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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 15 Dec 2012 06:19:24 -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/2012/Dec/15/t1355570447wegs7hwg9x7fkrk.htm/, Retrieved Tue, 30 Apr 2024 12:29:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199834, Retrieved Tue, 30 Apr 2024 12:29:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R P   [Multiple Regression] [Workshop 10: Mult...] [2012-12-09 10:16:25] [7bed0d115000febeef05f886574a3dac]
-   PD      [Multiple Regression] [Workshop 10 Peer ...] [2012-12-15 11:19:24] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12
2	5	39	32	16	11	18	11
2	5	30	35	19	15	11	14
1	5	31	33	15	6	12	12
2	8	34	37	14	13	16	21
2	6	35	29	13	10	18	12
2	5	39	31	19	12	14	22
2	6	34	36	15	14	14	11
2	5	36	35	14	12	15	10
2	4	37	38	15	6	15	13
1	6	38	31	16	10	17	10
2	5	36	34	16	12	19	8
1	5	38	35	16	12	10	15
2	6	39	38	16	11	16	14
2	7	33	37	17	15	18	10
1	6	32	33	15	12	14	14
1	7	36	32	15	10	14	14
2	6	38	38	20	12	17	11
1	8	39	38	18	11	14	10
2	7	32	32	16	12	16	13
1	5	32	33	16	11	18	7
2	5	31	31	16	12	11	14
2	7	39	38	19	13	14	12
2	7	37	39	16	11	12	14
1	5	39	32	17	9	17	11
2	4	41	32	17	13	9	9
1	10	36	35	16	10	16	11
2	6	33	37	15	14	14	15
2	5	33	33	16	12	15	14
1	5	34	33	14	10	11	13
2	5	31	28	15	12	16	9
1	5	27	32	12	8	13	15
2	6	37	31	14	10	17	10
2	5	34	37	16	12	15	11
1	5	34	30	14	12	14	13
1	5	32	33	7	7	16	8
1	5	29	31	10	6	9	20
1	5	36	33	14	12	15	12
2	5	29	31	16	10	17	10
1	5	35	33	16	10	13	10
1	5	37	32	16	10	15	9
2	7	34	33	14	12	16	14
1	5	38	32	20	15	16	8
1	6	35	33	14	10	12	14
2	7	38	28	14	10	12	11
2	7	37	35	11	12	11	13
2	5	38	39	14	13	15	9
2	5	33	34	15	11	15	11
2	4	36	38	16	11	17	15
1	5	38	32	14	12	13	11
2	4	32	38	16	14	16	10
1	5	32	30	14	10	14	14
1	5	32	33	12	12	11	18
2	7	34	38	16	13	12	14
1	5	32	32	9	5	12	11
2	5	37	32	14	6	15	12
2	6	39	34	16	12	16	13
2	4	29	34	16	12	15	9
1	6	37	36	15	11	12	10
2	6	35	34	16	10	12	15
1	5	30	28	12	7	8	20
1	7	38	34	16	12	13	12
2	6	34	35	16	14	11	12
2	8	31	35	14	11	14	14
2	7	34	31	16	12	15	13
1	5	35	37	17	13	10	11
2	6	36	35	18	14	11	17
1	6	30	27	18	11	12	12
2	5	39	40	12	12	15	13
1	5	35	37	16	12	15	14
1	5	38	36	10	8	14	13
2	5	31	38	14	11	16	15
2	4	34	39	18	14	15	13
1	6	38	41	18	14	15	10
1	6	34	27	16	12	13	11
2	6	39	30	17	9	12	19
2	6	37	37	16	13	17	13
2	7	34	31	16	11	13	17
1	5	28	31	13	12	15	13
1	7	37	27	16	12	13	9
1	6	33	36	16	12	15	11
1	5	37	38	20	12	16	10
2	5	35	37	16	12	15	9
1	4	37	33	15	12	16	12
2	8	32	34	15	11	15	12
2	8	33	31	16	10	14	13
1	5	38	39	14	9	15	13
2	5	33	34	16	12	14	12
2	6	29	32	16	12	13	15
2	4	33	33	15	12	7	22
2	5	31	36	12	9	17	13
2	5	36	32	17	15	13	15
2	5	35	41	16	12	15	13
2	5	32	28	15	12	14	15
2	6	29	30	13	12	13	10
2	6	39	36	16	10	16	11
2	5	37	35	16	13	12	16
2	6	35	31	16	9	14	11
1	5	37	34	16	12	17	11
1	7	32	36	14	10	15	10
2	5	38	36	16	14	17	10
1	6	37	35	16	11	12	16
2	6	36	37	20	15	16	12
1	6	32	28	15	11	11	11
2	4	33	39	16	11	15	16
1	5	40	32	13	12	9	19
2	5	38	35	17	12	16	11
1	7	41	39	16	12	15	16
1	6	36	35	16	11	10	15
2	9	43	42	12	7	10	24
2	6	30	34	16	12	15	14
2	6	31	33	16	14	11	15
2	5	32	41	17	11	13	11
1	6	32	33	13	11	14	15
2	5	37	34	12	10	18	12
1	8	37	32	18	13	16	10
2	7	33	40	14	13	14	14
2	5	34	40	14	8	14	13
2	7	33	35	13	11	14	9
2	6	38	36	16	12	14	15
2	6	33	37	13	11	12	15
2	9	31	27	16	13	14	14
2	7	38	39	13	12	15	11
2	6	37	38	16	14	15	8
2	5	33	31	15	13	15	11
2	5	31	33	16	15	13	11
1	6	39	32	15	10	17	8
2	6	44	39	17	11	17	10
2	7	33	36	15	9	19	11
2	5	35	33	12	11	15	13
1	5	32	33	16	10	13	11
1	5	28	32	10	11	9	20
2	6	40	37	16	8	15	10
1	4	27	30	12	11	15	15
1	5	37	38	14	12	15	12
2	7	32	29	15	12	16	14
1	5	28	22	13	9	11	23
1	7	34	35	15	11	14	14
2	7	30	35	11	10	11	16
2	6	35	34	12	8	15	11
1	5	31	35	8	9	13	12
2	8	32	34	16	8	15	10
1	5	30	34	15	9	16	14
2	5	30	35	17	15	14	12
1	5	31	23	16	11	15	12
2	6	40	31	10	8	16	11
2	4	32	27	18	13	16	12
1	5	36	36	13	12	11	13
1	5	32	31	16	12	12	11
1	7	35	32	13	9	9	19
2	6	38	39	10	7	16	12
2	7	42	37	15	13	13	17
1	10	34	38	16	9	16	9
2	6	35	39	16	6	12	12
2	8	35	34	14	8	9	19
2	4	33	31	10	8	13	18
2	5	36	32	17	15	13	15
2	6	32	37	13	6	14	14
2	7	33	36	15	9	19	11
2	7	34	32	16	11	13	9
2	6	32	35	12	8	12	18
2	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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depressionss[t] = + 25.5783010178719 + 1.10353609957969gender[t] + 0.0947908388073275age[t] -0.0219282718441132Connected[t] -0.0154350617985379Separate[t] -0.169063754185506Learning[t] -0.0767025300210166Software[t] -0.736472470929676Happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depressionss[t] =  +  25.5783010178719 +  1.10353609957969gender[t] +  0.0947908388073275age[t] -0.0219282718441132Connected[t] -0.0154350617985379Separate[t] -0.169063754185506Learning[t] -0.0767025300210166Software[t] -0.736472470929676Happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depressionss[t] =  +  25.5783010178719 +  1.10353609957969gender[t] +  0.0947908388073275age[t] -0.0219282718441132Connected[t] -0.0154350617985379Separate[t] -0.169063754185506Learning[t] -0.0767025300210166Software[t] -0.736472470929676Happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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
Depressionss[t] = + 25.5783010178719 + 1.10353609957969gender[t] + 0.0947908388073275age[t] -0.0219282718441132Connected[t] -0.0154350617985379Separate[t] -0.169063754185506Learning[t] -0.0767025300210166Software[t] -0.736472470929676Happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.57830101787192.8689558.915500
gender1.103536099579690.449492.45510.0151980.007599
age0.09479083880732750.1817940.52140.6028230.301412
Connected-0.02192827184411320.068027-0.32230.7476260.373813
Separate-0.01543506179853790.06466-0.23870.8116470.405823
Learning-0.1690637541855060.113548-1.48890.1385540.069277
Software-0.07670253002101660.117138-0.65480.5135710.256786
Happiness-0.7364724709296760.092057-8.000200

\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) & 25.5783010178719 & 2.868955 & 8.9155 & 0 & 0 \tabularnewline
gender & 1.10353609957969 & 0.44949 & 2.4551 & 0.015198 & 0.007599 \tabularnewline
age & 0.0947908388073275 & 0.181794 & 0.5214 & 0.602823 & 0.301412 \tabularnewline
Connected & -0.0219282718441132 & 0.068027 & -0.3223 & 0.747626 & 0.373813 \tabularnewline
Separate & -0.0154350617985379 & 0.06466 & -0.2387 & 0.811647 & 0.405823 \tabularnewline
Learning & -0.169063754185506 & 0.113548 & -1.4889 & 0.138554 & 0.069277 \tabularnewline
Software & -0.0767025300210166 & 0.117138 & -0.6548 & 0.513571 & 0.256786 \tabularnewline
Happiness & -0.736472470929676 & 0.092057 & -8.0002 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&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]25.5783010178719[/C][C]2.868955[/C][C]8.9155[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]gender[/C][C]1.10353609957969[/C][C]0.44949[/C][C]2.4551[/C][C]0.015198[/C][C]0.007599[/C][/ROW]
[ROW][C]age[/C][C]0.0947908388073275[/C][C]0.181794[/C][C]0.5214[/C][C]0.602823[/C][C]0.301412[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0219282718441132[/C][C]0.068027[/C][C]-0.3223[/C][C]0.747626[/C][C]0.373813[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0154350617985379[/C][C]0.06466[/C][C]-0.2387[/C][C]0.811647[/C][C]0.405823[/C][/ROW]
[ROW][C]Learning[/C][C]-0.169063754185506[/C][C]0.113548[/C][C]-1.4889[/C][C]0.138554[/C][C]0.069277[/C][/ROW]
[ROW][C]Software[/C][C]-0.0767025300210166[/C][C]0.117138[/C][C]-0.6548[/C][C]0.513571[/C][C]0.256786[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.736472470929676[/C][C]0.092057[/C][C]-8.0002[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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)25.57830101787192.8689558.915500
gender1.103536099579690.449492.45510.0151980.007599
age0.09479083880732750.1817940.52140.6028230.301412
Connected-0.02192827184411320.068027-0.32230.7476260.373813
Separate-0.01543506179853790.06466-0.23870.8116470.405823
Learning-0.1690637541855060.113548-1.48890.1385540.069277
Software-0.07670253002101660.117138-0.65480.5135710.256786
Happiness-0.7364724709296760.092057-8.000200






Multiple Linear Regression - Regression Statistics
Multiple R0.586341963309279
R-squared0.34379689793738
Adjusted R-squared0.313969484207261
F-TEST (value)11.526205424583
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value9.96369653449847e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.62243498956728
Sum Squared Residuals1059.08345227404

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.586341963309279 \tabularnewline
R-squared & 0.34379689793738 \tabularnewline
Adjusted R-squared & 0.313969484207261 \tabularnewline
F-TEST (value) & 11.526205424583 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 9.96369653449847e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.62243498956728 \tabularnewline
Sum Squared Residuals & 1059.08345227404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.586341963309279[/C][/ROW]
[ROW][C]R-squared[/C][C]0.34379689793738[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.313969484207261[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.526205424583[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]9.96369653449847e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.62243498956728[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1059.08345227404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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 R0.586341963309279
R-squared0.34379689793738
Adjusted R-squared0.313969484207261
F-TEST (value)11.526205424583
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value9.96369653449847e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.62243498956728
Sum Squared Residuals1059.08345227404






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.5344438370503-1.53444383705029
21110.10495045766080.895049542339187
31414.5973056327294-0.597305632729366
41214.1328167009041-2.13281670090414
52112.0794564144998.92054358550099
61210.91765336181771.08234663818226
72212.48238161060059.51761838939948
81113.1324884563357-2.13248845633571
91012.5952724789366-2.59527247893656
101312.72339960883010.276600391169896
11109.94674353148180.0532564685182
1289.32669014864539-1.32669014864538
131514.7921146819460.207885318053979
141411.58007586753632.41992413246373
15109.873052583077890.126947416922114
161412.27251914588191.72748085411809
171412.44843701915341.55156298084665
181110.11257412168770.887425878312338
191011.8009388790596-1.80093887905958
201311.84427245002261.1557275499774
2179.13947719919138-2.13947719919138
221415.374416460699-1.37441646069897
231212.4872153256044-0.487215325604394
241414.649178071952-0.649178071951987
25119.722228134867331.27777186513267
26916.2720864993048-7.27208649930481
271111.0444956541349-0.0444956541348613
281513.13898166638131.86101833361871
291412.3537999096951.64620009030503
301314.6657579904029-1.66575799040292
31911.9074230456317-2.90742304563171
321513.85327858166391.14672141833606
331011.4103354112766-1.41033541127661
341112.2701313906567-1.2701313906567
