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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 computationTue, 23 Nov 2010 18:36:14 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290537295nqx06hepanv1ics.htm/, Retrieved Thu, 28 Mar 2024 18:24:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99558, Retrieved Thu, 28 Mar 2024 18:24:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7: Mini-tutorial e] [2010-11-19 16:06:32] [1fd136673b2a4fecb5c545b9b4a05d64]
-   PD    [Multiple Regression] [ws7 trend] [2010-11-23 18:36:14] [2953e4eb3235e2fd3d6373a16d27c72f] [Current]
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Dataseries X:
1	28	6	6	6.06	6.06	3.53	3.53	48	48	5	5
1	40	5	5	8.1	8.1	4.52	4.52	63	63	11	11
1	79	3	3	79.38	79.38	3.72	3.72	113	113	13	13
1	16	2	2	26.26	26.26	3.17	3.17	104	104	1	1
1	90	2	2	39.56	39.56	3.39	3.39	89	89	11	11
1	87	5	5	65.61	65.61	4.15	4.15	97	97	3	3
1	53	5	5	80.3	80.3	3.09	3.09	114	114	11	11
1	23	5	5	34.68	34.68	2.76	2.76	57	57	9	9
1	42	6	6	7.17	7.17	5.14	5.14	127	127	10	10
1	64	4	4	65.88	65.88	4.78	4.78	64	64	4	4
1	87	6	6	42.69	42.69	4.22	4.22	91	91	2	2
1	77	2	2	54.94	54.94	3.93	3.93	127	127	2	2
1	70	4	4	89.99	89.99	3.01	3.01	45	45	10	10
1	82	4	4	72.64	72.64	5.12	5.12	40	40	9	9
1	44	3	3	24.96	24.96	5.82	5.82	33	33	1	1
1	36	2	2	57.52	57.52	2.83	2.83	60	60	7	7
0	73	2	0	71.91	0	5.11	0	50	0	3	0
0	75	3	0	65.34	0	5.99	0	128	0	11	0
0	21	3	0	34.62	0	3.15	0	52	0	7	0
0	81	2	0	60.92	0	3.5	0	40	0	1	0
0	99	3	0	56.49	0	4.5	0	29	0	9	0
0	54	3	0	56.19	0	3.31	0	36	0	5	0
0	6	4	0	61.2	0	5.31	0	49	0	9	0
0	71	5	0	58.2	0	4.24	0	57	0	7	0
0	93	6	0	75.91	0	5.06	0	82	0	4	0
0	82	3	0	73.66	0	4.72	0	34	0	10	0
0	32	4	0	73.87	0	4.58	0	36	0	13	0
0	93	4	0	87.21	0	5.3	0	89	0	9	0
0	24	4	0	64.29	0	5.11	0	69	0	5	0
0	96	5	0	71.82	0	4.05	0	35	0	8	0
0	88	4	0	89.31	0	4.62	0	65	0	12	0
0	83	2	0	1.41	0	4.66	0	70	0	8	0
0	23	6	0	35.17	0	4.66	0	60	0	5	0
0	20	5	0	41.08	0	5.1	0	127	0	11	0
0	33	3	0	30.57	0	4.97	0	96	0	8	0
0	88	2	0	68.84	0	2.87	0	61	0	9	0
0	98	2	0	71.05	0	4.98	0	36	0	1	0
0	34	4	0	23.32	0	4.55	0	55	0	9	0
0	59	3	0	61.39	0	5.45	0	75	0	2	0
0	26	6	0	8.41	0	4.36	0	42	0	3	0
0	13	1	0	64.06	0	4.74	0	83	0	3	0
0	6	2	0	26.8	0	5.44	0	56	0	1	0
0	49	4	0	12.78	0	5.78	0	114	0	5	0
0	3	5	0	23.84	0	2.92	0	33	0	4	0
0	76	4	0	5.68	0	3.22	0	80	0	6	0
0	12	2	0	45.92	0	3.04	0	115	0	7	0
0	63	5	0	18.17	0	3.11	0	127	0	13	0
0	35	1	0	29.12	0	3.87	0	45	0	9	0
0	69	1	0	40.08	0	3.75	0	74	0	11	0
0	10	5	0	1.08	0	4.82	0	105	0	10	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
slaagkans[t] = + 36.7863750286887 -70.406096372685klant[t] -1.04098616693031verzekeraar[t] + 1.39545275131342verzekeraar_klant[t] + 0.554792741023526kost[t] + 0.056752300999544kost_klant[t] -2.24035951676562grootte[t] + 13.5990278640766grootte_klant[t] -0.0426617485641796snelheid[t] + 0.221180838587168snelheid_klant[t] + 0.854266191255212maand[t] -0.612581041231767maand_klant[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
slaagkans[t] =  +  36.7863750286887 -70.406096372685klant[t] -1.04098616693031verzekeraar[t] +  1.39545275131342verzekeraar_klant[t] +  0.554792741023526kost[t] +  0.056752300999544kost_klant[t] -2.24035951676562grootte[t] +  13.5990278640766grootte_klant[t] -0.0426617485641796snelheid[t] +  0.221180838587168snelheid_klant[t] +  0.854266191255212maand[t] -0.612581041231767maand_klant[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]slaagkans[t] =  +  36.7863750286887 -70.406096372685klant[t] -1.04098616693031verzekeraar[t] +  1.39545275131342verzekeraar_klant[t] +  0.554792741023526kost[t] +  0.056752300999544kost_klant[t] -2.24035951676562grootte[t] +  13.5990278640766grootte_klant[t] -0.0426617485641796snelheid[t] +  0.221180838587168snelheid_klant[t] +  0.854266191255212maand[t] -0.612581041231767maand_klant[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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
