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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 computationTue, 17 Nov 2009 08:57:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/17/t1258473497cgrbse54l6svaa5.htm/, Retrieved Tue, 04 Nov 2025 07:14:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57372, Retrieved Tue, 04 Nov 2025 07:14:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact489

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [ws3] [2009-11-17 15:57:36] [94ba0ef70f5b330d175ff4daa1c9cd40] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
403,5	0	395,1	395,3
403,3	0	403,5	395,1
405,7	0	403,3	403,5
406,7	0	405,7	403,3
407,2	0	406,7	405,7
412,4	0	407,2	406,7
415,9	0	412,4	407,2
414,0	0	415,9	412,4
411,8	0	414,0	415,9
409,9	0	411,8	414,0
412,4	0	409,9	411,8
415,9	0	412,4	409,9
416,3	0	415,9	412,4
417,2	0	416,3	415,9
421,8	0	417,2	416,3
421,4	0	421,8	417,2
415,1	0	421,4	421,8
412,4	0	415,1	421,4
411,8	0	412,4	415,1
408,8	0	411,8	412,4
404,5	0	408,8	411,8
402,5	0	404,5	408,8
409,4	0	402,5	404,5
410,7	0	409,4	402,5
413,4	0	410,7	409,4
415,2	0	413,4	410,7
417,7	0	415,2	413,4
417,8	0	417,7	415,2
417,9	0	417,8	417,7
418,4	0	417,9	417,8
418,2	0	418,4	417,9
416,6	0	418,2	418,4
418,9	0	416,6	418,2
421,0	0	418,9	416,6
423,5	0	421,0	418,9
432,3	0	423,5	421,0
432,3	0	432,3	423,5
428,6	0	432,3	432,3
426,7	0	428,6	432,3
427,3	0	426,7	428,6
428,5	0	427,3	426,7
437,0	0	428,5	427,3
442,0	0	437,0	428,5
444,9	0	442,0	437,0
441,4	0	444,9	442,0
440,3	0	441,4	444,9
447,1	0	440,3	441,4
455,3	0	447,1	440,3
478,6	0	455,3	447,1
486,5	0	478,6	455,3
487,8	0	486,5	478,6
485,9	0	487,8	486,5
483,8	0	485,9	487,8
488,4	0	483,8	485,9
494,0	0	488,4	483,8
493,6	0	494,0	488,4
487,3	0	493,6	494,0
482,1	0	487,3	493,6
484,2	0	482,1	487,3
496,8	0	484,2	482,1
501,1	0	496,8	484,2
499,8	0	501,1	496,8
495,5	0	499,8	501,1
498,1	0	495,5	499,8
503,8	0	498,1	495,5
516,2	0	503,8	498,1
526,1	0	516,2	503,8
527,1	0	526,1	516,2
525,1	0	527,1	526,1
528,9	0	525,1	527,1
540,1	0	528,9	525,1
549,0	0	540,1	528,9
556,0	0	549,0	540,1
568,9	0	556,0	549,0
589,1	0	568,9	556,0
590,3	0	589,1	568,9
603,3	0	590,3	589,1
638,8	0	603,3	590,3
643,0	0	638,8	603,3
656,7	0	643,0	638,8
656,1	0	656,7	643,0
654,1	0	656,1	656,7
659,9	0	654,1	656,1
662,1	0	659,9	654,1
669,2	0	662,1	659,9
673,1	0	669,2	662,1
678,3	0	673,1	669,2
677,4	0	678,3	673,1
678,5	0	677,4	678,3
672,4	0	678,5	677,4
665,3	0	672,4	678,5
667,9	0	665,3	672,4
672,1	0	667,9	665,3
662,5	0	672,1	667,9
682,3	0	662,5	672,1
692,1	0	682,3	662,5
702,7	0	692,1	682,3
721,4	0	702,7	692,1
733,2	0	721,4	702,7
747,7	0	733,2	721,4
737,6	0	747,7	733,2
729,3	0	737,6	747,7
706,1	0	729,3	737,6
674,3	0	706,1	729,3
659,0	0	674,3	706,1
645,7	0	659,0	674,3
646,1	0	645,7	659,0
633,0	1	646,1	645,7
622,3	1	633,0	646,1
628,2	1	622,3	633,0
637,3	1	628,2	622,3
639,6	1	637,3	628,2
638,5	1	639,6	637,3
650,5	1	638,5	639,6
655,4	1	650,5	638,5

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 14.3225012216094 -3.18098980898201X[t] + 1.47915198643717Y1[t] -0.518658312352378Y2[t] + 0.133388825673378t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  14.3225012216094 -3.18098980898201X[t] +  1.47915198643717Y1[t] -0.518658312352378Y2[t] +  0.133388825673378t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  14.3225012216094 -3.18098980898201X[t] +  1.47915198643717Y1[t] -0.518658312352378Y2[t] +  0.133388825673378t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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
Y[t] = + 14.3225012216094 -3.18098980898201X[t] + 1.47915198643717Y1[t] -0.518658312352378Y2[t] + 0.133388825673378t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.32250122160946.279132.2810.0244760.012238
X-3.180989808982013.248082-0.97930.3295590.164779
Y11.479151986437170.08156718.134300
Y2-0.5186583123523780.08114-6.392100
t0.1333888256733780.0653372.04150.043590.021795

\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) & 14.3225012216094 & 6.27913 & 2.281 & 0.024476 & 0.012238 \tabularnewline
X & -3.18098980898201 & 3.248082 & -0.9793 & 0.329559 & 0.164779 \tabularnewline
Y1 & 1.47915198643717 & 0.081567 & 18.1343 & 0 & 0 \tabularnewline
Y2 & -0.518658312352378 & 0.08114 & -6.3921 & 0 & 0 \tabularnewline
t & 0.133388825673378 & 0.065337 & 2.0415 & 0.04359 & 0.021795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&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]14.3225012216094[/C][C]6.27913[/C][C]2.281[/C][C]0.024476[/C][C]0.012238[/C][/ROW]
[ROW][C]X[/C][C]-3.18098980898201[/C][C]3.248082[/C][C]-0.9793[/C][C]0.329559[/C][C]0.164779[/C][/ROW]
[ROW][C]Y1[/C][C]1.47915198643717[/C][C]0.081567[/C][C]18.1343[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.518658312352378[/C][C]0.08114[/C][C]-6.3921[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.133388825673378[/C][C]0.065337[/C][C]2.0415[/C][C]0.04359[/C][C]0.021795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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)14.32250122160946.279132.2810.0244760.012238
X-3.180989808982013.248082-0.97930.3295590.164779
Y11.479151986437170.08156718.134300
Y2-0.5186583123523780.08114-6.392100
t0.1333888256733780.0653372.04150.043590.021795






Multiple Linear Regression - Regression Statistics
Multiple R0.997939861436151
R-squared0.995883967043205
Adjusted R-squared0.995734293117503
F-TEST (value)6653.69042987728
F-TEST (DF numerator)4
F-TEST (DF denominator)110
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.27669185542326
Sum Squared Residuals5824.52687946615

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.997939861436151 \tabularnewline
R-squared & 0.995883967043205 \tabularnewline
Adjusted R-squared & 0.995734293117503 \tabularnewline
F-TEST (value) & 6653.69042987728 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 110 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.27669185542326 \tabularnewline
Sum Squared Residuals & 5824.52687946615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.997939861436151[/C][/ROW]
[ROW][C]R-squared[/C][C]0.995883967043205[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.995734293117503[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6653.69042987728[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]110[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.27669185542326[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5824.52687946615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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 R0.997939861436151
R-squared0.995883967043205
Adjusted R-squared0.995734293117503
F-TEST (value)6653.69042987728
F-TEST (DF numerator)4
F-TEST (DF denominator)110
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.27669185542326
Sum Squared Residuals5824.52687946615






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1403.5393.8432090157119.65679098428868
2403.3406.505206189928-3.2052061899278
3405.7401.9860347945543.71396520544628
4406.7405.7731200501470.926879949853032
5407.2406.1408809126121.05911908738821
6412.4406.4951874191515.9048125808486
7415.9414.0608374181221.83916258187818
8414416.674234972093-2.67423497209290
9411.8412.181930930302-0.381930930302359
10409.9410.046636179283-0.146636179283528
11412.4408.5106845179013.88931548209854
12415.9413.3274041031372.5725958968627
13416.3417.34117910046-1.04117910045979
14417.2416.2509246274750.949075372525225
15421.8417.5080869160014.29191308399942
16421.4423.978782398168-2.57878239816786
17415.1421.134682192445-6.03468219244532
18412.4412.1568768285060.243123171494364
19411.8411.5641026586190.235897341381475
20408.8412.210377735781-3.41037773578110
21404.5408.217505589554-3.71750558955441
22402.5403.546515810605-1.04651581060508
23409.4402.9518314065196.44816859348061
24410.7414.328685563314-3.62868556331393
25413.4412.8062296161240.59377038387574
