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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 18 Nov 2012 12:48:26 -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/Nov/18/t135326104811dkf9trxivae5q.htm/, Retrieved Mon, 29 Apr 2024 19:17:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190285, Retrieved Mon, 29 Apr 2024 19:17:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7-1] [2012-11-18 12:56:01] [f4f48270c576c45d216b84daa061a85b]
- R PD  [Multiple Regression] [WS7-4] [2012-11-18 15:59:32] [f4f48270c576c45d216b84daa061a85b]
-   P     [Multiple Regression] [WS7-5] [2012-11-18 16:04:18] [f4f48270c576c45d216b84daa061a85b]
-   P         [Multiple Regression] [WS7-7] [2012-11-18 17:48:26] [78cbff3691fb0cc454e192ff02249329] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	21	21	23	17	23	4
9	20	16	15	24	17	20	4
9	19	19	18	22	18	20	6
9	19	18	11	20	21	21	8
9	20	16	8	24	20	24	8
9	25	23	19	27	28	22	4
9	25	17	4	28	19	23	4
9	22	12	20	27	22	20	8
9	26	19	16	24	16	25	5
9	22	16	14	23	18	23	4
9	17	19	10	24	25	27	4
9	22	20	13	27	17	27	4
9	19	13	14	27	14	22	4
9	24	20	8	28	11	24	4
9	26	27	23	27	27	25	4
9	21	17	11	23	20	22	8
9	13	8	9	24	22	28	4
9	26	25	24	28	22	28	4
9	20	26	5	27	21	27	4
9	22	13	15	25	23	25	8
9	14	19	5	19	17	16	4
9	21	15	19	24	24	28	7
9	7	5	6	20	14	21	4
9	23	16	13	28	17	24	4
9	17	14	11	26	23	27	5
9	25	24	17	23	24	14	4
9	25	24	17	23	24	14	4
9	19	9	5	20	8	27	4
9	20	19	9	11	22	20	4
9	23	19	15	24	23	21	4
9	22	25	17	25	25	22	4
9	22	19	17	23	21	21	4
9	21	18	20	18	24	12	15
9	15	15	12	20	15	20	10
9	20	12	7	20	22	24	4
9	22	21	16	24	21	19	8
9	18	12	7	23	25	28	4
9	20	15	14	25	16	23	4
9	28	28	24	28	28	27	4
9	22	25	15	26	23	22	4
9	18	19	15	26	21	27	7
9	23	20	10	23	21	26	4
9	20	24	14	22	26	22	6
9	25	26	18	24	22	21	5
9	26	25	12	21	21	19	4
9	15	12	9	20	18	24	16
9	17	12	9	22	12	19	5
9	23	15	8	20	25	26	12
9	21	17	18	25	17	22	6
9	13	14	10	20	24	28	9
9	18	16	17	22	15	21	9
9	19	11	14	23	13	23	4
9	22	20	16	25	26	28	5
9	16	11	10	23	16	10	4
10	24	22	19	23	24	24	4
10	18	20	10	22	21	21	5
10	20	19	14	24	20	21	4
10	24	17	10	25	14	24	4
10	14	21	4	21	25	24	4
10	22	23	19	12	25	25	5
10	24	18	9	17	20	25	4
10	18	17	12	20	22	23	6
10	21	27	16	23	20	21	4
10	23	25	11	23	26	16	4
10	17	19	18	20	18	17	18
10	22	22	11	28	22	25	4
10	24	24	24	24	24	24	6
10	21	20	17	24	17	23	4
10	22	19	18	24	24	25	4
10	16	11	9	24	20	23	5
10	21	22	19	28	19	28	4
10	23	22	18	25	20	26	4
10	22	16	12	21	15	22	5
10	24	20	23	25	23	19	10
10	24	24	22	25	26	26	5
10	16	16	14	18	22	18	8
10	16	16	14	17	20	18	8
10	21	22	16	26	24	25	5
10	26	24	23	28	26	27	4
10	15	16	7	21	21	12	4
10	25	27	10	27	25	15	4
10	18	11	12	22	13	21	5
10	23	21	12	21	20	23	4
10	20	20	12	25	22	22	4
10	17	20	17	22	23	21	8
10	25	27	21	23	28	24	4
10	24	20	16	26	22	27	5
10	17	12	11	19	20	22	14
10	19	8	14	25	6	28	8
10	20	21	13	21	21	26	8
10	15	18	9	13	20	10	4
10	27	24	19	24	18	19	4
10	22	16	13	25	23	22	6
10	23	18	19	26	20	21	4
10	16	20	13	25	24	24	7
10	19	20	13	25	22	25	7
10	25	19	13	22	21	21	4
10	19	17	14	21	18	20	6
10	19	16	12	23	21	21	4
10	26	26	22	25	23	24	7
10	21	15	11	24	23	23	4
10	20	22	5	21	15	18	4
10	24	17	18	21	21	24	8
10	22	23	19	25	24	24	4
10	20	21	14	22	23	19	4
10	18	19	15	20	21	20	10
10	18	14	12	20	21	18	8
10	24	17	19	23	20	20	6
11	24	12	15	28	11	27	4
11	22	24	17	23	22	23	4
11	23	18	8	28	27	26	4
11	22	20	10	24	25	23	5
11	20	16	12	18	18	17	4
11	18	20	12	20	20	21	6
11	25	22	20	28	24	25	4
11	18	12	12	21	10	23	5
11	16	16	12	21	27	27	7
11	20	17	14	25	21	24	8
11	19	22	6	19	21	20	5
11	15	12	10	18	18	27	8
11	19	14	18	21	15	21	10
11	19	23	18	22	24	24	8
11	16	15	7	24	22	21	5
11	17	17	18	15	14	15	12
11	28	28	9	28	28	25	4
11	23	20	17	26	18	25	5
11	25	23	22	23	26	22	4
11	20	13	11	26	17	24	6
11	17	18	15	20	19	21	4
11	23	23	17	22	22	22	4
11	16	19	15	20	18	23	7
11	23	23	22	23	24	22	7
11	11	12	9	22	15	20	10
11	18	16	13	24	18	23	4
11	24	23	20	23	26	25	5
11	23	13	14	22	11	23	8
11	21	22	14	26	26	22	11
11	16	18	12	23	21	25	7
11	24	23	20	27	23	26	4
11	23	20	20	23	23	22	8
11	18	10	8	21	15	24	6
11	20	17	17	26	22	24	7
11	9	18	9	23	26	25	5
11	24	15	18	21	16	20	4
11	25	23	22	27	20	26	8
11	20	17	10	19	18	21	4
11	21	17	13	23	22	26	8
11	25	22	15	25	16	21	6
11	22	20	18	23	19	22	4
11	21	20	18	22	20	16	9
11	21	19	12	22	19	26	5
11	22	18	12	25	23	28	6
11	27	22	20	25	24	18	4
11	24	20	12	28	25	25	4
11	24	22	16	28	21	23	4
11	21	18	16	20	21	21	5
11	18	16	18	25	23	20	6
11	16	16	16	19	27	25	16
11	22	16	13	25	23	22	6
11	20	16	17	22	18	21	6
11	18	17	13	18	16	16	4
11	20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 9.07916875449423 -0.200719002705604month[t] + 0.362137374389289I2[t] + 0.255060178396081I3[t] + 0.257658182150737E1[t] -0.116546858431106E2[t] + 0.0407571883200762E3[t] -0.209826761474737A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  9.07916875449423 -0.200719002705604month[t] +  0.362137374389289I2[t] +  0.255060178396081I3[t] +  0.257658182150737E1[t] -0.116546858431106E2[t] +  0.0407571883200762E3[t] -0.209826761474737A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  9.07916875449423 -0.200719002705604month[t] +  0.362137374389289I2[t] +  0.255060178396081I3[t] +  0.257658182150737E1[t] -0.116546858431106E2[t] +  0.0407571883200762E3[t] -0.209826761474737A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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
I1[t] = + 9.07916875449423 -0.200719002705604month[t] + 0.362137374389289I2[t] + 0.255060178396081I3[t] + 0.257658182150737E1[t] -0.116546858431106E2[t] + 0.0407571883200762E3[t] -0.209826761474737A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.079168754494233.1358752.89530.004340.00217
month-0.2007190027056040.243827-0.82320.4116660.205833
I20.3621373743892890.0630285.745700
I30.2550601783960810.0505455.04621e-061e-06
E10.2576581821507370.0752253.42520.0007880.000394
E2-0.1165468584311060.058862-1.980.0494840.024742
E30.04075718832007620.0612960.66490.5070920.253546
A-0.2098267614747370.084154-2.49340.0137110.006855

\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) & 9.07916875449423 & 3.135875 & 2.8953 & 0.00434 & 0.00217 \tabularnewline
month & -0.200719002705604 & 0.243827 & -0.8232 & 0.411666 & 0.205833 \tabularnewline
I2 & 0.362137374389289 & 0.063028 & 5.7457 & 0 & 0 \tabularnewline
I3 & 0.255060178396081 & 0.050545 & 5.0462 & 1e-06 & 1e-06 \tabularnewline
E1 & 0.257658182150737 & 0.075225 & 3.4252 & 0.000788 & 0.000394 \tabularnewline
E2 & -0.116546858431106 & 0.058862 & -1.98 & 0.049484 & 0.024742 \tabularnewline
E3 & 0.0407571883200762 & 0.061296 & 0.6649 & 0.507092 & 0.253546 \tabularnewline
A & -0.209826761474737 & 0.084154 & -2.4934 & 0.013711 & 0.006855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&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]9.07916875449423[/C][C]3.135875[/C][C]2.8953[/C][C]0.00434[/C][C]0.00217[/C][/ROW]
[ROW][C]month[/C][C]-0.200719002705604[/C][C]0.243827[/C][C]-0.8232[/C][C]0.411666[/C][C]0.205833[/C][/ROW]
[ROW][C]I2[/C][C]0.362137374389289[/C][C]0.063028[/C][C]5.7457[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.255060178396081[/C][C]0.050545[/C][C]5.0462[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E1[/C][C]0.257658182150737[/C][C]0.075225[/C][C]3.4252[/C][C]0.000788[/C][C]0.000394[/C][/ROW]
[ROW][C]E2[/C][C]-0.116546858431106[/C][C]0.058862[/C][C]-1.98[/C][C]0.049484[/C][C]0.024742[/C][/ROW]
[ROW][C]E3[/C][C]0.0407571883200762[/C][C]0.061296[/C][C]0.6649[/C][C]0.507092[/C][C]0.253546[/C][/ROW]
[ROW][C]A[/C][C]-0.209826761474737[/C][C]0.084154[/C][C]-2.4934[/C][C]0.013711[/C][C]0.006855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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)9.079168754494233.1358752.89530.004340.00217
month-0.2007190027056040.243827-0.82320.4116660.205833
I20.3621373743892890.0630285.745700
I30.2550601783960810.0505455.04621e-061e-06
E10.2576581821507370.0752253.42520.0007880.000394
E2-0.1165468584311060.058862-1.980.0494840.024742
E30.04075718832007620.0612960.66490.5070920.253546
A-0.2098267614747370.084154-2.49340.0137110.006855






Multiple Linear Regression - Regression Statistics
Multiple R0.743004183219006
R-squared0.552055216280942
Adjusted R-squared0.531694089748258
F-TEST (value)27.1131960893599
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50263522516301
Sum Squared Residuals964.530192814917

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.743004183219006 \tabularnewline
R-squared & 0.552055216280942 \tabularnewline
Adjusted R-squared & 0.531694089748258 \tabularnewline
F-TEST (value) & 27.1131960893599 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.50263522516301 \tabularnewline
Sum Squared Residuals & 964.530192814917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.743004183219006[/C][/ROW]
[ROW][C]R-squared[/C][C]0.552055216280942[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.531694089748258[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.1131960893599[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.50263522516301[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]964.530192814917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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 R0.743004183219006
R-squared0.552055216280942
Adjusted R-squared0.531694089748258
F-TEST (value)27.1131960893599
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50263522516301
Sum Squared Residuals964.530192814917






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.27679622023751.72320377976249
22021.0711348951051-1.07113489510508
31921.8712108077791-2.87121080777913
41918.47979891039310.520201089606911
52018.25979477842061.74020522157945
62524.19881070976460.801189290235379
72519.54742088383845.45257911616156
82220.24881949792611.75118050207394
92622.52311323522583.47688676477423
102220.56414124108741.43585875891262
111720.2351715770842-3.23517157708423
122223.0678389005628-1.06783890056281
131920.9337920919268-1.93379209192681
142422.62720577635961.37279422364045
152626.9064193442974-0.906419344297432
162119.04794012920721.95205987079281
171316.3869980440194-3.38699804401936
182627.3998688131814-1.39986881318143
192022.7339942860055-2.73399428600548
202218.90757869920273.09242130079725
211418.1556255702781-4.15562557027815
222120.60998744741880.39001255258123
23714.1518472062686-7.15184720626862
242321.75467602019621.24532397980383
251719.2181282032228-2.21812820322281
262523.16032492592261.83967507407739
272523.16032492592261.83967507407739
281916.28916080593682.71083919406321
292016.69489528778143.30510471221864
302321.49902305600641.50097694399361
312224.2472893127429-2.24728931274288
322221.984578947510.0154210524899777
332118.07478155115922.92521844884082
341517.8873174049384-2.88731740493839
352016.13176570290113.86823429709889
362221.79063027750420.209369722495846
371816.71812842734031.2818715726597
382020.9504139478618-0.95041394786178
392827.74624259744260.253757402557416
402224.2279208549637-2.22792085496367
411821.8624959826663-3.86249598266632
422320.76508101472712.23491898527287
432021.8107964753326-1.81079647533256
