Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 25 Nov 2011 08:37:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/25/t13222283480a60a0uwpj0ppuh.htm/, Retrieved Thu, 28 Mar 2024 21:12:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147310, Retrieved Thu, 28 Mar 2024 21:12:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [jelle] [2011-11-25 13:37:48] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
11.73
11.75
11.39
11.54
9.62
9.82
9.94
9.9
9.8
9.86
10.5
10.33
10.16
9.91
9.96
10.03
9.55
9.51
9.8
10.08
10.2
10.23
10.2
10.07
10.01
10.05
9.92
10.03
10.18
10.1
10.16
10.15
10.13
10.09
10.18
10.06
9.65
9.74
9.53
9.5
9
9.15
9.32
9.62
9.59
9.37
9.35
9.32
9.49
9.52
9.59
9.35
9.2
9.57
9.78
9.79
9.57
9.53
9.65
9.36
9.4
9.32
9.31
9.19
9.39
9.28
9.28
9.31
9.28
9.31
9.35
9.19
9.07
8.96
8.69
8.58
8.56
8.47
8.46
8.75
8.95
9.33
9.51
9.561
9.94
9.9
9.275
9.56
9.779
9.746
9.991
9.98
10.195
10.31
10.25
9.871
10.06
9.894
9.59
9.64
9.89
9.53
9.388
9.16
9.418
9.57
9.857
9.877
9.76
9.76
9.695
9.475
9.262
9.097
8.55
8.16
7.532
7.325
6.749
7.13
6.995
7.346
7.73
7.837
7.514
7.58
6.83
6.617
6.715
6.63
6.891
7.002
7.09
7.36
7.477
7.826
7.79
7.578
7.204
7.198
7.685
7.795
7.46
7.274
7.33
7.655
7.767
7.84
7.424
7.54
7.351
6.735
6.777
6.679
7.34
6.978
6.92
6.628
6.385
5.984
6.268
6.596
6.395
6.715
6.804
6.929
6.846
6.992
6.774
6.75
6.485
6.27
6.47
6.78
6.71
6.141
6.72
6.68
6.371
6.097
6.27
6.447
6.37
6.446
6.54
6.374
6.33
6.63
6.498
6.485
6.36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=0

As an alternative you can also use a 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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
koers-nyrstar[t] = + 10.8154045352744 + 0.0586312932494904D1[t] + 0.0139454474976312D2[t] + 0.0145162632440349D3[t] + 0.00495550004307009D4[t] -0.0233339736411405t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
koers-nyrstar[t] =  +  10.8154045352744 +  0.0586312932494904D1[t] +  0.0139454474976312D2[t] +  0.0145162632440349D3[t] +  0.00495550004307009D4[t] -0.0233339736411405t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]koers-nyrstar[t] =  +  10.8154045352744 +  0.0586312932494904D1[t] +  0.0139454474976312D2[t] +  0.0145162632440349D3[t] +  0.00495550004307009D4[t] -0.0233339736411405t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
koers-nyrstar[t] = + 10.8154045352744 + 0.0586312932494904D1[t] + 0.0139454474976312D2[t] + 0.0145162632440349D3[t] + 0.00495550004307009D4[t] -0.0233339736411405t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.81540453527440.13717378.845200
D10.05863129324949040.151330.38740.6988760.349438
D20.01394544749763120.1523260.09150.9271550.463577
D30.01451626324403490.1523140.09530.9241760.462088
D40.004955500043070090.1523070.03250.9740790.48704
t-0.02333397364114050.000871-26.778300

\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) & 10.8154045352744 & 0.137173 & 78.8452 & 0 & 0 \tabularnewline
D1 & 0.0586312932494904 & 0.15133 & 0.3874 & 0.698876 & 0.349438 \tabularnewline
D2 & 0.0139454474976312 & 0.152326 & 0.0915 & 0.927155 & 0.463577 \tabularnewline
D3 & 0.0145162632440349 & 0.152314 & 0.0953 & 0.924176 & 0.462088 \tabularnewline
D4 & 0.00495550004307009 & 0.152307 & 0.0325 & 0.974079 & 0.48704 \tabularnewline
t & -0.0233339736411405 & 0.000871 & -26.7783 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&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]10.8154045352744[/C][C]0.137173[/C][C]78.8452[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D1[/C][C]0.0586312932494904[/C][C]0.15133[/C][C]0.3874[/C][C]0.698876[/C][C]0.349438[/C][/ROW]
[ROW][C]D2[/C][C]0.0139454474976312[/C][C]0.152326[/C][C]0.0915[/C][C]0.927155[/C][C]0.463577[/C][/ROW]
[ROW][C]D3[/C][C]0.0145162632440349[/C][C]0.152314[/C][C]0.0953[/C][C]0.924176[/C][C]0.462088[/C][/ROW]
[ROW][C]D4[/C][C]0.00495550004307009[/C][C]0.152307[/C][C]0.0325[/C][C]0.974079[/C][C]0.48704[/C][/ROW]
[ROW][C]t[/C][C]-0.0233339736411405[/C][C]0.000871[/C][C]-26.7783[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=2

As an alternative you can also use a 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)10.81540453527440.13717378.845200
D10.05863129324949040.151330.38740.6988760.349438
D20.01394544749763120.1523260.09150.9271550.463577
D30.01451626324403490.1523140.09530.9241760.462088
D40.004955500043070090.1523070.03250.9740790.48704
t-0.02333397364114050.000871-26.778300







Multiple Linear Regression - Regression Statistics
Multiple R0.891649004842128
R-squared0.795037947835958
Adjusted R-squared0.789498432912605
F-TEST (value)143.521221413157
F-TEST (DF numerator)5
F-TEST (DF denominator)185
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.663877908013157
Sum Squared Residuals81.5357671983662

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.891649004842128 \tabularnewline
R-squared & 0.795037947835958 \tabularnewline
Adjusted R-squared & 0.789498432912605 \tabularnewline
F-TEST (value) & 143.521221413157 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 185 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.663877908013157 \tabularnewline
Sum Squared Residuals & 81.5357671983662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.891649004842128[/C][/ROW]
[ROW][C]R-squared[/C][C]0.795037947835958[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.789498432912605[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]143.521221413157[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]185[/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]0.663877908013157[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]81.5357671983662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.891649004842128
R-squared0.795037947835958
Adjusted R-squared0.789498432912605
F-TEST (value)143.521221413157
F-TEST (DF numerator)5
F-TEST (DF denominator)185
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.663877908013157
Sum Squared Residuals81.5357671983662







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.7310.85070185488270.879298145117296
211.7510.78268203548970.967317964510294
311.3910.7599188775950.630081122405031