26415.2416.25907299912-1.05907299911985
27417.7417.6545579570290.0454420429712915
28417.8420.552241786561-2.75224178656070
29417.9419.536900029997-1.63690002999694
30418.4419.766338223079-1.36633822307872
31418.2420.587437210735-2.38743721073546
32416.6420.165666482945-3.5656664829452
33418.9418.0361437927900.863856207210331
34421422.401435487032-1.40143548703223
35423.5424.448129365813-0.948129365813238
36432.3427.190215701645.10978429836047
37432.3439.043496227079-6.74349622707904
38428.6434.612691904051-6.01269190405147
39426.7429.273218379907-2.57321837990738
40427.3428.515254187054-1.21525418705385
41428.5430.521584998059-2.02158499805912
42437432.1187612200464.88123877995437
43442444.202551955612-2.20255195561208
44444.9447.323105058476-2.4231050584761
45441.4449.152743083055-7.75274308305534
46440.3442.604990850377-2.30499085037671
47447.1442.9266165842034.17338341579742
48455.3453.6887630612361.61123693876370
49478.6462.42432165169816.1756783483017
50486.5492.768953600068-6.26895360006813
51487.8492.502904440785-4.70290444078466
52485.9490.461790181243-4.56179018124263
53483.8487.110534426627-3.31053442662721
54488.4485.1231548742523.27684512574784
55494493.1498252934760.850174706523593
56493.6499.180637006377-5.580637006377
57487.3495.817878488302-8.51787848830223
58482.1486.840073124362-4.74007312436237
59484.2482.5494189883831.65058101161747
60496.8488.4860502098068.31394979019375
61501.1506.167571608648-5.06757160864796
62499.8506.126219240361-6.3262192403612
63495.5502.106479740551-6.60647974055103
64498.1496.5537708306031.54622916939735
65503.8502.7631855641281.03681443587206
66516.2509.9792291003776.22077089962308
67526.1525.4977501774630.602249822537321
68527.1533.843380595694-6.74338059569445
69525.1530.321204115516-5.22120411551647
70528.9526.9776306559631.92236934403682
71540.1533.7691136548026.33088634519757
72549548.4981031416330.501896858366855
73556555.9869715482510.0130284517494041
74568.9561.8583652990487.04163470095198
75589.1577.44220656329411.6577934367059
76590.3600.763773285653-10.4637732856527
77603.3592.19524658553211.1047534144675
78638.8610.93522126006627.8647787399338
79643656.835947543678-13.8359475436781
80656.7644.76940462387811.9305953761219
81656.1662.988810751861-6.8888107518608
82654.1655.129089506444-1.02908950644422
83659.9652.6153693466557.28463065334526
84662.1662.365156318368-0.265156318368306
85669.2662.744461302566.45553869744028
86673.1672.2387809447620.8612190552382
87678.3674.4583884998383.84161150016176
88677.4680.26060023681-2.86060023681051
89678.5676.3657290504582.1342709495419
90672.4678.59297754233-6.19297754232958
91665.3669.133015107149-3.83301510714862
92667.9661.9282405344685.97175946553243
93672.1669.589898542582.51010145742053
94662.5674.587214099173-12.0872140991728
95682.3658.3423789431723.9576210568306
96692.1692.742096898881-0.642096898881373
97702.7697.1017406070625.59825939293798
98721.4707.83128902791613.5687109720839
99733.2730.1270418890293.07295811097089
100747.7738.0155137136729.6844862863283
101737.6753.476438256926-15.8764382569259
102729.3731.149846490474-1.84984649047443
103706.1724.244722783478-18.1447227834782
104674.3694.366649516334-20.0666495163343
105659659.49587801988-0.495878019880785
106645.7653.491575785871-7.79157578587121
107646.1641.8877153709224.21228462907827
108633646.329930736475-13.3299307364746
109622.3626.87896521488-4.57896521488015
110628.2617.97985167749210.2201483225081
111637.3632.3898811653154.91011883468478
112639.6642.923469024688-3.32346902468752
113638.5641.73911677676-3.23911677675991
114650.5639.05252429894211.4474757010581
115655.4657.506261105449-2.10626110544889

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 403.5 & 393.843209015711 & 9.65679098428868 \tabularnewline
2 & 403.3 & 406.505206189928 & -3.2052061899278 \tabularnewline
3 & 405.7 & 401.986034794554 & 3.71396520544628 \tabularnewline
4 & 406.7 & 405.773120050147 & 0.926879949853032 \tabularnewline
5 & 407.2 & 406.140880912612 & 1.05911908738821 \tabularnewline
6 & 412.4 & 406.495187419151 & 5.9048125808486 \tabularnewline
7 & 415.9 & 414.060837418122 & 1.83916258187818 \tabularnewline
8 & 414 & 416.674234972093 & -2.67423497209290 \tabularnewline
9 & 411.8 & 412.181930930302 & -0.381930930302359 \tabularnewline
10 & 409.9 & 410.046636179283 & -0.146636179283528 \tabularnewline
11 & 412.4 & 408.510684517901 & 3.88931548209854 \tabularnewline
12 & 415.9 & 413.327404103137 & 2.5725958968627 \tabularnewline
13 & 416.3 & 417.34117910046 & -1.04117910045979 \tabularnewline
14 & 417.2 & 416.250924627475 & 0.949075372525225 \tabularnewline
15 & 421.8 & 417.508086916001 & 4.29191308399942 \tabularnewline
16 & 421.4 & 423.978782398168 & -2.57878239816786 \tabularnewline
17 & 415.1 & 421.134682192445 & -6.03468219244532 \tabularnewline
18 & 412.4 & 412.156876828506 & 0.243123171494364 \tabularnewline
19 & 411.8 & 411.564102658619 & 0.235897341381475 \tabularnewline
20 & 408.8 & 412.210377735781 & -3.41037773578110 \tabularnewline
21 & 404.5 & 408.217505589554 & -3.71750558955441 \tabularnewline
22 & 402.5 & 403.546515810605 & -1.04651581060508 \tabularnewline
23 & 409.4 & 402.951831406519 & 6.44816859348061 \tabularnewline
24 & 410.7 & 414.328685563314 & -3.62868556331393 \tabularnewline
25 & 413.4 & 412.806229616124 & 0.59377038387574 \tabularnewline
26 & 415.2 & 416.25907299912 & -1.05907299911985 \tabularnewline
27 & 417.7 & 417.654557957029 & 0.0454420429712915 \tabularnewline
28 & 417.8 & 420.552241786561 & -2.75224178656070 \tabularnewline
29 & 417.9 & 419.536900029997 & -1.63690002999694 \tabularnewline
30 & 418.4 & 419.766338223079 & -1.36633822307872 \tabularnewline
31 & 418.2 & 420.587437210735 & -2.38743721073546 \tabularnewline
32 & 416.6 & 420.165666482945 & -3.5656664829452 \tabularnewline
33 & 418.9 & 418.036143792790 & 0.863856207210331 \tabularnewline
34 & 421 & 422.401435487032 & -1.40143548703223 \tabularnewline
35 & 423.5 & 424.448129365813 & -0.948129365813238 \tabularnewline
36 & 432.3 & 427.19021570164 & 5.10978429836047 \tabularnewline
37 & 432.3 & 439.043496227079 & -6.74349622707904 \tabularnewline
38 & 428.6 & 434.612691904051 & -6.01269190405147 \tabularnewline
39 & 426.7 & 429.273218379907 & -2.57321837990738 \tabularnewline
40 & 427.3 & 428.515254187054 & -1.21525418705385 \tabularnewline
41 & 428.5 & 430.521584998059 & -2.02158499805912 \tabularnewline
42 & 437 & 432.118761220046 & 4.88123877995437 \tabularnewline
43 & 442 & 444.202551955612 & -2.20255195561208 \tabularnewline
44 & 444.9 & 447.323105058476 & -2.4231050584761 \tabularnewline
45 & 441.4 & 449.152743083055 & -7.75274308305534 \tabularnewline
46 & 440.3 & 442.604990850377 & -2.30499085037671 \tabularnewline
47 & 447.1 & 442.926616584203 & 4.17338341579742 \tabularnewline
48 & 455.3 & 453.688763061236 & 1.61123693876370 \tabularnewline
49 & 478.6 & 462.424321651698 & 16.1756783483017 \tabularnewline
50 & 486.5 & 492.768953600068 & -6.26895360006813 \tabularnewline
51 & 487.8 & 492.502904440785 & -4.70290444078466 \tabularnewline
52 & 485.9 & 490.461790181243 & -4.56179018124263 \tabularnewline
53 & 483.8 & 487.110534426627 & -3.31053442662721 \tabularnewline
54 & 488.4 & 485.123154874252 & 3.27684512574784 \tabularnewline
55 & 494 & 493.149825293476 & 0.850174706523593 \tabularnewline
56 & 493.6 & 499.180637006377 & -5.580637006377 \tabularnewline
57 & 487.3 & 495.817878488302 & -8.51787848830223 \tabularnewline
58 & 482.1 & 486.840073124362 & -4.74007312436237 \tabularnewline
59 & 484.2 & 482.549418988383 & 1.65058101161747 \tabularnewline
60 & 496.8 & 488.486050209806 & 8.31394979019375 \tabularnewline
61 & 501.1 & 506.167571608648 & -5.06757160864796 \tabularnewline
62 & 499.8 & 506.126219240361 & -6.3262192403612 \tabularnewline
63 & 495.5 & 502.106479740551 & -6.60647974055103 \tabularnewline