442524.7058853088760.294114691123983
452622.28527156092373.71472843907628
461514.59015235572080.409847644279156
471717.9090583052307-0.909058305230681
482315.5264977140147.47350228598601
492122.117971840524-1.11797184052402
501316.5020222159123-3.50202221591233
511820.2906559854044-2.29065598540437
521919.3361886612965-0.336188661296469
532222.099711772415-0.0997117724149705
541617.4364639242578-1.43646392425784
552423.15302341443140.846976585568643
561819.8930911267957-1.89309112679567
572021.392884450198-1.39288445019802
582420.72757988553273.27242011446728
591418.3331201413683-4.33312014136828
602220.39530435357681.60469564642322
612418.11486766205355.88513233794653
621818.5566237528528-0.556623752852799
632124.5424456199538-3.54244561995375
642321.63980288700781.36019711299224
651718.514982738115-1.51498273811498
662222.6746838031987-0.674683803198681
672424.9906037143916-0.9906037143916
682122.951357311709-1.95135731170902
692222.1099664833382-0.109966483338223
701617.0921721782687-1.09217217826872
712125.1870773706209-4.18707737062087
722323.9609814107013-0.960981410701325
732219.43704214278662.56295785721335
742422.61810609152091.38189390847912
752424.7963889610029-0.796388961002854
761617.5658509064043-1.56585090640434
771617.5412864411158-1.54128644111582
782122.9917478525407-1.99174785254066
792626.075007635646-0.0750076356459574
801517.2647149784936-2.26471497849359
812523.21543985610421.78456014389577
821818.0763499815331-0.0763499815330774
832320.91557867237242.08442132762762
842021.3102231214037-1.31022312140375
851720.8159383742818-3.81593837428181
862525.0076432094455-0.0076432094455384
872422.58208119726451.41791880273555
881714.74694095710382.25305904289622
891918.74468080444360.255319195556355
902020.3370565113986-0.337056511398631
911516.4728771086494-1.47287710864938
922724.63045155434692.36954844565308
932219.58053342086212.41946657913791
942322.82136433209060.178635667909393
951620.7842236751536-4.78422367515356
961921.0580745803358-2.05807458033584
972520.50596104906944.49403895093064
981919.6683181605599-0.668318160559892
991919.4221469296561-0.422146929656149
1002625.36913638548510.630863614514879
1012118.91102821879952.08897178120054
1022019.87124314854420.128756851455753
1032420.08229352918173.91770647081827
1042224.0304771531221-2.03047715312212
1052021.1706878827417-1.17068788274165
1061819.2010472843915-1.20104728439155
1071816.96331902356621.03668097643382
1082421.22584169997962.77415830002044
1092421.23636158956682.76363841043317
1102223.3587953322543-1.3587953322543
1112319.71825766391223.28174233608777
1122219.82301543130732.17698456869273
1132018.11974883820991.88025116179013
1141819.5938962135371-1.59389621353712
1152524.53641268919560.463587310804407
1161818.4112651229995-0.411265122999495
1171617.6218932575587-1.62189325755868
1182019.89196654149470.108033458505253
1191918.5826744245120.417325575487969
1201515.7293438211624-0.72934382116237
1211918.95251846598520.0474815340148106
1221921.9624163796693-2.96241637966928
1231617.5152742228257-1.51527422282574
1241717.9453317018098-0.94533170180981
1252823.437387539454.56261246054999
1262323.0210954300392-0.021095430039197
1272523.76501422780091.23498577219908
1282018.8217356475741.17826435242602
1291720.1710023813274-3.17100238132736
1302322.69824258739420.301757412605798
1311620.1017207063637-4.1017207063637
1322323.3686276602389-0.36862766023892
1331116.1496031054725-5.14960310547254
1341820.1653012394308-2.16530123943083
1352423.16733867449420.83266132550575
1362318.79515389347644.20484610652364
1372120.6665826423720.333417357627966
1381619.4792511445852-3.47925114458521
1392424.7981959281853-0.798195928185327
1402321.67881527723531.32118472276474
1411815.91494579532642.08505420467359
1422021.0080851618774-1.00808516187736
143918.5509898401909-9.55098984019092
1442420.41631236247173.58368763752828
1452524.81864981437190.181350185628143
1462018.3924527912381.60754720876196
1472119.08655751700621.91344248299376
1482522.8378298419822.16217015801795
1492222.4741894000665-0.474189400066471
1502120.80630742219050.193692577809515
1512120.27723476495550.722765235044472
1522220.09357211845941.90642788154056
1532723.47813782450283.52186217549715
1542421.65510965481732.34489034518274
1552423.78429817426440.215701825735566
1562119.98314208138651.0168579186135
1571820.5736009334967-2.57360093349674
1581616.1568623769287-0.156862376928744
1592219.37981441815652.62018558184351
1602020.1690576891241-0.169057689124054
1611818.9292829195374-0.929282919537379
1622020.9084917484526-0.908491748452616

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.2767962202375 & 1.72320377976249 \tabularnewline
2 & 20 & 21.0711348951051 & -1.07113489510508 \tabularnewline
3 & 19 & 21.8712108077791 & -2.87121080777913 \tabularnewline
4 & 19 & 18.4797989103931 & 0.520201089606911 \tabularnewline
5 & 20 & 18.2597947784206 & 1.74020522157945 \tabularnewline
6 & 25 & 24.1988107097646 & 0.801189290235379 \tabularnewline
7 & 25 & 19.5474208838384 & 5.45257911616156 \tabularnewline
8 & 22 & 20.2488194979261 & 1.75118050207394 \tabularnewline
9 & 26 & 22.5231132352258 & 3.47688676477423 \tabularnewline
10 & 22 & 20.5641412410874 & 1.43585875891262 \tabularnewline
11 & 17 & 20.2351715770842 & -3.23517157708423 \tabularnewline
12 & 22 & 23.0678389005628 & -1.06783890056281 \tabularnewline
13 & 19 & 20.9337920919268 & -1.93379209192681 \tabularnewline
14 & 24 & 22.6272057763596 & 1.37279422364045 \tabularnewline
15 & 26 & 26.9064193442974 & -0.906419344297432 \tabularnewline
16 & 21 & 19.0479401292072 & 1.95205987079281 \tabularnewline
17 & 13 & 16.3869980440194 & -3.38699804401936 \tabularnewline
18 & 26 & 27.3998688131814 & -1.39986881318143 \tabularnewline
19 & 20 & 22.7339942860055 & -2.73399428600548 \tabularnewline
20 & 22 & 18.9075786992027 & 3.09242130079725 \tabularnewline
21 & 14 & 18.1556255702781 & -4.15562557027815 \tabularnewline
22 & 21 & 20.6099874474188 & 0.39001255258123 \tabularnewline
23 & 7 & 14.1518472062686 & -7.15184720626862 \tabularnewline
24 & 23 & 21.7546760201962 & 1.24532397980383 \tabularnewline
25 & 17 & 19.2181282032228 & -2.21812820322281 \tabularnewline
26 & 25 & 23.1603249259226 & 1.83967507407739 \tabularnewline
27 & 25 & 23.1603249259226 & 1.83967507407739 \tabularnewline
28 & 19 & 16.2891608059368 & 2.71083919406321 \tabularnewline
29 & 20 & 16.6948952877814 & 3.30510471221864 \tabularnewline
30 & 23 & 21.4990230560064 & 1.50097694399361 \tabularnewline
31 & 22 & 24.2472893127429 & -2.24728931274288 \tabularnewline
32 & 22 & 21.98457894751 & 0.0154210524899777 \tabularnewline
33 & 21 & 18.0747815511592 & 2.92521844884082 \tabularnewline
34 & 15 & 17.8873174049384 & -2.88731740493839 \tabularnewline
35 & 20 & 16.1317657029011 & 3.86823429709889 \tabularnewline
36 & 22 & 21.7906302775042 & 0.209369722495846 \tabularnewline
37 & 18 & 16.7181284273403 & 1.2818715726597 \tabularnewline
38 & 20 & 20.9504139478618 & -0.95041394786178 \tabularnewline
39 & 28 & 27.7462425974426 & 0.253757402557416 \tabularnewline
40 & 22 & 24.2279208549637 & -2.22792085496367 \tabularnewline
41 & 18 & 21.8624959826663 & -3.86249598266632 \tabularnewline
42 & 23 & 20.7650810147271 & 2.23491898527287 \tabularnewline
43 & 20 & 21.8107964753326 & -1.81079647533256 \tabularnewline
44 & 25 & 24.705885308876 & 0.294114691123983 \tabularnewline
45 & 26 & 22.2852715609237 & 3.71472843907628 \tabularnewline
46 & 15 & 14.5901523557208 & 0.409847644279156 \tabularnewline
47 & 17 & 17.9090583052307 & -0.909058305230681 \tabularnewline
48 & 23 & 15.526497714014 & 7.47350228598601 \tabularnewline
49 & 21 & 22.117971840524 & -1.11797184052402 \tabularnewline
50 & 13 & 16.5020222159123 & -3.50202221591233 \tabularnewline
51 & 18 & 20.2906559854044 & -2.29065598540437 \tabularnewline
52 & 19 & 19.3361886612965 & -0.336188661296469 \tabularnewline
53 & 22 & 22.099711772415 & -0.0997117724149705 \tabularnewline
54 & 16 & 17.4364639242578 & -1.43646392425784 \tabularnewline
55 & 24 & 23.1530234144314 & 0.846976585568643 \tabularnewline
56 & 18 & 19.8930911267957 & -1.89309112679567 \tabularnewline
57 & 20 & 21.392884450198 & -1.39288445019802 \tabularnewline
58 & 24 & 20.7275798855327 & 3.27242011446728 \tabularnewline
59 & 14 & 18.3331201413683 & -4.33312014136828 \tabularnewline
60 & 22 & 20.3953043535768 & 1.60469564642322 \tabularnewline
61 & 24 & 18.1148676620535 & 5.88513233794653 \tabularnewline
62 & 18 & 18.5566237528528 & -0.556623752852799 \tabularnewline
63 & 21 & 24.5424456199538 & -3.54244561995375 \tabularnewline
64 & 23 & 21.6398028870078 & 1.36019711299224 \tabularnewline
65 & 17 & 18.514982738115 & -1.51498273811498 \tabularnewline
66 & 22 & 22.6746838031987 & -0.674683803198681 \tabularnewline
67 & 24 & 24.9906037143916 & -0.9906037143916 \tabularnewline
68 & 21 & 22.951357311709 & -1.95135731170902 \tabularnewline
69 & 22 & 22.1099664833382 & -0.109966483338223 \tabularnewline
70 & 16 & 17.0921721782687 & -1.09217217826872 \tabularnewline
71 & 21 & 25.1870773706209 & -4.18707737062087 \tabularnewline
72 & 23 & 23.9609814107013 & -0.960981410701325 \tabularnewline
73 & 22 & 19.4370421427866 & 2.56295785721335 \tabularnewline
74 & 24 & 22.6181060915209 & 1.38189390847912 \tabularnewline
75 & 24 & 24.7963889610029 & -0.796388961002854 \tabularnewline
76 & 16 & 17.5658509064043 & -1.56585090640434 \tabularnewline
77 & 16 & 17.5412864411158 & -1.54128644111582 \tabularnewline
78 & 21 & 22.9917478525407 & -1.99174785254066 \tabularnewline
79 & 26 & 26.075007635646 & -0.0750076356459574 \tabularnewline
80 & 15 & 17.2647149784936 & -2.26471497849359 \tabularnewline
81 & 25 & 23.2154398561042 & 1.78456014389577 \tabularnewline
82 & 18 & 18.0763499815331 & -0.0763499815330774 \tabularnewline
83 & 23 & 20.9155786723724 & 2.08442132762762 \tabularnewline
84 & 20 & 21.3102231214037 & -1.31022312140375 \tabularnewline
85 & 17 & 20.8159383742818 & -3.81593837428181 \tabularnewline
86 & 25 & 25.0076432094455 & -0.0076432094455384 \tabularnewline
87 & 24 & 22.5820811972645 & 1.41791880273555 \tabularnewline
88 & 17 & 14.7469409571038 & 2.25305904289622 \tabularnewline
89 & 19 & 18.7446808044436 & 0.255319195556355 \tabularnewline
90 & 20 & 20.3370565113986 & -0.337056511398631 \tabularnewline
91 & 15 & 16.4728771086494 & -1.47287710864938 \tabularnewline
92 & 27 & 24.6304515543469 & 2.36954844565308 \tabularnewline
93 & 22 & 19.5805334208621 & 2.41946657913791 \tabularnewline
94 & 23 & 22.8213643320906 & 0.178635667909393 \tabularnewline
95 & 16 & 20.7842236751536 & -4.78422367515356 \tabularnewline
96 & 19 & 21.0580745803358 & -2.05807458033584 \tabularnewline
97 & 25 & 20.5059610490694 & 4.49403895093064 \tabularnewline
98 & 19 & 19.6683181605599 & -0.668318160559892 \tabularnewline
99 & 19 & 19.4221469296561 & -0.422146929656149 \tabularnewline
100 & 26 & 25.3691363854851 & 0.630863614514879 \tabularnewline
101 & 21 & 18.9110282187995 & 2.08897178120054 \tabularnewline
102 & 20 & 19.8712431485442 & 0.128756851455753 \tabularnewline