slaagkans[t] = + 36.7863750286887 -70.406096372685klant[t] -1.04098616693031verzekeraar[t] + 1.39545275131342verzekeraar_klant[t] + 0.554792741023526kost[t] + 0.056752300999544kost_klant[t] -2.24035951676562grootte[t] + 13.5990278640766grootte_klant[t] -0.0426617485641796snelheid[t] + 0.221180838587168snelheid_klant[t] + 0.854266191255212maand[t] -0.612581041231767maand_klant[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)36.786375028688730.3040061.21390.2322720.116136
klant-70.40609637268559.291374-1.18750.2424160.121208
verzekeraar-1.040986166930313.528783-0.2950.7696010.3848
verzekeraar_klant1.395452751313426.3725260.2190.8278380.413919
kost0.5547927410235260.215812.57070.0141890.007094
kost_klant0.0567523009995440.3629040.15640.8765580.438279
grootte-2.240359516765626.314635-0.35480.724710.362355
grootte_klant13.599027864076610.751921.26480.2136450.106823
snelheid-0.04266174856417960.193603-0.22040.8267730.413387
snelheid_klant0.2211808385871680.3049560.72530.4727190.236359
maand0.8542661912552121.5572870.54860.5865160.293258
maand_klant-0.6125810412317672.470105-0.2480.8054710.402736

\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) & 36.7863750286887 & 30.304006 & 1.2139 & 0.232272 & 0.116136 \tabularnewline
klant & -70.406096372685 & 59.291374 & -1.1875 & 0.242416 & 0.121208 \tabularnewline
verzekeraar & -1.04098616693031 & 3.528783 & -0.295 & 0.769601 & 0.3848 \tabularnewline
verzekeraar_klant & 1.39545275131342 & 6.372526 & 0.219 & 0.827838 & 0.413919 \tabularnewline
kost & 0.554792741023526 & 0.21581 & 2.5707 & 0.014189 & 0.007094 \tabularnewline
kost_klant & 0.056752300999544 & 0.362904 & 0.1564 & 0.876558 & 0.438279 \tabularnewline
grootte & -2.24035951676562 & 6.314635 & -0.3548 & 0.72471 & 0.362355 \tabularnewline
grootte_klant & 13.5990278640766 & 10.75192 & 1.2648 & 0.213645 & 0.106823 \tabularnewline
snelheid & -0.0426617485641796 & 0.193603 & -0.2204 & 0.826773 & 0.413387 \tabularnewline
snelheid_klant & 0.221180838587168 & 0.304956 & 0.7253 & 0.472719 & 0.236359 \tabularnewline
maand & 0.854266191255212 & 1.557287 & 0.5486 & 0.586516 & 0.293258 \tabularnewline
maand_klant & -0.612581041231767 & 2.470105 & -0.248 & 0.805471 & 0.402736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&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]36.7863750286887[/C][C]30.304006[/C][C]1.2139[/C][C]0.232272[/C][C]0.116136[/C][/ROW]
[ROW][C]klant[/C][C]-70.406096372685[/C][C]59.291374[/C][C]-1.1875[/C][C]0.242416[/C][C]0.121208[/C][/ROW]
[ROW][C]verzekeraar[/C][C]-1.04098616693031[/C][C]3.528783[/C][C]-0.295[/C][C]0.769601[/C][C]0.3848[/C][/ROW]
[ROW][C]verzekeraar_klant[/C][C]1.39545275131342[/C][C]6.372526[/C][C]0.219[/C][C]0.827838[/C][C]0.413919[/C][/ROW]
[ROW][C]kost[/C][C]0.554792741023526[/C][C]0.21581[/C][C]2.5707[/C][C]0.014189[/C][C]0.007094[/C][/ROW]
[ROW][C]kost_klant[/C][C]0.056752300999544[/C][C]0.362904[/C][C]0.1564[/C][C]0.876558[/C][C]0.438279[/C][/ROW]
[ROW][C]grootte[/C][C]-2.24035951676562[/C][C]6.314635[/C][C]-0.3548[/C][C]0.72471[/C][C]0.362355[/C][/ROW]
