20
7	38	34	16	12	13	12
6	34	35	16	14	11	12
8	31	35	14	11	14	14
7	34	31	16	12	15	13
5	35	37	17	13	10	11
6	36	35	18	14	11	17
6	30	27	18	11	12	12
5	39	40	12	12	15	13
5	35	37	16	12	15	14
5	38	36	10	8	14	13
5	31	38	14	11	16	15
4	34	39	18	14	15	13
6	38	41	18	14	15	10
6	34	27	16	12	13	11
6	39	30	17	9	12	19
6	37	37	16	13	17	13
7	34	31	16	11	13	17
5	28	31	13	12	15	13
7	37	27	16	12	13	9
6	33	36	16	12	15	11
5	37	38	20	12	16	10
5	35	37	16	12	15	9
4	37	33	15	12	16	12
8	32	34	15	11	15	12
8	33	31	16	10	14	13
5	38	39	14	9	15	13
5	33	34	16	12	14	12
6	29	32	16	12	13	15
4	33	33	15	12	7	22
5	31	36	12	9	17	13
5	36	32	17	15	13	15
5	35	41	16	12	15	13
5	32	28	15	12	14	15
6	29	30	13	12	13	10
6	39	36	16	10	16	11
5	37	35	16	13	12	16
6	35	31	16	9	14	11
5	37	34	16	12	17	11
7	32	36	14	10	15	10
5	38	36	16	14	17	10
6	37	35	16	11	12	16
6	36	37	20	15	16	12
6	32	28	15	11	11	11
4	33	39	16	11	15	16
5	40	32	13	12	9	19
5	38	35	17	12	16	11
7	41	39	16	12	15	16
6	36	35	16	11	10	15
9	43	42	12	7	10	24
6	30	34	16	12	15	14
6	31	33	16	14	11	15
5	32	41	17	11	13	11
6	32	33	13	11	14	15
5	37	34	12	10	18	12
8	37	32	18	13	16	10
7	33	40	14	13	14	14
5	34	40	14	8	14	13
7	33	35	13	11	14	9
6	38	36	16	12	14	15
6	33	37	13	11	12	15
9	31	27	16	13	14	14
7	38	39	13	12	15	11
6	37	38	16	14	15	8
5	33	31	15	13	15	11
5	31	33	16	15	13	11
6	39	32	15	10	17	8
6	44	39	17	11	17	10
7	33	36	15	9	19	11
5	35	33	12	11	15	13
5	32	33	16	10	13	11
5	28	32	10	11	9	20
6	40	37	16	8	15	10
4	27	30	12	11	15	15
5	37	38	14	12	15	12
7	32	29	15	12	16	14
5	28	22	13	9	11	23
7	34	35	15	11	14	14
7	30	35	11	10	11	16
6	35	34	12	8	15	11
5	31	35	8	9	13	12
8	32	34	16	8	15	10
5	30	34	15	9	16	14
5	30	35	17	15	14	12
5	31	23	16	11	15	12
6	40	31	10	8	16	11
4	32	27	18	13	16	12
5	36	36	13	12	11	13
5	32	31	16	12	12	11
7	35	32	13	9	9	19
6	38	39	10	7	16	12
7	42	37	15	13	13	17
10	34	38	16	9	16	9
6	35	39	16	6	12	12
8	35	34	14	8	9	19
4	33	31	10	8	13	18
5	36	32	17	15	13	15
6	32	37	13	6	14	14
7	33	36	15	9	19	11
7	34	32	16	11	13	9
6	32	35	12	8	12	18
6	34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.40277255177464 + 0.125528415943849Age[t] + 0.10984471782245Connected[t] -0.0266356448552552Separate[t] + 0.550024458362693Software[t] + 0.0627070496258956Happiness[t] -0.0801217386766543Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.40277255177464 +  0.125528415943849Age[t] +  0.10984471782245Connected[t] -0.0266356448552552Separate[t] +  0.550024458362693Software[t] +  0.0627070496258956Happiness[t] -0.0801217386766543Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.40277255177464 +  0.125528415943849Age[t] +  0.10984471782245Connected[t] -0.0266356448552552Separate[t] +  0.550024458362693Software[t] +  0.0627070496258956Happiness[t] -0.0801217386766543Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.40277255177464 + 0.125528415943849Age[t] + 0.10984471782245Connected[t] -0.0266356448552552Separate[t] + 0.550024458362693Software[t] + 0.0627070496258956Happiness[t] -0.0801217386766543Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.402772551774642.4244312.22850.0272880.013644
Age0.1255284159438490.1271920.98690.3252180.162609
Connected0.109844717822450.0469342.34040.0205360.010268
Separate-0.02663564485525520.044371-0.60030.549190.274595
Software0.5500244583626930.0687298.002800
Happiness0.06270704962589560.0748150.83820.4032280.201614
Depression-0.08012173867665430.055169-1.45230.1484390.074219

\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) & 5.40277255177464 & 2.424431 & 2.2285 & 0.027288 & 0.013644 \tabularnewline
Age & 0.125528415943849 & 0.127192 & 0.9869 & 0.325218 & 0.162609 \tabularnewline
Connected & 0.10984471782245 & 0.046934 & 2.3404 & 0.020536 & 0.010268 \tabularnewline
Separate & -0.0266356448552552 & 0.044371 & -0.6003 & 0.54919 & 0.274595 \tabularnewline
Software & 0.550024458362693 & 0.068729 & 8.0028 & 0 & 0 \tabularnewline
Happiness & 0.0627070496258956 & 0.074815 & 0.8382 & 0.403228 & 0.201614 \tabularnewline
Depression & -0.0801217386766543 & 0.055169 & -1.4523 & 0.148439 & 0.074219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&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]5.40277255177464[/C][C]2.424431[/C][C]2.2285[/C][C]0.027288[/C][C]0.013644[/C][/ROW]
[ROW][C]Age[/C][C]0.125528415943849[/C][C]0.127192[/C][C]0.9869[/C][C]0.325218[/C][C]0.162609[/C][/ROW]
[ROW][C]Connected[/C][C]0.10984471782245[/C][C]0.046934[/C][C]2.3404[/C][C]0.020536[/C][C]0.010268[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0266356448552552[/C][C]0.044371[/C][C]-0.6003[/C][C]0.54919[/C][C]0.274595[/C][/ROW]
[ROW][C]Software[/C][C]0.550024458362693[/C][C]0.068729[/C][C]8.0028[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0627070496258956[/C][C]0.074815[/C][C]0.8382[/C][C]0.403228[/C][C]0.201614[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0801217386766543[/C][C]0.055169[/C][C]-1.4523[/C][C]0.148439[/C][C]0.074219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&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)5.402772551774642.4244312.22850.0272880.013644
Age0.1255284159438490.1271920.98690.3252180.162609
Connected0.109844717822450.0469342.34040.0205360.010268
Separate-0.02663564485525520.044371-0.60030.549190.274595
Software0.5500244583626930.0687298.002800
Happiness0.06270704962589560.0748150.83820.4032280.201614
Depression-0.08012173867665430.055169-1.45230.1484390.074219






Multiple Linear Regression - Regression Statistics
Multiple R0.59826591264806
R-squared0.357922102236617
Adjusted R-squared0.333067473936099
F-TEST (value)14.4006218040749
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value5.12478948166972e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84259672265392
Sum Squared Residuals526.25021576192

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.59826591264806 \tabularnewline
R-squared & 0.357922102236617 \tabularnewline
Adjusted R-squared & 0.333067473936099 \tabularnewline
F-TEST (value) & 14.4006218040749 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 5.12478948166972e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84259672265392 \tabularnewline
Sum Squared Residuals & 526.25021576192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.59826591264806[/C][/ROW]
[ROW][C]R-squared[/C][C]0.357922102236617[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.333067473936099[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.4006218040749[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]5.12478948166972e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84259672265392[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]526.25021576192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&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 R0.59826591264806
R-squared0.357922102236617
Adjusted R-squared0.333067473936099
F-TEST (value)14.4006218040749
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value5.12478948166972e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84259672265392
Sum Squared Residuals526.25021576192






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2896817205973-3.28968172059735
21615.75967480101380.240325198986197
31916.21194867608552.78805132391447
41511.64779508533353.35220491466654
51415.6272756661639-1.62727566616388
61314.8955850831943-1.89558508319427
71915.2041675802853.79583241971503
81516.6286822250089-1.62868222500888
91415.7922587611423-1.79225876114235
101512.15615616224912.84384383775094
111615.26938437467850.730615625321477
121616.2299660818545-0.229966081854494
131615.29780425527450.702195744725502
141615.35961003254440.640389967455634
151717.498704673818-0.49870467381795
161515.1484855911744-0.148485591174394
171514.63997960653790.360020393462087
182016.10286203874053.89713796125953
191815.80573971988692.19426028011311
201615.50618548990190.493814510098056
211615.2846130861080.715386913891984
221614.77826259824091.22173740175908
231916.62001674331512.37998325668488
241614.98798516948451.01201483051552
251714.59691883466252.40308116533748
261716.54976476816050.450235231839514
271615.30243723508540.697562764914551
281516.1717149076246-1.17171490762455
291615.19550894267890.804491057321109
301414.034598283949-0.0345982839490274
311515.5723134743194-0.572313474319434
321212.1574426092202-0.157442609220231
331415.1595396568561-1.15953965685607
341615.43917629711030.560823702889717
351415.4026752841179-1.40267528411787
36712.8789794147288-5.8789794147288
371010.6522818811081-0.652281881108146
381415.6852865734995-1.68528657349955
391614.15525349833261.84474650166737
401614.51022231705321.48977768294677
411614.96208323548181.03791676451817
421415.6191175420149-1.61911754201493
432017.96487903342032.0351209665797
441414.2525567286646-0.252556728664567
451415.081162738382-1.081162738382
461115.6619668963189-4.66196689631895
471416.5355518144056-2.53555181440557
481514.85921405549090.140785944509094
491614.76160435813861.23839564186144
501415.8863192934246-1.88631929342457
511616.3102005056942-0.310200505694215
521414.0028151930709-0.0028151930709257
531214.5143490716462-2.51434907164624
541615.78513557759780.214864422402232
55911.3143727283251-2.31437272832512
561412.52162018600111.4783798139989
571616.0962988090047-0.096298809004734
581615.00457470397330.99542529602674
591515.262850642813-0.262850642813012
601614.14579934513271.85420065486734
611211.32935094223320.670649057766841
621616.0039830969251-0.00398309692510026
631616.3870749823098-0.387074982309791
641414.6864019571664-0.68640195716644
651615.68980352077620.310196479223797
661715.78551022516591.21448977483405
671816.20615572457141.79384427542858
681814.57341494439993.42658505560015
691215.7482494743035-3.74824947430346
701615.30865579890280.69134420109723
711013.4821424528254-3.48214245282536
721414.2752021353443-0.275202135344264
731816.2001820308281.799817969172
741817.0777116603250.92228833967505
751615.70564706235490.294352937645107
761713.82120938277423.17879061722583
771616.4094339467827-0.409433946782657
781614.69387800845511.3061219915449
791314.7796783819538-1.77967838195381
801616.3209531091194-0.3209531091194
811615.48149564008690.518504359913063
822015.88490359402494.11509640597507
831615.7092644922860.290735507713958
841515.732309925004-0.732309925004046
851515.0458328468233-0.0458328468233487
861614.54273125254631.45726874745368
871414.0149670262482-0.0149670262481858
881615.2664097255510.733590274448951
891614.70275829425981.29724170574025
901513.92735022031461.07264977968536
911213.4513750353086-1.45137503530859
921716.99621627816110.00378372183887183
931615.28223495815840.717765041841596
941515.0760136608302-0.0760136608301671
951315.1566382773535-2.15663827735353
961615.10322207992210.896777920077853