411.5410.72702414075290.812975859247135
59.6210.6987346670687-1.07873466706865
69.8210.734031986677-0.914031986677004
79.9410.666012167284-0.726012167284005
89.910.6432490093893-0.743249009389267
99.810.6103542725472-0.81035427254716
109.8610.582064798863-0.722064798862952
1110.510.6173621184713-0.117362118471301
1210.3310.5493422990783-0.219342299078301
1310.1610.5265791411836-0.366579141183564
149.9110.4936844043415-0.583684404341459
159.9610.4653949306572-0.505394930657248
1610.0310.5006922502656-0.470692250265599
179.5510.4326724308726-0.882672430872598
189.5110.4099092729779-0.899909272977862
199.810.3770145361358-0.577014536135756
2010.0810.3487250624515-0.268725062451546
2110.210.3840223820599-0.184022382059897
2210.2310.3160025626669-0.0860025626668962
2310.210.2932394047722-0.0932394047721604
2410.0710.2603446679301-0.190344667930054
2510.0110.2320551942458-0.222055194245844
2610.0510.2673525138542-0.217352513854193
279.9210.1993326944612-0.279332694461194
2810.0310.1765695365665-0.146569536566458
2910.1810.14367479972440.0363252002756479
3010.110.1153853260401-0.0153853260401416
3110.1610.15068264564850.00931735435150921
3210.1510.08266282625550.067337173744509
3310.1310.05989966836080.0701003316392464
3410.0910.02700493151860.0629950684813506
3510.189.998715457834440.181284542165561
3610.0610.03401277744280.025987222557212
379.659.96599295804979-0.315992958049789
389.749.94322980015505-0.203229800155052
399.539.91033506331295-0.380335063312948
409.59.88204558962874-0.382045589628736
4199.91734290923709-0.917342909237086
429.159.84932308984409-0.699323089844086
439.329.82655993194935-0.506559931949349
449.629.79366519510724-0.173665195107245
459.599.76537572142303-0.175375721423034
469.379.80067304103138-0.430673041031384
479.359.73265322163838-0.382653221638384
489.329.70989006374365-0.389890063743647
499.499.67699532690154-0.186995326901542
509.529.64870585321733-0.128705853217332
519.599.68400317282568-0.0940031728256814
529.359.61598335343268-0.265983353432682
539.29.59322019553795-0.393220195537945
549.579.560325458695840.00967454130416083
559.789.532035985011630.24796401498837
569.799.567333304619980.22266669538002
579.579.499313485226980.0706865147730212
589.539.476550327332240.0534496726677571
599.659.443655590490140.206344409509863
609.369.41536611680593-0.0553661168059269
619.49.45066343641428-0.0506634364142756
629.329.38264361702128-0.0626436170212761
639.319.35988045912654-0.0498804591265393
649.199.32698572228443-0.136985722284435
659.399.298696248600220.0913037513997767
669.289.33399356820857-0.0539935682085742
679.289.265973748815570.0140262511844255
689.319.243210590920840.0667894090791632
699.289.210315854078730.0696841459212674
709.319.182026380394520.127973619605479
719.359.217323700002870.132676299997129
729.199.149303880609870.0406961193901281
739.079.12654072271513-0.0565407227151345
748.969.09364598587303-0.133645985873029
758.699.06535651218882-0.375356512188819
768.589.10065383179717-0.520653831797169
778.569.03263401240417-0.472634012404169
788.479.00987085450943-0.539870854509432
798.468.97697611766733-0.516976117667326
808.758.94868664398312-0.198686643983117
818.958.98398396359147-0.0339839635914669
829.338.915964144198470.414035855801534
839.518.893200986303730.61679901369627
849.5618.860306249461620.700693750538375
859.948.832016775777421.10798322422259
869.98.867314095385761.03268590461424
879.2758.799294275992760.475705724007236
889.568.776531118098030.783468881901973
899.7798.743636381255921.03536361874408
909.7468.715346907571711.03065309242829
919.9918.750644227180061.24035577281994
929.988.682624407787061.29737559221294
9310.1958.659861249892321.53513875010768
9410.318.626966513050221.68303348694978
9510.258.598677039366011.65132296063399
969.8718.633974358974361.23702564102564
9710.068.565954539581361.49404546041864
989.8948.543191381686621.35080861831338
999.598.510296644844521.07970335515548
1009.648.48200717116031.15799282883969
1019.898.517304490768661.37269550923134
1029.538.449284671375661.08071532862434
1039.3888.426521513480920.96147848651908
1049.168.393626776638810.766373223361185
1059.4188.36533730295461.0526626970454
1069.578.400634622562951.16936537743705
1079.8578.332614803169951.52438519683004
1089.8778.309851645275221.56714835472478
1099.768.276956908433111.48304309156689
1109.768.24866743474891.5113325652511
1119.6958.283964754357251.41103524564275
1129.4758.215944934964251.25905506503575
1139.2628.193181777069521.06881822293049
1149.0978.160287040227410.93671295977259
1158.558.13199756654320.418002433456801
1168.168.16729488615155-0.00729488615154896
1177.5328.09927506675855-0.56727506675855
1187.3258.07651190886381-0.751511908863812
1196.7498.04361717202171-1.29461717202171
1207.138.0153276983375-0.885327698337497
1216.9958.05062501794585-1.05562501794585
1227.3467.98260519855285-0.636605198552847
1237.737.95984204065811-0.22984204065811
1247.8377.926947303816-0.0899473038160052
1257.5147.8986578301318-0.384657830131794
1267.587.93395514974014-0.353955149740144
1276.837.86593533034714-1.03593533034714
1286.6177.8431721724524-1.22617217245241
1296.7157.8102774356103-1.0952774356103
1306.637.78198796192609-1.15198796192609
1316.8917.81728528153444-0.926285281534442
1327.0027.74926546214144-0.747265462141443
1337.097.7265023042467-0.636502304246705
1347.367.6936075674046-0.3336075674046
1357.4777.66531809372039-0.188318093720389
1367.8267.700615413328740.12538458667126
1377.797.632595593935740.157404406064261
1387.5787.609832436041-0.0318324360410024
1397.2047.5769376991989-0.372937699198898
1407.1987.54864822551469-0.350648225514687
1417.6857.583945545123040.101054454876963
1427.7957.515925725730040.279074274269963
1437.467.4931625678353-0.0331625678353002
1447.2747.4602678309932-0.186267830993195
1457.337.43197835730898-0.101978357308984
1467.6557.467275676917330.187724323082666
1477.7677.399255857524330.367744142475666
1487.847.37649269962960.463507300370402
1497.4247.343597962787490.0804020372125076
1507.547.315308489103280.224691510896718
1517.3517.350605808711630.000394191288368287
1526.7357.28258598931863-0.547585989318632
