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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Dec 2011 12:03:36 -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/19/t1324314609ja7vve0as0xmyk3.htm/, Retrieved Fri, 31 May 2024 17:58:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157537, Retrieved Fri, 31 May 2024 17:58:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [paper statistiek,...] [2011-12-19 15:59:25] [4b648d52023f19d55c572f0eddd72b1f]
- R P   [Kendall tau Correlation Matrix] [Paper Kendall Tau] [2011-12-19 16:21:41] [74be16979710d4c4e7c6647856088456]
- RMPD      [Multiple Regression] [multiple regressi...] [2011-12-19 17:03:36] [2adf2d2c11e011c12275478b9efd18e5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	210907	79	94	112285	146283	30	-1
4	179321	108	103	101193	96933	30	3
0	149061	43	93	116174	95757	26	0
0	237213	78	123	66198	143983	38	3
-4	173326	86	148	71701	75851	44	4
4	133131	44	90	57793	59238	30	0
4	258873	104	124	80444	93163	40	0
0	324799	158	168	97668	151511	47	7
-1	230964	102	115	133824	136368	30	1
0	236785	77	71	101481	112642	31	0
1	344297	80	108	67654	127766	30	1
0	174724	123	120	69112	85646	34	4
3	174415	73	114	82753	98579	31	1
-1	223632	105	120	72654	131741	33	5
4	294424	107	124	101494	171975	33	13
3	325107	84	126	79215	159676	36	4
1	106408	33	37	31081	58391	14	0
0	96560	42	38	22996	31580	17	0
-2	265769	96	120	83122	136815	32	6
-3	269651	106	93	70106	120642	30	0
-4	149112	56	95	60578	69107	35	1
2	152871	59	90	79892	108016	28	3
2	362301	76	110	100708	79336	34	1
-4	183167	91	138	82875	93176	39	0
3	277965	115	133	139077	161632	39	2
2	218946	76	96	80670	102996	29	3
2	244052	101	164	143558	160604	44	4
0	341570	94	78	117105	158051	21	12
5	233328	92	102	120733	162647	28	0
-2	206161	75	99	73107	60622	28	3
0	311473	128	129	132068	179566	38	0
-2	207176	56	114	87011	96144	32	4
-3	196553	41	99	95260	129847	29	-1
2	143246	67	104	106671	71180	27	2
2	182192	77	138	70054	86767	40	1
2	194979	66	151	74011	93487	40	1
0	167488	69	72	83737	82981	28	0
4	143756	105	120	69094	73815	34	2
4	275541	116	115	93133	94552	33	0
2	152299	62	98	61370	67808	33	2
2	193339	100	71	84651	106175	35	4
-4	130585	67	107	95364	76669	29	0
3	112611	46	73	26706	57283	20	0
3	148446	135	129	126846	72413	37	6
2	182079	124	118	102860	96971	33	13
-1	243060	58	104	111813	120336	29	4
-3	162765	68	107	120293	93913	28	-1
0	85574	37	36	24266	32036	21	3
1	225060	93	139	109825	102255	41	0
-3	133328	56	56	40909	63506	20	2
3	100750	83	93	140867	68370	30	0
0	101523	59	87	61056	50517	22	1
0	243511	133	110	101338	103950	42	1
0	152474	106	83	65567	84396	32	0
3	132487	71	98	40735	55515	36	31
-3	317394	116	82	91413	209056	31	2
0	244749	98	115	76643	142775	33	5
-4	184510	64	140	110681	68847	40	1
2	128423	32	120	92696	20112	38	1
-1	97839	25	66	94785	61023	24	2
3	172494	46	139	86687	112494	43	13
2	229242	63	119	91721	78876	31	5
5	351619	95	141	115168	170745	40	3
2	324598	113	133	135777	122037	37	1
-2	195838	111	98	102372	112283	31	1
0	254488	120	117	103772	120691	39	4
3	199476	87	105	135400	122422	32	2
-2	92499	25	55	21399	25899	18	0
0	224330	131	132	130115	139296	39	4
6	181633	47	73	64466	89455	30	0
-3	271856	109	86	54990	147866	37	0
3	95227	37	48	34777	14336	32	0
0	98146	15	48	27114	30059	17	7
-2	118612	54	43	30080	41907	12	3
1	65475	16	46	69008	35885	13	4
0	108446	22	65	46300	55764	17	1
2	121848	37	52	30594	35619	17	0
2	76302	29	68	30976	40557	20	2
-3	98104	55	47	25568	44197	17	0
-2	30989	5	41	4154	4103	17	0
1	31774	0	47	4143	4694	17	0
-4	150580	27	71	45588	62991	22	2
0	54157	37	30	18625	24261	15	1
1	59382	29	24	26263	21425	12	0
0	84105	17	63	20055	27184	17	0

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
testscore[t] = -0.963134976978246 + 3.5485931497739e-06time_in_rfc[t] -0.00376727851632208blogged_computations[t] -0.00922102328437846feedback_messages_p120[t] + 1.69559259411212e-05totsize[t] -9.9637255784274e-06totseconds[t] + 0.0443887501829863compendiums_reviewed[t] + 0.098211691767925`difference_hyperlinks-blogs`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
testscore[t] =  -0.963134976978246 +  3.5485931497739e-06time_in_rfc[t] -0.00376727851632208blogged_computations[t] -0.00922102328437846feedback_messages_p120[t] +  1.69559259411212e-05totsize[t] -9.9637255784274e-06totseconds[t] +  0.0443887501829863compendiums_reviewed[t] +  0.098211691767925`difference_hyperlinks-blogs`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]testscore[t] =  -0.963134976978246 +  3.5485931497739e-06time_in_rfc[t] -0.00376727851632208blogged_computations[t] -0.00922102328437846feedback_messages_p120[t] +  1.69559259411212e-05totsize[t] -9.9637255784274e-06totseconds[t] +  0.0443887501829863compendiums_reviewed[t] +  0.098211691767925`difference_hyperlinks-blogs`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&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
testscore[t] = -0.963134976978246 + 3.5485931497739e-06time_in_rfc[t] -0.00376727851632208blogged_computations[t] -0.00922102328437846feedback_messages_p120[t] + 1.69559259411212e-05totsize[t] -9.9637255784274e-06totseconds[t] + 0.0443887501829863compendiums_reviewed[t] + 0.098211691767925`difference_hyperlinks-blogs`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9631349769782461.02124-0.94310.3485780.174289
time_in_rfc3.5485931497739e-067e-060.49830.6196880.309844
blogged_computations-0.003767278516322080.01342-0.28070.7796820.389841
feedback_messages_p120-0.009221023284378460.020182-0.45690.6490330.324517
totsize1.69559259411212e-051.3e-051.350.1809860.090493
totseconds-9.9637255784274e-061.3e-05-0.76320.4476650.223833
compendiums_reviewed0.04438875018298630.0757560.58590.5596280.279814
`difference_hyperlinks-blogs`0.0982116917679250.0647781.51610.1335810.06679

\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) & -0.963134976978246 & 1.02124 & -0.9431 & 0.348578 & 0.174289 \tabularnewline
time_in_rfc & 3.5485931497739e-06 & 7e-06 & 0.4983 & 0.619688 & 0.309844 \tabularnewline
blogged_computations & -0.00376727851632208 & 0.01342 & -0.2807 & 0.779682 & 0.389841 \tabularnewline
