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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 computationWed, 21 Dec 2011 18:46:47 -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/21/t1324511256bagbkvvkgu000tn.htm/, Retrieved Tue, 07 May 2024 17:23:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159142, Retrieved Tue, 07 May 2024 17:23:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

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] [Paper Mult. regre...] [2011-12-19 17:48:15] [25b6caf3839c2bdc14961e5bff2d6373]
-    D        [Multiple Regression] [PAPER - DEEL 3 - ...] [2011-12-21 23:46:47] [e524eb56e6915a531809c7eb50783bc6] [Current]
- R  D          [Multiple Regression] [PAPER - DEEL 3 - ...] [2011-12-22 13:02:31] [da10aa57c5e54f8a2ad733cadd93c4c3]
- RMPD          [Recursive Partitioning (Regression Trees)] [PAPER - DEEL 3 - ...] [2011-12-22 16:14:30] [da10aa57c5e54f8a2ad733cadd93c4c3]
- RMPD          [Recursive Partitioning (Regression Trees)] [PAPER - DEEL 3 - ...] [2011-12-22 16:37:34] [da10aa57c5e54f8a2ad733cadd93c4c3]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	210907	94	112285	144
4	179321	103	101193	135
0	149061	93	116174	84
0	237213	123	66198	130
-4	173326	148	71701	82
4	133131	90	57793	60
4	258873	124	80444	131
0	324799	168	97668	140
-1	230964	115	133824	151
0	236785	71	101481	91
1	344297	108	67654	119
0	174724	120	69112	123
3	174415	114	82753	90
-1	223632	120	72654	113
4	294424	124	101494	175
3	325107	126	79215	96
1	106408	37	31081	41
0	96560	38	22996	47
-2	265769	120	83122	126
-3	269651	93	70106	105
-4	149112	95	60578	80
2	152871	90	79892	73
2	362301	110	100708	68
-4	183167	138	82875	127
3	277965	133	139077	154
2	218946	96	80670	112
2	244052	164	143558	137
0	341570	78	117105	135
5	233328	102	120733	230
-2	206161	99	73107	71
0	311473	129	132068	147
-2	207176	114	87011	105
-3	196553	99	95260	107
2	143246	104	106671	116
2	182192	138	70054	89
2	194979	151	74011	84
0	167488	72	83737	113
4	143756	120	69094	120
4	275541	115	93133	110
2	152299	98	61370	78
2	193339	71	84651	145
-4	130585	107	95364	91
3	112611	73	26706	48
3	148446	129	126846	150
2	182079	118	102860	181
-1	243060	104	111813	121
-3	162765	107	120293	99
0	85574	36	24266	40
1	225060	139	109825	87
-3	133328	56	40909	66
3	100750	93	140867	58
0	101523	87	61056	77
0	243511	110	101338	130
0	152474	83	65567	101
3	132487	98	40735	120
-3	317394	82	91413	195
0	244749	115	76643	106
-4	184510	140	110681	83
2	128423	120	92696	37
-1	97839	66	94785	77
3	172494	139	86687	144
2	229242	119	91721	95
5	351619	141	115168	169
2	324598	133	135777	134
-2	195838	98	102372	197
0	254488	117	103772	140
3	199476	105	135400	125
-2	92499	55	21399	21
0	224330	132	130115	167
6	181633	73	64466	96
-3	271856	86	54990	151
3	95227	48	34777	23
0	98146	48	27114	21
-2	118612	43	30080	90
1	65475	46	69008	60
0	108446	65	46300	26
2	121848	52	30594	41
2	76302	68	30976	35
-3	98104	47	25568	68
-2	30989	41	4154	6
1	31774	47	4143	0
-4	150580	71	45588	41
0	54157	30	18625	38
1	59382	24	26263	47
0	84105	63	20055	34

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&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]5 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=159142&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
sum_testscore[t] = -0.552541834627129 -1.26978173330986e-06time_in_rfc[t] + 0.0034283371153375feedback_messages_p120[t] + 7.91009896633631e-06totsize[t] + 0.00370094917116098`tothyperlinks)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
sum_testscore[t] =  -0.552541834627129 -1.26978173330986e-06time_in_rfc[t] +  0.0034283371153375feedback_messages_p120[t] +  7.91009896633631e-06totsize[t] +  0.00370094917116098`tothyperlinks)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]sum_testscore[t] =  -0.552541834627129 -1.26978173330986e-06time_in_rfc[t] +  0.0034283371153375feedback_messages_p120[t] +  7.91009896633631e-06totsize[t] +  0.00370094917116098`tothyperlinks)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159142&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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
sum_testscore[t] = -0.552541834627129 -1.26978173330986e-06time_in_rfc[t] + 0.0034283371153375feedback_messages_p120[t] + 7.91009896633631e-06totsize[t] + 0.00370094917116098`tothyperlinks)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.5525418346271290.840835-0.65710.512980.25649
time_in_rfc-1.26978173330986e-065e-06-0.24440.8075250.403763
feedback_messages_p1200.00342833711533750.0123310.2780.7817080.390854
totsize7.91009896633631e-061.2e-050.63530.5270170.263509
`tothyperlinks)`0.003700949171160980.0091220.40570.6860210.343011

\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) & -0.552541834627129 & 0.840835 & -0.6571 & 0.51298 & 0.25649 \tabularnewline
time_in_rfc & -1.26978173330986e-06 & 5e-06 & -0.2444 & 0.807525 & 0.403763 \tabularnewline
feedback_messages_p120 & 0.0034283371153375 & 0.012331 & 0.278 & 0.781708 & 0.390854 \tabularnewline
totsize & 7.91009896633631e-06 & 1.2e-05 & 0.6353 & 0.527017 & 0.263509 \tabularnewline
`tothyperlinks)` & 0.00370094917116098 & 0.009122 & 0.4057 & 0.686021 & 0.343011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&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]-0.552541834627129[/C][C]0.840835[/C][C]-0.6571[/C][C]0.51298[/C][C]0.25649[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-1.26978173330986e-06[/C][C]5e-06[/C][C]-0.2444[/C][C]0.807525[/C][C]0.403763[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]0.0034283371153375[/C][C]0.012331[/C][C]0.278[/C][C]0.781708[/C][C]0.390854[/C][/ROW]
[ROW][C]totsize[/C][C]7.91009896633631e-06[/C][C]1.2e-05[/C][C]0.6353[/C][C]0.527017[/C][C]0.263509[/C][/ROW]
[ROW][C]`tothyperlinks)`[/C][C]0.00370094917116098[/C][C]0.009122[/C][C]0.4057[/C][C]0.686021[/C][C]0.343011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159142&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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)-0.5525418346271290.840835-0.65710.512980.25649
