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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 computationMon, 19 Nov 2012 05:28:54 -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/19/t1353320973c2sj6xmqke9ggf2.htm/, Retrieved Sat, 27 Apr 2024 20:52:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190425, Retrieved Sat, 27 Apr 2024 20:52:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
31/01/2001	98,8	101,5
28/02/2001	100,5	100,7
31/03/2001	110,4	110,6
30/04/2001	96,4	96,8
31/05/2001	101,9	100,0
30/06/2001	106,2	104,8
31/07/2001	81,0	86,8
31/08/2001	94,7	92,0
30/09/2001	101,0	100,2
31/10/2001	109,4	106,6
30/11/2001	102,3	102,1
31/12/2001	90,7	93,7
31/01/2002	96,2	97,6
28/02/2002	96,1	96,9
31/03/2002	106,0	105,6
30/04/2002	103,1	102,8
31/05/2002	102,0	101,7
30/06/2002	104,7	104,2
31/07/2002	86,0	92,7
31/08/2002	92,1	91,9
30/09/2002	106,9	106,5
31/10/2002	112,6	112,3
30/11/2002	101,7	102,8
31/12/2002	92,0	96,5
31/01/2003	97,4	101,0
28/02/2003	97,0	98,9
31/03/2003	105,4	105,1
30/04/2003	102,7	103,0
31/05/2003	98,1	99,0
30/06/2003	104,5	104,3
31/07/2003	87,4	94,6
31/08/2003	89,9	90,4
30/09/2003	109,8	108,9
31/10/2003	111,7	111,4
30/11/2003	98,6	100,8
31/12/2003	96,9	102,5
31/01/2004	95,1	98,2
29/02/2004	97,0	98,7
31/03/2004	112,7	113,3
30/04/2004	102,9	104,6
31/05/2004	97,4	99,3
30/06/2004	111,4	111,8
31/07/2004	87,4	97,3
31/08/2004	96,8	97,7
30/09/2004	114,1	115,6
31/10/2004	110,3	111,9
30/11/2004	103,9	107,0
31/12/2004	101,6	107,1
31/01/2005	94,6	100,6
28/02/2005	95,9	99,2
31/03/2005	104,7	108,4
30/04/2005	102,8	103,0
31/05/2005	98,1	99,8
30/06/2005	113,9	115,0
31/07/2005	80,9	90,8
31/08/2005	95,7	95,9
30/09/2005	113,2	114,4
31/10/2005	105,9	108,2
30/11/2005	108,8	112,6
31/12/2005	102,3	109,1
31/01/2006	99,0	105,0
28/02/2006	100,7	105,0
31/03/2006	115,5	118,5
30/04/2006	100,7	103,7
31/05/2006	109,9	112,5
30/06/2006	114,6	116,6
31/07/2006	85,4	96,6
31/08/2006	100,5	101,9
30/09/2006	114,8	116,5
31/10/2006	116,5	119,3
30/11/2006	112,9	115,4
31/12/2006	102,0	108,5
31/01/2007	106,0	111,5
28/02/2007	105,3	108,8
31/03/2007	118,8	121,8
30/04/2007	106,1	109,6
31/05/2007	109,3	112,2
30/06/2007	117,2	119,6
31/07/2007	92,5	104,1
31/08/2007	104,2	105,3
30/09/2007	112,5	115,0
31/10/2007	122,4	124,1
30/11/2007	113,3	116,8
31/12/2007	100,0	107,5
31/01/2008	110,7	115,6
29/02/2008	112,8	116,2
31/03/2008	109,8	116,3
30/04/2008	117,3	119,0
31/05/2008	109,1	111,9
30/06/2008	115,9	118,6
31/07/2008	96,0	106,9
31/08/2008	99,8	103,2
30/09/2008	116,8	118,6
31/10/2008	115,7	118,7
30/11/2008	99,4	102,8
31/12/2008	94,3	100,6
31/01/2009	91,0	94,9
28/02/2009	93,2	94,5
31/03/2009	103,1	102,9
30/04/2009	94,1	95,3
31/05/2009	91,8	92,5
30/06/2009	102,7	102,7
31/07/2009	82,6	91,5
31/08/2009	89,1	89,5
30/09/2009	104,5	104,2
31/10/2009	105,1	105,2
30/11/2009	95,1	99,0
31/12/2009	88,7	95,5
31/01/2010	86,3	90,5
28/02/2010	91,8	96,1
31/03/2010	111,5	113,0
30/04/2010	99,7	101,9
31/05/2010	97,5	101,4
30/06/2010	111,7	113,6
31/07/2010	86,2	96,6
31/08/2010	95,4	97,8
30/09/2010	113,0	114,9
31/10/2010	111,0	112,5
30/11/2010	104,5	108,4
31/12/2010	97,3	107,0
31/01/2011	97,1	103,5
28/02/2011	104,1	107,5
31/03/2011	119,3	122,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&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]9 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=190425&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TotaleIndustrieZonderBouwnijverheid[t] = + 16.1562962637093 + 33.3837324188536`periodes(2000=100)`[t] + 0.867868628284169TotaleIndustrie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotaleIndustrieZonderBouwnijverheid[t] =  +  16.1562962637093 +  33.3837324188536`periodes(2000=100)`[t] +  0.867868628284169TotaleIndustrie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotaleIndustrieZonderBouwnijverheid[t] =  +  16.1562962637093 +  33.3837324188536`periodes(2000=100)`[t] +  0.867868628284169TotaleIndustrie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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
TotaleIndustrieZonderBouwnijverheid[t] = + 16.1562962637093 + 33.3837324188536`periodes(2000=100)`[t] + 0.867868628284169TotaleIndustrie[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.15629626370932.8246475.719800
`periodes(2000=100)`33.383732418853662.1715260.5370.5922880.296144
TotaleIndustrie0.8678686282841690.02705432.078800

\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) & 16.1562962637093 & 2.824647 & 5.7198 & 0 & 0 \tabularnewline
`periodes(2000=100)` & 33.3837324188536 & 62.171526 & 0.537 & 0.592288 & 0.296144 \tabularnewline
TotaleIndustrie & 0.867868628284169 & 0.027054 & 32.0788 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&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]16.1562962637093[/C][C]2.824647[/C][C]5.7198[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`periodes(2000=100)`[/C][C]33.3837324188536[/C][C]62.171526[/C][C]0.537[/C][C]0.592288[/C][C]0.296144[/C][/ROW]
[ROW][C]TotaleIndustrie[/C][C]0.867868628284169[/C][C]0.027054[/C][C]32.0788[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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)16.15629626370932.8246475.719800
`periodes(2000=100)`33.383732418853662.1715260.5370.5922880.296144
TotaleIndustrie0.8678686282841690.02705432.078800






Multiple Linear Regression - Regression Statistics
Multiple R0.947094059493863
R-squared0.896987157528564
Adjusted R-squared0.895270276820707
F-TEST (value)522.45164933331
F-TEST (DF numerator)2
F-TEST (DF denominator)120
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6994787582425
Sum Squared Residuals874.462267944297

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.947094059493863 \tabularnewline
R-squared & 0.896987157528564 \tabularnewline
Adjusted R-squared & 0.895270276820707 \tabularnewline
F-TEST (value) & 522.45164933331 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 120 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.6994787582425 \tabularnewline
Sum Squared Residuals & 874.462267944297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.947094059493863[/C][/ROW]
[ROW][C]R-squared[/C][C]0.896987157528564[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.895270276820707[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]522.45164933331[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]120[/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]2.6994787582425[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]874.462267944297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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 R0.947094059493863
R-squared0.896987157528564
Adjusted R-squared0.895270276820707
F-TEST (value)522.45164933331
F-TEST (DF numerator)2
F-TEST (DF denominator)120
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6994787582425
Sum Squared Residuals874.462267944297






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.5102.418905996048-0.918905996048432
2100.7103.610662748529-2.91066274852908
3110.6112.141389245569-1.54138924556928
496.899.9439584636572-3.14395846365718
5100104.695547337439-4.69554733743874
6104.8108.407362209724-3.60736220972403
786.886.52753933442170.272460665578293
89298.408104019453-6.40810401945297
9100.2103.866639468568-3.66663946856767
10106.6111.152843123784-4.55284312378368
11102.1104.984757458399-2.88475745839939
1293.794.9150799539053-1.21507995390533
1397.6100.162189226217-2.56218922621698
1496.999.7919241160756-2.8919241160756
15105.6108.322681169021-2.7226811690214
16102.8105.758615772445-2.95861577244514
17101.7104.782282533009-3.08228253300862
18104.2107.105517600154-2.90551760015379
1992.790.86684557065791.83315442934212
2091.996.1516132938775-4.25161329387754
21106.5108.987036597348-2.48703659734829
22112.3113.929996900664-1.62999690066375
23102.8104.464013553896-1.66401355389582
2496.596.04328764265040.45671235734964
25101101.203373501815-0.203373501814711
2698.9100.572889330021-1.6728893300215
27105.1107.801873965936-2.70187396593648
28103105.411405882823-2.41140588282262
2999101.397543267032-2.39754326703171
30104.3106.931902248958-2.63190224895772
3194.692.0818247819212.51817521807902
3290.494.2422700518595-3.84227005185947
33108.9111.503827869013-2.60382786901287
34111.4113.148889327374-1.74888932737367
35100.8101.773598101375-0.973598101375302
36102.5100.2958224147142.20417758528581
3798.299.2070178359811-1.00701783598106
3898.7100.581102169386-1.88110216938611
39113.3114.137229012151-0.837229012150886