feedback_messages_p120 & -0.00922102328437846 & 0.020182 & -0.4569 & 0.649033 & 0.324517 \tabularnewline
totsize & 1.69559259411212e-05 & 1.3e-05 & 1.35 & 0.180986 & 0.090493 \tabularnewline
totseconds & -9.9637255784274e-06 & 1.3e-05 & -0.7632 & 0.447665 & 0.223833 \tabularnewline
compendiums_reviewed & 0.0443887501829863 & 0.075756 & 0.5859 & 0.559628 & 0.279814 \tabularnewline
`difference_hyperlinks-blogs` & 0.098211691767925 & 0.064778 & 1.5161 & 0.133581 & 0.06679 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&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]-0.963134976978246[/C][C]1.02124[/C][C]-0.9431[/C][C]0.348578[/C][C]0.174289[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]3.5485931497739e-06[/C][C]7e-06[/C][C]0.4983[/C][C]0.619688[/C][C]0.309844[/C][/ROW]
[ROW][C]blogged_computations[/C][C]-0.00376727851632208[/C][C]0.01342[/C][C]-0.2807[/C][C]0.779682[/C][C]0.389841[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]-0.00922102328437846[/C][C]0.020182[/C][C]-0.4569[/C][C]0.649033[/C][C]0.324517[/C][/ROW]
[ROW][C]totsize[/C][C]1.69559259411212e-05[/C][C]1.3e-05[/C][C]1.35[/C][C]0.180986[/C][C]0.090493[/C][/ROW]
[ROW][C]totseconds[/C][C]-9.9637255784274e-06[/C][C]1.3e-05[/C][C]-0.7632[/C][C]0.447665[/C][C]0.223833[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.0443887501829863[/C][C]0.075756[/C][C]0.5859[/C][C]0.559628[/C][C]0.279814[/C][/ROW]
[ROW][C]`difference_hyperlinks-blogs`[/C][C]0.098211691767925[/C][C]0.064778[/C][C]1.5161[/C][C]0.133581[/C][C]0.06679[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&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)-0.9631349769782461.02124-0.94310.3485780.174289
time_in_rfc3.5485931497739e-067e-060.49830.6196880.309844
blogged_computations-0.003767278516322080.01342-0.28070.7796820.389841
feedback_messages_p120-0.009221023284378460.020182-0.45690.6490330.324517
totsize1.69559259411212e-051.3e-051.350.1809860.090493
totseconds-9.9637255784274e-061.3e-05-0.76320.4476650.223833
compendiums_reviewed0.04438875018298630.0757560.58590.5596280.279814
`difference_hyperlinks-blogs`0.0982116917679250.0647781.51610.1335810.06679






Multiple Linear Regression - Regression Statistics
Multiple R0.261926189565139
R-squared0.0686053287801133
Adjusted R-squared-0.0160669140580583
F-TEST (value)0.810245795794427
F-TEST (DF numerator)7
F-TEST (DF denominator)77
p-value0.581456293634736
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48173653963539
Sum Squared Residuals474.244251416432

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.261926189565139 \tabularnewline
R-squared & 0.0686053287801133 \tabularnewline
Adjusted R-squared & -0.0160669140580583 \tabularnewline
F-TEST (value) & 0.810245795794427 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value & 0.581456293634736 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.48173653963539 \tabularnewline
Sum Squared Residuals & 474.244251416432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.261926189565139[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0686053287801133[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0160669140580583[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.810245795794427[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/C][/ROW]
[ROW][C]p-value[/C][C]0.581456293634736[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.48173653963539[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]474.244251416432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&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 R0.261926189565139
R-squared0.0686053287801133
Adjusted R-squared-0.0160669140580583
F-TEST (value)0.810245795794427
F-TEST (DF numerator)7
F-TEST (DF denominator)77
p-value0.581456293634736
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48173653963539
Sum Squared Residuals474.244251416432






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.3007202561714621.69927974382854
240.6928756002381343.30712439976187
300.716122499699143-0.716122499699143
400.119852729356281-0.119852729356281
5-40.769181152985885-4.76918115298588
640.2350055849219943.76499441507801
740.6315980721867213.36840192781328
800.96527306180084-0.96527306180084
9-10.752032905620889-1.75203290562089
1000.906767152541574-0.906767152541574
1110.2653672513155910.734632748684409
1200.307560346935828-0.307560346935828
1330.2247874941792222.77521250582078
14-10.203528854729546-1.20352885472955
1541.284144114181072.71585588581893
1630.4552747685867072.54472523141329
171-0.4843765931698941.48437659316989
180-0.2992346326419340.299234632641934
19-20.567723061680123-2.56772306168012
20-30.0551908945592925-3.05519089455929
21-40.469448882126154-4.46944888212615
2220.3431016119998091.65689838800019
2321.546442596433660.453557403566344
24-40.279530151268188-4.27953015126819
2531.038923651792531.96107634820747
2620.5658033928562571.43419660714374
2721.19005973820460.809940261795401
2801.69714495947101-1.69714495947101
2950.2471719045824884.75282809541751
30-20.746115327684181-2.74611532768418
3100.607393710316814-0.607393710316814
32-20.840570519063965-2.84057051906397
33-30.177535621574413-3.17753562157441
3420.8281999430546861.17180005694531
3520.3178782048676041.6821217951324
3620.2849591895183711.71504081048163
3700.543279361821412-0.543279361821412
3840.1866307746830123.81336922531699
3940.6191207683282423.38087923167176
4020.4662976718171121.53370232818289
4121.015416516211350.984583483788646
42-40.401550707846141-4.40155070784614
433-0.6401059957652473.64010599576525
4431.526486907339631.47351309266037
4521.647240889818460.352759110181543
46-11.09891608691101-2.09891608691101
47-30.62025650885044-3.62025650885044
4800.188239605282198-0.188239605282198
4910.866714823727290.13328517627271
50-3-0.0722610454071289-2.92773895459287
5131.263119507799981.73688049220002
520-0.02068192782837520.0206819278283752
5301.03071543416104-1.03071543416104
5400.104547377726924-0.104547377726924
5533.11599129624625-0.115991296246247
56-30.00952906272911624-3.00952906272912