971615.78327635638990.216723643610123
981614.12158287529411.87841712470586
991615.97403148439520.0259685156048103
1001414.4772521601596-0.477252160159605
1011617.2107755679092-1.21077556790917
1021614.80875585560831.19124414439166
1032017.41705283473642.58294716526365
1041514.78388342424030.216116575759746
1051614.19989872188751.80010127811249
1061315.2142066211527-2.21420662115265
1071715.99453350773651.00546649226351
1081616.0052661706613-0.00526617066134807
1091614.65361877721081.34638122278925
1101212.691474054272-0.691474054272004
1111614.96486756030011.03513243969986
1121615.8704469025230.129553097477011
1131714.43750572442992.56249427557012
1141314.518339394135-1.51833939413505
1151214.856567878619-2.85656787861905
1161816.97132716935071.0286728306493
1171415.7474336693166-1.7474336693166
1181412.93622100211451.06377899788546
1191315.1811716702508-2.18117167025076
1201615.64752522486670.352474775133326
1211314.3962274332847-1.39622743328469
1221616.1250644486777-0.125064448677715
1231316.0763407105773-3.07634071057727
1241617.2080173544216-1.20801735442157
1251516.0391699067821-1.03916990678206
1261616.7408439989002-0.740843998900241
1271515.512836924999-0.512836924999026
1281716.26539198113390.734608018866127
1291514.20777887944630.79222112055371
1301214.9452956586378-2.94529565863775
1311614.10056642490921.89943357509077
1321013.2659238102439-3.2659238102439
1331614.10379692521471.89620307478528
1341213.8606729573268-1.86067295732676
1351415.6619530670457-1.66195306704572
1361515.5059706857911-0.505970685791055
1371312.31728022529290.6827197747071
1381514.89040769468990.10959230531006
1391113.5526397388065-2.55263973880645
1401213.5543585319916-1.55435853199158
141813.3073042203369-5.30730422033692
1421613.55600294908862.44399705091142
1431513.25197281889411.7480271811059
1441716.56031330231650.439686697683478
1451614.85239497457721.14760502542284
1461014.2461961052955-4.24619610529549
1471815.8929246633862.10707533661398
1481315.2744297017535-2.27442970175355
1491615.19117958171920.808820418280772
1501313.26596648884-0.265966488840021
1511013.1832753137692-3.1832753137692
1521516.5128707986286-1.51287079862856
1531614.61305988386541.3869401161346
1541612.05288850343563.94711149656443
1551412.78819915671071.21180084328933
1561012.4772529290364-2.47725292903637
1571716.99621627816110.00378372183887183
1581311.74179626157721.25820373842278
1591514.20777887944630.79222112055371
1601615.30821627301310.691783726986918
1611212.4492154140547-0.449215414054705
1621312.86521973182360.134780268176446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2896817205973 & -3.28968172059735 \tabularnewline
2 & 16 & 15.7596748010138 & 0.240325198986197 \tabularnewline
3 & 19 & 16.2119486760855 & 2.78805132391447 \tabularnewline
4 & 15 & 11.6477950853335 & 3.35220491466654 \tabularnewline
5 & 14 & 15.6272756661639 & -1.62727566616388 \tabularnewline
6 & 13 & 14.8955850831943 & -1.89558508319427 \tabularnewline
7 & 19 & 15.204167580285 & 3.79583241971503 \tabularnewline
8 & 15 & 16.6286822250089 & -1.62868222500888 \tabularnewline
9 & 14 & 15.7922587611423 & -1.79225876114235 \tabularnewline
10 & 15 & 12.1561561622491 & 2.84384383775094 \tabularnewline
11 & 16 & 15.2693843746785 & 0.730615625321477 \tabularnewline
12 & 16 & 16.2299660818545 & -0.229966081854494 \tabularnewline
13 & 16 & 15.2978042552745 & 0.702195744725502 \tabularnewline
14 & 16 & 15.3596100325444 & 0.640389967455634 \tabularnewline
15 & 17 & 17.498704673818 & -0.49870467381795 \tabularnewline
16 & 15 & 15.1484855911744 & -0.148485591174394 \tabularnewline
17 & 15 & 14.6399796065379 & 0.360020393462087 \tabularnewline
18 & 20 & 16.1028620387405 & 3.89713796125953 \tabularnewline
19 & 18 & 15.8057397198869 & 2.19426028011311 \tabularnewline
20 & 16 & 15.5061854899019 & 0.493814510098056 \tabularnewline
21 & 16 & 15.284613086108 & 0.715386913891984 \tabularnewline
22 & 16 & 14.7782625982409 & 1.22173740175908 \tabularnewline
23 & 19 & 16.6200167433151 & 2.37998325668488 \tabularnewline
24 & 16 & 14.9879851694845 & 1.01201483051552 \tabularnewline
25 & 17 & 14.5969188346625 & 2.40308116533748 \tabularnewline
26 & 17 & 16.5497647681605 & 0.450235231839514 \tabularnewline
27 & 16 & 15.3024372350854 & 0.697562764914551 \tabularnewline
28 & 15 & 16.1717149076246 & -1.17171490762455 \tabularnewline
29 & 16 & 15.1955089426789 & 0.804491057321109 \tabularnewline
30 & 14 & 14.034598283949 & -0.0345982839490274 \tabularnewline
31 & 15 & 15.5723134743194 & -0.572313474319434 \tabularnewline
32 & 12 & 12.1574426092202 & -0.157442609220231 \tabularnewline
33 & 14 & 15.1595396568561 & -1.15953965685607 \tabularnewline
34 & 16 & 15.4391762971103 & 0.560823702889717 \tabularnewline
35 & 14 & 15.4026752841179 & -1.40267528411787 \tabularnewline
36 & 7 & 12.8789794147288 & -5.8789794147288 \tabularnewline
37 & 10 & 10.6522818811081 & -0.652281881108146 \tabularnewline
38 & 14 & 15.6852865734995 & -1.68528657349955 \tabularnewline
39 & 16 & 14.1552534983326 & 1.84474650166737 \tabularnewline
40 & 16 & 14.5102223170532 & 1.48977768294677 \tabularnewline
41 & 16 & 14.9620832354818 & 1.03791676451817 \tabularnewline
42 & 14 & 15.6191175420149 & -1.61911754201493 \tabularnewline
43 & 20 & 17.9648790334203 & 2.0351209665797 \tabularnewline
44 & 14 & 14.2525567286646 & -0.252556728664567 \tabularnewline
45 & 14 & 15.081162738382 & -1.081162738382 \tabularnewline
46 & 11 & 15.6619668963189 & -4.66196689631895 \tabularnewline
47 & 14 & 16.5355518144056 & -2.53555181440557 \tabularnewline
48 & 15 & 14.8592140554909 & 0.140785944509094 \tabularnewline
49 & 16 & 14.7616043581386 & 1.23839564186144 \tabularnewline
50 & 14 & 15.8863192934246 & -1.88631929342457 \tabularnewline
51 & 16 & 16.3102005056942 & -0.310200505694215 \tabularnewline
52 & 14 & 14.0028151930709 & -0.0028151930709257 \tabularnewline
53 & 12 & 14.5143490716462 & -2.51434907164624 \tabularnewline
54 & 16 & 15.7851355775978 & 0.214864422402232 \tabularnewline
55 & 9 & 11.3143727283251 & -2.31437272832512 \tabularnewline
56 & 14 & 12.5216201860011 & 1.4783798139989 \tabularnewline
57 & 16 & 16.0962988090047 & -0.096298809004734 \tabularnewline
58 & 16 & 15.0045747039733 & 0.99542529602674 \tabularnewline
59 & 15 & 15.262850642813 & -0.262850642813012 \tabularnewline
60 & 16 & 14.1457993451327 & 1.85420065486734 \tabularnewline
61 & 12 & 11.3293509422332 & 0.670649057766841 \tabularnewline
62 & 16 & 16.0039830969251 & -0.00398309692510026 \tabularnewline
63 & 16 & 16.3870749823098 & -0.387074982309791 \tabularnewline
64 & 14 & 14.6864019571664 & -0.68640195716644 \tabularnewline
65 & 16 & 15.6898035207762 & 0.310196479223797 \tabularnewline
66 & 17 & 15.7855102251659 & 1.21448977483405 \tabularnewline
67 & 18 & 16.2061557245714 & 1.79384427542858 \tabularnewline
68 & 18 & 14.5734149443999 & 3.42658505560015 \tabularnewline
69 & 12 & 15.7482494743035 & -3.74824947430346 \tabularnewline
70 & 16 & 15.3086557989028 & 0.69134420109723 \tabularnewline
71 & 10 & 13.4821424528254 & -3.48214245282536 \tabularnewline
72 & 14 & 14.2752021353443 & -0.275202135344264 \tabularnewline
73 & 18 & 16.200182030828 & 1.799817969172 \tabularnewline
74 & 18 & 17.077711660325 & 0.92228833967505 \tabularnewline
75 & 16 & 15.7056470623549 & 0.294352937645107 \tabularnewline
76 & 17 & 13.8212093827742 & 3.17879061722583 \tabularnewline
77 & 16 & 16.4094339467827 & -0.409433946782657 \tabularnewline
78 & 16 & 14.6938780084551 & 1.3061219915449 \tabularnewline
79 & 13 & 14.7796783819538 & -1.77967838195381 \tabularnewline
80 & 16 & 16.3209531091194 & -0.3209531091194 \tabularnewline
81 & 16 & 15.4814956400869 & 0.518504359913063 \tabularnewline
82 & 20 & 15.8849035940249 & 4.11509640597507 \tabularnewline
83 & 16 & 15.709264492286 & 0.290735507713958 \tabularnewline
84 & 15 & 15.732309925004 & -0.732309925004046 \tabularnewline
85 & 15 & 15.0458328468233 & -0.0458328468233487 \tabularnewline
86 & 16 & 14.5427312525463 & 1.45726874745368 \tabularnewline
87 & 14 & 14.0149670262482 & -0.0149670262481858 \tabularnewline
88 & 16 & 15.266409725551 & 0.733590274448951 \tabularnewline
89 & 16 & 14.7027582942598 & 1.29724170574025 \tabularnewline
90 & 15 & 13.9273502203146 & 1.07264977968536 \tabularnewline
91 & 12 & 13.4513750353086 & -1.45137503530859 \tabularnewline
92 & 17 & 16.9962162781611 & 0.00378372183887183 \tabularnewline
93 & 16 & 15.2822349581584 & 0.717765041841596 \tabularnewline
94 & 15 & 15.0760136608302 & -0.0760136608301671 \tabularnewline
95 & 13 & 15.1566382773535 & -2.15663827735353 \tabularnewline
96 & 16 & 15.1032220799221 & 0.896777920077853 \tabularnewline
97 & 16 & 15.7832763563899 & 0.216723643610123 \tabularnewline
98 & 16 & 14.1215828752941 & 1.87841712470586 \tabularnewline
99 & 16 & 15.9740314843952 & 0.0259685156048103 \tabularnewline
100 & 14 & 14.4772521601596 & -0.477252160159605 \tabularnewline
101 & 16 & 17.2107755679092 & -1.21077556790917 \tabularnewline
102 & 16 & 14.8087558556083 & 1.19124414439166 \tabularnewline
103 & 20 & 17.4170528347364 & 2.58294716526365 \tabularnewline
104 & 15 & 14.7838834242403 & 0.216116575759746 \tabularnewline
105 & 16 & 14.1998987218875 & 1.80010127811249 \tabularnewline
106 & 13 & 15.2142066211527 & -2.21420662115265 \tabularnewline
107 & 17 & 15.9945335077365 & 1.00546649226351 \tabularnewline
108 & 16 & 16.0052661706613 & -0.00526617066134807 \tabularnewline
109 & 16 & 14.6536187772108 & 1.34638122278925 \tabularnewline
110 & 12 & 12.691474054272 & -0.691474054272004 \tabularnewline
111 & 16 & 14.9648675603001 & 1.03513243969986 \tabularnewline
112 & 16 & 15.870446902523 & 0.129553097477011 \tabularnewline
113 & 17 & 14.4375057244299 & 2.56249427557012 \tabularnewline
114 & 13 & 14.518339394135 & -1.51833939413505 \tabularnewline
115 & 12 & 14.856567878619 & -2.85656787861905 \tabularnewline
116 & 18 & 16.9713271693507 & 1.0286728306493 \tabularnewline
117 & 14 & 15.7474336693166 & -1.7474336693166 \tabularnewline
118 & 14 & 12.9362210021145 & 1.06377899788546 \tabularnewline
119 & 13 & 15.1811716702508 & -2.18117167025076 \tabularnewline
120 & 16 & 15.6475252248667 & 0.352474775133326 \tabularnewline
121 & 13 & 14.3962274332847 & -1.39622743328469 \tabularnewline
122 & 16 & 16.1250644486777 & -0.125064448677715 \tabularnewline
123 & 13 & 16.0763407105773 & -3.07634071057727 \tabularnewline
124 & 16 & 17.2080173544216 & -1.20801735442157 \tabularnewline
125 & 15 & 16.0391699067821 & -1.03916990678206 \tabularnewline
126 & 16 & 16.7408439989002 & -0.740843998900241 \tabularnewline
127 & 15 & 15.512836924999 & -0.512836924999026 \tabularnewline
128 & 17 & 16.2653919811339 & 0.734608018866127 \tabularnewline