103 & 24 & 20.0822935291817 & 3.91770647081827 \tabularnewline
104 & 22 & 24.0304771531221 & -2.03047715312212 \tabularnewline
105 & 20 & 21.1706878827417 & -1.17068788274165 \tabularnewline
106 & 18 & 19.2010472843915 & -1.20104728439155 \tabularnewline
107 & 18 & 16.9633190235662 & 1.03668097643382 \tabularnewline
108 & 24 & 21.2258416999796 & 2.77415830002044 \tabularnewline
109 & 24 & 21.2363615895668 & 2.76363841043317 \tabularnewline
110 & 22 & 23.3587953322543 & -1.3587953322543 \tabularnewline
111 & 23 & 19.7182576639122 & 3.28174233608777 \tabularnewline
112 & 22 & 19.8230154313073 & 2.17698456869273 \tabularnewline
113 & 20 & 18.1197488382099 & 1.88025116179013 \tabularnewline
114 & 18 & 19.5938962135371 & -1.59389621353712 \tabularnewline
115 & 25 & 24.5364126891956 & 0.463587310804407 \tabularnewline
116 & 18 & 18.4112651229995 & -0.411265122999495 \tabularnewline
117 & 16 & 17.6218932575587 & -1.62189325755868 \tabularnewline
118 & 20 & 19.8919665414947 & 0.108033458505253 \tabularnewline
119 & 19 & 18.582674424512 & 0.417325575487969 \tabularnewline
120 & 15 & 15.7293438211624 & -0.72934382116237 \tabularnewline
121 & 19 & 18.9525184659852 & 0.0474815340148106 \tabularnewline
122 & 19 & 21.9624163796693 & -2.96241637966928 \tabularnewline
123 & 16 & 17.5152742228257 & -1.51527422282574 \tabularnewline
124 & 17 & 17.9453317018098 & -0.94533170180981 \tabularnewline
125 & 28 & 23.43738753945 & 4.56261246054999 \tabularnewline
126 & 23 & 23.0210954300392 & -0.021095430039197 \tabularnewline
127 & 25 & 23.7650142278009 & 1.23498577219908 \tabularnewline
128 & 20 & 18.821735647574 & 1.17826435242602 \tabularnewline
129 & 17 & 20.1710023813274 & -3.17100238132736 \tabularnewline
130 & 23 & 22.6982425873942 & 0.301757412605798 \tabularnewline
131 & 16 & 20.1017207063637 & -4.1017207063637 \tabularnewline
132 & 23 & 23.3686276602389 & -0.36862766023892 \tabularnewline
133 & 11 & 16.1496031054725 & -5.14960310547254 \tabularnewline
134 & 18 & 20.1653012394308 & -2.16530123943083 \tabularnewline
135 & 24 & 23.1673386744942 & 0.83266132550575 \tabularnewline
136 & 23 & 18.7951538934764 & 4.20484610652364 \tabularnewline
137 & 21 & 20.666582642372 & 0.333417357627966 \tabularnewline
138 & 16 & 19.4792511445852 & -3.47925114458521 \tabularnewline
139 & 24 & 24.7981959281853 & -0.798195928185327 \tabularnewline
140 & 23 & 21.6788152772353 & 1.32118472276474 \tabularnewline
141 & 18 & 15.9149457953264 & 2.08505420467359 \tabularnewline
142 & 20 & 21.0080851618774 & -1.00808516187736 \tabularnewline
143 & 9 & 18.5509898401909 & -9.55098984019092 \tabularnewline
144 & 24 & 20.4163123624717 & 3.58368763752828 \tabularnewline
145 & 25 & 24.8186498143719 & 0.181350185628143 \tabularnewline
146 & 20 & 18.392452791238 & 1.60754720876196 \tabularnewline
147 & 21 & 19.0865575170062 & 1.91344248299376 \tabularnewline
148 & 25 & 22.837829841982 & 2.16217015801795 \tabularnewline
149 & 22 & 22.4741894000665 & -0.474189400066471 \tabularnewline
150 & 21 & 20.8063074221905 & 0.193692577809515 \tabularnewline
151 & 21 & 20.2772347649555 & 0.722765235044472 \tabularnewline
152 & 22 & 20.0935721184594 & 1.90642788154056 \tabularnewline
153 & 27 & 23.4781378245028 & 3.52186217549715 \tabularnewline
154 & 24 & 21.6551096548173 & 2.34489034518274 \tabularnewline
155 & 24 & 23.7842981742644 & 0.215701825735566 \tabularnewline
156 & 21 & 19.9831420813865 & 1.0168579186135 \tabularnewline
157 & 18 & 20.5736009334967 & -2.57360093349674 \tabularnewline
158 & 16 & 16.1568623769287 & -0.156862376928744 \tabularnewline
159 & 22 & 19.3798144181565 & 2.62018558184351 \tabularnewline
160 & 20 & 20.1690576891241 & -0.169057689124054 \tabularnewline
161 & 18 & 18.9292829195374 & -0.929282919537379 \tabularnewline
162 & 20 & 20.9084917484526 & -0.908491748452616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&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]26[/C][C]24.2767962202375[/C][C]1.72320377976249[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]21.0711348951051[/C][C]-1.07113489510508[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]21.8712108077791[/C][C]-2.87121080777913[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.4797989103931[/C][C]0.520201089606911[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]18.2597947784206[/C][C]1.74020522157945[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.1988107097646[/C][C]0.801189290235379[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]19.5474208838384[/C][C]5.45257911616156[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.2488194979261[/C][C]1.75118050207394[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.5231132352258[/C][C]3.47688676477423[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.5641412410874[/C][C]1.43585875891262[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]20.2351715770842[/C][C]-3.23517157708423[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]23.0678389005628[/C][C]-1.06783890056281[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]20.9337920919268[/C][C]-1.93379209192681[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.6272057763596[/C][C]1.37279422364045[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.9064193442974[/C][C]-0.906419344297432[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]19.0479401292072[/C][C]1.95205987079281[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.3869980440194[/C][C]-3.38699804401936[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.3998688131814[/C][C]-1.39986881318143[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.7339942860055[/C][C]-2.73399428600548[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]18.9075786992027[/C][C]3.09242130079725[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]18.1556255702781[/C][C]-4.15562557027815[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.6099874474188[/C][C]0.39001255258123[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]14.1518472062686[/C][C]-7.15184720626862[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.7546760201962[/C][C]1.24532397980383[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]19.2181282032228[/C][C]-2.21812820322281[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]23.1603249259226[/C][C]1.83967507407739[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.1603249259226[/C][C]1.83967507407739[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]16.2891608059368[/C][C]2.71083919406321[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.6948952877814[/C][C]3.30510471221864[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.4990230560064[/C][C]1.50097694399361[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.2472893127429[/C][C]-2.24728931274288[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]21.98457894751[/C][C]0.0154210524899777[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]18.0747815511592[/C][C]2.92521844884082[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.8873174049384[/C][C]-2.88731740493839[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]16.1317657029011[/C][C]3.86823429709889[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.7906302775042[/C][C]0.209369722495846[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]16.7181284273403[/C][C]1.2818715726597[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]20.9504139478618[/C][C]-0.95041394786178[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.7462425974426[/C][C]0.253757402557416[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]24.2279208549637[/C][C]-2.22792085496367[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.8624959826663[/C][C]-3.86249598266632[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.7650810147271[/C][C]2.23491898527287[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.8107964753326[/C][C]-1.81079647533256[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.705885308876[/C][C]0.294114691123983[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.2852715609237[/C][C]3.71472843907628[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.5901523557208[/C][C]0.409847644279156[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]17.9090583052307[/C][C]-0.909058305230681[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.526497714014[/C][C]7.47350228598601[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]22.117971840524[/C][C]-1.11797184052402[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.5020222159123[/C][C]-3.50202221591233[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.2906559854044[/C][C]-2.29065598540437[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.3361886612965[/C][C]-0.336188661296469[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]22.099711772415[/C][C]-0.0997117724149705[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.4364639242578[/C][C]-1.43646392425784[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.1530234144314[/C][C]0.846976585568643[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]19.8930911267957[/C][C]-1.89309112679567[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.392884450198[/C][C]-1.39288445019802[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.7275798855327[/C][C]3.27242011446728[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.3331201413683[/C][C]-4.33312014136828[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]20.3953043535768[/C][C]1.60469564642322[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]18.1148676620535[/C][C]5.88513233794653[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.5566237528528[/C][C]-0.556623752852799[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.5424456199538[/C][C]-3.54244561995375[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]21.6398028870078[/C][C]1.36019711299224[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]18.514982738115[/C][C]-1.51498273811498[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]22.6746838031987[/C][C]-0.674683803198681[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]24.9906037143916[/C][C]-0.9906037143916[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.951357311709[/C][C]-1.95135731170902[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]22.1099664833382[/C][C]-0.109966483338223[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]17.0921721782687[/C][C]-1.09217217826872[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]25.1870773706209[/C][C]-4.18707737062087[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]23.9609814107013[/C][C]-0.960981410701325[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.4370421427866[/C][C]2.56295785721335[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.6181060915209[/C][C]1.38189390847912[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.7963889610029[/C][C]-0.796388961002854[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.5658509064043[/C][C]-1.56585090640434[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