5700.308638002856233-0.308638002856233
58-41.22405478350756-5.22405478350756
5921.421850555373690.57814944462631
60-10.942190730809773-1.94219073080977
6131.728426259209231.27157374079077
6221.152136675526630.847863324473366
6350.948270893125834.05172910687417
6421.363509719716820.63649028028318
65-20.501304209868733-2.50130420986873
6601.0900326174747-1.0900326174747
6731.141680063443281.85831993655672
68-2-0.332445069014063-1.66755493098594
6901.06455357499891-1.06455357499891
7060.3646480081595575.63535199184044
71-3-0.100576120495966-2.89942387950403
7230.6600647525587882.33993524744121
7300.488360895196319-0.488360895196319
74-2-0.222381805413551-1.77761819458645
7510.5672123965741860.432787603425814
760-0.1782882623043720.178288262304372
772-0.2311681047807692.23116810478077
782-0.2233245519118062.22332455191181
79-3-0.507821119192731-2.49217888080727
80-2-0.465903467679134-1.53409653232087
811-0.5056826461834241.50568264618342
82-40.133140607837802-4.1331406078378
830-0.3488567024933370.348856702493337
841-0.31846238968711.3184623896871
850-0.4858428200738480.485842820073848

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.300720256171462 & 1.69927974382854 \tabularnewline
2 & 4 & 0.692875600238134 & 3.30712439976187 \tabularnewline
3 & 0 & 0.716122499699143 & -0.716122499699143 \tabularnewline
4 & 0 & 0.119852729356281 & -0.119852729356281 \tabularnewline
5 & -4 & 0.769181152985885 & -4.76918115298588 \tabularnewline
6 & 4 & 0.235005584921994 & 3.76499441507801 \tabularnewline
7 & 4 & 0.631598072186721 & 3.36840192781328 \tabularnewline
8 & 0 & 0.96527306180084 & -0.96527306180084 \tabularnewline
9 & -1 & 0.752032905620889 & -1.75203290562089 \tabularnewline
10 & 0 & 0.906767152541574 & -0.906767152541574 \tabularnewline
11 & 1 & 0.265367251315591 & 0.734632748684409 \tabularnewline
12 & 0 & 0.307560346935828 & -0.307560346935828 \tabularnewline
13 & 3 & 0.224787494179222 & 2.77521250582078 \tabularnewline
14 & -1 & 0.203528854729546 & -1.20352885472955 \tabularnewline
15 & 4 & 1.28414411418107 & 2.71585588581893 \tabularnewline
16 & 3 & 0.455274768586707 & 2.54472523141329 \tabularnewline
17 & 1 & -0.484376593169894 & 1.48437659316989 \tabularnewline
18 & 0 & -0.299234632641934 & 0.299234632641934 \tabularnewline
19 & -2 & 0.567723061680123 & -2.56772306168012 \tabularnewline
20 & -3 & 0.0551908945592925 & -3.05519089455929 \tabularnewline
21 & -4 & 0.469448882126154 & -4.46944888212615 \tabularnewline
22 & 2 & 0.343101611999809 & 1.65689838800019 \tabularnewline
23 & 2 & 1.54644259643366 & 0.453557403566344 \tabularnewline
24 & -4 & 0.279530151268188 & -4.27953015126819 \tabularnewline
25 & 3 & 1.03892365179253 & 1.96107634820747 \tabularnewline
26 & 2 & 0.565803392856257 & 1.43419660714374 \tabularnewline
27 & 2 & 1.1900597382046 & 0.809940261795401 \tabularnewline
28 & 0 & 1.69714495947101 & -1.69714495947101 \tabularnewline
29 & 5 & 0.247171904582488 & 4.75282809541751 \tabularnewline
30 & -2 & 0.746115327684181 & -2.74611532768418 \tabularnewline
31 & 0 & 0.607393710316814 & -0.607393710316814 \tabularnewline
32 & -2 & 0.840570519063965 & -2.84057051906397 \tabularnewline
33 & -3 & 0.177535621574413 & -3.17753562157441 \tabularnewline
34 & 2 & 0.828199943054686 & 1.17180005694531 \tabularnewline
35 & 2 & 0.317878204867604 & 1.6821217951324 \tabularnewline
36 & 2 & 0.284959189518371 & 1.71504081048163 \tabularnewline
37 & 0 & 0.543279361821412 & -0.543279361821412 \tabularnewline
38 & 4 & 0.186630774683012 & 3.81336922531699 \tabularnewline
39 & 4 & 0.619120768328242 & 3.38087923167176 \tabularnewline
40 & 2 & 0.466297671817112 & 1.53370232818289 \tabularnewline
41 & 2 & 1.01541651621135 & 0.984583483788646 \tabularnewline
42 & -4 & 0.401550707846141 & -4.40155070784614 \tabularnewline
43 & 3 & -0.640105995765247 & 3.64010599576525 \tabularnewline
44 & 3 & 1.52648690733963 & 1.47351309266037 \tabularnewline
45 & 2 & 1.64724088981846 & 0.352759110181543 \tabularnewline
46 & -1 & 1.09891608691101 & -2.09891608691101 \tabularnewline
47 & -3 & 0.62025650885044 & -3.62025650885044 \tabularnewline
48 & 0 & 0.188239605282198 & -0.188239605282198 \tabularnewline
49 & 1 & 0.86671482372729 & 0.13328517627271 \tabularnewline
50 & -3 & -0.0722610454071289 & -2.92773895459287 \tabularnewline
51 & 3 & 1.26311950779998 & 1.73688049220002 \tabularnewline
52 & 0 & -0.0206819278283752 & 0.0206819278283752 \tabularnewline
53 & 0 & 1.03071543416104 & -1.03071543416104 \tabularnewline
54 & 0 & 0.104547377726924 & -0.104547377726924 \tabularnewline
55 & 3 & 3.11599129624625 & -0.115991296246247 \tabularnewline
56 & -3 & 0.00952906272911624 & -3.00952906272912 \tabularnewline
57 & 0 & 0.308638002856233 & -0.308638002856233 \tabularnewline
58 & -4 & 1.22405478350756 & -5.22405478350756 \tabularnewline
59 & 2 & 1.42185055537369 & 0.57814944462631 \tabularnewline
60 & -1 & 0.942190730809773 & -1.94219073080977 \tabularnewline
61 & 3 & 1.72842625920923 & 1.27157374079077 \tabularnewline
62 & 2 & 1.15213667552663 & 0.847863324473366 \tabularnewline
63 & 5 & 0.94827089312583 & 4.05172910687417 \tabularnewline
64 & 2 & 1.36350971971682 & 0.63649028028318 \tabularnewline
65 & -2 & 0.501304209868733 & -2.50130420986873 \tabularnewline
66 & 0 & 1.0900326174747 & -1.0900326174747 \tabularnewline
67 & 3 & 1.14168006344328 & 1.85831993655672 \tabularnewline
68 & -2 & -0.332445069014063 & -1.66755493098594 \tabularnewline
69 & 0 & 1.06455357499891 & -1.06455357499891 \tabularnewline
70 & 6 & 0.364648008159557 & 5.63535199184044 \tabularnewline
71 & -3 & -0.100576120495966 & -2.89942387950403 \tabularnewline
72 & 3 & 0.660064752558788 & 2.33993524744121 \tabularnewline
73 & 0 & 0.488360895196319 & -0.488360895196319 \tabularnewline
74 & -2 & -0.222381805413551 & -1.77761819458645 \tabularnewline
75 & 1 & 0.567212396574186 & 0.432787603425814 \tabularnewline
76 & 0 & -0.178288262304372 & 0.178288262304372 \tabularnewline
77 & 2 & -0.231168104780769 & 2.23116810478077 \tabularnewline
78 & 2 & -0.223324551911806 & 2.22332455191181 \tabularnewline
