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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 12 Dec 2011 07:00:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/12/t132369143884dipt1pdweium2.htm/, Retrieved Thu, 02 May 2024 05:02:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153930, Retrieved Thu, 02 May 2024 05:02:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2011-12-12 12:00:59] [2be7aedefc35278abdba659ba29c8de8] [Current]
-           [Multiple Regression] [] [2011-12-12 14:27:12] [aefb5c2d4042694c5b6b82f93ac1885a]
-           [Multiple Regression] [] [2011-12-12 14:28:12] [aefb5c2d4042694c5b6b82f93ac1885a]
-   PD      [Multiple Regression] [] [2011-12-18 00:47:01] [77e355412ccdb651b3c7eae41c3da865]
- RMP         [Kendall tau Correlation Matrix] [] [2011-12-20 18:45:28] [77e355412ccdb651b3c7eae41c3da865]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	210907	79	30	94	112285
4	179321	108	30	103	101193
0	149061	43	26	93	116174
0	237213	78	38	123	66198
-4	173326	86	44	148	71701
4	133131	44	30	90	57793
4	258873	104	40	124	80444
0	324799	158	47	168	97668
-1	230964	102	30	115	133824
0	236785	77	31	71	101481
1	344297	80	30	108	67654
0	174724	123	34	120	69112
3	174415	73	31	114	82753
-1	223632	105	33	120	72654
4	294424	107	33	124	101494
3	325107	84	36	126	79215
1	106408	33	14	37	31081
0	96560	42	17	38	22996
-2	265769	96	32	120	83122
-3	269651	106	30	93	70106
-4	149112	56	35	95	60578
2	152871	59	28	90	79892
2	362301	76	34	110	100708
-4	183167	91	39	138	82875
3	277965	115	39	133	139077
2	218946	76	29	96	80670
2	244052	101	44	164	143558
0	341570	94	21	78	117105
5	233328	92	28	102	120733
-2	206161	75	28	99	73107
0	311473	128	38	129	132068
-2	207176	56	32	114	87011
-3	196553	41	29	99	95260
2	143246	67	27	104	106671
2	182192	77	40	138	70054
2	194979	66	40	151	74011
0	167488	69	28	72	83737
4	143756	105	34	120	69094
4	275541	116	33	115	93133
2	152299	62	33	98	61370
2	193339	100	35	71	84651
-4	130585	67	29	107	95364
3	112611	46	20	73	26706
3	148446	135	37	129	126846
2	182079	124	33	118	102860
-1	243060	58	29	104	111813
-3	162765	68	28	107	120293
0	85574	37	21	36	24266
1	225060	93	41	139	109825
-3	133328	56	20	56	40909
3	100750	83	30	93	140867
0	101523	59	22	87	61056
0	243511	133	42	110	101338
0	152474	106	32	83	65567
3	132487	71	36	98	40735
-3	317394	116	31	82	91413
0	244749	98	33	115	76643
-4	184510	64	40	140	110681
2	128423	32	38	120	92696
-1	97839	25	24	66	94785
3	172494	46	43	139	86687
2	229242	63	31	119	91721
5	351619	95	40	141	115168
2	324598	113	37	133	135777
-2	195838	111	31	98	102372
0	254488	120	39	117	103772
3	199476	87	32	105	135400
-2	92499	25	18	55	21399
0	224330	131	39	132	130115
6	181633	47	30	73	64466
-3	271856	109	37	86	54990
3	95227	37	32	48	34777
0	98146	15	17	48	27114
-2	118612	54	12	43	30080
1	65475	16	13	46	69008
0	108446	22	17	65	46300
2	121848	37	17	52	30594
2	76302	29	20	68	30976
-3	98104	55	17	47	25568
-2	30989	5	17	41	4154
1	31774	0	17	47	4143
-4	150580	27	22	71	45588
0	54157	37	15	30	18625
1	59382	29	12	24	26263
0	84105	17	17	63	20055

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
SCORE[t] = -0.909188076342209 -1.66386590515146e-07time_in_rfc[t] -0.00387903602176033blogged_computations[t] + 0.0487441945745109compendiums_reviewed[t] -0.00593242209108413feedback_messages_p120[t] + 1.11144267903486e-05totsize[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SCORE[t] =  -0.909188076342209 -1.66386590515146e-07time_in_rfc[t] -0.00387903602176033blogged_computations[t] +  0.0487441945745109compendiums_reviewed[t] -0.00593242209108413feedback_messages_p120[t] +  1.11144267903486e-05totsize[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SCORE[t] =  -0.909188076342209 -1.66386590515146e-07time_in_rfc[t] -0.00387903602176033blogged_computations[t] +  0.0487441945745109compendiums_reviewed[t] -0.00593242209108413feedback_messages_p120[t] +  1.11144267903486e-05totsize[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
SCORE[t] = -0.909188076342209 -1.66386590515146e-07time_in_rfc[t] -0.00387903602176033blogged_computations[t] + 0.0487441945745109compendiums_reviewed[t] -0.00593242209108413feedback_messages_p120[t] + 1.11144267903486e-05totsize[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9091880763422091.024497-0.88740.3775320.188766
time_in_rfc-1.66386590515146e-075e-06-0.03140.9750060.487503
blogged_computations-0.003879036021760330.013148-0.2950.7687520.384376
compendiums_reviewed0.04874419457451090.075910.64210.5226470.261323
feedback_messages_p120-0.005932422091084130.020159-0.29430.7693180.384659
totsize1.11144267903486e-051.2e-050.94480.3476650.173832

\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) & -0.909188076342209 & 1.024497 & -0.8874 & 0.377532 & 0.188766 \tabularnewline
time_in_rfc & -1.66386590515146e-07 & 5e-06 & -0.0314 & 0.975006 & 0.487503 \tabularnewline
blogged_computations & -0.00387903602176033 & 0.013148 & -0.295 & 0.768752 & 0.384376 \tabularnewline
compendiums_reviewed & 0.0487441945745109 & 0.07591 & 0.6421 & 0.522647 & 0.261323 \tabularnewline
feedback_messages_p120 & -0.00593242209108413 & 0.020159 & -0.2943 & 0.769318 & 0.384659 \tabularnewline
totsize & 1.11144267903486e-05 & 1.2e-05 & 0.9448 & 0.347665 & 0.173832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&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]-0.909188076342209[/C][C]1.024497[/C][C]-0.8874[/C][C]0.377532[/C][C]0.188766[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-1.66386590515146e-07[/C][C]5e-06[/C][C]-0.0314[/C][C]0.975006[/C][C]0.487503[/C][/ROW]
[ROW][C]blogged_computations[/C][C]-0.00387903602176033[/C][C]0.013148[/C][C]-0.295[/C][C]0.768752[/C][C]0.384376[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.0487441945745109[/C][C]0.07591[/C][C]0.6421[/C][C]0.522647[/C][C]0.261323[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]-0.00593242209108413[/C][C]0.020159[/C][C]-0.2943[/C][C]0.769318[/C][C]0.384659[/C][/ROW]