351312.34924070296750.650759297032529
36812.4408060488044-4.44080604880436
372017.2622795519062.73772044809399
381211.5226065029540.477393497046045
391011.1528432388512-1.15284323885118
401012.8327572683284-2.83275726832844
41911.3313908445794-2.3313908445794
421412.12310835290681.87689164709315
4389.5132224349585-1.5132224349585
441414.0021480864365-0.00214808643645746
451115.2118655182838-4.21186551828382
461316.2160070309823-3.21600703098233
47912.4129731580332-3.41297315803317
481112.584131132103-1.58413113210295
491510.71980653452434.28019346547573
501112.9671299629236-1.96712996292362
511011.3138845027674-1.31388450276735
521412.54650230669771.45349769330227
531814.89433698242013.10566301757987
541414.5769928892408-0.576992889240831
551115.2174085459926-4.21740854599262
561213.0798645726142-1.07986457261417
571311.56511358470941.4348864152906
58912.3312870964656-3.33128709646555
591013.6662200731461-3.66622007314609
601514.75212161584660.247878384153408
612017.60829889799522.39170110200485
621212.7877140085702-0.787714008570185
631215.1882771767378-3.18827717673778
641413.80246135552980.197538644470194
651312.55232343906260.447676560937415
661114.5812630896748-3.58126308967476
671714.80629312467852.19370687532146
681213.4514422696852-1.45144226968521
691312.79043986278250.209560137217452
701411.14466701923292.8553329807671
711313.1519823816259-0.151982381625883
721511.99883871185293.00116128814714
731311.65293785983931.34706214016075
741010.6204002269007-0.620400226900694
751112.8886816897291-1.88868168972908
761914.63378755149984.3662124485002
771310.74948994205132.25051005794868
781714.1019709109432.89802908905704
791311.89796655548941.10203344451056
80912.9176877130041-3.91768771300406
811111.298749463527-0.298749463526995
82109.672647926074440.327352073925562
83912.2482031188126-3.24820311881259
841210.50035116718731.49964883281267
851212.890431920369-0.890431920369048
861313.5589200806857-0.558920080685735
871311.61624717853761.38375282146245
881213.0748373188261-1.07483731882611
891514.02468383953660.975316160463362
902218.31985259251063.68014740748945
911311.78476893293331.2152310670667
921513.3772237535721.62277624642804
931312.18646287161840.813537128381562
941513.3584397156471.64156028435305
951014.5627452256902-4.56274522569023
961111.6876485211544-0.687648521154359
971614.36793158148951.63206841851055
981113.4021843894039-2.40218438940387
99119.674170719080941.32582928091906
1001011.9070011425915-1.90700114259148
1011010.5715033631774-0.571503363177402
1021613.51259138075912.48740861924088
1031210.67823060804111.32176939195895
1041114.6358143976846-3.63581439768464
1051612.24310123011743.75689876988257
1061916.03822705713962.9617729428604
1071111.3077522017621-0.307752201762144
1081611.17180894218584.8281910578142
1091515.0074645944626-0.00746459446259088
1102417.11689501179186.88310498820821
1111412.49894050223611.5010594977639
1121515.2849321158672-0.284932115867192
1131113.6328314048456-2.63283140484564
1141512.68734918427392.31265081572607
1151210.8708944245151.12910557548499
1161010.3110557916376-0.311055791637644
1171413.43323360399950.566766396000471
1181313.6052363046458-0.605236304645844
119913.8328777272198-4.83287772721976
1201513.02911667481581.97088332518421
1211515.1801617066747-0.180161706674707
1221413.52920012031240.470799879687609
1231112.8483211198543-1.84832111985435
124812.1302972920911-4.13029729209112
1251112.4770312574565-1.47703125745653
1261113.6404938051795-2.6404938051795
127810.0784439520247-2.07844395202466
1281010.549463221402-0.54946322140198
129119.950357862443861.04964213755614
1301313.0629009127698-0.0629009127697832
1311112.8985420838608-1.89854208386078
1322016.88526011184653.1147398881535
1331012.5401627184834-2.54016271848342
1341512.08630533453132.91369466546871
1351211.42350292211720.576497077882848
1361412.05964138960371.94035861039628
1372315.2128795854587.78712041454196
1381412.36928584742491.63071415257505
1391616.5229099939332-0.522909993933156
1401113.3723642798416-2.37236427984162
1411214.3188127956129-2.31881279561289
1421012.9514757562466-2.95147575624659
1431410.9633124371683.03668756283204
1441212.7260157283114-0.726015728311351
1451211.52517350180990.474826498190097
1461112.910683143458-1.91068314345801
1471211.22224520420130.777754795798725
1481314.5912549554626-1.59125495546255
1491113.5124796183455-2.5124796183455
1501916.56755768403792.43244231596213
1511212.9077617227789-0.907761722778946
1521713.84959305954233.15040694045771
153911.1187495424485-2.11874954244849
1541214.981756426938-2.98175642693797
1551917.64265327466331.35734672533668
1561815.08401678154122.91598321845883
1571513.3772237535721.62277624642804
1581414.1126576867646-0.112657686764552
159119.950357862443861.04964213755614
160914.0865358491444-5.08653584914442
1611815.63213144636452.36786855363555
1621614.66730361576251.33269638423749

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.5344438370503 & -1.53444383705029 \tabularnewline
2 & 11 & 10.1049504576608 & 0.895049542339187 \tabularnewline
3 & 14 & 14.5973056327294 & -0.597305632729366 \tabularnewline
4 & 12 & 14.1328167009041 & -2.13281670090414 \tabularnewline
5 & 21 & 12.079456414499 & 8.92054358550099 \tabularnewline
6 & 12 & 10.9176533618177 & 1.08234663818226 \tabularnewline
7 & 22 & 12.4823816106005 & 9.51761838939948 \tabularnewline
8 & 11 & 13.1324884563357 & -2.13248845633571 \tabularnewline
9 & 10 & 12.5952724789366 & -2.59527247893656 \tabularnewline
10 & 13 & 12.7233996088301 & 0.276600391169896 \tabularnewline
11 & 10 & 9.9467435314818 & 0.0532564685182 \tabularnewline
12 & 8 & 9.32669014864539 & -1.32669014864538 \tabularnewline
13 & 15 & 14.792114681946 & 0.207885318053979 \tabularnewline
14 & 14 & 11.5800758675363 & 2.41992413246373 \tabularnewline
15 & 10 & 9.87305258307789 & 0.126947416922114 \tabularnewline
16 & 14 & 12.2725191458819 & 1.72748085411809 \tabularnewline
17 & 14 & 12.4484370191534 & 1.55156298084665 \tabularnewline
18 & 11 & 10.1125741216877 & 0.887425878312338 \tabularnewline
19 & 10 & 11.8009388790596 & -1.80093887905958 \tabularnewline
20 & 13 & 11.8442724500226 & 1.1557275499774 \tabularnewline
21 & 7 & 9.13947719919138 & -2.13947719919138 \tabularnewline
22 & 14 & 15.374416460699 & -1.37441646069897 \tabularnewline
23 & 12 & 12.4872153256044 & -0.487215325604394 \tabularnewline
24 & 14 & 14.649178071952 & -0.649178071951987 \tabularnewline
25 & 11 & 9.72222813486733 & 1.27777186513267 \tabularnewline
26 & 9 & 16.2720864993048 & -7.27208649930481 \tabularnewline
27 & 11 & 11.0444956541349 & -0.0444956541348613 \tabularnewline
28 & 15 & 13.1389816663813 & 1.86101833361871 \tabularnewline
29 & 14 & 12.353799909695 & 1.64620009030503 \tabularnewline
30 & 13 & 14.6657579904029 & -1.66575799040292 \tabularnewline
31 & 9 & 11.9074230456317 & -2.90742304563171 \tabularnewline
32 & 15 & 13.8532785816639 & 1.14672141833606 \tabularnewline
33 & 10 & 11.4103354112766 & -1.41033541127661 \tabularnewline
34 & 11 & 12.2701313906567 & -1.2701313906567 \tabularnewline
35 & 13 & 12.3492407029675 & 0.650759297032529 \tabularnewline
36 & 8 & 12.4408060488044 & -4.44080604880436 \tabularnewline
37 & 20 & 17.262279551906 & 2.73772044809399 \tabularnewline
38 & 12 & 11.522606502954 & 0.477393497046045 \tabularnewline
39 & 10 & 11.1528432388512 & -1.15284323885118 \tabularnewline
40 & 10 & 12.8327572683284 & -2.83275726832844 \tabularnewline
41 & 9 & 11.3313908445794 & -2.3313908445794 \tabularnewline
42 & 14 & 12.1231083529068 & 1.87689164709315 \tabularnewline
43 & 8 & 9.5132224349585 & -1.5132224349585 \tabularnewline
44 & 14 & 14.0021480864365 & -0.00214808643645746 \tabularnewline
45 & 11 & 15.2118655182838 & -4.21186551828382 \tabularnewline
46 & 13 & 16.2160070309823 & -3.21600703098233 \tabularnewline
47 & 9 & 12.4129731580332 & -3.41297315803317 \tabularnewline
48 & 11 & 12.584131132103 & -1.58413113210295 \tabularnewline
49 & 15 & 10.7198065345243 & 4.28019346547573 \tabularnewline
50 & 11 & 12.9671299629236 & -1.96712996292362 \tabularnewline
51 & 10 & 11.3138845027674 & -1.31388450276735 \tabularnewline
52 & 14 & 12.5465023066977 & 1.45349769330227 \tabularnewline
53 & 18 & 14.8943369824201 & 3.10566301757987 \tabularnewline
54 & 14 & 14.5769928892408 & -0.576992889240831 \tabularnewline
55 & 11 & 15.2174085459926 & -4.21740854599262 \tabularnewline
56 & 12 & 13.0798645726142 & -1.07986457261417 \tabularnewline
57 & 13 & 11.5651135847094 & 1.4348864152906 \tabularnewline
58 & 9 & 12.3312870964656 & -3.33128709646555 \tabularnewline
59 & 10 & 13.6662200731461 & -3.66622007314609 \tabularnewline
60 & 15 & 14.7521216158466 & 0.247878384153408 \tabularnewline
61 & 20 & 17.6082988979952 & 2.39170110200485 \tabularnewline
62 & 12 & 12.7877140085702 & -0.787714008570185 \tabularnewline
63 & 12 & 15.1882771767378 & -3.18827717673778 \tabularnewline
64 & 14 & 13.8024613555298 & 0.197538644470194 \tabularnewline
65 & 13 & 12.5523234390626 & 0.447676560937415 \tabularnewline
66 & 11 & 14.5812630896748 & -3.58126308967476 \tabularnewline
67 & 17 & 14.8062931246785 & 2.19370687532146 \tabularnewline
68 & 12 & 13.4514422696852 & -1.45144226968521 \tabularnewline
69 & 13 & 12.7904398627825 & 0.209560137217452 \tabularnewline
70 & 14 & 11.1446670192329 & 2.8553329807671 \tabularnewline
71 & 13 & 13.1519823816259 & -0.151982381625883 \tabularnewline
72 & 15 & 11.9988387118529 & 3.00116128814714 \tabularnewline
73 & 13 & 11.6529378598393 & 1.34706214016075 \tabularnewline
74 & 10 & 10.6204002269007 & -0.620400226900694 \tabularnewline
75 & 11 & 12.8886816897291 & -1.88868168972908 \tabularnewline
76 & 19 & 14.6337875514998 & 4.3662124485002 \tabularnewline
77 & 13 & 10.7494899420513 & 2.25051005794868 \tabularnewline
78 & 17 & 14.101970910943 & 2.89802908905704 \tabularnewline
79 & 13 & 11.8979665554894 & 1.10203344451056 \tabularnewline
80 & 9 & 12.9176877130041 & -3.91768771300406 \tabularnewline
81 & 11 & 11.298749463527 & -0.298749463526995 \tabularnewline
82 & 10 & 9.67264792607444 & 0.327352073925562 \tabularnewline
83 & 9 & 12.2482031188126 & -3.24820311881259 \tabularnewline
84 & 12 & 10.5003511671873 & 1.49964883281267 \tabularnewline
85 & 12 & 12.890431920369 & -0.890431920369048 \tabularnewline
86 & 13 & 13.5589200806857 & -0.558920080685735 \tabularnewline
87 & 13 & 11.6162471785376 & 1.38375282146245 \tabularnewline
88 & 12 & 13.0748373188261 & -1.07483731882611 \tabularnewline
89 & 15 & 14.0246838395366 & 0.975316160463362 \tabularnewline
90 & 22 & 18.3198525925106 & 3.68014740748945 \tabularnewline
91 & 13 & 11.7847689329333 & 1.2152310670667 \tabularnewline
92 & 15 & 13.377223753572 & 1.62277624642804 \tabularnewline
93 & 13 & 12.1864628716184 & 0.813537128381562 \tabularnewline
94 & 15 & 13.358439715647 & 1.64156028435305 \tabularnewline
95 & 10 & 14.5627452256902 & -4.56274522569023 \tabularnewline
96 & 11 & 11.6876485211544 & -0.687648521154359 \tabularnewline
97 & 16 & 14.3679315814895 & 1.63206841851055 \tabularnewline
98 & 11 & 13.4021843894039 & -2.40218438940387 \tabularnewline