[ROW][C]grootte_klant[/C][C]13.5990278640766[/C][C]10.75192[/C][C]1.2648[/C][C]0.213645[/C][C]0.106823[/C][/ROW]
[ROW][C]snelheid[/C][C]-0.0426617485641796[/C][C]0.193603[/C][C]-0.2204[/C][C]0.826773[/C][C]0.413387[/C][/ROW]
[ROW][C]snelheid_klant[/C][C]0.221180838587168[/C][C]0.304956[/C][C]0.7253[/C][C]0.472719[/C][C]0.236359[/C][/ROW]
[ROW][C]maand[/C][C]0.854266191255212[/C][C]1.557287[/C][C]0.5486[/C][C]0.586516[/C][C]0.293258[/C][/ROW]
[ROW][C]maand_klant[/C][C]-0.612581041231767[/C][C]2.470105[/C][C]-0.248[/C][C]0.805471[/C][C]0.402736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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)36.786375028688730.3040061.21390.2322720.116136
klant-70.40609637268559.291374-1.18750.2424160.121208
verzekeraar-1.040986166930313.528783-0.2950.7696010.3848
verzekeraar_klant1.395452751313426.3725260.2190.8278380.413919
kost0.5547927410235260.215812.57070.0141890.007094
kost_klant0.0567523009995440.3629040.15640.8765580.438279
grootte-2.240359516765626.314635-0.35480.724710.362355
grootte_klant13.599027864076610.751921.26480.2136450.106823
snelheid-0.04266174856417960.193603-0.22040.8267730.413387
snelheid_klant0.2211808385871680.3049560.72530.4727190.236359
maand0.8542661912552121.5572870.54860.5865160.293258
maand_klant-0.6125810412317672.470105-0.2480.8054710.402736







Multiple Linear Regression - Regression Statistics
Multiple R0.534618638587279
R-squared0.285817088724916
Adjusted R-squared0.0790799301979175
F-TEST (value)1.38251435185315
F-TEST (DF numerator)11
F-TEST (DF denominator)38
p-value0.220791410434633
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.8034976967944
Sum Squared Residuals31526.3762236312

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.534618638587279 \tabularnewline
R-squared & 0.285817088724916 \tabularnewline
Adjusted R-squared & 0.0790799301979175 \tabularnewline
F-TEST (value) & 1.38251435185315 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0.220791410434633 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 28.8034976967944 \tabularnewline
Sum Squared Residuals & 31526.3762236312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.534618638587279[/C][/ROW]
[ROW][C]R-squared[/C][C]0.285817088724916[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0790799301979175[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.38251435185315[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/C][/ROW]
[ROW][C]p-value[/C][C]0.220791410434633[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]28.8034976967944[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31526.3762236312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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 R0.534618638587279
R-squared0.285817088724916
Adjusted R-squared0.0790799301979175
F-TEST (value)1.38251435185315
F-TEST (DF numerator)11
F-TEST (DF denominator)38
p-value0.220791410434633
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.8034976967944
Sum Squared Residuals31526.3762236312







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12822.08648245419075.91351754580928
24038.3525466698581.647453330142
37981.5569342198438-2.55693421984381
41637.9630338016859-21.9630338016859
59048.334555046890841.6654449531092
68773.455962608693813.5440373913062
75375.3676765584414-22.3676765584414
82333.0616727553791-10.0616727553791
94256.3641873519403-14.3641873519403
106474.7731294237277-10.7731294237277
118759.276123924058527.7238760759415
127768.48235777141618.5176422285839