[ROW][C]totsize[/C][C]1.11144267903486e-05[/C][C]1.2e-05[/C][C]0.9448[/C][C]0.347665[/C][C]0.173832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9091880763422091.024497-0.88740.3775320.188766
time_in_rfc-1.66386590515146e-075e-06-0.03140.9750060.487503
blogged_computations-0.003879036021760330.013148-0.2950.7687520.384376
compendiums_reviewed0.04874419457451090.075910.64210.5226470.261323
feedback_messages_p120-0.005932422091084130.020159-0.29430.7693180.384659
totsize1.11144267903486e-051.2e-050.94480.3476650.173832






Multiple Linear Regression - Regression Statistics
Multiple R0.188547553243958
R-squared0.035550179834283
Adjusted R-squared-0.0254909480243066
F-TEST (value)0.582397165344651
F-TEST (DF numerator)5
F-TEST (DF denominator)79
p-value0.71334435972022
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49321904548164
Sum Squared Residuals491.075155491438

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.188547553243958 \tabularnewline
R-squared & 0.035550179834283 \tabularnewline
Adjusted R-squared & -0.0254909480243066 \tabularnewline
F-TEST (value) & 0.582397165344651 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 79 \tabularnewline
p-value & 0.71334435972022 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49321904548164 \tabularnewline
Sum Squared Residuals & 491.075155491438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.188547553243958[/C][/ROW]
[ROW][C]R-squared[/C][C]0.035550179834283[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0254909480243066[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.582397165344651[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]79[/C][/ROW]
[ROW][C]p-value[/C][C]0.71334435972022[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.49321904548164[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]491.075155491438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.188547553243958
R-squared0.035550179834283
Adjusted R-squared-0.0254909480243066
F-TEST (value)0.582397165344651
F-TEST (DF numerator)5
F-TEST (DF denominator)79
p-value0.71334435972022
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49321904548164
Sum Squared Residuals491.075155491438






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.901937554120661.09806244587934
240.6180279755593183.38197202444068
300.906052845561738-0.906052845561738
400.60712235296018-0.60712235296018
5-40.792037310691589-4.79203731069159
640.4687270420508383.53127295794916
740.7525555739570973.2474444260429
800.803736103466577-0.803736103466577
9-10.924195284498762-1.92419528449876
1000.970499509601349-0.970499509601349
1110.2967623194348580.703237680565142
1200.298170991277951-0.298170991277951
1330.5331480504925542.46685194950745
14-10.350479109417626-1.35047910941763
1540.6277525781276753.37224742187233
1630.5986145919505742.40138540804943
171-0.2465345236399711.24653452363997
180-0.229367251659940.22936725165994
19-20.445981026915791-2.44598102691579
20-30.32456638216088-3.32456638216088
21-40.664532126714946-4.66453212671495
2220.5553863589185551.44461364108144
2320.8597710365903221.14022896340968
24-40.71080057313917-4.71080057313917
2531.256245717535861.74375428246414
2620.5002454386512391.49975456134876
2721.425814524781140.574185475218856
2800.531803982134003-0.531803982134003
2950.7967264437390084.20327355626099
30-20.355651856569566-2.35565185656957
3101.09732744379427-1.09732744379427
32-20.689710095618428-2.68971009561843
33-30.78409981493219-3.78409981493219
3420.6917906728472381.30820932715276
3520.6715154330650181.32848456693498
3620.678915543596781.32108445640322
3700.663682494555797-0.663682494555797
3840.3729462399224833.62705376007752
3940.5564472083461823.44355279165382
4020.5342446071160941.46575539288391
4120.8964314883288591.10356851167114
42-40.647917592627048-4.64791759262705
433-0.267721732983563.26772173298356
4430.9905159670601462.00948403293985
4520.6312785068112941.36872149318871
46-10.864733057602397-1.8647330576024
47-30.8670115870046-3.8670115870046
4800.0128127960362154-0.0128127960362154
4911.08716183671703-0.0871618367170311
50-3-0.0512497449451126-2.94875025505489
5131.228355026297821.77164497370218
5200.0799107333715078-0.0799107333715078
5301.05538669191129-1.05538669191129
5400.450427290536143-0.450427290536143
5530.4195161209562262.58048387904377
56-30.628648156150706-3.62864815615071
5700.448116334860161-0.448116334860161
58-41.16123819026041-5.16123819026041
5921.115966554507210.884033445492793
60-10.809358680584274-1.80935868058427
6131.118545589330721.88145441066928
6220.6328280021124091.36717199788759
6351.057121387748663.94287861225134
6421.122078686146720.877921313853283
65-20.695152874394251-2.69515287439425
6600.943280711036672-0.943280711036672
6731.161948952468761.83805104753124
68-2-0.232604663904039-1.76739533609596
6901.10943021516595-1.10943021516595
7060.6240375970928175.37596240290718
71-30.527293032959768-3.52729303295977
7230.5930274814979372.40697251850206
730-0.1384521795931540.138452179593154
74-2-0.47423332496025-1.52576667503975
7510.1456206625227950.854379337477205
760-0.05492899677658440.0549289967765844
772-0.2127861501741942.21278615017419
782-0.118616077048412.11861607704841
79-3-0.304857113953561-2.69514288604644
80-2-0.302148079585141-1.69785192041486
811-0.3186003041910921.31860030419109
82-40.118878258961172-4.11887825896117
830-0.3015269528744880.301526952874488
841-0.2971100939881921.29711009398819
850-0.3113170875985840.311317087598584

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.90193755412066 & 1.09806244587934 \tabularnewline
2 & 4 & 0.618027975559318 & 3.38197202444068 \tabularnewline
3 & 0 & 0.906052845561738 & -0.906052845561738 \tabularnewline
4 & 0 & 0.60712235296018 & -0.60712235296018 \tabularnewline
5 & -4 & 0.792037310691589 & -4.79203731069159 \tabularnewline
6 & 4 & 0.468727042050838 & 3.53127295794916 \tabularnewline
7 & 4 & 0.752555573957097 & 3.2474444260429 \tabularnewline