129 & 15 & 14.2077788794463 & 0.79222112055371 \tabularnewline
130 & 12 & 14.9452956586378 & -2.94529565863775 \tabularnewline
131 & 16 & 14.1005664249092 & 1.89943357509077 \tabularnewline
132 & 10 & 13.2659238102439 & -3.2659238102439 \tabularnewline
133 & 16 & 14.1037969252147 & 1.89620307478528 \tabularnewline
134 & 12 & 13.8606729573268 & -1.86067295732676 \tabularnewline
135 & 14 & 15.6619530670457 & -1.66195306704572 \tabularnewline
136 & 15 & 15.5059706857911 & -0.505970685791055 \tabularnewline
137 & 13 & 12.3172802252929 & 0.6827197747071 \tabularnewline
138 & 15 & 14.8904076946899 & 0.10959230531006 \tabularnewline
139 & 11 & 13.5526397388065 & -2.55263973880645 \tabularnewline
140 & 12 & 13.5543585319916 & -1.55435853199158 \tabularnewline
141 & 8 & 13.3073042203369 & -5.30730422033692 \tabularnewline
142 & 16 & 13.5560029490886 & 2.44399705091142 \tabularnewline
143 & 15 & 13.2519728188941 & 1.7480271811059 \tabularnewline
144 & 17 & 16.5603133023165 & 0.439686697683478 \tabularnewline
145 & 16 & 14.8523949745772 & 1.14760502542284 \tabularnewline
146 & 10 & 14.2461961052955 & -4.24619610529549 \tabularnewline
147 & 18 & 15.892924663386 & 2.10707533661398 \tabularnewline
148 & 13 & 15.2744297017535 & -2.27442970175355 \tabularnewline
149 & 16 & 15.1911795817192 & 0.808820418280772 \tabularnewline
150 & 13 & 13.26596648884 & -0.265966488840021 \tabularnewline
151 & 10 & 13.1832753137692 & -3.1832753137692 \tabularnewline
152 & 15 & 16.5128707986286 & -1.51287079862856 \tabularnewline
153 & 16 & 14.6130598838654 & 1.3869401161346 \tabularnewline
154 & 16 & 12.0528885034356 & 3.94711149656443 \tabularnewline
155 & 14 & 12.7881991567107 & 1.21180084328933 \tabularnewline
156 & 10 & 12.4772529290364 & -2.47725292903637 \tabularnewline
157 & 17 & 16.9962162781611 & 0.00378372183887183 \tabularnewline
158 & 13 & 11.7417962615772 & 1.25820373842278 \tabularnewline
159 & 15 & 14.2077788794463 & 0.79222112055371 \tabularnewline
160 & 16 & 15.3082162730131 & 0.691783726986918 \tabularnewline
161 & 12 & 12.4492154140547 & -0.449215414054705 \tabularnewline
162 & 13 & 12.8652197318236 & 0.134780268176446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.2896817205973[/C][C]-3.28968172059735[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.7596748010138[/C][C]0.240325198986197[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2119486760855[/C][C]2.78805132391447[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.6477950853335[/C][C]3.35220491466654[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6272756661639[/C][C]-1.62727566616388[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8955850831943[/C][C]-1.89558508319427[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.204167580285[/C][C]3.79583241971503[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6286822250089[/C][C]-1.62868222500888[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.7922587611423[/C][C]-1.79225876114235[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.1561561622491[/C][C]2.84384383775094[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2693843746785[/C][C]0.730615625321477[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2299660818545[/C][C]-0.229966081854494[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.2978042552745[/C][C]0.702195744725502[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3596100325444[/C][C]0.640389967455634[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.498704673818[/C][C]-0.49870467381795[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1484855911744[/C][C]-0.148485591174394[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6399796065379[/C][C]0.360020393462087[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1028620387405[/C][C]3.89713796125953[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.8057397198869[/C][C]2.19426028011311[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.5061854899019[/C][C]0.493814510098056[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.284613086108[/C][C]0.715386913891984[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7782625982409[/C][C]1.22173740175908[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6200167433151[/C][C]2.37998325668488[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9879851694845[/C][C]1.01201483051552[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5969188346625[/C][C]2.40308116533748[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.5497647681605[/C][C]0.450235231839514[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.3024372350854[/C][C]0.697562764914551[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1717149076246[/C][C]-1.17171490762455[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.1955089426789[/C][C]0.804491057321109[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.034598283949[/C][C]-0.0345982839490274[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5723134743194[/C][C]-0.572313474319434[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1574426092202[/C][C]-0.157442609220231[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1595396568561[/C][C]-1.15953965685607[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4391762971103[/C][C]0.560823702889717[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4026752841179[/C][C]-1.40267528411787[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.8789794147288[/C][C]-5.8789794147288[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.6522818811081[/C][C]-0.652281881108146[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6852865734995[/C][C]-1.68528657349955[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1552534983326[/C][C]1.84474650166737[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5102223170532[/C][C]1.48977768294677[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9620832354818[/C][C]1.03791676451817[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6191175420149[/C][C]-1.61911754201493[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9648790334203[/C][C]2.0351209665797[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2525567286646[/C][C]-0.252556728664567[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.081162738382[/C][C]-1.081162738382[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6619668963189[/C][C]-4.66196689631895[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5355518144056[/C][C]-2.53555181440557[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8592140554909[/C][C]0.140785944509094[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.7616043581386[/C][C]1.23839564186144[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8863192934246[/C][C]-1.88631929342457[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.3102005056942[/C][C]-0.310200505694215[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0028151930709[/C][C]-0.0028151930709257[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5143490716462[/C][C]-2.51434907164624[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7851355775978[/C][C]0.214864422402232[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.3143727283251[/C][C]-2.31437272832512[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5216201860011[/C][C]1.4783798139989[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0962988090047[/C][C]-0.096298809004734[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0045747039733[/C][C]0.99542529602674[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.262850642813[/C][C]-0.262850642813012[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1457993451327[/C][C]1.85420065486734[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.3293509422332[/C][C]0.670649057766841[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0039830969251[/C][C]-0.00398309692510026[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3870749823098[/C][C]-0.387074982309791[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6864019571664[/C][C]-0.68640195716644[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.6898035207762[/C][C]0.310196479223797[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7855102251659[/C][C]1.21448977483405[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2061557245714[/C][C]1.79384427542858[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5734149443999[/C][C]3.42658505560015[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7482494743035[/C][C]-3.74824947430346[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3086557989028[/C][C]0.69134420109723[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4821424528254[/C][C]-3.48214245282536[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2752021353443[/C][C]-0.275202135344264[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.200182030828[/C][C]1.799817969172[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.077711660325[/C][C]0.92228833967505[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7056470623549[/C][C]0.294352937645107[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8212093827742[/C][C]3.17879061722583[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4094339467827[/C][C]-0.409433946782657[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6938780084551[/C][C]1.3061219915449[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7796783819538[/C][C]-1.77967838195381[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.3209531091194[/C][C]-0.3209531091194[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4814956400869[/C][C]0.518504359913063[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.8849035940249[/C][C]4.11509640597507[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.709264492286[/C][C]0.290735507713958[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.732309925004[/C][C]-0.732309925004046[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0458328468233[/C][C]-0.0458328468233487[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.5427312525463[/C][C]1.45726874745368[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.0149670262482[/C][C]-0.0149670262481858[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.266409725551[/C][C]0.733590274448951[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7027582942598[/C][C]1.29724170574025[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.9273502203146[/C][C]1.07264977968536[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4513750353086[/C][C]-1.45137503530859[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9962162781611[/C][C]0.00378372183887183[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2822349581584[/C][C]0.717765041841596[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