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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 02:25:14 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586228601x2i93giy6imt5x.htm/, Retrieved Thu, 28 Mar 2024 22:30:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57667, Retrieved Thu, 28 Mar 2024 22:30:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 09:25:14] [a1151e037da67acc5ce4bbcb8804d7f1] [Current]
-    D        [Multiple Regression] [] [2009-11-19 09:58:07] [69400782d28359bd00f6a8e8fb9347a1]
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Dataseries X:
3499	1	4164	3902	3186	3353
4145	1	3499	4164	3902	3186
3796	1	4145	3499	4164	3902
3711	1	3796	4145	3499	4164
3949	1	3711	3796	4145	3499
3740	1	3949	3711	3796	4145
3243	1	3740	3949	3711	3796
4407	1	3243	3740	3949	3711
4814	1	4407	3243	3740	3949
3908	1	4814	4407	3243	3740
5250	1	3908	4814	4407	3243
3937	1	5250	3908	4814	4407
4004	1	3937	5250	3908	4814
5560	1	4004	3937	5250	3908
3922	1	5560	4004	3937	5250
3759	1	3922	5560	4004	3937
4138	1	3759	3922	5560	4004
4634	1	4138	3759	3922	5560
3996	1	4634	4138	3759	3922
4308	1	3996	4634	4138	3759
4143	0	4308	3996	4634	4138
4429	0	4143	4308	3996	4634
5219	0	4429	4143	4308	3996
4929	0	5219	4429	4143	4308
5755	0	4929	5219	4429	4143
5592	0	5755	4929	5219	4429
4163	0	5592	5755	4929	5219
4962	0	4163	5592	5755	4929
5208	0	4962	4163	5592	5755
4755	0	5208	4962	4163	5592
4491	0	4755	5208	4962	4163
5732	0	4491	4755	5208	4962
5731	0	5732	4491	4755	5208
5040	0	5731	5732	4491	4755
6102	0	5040	5731	5732	4491
4904	0	6102	5040	5731	5732
5369	0	4904	6102	5040	5731
5578	0	5369	4904	6102	5040
4619	0	5578	5369	4904	6102
4731	0	4619	5578	5369	4904
5011	0	4731	4619	5578	5369
5299	0	5011	4731	4619	5578
4146	0	5299	5011	4731	4619
4625	0	4146	5299	5011	4731
4736	0	4625	4146	5299	5011
4219	0	4736	4625	4146	5299
5116	0	4219	4736	4625	4146
4205	0	5116	4219	4736	4625
4121	0	4205	5116	4219	4736
5103	1	4121	4205	5116	4219
4300	1	5103	4121	4205	5116
4578	1	4300	5103	4121	4205
3809	1	4578	4300	5103	4121
5526	1	3809	4578	4300	5103
4247	1	5526	3809	4578	4300
3830	1	4247	5526	3809	4578
4394	1	3830	4247	5526	3809




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=0

As an alternative you can also use a 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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 815.4400473079 -146.985860400957X[t] + 0.322066277846830Y1[t] -0.0980332042753272Y2[t] + 0.385737324810814Y3[t] + 0.120494337086232Y4[t] + 756.041550490447M1[t] + 1003.38137563738M2[t] -76.4336368920148M3[t] + 546.369797069734M4[t] + 196.75507538071M5[t] + 882.128458892924M6[t] + 58.5773967719282M7[t] + 847.254865489325M8[t] + 597.636910820808M9[t] + 482.014123979181M10[t] + 1430.83045970275M11[t] -1.58386023416724t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  815.4400473079 -146.985860400957X[t] +  0.322066277846830Y1[t] -0.0980332042753272Y2[t] +  0.385737324810814Y3[t] +  0.120494337086232Y4[t] +  756.041550490447M1[t] +  1003.38137563738M2[t] -76.4336368920148M3[t] +  546.369797069734M4[t] +  196.75507538071M5[t] +  882.128458892924M6[t] +  58.5773967719282M7[t] +  847.254865489325M8[t] +  597.636910820808M9[t] +  482.014123979181M10[t] +  1430.83045970275M11[t] -1.58386023416724t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  815.4400473079 -146.985860400957X[t] +  0.322066277846830Y1[t] -0.0980332042753272Y2[t] +  0.385737324810814Y3[t] +  0.120494337086232Y4[t] +  756.041550490447M1[t] +  1003.38137563738M2[t] -76.4336368920148M3[t] +  546.369797069734M4[t] +  196.75507538071M5[t] +  882.128458892924M6[t] +  58.5773967719282M7[t] +  847.254865489325M8[t] +  