8 & 0 & 0.803736103466577 & -0.803736103466577 \tabularnewline
9 & -1 & 0.924195284498762 & -1.92419528449876 \tabularnewline
10 & 0 & 0.970499509601349 & -0.970499509601349 \tabularnewline
11 & 1 & 0.296762319434858 & 0.703237680565142 \tabularnewline
12 & 0 & 0.298170991277951 & -0.298170991277951 \tabularnewline
13 & 3 & 0.533148050492554 & 2.46685194950745 \tabularnewline
14 & -1 & 0.350479109417626 & -1.35047910941763 \tabularnewline
15 & 4 & 0.627752578127675 & 3.37224742187233 \tabularnewline
16 & 3 & 0.598614591950574 & 2.40138540804943 \tabularnewline
17 & 1 & -0.246534523639971 & 1.24653452363997 \tabularnewline
18 & 0 & -0.22936725165994 & 0.22936725165994 \tabularnewline
19 & -2 & 0.445981026915791 & -2.44598102691579 \tabularnewline
20 & -3 & 0.32456638216088 & -3.32456638216088 \tabularnewline
21 & -4 & 0.664532126714946 & -4.66453212671495 \tabularnewline
22 & 2 & 0.555386358918555 & 1.44461364108144 \tabularnewline
23 & 2 & 0.859771036590322 & 1.14022896340968 \tabularnewline
24 & -4 & 0.71080057313917 & -4.71080057313917 \tabularnewline
25 & 3 & 1.25624571753586 & 1.74375428246414 \tabularnewline
26 & 2 & 0.500245438651239 & 1.49975456134876 \tabularnewline
27 & 2 & 1.42581452478114 & 0.574185475218856 \tabularnewline
28 & 0 & 0.531803982134003 & -0.531803982134003 \tabularnewline
29 & 5 & 0.796726443739008 & 4.20327355626099 \tabularnewline
30 & -2 & 0.355651856569566 & -2.35565185656957 \tabularnewline
31 & 0 & 1.09732744379427 & -1.09732744379427 \tabularnewline
32 & -2 & 0.689710095618428 & -2.68971009561843 \tabularnewline
33 & -3 & 0.78409981493219 & -3.78409981493219 \tabularnewline
34 & 2 & 0.691790672847238 & 1.30820932715276 \tabularnewline
35 & 2 & 0.671515433065018 & 1.32848456693498 \tabularnewline
36 & 2 & 0.67891554359678 & 1.32108445640322 \tabularnewline
37 & 0 & 0.663682494555797 & -0.663682494555797 \tabularnewline
38 & 4 & 0.372946239922483 & 3.62705376007752 \tabularnewline
39 & 4 & 0.556447208346182 & 3.44355279165382 \tabularnewline
40 & 2 & 0.534244607116094 & 1.46575539288391 \tabularnewline
41 & 2 & 0.896431488328859 & 1.10356851167114 \tabularnewline
42 & -4 & 0.647917592627048 & -4.64791759262705 \tabularnewline
43 & 3 & -0.26772173298356 & 3.26772173298356 \tabularnewline
44 & 3 & 0.990515967060146 & 2.00948403293985 \tabularnewline
45 & 2 & 0.631278506811294 & 1.36872149318871 \tabularnewline
46 & -1 & 0.864733057602397 & -1.8647330576024 \tabularnewline
47 & -3 & 0.8670115870046 & -3.8670115870046 \tabularnewline
48 & 0 & 0.0128127960362154 & -0.0128127960362154 \tabularnewline
49 & 1 & 1.08716183671703 & -0.0871618367170311 \tabularnewline
50 & -3 & -0.0512497449451126 & -2.94875025505489 \tabularnewline
51 & 3 & 1.22835502629782 & 1.77164497370218 \tabularnewline
52 & 0 & 0.0799107333715078 & -0.0799107333715078 \tabularnewline
53 & 0 & 1.05538669191129 & -1.05538669191129 \tabularnewline
54 & 0 & 0.450427290536143 & -0.450427290536143 \tabularnewline
55 & 3 & 0.419516120956226 & 2.58048387904377 \tabularnewline
56 & -3 & 0.628648156150706 & -3.62864815615071 \tabularnewline
57 & 0 & 0.448116334860161 & -0.448116334860161 \tabularnewline
58 & -4 & 1.16123819026041 & -5.16123819026041 \tabularnewline
59 & 2 & 1.11596655450721 & 0.884033445492793 \tabularnewline
60 & -1 & 0.809358680584274 & -1.80935868058427 \tabularnewline
61 & 3 & 1.11854558933072 & 1.88145441066928 \tabularnewline
62 & 2 & 0.632828002112409 & 1.36717199788759 \tabularnewline
63 & 5 & 1.05712138774866 & 3.94287861225134 \tabularnewline
64 & 2 & 1.12207868614672 & 0.877921313853283 \tabularnewline
65 & -2 & 0.695152874394251 & -2.69515287439425 \tabularnewline
66 & 0 & 0.943280711036672 & -0.943280711036672 \tabularnewline
67 & 3 & 1.16194895246876 & 1.83805104753124 \tabularnewline
68 & -2 & -0.232604663904039 & -1.76739533609596 \tabularnewline
69 & 0 & 1.10943021516595 & -1.10943021516595 \tabularnewline
70 & 6 & 0.624037597092817 & 5.37596240290718 \tabularnewline
71 & -3 & 0.527293032959768 & -3.52729303295977 \tabularnewline
72 & 3 & 0.593027481497937 & 2.40697251850206 \tabularnewline
73 & 0 & -0.138452179593154 & 0.138452179593154 \tabularnewline
74 & -2 & -0.47423332496025 & -1.52576667503975 \tabularnewline
75 & 1 & 0.145620662522795 & 0.854379337477205 \tabularnewline
76 & 0 & -0.0549289967765844 & 0.0549289967765844 \tabularnewline
77 & 2 & -0.212786150174194 & 2.21278615017419 \tabularnewline
78 & 2 & -0.11861607704841 & 2.11861607704841 \tabularnewline
79 & -3 & -0.304857113953561 & -2.69514288604644 \tabularnewline
80 & -2 & -0.302148079585141 & -1.69785192041486 \tabularnewline
81 & 1 & -0.318600304191092 & 1.31860030419109 \tabularnewline
82 & -4 & 0.118878258961172 & -4.11887825896117 \tabularnewline
83 & 0 & -0.301526952874488 & 0.301526952874488 \tabularnewline
84 & 1 & -0.297110093988192 & 1.29711009398819 \tabularnewline
85 & 0 & -0.311317087598584 & 0.311317087598584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&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]2[/C][C]0.90193755412066[/C][C]1.09806244587934[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]0.618027975559318[/C][C]3.38197202444068[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.906052845561738[/C][C]-0.906052845561738[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.60712235296018[/C][C]-0.60712235296018[/C][/ROW]