99 & 11 & 9.67417071908094 & 1.32582928091906 \tabularnewline
100 & 10 & 11.9070011425915 & -1.90700114259148 \tabularnewline
101 & 10 & 10.5715033631774 & -0.571503363177402 \tabularnewline
102 & 16 & 13.5125913807591 & 2.48740861924088 \tabularnewline
103 & 12 & 10.6782306080411 & 1.32176939195895 \tabularnewline
104 & 11 & 14.6358143976846 & -3.63581439768464 \tabularnewline
105 & 16 & 12.2431012301174 & 3.75689876988257 \tabularnewline
106 & 19 & 16.0382270571396 & 2.9617729428604 \tabularnewline
107 & 11 & 11.3077522017621 & -0.307752201762144 \tabularnewline
108 & 16 & 11.1718089421858 & 4.8281910578142 \tabularnewline
109 & 15 & 15.0074645944626 & -0.00746459446259088 \tabularnewline
110 & 24 & 17.1168950117918 & 6.88310498820821 \tabularnewline
111 & 14 & 12.4989405022361 & 1.5010594977639 \tabularnewline
112 & 15 & 15.2849321158672 & -0.284932115867192 \tabularnewline
113 & 11 & 13.6328314048456 & -2.63283140484564 \tabularnewline
114 & 15 & 12.6873491842739 & 2.31265081572607 \tabularnewline
115 & 12 & 10.870894424515 & 1.12910557548499 \tabularnewline
116 & 10 & 10.3110557916376 & -0.311055791637644 \tabularnewline
117 & 14 & 13.4332336039995 & 0.566766396000471 \tabularnewline
118 & 13 & 13.6052363046458 & -0.605236304645844 \tabularnewline
119 & 9 & 13.8328777272198 & -4.83287772721976 \tabularnewline
120 & 15 & 13.0291166748158 & 1.97088332518421 \tabularnewline
121 & 15 & 15.1801617066747 & -0.180161706674707 \tabularnewline
122 & 14 & 13.5292001203124 & 0.470799879687609 \tabularnewline
123 & 11 & 12.8483211198543 & -1.84832111985435 \tabularnewline
124 & 8 & 12.1302972920911 & -4.13029729209112 \tabularnewline
125 & 11 & 12.4770312574565 & -1.47703125745653 \tabularnewline
126 & 11 & 13.6404938051795 & -2.6404938051795 \tabularnewline
127 & 8 & 10.0784439520247 & -2.07844395202466 \tabularnewline
128 & 10 & 10.549463221402 & -0.54946322140198 \tabularnewline
129 & 11 & 9.95035786244386 & 1.04964213755614 \tabularnewline
130 & 13 & 13.0629009127698 & -0.0629009127697832 \tabularnewline
131 & 11 & 12.8985420838608 & -1.89854208386078 \tabularnewline
132 & 20 & 16.8852601118465 & 3.1147398881535 \tabularnewline
133 & 10 & 12.5401627184834 & -2.54016271848342 \tabularnewline
134 & 15 & 12.0863053345313 & 2.91369466546871 \tabularnewline
135 & 12 & 11.4235029221172 & 0.576497077882848 \tabularnewline
136 & 14 & 12.0596413896037 & 1.94035861039628 \tabularnewline
137 & 23 & 15.212879585458 & 7.78712041454196 \tabularnewline
138 & 14 & 12.3692858474249 & 1.63071415257505 \tabularnewline
139 & 16 & 16.5229099939332 & -0.522909993933156 \tabularnewline
140 & 11 & 13.3723642798416 & -2.37236427984162 \tabularnewline
141 & 12 & 14.3188127956129 & -2.31881279561289 \tabularnewline
142 & 10 & 12.9514757562466 & -2.95147575624659 \tabularnewline
143 & 14 & 10.963312437168 & 3.03668756283204 \tabularnewline
144 & 12 & 12.7260157283114 & -0.726015728311351 \tabularnewline
145 & 12 & 11.5251735018099 & 0.474826498190097 \tabularnewline
146 & 11 & 12.910683143458 & -1.91068314345801 \tabularnewline
147 & 12 & 11.2222452042013 & 0.777754795798725 \tabularnewline
148 & 13 & 14.5912549554626 & -1.59125495546255 \tabularnewline
149 & 11 & 13.5124796183455 & -2.5124796183455 \tabularnewline
150 & 19 & 16.5675576840379 & 2.43244231596213 \tabularnewline
151 & 12 & 12.9077617227789 & -0.907761722778946 \tabularnewline
152 & 17 & 13.8495930595423 & 3.15040694045771 \tabularnewline
153 & 9 & 11.1187495424485 & -2.11874954244849 \tabularnewline
154 & 12 & 14.981756426938 & -2.98175642693797 \tabularnewline
155 & 19 & 17.6426532746633 & 1.35734672533668 \tabularnewline
156 & 18 & 15.0840167815412 & 2.91598321845883 \tabularnewline
157 & 15 & 13.377223753572 & 1.62277624642804 \tabularnewline
158 & 14 & 14.1126576867646 & -0.112657686764552 \tabularnewline
159 & 11 & 9.95035786244386 & 1.04964213755614 \tabularnewline
160 & 9 & 14.0865358491444 & -5.08653584914442 \tabularnewline
161 & 18 & 15.6321314463645 & 2.36786855363555 \tabularnewline
162 & 16 & 14.6673036157625 & 1.33269638423749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&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]12[/C][C]13.5344438370503[/C][C]-1.53444383705029[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.1049504576608[/C][C]0.895049542339187[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.5973056327294[/C][C]-0.597305632729366[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.1328167009041[/C][C]-2.13281670090414[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.079456414499[/C][C]8.92054358550099[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.9176533618177[/C][C]1.08234663818226[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.4823816106005[/C][C]9.51761838939948[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.1324884563357[/C][C]-2.13248845633571[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.5952724789366[/C][C]-2.59527247893656[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.7233996088301[/C][C]0.276600391169896[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]9.9467435314818[/C][C]0.0532564685182[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.32669014864539[/C][C]-1.32669014864538[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]14.792114681946[/C][C]0.207885318053979[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.5800758675363[/C][C]2.41992413246373[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.87305258307789[/C][C]0.126947416922114[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.2725191458819[/C][C]1.72748085411809[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.4484370191534[/C][C]1.55156298084665[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.1125741216877[/C][C]0.887425878312338[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]11.8009388790596[/C][C]-1.80093887905958[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.8442724500226[/C][C]1.1557275499774[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]9.13947719919138[/C][C]-2.13947719919138[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.374416460699[/C][C]-1.37441646069897[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.4872153256044[/C][C]-0.487215325604394[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.649178071952[/C][C]-0.649178071951987[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.72222813486733[/C][C]1.27777186513267[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.2720864993048[/C][C]-7.27208649930481[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.0444956541349[/C][C]-0.0444956541348613[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1389816663813[/C][C]1.86101833361871[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.353799909695[/C][C]1.64620009030503[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]14.6657579904029[/C][C]-1.66575799040292[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.9074230456317[/C][C]-2.90742304563171[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]13.8532785816639[/C][C]1.14672141833606[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.4103354112766[/C][C]-1.41033541127661[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.2701313906567[/C][C]-1.2701313906567[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.3492407029675[/C][C]0.650759297032529[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.4408060488044[/C][C]-4.44080604880436[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.262279551906[/C][C]2.73772044809399[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.522606502954[/C][C]0.477393497046045[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.1528432388512[/C][C]-1.15284323885118[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8327572683284[/C][C]-2.83275726832844[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.3313908445794[/C][C]-2.3313908445794[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.1231083529068[/C][C]1.87689164709315[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]9.5132224349585[/C][C]-1.5132224349585[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.0021480864365[/C][C]-0.00214808643645746[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]15.2118655182838[/C][C]-4.21186551828382[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]16.2160070309823[/C][C]-3.21600703098233[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.4129731580332[/C][C]-3.41297315803317[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.584131132103[/C][C]-1.58413113210295[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.7198065345243[/C][C]4.28019346547573[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.9671299629236[/C][C]-1.96712996292362[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3138845027674[/C][C]-1.31388450276735[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.5465023066977[/C][C]1.45349769330227[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.8943369824201[/C][C]3.10566301757987[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.5769928892408[/C][C]-0.576992889240831[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.2174085459926[/C][C]-4.21740854599262[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.0798645726142[/C][C]-1.07986457261417[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.5651135847094[/C][C]1.4348864152906[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.3312870964656[/C][C]-3.33128709646555[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.6662200731461[/C][C]-3.66622007314609[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.7521216158466[/C][C]0.247878384153408[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.6082988979952[/C][C]2.39170110200485[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.7877140085702[/C][C]-0.787714008570185[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]15.1882771767378[/C][C]-3.18827717673778[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8024613555298[/C][C]0.197538644470194[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.5523234390626[/C][C]0.447676560937415[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.5812630896748[/C][C]-3.58126308967476[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.8062931246785[/C][C]2.19370687532146[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.4514422696852[/C][C]-1.45144226968521[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.7904398627825[/C][C]0.209560137217452[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]11.1446670192329[/C][C]2.8553329807671[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.1519823816259[/C][C]-0.151982381625883[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.9988387118529[/C][C]3.00116128814714[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.6529378598393[/C][C]1.34706214016075[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]10.6204002269007[/C][C]-0.620400226900694[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.8886816897291[/C][C]-1.88868168972908[