597.636910820808M9[t] +  482.014123979181M10[t] +  1430.83045970275M11[t] -1.58386023416724t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 815.4400473079 -146.985860400957X[t] + 0.322066277846830Y1[t] -0.0980332042753272Y2[t] + 0.385737324810814Y3[t] + 0.120494337086232Y4[t] + 756.041550490447M1[t] + 1003.38137563738M2[t] -76.4336368920148M3[t] + 546.369797069734M4[t] + 196.75507538071M5[t] + 882.128458892924M6[t] + 58.5773967719282M7[t] + 847.254865489325M8[t] + 597.636910820808M9[t] + 482.014123979181M10[t] + 1430.83045970275M11[t] -1.58386023416724t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)815.44004730791073.5564610.75960.4520790.22604
X-146.985860400957193.289434-0.76040.4515620.225781
Y10.3220662778468300.1560032.06450.0456680.022834
Y2-0.09803320427532720.156715-0.62560.5352540.267627
Y30.3857373248108140.1588172.42880.0198580.009929
Y40.1204943370862320.1706110.70630.484230.242115
M1756.041550490447385.7004591.96020.0571470.028574
M21003.38137563738327.0095593.06840.0039040.001952
M3-76.4336368920148327.677084-0.23330.816780.40839
M4546.369797069734392.6387661.39150.1719510.085975
M5196.75507538071335.5848010.58630.5610490.280524
M6882.128458892924378.262282.33210.0249540.012477
M758.5773967719282314.2799160.18640.8531080.426554
M8847.254865489325376.5745292.24990.0301720.015086
M9597.636910820808315.1133411.89660.0653080.032654
M10482.014123979181365.3548461.31930.1947610.09738
M111430.83045970275367.6030953.89230.0003770.000189
t-1.583860234167244.618476-0.34290.7334850.366743

\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) & 815.4400473079 & 1073.556461 & 0.7596 & 0.452079 & 0.22604 \tabularnewline
X & -146.985860400957 & 193.289434 & -0.7604 & 0.451562 & 0.225781 \tabularnewline
Y1 & 0.322066277846830 & 0.156003 & 2.0645 & 0.045668 & 0.022834 \tabularnewline
Y2 & -0.0980332042753272 & 0.156715 & -0.6256 & 0.535254 & 0.267627 \tabularnewline
Y3 & 0.385737324810814 & 0.158817 & 2.4288 & 0.019858 & 0.009929 \tabularnewline
Y4 & 0.120494337086232 & 0.170611 & 0.7063 & 0.48423 & 0.242115 \tabularnewline
M1 & 756.041550490447 & 385.700459 & 1.9602 & 0.057147 & 0.028574 \tabularnewline
M2 & 1003.38137563738 & 327.009559 & 3.0684 & 0.003904 & 0.001952 \tabularnewline
M3 & -76.4336368920148 & 327.677084 & -0.2333 & 0.81678 & 0.40839 \tabularnewline
M4 & 546.369797069734 & 392.638766 & 1.3915 & 0.171951 & 0.085975 \tabularnewline
M5 & 196.75507538071 & 335.584801 & 0.5863 & 0.561049 & 0.280524 \tabularnewline
M6 & 882.128458892924 & 378.26228 & 2.3321 & 0.024954 & 0.012477 \tabularnewline
M7 & 58.5773967719282 & 314.279916 & 0.1864 & 0.853108 & 0.426554 \tabularnewline
M8 & 847.254865489325 & 376.574529 & 2.2499 & 0.030172 & 0.015086 \tabularnewline
M9 & 597.636910820808 & 315.113341 & 1.8966 & 0.065308 & 0.032654 \tabularnewline
M10 & 482.014123979181 & 365.354846 & 1.3193 & 0.194761 & 0.09738 \tabularnewline
M11 & 1430.83045970275 & 367.603095 & 3.8923 & 0.000377 & 0.000189 \tabularnewline
t & -1.58386023416724 & 4.618476 & -0.3429 & 0.733485 & 0.366743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&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]815.4400473079[/C][C]1073.556461[/C][C]0.7596[/C][C]0.452079[/C][C]0.22604[/C][/ROW]
[ROW][C]X[/C][C]-146.985860400957[/C][C]193.289434[/C][C]-0.7604[/C][C]0.451562[/C][C]0.225781[/C][/ROW]
[ROW][C]Y1[/C][C]0.322066277846830[/C][C]0.156003[/C][C]2.0645[/C][C]0.045668[/C][C]0.022834[/C][/ROW]
[ROW][C]Y2[/C][C]-0.0980332042753272[/C][C]0.156715[/C][C]-0.6256[/C][C]0.535254[/C][C]0.267627[/C][/ROW]
[ROW][C]Y3[/C][C]0.385737324810814[/C][C]0.158817[/C][C]2.4288[/C][C]0.019858[/C][C]0.009929[/C][/ROW]