137067.47088560186732.52911439813267
148279.6930887354552.3069112645451
154454.948107560181-10.948107560181
163646.8132555163705-10.8132555163705
177363.58102271671429.4189772832858
187560.430045008541314.5699549914587
192149.5746611577697-28.5746611577697
208159.808914417989721.1910855820103
219961.373245655807237.6267543441928
225460.1571386534811-6.1571386534811
23660.2774071192339-54.2774071192339
247157.919401041148413.0805989588516
259361.237357226126831.7626427738732
268271.04711537392710.952884626073
273272.9137610915961-40.9137610915961
289373.023499965856319.9765000341437
292458.1694888560453-34.1694888560453
309667.694171141741428.3058288582586
318879.29868973271238.7013102672877
328328.894392242092654.1056077579074
332341.3240694232017-18.3240694232017
342046.9253824959355-26.9253824959355
353342.2274454905423-9.2274454905423
368871.552532232652316.4474677673477
379862.283879794001635.7161202059984
383440.7085608306196-6.70856083061955
395954.02108477315644.97891522684363
402626.2093026200867-0.209302620086662
411359.6879811851952-46.6879811851952
42635.8505006547148-29.8505006547148
434926.171295204302422.8287047956976
44340.2750804134853-37.2750804134853
457630.272352748392745.7276472516073
461254.4435546855668-42.4435546855668
476340.381928619960522.6180713800395
483548.9993791863894-13.9993791863894
496955.820092444168313.1799075558317
501025.4452657968455-15.4452657968455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 28 & 22.0864824541907 & 5.91351754580928 \tabularnewline
2 & 40 & 38.352546669858 & 1.647453330142 \tabularnewline
3 & 79 & 81.5569342198438 & -2.55693421984381 \tabularnewline
4 & 16 & 37.9630338016859 & -21.9630338016859 \tabularnewline
5 & 90 & 48.3345550468908 & 41.6654449531092 \tabularnewline
6 & 87 & 73.4559626086938 & 13.5440373913062 \tabularnewline
7 & 53 & 75.3676765584414 & -22.3676765584414 \tabularnewline
8 & 23 & 33.0616727553791 & -10.0616727553791 \tabularnewline
9 & 42 & 56.3641873519403 & -14.3641873519403 \tabularnewline
10 & 64 & 74.7731294237277 & -10.7731294237277 \tabularnewline
11 & 87 & 59.2761239240585 & 27.7238760759415 \tabularnewline
12 & 77 & 68.4823577714161 & 8.5176422285839 \tabularnewline
13 & 70 & 67.4708856018673 & 2.52911439813267 \tabularnewline
14 & 82 & 79.693088735455 & 2.3069112645451 \tabularnewline
15 & 44 & 54.948107560181 & -10.948107560181 \tabularnewline
16 & 36 & 46.8132555163705 & -10.8132555163705 \tabularnewline
17 & 73 & 63.5810227167142 & 9.4189772832858 \tabularnewline
18 & 75 & 60.4300450085413 & 14.5699549914587 \tabularnewline
19 & 21 & 49.5746611577697 & -28.5746611577697 \tabularnewline
20 & 81 & 59.8089144179897 & 21.1910855820103 \tabularnewline
21 & 99 & 61.3732456558072 & 37.6267543441928 \tabularnewline
22 & 54 & 60.1571386534811 & -6.1571386534811 \tabularnewline
23 & 6 & 60.2774071192339 & -54.2774071192339 \tabularnewline
24 & 71 & 57.9194010411484 & 13.0805989588516 \tabularnewline
25 & 93 & 61.2373572261268 & 31.7626427738732 \tabularnewline
26 & 82 & 71.047115373927 & 10.952884626073 \tabularnewline
27 & 32 & 72.9137610915961 & -40.9137610915961 \tabularnewline
28 & 93 & 73.0234999658563 & 19.9765000341437 \tabularnewline
29 & 24 & 58.1694888560453 & -34.1694888560453 \tabularnewline
30 & 96 & 67.6941711417414 & 28.3058288582586 \tabularnewline
31 & 88 & 79.2986897327123 & 8.7013102672877 \tabularnewline
32 & 83 & 28.8943922420926 & 54.1056077579074 \tabularnewline
33 & 23 & 41.3240694232017 & -18.3240694232017 \tabularnewline
