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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:34:54 -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/20/t1258742159j11bsaf9vbc5hk4.htm/, Retrieved Fri, 29 Mar 2024 10:12:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58400, Retrieved Fri, 29 Mar 2024 10:12:06 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7] [2009-11-20 18:34:54] [b243db81ea3e1f02fb3382887fb0f701] [Current]
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Dataseries X:
3016.70	2756.76
3052.40	2849.27
3099.60	2921.44
3103.30	2981.85
3119.80	3080.58
3093.70	3106.22
3164.90	3119.31
3311.50	3061.26
3410.60	3097.31
3392.60	3161.69
3338.20	3257.16
3285.10	3277.01
3294.80	3295.32
3611.20	3363.99
3611.30	3494.17
3521.00	3667.03
3519.30	3813.06
3438.30	3917.96
3534.90	3895.51
3705.80	3801.06
3807.60	3570.12
3663.00	3701.61
3604.50	3862.27
3563.80	3970.10
3511.40	4138.52
3546.50	4199.75
3525.40	4290.89
3529.90	4443.91
3591.60	4502.64
3668.30	4356.98
3728.80	4591.27
3853.60	4696.96
3897.70	4621.40
3640.70	4562.84
3495.50	4202.52
3495.10	4296.49
3268.00	4435.23
3479.10	4105.18
3417.80	4116.68
3521.30	3844.49
3487.10	3720.98
3529.90	3674.40
3544.30	3857.62
3710.80	3801.06
3641.90	3504.37
3447.10	3032.60
3386.80	3047.03
3438.50	2962.34
3364.30	2197.82
3462.70	2014.45
3291.90	1862.83
3550.00	1905.41
3611.00	1810.99
3708.60	1670.07
3771.10	1864.44
4042.70	2052.02
3988.40	2029.60
3851.20	2070.83
3876.70	2293.41




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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=58400&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=58400&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Zichtrekeningen[t] = + 2943.37797804525 + 0.0683218595762599`Bel20 `[t] -94.2882744750586M1[t] + 40.5455669616858M2[t] -11.2127651932675M3[t] + 34.0636735807399M4[t] + 45.0719224853597M5[t] + 61.357970088517M6[t] + 105.682285287828M7[t] + 272.128981094533M8[t] + 296.062320766578M9[t] + 141.266497108976M10[t] + 72.3889677968816M11[t] + 8.48262743431114t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Zichtrekeningen[t] =  +  2943.37797804525 +  0.0683218595762599`Bel20
`[t] -94.2882744750586M1[t] +  40.5455669616858M2[t] -11.2127651932675M3[t] +  34.0636735807399M4[t] +  45.0719224853597M5[t] +  61.357970088517M6[t] +  105.682285287828M7[t] +  272.128981094533M8[t] +  296.062320766578M9[t] +  141.266497108976M10[t] +  72.3889677968816M11[t] +  8.48262743431114t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Zichtrekeningen[t] =  +  2943.37797804525 +  0.0683218595762599`Bel20
`[t] -94.2882744750586M1[t] +  40.5455669616858M2[t] -11.2127651932675M3[t] +  34.0636735807399M4[t] +  45.0719224853597M5[t] +  61.357970088517M6[t] +  105.682285287828M7[t] +  272.128981094533M8[t] +  296.062320766578M9[t] +  141.266497108976M10[t] +  72.3889677968816M11[t] +  8.48262743431114t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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
Zichtrekeningen[t] = + 2943.37797804525 + 0.0683218595762599`Bel20 `[t] -94.2882744750586M1[t] + 40.5455669616858M2[t] -11.2127651932675M3[t] + 34.0636735807399M4[t] + 45.0719224853597M5[t] + 61.357970088517M6[t] + 105.682285287828M7[t] + 272.128981094533M8[t] + 296.062320766578M9[t] + 141.266497108976M10[t] + 72.3889677968816M11[t] + 8.48262743431114t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2943.37797804525133.93069721.976900
`Bel20 `0.06832185957625990.0252322.70780.0095420.004771
M1-94.2882744750586103.144258-0.91410.3655160.182758
M240.5455669616858103.1765760.3930.6961950.348098
M3-11.2127651932675103.023002-0.10880.9138150.456908
M434.0636735807399102.8955190.33110.7421430.371071
M545.0719224853597102.8178480.43840.6632180.331609
M661.357970088517102.8510130.59660.5537830.276892
M7105.682285287828102.6772051.02930.3088550.154427
M8272.128981094533102.6714392.65050.0110540.005527