40104.6105.584917232484-0.98491723248429
4199.3100.789983663077-1.48998366307679
42111.8112.920154200122-1.12015420012171
4397.392.0817879503815.21821204961903
4497.7100.230531359423-2.53053135942273
45115.6115.235635247970.364364752029713
46111.9111.933847465698-0.0338474656978215
47107106.3732791491010.626720850898659
48107.1104.3747834825852.72521651741523
49100.698.77282595823681.82717404176324
5099.299.6180010846718-0.418001084671763
51108.4107.1941941313431.20580586865655
52103105.498068055881-2.49806805588113
5399.8101.397440190155-1.59744019015526
54115115.089784228316-0.0897842283156451
5590.886.44060507173364.35939492826645
5695.999.2758436728599-3.37584367285987
57114.4114.454525787504-0.0545257875035409
58108.2108.1151997448870.0848002551127333
59112.6110.6258127681391.97418723186068
60109.1104.982270058754.11772994124983
61105102.5911906158782.40880938412189
62105103.7836542973611.21634570263926
63118.5116.5670895478761.93291045212385
64103.7103.6754816848370.0245183151629613
65112.5111.6382385425470.861761457453342
66116.6115.6972507670160.902749232983666
6796.690.34597714089676.25402285910326
68101.9103.441580925273-1.54158092527275
69116.5115.8430879253590.656912074640605
70119.3117.3145814740191.98541852598144
71115.4114.1840515071421.21594849285826
72108.5104.7218880280313.77811197196916
73111.5108.6660139634682.83398603653233
74108.8107.7757339001911.02426609980933
75121.8119.4309703377472.36902966225263
76109.6108.3619100879591.23808991204126
77112.2111.1174659554961.08253404450378
78119.6117.9536677408131.64633225918669
79104.196.50780768022877.59219231977132
80105.3106.652662718624-1.35266271862423
81115113.8469624404781.15303755952211
82124.1122.4349806758551.6650193241448
83116.8114.5311763440512.26882365594925
84107.5102.9861293505964.51387064940414
85115.6112.744739722032.85526027797005
86116.2114.2929453230451.90705467695532
87116.3111.6200670850654.67993291493459
88119118.081976597070.918023402929534
89111.9110.9438408709650.956159129035282
90118.6116.8253971055971.77460289440342
91106.999.54531119431287.35468880568721
92103.2102.8340086548770.365991345122786
93118.6117.5787699298021.02123007019838
94118.7116.6202351869142.07976481308607
95102.8102.467779819020.332220180979542
96100.698.0392567698452.56074323015501
9794.995.6474712061025-0.747471206102459
9894.597.2742916700994-2.77429167009941
99102.9105.805261762652-2.90526176265168
10095.397.9473623550561-2.64736235505613
10192.595.9296622939027-3.42966229390274
102102.7105.369489835031-2.66948983503113
10391.587.9158349269153.58416507308499
10489.593.5477822648954-4.04778226489544
105104.2106.903958217097-2.70395821709672
106105.2107.420802073229-2.22080207322879
1079998.73592214800880.26407785199116
10895.593.17917107322622.3208289267738
10990.591.5682323696999-1.06823236969994
11096.196.0591598495810.0408401504189759
111113113.095272812416-0.0952728124163982
112101.9102.807364669383-0.907364669382893
113101.4100.8764622184290.523537781570893
114113.6113.1802661535460.419733846454397
11596.691.04012537681425.55987462318581
11697.899.0153225876523-1.21532258765235
117114.9114.280814000150.619185999849879
118112.5112.541201351759-0.0412013517587051
119108.4106.8938647069471.50613529305255
120107100.6428199195146.35718008048553
121103.5100.9409575265842.55904247341636
122107.5106.7338283516640.766171648336429
123122.3119.8645627701712.43543722982885

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.5 & 102.418905996048 & -0.918905996048432 \tabularnewline
2 & 100.7 & 103.610662748529 & -2.91066274852908 \tabularnewline
3 & 110.6 & 112.141389245569 & -1.54138924556928 \tabularnewline
4 & 96.8 & 99.9439584636572 & -3.14395846365718 \tabularnewline
5 & 100 & 104.695547337439 & -4.69554733743874 \tabularnewline
6 & 104.8 & 108.407362209724 & -3.60736220972403 \tabularnewline
7 & 86.8 & 86.5275393344217 & 0.272460665578293 \tabularnewline
8 & 92 & 98.408104019453 & -6.40810401945297 \tabularnewline
9 & 100.2 & 103.866639468568 & -3.66663946856767 \tabularnewline
10 & 106.6 & 111.152843123784 & -4.55284312378368 \tabularnewline
11 & 102.1 & 104.984757458399 & -2.88475745839939 \tabularnewline
12 & 93.7 & 94.9150799539053 & -1.21507995390533 \tabularnewline
13 & 97.6 & 100.162189226217 & -2.56218922621698 \tabularnewline
14 & 96.9 & 99.7919241160756 & -2.8919241160756 \tabularnewline
15 & 105.6 & 108.322681169021 & -2.7226811690214 \tabularnewline
16 & 102.8 & 105.758615772445 & -2.95861577244514 \tabularnewline
17 & 101.7 & 104.782282533009 & -3.08228253300862 \tabularnewline
18 & 104.2 & 107.105517600154 & -2.90551760015379 \tabularnewline
19 & 92.7 & 90.8668455706579 & 1.83315442934212 \tabularnewline
20 & 91.9 & 96.1516132938775 & -4.25161329387754 \tabularnewline
21 & 106.5 & 108.987036597348 & -2.48703659734829 \tabularnewline
22 & 112.3 & 113.929996900664 & -1.62999690066375 \tabularnewline
23 & 102.8 & 104.464013553896 & -1.66401355389582 \tabularnewline
24 & 96.5 & 96.0432876426504 & 0.45671235734964 \tabularnewline
25 & 101 & 101.203373501815 & -0.203373501814711 \tabularnewline
26 & 98.9 & 100.572889330021 & -1.6728893300215 \tabularnewline
27 & 105.1 & 107.801873965936 & -2.70187396593648 \tabularnewline
28 & 103 & 105.411405882823 & -2.41140588282262 \tabularnewline
29 & 99 & 101.397543267032 & -2.39754326703171 \tabularnewline
30 & 104.3 & 106.931902248958 & -2.63190224895772 \tabularnewline
31 & 94.6 & 92.081824781921 & 2.51817521807902 \tabularnewline
32 & 90.4 & 94.2422700518595 & -3.84227005185947 \tabularnewline
33 & 108.9 & 111.503827869013 & -2.60382786901287 \tabularnewline
34 & 111.4 & 113.148889327374 & -1.74888932737367 \tabularnewline
35 & 100.8 & 101.773598101375 & -0.973598101375302 \tabularnewline
36 & 102.5 & 100.295822414714 & 2.20417758528581 \tabularnewline
37 & 98.2 & 99.2070178359811 & -1.00701783598106 \tabularnewline
38 & 98.7 & 100.581102169386 & -1.88110216938611 \tabularnewline
39 & 113.3 & 114.137229012151 & -0.837229012150886 \tabularnewline
40 & 104.6 & 105.584917232484 & -0.98491723248429 \tabularnewline
41 & 99.3 & 100.789983663077 & -1.48998366307679 \tabularnewline
42 & 111.8 & 112.920154200122 & -1.12015420012171 \tabularnewline
43 & 97.3 & 92.081787950381 & 5.21821204961903 \tabularnewline
44 & 97.7 & 100.230531359423 & -2.53053135942273 \tabularnewline
45 & 115.6 & 115.23563524797 & 0.364364752029713 \tabularnewline
46 & 111.9 & 111.933847465698 & -0.0338474656978215 \tabularnewline
47 & 107 & 106.373279149101 & 0.626720850898659 \tabularnewline
48 & 107.1 & 104.374783482585 & 2.72521651741523 \tabularnewline
49 & 100.6 & 98.7728259582368 & 1.82717404176324 \tabularnewline
50 & 99.2 & 99.6180010846718 & -0.418001084671763 \tabularnewline
51 & 108.4 & 107.194194131343 & 1.20580586865655 \tabularnewline
52 & 103 & 105.498068055881 & -2.49806805588113 \tabularnewline
53 & 99.8 & 101.397440190155 & -1.59744019015526 \tabularnewline
54 & 115 & 115.089784228316 & -0.0897842283156451 \tabularnewline
55 & 90.8 & 86.4406050717336 & 4.35939492826645 \tabularnewline
56 & 95.9 & 99.2758436728599 & -3.37584367285987 \tabularnewline
57 & 114.4 & 114.454525787504 & -0.0545257875035409 \tabularnewline
58 & 108.2 & 108.115199744887 & 0.0848002551127333 \tabularnewline
59 & 112.6 & 110.625812768139 & 1.97418723186068 \tabularnewline
60 & 109.1 & 104.98227005875 & 4.11772994124983 \tabularnewline
61 & 105 & 102.591190615878 & 2.40880938412189 \tabularnewline
62 & 105 & 103.783654297361 & 1.21634570263926 \tabularnewline
63 & 118.5 & 116.567089547876 & 1.93291045212385 \tabularnewline
64 & 103.7 & 103.675481684837 & 0.0245183151629613 \tabularnewline
65 & 112.5 & 111.638238542547 & 0.861761457453342 \tabularnewline
66 & 116.6 & 115.697250767016 & 0.902749232983666 \tabularnewline
67 & 96.6 & 90.3459771408967 & 6.25402285910326 \tabularnewline
68 & 101.9 & 103.441580925273 & -1.54158092527275 \tabularnewline
69 & 116.5 & 115.843087925359 & 0.656912074640605 \tabularnewline
70 & 119.3 & 117.314581474019 & 1.98541852598144 \tabularnewline
71 & 115.4 & 114.184051507142 & 1.21594849285826 \tabularnewline
72 & 108.5 & 104.721888028031 & 3.77811197196916 \tabularnewline
73 & 111.5 & 108.666013963468 & 2.83398603653233 \tabularnewline
74 & 108.8 & 107.775733900191 & 1.02426609980933 \tabularnewline
75 & 121.8 & 119.430970337747 & 2.36902966225263 \tabularnewline
76 & 109.6 & 108.361910087959 & 1.23808991204126 \tabularnewline
77 & 112.2 & 111.117465955496 & 1.08253404450378 \tabularnewline