[ROW][C]Y4[/C][C]0.120494337086232[/C][C]0.170611[/C][C]0.7063[/C][C]0.48423[/C][C]0.242115[/C][/ROW]
[ROW][C]M1[/C][C]756.041550490447[/C][C]385.700459[/C][C]1.9602[/C][C]0.057147[/C][C]0.028574[/C][/ROW]
[ROW][C]M2[/C][C]1003.38137563738[/C][C]327.009559[/C][C]3.0684[/C][C]0.003904[/C][C]0.001952[/C][/ROW]
[ROW][C]M3[/C][C]-76.4336368920148[/C][C]327.677084[/C][C]-0.2333[/C][C]0.81678[/C][C]0.40839[/C][/ROW]
[ROW][C]M4[/C][C]546.369797069734[/C][C]392.638766[/C][C]1.3915[/C][C]0.171951[/C][C]0.085975[/C][/ROW]
[ROW][C]M5[/C][C]196.75507538071[/C][C]335.584801[/C][C]0.5863[/C][C]0.561049[/C][C]0.280524[/C][/ROW]
[ROW][C]M6[/C][C]882.128458892924[/C][C]378.26228[/C][C]2.3321[/C][C]0.024954[/C][C]0.012477[/C][/ROW]
[ROW][C]M7[/C][C]58.5773967719282[/C][C]314.279916[/C][C]0.1864[/C][C]0.853108[/C][C]0.426554[/C][/ROW]
[ROW][C]M8[/C][C]847.254865489325[/C][C]376.574529[/C][C]2.2499[/C][C]0.030172[/C][C]0.015086[/C][/ROW]
[ROW][C]M9[/C][C]597.636910820808[/C][C]315.113341[/C][C]1.8966[/C][C]0.065308[/C][C]0.032654[/C][/ROW]
[ROW][C]M10[/C][C]482.014123979181[/C][C]365.354846[/C][C]1.3193[/C][C]0.194761[/C][C]0.09738[/C][/ROW]
[ROW][C]M11[/C][C]1430.83045970275[/C][C]367.603095[/C][C]3.8923[/C][C]0.000377[/C][C]0.000189[/C][/ROW]
[ROW][C]t[/C][C]-1.58386023416724[/C][C]4.618476[/C][C]-0.3429[/C][C]0.733485[/C][C]0.366743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=2

As an alternative you can also use a 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)815.44004730791073.5564610.75960.4520790.22604
X-146.985860400957193.289434-0.76040.4515620.225781
Y10.3220662778468300.1560032.06450.0456680.022834
Y2-0.09803320427532720.156715-0.62560.5352540.267627
Y30.3857373248108140.1588172.42880.0198580.009929
Y40.1204943370862320.1706110.70630.484230.242115
M1756.041550490447385.7004591.96020.0571470.028574
M21003.38137563738327.0095593.06840.0039040.001952
M3-76.4336368920148327.677084-0.23330.816780.40839
M4546.369797069734392.6387661.39150.1719510.085975
M5196.75507538071335.5848010.58630.5610490.280524
M6882.128458892924378.262282.33210.0249540.012477
M758.5773967719282314.2799160.18640.8531080.426554
M8847.254865489325376.5745292.24990.0301720.015086
M9597.636910820808315.1133411.89660.0653080.032654
M10482.014123979181365.3548461.31930.1947610.09738
M111430.83045970275367.6030953.89230.0003770.000189
t-1.583860234167244.618476-0.34290.7334850.366743







Multiple Linear Regression - Regression Statistics
Multiple R0.837277838225756
R-squared0.701034178383995
Adjusted R-squared0.570715743320609
F-TEST (value)5.37939377527833
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value7.01224103738518e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation438.580853085848
Sum Squared Residuals7501773.4230469

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.837277838225756 \tabularnewline
R-squared & 0.701034178383995 \tabularnewline
Adjusted R-squared & 0.570715743320609 \tabularnewline
F-TEST (value) & 5.37939377527833 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 7.01224103738518e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 438.580853085848 \tabularnewline
Sum Squared Residuals & 7501773.4230469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.837277838225756[/C][/ROW]
[ROW][C]R-squared[/C][C]0.701034178383995[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.570715743320609[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.37939377527833[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]7.01224103738518e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]438.580853085848[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7501773.4230469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.837277838225756