.5412864411158[/C][C]-1.54128644111582[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.9917478525407[/C][C]-1.99174785254066[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]26.075007635646[/C][C]-0.0750076356459574[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]17.2647149784936[/C][C]-2.26471497849359[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.2154398561042[/C][C]1.78456014389577[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]18.0763499815331[/C][C]-0.0763499815330774[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.9155786723724[/C][C]2.08442132762762[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.3102231214037[/C][C]-1.31022312140375[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.8159383742818[/C][C]-3.81593837428181[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]25.0076432094455[/C][C]-0.0076432094455384[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.5820811972645[/C][C]1.41791880273555[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.7469409571038[/C][C]2.25305904289622[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.7446808044436[/C][C]0.255319195556355[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.3370565113986[/C][C]-0.337056511398631[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.4728771086494[/C][C]-1.47287710864938[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.6304515543469[/C][C]2.36954844565308[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.5805334208621[/C][C]2.41946657913791[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.8213643320906[/C][C]0.178635667909393[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.7842236751536[/C][C]-4.78422367515356[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]21.0580745803358[/C][C]-2.05807458033584[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5059610490694[/C][C]4.49403895093064[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.6683181605599[/C][C]-0.668318160559892[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.4221469296561[/C][C]-0.422146929656149[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]25.3691363854851[/C][C]0.630863614514879[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]18.9110282187995[/C][C]2.08897178120054[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]19.8712431485442[/C][C]0.128756851455753[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]20.0822935291817[/C][C]3.91770647081827[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]24.0304771531221[/C][C]-2.03047715312212[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.1706878827417[/C][C]-1.17068788274165[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.2010472843915[/C][C]-1.20104728439155[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]16.9633190235662[/C][C]1.03668097643382[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.2258416999796[/C][C]2.77415830002044[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.2363615895668[/C][C]2.76363841043317[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]23.3587953322543[/C][C]-1.3587953322543[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.7182576639122[/C][C]3.28174233608777[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]19.8230154313073[/C][C]2.17698456869273[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]18.1197488382099[/C][C]1.88025116179013[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.5938962135371[/C][C]-1.59389621353712[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.5364126891956[/C][C]0.463587310804407[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.4112651229995[/C][C]-0.411265122999495[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.6218932575587[/C][C]-1.62189325755868[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.8919665414947[/C][C]0.108033458505253[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.582674424512[/C][C]0.417325575487969[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.7293438211624[/C][C]-0.72934382116237[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]18.9525184659852[/C][C]0.0474815340148106[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]21.9624163796693[/C][C]-2.96241637966928[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.5152742228257[/C][C]-1.51527422282574[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]17.9453317018098[/C][C]-0.94533170180981[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.43738753945[/C][C]4.56261246054999[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]23.0210954300392[/C][C]-0.021095430039197[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.7650142278009[/C][C]1.23498577219908[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]18.821735647574[/C][C]1.17826435242602[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.1710023813274[/C][C]-3.17100238132736[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.6982425873942[/C][C]0.301757412605798[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]20.1017207063637[/C][C]-4.1017207063637[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]23.3686276602389[/C][C]-0.36862766023892[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]16.1496031054725[/C][C]-5.14960310547254[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.1653012394308[/C][C]-2.16530123943083[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.1673386744942[/C][C]0.83266132550575[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]18.7951538934764[/C][C]4.20484610652364[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]20.666582642372[/C][C]0.333417357627966[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.4792511445852[/C][C]-3.47925114458521[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]24.7981959281853[/C][C]-0.798195928185327[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.6788152772353[/C][C]1.32118472276474[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]15.9149457953264[/C][C]2.08505420467359[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]21.0080851618774[/C][C]-1.00808516187736[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.5509898401909[/C][C]-9.55098984019092[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.4163123624717[/C][C]3.58368763752828[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.8186498143719[/C][C]0.181350185628143[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.392452791238[/C][C]1.60754720876196[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]19.0865575170062[/C][C]1.91344248299376[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]22.837829841982[/C][C]2.16217015801795[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.4741894000665[/C][C]-0.474189400066471[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]20.8063074221905[/C][C]0.193692577809515[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.2772347649555[/C][C]0.722765235044472[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.0935721184594[/C][C]1.90642788154056[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.4781378245028[/C][C]3.52186217549715[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.6551096548173[/C][C]2.34489034518274[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]23.7842981742644[/C][C]0.215701825735566[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]19.9831420813865[/C][C]1.0168579186135[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.5736009334967[/C][C]-2.57360093349674[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.1568623769287[/C][C]-0.156862376928744[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.3798144181565[/C][C]2.62018558184351[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]20.1690576891241[/C][C]-0.169057689124054[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]18.9292829195374[/C][C]-0.929282919537379[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]20.9084917484526[/C][C]-0.908491748452616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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
12624.27679622023751.72320377976249
22021.0711348951051-1.07113489510508
31921.8712108077791-2.87121080777913
41918.47979891039310.520201089606911
52018.25979477842061.74020522157945
62524.19881070976460.801189290235379
72519.54742088383845.45257911616156
82220.24881949792611.75118050207394
92622.52311323522583.47688676477423
102220.56414124108741.43585875891262
111720.2351715770842-3.23517157708423
122223.0678389005628-1.06783890056281
131920.9337920919268-1.93379209192681
142422.62720577635961.37279422364045
152626.9064193442974-0.906419344297432
162119.04794012920721.95205987079281
171316.3869980440194-3.38699804401936
182627.3998688131814-1.39986881318143
192022.7339942860055-2.73399428600548
202218.90757869920273.09242130079725
211418.1556255702781-4.15562557027815
222120.60998744741880.39001255258123
23714.1518472062686-7.15184720626862
242321.75467602019621.24532397980383
251719.2181282032228-2.21812820322281
262523.16032492592261.83967507407739
272523.16032492592261.83967507407739
281916.28916080593682.71083919406321
292016.69489528778143.30510471221864
302321.49902305600641.50097694399361
312224.2472893127429-2.24728931274288
322221.984578947510.0154210524899777
332118.07478155115922.92521844884082
341517.8873174049384-2.88731740493839
352016.13176570290113.86823429709889
362221.79063027750420.209369722495846
371816.71812842734031.2818715726597
382020.9504139478618-0.95041394786178
392827.74624259744260.253757402557416
402224.2279208549637-2.22792085496367
411821.8624959826663-3.86249598266632
422320.76508101472712.23491898527287
432021.8107964753326-1.81079647533256
442524.7058853088760.294114691123983
452622.28527156092373.71472843907628
461514.59015235572080.409847644279156
471717.9090583052307-0.909058305230681
482315.5264977140147.47350228598601
492122.117971840524-1.11797184052402
501316.5020222159123-3.50202221591233
511820.2906559854044-2.29065598540437
521919.3361886612965-0.336188661296469
532222.099711772415-0.0997117724149705
541617.4364639242578-1.43646392425784
552423.15302341443140.846976585568643
561819.8930911267957-1.89309112679567
572021.392884450198-1.39288445019802
582420.72757988553273.27242011446728
591418.3331201413683-4.33312014136828
602220.39530435357681.60469564642322
612418.11486766205355.88513233794653
621818.5566237528528-0.556623752852799
632124.5424456199538-3.54244561995375
642321.63980288700781.36019711299224
651718.514982738115-1.51498273811498
662222.6746838031987-0.674683803198681
672424.9906037143916-0.9906037143916
682122.951357311709-1.95135731170902
692222.1099664833382-0.109966483338223
701617.0921721782687-1.09217217826872
712125.1870773706209-4.18707737062087
722323.9609814107013-0.960981410701325
732219.43704214278662.56295785721335
742422.61810609152091.38189390847912
752424.7963889610029-0.796388961002854
761617.5658509064043-1.56585090640434
771617.5412864411158-1.54128644111582
782122.9917478525407-1.99174785254066