79 & -3 & -0.507821119192731 & -2.49217888080727 \tabularnewline
80 & -2 & -0.465903467679134 & -1.53409653232087 \tabularnewline
81 & 1 & -0.505682646183424 & 1.50568264618342 \tabularnewline
82 & -4 & 0.133140607837802 & -4.1331406078378 \tabularnewline
83 & 0 & -0.348856702493337 & 0.348856702493337 \tabularnewline
84 & 1 & -0.3184623896871 & 1.3184623896871 \tabularnewline
85 & 0 & -0.485842820073848 & 0.485842820073848 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&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]0.300720256171462[/C][C]1.69927974382854[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]0.692875600238134[/C][C]3.30712439976187[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.716122499699143[/C][C]-0.716122499699143[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.119852729356281[/C][C]-0.119852729356281[/C][/ROW]
[ROW][C]5[/C][C]-4[/C][C]0.769181152985885[/C][C]-4.76918115298588[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]0.235005584921994[/C][C]3.76499441507801[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]0.631598072186721[/C][C]3.36840192781328[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.96527306180084[/C][C]-0.96527306180084[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]0.752032905620889[/C][C]-1.75203290562089[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.906767152541574[/C][C]-0.906767152541574[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.265367251315591[/C][C]0.734632748684409[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.307560346935828[/C][C]-0.307560346935828[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]0.224787494179222[/C][C]2.77521250582078[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]0.203528854729546[/C][C]-1.20352885472955[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]1.28414411418107[/C][C]2.71585588581893[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]0.455274768586707[/C][C]2.54472523141329[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]-0.484376593169894[/C][C]1.48437659316989[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]-0.299234632641934[/C][C]0.299234632641934[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]0.567723061680123[/C][C]-2.56772306168012[/C][/ROW]
[ROW][C]20[/C][C]-3[/C][C]0.0551908945592925[/C][C]-3.05519089455929[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]0.469448882126154[/C][C]-4.46944888212615[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.343101611999809[/C][C]1.65689838800019[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.54644259643366[/C][C]0.453557403566344[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]0.279530151268188[/C][C]-4.27953015126819[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]1.03892365179253[/C][C]1.96107634820747[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.565803392856257[/C][C]1.43419660714374[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.1900597382046[/C][C]0.809940261795401[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]1.69714495947101[/C][C]-1.69714495947101[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]0.247171904582488[/C][C]4.75282809541751[/C][/ROW]
[ROW][C]30[/C][C]-2[/C][C]0.746115327684181[/C][C]-2.74611532768418[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.607393710316814[/C][C]-0.607393710316814[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]0.840570519063965[/C][C]-2.84057051906397[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]0.177535621574413[/C][C]-3.17753562157441[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]0.828199943054686[/C][C]1.17180005694531[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]0.317878204867604[/C][C]1.6821217951324[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.284959189518371[/C][C]1.71504081048163[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.543279361821412[/C][C]-0.543279361821412[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]0.186630774683012[/C][C]3.81336922531699[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]0.619120768328242[/C][C]3.38087923167176[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]0.466297671817112[/C][C]1.53370232818289[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]1.01541651621135[/C][C]0.984583483788646[/C][/ROW]
[ROW][C]42[/C][C]-4[/C][C]0.401550707846141[/C][C]-4.40155070784614[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-0.640105995765247[/C][C]3.64010599576525[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]1.52648690733963[/C][C]1.47351309266037[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.64724088981846[/C][C]0.352759110181543[/C][/ROW]
[ROW][C]46[/C][C]-1[/C][C]1.09891608691101[/C][C]-2.09891608691101[/C][/ROW]
[ROW][C]47[/C][C]-3[/C][C]0.62025650885044[/C][C]-3.62025650885044[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.188239605282198[/C][C]-0.188239605282198[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.86671482372729[/C][C]0.13328517627271[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]-0.0722610454071289[/C][C]-2.92773895459287[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]1.26311950779998[/C][C]1.73688049220002[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]-0.0206819278283752[/C][C]0.0206819278283752[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]1.03071543416104[/C][C]-1.03071543416104[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.104547377726924[/C][C]-0.104547377726924[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]3.11599129624625[/C][C]-0.115991296246247[/C][/ROW]
[ROW][C]56[/C][C]-3[/C][C]0.00952906272911624[/C][C]-3.00952906272912[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.308638002856233[/C][C]-0.308638002856233[/C][/ROW]