M9296.062320766578102.8074012.87980.0060730.003036
M10141.266497108976102.9163411.37260.1766690.088334
M1172.3889677968816102.9160290.70340.4854430.242722
t8.482627434311141.2595426.734700

\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) & 2943.37797804525 & 133.930697 & 21.9769 & 0 & 0 \tabularnewline
`Bel20
` & 0.0683218595762599 & 0.025232 & 2.7078 & 0.009542 & 0.004771 \tabularnewline
M1 & -94.2882744750586 & 103.144258 & -0.9141 & 0.365516 & 0.182758 \tabularnewline
M2 & 40.5455669616858 & 103.176576 & 0.393 & 0.696195 & 0.348098 \tabularnewline
M3 & -11.2127651932675 & 103.023002 & -0.1088 & 0.913815 & 0.456908 \tabularnewline
M4 & 34.0636735807399 & 102.895519 & 0.3311 & 0.742143 & 0.371071 \tabularnewline
M5 & 45.0719224853597 & 102.817848 & 0.4384 & 0.663218 & 0.331609 \tabularnewline
M6 & 61.357970088517 & 102.851013 & 0.5966 & 0.553783 & 0.276892 \tabularnewline
M7 & 105.682285287828 & 102.677205 & 1.0293 & 0.308855 & 0.154427 \tabularnewline
M8 & 272.128981094533 & 102.671439 & 2.6505 & 0.011054 & 0.005527 \tabularnewline
M9 & 296.062320766578 & 102.807401 & 2.8798 & 0.006073 & 0.003036 \tabularnewline
M10 & 141.266497108976 & 102.916341 & 1.3726 & 0.176669 & 0.088334 \tabularnewline
M11 & 72.3889677968816 & 102.916029 & 0.7034 & 0.485443 & 0.242722 \tabularnewline
t & 8.48262743431114 & 1.259542 & 6.7347 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&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]2943.37797804525[/C][C]133.930697[/C][C]21.9769[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Bel20
`[/C][C]0.0683218595762599[/C][C]0.025232[/C][C]2.7078[/C][C]0.009542[/C][C]0.004771[/C][/ROW]
[ROW][C]M1[/C][C]-94.2882744750586[/C][C]103.144258[/C][C]-0.9141[/C][C]0.365516[/C][C]0.182758[/C][/ROW]
[ROW][C]M2[/C][C]40.5455669616858[/C][C]103.176576[/C][C]0.393[/C][C]0.696195[/C][C]0.348098[/C][/ROW]
[ROW][C]M3[/C][C]-11.2127651932675[/C][C]103.023002[/C][C]-0.1088[/C][C]0.913815[/C][C]0.456908[/C][/ROW]
[ROW][C]M4[/C][C]34.0636735807399[/C][C]102.895519[/C][C]0.3311[/C][C]0.742143[/C][C]0.371071[/C][/ROW]
[ROW][C]M5[/C][C]45.0719224853597[/C][C]102.817848[/C][C]0.4384[/C][C]0.663218[/C][C]0.331609[/C][/ROW]
[ROW][C]M6[/C][C]61.357970088517[/C][C]102.851013[/C][C]0.5966[/C][C]0.553783[/C][C]0.276892[/C][/ROW]
[ROW][C]M7[/C][C]105.682285287828[/C][C]102.677205[/C][C]1.0293[/C][C]0.308855[/C][C]0.154427[/C][/ROW]
[ROW][C]M8[/C][C]272.128981094533[/C][C]102.671439[/C][C]2.6505[/C][C]0.011054[/C][C]0.005527[/C][/ROW]
[ROW][C]M9[/C][C]296.062320766578[/C][C]102.807401[/C][C]2.8798[/C][C]0.006073[/C][C]0.003036[/C][/ROW]
[ROW][C]M10[/C][C]141.266497108976[/C][C]102.916341[/C][C]1.3726[/C][C]0.176669[/C][C]0.088334[/C][/ROW]
[ROW][C]M11[/C][C]72.3889677968816[/C][C]102.916029[/C][C]0.7034[/C][C]0.485443[/C][C]0.242722[/C][/ROW]
[ROW][C]t[/C][C]8.48262743431114[/C][C]1.259542[/C][C]6.7347[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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)2943.37797804525133.93069721.976900
`Bel20 `0.06832185957625990.0252322.70780.0095420.004771
M1-94.2882744750586103.144258-0.91410.3655160.182758
M240.5455669616858103.1765760.3930.6961950.348098
M3-11.2127651932675103.023002-0.10880.9138150.456908
M434.0636735807399102.8955190.33110.7421430.371071
M545.0719224853597102.8178480.43840.6632180.331609
M661.357970088517102.8510130.59660.5537830.276892
M7105.682285287828102.6772051.02930.3088550.154427
M8272.128981094533102.6714392.65050.0110540.005527
M9296.062320766578102.8074012.87980.0060730.003036
M10141.266497108976102.9163411.37260.1766690.088334
M1172.3889677968816102.9160290.70340.4854430.242722
t8.482627434311141.2595426.734700







Multiple Linear Regression - Regression Statistics
Multiple R0.81148330821063
R-squared0.658505159504467
Adjusted R-squared0.559851094472425
F-TEST (value)6.67489129100342
F-TEST (DF numerator)13
F-TEST (DF denominator)45
p-value7.13518564787741e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.955565226744