792626.075007635646-0.0750076356459574
801517.2647149784936-2.26471497849359
812523.21543985610421.78456014389577
821818.0763499815331-0.0763499815330774
832320.91557867237242.08442132762762
842021.3102231214037-1.31022312140375
851720.8159383742818-3.81593837428181
862525.0076432094455-0.0076432094455384
872422.58208119726451.41791880273555
881714.74694095710382.25305904289622
891918.74468080444360.255319195556355
902020.3370565113986-0.337056511398631
911516.4728771086494-1.47287710864938
922724.63045155434692.36954844565308
932219.58053342086212.41946657913791
942322.82136433209060.178635667909393
951620.7842236751536-4.78422367515356
961921.0580745803358-2.05807458033584
972520.50596104906944.49403895093064
981919.6683181605599-0.668318160559892
991919.4221469296561-0.422146929656149
1002625.36913638548510.630863614514879
1012118.91102821879952.08897178120054
1022019.87124314854420.128756851455753
1032420.08229352918173.91770647081827
1042224.0304771531221-2.03047715312212
1052021.1706878827417-1.17068788274165
1061819.2010472843915-1.20104728439155
1071816.96331902356621.03668097643382
1082421.22584169997962.77415830002044
1092421.23636158956682.76363841043317
1102223.3587953322543-1.3587953322543
1112319.71825766391223.28174233608777
1122219.82301543130732.17698456869273
1132018.11974883820991.88025116179013
1141819.5938962135371-1.59389621353712
1152524.53641268919560.463587310804407
1161818.4112651229995-0.411265122999495
1171617.6218932575587-1.62189325755868
1182019.89196654149470.108033458505253
1191918.5826744245120.417325575487969
1201515.7293438211624-0.72934382116237
1211918.95251846598520.0474815340148106
1221921.9624163796693-2.96241637966928
1231617.5152742228257-1.51527422282574
1241717.9453317018098-0.94533170180981
1252823.437387539454.56261246054999
1262323.0210954300392-0.021095430039197
1272523.76501422780091.23498577219908
1282018.8217356475741.17826435242602
1291720.1710023813274-3.17100238132736
1302322.69824258739420.301757412605798
1311620.1017207063637-4.1017207063637
1322323.3686276602389-0.36862766023892
1331116.1496031054725-5.14960310547254
1341820.1653012394308-2.16530123943083
1352423.16733867449420.83266132550575
1362318.79515389347644.20484610652364
1372120.6665826423720.333417357627966
1381619.4792511445852-3.47925114458521
1392424.7981959281853-0.798195928185327
1402321.67881527723531.32118472276474
1411815.91494579532642.08505420467359
1422021.0080851618774-1.00808516187736
143918.5509898401909-9.55098984019092
1442420.41631236247173.58368763752828
1452524.81864981437190.181350185628143
1462018.3924527912381.60754720876196
1472119.08655751700621.91344248299376
1482522.8378298419822.16217015801795
1492222.4741894000665-0.474189400066471
1502120.80630742219050.193692577809515
1512120.27723476495550.722765235044472
1522220.09357211845941.90642788154056
1532723.47813782450283.52186217549715
1542421.65510965481732.34489034518274
1552423.78429817426440.215701825735566
1562119.98314208138651.0168579186135
1571820.5736009334967-2.57360093349674
1581616.1568623769287-0.156862376928744
1592219.37981441815652.62018558184351
1602020.1690576891241-0.169057689124054
1611818.9292829195374-0.929282919537379
1622020.9084917484526-0.908491748452616






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7487059802092650.5025880395814710.251294019790735
120.8761700865169110.2476598269661780.123829913483089
130.8942897762488580.2114204475022850.105710223751143
140.8621302609799150.275739478040170.137869739020085
150.8016275127179270.3967449745641470.198372487282073
160.7265441628504650.5469116742990710.273455837149536
170.6545027437810030.6909945124379930.345497256218997
180.5695588895153810.8608822209692380.430441110484619
190.6592227981371360.6815544037257280.340777201862864
200.6480345602310560.7039308795378870.351965439768944
210.6547979968270640.6904040063458720.345202003172936
220.5772907532897340.8454184934205320.422709246710266
230.7096845420771310.5806309158457380.290315457922869
240.6470833949139680.7058332101720640.352916605086032
250.6060827938676740.7878344122646520.393917206132326
260.5885972121339010.8228055757321980.411402787866099
270.5425508522746110.9148982954507780.457449147725389
280.7734539867394410.4530920265211190.226546013260559
290.89739792305780.20520415388440.1026020769422
300.8826013664253120.2347972671493760.117398633574688
310.8750646499132470.2498707001735060.124935350086753
320.8404547509123840.3190904981752320.159545249087616
330.8370777739683890.3258444520632230.162922226031611
340.890030764359980.2199384712800390.10996923564002
350.931419663802780.1371606723944410.0685803361972204
360.9118490111011960.1763019777976080.0881509888988038
370.8949076603606880.2101846792786230.105092339639312
380.868665681084970.2626686378300610.13133431891503
390.8366053661494610.3267892677010780.163394633850539
400.8282633110110210.3434733779779580.171736688988979
410.8709533198871020.2580933602257960.129046680112898
420.8626164901720870.2747670196558250.137383509827913
430.8521575264243140.2956849471513710.147842473575686
440.8191175891761770.3617648216476460.180882410823823
450.8455306797891970.3089386404216070.154469320210803
460.8140026644816060.3719946710367880.185997335518394
470.780790117732160.438419764535680.21920988226784
480.9401314857775370.1197370284449260.0598685142224632
490.9248784530020650.150243093995870.0751215469979351
500.945761879502840.108476240994320.0542381204971598
510.9415586346896480.1168827306207040.058441365310352
520.9267074457636610.1465851084726780.073292554236339
530.9076886607935750.1846226784128490.0923113392064245
540.8919523175836240.2160953648327510.108047682416376
550.8681407687755550.2637184624488890.131859231224445
560.857655564595810.284688870808380.14234443540419
570.8330705750952670.3338588498094660.166929424904733
580.8615906666154110.2768186667691780.138409333384589
590.9045976375312870.1908047249374260.0954023624687132
600.8931569625923190.2136860748153630.106843037407681
610.9638660891751990.07226782164960270.0361339108248013
620.9544783416636830.09104331667263430.0455216583363172
630.9630800555990290.07383988880194290.0369199444009715
640.9562647623814770.08747047523704660.0437352376185233
650.9501557719841380.09968845603172350.0498442280158617
660.9376852711967710.1246294576064580.062314728803229
670.9229127420059290.1541745159881420.077087257994071
680.913392539050750.17321492189850.0866074609492499
690.8935191113928280.2129617772143430.106480888607172
700.8737062602669880.2525874794660230.126293739733012
710.9077864982777060.1844270034445870.0922135017222935
720.8907231759013560.2185536481972880.109276824098644
730.895755225599080.2084895488018410.10424477440092
740.8822897542341940.2354204915316110.117710245765806
750.8592939351434350.281412129713130.140706064856565
760.8405403248710150.3189193502579690.159459675128985
770.8189677107558850.362064578488230.181032289244115
780.8062200934186950.387559813162610.193779906581305
790.7781245117145060.4437509765709880.221875488285494
800.7690904024603450.461819195079310.230909597539655
810.75239348170050.4952130365990010.2476065182995
820.7196493089423050.560701382115390.280350691057695
830.711738496428540.5765230071429210.28826150357146
840.6853523460496440.6292953079007110.314647653950356
850.7348997834792210.5302004330415570.265100216520779
860.6949902173917290.6100195652165430.305009782608271
870.6689131711665530.6621736576668940.331086828833447
880.6768282192778820.6463435614442360.323171780722118
890.6424087341106820.7151825317786350.357591265889318
900.5990037630740070.8019924738519860.400996236925993
910.5625342553273770.8749314893452460.437465744672623
920.5525608385495290.8948783229009410.447439161450471
930.5469615060238850.9060769879522290.453038493976115
940.5089878214912590.9820243570174810.491012178508741
950.6393848096852120.7212303806295770.360615190314788
960.6385433277726650.7229133444546710.361456672227336
970.7198765572907040.5602468854185910.280123442709296
980.683928174332120.6321436513357610.31607182566788
990.6484855481903560.7030289036192890.351514451809644
1000.6047365471567690.7905269056864620.395263452843231
1010.5798526058511890.8402947882976220.420147394148811
1020.5325375029359390.9349249941281220.467462497064061
1030.608999497992280.782001004015440.39100050200772
1040.6047356885331720.7905286229336570.395264311466828
1050.5830650343360210.8338699313279580.416934965663979
1060.5602089358688430.8795821282623150.439791064131157
1070.5145276870274910.9709446259450180.485472312972509
1080.4870957688298570.9741915376597150.512904231170143
1090.4732284575137520.9464569150275030.526771542486248
1100.4415232552479940.8830465104959870.558476744752006
1110.4665016966198380.9330033932396750.533498303380162
1120.456647621456060.9132952429121190.54335237854394
1130.449682369597280.899364739194560.55031763040272
1140.41408725654530.8281745130906010.5859127434547
1150.3657161389212620.7314322778425240.634283861078738
1160.3202803425929970.6405606851859930.679719657407003
1170.2839816107367340.5679632214734690.716018389263266
1180.2402544889708160.4805089779416320.759745511029184
1190.2091531539309910.4183063078619820.790846846069009
1200.1788630758161210.3577261516322420.821136924183879
1210.1452805129250890.2905610258501790.854719487074911
1220.1502267131110840.3004534262221670.849773286888916
1230.1236610870969670.2473221741939330.876338912903033
1240.09820082265346520.196401645306930.901799177346535
1250.1870541585035950.3741083170071890.812945841496405
1260.1565395422620740.3130790845241480.843460457737926
1270.1291563651338810.2583127302677620.870843634866119
1280.1025248376327970.2050496752655940.897475162367203
1290.1071944701572920.2143889403145830.892805529842708
1300.08291305193385150.1658261038677030.917086948066149
1310.1215975818234770.2431951636469540.878402418176523
1320.09768899582625180.1953779916525040.902311004173748
1330.2150220364769220.4300440729538440.784977963523078
1340.219069318218610.4381386364372190.78093068178139
1350.2014219510221290.4028439020442580.798578048977871
1360.1793160708885080.3586321417770160.820683929111492
1370.1401893956627890.2803787913255790.85981060433721
1380.1636408775008450.3272817550016890.836359122499155
1390.1277943501376330.2555887002752650.872205649862367
1400.09872326559855890.1974465311971180.901276734401441
1410.07219845735078630.1443969147015730.927801542649214
1420.06002000753737230.1200400150747450.939979992462628
1430.8587765894240430.2824468211519150.141223410575957
1440.9775355427496730.04492891450065420.0224644572503271
1450.9575684131542520.08486317369149650.0424315868457482
1460.9248995476443830.1502009047112340.075100452355617
1470.8934425508436490.2131148983127020.106557449156351
1480.8761891950838080.2476216098323830.123810804916192
1490.7941853527980470.4116292944039060.205814647201953
1500.6806173099900990.6387653800198020.319382690009901
1510.5155948677828250.9688102644343510.484405132217175

