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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 17:26:01 -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/2012/Nov/20/t1353450402ape46lfhjigutjv.htm/, Retrieved Mon, 29 Apr 2024 21:54:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191314, Retrieved Mon, 29 Apr 2024 21:54:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [ws7] [2012-11-18 10:59:23] [7722d8427d2b2c713c1f0d5525f2f86c]
- R PD  [Multiple Regression] [ws7 goed] [2012-11-18 13:15:15] [7722d8427d2b2c713c1f0d5525f2f86c]
-  M        [Multiple Regression] [] [2012-11-20 22:26:01] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,5	99,5	101,5	467
99	93,5	99,2	460
104,1	104,6	107,8	448
98,6	95,3	92,3	443
101,4	102,8	99,2	436
102,1	103,3	101,6	431
93	100,2	87	484
96,9	107,9	71,4	510
91,2	107,5	104,7	513
96,9	119,8	115,1	503
94	112	102,5	471
90,4	102,1	75,3	471
105,2	105,3	96,7	476
103,4	101,3	94,6	475
111,7	108,4	98,6	470
114,2	107,4	99,5	461
111,4	109,1	92	455
106,3	109,5	93,6	456
111,8	111,4	89,3	517
101,5	110,1	66,9	525
103	117	108,8	523
105,2	129,6	113,2	519
101,1	113,5	105,5	509
100,7	113,3	77,8	512
116,7	110,1	102,1	519
109	107,4	97	517
119,5	110,1	95,5	510
115,1	112,5	99,3	509
107,1	106	86,4	501
109,7	117,6	92,4	507
110,4	117,8	85,7	569
105	113,5	61,9	580
115,8	121,2	104,9	578
116,4	130,4	107,9	565
111,1	115,2	95,6	547
119,5	117,9	79,8	555
110,9	110,7	94,8	562
115,1	107,6	93,7	561
125,2	124,3	108,1	555
116	115,1	96,9	544
112,9	112,5	88,8	537
121,7	127,9	106,7	543
123,2	117,4	86,8	594
116,6	119,3	69,8	611
136,2	130,4	110,9	613
120,9	126	105,4	611
119,6	125,4	99,2	594
125,9	130,5	84,4	595
116,1	115,9	87,2	591
107,5	108,7	91,9	589
116,7	124	97,9	584
112,5	119,4	94,5	573
113	118,6	85	567
126,4	131,3	100,3	569
114,1	111,1	78,7	621
112,5	124,8	65,8	629
112,4	132,3	104,8	628
113,1	126,7	96	612
116,3	131,7	103,3	595
111,7	130,9	82,9	597
118,8	122,1	91,4	593
116,5	113,2	94,5	590
125,1	133,6	109,3	580
113,1	119,2	92,1	574
119,6	129,4	99,3	573
114,4	131,4	109,6	573
114	117,1	87,5	620
117,8	130,5	73,1	626
117	132,3	110,7	620
120,9	140,8	111,6	588
115	137,5	110,7	566
117,3	128,6	84	557
119,4	126,7	101,6	561
114,9	120,8	102,1	549
125,8	139,3	113,9	532
117,6	128,6	99	526
117,6	131,3	100,4	511
114,9	136,3	109,5	499
121,9	128,8	93,1	555
117	133,2	77	565
106,4	136,3	108	542
110,5	151,1	119,9	527
113,6	145	105,9	510
114,2	134,4	78,2	514
125,4	135,7	100,3	517
124,6	128,7	102,2	508
120,2	129,2	97	493
120,8	138,6	101,3	490
111,4	132,7	89,2	469
124,1	132,5	93,3	478
120,2	137,3	88,5	528
125,5	127,1	61,5	534
116	143,7	96,3	518
117	149,9	95,4	506
105,7	131,6	79,9	502
102	138,8	66,7	516
106,4	122,5	71,2	528
96,9	122	73,1	533
107,6	135,6	81	536
98,8	133,4	77,2	537
101,1	127,3	67,7	524
105,7	138,9	76,7	536
104,6	131,4	73,3	587
103,2	131,6	54,1	597
101,6	135,8	85	581
106,7	141,6	85,9	564
99,5	132,6	79,3	558
101	132,3	67,2	575
104,9	120,6	72,4	580
118,4	123,8	76,1	575
129	145,1	89,8	563
123,7	135	84	552
127,6	127,6	75,4	537
129,4	142	90	545
128,3	130,1	76,8	601
124,8	131	59,6	604
125,2	141,3	92,1	586
129,6	139,6	88,4	564
124,8	142,2	82,8	549
121,9	140	69,4	551
129,2	132	73,4	556

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 325.715341598458 + 1.70352742826429chemie[t] + 0.808573772076509vm[t] -0.839705808073971textiel[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  325.715341598458 +  1.70352742826429chemie[t] +  0.808573772076509vm[t] -0.839705808073971textiel[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  325.715341598458 +  1.70352742826429chemie[t] +  0.808573772076509vm[t] -0.839705808073971textiel[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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
werkloosheid[t] = + 325.715341598458 + 1.70352742826429chemie[t] + 0.808573772076509vm[t] -0.839705808073971textiel[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)325.71534159845850.8770696.40200
chemie1.703527428264290.4514353.77360.0002540.000127
vm0.8085737720765090.3369692.39950.0179940.008997
textiel-0.8397058080739710.266925-3.14580.00210.00105

\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) & 325.715341598458 & 50.877069 & 6.402 & 0 & 0 \tabularnewline
chemie & 1.70352742826429 & 0.451435 & 3.7736 & 0.000254 & 0.000127 \tabularnewline
vm & 0.808573772076509 & 0.336969 & 2.3995 & 0.017994 & 0.008997 \tabularnewline
textiel & -0.839705808073971 & 0.266925 & -3.1458 & 0.0021 & 0.00105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&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]325.715341598458[/C][C]50.877069[/C][C]6.402[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]chemie[/C][C]1.70352742826429[/C][C]0.451435[/C][C]3.7736[/C][C]0.000254[/C][C]0.000127[/C][/ROW]
[ROW][C]vm[/C][C]0.808573772076509[/C][C]0.336969[/C][C]2.3995[/C][C]0.017994[/C][C]0.008997[/C][/ROW]
[ROW][C]textiel[/C][C]-0.839705808073971[/C][C]0.266925[/C][C]-3.1458[/C][C]0.0021[/C][C]0.00105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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)325.71534159845850.8770696.40200
chemie1.703527428264290.4514353.77360.0002540.000127
vm0.8085737720765090.3369692.39950.0179940.008997
textiel-0.8397058080739710.266925-3.14580.00210.00105






Multiple Linear Regression - Regression Statistics
Multiple R0.530732354037242
R-squared0.281676831621912
Adjusted R-squared0.263258288842987
F-TEST (value)15.2931116757081
F-TEST (DF numerator)3
F-TEST (DF denominator)117
p-value1.8510441179842e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40.4265283305566
Sum Squared Residuals191213.590564772

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.530732354037242 \tabularnewline
R-squared & 0.281676831621912 \tabularnewline
Adjusted R-squared & 0.263258288842987 \tabularnewline
F-TEST (value) & 15.2931116757081 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 117 \tabularnewline
p-value & 1.8510441179842e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 40.4265283305566 \tabularnewline
Sum Squared Residuals & 191213.590564772 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.530732354037242[/C][/ROW]
[ROW][C]R-squared[/C][C]0.281676831621912[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.263258288842987[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.2931116757081[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]117[/C][/ROW]
[ROW][C]p-value[/C][C]1.8510441179842e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]40.4265283305566[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]191213.590564772[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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 R0.530732354037242
R-squared0.281676831621912
Adjusted R-squared0.263258288842987
F-TEST (value)15.2931116757081
F-TEST (DF numerator)3
F-TEST (DF denominator)117
p-value1.8510441179842e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40.4265283305566
Sum Squared Residuals191213.590564772






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1467492.142798941124-25.1427989411238
2460486.667388524839-26.6673885248385
3448497.109077329599-49.1090773295995
4443493.235380418981-50.2353804189809
5436498.275590432984-62.2755904329844
6431497.85705257943-66.8570525794301
7484492.108079086668-8.10807908666787
8510518.077264707842-8.07726470784169
9513480.08152544904132.9184745509586
10503491.0041487827211.9958512172804
11471490.337337000288-19.3373370002884
12471499.039755894592-28.0397558945916
13476508.869693610765-32.8696936107649
14475504.332431348538-29.3324313485385
15470520.853759552579-50.8537595525794
16461523.548269123897-62.548269123897
17455526.450761297842-71.4507612978419
18456516.742671629606-60.7426716296062
19517531.259097626723-14.2590976267233
20525531.471029312759-6.47102931275859
21523504.42180612418418.5781938758164
22519514.6628904390034.33710956099647
23509501.1261249748587.87387502514229
24512523.542850132786-11.5428501327857
25519527.807001778172-8.80700177817202
26517516.7891910171080.210808982892334
27510538.1189369106-28.1189369106002
28509529.37311120854-20.3731112085399
29501521.321367188082-20.3213671880825
30507530.091759409213-23.0917594092133
31569537.07197227750931.9280277224907