time_in_rfc-1.26978173330986e-065e-06-0.24440.8075250.403763
feedback_messages_p1200.00342833711533750.0123310.2780.7817080.390854
totsize7.91009896633631e-061.2e-050.63530.5270170.263509
`tothyperlinks)`0.003700949171160980.0091220.40570.6860210.343011






Multiple Linear Regression - Regression Statistics
Multiple R0.179804188890887
R-squared0.0323295463427098
Adjusted R-squared-0.0160539763401548
F-TEST (value)0.668193313550516
F-TEST (DF numerator)4
F-TEST (DF denominator)80
p-value0.615939031443569
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48172073944102
Sum Squared Residuals492.715026285736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.179804188890887 \tabularnewline
R-squared & 0.0323295463427098 \tabularnewline
Adjusted R-squared & -0.0160539763401548 \tabularnewline
F-TEST (value) & 0.668193313550516 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 0.615939031443569 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.48172073944102 \tabularnewline
Sum Squared Residuals & 492.715026285736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.179804188890887[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0323295463427098[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0160539763401548[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.668193313550516[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]0.615939031443569[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.48172073944102[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]492.715026285736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159142&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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 R0.179804188890887
R-squared0.0323295463427098
Adjusted R-squared-0.0160539763401548
F-TEST (value)0.668193313550516
F-TEST (DF numerator)4
F-TEST (DF denominator)80
p-value0.615939031443569
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48172073944102
Sum Squared Residuals492.715026285736






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120.9230381412696671.07696185873033
240.8729531408609793.12704685913902
300.806846149843035-0.806846149843035
400.57269101988021-0.57269101988021
5-40.605405707755637-4.60540570775564
640.2661664936471043.7338335063529
740.6650041036976443.33499589630236
800.901691393358934-0.901691393358934
9-11.1658474743008-2.1658474743008
1000.529715960618483-0.529715960618483
1110.3560993192326090.643900680767391
1200.638896783456775-0.638896783456775
1330.6044894606718242.39551053932818
14-10.567802377271209-1.56780237727121
1540.9492114400692073.05078855993079
1630.4485033219840122.55149667801599
171-0.1632155940473781.16321559404738
180-0.1890299015382680.189029901538268
19-20.645212839579433-2.64521283957943
20-30.367040664036398-3.3670406640364
21-40.359064406388233-4.35906440638823
2220.4640186185137271.53598138148627
2320.4128068466408441.58719315335916
24-40.813556573116845-4.81355657311684
2531.220531128513231.77946887148677
2620.5511788878483911.44882111215161
2721.342400704568850.657599295431154
2800.707089391282093-0.707089391282093
2951.307101206737283.69289879326272
30-20.366135064154768-2.36613506415477
3101.08292140587895-1.08292140587895
32-20.652085580272934-2.65208558027293
33-30.686801719611453-3.68680171961145
3420.8952023418910021.104797658109
3520.5727431629553071.42725683704469
3620.6138703621848491.38612963781515
3700.562200448213864-0.562200448213864
3840.6669741548790383.33302584512096
3940.6356356609182593.36436433908174
4020.3641655233892041.6358344766108
4120.6516071874431161.34839281255688
42-40.739600841471062-4.73960084147106
433-0.05637195276654383.05637195276654
4431.259726423226541.74027357677346
4521.104305936420860.895694063579139
46-10.827638822703117-1.82763882270312
47-30.925537715794235-3.92553771579423
480-0.1977975721576810.197797572157681
4910.8289291443749570.171070855625043
50-30.038004468803511-3.03800446880351
5130.9672899704865252.03271002951348
5200.384743532164442-0.384743532164442
5300.798086429701495-0.798086429701495
5400.430838771154228-0.430838771154228
5530.381537412107952.61846258789205
56-30.770331670554488-3.77033167055449
5700.529493451410806-0.529493451410806
58-40.875814378806551-4.87581437880655
5920.5659590927929861.43404090720701
60-10.584226056683426-1.58422605668343
6130.923606723841212.07639327615879
6220.6414553365422411.35854466345776
6351.0208250030323.979174996968
6421.061195086931660.938804913068339
65-21.0736233256885-3.0736233256885
6600.864409068017988-0.864409068017988
6731.087788627886651.91221137211335
68-2-0.234448693457984-1.76555130654202
6901.26242956695275-1.26242956695275
7060.332316069621535.66768393037847
71-30.390917041405352-3.39091704140535
723-0.1486878155198463.14868781551985
730-0.2204112951207350.220411295120735
74-20.0152865126929189-2.01528651269292
7510.2899397734285280.710060226571472
760-0.004940411389155710.00494041138915571
772-0.1352481854762252.13524818547623
782-0.04174535002732022.04174535002732
79-3-0.0620707033606633-2.93792929663934
80-2-0.396265032898704-1.6037349671013
811-0.3989844949829221.39898449498292
82-40.0120108748549761-4.01201087485498
830-0.2304976287457350.230497628745735
841-0.1639763825489781.16397638254898
850-0.1588822824515440.158882282451544

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.923038141269667 & 1.07696185873033 \tabularnewline
2 & 4 & 0.872953140860979 & 3.12704685913902 \tabularnewline
3 & 0 & 0.806846149843035 & -0.806846149843035 \tabularnewline
4 & 0 & 0.57269101988021 & -0.57269101988021 \tabularnewline
5 & -4 & 0.605405707755637 & -4.60540570775564 \tabularnewline
6 & 4 & 0.266166493647104 & 3.7338335063529 \tabularnewline
7 & 4 & 0.665004103697644 & 3.33499589630236 \tabularnewline
8 & 0 & 0.901691393358934 & -0.901691393358934 \tabularnewline
9 & -1 & 1.1658474743008 & -2.1658474743008 \tabularnewline
10 & 0 & 0.529715960618483 & -0.529715960618483 \tabularnewline
11 & 1 & 0.356099319232609 & 0.643900680767391 \tabularnewline