[ROW][C]58[/C][C]-4[/C][C]1.22405478350756[/C][C]-5.22405478350756[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.42185055537369[/C][C]0.57814944462631[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]0.942190730809773[/C][C]-1.94219073080977[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]1.72842625920923[/C][C]1.27157374079077[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.15213667552663[/C][C]0.847863324473366[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]0.94827089312583[/C][C]4.05172910687417[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.36350971971682[/C][C]0.63649028028318[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]0.501304209868733[/C][C]-2.50130420986873[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]1.0900326174747[/C][C]-1.0900326174747[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]1.14168006344328[/C][C]1.85831993655672[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-0.332445069014063[/C][C]-1.66755493098594[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]1.06455357499891[/C][C]-1.06455357499891[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]0.364648008159557[/C][C]5.63535199184044[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]-0.100576120495966[/C][C]-2.89942387950403[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]0.660064752558788[/C][C]2.33993524744121[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.488360895196319[/C][C]-0.488360895196319[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-0.222381805413551[/C][C]-1.77761819458645[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.567212396574186[/C][C]0.432787603425814[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]-0.178288262304372[/C][C]0.178288262304372[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]-0.231168104780769[/C][C]2.23116810478077[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]-0.223324551911806[/C][C]2.22332455191181[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C]-0.507821119192731[/C][C]-2.49217888080727[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-0.465903467679134[/C][C]-1.53409653232087[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.505682646183424[/C][C]1.50568264618342[/C][/ROW]
[ROW][C]82[/C][C]-4[/C][C]0.133140607837802[/C][C]-4.1331406078378[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.348856702493337[/C][C]0.348856702493337[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]-0.3184623896871[/C][C]1.3184623896871[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.485842820073848[/C][C]0.485842820073848[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&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
120.3007202561714621.69927974382854
240.6928756002381343.30712439976187
300.716122499699143-0.716122499699143
400.119852729356281-0.119852729356281
5-40.769181152985885-4.76918115298588
640.2350055849219943.76499441507801
740.6315980721867213.36840192781328
800.96527306180084-0.96527306180084
9-10.752032905620889-1.75203290562089
1000.906767152541574-0.906767152541574
1110.2653672513155910.734632748684409
1200.307560346935828-0.307560346935828
1330.2247874941792222.77521250582078
14-10.203528854729546-1.20352885472955
1541.284144114181072.71585588581893
1630.4552747685867072.54472523141329
171-0.4843765931698941.48437659316989
180-0.2992346326419340.299234632641934
19-20.567723061680123-2.56772306168012
20-30.0551908945592925-3.05519089455929
21-40.469448882126154-4.46944888212615
2220.3431016119998091.65689838800019
2321.546442596433660.453557403566344
24-40.279530151268188-4.27953015126819
2531.038923651792531.96107634820747
2620.5658033928562571.43419660714374
2721.19005973820460.809940261795401
2801.69714495947101-1.69714495947101
2950.2471719045824884.75282809541751
30-20.746115327684181-2.74611532768418
3100.607393710316814-0.607393710316814
32-20.840570519063965-2.84057051906397
33-30.177535621574413-3.17753562157441
3420.8281999430546861.17180005694531
3520.3178782048676041.6821217951324
3620.2849591895183711.71504081048163
3700.543279361821412-0.543279361821412
3840.1866307746830123.81336922531699
3940.6191207683282423.38087923167176
4020.4662976718171121.53370232818289
4121.015416516211350.984583483788646
42-40.401550707846141-4.40155070784614
433-0.6401059957652473.64010599576525
4431.526486907339631.47351309266037
4521.647240889818460.352759110181543
46-11.09891608691101-2.09891608691101
47-30.62025650885044-3.62025650885044
4800.188239605282198-0.188239605282198
4910.866714823727290.13328517627271
50-3-0.0722610454071289-2.92773895459287
5131.263119507799981.73688049220002
520-0.02068192782837520.0206819278283752
5301.03071543416104-1.03071543416104
5400.104547377726924-0.104547377726924
5533.11599129624625-0.115991296246247
56-30.00952906272911624-3.00952906272912
5700.308638002856233-0.308638002856233
58-41.22405478350756-5.22405478350756
5921.421850555373690.57814944462631
60-10.942190730809773-1.94219073080977
6131.728426259209231.27157374079077
6221.152136675526630.847863324473366
6350.948270893125834.05172910687417
6421.363509719716820.63649028028318
65-20.501304209868733-2.50130420986873
6601.0900326174747-1.0900326174747
6731.141680063443281.85831993655672
68-2-0.332445069014063-1.66755493098594
6901.06455357499891-1.06455357499891
7060.3646480081595575.63535199184044
71-3-0.100576120495966-2.89942387950403
7230.6600647525587882.33993524744121
7300.488360895196319-0.488360895196319
74-2-0.222381805413551-1.77761819458645
7510.5672123965741860.432787603425814
760-0.1782882623043720.178288262304372
772-0.2311681047807692.23116810478077
782-0.2233245519118062.22332455191181
79-3-0.507821119192731-2.49217888080727
80-2-0.465903467679134-1.53409653232087
811-0.5056826461834241.50568264618342
82-40.133140607837802-4.1331406078378
830-0.3488567024933370.348856702493337
841-0.31846238968711.3184623896871
850-0.4858428200738480.485842820073848






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5190183791887260.9619632416225470.480981620811274
120.7483563064593230.5032873870813530.251643693540677
130.668092639590220.663814720819560.33190736040978
140.5641521060754840.8716957878490320.435847893924516
150.7825135417561730.4349729164876550.217486458243827
160.745617787000450.5087644259990990.25438221299955