32580544.38105517711435.6189448228864
33578532.89781970017645.1021802998237
34565538.83969743601726.1603025639832
35547527.84906216996319.150937830037
36555557.609193519558-2.60919351955837
37562524.54153935642537.458460643575
38561530.11345225057930.8865477494208
39555548.7304976334616.26950236653891
40544535.0238716407548.9761283592458
41537534.4422618511352.55773814886483
42543546.854605345315-3.85460534531507
43594557.6300174615836.3699825384198
44611562.19802533923948.8019746607613
45613570.05042309142842.9495769085722
46611545.04711078625465.9528892137456
47594547.55355687632446.4464431236764
48595574.83715187147420.1628481285264
49591543.98622973955947.0137702604406
50589519.56754539958869.4324546004121
51584542.57294160394641.4270583960538
52573534.55368680113638.4463131988643
53567542.73579667430924.2642033256906
54569562.9844522548916.0155477451092
55621543.83552014569277.1644798543077
56629563.01954186207265.9804581379282
57628536.16496589493491.8350341050656
58612540.21883308214271.7811669178581
59595543.5831373140351.4168626859699
60597552.23005061106244.7699493889378
61593550.07214678883742.9278532111634
62590536.35463912731853.6453608726815
63580555.07223400125724.9277659987426
64574537.42938244305736.5706175569435
65573550.70388138382222.2961186161778
66573534.81371647783938.186283522161
67620541.12719892427478.872801075726
68626570.52725533376955.4727446662313
69620539.04692779731480.9530722026863
70588551.80782660292836.1921733970718
71566539.84445655558326.1555434444171
72557558.986408144685-1.98640814468488
73561546.24870335499314.7512966450073
74549533.39239176851515.607608231485
75532557.010926984738-25.0109269847383
76526546.901879252055-20.9018792520546
77511547.909440305358-36.9094403053576
78499539.711462255954-40.7114622559535
79555559.343026215643-4.34302621564281
80565568.072729924275-3.07272992427534
81542526.49103782781815.508962172182
82527535.449892994354-8.4498929943536
83510547.554409325342-37.5544093253418
84514563.265494681938-49.2654946819384
85517564.838649423763-47.8386494237631
86508556.220370041276-48.2203700412756
87493553.495606444936-60.4956064449356
88490558.507581384695-68.5075813846953
89469547.884278581455-78.8842785814546
90478565.914568352893-87.9145683528925
91528567.182553367384-39.1825533673841
92534590.635853080002-56.6358530800017
93518558.652905006987-40.6529050069867
94506566.125325049392-60.125325049392
95502545.094005106152-43.0940051061519
96516555.696801447101-39.6968014471014
97528546.233893510284-18.2338935102843
98533528.0506550203954.94934497960528
99536550.641325919279-14.6413259192787
100537537.062304322666-0.0623043226657736
101524544.02532257471-20.0253225747096
102536553.683652228147-17.6836522281472
103587548.60046851393438.3995314860659
104597562.499596383834.5004036162003
105581537.22305287181243.7769471281876
106564549.84503540673714.1549645932625
107558535.84453230783422.1554676921658
108575548.31769159630326.6823084036972
109580541.13466523125438.8653347687463
110575563.61281009359311.3871899064072
111563587.38885260781-24.3888526078104
112552575.063855826866-23.063855826866
113537582.945636833167-45.9456368331667
114545585.395743724064-40.3957437240642
115601584.98395233183916.0160476681605
116604594.1922626266569.80773737334443
117586575.91154468794510.0884553120547
118564585.139401449652-21.1394014496518
119549583.767114126596-34.7671141265963
120551588.300080114253-37.3000801142528
121556590.908416931674-34.9084169316741

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 467 & 492.142798941124 & -25.1427989411238 \tabularnewline
2 & 460 & 486.667388524839 & -26.6673885248385 \tabularnewline
3 & 448 & 497.109077329599 & -49.1090773295995 \tabularnewline
4 & 443 & 493.235380418981 & -50.2353804189809 \tabularnewline
5 & 436 & 498.275590432984 & -62.2755904329844 \tabularnewline
6 & 431 & 497.85705257943 & -66.8570525794301 \tabularnewline
7 & 484 & 492.108079086668 & -8.10807908666787 \tabularnewline
8 & 510 & 518.077264707842 & -8.07726470784169 \tabularnewline
9 & 513 & 480.081525449041 & 32.9184745509586 \tabularnewline
10 & 503 & 491.00414878272 & 11.9958512172804 \tabularnewline
11 & 471 & 490.337337000288 & -19.3373370002884 \tabularnewline
12 & 471 & 499.039755894592 & -28.0397558945916 \tabularnewline
13 & 476 & 508.869693610765 & -32.8696936107649 \tabularnewline
14 & 475 & 504.332431348538 & -29.3324313485385 \tabularnewline
15 & 470 & 520.853759552579 & -50.8537595525794 \tabularnewline
16 & 461 & 523.548269123897 & -62.548269123897 \tabularnewline
17 & 455 & 526.450761297842 & -71.4507612978419 \tabularnewline
18 & 456 & 516.742671629606 & -60.7426716296062 \tabularnewline
19 & 517 & 531.259097626723 & -14.2590976267233 \tabularnewline
20 & 525 & 531.471029312759 & -6.47102931275859 \tabularnewline
21 & 523 & 504.421806124184 & 18.5781938758164 \tabularnewline
22 & 519 & 514.662890439003 & 4.33710956099647 \tabularnewline
23 & 509 & 501.126124974858 & 7.87387502514229 \tabularnewline
24 & 512 & 523.542850132786 & -11.5428501327857 \tabularnewline
25 & 519 & 527.807001778172 & -8.80700177817202 \tabularnewline
26 & 517 & 516.789191017108 & 0.210808982892334 \tabularnewline
27 & 510 & 538.1189369106 & -28.1189369106002 \tabularnewline
28 & 509 & 529.37311120854 & -20.3731112085399 \tabularnewline
29 & 501 & 521.321367188082 & -20.3213671880825 \tabularnewline
30 & 507 & 530.091759409213 & -23.0917594092133 \tabularnewline
31 & 569 & 537.071972277509 & 31.9280277224907 \tabularnewline
32 & 580 & 544.381055177114 & 35.6189448228864 \tabularnewline
33 & 578 & 532.897819700176 & 45.1021802998237 \tabularnewline
34 & 565 & 538.839697436017 & 26.1603025639832 \tabularnewline
35 & 547 & 527.849062169963 & 19.150937830037 \tabularnewline
36 & 555 & 557.609193519558 & -2.60919351955837 \tabularnewline
37 & 562 & 524.541539356425 & 37.458460643575 \tabularnewline
38 & 561 & 530.113452250579 & 30.8865477494208 \tabularnewline
39 & 555 & 548.730497633461 & 6.26950236653891 \tabularnewline
40 & 544 & 535.023871640754 & 8.9761283592458 \tabularnewline
41 & 537 & 534.442261851135 & 2.55773814886483 \tabularnewline
42 & 543 & 546.854605345315 & -3.85460534531507 \tabularnewline
43 & 594 & 557.63001746158 & 36.3699825384198 \tabularnewline
44 & 611 & 562.198025339239 & 48.8019746607613 \tabularnewline
45 & 613 & 570.050423091428 & 42.9495769085722 \tabularnewline
46 & 611 & 545.047110786254 & 65.9528892137456 \tabularnewline
47 & 594 & 547.553556876324 & 46.4464431236764 \tabularnewline
48 & 595 & 574.837151871474 & 20.1628481285264 \tabularnewline
49 & 591 & 543.986229739559 & 47.0137702604406 \tabularnewline
50 & 589 & 519.567545399588 & 69.4324546004121 \tabularnewline
51 & 584 & 542.572941603946 & 41.4270583960538 \tabularnewline
52 & 573 & 534.553686801136 & 38.4463131988643 \tabularnewline
53 & 567 & 542.735796674309 & 24.2642033256906 \tabularnewline
54 & 569 & 562.984452254891 & 6.0155477451092 \tabularnewline
55 & 621 & 543.835520145692 & 77.1644798543077 \tabularnewline
56 & 629 & 563.019541862072 & 65.9804581379282 \tabularnewline
57 & 628 & 536.164965894934 & 91.8350341050656 \tabularnewline
58 & 612 & 540.218833082142 & 71.7811669178581 \tabularnewline
59 & 595 & 543.58313731403 & 51.4168626859699 \tabularnewline
60 & 597 & 552.230050611062 & 44.7699493889378 \tabularnewline
61 & 593 & 550.072146788837 & 42.9278532111634 \tabularnewline
62 & 590 & 536.354639127318 & 53.6453608726815 \tabularnewline
63 & 580 & 555.072234001257 & 24.9277659987426 \tabularnewline
64 & 574 & 537.429382443057 & 36.5706175569435 \tabularnewline
65 & 573 & 550.703881383822 & 22.2961186161778 \tabularnewline
66 & 573 & 534.813716477839 & 38.186283522161 \tabularnewline
67 & 620 & 541.127198924274 & 78.872801075726 \tabularnewline
68 & 626 & 570.527255333769 & 55.4727446662313 \tabularnewline
69 & 620 & 539.046927797314 & 80.9530722026863 \tabularnewline
70 & 588 & 551.807826602928 & 36.1921733970718 \tabularnewline
71 & 566 & 539.844456555583 & 26.1555434444171 \tabularnewline
72 & 557 & 558.986408144685 & -1.98640814468488 \tabularnewline
73 & 561 & 546.248703354993 & 14.7512966450073 \tabularnewline
74 & 549 & 533.392391768515 & 15.607608231485 \tabularnewline