Sum Squared Residuals1052793.22202247

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.81148330821063 \tabularnewline
R-squared & 0.658505159504467 \tabularnewline
Adjusted R-squared & 0.559851094472425 \tabularnewline
F-TEST (value) & 6.67489129100342 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 7.13518564787741e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 152.955565226744 \tabularnewline
Sum Squared Residuals & 1052793.22202247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.81148330821063[/C][/ROW]
[ROW][C]R-squared[/C][C]0.658505159504467[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.559851094472425[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.67489129100342[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]7.13518564787741e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]152.955565226744[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1052793.22202247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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 R0.81148330821063
R-squared0.658505159504467
Adjusted R-squared0.559851094472425
F-TEST (value)6.67489129100342
F-TEST (DF numerator)13
F-TEST (DF denominator)45
p-value7.13518564787741e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.955565226744
Sum Squared Residuals1052793.22202247







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13016.73045.91930060995-29.2193006099471
23052.43195.55622471041-143.156224710411
33099.63157.21130859539-57.6113085953879
43103.33215.09769834071-111.797698340707
53119.83241.3339918756-121.533991875603
63093.73267.85443939261-174.154439392607
73164.93321.55571516808-156.655715168082
83311.53492.51895446070-181.018954460697
93410.63527.39792460478-116.797924604777
103392.63385.483289701017.11671029899395
113338.23331.611075756976.58892424303202
123285.13269.0609243069916.0390756930139
133294.83184.50625051508110.293749484920
143611.23332.51438148324278.685618516763
153611.33298.13281644223313.167183557767
1635213363.70199929690157.298000703097
173519.33393.16991678976126.130083210245
183438.33425.1055548967713.1944451032266
193534.93476.3786717829158.5213282170912
203705.83644.8549953869560.9450046130524
213807.63661.49271224276146.107287757238
2236633524.16315733515138.836842664846
233604.53474.74484541689129.755154583108
243563.83418.20565117243145.594348827570
253511.43343.90677172152167.493228278484
263546.53491.4065880544355.0934119455739
273525.43454.3577376155671.0422623844358
283529.93518.5714147762411.3285852237581
293591.63542.0748339280949.5251660719132
303668.33556.89174689968111.408253100323
313728.83625.70581801342103.094181986579
323853.63807.8560785930545.7439214069471
333897.73835.1096459898362.5903540101733
343640.73684.79552166975-44.0955216697502
353495.53599.78288734945-104.282887349448
363495.13542.29675213126-47.1967521312591
3732683465.97007988812-197.970079888122
383479.13586.73691900603-107.636919006033
393417.83544.24691567052-126.446915670517
403521.33579.40945492077-58.1094549207738
413487.13590.46189838344-103.361898383441
423529.93612.04814120185-82.1481412018471
433544.33677.37301494703-133.073014947032
443710.83848.43805381042-137.638053810415
453641.93860.58360839909-218.683608399091
463447.13682.03820848351-234.938208483508
473386.83622.62919103941-235.829191039409
483438.53552.93667238933-114.436672389325
493364.33414.89759726534-50.5975972653355
503462.73545.68588674589-82.9858867458928
513291.93492.0512216763-200.151221676298
5235503548.719432665371.28056733462649
5336113561.7593590231149.240640976886
543708.63576.90011760910131.699882390904
553771.13642.98678008856128.113219911444
564042.73830.73191774889211.968082251113
573988.43861.61610876354126.783891236457
583851.23718.11982281058133.080177189418
593876.73672.93200043728203.767999562718

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3016.7 & 3045.91930060995 & -29.2193006099471 \tabularnewline