34 & 20 & 46.9253824959355 & -26.9253824959355 \tabularnewline
35 & 33 & 42.2274454905423 & -9.2274454905423 \tabularnewline
36 & 88 & 71.5525322326523 & 16.4474677673477 \tabularnewline
37 & 98 & 62.2838797940016 & 35.7161202059984 \tabularnewline
38 & 34 & 40.7085608306196 & -6.70856083061955 \tabularnewline
39 & 59 & 54.0210847731564 & 4.97891522684363 \tabularnewline
40 & 26 & 26.2093026200867 & -0.209302620086662 \tabularnewline
41 & 13 & 59.6879811851952 & -46.6879811851952 \tabularnewline
42 & 6 & 35.8505006547148 & -29.8505006547148 \tabularnewline
43 & 49 & 26.1712952043024 & 22.8287047956976 \tabularnewline
44 & 3 & 40.2750804134853 & -37.2750804134853 \tabularnewline
45 & 76 & 30.2723527483927 & 45.7276472516073 \tabularnewline
46 & 12 & 54.4435546855668 & -42.4435546855668 \tabularnewline
47 & 63 & 40.3819286199605 & 22.6180713800395 \tabularnewline
48 & 35 & 48.9993791863894 & -13.9993791863894 \tabularnewline
49 & 69 & 55.8200924441683 & 13.1799075558317 \tabularnewline
50 & 10 & 25.4452657968455 & -15.4452657968455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&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]28[/C][C]22.0864824541907[/C][C]5.91351754580928[/C][/ROW]
[ROW][C]2[/C][C]40[/C][C]38.352546669858[/C][C]1.647453330142[/C][/ROW]
[ROW][C]3[/C][C]79[/C][C]81.5569342198438[/C][C]-2.55693421984381[/C][/ROW]
[ROW][C]4[/C][C]16[/C][C]37.9630338016859[/C][C]-21.9630338016859[/C][/ROW]
[ROW][C]5[/C][C]90[/C][C]48.3345550468908[/C][C]41.6654449531092[/C][/ROW]
[ROW][C]6[/C][C]87[/C][C]73.4559626086938[/C][C]13.5440373913062[/C][/ROW]
[ROW][C]7[/C][C]53[/C][C]75.3676765584414[/C][C]-22.3676765584414[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]33.0616727553791[/C][C]-10.0616727553791[/C][/ROW]
[ROW][C]9[/C][C]42[/C][C]56.3641873519403[/C][C]-14.3641873519403[/C][/ROW]
[ROW][C]10[/C][C]64[/C][C]74.7731294237277[/C][C]-10.7731294237277[/C][/ROW]
[ROW][C]11[/C][C]87[/C][C]59.2761239240585[/C][C]27.7238760759415[/C][/ROW]
[ROW][C]12[/C][C]77[/C][C]68.4823577714161[/C][C]8.5176422285839[/C][/ROW]
[ROW][C]13[/C][C]70[/C][C]67.4708856018673[/C][C]2.52911439813267[/C][/ROW]
[ROW][C]14[/C][C]82[/C][C]79.693088735455[/C][C]2.3069112645451[/C][/ROW]
[ROW][C]15[/C][C]44[/C][C]54.948107560181[/C][C]-10.948107560181[/C][/ROW]
[ROW][C]16[/C][C]36[/C][C]46.8132555163705[/C][C]-10.8132555163705[/C][/ROW]
[ROW][C]17[/C][C]73[/C][C]63.5810227167142[/C][C]9.4189772832858[/C][/ROW]
[ROW][C]18[/C][C]75[/C][C]60.4300450085413[/C][C]14.5699549914587[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]49.5746611577697[/C][C]-28.5746611577697[/C][/ROW]
[ROW][C]20[/C][C]81[/C][C]59.8089144179897[/C][C]21.1910855820103[/C][/ROW]
[ROW][C]21[/C][C]99[/C][C]61.3732456558072[/C][C]37.6267543441928[/C][/ROW]
[ROW][C]22[/C][C]54[/C][C]60.1571386534811[/C][C]-6.1571386534811[/C][/ROW]
[ROW][C]23[/C][C]6[/C][C]60.2774071192339[/C][C]-54.2774071192339[/C][/ROW]
[ROW][C]24[/C][C]71[/C][C]57.9194010411484[/C][C]13.0805989588516[/C][/ROW]
[ROW][C]25[/C][C]93[/C][C]61.2373572261268[/C][C]31.7626427738732[/C][/ROW]
[ROW][C]26[/C][C]82[/C][C]71.047115373927[/C][C]10.952884626073[/C][/ROW]
[ROW][C]27[/C][C]32[/C][C]72.9137610915961[/C][C]-40.9137610915961[/C][/ROW]
[ROW][C]28[/C][C]93[/C][C]73.0234999658563[/C][C]19.9765000341437[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]58.1694888560453[/C][C]-34.1694888560453[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]67.6941711417414[/C][C]28.3058288582586[/C][/ROW]