75 & 532 & 557.010926984738 & -25.0109269847383 \tabularnewline
76 & 526 & 546.901879252055 & -20.9018792520546 \tabularnewline
77 & 511 & 547.909440305358 & -36.9094403053576 \tabularnewline
78 & 499 & 539.711462255954 & -40.7114622559535 \tabularnewline
79 & 555 & 559.343026215643 & -4.34302621564281 \tabularnewline
80 & 565 & 568.072729924275 & -3.07272992427534 \tabularnewline
81 & 542 & 526.491037827818 & 15.508962172182 \tabularnewline
82 & 527 & 535.449892994354 & -8.4498929943536 \tabularnewline
83 & 510 & 547.554409325342 & -37.5544093253418 \tabularnewline
84 & 514 & 563.265494681938 & -49.2654946819384 \tabularnewline
85 & 517 & 564.838649423763 & -47.8386494237631 \tabularnewline
86 & 508 & 556.220370041276 & -48.2203700412756 \tabularnewline
87 & 493 & 553.495606444936 & -60.4956064449356 \tabularnewline
88 & 490 & 558.507581384695 & -68.5075813846953 \tabularnewline
89 & 469 & 547.884278581455 & -78.8842785814546 \tabularnewline
90 & 478 & 565.914568352893 & -87.9145683528925 \tabularnewline
91 & 528 & 567.182553367384 & -39.1825533673841 \tabularnewline
92 & 534 & 590.635853080002 & -56.6358530800017 \tabularnewline
93 & 518 & 558.652905006987 & -40.6529050069867 \tabularnewline
94 & 506 & 566.125325049392 & -60.125325049392 \tabularnewline
95 & 502 & 545.094005106152 & -43.0940051061519 \tabularnewline
96 & 516 & 555.696801447101 & -39.6968014471014 \tabularnewline
97 & 528 & 546.233893510284 & -18.2338935102843 \tabularnewline
98 & 533 & 528.050655020395 & 4.94934497960528 \tabularnewline
99 & 536 & 550.641325919279 & -14.6413259192787 \tabularnewline
100 & 537 & 537.062304322666 & -0.0623043226657736 \tabularnewline
101 & 524 & 544.02532257471 & -20.0253225747096 \tabularnewline
102 & 536 & 553.683652228147 & -17.6836522281472 \tabularnewline
103 & 587 & 548.600468513934 & 38.3995314860659 \tabularnewline
104 & 597 & 562.4995963838 & 34.5004036162003 \tabularnewline
105 & 581 & 537.223052871812 & 43.7769471281876 \tabularnewline
106 & 564 & 549.845035406737 & 14.1549645932625 \tabularnewline
107 & 558 & 535.844532307834 & 22.1554676921658 \tabularnewline
108 & 575 & 548.317691596303 & 26.6823084036972 \tabularnewline
109 & 580 & 541.134665231254 & 38.8653347687463 \tabularnewline
110 & 575 & 563.612810093593 & 11.3871899064072 \tabularnewline
111 & 563 & 587.38885260781 & -24.3888526078104 \tabularnewline
112 & 552 & 575.063855826866 & -23.063855826866 \tabularnewline
113 & 537 & 582.945636833167 & -45.9456368331667 \tabularnewline
114 & 545 & 585.395743724064 & -40.3957437240642 \tabularnewline
115 & 601 & 584.983952331839 & 16.0160476681605 \tabularnewline
116 & 604 & 594.192262626656 & 9.80773737334443 \tabularnewline
117 & 586 & 575.911544687945 & 10.0884553120547 \tabularnewline
118 & 564 & 585.139401449652 & -21.1394014496518 \tabularnewline
119 & 549 & 583.767114126596 & -34.7671141265963 \tabularnewline
120 & 551 & 588.300080114253 & -37.3000801142528 \tabularnewline
121 & 556 & 590.908416931674 & -34.9084169316741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&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]467[/C][C]492.142798941124[/C][C]-25.1427989411238[/C][/ROW]
[ROW][C]2[/C][C]460[/C][C]486.667388524839[/C][C]-26.6673885248385[/C][/ROW]
[ROW][C]3[/C][C]448[/C][C]497.109077329599[/C][C]-49.1090773295995[/C][/ROW]
[ROW][C]4[/C][C]443[/C][C]493.235380418981[/C][C]-50.2353804189809[/C][/ROW]
[ROW][C]5[/C][C]436[/C][C]498.275590432984[/C][C]-62.2755904329844[/C][/ROW]
[ROW][C]6[/C][C]431[/C][C]497.85705257943[/C][C]-66.8570525794301[/C][/ROW]
[ROW][C]7[/C][C]484[/C][C]492.108079086668[/C][C]-8.10807908666787[/C][/ROW]
[ROW][C]8[/C][C]510[/C][C]518.077264707842[/C][C]-8.07726470784169[/C][/ROW]
[ROW][C]9[/C][C]513[/C][C]480.081525449041[/C][C]32.9184745509586[/C][/ROW]
[ROW][C]10[/C][C]503[/C][C]491.00414878272[/C][C]11.9958512172804[/C][/ROW]
[ROW][C]11[/C][C]471[/C][C]490.337337000288[/C][C]-19.3373370002884[/C][/ROW]
[ROW][C]12[/C][C]471[/C][C]499.039755894592[/C][C]-28.0397558945916[/C][/ROW]
[ROW][C]13[/C][C]476[/C][C]508.869693610765[/C][C]-32.8696936107649[/C][/ROW]
[ROW][C]14[/C][C]475[/C][C]504.332431348538[/C][C]-29.3324313485385[/C][/ROW]
[ROW][C]15[/C][C]470[/C][C]520.853759552579[/C][C]-50.8537595525794[/C][/ROW]
[ROW][C]16[/C][C]461[/C][C]523.548269123897[/C][C]-62.548269123897[/C][/ROW]
[ROW][C]17[/C][C]455[/C][C]526.450761297842[/C][C]-71.4507612978419[/C][/ROW]
[ROW][C]18[/C][C]456[/C][C]516.742671629606[/C][C]-60.7426716296062[/C][/ROW]
[ROW][C]19[/C][C]517[/C][C]531.259097626723[/C][C]-14.2590976267233[/C][/ROW]
[ROW][C]20[/C][C]525[/C][C]531.471029312759[/C][C]-6.47102931275859[/C][/ROW]
[ROW][C]21[/C][C]523[/C][C]504.421806124184[/C][C]18.5781938758164[/C][/ROW]
[ROW][C]22[/C][C]519[/C][C]514.662890439003[/C][C]4.33710956099647[/C][/ROW]
[ROW][C]23[/C][C]509[/C][C]501.126124974858[/C][C]7.87387502514229[/C][/ROW]
[ROW][C]24[/C][C]512[/C][C]523.542850132786[/C][C]-11.5428501327857[/C][/ROW]
[ROW][C]25[/C][C]519[/C][C]527.807001778172[/C][C]-8.80700177817202[/C][/ROW]
[ROW][C]26[/C][C]517[/C][C]516.789191017108[/C][C]0.210808982892334[/C][/ROW]
[ROW][C]27[/C][C]510[/C][C]538.1189369106[/C][C]-28.1189369106002[/C][/ROW]
[ROW][C]28[/C][C]509[/C][C]529.37311120854[/C][C]-20.3731112085399[/C][/ROW]
[ROW][C]29[/C][C]501[/C][C]521.321367188082[/C][C]-20.3213671880825[/C][/ROW]
[ROW][C]30[/C][C]507[/C][C]530.091759409213[/C][C]-23.0917594092133[/C][/ROW]
[ROW][C]31[/C][C]569[/C][C]537.071972277509[/C][C]31.9280277224907[/C][/ROW]
[ROW][C]32[/C][C]580[/C][C]544.381055177114[/C][C]35.6189448228864[/C][/ROW]
[ROW][C]33[/C][C]578[/C][C]532.897819700176[/C][C]45.1021802998237[/C][/ROW]
[ROW][C]34[/C][C]565[/C][C]538.839697436017[/C][C]26.1603025639832[/C][/ROW]
[ROW][C]35[/C][C]547[/C][C]527.849062169963[/C][C]19.150937830037[/C][/ROW]
[ROW][C]36[/C][C]555[/C][C]557.609193519558[/C][C]-2.60919351955837[/C][/ROW]
[ROW][C]37[/C][C]562[/C][C]524.541539356425[/C][C]37.458460643575[/C][/ROW]
[ROW][C]38[/C][C]561[/C][C]530.113452250579[/C][C]30.8865477494208[/C][/ROW]
[ROW][C]39[/C][C]555[/C][C]548.730497633461[/C][C]6.26950236653891[/C][/ROW]
[ROW][C]40[/C][C]544[/C][C]535.023871640754[/C][C]8.9761283592458[/C][/ROW]
[ROW][C]41[/C][C]537[/C][C]534.442261851135[/C][C]2.55773814886483[/C][/ROW]
[ROW][C]42[/C][C]543[/C][C]546.854605345315[/C][C]-3.85460534531507[/C][/ROW]
[ROW][C]43[/C][C]594[/C][C]557.63001746158[/C][C]36.3699825384198[/C][/ROW]
[ROW][C]44[/C][C]611[/C][C]562.198025339239[/C][C]48.8019746607613[/C][/ROW]
[ROW][C]45[/C][C]613[/C][C]570.050423091428[/C][C]42.9495769085722[/C][/ROW]
[ROW][C]46[/C][C]611[/C][C]545.047110786254[/C][C]65.9528892137456[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]547.553556876324[/C][C]46.4464431236764[/C][/ROW]
[ROW][C]48[/C][C]595[/C][C]574.837151871474[/C][C]20.1628481285264[/C][/ROW]
[ROW][C]49[/C][C]591[/C][C]543.986229739559[/C][C]47.0137702604406[/C][/ROW]
[ROW][C]50[/C][C]589[/C][C]519.567545399588[/C][C]69.4324546004121[/C][/ROW]
[ROW][C]51[/C][C]584[/C][C]542.572941603946[/C][C]41.4270583960538[/C][/ROW]
[ROW][C]52[/C][C]573[/C][C]534.553686801136[/C][C]38.4463131988643[/C][/ROW]
[ROW][C]53[/C][C]567[/C][C]542.735796674309[/C][C]24.2642033256906[/C][/ROW]
[ROW][C]54[/C][C]569[/C][C]562.984452254891[/C][C]6.0155477451092[/C][/ROW]
[ROW][C]55[/C][C]621[/C][C]543.835520145692[/C][C]77.1644798543077[/C][/ROW]
[ROW][C]56[/C][C]629[/C][C]563.019541862072[/C][C]65.9804581379282[/C][/ROW]
[ROW][C]57[/C][C]628[/C][C]536.164965894934[/C][C]91.8350341050656[/C][/ROW]
[ROW][C]58[/C][C]612[/C][C]540.218833082142[/C][C]71.7811669178581[/C][/ROW]
[ROW][C]59[/C][C]595[/C][C]543.58313731403[/C][C]51.4168626859699[/C][/ROW]
[ROW][C]60[/C][C]597[/C][C]552.230050611062[/C][C]44.7699493889378[/C][/ROW]
[ROW][C]61[/C][C]593[/C][C]550.072146788837[/C][C]42.9278532111634[/C][/ROW]
[ROW][C]62[/C][C]590[/C][C]536.354639127318[/C][C]53.6453608726815[/C][/ROW]
[ROW][C]63[/C][C]580[/C][C]555.072234001257[/C][C]24.9277659987426[/C][/ROW]
[ROW][C]64[/C][C]574[/C][C]537.429382443057[/C][C]36.5706175569435[/C][/ROW]
[ROW][C]65[/C][C]573[/C][C]550.703881383822[/C][C]22.2961186161778[/C][/ROW]
[ROW][C]66[/C][C]573[/C][C]534.813716477839[/C][C]38.186283522161[/C][/ROW]