78 & 119.6 & 117.953667740813 & 1.64633225918669 \tabularnewline
79 & 104.1 & 96.5078076802287 & 7.59219231977132 \tabularnewline
80 & 105.3 & 106.652662718624 & -1.35266271862423 \tabularnewline
81 & 115 & 113.846962440478 & 1.15303755952211 \tabularnewline
82 & 124.1 & 122.434980675855 & 1.6650193241448 \tabularnewline
83 & 116.8 & 114.531176344051 & 2.26882365594925 \tabularnewline
84 & 107.5 & 102.986129350596 & 4.51387064940414 \tabularnewline
85 & 115.6 & 112.74473972203 & 2.85526027797005 \tabularnewline
86 & 116.2 & 114.292945323045 & 1.90705467695532 \tabularnewline
87 & 116.3 & 111.620067085065 & 4.67993291493459 \tabularnewline
88 & 119 & 118.08197659707 & 0.918023402929534 \tabularnewline
89 & 111.9 & 110.943840870965 & 0.956159129035282 \tabularnewline
90 & 118.6 & 116.825397105597 & 1.77460289440342 \tabularnewline
91 & 106.9 & 99.5453111943128 & 7.35468880568721 \tabularnewline
92 & 103.2 & 102.834008654877 & 0.365991345122786 \tabularnewline
93 & 118.6 & 117.578769929802 & 1.02123007019838 \tabularnewline
94 & 118.7 & 116.620235186914 & 2.07976481308607 \tabularnewline
95 & 102.8 & 102.46777981902 & 0.332220180979542 \tabularnewline
96 & 100.6 & 98.039256769845 & 2.56074323015501 \tabularnewline
97 & 94.9 & 95.6474712061025 & -0.747471206102459 \tabularnewline
98 & 94.5 & 97.2742916700994 & -2.77429167009941 \tabularnewline
99 & 102.9 & 105.805261762652 & -2.90526176265168 \tabularnewline
100 & 95.3 & 97.9473623550561 & -2.64736235505613 \tabularnewline
101 & 92.5 & 95.9296622939027 & -3.42966229390274 \tabularnewline
102 & 102.7 & 105.369489835031 & -2.66948983503113 \tabularnewline
103 & 91.5 & 87.915834926915 & 3.58416507308499 \tabularnewline
104 & 89.5 & 93.5477822648954 & -4.04778226489544 \tabularnewline
105 & 104.2 & 106.903958217097 & -2.70395821709672 \tabularnewline
106 & 105.2 & 107.420802073229 & -2.22080207322879 \tabularnewline
107 & 99 & 98.7359221480088 & 0.26407785199116 \tabularnewline
108 & 95.5 & 93.1791710732262 & 2.3208289267738 \tabularnewline
109 & 90.5 & 91.5682323696999 & -1.06823236969994 \tabularnewline
110 & 96.1 & 96.059159849581 & 0.0408401504189759 \tabularnewline
111 & 113 & 113.095272812416 & -0.0952728124163982 \tabularnewline
112 & 101.9 & 102.807364669383 & -0.907364669382893 \tabularnewline
113 & 101.4 & 100.876462218429 & 0.523537781570893 \tabularnewline
114 & 113.6 & 113.180266153546 & 0.419733846454397 \tabularnewline
115 & 96.6 & 91.0401253768142 & 5.55987462318581 \tabularnewline
116 & 97.8 & 99.0153225876523 & -1.21532258765235 \tabularnewline
117 & 114.9 & 114.28081400015 & 0.619185999849879 \tabularnewline
118 & 112.5 & 112.541201351759 & -0.0412013517587051 \tabularnewline
119 & 108.4 & 106.893864706947 & 1.50613529305255 \tabularnewline
120 & 107 & 100.642819919514 & 6.35718008048553 \tabularnewline
121 & 103.5 & 100.940957526584 & 2.55904247341636 \tabularnewline
122 & 107.5 & 106.733828351664 & 0.766171648336429 \tabularnewline
123 & 122.3 & 119.864562770171 & 2.43543722982885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&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]101.5[/C][C]102.418905996048[/C][C]-0.918905996048432[/C][/ROW]
[ROW][C]2[/C][C]100.7[/C][C]103.610662748529[/C][C]-2.91066274852908[/C][/ROW]
[ROW][C]3[/C][C]110.6[/C][C]112.141389245569[/C][C]-1.54138924556928[/C][/ROW]
[ROW][C]4[/C][C]96.8[/C][C]99.9439584636572[/C][C]-3.14395846365718[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]104.695547337439[/C][C]-4.69554733743874[/C][/ROW]
[ROW][C]6[/C][C]104.8[/C][C]108.407362209724[/C][C]-3.60736220972403[/C][/ROW]
[ROW][C]7[/C][C]86.8[/C][C]86.5275393344217[/C][C]0.272460665578293[/C][/ROW]
[ROW][C]8[/C][C]92[/C][C]98.408104019453[/C][C]-6.40810401945297[/C][/ROW]
[ROW][C]9[/C][C]100.2[/C][C]103.866639468568[/C][C]-3.66663946856767[/C][/ROW]
[ROW][C]10[/C][C]106.6[/C][C]111.152843123784[/C][C]-4.55284312378368[/C][/ROW]
[ROW][C]11[/C][C]102.1[/C][C]104.984757458399[/C][C]-2.88475745839939[/C][/ROW]
[ROW][C]12[/C][C]93.7[/C][C]94.9150799539053[/C][C]-1.21507995390533[/C][/ROW]
[ROW][C]13[/C][C]97.6[/C][C]100.162189226217[/C][C]-2.56218922621698[/C][/ROW]
[ROW][C]14[/C][C]96.9[/C][C]99.7919241160756[/C][C]-2.8919241160756[/C][/ROW]
[ROW][C]15[/C][C]105.6[/C][C]108.322681169021[/C][C]-2.7226811690214[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]105.758615772445[/C][C]-2.95861577244514[/C][/ROW]
[ROW][C]17[/C][C]101.7[/C][C]104.782282533009[/C][C]-3.08228253300862[/C][/ROW]
[ROW][C]18[/C][C]104.2[/C][C]107.105517600154[/C][C]-2.90551760015379[/C][/ROW]
[ROW][C]19[/C][C]92.7[/C][C]90.8668455706579[/C][C]1.83315442934212[/C][/ROW]
[ROW][C]20[/C][C]91.9[/C][C]96.1516132938775[/C][C]-4.25161329387754[/C][/ROW]
[ROW][C]21[/C][C]106.5[/C][C]108.987036597348[/C][C]-2.48703659734829[/C][/ROW]
[ROW][C]22[/C][C]112.3[/C][C]113.929996900664[/C][C]-1.62999690066375[/C][/ROW]
[ROW][C]23[/C][C]102.8[/C][C]104.464013553896[/C][C]-1.66401355389582[/C][/ROW]
[ROW][C]24[/C][C]96.5[/C][C]96.0432876426504[/C][C]0.45671235734964[/C][/ROW]
[ROW][C]25[/C][C]101[/C][C]101.203373501815[/C][C]-0.203373501814711[/C][/ROW]
[ROW][C]26[/C][C]98.9[/C][C]100.572889330021[/C][C]-1.6728893300215[/C][/ROW]
[ROW][C]27[/C][C]105.1[/C][C]107.801873965936[/C][C]-2.70187396593648[/C][/ROW]
[ROW][C]28[/C][C]103[/C][C]105.411405882823[/C][C]-2.41140588282262[/C][/ROW]
[ROW][C]29[/C][C]99[/C][C]101.397543267032[/C][C]-2.39754326703171[/C][/ROW]
[ROW][C]30[/C][C]104.3[/C][C]106.931902248958[/C][C]-2.63190224895772[/C][/ROW]
[ROW][C]31[/C][C]94.6[/C][C]92.081824781921[/C][C]2.51817521807902[/C][/ROW]
[ROW][C]32[/C][C]90.4[/C][C]94.2422700518595[/C][C]-3.84227005185947[/C][/ROW]
[ROW][C]33[/C][C]108.9[/C][C]111.503827869013[/C][C]-2.60382786901287[/C][/ROW]
[ROW][C]34[/C][C]111.4[/C][C]113.148889327374[/C][C]-1.74888932737367[/C][/ROW]
[ROW][C]35[/C][C]100.8[/C][C]101.773598101375[/C][C]-0.973598101375302[/C][/ROW]
[ROW][C]36[/C][C]102.5[/C][C]100.295822414714[/C][C]2.20417758528581[/C][/ROW]
[ROW][C]37[/C][C]98.2[/C][C]99.2070178359811[/C][C]-1.00701783598106[/C][/ROW]
[ROW][C]38[/C][C]98.7[/C][C]100.581102169386[/C][C]-1.88110216938611[/C][/ROW]
[ROW][C]39[/C][C]113.3[/C][C]114.137229012151[/C][C]-0.837229012150886[/C][/ROW]
[ROW][C]40[/C][C]104.6[/C][C]105.584917232484[/C][C]-0.98491723248429[/C][/ROW]
[ROW][C]41[/C][C]99.3[/C][C]100.789983663077[/C][C]-1.48998366307679[/C][/ROW]
[ROW][C]42[/C][C]111.8[/C][C]112.920154200122[/C][C]-1.12015420012171[/C][/ROW]
[ROW][C]43[/C][C]97.3[/C][C]92.081787950381[/C][C]5.21821204961903[/C][/ROW]
[ROW][C]44[/C][C]97.7[/C][C]100.230531359423[/C][C]-2.53053135942273[/C][/ROW]
[ROW][C]45[/C][C]115.6[/C][C]115.23563524797[/C][C]0.364364752029713[/C][/ROW]
[ROW][C]46[/C][C]111.9[/C][C]111.933847465698[/C][C]-0.0338474656978215[/C][/ROW]
[ROW][C]47[/C][C]107[/C][C]106.373279149101[/C][C]0.626720850898659[/C][/ROW]
[ROW][C]48[/C][C]107.1[/C][C]104.374783482585[/C][C]2.72521651741523[/C][/ROW]
[ROW][C]49[/C][C]100.6[/C][C]98.7728259582368[/C][C]1.82717404176324[/C][/ROW]
[ROW][C]50[/C][C]99.2[/C][C]99.6180010846718[/C][C]-0.418001084671763[/C][/ROW]
[ROW][C]51[/C][C]108.4[/C][C]107.194194131343[/C][C]1.20580586865655[/C][/ROW]
[ROW][C]52[/C][C]103[/C][C]105.498068055881[/C][C]-2.49806805588113[/C][/ROW]
[ROW][C]53[/C][C]99.8[/C][C]101.397440190155[/C][C]-1.59744019015526[/C][/ROW]
[ROW][C]54[/C][C]115[/C][C]115.089784228316[/C][C]-0.0897842283156451[/C][/ROW]
[ROW][C]55[/C][C]90.8[/C][C]86.4406050717336[/C][C]4.35939492826645[/C][/ROW]
[ROW][C]56[/C][C]95.9[/C][C]99.2758436728599[/C][C]-3.37584367285987[/C][/ROW]
[ROW][C]57[/C][C]114.4[/C][C]114.454525787504[/C][C]-0.0545257875035409[/C][/ROW]
[ROW][C]58[/C][C]108.2[/C][C]108.115199744887[/C][C]0.0848002551127333[/C][/ROW]
[ROW][C]59[/C][C]112.6[/C][C]110.625812768139[/C][C]1.97418723186068[/C][/ROW]
[ROW][C]60[/C][C]109.1[/C][C]104.98227005875[/C][C]4.11772994124983[/C][/ROW]
[ROW][C]61[/C][C]105[/C][C]102.591190615878[/C][C]2.40880938412189[/C][/ROW]
[ROW][C]62[/C][C]105[/C][C]103.783654297361[/C][C]1.21634570263926[/C][/ROW]
[ROW][C]63[/C][C]118.5[/C][C]116.567089547876[/C][C]1.93291045212385[/C][/ROW]
[ROW][C]64[/C][C]103.7[/C][C]103.675481684837[/C][C]0.0245183151629613[/C][/ROW]
[ROW][C]65[/C][C]112.5[/C][C]111.638238542547[/C][C]0.861761457453342[/C][/ROW]
[ROW][C]66[/C][C]116.6[/C][C]115.697250767016[/C][C]0.902749232983666[/C][/ROW]
[ROW][C]67[/C][C]96.6[/C][C]90.3459771408967[/C][C]6.25402285910326[/C][/ROW]
[ROW][C]68[/C][C]101.9[/C][C]103.441580925273[/C][C]-1.54158092527275[/C][/ROW]