.0760136608302[/C][C]-0.0760136608301671[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1566382773535[/C][C]-2.15663827735353[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1032220799221[/C][C]0.896777920077853[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7832763563899[/C][C]0.216723643610123[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1215828752941[/C][C]1.87841712470586[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9740314843952[/C][C]0.0259685156048103[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4772521601596[/C][C]-0.477252160159605[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2107755679092[/C][C]-1.21077556790917[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8087558556083[/C][C]1.19124414439166[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4170528347364[/C][C]2.58294716526365[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7838834242403[/C][C]0.216116575759746[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.1998987218875[/C][C]1.80010127811249[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2142066211527[/C][C]-2.21420662115265[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9945335077365[/C][C]1.00546649226351[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]16.0052661706613[/C][C]-0.00526617066134807[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6536187772108[/C][C]1.34638122278925[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.691474054272[/C][C]-0.691474054272004[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9648675603001[/C][C]1.03513243969986[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.870446902523[/C][C]0.129553097477011[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4375057244299[/C][C]2.56249427557012[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.518339394135[/C][C]-1.51833939413505[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.856567878619[/C][C]-2.85656787861905[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.9713271693507[/C][C]1.0286728306493[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.7474336693166[/C][C]-1.7474336693166[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9362210021145[/C][C]1.06377899788546[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.1811716702508[/C][C]-2.18117167025076[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6475252248667[/C][C]0.352474775133326[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3962274332847[/C][C]-1.39622743328469[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.1250644486777[/C][C]-0.125064448677715[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0763407105773[/C][C]-3.07634071057727[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.2080173544216[/C][C]-1.20801735442157[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0391699067821[/C][C]-1.03916990678206[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.7408439989002[/C][C]-0.740843998900241[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.512836924999[/C][C]-0.512836924999026[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2653919811339[/C][C]0.734608018866127[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.2077788794463[/C][C]0.79222112055371[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9452956586378[/C][C]-2.94529565863775[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1005664249092[/C][C]1.89943357509077[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2659238102439[/C][C]-3.2659238102439[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1037969252147[/C][C]1.89620307478528[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8606729573268[/C][C]-1.86067295732676[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.6619530670457[/C][C]-1.66195306704572[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.5059706857911[/C][C]-0.505970685791055[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3172802252929[/C][C]0.6827197747071[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.8904076946899[/C][C]0.10959230531006[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.5526397388065[/C][C]-2.55263973880645[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5543585319916[/C][C]-1.55435853199158[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3073042203369[/C][C]-5.30730422033692[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.5560029490886[/C][C]2.44399705091142[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2519728188941[/C][C]1.7480271811059[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5603133023165[/C][C]0.439686697683478[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8523949745772[/C][C]1.14760502542284[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2461961052955[/C][C]-4.24619610529549[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.892924663386[/C][C]2.10707533661398[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2744297017535[/C][C]-2.27442970175355[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1911795817192[/C][C]0.808820418280772[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.26596648884[/C][C]-0.265966488840021[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1832753137692[/C][C]-3.1832753137692[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5128707986286[/C][C]-1.51287079862856[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.6130598838654[/C][C]1.3869401161346[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0528885034356[/C][C]3.94711149656443[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.7881991567107[/C][C]1.21180084328933[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4772529290364[/C][C]-2.47725292903637[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.9962162781611[/C][C]0.00378372183887183[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7417962615772[/C][C]1.25820373842278[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.2077788794463[/C][C]0.79222112055371[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.3082162730131[/C][C]0.691783726986918[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4492154140547[/C][C]-0.449215414054705[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8652197318236[/C][C]0.134780268176446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2896817205973-3.28968172059735
21615.75967480101380.240325198986197
31916.21194867608552.78805132391447
41511.64779508533353.35220491466654
51415.6272756661639-1.62727566616388
61314.8955850831943-1.89558508319427
71915.2041675802853.79583241971503
81516.6286822250089-1.62868222500888
91415.7922587611423-1.79225876114235
101512.15615616224912.84384383775094
111615.26938437467850.730615625321477
121616.2299660818545-0.229966081854494
131615.29780425527450.702195744725502
141615.35961003254440.640389967455634
151717.498704673818-0.49870467381795
161515.1484855911744-0.148485591174394
171514.63997960653790.360020393462087
182016.10286203874053.89713796125953
191815.80573971988692.19426028011311
201615.50618548990190.493814510098056
211615.2846130861080.715386913891984
221614.77826259824091.22173740175908
231916.62001674331512.37998325668488
241614.98798516948451.01201483051552
251714.59691883466252.40308116533748
261716.54976476816050.450235231839514
271615.30243723508540.697562764914551
281516.1717149076246-1.17171490762455
291615.19550894267890.804491057321109
301414.034598283949-0.0345982839490274
311515.5723134743194-0.572313474319434
321212.1574426092202-0.157442609220231
331415.1595396568561-1.15953965685607
341615.43917629711030.560823702889717
351415.4026752841179-1.40267528411787
36712.8789794147288-5.8789794147288
371010.6522818811081-0.652281881108146
381415.6852865734995-1.68528657349955
391614.15525349833261.84474650166737
401614.51022231705321.48977768294677
411614.96208323548181.03791676451817
421415.6191175420149-1.61911754201493
432017.96487903342032.0351209665797
441414.2525567286646-0.252556728664567
451415.081162738382-1.081162738382
461115.6619668963189-4.66196689631895
471416.5355518144056-2.53555181440557
481514.85921405549090.140785944509094
491614.76160435813861.23839564186144
501415.8863192934246-1.88631929342457
511616.3102005056942-0.310200505694215
521414.0028151930709-0.0028151930709257
531214.5143490716462-2.51434907164624
541615.78513557759780.214864422402232
55911.3143727283251-2.31437272832512
561412.52162018600111.4783798139989
571616.0962988090047-0.096298809004734
581615.00457470397330.99542529602674
591515.262850642813-0.262850642813012
601614.14579934513271.85420065486734
611211.32935094223320.670649057766841
621616.0039830969251-0.00398309692510026
631616.3870749823098-0.387074982309791
641414.6864019571664-0.68640195716644
651615.68980352077620.310196479223797
661715.78551022516591.21448977483405
671816.20615572457141.79384427542858
681814.57341494439993.42658505560015
691215.7482494743035-3.74824947430346
701615.30865579890280.69134420109723
711013.4821424528254-3.48214245282536
721414.2752021353443-0.275202135344264
731816.2001820308281.799817969172
741817.0777116603250.92228833967505
751615.70564706235490.294352937645107
761713.82120938277423.17879061722583
771616.4094339467827-0.409433946782657
781614.69387800845511.3061219915449
791314.7796783819538-1.77967838195381
801616.3209531091194-0.3209531091194
811615.48149564008690.518504359913063
822015.88490359402494.11509640597507
831615.7092644922860.290735507713958
841515.732309925004-0.732309925004046
851515.0458328468233-0.0458328468233487
861614.54273125254631.45726874745368
871414.0149670262482-0.0149670262481858
881615.2664097255510.733590274448951
891614.70275829425981.29724170574025
901513.92735022031461.07264977968536
911213.4513750353086-1.45137503530859
921716.99621627816110.00378372183887183
931615.28223495815840.717765041841596
941515.0760136608302-0.0760136608301671
951315.1566382773535-2.15663827735353
961615.10322207992210.896777920077853
971615.78327635638990.216723643610123
981614.12158287529411.87841712470586
991615.97403148439520.0259685156048103
1001414.4772521601596-0.477252160159605
1011617.2107755679092-1.21077556790917
1021614.80875585560831.19124414439166
1032017.41705283473642.58294716526365
1041514.78388342424030.216116575759746
1051614.19989872188751.80010127811249
1061315.2142066211527-2.21420662115265
1071715.99453350773651.00546649226351
1081616.0052661706613-0.00526617066134807
1091614.65361877721081.34638122278925
1101212.691474054272-0.691474054272004
1111614.96486756030011.03513243969986
1121615.8704469025230.129553097477011
1131714.43750572442992.56249427557012
1141314.518339394135-1.51833939413505
1151214.856567878619-2.85656787861905