170.7299058593523230.5401882812953540.270094140647677
180.6762090557894810.6475818884210380.323790944210519
190.7093253532255740.5813492935488530.290674646774426
200.7610058207093160.4779883585813690.238994179290684
210.8447199497283280.3105601005433440.155280050271672
220.8063089814238140.3873820371523720.193691018576186
230.7452903246042150.5094193507915690.254709675395785
240.7978569203559640.4042861592880720.202143079644036
250.7743311934437560.4513376131124880.225668806556244
260.7237894357203860.5524211285592270.276210564279614
270.6656853485942960.6686293028114090.334314651405704
280.6971128612966190.6057742774067630.302887138703382
290.7905600959113130.4188798081773730.209439904088687
300.788160150758380.4236796984832390.21183984924162
310.754603598608770.490792802782460.24539640139123
320.756542500364890.486914999270220.24345749963511
330.7971613508318480.4056772983363040.202838649168152
340.7618778036916730.4762443926166550.238122196308327
350.7437692020845640.5124615958308710.256230797915436
360.7129024093016150.574195181396770.287097590698385
370.6515383566993980.6969232866012050.348461643300602
380.7156891231488250.568621753702350.284310876851175
390.7328822212373450.534235557525310.267117778762655
400.7059164829999680.5881670340000640.294083517000032
410.6617574701921720.6764850596156560.338242529807828
420.7712399035378960.4575201929242080.228760096462104
430.8213849920255970.3572300159488060.178615007974403
440.8016178044366070.3967643911267860.198382195563393
450.7646604100519480.4706791798961030.235339589948052
460.7464828492023970.5070343015952070.253517150797603
470.7942837242467520.4114325515064960.205716275753248
480.7417950057841190.5164099884317620.258204994215881
490.6845441267441990.6309117465116030.315455873255801
500.7047343067832740.5905313864334520.295265693216726
510.6823076695487890.6353846609024210.317692330451211
520.6283189622126150.7433620755747690.371681037787385
530.568439957142650.8631200857146990.43156004285735
540.5206884361616830.9586231276766350.479311563838317
550.4560336931058330.9120673862116670.543966306894167
560.5314042549771020.9371914900457960.468595745022898
570.4573607508316230.9147215016632450.542639249168377
580.6798186332238550.640362733552290.320181366776145
590.648881821373740.7022363572525210.35111817862626
600.7717426517881280.4565146964237430.228257348211872
610.7195371636218490.5609256727563020.280462836378151
620.6476301704717850.704739659056430.352369829528215
630.7457487845110140.5085024309779730.254251215488986
640.6701778841555090.6596442316889820.329822115844491
650.6501036428601460.6997927142797080.349896357139854
660.561915401001620.876169197996760.43808459899838
670.4767887223993010.9535774447986020.523211277600699
680.4241414397560040.8482828795120070.575858560243996
690.3334804984348920.6669609968697840.666519501565108
700.6724342009032030.6551315981935940.327565799096797
710.6535975252922230.6928049494155540.346402474707777
720.5329379214242460.9341241571515070.467062078575754
730.4925758517298060.9851517034596120.507424148270194
740.3669409359607630.7338818719215270.633059064039237

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.519018379188726 & 0.961963241622547 & 0.480981620811274 \tabularnewline
12 & 0.748356306459323 & 0.503287387081353 & 0.251643693540677 \tabularnewline
13 & 0.66809263959022 & 0.66381472081956 & 0.33190736040978 \tabularnewline
14 & 0.564152106075484 & 0.871695787849032 & 0.435847893924516 \tabularnewline
15 & 0.782513541756173 & 0.434972916487655 & 0.217486458243827 \tabularnewline
16 & 0.74561778700045 & 0.508764425999099 & 0.25438221299955 \tabularnewline
17 & 0.729905859352323 & 0.540188281295354 & 0.270094140647677 \tabularnewline
18 & 0.676209055789481 & 0.647581888421038 & 0.323790944210519 \tabularnewline
19 & 0.709325353225574 & 0.581349293548853 & 0.290674646774426 \tabularnewline
20 & 0.761005820709316 & 0.477988358581369 & 0.238994179290684 \tabularnewline
21 & 0.844719949728328 & 0.310560100543344 & 0.155280050271672 \tabularnewline
22 & 0.806308981423814 & 0.387382037152372 & 0.193691018576186 \tabularnewline
23 & 0.745290324604215 & 0.509419350791569 & 0.254709675395785 \tabularnewline
24 & 0.797856920355964 & 0.404286159288072 & 0.202143079644036 \tabularnewline
25 & 0.774331193443756 & 0.451337613112488 & 0.225668806556244 \tabularnewline
26 & 0.723789435720386 & 0.552421128559227 & 0.276210564279614 \tabularnewline
27 & 0.665685348594296 & 0.668629302811409 & 0.334314651405704 \tabularnewline
28 & 0.697112861296619 & 0.605774277406763 & 0.302887138703382 \tabularnewline
29 & 0.790560095911313 & 0.418879808177373 & 0.209439904088687 \tabularnewline
30 & 0.78816015075838 & 0.423679698483239 & 0.21183984924162 \tabularnewline
31 & 0.75460359860877 & 0.49079280278246 & 0.24539640139123 \tabularnewline
32 & 0.75654250036489 & 0.48691499927022 & 0.24345749963511 \tabularnewline
33 & 0.797161350831848 & 0.405677298336304 & 0.202838649168152 \tabularnewline
34 & 0.761877803691673 & 0.476244392616655 & 0.238122196308327 \tabularnewline
35 & 0.743769202084564 & 0.512461595830871 & 0.256230797915436 \tabularnewline
36 & 0.712902409301615 & 0.57419518139677 & 0.287097590698385 \tabularnewline
37 & 0.651538356699398 & 0.696923286601205 & 0.348461643300602 \tabularnewline
38 & 0.715689123148825 & 0.56862175370235 & 0.284310876851175 \tabularnewline
39 & 0.732882221237345 & 0.53423555752531 & 0.267117778762655 \tabularnewline
40 & 0.705916482999968 & 0.588167034000064 & 0.294083517000032 \tabularnewline
41 & 0.661757470192172 & 0.676485059615656 & 0.338242529807828 \tabularnewline
42 & 0.771239903537896 & 0.457520192924208 & 0.228760096462104 \tabularnewline
43 & 0.821384992025597 & 0.357230015948806 & 0.178615007974403 \tabularnewline
44 & 0.801617804436607 & 0.396764391126786 & 0.198382195563393 \tabularnewline
45 & 0.764660410051948 & 0.470679179896103 & 0.235339589948052 \tabularnewline
46 & 0.746482849202397 & 0.507034301595207 & 0.253517150797603 \tabularnewline
47 & 0.794283724246752 & 0.411432551506496 & 0.205716275753248 \tabularnewline