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]14.6337875514998[/C][C]4.3662124485002[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.7494899420513[/C][C]2.25051005794868[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.101970910943[/C][C]2.89802908905704[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.8979665554894[/C][C]1.10203344451056[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.9176877130041[/C][C]-3.91768771300406[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.298749463527[/C][C]-0.298749463526995[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]9.67264792607444[/C][C]0.327352073925562[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.2482031188126[/C][C]-3.24820311881259[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.5003511671873[/C][C]1.49964883281267[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.890431920369[/C][C]-0.890431920369048[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.5589200806857[/C][C]-0.558920080685735[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]11.6162471785376[/C][C]1.38375282146245[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.0748373188261[/C][C]-1.07483731882611[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.0246838395366[/C][C]0.975316160463362[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]18.3198525925106[/C][C]3.68014740748945[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.7847689329333[/C][C]1.2152310670667[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.377223753572[/C][C]1.62277624642804[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.1864628716184[/C][C]0.813537128381562[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.358439715647[/C][C]1.64156028435305[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]14.5627452256902[/C][C]-4.56274522569023[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.6876485211544[/C][C]-0.687648521154359[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.3679315814895[/C][C]1.63206841851055[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]13.4021843894039[/C][C]-2.40218438940387[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]9.67417071908094[/C][C]1.32582928091906[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.9070011425915[/C][C]-1.90700114259148[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.5715033631774[/C][C]-0.571503363177402[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.5125913807591[/C][C]2.48740861924088[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.6782306080411[/C][C]1.32176939195895[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.6358143976846[/C][C]-3.63581439768464[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.2431012301174[/C][C]3.75689876988257[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.0382270571396[/C][C]2.9617729428604[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.3077522017621[/C][C]-0.307752201762144[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.1718089421858[/C][C]4.8281910578142[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.0074645944626[/C][C]-0.00746459446259088[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]17.1168950117918[/C][C]6.88310498820821[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.4989405022361[/C][C]1.5010594977639[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]15.2849321158672[/C][C]-0.284932115867192[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.6328314048456[/C][C]-2.63283140484564[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]12.6873491842739[/C][C]2.31265081572607[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.870894424515[/C][C]1.12910557548499[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.3110557916376[/C][C]-0.311055791637644[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.4332336039995[/C][C]0.566766396000471[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.6052363046458[/C][C]-0.605236304645844[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.8328777272198[/C][C]-4.83287772721976[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.0291166748158[/C][C]1.97088332518421[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.1801617066747[/C][C]-0.180161706674707[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.5292001203124[/C][C]0.470799879687609[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.8483211198543[/C][C]-1.84832111985435[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.1302972920911[/C][C]-4.13029729209112[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.4770312574565[/C][C]-1.47703125745653[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.6404938051795[/C][C]-2.6404938051795[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.0784439520247[/C][C]-2.07844395202466[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.549463221402[/C][C]-0.54946322140198[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.95035786244386[/C][C]1.04964213755614[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.0629009127698[/C][C]-0.0629009127697832[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.8985420838608[/C][C]-1.89854208386078[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]16.8852601118465[/C][C]3.1147398881535[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.5401627184834[/C][C]-2.54016271848342[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.0863053345313[/C][C]2.91369466546871[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.4235029221172[/C][C]0.576497077882848[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.0596413896037[/C][C]1.94035861039628[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.212879585458[/C][C]7.78712041454196[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.3692858474249[/C][C]1.63071415257505[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]16.5229099939332[/C][C]-0.522909993933156[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.3723642798416[/C][C]-2.37236427984162[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.3188127956129[/C][C]-2.31881279561289[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.9514757562466[/C][C]-2.95147575624659[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.963312437168[/C][C]3.03668756283204[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.7260157283114[/C][C]-0.726015728311351[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.5251735018099[/C][C]0.474826498190097[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.910683143458[/C][C]-1.91068314345801[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.2222452042013[/C][C]0.777754795798725[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.5912549554626[/C][C]-1.59125495546255[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.5124796183455[/C][C]-2.5124796183455[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.5675576840379[/C][C]2.43244231596213[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.9077617227789[/C][C]-0.907761722778946[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.8495930595423[/C][C]3.15040694045771[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.1187495424485[/C][C]-2.11874954244849[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.981756426938[/C][C]-2.98175642693797[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]17.6426532746633[/C][C]1.35734672533668[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]15.0840167815412[/C][C]2.91598321845883[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.377223753572[/C][C]1.62277624642804[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]14.1126576867646[/C][C]-0.112657686764552[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.95035786244386[/C][C]1.04964213755614[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]14.0865358491444[/C][C]-5.08653584914442[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]15.6321314463645[/C][C]2.36786855363555[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.6673036157625[/C][C]1.33269638423749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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
11213.5344438370503-1.53444383705029
21110.10495045766080.895049542339187
31414.5973056327294-0.597305632729366
41214.1328167009041-2.13281670090414
52112.0794564144998.92054358550099
61210.91765336181771.08234663818226
72212.48238161060059.51761838939948
81113.1324884563357-2.13248845633571
91012.5952724789366-2.59527247893656
101312.72339960883010.276600391169896
11109.94674353148180.0532564685182
1289.32669014864539-1.32669014864538
131514.7921146819460.207885318053979
141411.58007586753632.41992413246373
15109.873052583077890.126947416922114
161412.27251914588191.72748085411809
171412.44843701915341.55156298084665
181110.11257412168770.887425878312338
191011.8009388790596-1.80093887905958
201311.84427245002261.1557275499774
2179.13947719919138-2.13947719919138
221415.374416460699-1.37441646069897
231212.4872153256044-0.487215325604394
241414.649178071952-0.649178071951987
25119.722228134867331.27777186513267
26916.2720864993048-7.27208649930481
271111.0444956541349-0.0444956541348613
281513.13898166638131.86101833361871
291412.3537999096951.64620009030503
301314.6657579904029-1.66575799040292
31911.9074230456317-2.90742304563171
321513.85327858166391.14672141833606
331011.4103354112766-1.41033541127661
341112.2701313906567-1.2701313906567
351312.34924070296750.650759297032529
36812.4408060488044-4.44080604880436
372017.2622795519062.73772044809399
381211.5226065029540.477393497046045
391011.1528432388512-1.15284323885118
401012.8327572683284-2.83275726832844
41911.3313908445794-2.3313908445794
421412.12310835290681.87689164709315
4389.5132224349585-1.5132224349585
441414.0021480864365-0.00214808643645746
451115.2118655182838-4.21186551828382
461316.2160070309823-3.21600703098233
47912.4129731580332-3.41297315803317
481112.584131132103-1.58413113210295
491510.71980653452434.28019346547573
501112.9671299629236-1.96712996292362
511011.3138845027674-1.31388450276735
521412.54650230669771.45349769330227
531814.89433698242013.10566301757987
541414.5769928892408-0.576992889240831
551115.2174085459926-4.21740854599262
561213.0798645726142-1.07986457261417
571311.56511358470941.4348864152906
58912.3312870964656-3.33128709646555
591013.6662200731461-3.66622007314609
601514.75212161584660.247878384153408
612017.60829889799522.39170110200485
621212.7877140085702-0.787714008570185
631215.1882771767378-3.18827717673778
641413.80246135552980.197538644470194
651312.55232343906260.447676560937415
661114.5812630896748-3.58126308967476
671714.80629312467852.19370687532146
681213.4514422696852-1.45144226968521
691312.79043986278250.209560137217452
701411.14466701923292.8553329807671
711313.1519823816259-0.151982381625883
721511.99883871185293.00116128814714
731311.65293785983931.34706214016075
741010.6204002269007-0.620400226900694
751112.8886816897291-1.88868168972908
761914.63378755149984.3662124485002
771310.74948994205132.25051005794868
781714.1019709109432.89802908905704