[ROW][C]5[/C][C]-4[/C][C]0.792037310691589[/C][C]-4.79203731069159[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]0.468727042050838[/C][C]3.53127295794916[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]0.752555573957097[/C][C]3.2474444260429[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.803736103466577[/C][C]-0.803736103466577[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]0.924195284498762[/C][C]-1.92419528449876[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.970499509601349[/C][C]-0.970499509601349[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.296762319434858[/C][C]0.703237680565142[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.298170991277951[/C][C]-0.298170991277951[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]0.533148050492554[/C][C]2.46685194950745[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]0.350479109417626[/C][C]-1.35047910941763[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]0.627752578127675[/C][C]3.37224742187233[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]0.598614591950574[/C][C]2.40138540804943[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]-0.246534523639971[/C][C]1.24653452363997[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]-0.22936725165994[/C][C]0.22936725165994[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]0.445981026915791[/C][C]-2.44598102691579[/C][/ROW]
[ROW][C]20[/C][C]-3[/C][C]0.32456638216088[/C][C]-3.32456638216088[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]0.664532126714946[/C][C]-4.66453212671495[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.555386358918555[/C][C]1.44461364108144[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]0.859771036590322[/C][C]1.14022896340968[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]0.71080057313917[/C][C]-4.71080057313917[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]1.25624571753586[/C][C]1.74375428246414[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.500245438651239[/C][C]1.49975456134876[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.42581452478114[/C][C]0.574185475218856[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.531803982134003[/C][C]-0.531803982134003[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]0.796726443739008[/C][C]4.20327355626099[/C][/ROW]
[ROW][C]30[/C][C]-2[/C][C]0.355651856569566[/C][C]-2.35565185656957[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]1.09732744379427[/C][C]-1.09732744379427[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]0.689710095618428[/C][C]-2.68971009561843[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]0.78409981493219[/C][C]-3.78409981493219[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]0.691790672847238[/C][C]1.30820932715276[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]0.671515433065018[/C][C]1.32848456693498[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.67891554359678[/C][C]1.32108445640322[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.663682494555797[/C][C]-0.663682494555797[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]0.372946239922483[/C][C]3.62705376007752[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]0.556447208346182[/C][C]3.44355279165382[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]0.534244607116094[/C][C]1.46575539288391[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]0.896431488328859[/C][C]1.10356851167114[/C][/ROW]
[ROW][C]42[/C][C]-4[/C][C]0.647917592627048[/C][C]-4.64791759262705[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-0.26772173298356[/C][C]3.26772173298356[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]0.990515967060146[/C][C]2.00948403293985[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]0.631278506811294[/C][C]1.36872149318871[/C][/ROW]
[ROW][C]46[/C][C]-1[/C][C]0.864733057602397[/C][C]-1.8647330576024[/C][/ROW]
[ROW][C]47[/C][C]-3[/C][C]0.8670115870046[/C][C]-3.8670115870046[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0128127960362154[/C][C]-0.0128127960362154[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.08716183671703[/C][C]-0.0871618367170311[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]-0.0512497449451126[/C][C]-2.94875025505489[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]1.22835502629782[/C][C]1.77164497370218[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.0799107333715078[/C][C]-0.0799107333715078[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]1.05538669191129[/C][C]-1.05538669191129[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.450427290536143[/C][C]-0.450427290536143[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]0.419516120956226[/C][C]2.58048387904377[/C][/ROW]
[ROW][C]56[/C][C]-3[/C][C]0.628648156150706[/C][C]-3.62864815615071[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.448116334860161[/C][C]-0.448116334860161[/C][/ROW]
[ROW][C]58[/C][C]-4[/C][C]1.16123819026041[/C][C]-5.16123819026041[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.11596655450721[/C][C]0.884033445492793[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]0.809358680584274[/C][C]-1.80935868058427[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]1.11854558933072[/C][C]1.88145441066928[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]0.632828002112409[/C][C]1.36717199788759[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]1.05712138774866[/C][C]3.94287861225134[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.12207868614672[/C][C]0.877921313853283[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]0.695152874394251[/C][C]-2.69515287439425[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.943280711036672[/C][C]-0.943280711036672[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]1.16194895246876[/C][C]1.83805104753124[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-0.232604663904039[/C][C]-1.76739533609596[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]1.10943021516595[/C][C]-1.10943021516595[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]0.624037597092817[/C][C]5.37596240290718[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]0.527293032959768[/C][C]-3.52729303295977[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]0.593027481497937[/C][C]2.40697251850206[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]-0.138452179593154[/C][C]0.138452179593154[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-0.47423332496025[/C][C]-1.52576667503975[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.145620662522795[/C][C]0.854379337477205[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]-0.0549289967765844[/C][C]0.0549289967765844[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]-0.212786150174194[/C][C]2.21278615017419[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]-0.11861607704841[/C][C]2.11861607704841[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C]-0.304857113953561[/C][C]-2.69514288604644[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-0.302148079585141[/C][C]-1.69785192041486[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.318600304191092[/C][C]1.31860030419109[/C][/ROW]