[ROW][C]69[/C][C]116.5[/C][C]115.843087925359[/C][C]0.656912074640605[/C][/ROW]
[ROW][C]70[/C][C]119.3[/C][C]117.314581474019[/C][C]1.98541852598144[/C][/ROW]
[ROW][C]71[/C][C]115.4[/C][C]114.184051507142[/C][C]1.21594849285826[/C][/ROW]
[ROW][C]72[/C][C]108.5[/C][C]104.721888028031[/C][C]3.77811197196916[/C][/ROW]
[ROW][C]73[/C][C]111.5[/C][C]108.666013963468[/C][C]2.83398603653233[/C][/ROW]
[ROW][C]74[/C][C]108.8[/C][C]107.775733900191[/C][C]1.02426609980933[/C][/ROW]
[ROW][C]75[/C][C]121.8[/C][C]119.430970337747[/C][C]2.36902966225263[/C][/ROW]
[ROW][C]76[/C][C]109.6[/C][C]108.361910087959[/C][C]1.23808991204126[/C][/ROW]
[ROW][C]77[/C][C]112.2[/C][C]111.117465955496[/C][C]1.08253404450378[/C][/ROW]
[ROW][C]78[/C][C]119.6[/C][C]117.953667740813[/C][C]1.64633225918669[/C][/ROW]
[ROW][C]79[/C][C]104.1[/C][C]96.5078076802287[/C][C]7.59219231977132[/C][/ROW]
[ROW][C]80[/C][C]105.3[/C][C]106.652662718624[/C][C]-1.35266271862423[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]113.846962440478[/C][C]1.15303755952211[/C][/ROW]
[ROW][C]82[/C][C]124.1[/C][C]122.434980675855[/C][C]1.6650193241448[/C][/ROW]
[ROW][C]83[/C][C]116.8[/C][C]114.531176344051[/C][C]2.26882365594925[/C][/ROW]
[ROW][C]84[/C][C]107.5[/C][C]102.986129350596[/C][C]4.51387064940414[/C][/ROW]
[ROW][C]85[/C][C]115.6[/C][C]112.74473972203[/C][C]2.85526027797005[/C][/ROW]
[ROW][C]86[/C][C]116.2[/C][C]114.292945323045[/C][C]1.90705467695532[/C][/ROW]
[ROW][C]87[/C][C]116.3[/C][C]111.620067085065[/C][C]4.67993291493459[/C][/ROW]
[ROW][C]88[/C][C]119[/C][C]118.08197659707[/C][C]0.918023402929534[/C][/ROW]
[ROW][C]89[/C][C]111.9[/C][C]110.943840870965[/C][C]0.956159129035282[/C][/ROW]
[ROW][C]90[/C][C]118.6[/C][C]116.825397105597[/C][C]1.77460289440342[/C][/ROW]
[ROW][C]91[/C][C]106.9[/C][C]99.5453111943128[/C][C]7.35468880568721[/C][/ROW]
[ROW][C]92[/C][C]103.2[/C][C]102.834008654877[/C][C]0.365991345122786[/C][/ROW]
[ROW][C]93[/C][C]118.6[/C][C]117.578769929802[/C][C]1.02123007019838[/C][/ROW]
[ROW][C]94[/C][C]118.7[/C][C]116.620235186914[/C][C]2.07976481308607[/C][/ROW]
[ROW][C]95[/C][C]102.8[/C][C]102.46777981902[/C][C]0.332220180979542[/C][/ROW]
[ROW][C]96[/C][C]100.6[/C][C]98.039256769845[/C][C]2.56074323015501[/C][/ROW]
[ROW][C]97[/C][C]94.9[/C][C]95.6474712061025[/C][C]-0.747471206102459[/C][/ROW]
[ROW][C]98[/C][C]94.5[/C][C]97.2742916700994[/C][C]-2.77429167009941[/C][/ROW]
[ROW][C]99[/C][C]102.9[/C][C]105.805261762652[/C][C]-2.90526176265168[/C][/ROW]
[ROW][C]100[/C][C]95.3[/C][C]97.9473623550561[/C][C]-2.64736235505613[/C][/ROW]
[ROW][C]101[/C][C]92.5[/C][C]95.9296622939027[/C][C]-3.42966229390274[/C][/ROW]
[ROW][C]102[/C][C]102.7[/C][C]105.369489835031[/C][C]-2.66948983503113[/C][/ROW]
[ROW][C]103[/C][C]91.5[/C][C]87.915834926915[/C][C]3.58416507308499[/C][/ROW]
[ROW][C]104[/C][C]89.5[/C][C]93.5477822648954[/C][C]-4.04778226489544[/C][/ROW]
[ROW][C]105[/C][C]104.2[/C][C]106.903958217097[/C][C]-2.70395821709672[/C][/ROW]
[ROW][C]106[/C][C]105.2[/C][C]107.420802073229[/C][C]-2.22080207322879[/C][/ROW]
[ROW][C]107[/C][C]99[/C][C]98.7359221480088[/C][C]0.26407785199116[/C][/ROW]
[ROW][C]108[/C][C]95.5[/C][C]93.1791710732262[/C][C]2.3208289267738[/C][/ROW]
[ROW][C]109[/C][C]90.5[/C][C]91.5682323696999[/C][C]-1.06823236969994[/C][/ROW]
[ROW][C]110[/C][C]96.1[/C][C]96.059159849581[/C][C]0.0408401504189759[/C][/ROW]
[ROW][C]111[/C][C]113[/C][C]113.095272812416[/C][C]-0.0952728124163982[/C][/ROW]
[ROW][C]112[/C][C]101.9[/C][C]102.807364669383[/C][C]-0.907364669382893[/C][/ROW]
[ROW][C]113[/C][C]101.4[/C][C]100.876462218429[/C][C]0.523537781570893[/C][/ROW]
[ROW][C]114[/C][C]113.6[/C][C]113.180266153546[/C][C]0.419733846454397[/C][/ROW]
[ROW][C]115[/C][C]96.6[/C][C]91.0401253768142[/C][C]5.55987462318581[/C][/ROW]
[ROW][C]116[/C][C]97.8[/C][C]99.0153225876523[/C][C]-1.21532258765235[/C][/ROW]
[ROW][C]117[/C][C]114.9[/C][C]114.28081400015[/C][C]0.619185999849879[/C][/ROW]
[ROW][C]118[/C][C]112.5[/C][C]112.541201351759[/C][C]-0.0412013517587051[/C][/ROW]
[ROW][C]119[/C][C]108.4[/C][C]106.893864706947[/C][C]1.50613529305255[/C][/ROW]
[ROW][C]120[/C][C]107[/C][C]100.642819919514[/C][C]6.35718008048553[/C][/ROW]
[ROW][C]121[/C][C]103.5[/C][C]100.940957526584[/C][C]2.55904247341636[/C][/ROW]
[ROW][C]122[/C][C]107.5[/C][C]106.733828351664[/C][C]0.766171648336429[/C][/ROW]
[ROW][C]123[/C][C]122.3[/C][C]119.864562770171[/C][C]2.43543722982885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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
1101.5102.418905996048-0.918905996048432
2100.7103.610662748529-2.91066274852908
3110.6112.141389245569-1.54138924556928
496.899.9439584636572-3.14395846365718
5100104.695547337439-4.69554733743874
6104.8108.407362209724-3.60736220972403
786.886.52753933442170.272460665578293
89298.408104019453-6.40810401945297
9100.2103.866639468568-3.66663946856767
10106.6111.152843123784-4.55284312378368
11102.1104.984757458399-2.88475745839939
1293.794.9150799539053-1.21507995390533
1397.6100.162189226217-2.56218922621698
1496.999.7919241160756-2.8919241160756
15105.6108.322681169021-2.7226811690214
16102.8105.758615772445-2.95861577244514
17101.7104.782282533009-3.08228253300862
18104.2107.105517600154-2.90551760015379
1992.790.86684557065791.83315442934212
2091.996.1516132938775-4.25161329387754
21106.5108.987036597348-2.48703659734829
22112.3113.929996900664-1.62999690066375
23102.8104.464013553896-1.66401355389582
2496.596.04328764265040.45671235734964
25101101.203373501815-0.203373501814711
2698.9100.572889330021-1.6728893300215
27105.1107.801873965936-2.70187396593648
28103105.411405882823-2.41140588282262
2999101.397543267032-2.39754326703171
30104.3106.931902248958-2.63190224895772
3194.692.0818247819212.51817521807902
3290.494.2422700518595-3.84227005185947
33108.9111.503827869013-2.60382786901287
34111.4113.148889327374-1.74888932737367
35100.8101.773598101375-0.973598101375302
36102.5100.2958224147142.20417758528581
3798.299.2070178359811-1.00701783598106
3898.7100.581102169386-1.88110216938611
39113.3114.137229012151-0.837229012150886
40104.6105.584917232484-0.98491723248429
4199.3100.789983663077-1.48998366307679
42111.8112.920154200122-1.12015420012171
4397.392.0817879503815.21821204961903
4497.7100.230531359423-2.53053135942273
45115.6115.235635247970.364364752029713
46111.9111.933847465698-0.0338474656978215
47107106.3732791491010.626720850898659
48107.1104.3747834825852.72521651741523
49100.698.77282595823681.82717404176324
5099.299.6180010846718-0.418001084671763
51108.4107.1941941313431.20580586865655
52103105.498068055881-2.49806805588113
5399.8101.397440190155-1.59744019015526
54115115.089784228316-0.0897842283156451
5590.886.44060507173364.35939492826645
5695.999.2758436728599-3.37584367285987
57114.4114.454525787504-0.0545257875035409
58108.2108.1151997448870.0848002551127333
59112.6110.6258127681391.97418723186068
60109.1104.982270058754.11772994124983
61105102.5911906158782.40880938412189
62105103.7836542973611.21634570263926
63118.5116.5670895478761.93291045212385
64103.7103.6754816848370.0245183151629613
65112.5111.6382385425470.861761457453342
66116.6115.6972507670160.902749232983666
6796.690.34597714089676.25402285910326
68101.9103.441580925273-1.54158092527275
69116.5115.8430879253590.656912074640605
70119.3117.3145814740191.98541852598144
71115.4114.1840515071421.21594849285826
72108.5104.7218880280313.77811197196916
73111.5108.6660139634682.83398603653233
74108.8107.7757339001911.02426609980933
75121.8119.4309703377472.36902966225263
76109.6108.3619100879591.23808991204126
77112.2111.1174659554961.08253404450378
78119.6117.9536677408131.64633225918669
79104.196.50780768022877.59219231977132
80105.3106.652662718624-1.35266271862423
81115113.8469624404781.15303755952211
82124.1122.4349806758551.6650193241448
83116.8114.5311763440512.26882365594925
84107.5102.9861293505964.51387064940414
85115.6112.744739722032.85526027797005
86116.2114.2929453230451.90705467695532
87116.3111.6200670850654.67993291493459
88119118.081976597070.918023402929534
89111.9110.9438408709650.956159129035282
90118.6116.8253971055971.77460289440342
91106.999.54531119431287.35468880568721
92103.2102.8340086548770.365991345122786
93118.6117.5787699298021.02123007019838
94118.7116.6202351869142.07976481308607
95102.8102.467779819020.332220180979542
96100.698.0392567698452.56074323015501
9794.995.6474712061025-0.747471206102459
9894.597.2742916700994-2.77429167009941
99102.9105.805261762652-2.90526176265168