1536.7777.2598228314239-0.482822831423895
1546.6797.2269280945818-0.54792809458179
1557.347.198638620897580.14136137910242
1566.9787.23393594050593-0.25593594050593
1576.927.16591612111293-0.24591612111293
1586.6287.1431529632182-0.515152963218193
1596.3857.11025822637609-0.725258226376088
1605.9847.08196875269188-1.09796875269188
1616.2687.11726607230023-0.849266072300227
1626.5967.04924625290723-0.453246252907227
1636.3957.02648309501249-0.631483095012491
1646.7156.99358835817038-0.278588358170385
1656.8046.96529888448617-0.161298884486174
1666.9297.00059620409452-0.0715962040945244
1676.8466.93257638470152-0.086576384701525
1686.9926.909813226806790.0821867731932114
1696.7746.87691848996468-0.102918489964683
1706.756.84862901628047-0.0986290162804717
1716.4856.88392633588882-0.398926335888821
1726.276.81590651649582-0.545906516495823
1736.476.79314335860109-0.323143358601086
1746.786.760248621758980.0197513782410201
1756.716.73195914807477-0.0219591480747694
1766.1416.76725646768312-0.62625646768312
1776.726.699236648290120.02076335170988
1786.686.676473490395380.0035265096046167
1796.3716.64357875355328-0.272578753553277
1806.0976.61528927986907-0.518289279869067
1816.276.65058659947742-0.380586599477418
1826.4476.58256678008442-0.135566780084417
1836.376.55980362218968-0.18980362218968
1846.4466.52690888534758-0.0809088853475753
1856.546.498619411663360.0413805883366355
1866.3746.53391673127171-0.159916731271715
1876.336.46589691187871-0.135896911878715
1886.636.443133753983980.186866246016022
1896.4986.410239017141870.0877609828581275
1906.4856.381949543457660.103050456542338
1916.366.41724686306601-0.0572468630660119

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.73 & 10.8507018548827 & 0.879298145117296 \tabularnewline
2 & 11.75 & 10.7826820354897 & 0.967317964510294 \tabularnewline
3 & 11.39 & 10.759918877595 & 0.630081122405031 \tabularnewline
4 & 11.54 & 10.7270241407529 & 0.812975859247135 \tabularnewline
5 & 9.62 & 10.6987346670687 & -1.07873466706865 \tabularnewline
6 & 9.82 & 10.734031986677 & -0.914031986677004 \tabularnewline
7 & 9.94 & 10.666012167284 & -0.726012167284005 \tabularnewline
8 & 9.9 & 10.6432490093893 & -0.743249009389267 \tabularnewline
9 & 9.8 & 10.6103542725472 & -0.81035427254716 \tabularnewline
10 & 9.86 & 10.582064798863 & -0.722064798862952 \tabularnewline
11 & 10.5 & 10.6173621184713 & -0.117362118471301 \tabularnewline
12 & 10.33 & 10.5493422990783 & -0.219342299078301 \tabularnewline
13 & 10.16 & 10.5265791411836 & -0.366579141183564 \tabularnewline
14 & 9.91 & 10.4936844043415 & -0.583684404341459 \tabularnewline
15 & 9.96 & 10.4653949306572 & -0.505394930657248 \tabularnewline
16 & 10.03 & 10.5006922502656 & -0.470692250265599 \tabularnewline
17 & 9.55 & 10.4326724308726 & -0.882672430872598 \tabularnewline
18 & 9.51 & 10.4099092729779 & -0.899909272977862 \tabularnewline
19 & 9.8 & 10.3770145361358 & -0.577014536135756 \tabularnewline
20 & 10.08 & 10.3487250624515 & -0.268725062451546 \tabularnewline
21 & 10.2 & 10.3840223820599 & -0.184022382059897 \tabularnewline
22 & 10.23 & 10.3160025626669 & -0.0860025626668962 \tabularnewline
23 & 10.2 & 10.2932394047722 & -0.0932394047721604 \tabularnewline
24 & 10.07 & 10.2603446679301 & -0.190344667930054 \tabularnewline
25 & 10.01 & 10.2320551942458 & -0.222055194245844 \tabularnewline
26 & 10.05 & 10.2673525138542 & -0.217352513854193 \tabularnewline
27 & 9.92 & 10.1993326944612 & -0.279332694461194 \tabularnewline
28 & 10.03 & 10.1765695365665 & -0.146569536566458 \tabularnewline
29 & 10.18 & 10.1436747997244 & 0.0363252002756479 \tabularnewline
30 & 10.1 & 10.1153853260401 & -0.0153853260401416 \tabularnewline
31 & 10.16 & 10.1506826456485 & 0.00931735435150921 \tabularnewline
32 & 10.15 & 10.0826628262555 & 0.067337173744509 \tabularnewline
33 & 10.13 & 10.0598996683608 & 0.0701003316392464 \tabularnewline
34 & 10.09 & 10.0270049315186 & 0.0629950684813506 \tabularnewline
35 & 10.18 & 9.99871545783444 & 0.181284542165561 \tabularnewline
36 & 10.06 & 10.0340127774428 & 0.025987222557212 \tabularnewline
37 & 9.65 & 9.96599295804979 & -0.315992958049789 \tabularnewline
38 & 9.74 & 9.94322980015505 & -0.203229800155052 \tabularnewline
39 & 9.53 & 9.91033506331295 & -0.380335063312948 \tabularnewline
40 & 9.5 & 9.88204558962874 & -0.382045589628736 \tabularnewline
41 & 9 & 9.91734290923709 & -0.917342909237086 \tabularnewline
42 & 9.15 & 9.84932308984409 & -0.699323089844086 \tabularnewline
43 & 9.32 & 9.82655993194935 & -0.506559931949349 \tabularnewline
44 & 9.62 & 9.79366519510724 & -0.173665195107245 \tabularnewline
45 & 9.59 & 9.76537572142303 & -0.175375721423034 \tabularnewline
46 & 9.37 & 9.80067304103138 & -0.430673041031384 \tabularnewline
47 & 9.35 & 9.73265322163838 & -0.382653221638384 \tabularnewline
48 & 9.32 & 9.70989006374365 & -0.389890063743647 \tabularnewline
49 & 9.49 & 9.67699532690154 & -0.186995326901542 \tabularnewline
50 & 9.52 & 9.64870585321733 & -0.128705853217332 \tabularnewline
51 & 9.59 & 9.68400317282568 & -0.0940031728256814 \tabularnewline
52 & 9.35 & 9.61598335343268 & -0.265983353432682 \tabularnewline
53 & 9.2 & 9.59322019553795 & -0.393220195537945 \tabularnewline
54 & 9.57 & 9.56032545869584 & 0.00967454130416083 \tabularnewline
55 & 9.78 & 9.53203598501163 & 0.24796401498837 \tabularnewline
56 & 9.79 & 9.56733330461998 & 0.22266669538002 \tabularnewline
57 & 9.57 & 9.49931348522698 & 0.0706865147730212 \tabularnewline
58 & 9.53 & 9.47655032733224 & 0.0534496726677571 \tabularnewline
59 & 9.65 & 9.44365559049014 & 0.206344409509863 \tabularnewline
60 & 9.36 & 9.41536611680593 & -0.0553661168059269 \tabularnewline
61 & 9.4 & 9.45066343641428 & -0.0506634364142756 \tabularnewline
62 & 9.32 & 9.38264361702128 & -0.0626436170212761 \tabularnewline
63 & 9.31 & 9.35988045912654 & -0.0498804591265393 \tabularnewline
64 & 9.19 & 9.32698572228443 & -0.136985722284435 \tabularnewline
65 & 9.39 & 9.29869624860022 & 0.0913037513997767 \tabularnewline
66 & 9.28 & 9.33399356820857 & -0.0539935682085742 \tabularnewline
67 & 9.28 & 9.26597374881557 & 0.0140262511844255 \tabularnewline