1161816.97132716935071.0286728306493
1171415.7474336693166-1.7474336693166
1181412.93622100211451.06377899788546
1191315.1811716702508-2.18117167025076
1201615.64752522486670.352474775133326
1211314.3962274332847-1.39622743328469
1221616.1250644486777-0.125064448677715
1231316.0763407105773-3.07634071057727
1241617.2080173544216-1.20801735442157
1251516.0391699067821-1.03916990678206
1261616.7408439989002-0.740843998900241
1271515.512836924999-0.512836924999026
1281716.26539198113390.734608018866127
1291514.20777887944630.79222112055371
1301214.9452956586378-2.94529565863775
1311614.10056642490921.89943357509077
1321013.2659238102439-3.2659238102439
1331614.10379692521471.89620307478528
1341213.8606729573268-1.86067295732676
1351415.6619530670457-1.66195306704572
1361515.5059706857911-0.505970685791055
1371312.31728022529290.6827197747071
1381514.89040769468990.10959230531006
1391113.5526397388065-2.55263973880645
1401213.5543585319916-1.55435853199158
141813.3073042203369-5.30730422033692
1421613.55600294908862.44399705091142
1431513.25197281889411.7480271811059
1441716.56031330231650.439686697683478
1451614.85239497457721.14760502542284
1461014.2461961052955-4.24619610529549
1471815.8929246633862.10707533661398
1481315.2744297017535-2.27442970175355
1491615.19117958171920.808820418280772
1501313.26596648884-0.265966488840021
1511013.1832753137692-3.1832753137692
1521516.5128707986286-1.51287079862856
1531614.61305988386541.3869401161346
1541612.05288850343563.94711149656443
1551412.78819915671071.21180084328933
1561012.4772529290364-2.47725292903637
1571716.99621627816110.00378372183887183
1581311.74179626157721.25820373842278
1591514.20777887944630.79222112055371
1601615.30821627301310.691783726986918
1611212.4492154140547-0.449215414054705
1621312.86521973182360.134780268176446






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2608278499577660.5216556999155320.739172150042234
110.4529523065895340.9059046131790680.547047693410466
120.3924852771092060.7849705542184130.607514722890794
130.3182100639337230.6364201278674470.681789936066276
140.2987693059695510.5975386119391020.701230694030449
150.3626860880037680.7253721760075350.637313911996232
160.2876193438850710.5752386877701430.712380656114929
170.2563645178148250.5127290356296490.743635482185175
180.7180133627282170.5639732745435660.281986637271783
190.86901550837510.2619689832497990.130984491624899
200.8305365361816580.3389269276366840.169463463818342
210.7786127444384820.4427745111230360.221387255561518
220.7209736415459840.5580527169080330.279026358454016
230.7562146492578830.4875707014842340.243785350742117
240.6990741113883820.6018517772232360.300925888611618
250.683505033968570.632989932062860.31649496603143
260.6278534629953410.7442930740093180.372146537004659
270.5929142482626270.8141715034747460.407085751737373
280.5740756661372110.8518486677255790.425924333862789
290.5102529364611450.979494127077710.489747063538855
300.4897306118130190.9794612236260380.510269388186981
310.4286885977718280.8573771955436560.571311402228172
320.4158835605964350.8317671211928710.584116439403565
330.3913246601538730.7826493203077450.608675339846127
340.3348681948225180.6697363896450360.665131805177482
350.3184115446972720.6368230893945430.681588455302728
360.8425996445603520.3148007108792970.157400355439649
370.8299298200045480.3401403599909030.170070179995452
380.8296984669615210.3406030660769580.170301533038479
390.8475677035420420.3048645929159150.152432296457958
400.830269877677030.339460244645940.16973012232297
410.802461807796120.3950763844077610.197538192203881
420.7886167386894940.4227665226210120.211383261310506
430.7932884598015470.4134230803969050.206711540198453
440.7567071482258520.4865857035482960.243292851774148
450.7206663128657520.5586673742684970.279333687134248
460.8919176737990480.2161646524019040.108082326200952
470.9221998090782750.155600381843450.0778001909217251
480.9018623565550080.1962752868899850.0981376434449924
490.8849541222384260.2300917555231490.115045877761574
500.8864665726918030.2270668546163950.113533427308197
510.8627986842665430.2744026314669140.137201315733457
520.833468175212970.333063649574060.16653182478703
530.8609681841304810.2780636317390370.139031815869519
540.8334853243463670.3330293513072660.166514675653633
550.8460513612526380.3078972774947230.153948638747362
560.8300566586781240.3398866826437530.169943341321876
570.7989456416655560.4021087166688870.201054358334444
580.7756748144222860.4486503711554280.224325185577714
590.7384250878132120.5231498243735760.261574912186788
600.7367519182319270.5264961635361450.263248081768073
610.7016478765201090.5967042469597830.298352123479891
620.6594141703362870.6811716593274260.340585829663713
630.6167774716313150.766445056737370.383222528368685
640.5757844183011190.8484311633977620.424215581698881
650.5336563787419090.9326872425161820.466343621258091
660.5051453447022460.9897093105955090.494854655297754
670.4941576077280980.9883152154561970.505842392271902
680.6327900860967940.7344198278064110.367209913903206
690.7663290009719890.4673419980560220.233670999028011
700.7332428398394030.5335143203211950.266757160160597
710.8155014901358650.368997019728270.184498509864135
720.7832429112814510.4335141774370980.216757088718549
730.7790615690556470.4418768618887060.220938430944353
740.7537757187260610.4924485625478790.246224281273939
750.7160850099686740.5678299800626510.283914990031326
760.7811799698504370.4376400602991270.218820030149563
770.7471119882961470.5057760234077060.252888011703853
780.7276393128626380.5447213742747240.272360687137362
790.7262760964949830.5474478070100330.273723903505017
800.6870463820058610.6259072359882770.312953617994139
810.6490229311453350.701954137709330.350977068854665
820.8026287437710020.3947425124579970.197371256228998
830.7698348280586840.4603303438826320.230165171941316
840.7416158785366930.5167682429266130.258384121463307
850.7037234049588520.5925531900822960.296276595041148
860.6889046808127810.6221906383744380.311095319187219
870.6471370485739790.7057259028520430.352862951426021
880.610691772338480.778616455323040.38930822766152
890.5865388169862380.8269223660275240.413461183013762
900.5689973301522490.8620053396955020.431002669847751
910.5492951754585820.9014096490828350.450704824541418
920.509360076806370.981279846387260.49063992319363
930.4719160572085090.9438321144170170.528083942791491
940.4284242351289710.8568484702579420.571575764871029
950.4444927477500640.8889854955001280.555507252249936
960.4115734833721990.8231469667443980.588426516627801
970.3753362554800210.7506725109600420.624663744519979
980.3794509585728430.7589019171456860.620549041427157
990.3370764133793570.6741528267587140.662923586620643
1000.300070266451960.600140532903920.69992973354804
1010.2715896606797280.5431793213594550.728410339320273
1020.2579577356003010.5159154712006010.7420422643997
1030.3127875974013560.6255751948027120.687212402598644
1040.2716110497810010.5432220995620010.728388950218999
1050.2973271685816560.5946543371633120.702672831418344
1060.3019168539532120.6038337079064250.698083146046787
1070.2898614522535390.5797229045070770.710138547746461
1080.2620865989766510.5241731979533030.737913401023348
1090.2546967468943070.5093934937886140.745303253105693
1100.2249308052628420.4498616105256840.775069194737158
1110.2051362645134470.4102725290268930.794863735486553
1120.1751270402737540.3502540805475080.824872959726246
1130.2329487977301470.4658975954602950.767051202269852
1140.2114950831493060.4229901662986130.788504916850694
1150.2388866226587990.4777732453175980.7611133773412
1160.212108435672840.424216871345680.78789156432716
1170.1910682486270950.3821364972541910.808931751372905
1180.1819908430853160.3639816861706320.818009156914684
1190.2004408418031240.4008816836062490.799559158196876
1200.1858897602663820.3717795205327650.814110239733618
1210.1593864684243150.3187729368486290.840613531575685
1220.1371541284188610.2743082568377220.86284587158114
1230.167270138422350.33454027684470.83272986157765
1240.1411864371983060.2823728743966120.858813562801694
1250.1175324299914480.2350648599828970.882467570008552
1260.09376323765164820.1875264753032960.906236762348352
1270.0749241626333650.149848325266730.925075837366635
1280.07096695926512030.1419339185302410.92903304073488
1290.0551898629146350.110379725829270.944810137085365
1300.0635687118348430.1271374236696860.936431288165157
1310.06333545819652440.1266709163930490.936664541803476
1320.08477651698299560.1695530339659910.915223483017004
1330.1165266205690220.2330532411380450.883473379430977
1340.1189702155675060.2379404311350110.881029784432494
1350.0940758217310520.1881516434621040.905924178268948
1360.08002242064541090.1600448412908220.919977579354589
1370.05826069072085370.1165213814417070.941739309279146
1380.04093639692357970.08187279384715930.95906360307642
1390.1015908253173760.2031816506347520.898409174682624
1400.07905326107436290.1581065221487260.920946738925637
1410.6218283135015010.7563433729969980.378171686498499
1420.5621852553477940.8756294893044120.437814744652206
1430.4989968886778590.9979937773557170.501003111322141
1440.4876418799442330.9752837598884670.512358120055767
1450.4146129913407470.8292259826814940.585387008659253
1460.402841711702670.805683423405340.59715828829733
1470.5226916634044590.9546166731910810.477308336595541
1480.7841566970622180.4316866058755640.215843302937782
1490.7181124654828060.5637750690343880.281887534517194
1500.5928446615457680.8143106769084640.407155338454232
1510.7867132999503140.4265734000993710.213286700049686
1520.8451185803922350.309762839215530.154881419607765

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.260827849957766 & 0.521655699915532 & 0.739172150042234 \tabularnewline
11 & 0.452952306589534 & 0.905904613179068 & 0.547047693410466 \tabularnewline
12 & 0.392485277109206 & 0.784970554218413 & 0.607514722890794 \tabularnewline
13 & 0.318210063933723 & 0.636420127867447 & 0.681789936066276 \tabularnewline
14 & 0.298769305969551 & 0.597538611939102 & 0.701230694030449 \tabularnewline
15 & 0.362686088003768 & 0.725372176007535 & 0.637313911996232 \tabularnewline
16 & 0.287619343885071 & 0.575238687770143 & 0.712380656114929 \tabularnewline
17 & 0.256364517814825 & 0.512729035629649 & 0.743635482185175 \tabularnewline
18 & 0.718013362728217 & 0.563973274543566 & 0.281986637271783 \tabularnewline
19 & 0.8690155083751 & 0.261968983249799 & 0.130984491624899 \tabularnewline
20 & 0.830536536181658 & 0.338926927636684 & 0.169463463818342 \tabularnewline
21 & 0.778612744438482 & 0.442774511123036 & 0.221387255561518 \tabularnewline
22 & 0.720973641545984 & 0.558052716908033 & 0.279026358454016 \tabularnewline