64 & 498.1 & 496.553770830603 & 1.54622916939735 \tabularnewline
65 & 503.8 & 502.763185564128 & 1.03681443587206 \tabularnewline
66 & 516.2 & 509.979229100377 & 6.22077089962308 \tabularnewline
67 & 526.1 & 525.497750177463 & 0.602249822537321 \tabularnewline
68 & 527.1 & 533.843380595694 & -6.74338059569445 \tabularnewline
69 & 525.1 & 530.321204115516 & -5.22120411551647 \tabularnewline
70 & 528.9 & 526.977630655963 & 1.92236934403682 \tabularnewline
71 & 540.1 & 533.769113654802 & 6.33088634519757 \tabularnewline
72 & 549 & 548.498103141633 & 0.501896858366855 \tabularnewline
73 & 556 & 555.986971548251 & 0.0130284517494041 \tabularnewline
74 & 568.9 & 561.858365299048 & 7.04163470095198 \tabularnewline
75 & 589.1 & 577.442206563294 & 11.6577934367059 \tabularnewline
76 & 590.3 & 600.763773285653 & -10.4637732856527 \tabularnewline
77 & 603.3 & 592.195246585532 & 11.1047534144675 \tabularnewline
78 & 638.8 & 610.935221260066 & 27.8647787399338 \tabularnewline
79 & 643 & 656.835947543678 & -13.8359475436781 \tabularnewline
80 & 656.7 & 644.769404623878 & 11.9305953761219 \tabularnewline
81 & 656.1 & 662.988810751861 & -6.8888107518608 \tabularnewline
82 & 654.1 & 655.129089506444 & -1.02908950644422 \tabularnewline
83 & 659.9 & 652.615369346655 & 7.28463065334526 \tabularnewline
84 & 662.1 & 662.365156318368 & -0.265156318368306 \tabularnewline
85 & 669.2 & 662.74446130256 & 6.45553869744028 \tabularnewline
86 & 673.1 & 672.238780944762 & 0.8612190552382 \tabularnewline
87 & 678.3 & 674.458388499838 & 3.84161150016176 \tabularnewline
88 & 677.4 & 680.26060023681 & -2.86060023681051 \tabularnewline
89 & 678.5 & 676.365729050458 & 2.1342709495419 \tabularnewline
90 & 672.4 & 678.59297754233 & -6.19297754232958 \tabularnewline
91 & 665.3 & 669.133015107149 & -3.83301510714862 \tabularnewline
92 & 667.9 & 661.928240534468 & 5.97175946553243 \tabularnewline
93 & 672.1 & 669.58989854258 & 2.51010145742053 \tabularnewline
94 & 662.5 & 674.587214099173 & -12.0872140991728 \tabularnewline
95 & 682.3 & 658.34237894317 & 23.9576210568306 \tabularnewline
96 & 692.1 & 692.742096898881 & -0.642096898881373 \tabularnewline
97 & 702.7 & 697.101740607062 & 5.59825939293798 \tabularnewline
98 & 721.4 & 707.831289027916 & 13.5687109720839 \tabularnewline
99 & 733.2 & 730.127041889029 & 3.07295811097089 \tabularnewline
100 & 747.7 & 738.015513713672 & 9.6844862863283 \tabularnewline
101 & 737.6 & 753.476438256926 & -15.8764382569259 \tabularnewline
102 & 729.3 & 731.149846490474 & -1.84984649047443 \tabularnewline
103 & 706.1 & 724.244722783478 & -18.1447227834782 \tabularnewline
104 & 674.3 & 694.366649516334 & -20.0666495163343 \tabularnewline
105 & 659 & 659.49587801988 & -0.495878019880785 \tabularnewline
106 & 645.7 & 653.491575785871 & -7.79157578587121 \tabularnewline
107 & 646.1 & 641.887715370922 & 4.21228462907827 \tabularnewline
108 & 633 & 646.329930736475 & -13.3299307364746 \tabularnewline
109 & 622.3 & 626.87896521488 & -4.57896521488015 \tabularnewline
110 & 628.2 & 617.979851677492 & 10.2201483225081 \tabularnewline
111 & 637.3 & 632.389881165315 & 4.91011883468478 \tabularnewline
112 & 639.6 & 642.923469024688 & -3.32346902468752 \tabularnewline
113 & 638.5 & 641.73911677676 & -3.23911677675991 \tabularnewline
114 & 650.5 & 639.052524298942 & 11.4474757010581 \tabularnewline
115 & 655.4 & 657.506261105449 & -2.10626110544889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&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]403.5[/C][C]393.843209015711[/C][C]9.65679098428868[/C][/ROW]
[ROW][C]2[/C][C]403.3[/C][C]406.505206189928[/C][C]-3.2052061899278[/C][/ROW]
[ROW][C]3[/C][C]405.7[/C][C]401.986034794554[/C][C]3.71396520544628[/C][/ROW]
[ROW][C]4[/C][C]406.7[/C][C]405.773120050147[/C][C]0.926879949853032[/C][/ROW]
[ROW][C]5[/C][C]407.2[/C][C]406.140880912612[/C][C]1.05911908738821[/C][/ROW]
[ROW][C]6[/C][C]412.4[/C][C]406.495187419151[/C][C]5.9048125808486[/C][/ROW]
[ROW][C]7[/C][C]415.9[/C][C]414.060837418122[/C][C]1.83916258187818[/C][/ROW]
[ROW][C]8[/C][C]414[/C][C]416.674234972093[/C][C]-2.67423497209290[/C][/ROW]
[ROW][C]9[/C][C]411.8[/C][C]412.181930930302[/C][C]-0.381930930302359[/C][/ROW]
[ROW][C]10[/C][C]409.9[/C][C]410.046636179283[/C][C]-0.146636179283528[/C][/ROW]
[ROW][C]11[/C][C]412.4[/C][C]408.510684517901[/C][C]3.88931548209854[/C][/ROW]
[ROW][C]12[/C][C]415.9[/C][C]413.327404103137[/C][C]2.5725958968627[/C][/ROW]
[ROW][C]13[/C][C]416.3[/C][C]417.34117910046[/C][C]-1.04117910045979[/C][/ROW]
[ROW][C]14[/C][C]417.2[/C][C]416.250924627475[/C][C]0.949075372525225[/C][/ROW]
[ROW][C]15[/C][C]421.8[/C][C]417.508086916001[/C][C]4.29191308399942[/C][/ROW]
[ROW][C]16[/C][C]421.4[/C][C]423.978782398168[/C][C]-2.57878239816786[/C][/ROW]
[ROW][C]17[/C][C]415.1[/C][C]421.134682192445[/C][C]-6.03468219244532[/C][/ROW]
[ROW][C]18[/C][C]412.4[/C][C]412.156876828506[/C][C]0.243123171494364[/C][/ROW]
[ROW][C]19[/C][C]411.8[/C][C]411.564102658619[/C][C]0.235897341381475[/C][/ROW]
[ROW][C]20[/C][C]408.8[/C][C]412.210377735781[/C][C]-3.41037773578110[/C][/ROW]
[ROW][C]21[/C][C]404.5[/C][C]408.217505589554[/C][C]-3.71750558955441[/C][/ROW]
[ROW][C]22[/C][C]402.5[/C][C]403.546515810605[/C][C]-1.04651581060508[/C][/ROW]
[ROW][C]23[/C][C]409.4[/C][C]402.951831406519[/C][C]6.44816859348061[/C][/ROW]
[ROW][C]24[/C][C]410.7[/C][C]414.328685563314[/C][C]-3.62868556331393[/C][/ROW]
[ROW][C]25[/C][C]413.4[/C][C]412.806229616124[/C][C]0.59377038387574[/C][/ROW]
[ROW][C]26[/C][C]415.2[/C][C]416.25907299912[/C][C]-1.05907299911985[/C][/ROW]
[ROW][C]27[/C][C]417.7[/C][C]417.654557957029[/C][C]0.0454420429712915[/C][/ROW]
[ROW][C]28[/C][C]417.8[/C][C]420.552241786561[/C][C]-2.75224178656070[/C][/ROW]
[ROW][C]29[/C][C]417.9[/C][C]419.536900029997[/C][C]-1.63690002999694[/C][/ROW]
[ROW][C]30[/C][C]418.4[/C][C]419.766338223079[/C][C]-1.36633822307872[/C][/ROW]
[ROW][C]31[/C][C]418.2[/C][C]420.587437210735[/C][C]-2.38743721073546[/C][/ROW]
[ROW][C]32[/C][C]416.6[/C][C]420.165666482945[/C][C]-3.5656664829452[/C][/ROW]
[ROW][C]33[/C][C]418.9[/C][C]418.036143792790[/C][C]0.863856207210331[/C][/ROW]
[ROW][C]34[/C][C]421[/C][C]422.401435487032[/C][C]-1.40143548703223[/C][/ROW]
[ROW][C]35[/C][C]423.5[/C][C]424.448129365813[/C][C]-0.948129365813238[/C][/ROW]
[ROW][C]36[/C][C]432.3[/C][C]427.19021570164[/C][C]5.10978429836047[/C][/ROW]
[ROW][C]37[/C][C]432.3[/C][C]439.043496227079[/C][C]-6.74349622707904[/C][/ROW]
[ROW][C]38[/C][C]428.6[/C][C]434.612691904051[/C][C]-6.01269190405147[/C][/ROW]
[ROW][C]39[/C][C]426.7[/C][C]429.273218379907[/C][C]-2.57321837990738[/C][/ROW]
[ROW][C]40[/C][C]427.3[/C][C]428.515254187054[/C][C]-1.21525418705385[/C][/ROW]
[ROW][C]41[/C][C]428.5[/C][C]430.521584998059[/C][C]-2.02158499805912[/C][/ROW]
[ROW][C]42[/C][C]437[/C][C]432.118761220046[/C][C]4.88123877995437[/C][/ROW]
[ROW][C]43[/C][C]442[/C][C]444.202551955612[/C][C]-2.20255195561208[/C][/ROW]
[ROW][C]44[/C][C]444.9[/C][C]447.323105058476[/C][C]-2.4231050584761[/C][/ROW]
[ROW][C]45[/C][C]441.4[/C][C]449.152743083055[/C][C]-7.75274308305534[/C][/ROW]
[ROW][C]46[/C][C]440.3[/C][C]442.604990850377[/C][C]-2.30499085037671[/C][/ROW]
[ROW][C]47[/C][C]447.1[/C][C]442.926616584203[/C][C]4.17338341579742[/C][/ROW]
[ROW][C]48[/C][C]455.3[/C][C]453.688763061236[/C][C]1.61123693876370[/C][/ROW]
[ROW][C]49[/C][C]478.6[/C][C]462.424321651698[/C][C]16.1756783483017[/C][/ROW]
[ROW][C]50[/C][C]486.5[/C][C]492.768953600068[/C][C]-6.26895360006813[/C][/ROW]
[ROW][C]51[/C][C]487.8[/C][C]492.502904440785[/C][C]-4.70290444078466[/C][/ROW]
[ROW][C]52[/C][C]485.9[/C][C]490.461790181243[/C][C]-4.56179018124263[/C][/ROW]
[ROW][C]53[/C][C]483.8[/C][C]487.110534426627[/C][C]-3.31053442662721[/C][/ROW]
[ROW][C]54[/C][C]488.4[/C][C]485.123154874252[/C][C]3.27684512574784[/C][/ROW]