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.748705980209265 & 0.502588039581471 & 0.251294019790735 \tabularnewline
12 & 0.876170086516911 & 0.247659826966178 & 0.123829913483089 \tabularnewline
13 & 0.894289776248858 & 0.211420447502285 & 0.105710223751143 \tabularnewline
14 & 0.862130260979915 & 0.27573947804017 & 0.137869739020085 \tabularnewline
15 & 0.801627512717927 & 0.396744974564147 & 0.198372487282073 \tabularnewline
16 & 0.726544162850465 & 0.546911674299071 & 0.273455837149536 \tabularnewline
17 & 0.654502743781003 & 0.690994512437993 & 0.345497256218997 \tabularnewline
18 & 0.569558889515381 & 0.860882220969238 & 0.430441110484619 \tabularnewline
19 & 0.659222798137136 & 0.681554403725728 & 0.340777201862864 \tabularnewline
20 & 0.648034560231056 & 0.703930879537887 & 0.351965439768944 \tabularnewline
21 & 0.654797996827064 & 0.690404006345872 & 0.345202003172936 \tabularnewline
22 & 0.577290753289734 & 0.845418493420532 & 0.422709246710266 \tabularnewline
23 & 0.709684542077131 & 0.580630915845738 & 0.290315457922869 \tabularnewline
24 & 0.647083394913968 & 0.705833210172064 & 0.352916605086032 \tabularnewline
25 & 0.606082793867674 & 0.787834412264652 & 0.393917206132326 \tabularnewline
26 & 0.588597212133901 & 0.822805575732198 & 0.411402787866099 \tabularnewline
27 & 0.542550852274611 & 0.914898295450778 & 0.457449147725389 \tabularnewline
28 & 0.773453986739441 & 0.453092026521119 & 0.226546013260559 \tabularnewline
29 & 0.8973979230578 & 0.2052041538844 & 0.1026020769422 \tabularnewline
30 & 0.882601366425312 & 0.234797267149376 & 0.117398633574688 \tabularnewline
31 & 0.875064649913247 & 0.249870700173506 & 0.124935350086753 \tabularnewline
32 & 0.840454750912384 & 0.319090498175232 & 0.159545249087616 \tabularnewline
33 & 0.837077773968389 & 0.325844452063223 & 0.162922226031611 \tabularnewline
34 & 0.89003076435998 & 0.219938471280039 & 0.10996923564002 \tabularnewline
35 & 0.93141966380278 & 0.137160672394441 & 0.0685803361972204 \tabularnewline
36 & 0.911849011101196 & 0.176301977797608 & 0.0881509888988038 \tabularnewline
37 & 0.894907660360688 & 0.210184679278623 & 0.105092339639312 \tabularnewline
38 & 0.86866568108497 & 0.262668637830061 & 0.13133431891503 \tabularnewline
39 & 0.836605366149461 & 0.326789267701078 & 0.163394633850539 \tabularnewline
40 & 0.828263311011021 & 0.343473377977958 & 0.171736688988979 \tabularnewline
41 & 0.870953319887102 & 0.258093360225796 & 0.129046680112898 \tabularnewline
42 & 0.862616490172087 & 0.274767019655825 & 0.137383509827913 \tabularnewline
43 & 0.852157526424314 & 0.295684947151371 & 0.147842473575686 \tabularnewline
44 & 0.819117589176177 & 0.361764821647646 & 0.180882410823823 \tabularnewline
45 & 0.845530679789197 & 0.308938640421607 & 0.154469320210803 \tabularnewline
46 & 0.814002664481606 & 0.371994671036788 & 0.185997335518394 \tabularnewline
47 & 0.78079011773216 & 0.43841976453568 & 0.21920988226784 \tabularnewline
48 & 0.940131485777537 & 0.119737028444926 & 0.0598685142224632 \tabularnewline
49 & 0.924878453002065 & 0.15024309399587 & 0.0751215469979351 \tabularnewline
50 & 0.94576187950284 & 0.10847624099432 & 0.0542381204971598 \tabularnewline
51 & 0.941558634689648 & 0.116882730620704 & 0.058441365310352 \tabularnewline
52 & 0.926707445763661 & 0.146585108472678 & 0.073292554236339 \tabularnewline
53 & 0.907688660793575 & 0.184622678412849 & 0.0923113392064245 \tabularnewline
54 & 0.891952317583624 & 0.216095364832751 & 0.108047682416376 \tabularnewline
55 & 0.868140768775555 & 0.263718462448889 & 0.131859231224445 \tabularnewline
56 & 0.85765556459581 & 0.28468887080838 & 0.14234443540419 \tabularnewline
57 & 0.833070575095267 & 0.333858849809466 & 0.166929424904733 \tabularnewline
58 & 0.861590666615411 & 0.276818666769178 & 0.138409333384589 \tabularnewline
59 & 0.904597637531287 & 0.190804724937426 & 0.0954023624687132 \tabularnewline
60 & 0.893156962592319 & 0.213686074815363 & 0.106843037407681 \tabularnewline
61 & 0.963866089175199 & 0.0722678216496027 & 0.0361339108248013 \tabularnewline
62 & 0.954478341663683 & 0.0910433166726343 & 0.0455216583363172 \tabularnewline
63 & 0.963080055599029 & 0.0738398888019429 & 0.0369199444009715 \tabularnewline
64 & 0.956264762381477 & 0.0874704752370466 & 0.0437352376185233 \tabularnewline
65 & 0.950155771984138 & 0.0996884560317235 & 0.0498442280158617 \tabularnewline
66 & 0.937685271196771 & 0.124629457606458 & 0.062314728803229 \tabularnewline
67 & 0.922912742005929 & 0.154174515988142 & 0.077087257994071 \tabularnewline
68 & 0.91339253905075 & 0.1732149218985 & 0.0866074609492499 \tabularnewline
69 & 0.893519111392828 & 0.212961777214343 & 0.106480888607172 \tabularnewline
70 & 0.873706260266988 & 0.252587479466023 & 0.126293739733012 \tabularnewline
71 & 0.907786498277706 & 0.184427003444587 & 0.0922135017222935 \tabularnewline
72 & 0.890723175901356 & 0.218553648197288 & 0.109276824098644 \tabularnewline
73 & 0.89575522559908 & 0.208489548801841 & 0.10424477440092 \tabularnewline
74 & 0.882289754234194 & 0.235420491531611 & 0.117710245765806 \tabularnewline
75 & 0.859293935143435 & 0.28141212971313 & 0.140706064856565 \tabularnewline
76 & 0.840540324871015 & 0.318919350257969 & 0.159459675128985 \tabularnewline
77 & 0.818967710755885 & 0.36206457848823 & 0.181032289244115 \tabularnewline
78 & 0.806220093418695 & 0.38755981316261 & 0.193779906581305 \tabularnewline
79 & 0.778124511714506 & 0.443750976570988 & 0.221875488285494 \tabularnewline
80 & 0.769090402460345 & 0.46181919507931 & 0.230909597539655 \tabularnewline
81 & 0.7523934817005 & 0.495213036599001 & 0.2476065182995 \tabularnewline
82 & 0.719649308942305 & 0.56070138211539 & 0.280350691057695 \tabularnewline
83 & 0.71173849642854 & 0.576523007142921 & 0.28826150357146 \tabularnewline
84 & 0.685352346049644 & 0.629295307900711 & 0.314647653950356 \tabularnewline
85 & 0.734899783479221 & 0.530200433041557 & 0.265100216520779 \tabularnewline
86 & 0.694990217391729 & 0.610019565216543 & 0.305009782608271 \tabularnewline
87 & 0.668913171166553 & 0.662173657666894 & 0.331086828833447 \tabularnewline
88 & 0.676828219277882 & 0.646343561444236 & 0.323171780722118 \tabularnewline
89 & 0.642408734110682 & 0.715182531778635 & 0.357591265889318 \tabularnewline
90 & 0.599003763074007 & 0.801992473851986 & 0.400996236925993 \tabularnewline
91 & 0.562534255327377 & 0.874931489345246 & 0.437465744672623 \tabularnewline
92 & 0.552560838549529 & 0.894878322900941 & 0.447439161450471 \tabularnewline
93 & 0.546961506023885 & 0.906076987952229 & 0.453038493976115 \tabularnewline
94 & 0.508987821491259 & 0.982024357017481 & 0.491012178508741 \tabularnewline
95 & 0.639384809685212 & 0.721230380629577 & 0.360615190314788 \tabularnewline
96 & 0.638543327772665 & 0.722913344454671 & 0.361456672227336 \tabularnewline
97 & 0.719876557290704 & 0.560246885418591 & 0.280123442709296 \tabularnewline
98 & 0.68392817433212 & 0.632143651335761 & 0.31607182566788 \tabularnewline
99 & 0.648485548190356 & 0.703028903619289 & 0.351514451809644 \tabularnewline
100 & 0.604736547156769 & 0.790526905686462 & 0.395263452843231 \tabularnewline
101 & 0.579852605851189 & 0.840294788297622 & 0.420147394148811 \tabularnewline
102 & 0.532537502935939 & 0.934924994128122 & 0.467462497064061 \tabularnewline
103 & 0.60899949799228 & 0.78200100401544 & 0.39100050200772 \tabularnewline
104 & 0.604735688533172 & 0.790528622933657 & 0.395264311466828 \tabularnewline
105 & 0.583065034336021 & 0.833869931327958 & 0.416934965663979 \tabularnewline
106 & 0.560208935868843 & 0.879582128262315 & 0.439791064131157 \tabularnewline
107 & 0.514527687027491 & 0.970944625945018 & 0.485472312972509 \tabularnewline
108 & 0.487095768829857 & 0.974191537659715 & 0.512904231170143 \tabularnewline
109 & 0.473228457513752 & 0.946456915027503 & 0.526771542486248 \tabularnewline
110 & 0.441523255247994 & 0.883046510495987 & 0.558476744752006 \tabularnewline
111 & 0.466501696619838 & 0.933003393239675 & 0.533498303380162 \tabularnewline
112 & 0.45664762145606 & 0.913295242912119 & 0.54335237854394 \tabularnewline
113 & 0.44968236959728 & 0.89936473919456 & 0.55031763040272 \tabularnewline
114 & 0.4140872565453 & 0.828174513090601 & 0.5859127434547 \tabularnewline
115 & 0.365716138921262 & 0.731432277842524 & 0.634283861078738 \tabularnewline
116 & 0.320280342592997 & 0.640560685185993 & 0.679719657407003 \tabularnewline
117 & 0.283981610736734 & 0.567963221473469 & 0.716018389263266 \tabularnewline
118 & 0.240254488970816 & 0.480508977941632 & 0.759745511029184 \tabularnewline
119 & 0.209153153930991 & 0.418306307861982 & 0.790846846069009 \tabularnewline
120 & 0.178863075816121 & 0.357726151632242 & 0.821136924183879 \tabularnewline
121 & 0.145280512925089 & 0.290561025850179 & 0.854719487074911 \tabularnewline
122 & 0.150226713111084 & 0.300453426222167 & 0.849773286888916 \tabularnewline
123 & 0.123661087096967 & 0.247322174193933 & 0.876338912903033 \tabularnewline
124 & 0.0982008226534652 & 0.19640164530693 & 0.901799177346535 \tabularnewline
125 & 0.187054158503595 & 0.374108317007189 & 0.812945841496405 \tabularnewline
126 & 0.156539542262074 & 0.313079084524148 & 0.843460457737926 \tabularnewline
127 & 0.129156365133881 & 0.258312730267762 & 0.870843634866119 \tabularnewline
128 & 0.102524837632797 & 0.205049675265594 & 0.897475162367203 \tabularnewline
129 & 0.107194470157292 & 0.214388940314583 & 0.892805529842708 \tabularnewline
130 & 0.0829130519338515 & 0.165826103867703 & 0.917086948066149 \tabularnewline
131 & 0.121597581823477 & 0.243195163646954 & 0.878402418176523 \tabularnewline
132 & 0.0976889958262518 & 0.195377991652504 & 0.902311004173748 \tabularnewline
133 & 0.215022036476922 & 0.430044072953844 & 0.784977963523078 \tabularnewline
134 & 0.21906931821861 & 0.438138636437219 & 0.78093068178139 \tabularnewline
135 & 0.201421951022129 & 0.402843902044258 & 0.798578048977871 \tabularnewline
136 & 0.179316070888508 & 0.358632141777016 & 0.820683929111492 \tabularnewline
137 & 0.140189395662789 & 0.280378791325579 & 0.85981060433721 \tabularnewline
138 & 0.163640877500845 & 0.327281755001689 & 0.836359122499155 \tabularnewline
139 & 0.127794350137633 & 0.255588700275265 & 0.872205649862367 \tabularnewline
140 & 0.0987232655985589 & 0.197446531197118 & 0.901276734401441 \tabularnewline
141 & 0.0721984573507863 & 0.144396914701573 & 0.927801542649214 \tabularnewline
142 & 0.0600200075373723 & 0.120040015074745 & 0.939979992462628 \tabularnewline
143 & 0.858776589424043 & 0.282446821151915 & 0.141223410575957 \tabularnewline
144 & 0.977535542749673 & 0.0449289145006542 & 0.0224644572503271 \tabularnewline
145 & 0.957568413154252 & 0.0848631736914965 & 0.0424315868457482 \tabularnewline
146 & 0.924899547644383 & 0.150200904711234 & 0.075100452355617 \tabularnewline
147 & 0.893442550843649 & 0.213114898312702 & 0.106557449156351 \tabularnewline
148 & 0.876189195083808 & 0.247621609832383 & 0.123810804916192 \tabularnewline
149 & 0.794185352798047 & 0.411629294403906 & 0.205814647201953 \tabularnewline