R-squared0.701034178383995
Adjusted R-squared0.570715743320609
F-TEST (value)5.37939377527833
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value7.01224103738518e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation438.580853085848
Sum Squared Residuals7501773.4230469







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134994014.44692413249-515.44692413249
241454276.40948502811-131.409485028112
337963655.59463305087140.405366949125
437113876.13782116545-165.137821165453
539493700.83477158281248.165228417188
637404412.82590675054-672.82590675054
732433422.20703345585-179.207033455847
844074151.28610619539255.713893804612
948144271.75049257232542.249507427683
1039083954.6194039217-46.6194039217033
1152504959.27287808975290.727121910246
1239374345.1400856631-408.140085663099
1340044244.7273718845-240.727371884502
1455605049.27099512249510.729004877507
1539224117.66931889528-195.669318895280
1637593926.43999982545-167.439999825446
1741384291.60340120661-153.603401206612
1846344669.08690655155-35.086906551549
1939963706.29736549665289.702634503346
2043084365.84208855129-57.8420885512853
2141434661.64906392728-518.649063927276
2244294274.37989923832154.620100761676
2352195373.37346717731-154.373467177309
2449294141.30158489376787.698415106238
2557554815.35313247362939.646867526383
2655925694.75933913491-102.759339134912
2741634463.21493845389-300.214938453886
2849624923.8568859739538.1431140260491
2952085006.73184744888201.268152551116
3047555120.56193076156-365.561930761556
3144914261.43253111766229.567468882337
3257325199.07604102141532.923958978588
3357315228.34184163925502.658158360755
3450404832.7353333298207.264666670195
3561026004.4075591307897.5924408692193
3649045130.91630542065-226.916305420648
3753695128.76234609467240.237653905330
3855785968.11436095055-390.114360950549
3946194574.2935711311744.7064288688274
4047314901.17828491782-170.178284917823
4150114816.71393664407194.286063355926
4252995234.9635207978564.0364792021519
4341464402.78290037860-256.782900378595
4446254911.80234437382-286.802344373821
4547365072.73332501888-336.733325018880
4642194534.26536351017-315.265363510168
4751165349.94609560216-233.946095602156
4842054357.64202402249-152.642024022491
4941214544.71022541472-423.710225414722
5051034989.44581976393113.554180236066
5143003989.22753846879310.772461531213
5245784113.38700811733464.612991882673
5338094299.11604311762-490.116043117618
5455264516.561735138511009.43826486149
5542474330.28016955124-83.2801695512407
5638304273.99341985809-443.993419858094
5743944583.52527684228-189.525276842281

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3499 & 4014.44692413249 & -515.44692413249 \tabularnewline
2 & 4145 & 4276.40948502811 & -131.409485028112 \tabularnewline
3 & 3796 & 3655.59463305087 & 140.405366949125 \tabularnewline
4 & 3711 & 3876.13782116545 & -165.137821165453 \tabularnewline
5 & 3949 & 3700.83477158281 & 248.165228417188 \tabularnewline
6 & 3740 & 4412.82590675054 & -672.82590675054 \tabularnewline
7 & 3243 & 3422.20703345585 & -179.207033455847 \tabularnewline
8 & 4407 & 4151.28610619539 & 255.713893804612 \tabularnewline
9 & 4814 & 4271.75049257232 & 542.249507427683 \tabularnewline
10 & 3908 & 3954.6194039217 & -46.6194039217033 \tabularnewline
11 & 5250 & 4959.27287808975 & 290.727121910246 \tabularnewline
12 & 3937 & 4345.1400856631 & -408.140085663099 \tabularnewline
13 & 4004 & 4244.7273718845 & -240.727371884502 \tabularnewline
14 & 5560 & 5049.27099512249 & 510.729004877507 \tabularnewline