2 & 3052.4 & 3195.55622471041 & -143.156224710411 \tabularnewline
3 & 3099.6 & 3157.21130859539 & -57.6113085953879 \tabularnewline
4 & 3103.3 & 3215.09769834071 & -111.797698340707 \tabularnewline
5 & 3119.8 & 3241.3339918756 & -121.533991875603 \tabularnewline
6 & 3093.7 & 3267.85443939261 & -174.154439392607 \tabularnewline
7 & 3164.9 & 3321.55571516808 & -156.655715168082 \tabularnewline
8 & 3311.5 & 3492.51895446070 & -181.018954460697 \tabularnewline
9 & 3410.6 & 3527.39792460478 & -116.797924604777 \tabularnewline
10 & 3392.6 & 3385.48328970101 & 7.11671029899395 \tabularnewline
11 & 3338.2 & 3331.61107575697 & 6.58892424303202 \tabularnewline
12 & 3285.1 & 3269.06092430699 & 16.0390756930139 \tabularnewline
13 & 3294.8 & 3184.50625051508 & 110.293749484920 \tabularnewline
14 & 3611.2 & 3332.51438148324 & 278.685618516763 \tabularnewline
15 & 3611.3 & 3298.13281644223 & 313.167183557767 \tabularnewline
16 & 3521 & 3363.70199929690 & 157.298000703097 \tabularnewline
17 & 3519.3 & 3393.16991678976 & 126.130083210245 \tabularnewline
18 & 3438.3 & 3425.10555489677 & 13.1944451032266 \tabularnewline
19 & 3534.9 & 3476.37867178291 & 58.5213282170912 \tabularnewline
20 & 3705.8 & 3644.85499538695 & 60.9450046130524 \tabularnewline
21 & 3807.6 & 3661.49271224276 & 146.107287757238 \tabularnewline
22 & 3663 & 3524.16315733515 & 138.836842664846 \tabularnewline
23 & 3604.5 & 3474.74484541689 & 129.755154583108 \tabularnewline
24 & 3563.8 & 3418.20565117243 & 145.594348827570 \tabularnewline
25 & 3511.4 & 3343.90677172152 & 167.493228278484 \tabularnewline
26 & 3546.5 & 3491.40658805443 & 55.0934119455739 \tabularnewline
27 & 3525.4 & 3454.35773761556 & 71.0422623844358 \tabularnewline
28 & 3529.9 & 3518.57141477624 & 11.3285852237581 \tabularnewline
29 & 3591.6 & 3542.07483392809 & 49.5251660719132 \tabularnewline
30 & 3668.3 & 3556.89174689968 & 111.408253100323 \tabularnewline
31 & 3728.8 & 3625.70581801342 & 103.094181986579 \tabularnewline
32 & 3853.6 & 3807.85607859305 & 45.7439214069471 \tabularnewline
33 & 3897.7 & 3835.10964598983 & 62.5903540101733 \tabularnewline
34 & 3640.7 & 3684.79552166975 & -44.0955216697502 \tabularnewline
35 & 3495.5 & 3599.78288734945 & -104.282887349448 \tabularnewline
36 & 3495.1 & 3542.29675213126 & -47.1967521312591 \tabularnewline
37 & 3268 & 3465.97007988812 & -197.970079888122 \tabularnewline
38 & 3479.1 & 3586.73691900603 & -107.636919006033 \tabularnewline
39 & 3417.8 & 3544.24691567052 & -126.446915670517 \tabularnewline
40 & 3521.3 & 3579.40945492077 & -58.1094549207738 \tabularnewline
41 & 3487.1 & 3590.46189838344 & -103.361898383441 \tabularnewline
42 & 3529.9 & 3612.04814120185 & -82.1481412018471 \tabularnewline
43 & 3544.3 & 3677.37301494703 & -133.073014947032 \tabularnewline
44 & 3710.8 & 3848.43805381042 & -137.638053810415 \tabularnewline
45 & 3641.9 & 3860.58360839909 & -218.683608399091 \tabularnewline
46 & 3447.1 & 3682.03820848351 & -234.938208483508 \tabularnewline
47 & 3386.8 & 3622.62919103941 & -235.829191039409 \tabularnewline
48 & 3438.5 & 3552.93667238933 & -114.436672389325 \tabularnewline
49 & 3364.3 & 3414.89759726534 & -50.5975972653355 \tabularnewline
50 & 3462.7 & 3545.68588674589 & -82.9858867458928 \tabularnewline
51 & 3291.9 & 3492.0512216763 & -200.151221676298 \tabularnewline
52 & 3550 & 3548.71943266537 & 1.28056733462649 \tabularnewline
53 & 3611 & 3561.75935902311 & 49.240640976886 \tabularnewline
54 & 3708.6 & 3576.90011760910 & 131.699882390904 \tabularnewline
55 & 3771.1 & 3642.98678008856 & 128.113219911444 \tabularnewline
56 & 4042.7 & 3830.73191774889 & 211.968082251113 \tabularnewline
57 & 3988.4 & 3861.61610876354 & 126.783891236457 \tabularnewline
58 & 3851.2 & 3718.11982281058 & 133.080177189418 \tabularnewline
59 & 3876.7 & 3672.93200043728 & 203.767999562718 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&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]3016.7[/C][C]3045.91930060995[/C][C]-29.2193006099471[/C][/ROW]