10095.397.9473623550561-2.64736235505613
10192.595.9296622939027-3.42966229390274
102102.7105.369489835031-2.66948983503113
10391.587.9158349269153.58416507308499
10489.593.5477822648954-4.04778226489544
105104.2106.903958217097-2.70395821709672
106105.2107.420802073229-2.22080207322879
1079998.73592214800880.26407785199116
10895.593.17917107322622.3208289267738
10990.591.5682323696999-1.06823236969994
11096.196.0591598495810.0408401504189759
111113113.095272812416-0.0952728124163982
112101.9102.807364669383-0.907364669382893
113101.4100.8764622184290.523537781570893
114113.6113.1802661535460.419733846454397
11596.691.04012537681425.55987462318581
11697.899.0153225876523-1.21532258765235
117114.9114.280814000150.619185999849879
118112.5112.541201351759-0.0412013517587051
119108.4106.8938647069471.50613529305255
120107100.6428199195146.35718008048553
121103.5100.9409575265842.55904247341636
122107.5106.7338283516640.766171648336429
123122.3119.8645627701712.43543722982885






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.04964911015095990.09929822030191990.95035088984904
70.1441650607946230.2883301215892470.855834939205377
80.3359937468069120.6719874936138240.664006253193088
90.2280006253983520.4560012507967050.771999374601648
100.1516148260767170.3032296521534340.848385173923283
110.1059124430069310.2118248860138620.894087556993069
120.08175665646443440.1635133129288690.918243343535566
130.06516326470831920.1303265294166380.934836735291681
140.04114724021718110.08229448043436230.958852759782819
150.02655196481102730.05310392962205460.973448035188973
160.01602754072518510.03205508145037030.983972459274815
170.009441095858424850.01888219171684970.990558904141575
180.005787382404691850.01157476480938370.994212617595308
190.01805643174823150.0361128634964630.981943568251768
200.02326513783996610.04653027567993220.976734862160034
210.01869094828709060.03738189657418120.981309051712909
220.02314318673817030.04628637347634050.97685681326183
230.01799402183651160.03598804367302310.982005978163488
240.02084051440212410.04168102880424830.979159485597876
250.01627013812900520.03254027625801030.983729861870995
260.01084962501851390.02169925003702780.989150374981486
270.007577026169401190.01515405233880240.992422973830599
280.005212335595454010.0104246711909080.994787664404546
290.003547074215436260.007094148430872520.996452925784564
300.002486092023660260.004972184047320530.99751390797634
310.007012227852253980.0140244557045080.992987772147746
320.01128985570474510.02257971140949030.988710144295255
330.009357012785103740.01871402557020750.990642987214896
340.009137742866726380.01827548573345280.990862257133274
350.007359049235394820.01471809847078960.992640950764605
360.02059902470187340.04119804940374680.979400975298127
370.01487797455305250.0297559491061050.985122025446947
380.01143961887952070.02287923775904150.988560381120479
390.0123637976983730.02472759539674610.987636202301627
400.01012267753339780.02024535506679550.989877322466602
410.007681728149647510.0153634562992950.992318271850352
420.007123396177031140.01424679235406230.992876603822969
430.05594195282836570.1118839056567310.944058047171634
440.05308768728260890.1061753745652180.946912312717391
450.06884161261447850.1376832252289570.931158387385521
460.06908167658180810.1381633531636160.930918323418192
470.06851381528634610.1370276305726920.931486184713654
480.1072708573986480.2145417147972970.892729142601352
490.1137021466867870.2274042933735750.886297853313213
500.09387430500534910.1877486100106980.906125694994651
510.09691623320130280.1938324664026060.903083766798697
520.0936878358644820.1873756717289640.906312164135518
530.08260376942763320.1652075388552660.917396230572367
540.07764591766678280.1552918353335660.922354082333217
550.1196429393900160.2392858787800330.880357060609984
560.149896532756070.2997930655121390.85010346724393
570.1424735885337280.2849471770674560.857526411466272
580.1274704935099570.2549409870199130.872529506490043
590.1457664945257780.2915329890515560.854233505474222
600.2359804531387360.4719609062774720.764019546861264
610.249214716049240.498429432098480.75078528395076
620.2278196266820850.4556392533641690.772180373317915
630.2412765986267160.4825531972534330.758723401373284
640.2093426092174720.4186852184349450.790657390782528
650.189738692856860.379477385713720.81026130714314
660.1729694167753680.3459388335507370.827030583224632
670.3604890211567340.7209780423134670.639510978843266
680.340169746535230.6803394930704610.65983025346477
690.3129837297337190.6259674594674380.687016270266281
700.3098824625044550.6197649250089090.690117537495545
710.283360898642460.5667217972849190.71663910135754
720.3286121008593240.6572242017186470.671387899140677
730.3386368157566710.6772736315133420.661363184243329
740.2997008911505720.5994017823011450.700299108849427
750.2918646387961360.5837292775922730.708135361203864
760.2570952064432690.5141904128865380.742904793556731
770.223167296281990.446334592563980.77683270371801
780.1981394651420390.3962789302840780.801860534857961
790.5118486702977660.9763026594044670.488151329702234
800.4844220962130730.9688441924261460.515577903786927
810.4381765570915730.8763531141831450.561823442908427
820.3990483850559840.7980967701119690.600951614944016
830.3709288189401460.7418576378802920.629071181059854
840.4443214801328880.8886429602657760.555678519867112
850.4413373549426040.8826747098852080.558662645057396
860.4055223863728560.8110447727457110.594477613627144
870.4996500057692830.9993000115385670.500349994230717
880.4451136320494460.8902272640988910.554886367950554
890.3913739142794180.7827478285588360.608626085720582
900.3529508176165990.7059016352331970.647049182383401
910.6884293467473430.6231413065053130.311570653252657
920.631823688918440.7363526221631190.36817631108156
930.5778197735852170.8443604528295660.422180226414783
940.5518811162623160.8962377674753690.448118883737684
950.4883289652743880.9766579305487770.511671034725612
960.4719302899110340.9438605798220690.528069710088966
970.4100193133396970.8200386266793950.589980686660303
980.4151445301018090.8302890602036190.584855469898191
990.4254788226365150.8509576452730290.574521177363485
1000.4359823644760090.8719647289520180.564017635523991
1010.516443313708930.967113372582140.48355668629107
1020.5386700248889290.9226599502221430.461329975111071
1030.539257043975870.921485912048260.46074295602413
1040.7265397793021530.5469204413956940.273460220697847
1050.781270658729190.437458682541620.21872934127081
1060.8225387106299090.3549225787401820.177461289370091
1070.7848650314258960.4302699371482080.215134968574104
1080.7194075298937440.5611849402125110.280592470106256
1090.7052293669274010.5895412661451970.294770633072599
1100.6818132223715390.6363735552569230.318186777628461
1110.5961302994468040.8077394011063920.403869700553196
1120.6139622785368880.7720754429262240.386037721463112
1130.5634145412452060.8731709175095890.436585458754794
1140.4506879573047470.9013759146094930.549312042695253
1150.4383328045076170.8766656090152340.561667195492383
1160.6188465424623390.7623069150753220.381153457537661
1170.4639554296753790.9279108593507590.536044570324621

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0496491101509599 & 0.0992982203019199 & 0.95035088984904 \tabularnewline
7 & 0.144165060794623 & 0.288330121589247 & 0.855834939205377 \tabularnewline
8 & 0.335993746806912 & 0.671987493613824 & 0.664006253193088 \tabularnewline
9 & 0.228000625398352 & 0.456001250796705 & 0.771999374601648 \tabularnewline
10 & 0.151614826076717 & 0.303229652153434 & 0.848385173923283 \tabularnewline
11 & 0.105912443006931 & 0.211824886013862 & 0.894087556993069 \tabularnewline
12 & 0.0817566564644344 & 0.163513312928869 & 0.918243343535566 \tabularnewline
13 & 0.0651632647083192 & 0.130326529416638 & 0.934836735291681 \tabularnewline
14 & 0.0411472402171811 & 0.0822944804343623 & 0.958852759782819 \tabularnewline
15 & 0.0265519648110273 & 0.0531039296220546 & 0.973448035188973 \tabularnewline
16 & 0.0160275407251851 & 0.0320550814503703 & 0.983972459274815 \tabularnewline
17 & 0.00944109585842485 & 0.0188821917168497 & 0.990558904141575 \tabularnewline
18 & 0.00578738240469185 & 0.0115747648093837 & 0.994212617595308 \tabularnewline
19 & 0.0180564317482315 & 0.036112863496463 & 0.981943568251768 \tabularnewline
20 & 0.0232651378399661 & 0.0465302756799322 & 0.976734862160034 \tabularnewline
21 & 0.0186909482870906 & 0.0373818965741812 & 0.981309051712909 \tabularnewline