[ROW][C]31[/C][C]88[/C][C]79.2986897327123[/C][C]8.7013102672877[/C][/ROW]
[ROW][C]32[/C][C]83[/C][C]28.8943922420926[/C][C]54.1056077579074[/C][/ROW]
[ROW][C]33[/C][C]23[/C][C]41.3240694232017[/C][C]-18.3240694232017[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]46.9253824959355[/C][C]-26.9253824959355[/C][/ROW]
[ROW][C]35[/C][C]33[/C][C]42.2274454905423[/C][C]-9.2274454905423[/C][/ROW]
[ROW][C]36[/C][C]88[/C][C]71.5525322326523[/C][C]16.4474677673477[/C][/ROW]
[ROW][C]37[/C][C]98[/C][C]62.2838797940016[/C][C]35.7161202059984[/C][/ROW]
[ROW][C]38[/C][C]34[/C][C]40.7085608306196[/C][C]-6.70856083061955[/C][/ROW]
[ROW][C]39[/C][C]59[/C][C]54.0210847731564[/C][C]4.97891522684363[/C][/ROW]
[ROW][C]40[/C][C]26[/C][C]26.2093026200867[/C][C]-0.209302620086662[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]59.6879811851952[/C][C]-46.6879811851952[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]35.8505006547148[/C][C]-29.8505006547148[/C][/ROW]
[ROW][C]43[/C][C]49[/C][C]26.1712952043024[/C][C]22.8287047956976[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]40.2750804134853[/C][C]-37.2750804134853[/C][/ROW]
[ROW][C]45[/C][C]76[/C][C]30.2723527483927[/C][C]45.7276472516073[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]54.4435546855668[/C][C]-42.4435546855668[/C][/ROW]
[ROW][C]47[/C][C]63[/C][C]40.3819286199605[/C][C]22.6180713800395[/C][/ROW]
[ROW][C]48[/C][C]35[/C][C]48.9993791863894[/C][C]-13.9993791863894[/C][/ROW]
[ROW][C]49[/C][C]69[/C][C]55.8200924441683[/C][C]13.1799075558317[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]25.4452657968455[/C][C]-15.4452657968455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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
12822.08648245419075.91351754580928
24038.3525466698581.647453330142
37981.5569342198438-2.55693421984381
41637.9630338016859-21.9630338016859
59048.334555046890841.6654449531092
68773.455962608693813.5440373913062
75375.3676765584414-22.3676765584414
82333.0616727553791-10.0616727553791
94256.3641873519403-14.3641873519403
106474.7731294237277-10.7731294237277
118759.276123924058527.7238760759415
127768.48235777141618.5176422285839
137067.47088560186732.52911439813267
148279.6930887354552.3069112645451
154454.948107560181-10.948107560181
163646.8132555163705-10.8132555163705
177363.58102271671429.4189772832858
187560.430045008541314.5699549914587
192149.5746611577697-28.5746611577697
208159.808914417989721.1910855820103
219961.373245655807237.6267543441928
225460.1571386534811-6.1571386534811
23660.2774071192339-54.2774071192339
247157.919401041148413.0805989588516
259361.237357226126831.7626427738732
268271.04711537392710.952884626073
273272.9137610915961-40.9137610915961
289373.023499965856319.9765000341437
292458.1694888560453-34.1694888560453
309667.694171141741428.3058288582586
318879.29868973271238.7013102672877
328328.894392242092654.1056077579074
332341.3240694232017-18.3240694232017
342046.9253824959355-26.9253824959355
353342.2274454905423-9.2274454905423
368871.552532232652316.4474677673477
379862.283879794001635.7161202059984
383440.7085608306196-6.70856083061955
395954.02108477315644.97891522684363
402626.2093026200867-0.209302620086662
411359.6879811851952-46.6879811851952
42635.8505006547148-29.8505006547148
434926.171295204302422.8287047956976
44340.2750804134853-37.2750804134853
457630.272352748392745.7276472516073
461254.4435546855668-42.4435546855668