68 & 9.31 & 9.24321059092084 & 0.0667894090791632 \tabularnewline
69 & 9.28 & 9.21031585407873 & 0.0696841459212674 \tabularnewline
70 & 9.31 & 9.18202638039452 & 0.127973619605479 \tabularnewline
71 & 9.35 & 9.21732370000287 & 0.132676299997129 \tabularnewline
72 & 9.19 & 9.14930388060987 & 0.0406961193901281 \tabularnewline
73 & 9.07 & 9.12654072271513 & -0.0565407227151345 \tabularnewline
74 & 8.96 & 9.09364598587303 & -0.133645985873029 \tabularnewline
75 & 8.69 & 9.06535651218882 & -0.375356512188819 \tabularnewline
76 & 8.58 & 9.10065383179717 & -0.520653831797169 \tabularnewline
77 & 8.56 & 9.03263401240417 & -0.472634012404169 \tabularnewline
78 & 8.47 & 9.00987085450943 & -0.539870854509432 \tabularnewline
79 & 8.46 & 8.97697611766733 & -0.516976117667326 \tabularnewline
80 & 8.75 & 8.94868664398312 & -0.198686643983117 \tabularnewline
81 & 8.95 & 8.98398396359147 & -0.0339839635914669 \tabularnewline
82 & 9.33 & 8.91596414419847 & 0.414035855801534 \tabularnewline
83 & 9.51 & 8.89320098630373 & 0.61679901369627 \tabularnewline
84 & 9.561 & 8.86030624946162 & 0.700693750538375 \tabularnewline
85 & 9.94 & 8.83201677577742 & 1.10798322422259 \tabularnewline
86 & 9.9 & 8.86731409538576 & 1.03268590461424 \tabularnewline
87 & 9.275 & 8.79929427599276 & 0.475705724007236 \tabularnewline
88 & 9.56 & 8.77653111809803 & 0.783468881901973 \tabularnewline
89 & 9.779 & 8.74363638125592 & 1.03536361874408 \tabularnewline
90 & 9.746 & 8.71534690757171 & 1.03065309242829 \tabularnewline
91 & 9.991 & 8.75064422718006 & 1.24035577281994 \tabularnewline
92 & 9.98 & 8.68262440778706 & 1.29737559221294 \tabularnewline
93 & 10.195 & 8.65986124989232 & 1.53513875010768 \tabularnewline
94 & 10.31 & 8.62696651305022 & 1.68303348694978 \tabularnewline
95 & 10.25 & 8.59867703936601 & 1.65132296063399 \tabularnewline
96 & 9.871 & 8.63397435897436 & 1.23702564102564 \tabularnewline
97 & 10.06 & 8.56595453958136 & 1.49404546041864 \tabularnewline
98 & 9.894 & 8.54319138168662 & 1.35080861831338 \tabularnewline
99 & 9.59 & 8.51029664484452 & 1.07970335515548 \tabularnewline
100 & 9.64 & 8.4820071711603 & 1.15799282883969 \tabularnewline
101 & 9.89 & 8.51730449076866 & 1.37269550923134 \tabularnewline
102 & 9.53 & 8.44928467137566 & 1.08071532862434 \tabularnewline
103 & 9.388 & 8.42652151348092 & 0.96147848651908 \tabularnewline
104 & 9.16 & 8.39362677663881 & 0.766373223361185 \tabularnewline
105 & 9.418 & 8.3653373029546 & 1.0526626970454 \tabularnewline
106 & 9.57 & 8.40063462256295 & 1.16936537743705 \tabularnewline
107 & 9.857 & 8.33261480316995 & 1.52438519683004 \tabularnewline
108 & 9.877 & 8.30985164527522 & 1.56714835472478 \tabularnewline
109 & 9.76 & 8.27695690843311 & 1.48304309156689 \tabularnewline
110 & 9.76 & 8.2486674347489 & 1.5113325652511 \tabularnewline
111 & 9.695 & 8.28396475435725 & 1.41103524564275 \tabularnewline
112 & 9.475 & 8.21594493496425 & 1.25905506503575 \tabularnewline
113 & 9.262 & 8.19318177706952 & 1.06881822293049 \tabularnewline
114 & 9.097 & 8.16028704022741 & 0.93671295977259 \tabularnewline
115 & 8.55 & 8.1319975665432 & 0.418002433456801 \tabularnewline
116 & 8.16 & 8.16729488615155 & -0.00729488615154896 \tabularnewline
117 & 7.532 & 8.09927506675855 & -0.56727506675855 \tabularnewline
118 & 7.325 & 8.07651190886381 & -0.751511908863812 \tabularnewline
119 & 6.749 & 8.04361717202171 & -1.29461717202171 \tabularnewline
120 & 7.13 & 8.0153276983375 & -0.885327698337497 \tabularnewline
121 & 6.995 & 8.05062501794585 & -1.05562501794585 \tabularnewline
122 & 7.346 & 7.98260519855285 & -0.636605198552847 \tabularnewline
123 & 7.73 & 7.95984204065811 & -0.22984204065811 \tabularnewline
124 & 7.837 & 7.926947303816 & -0.0899473038160052 \tabularnewline
125 & 7.514 & 7.8986578301318 & -0.384657830131794 \tabularnewline
126 & 7.58 & 7.93395514974014 & -0.353955149740144 \tabularnewline
127 & 6.83 & 7.86593533034714 & -1.03593533034714 \tabularnewline
128 & 6.617 & 7.8431721724524 & -1.22617217245241 \tabularnewline
129 & 6.715 & 7.8102774356103 & -1.0952774356103 \tabularnewline
130 & 6.63 & 7.78198796192609 & -1.15198796192609 \tabularnewline
131 & 6.891 & 7.81728528153444 & -0.926285281534442 \tabularnewline
132 & 7.002 & 7.74926546214144 & -0.747265462141443 \tabularnewline
133 & 7.09 & 7.7265023042467 & -0.636502304246705 \tabularnewline
134 & 7.36 & 7.6936075674046 & -0.3336075674046 \tabularnewline
135 & 7.477 & 7.66531809372039 & -0.188318093720389 \tabularnewline
136 & 7.826 & 7.70061541332874 & 0.12538458667126 \tabularnewline
137 & 7.79 & 7.63259559393574 & 0.157404406064261 \tabularnewline
138 & 7.578 & 7.609832436041 & -0.0318324360410024 \tabularnewline
139 & 7.204 & 7.5769376991989 & -0.372937699198898 \tabularnewline
140 & 7.198 & 7.54864822551469 & -0.350648225514687 \tabularnewline
141 & 7.685 & 7.58394554512304 & 0.101054454876963 \tabularnewline
142 & 7.795 & 7.51592572573004 & 0.279074274269963 \tabularnewline
143 & 7.46 & 7.4931625678353 & -0.0331625678353002 \tabularnewline
144 & 7.274 & 7.4602678309932 & -0.186267830993195 \tabularnewline
145 & 7.33 & 7.43197835730898 & -0.101978357308984 \tabularnewline
146 & 7.655 & 7.46727567691733 & 0.187724323082666 \tabularnewline
147 & 7.767 & 7.39925585752433 & 0.367744142475666 \tabularnewline
148 & 7.84 & 7.3764926996296 & 0.463507300370402 \tabularnewline
149 & 7.424 & 7.34359796278749 & 0.0804020372125076 \tabularnewline
150 & 7.54 & 7.31530848910328 & 0.224691510896718 \tabularnewline
151 & 7.351 & 7.35060580871163 & 0.000394191288368287 \tabularnewline
152 & 6.735 & 7.28258598931863 & -0.547585989318632 \tabularnewline
153 & 6.777 & 7.2598228314239 & -0.482822831423895 \tabularnewline
154 & 6.679 & 7.2269280945818 & -0.54792809458179 \tabularnewline
155 & 7.34 & 7.19863862089758 & 0.14136137910242 \tabularnewline
156 & 6.978 & 7.23393594050593 & -0.25593594050593 \tabularnewline
157 & 6.92 & 7.16591612111293 & -0.24591612111293 \tabularnewline
158 & 6.628 & 7.1431529632182 & -0.515152963218193 \tabularnewline
159 & 6.385 & 7.11025822637609 & -0.725258226376088 \tabularnewline