12 & 0 & 0.638896783456775 & -0.638896783456775 \tabularnewline
13 & 3 & 0.604489460671824 & 2.39551053932818 \tabularnewline
14 & -1 & 0.567802377271209 & -1.56780237727121 \tabularnewline
15 & 4 & 0.949211440069207 & 3.05078855993079 \tabularnewline
16 & 3 & 0.448503321984012 & 2.55149667801599 \tabularnewline
17 & 1 & -0.163215594047378 & 1.16321559404738 \tabularnewline
18 & 0 & -0.189029901538268 & 0.189029901538268 \tabularnewline
19 & -2 & 0.645212839579433 & -2.64521283957943 \tabularnewline
20 & -3 & 0.367040664036398 & -3.3670406640364 \tabularnewline
21 & -4 & 0.359064406388233 & -4.35906440638823 \tabularnewline
22 & 2 & 0.464018618513727 & 1.53598138148627 \tabularnewline
23 & 2 & 0.412806846640844 & 1.58719315335916 \tabularnewline
24 & -4 & 0.813556573116845 & -4.81355657311684 \tabularnewline
25 & 3 & 1.22053112851323 & 1.77946887148677 \tabularnewline
26 & 2 & 0.551178887848391 & 1.44882111215161 \tabularnewline
27 & 2 & 1.34240070456885 & 0.657599295431154 \tabularnewline
28 & 0 & 0.707089391282093 & -0.707089391282093 \tabularnewline
29 & 5 & 1.30710120673728 & 3.69289879326272 \tabularnewline
30 & -2 & 0.366135064154768 & -2.36613506415477 \tabularnewline
31 & 0 & 1.08292140587895 & -1.08292140587895 \tabularnewline
32 & -2 & 0.652085580272934 & -2.65208558027293 \tabularnewline
33 & -3 & 0.686801719611453 & -3.68680171961145 \tabularnewline
34 & 2 & 0.895202341891002 & 1.104797658109 \tabularnewline
35 & 2 & 0.572743162955307 & 1.42725683704469 \tabularnewline
36 & 2 & 0.613870362184849 & 1.38612963781515 \tabularnewline
37 & 0 & 0.562200448213864 & -0.562200448213864 \tabularnewline
38 & 4 & 0.666974154879038 & 3.33302584512096 \tabularnewline
39 & 4 & 0.635635660918259 & 3.36436433908174 \tabularnewline
40 & 2 & 0.364165523389204 & 1.6358344766108 \tabularnewline
41 & 2 & 0.651607187443116 & 1.34839281255688 \tabularnewline
42 & -4 & 0.739600841471062 & -4.73960084147106 \tabularnewline
43 & 3 & -0.0563719527665438 & 3.05637195276654 \tabularnewline
44 & 3 & 1.25972642322654 & 1.74027357677346 \tabularnewline
45 & 2 & 1.10430593642086 & 0.895694063579139 \tabularnewline
46 & -1 & 0.827638822703117 & -1.82763882270312 \tabularnewline
47 & -3 & 0.925537715794235 & -3.92553771579423 \tabularnewline
48 & 0 & -0.197797572157681 & 0.197797572157681 \tabularnewline
49 & 1 & 0.828929144374957 & 0.171070855625043 \tabularnewline
50 & -3 & 0.038004468803511 & -3.03800446880351 \tabularnewline
51 & 3 & 0.967289970486525 & 2.03271002951348 \tabularnewline
52 & 0 & 0.384743532164442 & -0.384743532164442 \tabularnewline
53 & 0 & 0.798086429701495 & -0.798086429701495 \tabularnewline
54 & 0 & 0.430838771154228 & -0.430838771154228 \tabularnewline
55 & 3 & 0.38153741210795 & 2.61846258789205 \tabularnewline
56 & -3 & 0.770331670554488 & -3.77033167055449 \tabularnewline
57 & 0 & 0.529493451410806 & -0.529493451410806 \tabularnewline
58 & -4 & 0.875814378806551 & -4.87581437880655 \tabularnewline
59 & 2 & 0.565959092792986 & 1.43404090720701 \tabularnewline
60 & -1 & 0.584226056683426 & -1.58422605668343 \tabularnewline
61 & 3 & 0.92360672384121 & 2.07639327615879 \tabularnewline
62 & 2 & 0.641455336542241 & 1.35854466345776 \tabularnewline
63 & 5 & 1.020825003032 & 3.979174996968 \tabularnewline
64 & 2 & 1.06119508693166 & 0.938804913068339 \tabularnewline
65 & -2 & 1.0736233256885 & -3.0736233256885 \tabularnewline
66 & 0 & 0.864409068017988 & -0.864409068017988 \tabularnewline
67 & 3 & 1.08778862788665 & 1.91221137211335 \tabularnewline
68 & -2 & -0.234448693457984 & -1.76555130654202 \tabularnewline
69 & 0 & 1.26242956695275 & -1.26242956695275 \tabularnewline
70 & 6 & 0.33231606962153 & 5.66768393037847 \tabularnewline
71 & -3 & 0.390917041405352 & -3.39091704140535 \tabularnewline
72 & 3 & -0.148687815519846 & 3.14868781551985 \tabularnewline
73 & 0 & -0.220411295120735 & 0.220411295120735 \tabularnewline
74 & -2 & 0.0152865126929189 & -2.01528651269292 \tabularnewline
75 & 1 & 0.289939773428528 & 0.710060226571472 \tabularnewline
76 & 0 & -0.00494041138915571 & 0.00494041138915571 \tabularnewline
77 & 2 & -0.135248185476225 & 2.13524818547623 \tabularnewline
78 & 2 & -0.0417453500273202 & 2.04174535002732 \tabularnewline
79 & -3 & -0.0620707033606633 & -2.93792929663934 \tabularnewline
80 & -2 & -0.396265032898704 & -1.6037349671013 \tabularnewline
81 & 1 & -0.398984494982922 & 1.39898449498292 \tabularnewline
82 & -4 & 0.0120108748549761 & -4.01201087485498 \tabularnewline
83 & 0 & -0.230497628745735 & 0.230497628745735 \tabularnewline
84 & 1 & -0.163976382548978 & 1.16397638254898 \tabularnewline
85 & 0 & -0.158882282451544 & 0.158882282451544 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&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]0.923038141269667[/C][C]1.07696185873033[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]0.872953140860979[/C][C]3.12704685913902[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.806846149843035[/C][C]-0.806846149843035[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.57269101988021[/C][C]-0.57269101988021[/C][/ROW]
[ROW][C]5[/C][C]-4[/C][C]0.605405707755637[/C][C]-4.60540570775564[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]0.266166493647104[/C][C]3.7338335063529[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]0.665004103697644[/C][C]3.33499589630236[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.901691393358934[/C][C]-0.901691393358934[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]1.1658474743008[/C][C]-2.1658474743008[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.529715960618483[/C][C]-0.529715960618483[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.356099319232609[/C][C]0.643900680767391[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.638896783456775[/C][C]-0.638896783456775[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]0.604489460671824[/C][C]2.39551053932818[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]0.567802377271209[/C][C]-1.56780237727121[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]0.949211440069207[/C][C]3.05078855993079[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]0.448503321984012[/C][C]2.55149667801599[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]-0.163215594047378[/C][C]1.16321559404738[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]-0.189029901538268[/C][C]0.189029901538268[/C][/ROW]