[ROW][C]67[/C][C]620[/C][C]541.127198924274[/C][C]78.872801075726[/C][/ROW]
[ROW][C]68[/C][C]626[/C][C]570.527255333769[/C][C]55.4727446662313[/C][/ROW]
[ROW][C]69[/C][C]620[/C][C]539.046927797314[/C][C]80.9530722026863[/C][/ROW]
[ROW][C]70[/C][C]588[/C][C]551.807826602928[/C][C]36.1921733970718[/C][/ROW]
[ROW][C]71[/C][C]566[/C][C]539.844456555583[/C][C]26.1555434444171[/C][/ROW]
[ROW][C]72[/C][C]557[/C][C]558.986408144685[/C][C]-1.98640814468488[/C][/ROW]
[ROW][C]73[/C][C]561[/C][C]546.248703354993[/C][C]14.7512966450073[/C][/ROW]
[ROW][C]74[/C][C]549[/C][C]533.392391768515[/C][C]15.607608231485[/C][/ROW]
[ROW][C]75[/C][C]532[/C][C]557.010926984738[/C][C]-25.0109269847383[/C][/ROW]
[ROW][C]76[/C][C]526[/C][C]546.901879252055[/C][C]-20.9018792520546[/C][/ROW]
[ROW][C]77[/C][C]511[/C][C]547.909440305358[/C][C]-36.9094403053576[/C][/ROW]
[ROW][C]78[/C][C]499[/C][C]539.711462255954[/C][C]-40.7114622559535[/C][/ROW]
[ROW][C]79[/C][C]555[/C][C]559.343026215643[/C][C]-4.34302621564281[/C][/ROW]
[ROW][C]80[/C][C]565[/C][C]568.072729924275[/C][C]-3.07272992427534[/C][/ROW]
[ROW][C]81[/C][C]542[/C][C]526.491037827818[/C][C]15.508962172182[/C][/ROW]
[ROW][C]82[/C][C]527[/C][C]535.449892994354[/C][C]-8.4498929943536[/C][/ROW]
[ROW][C]83[/C][C]510[/C][C]547.554409325342[/C][C]-37.5544093253418[/C][/ROW]
[ROW][C]84[/C][C]514[/C][C]563.265494681938[/C][C]-49.2654946819384[/C][/ROW]
[ROW][C]85[/C][C]517[/C][C]564.838649423763[/C][C]-47.8386494237631[/C][/ROW]
[ROW][C]86[/C][C]508[/C][C]556.220370041276[/C][C]-48.2203700412756[/C][/ROW]
[ROW][C]87[/C][C]493[/C][C]553.495606444936[/C][C]-60.4956064449356[/C][/ROW]
[ROW][C]88[/C][C]490[/C][C]558.507581384695[/C][C]-68.5075813846953[/C][/ROW]
[ROW][C]89[/C][C]469[/C][C]547.884278581455[/C][C]-78.8842785814546[/C][/ROW]
[ROW][C]90[/C][C]478[/C][C]565.914568352893[/C][C]-87.9145683528925[/C][/ROW]
[ROW][C]91[/C][C]528[/C][C]567.182553367384[/C][C]-39.1825533673841[/C][/ROW]
[ROW][C]92[/C][C]534[/C][C]590.635853080002[/C][C]-56.6358530800017[/C][/ROW]
[ROW][C]93[/C][C]518[/C][C]558.652905006987[/C][C]-40.6529050069867[/C][/ROW]
[ROW][C]94[/C][C]506[/C][C]566.125325049392[/C][C]-60.125325049392[/C][/ROW]
[ROW][C]95[/C][C]502[/C][C]545.094005106152[/C][C]-43.0940051061519[/C][/ROW]
[ROW][C]96[/C][C]516[/C][C]555.696801447101[/C][C]-39.6968014471014[/C][/ROW]
[ROW][C]97[/C][C]528[/C][C]546.233893510284[/C][C]-18.2338935102843[/C][/ROW]
[ROW][C]98[/C][C]533[/C][C]528.050655020395[/C][C]4.94934497960528[/C][/ROW]
[ROW][C]99[/C][C]536[/C][C]550.641325919279[/C][C]-14.6413259192787[/C][/ROW]
[ROW][C]100[/C][C]537[/C][C]537.062304322666[/C][C]-0.0623043226657736[/C][/ROW]
[ROW][C]101[/C][C]524[/C][C]544.02532257471[/C][C]-20.0253225747096[/C][/ROW]
[ROW][C]102[/C][C]536[/C][C]553.683652228147[/C][C]-17.6836522281472[/C][/ROW]
[ROW][C]103[/C][C]587[/C][C]548.600468513934[/C][C]38.3995314860659[/C][/ROW]
[ROW][C]104[/C][C]597[/C][C]562.4995963838[/C][C]34.5004036162003[/C][/ROW]
[ROW][C]105[/C][C]581[/C][C]537.223052871812[/C][C]43.7769471281876[/C][/ROW]
[ROW][C]106[/C][C]564[/C][C]549.845035406737[/C][C]14.1549645932625[/C][/ROW]
[ROW][C]107[/C][C]558[/C][C]535.844532307834[/C][C]22.1554676921658[/C][/ROW]
[ROW][C]108[/C][C]575[/C][C]548.317691596303[/C][C]26.6823084036972[/C][/ROW]
[ROW][C]109[/C][C]580[/C][C]541.134665231254[/C][C]38.8653347687463[/C][/ROW]
[ROW][C]110[/C][C]575[/C][C]563.612810093593[/C][C]11.3871899064072[/C][/ROW]
[ROW][C]111[/C][C]563[/C][C]587.38885260781[/C][C]-24.3888526078104[/C][/ROW]
[ROW][C]112[/C][C]552[/C][C]575.063855826866[/C][C]-23.063855826866[/C][/ROW]
[ROW][C]113[/C][C]537[/C][C]582.945636833167[/C][C]-45.9456368331667[/C][/ROW]
[ROW][C]114[/C][C]545[/C][C]585.395743724064[/C][C]-40.3957437240642[/C][/ROW]
[ROW][C]115[/C][C]601[/C][C]584.983952331839[/C][C]16.0160476681605[/C][/ROW]
[ROW][C]116[/C][C]604[/C][C]594.192262626656[/C][C]9.80773737334443[/C][/ROW]
[ROW][C]117[/C][C]586[/C][C]575.911544687945[/C][C]10.0884553120547[/C][/ROW]
[ROW][C]118[/C][C]564[/C][C]585.139401449652[/C][C]-21.1394014496518[/C][/ROW]
[ROW][C]119[/C][C]549[/C][C]583.767114126596[/C][C]-34.7671141265963[/C][/ROW]
[ROW][C]120[/C][C]551[/C][C]588.300080114253[/C][C]-37.3000801142528[/C][/ROW]
[ROW][C]121[/C][C]556[/C][C]590.908416931674[/C][C]-34.9084169316741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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
1467492.142798941124-25.1427989411238
2460486.667388524839-26.6673885248385
3448497.109077329599-49.1090773295995
4443493.235380418981-50.2353804189809
5436498.275590432984-62.2755904329844
6431497.85705257943-66.8570525794301
7484492.108079086668-8.10807908666787
8510518.077264707842-8.07726470784169
9513480.08152544904132.9184745509586
10503491.0041487827211.9958512172804
11471490.337337000288-19.3373370002884
12471499.039755894592-28.0397558945916
13476508.869693610765-32.8696936107649
14475504.332431348538-29.3324313485385
15470520.853759552579-50.8537595525794
16461523.548269123897-62.548269123897
17455526.450761297842-71.4507612978419
18456516.742671629606-60.7426716296062
19517531.259097626723-14.2590976267233
20525531.471029312759-6.47102931275859
21523504.42180612418418.5781938758164
22519514.6628904390034.33710956099647
23509501.1261249748587.87387502514229
24512523.542850132786-11.5428501327857
25519527.807001778172-8.80700177817202
26517516.7891910171080.210808982892334
27510538.1189369106-28.1189369106002
28509529.37311120854-20.3731112085399
29501521.321367188082-20.3213671880825
30507530.091759409213-23.0917594092133
31569537.07197227750931.9280277224907
32580544.38105517711435.6189448228864
33578532.89781970017645.1021802998237
34565538.83969743601726.1603025639832
35547527.84906216996319.150937830037
36555557.609193519558-2.60919351955837
37562524.54153935642537.458460643575
38561530.11345225057930.8865477494208
39555548.7304976334616.26950236653891
40544535.0238716407548.9761283592458
41537534.4422618511352.55773814886483
42543546.854605345315-3.85460534531507
43594557.6300174615836.3699825384198
44611562.19802533923948.8019746607613
45613570.05042309142842.9495769085722
46611545.04711078625465.9528892137456
47594547.55355687632446.4464431236764
48595574.83715187147420.1628481285264
49591543.98622973955947.0137702604406
50589519.56754539958869.4324546004121
51584542.57294160394641.4270583960538
52573534.55368680113638.4463131988643
53567542.73579667430924.2642033256906
54569562.9844522548916.0155477451092
55621543.83552014569277.1644798543077
56629563.01954186207265.9804581379282
57628536.16496589493491.8350341050656
58612540.21883308214271.7811669178581
59595543.5831373140351.4168626859699
60597552.23005061106244.7699493889378
61593550.07214678883742.9278532111634
62590536.35463912731853.6453608726815
63580555.07223400125724.9277659987426
64574537.42938244305736.5706175569435
65573550.70388138382222.2961186161778
66573534.81371647783938.186283522161
67620541.12719892427478.872801075726
68626570.52725533376955.4727446662313
69620539.04692779731480.9530722026863
70588551.80782660292836.1921733970718
71566539.84445655558326.1555434444171
72557558.986408144685-1.98640814468488
73561546.24870335499314.7512966450073
74549533.39239176851515.607608231485
75532557.010926984738-25.0109269847383
76526546.901879252055-20.9018792520546
77511547.909440305358-36.9094403053576
78499539.711462255954-40.7114622559535
79555559.343026215643-4.34302621564281
80565568.072729924275-3.07272992427534
81542526.49103782781815.508962172182
82527535.449892994354-8.4498929943536
83510547.554409325342-37.5544093253418
84514563.265494681938-49.2654946819384
85517564.838649423763-47.8386494237631
86508556.220370041276-48.2203700412756
87493553.495606444936-60.4956064449356
88490558.507581384695-68.5075813846953
89469547.884278581455-78.8842785814546
90478565.914568352893-87.9145683528925
91528567.182553367384-39.1825533673841
92534590.635853080002-56.6358530800017
93518558.652905006987-40.6529050069867
94506566.125325049392-60.125325049392
95502545.094005106152-43.0940051061519
96516555.696801447101-39.6968014471014
97528546.233893510284-18.2338935102843
98533528.0506550203954.94934497960528
99536550.641325919279-14.6413259192787