150 & 0.680617309990099 & 0.638765380019802 & 0.319382690009901 \tabularnewline
151 & 0.515594867782825 & 0.968810264434351 & 0.484405132217175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&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]0.748705980209265[/C][C]0.502588039581471[/C][C]0.251294019790735[/C][/ROW]
[ROW][C]12[/C][C]0.876170086516911[/C][C]0.247659826966178[/C][C]0.123829913483089[/C][/ROW]
[ROW][C]13[/C][C]0.894289776248858[/C][C]0.211420447502285[/C][C]0.105710223751143[/C][/ROW]
[ROW][C]14[/C][C]0.862130260979915[/C][C]0.27573947804017[/C][C]0.137869739020085[/C][/ROW]
[ROW][C]15[/C][C]0.801627512717927[/C][C]0.396744974564147[/C][C]0.198372487282073[/C][/ROW]
[ROW][C]16[/C][C]0.726544162850465[/C][C]0.546911674299071[/C][C]0.273455837149536[/C][/ROW]
[ROW][C]17[/C][C]0.654502743781003[/C][C]0.690994512437993[/C][C]0.345497256218997[/C][/ROW]
[ROW][C]18[/C][C]0.569558889515381[/C][C]0.860882220969238[/C][C]0.430441110484619[/C][/ROW]
[ROW][C]19[/C][C]0.659222798137136[/C][C]0.681554403725728[/C][C]0.340777201862864[/C][/ROW]
[ROW][C]20[/C][C]0.648034560231056[/C][C]0.703930879537887[/C][C]0.351965439768944[/C][/ROW]
[ROW][C]21[/C][C]0.654797996827064[/C][C]0.690404006345872[/C][C]0.345202003172936[/C][/ROW]
[ROW][C]22[/C][C]0.577290753289734[/C][C]0.845418493420532[/C][C]0.422709246710266[/C][/ROW]
[ROW][C]23[/C][C]0.709684542077131[/C][C]0.580630915845738[/C][C]0.290315457922869[/C][/ROW]
[ROW][C]24[/C][C]0.647083394913968[/C][C]0.705833210172064[/C][C]0.352916605086032[/C][/ROW]
[ROW][C]25[/C][C]0.606082793867674[/C][C]0.787834412264652[/C][C]0.393917206132326[/C][/ROW]
[ROW][C]26[/C][C]0.588597212133901[/C][C]0.822805575732198[/C][C]0.411402787866099[/C][/ROW]
[ROW][C]27[/C][C]0.542550852274611[/C][C]0.914898295450778[/C][C]0.457449147725389[/C][/ROW]
[ROW][C]28[/C][C]0.773453986739441[/C][C]0.453092026521119[/C][C]0.226546013260559[/C][/ROW]
[ROW][C]29[/C][C]0.8973979230578[/C][C]0.2052041538844[/C][C]0.1026020769422[/C][/ROW]
[ROW][C]30[/C][C]0.882601366425312[/C][C]0.234797267149376[/C][C]0.117398633574688[/C][/ROW]
[ROW][C]31[/C][C]0.875064649913247[/C][C]0.249870700173506[/C][C]0.124935350086753[/C][/ROW]
[ROW][C]32[/C][C]0.840454750912384[/C][C]0.319090498175232[/C][C]0.159545249087616[/C][/ROW]
[ROW][C]33[/C][C]0.837077773968389[/C][C]0.325844452063223[/C][C]0.162922226031611[/C][/ROW]
[ROW][C]34[/C][C]0.89003076435998[/C][C]0.219938471280039[/C][C]0.10996923564002[/C][/ROW]
[ROW][C]35[/C][C]0.93141966380278[/C][C]0.137160672394441[/C][C]0.0685803361972204[/C][/ROW]
[ROW][C]36[/C][C]0.911849011101196[/C][C]0.176301977797608[/C][C]0.0881509888988038[/C][/ROW]
[ROW][C]37[/C][C]0.894907660360688[/C][C]0.210184679278623[/C][C]0.105092339639312[/C][/ROW]
[ROW][C]38[/C][C]0.86866568108497[/C][C]0.262668637830061[/C][C]0.13133431891503[/C][/ROW]
[ROW][C]39[/C][C]0.836605366149461[/C][C]0.326789267701078[/C][C]0.163394633850539[/C][/ROW]
[ROW][C]40[/C][C]0.828263311011021[/C][C]0.343473377977958[/C][C]0.171736688988979[/C][/ROW]
[ROW][C]41[/C][C]0.870953319887102[/C][C]0.258093360225796[/C][C]0.129046680112898[/C][/ROW]
[ROW][C]42[/C][C]0.862616490172087[/C][C]0.274767019655825[/C][C]0.137383509827913[/C][/ROW]
[ROW][C]43[/C][C]0.852157526424314[/C][C]0.295684947151371[/C][C]0.147842473575686[/C][/ROW]
[ROW][C]44[/C][C]0.819117589176177[/C][C]0.361764821647646[/C][C]0.180882410823823[/C][/ROW]
[ROW][C]45[/C][C]0.845530679789197[/C][C]0.308938640421607[/C][C]0.154469320210803[/C][/ROW]
[ROW][C]46[/C][C]0.814002664481606[/C][C]0.371994671036788[/C][C]0.185997335518394[/C][/ROW]
[ROW][C]47[/C][C]0.78079011773216[/C][C]0.43841976453568[/C][C]0.21920988226784[/C][/ROW]
[ROW][C]48[/C][C]0.940131485777537[/C][C]0.119737028444926[/C][C]0.0598685142224632[/C][/ROW]
[ROW][C]49[/C][C]0.924878453002065[/C][C]0.15024309399587[/C][C]0.0751215469979351[/C][/ROW]
[ROW][C]50[/C][C]0.94576187950284[/C][C]0.10847624099432[/C][C]0.0542381204971598[/C][/ROW]
[ROW][C]51[/C][C]0.941558634689648[/C][C]0.116882730620704[/C][C]0.058441365310352[/C][/ROW]
[ROW][C]52[/C][C]0.926707445763661[/C][C]0.146585108472678[/C][C]0.073292554236339[/C][/ROW]
[ROW][C]53[/C][C]0.907688660793575[/C][C]0.184622678412849[/C][C]0.0923113392064245[/C][/ROW]
[ROW][C]54[/C][C]0.891952317583624[/C][C]0.216095364832751[/C][C]0.108047682416376[/C][/ROW]
[ROW][C]55[/C][C]0.868140768775555[/C][C]0.263718462448889[/C][C]0.131859231224445[/C][/ROW]
[ROW][C]56[/C][C]0.85765556459581[/C][C]0.28468887080838[/C][C]0.14234443540419[/C][/ROW]
[ROW][C]57[/C][C]0.833070575095267[/C][C]0.333858849809466[/C][C]0.166929424904733[/C][/ROW]
[ROW][C]58[/C][C]0.861590666615411[/C][C]0.276818666769178[/C][C]0.138409333384589[/C][/ROW]
[ROW][C]59[/C][C]0.904597637531287[/C][C]0.190804724937426[/C][C]0.0954023624687132[/C][/ROW]
[ROW][C]60[/C][C]0.893156962592319[/C][C]0.213686074815363[/C][C]0.106843037407681[/C][/ROW]
[ROW][C]61[/C][C]0.963866089175199[/C][C]0.0722678216496027[/C][C]0.0361339108248013[/C][/ROW]
[ROW][C]62[/C][C]0.954478341663683[/C][C]0.0910433166726343[/C][C]0.0455216583363172[/C][/ROW]
[ROW][C]63[/C][C]0.963080055599029[/C][C]0.0738398888019429[/C][C]0.0369199444009715[/C][/ROW]
[ROW][C]64[/C][C]0.956264762381477[/C][C]0.0874704752370466[/C][C]0.0437352376185233[/C][/ROW]
[ROW][C]65[/C][C]0.950155771984138[/C][C]0.0996884560317235[/C][C]0.0498442280158617[/C][/ROW]
[ROW][C]66[/C][C]0.937685271196771[/C][C]0.124629457606458[/C][C]0.062314728803229[/C][/ROW]
[ROW][C]67[/C][C]0.922912742005929[/C][C]0.154174515988142[/C][C]0.077087257994071[/C][/ROW]
[ROW][C]68[/C][C]0.91339253905075[/C][C]0.1732149218985[/C][C]0.0866074609492499[/C][/ROW]
[ROW][C]69[/C][C]0.893519111392828[/C][C]0.212961777214343[/C][C]0.106480888607172[/C][/ROW]
[ROW][C]70[/C][C]0.873706260266988[/C][C]0.252587479466023[/C][C]0.126293739733012[/C][/ROW]
[ROW][C]71[/C][C]0.907786498277706[/C][C]0.184427003444587[/C][C]0.0922135017222935[/C][/ROW]
[ROW][C]72[/C][C]0.890723175901356[/C][C]0.218553648197288[/C][C]0.109276824098644[/C][/ROW]
[ROW][C]73[/C][C]0.89575522559908[/C][C]0.208489548801841[/C][C]0.10424477440092[/C][/ROW]
[ROW][C]74[/C][C]0.882289754234194[/C][C]0.235420491531611[/C][C]0.117710245765806[/C][/ROW]
[ROW][C]75[/C][C]0.859293935143435[/C][C]0.28141212971313[/C][C]0.140706064856565[/C][/ROW]
[ROW][C]76[/C][C]0.840540324871015[/C][C]0.318919350257969[/C][C]0.159459675128985[/C][/ROW]
[ROW][C]77[/C][C]0.818967710755885[/C][C]0.36206457848823[/C][C]0.181032289244115[/C][/ROW]
[ROW][C]78[/C][C]0.806220093418695[/C][C]0.38755981316261[/C][C]0.193779906581305[/C][/ROW]
[ROW][C]79[/C][C]0.778124511714506[/C][C]0.443750976570988[/C][C]0.221875488285494[/C][/ROW]
[ROW][C]80[/C][C]0.769090402460345[/C][C]0.46181919507931[/C][C]0.230909597539655[/C][/ROW]
[ROW][C]81[/C][C]0.7523934817005[/C][C]0.495213036599001[/C][C]0.2476065182995[/C][/ROW]
[ROW][C]82[/C][C]0.719649308942305[/C][C]0.56070138211539[/C][C]0.280350691057695[/C][/ROW]
[ROW][C]83[/C][C]0.71173849642854[/C][C]0.576523007142921[/C][C]0.28826150357146[/C][/ROW]
[ROW][C]84[/C][C]0.685352346049644[/C][C]0.629295307900711[/C][C]0.314647653950356[/C][/ROW]
[ROW][C]85[/C][C]0.734899783479221[/C][C]0.530200433041557[/C][C]0.265100216520779[/C][/ROW]
[ROW][C]86[/C][C]0.694990217391729[/C][C]0.610019565216543[/C][C]0.305009782608271[/C][/ROW]
[ROW][C]87[/C][C]0.668913171166553[/C][C]0.662173657666894[/C][C]0.331086828833447[/C][/ROW]
[ROW][C]88[/C][C]0.676828219277882[/C][C]0.646343561444236[/C][C]0.323171780722118[/C][/ROW]
[ROW][C]89[/C][C]0.642408734110682[/C][C]0.715182531778635[/C][C]0.357591265889318[/C][/ROW]
[ROW][C]90[/C][C]0.599003763074007[/C][C]0.801992473851986[/C][C]0.400996236925993[/C][/ROW]
[ROW][C]91[/C][C]0.562534255327377[/C][C]0.874931489345246[/C][C]0.437465744672623[/C][/ROW]
[ROW][C]92[/C][C]0.552560838549529[/C][C]0.894878322900941[/C][C]0.447439161450471[/C][/ROW]
[ROW][C]93[/C][C]0.546961506023885[/C][C]0.906076987952229[/C][C]0.453038493976115[/C][/ROW]
[ROW][C]94[/C][C]0.508987821491259[/C][C]0.982024357017481[/C][C]0.491012178508741[/C][/ROW]
[ROW][C]95[/C][C]0.639384809685212[/C][C]0.721230380629577[/C][C]0.360615190314788[/C][/ROW]
[ROW][C]96[/C][C]0.638543327772665[/C][C]0.722913344454671[/C][C]0.361456672227336[/C][/ROW]
[ROW][C]97[/C][C]0.719876557290704[/C][C]0.560246885418591[/C][C]0.280123442709296[/C][/ROW]
[ROW][C]98[/C][C]0.68392817433212[/C][C]0.632143651335761[/C][C]0.31607182566788[/C][/ROW]
[ROW][C]99[/C][C]0.648485548190356[/C][C]0.703028903619289[/C][C]0.351514451809644[/C][/ROW]
[ROW][C]100[/C][C]0.604736547156769[/C][C]0.790526905686462[/C][C]0.395263452843231[/C][/ROW]
[ROW][C]101[/C][C]0.579852605851189[/C][C]0.840294788297622[/C][C]0.420147394148811[/C][/ROW]
[ROW][C]102[/C][C]0.532537502935939[/C][C]0.934924994128122[/C][C]0.467462497064061[/C][/ROW]
[ROW][C]103[/C][C]0.60899949799228[/C][C]0.78200100401544[/C][C]0.39100050200772[/C][/ROW]
[ROW][C]104[/C][C]0.604735688533172[/C][C]0.790528622933657[/C][C]0.395264311466828[/C][/ROW]
[ROW][C]105[/C][C]0.583065034336021[/C][C]0.833869931327958[/C][C]0.416934965663979[/C][/ROW]
[ROW][C]106[/C][C]0.560208935868843[/C][C]0.879582128262315[/C][C]0.439791064131157[/C][/ROW]
[ROW][C]107[/C][C]0.514527687027491[/C][C]0.970944625945018[/C][C]0.485472312972509[/C][/ROW]
[ROW][C]108[/C][C]0.487095768829857[/C][C]0.974191537659715[/C][C]0.512904231170143[/C][/ROW]
[ROW][C]109[/C][C]0.473228457513752[/C][C]0.946456915027503[/C][C]0.526771542486248[/C][/ROW]
[ROW][C]110[/C][C]0.441523255247994[/C][C]0.883046510495987[/C][C]0.558476744752006[/C][/ROW]
[ROW][C]111[/C][C]0.466501696619838[/C][C]0.933003393239675[/C][C]0.533498303380162[/C][/ROW]
[ROW][C]112[/C][C]0.45664762145606[/C][C]0.913295242912119[/C][C]0.54335237854394[/C][/ROW]
[ROW][C]113[/C][C]0.44968236959728[/C][C]0.89936473919456[/C][C]0.55031763040272[/C][/ROW]
[ROW][C]114[/C][C]0.4140872565453[/C][C]0.828174513090601[/C][C]0.5859127434547[/C][/ROW]
[ROW][C]115[/C][C]0.365716138921262[/C][C]0.731432277842524[/C][C]0.634283861078738[/C][/ROW]
[ROW][C]116[/C][C]0.320280342592997[/C][C]0.640560685185993[/C][C]0.679719657407003[/C][/ROW]
[ROW][C]117[/C][C]0.283981610736734[/C][C]0.567963221473469[/C][C]0.716018389263266[/C][/ROW]
[ROW][C]118[/C][C]0.240254488970816[/C][C]0.480508977941632[/C][C]0.759745511029184[/C][/ROW]
[ROW][C]119[/C][C]0.209153153930991[/C][C]0.418306307861982[/C][C]0.790846846069009[/C][/ROW]
[ROW][C]120[/C][C]0.178863075816121[/C][C]0.357726151632242[/C][C]0.821136924183879[/C][/ROW]
[ROW][C]121[/C][C]0.145280512925089[/C][C]0.290561025850179[/C][C]0.854719487074911[/C][/ROW]
[ROW][C]122[/C][C]0.150226713111084[/C][C]0.300453426222167[/C][C]0.849773286888916[/C][/ROW]
[ROW][C]123[/C][C]0.123661087096967[/C][C]0.247322174193933[/C][C]0.876338912903033[/C][/ROW]
[ROW][C]124[/C][C]0.0982008226534652[/C][C]0.19640164530693[/C][C]0.901799177346535[/C][/ROW]
[ROW][C]125[/C][C]0.187054158503595[/C][C]0.374108317007189[/C][C]0.812945841496405[/C][/ROW]