160 & 5.984 & 7.08196875269188 & -1.09796875269188 \tabularnewline
161 & 6.268 & 7.11726607230023 & -0.849266072300227 \tabularnewline
162 & 6.596 & 7.04924625290723 & -0.453246252907227 \tabularnewline
163 & 6.395 & 7.02648309501249 & -0.631483095012491 \tabularnewline
164 & 6.715 & 6.99358835817038 & -0.278588358170385 \tabularnewline
165 & 6.804 & 6.96529888448617 & -0.161298884486174 \tabularnewline
166 & 6.929 & 7.00059620409452 & -0.0715962040945244 \tabularnewline
167 & 6.846 & 6.93257638470152 & -0.086576384701525 \tabularnewline
168 & 6.992 & 6.90981322680679 & 0.0821867731932114 \tabularnewline
169 & 6.774 & 6.87691848996468 & -0.102918489964683 \tabularnewline
170 & 6.75 & 6.84862901628047 & -0.0986290162804717 \tabularnewline
171 & 6.485 & 6.88392633588882 & -0.398926335888821 \tabularnewline
172 & 6.27 & 6.81590651649582 & -0.545906516495823 \tabularnewline
173 & 6.47 & 6.79314335860109 & -0.323143358601086 \tabularnewline
174 & 6.78 & 6.76024862175898 & 0.0197513782410201 \tabularnewline
175 & 6.71 & 6.73195914807477 & -0.0219591480747694 \tabularnewline
176 & 6.141 & 6.76725646768312 & -0.62625646768312 \tabularnewline
177 & 6.72 & 6.69923664829012 & 0.02076335170988 \tabularnewline
178 & 6.68 & 6.67647349039538 & 0.0035265096046167 \tabularnewline
179 & 6.371 & 6.64357875355328 & -0.272578753553277 \tabularnewline
180 & 6.097 & 6.61528927986907 & -0.518289279869067 \tabularnewline
181 & 6.27 & 6.65058659947742 & -0.380586599477418 \tabularnewline
182 & 6.447 & 6.58256678008442 & -0.135566780084417 \tabularnewline
183 & 6.37 & 6.55980362218968 & -0.18980362218968 \tabularnewline
184 & 6.446 & 6.52690888534758 & -0.0809088853475753 \tabularnewline
185 & 6.54 & 6.49861941166336 & 0.0413805883366355 \tabularnewline
186 & 6.374 & 6.53391673127171 & -0.159916731271715 \tabularnewline
187 & 6.33 & 6.46589691187871 & -0.135896911878715 \tabularnewline
188 & 6.63 & 6.44313375398398 & 0.186866246016022 \tabularnewline
189 & 6.498 & 6.41023901714187 & 0.0877609828581275 \tabularnewline
190 & 6.485 & 6.38194954345766 & 0.103050456542338 \tabularnewline
191 & 6.36 & 6.41724686306601 & -0.0572468630660119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&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]11.73[/C][C]10.8507018548827[/C][C]0.879298145117296[/C][/ROW]
[ROW][C]2[/C][C]11.75[/C][C]10.7826820354897[/C][C]0.967317964510294[/C][/ROW]
[ROW][C]3[/C][C]11.39[/C][C]10.759918877595[/C][C]0.630081122405031[/C][/ROW]
[ROW][C]4[/C][C]11.54[/C][C]10.7270241407529[/C][C]0.812975859247135[/C][/ROW]
[ROW][C]5[/C][C]9.62[/C][C]10.6987346670687[/C][C]-1.07873466706865[/C][/ROW]
[ROW][C]6[/C][C]9.82[/C][C]10.734031986677[/C][C]-0.914031986677004[/C][/ROW]
[ROW][C]7[/C][C]9.94[/C][C]10.666012167284[/C][C]-0.726012167284005[/C][/ROW]
[ROW][C]8[/C][C]9.9[/C][C]10.6432490093893[/C][C]-0.743249009389267[/C][/ROW]
[ROW][C]9[/C][C]9.8[/C][C]10.6103542725472[/C][C]-0.81035427254716[/C][/ROW]
[ROW][C]10[/C][C]9.86[/C][C]10.582064798863[/C][C]-0.722064798862952[/C][/ROW]
[ROW][C]11[/C][C]10.5[/C][C]10.6173621184713[/C][C]-0.117362118471301[/C][/ROW]
[ROW][C]12[/C][C]10.33[/C][C]10.5493422990783[/C][C]-0.219342299078301[/C][/ROW]
[ROW][C]13[/C][C]10.16[/C][C]10.5265791411836[/C][C]-0.366579141183564[/C][/ROW]
[ROW][C]14[/C][C]9.91[/C][C]10.4936844043415[/C][C]-0.583684404341459[/C][/ROW]
[ROW][C]15[/C][C]9.96[/C][C]10.4653949306572[/C][C]-0.505394930657248[/C][/ROW]
[ROW][C]16[/C][C]10.03[/C][C]10.5006922502656[/C][C]-0.470692250265599[/C][/ROW]
[ROW][C]17[/C][C]9.55[/C][C]10.4326724308726[/C][C]-0.882672430872598[/C][/ROW]
[ROW][C]18[/C][C]9.51[/C][C]10.4099092729779[/C][C]-0.899909272977862[/C][/ROW]
[ROW][C]19[/C][C]9.8[/C][C]10.3770145361358[/C][C]-0.577014536135756[/C][/ROW]
[ROW][C]20[/C][C]10.08[/C][C]10.3487250624515[/C][C]-0.268725062451546[/C][/ROW]
[ROW][C]21[/C][C]10.2[/C][C]10.3840223820599[/C][C]-0.184022382059897[/C][/ROW]
[ROW][C]22[/C][C]10.23[/C][C]10.3160025626669[/C][C]-0.0860025626668962[/C][/ROW]
[ROW][C]23[/C][C]10.2[/C][C]10.2932394047722[/C][C]-0.0932394047721604[/C][/ROW]
[ROW][C]24[/C][C]10.07[/C][C]10.2603446679301[/C][C]-0.190344667930054[/C][/ROW]
[ROW][C]25[/C][C]10.01[/C][C]10.2320551942458[/C][C]-0.222055194245844[/C][/ROW]
[ROW][C]26[/C][C]10.05[/C][C]10.2673525138542[/C][C]-0.217352513854193[/C][/ROW]
[ROW][C]27[/C][C]9.92[/C][C]10.1993326944612[/C][C]-0.279332694461194[/C][/ROW]
[ROW][C]28[/C][C]10.03[/C][C]10.1765695365665[/C][C]-0.146569536566458[/C][/ROW]
[ROW][C]29[/C][C]10.18[/C][C]10.1436747997244[/C][C]0.0363252002756479[/C][/ROW]
[ROW][C]30[/C][C]10.1[/C][C]10.1153853260401[/C][C]-0.0153853260401416[/C][/ROW]
[ROW][C]31[/C][C]10.16[/C][C]10.1506826456485[/C][C]0.00931735435150921[/C][/ROW]
[ROW][C]32[/C][C]10.15[/C][C]10.0826628262555[/C][C]0.067337173744509[/C][/ROW]
[ROW][C]33[/C][C]10.13[/C][C]10.0598996683608[/C][C]0.0701003316392464[/C][/ROW]
[ROW][C]34[/C][C]10.09[/C][C]10.0270049315186[/C][C]0.0629950684813506[/C][/ROW]
[ROW][C]35[/C][C]10.18[/C][C]9.99871545783444[/C][C]0.181284542165561[/C][/ROW]
[ROW][C]36[/C][C]10.06[/C][C]10.0340127774428[/C][C]0.025987222557212[/C][/ROW]
[ROW][C]37[/C][C]9.65[/C][C]9.96599295804979[/C][C]-0.315992958049789[/C][/ROW]
[ROW][C]38[/C][C]9.74[/C][C]9.94322980015505[/C][C]-0.203229800155052[/C][/ROW]
[ROW][C]39[/C][C]9.53[/C][C]9.91033506331295[/C][C]-0.380335063312948[/C][/ROW]
[ROW][C]40[/C][C]9.5[/C][C]9.88204558962874[/C][C]-0.382045589628736[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.91734290923709[/C][C]-0.917342909237086[/C][/ROW]
[ROW][C]42[/C][C]9.15[/C][C]9.84932308984409[/C][C]-0.699323089844086[/C][/ROW]
[ROW][C]43[/C][C]9.32[/C][C]9.82655993194935[/C][C]-0.506559931949349[/C][/ROW]
[ROW][C]44[/C][C]9.62[/C][C]9.79366519510724[/C][C]-0.173665195107245[/C][/ROW]
[ROW][C]45[/C][C]9.59[/C][C]9.76537572142303[/C][C]-0.175375721423034[/C][/ROW]
[ROW][C]46[/C][C]9.37[/C][C]9.80067304103138[/C][C]-0.430673041031384[/C][/ROW]
[ROW][C]47[/C][C]9.35[/C][C]9.73265322163838[/C][C]-0.382653221638384[/C][/ROW]
[ROW][C]48[/C][C]9.32[/C][C]9.70989006374365[/C][C]-0.389890063743647[/C][/ROW]
[ROW][C]49[/C][C]9.49[/C][C]9.67699532690154[/C][C]-0.186995326901542[/C][/ROW]