100537537.062304322666-0.0623043226657736
101524544.02532257471-20.0253225747096
102536553.683652228147-17.6836522281472
103587548.60046851393438.3995314860659
104597562.499596383834.5004036162003
105581537.22305287181243.7769471281876
106564549.84503540673714.1549645932625
107558535.84453230783422.1554676921658
108575548.31769159630326.6823084036972
109580541.13466523125438.8653347687463
110575563.61281009359311.3871899064072
111563587.38885260781-24.3888526078104
112552575.063855826866-23.063855826866
113537582.945636833167-45.9456368331667
114545585.395743724064-40.3957437240642
115601584.98395233183916.0160476681605
116604594.1922626266569.80773737334443
117586575.91154468794510.0884553120547
118564585.139401449652-21.1394014496518
119549583.767114126596-34.7671141265963
120551588.300080114253-37.3000801142528
121556590.908416931674-34.9084169316741






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.01307202356226570.02614404712453140.986927976437734
80.08352186240026260.1670437248005250.916478137599737
90.03883471791953480.07766943583906960.961165282080465
100.01488813384271860.02977626768543720.985111866157281
110.02197226726444440.04394453452888880.978027732735556
120.02636308189444760.05272616378889520.973636918105552
130.02235179166565120.04470358333130240.977648208334349
140.01649779292560140.03299558585120280.983502207074399
150.0101637387114230.02032747742284590.989836261288577
160.006128683053124090.01225736610624820.993871316946876
170.004695804558662140.009391609117324270.995304195441338
180.004090773498998390.008181546997996790.995909226501002
190.01106500370353820.02213000740707630.988934996296462
200.007770775044453650.01554155008890730.992229224955546
210.008459076083589910.01691815216717980.99154092391641
220.004921038768795750.009842077537591510.995078961231204
230.003748967065804560.007497934131609110.996251032934195
240.002253826731949980.004507653463899970.99774617326805
250.006758727742194460.01351745548438890.993241272257806
260.01064756849988520.02129513699977040.989352431500115
270.01038422752081690.02076845504163380.989615772479183
280.008778905164019040.01755781032803810.991221094835981
290.008852624568253510.0177052491365070.991147375431746
300.007598604942380620.01519720988476120.992401395057619
310.01280706838874220.02561413677748440.987192931611258
320.01516996524877130.03033993049754250.984830034751229
330.02908660079251730.05817320158503470.970913399207483
340.02018851718201890.04037703436403780.979811482817981
350.0191895270567070.0383790541134140.980810472943293
360.01360511609537970.02721023219075950.98639488390462
370.02894938038849540.05789876077699080.971050619611505
380.05679637925160020.11359275850320.9432036207484
390.04231134396156730.08462268792313470.957688656038433
400.03670336593464110.07340673186928210.963296634065359
410.03405484278161140.06810968556322280.965945157218389
420.0293517955977260.05870359119545190.970648204402274
430.02982672695137040.05965345390274070.97017327304863
440.02768608141142160.05537216282284320.972313918588578
450.02610430248588350.0522086049717670.973895697514117
460.03605083380169710.07210166760339420.963949166198303
470.03061104113239770.06122208226479540.969388958867602
480.02867099461728110.05734198923456220.971329005382719
490.0305387386436340.0610774772872680.969461261356366
500.08223969482883840.1644793896576770.917760305171162
510.06753686241466470.1350737248293290.932463137585335
520.05614260100349290.1122852020069860.943857398996507
530.04294224959678860.08588449919357720.957057750403211
540.04067892478439590.08135784956879190.959321075215604
550.07736797180871450.1547359436174290.922632028191286
560.08311509274701980.166230185494040.91688490725298
570.1436884689035910.2873769378071810.856311531096409
580.1659241972704980.3318483945409950.834075802729502
590.1612339172151580.3224678344303150.838766082784842
600.1594660436952650.3189320873905310.840533956304735
610.1433421010618110.2866842021236220.856657898938189
620.1622990806008520.3245981612017040.837700919399148
630.1562513908072480.3125027816144970.843748609192752
640.1329641676482420.2659283352964840.867035832351758
650.1191255370766930.2382510741533870.880874462923307
660.108678993948360.217357987896720.89132100605164
670.1956081444473820.3912162888947630.804391855552618
680.2597380193391130.5194760386782260.740261980660887
690.4852386616778120.9704773233556230.514761338322188
700.5991662548794740.8016674902410520.400833745120526
710.6528859128110460.6942281743779080.347114087188954
720.6663252772524460.6673494454951090.333674722747554
730.6743008474281350.6513983051437310.325699152571865
740.6722681076278940.6554637847442110.327731892372106
750.7441601227937510.5116797544124980.255839877206249
760.7487164574502280.5025670850995430.251283542549772
770.7817360084175480.4365279831649040.218263991582452
780.8121643842944890.3756712314110210.18783561570551
790.8129235892445530.3741528215108940.187076410755447
800.8125147639842870.3749704720314260.187485236015713
810.8222987428212040.3554025143575920.177701257178796
820.8452868653202130.3094262693595750.154713134679787
830.8542881902293060.2914236195413870.145711809770694
840.8975470993362220.2049058013275570.102452900663778
850.9017795559649620.1964408880700770.0982204440350385
860.897000155078150.2059996898436990.10299984492185
870.9043722702432250.191255459513550.0956277297567751
880.9208617546989370.1582764906021260.079138245301063
890.9697075284891030.06058494302179460.0302924715108973
900.9923816235065680.01523675298686440.00761837649343219
910.9909847062888340.01803058742233190.00901529371116593
920.99490740863890.01018518272220050.00509259136110027
930.9933556858463360.01328862830732710.00664431415366354
940.9944951980062960.01100960398740780.00550480199370388
950.9970512202472330.005897559505533430.00294877975276671
960.9982632309010080.003473538197984340.00173676909899217
970.9982449930914720.003510013817056130.00175500690852806
980.9979040579911980.004191884017603440.00209594200880172
990.9971318343308250.005736331338349080.00286816566917454
1000.9963502556934880.007299488613023240.00364974430651162
1010.9990493593872240.00190128122555190.00095064061277595
1020.9993356075898710.0013287848202590.000664392410129499
1030.9987456094105010.002508781178998040.00125439058949902
1040.9977006314279350.004598737144129390.0022993685720647
1050.9962813393420830.007437321315833180.00371866065791659
1060.992200225449230.01559954910153940.00779977455076971
1070.9850557886263670.02988842274726590.014944211373633
1080.9704080692784280.0591838614431440.029591930721572
1090.9447772554645460.1104454890709070.0552227445354535
1100.9010401775088010.1979196449823990.0989598224911993
1110.8340480762396260.3319038475207480.165951923760374
1120.7489457028196390.5021085943607220.251054297180361
1130.9513893617147920.09722127657041650.0486106382852083
1140.8783176078274890.2433647843450210.121682392172511

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0130720235622657 & 0.0261440471245314 & 0.986927976437734 \tabularnewline
8 & 0.0835218624002626 & 0.167043724800525 & 0.916478137599737 \tabularnewline
9 & 0.0388347179195348 & 0.0776694358390696 & 0.961165282080465 \tabularnewline
10 & 0.0148881338427186 & 0.0297762676854372 & 0.985111866157281 \tabularnewline
11 & 0.0219722672644444 & 0.0439445345288888 & 0.978027732735556 \tabularnewline
12 & 0.0263630818944476 & 0.0527261637888952 & 0.973636918105552 \tabularnewline
13 & 0.0223517916656512 & 0.0447035833313024 & 0.977648208334349 \tabularnewline
14 & 0.0164977929256014 & 0.0329955858512028 & 0.983502207074399 \tabularnewline
15 & 0.010163738711423 & 0.0203274774228459 & 0.989836261288577 \tabularnewline
16 & 0.00612868305312409 & 0.0122573661062482 & 0.993871316946876 \tabularnewline
17 & 0.00469580455866214 & 0.00939160911732427 & 0.995304195441338 \tabularnewline
18 & 0.00409077349899839 & 0.00818154699799679 & 0.995909226501002 \tabularnewline
19 & 0.0110650037035382 & 0.0221300074070763 & 0.988934996296462 \tabularnewline
20 & 0.00777077504445365 & 0.0155415500889073 & 0.992229224955546 \tabularnewline
21 & 0.00845907608358991 & 0.0169181521671798 & 0.99154092391641 \tabularnewline