791311.89796655548941.10203344451056
80912.9176877130041-3.91768771300406
811111.298749463527-0.298749463526995
82109.672647926074440.327352073925562
83912.2482031188126-3.24820311881259
841210.50035116718731.49964883281267
851212.890431920369-0.890431920369048
861313.5589200806857-0.558920080685735
871311.61624717853761.38375282146245
881213.0748373188261-1.07483731882611
891514.02468383953660.975316160463362
902218.31985259251063.68014740748945
911311.78476893293331.2152310670667
921513.3772237535721.62277624642804
931312.18646287161840.813537128381562
941513.3584397156471.64156028435305
951014.5627452256902-4.56274522569023
961111.6876485211544-0.687648521154359
971614.36793158148951.63206841851055
981113.4021843894039-2.40218438940387
99119.674170719080941.32582928091906
1001011.9070011425915-1.90700114259148
1011010.5715033631774-0.571503363177402
1021613.51259138075912.48740861924088
1031210.67823060804111.32176939195895
1041114.6358143976846-3.63581439768464
1051612.24310123011743.75689876988257
1061916.03822705713962.9617729428604
1071111.3077522017621-0.307752201762144
1081611.17180894218584.8281910578142
1091515.0074645944626-0.00746459446259088
1102417.11689501179186.88310498820821
1111412.49894050223611.5010594977639
1121515.2849321158672-0.284932115867192
1131113.6328314048456-2.63283140484564
1141512.68734918427392.31265081572607
1151210.8708944245151.12910557548499
1161010.3110557916376-0.311055791637644
1171413.43323360399950.566766396000471
1181313.6052363046458-0.605236304645844
119913.8328777272198-4.83287772721976
1201513.02911667481581.97088332518421
1211515.1801617066747-0.180161706674707
1221413.52920012031240.470799879687609
1231112.8483211198543-1.84832111985435
124812.1302972920911-4.13029729209112
1251112.4770312574565-1.47703125745653
1261113.6404938051795-2.6404938051795
127810.0784439520247-2.07844395202466
1281010.549463221402-0.54946322140198
129119.950357862443861.04964213755614
1301313.0629009127698-0.0629009127697832
1311112.8985420838608-1.89854208386078
1322016.88526011184653.1147398881535
1331012.5401627184834-2.54016271848342
1341512.08630533453132.91369466546871
1351211.42350292211720.576497077882848
1361412.05964138960371.94035861039628
1372315.2128795854587.78712041454196
1381412.36928584742491.63071415257505
1391616.5229099939332-0.522909993933156
1401113.3723642798416-2.37236427984162
1411214.3188127956129-2.31881279561289
1421012.9514757562466-2.95147575624659
1431410.9633124371683.03668756283204
1441212.7260157283114-0.726015728311351
1451211.52517350180990.474826498190097
1461112.910683143458-1.91068314345801
1471211.22224520420130.777754795798725
1481314.5912549554626-1.59125495546255
1491113.5124796183455-2.5124796183455
1501916.56755768403792.43244231596213
1511212.9077617227789-0.907761722778946
1521713.84959305954233.15040694045771
153911.1187495424485-2.11874954244849
1541214.981756426938-2.98175642693797
1551917.64265327466331.35734672533668
1561815.08401678154122.91598321845883
1571513.3772237535721.62277624642804
1581414.1126576867646-0.112657686764552
159119.950357862443861.04964213755614
160914.0865358491444-5.08653584914442
1611815.63213144636452.36786855363555
1621614.66730361576251.33269638423749






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7855713930983250.4288572138033490.214428606901675
120.6878577895920330.6242844208159350.312142210407967
130.9225829461907870.1548341076184270.0774170538092135
140.885859030461440.2282819390771190.11414096953856
150.8565218566938260.2869562866123470.143478143306174
160.9113350215645790.1773299568708410.0886649784354206
170.9010915046580780.1978169906838440.0989084953419219
180.9200060313182920.1599879373634170.0799939686817084
190.9562714391562660.08745712168746730.0437285608437337
200.95604338488510.08791323022980040.0439566151149002
210.9400199882089840.1199600235820310.0599800117910155
220.9430353519951820.1139292960096360.0569646480048181
230.9434476054152190.1131047891695620.0565523945847809
240.9256871661539570.1486256676920860.0743128338460432
250.9008158700699370.1983682598601260.0991841299300631
260.9799510157242260.04009796855154850.0200489842757743
270.9826683408302910.03466331833941770.0173316591697088
280.9799533186529230.04009336269415390.020046681347077
290.9729548302837820.05409033943243530.0270451697162176
300.9635779072283780.07284418554324410.0364220927716221
310.9710092935411320.05798141291773570.0289907064588679
320.9658752313364540.06824953732709110.0341247686635455
330.9599941207180370.08001175856392560.0400058792819628
340.9484402961951950.1031194076096110.0515597038048053
350.9365555361345010.1268889277309980.0634444638654988
360.9406087287488970.1187825425022050.0593912712511026
370.9539667923136710.09206641537265730.0460332076863287
380.9441824107218260.1116351785563490.0558175892781743
390.9354429280522720.1291141438954570.0645570719477283
400.9319328816055430.1361342367889130.0680671183944565
410.9224296205300060.1551407589399890.0775703794699943
420.9072633474610190.1854733050779620.0927366525389811
430.8864562628012150.2270874743975690.113543737198785
440.8593264485478950.2813471029042110.140673551452105
450.9036277397405390.1927445205189210.0963722602594606
460.901330282913080.197339434173840.0986697170869199
470.9019955172669080.1960089654661850.0980044827330925
480.8853519180954670.2292961638090670.114648081904533
490.9224360572627110.1551278854745790.0775639427372894
500.9103932223265160.1792135553469680.089606777673484
510.8930277709040530.2139444581918940.106972229095947
520.8802468826612950.2395062346774110.119753117338705
530.9077434032037890.1845131935924210.0922565967962106
540.8871063585798220.2257872828403550.112893641420178
550.91089635584870.1782072883025990.0891036441512997
560.8940508090253310.2118983819493380.105949190974669
570.8787703323767590.2424593352464820.121229667623241
580.8937142225091120.2125715549817770.106285777490888
590.9087444252442640.1825111495114710.0912555747557357
600.8880412507590770.2239174984818450.111958749240923
610.8934260326515750.2131479346968490.106573967348425
620.8716412278422580.2567175443154830.128358772157742
630.8789775011034530.2420449977930950.121022498896547
640.8554643939063970.2890712121872060.144535606093603
650.8276675927286380.3446648145427240.172332407271362
660.8509277142812760.2981445714374470.149072285718724
670.8454173925406890.3091652149186220.154582607459311
680.8326680579039770.3346638841920460.167331942096023
690.8088036967410980.3823926065178030.191196303258902
700.8165608440361650.366878311927670.183439155963835
710.7920325607919890.4159348784160220.207967439208011
720.8017921274952460.3964157450095080.198207872504754
730.7768585748882090.4462828502235810.223141425111791
740.7431018523470770.5137962953058460.256898147652923
750.7273874418880710.5452251162238570.272612558111928
760.7879412168449060.4241175663101870.212058783155094
770.782208082024030.4355838359519410.21779191797597
780.788723287096290.422553425807420.21127671290371
790.7612189126743960.4775621746512080.238781087325604
800.8123417339447440.3753165321105120.187658266055256
810.7798458619877030.4403082760245930.220154138012297
820.7436098509502370.5127802980995260.256390149049763
830.7644135499357790.4711729001284420.235586450064221
840.7444521405990850.5110957188018290.255547859400915
850.7150034566205020.5699930867589970.284996543379498
860.6785602980610890.6428794038778230.321439701938912
870.6438220178750830.7123559642498340.356177982124917
880.6061765834388110.7876468331223790.393823416561189
890.5692503135681280.8614993728637450.430749686431872
900.6168745580127930.7662508839744130.383125441987207
910.5843389081611090.8313221836777810.415661091838891
920.5573830434925030.8852339130149930.442616956507497
930.5178312628819670.9643374742360660.482168737118033
940.4897027456325030.9794054912650060.510297254367497
950.5773534497986470.8452931004027050.422646550201353
960.5337494275547270.9325011448905460.466250572445273
970.5035418080108590.9929163839782820.496458191989141
980.4997232606168270.9994465212336550.500276739383173
990.4618632364281170.9237264728562330.538136763571883
1000.4396203069140130.8792406138280260.560379693085987
1010.3934152793136040.7868305586272080.606584720686396
1020.3799048937881720.7598097875763430.620095106211828
1030.3620292534124910.7240585068249830.637970746587509
1040.444756591052270.8895131821045410.55524340894773
1050.5428625967043940.9142748065912120.457137403295606
1060.5415570372891880.9168859254216240.458442962710812
1070.4942848475230710.9885696950461430.505715152476929
1080.627998451800750.7440030963985010.372001548199251
1090.5809665863549540.8380668272900920.419033413645046
1100.8500256300025050.299948739994990.149974369997495
1110.8359520198598410.3280959602803180.164047980140159
1120.8011923966561980.3976152066876030.198807603343802
1130.7853809284623270.4292381430753460.214619071537673
1140.7709309208478110.4581381583043780.229069079152189
1150.7400506027295770.5198987945408460.259949397270423
1160.6953838803920420.6092322392159170.304616119607958
1170.6789675257438230.6420649485123540.321032474256177
1180.6341388742847560.7317222514304880.365861125715244
1190.7392835046871330.5214329906257340.260716495312867
1200.7553092594594590.4893814810810820.244690740540541
1210.7094376594124440.5811246811751120.290562340587556
1220.6600607423651240.6798785152697520.339939257634876
1230.6160075115495580.7679849769008840.383992488450442
1240.6317638722044860.7364722555910270.368236127795514
1250.6001644224304170.7996711551391660.399835577569583
1260.6201276717729310.7597446564541370.379872328227069
1270.5953677727774210.8092644544451590.404632227222579
1280.5616988368819260.8766023262361480.438301163118074
1290.5382418808288410.9235162383423180.461758119171159
1300.4781732473536890.9563464947073790.521826752646311
1310.4586588748443160.9173177496886320.541341125155684
1320.4228148970651720.8456297941303440.577185102934828
1330.3816058589647520.7632117179295040.618394141035248
1340.3465894550316550.693178910063310.653410544968345
1350.2987139551725870.5974279103451740.701286044827413
1360.2588191612101130.5176383224202250.741180838789887
1370.5274441383172050.9451117233655890.472555861682795
1380.5000800388891650.999839922221670.499919961110835
1390.4241936812739220.8483873625478440.575806318726078
1400.4021210659420040.8042421318840090.597878934057996
1410.441139242499890.8822784849997810.55886075750011
1420.4079412582513330.8158825165026660.592058741748667
1430.5044700675193460.9910598649613070.495529932480654
1440.4536628663645260.9073257327290520.546337133635474
1450.4110406294414950.8220812588829910.588959370558505
1460.4402989509943290.8805979019886590.559701049005671
1470.3637628272671190.7275256545342380.636237172732881
1480.3557842761155720.7115685522311430.644215723884428
1490.3270992180330340.6541984360660690.672900781966965
1500.2484951098672740.4969902197345480.751504890132726
1510.4451097867008490.8902195734016990.554890213299151

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.785571393098325 & 0.428857213803349 & 0.214428606901675 \tabularnewline