22 & 0.0231431867381703 & 0.0462863734763405 & 0.97685681326183 \tabularnewline
23 & 0.0179940218365116 & 0.0359880436730231 & 0.982005978163488 \tabularnewline
24 & 0.0208405144021241 & 0.0416810288042483 & 0.979159485597876 \tabularnewline
25 & 0.0162701381290052 & 0.0325402762580103 & 0.983729861870995 \tabularnewline
26 & 0.0108496250185139 & 0.0216992500370278 & 0.989150374981486 \tabularnewline
27 & 0.00757702616940119 & 0.0151540523388024 & 0.992422973830599 \tabularnewline
28 & 0.00521233559545401 & 0.010424671190908 & 0.994787664404546 \tabularnewline
29 & 0.00354707421543626 & 0.00709414843087252 & 0.996452925784564 \tabularnewline
30 & 0.00248609202366026 & 0.00497218404732053 & 0.99751390797634 \tabularnewline
31 & 0.00701222785225398 & 0.014024455704508 & 0.992987772147746 \tabularnewline
32 & 0.0112898557047451 & 0.0225797114094903 & 0.988710144295255 \tabularnewline
33 & 0.00935701278510374 & 0.0187140255702075 & 0.990642987214896 \tabularnewline
34 & 0.00913774286672638 & 0.0182754857334528 & 0.990862257133274 \tabularnewline
35 & 0.00735904923539482 & 0.0147180984707896 & 0.992640950764605 \tabularnewline
36 & 0.0205990247018734 & 0.0411980494037468 & 0.979400975298127 \tabularnewline
37 & 0.0148779745530525 & 0.029755949106105 & 0.985122025446947 \tabularnewline
38 & 0.0114396188795207 & 0.0228792377590415 & 0.988560381120479 \tabularnewline
39 & 0.012363797698373 & 0.0247275953967461 & 0.987636202301627 \tabularnewline
40 & 0.0101226775333978 & 0.0202453550667955 & 0.989877322466602 \tabularnewline
41 & 0.00768172814964751 & 0.015363456299295 & 0.992318271850352 \tabularnewline
42 & 0.00712339617703114 & 0.0142467923540623 & 0.992876603822969 \tabularnewline
43 & 0.0559419528283657 & 0.111883905656731 & 0.944058047171634 \tabularnewline
44 & 0.0530876872826089 & 0.106175374565218 & 0.946912312717391 \tabularnewline
45 & 0.0688416126144785 & 0.137683225228957 & 0.931158387385521 \tabularnewline
46 & 0.0690816765818081 & 0.138163353163616 & 0.930918323418192 \tabularnewline
47 & 0.0685138152863461 & 0.137027630572692 & 0.931486184713654 \tabularnewline
48 & 0.107270857398648 & 0.214541714797297 & 0.892729142601352 \tabularnewline
49 & 0.113702146686787 & 0.227404293373575 & 0.886297853313213 \tabularnewline
50 & 0.0938743050053491 & 0.187748610010698 & 0.906125694994651 \tabularnewline
51 & 0.0969162332013028 & 0.193832466402606 & 0.903083766798697 \tabularnewline
52 & 0.093687835864482 & 0.187375671728964 & 0.906312164135518 \tabularnewline
53 & 0.0826037694276332 & 0.165207538855266 & 0.917396230572367 \tabularnewline
54 & 0.0776459176667828 & 0.155291835333566 & 0.922354082333217 \tabularnewline
55 & 0.119642939390016 & 0.239285878780033 & 0.880357060609984 \tabularnewline
56 & 0.14989653275607 & 0.299793065512139 & 0.85010346724393 \tabularnewline
57 & 0.142473588533728 & 0.284947177067456 & 0.857526411466272 \tabularnewline
58 & 0.127470493509957 & 0.254940987019913 & 0.872529506490043 \tabularnewline
59 & 0.145766494525778 & 0.291532989051556 & 0.854233505474222 \tabularnewline
60 & 0.235980453138736 & 0.471960906277472 & 0.764019546861264 \tabularnewline
61 & 0.24921471604924 & 0.49842943209848 & 0.75078528395076 \tabularnewline
62 & 0.227819626682085 & 0.455639253364169 & 0.772180373317915 \tabularnewline
63 & 0.241276598626716 & 0.482553197253433 & 0.758723401373284 \tabularnewline
64 & 0.209342609217472 & 0.418685218434945 & 0.790657390782528 \tabularnewline
65 & 0.18973869285686 & 0.37947738571372 & 0.81026130714314 \tabularnewline
66 & 0.172969416775368 & 0.345938833550737 & 0.827030583224632 \tabularnewline
67 & 0.360489021156734 & 0.720978042313467 & 0.639510978843266 \tabularnewline
68 & 0.34016974653523 & 0.680339493070461 & 0.65983025346477 \tabularnewline
69 & 0.312983729733719 & 0.625967459467438 & 0.687016270266281 \tabularnewline
70 & 0.309882462504455 & 0.619764925008909 & 0.690117537495545 \tabularnewline
71 & 0.28336089864246 & 0.566721797284919 & 0.71663910135754 \tabularnewline
72 & 0.328612100859324 & 0.657224201718647 & 0.671387899140677 \tabularnewline
73 & 0.338636815756671 & 0.677273631513342 & 0.661363184243329 \tabularnewline
74 & 0.299700891150572 & 0.599401782301145 & 0.700299108849427 \tabularnewline
75 & 0.291864638796136 & 0.583729277592273 & 0.708135361203864 \tabularnewline
76 & 0.257095206443269 & 0.514190412886538 & 0.742904793556731 \tabularnewline
77 & 0.22316729628199 & 0.44633459256398 & 0.77683270371801 \tabularnewline
78 & 0.198139465142039 & 0.396278930284078 & 0.801860534857961 \tabularnewline
79 & 0.511848670297766 & 0.976302659404467 & 0.488151329702234 \tabularnewline
80 & 0.484422096213073 & 0.968844192426146 & 0.515577903786927 \tabularnewline
81 & 0.438176557091573 & 0.876353114183145 & 0.561823442908427 \tabularnewline
82 & 0.399048385055984 & 0.798096770111969 & 0.600951614944016 \tabularnewline
83 & 0.370928818940146 & 0.741857637880292 & 0.629071181059854 \tabularnewline
84 & 0.444321480132888 & 0.888642960265776 & 0.555678519867112 \tabularnewline
85 & 0.441337354942604 & 0.882674709885208 & 0.558662645057396 \tabularnewline
86 & 0.405522386372856 & 0.811044772745711 & 0.594477613627144 \tabularnewline
87 & 0.499650005769283 & 0.999300011538567 & 0.500349994230717 \tabularnewline
88 & 0.445113632049446 & 0.890227264098891 & 0.554886367950554 \tabularnewline
89 & 0.391373914279418 & 0.782747828558836 & 0.608626085720582 \tabularnewline
90 & 0.352950817616599 & 0.705901635233197 & 0.647049182383401 \tabularnewline
91 & 0.688429346747343 & 0.623141306505313 & 0.311570653252657 \tabularnewline
92 & 0.63182368891844 & 0.736352622163119 & 0.36817631108156 \tabularnewline
93 & 0.577819773585217 & 0.844360452829566 & 0.422180226414783 \tabularnewline
94 & 0.551881116262316 & 0.896237767475369 & 0.448118883737684 \tabularnewline
95 & 0.488328965274388 & 0.976657930548777 & 0.511671034725612 \tabularnewline
96 & 0.471930289911034 & 0.943860579822069 & 0.528069710088966 \tabularnewline
97 & 0.410019313339697 & 0.820038626679395 & 0.589980686660303 \tabularnewline
98 & 0.415144530101809 & 0.830289060203619 & 0.584855469898191 \tabularnewline
99 & 0.425478822636515 & 0.850957645273029 & 0.574521177363485 \tabularnewline
100 & 0.435982364476009 & 0.871964728952018 & 0.564017635523991 \tabularnewline
101 & 0.51644331370893 & 0.96711337258214 & 0.48355668629107 \tabularnewline
102 & 0.538670024888929 & 0.922659950222143 & 0.461329975111071 \tabularnewline
103 & 0.53925704397587 & 0.92148591204826 & 0.46074295602413 \tabularnewline
104 & 0.726539779302153 & 0.546920441395694 & 0.273460220697847 \tabularnewline
105 & 0.78127065872919 & 0.43745868254162 & 0.21872934127081 \tabularnewline
106 & 0.822538710629909 & 0.354922578740182 & 0.177461289370091 \tabularnewline
107 & 0.784865031425896 & 0.430269937148208 & 0.215134968574104 \tabularnewline
108 & 0.719407529893744 & 0.561184940212511 & 0.280592470106256 \tabularnewline
109 & 0.705229366927401 & 0.589541266145197 & 0.294770633072599 \tabularnewline
110 & 0.681813222371539 & 0.636373555256923 & 0.318186777628461 \tabularnewline
111 & 0.596130299446804 & 0.807739401106392 & 0.403869700553196 \tabularnewline
112 & 0.613962278536888 & 0.772075442926224 & 0.386037721463112 \tabularnewline
113 & 0.563414541245206 & 0.873170917509589 & 0.436585458754794 \tabularnewline
114 & 0.450687957304747 & 0.901375914609493 & 0.549312042695253 \tabularnewline
115 & 0.438332804507617 & 0.876665609015234 & 0.561667195492383 \tabularnewline
116 & 0.618846542462339 & 0.762306915075322 & 0.381153457537661 \tabularnewline
117 & 0.463955429675379 & 0.927910859350759 & 0.536044570324621 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&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]6[/C][C]0.0496491101509599[/C][C]0.0992982203019199[/C][C]0.95035088984904[/C][/ROW]
[ROW][C]7[/C][C]0.144165060794623[/C][C]0.288330121589247[/C][C]0.855834939205377[/C][/ROW]
[ROW][C]8[/C][C]0.335993746806912[/C][C]0.671987493613824[/C][C]0.664006253193088[/C][/ROW]
[ROW][C]9[/C][C]0.228000625398352[/C][C]0.456001250796705[/C][C]0.771999374601648[/C][/ROW]
[ROW][C]10[/C][C]0.151614826076717[/C][C]0.303229652153434[/C][C]0.848385173923283[/C][/ROW]
[ROW][C]11[/C][C]0.105912443006931[/C][C]0.211824886013862[/C][C]0.894087556993069[/C][/ROW]
[ROW][C]12[/C][C]0.0817566564644344[/C][C]0.163513312928869[/C][C]0.918243343535566[/C][/ROW]
[ROW][C]13[/C][C]0.0651632647083192[/C][C]0.130326529416638[/C][C]0.934836735291681[/C][/ROW]