[ROW][C]19[/C][C]-2[/C][C]0.645212839579433[/C][C]-2.64521283957943[/C][/ROW]
[ROW][C]20[/C][C]-3[/C][C]0.367040664036398[/C][C]-3.3670406640364[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]0.359064406388233[/C][C]-4.35906440638823[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]0.464018618513727[/C][C]1.53598138148627[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]0.412806846640844[/C][C]1.58719315335916[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]0.813556573116845[/C][C]-4.81355657311684[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]1.22053112851323[/C][C]1.77946887148677[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.551178887848391[/C][C]1.44882111215161[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.34240070456885[/C][C]0.657599295431154[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.707089391282093[/C][C]-0.707089391282093[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]1.30710120673728[/C][C]3.69289879326272[/C][/ROW]
[ROW][C]30[/C][C]-2[/C][C]0.366135064154768[/C][C]-2.36613506415477[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]1.08292140587895[/C][C]-1.08292140587895[/C][/ROW]
[ROW][C]32[/C][C]-2[/C][C]0.652085580272934[/C][C]-2.65208558027293[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]0.686801719611453[/C][C]-3.68680171961145[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]0.895202341891002[/C][C]1.104797658109[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]0.572743162955307[/C][C]1.42725683704469[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.613870362184849[/C][C]1.38612963781515[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.562200448213864[/C][C]-0.562200448213864[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]0.666974154879038[/C][C]3.33302584512096[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]0.635635660918259[/C][C]3.36436433908174[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]0.364165523389204[/C][C]1.6358344766108[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]0.651607187443116[/C][C]1.34839281255688[/C][/ROW]
[ROW][C]42[/C][C]-4[/C][C]0.739600841471062[/C][C]-4.73960084147106[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]-0.0563719527665438[/C][C]3.05637195276654[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]1.25972642322654[/C][C]1.74027357677346[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.10430593642086[/C][C]0.895694063579139[/C][/ROW]
[ROW][C]46[/C][C]-1[/C][C]0.827638822703117[/C][C]-1.82763882270312[/C][/ROW]
[ROW][C]47[/C][C]-3[/C][C]0.925537715794235[/C][C]-3.92553771579423[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.197797572157681[/C][C]0.197797572157681[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.828929144374957[/C][C]0.171070855625043[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]0.038004468803511[/C][C]-3.03800446880351[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]0.967289970486525[/C][C]2.03271002951348[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.384743532164442[/C][C]-0.384743532164442[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.798086429701495[/C][C]-0.798086429701495[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.430838771154228[/C][C]-0.430838771154228[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]0.38153741210795[/C][C]2.61846258789205[/C][/ROW]
[ROW][C]56[/C][C]-3[/C][C]0.770331670554488[/C][C]-3.77033167055449[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.529493451410806[/C][C]-0.529493451410806[/C][/ROW]
[ROW][C]58[/C][C]-4[/C][C]0.875814378806551[/C][C]-4.87581437880655[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]0.565959092792986[/C][C]1.43404090720701[/C][/ROW]
[ROW][C]60[/C][C]-1[/C][C]0.584226056683426[/C][C]-1.58422605668343[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]0.92360672384121[/C][C]2.07639327615879[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]0.641455336542241[/C][C]1.35854466345776[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]1.020825003032[/C][C]3.979174996968[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.06119508693166[/C][C]0.938804913068339[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]1.0736233256885[/C][C]-3.0736233256885[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.864409068017988[/C][C]-0.864409068017988[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]1.08778862788665[/C][C]1.91221137211335[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-0.234448693457984[/C][C]-1.76555130654202[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]1.26242956695275[/C][C]-1.26242956695275[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]0.33231606962153[/C][C]5.66768393037847[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]0.390917041405352[/C][C]-3.39091704140535[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]-0.148687815519846[/C][C]3.14868781551985[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]-0.220411295120735[/C][C]0.220411295120735[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]0.0152865126929189[/C][C]-2.01528651269292[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.289939773428528[/C][C]0.710060226571472[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]-0.00494041138915571[/C][C]0.00494041138915571[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]-0.135248185476225[/C][C]2.13524818547623[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]-0.0417453500273202[/C][C]2.04174535002732[/C][/ROW]