[ROW][C]2[/C][C]3052.4[/C][C]3195.55622471041[/C][C]-143.156224710411[/C][/ROW]
[ROW][C]3[/C][C]3099.6[/C][C]3157.21130859539[/C][C]-57.6113085953879[/C][/ROW]
[ROW][C]4[/C][C]3103.3[/C][C]3215.09769834071[/C][C]-111.797698340707[/C][/ROW]
[ROW][C]5[/C][C]3119.8[/C][C]3241.3339918756[/C][C]-121.533991875603[/C][/ROW]
[ROW][C]6[/C][C]3093.7[/C][C]3267.85443939261[/C][C]-174.154439392607[/C][/ROW]
[ROW][C]7[/C][C]3164.9[/C][C]3321.55571516808[/C][C]-156.655715168082[/C][/ROW]
[ROW][C]8[/C][C]3311.5[/C][C]3492.51895446070[/C][C]-181.018954460697[/C][/ROW]
[ROW][C]9[/C][C]3410.6[/C][C]3527.39792460478[/C][C]-116.797924604777[/C][/ROW]
[ROW][C]10[/C][C]3392.6[/C][C]3385.48328970101[/C][C]7.11671029899395[/C][/ROW]
[ROW][C]11[/C][C]3338.2[/C][C]3331.61107575697[/C][C]6.58892424303202[/C][/ROW]
[ROW][C]12[/C][C]3285.1[/C][C]3269.06092430699[/C][C]16.0390756930139[/C][/ROW]
[ROW][C]13[/C][C]3294.8[/C][C]3184.50625051508[/C][C]110.293749484920[/C][/ROW]
[ROW][C]14[/C][C]3611.2[/C][C]3332.51438148324[/C][C]278.685618516763[/C][/ROW]
[ROW][C]15[/C][C]3611.3[/C][C]3298.13281644223[/C][C]313.167183557767[/C][/ROW]
[ROW][C]16[/C][C]3521[/C][C]3363.70199929690[/C][C]157.298000703097[/C][/ROW]
[ROW][C]17[/C][C]3519.3[/C][C]3393.16991678976[/C][C]126.130083210245[/C][/ROW]
[ROW][C]18[/C][C]3438.3[/C][C]3425.10555489677[/C][C]13.1944451032266[/C][/ROW]
[ROW][C]19[/C][C]3534.9[/C][C]3476.37867178291[/C][C]58.5213282170912[/C][/ROW]
[ROW][C]20[/C][C]3705.8[/C][C]3644.85499538695[/C][C]60.9450046130524[/C][/ROW]
[ROW][C]21[/C][C]3807.6[/C][C]3661.49271224276[/C][C]146.107287757238[/C][/ROW]
[ROW][C]22[/C][C]3663[/C][C]3524.16315733515[/C][C]138.836842664846[/C][/ROW]
[ROW][C]23[/C][C]3604.5[/C][C]3474.74484541689[/C][C]129.755154583108[/C][/ROW]
[ROW][C]24[/C][C]3563.8[/C][C]3418.20565117243[/C][C]145.594348827570[/C][/ROW]
[ROW][C]25[/C][C]3511.4[/C][C]3343.90677172152[/C][C]167.493228278484[/C][/ROW]
[ROW][C]26[/C][C]3546.5[/C][C]3491.40658805443[/C][C]55.0934119455739[/C][/ROW]
[ROW][C]27[/C][C]3525.4[/C][C]3454.35773761556[/C][C]71.0422623844358[/C][/ROW]
[ROW][C]28[/C][C]3529.9[/C][C]3518.57141477624[/C][C]11.3285852237581[/C][/ROW]
[ROW][C]29[/C][C]3591.6[/C][C]3542.07483392809[/C][C]49.5251660719132[/C][/ROW]
[ROW][C]30[/C][C]3668.3[/C][C]3556.89174689968[/C][C]111.408253100323[/C][/ROW]
[ROW][C]31[/C][C]3728.8[/C][C]3625.70581801342[/C][C]103.094181986579[/C][/ROW]
[ROW][C]32[/C][C]3853.6[/C][C]3807.85607859305[/C][C]45.7439214069471[/C][/ROW]
[ROW][C]33[/C][C]3897.7[/C][C]3835.10964598983[/C][C]62.5903540101733[/C][/ROW]
[ROW][C]34[/C][C]3640.7[/C][C]3684.79552166975[/C][C]-44.0955216697502[/C][/ROW]
[ROW][C]35[/C][C]3495.5[/C][C]3599.78288734945[/C][C]-104.282887349448[/C][/ROW]
[ROW][C]36[/C][C]3495.1[/C][C]3542.29675213126[/C][C]-47.1967521312591[/C][/ROW]
[ROW][C]37[/C][C]3268[/C][C]3465.97007988812[/C][C]-197.970079888122[/C][/ROW]
[ROW][C]38[/C][C]3479.1[/C][C]3586.73691900603[/C][C]-107.636919006033[/C][/ROW]
[ROW][C]39[/C][C]3417.8[/C][C]3544.24691567052[/C][C]-126.446915670517[/C][/ROW]
[ROW][C]40[/C][C]3521.3[/C][C]3579.40945492077[/C][C]-58.1094549207738[/C][/ROW]
[ROW][C]41[/C][C]3487.1[/C][C]3590.46189838344[/C][C]-103.361898383441[/C][/ROW]
[ROW][C]42[/C][C]3529.9[/C][C]3612.04814120185[/C][C]-82.1481412018471[/C][/ROW]
[ROW][C]43[/C][C]3544.3[/C][C]3677.37301494703[/C][C]-133.073014947032[/C][/ROW]
[ROW][C]44[/C][C]3710.8[/C][C]3848.43805381042[/C][C]-137.638053810415[/C][/ROW]
[ROW][C]45[/C][C]3641.9[/C][C]3860.58360839909[/C][C]-218.683608399091[/C][/ROW]
[ROW][C]46[/C][C]3447.1[/C][C]3682.03820848351[/C][C]-234.938208483508[/C][/ROW]
[ROW][C]47[/C][C]3386.8[/C][C]3622.62919103941[/C][C]-235.829191039409[/C][/ROW]
[ROW][C]48[/C][C]3438.5[/C][C]3552.93667238933[/C][C]-114.436672389325[/C][/ROW]
[ROW][C]49[/C][C]3364.3[/C][C]3414.89759726534[/C][C]-50.5975972653355[/C][/ROW]