476340.381928619960522.6180713800395
483548.9993791863894-13.9993791863894
496955.820092444168313.1799075558317
501025.4452657968455-15.4452657968455







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.6738823920355490.6522352159289020.326117607964451
160.5225317558211120.9549364883577760.477468244178888
170.3667227577007080.7334455154014160.633277242299292
180.2413828955629990.4827657911259980.758617104437001
190.1569228647128490.3138457294256990.84307713528715
200.09239674987916380.1847934997583280.907603250120836
210.05889685819076920.1177937163815380.94110314180923
220.02992165899418250.0598433179883650.970078341005817
230.02847257534839570.05694515069679130.971527424651604
240.09671493283535850.1934298656707170.903285067164642
250.08325173115517330.1665034623103470.916748268844827
260.05862842255796040.1172568451159210.94137157744204
270.09677436221488020.193548724429760.90322563778512
280.06433421913229380.1286684382645880.935665780867706
290.0672846768145650.134569353629130.932715323185435
300.05195056269351560.1039011253870310.948049437306484
310.03023170170281230.06046340340562460.969768298297188
320.06943094577755050.1388618915551010.93056905422245
330.04222474089521140.08444948179042280.957775259104789
340.03432927667745160.06865855335490320.965670723322548
350.01537505955305540.03075011910611080.984624940446945

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.673882392035549 & 0.652235215928902 & 0.326117607964451 \tabularnewline
16 & 0.522531755821112 & 0.954936488357776 & 0.477468244178888 \tabularnewline
17 & 0.366722757700708 & 0.733445515401416 & 0.633277242299292 \tabularnewline
18 & 0.241382895562999 & 0.482765791125998 & 0.758617104437001 \tabularnewline
19 & 0.156922864712849 & 0.313845729425699 & 0.84307713528715 \tabularnewline
20 & 0.0923967498791638 & 0.184793499758328 & 0.907603250120836 \tabularnewline
21 & 0.0588968581907692 & 0.117793716381538 & 0.94110314180923 \tabularnewline
22 & 0.0299216589941825 & 0.059843317988365 & 0.970078341005817 \tabularnewline
23 & 0.0284725753483957 & 0.0569451506967913 & 0.971527424651604 \tabularnewline
24 & 0.0967149328353585 & 0.193429865670717 & 0.903285067164642 \tabularnewline
25 & 0.0832517311551733 & 0.166503462310347 & 0.916748268844827 \tabularnewline
26 & 0.0586284225579604 & 0.117256845115921 & 0.94137157744204 \tabularnewline
27 & 0.0967743622148802 & 0.19354872442976 & 0.90322563778512 \tabularnewline
28 & 0.0643342191322938 & 0.128668438264588 & 0.935665780867706 \tabularnewline
29 & 0.067284676814565 & 0.13456935362913 & 0.932715323185435 \tabularnewline
30 & 0.0519505626935156 & 0.103901125387031 & 0.948049437306484 \tabularnewline
31 & 0.0302317017028123 & 0.0604634034056246 & 0.969768298297188 \tabularnewline
32 & 0.0694309457775505 & 0.138861891555101 & 0.93056905422245 \tabularnewline
33 & 0.0422247408952114 & 0.0844494817904228 & 0.957775259104789 \tabularnewline
34 & 0.0343292766774516 & 0.0686585533549032 & 0.965670723322548 \tabularnewline
35 & 0.0153750595530554 & 0.0307501191061108 & 0.984624940446945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&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]15[/C][C]0.673882392035549[/C][C]0.652235215928902[/C][C]0.326117607964451[/C][/ROW]
[ROW][C]16[/C][C]0.522531755821112[/C][C]0.954936488357776[/C][C]0.477468244178888[/C][/ROW]
[ROW][C]17[/C][C]0.366722757700708[/C][C]0.733445515401416[/C][C]0.633277242299292[/C][/ROW]
[ROW][C]18[/C][C]0.241382895562999[/C][C]0.482765791125998[/C][C]0.758617104437001[/C][/ROW]