[ROW][C]79[/C][C]-3[/C][C]-0.0620707033606633[/C][C]-2.93792929663934[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-0.396265032898704[/C][C]-1.6037349671013[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-0.398984494982922[/C][C]1.39898449498292[/C][/ROW]
[ROW][C]82[/C][C]-4[/C][C]0.0120108748549761[/C][C]-4.01201087485498[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.230497628745735[/C][C]0.230497628745735[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]-0.163976382548978[/C][C]1.16397638254898[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.158882282451544[/C][C]0.158882282451544[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159142&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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
120.9230381412696671.07696185873033
240.8729531408609793.12704685913902
300.806846149843035-0.806846149843035
400.57269101988021-0.57269101988021
5-40.605405707755637-4.60540570775564
640.2661664936471043.7338335063529
740.6650041036976443.33499589630236
800.901691393358934-0.901691393358934
9-11.1658474743008-2.1658474743008
1000.529715960618483-0.529715960618483
1110.3560993192326090.643900680767391
1200.638896783456775-0.638896783456775
1330.6044894606718242.39551053932818
14-10.567802377271209-1.56780237727121
1540.9492114400692073.05078855993079
1630.4485033219840122.55149667801599
171-0.1632155940473781.16321559404738
180-0.1890299015382680.189029901538268
19-20.645212839579433-2.64521283957943
20-30.367040664036398-3.3670406640364
21-40.359064406388233-4.35906440638823
2220.4640186185137271.53598138148627
2320.4128068466408441.58719315335916
24-40.813556573116845-4.81355657311684
2531.220531128513231.77946887148677
2620.5511788878483911.44882111215161
2721.342400704568850.657599295431154
2800.707089391282093-0.707089391282093
2951.307101206737283.69289879326272
30-20.366135064154768-2.36613506415477
3101.08292140587895-1.08292140587895
32-20.652085580272934-2.65208558027293
33-30.686801719611453-3.68680171961145
3420.8952023418910021.104797658109
3520.5727431629553071.42725683704469
3620.6138703621848491.38612963781515
3700.562200448213864-0.562200448213864
3840.6669741548790383.33302584512096
3940.6356356609182593.36436433908174
4020.3641655233892041.6358344766108
4120.6516071874431161.34839281255688
42-40.739600841471062-4.73960084147106
433-0.05637195276654383.05637195276654
4431.259726423226541.74027357677346
4521.104305936420860.895694063579139
46-10.827638822703117-1.82763882270312
47-30.925537715794235-3.92553771579423
480-0.1977975721576810.197797572157681
4910.8289291443749570.171070855625043
50-30.038004468803511-3.03800446880351
5130.9672899704865252.03271002951348
5200.384743532164442-0.384743532164442
5300.798086429701495-0.798086429701495
5400.430838771154228-0.430838771154228
5530.381537412107952.61846258789205
56-30.770331670554488-3.77033167055449
5700.529493451410806-0.529493451410806
58-40.875814378806551-4.87581437880655
5920.5659590927929861.43404090720701
60-10.584226056683426-1.58422605668343
6130.923606723841212.07639327615879
6220.6414553365422411.35854466345776
6351.0208250030323.979174996968
6421.061195086931660.938804913068339
65-21.0736233256885-3.0736233256885
6600.864409068017988-0.864409068017988
6731.087788627886651.91221137211335
68-2-0.234448693457984-1.76555130654202
6901.26242956695275-1.26242956695275
7060.332316069621535.66768393037847
71-30.390917041405352-3.39091704140535
723-0.1486878155198463.14868781551985
730-0.2204112951207350.220411295120735
74-20.0152865126929189-2.01528651269292
7510.2899397734285280.710060226571472
760-0.004940411389155710.00494041138915571
772-0.1352481854762252.13524818547623
782-0.04174535002732022.04174535002732
79-3-0.0620707033606633-2.93792929663934
80-2-0.396265032898704-1.6037349671013
811-0.3989844949829221.39898449498292
82-40.0120108748549761-4.01201087485498
830-0.2304976287457350.230497628745735
841-0.1639763825489781.16397638254898
850-0.1588822824515440.158882282451544






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.6459083263876790.7081833472246420.354091673612321
90.5322186128833860.9355627742332270.467781387116614
100.640410337669680.719179324660640.35958966233032
110.5319148733822330.9361702532355340.468085126617767
120.4997555576195440.9995111152390880.500244442380456
130.5081584752843670.9836830494312660.491841524715633
140.475201379898470.9504027597969410.52479862010153
150.4505348418415890.9010696836831790.549465158158411
160.4828599327488440.9657198654976880.517140067251156
170.4481959154341990.8963918308683990.551804084565801
180.4043615633551190.8087231267102390.595638436644881
190.4550468408873580.9100936817747170.544953159112642
200.5790219896327810.8419560207344390.420978010367219
210.7034416128497170.5931167743005650.296558387150283
220.6676922470943960.6646155058112080.332307752905604
230.620388242606810.759223514786380.37961175739319
240.732126268989550.5357474620209010.26787373101045
250.6950653687014210.6098692625971580.304934631298579
260.6441152435882850.711769512823430.355884756411715
270.591781045936260.816437908127480.40821895406374
280.5879953336787280.8240093326425430.412004666321272
290.6146374482985150.7707251034029710.385362551701485
300.5950301842800280.8099396314399440.404969815719972
310.547392194363860.905215611272280.45260780563614
320.5477964690685070.9044070618629860.452203530931493
330.620996916455050.75800616708990.37900308354495
340.5725524761886250.8548950476227510.427447523811375
350.5576333998160240.8847332003679530.442366600183976
360.5361725250106520.9276549499786950.463827474989348
370.4754884685480180.9509769370960370.524511531451982
380.5129969643928390.9740060712143230.487003035607161
390.5616336576036290.8767326847927420.438366342396371
400.5233830253477270.9532339493045460.476616974652273
410.4830837489168570.9661674978337140.516916251083143
420.6388394301035120.7223211397929750.361160569896488
430.6599778958555060.6800442082889880.340022104144494
440.6285469811769550.742906037646090.371453018823045
450.5796679422184860.8406641155630280.420332057781514