48 & 0.741795005784119 & 0.516409988431762 & 0.258204994215881 \tabularnewline
49 & 0.684544126744199 & 0.630911746511603 & 0.315455873255801 \tabularnewline
50 & 0.704734306783274 & 0.590531386433452 & 0.295265693216726 \tabularnewline
51 & 0.682307669548789 & 0.635384660902421 & 0.317692330451211 \tabularnewline
52 & 0.628318962212615 & 0.743362075574769 & 0.371681037787385 \tabularnewline
53 & 0.56843995714265 & 0.863120085714699 & 0.43156004285735 \tabularnewline
54 & 0.520688436161683 & 0.958623127676635 & 0.479311563838317 \tabularnewline
55 & 0.456033693105833 & 0.912067386211667 & 0.543966306894167 \tabularnewline
56 & 0.531404254977102 & 0.937191490045796 & 0.468595745022898 \tabularnewline
57 & 0.457360750831623 & 0.914721501663245 & 0.542639249168377 \tabularnewline
58 & 0.679818633223855 & 0.64036273355229 & 0.320181366776145 \tabularnewline
59 & 0.64888182137374 & 0.702236357252521 & 0.35111817862626 \tabularnewline
60 & 0.771742651788128 & 0.456514696423743 & 0.228257348211872 \tabularnewline
61 & 0.719537163621849 & 0.560925672756302 & 0.280462836378151 \tabularnewline
62 & 0.647630170471785 & 0.70473965905643 & 0.352369829528215 \tabularnewline
63 & 0.745748784511014 & 0.508502430977973 & 0.254251215488986 \tabularnewline
64 & 0.670177884155509 & 0.659644231688982 & 0.329822115844491 \tabularnewline
65 & 0.650103642860146 & 0.699792714279708 & 0.349896357139854 \tabularnewline
66 & 0.56191540100162 & 0.87616919799676 & 0.43808459899838 \tabularnewline
67 & 0.476788722399301 & 0.953577444798602 & 0.523211277600699 \tabularnewline
68 & 0.424141439756004 & 0.848282879512007 & 0.575858560243996 \tabularnewline
69 & 0.333480498434892 & 0.666960996869784 & 0.666519501565108 \tabularnewline
70 & 0.672434200903203 & 0.655131598193594 & 0.327565799096797 \tabularnewline
71 & 0.653597525292223 & 0.692804949415554 & 0.346402474707777 \tabularnewline
72 & 0.532937921424246 & 0.934124157151507 & 0.467062078575754 \tabularnewline
73 & 0.492575851729806 & 0.985151703459612 & 0.507424148270194 \tabularnewline
74 & 0.366940935960763 & 0.733881871921527 & 0.633059064039237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157537&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]11[/C][C]0.519018379188726[/C][C]0.961963241622547[/C][C]0.480981620811274[/C][/ROW]
[ROW][C]12[/C][C]0.748356306459323[/C][C]0.503287387081353[/C][C]0.251643693540677[/C][/ROW]
[ROW][C]13[/C][C]0.66809263959022[/C][C]0.66381472081956[/C][C]0.33190736040978[/C][/ROW]
[ROW][C]14[/C][C]0.564152106075484[/C][C]0.871695787849032[/C][C]0.435847893924516[/C][/ROW]
[ROW][C]15[/C][C]0.782513541756173[/C][C]0.434972916487655[/C][C]0.217486458243827[/C][/ROW]
[ROW][C]16[/C][C]0.74561778700045[/C][C]0.508764425999099[/C][C]0.25438221299955[/C][/ROW]
[ROW][C]17[/C][C]0.729905859352323[/C][C]0.540188281295354[/C][C]0.270094140647677[/C][/ROW]
[ROW][C]18[/C][C]0.676209055789481[/C][C]0.647581888421038[/C][C]0.323790944210519[/C][/ROW]
[ROW][C]19[/C][C]0.709325353225574[/C][C]0.581349293548853[/C][C]0.290674646774426[/C][/ROW]
[ROW][C]20[/C][C]0.761005820709316[/C][C]0.477988358581369[/C][C]0.238994179290684[/C][/ROW]
[ROW][C]21[/C][C]0.844719949728328[/C][C]0.310560100543344[/C][C]0.155280050271672[/C][/ROW]
[ROW][C]22[/C][C]0.806308981423814[/C][C]0.387382037152372[/C][C]0.193691018576186[/C][/ROW]
[ROW][C]23[/C][C]0.745290324604215[/C][C]0.509419350791569[/C][C]0.254709675395785[/C][/ROW]
[ROW][C]24[/C][C]0.797856920355964[/C][C]0.404286159288072[/C][C]0.202143079644036[/C][/ROW]
[ROW][C]25[/C][C]0.774331193443756[/C][C]0.451337613112488[/C][C]0.225668806556244[/C][/ROW]
[ROW][C]26[/C][C]0.723789435720386[/C][C]0.552421128559227[/C][C]0.276210564279614[/C][/ROW]
[ROW][C]27[/C][C]0.665685348594296[/C][C]0.668629302811409[/C][C]0.334314651405704[/C][/ROW]
[ROW][C]28[/C][C]0.697112861296619[/C][C]0.605774277406763[/C][C]0.302887138703382[/C][/ROW]
[ROW][C]29[/C][C]0.790560095911313[/C][C]0.418879808177373[/C][C]0.209439904088687[/C][/ROW]
[ROW][C]30[/C][C]0.78816015075838[/C][C]0.423679698483239[/C][C]0.21183984924162[/C][/ROW]
[ROW][C]31[/C][C]0.75460359860877[/C][C]0.49079280278246[/C][C]0.24539640139123[/C][/ROW]
[ROW][C]32[/C][C]0.75654250036489[/C][C]0.48691499927022[/C][C]0.24345749963511[/C][/ROW]
[ROW][C]33[/C][C]0.797161350831848[/C][C]0.405677298336304[/C][C]0.202838649168152[/C][/ROW]
[ROW][C]34[/C][C]0.761877803691673[/C][C]0.476244392616655[/C][C]0.238122196308327[/C][/ROW]
[ROW][C]35[/C][C]0.743769202084564[/C][C]0.512461595830871[/C][C]0.256230797915436[/C][/ROW]
[ROW][C]36[/C][C]0.712902409301615[/C][C]0.57419518139677[/C][C]0.287097590698385[/C][/ROW]
[ROW][C]37[/C][C]0.651538356699398[/C][C]0.696923286601205[/C][C]0.348461643300602[/C][/ROW]
[ROW][C]38[/C][C]0.715689123148825[/C][C]0.56862175370235[/C][C]0.284310876851175[/C][/ROW]
[ROW][C]39[/C][C]0.732882221237345[/C][C]0.53423555752531[/C][C]0.267117778762655[/C][/ROW]
[ROW][C]40[/C][C]0.705916482999968[/C][C]0.588167034000064[/C][C]0.294083517000032[/C][/ROW]
[ROW][C]41[/C][C]0.661757470192172[/C][C]0.676485059615656[/C][C]0.338242529807828[/C][/ROW]
[ROW][C]42[/C][C]0.771239903537896[/C][C]0.457520192924208[/C][C]0.228760096462104[/C][/ROW]
[ROW][C]43[/C][C]0.821384992025597[/C][C]0.357230015948806[/C][C]0.178615007974403[/C][/ROW]
[ROW][C]44[/C][C]0.801617804436607[/C][C]0.396764391126786[/C][C]0.198382195563393[/C][/ROW]
[ROW][C]45[/C][C]0.764660410051948[/C][C]0.470679179896103[/C][C]0.235339589948052[/C][/ROW]
[ROW][C]46[/C][C]0.746482849202397[/C][C]0.507034301595207[/C][C]0.253517150797603[/C][/ROW]
[ROW][C]47[/C][C]0.794283724246752[/C][C]0.411432551506496[/C][C]0.205716275753248[/C][/ROW]
[ROW][C]48[/C][C]0.741795005784119[/C][C]0.516409988431762[/C][C]0.258204994215881[/C][/ROW]
[ROW][C]49[/C][C]0.684544126744199[/C][C]0.630911746511603[/C][C]0.315455873255801[/C][/ROW]
[ROW][C]50[/C][C]0.704734306783274[/C][C]0.590531386433452[/C][C]0.295265693216726[/C][/ROW]
[ROW][C]51[/C][C]0.682307669548789[/C][C]0.635384660902421[/C][C]0.317692330451211[/C][/ROW]