22 & 0.00492103876879575 & 0.00984207753759151 & 0.995078961231204 \tabularnewline
23 & 0.00374896706580456 & 0.00749793413160911 & 0.996251032934195 \tabularnewline
24 & 0.00225382673194998 & 0.00450765346389997 & 0.99774617326805 \tabularnewline
25 & 0.00675872774219446 & 0.0135174554843889 & 0.993241272257806 \tabularnewline
26 & 0.0106475684998852 & 0.0212951369997704 & 0.989352431500115 \tabularnewline
27 & 0.0103842275208169 & 0.0207684550416338 & 0.989615772479183 \tabularnewline
28 & 0.00877890516401904 & 0.0175578103280381 & 0.991221094835981 \tabularnewline
29 & 0.00885262456825351 & 0.017705249136507 & 0.991147375431746 \tabularnewline
30 & 0.00759860494238062 & 0.0151972098847612 & 0.992401395057619 \tabularnewline
31 & 0.0128070683887422 & 0.0256141367774844 & 0.987192931611258 \tabularnewline
32 & 0.0151699652487713 & 0.0303399304975425 & 0.984830034751229 \tabularnewline
33 & 0.0290866007925173 & 0.0581732015850347 & 0.970913399207483 \tabularnewline
34 & 0.0201885171820189 & 0.0403770343640378 & 0.979811482817981 \tabularnewline
35 & 0.019189527056707 & 0.038379054113414 & 0.980810472943293 \tabularnewline
36 & 0.0136051160953797 & 0.0272102321907595 & 0.98639488390462 \tabularnewline
37 & 0.0289493803884954 & 0.0578987607769908 & 0.971050619611505 \tabularnewline
38 & 0.0567963792516002 & 0.1135927585032 & 0.9432036207484 \tabularnewline
39 & 0.0423113439615673 & 0.0846226879231347 & 0.957688656038433 \tabularnewline
40 & 0.0367033659346411 & 0.0734067318692821 & 0.963296634065359 \tabularnewline
41 & 0.0340548427816114 & 0.0681096855632228 & 0.965945157218389 \tabularnewline
42 & 0.029351795597726 & 0.0587035911954519 & 0.970648204402274 \tabularnewline
43 & 0.0298267269513704 & 0.0596534539027407 & 0.97017327304863 \tabularnewline
44 & 0.0276860814114216 & 0.0553721628228432 & 0.972313918588578 \tabularnewline
45 & 0.0261043024858835 & 0.052208604971767 & 0.973895697514117 \tabularnewline
46 & 0.0360508338016971 & 0.0721016676033942 & 0.963949166198303 \tabularnewline
47 & 0.0306110411323977 & 0.0612220822647954 & 0.969388958867602 \tabularnewline
48 & 0.0286709946172811 & 0.0573419892345622 & 0.971329005382719 \tabularnewline
49 & 0.030538738643634 & 0.061077477287268 & 0.969461261356366 \tabularnewline
50 & 0.0822396948288384 & 0.164479389657677 & 0.917760305171162 \tabularnewline
51 & 0.0675368624146647 & 0.135073724829329 & 0.932463137585335 \tabularnewline
52 & 0.0561426010034929 & 0.112285202006986 & 0.943857398996507 \tabularnewline
53 & 0.0429422495967886 & 0.0858844991935772 & 0.957057750403211 \tabularnewline
54 & 0.0406789247843959 & 0.0813578495687919 & 0.959321075215604 \tabularnewline
55 & 0.0773679718087145 & 0.154735943617429 & 0.922632028191286 \tabularnewline
56 & 0.0831150927470198 & 0.16623018549404 & 0.91688490725298 \tabularnewline
57 & 0.143688468903591 & 0.287376937807181 & 0.856311531096409 \tabularnewline
58 & 0.165924197270498 & 0.331848394540995 & 0.834075802729502 \tabularnewline
59 & 0.161233917215158 & 0.322467834430315 & 0.838766082784842 \tabularnewline
60 & 0.159466043695265 & 0.318932087390531 & 0.840533956304735 \tabularnewline
61 & 0.143342101061811 & 0.286684202123622 & 0.856657898938189 \tabularnewline
62 & 0.162299080600852 & 0.324598161201704 & 0.837700919399148 \tabularnewline
63 & 0.156251390807248 & 0.312502781614497 & 0.843748609192752 \tabularnewline
64 & 0.132964167648242 & 0.265928335296484 & 0.867035832351758 \tabularnewline
65 & 0.119125537076693 & 0.238251074153387 & 0.880874462923307 \tabularnewline
66 & 0.10867899394836 & 0.21735798789672 & 0.89132100605164 \tabularnewline
67 & 0.195608144447382 & 0.391216288894763 & 0.804391855552618 \tabularnewline
68 & 0.259738019339113 & 0.519476038678226 & 0.740261980660887 \tabularnewline
69 & 0.485238661677812 & 0.970477323355623 & 0.514761338322188 \tabularnewline
70 & 0.599166254879474 & 0.801667490241052 & 0.400833745120526 \tabularnewline
71 & 0.652885912811046 & 0.694228174377908 & 0.347114087188954 \tabularnewline
72 & 0.666325277252446 & 0.667349445495109 & 0.333674722747554 \tabularnewline
73 & 0.674300847428135 & 0.651398305143731 & 0.325699152571865 \tabularnewline
74 & 0.672268107627894 & 0.655463784744211 & 0.327731892372106 \tabularnewline
75 & 0.744160122793751 & 0.511679754412498 & 0.255839877206249 \tabularnewline
76 & 0.748716457450228 & 0.502567085099543 & 0.251283542549772 \tabularnewline
77 & 0.781736008417548 & 0.436527983164904 & 0.218263991582452 \tabularnewline
78 & 0.812164384294489 & 0.375671231411021 & 0.18783561570551 \tabularnewline
79 & 0.812923589244553 & 0.374152821510894 & 0.187076410755447 \tabularnewline
80 & 0.812514763984287 & 0.374970472031426 & 0.187485236015713 \tabularnewline
81 & 0.822298742821204 & 0.355402514357592 & 0.177701257178796 \tabularnewline
82 & 0.845286865320213 & 0.309426269359575 & 0.154713134679787 \tabularnewline
83 & 0.854288190229306 & 0.291423619541387 & 0.145711809770694 \tabularnewline
84 & 0.897547099336222 & 0.204905801327557 & 0.102452900663778 \tabularnewline
85 & 0.901779555964962 & 0.196440888070077 & 0.0982204440350385 \tabularnewline
86 & 0.89700015507815 & 0.205999689843699 & 0.10299984492185 \tabularnewline
87 & 0.904372270243225 & 0.19125545951355 & 0.0956277297567751 \tabularnewline
88 & 0.920861754698937 & 0.158276490602126 & 0.079138245301063 \tabularnewline
89 & 0.969707528489103 & 0.0605849430217946 & 0.0302924715108973 \tabularnewline
90 & 0.992381623506568 & 0.0152367529868644 & 0.00761837649343219 \tabularnewline
91 & 0.990984706288834 & 0.0180305874223319 & 0.00901529371116593 \tabularnewline
92 & 0.9949074086389 & 0.0101851827222005 & 0.00509259136110027 \tabularnewline
93 & 0.993355685846336 & 0.0132886283073271 & 0.00664431415366354 \tabularnewline
94 & 0.994495198006296 & 0.0110096039874078 & 0.00550480199370388 \tabularnewline
95 & 0.997051220247233 & 0.00589755950553343 & 0.00294877975276671 \tabularnewline
96 & 0.998263230901008 & 0.00347353819798434 & 0.00173676909899217 \tabularnewline
97 & 0.998244993091472 & 0.00351001381705613 & 0.00175500690852806 \tabularnewline
98 & 0.997904057991198 & 0.00419188401760344 & 0.00209594200880172 \tabularnewline
99 & 0.997131834330825 & 0.00573633133834908 & 0.00286816566917454 \tabularnewline
100 & 0.996350255693488 & 0.00729948861302324 & 0.00364974430651162 \tabularnewline
101 & 0.999049359387224 & 0.0019012812255519 & 0.00095064061277595 \tabularnewline
102 & 0.999335607589871 & 0.001328784820259 & 0.000664392410129499 \tabularnewline
103 & 0.998745609410501 & 0.00250878117899804 & 0.00125439058949902 \tabularnewline
104 & 0.997700631427935 & 0.00459873714412939 & 0.0022993685720647 \tabularnewline
105 & 0.996281339342083 & 0.00743732131583318 & 0.00371866065791659 \tabularnewline
106 & 0.99220022544923 & 0.0155995491015394 & 0.00779977455076971 \tabularnewline
107 & 0.985055788626367 & 0.0298884227472659 & 0.014944211373633 \tabularnewline
108 & 0.970408069278428 & 0.059183861443144 & 0.029591930721572 \tabularnewline
109 & 0.944777255464546 & 0.110445489070907 & 0.0552227445354535 \tabularnewline
110 & 0.901040177508801 & 0.197919644982399 & 0.0989598224911993 \tabularnewline
111 & 0.834048076239626 & 0.331903847520748 & 0.165951923760374 \tabularnewline
112 & 0.748945702819639 & 0.502108594360722 & 0.251054297180361 \tabularnewline
113 & 0.951389361714792 & 0.0972212765704165 & 0.0486106382852083 \tabularnewline
114 & 0.878317607827489 & 0.243364784345021 & 0.121682392172511 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&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]7[/C][C]0.0130720235622657[/C][C]0.0261440471245314[/C][C]0.986927976437734[/C][/ROW]
[ROW][C]8[/C][C]0.0835218624002626[/C][C]0.167043724800525[/C][C]0.916478137599737[/C][/ROW]
[ROW][C]9[/C][C]0.0388347179195348[/C][C]0.0776694358390696[/C][C]0.961165282080465[/C][/ROW]
[ROW][C]10[/C][C]0.0148881338427186[/C][C]0.0297762676854372[/C][C]0.985111866157281[/C][/ROW]
[ROW][C]11[/C][C]0.0219722672644444[/C][C]0.0439445345288888[/C][C]0.978027732735556[/C][/ROW]
[ROW][C]12[/C][C]0.0263630818944476[/C][C]0.0527261637888952[/C][C]0.973636918105552[/C][/ROW]