460.542614119658210.914771760683580.45738588034179
470.6190860963617380.7618278072765230.380913903638262
480.5558552982229490.8882894035541010.444144701777051
490.5003784774639470.9992430450721050.499621522536053
500.5295060884051990.9409878231896030.470493911594802
510.5219909307283290.9560181385433420.478009069271671
520.4559485105141410.9118970210282820.544051489485859
530.3955651137109410.7911302274218820.604434886289059
540.3327193035844130.6654386071688260.667280696415587
550.3555390792461320.7110781584922650.644460920753868
560.4259307796010520.8518615592021050.574069220398948
570.3628287602690440.7256575205380880.637171239730956
580.6296004305417530.7407991389164930.370399569458247
590.5818348620560620.8363302758878750.418165137943938
600.5637895303218280.8724209393563440.436210469678172
610.5809078963205840.8381842073588320.419092103679416
620.513666542340510.972666915318980.48633345765949
630.6845076010773920.6309847978452170.315492398922609
640.6117686077245780.7764627845508430.388231392275422
650.5708509648978410.8582980702043190.429149035102159
660.4890388205557870.9780776411115740.510961179444213
670.4147446374786970.8294892749573940.585255362521303
680.3731658531333150.7463317062666290.626834146866685
690.3165550415427080.6331100830854150.683444958457292
700.7699088060724250.4601823878551490.230091193927575
710.7397314782065130.5205370435869730.260268521793487
720.7675567473739440.4648865052521120.232443252626056
730.6700946097275970.6598107805448050.329905390272403
740.5562376851416880.8875246297166230.443762314858312
750.4425280601268920.8850561202537830.557471939873108
760.3128152515044320.6256305030088640.687184748495568
770.7810882403770210.4378235192459580.218911759622979

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.645908326387679 & 0.708183347224642 & 0.354091673612321 \tabularnewline
9 & 0.532218612883386 & 0.935562774233227 & 0.467781387116614 \tabularnewline
10 & 0.64041033766968 & 0.71917932466064 & 0.35958966233032 \tabularnewline
11 & 0.531914873382233 & 0.936170253235534 & 0.468085126617767 \tabularnewline
12 & 0.499755557619544 & 0.999511115239088 & 0.500244442380456 \tabularnewline
13 & 0.508158475284367 & 0.983683049431266 & 0.491841524715633 \tabularnewline
14 & 0.47520137989847 & 0.950402759796941 & 0.52479862010153 \tabularnewline
15 & 0.450534841841589 & 0.901069683683179 & 0.549465158158411 \tabularnewline
16 & 0.482859932748844 & 0.965719865497688 & 0.517140067251156 \tabularnewline
17 & 0.448195915434199 & 0.896391830868399 & 0.551804084565801 \tabularnewline
18 & 0.404361563355119 & 0.808723126710239 & 0.595638436644881 \tabularnewline
19 & 0.455046840887358 & 0.910093681774717 & 0.544953159112642 \tabularnewline
20 & 0.579021989632781 & 0.841956020734439 & 0.420978010367219 \tabularnewline
21 & 0.703441612849717 & 0.593116774300565 & 0.296558387150283 \tabularnewline
22 & 0.667692247094396 & 0.664615505811208 & 0.332307752905604 \tabularnewline
23 & 0.62038824260681 & 0.75922351478638 & 0.37961175739319 \tabularnewline
24 & 0.73212626898955 & 0.535747462020901 & 0.26787373101045 \tabularnewline
25 & 0.695065368701421 & 0.609869262597158 & 0.304934631298579 \tabularnewline
26 & 0.644115243588285 & 0.71176951282343 & 0.355884756411715 \tabularnewline
27 & 0.59178104593626 & 0.81643790812748 & 0.40821895406374 \tabularnewline
28 & 0.587995333678728 & 0.824009332642543 & 0.412004666321272 \tabularnewline
29 & 0.614637448298515 & 0.770725103402971 & 0.385362551701485 \tabularnewline
30 & 0.595030184280028 & 0.809939631439944 & 0.404969815719972 \tabularnewline
31 & 0.54739219436386 & 0.90521561127228 & 0.45260780563614 \tabularnewline
32 & 0.547796469068507 & 0.904407061862986 & 0.452203530931493 \tabularnewline
33 & 0.62099691645505 & 0.7580061670899 & 0.37900308354495 \tabularnewline
34 & 0.572552476188625 & 0.854895047622751 & 0.427447523811375 \tabularnewline
35 & 0.557633399816024 & 0.884733200367953 & 0.442366600183976 \tabularnewline
36 & 0.536172525010652 & 0.927654949978695 & 0.463827474989348 \tabularnewline
37 & 0.475488468548018 & 0.950976937096037 & 0.524511531451982 \tabularnewline
38 & 0.512996964392839 & 0.974006071214323 & 0.487003035607161 \tabularnewline
39 & 0.561633657603629 & 0.876732684792742 & 0.438366342396371 \tabularnewline
40 & 0.523383025347727 & 0.953233949304546 & 0.476616974652273 \tabularnewline
41 & 0.483083748916857 & 0.966167497833714 & 0.516916251083143 \tabularnewline
42 & 0.638839430103512 & 0.722321139792975 & 0.361160569896488 \tabularnewline
43 & 0.659977895855506 & 0.680044208288988 & 0.340022104144494 \tabularnewline
44 & 0.628546981176955 & 0.74290603764609 & 0.371453018823045 \tabularnewline
45 & 0.579667942218486 & 0.840664115563028 & 0.420332057781514 \tabularnewline
46 & 0.54261411965821 & 0.91477176068358 & 0.45738588034179 \tabularnewline
47 & 0.619086096361738 & 0.761827807276523 & 0.380913903638262 \tabularnewline
48 & 0.555855298222949 & 0.888289403554101 & 0.444144701777051 \tabularnewline
49 & 0.500378477463947 & 0.999243045072105 & 0.499621522536053 \tabularnewline
50 & 0.529506088405199 & 0.940987823189603 & 0.470493911594802 \tabularnewline
51 & 0.521990930728329 & 0.956018138543342 & 0.478009069271671 \tabularnewline
52 & 0.455948510514141 & 0.911897021028282 & 0.544051489485859 \tabularnewline
53 & 0.395565113710941 & 0.791130227421882 & 0.604434886289059 \tabularnewline
54 & 0.332719303584413 & 0.665438607168826 & 0.667280696415587 \tabularnewline
55 & 0.355539079246132 & 0.711078158492265 & 0.644460920753868 \tabularnewline
56 & 0.425930779601052 & 0.851861559202105 & 0.574069220398948 \tabularnewline
57 & 0.362828760269044 & 0.725657520538088 & 0.637171239730956 \tabularnewline
58 & 0.629600430541753 & 0.740799138916493 & 0.370399569458247 \tabularnewline
59 & 0.581834862056062 & 0.836330275887875 & 0.418165137943938 \tabularnewline
60 & 0.563789530321828 & 0.872420939356344 & 0.436210469678172 \tabularnewline
61 & 0.580907896320584 & 0.838184207358832 & 0.419092103679416 \tabularnewline