[ROW][C]19[/C][C]0.156922864712849[/C][C]0.313845729425699[/C][C]0.84307713528715[/C][/ROW]
[ROW][C]20[/C][C]0.0923967498791638[/C][C]0.184793499758328[/C][C]0.907603250120836[/C][/ROW]
[ROW][C]21[/C][C]0.0588968581907692[/C][C]0.117793716381538[/C][C]0.94110314180923[/C][/ROW]
[ROW][C]22[/C][C]0.0299216589941825[/C][C]0.059843317988365[/C][C]0.970078341005817[/C][/ROW]
[ROW][C]23[/C][C]0.0284725753483957[/C][C]0.0569451506967913[/C][C]0.971527424651604[/C][/ROW]
[ROW][C]24[/C][C]0.0967149328353585[/C][C]0.193429865670717[/C][C]0.903285067164642[/C][/ROW]
[ROW][C]25[/C][C]0.0832517311551733[/C][C]0.166503462310347[/C][C]0.916748268844827[/C][/ROW]
[ROW][C]26[/C][C]0.0586284225579604[/C][C]0.117256845115921[/C][C]0.94137157744204[/C][/ROW]
[ROW][C]27[/C][C]0.0967743622148802[/C][C]0.19354872442976[/C][C]0.90322563778512[/C][/ROW]
[ROW][C]28[/C][C]0.0643342191322938[/C][C]0.128668438264588[/C][C]0.935665780867706[/C][/ROW]
[ROW][C]29[/C][C]0.067284676814565[/C][C]0.13456935362913[/C][C]0.932715323185435[/C][/ROW]
[ROW][C]30[/C][C]0.0519505626935156[/C][C]0.103901125387031[/C][C]0.948049437306484[/C][/ROW]
[ROW][C]31[/C][C]0.0302317017028123[/C][C]0.0604634034056246[/C][C]0.969768298297188[/C][/ROW]
[ROW][C]32[/C][C]0.0694309457775505[/C][C]0.138861891555101[/C][C]0.93056905422245[/C][/ROW]
[ROW][C]33[/C][C]0.0422247408952114[/C][C]0.0844494817904228[/C][C]0.957775259104789[/C][/ROW]
[ROW][C]34[/C][C]0.0343292766774516[/C][C]0.0686585533549032[/C][C]0.965670723322548[/C][/ROW]
[ROW][C]35[/C][C]0.0153750595530554[/C][C]0.0307501191061108[/C][C]0.984624940446945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99558&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
150.6738823920355490.6522352159289020.326117607964451
160.5225317558211120.9549364883577760.477468244178888
170.3667227577007080.7334455154014160.633277242299292
180.2413828955629990.4827657911259980.758617104437001
190.1569228647128490.3138457294256990.84307713528715
200.09239674987916380.1847934997583280.907603250120836
210.05889685819076920.1177937163815380.94110314180923
220.02992165899418250.0598433179883650.970078341005817
230.02847257534839570.05694515069679130.971527424651604
240.09671493283535850.1934298656707170.903285067164642
250.08325173115517330.1665034623103470.916748268844827
260.05862842255796040.1172568451159210.94137157744204
270.09677436221488020.193548724429760.90322563778512
280.06433421913229380.1286684382645880.935665780867706
290.0672846768145650.134569353629130.932715323185435
300.05195056269351560.1039011253870310.948049437306484
310.03023170170281230.06046340340562460.969768298297188
320.06943094577755050.1388618915551010.93056905422245
330.04222474089521140.08444948179042280.957775259104789
340.03432927667745160.06865855335490320.965670723322548
350.01537505955305540.03075011910611080.984624940446945







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0476190476190476OK
10% type I error level60.285714285714286NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0476190476190476 & OK \tabularnewline
10% type I error level & 6 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99558&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0476190476190476[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99558&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0476190476190476OK
10% type I error level60.285714285714286NOK



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