[ROW][C]82[/C][C]-4[/C][C]0.118878258961172[/C][C]-4.11887825896117[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.301526952874488[/C][C]0.301526952874488[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]-0.297110093988192[/C][C]1.29711009398819[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.311317087598584[/C][C]0.311317087598584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.901937554120661.09806244587934
240.6180279755593183.38197202444068
300.906052845561738-0.906052845561738
400.60712235296018-0.60712235296018
5-40.792037310691589-4.79203731069159
640.4687270420508383.53127295794916
740.7525555739570973.2474444260429
800.803736103466577-0.803736103466577
9-10.924195284498762-1.92419528449876
1000.970499509601349-0.970499509601349
1110.2967623194348580.703237680565142
1200.298170991277951-0.298170991277951
1330.5331480504925542.46685194950745
14-10.350479109417626-1.35047910941763
1540.6277525781276753.37224742187233
1630.5986145919505742.40138540804943
171-0.2465345236399711.24653452363997
180-0.229367251659940.22936725165994
19-20.445981026915791-2.44598102691579
20-30.32456638216088-3.32456638216088
21-40.664532126714946-4.66453212671495
2220.5553863589185551.44461364108144
2320.8597710365903221.14022896340968
24-40.71080057313917-4.71080057313917
2531.256245717535861.74375428246414
2620.5002454386512391.49975456134876
2721.425814524781140.574185475218856
2800.531803982134003-0.531803982134003
2950.7967264437390084.20327355626099
30-20.355651856569566-2.35565185656957
3101.09732744379427-1.09732744379427
32-20.689710095618428-2.68971009561843
33-30.78409981493219-3.78409981493219
3420.6917906728472381.30820932715276
3520.6715154330650181.32848456693498
3620.678915543596781.32108445640322
3700.663682494555797-0.663682494555797
3840.3729462399224833.62705376007752
3940.5564472083461823.44355279165382
4020.5342446071160941.46575539288391
4120.8964314883288591.10356851167114
42-40.647917592627048-4.64791759262705
433-0.267721732983563.26772173298356
4430.9905159670601462.00948403293985
4520.6312785068112941.36872149318871
46-10.864733057602397-1.8647330576024
47-30.8670115870046-3.8670115870046
4800.0128127960362154-0.0128127960362154
4911.08716183671703-0.0871618367170311
50-3-0.0512497449451126-2.94875025505489
5131.228355026297821.77164497370218
5200.0799107333715078-0.0799107333715078
5301.05538669191129-1.05538669191129
5400.450427290536143-0.450427290536143
5530.4195161209562262.58048387904377
56-30.628648156150706-3.62864815615071
5700.448116334860161-0.448116334860161
58-41.16123819026041-5.16123819026041
5921.115966554507210.884033445492793
60-10.809358680584274-1.80935868058427
6131.118545589330721.88145441066928
6220.6328280021124091.36717199788759
6351.057121387748663.94287861225134
6421.122078686146720.877921313853283
65-20.695152874394251-2.69515287439425
6600.943280711036672-0.943280711036672
6731.161948952468761.83805104753124
68-2-0.232604663904039-1.76739533609596
6901.10943021516595-1.10943021516595
7060.6240375970928175.37596240290718
71-30.527293032959768-3.52729303295977
7230.5930274814979372.40697251850206
730-0.1384521795931540.138452179593154
74-2-0.47423332496025-1.52576667503975
7510.1456206625227950.854379337477205
760-0.05492899677658440.0549289967765844
772-0.2127861501741942.21278615017419
782-0.118616077048412.11861607704841
79-3-0.304857113953561-2.69514288604644
80-2-0.302148079585141-1.69785192041486
811-0.3186003041910921.31860030419109
82-40.118878258961172-4.11887825896117
830-0.3015269528744880.301526952874488
841-0.2971100939881921.29711009398819
850-0.3113170875985840.311317087598584






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.249165535742180.4983310714843610.75083446425782
100.4576801247476360.9153602494952720.542319875252364
110.4807009582544660.9614019165089320.519299041745534
120.6429566985444420.7140866029111160.357043301455558
130.6035072772177050.7929854455645890.396492722782295
140.5990327686343930.8019344627312140.400967231365607
150.6428208900582240.7143582198835520.357179109941776
160.5984752402405390.8030495195189230.401524759759461
170.582064548368620.8358709032627610.41793545163138
180.5206012895947010.9587974208105980.479398710405299
190.5591561574038390.8816876851923230.440843842596161
200.6581056055047260.6837887889905490.341894394495274
210.759157976407050.4816840471859010.24084202359295
220.7114228737238690.5771542525522620.288577126276131
230.6479742158620710.7040515682758580.352025784137929
240.749954935608920.500090128782160.25004506439108
250.7185208546936360.5629582906127290.281479145306364
260.6688918085669390.6622163828661210.331108191433061
270.6090960432552340.7818079134895320.390903956744766
280.6166844908554150.7666310182891690.383315509144585
290.7088799868616530.5822400262766950.291120013138347
300.7050611734511270.5898776530977450.294938826548873
310.6563532750130120.6872934499739760.343646724986988
320.6622439183038090.6755121633923830.337756081696191
330.7193808583127730.5612382833744540.280619141687227
340.6758316219383710.6483367561232590.324168378061629
350.6550150248333610.6899699503332780.344984975166639
360.6189839121130550.762032175773890.381016087886945
370.5540651167024720.8918697665950560.445934883297528
380.6008279416100990.7983441167798030.399172058389901
390.6550381561390540.6899236877218920.344961843860946