23 & 0.756214649257883 & 0.487570701484234 & 0.243785350742117 \tabularnewline
24 & 0.699074111388382 & 0.601851777223236 & 0.300925888611618 \tabularnewline
25 & 0.68350503396857 & 0.63298993206286 & 0.31649496603143 \tabularnewline
26 & 0.627853462995341 & 0.744293074009318 & 0.372146537004659 \tabularnewline
27 & 0.592914248262627 & 0.814171503474746 & 0.407085751737373 \tabularnewline
28 & 0.574075666137211 & 0.851848667725579 & 0.425924333862789 \tabularnewline
29 & 0.510252936461145 & 0.97949412707771 & 0.489747063538855 \tabularnewline
30 & 0.489730611813019 & 0.979461223626038 & 0.510269388186981 \tabularnewline
31 & 0.428688597771828 & 0.857377195543656 & 0.571311402228172 \tabularnewline
32 & 0.415883560596435 & 0.831767121192871 & 0.584116439403565 \tabularnewline
33 & 0.391324660153873 & 0.782649320307745 & 0.608675339846127 \tabularnewline
34 & 0.334868194822518 & 0.669736389645036 & 0.665131805177482 \tabularnewline
35 & 0.318411544697272 & 0.636823089394543 & 0.681588455302728 \tabularnewline
36 & 0.842599644560352 & 0.314800710879297 & 0.157400355439649 \tabularnewline
37 & 0.829929820004548 & 0.340140359990903 & 0.170070179995452 \tabularnewline
38 & 0.829698466961521 & 0.340603066076958 & 0.170301533038479 \tabularnewline
39 & 0.847567703542042 & 0.304864592915915 & 0.152432296457958 \tabularnewline
40 & 0.83026987767703 & 0.33946024464594 & 0.16973012232297 \tabularnewline
41 & 0.80246180779612 & 0.395076384407761 & 0.197538192203881 \tabularnewline
42 & 0.788616738689494 & 0.422766522621012 & 0.211383261310506 \tabularnewline
43 & 0.793288459801547 & 0.413423080396905 & 0.206711540198453 \tabularnewline
44 & 0.756707148225852 & 0.486585703548296 & 0.243292851774148 \tabularnewline
45 & 0.720666312865752 & 0.558667374268497 & 0.279333687134248 \tabularnewline
46 & 0.891917673799048 & 0.216164652401904 & 0.108082326200952 \tabularnewline
47 & 0.922199809078275 & 0.15560038184345 & 0.0778001909217251 \tabularnewline
48 & 0.901862356555008 & 0.196275286889985 & 0.0981376434449924 \tabularnewline
49 & 0.884954122238426 & 0.230091755523149 & 0.115045877761574 \tabularnewline
50 & 0.886466572691803 & 0.227066854616395 & 0.113533427308197 \tabularnewline
51 & 0.862798684266543 & 0.274402631466914 & 0.137201315733457 \tabularnewline
52 & 0.83346817521297 & 0.33306364957406 & 0.16653182478703 \tabularnewline
53 & 0.860968184130481 & 0.278063631739037 & 0.139031815869519 \tabularnewline
54 & 0.833485324346367 & 0.333029351307266 & 0.166514675653633 \tabularnewline
55 & 0.846051361252638 & 0.307897277494723 & 0.153948638747362 \tabularnewline
56 & 0.830056658678124 & 0.339886682643753 & 0.169943341321876 \tabularnewline
57 & 0.798945641665556 & 0.402108716668887 & 0.201054358334444 \tabularnewline
58 & 0.775674814422286 & 0.448650371155428 & 0.224325185577714 \tabularnewline
59 & 0.738425087813212 & 0.523149824373576 & 0.261574912186788 \tabularnewline
60 & 0.736751918231927 & 0.526496163536145 & 0.263248081768073 \tabularnewline
61 & 0.701647876520109 & 0.596704246959783 & 0.298352123479891 \tabularnewline
62 & 0.659414170336287 & 0.681171659327426 & 0.340585829663713 \tabularnewline
63 & 0.616777471631315 & 0.76644505673737 & 0.383222528368685 \tabularnewline
64 & 0.575784418301119 & 0.848431163397762 & 0.424215581698881 \tabularnewline
65 & 0.533656378741909 & 0.932687242516182 & 0.466343621258091 \tabularnewline
66 & 0.505145344702246 & 0.989709310595509 & 0.494854655297754 \tabularnewline
67 & 0.494157607728098 & 0.988315215456197 & 0.505842392271902 \tabularnewline
68 & 0.632790086096794 & 0.734419827806411 & 0.367209913903206 \tabularnewline
69 & 0.766329000971989 & 0.467341998056022 & 0.233670999028011 \tabularnewline
70 & 0.733242839839403 & 0.533514320321195 & 0.266757160160597 \tabularnewline
71 & 0.815501490135865 & 0.36899701972827 & 0.184498509864135 \tabularnewline
72 & 0.783242911281451 & 0.433514177437098 & 0.216757088718549 \tabularnewline
73 & 0.779061569055647 & 0.441876861888706 & 0.220938430944353 \tabularnewline
74 & 0.753775718726061 & 0.492448562547879 & 0.246224281273939 \tabularnewline
75 & 0.716085009968674 & 0.567829980062651 & 0.283914990031326 \tabularnewline
76 & 0.781179969850437 & 0.437640060299127 & 0.218820030149563 \tabularnewline
77 & 0.747111988296147 & 0.505776023407706 & 0.252888011703853 \tabularnewline
78 & 0.727639312862638 & 0.544721374274724 & 0.272360687137362 \tabularnewline
79 & 0.726276096494983 & 0.547447807010033 & 0.273723903505017 \tabularnewline
80 & 0.687046382005861 & 0.625907235988277 & 0.312953617994139 \tabularnewline
81 & 0.649022931145335 & 0.70195413770933 & 0.350977068854665 \tabularnewline
82 & 0.802628743771002 & 0.394742512457997 & 0.197371256228998 \tabularnewline
83 & 0.769834828058684 & 0.460330343882632 & 0.230165171941316 \tabularnewline
84 & 0.741615878536693 & 0.516768242926613 & 0.258384121463307 \tabularnewline
85 & 0.703723404958852 & 0.592553190082296 & 0.296276595041148 \tabularnewline
86 & 0.688904680812781 & 0.622190638374438 & 0.311095319187219 \tabularnewline
87 & 0.647137048573979 & 0.705725902852043 & 0.352862951426021 \tabularnewline
88 & 0.61069177233848 & 0.77861645532304 & 0.38930822766152 \tabularnewline
89 & 0.586538816986238 & 0.826922366027524 & 0.413461183013762 \tabularnewline
90 & 0.568997330152249 & 0.862005339695502 & 0.431002669847751 \tabularnewline
91 & 0.549295175458582 & 0.901409649082835 & 0.450704824541418 \tabularnewline
92 & 0.50936007680637 & 0.98127984638726 & 0.49063992319363 \tabularnewline
93 & 0.471916057208509 & 0.943832114417017 & 0.528083942791491 \tabularnewline
94 & 0.428424235128971 & 0.856848470257942 & 0.571575764871029 \tabularnewline
95 & 0.444492747750064 & 0.888985495500128 & 0.555507252249936 \tabularnewline
96 & 0.411573483372199 & 0.823146966744398 & 0.588426516627801 \tabularnewline
97 & 0.375336255480021 & 0.750672510960042 & 0.624663744519979 \tabularnewline
98 & 0.379450958572843 & 0.758901917145686 & 0.620549041427157 \tabularnewline
99 & 0.337076413379357 & 0.674152826758714 & 0.662923586620643 \tabularnewline
100 & 0.30007026645196 & 0.60014053290392 & 0.69992973354804 \tabularnewline
101 & 0.271589660679728 & 0.543179321359455 & 0.728410339320273 \tabularnewline
102 & 0.257957735600301 & 0.515915471200601 & 0.7420422643997 \tabularnewline
103 & 0.312787597401356 & 0.625575194802712 & 0.687212402598644 \tabularnewline
104 & 0.271611049781001 & 0.543222099562001 & 0.728388950218999 \tabularnewline
105 & 0.297327168581656 & 0.594654337163312 & 0.702672831418344 \tabularnewline
106 & 0.301916853953212 & 0.603833707906425 & 0.698083146046787 \tabularnewline
107 & 0.289861452253539 & 0.579722904507077 & 0.710138547746461 \tabularnewline
108 & 0.262086598976651 & 0.524173197953303 & 0.737913401023348 \tabularnewline
109 & 0.254696746894307 & 0.509393493788614 & 0.745303253105693 \tabularnewline
110 & 0.224930805262842 & 0.449861610525684 & 0.775069194737158 \tabularnewline
111 & 0.205136264513447 & 0.410272529026893 & 0.794863735486553 \tabularnewline
112 & 0.175127040273754 & 0.350254080547508 & 0.824872959726246 \tabularnewline
113 & 0.232948797730147 & 0.465897595460295 & 0.767051202269852 \tabularnewline
114 & 0.211495083149306 & 0.422990166298613 & 0.788504916850694 \tabularnewline
115 & 0.238886622658799 & 0.477773245317598 & 0.7611133773412 \tabularnewline
116 & 0.21210843567284 & 0.42421687134568 & 0.78789156432716 \tabularnewline
117 & 0.191068248627095 & 0.382136497254191 & 0.808931751372905 \tabularnewline
118 & 0.181990843085316 & 0.363981686170632 & 0.818009156914684 \tabularnewline
119 & 0.200440841803124 & 0.400881683606249 & 0.799559158196876 \tabularnewline
120 & 0.185889760266382 & 0.371779520532765 & 0.814110239733618 \tabularnewline
121 & 0.159386468424315 & 0.318772936848629 & 0.840613531575685 \tabularnewline
122 & 0.137154128418861 & 0.274308256837722 & 0.86284587158114 \tabularnewline
123 & 0.16727013842235 & 0.3345402768447 & 0.83272986157765 \tabularnewline
124 & 0.141186437198306 & 0.282372874396612 & 0.858813562801694 \tabularnewline
125 & 0.117532429991448 & 0.235064859982897 & 0.882467570008552 \tabularnewline
126 & 0.0937632376516482 & 0.187526475303296 & 0.906236762348352 \tabularnewline
127 & 0.074924162633365 & 0.14984832526673 & 0.925075837366635 \tabularnewline
128 & 0.0709669592651203 & 0.141933918530241 & 0.92903304073488 \tabularnewline
129 & 0.055189862914635 & 0.11037972582927 & 0.944810137085365 \tabularnewline
130 & 0.063568711834843 & 0.127137423669686 & 0.936431288165157 \tabularnewline
131 & 0.0633354581965244 & 0.126670916393049 & 0.936664541803476 \tabularnewline
132 & 0.0847765169829956 & 0.169553033965991 & 0.915223483017004 \tabularnewline
133 & 0.116526620569022 & 0.233053241138045 & 0.883473379430977 \tabularnewline
134 & 0.118970215567506 & 0.237940431135011 & 0.881029784432494 \tabularnewline
135 & 0.094075821731052 & 0.188151643462104 & 0.905924178268948 \tabularnewline
136 & 0.0800224206454109 & 0.160044841290822 & 0.919977579354589 \tabularnewline
137 & 0.0582606907208537 & 0.116521381441707 & 0.941739309279146 \tabularnewline
138 & 0.0409363969235797 & 0.0818727938471593 & 0.95906360307642 \tabularnewline
139 & 0.101590825317376 & 0.203181650634752 & 0.898409174682624 \tabularnewline
140 & 0.0790532610743629 & 0.158106522148726 & 0.920946738925637 \tabularnewline
141 & 0.621828313501501 & 0.756343372996998 & 0.378171686498499 \tabularnewline
142 & 0.562185255347794 & 0.875629489304412 & 0.437814744652206 \tabularnewline
143 & 0.498996888677859 & 0.997993777355717 & 0.501003111322141 \tabularnewline
144 & 0.487641879944233 & 0.975283759888467 & 0.512358120055767 \tabularnewline
145 & 0.414612991340747 & 0.829225982681494 & 0.585387008659253 \tabularnewline
146 & 0.40284171170267 & 0.80568342340534 & 0.59715828829733 \tabularnewline
147 & 0.522691663404459 & 0.954616673191081 & 0.477308336595541 \tabularnewline
148 & 0.784156697062218 & 0.431686605875564 & 0.215843302937782 \tabularnewline
149 & 0.718112465482806 & 0.563775069034388 & 0.281887534517194 \tabularnewline
150 & 0.592844661545768 & 0.814310676908464 & 0.407155338454232 \tabularnewline
151 & 0.786713299950314 & 0.426573400099371 & 0.213286700049686 \tabularnewline
152 & 0.845118580392235 & 0.30976283921553 & 0.154881419607765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.260827849957766[/C][C]0.521655699915532[/C][C]0.739172150042234[/C][/ROW]
[ROW][C]11[/C][C]0.452952306589534[/C][C]0.905904613179068[/C][C]0.547047693410466[/C][/ROW]
[ROW][C]12[/C][C]0.392485277109206[/C][C]0.784970554218413[/C][C]0.607514722890794[/C][/ROW]