15 & 3922 & 4117.66931889528 & -195.669318895280 \tabularnewline
16 & 3759 & 3926.43999982545 & -167.439999825446 \tabularnewline
17 & 4138 & 4291.60340120661 & -153.603401206612 \tabularnewline
18 & 4634 & 4669.08690655155 & -35.086906551549 \tabularnewline
19 & 3996 & 3706.29736549665 & 289.702634503346 \tabularnewline
20 & 4308 & 4365.84208855129 & -57.8420885512853 \tabularnewline
21 & 4143 & 4661.64906392728 & -518.649063927276 \tabularnewline
22 & 4429 & 4274.37989923832 & 154.620100761676 \tabularnewline
23 & 5219 & 5373.37346717731 & -154.373467177309 \tabularnewline
24 & 4929 & 4141.30158489376 & 787.698415106238 \tabularnewline
25 & 5755 & 4815.35313247362 & 939.646867526383 \tabularnewline
26 & 5592 & 5694.75933913491 & -102.759339134912 \tabularnewline
27 & 4163 & 4463.21493845389 & -300.214938453886 \tabularnewline
28 & 4962 & 4923.85688597395 & 38.1431140260491 \tabularnewline
29 & 5208 & 5006.73184744888 & 201.268152551116 \tabularnewline
30 & 4755 & 5120.56193076156 & -365.561930761556 \tabularnewline
31 & 4491 & 4261.43253111766 & 229.567468882337 \tabularnewline
32 & 5732 & 5199.07604102141 & 532.923958978588 \tabularnewline
33 & 5731 & 5228.34184163925 & 502.658158360755 \tabularnewline
34 & 5040 & 4832.7353333298 & 207.264666670195 \tabularnewline
35 & 6102 & 6004.40755913078 & 97.5924408692193 \tabularnewline
36 & 4904 & 5130.91630542065 & -226.916305420648 \tabularnewline
37 & 5369 & 5128.76234609467 & 240.237653905330 \tabularnewline
38 & 5578 & 5968.11436095055 & -390.114360950549 \tabularnewline
39 & 4619 & 4574.29357113117 & 44.7064288688274 \tabularnewline
40 & 4731 & 4901.17828491782 & -170.178284917823 \tabularnewline
41 & 5011 & 4816.71393664407 & 194.286063355926 \tabularnewline
42 & 5299 & 5234.96352079785 & 64.0364792021519 \tabularnewline
43 & 4146 & 4402.78290037860 & -256.782900378595 \tabularnewline
44 & 4625 & 4911.80234437382 & -286.802344373821 \tabularnewline
45 & 4736 & 5072.73332501888 & -336.733325018880 \tabularnewline
46 & 4219 & 4534.26536351017 & -315.265363510168 \tabularnewline
47 & 5116 & 5349.94609560216 & -233.946095602156 \tabularnewline
48 & 4205 & 4357.64202402249 & -152.642024022491 \tabularnewline
49 & 4121 & 4544.71022541472 & -423.710225414722 \tabularnewline
50 & 5103 & 4989.44581976393 & 113.554180236066 \tabularnewline
51 & 4300 & 3989.22753846879 & 310.772461531213 \tabularnewline
52 & 4578 & 4113.38700811733 & 464.612991882673 \tabularnewline
53 & 3809 & 4299.11604311762 & -490.116043117618 \tabularnewline
54 & 5526 & 4516.56173513851 & 1009.43826486149 \tabularnewline
55 & 4247 & 4330.28016955124 & -83.2801695512407 \tabularnewline
56 & 3830 & 4273.99341985809 & -443.993419858094 \tabularnewline
57 & 4394 & 4583.52527684228 & -189.525276842281 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&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]3499[/C][C]4014.44692413249[/C][C]-515.44692413249[/C][/ROW]
[ROW][C]2[/C][C]4145[/C][C]4276.40948502811[/C][C]-131.409485028112[/C][/ROW]
[ROW][C]3[/C][C]3796[/C][C]3655.59463305087[/C][C]140.405366949125[/C][/ROW]
[ROW][C]4[/C][C]3711[/C][C]3876.13782116545[/C][C]-165.137821165453[/C][/ROW]
[ROW][C]5[/C][C]3949[/C][C]3700.83477158281[/C][C]248.165228417188[/C][/ROW]
[ROW][C]6[/C][C]3740[/C][C]4412.82590675054[/C][C]-672.82590675054[/C][/ROW]
[ROW][C]7[/C][C]3243[/C][C]3422.20703345585[/C][C]-179.207033455847[/C][/ROW]
[ROW][C]8[/C][C]4407[/C][C]4151.28610619539[/C][C]255.713893804612[/C][/ROW]
[ROW][C]9[/C][C]4814[/C][C]4271.75049257232[/C][C]542.249507427683[/C][/ROW]
[ROW][C]10[/C][C]3908[/C][C]3954.6194039217[/C][C]-46.6194039217033[/C][/ROW]