[ROW][C]14[/C][C]0.0411472402171811[/C][C]0.0822944804343623[/C][C]0.958852759782819[/C][/ROW]
[ROW][C]15[/C][C]0.0265519648110273[/C][C]0.0531039296220546[/C][C]0.973448035188973[/C][/ROW]
[ROW][C]16[/C][C]0.0160275407251851[/C][C]0.0320550814503703[/C][C]0.983972459274815[/C][/ROW]
[ROW][C]17[/C][C]0.00944109585842485[/C][C]0.0188821917168497[/C][C]0.990558904141575[/C][/ROW]
[ROW][C]18[/C][C]0.00578738240469185[/C][C]0.0115747648093837[/C][C]0.994212617595308[/C][/ROW]
[ROW][C]19[/C][C]0.0180564317482315[/C][C]0.036112863496463[/C][C]0.981943568251768[/C][/ROW]
[ROW][C]20[/C][C]0.0232651378399661[/C][C]0.0465302756799322[/C][C]0.976734862160034[/C][/ROW]
[ROW][C]21[/C][C]0.0186909482870906[/C][C]0.0373818965741812[/C][C]0.981309051712909[/C][/ROW]
[ROW][C]22[/C][C]0.0231431867381703[/C][C]0.0462863734763405[/C][C]0.97685681326183[/C][/ROW]
[ROW][C]23[/C][C]0.0179940218365116[/C][C]0.0359880436730231[/C][C]0.982005978163488[/C][/ROW]
[ROW][C]24[/C][C]0.0208405144021241[/C][C]0.0416810288042483[/C][C]0.979159485597876[/C][/ROW]
[ROW][C]25[/C][C]0.0162701381290052[/C][C]0.0325402762580103[/C][C]0.983729861870995[/C][/ROW]
[ROW][C]26[/C][C]0.0108496250185139[/C][C]0.0216992500370278[/C][C]0.989150374981486[/C][/ROW]
[ROW][C]27[/C][C]0.00757702616940119[/C][C]0.0151540523388024[/C][C]0.992422973830599[/C][/ROW]
[ROW][C]28[/C][C]0.00521233559545401[/C][C]0.010424671190908[/C][C]0.994787664404546[/C][/ROW]
[ROW][C]29[/C][C]0.00354707421543626[/C][C]0.00709414843087252[/C][C]0.996452925784564[/C][/ROW]
[ROW][C]30[/C][C]0.00248609202366026[/C][C]0.00497218404732053[/C][C]0.99751390797634[/C][/ROW]
[ROW][C]31[/C][C]0.00701222785225398[/C][C]0.014024455704508[/C][C]0.992987772147746[/C][/ROW]
[ROW][C]32[/C][C]0.0112898557047451[/C][C]0.0225797114094903[/C][C]0.988710144295255[/C][/ROW]
[ROW][C]33[/C][C]0.00935701278510374[/C][C]0.0187140255702075[/C][C]0.990642987214896[/C][/ROW]
[ROW][C]34[/C][C]0.00913774286672638[/C][C]0.0182754857334528[/C][C]0.990862257133274[/C][/ROW]
[ROW][C]35[/C][C]0.00735904923539482[/C][C]0.0147180984707896[/C][C]0.992640950764605[/C][/ROW]
[ROW][C]36[/C][C]0.0205990247018734[/C][C]0.0411980494037468[/C][C]0.979400975298127[/C][/ROW]
[ROW][C]37[/C][C]0.0148779745530525[/C][C]0.029755949106105[/C][C]0.985122025446947[/C][/ROW]
[ROW][C]38[/C][C]0.0114396188795207[/C][C]0.0228792377590415[/C][C]0.988560381120479[/C][/ROW]
[ROW][C]39[/C][C]0.012363797698373[/C][C]0.0247275953967461[/C][C]0.987636202301627[/C][/ROW]
[ROW][C]40[/C][C]0.0101226775333978[/C][C]0.0202453550667955[/C][C]0.989877322466602[/C][/ROW]
[ROW][C]41[/C][C]0.00768172814964751[/C][C]0.015363456299295[/C][C]0.992318271850352[/C][/ROW]
[ROW][C]42[/C][C]0.00712339617703114[/C][C]0.0142467923540623[/C][C]0.992876603822969[/C][/ROW]
[ROW][C]43[/C][C]0.0559419528283657[/C][C]0.111883905656731[/C][C]0.944058047171634[/C][/ROW]
[ROW][C]44[/C][C]0.0530876872826089[/C][C]0.106175374565218[/C][C]0.946912312717391[/C][/ROW]
[ROW][C]45[/C][C]0.0688416126144785[/C][C]0.137683225228957[/C][C]0.931158387385521[/C][/ROW]
[ROW][C]46[/C][C]0.0690816765818081[/C][C]0.138163353163616[/C][C]0.930918323418192[/C][/ROW]
[ROW][C]47[/C][C]0.0685138152863461[/C][C]0.137027630572692[/C][C]0.931486184713654[/C][/ROW]
[ROW][C]48[/C][C]0.107270857398648[/C][C]0.214541714797297[/C][C]0.892729142601352[/C][/ROW]
[ROW][C]49[/C][C]0.113702146686787[/C][C]0.227404293373575[/C][C]0.886297853313213[/C][/ROW]
[ROW][C]50[/C][C]0.0938743050053491[/C][C]0.187748610010698[/C][C]0.906125694994651[/C][/ROW]
[ROW][C]51[/C][C]0.0969162332013028[/C][C]0.193832466402606[/C][C]0.903083766798697[/C][/ROW]
[ROW][C]52[/C][C]0.093687835864482[/C][C]0.187375671728964[/C][C]0.906312164135518[/C][/ROW]
[ROW][C]53[/C][C]0.0826037694276332[/C][C]0.165207538855266[/C][C]0.917396230572367[/C][/ROW]
[ROW][C]54[/C][C]0.0776459176667828[/C][C]0.155291835333566[/C][C]0.922354082333217[/C][/ROW]
[ROW][C]55[/C][C]0.119642939390016[/C][C]0.239285878780033[/C][C]0.880357060609984[/C][/ROW]
[ROW][C]56[/C][C]0.14989653275607[/C][C]0.299793065512139[/C][C]0.85010346724393[/C][/ROW]
[ROW][C]57[/C][C]0.142473588533728[/C][C]0.284947177067456[/C][C]0.857526411466272[/C][/ROW]
[ROW][C]58[/C][C]0.127470493509957[/C][C]0.254940987019913[/C][C]0.872529506490043[/C][/ROW]
[ROW][C]59[/C][C]0.145766494525778[/C][C]0.291532989051556[/C][C]0.854233505474222[/C][/ROW]
[ROW][C]60[/C][C]0.235980453138736[/C][C]0.471960906277472[/C][C]0.764019546861264[/C][/ROW]
[ROW][C]61[/C][C]0.24921471604924[/C][C]0.49842943209848[/C][C]0.75078528395076[/C][/ROW]
[ROW][C]62[/C][C]0.227819626682085[/C][C]0.455639253364169[/C][C]0.772180373317915[/C][/ROW]
[ROW][C]63[/C][C]0.241276598626716[/C][C]0.482553197253433[/C][C]0.758723401373284[/C][/ROW]
[ROW][C]64[/C][C]0.209342609217472[/C][C]0.418685218434945[/C][C]0.790657390782528[/C][/ROW]
[ROW][C]65[/C][C]0.18973869285686[/C][C]0.37947738571372[/C][C]0.81026130714314[/C][/ROW]
[ROW][C]66[/C][C]0.172969416775368[/C][C]0.345938833550737[/C][C]0.827030583224632[/C][/ROW]
[ROW][C]67[/C][C]0.360489021156734[/C][C]0.720978042313467[/C][C]0.639510978843266[/C][/ROW]
[ROW][C]68[/C][C]0.34016974653523[/C][C]0.680339493070461[/C][C]0.65983025346477[/C][/ROW]
[ROW][C]69[/C][C]0.312983729733719[/C][C]0.625967459467438[/C][C]0.687016270266281[/C][/ROW]
[ROW][C]70[/C][C]0.309882462504455[/C][C]0.619764925008909[/C][C]0.690117537495545[/C][/ROW]
[ROW][C]71[/C][C]0.28336089864246[/C][C]0.566721797284919[/C][C]0.71663910135754[/C][/ROW]
[ROW][C]72[/C][C]0.328612100859324[/C][C]0.657224201718647[/C][C]0.671387899140677[/C][/ROW]
[ROW][C]73[/C][C]0.338636815756671[/C][C]0.677273631513342[/C][C]0.661363184243329[/C][/ROW]
[ROW][C]74[/C][C]0.299700891150572[/C][C]0.599401782301145[/C][C]0.700299108849427[/C][/ROW]
[ROW][C]75[/C][C]0.291864638796136[/C][C]0.583729277592273[/C][C]0.708135361203864[/C][/ROW]
[ROW][C]76[/C][C]0.257095206443269[/C][C]0.514190412886538[/C][C]0.742904793556731[/C][/ROW]
[ROW][C]77[/C][C]0.22316729628199[/C][C]0.44633459256398[/C][C]0.77683270371801[/C][/ROW]
[ROW][C]78[/C][C]0.198139465142039[/C][C]0.396278930284078[/C][C]0.801860534857961[/C][/ROW]
[ROW][C]79[/C][C]0.511848670297766[/C][C]0.976302659404467[/C][C]0.488151329702234[/C][/ROW]
[ROW][C]80[/C][C]0.484422096213073[/C][C]0.968844192426146[/C][C]0.515577903786927[/C][/ROW]
[ROW][C]81[/C][C]0.438176557091573[/C][C]0.876353114183145[/C][C]0.561823442908427[/C][/ROW]
[ROW][C]82[/C][C]0.399048385055984[/C][C]0.798096770111969[/C][C]0.600951614944016[/C][/ROW]
[ROW][C]83[/C][C]0.370928818940146[/C][C]0.741857637880292[/C][C]0.629071181059854[/C][/ROW]
[ROW][C]84[/C][C]0.444321480132888[/C][C]0.888642960265776[/C][C]0.555678519867112[/C][/ROW]
[ROW][C]85[/C][C]0.441337354942604[/C][C]0.882674709885208[/C][C]0.558662645057396[/C][/ROW]
[ROW][C]86[/C][C]0.405522386372856[/C][C]0.811044772745711[/C][C]0.594477613627144[/C][/ROW]
[ROW][C]87[/C][C]0.499650005769283[/C][C]0.999300011538567[/C][C]0.500349994230717[/C][/ROW]
[ROW][C]88[/C][C]0.445113632049446[/C][C]0.890227264098891[/C][C]0.554886367950554[/C][/ROW]
[ROW][C]89[/C][C]0.391373914279418[/C][C]0.782747828558836[/C][C]0.608626085720582[/C][/ROW]
[ROW][C]90[/C][C]0.352950817616599[/C][C]0.705901635233197[/C][C]0.647049182383401[/C][/ROW]
[ROW][C]91[/C][C]0.688429346747343[/C][C]0.623141306505313[/C][C]0.311570653252657[/C][/ROW]
[ROW][C]92[/C][C]0.63182368891844[/C][C]0.736352622163119[/C][C]0.36817631108156[/C][/ROW]
[ROW][C]93[/C][C]0.577819773585217[/C][C]0.844360452829566[/C][C]0.422180226414783[/C][/ROW]
[ROW][C]94[/C][C]0.551881116262316[/C][C]0.896237767475369[/C][C]0.448118883737684[/C][/ROW]
[ROW][C]95[/C][C]0.488328965274388[/C][C]0.976657930548777[/C][C]0.511671034725612[/C][/ROW]
[ROW][C]96[/C][C]0.471930289911034[/C][C]0.943860579822069[/C][C]0.528069710088966[/C][/ROW]
[ROW][C]97[/C][C]0.410019313339697[/C][C]0.820038626679395[/C][C]0.589980686660303[/C][/ROW]
[ROW][C]98[/C][C]0.415144530101809[/C][C]0.830289060203619[/C][C]0.584855469898191[/C][/ROW]
[ROW][C]99[/C][C]0.425478822636515[/C][C]0.850957645273029[/C][C]0.574521177363485[/C][/ROW]
[ROW][C]100[/C][C]0.435982364476009[/C][C]0.871964728952018[/C][C]0.564017635523991[/C][/ROW]
[ROW][C]101[/C][C]0.51644331370893[/C][C]0.96711337258214[/C][C]0.48355668629107[/C][/ROW]
[ROW][C]102[/C][C]0.538670024888929[/C][C]0.922659950222143[/C][C]0.461329975111071[/C][/ROW]
[ROW][C]103[/C][C]0.53925704397587[/C][C]0.92148591204826[/C][C]0.46074295602413[/C][/ROW]