[ROW][C]126[/C][C]0.156539542262074[/C][C]0.313079084524148[/C][C]0.843460457737926[/C][/ROW]
[ROW][C]127[/C][C]0.129156365133881[/C][C]0.258312730267762[/C][C]0.870843634866119[/C][/ROW]
[ROW][C]128[/C][C]0.102524837632797[/C][C]0.205049675265594[/C][C]0.897475162367203[/C][/ROW]
[ROW][C]129[/C][C]0.107194470157292[/C][C]0.214388940314583[/C][C]0.892805529842708[/C][/ROW]
[ROW][C]130[/C][C]0.0829130519338515[/C][C]0.165826103867703[/C][C]0.917086948066149[/C][/ROW]
[ROW][C]131[/C][C]0.121597581823477[/C][C]0.243195163646954[/C][C]0.878402418176523[/C][/ROW]
[ROW][C]132[/C][C]0.0976889958262518[/C][C]0.195377991652504[/C][C]0.902311004173748[/C][/ROW]
[ROW][C]133[/C][C]0.215022036476922[/C][C]0.430044072953844[/C][C]0.784977963523078[/C][/ROW]
[ROW][C]134[/C][C]0.21906931821861[/C][C]0.438138636437219[/C][C]0.78093068178139[/C][/ROW]
[ROW][C]135[/C][C]0.201421951022129[/C][C]0.402843902044258[/C][C]0.798578048977871[/C][/ROW]
[ROW][C]136[/C][C]0.179316070888508[/C][C]0.358632141777016[/C][C]0.820683929111492[/C][/ROW]
[ROW][C]137[/C][C]0.140189395662789[/C][C]0.280378791325579[/C][C]0.85981060433721[/C][/ROW]
[ROW][C]138[/C][C]0.163640877500845[/C][C]0.327281755001689[/C][C]0.836359122499155[/C][/ROW]
[ROW][C]139[/C][C]0.127794350137633[/C][C]0.255588700275265[/C][C]0.872205649862367[/C][/ROW]
[ROW][C]140[/C][C]0.0987232655985589[/C][C]0.197446531197118[/C][C]0.901276734401441[/C][/ROW]
[ROW][C]141[/C][C]0.0721984573507863[/C][C]0.144396914701573[/C][C]0.927801542649214[/C][/ROW]
[ROW][C]142[/C][C]0.0600200075373723[/C][C]0.120040015074745[/C][C]0.939979992462628[/C][/ROW]
[ROW][C]143[/C][C]0.858776589424043[/C][C]0.282446821151915[/C][C]0.141223410575957[/C][/ROW]
[ROW][C]144[/C][C]0.977535542749673[/C][C]0.0449289145006542[/C][C]0.0224644572503271[/C][/ROW]
[ROW][C]145[/C][C]0.957568413154252[/C][C]0.0848631736914965[/C][C]0.0424315868457482[/C][/ROW]
[ROW][C]146[/C][C]0.924899547644383[/C][C]0.150200904711234[/C][C]0.075100452355617[/C][/ROW]
[ROW][C]147[/C][C]0.893442550843649[/C][C]0.213114898312702[/C][C]0.106557449156351[/C][/ROW]
[ROW][C]148[/C][C]0.876189195083808[/C][C]0.247621609832383[/C][C]0.123810804916192[/C][/ROW]
[ROW][C]149[/C][C]0.794185352798047[/C][C]0.411629294403906[/C][C]0.205814647201953[/C][/ROW]
[ROW][C]150[/C][C]0.680617309990099[/C][C]0.638765380019802[/C][C]0.319382690009901[/C][/ROW]
[ROW][C]151[/C][C]0.515594867782825[/C][C]0.968810264434351[/C][C]0.484405132217175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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
110.7487059802092650.5025880395814710.251294019790735
120.8761700865169110.2476598269661780.123829913483089
130.8942897762488580.2114204475022850.105710223751143
140.8621302609799150.275739478040170.137869739020085
150.8016275127179270.3967449745641470.198372487282073
160.7265441628504650.5469116742990710.273455837149536
170.6545027437810030.6909945124379930.345497256218997
180.5695588895153810.8608822209692380.430441110484619
190.6592227981371360.6815544037257280.340777201862864
200.6480345602310560.7039308795378870.351965439768944
210.6547979968270640.6904040063458720.345202003172936
220.5772907532897340.8454184934205320.422709246710266
230.7096845420771310.5806309158457380.290315457922869
240.6470833949139680.7058332101720640.352916605086032
250.6060827938676740.7878344122646520.393917206132326
260.5885972121339010.8228055757321980.411402787866099
270.5425508522746110.9148982954507780.457449147725389
280.7734539867394410.4530920265211190.226546013260559
290.89739792305780.20520415388440.1026020769422
300.8826013664253120.2347972671493760.117398633574688
310.8750646499132470.2498707001735060.124935350086753
320.8404547509123840.3190904981752320.159545249087616
330.8370777739683890.3258444520632230.162922226031611
340.890030764359980.2199384712800390.10996923564002
350.931419663802780.1371606723944410.0685803361972204
360.9118490111011960.1763019777976080.0881509888988038
370.8949076603606880.2101846792786230.105092339639312
380.868665681084970.2626686378300610.13133431891503
390.8366053661494610.3267892677010780.163394633850539
400.8282633110110210.3434733779779580.171736688988979
410.8709533198871020.2580933602257960.129046680112898
420.8626164901720870.2747670196558250.137383509827913
430.8521575264243140.2956849471513710.147842473575686
440.8191175891761770.3617648216476460.180882410823823
450.8455306797891970.3089386404216070.154469320210803
460.8140026644816060.3719946710367880.185997335518394
470.780790117732160.438419764535680.21920988226784
480.9401314857775370.1197370284449260.0598685142224632
490.9248784530020650.150243093995870.0751215469979351
500.945761879502840.108476240994320.0542381204971598
510.9415586346896480.1168827306207040.058441365310352
520.9267074457636610.1465851084726780.073292554236339
530.9076886607935750.1846226784128490.0923113392064245
540.8919523175836240.2160953648327510.108047682416376
550.8681407687755550.2637184624488890.131859231224445
560.857655564595810.284688870808380.14234443540419
570.8330705750952670.3338588498094660.166929424904733
580.8615906666154110.2768186667691780.138409333384589
590.9045976375312870.1908047249374260.0954023624687132
600.8931569625923190.2136860748153630.106843037407681
610.9638660891751990.07226782164960270.0361339108248013
620.9544783416636830.09104331667263430.0455216583363172
630.9630800555990290.07383988880194290.0369199444009715
640.9562647623814770.08747047523704660.0437352376185233
650.9501557719841380.09968845603172350.0498442280158617
660.9376852711967710.1246294576064580.062314728803229
670.9229127420059290.1541745159881420.077087257994071
680.913392539050750.17321492189850.0866074609492499
690.8935191113928280.2129617772143430.106480888607172
700.8737062602669880.2525874794660230.126293739733012
710.9077864982777060.1844270034445870.0922135017222935
720.8907231759013560.2185536481972880.109276824098644
730.895755225599080.2084895488018410.10424477440092
740.8822897542341940.2354204915316110.117710245765806
750.8592939351434350.281412129713130.140706064856565
760.8405403248710150.3189193502579690.159459675128985
770.8189677107558850.362064578488230.181032289244115
780.8062200934186950.387559813162610.193779906581305
790.7781245117145060.4437509765709880.221875488285494
800.7690904024603450.461819195079310.230909597539655
810.75239348170050.4952130365990010.2476065182995
820.7196493089423050.560701382115390.280350691057695
830.711738496428540.5765230071429210.28826150357146
840.6853523460496440.6292953079007110.314647653950356
850.7348997834792210.5302004330415570.265100216520779
860.6949902173917290.6100195652165430.305009782608271
870.6689131711665530.6621736576668940.331086828833447
880.6768282192778820.6463435614442360.323171780722118
890.6424087341106820.7151825317786350.357591265889318
900.5990037630740070.8019924738519860.400996236925993
910.5625342553273770.8749314893452460.437465744672623
920.5525608385495290.8948783229009410.447439161450471
930.5469615060238850.9060769879522290.453038493976115
940.5089878214912590.9820243570174810.491012178508741
950.6393848096852120.7212303806295770.360615190314788
960.6385433277726650.7229133444546710.361456672227336
970.7198765572907040.5602468854185910.280123442709296
980.683928174332120.6321436513357610.31607182566788
990.6484855481903560.7030289036192890.351514451809644
1000.6047365471567690.7905269056864620.395263452843231
1010.5798526058511890.8402947882976220.420147394148811
1020.5325375029359390.9349249941281220.467462497064061
1030.608999497992280.782001004015440.39100050200772
1040.6047356885331720.7905286229336570.395264311466828
1050.5830650343360210.8338699313279580.416934965663979
1060.5602089358688430.8795821282623150.439791064131157
1070.5145276870274910.9709446259450180.485472312972509
1080.4870957688298570.9741915376597150.512904231170143
1090.4732284575137520.9464569150275030.526771542486248
1100.4415232552479940.8830465104959870.558476744752006
1110.4665016966198380.9330033932396750.533498303380162
1120.456647621456060.9132952429121190.54335237854394
1130.449682369597280.899364739194560.55031763040272
1140.41408725654530.8281745130906010.5859127434547
1150.3657161389212620.7314322778425240.634283861078738
1160.3202803425929970.6405606851859930.679719657407003
1170.2839816107367340.5679632214734690.716018389263266
1180.2402544889708160.4805089779416320.759745511029184
1190.2091531539309910.4183063078619820.790846846069009
1200.1788630758161210.3577261516322420.821136924183879
1210.1452805129250890.2905610258501790.854719487074911
1220.1502267131110840.3004534262221670.849773286888916
1230.1236610870969670.2473221741939330.876338912903033
1240.09820082265346520.196401645306930.901799177346535
1250.1870541585035950.3741083170071890.812945841496405
1260.1565395422620740.3130790845241480.843460457737926
1270.1291563651338810.2583127302677620.870843634866119
1280.1025248376327970.2050496752655940.897475162367203
1290.1071944701572920.2143889403145830.892805529842708
1300.08291305193385150.1658261038677030.917086948066149
1310.1215975818234770.2431951636469540.878402418176523
1320.09768899582625180.1953779916525040.902311004173748
1330.2150220364769220.4300440729538440.784977963523078
1340.219069318218610.4381386364372190.78093068178139
1350.2014219510221290.4028439020442580.798578048977871
1360.1793160708885080.3586321417770160.820683929111492
1370.1401893956627890.2803787913255790.85981060433721
1380.1636408775008450.3272817550016890.836359122499155
1390.1277943501376330.2555887002752650.872205649862367
1400.09872326559855890.1974465311971180.901276734401441
1410.07219845735078630.1443969147015730.927801542649214
1420.06002000753737230.1200400150747450.939979992462628
1430.8587765894240430.2824468211519150.141223410575957
1440.9775355427496730.04492891450065420.0224644572503271
1450.9575684131542520.08486317369149650.0424315868457482
1460.9248995476443830.1502009047112340.075100452355617
1470.8934425508436490.2131148983127020.106557449156351
1480.8761891950838080.2476216098323830.123810804916192
1490.7941853527980470.4116292944039060.205814647201953
1500.6806173099900990.6387653800198020.319382690009901
1510.5155948677828250.9688102644343510.484405132217175






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00709219858156028OK
10% type I error level70.049645390070922OK

\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 & 1 & 0.00709219858156028 & OK \tabularnewline
10% type I error level & 7 & 0.049645390070922 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190285&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]1[/C][C]0.00709219858156028[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.049645390070922[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190285&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190285&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 level10.00709219858156028OK
10% type I error level70.049645390070922OK


Figures (Output of Computation)

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