[ROW][C]11[/C][C]5250[/C][C]4959.27287808975[/C][C]290.727121910246[/C][/ROW]
[ROW][C]12[/C][C]3937[/C][C]4345.1400856631[/C][C]-408.140085663099[/C][/ROW]
[ROW][C]13[/C][C]4004[/C][C]4244.7273718845[/C][C]-240.727371884502[/C][/ROW]
[ROW][C]14[/C][C]5560[/C][C]5049.27099512249[/C][C]510.729004877507[/C][/ROW]
[ROW][C]15[/C][C]3922[/C][C]4117.66931889528[/C][C]-195.669318895280[/C][/ROW]
[ROW][C]16[/C][C]3759[/C][C]3926.43999982545[/C][C]-167.439999825446[/C][/ROW]
[ROW][C]17[/C][C]4138[/C][C]4291.60340120661[/C][C]-153.603401206612[/C][/ROW]
[ROW][C]18[/C][C]4634[/C][C]4669.08690655155[/C][C]-35.086906551549[/C][/ROW]
[ROW][C]19[/C][C]3996[/C][C]3706.29736549665[/C][C]289.702634503346[/C][/ROW]
[ROW][C]20[/C][C]4308[/C][C]4365.84208855129[/C][C]-57.8420885512853[/C][/ROW]
[ROW][C]21[/C][C]4143[/C][C]4661.64906392728[/C][C]-518.649063927276[/C][/ROW]
[ROW][C]22[/C][C]4429[/C][C]4274.37989923832[/C][C]154.620100761676[/C][/ROW]
[ROW][C]23[/C][C]5219[/C][C]5373.37346717731[/C][C]-154.373467177309[/C][/ROW]
[ROW][C]24[/C][C]4929[/C][C]4141.30158489376[/C][C]787.698415106238[/C][/ROW]
[ROW][C]25[/C][C]5755[/C][C]4815.35313247362[/C][C]939.646867526383[/C][/ROW]
[ROW][C]26[/C][C]5592[/C][C]5694.75933913491[/C][C]-102.759339134912[/C][/ROW]
[ROW][C]27[/C][C]4163[/C][C]4463.21493845389[/C][C]-300.214938453886[/C][/ROW]
[ROW][C]28[/C][C]4962[/C][C]4923.85688597395[/C][C]38.1431140260491[/C][/ROW]
[ROW][C]29[/C][C]5208[/C][C]5006.73184744888[/C][C]201.268152551116[/C][/ROW]
[ROW][C]30[/C][C]4755[/C][C]5120.56193076156[/C][C]-365.561930761556[/C][/ROW]
[ROW][C]31[/C][C]4491[/C][C]4261.43253111766[/C][C]229.567468882337[/C][/ROW]
[ROW][C]32[/C][C]5732[/C][C]5199.07604102141[/C][C]532.923958978588[/C][/ROW]
[ROW][C]33[/C][C]5731[/C][C]5228.34184163925[/C][C]502.658158360755[/C][/ROW]
[ROW][C]34[/C][C]5040[/C][C]4832.7353333298[/C][C]207.264666670195[/C][/ROW]
[ROW][C]35[/C][C]6102[/C][C]6004.40755913078[/C][C]97.5924408692193[/C][/ROW]
[ROW][C]36[/C][C]4904[/C][C]5130.91630542065[/C][C]-226.916305420648[/C][/ROW]
[ROW][C]37[/C][C]5369[/C][C]5128.76234609467[/C][C]240.237653905330[/C][/ROW]
[ROW][C]38[/C][C]5578[/C][C]5968.11436095055[/C][C]-390.114360950549[/C][/ROW]
[ROW][C]39[/C][C]4619[/C][C]4574.29357113117[/C][C]44.7064288688274[/C][/ROW]
[ROW][C]40[/C][C]4731[/C][C]4901.17828491782[/C][C]-170.178284917823[/C][/ROW]
[ROW][C]41[/C][C]5011[/C][C]4816.71393664407[/C][C]194.286063355926[/C][/ROW]
[ROW][C]42[/C][C]5299[/C][C]5234.96352079785[/C][C]64.0364792021519[/C][/ROW]
[ROW][C]43[/C][C]4146[/C][C]4402.78290037860[/C][C]-256.782900378595[/C][/ROW]
[ROW][C]44[/C][C]4625[/C][C]4911.80234437382[/C][C]-286.802344373821[/C][/ROW]
[ROW][C]45[/C][C]4736[/C][C]5072.73332501888[/C][C]-336.733325018880[/C][/ROW]
[ROW][C]46[/C][C]4219[/C][C]4534.26536351017[/C][C]-315.265363510168[/C][/ROW]
[ROW][C]47[/C][C]5116[/C][C]5349.94609560216[/C][C]-233.946095602156[/C][/ROW]
[ROW][C]48[/C][C]4205[/C][C]4357.64202402249[/C][C]-152.642024022491[/C][/ROW]
[ROW][C]49[/C][C]4121[/C][C]4544.71022541472[/C][C]-423.710225414722[/C][/ROW]
[ROW][C]50[/C][C]5103[/C][C]4989.44581976393[/C][C]113.554180236066[/C][/ROW]
[ROW][C]51[/C][C]4300[/C][C]3989.22753846879[/C][C]310.772461531213[/C][/ROW]
[ROW][C]52[/C][C]4578[/C][C]4113.38700811733[/C][C]464.612991882673[/C][/ROW]
[ROW][C]53[/C][C]3809[/C][C]4299.11604311762[/C][C]-490.116043117618[/C][/ROW]
[ROW][C]54[/C][C]5526[/C][C]4516.56173513851[/C][C]1009.43826486149[/C][/ROW]
[ROW][C]55[/C][C]4247[/C][C]4330.28016955124[/C][C]-83.2801695512407[/C][/ROW]
[ROW][C]56[/C][C]3830[/C][C]4273.99341985809[/C][C]-443.993419858094[/C][/ROW]