[ROW][C]52[/C][C]0.628318962212615[/C][C]0.743362075574769[/C][C]0.371681037787385[/C][/ROW]
[ROW][C]53[/C][C]0.56843995714265[/C][C]0.863120085714699[/C][C]0.43156004285735[/C][/ROW]
[ROW][C]54[/C][C]0.520688436161683[/C][C]0.958623127676635[/C][C]0.479311563838317[/C][/ROW]
[ROW][C]55[/C][C]0.456033693105833[/C][C]0.912067386211667[/C][C]0.543966306894167[/C][/ROW]
[ROW][C]56[/C][C]0.531404254977102[/C][C]0.937191490045796[/C][C]0.468595745022898[/C][/ROW]
[ROW][C]57[/C][C]0.457360750831623[/C][C]0.914721501663245[/C][C]0.542639249168377[/C][/ROW]
[ROW][C]58[/C][C]0.679818633223855[/C][C]0.64036273355229[/C][C]0.320181366776145[/C][/ROW]
[ROW][C]59[/C][C]0.64888182137374[/C][C]0.702236357252521[/C][C]0.35111817862626[/C][/ROW]
[ROW][C]60[/C][C]0.771742651788128[/C][C]0.456514696423743[/C][C]0.228257348211872[/C][/ROW]
[ROW][C]61[/C][C]0.719537163621849[/C][C]0.560925672756302[/C][C]0.280462836378151[/C][/ROW]
[ROW][C]62[/C][C]0.647630170471785[/C][C]0.70473965905643[/C][C]0.352369829528215[/C][/ROW]
[ROW][C]63[/C][C]0.745748784511014[/C][C]0.508502430977973[/C][C]0.254251215488986[/C][/ROW]
[ROW][C]64[/C][C]0.670177884155509[/C][C]0.659644231688982[/C][C]0.329822115844491[/C][/ROW]
[ROW][C]65[/C][C]0.650103642860146[/C][C]0.699792714279708[/C][C]0.349896357139854[/C][/ROW]
[ROW][C]66[/C][C]0.56191540100162[/C][C]0.87616919799676[/C][C]0.43808459899838[/C][/ROW]
[ROW][C]67[/C][C]0.476788722399301[/C][C]0.953577444798602[/C][C]0.523211277600699[/C][/ROW]
[ROW][C]68[/C][C]0.424141439756004[/C][C]0.848282879512007[/C][C]0.575858560243996[/C][/ROW]
[ROW][C]69[/C][C]0.333480498434892[/C][C]0.666960996869784[/C][C]0.666519501565108[/C][/ROW]
[ROW][C]70[/C][C]0.672434200903203[/C][C]0.655131598193594[/C][C]0.327565799096797[/C][/ROW]
[ROW][C]71[/C][C]0.653597525292223[/C][C]0.692804949415554[/C][C]0.346402474707777[/C][/ROW]
[ROW][C]72[/C][C]0.532937921424246[/C][C]0.934124157151507[/C][C]0.467062078575754[/C][/ROW]
[ROW][C]73[/C][C]0.492575851729806[/C][C]0.985151703459612[/C][C]0.507424148270194[/C][/ROW]
[ROW][C]74[/C][C]0.366940935960763[/C][C]0.733881871921527[/C][C]0.633059064039237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157537&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&T=5

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5190183791887260.9619632416225470.480981620811274
120.7483563064593230.5032873870813530.251643693540677
130.668092639590220.663814720819560.33190736040978
140.5641521060754840.8716957878490320.435847893924516
150.7825135417561730.4349729164876550.217486458243827
160.745617787000450.5087644259990990.25438221299955
170.7299058593523230.5401882812953540.270094140647677
180.6762090557894810.6475818884210380.323790944210519
190.7093253532255740.5813492935488530.290674646774426
200.7610058207093160.4779883585813690.238994179290684
210.8447199497283280.3105601005433440.155280050271672
220.8063089814238140.3873820371523720.193691018576186
230.7452903246042150.5094193507915690.254709675395785
240.7978569203559640.4042861592880720.202143079644036
250.7743311934437560.4513376131124880.225668806556244
260.7237894357203860.5524211285592270.276210564279614
270.6656853485942960.6686293028114090.334314651405704
280.6971128612966190.6057742774067630.302887138703382
290.7905600959113130.4188798081773730.209439904088687
300.788160150758380.4236796984832390.21183984924162
310.754603598608770.490792802782460.24539640139123
320.756542500364890.486914999270220.24345749963511
330.7971613508318480.4056772983363040.202838649168152
340.7618778036916730.4762443926166550.238122196308327
350.7437692020845640.5124615958308710.256230797915436
360.7129024093016150.574195181396770.287097590698385
370.6515383566993980.6969232866012050.348461643300602
380.7156891231488250.568621753702350.284310876851175
390.7328822212373450.534235557525310.267117778762655
400.7059164829999680.5881670340000640.294083517000032
410.6617574701921720.6764850596156560.338242529807828
420.7712399035378960.4575201929242080.228760096462104
430.8213849920255970.3572300159488060.178615007974403
440.8016178044366070.3967643911267860.198382195563393
450.7646604100519480.4706791798961030.235339589948052
460.7464828492023970.5070343015952070.253517150797603
470.7942837242467520.4114325515064960.205716275753248
480.7417950057841190.5164099884317620.258204994215881
490.6845441267441990.6309117465116030.315455873255801
500.7047343067832740.5905313864334520.295265693216726
510.6823076695487890.6353846609024210.317692330451211
520.6283189622126150.7433620755747690.371681037787385
530.568439957142650.8631200857146990.43156004285735
540.5206884361616830.9586231276766350.479311563838317
550.4560336931058330.9120673862116670.543966306894167
560.5314042549771020.9371914900457960.468595745022898
570.4573607508316230.9147215016632450.542639249168377
580.6798186332238550.640362733552290.320181366776145
590.648881821373740.7022363572525210.35111817862626
600.7717426517881280.4565146964237430.228257348211872
610.7195371636218490.5609256727563020.280462836378151
620.6476301704717850.704739659056430.352369829528215
630.7457487845110140.5085024309779730.254251215488986
640.6701778841555090.6596442316889820.329822115844491
650.6501036428601460.6997927142797080.349896357139854
660.561915401001620.876169197996760.43808459899838
670.4767887223993010.9535774447986020.523211277600699
680.4241414397560040.8482828795120070.575858560243996
690.3334804984348920.6669609968697840.666519501565108
700.6724342009032030.6551315981935940.327565799096797
710.6535975252922230.6928049494155540.346402474707777
720.5329379214242460.9341241571515070.467062078575754
730.4925758517298060.9851517034596120.507424148270194
740.3669409359607630.7338818719215270.633059064039237






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=157537&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=157537&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157537&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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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')
}