[ROW][C]104[/C][C]0.726539779302153[/C][C]0.546920441395694[/C][C]0.273460220697847[/C][/ROW]
[ROW][C]105[/C][C]0.78127065872919[/C][C]0.43745868254162[/C][C]0.21872934127081[/C][/ROW]
[ROW][C]106[/C][C]0.822538710629909[/C][C]0.354922578740182[/C][C]0.177461289370091[/C][/ROW]
[ROW][C]107[/C][C]0.784865031425896[/C][C]0.430269937148208[/C][C]0.215134968574104[/C][/ROW]
[ROW][C]108[/C][C]0.719407529893744[/C][C]0.561184940212511[/C][C]0.280592470106256[/C][/ROW]
[ROW][C]109[/C][C]0.705229366927401[/C][C]0.589541266145197[/C][C]0.294770633072599[/C][/ROW]
[ROW][C]110[/C][C]0.681813222371539[/C][C]0.636373555256923[/C][C]0.318186777628461[/C][/ROW]
[ROW][C]111[/C][C]0.596130299446804[/C][C]0.807739401106392[/C][C]0.403869700553196[/C][/ROW]
[ROW][C]112[/C][C]0.613962278536888[/C][C]0.772075442926224[/C][C]0.386037721463112[/C][/ROW]
[ROW][C]113[/C][C]0.563414541245206[/C][C]0.873170917509589[/C][C]0.436585458754794[/C][/ROW]
[ROW][C]114[/C][C]0.450687957304747[/C][C]0.901375914609493[/C][C]0.549312042695253[/C][/ROW]
[ROW][C]115[/C][C]0.438332804507617[/C][C]0.876665609015234[/C][C]0.561667195492383[/C][/ROW]
[ROW][C]116[/C][C]0.618846542462339[/C][C]0.762306915075322[/C][C]0.381153457537661[/C][/ROW]
[ROW][C]117[/C][C]0.463955429675379[/C][C]0.927910859350759[/C][C]0.536044570324621[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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
60.04964911015095990.09929822030191990.95035088984904
70.1441650607946230.2883301215892470.855834939205377
80.3359937468069120.6719874936138240.664006253193088
90.2280006253983520.4560012507967050.771999374601648
100.1516148260767170.3032296521534340.848385173923283
110.1059124430069310.2118248860138620.894087556993069
120.08175665646443440.1635133129288690.918243343535566
130.06516326470831920.1303265294166380.934836735291681
140.04114724021718110.08229448043436230.958852759782819
150.02655196481102730.05310392962205460.973448035188973
160.01602754072518510.03205508145037030.983972459274815
170.009441095858424850.01888219171684970.990558904141575
180.005787382404691850.01157476480938370.994212617595308
190.01805643174823150.0361128634964630.981943568251768
200.02326513783996610.04653027567993220.976734862160034
210.01869094828709060.03738189657418120.981309051712909
220.02314318673817030.04628637347634050.97685681326183
230.01799402183651160.03598804367302310.982005978163488
240.02084051440212410.04168102880424830.979159485597876
250.01627013812900520.03254027625801030.983729861870995
260.01084962501851390.02169925003702780.989150374981486
270.007577026169401190.01515405233880240.992422973830599
280.005212335595454010.0104246711909080.994787664404546
290.003547074215436260.007094148430872520.996452925784564
300.002486092023660260.004972184047320530.99751390797634
310.007012227852253980.0140244557045080.992987772147746
320.01128985570474510.02257971140949030.988710144295255
330.009357012785103740.01871402557020750.990642987214896
340.009137742866726380.01827548573345280.990862257133274
350.007359049235394820.01471809847078960.992640950764605
360.02059902470187340.04119804940374680.979400975298127
370.01487797455305250.0297559491061050.985122025446947
380.01143961887952070.02287923775904150.988560381120479
390.0123637976983730.02472759539674610.987636202301627
400.01012267753339780.02024535506679550.989877322466602
410.007681728149647510.0153634562992950.992318271850352
420.007123396177031140.01424679235406230.992876603822969
430.05594195282836570.1118839056567310.944058047171634
440.05308768728260890.1061753745652180.946912312717391
450.06884161261447850.1376832252289570.931158387385521
460.06908167658180810.1381633531636160.930918323418192
470.06851381528634610.1370276305726920.931486184713654
480.1072708573986480.2145417147972970.892729142601352
490.1137021466867870.2274042933735750.886297853313213
500.09387430500534910.1877486100106980.906125694994651
510.09691623320130280.1938324664026060.903083766798697
520.0936878358644820.1873756717289640.906312164135518
530.08260376942763320.1652075388552660.917396230572367
540.07764591766678280.1552918353335660.922354082333217
550.1196429393900160.2392858787800330.880357060609984
560.149896532756070.2997930655121390.85010346724393
570.1424735885337280.2849471770674560.857526411466272
580.1274704935099570.2549409870199130.872529506490043
590.1457664945257780.2915329890515560.854233505474222
600.2359804531387360.4719609062774720.764019546861264
610.249214716049240.498429432098480.75078528395076
620.2278196266820850.4556392533641690.772180373317915
630.2412765986267160.4825531972534330.758723401373284
640.2093426092174720.4186852184349450.790657390782528
650.189738692856860.379477385713720.81026130714314
660.1729694167753680.3459388335507370.827030583224632
670.3604890211567340.7209780423134670.639510978843266
680.340169746535230.6803394930704610.65983025346477
690.3129837297337190.6259674594674380.687016270266281
700.3098824625044550.6197649250089090.690117537495545
710.283360898642460.5667217972849190.71663910135754
720.3286121008593240.6572242017186470.671387899140677
730.3386368157566710.6772736315133420.661363184243329
740.2997008911505720.5994017823011450.700299108849427
750.2918646387961360.5837292775922730.708135361203864
760.2570952064432690.5141904128865380.742904793556731
770.223167296281990.446334592563980.77683270371801
780.1981394651420390.3962789302840780.801860534857961
790.5118486702977660.9763026594044670.488151329702234
800.4844220962130730.9688441924261460.515577903786927
810.4381765570915730.8763531141831450.561823442908427
820.3990483850559840.7980967701119690.600951614944016
830.3709288189401460.7418576378802920.629071181059854
840.4443214801328880.8886429602657760.555678519867112
850.4413373549426040.8826747098852080.558662645057396
860.4055223863728560.8110447727457110.594477613627144
870.4996500057692830.9993000115385670.500349994230717
880.4451136320494460.8902272640988910.554886367950554
890.3913739142794180.7827478285588360.608626085720582
900.3529508176165990.7059016352331970.647049182383401
910.6884293467473430.6231413065053130.311570653252657
920.631823688918440.7363526221631190.36817631108156
930.5778197735852170.8443604528295660.422180226414783
940.5518811162623160.8962377674753690.448118883737684
950.4883289652743880.9766579305487770.511671034725612
960.4719302899110340.9438605798220690.528069710088966
970.4100193133396970.8200386266793950.589980686660303
980.4151445301018090.8302890602036190.584855469898191
990.4254788226365150.8509576452730290.574521177363485
1000.4359823644760090.8719647289520180.564017635523991
1010.516443313708930.967113372582140.48355668629107
1020.5386700248889290.9226599502221430.461329975111071
1030.539257043975870.921485912048260.46074295602413
1040.7265397793021530.5469204413956940.273460220697847
1050.781270658729190.437458682541620.21872934127081
1060.8225387106299090.3549225787401820.177461289370091
1070.7848650314258960.4302699371482080.215134968574104
1080.7194075298937440.5611849402125110.280592470106256
1090.7052293669274010.5895412661451970.294770633072599
1100.6818132223715390.6363735552569230.318186777628461
1110.5961302994468040.8077394011063920.403869700553196
1120.6139622785368880.7720754429262240.386037721463112
1130.5634145412452060.8731709175095890.436585458754794
1140.4506879573047470.9013759146094930.549312042695253
1150.4383328045076170.8766656090152340.561667195492383
1160.6188465424623390.7623069150753220.381153457537661
1170.4639554296753790.9279108593507590.536044570324621






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0178571428571429NOK
5% type I error level270.241071428571429NOK
10% type I error level300.267857142857143NOK

\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 & 2 & 0.0178571428571429 & NOK \tabularnewline
5% type I error level & 27 & 0.241071428571429 & NOK \tabularnewline
10% type I error level & 30 & 0.267857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190425&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]2[/C][C]0.0178571428571429[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.241071428571429[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.267857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190425&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190425&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 level20.0178571428571429NOK
5% type I error level270.241071428571429NOK
10% type I error level300.267857142857143NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}