[ROW][C]50[/C][C]3462.7[/C][C]3545.68588674589[/C][C]-82.9858867458928[/C][/ROW]
[ROW][C]51[/C][C]3291.9[/C][C]3492.0512216763[/C][C]-200.151221676298[/C][/ROW]
[ROW][C]52[/C][C]3550[/C][C]3548.71943266537[/C][C]1.28056733462649[/C][/ROW]
[ROW][C]53[/C][C]3611[/C][C]3561.75935902311[/C][C]49.240640976886[/C][/ROW]
[ROW][C]54[/C][C]3708.6[/C][C]3576.90011760910[/C][C]131.699882390904[/C][/ROW]
[ROW][C]55[/C][C]3771.1[/C][C]3642.98678008856[/C][C]128.113219911444[/C][/ROW]
[ROW][C]56[/C][C]4042.7[/C][C]3830.73191774889[/C][C]211.968082251113[/C][/ROW]
[ROW][C]57[/C][C]3988.4[/C][C]3861.61610876354[/C][C]126.783891236457[/C][/ROW]
[ROW][C]58[/C][C]3851.2[/C][C]3718.11982281058[/C][C]133.080177189418[/C][/ROW]
[ROW][C]59[/C][C]3876.7[/C][C]3672.93200043728[/C][C]203.767999562718[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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
13016.73045.91930060995-29.2193006099471
23052.43195.55622471041-143.156224710411
33099.63157.21130859539-57.6113085953879
43103.33215.09769834071-111.797698340707
53119.83241.3339918756-121.533991875603
63093.73267.85443939261-174.154439392607
73164.93321.55571516808-156.655715168082
83311.53492.51895446070-181.018954460697
93410.63527.39792460478-116.797924604777
103392.63385.483289701017.11671029899395
113338.23331.611075756976.58892424303202
123285.13269.0609243069916.0390756930139
133294.83184.50625051508110.293749484920
143611.23332.51438148324278.685618516763
153611.33298.13281644223313.167183557767
1635213363.70199929690157.298000703097
173519.33393.16991678976126.130083210245
183438.33425.1055548967713.1944451032266
193534.93476.3786717829158.5213282170912
203705.83644.8549953869560.9450046130524
213807.63661.49271224276146.107287757238
2236633524.16315733515138.836842664846
233604.53474.74484541689129.755154583108
243563.83418.20565117243145.594348827570
253511.43343.90677172152167.493228278484
263546.53491.4065880544355.0934119455739
273525.43454.3577376155671.0422623844358
283529.93518.5714147762411.3285852237581
293591.63542.0748339280949.5251660719132
303668.33556.89174689968111.408253100323
313728.83625.70581801342103.094181986579
323853.63807.8560785930545.7439214069471
333897.73835.1096459898362.5903540101733
343640.73684.79552166975-44.0955216697502
353495.53599.78288734945-104.282887349448
363495.13542.29675213126-47.1967521312591
3732683465.97007988812-197.970079888122
383479.13586.73691900603-107.636919006033
393417.83544.24691567052-126.446915670517
403521.33579.40945492077-58.1094549207738
413487.13590.46189838344-103.361898383441
423529.93612.04814120185-82.1481412018471
433544.33677.37301494703-133.073014947032
443710.83848.43805381042-137.638053810415
453641.93860.58360839909-218.683608399091
463447.13682.03820848351-234.938208483508
473386.83622.62919103941-235.829191039409
483438.53552.93667238933-114.436672389325
493364.33414.89759726534-50.5975972653355
503462.73545.68588674589-82.9858867458928
513291.93492.0512216763-200.151221676298
5235503548.719432665371.28056733462649
5336113561.7593590231149.240640976886
543708.63576.90011760910131.699882390904
553771.13642.98678008856128.113219911444
564042.73830.73191774889211.968082251113
573988.43861.61610876354126.783891236457
583851.23718.11982281058133.080177189418
593876.73672.93200043728203.767999562718







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.266564861856330.533129723712660.73343513814367
180.1435840594052760.2871681188105510.856415940594724
190.06981163225274390.1396232645054880.930188367747256
200.03453022621879980.06906045243759960.9654697737812
210.018661166696970.037322333393940.98133883330303
220.02367147144945630.04734294289891250.976328528550544
230.02066993246768160.04133986493536320.979330067532318
240.01312166638149020.02624333276298030.98687833361851
250.02092640438026240.04185280876052470.979073595619738
260.06372207519209670.1274441503841930.936277924807903