[ROW][C]57[/C][C]4394[/C][C]4583.52527684228[/C][C]-189.525276842281[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134994014.44692413249-515.44692413249
241454276.40948502811-131.409485028112
337963655.59463305087140.405366949125
437113876.13782116545-165.137821165453
539493700.83477158281248.165228417188
637404412.82590675054-672.82590675054
732433422.20703345585-179.207033455847
844074151.28610619539255.713893804612
948144271.75049257232542.249507427683
1039083954.6194039217-46.6194039217033
1152504959.27287808975290.727121910246
1239374345.1400856631-408.140085663099
1340044244.7273718845-240.727371884502
1455605049.27099512249510.729004877507
1539224117.66931889528-195.669318895280
1637593926.43999982545-167.439999825446
1741384291.60340120661-153.603401206612
1846344669.08690655155-35.086906551549
1939963706.29736549665289.702634503346
2043084365.84208855129-57.8420885512853
2141434661.64906392728-518.649063927276
2244294274.37989923832154.620100761676
2352195373.37346717731-154.373467177309
2449294141.30158489376787.698415106238
2557554815.35313247362939.646867526383
2655925694.75933913491-102.759339134912
2741634463.21493845389-300.214938453886
2849624923.8568859739538.1431140260491
2952085006.73184744888201.268152551116
3047555120.56193076156-365.561930761556
3144914261.43253111766229.567468882337
3257325199.07604102141532.923958978588
3357315228.34184163925502.658158360755
3450404832.7353333298207.264666670195
3561026004.4075591307897.5924408692193
3649045130.91630542065-226.916305420648
3753695128.76234609467240.237653905330
3855785968.11436095055-390.114360950549
3946194574.2935711311744.7064288688274
4047314901.17828491782-170.178284917823
4150114816.71393664407194.286063355926
4252995234.9635207978564.0364792021519
4341464402.78290037860-256.782900378595
4446254911.80234437382-286.802344373821
4547365072.73332501888-336.733325018880
4642194534.26536351017-315.265363510168
4751165349.94609560216-233.946095602156
4842054357.64202402249-152.642024022491
4941214544.71022541472-423.710225414722
5051034989.44581976393113.554180236066
5143003989.22753846879310.772461531213
5245784113.38700811733464.612991882673
5338094299.11604311762-490.116043117618
5455264516.561735138511009.43826486149
5542474330.28016955124-83.2801695512407
5638304273.99341985809-443.993419858094
5743944583.52527684228-189.525276842281







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4611570421694150.922314084338830.538842957830585
220.3222645881151250.644529176230250.677735411884875
230.2823878612628160.5647757225256310.717612138737184
240.4572472291821250.914494458364250.542752770817875
250.8986205170923090.2027589658153830.101379482907691
260.8298376873805630.3403246252388740.170162312619437
270.7611461768886720.4777076462226560.238853823111328
280.7285397684760590.5429204630478830.271460231523941
290.6465258290164070.7069483419671870.353474170983593
300.9146311583277350.1707376833445310.0853688416722653
310.9338303143052150.1323393713895700.0661696856947852
320.8917502769102480.2164994461795040.108249723089752
330.8203168980652990.3593662038694010.179683101934701
340.8098167742900420.3803664514199160.190183225709958
350.7428165513321030.5143668973357940.257183448667897
360.7117627255736350.576474548852730.288237274426365

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.461157042169415 & 0.92231408433883 & 0.538842957830585 \tabularnewline
22 & 0.322264588115125 & 0.64452917623025 & 0.677735411884875 \tabularnewline
23 & 0.282387861262816 & 0.564775722525631 & 0.717612138737184 \tabularnewline
24 & 0.457247229182125 & 0.91449445836425 & 0.542752770817875 \tabularnewline