[ROW][C]55[/C][C]494[/C][C]493.149825293476[/C][C]0.850174706523593[/C][/ROW]
[ROW][C]56[/C][C]493.6[/C][C]499.180637006377[/C][C]-5.580637006377[/C][/ROW]
[ROW][C]57[/C][C]487.3[/C][C]495.817878488302[/C][C]-8.51787848830223[/C][/ROW]
[ROW][C]58[/C][C]482.1[/C][C]486.840073124362[/C][C]-4.74007312436237[/C][/ROW]
[ROW][C]59[/C][C]484.2[/C][C]482.549418988383[/C][C]1.65058101161747[/C][/ROW]
[ROW][C]60[/C][C]496.8[/C][C]488.486050209806[/C][C]8.31394979019375[/C][/ROW]
[ROW][C]61[/C][C]501.1[/C][C]506.167571608648[/C][C]-5.06757160864796[/C][/ROW]
[ROW][C]62[/C][C]499.8[/C][C]506.126219240361[/C][C]-6.3262192403612[/C][/ROW]
[ROW][C]63[/C][C]495.5[/C][C]502.106479740551[/C][C]-6.60647974055103[/C][/ROW]
[ROW][C]64[/C][C]498.1[/C][C]496.553770830603[/C][C]1.54622916939735[/C][/ROW]
[ROW][C]65[/C][C]503.8[/C][C]502.763185564128[/C][C]1.03681443587206[/C][/ROW]
[ROW][C]66[/C][C]516.2[/C][C]509.979229100377[/C][C]6.22077089962308[/C][/ROW]
[ROW][C]67[/C][C]526.1[/C][C]525.497750177463[/C][C]0.602249822537321[/C][/ROW]
[ROW][C]68[/C][C]527.1[/C][C]533.843380595694[/C][C]-6.74338059569445[/C][/ROW]
[ROW][C]69[/C][C]525.1[/C][C]530.321204115516[/C][C]-5.22120411551647[/C][/ROW]
[ROW][C]70[/C][C]528.9[/C][C]526.977630655963[/C][C]1.92236934403682[/C][/ROW]
[ROW][C]71[/C][C]540.1[/C][C]533.769113654802[/C][C]6.33088634519757[/C][/ROW]
[ROW][C]72[/C][C]549[/C][C]548.498103141633[/C][C]0.501896858366855[/C][/ROW]
[ROW][C]73[/C][C]556[/C][C]555.986971548251[/C][C]0.0130284517494041[/C][/ROW]
[ROW][C]74[/C][C]568.9[/C][C]561.858365299048[/C][C]7.04163470095198[/C][/ROW]
[ROW][C]75[/C][C]589.1[/C][C]577.442206563294[/C][C]11.6577934367059[/C][/ROW]
[ROW][C]76[/C][C]590.3[/C][C]600.763773285653[/C][C]-10.4637732856527[/C][/ROW]
[ROW][C]77[/C][C]603.3[/C][C]592.195246585532[/C][C]11.1047534144675[/C][/ROW]
[ROW][C]78[/C][C]638.8[/C][C]610.935221260066[/C][C]27.8647787399338[/C][/ROW]
[ROW][C]79[/C][C]643[/C][C]656.835947543678[/C][C]-13.8359475436781[/C][/ROW]
[ROW][C]80[/C][C]656.7[/C][C]644.769404623878[/C][C]11.9305953761219[/C][/ROW]
[ROW][C]81[/C][C]656.1[/C][C]662.988810751861[/C][C]-6.8888107518608[/C][/ROW]
[ROW][C]82[/C][C]654.1[/C][C]655.129089506444[/C][C]-1.02908950644422[/C][/ROW]
[ROW][C]83[/C][C]659.9[/C][C]652.615369346655[/C][C]7.28463065334526[/C][/ROW]
[ROW][C]84[/C][C]662.1[/C][C]662.365156318368[/C][C]-0.265156318368306[/C][/ROW]
[ROW][C]85[/C][C]669.2[/C][C]662.74446130256[/C][C]6.45553869744028[/C][/ROW]
[ROW][C]86[/C][C]673.1[/C][C]672.238780944762[/C][C]0.8612190552382[/C][/ROW]
[ROW][C]87[/C][C]678.3[/C][C]674.458388499838[/C][C]3.84161150016176[/C][/ROW]
[ROW][C]88[/C][C]677.4[/C][C]680.26060023681[/C][C]-2.86060023681051[/C][/ROW]
[ROW][C]89[/C][C]678.5[/C][C]676.365729050458[/C][C]2.1342709495419[/C][/ROW]
[ROW][C]90[/C][C]672.4[/C][C]678.59297754233[/C][C]-6.19297754232958[/C][/ROW]
[ROW][C]91[/C][C]665.3[/C][C]669.133015107149[/C][C]-3.83301510714862[/C][/ROW]
[ROW][C]92[/C][C]667.9[/C][C]661.928240534468[/C][C]5.97175946553243[/C][/ROW]
[ROW][C]93[/C][C]672.1[/C][C]669.58989854258[/C][C]2.51010145742053[/C][/ROW]
[ROW][C]94[/C][C]662.5[/C][C]674.587214099173[/C][C]-12.0872140991728[/C][/ROW]
[ROW][C]95[/C][C]682.3[/C][C]658.34237894317[/C][C]23.9576210568306[/C][/ROW]
[ROW][C]96[/C][C]692.1[/C][C]692.742096898881[/C][C]-0.642096898881373[/C][/ROW]
[ROW][C]97[/C][C]702.7[/C][C]697.101740607062[/C][C]5.59825939293798[/C][/ROW]
[ROW][C]98[/C][C]721.4[/C][C]707.831289027916[/C][C]13.5687109720839[/C][/ROW]
[ROW][C]99[/C][C]733.2[/C][C]730.127041889029[/C][C]3.07295811097089[/C][/ROW]
[ROW][C]100[/C][C]747.7[/C][C]738.015513713672[/C][C]9.6844862863283[/C][/ROW]
[ROW][C]101[/C][C]737.6[/C][C]753.476438256926[/C][C]-15.8764382569259[/C][/ROW]
[ROW][C]102[/C][C]729.3[/C][C]731.149846490474[/C][C]-1.84984649047443[/C][/ROW]
[ROW][C]103[/C][C]706.1[/C][C]724.244722783478[/C][C]-18.1447227834782[/C][/ROW]
[ROW][C]104[/C][C]674.3[/C][C]694.366649516334[/C][C]-20.0666495163343[/C][/ROW]
[ROW][C]105[/C][C]659[/C][C]659.49587801988[/C][C]-0.495878019880785[/C][/ROW]
[ROW][C]106[/C][C]645.7[/C][C]653.491575785871[/C][C]-7.79157578587121[/C][/ROW]
[ROW][C]107[/C][C]646.1[/C][C]641.887715370922[/C][C]4.21228462907827[/C][/ROW]
[ROW][C]108[/C][C]633[/C][C]646.329930736475[/C][C]-13.3299307364746[/C][/ROW]
[ROW][C]109[/C][C]622.3[/C][C]626.87896521488[/C][C]-4.57896521488015[/C][/ROW]
[ROW][C]110[/C][C]628.2[/C][C]617.979851677492[/C][C]10.2201483225081[/C][/ROW]
[ROW][C]111[/C][C]637.3[/C][C]632.389881165315[/C][C]4.91011883468478[/C][/ROW]
[ROW][C]112[/C][C]639.6[/C][C]642.923469024688[/C][C]-3.32346902468752[/C][/ROW]
[ROW][C]113[/C][C]638.5[/C][C]641.73911677676[/C][C]-3.23911677675991[/C][/ROW]
[ROW][C]114[/C][C]650.5[/C][C]639.052524298942[/C][C]11.4474757010581[/C][/ROW]
[ROW][C]115[/C][C]655.4[/C][C]657.506261105449[/C][C]-2.10626110544889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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
1403.5393.8432090157119.65679098428868
2403.3406.505206189928-3.2052061899278
3405.7401.9860347945543.71396520544628
4406.7405.7731200501470.926879949853032
5407.2406.1408809126121.05911908738821
6412.4406.4951874191515.9048125808486
7415.9414.0608374181221.83916258187818
8414416.674234972093-2.67423497209290
9411.8412.181930930302-0.381930930302359
10409.9410.046636179283-0.146636179283528
11412.4408.5106845179013.88931548209854
12415.9413.3274041031372.5725958968627
13416.3417.34117910046-1.04117910045979
14417.2416.2509246274750.949075372525225
15421.8417.5080869160014.29191308399942
16421.4423.978782398168-2.57878239816786
17415.1421.134682192445-6.03468219244532
18412.4412.1568768285060.243123171494364
19411.8411.5641026586190.235897341381475
20408.8412.210377735781-3.41037773578110
21404.5408.217505589554-3.71750558955441
22402.5403.546515810605-1.04651581060508
23409.4402.9518314065196.44816859348061
24410.7414.328685563314-3.62868556331393
25413.4412.8062296161240.59377038387574
26415.2416.25907299912-1.05907299911985
27417.7417.6545579570290.0454420429712915
28417.8420.552241786561-2.75224178656070
29417.9419.536900029997-1.63690002999694
30418.4419.766338223079-1.36633822307872
31418.2420.587437210735-2.38743721073546
32416.6420.165666482945-3.5656664829452
33418.9418.0361437927900.863856207210331
34421422.401435487032-1.40143548703223
35423.5424.448129365813-0.948129365813238
36432.3427.190215701645.10978429836047
37432.3439.043496227079-6.74349622707904
38428.6434.612691904051-6.01269190405147
39426.7429.273218379907-2.57321837990738
40427.3428.515254187054-1.21525418705385
41428.5430.521584998059-2.02158499805912
42437432.1187612200464.88123877995437
43442444.202551955612-2.20255195561208
44444.9447.323105058476-2.4231050584761
45441.4449.152743083055-7.75274308305534
46440.3442.604990850377-2.30499085037671
47447.1442.9266165842034.17338341579742
48455.3453.6887630612361.61123693876370
49478.6462.42432165169816.1756783483017
50486.5492.768953600068-6.26895360006813
51487.8492.502904440785-4.70290444078466
52485.9490.461790181243-4.56179018124263
53483.8487.110534426627-3.31053442662721
54488.4485.1231548742523.27684512574784
55494493.1498252934760.850174706523593
56493.6499.180637006377-5.580637006377
57487.3495.817878488302-8.51787848830223
58482.1486.840073124362-4.74007312436237
59484.2482.5494189883831.65058101161747
60496.8488.4860502098068.31394979019375
61501.1506.167571608648-5.06757160864796
62499.8506.126219240361-6.3262192403612
63495.5502.106479740551-6.60647974055103
64498.1496.5537708306031.54622916939735
65503.8502.7631855641281.03681443587206
66516.2509.9792291003776.22077089962308
67526.1525.4977501774630.602249822537321
68527.1533.843380595694-6.74338059569445
69525.1530.321204115516-5.22120411551647
70528.9526.9776306559631.92236934403682
71540.1533.7691136548026.33088634519757