270.1385180775895910.2770361551791820.861481922410409
280.1176650068679430.2353300137358850.882334993132057
290.09711450277179170.1942290055435830.902885497228208
300.08394432257650950.1678886451530190.91605567742349
310.0754735501883650.150947100376730.924526449811635
320.06244535540201660.1248907108040330.937554644597983
330.09439202328536230.1887840465707250.905607976714638
340.2018550593289630.4037101186579260.798144940671037
350.7076440098455120.5847119803089750.292355990154488
360.9647205674447560.07055886511048840.0352794325552442
370.9683158985512860.06336820289742740.0316841014487137
380.9508330043425560.09833399131488730.0491669956574436
390.9741554322349070.05168913553018680.0258445677650934
400.992272580500980.01545483899804120.00772741949902059
410.995985033650770.00802993269845920.0040149663492296
420.9928247083619720.01435058327605630.00717529163802814

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.26656486185633 & 0.53312972371266 & 0.73343513814367 \tabularnewline
18 & 0.143584059405276 & 0.287168118810551 & 0.856415940594724 \tabularnewline
19 & 0.0698116322527439 & 0.139623264505488 & 0.930188367747256 \tabularnewline
20 & 0.0345302262187998 & 0.0690604524375996 & 0.9654697737812 \tabularnewline
21 & 0.01866116669697 & 0.03732233339394 & 0.98133883330303 \tabularnewline
22 & 0.0236714714494563 & 0.0473429428989125 & 0.976328528550544 \tabularnewline
23 & 0.0206699324676816 & 0.0413398649353632 & 0.979330067532318 \tabularnewline
24 & 0.0131216663814902 & 0.0262433327629803 & 0.98687833361851 \tabularnewline
25 & 0.0209264043802624 & 0.0418528087605247 & 0.979073595619738 \tabularnewline
26 & 0.0637220751920967 & 0.127444150384193 & 0.936277924807903 \tabularnewline
27 & 0.138518077589591 & 0.277036155179182 & 0.861481922410409 \tabularnewline
28 & 0.117665006867943 & 0.235330013735885 & 0.882334993132057 \tabularnewline
29 & 0.0971145027717917 & 0.194229005543583 & 0.902885497228208 \tabularnewline
30 & 0.0839443225765095 & 0.167888645153019 & 0.91605567742349 \tabularnewline
31 & 0.075473550188365 & 0.15094710037673 & 0.924526449811635 \tabularnewline
32 & 0.0624453554020166 & 0.124890710804033 & 0.937554644597983 \tabularnewline
33 & 0.0943920232853623 & 0.188784046570725 & 0.905607976714638 \tabularnewline
34 & 0.201855059328963 & 0.403710118657926 & 0.798144940671037 \tabularnewline
35 & 0.707644009845512 & 0.584711980308975 & 0.292355990154488 \tabularnewline
36 & 0.964720567444756 & 0.0705588651104884 & 0.0352794325552442 \tabularnewline
37 & 0.968315898551286 & 0.0633682028974274 & 0.0316841014487137 \tabularnewline
38 & 0.950833004342556 & 0.0983339913148873 & 0.0491669956574436 \tabularnewline
39 & 0.974155432234907 & 0.0516891355301868 & 0.0258445677650934 \tabularnewline
40 & 0.99227258050098 & 0.0154548389980412 & 0.00772741949902059 \tabularnewline
41 & 0.99598503365077 & 0.0080299326984592 & 0.0040149663492296 \tabularnewline
42 & 0.992824708361972 & 0.0143505832760563 & 0.00717529163802814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&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]17[/C][C]0.26656486185633[/C][C]0.53312972371266[/C][C]0.73343513814367[/C][/ROW]
[ROW][C]18[/C][C]0.143584059405276[/C][C]0.287168118810551[/C][C]0.856415940594724[/C][/ROW]
[ROW][C]19[/C][C]0.0698116322527439[/C][C]0.139623264505488[/C][C]0.930188367747256[/C][/ROW]
[ROW][C]20[/C][C]0.0345302262187998[/C][C]0.0690604524375996[/C][C]0.9654697737812[/C][/ROW]
[ROW][C]21[/C][C]0.01866116669697[/C][C]0.03732233339394[/C][C]0.98133883330303[/C][/ROW]
[ROW][C]22[/C][C]0.0236714714494563[/C][C]0.0473429428989125[/C][C]0.976328528550544[/C][/ROW]
[ROW][C]23[/C][C]0.0206699324676816[/C][C]0.0413398649353632[/C][C]0.979330067532318[/C][/ROW]
[ROW][C]24[/C][C]0.0131216663814902[/C][C]0.0262433327629803[/C][C]0.98687833361851[/C][/ROW]
[ROW][C]25[/C][C]0.0209264043802624[/C][C]0.0418528087605247[/C][C]0.979073595619738[/C][/ROW]