400.6185780029574980.7628439940850040.381421997042502
410.5698751156672510.8602497686654980.430124884332749
420.719786891784970.560426216430060.28021310821503
430.7506728616879440.4986542766241130.249327138312056
440.7312088272083840.5375823455832330.268791172791617
450.7137916112987240.5724167774025520.286208388701276
460.684583635639270.630832728721460.31541636436073
470.7526406870494420.4947186259011170.247359312950558
480.6965276399036730.6069447201926550.303472360096327
490.6360827578616320.7278344842767360.363917242138368
500.6569712384837880.6860575230324240.343028761516212
510.6326177516796590.7347644966406820.367382248320341
520.5710035920907180.8579928158185640.428996407909282
530.5125341448907580.9749317102184840.487465855109242
540.4528200634938760.9056401269877520.547179936506124
550.4926563255002240.9853126510004490.507343674499776
560.6009917343191950.7980165313616110.399008265680805
570.5333816815012510.9332366369974980.466618318498749
580.7627430847852690.4745138304294630.237256915214731
590.7258308003865710.5483383992268570.274169199613429
600.8389729582820640.3220540834358720.161027041717936
610.806665087664220.3866698246715590.19333491233578
620.752360344464130.495279311071740.24763965553587
630.8142742651316570.3714514697366860.185725734868343
640.7782800945348640.4434398109302720.221719905465136
650.7565317044078570.4869365911842860.243468295592143
660.6836270736579190.6327458526841630.316372926342081
670.607445041131970.7851099177360610.39255495886803
680.5433393228632370.9133213542735260.456660677136763
690.4569333693397820.9138667386795640.543066630660218
700.7686081608688350.4627836782623290.231391839131165
710.6967482605453250.6065034789093510.303251739454675
720.7001695590403170.5996608819193670.299830440959683
730.6016726317392430.7966547365215150.398327368260757
740.584026537133990.831946925732020.41597346286601
750.4554424875003370.9108849750006740.544557512499663
760.3635388665201060.7270777330402120.636461133479894

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.24916553574218 & 0.498331071484361 & 0.75083446425782 \tabularnewline
10 & 0.457680124747636 & 0.915360249495272 & 0.542319875252364 \tabularnewline
11 & 0.480700958254466 & 0.961401916508932 & 0.519299041745534 \tabularnewline
12 & 0.642956698544442 & 0.714086602911116 & 0.357043301455558 \tabularnewline
13 & 0.603507277217705 & 0.792985445564589 & 0.396492722782295 \tabularnewline
14 & 0.599032768634393 & 0.801934462731214 & 0.400967231365607 \tabularnewline
15 & 0.642820890058224 & 0.714358219883552 & 0.357179109941776 \tabularnewline
16 & 0.598475240240539 & 0.803049519518923 & 0.401524759759461 \tabularnewline
17 & 0.58206454836862 & 0.835870903262761 & 0.41793545163138 \tabularnewline
18 & 0.520601289594701 & 0.958797420810598 & 0.479398710405299 \tabularnewline
19 & 0.559156157403839 & 0.881687685192323 & 0.440843842596161 \tabularnewline
20 & 0.658105605504726 & 0.683788788990549 & 0.341894394495274 \tabularnewline
21 & 0.75915797640705 & 0.481684047185901 & 0.24084202359295 \tabularnewline
22 & 0.711422873723869 & 0.577154252552262 & 0.288577126276131 \tabularnewline
23 & 0.647974215862071 & 0.704051568275858 & 0.352025784137929 \tabularnewline
24 & 0.74995493560892 & 0.50009012878216 & 0.25004506439108 \tabularnewline
25 & 0.718520854693636 & 0.562958290612729 & 0.281479145306364 \tabularnewline
26 & 0.668891808566939 & 0.662216382866121 & 0.331108191433061 \tabularnewline
27 & 0.609096043255234 & 0.781807913489532 & 0.390903956744766 \tabularnewline
28 & 0.616684490855415 & 0.766631018289169 & 0.383315509144585 \tabularnewline
29 & 0.708879986861653 & 0.582240026276695 & 0.291120013138347 \tabularnewline
30 & 0.705061173451127 & 0.589877653097745 & 0.294938826548873 \tabularnewline
31 & 0.656353275013012 & 0.687293449973976 & 0.343646724986988 \tabularnewline
32 & 0.662243918303809 & 0.675512163392383 & 0.337756081696191 \tabularnewline
33 & 0.719380858312773 & 0.561238283374454 & 0.280619141687227 \tabularnewline
34 & 0.675831621938371 & 0.648336756123259 & 0.324168378061629 \tabularnewline
35 & 0.655015024833361 & 0.689969950333278 & 0.344984975166639 \tabularnewline
36 & 0.618983912113055 & 0.76203217577389 & 0.381016087886945 \tabularnewline
37 & 0.554065116702472 & 0.891869766595056 & 0.445934883297528 \tabularnewline
38 & 0.600827941610099 & 0.798344116779803 & 0.399172058389901 \tabularnewline
39 & 0.655038156139054 & 0.689923687721892 & 0.344961843860946 \tabularnewline
40 & 0.618578002957498 & 0.762843994085004 & 0.381421997042502 \tabularnewline
41 & 0.569875115667251 & 0.860249768665498 & 0.430124884332749 \tabularnewline
42 & 0.71978689178497 & 0.56042621643006 & 0.28021310821503 \tabularnewline
43 & 0.750672861687944 & 0.498654276624113 & 0.249327138312056 \tabularnewline
44 & 0.731208827208384 & 0.537582345583233 & 0.268791172791617 \tabularnewline
45 & 0.713791611298724 & 0.572416777402552 & 0.286208388701276 \tabularnewline
46 & 0.68458363563927 & 0.63083272872146 & 0.31541636436073 \tabularnewline
47 & 0.752640687049442 & 0.494718625901117 & 0.247359312950558 \tabularnewline
48 & 0.696527639903673 & 0.606944720192655 & 0.303472360096327 \tabularnewline
49 & 0.636082757861632 & 0.727834484276736 & 0.363917242138368 \tabularnewline
50 & 0.656971238483788 & 0.686057523032424 & 0.343028761516212 \tabularnewline
51 & 0.632617751679659 & 0.734764496640682 & 0.367382248320341 \tabularnewline
52 & 0.571003592090718 & 0.857992815818564 & 0.428996407909282 \tabularnewline
53 & 0.512534144890758 & 0.974931710218484 & 0.487465855109242 \tabularnewline
54 & 0.452820063493876 & 0.905640126987752 & 0.547179936506124 \tabularnewline
55 & 0.492656325500224 & 0.985312651000449 & 0.507343674499776 \tabularnewline
56 & 0.600991734319195 & 0.798016531361611 & 0.399008265680805 \tabularnewline
57 & 0.533381681501251 & 0.933236636997498 & 0.466618318498749 \tabularnewline
58 & 0.762743084785269 & 0.474513830429463 & 0.237256915214731 \tabularnewline