25 & 0.898620517092309 & 0.202758965815383 & 0.101379482907691 \tabularnewline
26 & 0.829837687380563 & 0.340324625238874 & 0.170162312619437 \tabularnewline
27 & 0.761146176888672 & 0.477707646222656 & 0.238853823111328 \tabularnewline
28 & 0.728539768476059 & 0.542920463047883 & 0.271460231523941 \tabularnewline
29 & 0.646525829016407 & 0.706948341967187 & 0.353474170983593 \tabularnewline
30 & 0.914631158327735 & 0.170737683344531 & 0.0853688416722653 \tabularnewline
31 & 0.933830314305215 & 0.132339371389570 & 0.0661696856947852 \tabularnewline
32 & 0.891750276910248 & 0.216499446179504 & 0.108249723089752 \tabularnewline
33 & 0.820316898065299 & 0.359366203869401 & 0.179683101934701 \tabularnewline
34 & 0.809816774290042 & 0.380366451419916 & 0.190183225709958 \tabularnewline
35 & 0.742816551332103 & 0.514366897335794 & 0.257183448667897 \tabularnewline
36 & 0.711762725573635 & 0.57647454885273 & 0.288237274426365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&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]21[/C][C]0.461157042169415[/C][C]0.92231408433883[/C][C]0.538842957830585[/C][/ROW]
[ROW][C]22[/C][C]0.322264588115125[/C][C]0.64452917623025[/C][C]0.677735411884875[/C][/ROW]
[ROW][C]23[/C][C]0.282387861262816[/C][C]0.564775722525631[/C][C]0.717612138737184[/C][/ROW]
[ROW][C]24[/C][C]0.457247229182125[/C][C]0.91449445836425[/C][C]0.542752770817875[/C][/ROW]
[ROW][C]25[/C][C]0.898620517092309[/C][C]0.202758965815383[/C][C]0.101379482907691[/C][/ROW]
[ROW][C]26[/C][C]0.829837687380563[/C][C]0.340324625238874[/C][C]0.170162312619437[/C][/ROW]
[ROW][C]27[/C][C]0.761146176888672[/C][C]0.477707646222656[/C][C]0.238853823111328[/C][/ROW]
[ROW][C]28[/C][C]0.728539768476059[/C][C]0.542920463047883[/C][C]0.271460231523941[/C][/ROW]
[ROW][C]29[/C][C]0.646525829016407[/C][C]0.706948341967187[/C][C]0.353474170983593[/C][/ROW]
[ROW][C]30[/C][C]0.914631158327735[/C][C]0.170737683344531[/C][C]0.0853688416722653[/C][/ROW]
[ROW][C]31[/C][C]0.933830314305215[/C][C]0.132339371389570[/C][C]0.0661696856947852[/C][/ROW]
[ROW][C]32[/C][C]0.891750276910248[/C][C]0.216499446179504[/C][C]0.108249723089752[/C][/ROW]
[ROW][C]33[/C][C]0.820316898065299[/C][C]0.359366203869401[/C][C]0.179683101934701[/C][/ROW]
[ROW][C]34[/C][C]0.809816774290042[/C][C]0.380366451419916[/C][C]0.190183225709958[/C][/ROW]
[ROW][C]35[/C][C]0.742816551332103[/C][C]0.514366897335794[/C][C]0.257183448667897[/C][/ROW]
[ROW][C]36[/C][C]0.711762725573635[/C][C]0.57647454885273[/C][C]0.288237274426365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4611570421694150.922314084338830.538842957830585
220.3222645881151250.644529176230250.677735411884875
230.2823878612628160.5647757225256310.717612138737184
240.4572472291821250.914494458364250.542752770817875
250.8986205170923090.2027589658153830.101379482907691
260.8298376873805630.3403246252388740.170162312619437
270.7611461768886720.4777076462226560.238853823111328
280.7285397684760590.5429204630478830.271460231523941
290.6465258290164070.7069483419671870.353474170983593
300.9146311583277350.1707376833445310.0853688416722653
310.9338303143052150.1323393713895700.0661696856947852
320.8917502769102480.2164994461795040.108249723089752
330.8203168980652990.3593662038694010.179683101934701
340.8098167742900420.3803664514199160.190183225709958
350.7428165513321030.5143668973357940.257183448667897
360.7117627255736350.576474548852730.288237274426365







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57667&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57667&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57667&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}