[ROW][C]13[/C][C]0.0223517916656512[/C][C]0.0447035833313024[/C][C]0.977648208334349[/C][/ROW]
[ROW][C]14[/C][C]0.0164977929256014[/C][C]0.0329955858512028[/C][C]0.983502207074399[/C][/ROW]
[ROW][C]15[/C][C]0.010163738711423[/C][C]0.0203274774228459[/C][C]0.989836261288577[/C][/ROW]
[ROW][C]16[/C][C]0.00612868305312409[/C][C]0.0122573661062482[/C][C]0.993871316946876[/C][/ROW]
[ROW][C]17[/C][C]0.00469580455866214[/C][C]0.00939160911732427[/C][C]0.995304195441338[/C][/ROW]
[ROW][C]18[/C][C]0.00409077349899839[/C][C]0.00818154699799679[/C][C]0.995909226501002[/C][/ROW]
[ROW][C]19[/C][C]0.0110650037035382[/C][C]0.0221300074070763[/C][C]0.988934996296462[/C][/ROW]
[ROW][C]20[/C][C]0.00777077504445365[/C][C]0.0155415500889073[/C][C]0.992229224955546[/C][/ROW]
[ROW][C]21[/C][C]0.00845907608358991[/C][C]0.0169181521671798[/C][C]0.99154092391641[/C][/ROW]
[ROW][C]22[/C][C]0.00492103876879575[/C][C]0.00984207753759151[/C][C]0.995078961231204[/C][/ROW]
[ROW][C]23[/C][C]0.00374896706580456[/C][C]0.00749793413160911[/C][C]0.996251032934195[/C][/ROW]
[ROW][C]24[/C][C]0.00225382673194998[/C][C]0.00450765346389997[/C][C]0.99774617326805[/C][/ROW]
[ROW][C]25[/C][C]0.00675872774219446[/C][C]0.0135174554843889[/C][C]0.993241272257806[/C][/ROW]
[ROW][C]26[/C][C]0.0106475684998852[/C][C]0.0212951369997704[/C][C]0.989352431500115[/C][/ROW]
[ROW][C]27[/C][C]0.0103842275208169[/C][C]0.0207684550416338[/C][C]0.989615772479183[/C][/ROW]
[ROW][C]28[/C][C]0.00877890516401904[/C][C]0.0175578103280381[/C][C]0.991221094835981[/C][/ROW]
[ROW][C]29[/C][C]0.00885262456825351[/C][C]0.017705249136507[/C][C]0.991147375431746[/C][/ROW]
[ROW][C]30[/C][C]0.00759860494238062[/C][C]0.0151972098847612[/C][C]0.992401395057619[/C][/ROW]
[ROW][C]31[/C][C]0.0128070683887422[/C][C]0.0256141367774844[/C][C]0.987192931611258[/C][/ROW]
[ROW][C]32[/C][C]0.0151699652487713[/C][C]0.0303399304975425[/C][C]0.984830034751229[/C][/ROW]
[ROW][C]33[/C][C]0.0290866007925173[/C][C]0.0581732015850347[/C][C]0.970913399207483[/C][/ROW]
[ROW][C]34[/C][C]0.0201885171820189[/C][C]0.0403770343640378[/C][C]0.979811482817981[/C][/ROW]
[ROW][C]35[/C][C]0.019189527056707[/C][C]0.038379054113414[/C][C]0.980810472943293[/C][/ROW]
[ROW][C]36[/C][C]0.0136051160953797[/C][C]0.0272102321907595[/C][C]0.98639488390462[/C][/ROW]
[ROW][C]37[/C][C]0.0289493803884954[/C][C]0.0578987607769908[/C][C]0.971050619611505[/C][/ROW]
[ROW][C]38[/C][C]0.0567963792516002[/C][C]0.1135927585032[/C][C]0.9432036207484[/C][/ROW]
[ROW][C]39[/C][C]0.0423113439615673[/C][C]0.0846226879231347[/C][C]0.957688656038433[/C][/ROW]
[ROW][C]40[/C][C]0.0367033659346411[/C][C]0.0734067318692821[/C][C]0.963296634065359[/C][/ROW]
[ROW][C]41[/C][C]0.0340548427816114[/C][C]0.0681096855632228[/C][C]0.965945157218389[/C][/ROW]
[ROW][C]42[/C][C]0.029351795597726[/C][C]0.0587035911954519[/C][C]0.970648204402274[/C][/ROW]
[ROW][C]43[/C][C]0.0298267269513704[/C][C]0.0596534539027407[/C][C]0.97017327304863[/C][/ROW]
[ROW][C]44[/C][C]0.0276860814114216[/C][C]0.0553721628228432[/C][C]0.972313918588578[/C][/ROW]
[ROW][C]45[/C][C]0.0261043024858835[/C][C]0.052208604971767[/C][C]0.973895697514117[/C][/ROW]
[ROW][C]46[/C][C]0.0360508338016971[/C][C]0.0721016676033942[/C][C]0.963949166198303[/C][/ROW]
[ROW][C]47[/C][C]0.0306110411323977[/C][C]0.0612220822647954[/C][C]0.969388958867602[/C][/ROW]
[ROW][C]48[/C][C]0.0286709946172811[/C][C]0.0573419892345622[/C][C]0.971329005382719[/C][/ROW]
[ROW][C]49[/C][C]0.030538738643634[/C][C]0.061077477287268[/C][C]0.969461261356366[/C][/ROW]
[ROW][C]50[/C][C]0.0822396948288384[/C][C]0.164479389657677[/C][C]0.917760305171162[/C][/ROW]
[ROW][C]51[/C][C]0.0675368624146647[/C][C]0.135073724829329[/C][C]0.932463137585335[/C][/ROW]
[ROW][C]52[/C][C]0.0561426010034929[/C][C]0.112285202006986[/C][C]0.943857398996507[/C][/ROW]
[ROW][C]53[/C][C]0.0429422495967886[/C][C]0.0858844991935772[/C][C]0.957057750403211[/C][/ROW]
[ROW][C]54[/C][C]0.0406789247843959[/C][C]0.0813578495687919[/C][C]0.959321075215604[/C][/ROW]
[ROW][C]55[/C][C]0.0773679718087145[/C][C]0.154735943617429[/C][C]0.922632028191286[/C][/ROW]
[ROW][C]56[/C][C]0.0831150927470198[/C][C]0.16623018549404[/C][C]0.91688490725298[/C][/ROW]
[ROW][C]57[/C][C]0.143688468903591[/C][C]0.287376937807181[/C][C]0.856311531096409[/C][/ROW]
[ROW][C]58[/C][C]0.165924197270498[/C][C]0.331848394540995[/C][C]0.834075802729502[/C][/ROW]
[ROW][C]59[/C][C]0.161233917215158[/C][C]0.322467834430315[/C][C]0.838766082784842[/C][/ROW]
[ROW][C]60[/C][C]0.159466043695265[/C][C]0.318932087390531[/C][C]0.840533956304735[/C][/ROW]
[ROW][C]61[/C][C]0.143342101061811[/C][C]0.286684202123622[/C][C]0.856657898938189[/C][/ROW]
[ROW][C]62[/C][C]0.162299080600852[/C][C]0.324598161201704[/C][C]0.837700919399148[/C][/ROW]
[ROW][C]63[/C][C]0.156251390807248[/C][C]0.312502781614497[/C][C]0.843748609192752[/C][/ROW]
[ROW][C]64[/C][C]0.132964167648242[/C][C]0.265928335296484[/C][C]0.867035832351758[/C][/ROW]
[ROW][C]65[/C][C]0.119125537076693[/C][C]0.238251074153387[/C][C]0.880874462923307[/C][/ROW]
[ROW][C]66[/C][C]0.10867899394836[/C][C]0.21735798789672[/C][C]0.89132100605164[/C][/ROW]
[ROW][C]67[/C][C]0.195608144447382[/C][C]0.391216288894763[/C][C]0.804391855552618[/C][/ROW]
[ROW][C]68[/C][C]0.259738019339113[/C][C]0.519476038678226[/C][C]0.740261980660887[/C][/ROW]
[ROW][C]69[/C][C]0.485238661677812[/C][C]0.970477323355623[/C][C]0.514761338322188[/C][/ROW]
[ROW][C]70[/C][C]0.599166254879474[/C][C]0.801667490241052[/C][C]0.400833745120526[/C][/ROW]
[ROW][C]71[/C][C]0.652885912811046[/C][C]0.694228174377908[/C][C]0.347114087188954[/C][/ROW]
[ROW][C]72[/C][C]0.666325277252446[/C][C]0.667349445495109[/C][C]0.333674722747554[/C][/ROW]
[ROW][C]73[/C][C]0.674300847428135[/C][C]0.651398305143731[/C][C]0.325699152571865[/C][/ROW]
[ROW][C]74[/C][C]0.672268107627894[/C][C]0.655463784744211[/C][C]0.327731892372106[/C][/ROW]
[ROW][C]75[/C][C]0.744160122793751[/C][C]0.511679754412498[/C][C]0.255839877206249[/C][/ROW]
[ROW][C]76[/C][C]0.748716457450228[/C][C]0.502567085099543[/C][C]0.251283542549772[/C][/ROW]
[ROW][C]77[/C][C]0.781736008417548[/C][C]0.436527983164904[/C][C]0.218263991582452[/C][/ROW]
[ROW][C]78[/C][C]0.812164384294489[/C][C]0.375671231411021[/C][C]0.18783561570551[/C][/ROW]
[ROW][C]79[/C][C]0.812923589244553[/C][C]0.374152821510894[/C][C]0.187076410755447[/C][/ROW]
[ROW][C]80[/C][C]0.812514763984287[/C][C]0.374970472031426[/C][C]0.187485236015713[/C][/ROW]
[ROW][C]81[/C][C]0.822298742821204[/C][C]0.355402514357592[/C][C]0.177701257178796[/C][/ROW]
[ROW][C]82[/C][C]0.845286865320213[/C][C]0.309426269359575[/C][C]0.154713134679787[/C][/ROW]
[ROW][C]83[/C][C]0.854288190229306[/C][C]0.291423619541387[/C][C]0.145711809770694[/C][/ROW]
[ROW][C]84[/C][C]0.897547099336222[/C][C]0.204905801327557[/C][C]0.102452900663778[/C][/ROW]
[ROW][C]85[/C][C]0.901779555964962[/C][C]0.196440888070077[/C][C]0.0982204440350385[/C][/ROW]
[ROW][C]86[/C][C]0.89700015507815[/C][C]0.205999689843699[/C][C]0.10299984492185[/C][/ROW]
[ROW][C]87[/C][C]0.904372270243225[/C][C]0.19125545951355[/C][C]0.0956277297567751[/C][/ROW]
[ROW][C]88[/C][C]0.920861754698937[/C][C]0.158276490602126[/C][C]0.079138245301063[/C][/ROW]
[ROW][C]89[/C][C]0.969707528489103[/C][C]0.0605849430217946[/C][C]0.0302924715108973[/C][/ROW]
[ROW][C]90[/C][C]0.992381623506568[/C][C]0.0152367529868644[/C][C]0.00761837649343219[/C][/ROW]
[ROW][C]91[/C][C]0.990984706288834[/C][C]0.0180305874223319[/C][C]0.00901529371116593[/C][/ROW]
[ROW][C]92[/C][C]0.9949074086389[/C][C]0.0101851827222005[/C][C]0.00509259136110027[/C][/ROW]
[ROW][C]93[/C][C]0.993355685846336[/C][C]0.0132886283073271[/C][C]0.00664431415366354[/C][/ROW]
[ROW][C]94[/C][C]0.994495198006296[/C][C]0.0110096039874078[/C][C]0.00550480199370388[/C][/ROW]
[ROW][C]95[/C][C]0.997051220247233[/C][C]0.00589755950553343[/C][C]0.00294877975276671[/C][/ROW]
[ROW][C]96[/C][C]0.998263230901008[/C][C]0.00347353819798434[/C][C]0.00173676909899217[/C][/ROW]
[ROW][C]97[/C][C]0.998244993091472[/C][C]0.00351001381705613[/C][C]0.00175500690852806[/C][/ROW]
[ROW][C]98[/C][C]0.997904057991198[/C][C]0.00419188401760344[/C][C]0.00209594200880172[/C][/ROW]
[ROW][C]99[/C][C]0.997131834330825[/C][C]0.00573633133834908[/C][C]0.00286816566917454[/C][/ROW]