[ROW][C]50[/C][C]9.52[/C][C]9.64870585321733[/C][C]-0.128705853217332[/C][/ROW]
[ROW][C]51[/C][C]9.59[/C][C]9.68400317282568[/C][C]-0.0940031728256814[/C][/ROW]
[ROW][C]52[/C][C]9.35[/C][C]9.61598335343268[/C][C]-0.265983353432682[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]9.59322019553795[/C][C]-0.393220195537945[/C][/ROW]
[ROW][C]54[/C][C]9.57[/C][C]9.56032545869584[/C][C]0.00967454130416083[/C][/ROW]
[ROW][C]55[/C][C]9.78[/C][C]9.53203598501163[/C][C]0.24796401498837[/C][/ROW]
[ROW][C]56[/C][C]9.79[/C][C]9.56733330461998[/C][C]0.22266669538002[/C][/ROW]
[ROW][C]57[/C][C]9.57[/C][C]9.49931348522698[/C][C]0.0706865147730212[/C][/ROW]
[ROW][C]58[/C][C]9.53[/C][C]9.47655032733224[/C][C]0.0534496726677571[/C][/ROW]
[ROW][C]59[/C][C]9.65[/C][C]9.44365559049014[/C][C]0.206344409509863[/C][/ROW]
[ROW][C]60[/C][C]9.36[/C][C]9.41536611680593[/C][C]-0.0553661168059269[/C][/ROW]
[ROW][C]61[/C][C]9.4[/C][C]9.45066343641428[/C][C]-0.0506634364142756[/C][/ROW]
[ROW][C]62[/C][C]9.32[/C][C]9.38264361702128[/C][C]-0.0626436170212761[/C][/ROW]
[ROW][C]63[/C][C]9.31[/C][C]9.35988045912654[/C][C]-0.0498804591265393[/C][/ROW]
[ROW][C]64[/C][C]9.19[/C][C]9.32698572228443[/C][C]-0.136985722284435[/C][/ROW]
[ROW][C]65[/C][C]9.39[/C][C]9.29869624860022[/C][C]0.0913037513997767[/C][/ROW]
[ROW][C]66[/C][C]9.28[/C][C]9.33399356820857[/C][C]-0.0539935682085742[/C][/ROW]
[ROW][C]67[/C][C]9.28[/C][C]9.26597374881557[/C][C]0.0140262511844255[/C][/ROW]
[ROW][C]68[/C][C]9.31[/C][C]9.24321059092084[/C][C]0.0667894090791632[/C][/ROW]
[ROW][C]69[/C][C]9.28[/C][C]9.21031585407873[/C][C]0.0696841459212674[/C][/ROW]
[ROW][C]70[/C][C]9.31[/C][C]9.18202638039452[/C][C]0.127973619605479[/C][/ROW]
[ROW][C]71[/C][C]9.35[/C][C]9.21732370000287[/C][C]0.132676299997129[/C][/ROW]
[ROW][C]72[/C][C]9.19[/C][C]9.14930388060987[/C][C]0.0406961193901281[/C][/ROW]
[ROW][C]73[/C][C]9.07[/C][C]9.12654072271513[/C][C]-0.0565407227151345[/C][/ROW]
[ROW][C]74[/C][C]8.96[/C][C]9.09364598587303[/C][C]-0.133645985873029[/C][/ROW]
[ROW][C]75[/C][C]8.69[/C][C]9.06535651218882[/C][C]-0.375356512188819[/C][/ROW]
[ROW][C]76[/C][C]8.58[/C][C]9.10065383179717[/C][C]-0.520653831797169[/C][/ROW]
[ROW][C]77[/C][C]8.56[/C][C]9.03263401240417[/C][C]-0.472634012404169[/C][/ROW]
[ROW][C]78[/C][C]8.47[/C][C]9.00987085450943[/C][C]-0.539870854509432[/C][/ROW]
[ROW][C]79[/C][C]8.46[/C][C]8.97697611766733[/C][C]-0.516976117667326[/C][/ROW]
[ROW][C]80[/C][C]8.75[/C][C]8.94868664398312[/C][C]-0.198686643983117[/C][/ROW]
[ROW][C]81[/C][C]8.95[/C][C]8.98398396359147[/C][C]-0.0339839635914669[/C][/ROW]
[ROW][C]82[/C][C]9.33[/C][C]8.91596414419847[/C][C]0.414035855801534[/C][/ROW]
[ROW][C]83[/C][C]9.51[/C][C]8.89320098630373[/C][C]0.61679901369627[/C][/ROW]
[ROW][C]84[/C][C]9.561[/C][C]8.86030624946162[/C][C]0.700693750538375[/C][/ROW]
[ROW][C]85[/C][C]9.94[/C][C]8.83201677577742[/C][C]1.10798322422259[/C][/ROW]
[ROW][C]86[/C][C]9.9[/C][C]8.86731409538576[/C][C]1.03268590461424[/C][/ROW]
[ROW][C]87[/C][C]9.275[/C][C]8.79929427599276[/C][C]0.475705724007236[/C][/ROW]
[ROW][C]88[/C][C]9.56[/C][C]8.77653111809803[/C][C]0.783468881901973[/C][/ROW]
[ROW][C]89[/C][C]9.779[/C][C]8.74363638125592[/C][C]1.03536361874408[/C][/ROW]
[ROW][C]90[/C][C]9.746[/C][C]8.71534690757171[/C][C]1.03065309242829[/C][/ROW]
[ROW][C]91[/C][C]9.991[/C][C]8.75064422718006[/C][C]1.24035577281994[/C][/ROW]
[ROW][C]92[/C][C]9.98[/C][C]8.68262440778706[/C][C]1.29737559221294[/C][/ROW]
[ROW][C]93[/C][C]10.195[/C][C]8.65986124989232[/C][C]1.53513875010768[/C][/ROW]
[ROW][C]94[/C][C]10.31[/C][C]8.62696651305022[/C][C]1.68303348694978[/C][/ROW]
[ROW][C]95[/C][C]10.25[/C][C]8.59867703936601[/C][C]1.65132296063399[/C][/ROW]
[ROW][C]96[/C][C]9.871[/C][C]8.63397435897436[/C][C]1.23702564102564[/C][/ROW]
[ROW][C]97[/C][C]10.06[/C][C]8.56595453958136[/C][C]1.49404546041864[/C][/ROW]
[ROW][C]98[/C][C]9.894[/C][C]8.54319138168662[/C][C]1.35080861831338[/C][/ROW]
[ROW][C]99[/C][C]9.59[/C][C]8.51029664484452[/C][C]1.07970335515548[/C][/ROW]
[ROW][C]100[/C][C]9.64[/C][C]8.4820071711603[/C][C]1.15799282883969[/C][/ROW]
[ROW][C]101[/C][C]9.89[/C][C]8.51730449076866[/C][C]1.37269550923134[/C][/ROW]
[ROW][C]102[/C][C]9.53[/C][C]8.44928467137566[/C][C]1.08071532862434[/C][/ROW]
[ROW][C]103[/C][C]9.388[/C][C]8.42652151348092[/C][C]0.96147848651908[/C][/ROW]
[ROW][C]104[/C][C]9.16[/C][C]8.39362677663881[/C][C]0.766373223361185[/C][/ROW]
[ROW][C]105[/C][C]9.418[/C][C]8.3653373029546[/C][C]1.0526626970454[/C][/ROW]
[ROW][C]106[/C][C]9.57[/C][C]8.40063462256295[/C][C]1.16936537743705[/C][/ROW]
[ROW][C]107[/C][C]9.857[/C][C]8.33261480316995[/C][C]1.52438519683004[/C][/ROW]
[ROW][C]108[/C][C]9.877[/C][C]8.30985164527522[/C][C]1.56714835472478[/C][/ROW]
[ROW][C]109[/C][C]9.76[/C][C]8.27695690843311[/C][C]1.48304309156689[/C][/ROW]
[ROW][C]110[/C][C]9.76[/C][C]8.2486674347489[/C][C]1.5113325652511[/C][/ROW]
[ROW][C]111[/C][C]9.695[/C][C]8.28396475435725[/C][C]1.41103524564275[/C][/ROW]
[ROW][C]112[/C][C]9.475[/C][C]8.21594493496425[/C][C]1.25905506503575[/C][/ROW]
[ROW][C]113[/C][C]9.262[/C][C]8.19318177706952[/C][C]1.06881822293049[/C][/ROW]
[ROW][C]114[/C][C]9.097[/C][C]8.16028704022741[/C][C]0.93671295977259[/C][/ROW]
[ROW][C]115[/C][C]8.55[/C][C]8.1319975665432[/C][C]0.418002433456801[/C][/ROW]
[ROW][C]116[/C][C]8.16[/C][C]8.16729488615155[/C][C]-0.00729488615154896[/C][/ROW]
[ROW][C]117[/C][C]7.532[/C][C]8.09927506675855[/C][C]-0.56727506675855[/C][/ROW]
[ROW][C]118[/C][C]7.325[/C][C]8.07651190886381[/C][C]-0.751511908863812[/C][/ROW]
[ROW][C]119[/C][C]6.749[/C][C]8.04361717202171[/C][C]-1.29461717202171[/C][/ROW]
[ROW][C]120[/C][C]7.13[/C][C]8.0153276983375[/C][C]-0.885327698337497[/C][/ROW]
[ROW][C]121[/C][C]6.995[/C][C]8.05062501794585[/C][C]-1.05562501794585[/C][/ROW]
[ROW][C]122[/C][C]7.346[/C][C]7.98260519855285[/C][C]-0.636605198552847[/C][/ROW]
[ROW][C]123[/C][C]7.73[/C][C]7.95984204065811[/C][C]-0.22984204065811[/C][/ROW]
[ROW][C]124[/C][C]7.837[/C][C]7.926947303816[/C][C]-0.0899473038160052[/C][/ROW]
[ROW][C]125[/C][C]7.514[/C][C]7.8986578301318[/C][C]-0.384657830131794[/C][/ROW]