62 & 0.51366654234051 & 0.97266691531898 & 0.48633345765949 \tabularnewline
63 & 0.684507601077392 & 0.630984797845217 & 0.315492398922609 \tabularnewline
64 & 0.611768607724578 & 0.776462784550843 & 0.388231392275422 \tabularnewline
65 & 0.570850964897841 & 0.858298070204319 & 0.429149035102159 \tabularnewline
66 & 0.489038820555787 & 0.978077641111574 & 0.510961179444213 \tabularnewline
67 & 0.414744637478697 & 0.829489274957394 & 0.585255362521303 \tabularnewline
68 & 0.373165853133315 & 0.746331706266629 & 0.626834146866685 \tabularnewline
69 & 0.316555041542708 & 0.633110083085415 & 0.683444958457292 \tabularnewline
70 & 0.769908806072425 & 0.460182387855149 & 0.230091193927575 \tabularnewline
71 & 0.739731478206513 & 0.520537043586973 & 0.260268521793487 \tabularnewline
72 & 0.767556747373944 & 0.464886505252112 & 0.232443252626056 \tabularnewline
73 & 0.670094609727597 & 0.659810780544805 & 0.329905390272403 \tabularnewline
74 & 0.556237685141688 & 0.887524629716623 & 0.443762314858312 \tabularnewline
75 & 0.442528060126892 & 0.885056120253783 & 0.557471939873108 \tabularnewline
76 & 0.312815251504432 & 0.625630503008864 & 0.687184748495568 \tabularnewline
77 & 0.781088240377021 & 0.437823519245958 & 0.218911759622979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159142&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]8[/C][C]0.645908326387679[/C][C]0.708183347224642[/C][C]0.354091673612321[/C][/ROW]
[ROW][C]9[/C][C]0.532218612883386[/C][C]0.935562774233227[/C][C]0.467781387116614[/C][/ROW]
[ROW][C]10[/C][C]0.64041033766968[/C][C]0.71917932466064[/C][C]0.35958966233032[/C][/ROW]
[ROW][C]11[/C][C]0.531914873382233[/C][C]0.936170253235534[/C][C]0.468085126617767[/C][/ROW]
[ROW][C]12[/C][C]0.499755557619544[/C][C]0.999511115239088[/C][C]0.500244442380456[/C][/ROW]
[ROW][C]13[/C][C]0.508158475284367[/C][C]0.983683049431266[/C][C]0.491841524715633[/C][/ROW]
[ROW][C]14[/C][C]0.47520137989847[/C][C]0.950402759796941[/C][C]0.52479862010153[/C][/ROW]
[ROW][C]15[/C][C]0.450534841841589[/C][C]0.901069683683179[/C][C]0.549465158158411[/C][/ROW]
[ROW][C]16[/C][C]0.482859932748844[/C][C]0.965719865497688[/C][C]0.517140067251156[/C][/ROW]
[ROW][C]17[/C][C]0.448195915434199[/C][C]0.896391830868399[/C][C]0.551804084565801[/C][/ROW]
[ROW][C]18[/C][C]0.404361563355119[/C][C]0.808723126710239[/C][C]0.595638436644881[/C][/ROW]
[ROW][C]19[/C][C]0.455046840887358[/C][C]0.910093681774717[/C][C]0.544953159112642[/C][/ROW]
[ROW][C]20[/C][C]0.579021989632781[/C][C]0.841956020734439[/C][C]0.420978010367219[/C][/ROW]
[ROW][C]21[/C][C]0.703441612849717[/C][C]0.593116774300565[/C][C]0.296558387150283[/C][/ROW]
[ROW][C]22[/C][C]0.667692247094396[/C][C]0.664615505811208[/C][C]0.332307752905604[/C][/ROW]
[ROW][C]23[/C][C]0.62038824260681[/C][C]0.75922351478638[/C][C]0.37961175739319[/C][/ROW]
[ROW][C]24[/C][C]0.73212626898955[/C][C]0.535747462020901[/C][C]0.26787373101045[/C][/ROW]
[ROW][C]25[/C][C]0.695065368701421[/C][C]0.609869262597158[/C][C]0.304934631298579[/C][/ROW]
[ROW][C]26[/C][C]0.644115243588285[/C][C]0.71176951282343[/C][C]0.355884756411715[/C][/ROW]
[ROW][C]27[/C][C]0.59178104593626[/C][C]0.81643790812748[/C][C]0.40821895406374[/C][/ROW]
[ROW][C]28[/C][C]0.587995333678728[/C][C]0.824009332642543[/C][C]0.412004666321272[/C][/ROW]
[ROW][C]29[/C][C]0.614637448298515[/C][C]0.770725103402971[/C][C]0.385362551701485[/C][/ROW]
[ROW][C]30[/C][C]0.595030184280028[/C][C]0.809939631439944[/C][C]0.404969815719972[/C][/ROW]
[ROW][C]31[/C][C]0.54739219436386[/C][C]0.90521561127228[/C][C]0.45260780563614[/C][/ROW]
[ROW][C]32[/C][C]0.547796469068507[/C][C]0.904407061862986[/C][C]0.452203530931493[/C][/ROW]
[ROW][C]33[/C][C]0.62099691645505[/C][C]0.7580061670899[/C][C]0.37900308354495[/C][/ROW]
[ROW][C]34[/C][C]0.572552476188625[/C][C]0.854895047622751[/C][C]0.427447523811375[/C][/ROW]
[ROW][C]35[/C][C]0.557633399816024[/C][C]0.884733200367953[/C][C]0.442366600183976[/C][/ROW]
[ROW][C]36[/C][C]0.536172525010652[/C][C]0.927654949978695[/C][C]0.463827474989348[/C][/ROW]
[ROW][C]37[/C][C]0.475488468548018[/C][C]0.950976937096037[/C][C]0.524511531451982[/C][/ROW]
[ROW][C]38[/C][C]0.512996964392839[/C][C]0.974006071214323[/C][C]0.487003035607161[/C][/ROW]
[ROW][C]39[/C][C]0.561633657603629[/C][C]0.876732684792742[/C][C]0.438366342396371[/C][/ROW]
[ROW][C]40[/C][C]0.523383025347727[/C][C]0.953233949304546[/C][C]0.476616974652273[/C][/ROW]
[ROW][C]41[/C][C]0.483083748916857[/C][C]0.966167497833714[/C][C]0.516916251083143[/C][/ROW]
[ROW][C]42[/C][C]0.638839430103512[/C][C]0.722321139792975[/C][C]0.361160569896488[/C][/ROW]
[ROW][C]43[/C][C]0.659977895855506[/C][C]0.680044208288988[/C][C]0.340022104144494[/C][/ROW]
[ROW][C]44[/C][C]0.628546981176955[/C][C]0.74290603764609[/C][C]0.371453018823045[/C][/ROW]
[ROW][C]45[/C][C]0.579667942218486[/C][C]0.840664115563028[/C][C]0.420332057781514[/C][/ROW]
[ROW][C]46[/C][C]0.54261411965821[/C][C]0.91477176068358[/C][C]0.45738588034179[/C][/ROW]
[ROW][C]47[/C][C]0.619086096361738[/C][C]0.761827807276523[/C][C]0.380913903638262[/C][/ROW]
[ROW][C]48[/C][C]0.555855298222949[/C][C]0.888289403554101[/C][C]0.444144701777051[/C][/ROW]
[ROW][C]49[/C][C]0.500378477463947[/C][C]0.999243045072105[/C][C]0.499621522536053[/C][/ROW]
[ROW][C]50[/C][C]0.529506088405199[/C][C]0.940987823189603[/C][C]0.470493911594802[/C][/ROW]
[ROW][C]51[/C][C]0.521990930728329[/C][C]0.956018138543342[/C][C]0.478009069271671[/C][/ROW]
[ROW][C]52[/C][C]0.455948510514141[/C][C]0.911897021028282[/C][C]0.544051489485859[/C][/ROW]
[ROW][C]53[/C][C]0.395565113710941[/C][C]0.791130227421882[/C][C]0.604434886289059[/C][/ROW]
[ROW][C]54[/C][C]0.332719303584413[/C][C]0.665438607168826[/C][C]0.667280696415587[/C][/ROW]
[ROW][C]55[/C][C]0.355539079246132[/C][C]0.711078158492265[/C][C]0.644460920753868[/C][/ROW]
[ROW][C]56[/C][C]0.425930779601052[/C][C]0.851861559202105[/C][C]0.574069220398948[/C][/ROW]
[ROW][C]57[/C][C]0.362828760269044[/C][C]0.725657520538088[/C][C]0.637171239730956[/C][/ROW]