72549548.4981031416330.501896858366855
73556555.9869715482510.0130284517494041
74568.9561.8583652990487.04163470095198
75589.1577.44220656329411.6577934367059
76590.3600.763773285653-10.4637732856527
77603.3592.19524658553211.1047534144675
78638.8610.93522126006627.8647787399338
79643656.835947543678-13.8359475436781
80656.7644.76940462387811.9305953761219
81656.1662.988810751861-6.8888107518608
82654.1655.129089506444-1.02908950644422
83659.9652.6153693466557.28463065334526
84662.1662.365156318368-0.265156318368306
85669.2662.744461302566.45553869744028
86673.1672.2387809447620.8612190552382
87678.3674.4583884998383.84161150016176
88677.4680.26060023681-2.86060023681051
89678.5676.3657290504582.1342709495419
90672.4678.59297754233-6.19297754232958
91665.3669.133015107149-3.83301510714862
92667.9661.9282405344685.97175946553243
93672.1669.589898542582.51010145742053
94662.5674.587214099173-12.0872140991728
95682.3658.3423789431723.9576210568306
96692.1692.742096898881-0.642096898881373
97702.7697.1017406070625.59825939293798
98721.4707.83128902791613.5687109720839
99733.2730.1270418890293.07295811097089
100747.7738.0155137136729.6844862863283
101737.6753.476438256926-15.8764382569259
102729.3731.149846490474-1.84984649047443
103706.1724.244722783478-18.1447227834782
104674.3694.366649516334-20.0666495163343
105659659.49587801988-0.495878019880785
106645.7653.491575785871-7.79157578587121
107646.1641.8877153709224.21228462907827
108633646.329930736475-13.3299307364746
109622.3626.87896521488-4.57896521488015
110628.2617.97985167749210.2201483225081
111637.3632.3898811653154.91011883468478
112639.6642.923469024688-3.32346902468752
113638.5641.73911677676-3.23911677675991
114650.5639.05252429894211.4474757010581
115655.4657.506261105449-2.10626110544889






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.02297690076638640.04595380153277270.977023099233614
90.02433114960719240.04866229921438470.975668850392808
100.02909905405628450.0581981081125690.970900945943715
110.01060034753790320.02120069507580650.989399652462097
120.003821292397840470.007642584795680950.99617870760216
130.001256333490491510.002512666980983030.998743666509508
140.000381203176906390.000762406353812780.999618796823094
150.0002536671649788310.0005073343299576630.99974633283502
167.64227095512754e-050.0001528454191025510.999923577290449
170.0002002523561648350.000400504712329670.999799747643835
180.0001624485317416240.0003248970634832490.999837551468258
199.84752373237073e-050.0001969504746474150.999901524762676
208.99652214839157e-050.0001799304429678310.999910034778516
216.61173570651626e-050.0001322347141303250.999933882642935
222.51752830949185e-055.0350566189837e-050.999974824716905
235.44706700090183e-050.0001089413400180370.999945529329991
242.16351251372732e-054.32702502745463e-050.999978364874863
251.03419218973295e-052.06838437946589e-050.999989658078103
264.24381524605465e-068.4876304921093e-060.999995756184754
272.10813094042634e-064.21626188085268e-060.99999789186906
287.65125253933757e-071.53025050786751e-060.999999234874746
292.82221435008822e-075.64442870017644e-070.999999717778565
301.04818567692531e-072.09637135385062e-070.999999895181432
313.54645895210230e-087.09291790420461e-080.99999996453541
321.21691066237309e-082.43382132474618e-080.999999987830893
336.2125915281806e-091.24251830563612e-080.999999993787408
342.41285499992283e-094.82570999984566e-090.999999997587145
351.11039557073494e-092.22079114146989e-090.999999998889604
361.61868498973029e-083.23736997946058e-080.99999998381315
376.22498818298106e-091.24499763659621e-080.999999993775012
382.44082067697163e-094.88164135394326e-090.99999999755918
398.52322197621955e-101.70464439524391e-090.999999999147678
403.32368755954365e-106.6473751190873e-100.999999999667631
411.21441164808283e-102.42882329616565e-100.99999999987856
429.44681034775554e-101.88936206955111e-090.999999999055319
435.2037380313025e-101.0407476062605e-090.999999999479626
442.51296371783233e-105.02592743566465e-100.999999999748704
451.30371956793445e-102.60743913586890e-100.999999999869628
465.17031525165696e-111.03406305033139e-100.999999999948297
471.89534546738177e-103.79069093476353e-100.999999999810465
482.89059783341128e-105.78119566682257e-100.99999999971094
491.34192142431997e-062.68384284863993e-060.999998658078576
501.01417662179946e-062.02835324359892e-060.999998985823378
515.54431051498493e-071.10886210299699e-060.999999445568948
522.83409223541876e-075.66818447083752e-070.999999716590776
531.33436588711018e-072.66873177422035e-070.999999866563411
541.11393329866957e-072.22786659733914e-070.99999988860667
555.93153965102735e-081.18630793020547e-070.999999940684604
563.59740280617694e-087.19480561235387e-080.999999964025972
574.18304785857955e-088.3660957171591e-080.999999958169521
582.33944848220043e-084.67889696440087e-080.999999976605515
591.41177359104766e-082.82354718209531e-080.999999985882264
605.32745674252418e-081.06549134850484e-070.999999946725433
613.11310750371321e-086.22621500742642e-080.999999968868925
622.33839260360350e-084.67678520720700e-080.999999976616074
632.06878358325708e-084.13756716651416e-080.999999979312164
641.23615452939668e-082.47230905879335e-080.999999987638455
657.41913125898957e-091.48382625179791e-080.999999992580869
661.21255118451460e-082.42510236902919e-080.999999987874488
677.0187221479334e-091.40374442958668e-080.999999992981278
687.47232670782399e-091.49446534156480e-080.999999992527673
697.31919566791137e-091.46383913358227e-080.999999992680804
705.27021890098909e-091.05404378019782e-080.999999994729781
717.30962728519218e-091.46192545703844e-080.999999992690373
725.3258558927286e-091.06517117854572e-080.999999994674144
734.37293981294657e-098.74587962589314e-090.99999999562706
745.61944206535925e-091.12388841307185e-080.999999994380558
751.49598990829468e-082.99197981658937e-080.9999999850401
762.83961592242696e-075.67923184485392e-070.999999716038408
773.42152411152521e-076.84304822305042e-070.99999965784759
780.0001024142714518230.0002048285429036470.999897585728548
790.001679254122212610.003358508244425220.998320745877787
800.001398623099421740.002797246198843490.998601376900578
810.003150577704059780.006301155408119550.99684942229594
820.002792961221995790.005585922443991580.997207038778004
830.001873437466178660.003746874932357320.998126562533821
840.001391428290568820.002782856581137630.998608571709431
850.0008822912519918070.001764582503983610.999117708748008
860.0005589378488970.0011178756977940.999441062151103
870.0003251389599975480.0006502779199950970.999674861040002
880.0002573194623244910.0005146389246489830.999742680537675
890.00014606401306630.00029212802613260.999853935986934
900.0001889912600062130.0003779825200124260.999811008739994
910.0001862115810999810.0003724231621999610.9998137884189
920.0001008492200948990.0002016984401897990.999899150779905
935.91216598580282e-050.0001182433197160560.999940878340142
940.001650844616312830.003301689232625660.998349155383687
950.009942214852618840.01988442970523770.990057785147381
960.01415752502020650.02831505004041310.985842474979793
970.009648273723070080.01929654744614020.99035172627693
980.01226678371494480.02453356742988960.987733216285055
990.00778319753485610.01556639506971220.992216802465144
1000.07863660723030450.1572732144606090.921363392769695
1010.08006865782440580.1601373156488120.919931342175594
1020.5041311671984710.9917376656030590.495868832801529
1030.8472234522860710.3055530954278570.152776547713929
1040.8221473331599030.3557053336801950.177852666840097
1050.8991385194202070.2017229611595860.100861480579793
1060.8073661229113880.3852677541772240.192633877088612
1070.6573459888861920.6853080222276160.342654011113808

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0229769007663864 & 0.0459538015327727 & 0.977023099233614 \tabularnewline