59 & 0.725830800386571 & 0.548338399226857 & 0.274169199613429 \tabularnewline
60 & 0.838972958282064 & 0.322054083435872 & 0.161027041717936 \tabularnewline
61 & 0.80666508766422 & 0.386669824671559 & 0.19333491233578 \tabularnewline
62 & 0.75236034446413 & 0.49527931107174 & 0.24763965553587 \tabularnewline
63 & 0.814274265131657 & 0.371451469736686 & 0.185725734868343 \tabularnewline
64 & 0.778280094534864 & 0.443439810930272 & 0.221719905465136 \tabularnewline
65 & 0.756531704407857 & 0.486936591184286 & 0.243468295592143 \tabularnewline
66 & 0.683627073657919 & 0.632745852684163 & 0.316372926342081 \tabularnewline
67 & 0.60744504113197 & 0.785109917736061 & 0.39255495886803 \tabularnewline
68 & 0.543339322863237 & 0.913321354273526 & 0.456660677136763 \tabularnewline
69 & 0.456933369339782 & 0.913866738679564 & 0.543066630660218 \tabularnewline
70 & 0.768608160868835 & 0.462783678262329 & 0.231391839131165 \tabularnewline
71 & 0.696748260545325 & 0.606503478909351 & 0.303251739454675 \tabularnewline
72 & 0.700169559040317 & 0.599660881919367 & 0.299830440959683 \tabularnewline
73 & 0.601672631739243 & 0.796654736521515 & 0.398327368260757 \tabularnewline
74 & 0.58402653713399 & 0.83194692573202 & 0.41597346286601 \tabularnewline
75 & 0.455442487500337 & 0.910884975000674 & 0.544557512499663 \tabularnewline
76 & 0.363538866520106 & 0.727077733040212 & 0.636461133479894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153930&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]9[/C][C]0.24916553574218[/C][C]0.498331071484361[/C][C]0.75083446425782[/C][/ROW]
[ROW][C]10[/C][C]0.457680124747636[/C][C]0.915360249495272[/C][C]0.542319875252364[/C][/ROW]
[ROW][C]11[/C][C]0.480700958254466[/C][C]0.961401916508932[/C][C]0.519299041745534[/C][/ROW]
[ROW][C]12[/C][C]0.642956698544442[/C][C]0.714086602911116[/C][C]0.357043301455558[/C][/ROW]
[ROW][C]13[/C][C]0.603507277217705[/C][C]0.792985445564589[/C][C]0.396492722782295[/C][/ROW]
[ROW][C]14[/C][C]0.599032768634393[/C][C]0.801934462731214[/C][C]0.400967231365607[/C][/ROW]
[ROW][C]15[/C][C]0.642820890058224[/C][C]0.714358219883552[/C][C]0.357179109941776[/C][/ROW]
[ROW][C]16[/C][C]0.598475240240539[/C][C]0.803049519518923[/C][C]0.401524759759461[/C][/ROW]
[ROW][C]17[/C][C]0.58206454836862[/C][C]0.835870903262761[/C][C]0.41793545163138[/C][/ROW]
[ROW][C]18[/C][C]0.520601289594701[/C][C]0.958797420810598[/C][C]0.479398710405299[/C][/ROW]
[ROW][C]19[/C][C]0.559156157403839[/C][C]0.881687685192323[/C][C]0.440843842596161[/C][/ROW]
[ROW][C]20[/C][C]0.658105605504726[/C][C]0.683788788990549[/C][C]0.341894394495274[/C][/ROW]
[ROW][C]21[/C][C]0.75915797640705[/C][C]0.481684047185901[/C][C]0.24084202359295[/C][/ROW]
[ROW][C]22[/C][C]0.711422873723869[/C][C]0.577154252552262[/C][C]0.288577126276131[/C][/ROW]
[ROW][C]23[/C][C]0.647974215862071[/C][C]0.704051568275858[/C][C]0.352025784137929[/C][/ROW]
[ROW][C]24[/C][C]0.74995493560892[/C][C]0.50009012878216[/C][C]0.25004506439108[/C][/ROW]
[ROW][C]25[/C][C]0.718520854693636[/C][C]0.562958290612729[/C][C]0.281479145306364[/C][/ROW]
[ROW][C]26[/C][C]0.668891808566939[/C][C]0.662216382866121[/C][C]0.331108191433061[/C][/ROW]
[ROW][C]27[/C][C]0.609096043255234[/C][C]0.781807913489532[/C][C]0.390903956744766[/C][/ROW]
[ROW][C]28[/C][C]0.616684490855415[/C][C]0.766631018289169[/C][C]0.383315509144585[/C][/ROW]
[ROW][C]29[/C][C]0.708879986861653[/C][C]0.582240026276695[/C][C]0.291120013138347[/C][/ROW]
[ROW][C]30[/C][C]0.705061173451127[/C][C]0.589877653097745[/C][C]0.294938826548873[/C][/ROW]
[ROW][C]31[/C][C]0.656353275013012[/C][C]0.687293449973976[/C][C]0.343646724986988[/C][/ROW]
[ROW][C]32[/C][C]0.662243918303809[/C][C]0.675512163392383[/C][C]0.337756081696191[/C][/ROW]
[ROW][C]33[/C][C]0.719380858312773[/C][C]0.561238283374454[/C][C]0.280619141687227[/C][/ROW]
[ROW][C]34[/C][C]0.675831621938371[/C][C]0.648336756123259[/C][C]0.324168378061629[/C][/ROW]
[ROW][C]35[/C][C]0.655015024833361[/C][C]0.689969950333278[/C][C]0.344984975166639[/C][/ROW]
[ROW][C]36[/C][C]0.618983912113055[/C][C]0.76203217577389[/C][C]0.381016087886945[/C][/ROW]
[ROW][C]37[/C][C]0.554065116702472[/C][C]0.891869766595056[/C][C]0.445934883297528[/C][/ROW]
[ROW][C]38[/C][C]0.600827941610099[/C][C]0.798344116779803[/C][C]0.399172058389901[/C][/ROW]
[ROW][C]39[/C][C]0.655038156139054[/C][C]0.689923687721892[/C][C]0.344961843860946[/C][/ROW]
[ROW][C]40[/C][C]0.618578002957498[/C][C]0.762843994085004[/C][C]0.381421997042502[/C][/ROW]
[ROW][C]41[/C][C]0.569875115667251[/C][C]0.860249768665498[/C][C]0.430124884332749[/C][/ROW]
[ROW][C]42[/C][C]0.71978689178497[/C][C]0.56042621643006[/C][C]0.28021310821503[/C][/ROW]
[ROW][C]43[/C][C]0.750672861687944[/C][C]0.498654276624113[/C][C]0.249327138312056[/C][/ROW]
[ROW][C]44[/C][C]0.731208827208384[/C][C]0.537582345583233[/C][C]0.268791172791617[/C][/ROW]
[ROW][C]45[/C][C]0.713791611298724[/C][C]0.572416777402552[/C][C]0.286208388701276[/C][/ROW]
[ROW][C]46[/C][C]0.68458363563927[/C][C]0.63083272872146[/C][C]0.31541636436073[/C][/ROW]
[ROW][C]47[/C][C]0.752640687049442[/C][C]0.494718625901117[/C][C]0.247359312950558[/C][/ROW]
[ROW][C]48[/C][C]0.696527639903673[/C][C]0.606944720192655[/C][C]0.303472360096327[/C][/ROW]
[ROW][C]49[/C][C]0.636082757861632[/C][C]0.727834484276736[/C][C]0.363917242138368[/C][/ROW]
[ROW][C]50[/C][C]0.656971238483788[/C][C]0.686057523032424[/C][C]0.343028761516212[/C][/ROW]
[ROW][C]51[/C][C]0.632617751679659[/C][C]0.734764496640682[/C][C]0.367382248320341[/C][/ROW]
[ROW][C]52[/C][C]0.571003592090718[/C][C]0.857992815818564[/C][C]0.428996407909282[/C][/ROW]
[ROW][C]53[/C][C]0.512534144890758[/C][C]0.974931710218484[/C][C]0.487465855109242[/C][/ROW]
[ROW][C]54[/C][C]0.452820063493876[/C][C]0.905640126987752[/C][C]0.547179936506124[/C][/ROW]
[ROW][C]55[/C][C]0.492656325500224[/C][C]0.985312651000449[/C][C]0.507343674499776[/C][/ROW]
[ROW][C]56[/C][C]0.600991734319195[/C][C]0.798016531361611[/C][C]0.399008265680805[/C][/ROW]