[ROW][C]13[/C][C]0.318210063933723[/C][C]0.636420127867447[/C][C]0.681789936066276[/C][/ROW]
[ROW][C]14[/C][C]0.298769305969551[/C][C]0.597538611939102[/C][C]0.701230694030449[/C][/ROW]
[ROW][C]15[/C][C]0.362686088003768[/C][C]0.725372176007535[/C][C]0.637313911996232[/C][/ROW]
[ROW][C]16[/C][C]0.287619343885071[/C][C]0.575238687770143[/C][C]0.712380656114929[/C][/ROW]
[ROW][C]17[/C][C]0.256364517814825[/C][C]0.512729035629649[/C][C]0.743635482185175[/C][/ROW]
[ROW][C]18[/C][C]0.718013362728217[/C][C]0.563973274543566[/C][C]0.281986637271783[/C][/ROW]
[ROW][C]19[/C][C]0.8690155083751[/C][C]0.261968983249799[/C][C]0.130984491624899[/C][/ROW]
[ROW][C]20[/C][C]0.830536536181658[/C][C]0.338926927636684[/C][C]0.169463463818342[/C][/ROW]
[ROW][C]21[/C][C]0.778612744438482[/C][C]0.442774511123036[/C][C]0.221387255561518[/C][/ROW]
[ROW][C]22[/C][C]0.720973641545984[/C][C]0.558052716908033[/C][C]0.279026358454016[/C][/ROW]
[ROW][C]23[/C][C]0.756214649257883[/C][C]0.487570701484234[/C][C]0.243785350742117[/C][/ROW]
[ROW][C]24[/C][C]0.699074111388382[/C][C]0.601851777223236[/C][C]0.300925888611618[/C][/ROW]
[ROW][C]25[/C][C]0.68350503396857[/C][C]0.63298993206286[/C][C]0.31649496603143[/C][/ROW]
[ROW][C]26[/C][C]0.627853462995341[/C][C]0.744293074009318[/C][C]0.372146537004659[/C][/ROW]
[ROW][C]27[/C][C]0.592914248262627[/C][C]0.814171503474746[/C][C]0.407085751737373[/C][/ROW]
[ROW][C]28[/C][C]0.574075666137211[/C][C]0.851848667725579[/C][C]0.425924333862789[/C][/ROW]
[ROW][C]29[/C][C]0.510252936461145[/C][C]0.97949412707771[/C][C]0.489747063538855[/C][/ROW]
[ROW][C]30[/C][C]0.489730611813019[/C][C]0.979461223626038[/C][C]0.510269388186981[/C][/ROW]
[ROW][C]31[/C][C]0.428688597771828[/C][C]0.857377195543656[/C][C]0.571311402228172[/C][/ROW]
[ROW][C]32[/C][C]0.415883560596435[/C][C]0.831767121192871[/C][C]0.584116439403565[/C][/ROW]
[ROW][C]33[/C][C]0.391324660153873[/C][C]0.782649320307745[/C][C]0.608675339846127[/C][/ROW]
[ROW][C]34[/C][C]0.334868194822518[/C][C]0.669736389645036[/C][C]0.665131805177482[/C][/ROW]
[ROW][C]35[/C][C]0.318411544697272[/C][C]0.636823089394543[/C][C]0.681588455302728[/C][/ROW]
[ROW][C]36[/C][C]0.842599644560352[/C][C]0.314800710879297[/C][C]0.157400355439649[/C][/ROW]
[ROW][C]37[/C][C]0.829929820004548[/C][C]0.340140359990903[/C][C]0.170070179995452[/C][/ROW]
[ROW][C]38[/C][C]0.829698466961521[/C][C]0.340603066076958[/C][C]0.170301533038479[/C][/ROW]
[ROW][C]39[/C][C]0.847567703542042[/C][C]0.304864592915915[/C][C]0.152432296457958[/C][/ROW]
[ROW][C]40[/C][C]0.83026987767703[/C][C]0.33946024464594[/C][C]0.16973012232297[/C][/ROW]
[ROW][C]41[/C][C]0.80246180779612[/C][C]0.395076384407761[/C][C]0.197538192203881[/C][/ROW]
[ROW][C]42[/C][C]0.788616738689494[/C][C]0.422766522621012[/C][C]0.211383261310506[/C][/ROW]
[ROW][C]43[/C][C]0.793288459801547[/C][C]0.413423080396905[/C][C]0.206711540198453[/C][/ROW]
[ROW][C]44[/C][C]0.756707148225852[/C][C]0.486585703548296[/C][C]0.243292851774148[/C][/ROW]
[ROW][C]45[/C][C]0.720666312865752[/C][C]0.558667374268497[/C][C]0.279333687134248[/C][/ROW]
[ROW][C]46[/C][C]0.891917673799048[/C][C]0.216164652401904[/C][C]0.108082326200952[/C][/ROW]
[ROW][C]47[/C][C]0.922199809078275[/C][C]0.15560038184345[/C][C]0.0778001909217251[/C][/ROW]
[ROW][C]48[/C][C]0.901862356555008[/C][C]0.196275286889985[/C][C]0.0981376434449924[/C][/ROW]
[ROW][C]49[/C][C]0.884954122238426[/C][C]0.230091755523149[/C][C]0.115045877761574[/C][/ROW]
[ROW][C]50[/C][C]0.886466572691803[/C][C]0.227066854616395[/C][C]0.113533427308197[/C][/ROW]
[ROW][C]51[/C][C]0.862798684266543[/C][C]0.274402631466914[/C][C]0.137201315733457[/C][/ROW]
[ROW][C]52[/C][C]0.83346817521297[/C][C]0.33306364957406[/C][C]0.16653182478703[/C][/ROW]
[ROW][C]53[/C][C]0.860968184130481[/C][C]0.278063631739037[/C][C]0.139031815869519[/C][/ROW]
[ROW][C]54[/C][C]0.833485324346367[/C][C]0.333029351307266[/C][C]0.166514675653633[/C][/ROW]
[ROW][C]55[/C][C]0.846051361252638[/C][C]0.307897277494723[/C][C]0.153948638747362[/C][/ROW]
[ROW][C]56[/C][C]0.830056658678124[/C][C]0.339886682643753[/C][C]0.169943341321876[/C][/ROW]
[ROW][C]57[/C][C]0.798945641665556[/C][C]0.402108716668887[/C][C]0.201054358334444[/C][/ROW]
[ROW][C]58[/C][C]0.775674814422286[/C][C]0.448650371155428[/C][C]0.224325185577714[/C][/ROW]
[ROW][C]59[/C][C]0.738425087813212[/C][C]0.523149824373576[/C][C]0.261574912186788[/C][/ROW]
[ROW][C]60[/C][C]0.736751918231927[/C][C]0.526496163536145[/C][C]0.263248081768073[/C][/ROW]
[ROW][C]61[/C][C]0.701647876520109[/C][C]0.596704246959783[/C][C]0.298352123479891[/C][/ROW]
[ROW][C]62[/C][C]0.659414170336287[/C][C]0.681171659327426[/C][C]0.340585829663713[/C][/ROW]
[ROW][C]63[/C][C]0.616777471631315[/C][C]0.76644505673737[/C][C]0.383222528368685[/C][/ROW]
[ROW][C]64[/C][C]0.575784418301119[/C][C]0.848431163397762[/C][C]0.424215581698881[/C][/ROW]
[ROW][C]65[/C][C]0.533656378741909[/C][C]0.932687242516182[/C][C]0.466343621258091[/C][/ROW]
[ROW][C]66[/C][C]0.505145344702246[/C][C]0.989709310595509[/C][C]0.494854655297754[/C][/ROW]
[ROW][C]67[/C][C]0.494157607728098[/C][C]0.988315215456197[/C][C]0.505842392271902[/C][/ROW]
[ROW][C]68[/C][C]0.632790086096794[/C][C]0.734419827806411[/C][C]0.367209913903206[/C][/ROW]
[ROW][C]69[/C][C]0.766329000971989[/C][C]0.467341998056022[/C][C]0.233670999028011[/C][/ROW]
[ROW][C]70[/C][C]0.733242839839403[/C][C]0.533514320321195[/C][C]0.266757160160597[/C][/ROW]
[ROW][C]71[/C][C]0.815501490135865[/C][C]0.36899701972827[/C][C]0.184498509864135[/C][/ROW]
[ROW][C]72[/C][C]0.783242911281451[/C][C]0.433514177437098[/C][C]0.216757088718549[/C][/ROW]
[ROW][C]73[/C][C]0.779061569055647[/C][C]0.441876861888706[/C][C]0.220938430944353[/C][/ROW]
[ROW][C]74[/C][C]0.753775718726061[/C][C]0.492448562547879[/C][C]0.246224281273939[/C][/ROW]
[ROW][C]75[/C][C]0.716085009968674[/C][C]0.567829980062651[/C][C]0.283914990031326[/C][/ROW]
[ROW][C]76[/C][C]0.781179969850437[/C][C]0.437640060299127[/C][C]0.218820030149563[/C][/ROW]
[ROW][C]77[/C][C]0.747111988296147[/C][C]0.505776023407706[/C][C]0.252888011703853[/C][/ROW]
[ROW][C]78[/C][C]0.727639312862638[/C][C]0.544721374274724[/C][C]0.272360687137362[/C][/ROW]
[ROW][C]79[/C][C]0.726276096494983[/C][C]0.547447807010033[/C][C]0.273723903505017[/C][/ROW]
[ROW][C]80[/C][C]0.687046382005861[/C][C]0.625907235988277[/C][C]0.312953617994139[/C][/ROW]
[ROW][C]81[/C][C]0.649022931145335[/C][C]0.70195413770933[/C][C]0.350977068854665[/C][/ROW]
[ROW][C]82[/C][C]0.802628743771002[/C][C]0.394742512457997[/C][C]0.197371256228998[/C][/ROW]
[ROW][C]83[/C][C]0.769834828058684[/C][C]0.460330343882632[/C][C]0.230165171941316[/C][/ROW]
[ROW][C]84[/C][C]0.741615878536693[/C][C]0.516768242926613[/C][C]0.258384121463307[/C][/ROW]
[ROW][C]85[/C][C]0.703723404958852[/C][C]0.592553190082296[/C][C]0.296276595041148[/C][/ROW]
[ROW][C]86[/C][C]0.688904680812781[/C][C]0.622190638374438[/C][C]0.311095319187219[/C][/ROW]
[ROW][C]87[/C][C]0.647137048573979[/C][C]0.705725902852043[/C][C]0.352862951426021[/C][/ROW]
[ROW][C]88[/C][C]0.61069177233848[/C][C]0.77861645532304[/C][C]0.38930822766152[/C][/ROW]
[ROW][C]89[/C][C]0.586538816986238[/C][C]0.826922366027524[/C][C]0.413461183013762[/C][/ROW]
[ROW][C]90[/C][C]0.568997330152249[/C][C]0.862005339695502[/C][C]0.431002669847751[/C][/ROW]
[ROW][C]91[/C][C]0.549295175458582[/C][C]0.901409649082835[/C][C]0.450704824541418[/C][/ROW]
[ROW][C]92[/C][C]0.50936007680637[/C][C]0.98127984638726[/C][C]0.49063992319363[/C][/ROW]
[ROW][C]93[/C][C]0.471916057208509[/C][C]0.943832114417017[/C][C]0.528083942791491[/C][/ROW]
[ROW][C]94[/C][C]0.428424235128971[/C][C]0.856848470257942[/C][C]0.571575764871029[/C][/ROW]
[ROW][C]95[/C][C]0.444492747750064[/C][C]0.888985495500128[/C][C]0.555507252249936[/C][/ROW]
[ROW][C]96[/C][C]0.411573483372199[/C][C]0.823146966744398[/C][C]0.588426516627801[/C][/ROW]
[ROW][C]97[/C][C]0.375336255480021[/C][C]0.750672510960042[/C][C]0.624663744519979[/C][/ROW]
[ROW][C]98[/C][C]0.379450958572843[/C][C]0.758901917145686[/C][C]0.620549041427157[/C][/ROW]
[ROW][C]99[/C][C]0.337076413379357[/C][C]0.674152826758714[/C][C]0.662923586620643[/C][/ROW]
[ROW][C]100[/C][C]0.30007026645196[/C][C]0.60014053290392[/C][C]0.69992973354804[/C][/ROW]
[ROW][C]101[/C][C]0.271589660679728[/C][C]0.543179321359455[/C][C]0.728410339320273[/C][/ROW]
[ROW][C]102[/C][C]0.257957735600301[/C][C]0.515915471200601[/C][C]0.7420422643997[/C][/ROW]
[ROW][C]103[/C][C]0.312787597401356[/C][C]0.625575194802712[/C][C]0.687212402598644[/C][/ROW]
[ROW][C]104[/C][C]0.271611049781001[/C][C]0.543222099562001[/C][C]0.728388950218999[/C][/ROW]
[ROW][C]105[/C][C]0.297327168581656[/C][C]0.594654337163312[/C][C]0.702672831418344[/C][/ROW]
[ROW][C]106[/C][C]0.301916853953212[/C][C]0.603833707906425[/C][C]0.698083146046787[/C][/ROW]
[ROW][C]107[/C][C]0.289861452253539[/C][C]0.579722904507077[/C][C]0.710138547746461[/C][/ROW]
[ROW][C]108[/C][C]0.262086598976651[/C][C]0.524173197953303[/C][C]0.737913401023348[/C][/ROW]
[ROW][C]109[/C][C]0.254696746894307[/C][C]0.509393493788614[/C][C]0.745303253105693[/C][/ROW]
[ROW][C]110[/C][C]0.224930805262842[/C][C]0.449861610525684[/C][C]0.775069194737158[/C][/ROW]
[ROW][C]111[/C][C]0.205136264513447[/C][C]0.410272529026893[/C][C]0.794863735486553[/C][/ROW]
[ROW][C]112[/C][C]0.175127040273754[/C][C]0.350254080547508[/C][C]0.824872959726246[/C][/ROW]
[ROW][C]113[/C][C]0.232948797730147[/C][C]0.465897595460295[/C][C]0.767051202269852[/C][/ROW]
[ROW][C]114[/C][C]0.211495083149306[/C][C]0.422990166298613[/C][C]0.788504916850694[/C][/ROW]
[ROW][C]115[/C][C]0.238886622658799[/C][C]0.477773245317598[/C][C]0.7611133773412[/C][/ROW]
[ROW][C]116[/C][C]0.21210843567284[/C][C]0.42421687134568[/C][C]0.78789156432716[/C][/ROW]
[ROW][C]117[/C][C]0.191068248627095[/C][C]0.382136497254191[/C][C]0.808931751372905[/C][/ROW]
[ROW][C]118[/C][C]0.181990843085316[/C][C]0.363981686170632[/C][C]0.818009156914684[/C][/ROW]
[ROW][C]119[/C][C]0.200440841803124[/C][C]0.400881683606249[/C][C]0.799559158196876[/C][/ROW]
[ROW][C]120[/C][C]0.185889760266382[/C][C]0.371779520532765[/C][C]0.814110239733618[/C][/ROW]
[ROW][C]121[/C][C]0.159386468424315[/C][C]0.318772936848629[/C][C]0.840613531575685[/C][/ROW]
[ROW][C]122[/C][C]0.137154128418861[/C][C]0.274308256837722[/C][C]0.86284587158114[/C][/ROW]
[ROW][C]123[/C][C]0.16727013842235[/C][C]0.3345402768447[/C][C]0.83272986157765[/C][/ROW]
[ROW][C]124[/C][C]0.141186437198306[/C][C]0.282372874396612[/C][C]0.858813562801694[/C][/ROW]
[ROW][C]125[/C][C]0.117532429991448[/C][C]0.235064859982897[/C][C]0.882467570008552[/C][/ROW]
[ROW][C]126[/C][C]0.0937632376516482[/C][C]0.187526475303296[/C][C]0.906236762348352[/C][/ROW]
[ROW][C]127[/C][C]0.074924162633365[/C][C]0.14984832526673[/C][C]0.925075837366635[/C][/ROW]