[ROW][C]58[/C][C]0.629600430541753[/C][C]0.740799138916493[/C][C]0.370399569458247[/C][/ROW]
[ROW][C]59[/C][C]0.581834862056062[/C][C]0.836330275887875[/C][C]0.418165137943938[/C][/ROW]
[ROW][C]60[/C][C]0.563789530321828[/C][C]0.872420939356344[/C][C]0.436210469678172[/C][/ROW]
[ROW][C]61[/C][C]0.580907896320584[/C][C]0.838184207358832[/C][C]0.419092103679416[/C][/ROW]
[ROW][C]62[/C][C]0.51366654234051[/C][C]0.97266691531898[/C][C]0.48633345765949[/C][/ROW]
[ROW][C]63[/C][C]0.684507601077392[/C][C]0.630984797845217[/C][C]0.315492398922609[/C][/ROW]
[ROW][C]64[/C][C]0.611768607724578[/C][C]0.776462784550843[/C][C]0.388231392275422[/C][/ROW]
[ROW][C]65[/C][C]0.570850964897841[/C][C]0.858298070204319[/C][C]0.429149035102159[/C][/ROW]
[ROW][C]66[/C][C]0.489038820555787[/C][C]0.978077641111574[/C][C]0.510961179444213[/C][/ROW]
[ROW][C]67[/C][C]0.414744637478697[/C][C]0.829489274957394[/C][C]0.585255362521303[/C][/ROW]
[ROW][C]68[/C][C]0.373165853133315[/C][C]0.746331706266629[/C][C]0.626834146866685[/C][/ROW]
[ROW][C]69[/C][C]0.316555041542708[/C][C]0.633110083085415[/C][C]0.683444958457292[/C][/ROW]
[ROW][C]70[/C][C]0.769908806072425[/C][C]0.460182387855149[/C][C]0.230091193927575[/C][/ROW]
[ROW][C]71[/C][C]0.739731478206513[/C][C]0.520537043586973[/C][C]0.260268521793487[/C][/ROW]
[ROW][C]72[/C][C]0.767556747373944[/C][C]0.464886505252112[/C][C]0.232443252626056[/C][/ROW]
[ROW][C]73[/C][C]0.670094609727597[/C][C]0.659810780544805[/C][C]0.329905390272403[/C][/ROW]
[ROW][C]74[/C][C]0.556237685141688[/C][C]0.887524629716623[/C][C]0.443762314858312[/C][/ROW]
[ROW][C]75[/C][C]0.442528060126892[/C][C]0.885056120253783[/C][C]0.557471939873108[/C][/ROW]
[ROW][C]76[/C][C]0.312815251504432[/C][C]0.625630503008864[/C][C]0.687184748495568[/C][/ROW]
[ROW][C]77[/C][C]0.781088240377021[/C][C]0.437823519245958[/C][C]0.218911759622979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159142&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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
80.6459083263876790.7081833472246420.354091673612321
90.5322186128833860.9355627742332270.467781387116614
100.640410337669680.719179324660640.35958966233032
110.5319148733822330.9361702532355340.468085126617767
120.4997555576195440.9995111152390880.500244442380456
130.5081584752843670.9836830494312660.491841524715633
140.475201379898470.9504027597969410.52479862010153
150.4505348418415890.9010696836831790.549465158158411
160.4828599327488440.9657198654976880.517140067251156
170.4481959154341990.8963918308683990.551804084565801
180.4043615633551190.8087231267102390.595638436644881
190.4550468408873580.9100936817747170.544953159112642
200.5790219896327810.8419560207344390.420978010367219
210.7034416128497170.5931167743005650.296558387150283
220.6676922470943960.6646155058112080.332307752905604
230.620388242606810.759223514786380.37961175739319
240.732126268989550.5357474620209010.26787373101045
250.6950653687014210.6098692625971580.304934631298579
260.6441152435882850.711769512823430.355884756411715
270.591781045936260.816437908127480.40821895406374
280.5879953336787280.8240093326425430.412004666321272
290.6146374482985150.7707251034029710.385362551701485
300.5950301842800280.8099396314399440.404969815719972
310.547392194363860.905215611272280.45260780563614
320.5477964690685070.9044070618629860.452203530931493
330.620996916455050.75800616708990.37900308354495
340.5725524761886250.8548950476227510.427447523811375
350.5576333998160240.8847332003679530.442366600183976
360.5361725250106520.9276549499786950.463827474989348
370.4754884685480180.9509769370960370.524511531451982
380.5129969643928390.9740060712143230.487003035607161
390.5616336576036290.8767326847927420.438366342396371
400.5233830253477270.9532339493045460.476616974652273
410.4830837489168570.9661674978337140.516916251083143
420.6388394301035120.7223211397929750.361160569896488
430.6599778958555060.6800442082889880.340022104144494
440.6285469811769550.742906037646090.371453018823045
450.5796679422184860.8406641155630280.420332057781514
460.542614119658210.914771760683580.45738588034179
470.6190860963617380.7618278072765230.380913903638262
480.5558552982229490.8882894035541010.444144701777051
490.5003784774639470.9992430450721050.499621522536053
500.5295060884051990.9409878231896030.470493911594802
510.5219909307283290.9560181385433420.478009069271671
520.4559485105141410.9118970210282820.544051489485859
530.3955651137109410.7911302274218820.604434886289059
540.3327193035844130.6654386071688260.667280696415587
550.3555390792461320.7110781584922650.644460920753868
560.4259307796010520.8518615592021050.574069220398948
570.3628287602690440.7256575205380880.637171239730956
580.6296004305417530.7407991389164930.370399569458247
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600.5637895303218280.8724209393563440.436210469678172
610.5809078963205840.8381842073588320.419092103679416
620.513666542340510.972666915318980.48633345765949
630.6845076010773920.6309847978452170.315492398922609
640.6117686077245780.7764627845508430.388231392275422
650.5708509648978410.8582980702043190.429149035102159
660.4890388205557870.9780776411115740.510961179444213
670.4147446374786970.8294892749573940.585255362521303
680.3731658531333150.7463317062666290.626834146866685
690.3165550415427080.6331100830854150.683444958457292
700.7699088060724250.4601823878551490.230091193927575
710.7397314782065130.5205370435869730.260268521793487
720.7675567473739440.4648865052521120.232443252626056
730.6700946097275970.6598107805448050.329905390272403
740.5562376851416880.8875246297166230.443762314858312
750.4425280601268920.8850561202537830.557471939873108
760.3128152515044320.6256305030088640.687184748495568
770.7810882403770210.4378235192459580.218911759622979






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=159142&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=159142&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159142&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')
}