[ROW][C]26[/C][C]0.0637220751920967[/C][C]0.127444150384193[/C][C]0.936277924807903[/C][/ROW]
[ROW][C]27[/C][C]0.138518077589591[/C][C]0.277036155179182[/C][C]0.861481922410409[/C][/ROW]
[ROW][C]28[/C][C]0.117665006867943[/C][C]0.235330013735885[/C][C]0.882334993132057[/C][/ROW]
[ROW][C]29[/C][C]0.0971145027717917[/C][C]0.194229005543583[/C][C]0.902885497228208[/C][/ROW]
[ROW][C]30[/C][C]0.0839443225765095[/C][C]0.167888645153019[/C][C]0.91605567742349[/C][/ROW]
[ROW][C]31[/C][C]0.075473550188365[/C][C]0.15094710037673[/C][C]0.924526449811635[/C][/ROW]
[ROW][C]32[/C][C]0.0624453554020166[/C][C]0.124890710804033[/C][C]0.937554644597983[/C][/ROW]
[ROW][C]33[/C][C]0.0943920232853623[/C][C]0.188784046570725[/C][C]0.905607976714638[/C][/ROW]
[ROW][C]34[/C][C]0.201855059328963[/C][C]0.403710118657926[/C][C]0.798144940671037[/C][/ROW]
[ROW][C]35[/C][C]0.707644009845512[/C][C]0.584711980308975[/C][C]0.292355990154488[/C][/ROW]
[ROW][C]36[/C][C]0.964720567444756[/C][C]0.0705588651104884[/C][C]0.0352794325552442[/C][/ROW]
[ROW][C]37[/C][C]0.968315898551286[/C][C]0.0633682028974274[/C][C]0.0316841014487137[/C][/ROW]
[ROW][C]38[/C][C]0.950833004342556[/C][C]0.0983339913148873[/C][C]0.0491669956574436[/C][/ROW]
[ROW][C]39[/C][C]0.974155432234907[/C][C]0.0516891355301868[/C][C]0.0258445677650934[/C][/ROW]
[ROW][C]40[/C][C]0.99227258050098[/C][C]0.0154548389980412[/C][C]0.00772741949902059[/C][/ROW]
[ROW][C]41[/C][C]0.99598503365077[/C][C]0.0080299326984592[/C][C]0.0040149663492296[/C][/ROW]
[ROW][C]42[/C][C]0.992824708361972[/C][C]0.0143505832760563[/C][C]0.00717529163802814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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
170.266564861856330.533129723712660.73343513814367
180.1435840594052760.2871681188105510.856415940594724
190.06981163225274390.1396232645054880.930188367747256
200.03453022621879980.06906045243759960.9654697737812
210.018661166696970.037322333393940.98133883330303
220.02367147144945630.04734294289891250.976328528550544
230.02066993246768160.04133986493536320.979330067532318
240.01312166638149020.02624333276298030.98687833361851
250.02092640438026240.04185280876052470.979073595619738
260.06372207519209670.1274441503841930.936277924807903
270.1385180775895910.2770361551791820.861481922410409
280.1176650068679430.2353300137358850.882334993132057
290.09711450277179170.1942290055435830.902885497228208
300.08394432257650950.1678886451530190.91605567742349
310.0754735501883650.150947100376730.924526449811635
320.06244535540201660.1248907108040330.937554644597983
330.09439202328536230.1887840465707250.905607976714638
340.2018550593289630.4037101186579260.798144940671037
350.7076440098455120.5847119803089750.292355990154488
360.9647205674447560.07055886511048840.0352794325552442
370.9683158985512860.06336820289742740.0316841014487137
380.9508330043425560.09833399131488730.0491669956574436
390.9741554322349070.05168913553018680.0258445677650934
400.992272580500980.01545483899804120.00772741949902059
410.995985033650770.00802993269845920.0040149663492296
420.9928247083619720.01435058327605630.00717529163802814







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0384615384615385NOK
5% type I error level80.307692307692308NOK
10% type I error level130.5NOK

\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 & 1 & 0.0384615384615385 & NOK \tabularnewline
5% type I error level & 8 & 0.307692307692308 & NOK \tabularnewline
10% type I error level & 13 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58400&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]1[/C][C]0.0384615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58400&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58400&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 level10.0384615384615385NOK
5% type I error level80.307692307692308NOK
10% type I error level130.5NOK



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')
}