12 & 0.687857789592033 & 0.624284420815935 & 0.312142210407967 \tabularnewline
13 & 0.922582946190787 & 0.154834107618427 & 0.0774170538092135 \tabularnewline
14 & 0.88585903046144 & 0.228281939077119 & 0.11414096953856 \tabularnewline
15 & 0.856521856693826 & 0.286956286612347 & 0.143478143306174 \tabularnewline
16 & 0.911335021564579 & 0.177329956870841 & 0.0886649784354206 \tabularnewline
17 & 0.901091504658078 & 0.197816990683844 & 0.0989084953419219 \tabularnewline
18 & 0.920006031318292 & 0.159987937363417 & 0.0799939686817084 \tabularnewline
19 & 0.956271439156266 & 0.0874571216874673 & 0.0437285608437337 \tabularnewline
20 & 0.9560433848851 & 0.0879132302298004 & 0.0439566151149002 \tabularnewline
21 & 0.940019988208984 & 0.119960023582031 & 0.0599800117910155 \tabularnewline
22 & 0.943035351995182 & 0.113929296009636 & 0.0569646480048181 \tabularnewline
23 & 0.943447605415219 & 0.113104789169562 & 0.0565523945847809 \tabularnewline
24 & 0.925687166153957 & 0.148625667692086 & 0.0743128338460432 \tabularnewline
25 & 0.900815870069937 & 0.198368259860126 & 0.0991841299300631 \tabularnewline
26 & 0.979951015724226 & 0.0400979685515485 & 0.0200489842757743 \tabularnewline
27 & 0.982668340830291 & 0.0346633183394177 & 0.0173316591697088 \tabularnewline
28 & 0.979953318652923 & 0.0400933626941539 & 0.020046681347077 \tabularnewline
29 & 0.972954830283782 & 0.0540903394324353 & 0.0270451697162176 \tabularnewline
30 & 0.963577907228378 & 0.0728441855432441 & 0.0364220927716221 \tabularnewline
31 & 0.971009293541132 & 0.0579814129177357 & 0.0289907064588679 \tabularnewline
32 & 0.965875231336454 & 0.0682495373270911 & 0.0341247686635455 \tabularnewline
33 & 0.959994120718037 & 0.0800117585639256 & 0.0400058792819628 \tabularnewline
34 & 0.948440296195195 & 0.103119407609611 & 0.0515597038048053 \tabularnewline
35 & 0.936555536134501 & 0.126888927730998 & 0.0634444638654988 \tabularnewline
36 & 0.940608728748897 & 0.118782542502205 & 0.0593912712511026 \tabularnewline
37 & 0.953966792313671 & 0.0920664153726573 & 0.0460332076863287 \tabularnewline
38 & 0.944182410721826 & 0.111635178556349 & 0.0558175892781743 \tabularnewline
39 & 0.935442928052272 & 0.129114143895457 & 0.0645570719477283 \tabularnewline
40 & 0.931932881605543 & 0.136134236788913 & 0.0680671183944565 \tabularnewline
41 & 0.922429620530006 & 0.155140758939989 & 0.0775703794699943 \tabularnewline
42 & 0.907263347461019 & 0.185473305077962 & 0.0927366525389811 \tabularnewline
43 & 0.886456262801215 & 0.227087474397569 & 0.113543737198785 \tabularnewline
44 & 0.859326448547895 & 0.281347102904211 & 0.140673551452105 \tabularnewline
45 & 0.903627739740539 & 0.192744520518921 & 0.0963722602594606 \tabularnewline
46 & 0.90133028291308 & 0.19733943417384 & 0.0986697170869199 \tabularnewline
47 & 0.901995517266908 & 0.196008965466185 & 0.0980044827330925 \tabularnewline
48 & 0.885351918095467 & 0.229296163809067 & 0.114648081904533 \tabularnewline
49 & 0.922436057262711 & 0.155127885474579 & 0.0775639427372894 \tabularnewline
50 & 0.910393222326516 & 0.179213555346968 & 0.089606777673484 \tabularnewline
51 & 0.893027770904053 & 0.213944458191894 & 0.106972229095947 \tabularnewline
52 & 0.880246882661295 & 0.239506234677411 & 0.119753117338705 \tabularnewline
53 & 0.907743403203789 & 0.184513193592421 & 0.0922565967962106 \tabularnewline
54 & 0.887106358579822 & 0.225787282840355 & 0.112893641420178 \tabularnewline
55 & 0.9108963558487 & 0.178207288302599 & 0.0891036441512997 \tabularnewline
56 & 0.894050809025331 & 0.211898381949338 & 0.105949190974669 \tabularnewline
57 & 0.878770332376759 & 0.242459335246482 & 0.121229667623241 \tabularnewline
58 & 0.893714222509112 & 0.212571554981777 & 0.106285777490888 \tabularnewline
59 & 0.908744425244264 & 0.182511149511471 & 0.0912555747557357 \tabularnewline
60 & 0.888041250759077 & 0.223917498481845 & 0.111958749240923 \tabularnewline
61 & 0.893426032651575 & 0.213147934696849 & 0.106573967348425 \tabularnewline
62 & 0.871641227842258 & 0.256717544315483 & 0.128358772157742 \tabularnewline
63 & 0.878977501103453 & 0.242044997793095 & 0.121022498896547 \tabularnewline
64 & 0.855464393906397 & 0.289071212187206 & 0.144535606093603 \tabularnewline
65 & 0.827667592728638 & 0.344664814542724 & 0.172332407271362 \tabularnewline
66 & 0.850927714281276 & 0.298144571437447 & 0.149072285718724 \tabularnewline
67 & 0.845417392540689 & 0.309165214918622 & 0.154582607459311 \tabularnewline
68 & 0.832668057903977 & 0.334663884192046 & 0.167331942096023 \tabularnewline
69 & 0.808803696741098 & 0.382392606517803 & 0.191196303258902 \tabularnewline
70 & 0.816560844036165 & 0.36687831192767 & 0.183439155963835 \tabularnewline
71 & 0.792032560791989 & 0.415934878416022 & 0.207967439208011 \tabularnewline
72 & 0.801792127495246 & 0.396415745009508 & 0.198207872504754 \tabularnewline
73 & 0.776858574888209 & 0.446282850223581 & 0.223141425111791 \tabularnewline
74 & 0.743101852347077 & 0.513796295305846 & 0.256898147652923 \tabularnewline
75 & 0.727387441888071 & 0.545225116223857 & 0.272612558111928 \tabularnewline
76 & 0.787941216844906 & 0.424117566310187 & 0.212058783155094 \tabularnewline
77 & 0.78220808202403 & 0.435583835951941 & 0.21779191797597 \tabularnewline
78 & 0.78872328709629 & 0.42255342580742 & 0.21127671290371 \tabularnewline
79 & 0.761218912674396 & 0.477562174651208 & 0.238781087325604 \tabularnewline
80 & 0.812341733944744 & 0.375316532110512 & 0.187658266055256 \tabularnewline
81 & 0.779845861987703 & 0.440308276024593 & 0.220154138012297 \tabularnewline
82 & 0.743609850950237 & 0.512780298099526 & 0.256390149049763 \tabularnewline
83 & 0.764413549935779 & 0.471172900128442 & 0.235586450064221 \tabularnewline
84 & 0.744452140599085 & 0.511095718801829 & 0.255547859400915 \tabularnewline
85 & 0.715003456620502 & 0.569993086758997 & 0.284996543379498 \tabularnewline
86 & 0.678560298061089 & 0.642879403877823 & 0.321439701938912 \tabularnewline
87 & 0.643822017875083 & 0.712355964249834 & 0.356177982124917 \tabularnewline
88 & 0.606176583438811 & 0.787646833122379 & 0.393823416561189 \tabularnewline
89 & 0.569250313568128 & 0.861499372863745 & 0.430749686431872 \tabularnewline
90 & 0.616874558012793 & 0.766250883974413 & 0.383125441987207 \tabularnewline
91 & 0.584338908161109 & 0.831322183677781 & 0.415661091838891 \tabularnewline
92 & 0.557383043492503 & 0.885233913014993 & 0.442616956507497 \tabularnewline
93 & 0.517831262881967 & 0.964337474236066 & 0.482168737118033 \tabularnewline
94 & 0.489702745632503 & 0.979405491265006 & 0.510297254367497 \tabularnewline
95 & 0.577353449798647 & 0.845293100402705 & 0.422646550201353 \tabularnewline
96 & 0.533749427554727 & 0.932501144890546 & 0.466250572445273 \tabularnewline
97 & 0.503541808010859 & 0.992916383978282 & 0.496458191989141 \tabularnewline
98 & 0.499723260616827 & 0.999446521233655 & 0.500276739383173 \tabularnewline
99 & 0.461863236428117 & 0.923726472856233 & 0.538136763571883 \tabularnewline
100 & 0.439620306914013 & 0.879240613828026 & 0.560379693085987 \tabularnewline
101 & 0.393415279313604 & 0.786830558627208 & 0.606584720686396 \tabularnewline
102 & 0.379904893788172 & 0.759809787576343 & 0.620095106211828 \tabularnewline
103 & 0.362029253412491 & 0.724058506824983 & 0.637970746587509 \tabularnewline
104 & 0.44475659105227 & 0.889513182104541 & 0.55524340894773 \tabularnewline
105 & 0.542862596704394 & 0.914274806591212 & 0.457137403295606 \tabularnewline
106 & 0.541557037289188 & 0.916885925421624 & 0.458442962710812 \tabularnewline
107 & 0.494284847523071 & 0.988569695046143 & 0.505715152476929 \tabularnewline
108 & 0.62799845180075 & 0.744003096398501 & 0.372001548199251 \tabularnewline
109 & 0.580966586354954 & 0.838066827290092 & 0.419033413645046 \tabularnewline
110 & 0.850025630002505 & 0.29994873999499 & 0.149974369997495 \tabularnewline
111 & 0.835952019859841 & 0.328095960280318 & 0.164047980140159 \tabularnewline
112 & 0.801192396656198 & 0.397615206687603 & 0.198807603343802 \tabularnewline
113 & 0.785380928462327 & 0.429238143075346 & 0.214619071537673 \tabularnewline
114 & 0.770930920847811 & 0.458138158304378 & 0.229069079152189 \tabularnewline
115 & 0.740050602729577 & 0.519898794540846 & 0.259949397270423 \tabularnewline
116 & 0.695383880392042 & 0.609232239215917 & 0.304616119607958 \tabularnewline
117 & 0.678967525743823 & 0.642064948512354 & 0.321032474256177 \tabularnewline
118 & 0.634138874284756 & 0.731722251430488 & 0.365861125715244 \tabularnewline
119 & 0.739283504687133 & 0.521432990625734 & 0.260716495312867 \tabularnewline
120 & 0.755309259459459 & 0.489381481081082 & 0.244690740540541 \tabularnewline
121 & 0.709437659412444 & 0.581124681175112 & 0.290562340587556 \tabularnewline
122 & 0.660060742365124 & 0.679878515269752 & 0.339939257634876 \tabularnewline
123 & 0.616007511549558 & 0.767984976900884 & 0.383992488450442 \tabularnewline
124 & 0.631763872204486 & 0.736472255591027 & 0.368236127795514 \tabularnewline
125 & 0.600164422430417 & 0.799671155139166 & 0.399835577569583 \tabularnewline
126 & 0.620127671772931 & 0.759744656454137 & 0.379872328227069 \tabularnewline
127 & 0.595367772777421 & 0.809264454445159 & 0.404632227222579 \tabularnewline
128 & 0.561698836881926 & 0.876602326236148 & 0.438301163118074 \tabularnewline
129 & 0.538241880828841 & 0.923516238342318 & 0.461758119171159 \tabularnewline
130 & 0.478173247353689 & 0.956346494707379 & 0.521826752646311 \tabularnewline
131 & 0.458658874844316 & 0.917317749688632 & 0.541341125155684 \tabularnewline
132 & 0.422814897065172 & 0.845629794130344 & 0.577185102934828 \tabularnewline
133 & 0.381605858964752 & 0.763211717929504 & 0.618394141035248 \tabularnewline
134 & 0.346589455031655 & 0.69317891006331 & 0.653410544968345 \tabularnewline
135 & 0.298713955172587 & 0.597427910345174 & 0.701286044827413 \tabularnewline
136 & 0.258819161210113 & 0.517638322420225 & 0.741180838789887 \tabularnewline
137 & 0.527444138317205 & 0.945111723365589 & 0.472555861682795 \tabularnewline
138 & 0.500080038889165 & 0.99983992222167 & 0.499919961110835 \tabularnewline
139 & 0.424193681273922 & 0.848387362547844 & 0.575806318726078 \tabularnewline
140 & 0.402121065942004 & 0.804242131884009 & 0.597878934057996 \tabularnewline
141 & 0.44113924249989 & 0.882278484999781 & 0.55886075750011 \tabularnewline
142 & 0.407941258251333 & 0.815882516502666 & 0.592058741748667 \tabularnewline
143 & 0.504470067519346 & 0.991059864961307 & 0.495529932480654 \tabularnewline
144 & 0.453662866364526 & 0.907325732729052 & 0.546337133635474 \tabularnewline
145 & 0.411040629441495 & 0.822081258882991 & 0.588959370558505 \tabularnewline
146 & 0.440298950994329 & 0.880597901988659 & 0.559701049005671 \tabularnewline
147 & 0.363762827267119 & 0.727525654534238 & 0.636237172732881 \tabularnewline
148 & 0.355784276115572 & 0.711568552231143 & 0.644215723884428 \tabularnewline
149 & 0.327099218033034 & 0.654198436066069 & 0.672900781966965 \tabularnewline