9 & 0.0243311496071924 & 0.0486622992143847 & 0.975668850392808 \tabularnewline
10 & 0.0290990540562845 & 0.058198108112569 & 0.970900945943715 \tabularnewline
11 & 0.0106003475379032 & 0.0212006950758065 & 0.989399652462097 \tabularnewline
12 & 0.00382129239784047 & 0.00764258479568095 & 0.99617870760216 \tabularnewline
13 & 0.00125633349049151 & 0.00251266698098303 & 0.998743666509508 \tabularnewline
14 & 0.00038120317690639 & 0.00076240635381278 & 0.999618796823094 \tabularnewline
15 & 0.000253667164978831 & 0.000507334329957663 & 0.99974633283502 \tabularnewline
16 & 7.64227095512754e-05 & 0.000152845419102551 & 0.999923577290449 \tabularnewline
17 & 0.000200252356164835 & 0.00040050471232967 & 0.999799747643835 \tabularnewline
18 & 0.000162448531741624 & 0.000324897063483249 & 0.999837551468258 \tabularnewline
19 & 9.84752373237073e-05 & 0.000196950474647415 & 0.999901524762676 \tabularnewline
20 & 8.99652214839157e-05 & 0.000179930442967831 & 0.999910034778516 \tabularnewline
21 & 6.61173570651626e-05 & 0.000132234714130325 & 0.999933882642935 \tabularnewline
22 & 2.51752830949185e-05 & 5.0350566189837e-05 & 0.999974824716905 \tabularnewline
23 & 5.44706700090183e-05 & 0.000108941340018037 & 0.999945529329991 \tabularnewline
24 & 2.16351251372732e-05 & 4.32702502745463e-05 & 0.999978364874863 \tabularnewline
25 & 1.03419218973295e-05 & 2.06838437946589e-05 & 0.999989658078103 \tabularnewline
26 & 4.24381524605465e-06 & 8.4876304921093e-06 & 0.999995756184754 \tabularnewline
27 & 2.10813094042634e-06 & 4.21626188085268e-06 & 0.99999789186906 \tabularnewline
28 & 7.65125253933757e-07 & 1.53025050786751e-06 & 0.999999234874746 \tabularnewline
29 & 2.82221435008822e-07 & 5.64442870017644e-07 & 0.999999717778565 \tabularnewline
30 & 1.04818567692531e-07 & 2.09637135385062e-07 & 0.999999895181432 \tabularnewline
31 & 3.54645895210230e-08 & 7.09291790420461e-08 & 0.99999996453541 \tabularnewline
32 & 1.21691066237309e-08 & 2.43382132474618e-08 & 0.999999987830893 \tabularnewline
33 & 6.2125915281806e-09 & 1.24251830563612e-08 & 0.999999993787408 \tabularnewline
34 & 2.41285499992283e-09 & 4.82570999984566e-09 & 0.999999997587145 \tabularnewline
35 & 1.11039557073494e-09 & 2.22079114146989e-09 & 0.999999998889604 \tabularnewline
36 & 1.61868498973029e-08 & 3.23736997946058e-08 & 0.99999998381315 \tabularnewline
37 & 6.22498818298106e-09 & 1.24499763659621e-08 & 0.999999993775012 \tabularnewline
38 & 2.44082067697163e-09 & 4.88164135394326e-09 & 0.99999999755918 \tabularnewline
39 & 8.52322197621955e-10 & 1.70464439524391e-09 & 0.999999999147678 \tabularnewline
40 & 3.32368755954365e-10 & 6.6473751190873e-10 & 0.999999999667631 \tabularnewline
41 & 1.21441164808283e-10 & 2.42882329616565e-10 & 0.99999999987856 \tabularnewline
42 & 9.44681034775554e-10 & 1.88936206955111e-09 & 0.999999999055319 \tabularnewline
43 & 5.2037380313025e-10 & 1.0407476062605e-09 & 0.999999999479626 \tabularnewline
44 & 2.51296371783233e-10 & 5.02592743566465e-10 & 0.999999999748704 \tabularnewline
45 & 1.30371956793445e-10 & 2.60743913586890e-10 & 0.999999999869628 \tabularnewline
46 & 5.17031525165696e-11 & 1.03406305033139e-10 & 0.999999999948297 \tabularnewline
47 & 1.89534546738177e-10 & 3.79069093476353e-10 & 0.999999999810465 \tabularnewline
48 & 2.89059783341128e-10 & 5.78119566682257e-10 & 0.99999999971094 \tabularnewline
49 & 1.34192142431997e-06 & 2.68384284863993e-06 & 0.999998658078576 \tabularnewline
50 & 1.01417662179946e-06 & 2.02835324359892e-06 & 0.999998985823378 \tabularnewline
51 & 5.54431051498493e-07 & 1.10886210299699e-06 & 0.999999445568948 \tabularnewline
52 & 2.83409223541876e-07 & 5.66818447083752e-07 & 0.999999716590776 \tabularnewline
53 & 1.33436588711018e-07 & 2.66873177422035e-07 & 0.999999866563411 \tabularnewline
54 & 1.11393329866957e-07 & 2.22786659733914e-07 & 0.99999988860667 \tabularnewline
55 & 5.93153965102735e-08 & 1.18630793020547e-07 & 0.999999940684604 \tabularnewline
56 & 3.59740280617694e-08 & 7.19480561235387e-08 & 0.999999964025972 \tabularnewline
57 & 4.18304785857955e-08 & 8.3660957171591e-08 & 0.999999958169521 \tabularnewline
58 & 2.33944848220043e-08 & 4.67889696440087e-08 & 0.999999976605515 \tabularnewline
59 & 1.41177359104766e-08 & 2.82354718209531e-08 & 0.999999985882264 \tabularnewline
60 & 5.32745674252418e-08 & 1.06549134850484e-07 & 0.999999946725433 \tabularnewline
61 & 3.11310750371321e-08 & 6.22621500742642e-08 & 0.999999968868925 \tabularnewline
62 & 2.33839260360350e-08 & 4.67678520720700e-08 & 0.999999976616074 \tabularnewline
63 & 2.06878358325708e-08 & 4.13756716651416e-08 & 0.999999979312164 \tabularnewline
64 & 1.23615452939668e-08 & 2.47230905879335e-08 & 0.999999987638455 \tabularnewline
65 & 7.41913125898957e-09 & 1.48382625179791e-08 & 0.999999992580869 \tabularnewline
66 & 1.21255118451460e-08 & 2.42510236902919e-08 & 0.999999987874488 \tabularnewline
67 & 7.0187221479334e-09 & 1.40374442958668e-08 & 0.999999992981278 \tabularnewline
68 & 7.47232670782399e-09 & 1.49446534156480e-08 & 0.999999992527673 \tabularnewline
69 & 7.31919566791137e-09 & 1.46383913358227e-08 & 0.999999992680804 \tabularnewline
70 & 5.27021890098909e-09 & 1.05404378019782e-08 & 0.999999994729781 \tabularnewline
71 & 7.30962728519218e-09 & 1.46192545703844e-08 & 0.999999992690373 \tabularnewline
72 & 5.3258558927286e-09 & 1.06517117854572e-08 & 0.999999994674144 \tabularnewline
73 & 4.37293981294657e-09 & 8.74587962589314e-09 & 0.99999999562706 \tabularnewline
74 & 5.61944206535925e-09 & 1.12388841307185e-08 & 0.999999994380558 \tabularnewline
75 & 1.49598990829468e-08 & 2.99197981658937e-08 & 0.9999999850401 \tabularnewline
76 & 2.83961592242696e-07 & 5.67923184485392e-07 & 0.999999716038408 \tabularnewline
77 & 3.42152411152521e-07 & 6.84304822305042e-07 & 0.99999965784759 \tabularnewline
78 & 0.000102414271451823 & 0.000204828542903647 & 0.999897585728548 \tabularnewline
79 & 0.00167925412221261 & 0.00335850824442522 & 0.998320745877787 \tabularnewline
80 & 0.00139862309942174 & 0.00279724619884349 & 0.998601376900578 \tabularnewline
81 & 0.00315057770405978 & 0.00630115540811955 & 0.99684942229594 \tabularnewline
82 & 0.00279296122199579 & 0.00558592244399158 & 0.997207038778004 \tabularnewline
83 & 0.00187343746617866 & 0.00374687493235732 & 0.998126562533821 \tabularnewline
84 & 0.00139142829056882 & 0.00278285658113763 & 0.998608571709431 \tabularnewline
85 & 0.000882291251991807 & 0.00176458250398361 & 0.999117708748008 \tabularnewline
86 & 0.000558937848897 & 0.001117875697794 & 0.999441062151103 \tabularnewline
87 & 0.000325138959997548 & 0.000650277919995097 & 0.999674861040002 \tabularnewline
88 & 0.000257319462324491 & 0.000514638924648983 & 0.999742680537675 \tabularnewline
89 & 0.0001460640130663 & 0.0002921280261326 & 0.999853935986934 \tabularnewline
90 & 0.000188991260006213 & 0.000377982520012426 & 0.999811008739994 \tabularnewline
91 & 0.000186211581099981 & 0.000372423162199961 & 0.9998137884189 \tabularnewline
92 & 0.000100849220094899 & 0.000201698440189799 & 0.999899150779905 \tabularnewline
93 & 5.91216598580282e-05 & 0.000118243319716056 & 0.999940878340142 \tabularnewline
94 & 0.00165084461631283 & 0.00330168923262566 & 0.998349155383687 \tabularnewline
95 & 0.00994221485261884 & 0.0198844297052377 & 0.990057785147381 \tabularnewline
96 & 0.0141575250202065 & 0.0283150500404131 & 0.985842474979793 \tabularnewline
97 & 0.00964827372307008 & 0.0192965474461402 & 0.99035172627693 \tabularnewline
98 & 0.0122667837149448 & 0.0245335674298896 & 0.987733216285055 \tabularnewline
99 & 0.0077831975348561 & 0.0155663950697122 & 0.992216802465144 \tabularnewline
100 & 0.0786366072303045 & 0.157273214460609 & 0.921363392769695 \tabularnewline
101 & 0.0800686578244058 & 0.160137315648812 & 0.919931342175594 \tabularnewline