[ROW][C]126[/C][C]7.58[/C][C]7.93395514974014[/C][C]-0.353955149740144[/C][/ROW]
[ROW][C]127[/C][C]6.83[/C][C]7.86593533034714[/C][C]-1.03593533034714[/C][/ROW]
[ROW][C]128[/C][C]6.617[/C][C]7.8431721724524[/C][C]-1.22617217245241[/C][/ROW]
[ROW][C]129[/C][C]6.715[/C][C]7.8102774356103[/C][C]-1.0952774356103[/C][/ROW]
[ROW][C]130[/C][C]6.63[/C][C]7.78198796192609[/C][C]-1.15198796192609[/C][/ROW]
[ROW][C]131[/C][C]6.891[/C][C]7.81728528153444[/C][C]-0.926285281534442[/C][/ROW]
[ROW][C]132[/C][C]7.002[/C][C]7.74926546214144[/C][C]-0.747265462141443[/C][/ROW]
[ROW][C]133[/C][C]7.09[/C][C]7.7265023042467[/C][C]-0.636502304246705[/C][/ROW]
[ROW][C]134[/C][C]7.36[/C][C]7.6936075674046[/C][C]-0.3336075674046[/C][/ROW]
[ROW][C]135[/C][C]7.477[/C][C]7.66531809372039[/C][C]-0.188318093720389[/C][/ROW]
[ROW][C]136[/C][C]7.826[/C][C]7.70061541332874[/C][C]0.12538458667126[/C][/ROW]
[ROW][C]137[/C][C]7.79[/C][C]7.63259559393574[/C][C]0.157404406064261[/C][/ROW]
[ROW][C]138[/C][C]7.578[/C][C]7.609832436041[/C][C]-0.0318324360410024[/C][/ROW]
[ROW][C]139[/C][C]7.204[/C][C]7.5769376991989[/C][C]-0.372937699198898[/C][/ROW]
[ROW][C]140[/C][C]7.198[/C][C]7.54864822551469[/C][C]-0.350648225514687[/C][/ROW]
[ROW][C]141[/C][C]7.685[/C][C]7.58394554512304[/C][C]0.101054454876963[/C][/ROW]
[ROW][C]142[/C][C]7.795[/C][C]7.51592572573004[/C][C]0.279074274269963[/C][/ROW]
[ROW][C]143[/C][C]7.46[/C][C]7.4931625678353[/C][C]-0.0331625678353002[/C][/ROW]
[ROW][C]144[/C][C]7.274[/C][C]7.4602678309932[/C][C]-0.186267830993195[/C][/ROW]
[ROW][C]145[/C][C]7.33[/C][C]7.43197835730898[/C][C]-0.101978357308984[/C][/ROW]
[ROW][C]146[/C][C]7.655[/C][C]7.46727567691733[/C][C]0.187724323082666[/C][/ROW]
[ROW][C]147[/C][C]7.767[/C][C]7.39925585752433[/C][C]0.367744142475666[/C][/ROW]
[ROW][C]148[/C][C]7.84[/C][C]7.3764926996296[/C][C]0.463507300370402[/C][/ROW]
[ROW][C]149[/C][C]7.424[/C][C]7.34359796278749[/C][C]0.0804020372125076[/C][/ROW]
[ROW][C]150[/C][C]7.54[/C][C]7.31530848910328[/C][C]0.224691510896718[/C][/ROW]
[ROW][C]151[/C][C]7.351[/C][C]7.35060580871163[/C][C]0.000394191288368287[/C][/ROW]
[ROW][C]152[/C][C]6.735[/C][C]7.28258598931863[/C][C]-0.547585989318632[/C][/ROW]
[ROW][C]153[/C][C]6.777[/C][C]7.2598228314239[/C][C]-0.482822831423895[/C][/ROW]
[ROW][C]154[/C][C]6.679[/C][C]7.2269280945818[/C][C]-0.54792809458179[/C][/ROW]
[ROW][C]155[/C][C]7.34[/C][C]7.19863862089758[/C][C]0.14136137910242[/C][/ROW]
[ROW][C]156[/C][C]6.978[/C][C]7.23393594050593[/C][C]-0.25593594050593[/C][/ROW]
[ROW][C]157[/C][C]6.92[/C][C]7.16591612111293[/C][C]-0.24591612111293[/C][/ROW]
[ROW][C]158[/C][C]6.628[/C][C]7.1431529632182[/C][C]-0.515152963218193[/C][/ROW]
[ROW][C]159[/C][C]6.385[/C][C]7.11025822637609[/C][C]-0.725258226376088[/C][/ROW]
[ROW][C]160[/C][C]5.984[/C][C]7.08196875269188[/C][C]-1.09796875269188[/C][/ROW]
[ROW][C]161[/C][C]6.268[/C][C]7.11726607230023[/C][C]-0.849266072300227[/C][/ROW]
[ROW][C]162[/C][C]6.596[/C][C]7.04924625290723[/C][C]-0.453246252907227[/C][/ROW]
[ROW][C]163[/C][C]6.395[/C][C]7.02648309501249[/C][C]-0.631483095012491[/C][/ROW]
[ROW][C]164[/C][C]6.715[/C][C]6.99358835817038[/C][C]-0.278588358170385[/C][/ROW]
[ROW][C]165[/C][C]6.804[/C][C]6.96529888448617[/C][C]-0.161298884486174[/C][/ROW]
[ROW][C]166[/C][C]6.929[/C][C]7.00059620409452[/C][C]-0.0715962040945244[/C][/ROW]
[ROW][C]167[/C][C]6.846[/C][C]6.93257638470152[/C][C]-0.086576384701525[/C][/ROW]
[ROW][C]168[/C][C]6.992[/C][C]6.90981322680679[/C][C]0.0821867731932114[/C][/ROW]
[ROW][C]169[/C][C]6.774[/C][C]6.87691848996468[/C][C]-0.102918489964683[/C][/ROW]
[ROW][C]170[/C][C]6.75[/C][C]6.84862901628047[/C][C]-0.0986290162804717[/C][/ROW]
[ROW][C]171[/C][C]6.485[/C][C]6.88392633588882[/C][C]-0.398926335888821[/C][/ROW]
[ROW][C]172[/C][C]6.27[/C][C]6.81590651649582[/C][C]-0.545906516495823[/C][/ROW]
[ROW][C]173[/C][C]6.47[/C][C]6.79314335860109[/C][C]-0.323143358601086[/C][/ROW]
[ROW][C]174[/C][C]6.78[/C][C]6.76024862175898[/C][C]0.0197513782410201[/C][/ROW]
[ROW][C]175[/C][C]6.71[/C][C]6.73195914807477[/C][C]-0.0219591480747694[/C][/ROW]
[ROW][C]176[/C][C]6.141[/C][C]6.76725646768312[/C][C]-0.62625646768312[/C][/ROW]
[ROW][C]177[/C][C]6.72[/C][C]6.69923664829012[/C][C]0.02076335170988[/C][/ROW]
[ROW][C]178[/C][C]6.68[/C][C]6.67647349039538[/C][C]0.0035265096046167[/C][/ROW]
[ROW][C]179[/C][C]6.371[/C][C]6.64357875355328[/C][C]-0.272578753553277[/C][/ROW]
[ROW][C]180[/C][C]6.097[/C][C]6.61528927986907[/C][C]-0.518289279869067[/C][/ROW]
[ROW][C]181[/C][C]6.27[/C][C]6.65058659947742[/C][C]-0.380586599477418[/C][/ROW]
[ROW][C]182[/C][C]6.447[/C][C]6.58256678008442[/C][C]-0.135566780084417[/C][/ROW]
[ROW][C]183[/C][C]6.37[/C][C]6.55980362218968[/C][C]-0.18980362218968[/C][/ROW]
[ROW][C]184[/C][C]6.446[/C][C]6.52690888534758[/C][C]-0.0809088853475753[/C][/ROW]
[ROW][C]185[/C][C]6.54[/C][C]6.49861941166336[/C][C]0.0413805883366355[/C][/ROW]
[ROW][C]186[/C][C]6.374[/C][C]6.53391673127171[/C][C]-0.159916731271715[/C][/ROW]
[ROW][C]187[/C][C]6.33[/C][C]6.46589691187871[/C][C]-0.135896911878715[/C][/ROW]
[ROW][C]188[/C][C]6.63[/C][C]6.44313375398398[/C][C]0.186866246016022[/C][/ROW]
[ROW][C]189[/C][C]6.498[/C][C]6.41023901714187[/C][C]0.0877609828581275[/C][/ROW]
[ROW][C]190[/C][C]6.485[/C][C]6.38194954345766[/C][C]0.103050456542338[/C][/ROW]
[ROW][C]191[/C][C]6.36[/C][C]6.41724686306601[/C][C]-0.0572468630660119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.7310.85070185488270.879298145117296
211.7510.78268203548970.967317964510294
311.3910.7599188775950.630081122405031
411.5410.72702414075290.812975859247135
59.6210.6987346670687-1.07873466706865
69.8210.734031986677-0.914031986677004
79.9410.666012167284-0.726012167284005
89.910.6432490093893-0.743249009389267
99.810.6103542725472-0.81035427254716
109.8610.582064798863-0.722064798862952
1110.510.6173621184713-0.117362118471301
1210.3310.5493422990783-0.219342299078301
1310.1610.5265791411836-0.366579141183564
149.9110.4936844043415-0.583684404341459
159.9610.4653949306572-0.505394930657248
1610.0310.5006922502656-0.470692250265599
179.5510.4326724308726-0.882672430872598
189.5110.4099092729779-0.899909272977862