150 & 0.248495109867274 & 0.496990219734548 & 0.751504890132726 \tabularnewline
151 & 0.445109786700849 & 0.890219573401699 & 0.554890213299151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&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]11[/C][C]0.785571393098325[/C][C]0.428857213803349[/C][C]0.214428606901675[/C][/ROW]
[ROW][C]12[/C][C]0.687857789592033[/C][C]0.624284420815935[/C][C]0.312142210407967[/C][/ROW]
[ROW][C]13[/C][C]0.922582946190787[/C][C]0.154834107618427[/C][C]0.0774170538092135[/C][/ROW]
[ROW][C]14[/C][C]0.88585903046144[/C][C]0.228281939077119[/C][C]0.11414096953856[/C][/ROW]
[ROW][C]15[/C][C]0.856521856693826[/C][C]0.286956286612347[/C][C]0.143478143306174[/C][/ROW]
[ROW][C]16[/C][C]0.911335021564579[/C][C]0.177329956870841[/C][C]0.0886649784354206[/C][/ROW]
[ROW][C]17[/C][C]0.901091504658078[/C][C]0.197816990683844[/C][C]0.0989084953419219[/C][/ROW]
[ROW][C]18[/C][C]0.920006031318292[/C][C]0.159987937363417[/C][C]0.0799939686817084[/C][/ROW]
[ROW][C]19[/C][C]0.956271439156266[/C][C]0.0874571216874673[/C][C]0.0437285608437337[/C][/ROW]
[ROW][C]20[/C][C]0.9560433848851[/C][C]0.0879132302298004[/C][C]0.0439566151149002[/C][/ROW]
[ROW][C]21[/C][C]0.940019988208984[/C][C]0.119960023582031[/C][C]0.0599800117910155[/C][/ROW]
[ROW][C]22[/C][C]0.943035351995182[/C][C]0.113929296009636[/C][C]0.0569646480048181[/C][/ROW]
[ROW][C]23[/C][C]0.943447605415219[/C][C]0.113104789169562[/C][C]0.0565523945847809[/C][/ROW]
[ROW][C]24[/C][C]0.925687166153957[/C][C]0.148625667692086[/C][C]0.0743128338460432[/C][/ROW]
[ROW][C]25[/C][C]0.900815870069937[/C][C]0.198368259860126[/C][C]0.0991841299300631[/C][/ROW]
[ROW][C]26[/C][C]0.979951015724226[/C][C]0.0400979685515485[/C][C]0.0200489842757743[/C][/ROW]
[ROW][C]27[/C][C]0.982668340830291[/C][C]0.0346633183394177[/C][C]0.0173316591697088[/C][/ROW]
[ROW][C]28[/C][C]0.979953318652923[/C][C]0.0400933626941539[/C][C]0.020046681347077[/C][/ROW]
[ROW][C]29[/C][C]0.972954830283782[/C][C]0.0540903394324353[/C][C]0.0270451697162176[/C][/ROW]
[ROW][C]30[/C][C]0.963577907228378[/C][C]0.0728441855432441[/C][C]0.0364220927716221[/C][/ROW]
[ROW][C]31[/C][C]0.971009293541132[/C][C]0.0579814129177357[/C][C]0.0289907064588679[/C][/ROW]
[ROW][C]32[/C][C]0.965875231336454[/C][C]0.0682495373270911[/C][C]0.0341247686635455[/C][/ROW]
[ROW][C]33[/C][C]0.959994120718037[/C][C]0.0800117585639256[/C][C]0.0400058792819628[/C][/ROW]
[ROW][C]34[/C][C]0.948440296195195[/C][C]0.103119407609611[/C][C]0.0515597038048053[/C][/ROW]
[ROW][C]35[/C][C]0.936555536134501[/C][C]0.126888927730998[/C][C]0.0634444638654988[/C][/ROW]
[ROW][C]36[/C][C]0.940608728748897[/C][C]0.118782542502205[/C][C]0.0593912712511026[/C][/ROW]
[ROW][C]37[/C][C]0.953966792313671[/C][C]0.0920664153726573[/C][C]0.0460332076863287[/C][/ROW]
[ROW][C]38[/C][C]0.944182410721826[/C][C]0.111635178556349[/C][C]0.0558175892781743[/C][/ROW]
[ROW][C]39[/C][C]0.935442928052272[/C][C]0.129114143895457[/C][C]0.0645570719477283[/C][/ROW]
[ROW][C]40[/C][C]0.931932881605543[/C][C]0.136134236788913[/C][C]0.0680671183944565[/C][/ROW]
[ROW][C]41[/C][C]0.922429620530006[/C][C]0.155140758939989[/C][C]0.0775703794699943[/C][/ROW]
[ROW][C]42[/C][C]0.907263347461019[/C][C]0.185473305077962[/C][C]0.0927366525389811[/C][/ROW]
[ROW][C]43[/C][C]0.886456262801215[/C][C]0.227087474397569[/C][C]0.113543737198785[/C][/ROW]
[ROW][C]44[/C][C]0.859326448547895[/C][C]0.281347102904211[/C][C]0.140673551452105[/C][/ROW]
[ROW][C]45[/C][C]0.903627739740539[/C][C]0.192744520518921[/C][C]0.0963722602594606[/C][/ROW]
[ROW][C]46[/C][C]0.90133028291308[/C][C]0.19733943417384[/C][C]0.0986697170869199[/C][/ROW]
[ROW][C]47[/C][C]0.901995517266908[/C][C]0.196008965466185[/C][C]0.0980044827330925[/C][/ROW]
[ROW][C]48[/C][C]0.885351918095467[/C][C]0.229296163809067[/C][C]0.114648081904533[/C][/ROW]
[ROW][C]49[/C][C]0.922436057262711[/C][C]0.155127885474579[/C][C]0.0775639427372894[/C][/ROW]
[ROW][C]50[/C][C]0.910393222326516[/C][C]0.179213555346968[/C][C]0.089606777673484[/C][/ROW]
[ROW][C]51[/C][C]0.893027770904053[/C][C]0.213944458191894[/C][C]0.106972229095947[/C][/ROW]
[ROW][C]52[/C][C]0.880246882661295[/C][C]0.239506234677411[/C][C]0.119753117338705[/C][/ROW]
[ROW][C]53[/C][C]0.907743403203789[/C][C]0.184513193592421[/C][C]0.0922565967962106[/C][/ROW]
[ROW][C]54[/C][C]0.887106358579822[/C][C]0.225787282840355[/C][C]0.112893641420178[/C][/ROW]
[ROW][C]55[/C][C]0.9108963558487[/C][C]0.178207288302599[/C][C]0.0891036441512997[/C][/ROW]
[ROW][C]56[/C][C]0.894050809025331[/C][C]0.211898381949338[/C][C]0.105949190974669[/C][/ROW]
[ROW][C]57[/C][C]0.878770332376759[/C][C]0.242459335246482[/C][C]0.121229667623241[/C][/ROW]
[ROW][C]58[/C][C]0.893714222509112[/C][C]0.212571554981777[/C][C]0.106285777490888[/C][/ROW]
[ROW][C]59[/C][C]0.908744425244264[/C][C]0.182511149511471[/C][C]0.0912555747557357[/C][/ROW]
[ROW][C]60[/C][C]0.888041250759077[/C][C]0.223917498481845[/C][C]0.111958749240923[/C][/ROW]
[ROW][C]61[/C][C]0.893426032651575[/C][C]0.213147934696849[/C][C]0.106573967348425[/C][/ROW]
[ROW][C]62[/C][C]0.871641227842258[/C][C]0.256717544315483[/C][C]0.128358772157742[/C][/ROW]
[ROW][C]63[/C][C]0.878977501103453[/C][C]0.242044997793095[/C][C]0.121022498896547[/C][/ROW]
[ROW][C]64[/C][C]0.855464393906397[/C][C]0.289071212187206[/C][C]0.144535606093603[/C][/ROW]
[ROW][C]65[/C][C]0.827667592728638[/C][C]0.344664814542724[/C][C]0.172332407271362[/C][/ROW]
[ROW][C]66[/C][C]0.850927714281276[/C][C]0.298144571437447[/C][C]0.149072285718724[/C][/ROW]
[ROW][C]67[/C][C]0.845417392540689[/C][C]0.309165214918622[/C][C]0.154582607459311[/C][/ROW]
[ROW][C]68[/C][C]0.832668057903977[/C][C]0.334663884192046[/C][C]0.167331942096023[/C][/ROW]
[ROW][C]69[/C][C]0.808803696741098[/C][C]0.382392606517803[/C][C]0.191196303258902[/C][/ROW]
[ROW][C]70[/C][C]0.816560844036165[/C][C]0.36687831192767[/C][C]0.183439155963835[/C][/ROW]
[ROW][C]71[/C][C]0.792032560791989[/C][C]0.415934878416022[/C][C]0.207967439208011[/C][/ROW]
[ROW][C]72[/C][C]0.801792127495246[/C][C]0.396415745009508[/C][C]0.198207872504754[/C][/ROW]
[ROW][C]73[/C][C]0.776858574888209[/C][C]0.446282850223581[/C][C]0.223141425111791[/C][/ROW]
[ROW][C]74[/C][C]0.743101852347077[/C][C]0.513796295305846[/C][C]0.256898147652923[/C][/ROW]
[ROW][C]75[/C][C]0.727387441888071[/C][C]0.545225116223857[/C][C]0.272612558111928[/C][/ROW]
[ROW][C]76[/C][C]0.787941216844906[/C][C]0.424117566310187[/C][C]0.212058783155094[/C][/ROW]
[ROW][C]77[/C][C]0.78220808202403[/C][C]0.435583835951941[/C][C]0.21779191797597[/C][/ROW]
[ROW][C]78[/C][C]0.78872328709629[/C][C]0.42255342580742[/C][C]0.21127671290371[/C][/ROW]
[ROW][C]79[/C][C]0.761218912674396[/C][C]0.477562174651208[/C][C]0.238781087325604[/C][/ROW]
[ROW][C]80[/C][C]0.812341733944744[/C][C]0.375316532110512[/C][C]0.187658266055256[/C][/ROW]
[ROW][C]81[/C][C]0.779845861987703[/C][C]0.440308276024593[/C][C]0.220154138012297[/C][/ROW]
[ROW][C]82[/C][C]0.743609850950237[/C][C]0.512780298099526[/C][C]0.256390149049763[/C][/ROW]
[ROW][C]83[/C][C]0.764413549935779[/C][C]0.471172900128442[/C][C]0.235586450064221[/C][/ROW]
[ROW][C]84[/C][C]0.744452140599085[/C][C]0.511095718801829[/C][C]0.255547859400915[/C][/ROW]
[ROW][C]85[/C][C]0.715003456620502[/C][C]0.569993086758997[/C][C]0.284996543379498[/C][/ROW]
[ROW][C]86[/C][C]0.678560298061089[/C][C]0.642879403877823[/C][C]0.321439701938912[/C][/ROW]
[ROW][C]87[/C][C]0.643822017875083[/C][C]0.712355964249834[/C][C]0.356177982124917[/C][/ROW]
[ROW][C]88[/C][C]0.606176583438811[/C][C]0.787646833122379[/C][C]0.393823416561189[/C][/ROW]
[ROW][C]89[/C][C]0.569250313568128[/C][C]0.861499372863745[/C][C]0.430749686431872[/C][/ROW]
[ROW][C]90[/C][C]0.616874558012793[/C][C]0.766250883974413[/C][C]0.383125441987207[/C][/ROW]
[ROW][C]91[/C][C]0.584338908161109[/C][C]0.831322183677781[/C][C]0.415661091838891[/C][/ROW]
[ROW][C]92[/C][C]0.557383043492503[/C][C]0.885233913014993[/C][C]0.442616956507497[/C][/ROW]
[ROW][C]93[/C][C]0.517831262881967[/C][C]0.964337474236066[/C][C]0.482168737118033[/C][/ROW]
[ROW][C]94[/C][C]0.489702745632503[/C][C]0.979405491265006[/C][C]0.510297254367497[/C][/ROW]
[ROW][C]95[/C][C]0.577353449798647[/C][C]0.845293100402705[/C][C]0.422646550201353[/C][/ROW]
[ROW][C]96[/C][C]0.533749427554727[/C][C]0.932501144890546[/C][C]0.466250572445273[/C][/ROW]
[ROW][C]97[/C][C]0.503541808010859[/C][C]0.992916383978282[/C][C]0.496458191989141[/C][/ROW]
[ROW][C]98[/C][C]0.499723260616827[/C][C]0.999446521233655[/C][C]0.500276739383173[/C][/ROW]
[ROW][C]99[/C][C]0.461863236428117[/C][C]0.923726472856233[/C][C]0.538136763571883[/C][/ROW]
[ROW][C]100[/C][C]0.439620306914013[/C][C]0.879240613828026[/C][C]0.560379693085987[/C][/ROW]
[ROW][C]101[/C][C]0.393415279313604[/C][C]0.786830558627208[/C][C]0.606584720686396[/C][/ROW]
[ROW][C]102[/C][C]0.379904893788172[/C][C]0.759809787576343[/C][C]0.620095106211828[/C][/ROW]
[ROW][C]103[/C][C]0.362029253412491[/C][C]0.724058506824983[/C][C]0.637970746587509[/C][/ROW]
[ROW][C]104[/C][C]0.44475659105227[/C][C]0.889513182104541[/C][C]0.55524340894773[/C][/ROW]
[ROW][C]105[/C][C]0.542862596704394[/C][C]0.914274806591212[/C][C]0.457137403295606[/C][/ROW]
[ROW][C]106[/C][C]0.541557037289188[/C][C]0.916885925421624[/C][C]0.458442962710812[/C][/ROW]
[ROW][C]107[/C][C]0.494284847523071[/C][C]0.988569695046143[/C][C]0.505715152476929[/C][/ROW]
[ROW][C]108[/C][C]0.62799845180075[/C][C]0.744003096398501[/C][C]0.372001548199251[/C][/ROW]
[ROW][C]109[/C][C]0.580966586354954[/C][C]0.838066827290092[/C][C]0.419033413645046[/C][/ROW]
[ROW][C]110[/C][C]0.850025630002505[/C][C]0.29994873999499[/C][C]0.149974369997495[/C][/ROW]
[ROW][C]111[/C][C]0.835952019859841[/C][C]0.328095960280318[/C][C]0.164047980140159[/C][/ROW]
[ROW][C]112[/C][C]0.801192396656198[/C][C]0.397615206687603[/C][C]0.198807603343802[/C][/ROW]
[ROW][C]113[/C][C]0.785380928462327[/C][C]0.429238143075346[/C][C]0.214619071537673[/C][/ROW]
[ROW][C]114[/C][C]0.770930920847811[/C][C]0.458138158304378[/C][C]0.229069079152189[/C][/ROW]
[ROW][C]115[/C][C]0.740050602729577[/C][C]0.519898794540846[/C][C]0.259949397270423[/C][/ROW]
[ROW][C]116[/C][C]0.695383880392042[/C][C]0.609232239215917[/C][C]0.304616119607958[/C][/ROW]
[ROW][C]117[/C][C]0.678967525743823[/C][C]0.642064948512354[/C][C]0.321032474256177[/C][/ROW]
[ROW][C]118[/C][C]0.634138874284756[/C][C]0.731722251430488[/C][C]0.365861125715244[/C][/ROW]
[ROW][C]119[/C][C]0.739283504687133[/C][C]0.521432990625734[/C][C]0.260716495312867[/C][/ROW]
[ROW][C]120[/C][C]0.755309259459459[/C][C]0.489381481081082[/C][C]0.244690740540541[/C][/ROW]
[ROW][C]121[/C][C]0.709437659412444[/C][C]0.581124681175112[/C][C]0.290562340587556[/C][/ROW]
[ROW][C]122[/C][C]0.660060742365124[/C][C]0.679878515269752[/C][C]0.339939257634876[/C][/ROW]
[ROW][C]123[/C][C]0.616007511549558[/C][C]0.767984976900884[/C][C]0.383992488450442[/C][/ROW]
[ROW][C]124[/C][C]0.631763872204486[/C][C]0.736472255591027[/C][C]0.368236127795514[/C][/ROW]
[ROW][C]125[/C][C]0.600164422430417[/C][C]0.799671155139166[/C][C]0.399835577569583[/C][/ROW]