[ROW][C]57[/C][C]0.533381681501251[/C][C]0.933236636997498[/C][C]0.466618318498749[/C][/ROW]
[ROW][C]58[/C][C]0.762743084785269[/C][C]0.474513830429463[/C][C]0.237256915214731[/C][/ROW]
[ROW][C]59[/C][C]0.725830800386571[/C][C]0.548338399226857[/C][C]0.274169199613429[/C][/ROW]
[ROW][C]60[/C][C]0.838972958282064[/C][C]0.322054083435872[/C][C]0.161027041717936[/C][/ROW]
[ROW][C]61[/C][C]0.80666508766422[/C][C]0.386669824671559[/C][C]0.19333491233578[/C][/ROW]
[ROW][C]62[/C][C]0.75236034446413[/C][C]0.49527931107174[/C][C]0.24763965553587[/C][/ROW]
[ROW][C]63[/C][C]0.814274265131657[/C][C]0.371451469736686[/C][C]0.185725734868343[/C][/ROW]
[ROW][C]64[/C][C]0.778280094534864[/C][C]0.443439810930272[/C][C]0.221719905465136[/C][/ROW]
[ROW][C]65[/C][C]0.756531704407857[/C][C]0.486936591184286[/C][C]0.243468295592143[/C][/ROW]
[ROW][C]66[/C][C]0.683627073657919[/C][C]0.632745852684163[/C][C]0.316372926342081[/C][/ROW]
[ROW][C]67[/C][C]0.60744504113197[/C][C]0.785109917736061[/C][C]0.39255495886803[/C][/ROW]
[ROW][C]68[/C][C]0.543339322863237[/C][C]0.913321354273526[/C][C]0.456660677136763[/C][/ROW]
[ROW][C]69[/C][C]0.456933369339782[/C][C]0.913866738679564[/C][C]0.543066630660218[/C][/ROW]
[ROW][C]70[/C][C]0.768608160868835[/C][C]0.462783678262329[/C][C]0.231391839131165[/C][/ROW]
[ROW][C]71[/C][C]0.696748260545325[/C][C]0.606503478909351[/C][C]0.303251739454675[/C][/ROW]
[ROW][C]72[/C][C]0.700169559040317[/C][C]0.599660881919367[/C][C]0.299830440959683[/C][/ROW]
[ROW][C]73[/C][C]0.601672631739243[/C][C]0.796654736521515[/C][C]0.398327368260757[/C][/ROW]
[ROW][C]74[/C][C]0.58402653713399[/C][C]0.83194692573202[/C][C]0.41597346286601[/C][/ROW]
[ROW][C]75[/C][C]0.455442487500337[/C][C]0.910884975000674[/C][C]0.544557512499663[/C][/ROW]
[ROW][C]76[/C][C]0.363538866520106[/C][C]0.727077733040212[/C][C]0.636461133479894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153930&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.249165535742180.4983310714843610.75083446425782
100.4576801247476360.9153602494952720.542319875252364
110.4807009582544660.9614019165089320.519299041745534
120.6429566985444420.7140866029111160.357043301455558
130.6035072772177050.7929854455645890.396492722782295
140.5990327686343930.8019344627312140.400967231365607
150.6428208900582240.7143582198835520.357179109941776
160.5984752402405390.8030495195189230.401524759759461
170.582064548368620.8358709032627610.41793545163138
180.5206012895947010.9587974208105980.479398710405299
190.5591561574038390.8816876851923230.440843842596161
200.6581056055047260.6837887889905490.341894394495274
210.759157976407050.4816840471859010.24084202359295
220.7114228737238690.5771542525522620.288577126276131
230.6479742158620710.7040515682758580.352025784137929
240.749954935608920.500090128782160.25004506439108
250.7185208546936360.5629582906127290.281479145306364
260.6688918085669390.6622163828661210.331108191433061
270.6090960432552340.7818079134895320.390903956744766
280.6166844908554150.7666310182891690.383315509144585
290.7088799868616530.5822400262766950.291120013138347
300.7050611734511270.5898776530977450.294938826548873
310.6563532750130120.6872934499739760.343646724986988
320.6622439183038090.6755121633923830.337756081696191
330.7193808583127730.5612382833744540.280619141687227
340.6758316219383710.6483367561232590.324168378061629
350.6550150248333610.6899699503332780.344984975166639
360.6189839121130550.762032175773890.381016087886945
370.5540651167024720.8918697665950560.445934883297528
380.6008279416100990.7983441167798030.399172058389901
390.6550381561390540.6899236877218920.344961843860946
400.6185780029574980.7628439940850040.381421997042502
410.5698751156672510.8602497686654980.430124884332749
420.719786891784970.560426216430060.28021310821503
430.7506728616879440.4986542766241130.249327138312056
440.7312088272083840.5375823455832330.268791172791617
450.7137916112987240.5724167774025520.286208388701276
460.684583635639270.630832728721460.31541636436073
470.7526406870494420.4947186259011170.247359312950558
480.6965276399036730.6069447201926550.303472360096327
490.6360827578616320.7278344842767360.363917242138368
500.6569712384837880.6860575230324240.343028761516212
510.6326177516796590.7347644966406820.367382248320341
520.5710035920907180.8579928158185640.428996407909282
530.5125341448907580.9749317102184840.487465855109242
540.4528200634938760.9056401269877520.547179936506124
550.4926563255002240.9853126510004490.507343674499776
560.6009917343191950.7980165313616110.399008265680805
570.5333816815012510.9332366369974980.466618318498749
580.7627430847852690.4745138304294630.237256915214731
590.7258308003865710.5483383992268570.274169199613429
600.8389729582820640.3220540834358720.161027041717936
610.806665087664220.3866698246715590.19333491233578
620.752360344464130.495279311071740.24763965553587
630.8142742651316570.3714514697366860.185725734868343
640.7782800945348640.4434398109302720.221719905465136
650.7565317044078570.4869365911842860.243468295592143
660.6836270736579190.6327458526841630.316372926342081
670.607445041131970.7851099177360610.39255495886803
680.5433393228632370.9133213542735260.456660677136763
690.4569333693397820.9138667386795640.543066630660218
700.7686081608688350.4627836782623290.231391839131165
710.6967482605453250.6065034789093510.303251739454675
720.7001695590403170.5996608819193670.299830440959683
730.6016726317392430.7966547365215150.398327368260757
740.584026537133990.831946925732020.41597346286601
750.4554424875003370.9108849750006740.544557512499663
760.3635388665201060.7270777330402120.636461133479894






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=153930&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=153930&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153930&T=6

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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


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}