102 & 0.504131167198471 & 0.991737665603059 & 0.495868832801529 \tabularnewline
103 & 0.847223452286071 & 0.305553095427857 & 0.152776547713929 \tabularnewline
104 & 0.822147333159903 & 0.355705333680195 & 0.177852666840097 \tabularnewline
105 & 0.899138519420207 & 0.201722961159586 & 0.100861480579793 \tabularnewline
106 & 0.807366122911388 & 0.385267754177224 & 0.192633877088612 \tabularnewline
107 & 0.657345988886192 & 0.685308022227616 & 0.342654011113808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&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]0.0229769007663864[/C][C]0.0459538015327727[/C][C]0.977023099233614[/C][/ROW]
[ROW][C]9[/C][C]0.0243311496071924[/C][C]0.0486622992143847[/C][C]0.975668850392808[/C][/ROW]
[ROW][C]10[/C][C]0.0290990540562845[/C][C]0.058198108112569[/C][C]0.970900945943715[/C][/ROW]
[ROW][C]11[/C][C]0.0106003475379032[/C][C]0.0212006950758065[/C][C]0.989399652462097[/C][/ROW]
[ROW][C]12[/C][C]0.00382129239784047[/C][C]0.00764258479568095[/C][C]0.99617870760216[/C][/ROW]
[ROW][C]13[/C][C]0.00125633349049151[/C][C]0.00251266698098303[/C][C]0.998743666509508[/C][/ROW]
[ROW][C]14[/C][C]0.00038120317690639[/C][C]0.00076240635381278[/C][C]0.999618796823094[/C][/ROW]
[ROW][C]15[/C][C]0.000253667164978831[/C][C]0.000507334329957663[/C][C]0.99974633283502[/C][/ROW]
[ROW][C]16[/C][C]7.64227095512754e-05[/C][C]0.000152845419102551[/C][C]0.999923577290449[/C][/ROW]
[ROW][C]17[/C][C]0.000200252356164835[/C][C]0.00040050471232967[/C][C]0.999799747643835[/C][/ROW]
[ROW][C]18[/C][C]0.000162448531741624[/C][C]0.000324897063483249[/C][C]0.999837551468258[/C][/ROW]
[ROW][C]19[/C][C]9.84752373237073e-05[/C][C]0.000196950474647415[/C][C]0.999901524762676[/C][/ROW]
[ROW][C]20[/C][C]8.99652214839157e-05[/C][C]0.000179930442967831[/C][C]0.999910034778516[/C][/ROW]
[ROW][C]21[/C][C]6.61173570651626e-05[/C][C]0.000132234714130325[/C][C]0.999933882642935[/C][/ROW]
[ROW][C]22[/C][C]2.51752830949185e-05[/C][C]5.0350566189837e-05[/C][C]0.999974824716905[/C][/ROW]
[ROW][C]23[/C][C]5.44706700090183e-05[/C][C]0.000108941340018037[/C][C]0.999945529329991[/C][/ROW]
[ROW][C]24[/C][C]2.16351251372732e-05[/C][C]4.32702502745463e-05[/C][C]0.999978364874863[/C][/ROW]
[ROW][C]25[/C][C]1.03419218973295e-05[/C][C]2.06838437946589e-05[/C][C]0.999989658078103[/C][/ROW]
[ROW][C]26[/C][C]4.24381524605465e-06[/C][C]8.4876304921093e-06[/C][C]0.999995756184754[/C][/ROW]
[ROW][C]27[/C][C]2.10813094042634e-06[/C][C]4.21626188085268e-06[/C][C]0.99999789186906[/C][/ROW]
[ROW][C]28[/C][C]7.65125253933757e-07[/C][C]1.53025050786751e-06[/C][C]0.999999234874746[/C][/ROW]
[ROW][C]29[/C][C]2.82221435008822e-07[/C][C]5.64442870017644e-07[/C][C]0.999999717778565[/C][/ROW]
[ROW][C]30[/C][C]1.04818567692531e-07[/C][C]2.09637135385062e-07[/C][C]0.999999895181432[/C][/ROW]
[ROW][C]31[/C][C]3.54645895210230e-08[/C][C]7.09291790420461e-08[/C][C]0.99999996453541[/C][/ROW]
[ROW][C]32[/C][C]1.21691066237309e-08[/C][C]2.43382132474618e-08[/C][C]0.999999987830893[/C][/ROW]
[ROW][C]33[/C][C]6.2125915281806e-09[/C][C]1.24251830563612e-08[/C][C]0.999999993787408[/C][/ROW]
[ROW][C]34[/C][C]2.41285499992283e-09[/C][C]4.82570999984566e-09[/C][C]0.999999997587145[/C][/ROW]
[ROW][C]35[/C][C]1.11039557073494e-09[/C][C]2.22079114146989e-09[/C][C]0.999999998889604[/C][/ROW]
[ROW][C]36[/C][C]1.61868498973029e-08[/C][C]3.23736997946058e-08[/C][C]0.99999998381315[/C][/ROW]
[ROW][C]37[/C][C]6.22498818298106e-09[/C][C]1.24499763659621e-08[/C][C]0.999999993775012[/C][/ROW]
[ROW][C]38[/C][C]2.44082067697163e-09[/C][C]4.88164135394326e-09[/C][C]0.99999999755918[/C][/ROW]
[ROW][C]39[/C][C]8.52322197621955e-10[/C][C]1.70464439524391e-09[/C][C]0.999999999147678[/C][/ROW]
[ROW][C]40[/C][C]3.32368755954365e-10[/C][C]6.6473751190873e-10[/C][C]0.999999999667631[/C][/ROW]
[ROW][C]41[/C][C]1.21441164808283e-10[/C][C]2.42882329616565e-10[/C][C]0.99999999987856[/C][/ROW]
[ROW][C]42[/C][C]9.44681034775554e-10[/C][C]1.88936206955111e-09[/C][C]0.999999999055319[/C][/ROW]
[ROW][C]43[/C][C]5.2037380313025e-10[/C][C]1.0407476062605e-09[/C][C]0.999999999479626[/C][/ROW]
[ROW][C]44[/C][C]2.51296371783233e-10[/C][C]5.02592743566465e-10[/C][C]0.999999999748704[/C][/ROW]
[ROW][C]45[/C][C]1.30371956793445e-10[/C][C]2.60743913586890e-10[/C][C]0.999999999869628[/C][/ROW]
[ROW][C]46[/C][C]5.17031525165696e-11[/C][C]1.03406305033139e-10[/C][C]0.999999999948297[/C][/ROW]
[ROW][C]47[/C][C]1.89534546738177e-10[/C][C]3.79069093476353e-10[/C][C]0.999999999810465[/C][/ROW]
[ROW][C]48[/C][C]2.89059783341128e-10[/C][C]5.78119566682257e-10[/C][C]0.99999999971094[/C][/ROW]
[ROW][C]49[/C][C]1.34192142431997e-06[/C][C]2.68384284863993e-06[/C][C]0.999998658078576[/C][/ROW]
[ROW][C]50[/C][C]1.01417662179946e-06[/C][C]2.02835324359892e-06[/C][C]0.999998985823378[/C][/ROW]
[ROW][C]51[/C][C]5.54431051498493e-07[/C][C]1.10886210299699e-06[/C][C]0.999999445568948[/C][/ROW]
[ROW][C]52[/C][C]2.83409223541876e-07[/C][C]5.66818447083752e-07[/C][C]0.999999716590776[/C][/ROW]
[ROW][C]53[/C][C]1.33436588711018e-07[/C][C]2.66873177422035e-07[/C][C]0.999999866563411[/C][/ROW]
[ROW][C]54[/C][C]1.11393329866957e-07[/C][C]2.22786659733914e-07[/C][C]0.99999988860667[/C][/ROW]
[ROW][C]55[/C][C]5.93153965102735e-08[/C][C]1.18630793020547e-07[/C][C]0.999999940684604[/C][/ROW]
[ROW][C]56[/C][C]3.59740280617694e-08[/C][C]7.19480561235387e-08[/C][C]0.999999964025972[/C][/ROW]
[ROW][C]57[/C][C]4.18304785857955e-08[/C][C]8.3660957171591e-08[/C][C]0.999999958169521[/C][/ROW]
[ROW][C]58[/C][C]2.33944848220043e-08[/C][C]4.67889696440087e-08[/C][C]0.999999976605515[/C][/ROW]
[ROW][C]59[/C][C]1.41177359104766e-08[/C][C]2.82354718209531e-08[/C][C]0.999999985882264[/C][/ROW]
[ROW][C]60[/C][C]5.32745674252418e-08[/C][C]1.06549134850484e-07[/C][C]0.999999946725433[/C][/ROW]
[ROW][C]61[/C][C]3.11310750371321e-08[/C][C]6.22621500742642e-08[/C][C]0.999999968868925[/C][/ROW]
[ROW][C]62[/C][C]2.33839260360350e-08[/C][C]4.67678520720700e-08[/C][C]0.999999976616074[/C][/ROW]
[ROW][C]63[/C][C]2.06878358325708e-08[/C][C]4.13756716651416e-08[/C][C]0.999999979312164[/C][/ROW]
[ROW][C]64[/C][C]1.23615452939668e-08[/C][C]2.47230905879335e-08[/C][C]0.999999987638455[/C][/ROW]
[ROW][C]65[/C][C]7.41913125898957e-09[/C][C]1.48382625179791e-08[/C][C]0.999999992580869[/C][/ROW]
[ROW][C]66[/C][C]1.21255118451460e-08[/C][C]2.42510236902919e-08[/C][C]0.999999987874488[/C][/ROW]
[ROW][C]67[/C][C]7.0187221479334e-09[/C][C]1.40374442958668e-08[/C][C]0.999999992981278[/C][/ROW]
[ROW][C]68[/C][C]7.47232670782399e-09[/C][C]1.49446534156480e-08[/C][C]0.999999992527673[/C][/ROW]
[ROW][C]69[/C][C]7.31919566791137e-09[/C][C]1.46383913358227e-08[/C][C]0.999999992680804[/C][/ROW]
[ROW][C]70[/C][C]5.27021890098909e-09[/C][C]1.05404378019782e-08[/C][C]0.999999994729781[/C][/ROW]
[ROW][C]71[/C][C]7.30962728519218e-09[/C][C]1.46192545703844e-08[/C][C]0.999999992690373[/C][/ROW]
[ROW][C]72[/C][C]5.3258558927286e-09[/C][C]1.06517117854572e-08[/C][C]0.999999994674144[/C][/ROW]
[ROW][C]73[/C][C]4.37293981294657e-09[/C][C]8.74587962589314e-09[/C][C]0.99999999562706[/C][/ROW]
[ROW][C]74[/C][C]5.61944206535925e-09[/C][C]1.12388841307185e-08[/C][C]0.999999994380558[/C][/ROW]
[ROW][C]75[/C][C]1.49598990829468e-08[/C][C]2.99197981658937e-08[/C][C]0.9999999850401[/C][/ROW]
[ROW][C]76[/C][C]2.83961592242696e-07[/C][C]5.67923184485392e-07[/C][C]0.999999716038408[/C][/ROW]
[ROW][C]77[/C][C]3.42152411152521e-07[/C][C]6.84304822305042e-07[/C][C]0.99999965784759[/C][/ROW]
[ROW][C]78[/C][C]0.000102414271451823[/C][C]0.000204828542903647[/C][C]0.999897585728548[/C][/ROW]
[ROW][C]79[/C][C]0.00167925412221261[/C][C]0.00335850824442522[/C][C]0.998320745877787[/C][/ROW]
[ROW][C]80[/C][C]0.00139862309942174[/C][C]0.00279724619884349[/C][C]0.998601376900578[/C][/ROW]
[ROW][C]81[/C][C]0.00315057770405978[/C][C]0.00630115540811955[/C][C]0.99684942229594[/C][/ROW]