[ROW][C]128[/C][C]0.0709669592651203[/C][C]0.141933918530241[/C][C]0.92903304073488[/C][/ROW]
[ROW][C]129[/C][C]0.055189862914635[/C][C]0.11037972582927[/C][C]0.944810137085365[/C][/ROW]
[ROW][C]130[/C][C]0.063568711834843[/C][C]0.127137423669686[/C][C]0.936431288165157[/C][/ROW]
[ROW][C]131[/C][C]0.0633354581965244[/C][C]0.126670916393049[/C][C]0.936664541803476[/C][/ROW]
[ROW][C]132[/C][C]0.0847765169829956[/C][C]0.169553033965991[/C][C]0.915223483017004[/C][/ROW]
[ROW][C]133[/C][C]0.116526620569022[/C][C]0.233053241138045[/C][C]0.883473379430977[/C][/ROW]
[ROW][C]134[/C][C]0.118970215567506[/C][C]0.237940431135011[/C][C]0.881029784432494[/C][/ROW]
[ROW][C]135[/C][C]0.094075821731052[/C][C]0.188151643462104[/C][C]0.905924178268948[/C][/ROW]
[ROW][C]136[/C][C]0.0800224206454109[/C][C]0.160044841290822[/C][C]0.919977579354589[/C][/ROW]
[ROW][C]137[/C][C]0.0582606907208537[/C][C]0.116521381441707[/C][C]0.941739309279146[/C][/ROW]
[ROW][C]138[/C][C]0.0409363969235797[/C][C]0.0818727938471593[/C][C]0.95906360307642[/C][/ROW]
[ROW][C]139[/C][C]0.101590825317376[/C][C]0.203181650634752[/C][C]0.898409174682624[/C][/ROW]
[ROW][C]140[/C][C]0.0790532610743629[/C][C]0.158106522148726[/C][C]0.920946738925637[/C][/ROW]
[ROW][C]141[/C][C]0.621828313501501[/C][C]0.756343372996998[/C][C]0.378171686498499[/C][/ROW]
[ROW][C]142[/C][C]0.562185255347794[/C][C]0.875629489304412[/C][C]0.437814744652206[/C][/ROW]
[ROW][C]143[/C][C]0.498996888677859[/C][C]0.997993777355717[/C][C]0.501003111322141[/C][/ROW]
[ROW][C]144[/C][C]0.487641879944233[/C][C]0.975283759888467[/C][C]0.512358120055767[/C][/ROW]
[ROW][C]145[/C][C]0.414612991340747[/C][C]0.829225982681494[/C][C]0.585387008659253[/C][/ROW]
[ROW][C]146[/C][C]0.40284171170267[/C][C]0.80568342340534[/C][C]0.59715828829733[/C][/ROW]
[ROW][C]147[/C][C]0.522691663404459[/C][C]0.954616673191081[/C][C]0.477308336595541[/C][/ROW]
[ROW][C]148[/C][C]0.784156697062218[/C][C]0.431686605875564[/C][C]0.215843302937782[/C][/ROW]
[ROW][C]149[/C][C]0.718112465482806[/C][C]0.563775069034388[/C][C]0.281887534517194[/C][/ROW]
[ROW][C]150[/C][C]0.592844661545768[/C][C]0.814310676908464[/C][C]0.407155338454232[/C][/ROW]
[ROW][C]151[/C][C]0.786713299950314[/C][C]0.426573400099371[/C][C]0.213286700049686[/C][/ROW]
[ROW][C]152[/C][C]0.845118580392235[/C][C]0.30976283921553[/C][C]0.154881419607765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2608278499577660.5216556999155320.739172150042234
110.4529523065895340.9059046131790680.547047693410466
120.3924852771092060.7849705542184130.607514722890794
130.3182100639337230.6364201278674470.681789936066276
140.2987693059695510.5975386119391020.701230694030449
150.3626860880037680.7253721760075350.637313911996232
160.2876193438850710.5752386877701430.712380656114929
170.2563645178148250.5127290356296490.743635482185175
180.7180133627282170.5639732745435660.281986637271783
190.86901550837510.2619689832497990.130984491624899
200.8305365361816580.3389269276366840.169463463818342
210.7786127444384820.4427745111230360.221387255561518
220.7209736415459840.5580527169080330.279026358454016
230.7562146492578830.4875707014842340.243785350742117
240.6990741113883820.6018517772232360.300925888611618
250.683505033968570.632989932062860.31649496603143
260.6278534629953410.7442930740093180.372146537004659
270.5929142482626270.8141715034747460.407085751737373
280.5740756661372110.8518486677255790.425924333862789
290.5102529364611450.979494127077710.489747063538855
300.4897306118130190.9794612236260380.510269388186981
310.4286885977718280.8573771955436560.571311402228172
320.4158835605964350.8317671211928710.584116439403565
330.3913246601538730.7826493203077450.608675339846127
340.3348681948225180.6697363896450360.665131805177482
350.3184115446972720.6368230893945430.681588455302728
360.8425996445603520.3148007108792970.157400355439649
370.8299298200045480.3401403599909030.170070179995452
380.8296984669615210.3406030660769580.170301533038479
390.8475677035420420.3048645929159150.152432296457958
400.830269877677030.339460244645940.16973012232297
410.802461807796120.3950763844077610.197538192203881
420.7886167386894940.4227665226210120.211383261310506
430.7932884598015470.4134230803969050.206711540198453
440.7567071482258520.4865857035482960.243292851774148
450.7206663128657520.5586673742684970.279333687134248
460.8919176737990480.2161646524019040.108082326200952
470.9221998090782750.155600381843450.0778001909217251
480.9018623565550080.1962752868899850.0981376434449924
490.8849541222384260.2300917555231490.115045877761574
500.8864665726918030.2270668546163950.113533427308197
510.8627986842665430.2744026314669140.137201315733457
520.833468175212970.333063649574060.16653182478703
530.8609681841304810.2780636317390370.139031815869519
540.8334853243463670.3330293513072660.166514675653633
550.8460513612526380.3078972774947230.153948638747362
560.8300566586781240.3398866826437530.169943341321876
570.7989456416655560.4021087166688870.201054358334444
580.7756748144222860.4486503711554280.224325185577714
590.7384250878132120.5231498243735760.261574912186788
600.7367519182319270.5264961635361450.263248081768073
610.7016478765201090.5967042469597830.298352123479891
620.6594141703362870.6811716593274260.340585829663713
630.6167774716313150.766445056737370.383222528368685
640.5757844183011190.8484311633977620.424215581698881
650.5336563787419090.9326872425161820.466343621258091
660.5051453447022460.9897093105955090.494854655297754
670.4941576077280980.9883152154561970.505842392271902
680.6327900860967940.7344198278064110.367209913903206
690.7663290009719890.4673419980560220.233670999028011
700.7332428398394030.5335143203211950.266757160160597
710.8155014901358650.368997019728270.184498509864135
720.7832429112814510.4335141774370980.216757088718549
730.7790615690556470.4418768618887060.220938430944353
740.7537757187260610.4924485625478790.246224281273939
750.7160850099686740.5678299800626510.283914990031326
760.7811799698504370.4376400602991270.218820030149563
770.7471119882961470.5057760234077060.252888011703853
780.7276393128626380.5447213742747240.272360687137362
790.7262760964949830.5474478070100330.273723903505017
800.6870463820058610.6259072359882770.312953617994139
810.6490229311453350.701954137709330.350977068854665
820.8026287437710020.3947425124579970.197371256228998
830.7698348280586840.4603303438826320.230165171941316
840.7416158785366930.5167682429266130.258384121463307
850.7037234049588520.5925531900822960.296276595041148
860.6889046808127810.6221906383744380.311095319187219
870.6471370485739790.7057259028520430.352862951426021
880.610691772338480.778616455323040.38930822766152
890.5865388169862380.8269223660275240.413461183013762
900.5689973301522490.8620053396955020.431002669847751
910.5492951754585820.9014096490828350.450704824541418
920.509360076806370.981279846387260.49063992319363
930.4719160572085090.9438321144170170.528083942791491
940.4284242351289710.8568484702579420.571575764871029
950.4444927477500640.8889854955001280.555507252249936
960.4115734833721990.8231469667443980.588426516627801
970.3753362554800210.7506725109600420.624663744519979
980.3794509585728430.7589019171456860.620549041427157
990.3370764133793570.6741528267587140.662923586620643
1000.300070266451960.600140532903920.69992973354804
1010.2715896606797280.5431793213594550.728410339320273
1020.2579577356003010.5159154712006010.7420422643997
1030.3127875974013560.6255751948027120.687212402598644
1040.2716110497810010.5432220995620010.728388950218999
1050.2973271685816560.5946543371633120.702672831418344
1060.3019168539532120.6038337079064250.698083146046787
1070.2898614522535390.5797229045070770.710138547746461
1080.2620865989766510.5241731979533030.737913401023348
1090.2546967468943070.5093934937886140.745303253105693
1100.2249308052628420.4498616105256840.775069194737158
1110.2051362645134470.4102725290268930.794863735486553
1120.1751270402737540.3502540805475080.824872959726246
1130.2329487977301470.4658975954602950.767051202269852
1140.2114950831493060.4229901662986130.788504916850694
1150.2388866226587990.4777732453175980.7611133773412
1160.212108435672840.424216871345680.78789156432716
1170.1910682486270950.3821364972541910.808931751372905
1180.1819908430853160.3639816861706320.818009156914684
1190.2004408418031240.4008816836062490.799559158196876
1200.1858897602663820.3717795205327650.814110239733618
1210.1593864684243150.3187729368486290.840613531575685
1220.1371541284188610.2743082568377220.86284587158114
1230.167270138422350.33454027684470.83272986157765
1240.1411864371983060.2823728743966120.858813562801694
1250.1175324299914480.2350648599828970.882467570008552
1260.09376323765164820.1875264753032960.906236762348352
1270.0749241626333650.149848325266730.925075837366635
1280.07096695926512030.1419339185302410.92903304073488
1290.0551898629146350.110379725829270.944810137085365
1300.0635687118348430.1271374236696860.936431288165157
1310.06333545819652440.1266709163930490.936664541803476
1320.08477651698299560.1695530339659910.915223483017004
1330.1165266205690220.2330532411380450.883473379430977
1340.1189702155675060.2379404311350110.881029784432494
1350.0940758217310520.1881516434621040.905924178268948
1360.08002242064541090.1600448412908220.919977579354589
1370.05826069072085370.1165213814417070.941739309279146
1380.04093639692357970.08187279384715930.95906360307642
1390.1015908253173760.2031816506347520.898409174682624
1400.07905326107436290.1581065221487260.920946738925637
1410.6218283135015010.7563433729969980.378171686498499
1420.5621852553477940.8756294893044120.437814744652206
1430.4989968886778590.9979937773557170.501003111322141
1440.4876418799442330.9752837598884670.512358120055767
1450.4146129913407470.8292259826814940.585387008659253
1460.402841711702670.805683423405340.59715828829733
1470.5226916634044590.9546166731910810.477308336595541
1480.7841566970622180.4316866058755640.215843302937782
1490.7181124654828060.5637750690343880.281887534517194
1500.5928446615457680.8143106769084640.407155338454232
1510.7867132999503140.4265734000993710.213286700049686
1520.8451185803922350.309762839215530.154881419607765






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.006993006993007OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.006993006993007 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146311&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.006993006993007[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146311&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146311&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.006993006993007OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}