[ROW][C]126[/C][C]0.620127671772931[/C][C]0.759744656454137[/C][C]0.379872328227069[/C][/ROW]
[ROW][C]127[/C][C]0.595367772777421[/C][C]0.809264454445159[/C][C]0.404632227222579[/C][/ROW]
[ROW][C]128[/C][C]0.561698836881926[/C][C]0.876602326236148[/C][C]0.438301163118074[/C][/ROW]
[ROW][C]129[/C][C]0.538241880828841[/C][C]0.923516238342318[/C][C]0.461758119171159[/C][/ROW]
[ROW][C]130[/C][C]0.478173247353689[/C][C]0.956346494707379[/C][C]0.521826752646311[/C][/ROW]
[ROW][C]131[/C][C]0.458658874844316[/C][C]0.917317749688632[/C][C]0.541341125155684[/C][/ROW]
[ROW][C]132[/C][C]0.422814897065172[/C][C]0.845629794130344[/C][C]0.577185102934828[/C][/ROW]
[ROW][C]133[/C][C]0.381605858964752[/C][C]0.763211717929504[/C][C]0.618394141035248[/C][/ROW]
[ROW][C]134[/C][C]0.346589455031655[/C][C]0.69317891006331[/C][C]0.653410544968345[/C][/ROW]
[ROW][C]135[/C][C]0.298713955172587[/C][C]0.597427910345174[/C][C]0.701286044827413[/C][/ROW]
[ROW][C]136[/C][C]0.258819161210113[/C][C]0.517638322420225[/C][C]0.741180838789887[/C][/ROW]
[ROW][C]137[/C][C]0.527444138317205[/C][C]0.945111723365589[/C][C]0.472555861682795[/C][/ROW]
[ROW][C]138[/C][C]0.500080038889165[/C][C]0.99983992222167[/C][C]0.499919961110835[/C][/ROW]
[ROW][C]139[/C][C]0.424193681273922[/C][C]0.848387362547844[/C][C]0.575806318726078[/C][/ROW]
[ROW][C]140[/C][C]0.402121065942004[/C][C]0.804242131884009[/C][C]0.597878934057996[/C][/ROW]
[ROW][C]141[/C][C]0.44113924249989[/C][C]0.882278484999781[/C][C]0.55886075750011[/C][/ROW]
[ROW][C]142[/C][C]0.407941258251333[/C][C]0.815882516502666[/C][C]0.592058741748667[/C][/ROW]
[ROW][C]143[/C][C]0.504470067519346[/C][C]0.991059864961307[/C][C]0.495529932480654[/C][/ROW]
[ROW][C]144[/C][C]0.453662866364526[/C][C]0.907325732729052[/C][C]0.546337133635474[/C][/ROW]
[ROW][C]145[/C][C]0.411040629441495[/C][C]0.822081258882991[/C][C]0.588959370558505[/C][/ROW]
[ROW][C]146[/C][C]0.440298950994329[/C][C]0.880597901988659[/C][C]0.559701049005671[/C][/ROW]
[ROW][C]147[/C][C]0.363762827267119[/C][C]0.727525654534238[/C][C]0.636237172732881[/C][/ROW]
[ROW][C]148[/C][C]0.355784276115572[/C][C]0.711568552231143[/C][C]0.644215723884428[/C][/ROW]
[ROW][C]149[/C][C]0.327099218033034[/C][C]0.654198436066069[/C][C]0.672900781966965[/C][/ROW]
[ROW][C]150[/C][C]0.248495109867274[/C][C]0.496990219734548[/C][C]0.751504890132726[/C][/ROW]
[ROW][C]151[/C][C]0.445109786700849[/C][C]0.890219573401699[/C][C]0.554890213299151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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
110.7855713930983250.4288572138033490.214428606901675
120.6878577895920330.6242844208159350.312142210407967
130.9225829461907870.1548341076184270.0774170538092135
140.885859030461440.2282819390771190.11414096953856
150.8565218566938260.2869562866123470.143478143306174
160.9113350215645790.1773299568708410.0886649784354206
170.9010915046580780.1978169906838440.0989084953419219
180.9200060313182920.1599879373634170.0799939686817084
190.9562714391562660.08745712168746730.0437285608437337
200.95604338488510.08791323022980040.0439566151149002
210.9400199882089840.1199600235820310.0599800117910155
220.9430353519951820.1139292960096360.0569646480048181
230.9434476054152190.1131047891695620.0565523945847809
240.9256871661539570.1486256676920860.0743128338460432
250.9008158700699370.1983682598601260.0991841299300631
260.9799510157242260.04009796855154850.0200489842757743
270.9826683408302910.03466331833941770.0173316591697088
280.9799533186529230.04009336269415390.020046681347077
290.9729548302837820.05409033943243530.0270451697162176
300.9635779072283780.07284418554324410.0364220927716221
310.9710092935411320.05798141291773570.0289907064588679
320.9658752313364540.06824953732709110.0341247686635455
330.9599941207180370.08001175856392560.0400058792819628
340.9484402961951950.1031194076096110.0515597038048053
350.9365555361345010.1268889277309980.0634444638654988
360.9406087287488970.1187825425022050.0593912712511026
370.9539667923136710.09206641537265730.0460332076863287
380.9441824107218260.1116351785563490.0558175892781743
390.9354429280522720.1291141438954570.0645570719477283
400.9319328816055430.1361342367889130.0680671183944565
410.9224296205300060.1551407589399890.0775703794699943
420.9072633474610190.1854733050779620.0927366525389811
430.8864562628012150.2270874743975690.113543737198785
440.8593264485478950.2813471029042110.140673551452105
450.9036277397405390.1927445205189210.0963722602594606
460.901330282913080.197339434173840.0986697170869199
470.9019955172669080.1960089654661850.0980044827330925
480.8853519180954670.2292961638090670.114648081904533
490.9224360572627110.1551278854745790.0775639427372894
500.9103932223265160.1792135553469680.089606777673484
510.8930277709040530.2139444581918940.106972229095947
520.8802468826612950.2395062346774110.119753117338705
530.9077434032037890.1845131935924210.0922565967962106
540.8871063585798220.2257872828403550.112893641420178
550.91089635584870.1782072883025990.0891036441512997
560.8940508090253310.2118983819493380.105949190974669
570.8787703323767590.2424593352464820.121229667623241
580.8937142225091120.2125715549817770.106285777490888
590.9087444252442640.1825111495114710.0912555747557357
600.8880412507590770.2239174984818450.111958749240923
610.8934260326515750.2131479346968490.106573967348425
620.8716412278422580.2567175443154830.128358772157742
630.8789775011034530.2420449977930950.121022498896547
640.8554643939063970.2890712121872060.144535606093603
650.8276675927286380.3446648145427240.172332407271362
660.8509277142812760.2981445714374470.149072285718724
670.8454173925406890.3091652149186220.154582607459311
680.8326680579039770.3346638841920460.167331942096023
690.8088036967410980.3823926065178030.191196303258902
700.8165608440361650.366878311927670.183439155963835
710.7920325607919890.4159348784160220.207967439208011
720.8017921274952460.3964157450095080.198207872504754
730.7768585748882090.4462828502235810.223141425111791
740.7431018523470770.5137962953058460.256898147652923
750.7273874418880710.5452251162238570.272612558111928
760.7879412168449060.4241175663101870.212058783155094
770.782208082024030.4355838359519410.21779191797597
780.788723287096290.422553425807420.21127671290371
790.7612189126743960.4775621746512080.238781087325604
800.8123417339447440.3753165321105120.187658266055256
810.7798458619877030.4403082760245930.220154138012297
820.7436098509502370.5127802980995260.256390149049763
830.7644135499357790.4711729001284420.235586450064221
840.7444521405990850.5110957188018290.255547859400915
850.7150034566205020.5699930867589970.284996543379498
860.6785602980610890.6428794038778230.321439701938912
870.6438220178750830.7123559642498340.356177982124917
880.6061765834388110.7876468331223790.393823416561189
890.5692503135681280.8614993728637450.430749686431872
900.6168745580127930.7662508839744130.383125441987207
910.5843389081611090.8313221836777810.415661091838891
920.5573830434925030.8852339130149930.442616956507497
930.5178312628819670.9643374742360660.482168737118033
940.4897027456325030.9794054912650060.510297254367497
950.5773534497986470.8452931004027050.422646550201353
960.5337494275547270.9325011448905460.466250572445273
970.5035418080108590.9929163839782820.496458191989141
980.4997232606168270.9994465212336550.500276739383173
990.4618632364281170.9237264728562330.538136763571883
1000.4396203069140130.8792406138280260.560379693085987
1010.3934152793136040.7868305586272080.606584720686396
1020.3799048937881720.7598097875763430.620095106211828
1030.3620292534124910.7240585068249830.637970746587509
1040.444756591052270.8895131821045410.55524340894773
1050.5428625967043940.9142748065912120.457137403295606
1060.5415570372891880.9168859254216240.458442962710812
1070.4942848475230710.9885696950461430.505715152476929
1080.627998451800750.7440030963985010.372001548199251
1090.5809665863549540.8380668272900920.419033413645046
1100.8500256300025050.299948739994990.149974369997495
1110.8359520198598410.3280959602803180.164047980140159
1120.8011923966561980.3976152066876030.198807603343802
1130.7853809284623270.4292381430753460.214619071537673
1140.7709309208478110.4581381583043780.229069079152189
1150.7400506027295770.5198987945408460.259949397270423
1160.6953838803920420.6092322392159170.304616119607958
1170.6789675257438230.6420649485123540.321032474256177
1180.6341388742847560.7317222514304880.365861125715244
1190.7392835046871330.5214329906257340.260716495312867
1200.7553092594594590.4893814810810820.244690740540541
1210.7094376594124440.5811246811751120.290562340587556
1220.6600607423651240.6798785152697520.339939257634876
1230.6160075115495580.7679849769008840.383992488450442
1240.6317638722044860.7364722555910270.368236127795514
1250.6001644224304170.7996711551391660.399835577569583
1260.6201276717729310.7597446564541370.379872328227069
1270.5953677727774210.8092644544451590.404632227222579
1280.5616988368819260.8766023262361480.438301163118074
1290.5382418808288410.9235162383423180.461758119171159
1300.4781732473536890.9563464947073790.521826752646311
1310.4586588748443160.9173177496886320.541341125155684
1320.4228148970651720.8456297941303440.577185102934828
1330.3816058589647520.7632117179295040.618394141035248
1340.3465894550316550.693178910063310.653410544968345
1350.2987139551725870.5974279103451740.701286044827413
1360.2588191612101130.5176383224202250.741180838789887
1370.5274441383172050.9451117233655890.472555861682795
1380.5000800388891650.999839922221670.499919961110835
1390.4241936812739220.8483873625478440.575806318726078
1400.4021210659420040.8042421318840090.597878934057996
1410.441139242499890.8822784849997810.55886075750011
1420.4079412582513330.8158825165026660.592058741748667
1430.5044700675193460.9910598649613070.495529932480654
1440.4536628663645260.9073257327290520.546337133635474
1450.4110406294414950.8220812588829910.588959370558505
1460.4402989509943290.8805979019886590.559701049005671
1470.3637628272671190.7275256545342380.636237172732881
1480.3557842761155720.7115685522311430.644215723884428
1490.3270992180330340.6541984360660690.672900781966965
1500.2484951098672740.4969902197345480.751504890132726
1510.4451097867008490.8902195734016990.554890213299151






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0212765957446809OK
10% type I error level110.0780141843971631OK

\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 & 3 & 0.0212765957446809 & OK \tabularnewline
10% type I error level & 11 & 0.0780141843971631 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199834&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]3[/C][C]0.0212765957446809[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0780141843971631[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199834&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199834&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 level30.0212765957446809OK
10% type I error level110.0780141843971631OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 8 ; 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')
}