[ROW][C]82[/C][C]0.00279296122199579[/C][C]0.00558592244399158[/C][C]0.997207038778004[/C][/ROW]
[ROW][C]83[/C][C]0.00187343746617866[/C][C]0.00374687493235732[/C][C]0.998126562533821[/C][/ROW]
[ROW][C]84[/C][C]0.00139142829056882[/C][C]0.00278285658113763[/C][C]0.998608571709431[/C][/ROW]
[ROW][C]85[/C][C]0.000882291251991807[/C][C]0.00176458250398361[/C][C]0.999117708748008[/C][/ROW]
[ROW][C]86[/C][C]0.000558937848897[/C][C]0.001117875697794[/C][C]0.999441062151103[/C][/ROW]
[ROW][C]87[/C][C]0.000325138959997548[/C][C]0.000650277919995097[/C][C]0.999674861040002[/C][/ROW]
[ROW][C]88[/C][C]0.000257319462324491[/C][C]0.000514638924648983[/C][C]0.999742680537675[/C][/ROW]
[ROW][C]89[/C][C]0.0001460640130663[/C][C]0.0002921280261326[/C][C]0.999853935986934[/C][/ROW]
[ROW][C]90[/C][C]0.000188991260006213[/C][C]0.000377982520012426[/C][C]0.999811008739994[/C][/ROW]
[ROW][C]91[/C][C]0.000186211581099981[/C][C]0.000372423162199961[/C][C]0.9998137884189[/C][/ROW]
[ROW][C]92[/C][C]0.000100849220094899[/C][C]0.000201698440189799[/C][C]0.999899150779905[/C][/ROW]
[ROW][C]93[/C][C]5.91216598580282e-05[/C][C]0.000118243319716056[/C][C]0.999940878340142[/C][/ROW]
[ROW][C]94[/C][C]0.00165084461631283[/C][C]0.00330168923262566[/C][C]0.998349155383687[/C][/ROW]
[ROW][C]95[/C][C]0.00994221485261884[/C][C]0.0198844297052377[/C][C]0.990057785147381[/C][/ROW]
[ROW][C]96[/C][C]0.0141575250202065[/C][C]0.0283150500404131[/C][C]0.985842474979793[/C][/ROW]
[ROW][C]97[/C][C]0.00964827372307008[/C][C]0.0192965474461402[/C][C]0.99035172627693[/C][/ROW]
[ROW][C]98[/C][C]0.0122667837149448[/C][C]0.0245335674298896[/C][C]0.987733216285055[/C][/ROW]
[ROW][C]99[/C][C]0.0077831975348561[/C][C]0.0155663950697122[/C][C]0.992216802465144[/C][/ROW]
[ROW][C]100[/C][C]0.0786366072303045[/C][C]0.157273214460609[/C][C]0.921363392769695[/C][/ROW]
[ROW][C]101[/C][C]0.0800686578244058[/C][C]0.160137315648812[/C][C]0.919931342175594[/C][/ROW]
[ROW][C]102[/C][C]0.504131167198471[/C][C]0.991737665603059[/C][C]0.495868832801529[/C][/ROW]
[ROW][C]103[/C][C]0.847223452286071[/C][C]0.305553095427857[/C][C]0.152776547713929[/C][/ROW]
[ROW][C]104[/C][C]0.822147333159903[/C][C]0.355705333680195[/C][C]0.177852666840097[/C][/ROW]
[ROW][C]105[/C][C]0.899138519420207[/C][C]0.201722961159586[/C][C]0.100861480579793[/C][/ROW]
[ROW][C]106[/C][C]0.807366122911388[/C][C]0.385267754177224[/C][C]0.192633877088612[/C][/ROW]
[ROW][C]107[/C][C]0.657345988886192[/C][C]0.685308022227616[/C][C]0.342654011113808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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
80.02297690076638640.04595380153277270.977023099233614
90.02433114960719240.04866229921438470.975668850392808
100.02909905405628450.0581981081125690.970900945943715
110.01060034753790320.02120069507580650.989399652462097
120.003821292397840470.007642584795680950.99617870760216
130.001256333490491510.002512666980983030.998743666509508
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222.51752830949185e-055.0350566189837e-050.999974824716905
235.44706700090183e-050.0001089413400180370.999945529329991
242.16351251372732e-054.32702502745463e-050.999978364874863
251.03419218973295e-052.06838437946589e-050.999989658078103
264.24381524605465e-068.4876304921093e-060.999995756184754
272.10813094042634e-064.21626188085268e-060.99999789186906
287.65125253933757e-071.53025050786751e-060.999999234874746
292.82221435008822e-075.64442870017644e-070.999999717778565
301.04818567692531e-072.09637135385062e-070.999999895181432
313.54645895210230e-087.09291790420461e-080.99999996453541
321.21691066237309e-082.43382132474618e-080.999999987830893
336.2125915281806e-091.24251830563612e-080.999999993787408
342.41285499992283e-094.82570999984566e-090.999999997587145
351.11039557073494e-092.22079114146989e-090.999999998889604
361.61868498973029e-083.23736997946058e-080.99999998381315
376.22498818298106e-091.24499763659621e-080.999999993775012
382.44082067697163e-094.88164135394326e-090.99999999755918
398.52322197621955e-101.70464439524391e-090.999999999147678
403.32368755954365e-106.6473751190873e-100.999999999667631
411.21441164808283e-102.42882329616565e-100.99999999987856
429.44681034775554e-101.88936206955111e-090.999999999055319
435.2037380313025e-101.0407476062605e-090.999999999479626
442.51296371783233e-105.02592743566465e-100.999999999748704
451.30371956793445e-102.60743913586890e-100.999999999869628
465.17031525165696e-111.03406305033139e-100.999999999948297
471.89534546738177e-103.79069093476353e-100.999999999810465
482.89059783341128e-105.78119566682257e-100.99999999971094
491.34192142431997e-062.68384284863993e-060.999998658078576
501.01417662179946e-062.02835324359892e-060.999998985823378
515.54431051498493e-071.10886210299699e-060.999999445568948
522.83409223541876e-075.66818447083752e-070.999999716590776
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555.93153965102735e-081.18630793020547e-070.999999940684604
563.59740280617694e-087.19480561235387e-080.999999964025972
574.18304785857955e-088.3660957171591e-080.999999958169521
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591.41177359104766e-082.82354718209531e-080.999999985882264
605.32745674252418e-081.06549134850484e-070.999999946725433
613.11310750371321e-086.22621500742642e-080.999999968868925
622.33839260360350e-084.67678520720700e-080.999999976616074
632.06878358325708e-084.13756716651416e-080.999999979312164
641.23615452939668e-082.47230905879335e-080.999999987638455
657.41913125898957e-091.48382625179791e-080.999999992580869
661.21255118451460e-082.42510236902919e-080.999999987874488
677.0187221479334e-091.40374442958668e-080.999999992981278
687.47232670782399e-091.49446534156480e-080.999999992527673
697.31919566791137e-091.46383913358227e-080.999999992680804
705.27021890098909e-091.05404378019782e-080.999999994729781
717.30962728519218e-091.46192545703844e-080.999999992690373
725.3258558927286e-091.06517117854572e-080.999999994674144
734.37293981294657e-098.74587962589314e-090.99999999562706
745.61944206535925e-091.12388841307185e-080.999999994380558
751.49598990829468e-082.99197981658937e-080.9999999850401
762.83961592242696e-075.67923184485392e-070.999999716038408
773.42152411152521e-076.84304822305042e-070.99999965784759
780.0001024142714518230.0002048285429036470.999897585728548
790.001679254122212610.003358508244425220.998320745877787
800.001398623099421740.002797246198843490.998601376900578
810.003150577704059780.006301155408119550.99684942229594
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840.001391428290568820.002782856581137630.998608571709431
850.0008822912519918070.001764582503983610.999117708748008
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880.0002573194623244910.0005146389246489830.999742680537675
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1070.6573459888861920.6853080222276160.342654011113808






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level830.83NOK
5% type I error level910.91NOK
10% type I error level920.92NOK

\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 & 83 & 0.83 & NOK \tabularnewline
5% type I error level & 91 & 0.91 & NOK \tabularnewline
10% type I error level & 92 & 0.92 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57372&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]83[/C][C]0.83[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]91[/C][C]0.91[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]92[/C][C]0.92[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57372&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57372&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 level830.83NOK
5% type I error level910.91NOK
10% type I error level920.92NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}