[ROW][C]100[/C][C]0.996350255693488[/C][C]0.00729948861302324[/C][C]0.00364974430651162[/C][/ROW]
[ROW][C]101[/C][C]0.999049359387224[/C][C]0.0019012812255519[/C][C]0.00095064061277595[/C][/ROW]
[ROW][C]102[/C][C]0.999335607589871[/C][C]0.001328784820259[/C][C]0.000664392410129499[/C][/ROW]
[ROW][C]103[/C][C]0.998745609410501[/C][C]0.00250878117899804[/C][C]0.00125439058949902[/C][/ROW]
[ROW][C]104[/C][C]0.997700631427935[/C][C]0.00459873714412939[/C][C]0.0022993685720647[/C][/ROW]
[ROW][C]105[/C][C]0.996281339342083[/C][C]0.00743732131583318[/C][C]0.00371866065791659[/C][/ROW]
[ROW][C]106[/C][C]0.99220022544923[/C][C]0.0155995491015394[/C][C]0.00779977455076971[/C][/ROW]
[ROW][C]107[/C][C]0.985055788626367[/C][C]0.0298884227472659[/C][C]0.014944211373633[/C][/ROW]
[ROW][C]108[/C][C]0.970408069278428[/C][C]0.059183861443144[/C][C]0.029591930721572[/C][/ROW]
[ROW][C]109[/C][C]0.944777255464546[/C][C]0.110445489070907[/C][C]0.0552227445354535[/C][/ROW]
[ROW][C]110[/C][C]0.901040177508801[/C][C]0.197919644982399[/C][C]0.0989598224911993[/C][/ROW]
[ROW][C]111[/C][C]0.834048076239626[/C][C]0.331903847520748[/C][C]0.165951923760374[/C][/ROW]
[ROW][C]112[/C][C]0.748945702819639[/C][C]0.502108594360722[/C][C]0.251054297180361[/C][/ROW]
[ROW][C]113[/C][C]0.951389361714792[/C][C]0.0972212765704165[/C][C]0.0486106382852083[/C][/ROW]
[ROW][C]114[/C][C]0.878317607827489[/C][C]0.243364784345021[/C][C]0.121682392172511[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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
70.01307202356226570.02614404712453140.986927976437734
80.08352186240026260.1670437248005250.916478137599737
90.03883471791953480.07766943583906960.961165282080465
100.01488813384271860.02977626768543720.985111866157281
110.02197226726444440.04394453452888880.978027732735556
120.02636308189444760.05272616378889520.973636918105552
130.02235179166565120.04470358333130240.977648208334349
140.01649779292560140.03299558585120280.983502207074399
150.0101637387114230.02032747742284590.989836261288577
160.006128683053124090.01225736610624820.993871316946876
170.004695804558662140.009391609117324270.995304195441338
180.004090773498998390.008181546997996790.995909226501002
190.01106500370353820.02213000740707630.988934996296462
200.007770775044453650.01554155008890730.992229224955546
210.008459076083589910.01691815216717980.99154092391641
220.004921038768795750.009842077537591510.995078961231204
230.003748967065804560.007497934131609110.996251032934195
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250.006758727742194460.01351745548438890.993241272257806
260.01064756849988520.02129513699977040.989352431500115
270.01038422752081690.02076845504163380.989615772479183
280.008778905164019040.01755781032803810.991221094835981
290.008852624568253510.0177052491365070.991147375431746
300.007598604942380620.01519720988476120.992401395057619
310.01280706838874220.02561413677748440.987192931611258
320.01516996524877130.03033993049754250.984830034751229
330.02908660079251730.05817320158503470.970913399207483
340.02018851718201890.04037703436403780.979811482817981
350.0191895270567070.0383790541134140.980810472943293
360.01360511609537970.02721023219075950.98639488390462
370.02894938038849540.05789876077699080.971050619611505
380.05679637925160020.11359275850320.9432036207484
390.04231134396156730.08462268792313470.957688656038433
400.03670336593464110.07340673186928210.963296634065359
410.03405484278161140.06810968556322280.965945157218389
420.0293517955977260.05870359119545190.970648204402274
430.02982672695137040.05965345390274070.97017327304863
440.02768608141142160.05537216282284320.972313918588578
450.02610430248588350.0522086049717670.973895697514117
460.03605083380169710.07210166760339420.963949166198303
470.03061104113239770.06122208226479540.969388958867602
480.02867099461728110.05734198923456220.971329005382719
490.0305387386436340.0610774772872680.969461261356366
500.08223969482883840.1644793896576770.917760305171162
510.06753686241466470.1350737248293290.932463137585335
520.05614260100349290.1122852020069860.943857398996507
530.04294224959678860.08588449919357720.957057750403211
540.04067892478439590.08135784956879190.959321075215604
550.07736797180871450.1547359436174290.922632028191286
560.08311509274701980.166230185494040.91688490725298
570.1436884689035910.2873769378071810.856311531096409
580.1659241972704980.3318483945409950.834075802729502
590.1612339172151580.3224678344303150.838766082784842
600.1594660436952650.3189320873905310.840533956304735
610.1433421010618110.2866842021236220.856657898938189
620.1622990806008520.3245981612017040.837700919399148
630.1562513908072480.3125027816144970.843748609192752
640.1329641676482420.2659283352964840.867035832351758
650.1191255370766930.2382510741533870.880874462923307
660.108678993948360.217357987896720.89132100605164
670.1956081444473820.3912162888947630.804391855552618
680.2597380193391130.5194760386782260.740261980660887
690.4852386616778120.9704773233556230.514761338322188
700.5991662548794740.8016674902410520.400833745120526
710.6528859128110460.6942281743779080.347114087188954
720.6663252772524460.6673494454951090.333674722747554
730.6743008474281350.6513983051437310.325699152571865
740.6722681076278940.6554637847442110.327731892372106
750.7441601227937510.5116797544124980.255839877206249
760.7487164574502280.5025670850995430.251283542549772
770.7817360084175480.4365279831649040.218263991582452
780.8121643842944890.3756712314110210.18783561570551
790.8129235892445530.3741528215108940.187076410755447
800.8125147639842870.3749704720314260.187485236015713
810.8222987428212040.3554025143575920.177701257178796
820.8452868653202130.3094262693595750.154713134679787
830.8542881902293060.2914236195413870.145711809770694
840.8975470993362220.2049058013275570.102452900663778
850.9017795559649620.1964408880700770.0982204440350385
860.897000155078150.2059996898436990.10299984492185
870.9043722702432250.191255459513550.0956277297567751
880.9208617546989370.1582764906021260.079138245301063
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900.9923816235065680.01523675298686440.00761837649343219
910.9909847062888340.01803058742233190.00901529371116593
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930.9933556858463360.01328862830732710.00664431415366354
940.9944951980062960.01100960398740780.00550480199370388
950.9970512202472330.005897559505533430.00294877975276671
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1000.9963502556934880.007299488613023240.00364974430651162
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1030.9987456094105010.002508781178998040.00125439058949902
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1060.992200225449230.01559954910153940.00779977455076971
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1080.9704080692784280.0591838614431440.029591930721572
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1100.9010401775088010.1979196449823990.0989598224911993
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1120.7489457028196390.5021085943607220.251054297180361
1130.9513893617147920.09722127657041650.0486106382852083
1140.8783176078274890.2433647843450210.121682392172511






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.148148148148148NOK
5% type I error level440.407407407407407NOK
10% type I error level640.592592592592593NOK

\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 & 16 & 0.148148148148148 & NOK \tabularnewline
5% type I error level & 44 & 0.407407407407407 & NOK \tabularnewline
10% type I error level & 64 & 0.592592592592593 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191314&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]16[/C][C]0.148148148148148[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.407407407407407[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]64[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191314&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191314&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 level160.148148148148148NOK
5% type I error level440.407407407407407NOK
10% type I error level640.592592592592593NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}