199.810.3770145361358-0.577014536135756
2010.0810.3487250624515-0.268725062451546
2110.210.3840223820599-0.184022382059897
2210.2310.3160025626669-0.0860025626668962
2310.210.2932394047722-0.0932394047721604
2410.0710.2603446679301-0.190344667930054
2510.0110.2320551942458-0.222055194245844
2610.0510.2673525138542-0.217352513854193
279.9210.1993326944612-0.279332694461194
2810.0310.1765695365665-0.146569536566458
2910.1810.14367479972440.0363252002756479
3010.110.1153853260401-0.0153853260401416
3110.1610.15068264564850.00931735435150921
3210.1510.08266282625550.067337173744509
3310.1310.05989966836080.0701003316392464
3410.0910.02700493151860.0629950684813506
3510.189.998715457834440.181284542165561
3610.0610.03401277744280.025987222557212
379.659.96599295804979-0.315992958049789
389.749.94322980015505-0.203229800155052
399.539.91033506331295-0.380335063312948
409.59.88204558962874-0.382045589628736
4199.91734290923709-0.917342909237086
429.159.84932308984409-0.699323089844086
439.329.82655993194935-0.506559931949349
449.629.79366519510724-0.173665195107245
459.599.76537572142303-0.175375721423034
469.379.80067304103138-0.430673041031384
479.359.73265322163838-0.382653221638384
489.329.70989006374365-0.389890063743647
499.499.67699532690154-0.186995326901542
509.529.64870585321733-0.128705853217332
519.599.68400317282568-0.0940031728256814
529.359.61598335343268-0.265983353432682
539.29.59322019553795-0.393220195537945
549.579.560325458695840.00967454130416083
559.789.532035985011630.24796401498837
569.799.567333304619980.22266669538002
579.579.499313485226980.0706865147730212
589.539.476550327332240.0534496726677571
599.659.443655590490140.206344409509863
609.369.41536611680593-0.0553661168059269
619.49.45066343641428-0.0506634364142756
629.329.38264361702128-0.0626436170212761
639.319.35988045912654-0.0498804591265393
649.199.32698572228443-0.136985722284435
659.399.298696248600220.0913037513997767
669.289.33399356820857-0.0539935682085742
679.289.265973748815570.0140262511844255
689.319.243210590920840.0667894090791632
699.289.210315854078730.0696841459212674
709.319.182026380394520.127973619605479
719.359.217323700002870.132676299997129
729.199.149303880609870.0406961193901281
739.079.12654072271513-0.0565407227151345
748.969.09364598587303-0.133645985873029
758.699.06535651218882-0.375356512188819
768.589.10065383179717-0.520653831797169
778.569.03263401240417-0.472634012404169
788.479.00987085450943-0.539870854509432
798.468.97697611766733-0.516976117667326
808.758.94868664398312-0.198686643983117
818.958.98398396359147-0.0339839635914669
829.338.915964144198470.414035855801534
839.518.893200986303730.61679901369627
849.5618.860306249461620.700693750538375
859.948.832016775777421.10798322422259
869.98.867314095385761.03268590461424
879.2758.799294275992760.475705724007236
889.568.776531118098030.783468881901973
899.7798.743636381255921.03536361874408
909.7468.715346907571711.03065309242829
919.9918.750644227180061.24035577281994
929.988.682624407787061.29737559221294
9310.1958.659861249892321.53513875010768
9410.318.626966513050221.68303348694978
9510.258.598677039366011.65132296063399
969.8718.633974358974361.23702564102564
9710.068.565954539581361.49404546041864
989.8948.543191381686621.35080861831338
999.598.510296644844521.07970335515548
1009.648.48200717116031.15799282883969
1019.898.517304490768661.37269550923134
1029.538.449284671375661.08071532862434
1039.3888.426521513480920.96147848651908
1049.168.393626776638810.766373223361185
1059.4188.36533730295461.0526626970454
1069.578.400634622562951.16936537743705
1079.8578.332614803169951.52438519683004
1089.8778.309851645275221.56714835472478
1099.768.276956908433111.48304309156689
1109.768.24866743474891.5113325652511
1119.6958.283964754357251.41103524564275
1129.4758.215944934964251.25905506503575
1139.2628.193181777069521.06881822293049
1149.0978.160287040227410.93671295977259
1158.558.13199756654320.418002433456801
1168.168.16729488615155-0.00729488615154896
1177.5328.09927506675855-0.56727506675855
1187.3258.07651190886381-0.751511908863812
1196.7498.04361717202171-1.29461717202171
1207.138.0153276983375-0.885327698337497
1216.9958.05062501794585-1.05562501794585
1227.3467.98260519855285-0.636605198552847
1237.737.95984204065811-0.22984204065811
1247.8377.926947303816-0.0899473038160052
1257.5147.8986578301318-0.384657830131794
1267.587.93395514974014-0.353955149740144
1276.837.86593533034714-1.03593533034714
1286.6177.8431721724524-1.22617217245241
1296.7157.8102774356103-1.0952774356103
1306.637.78198796192609-1.15198796192609
1316.8917.81728528153444-0.926285281534442
1327.0027.74926546214144-0.747265462141443
1337.097.7265023042467-0.636502304246705
1347.367.6936075674046-0.3336075674046
1357.4777.66531809372039-0.188318093720389
1367.8267.700615413328740.12538458667126
1377.797.632595593935740.157404406064261
1387.5787.609832436041-0.0318324360410024
1397.2047.5769376991989-0.372937699198898
1407.1987.54864822551469-0.350648225514687
1417.6857.583945545123040.101054454876963
1427.7957.515925725730040.279074274269963
1437.467.4931625678353-0.0331625678353002
1447.2747.4602678309932-0.186267830993195
1457.337.43197835730898-0.101978357308984
1467.6557.467275676917330.187724323082666
1477.7677.399255857524330.367744142475666
1487.847.37649269962960.463507300370402
1497.4247.343597962787490.0804020372125076
1507.547.315308489103280.224691510896718
1517.3517.350605808711630.000394191288368287
1526.7357.28258598931863-0.547585989318632
1536.7777.2598228314239-0.482822831423895
1546.6797.2269280945818-0.54792809458179
1557.347.198638620897580.14136137910242
1566.9787.23393594050593-0.25593594050593
1576.927.16591612111293-0.24591612111293
1586.6287.1431529632182-0.515152963218193
1596.3857.11025822637609-0.725258226376088
1605.9847.08196875269188-1.09796875269188
1616.2687.11726607230023-0.849266072300227
1626.5967.04924625290723-0.453246252907227
1636.3957.02648309501249-0.631483095012491
1646.7156.99358835817038-0.278588358170385
1656.8046.96529888448617-0.161298884486174
1666.9297.00059620409452-0.0715962040945244
1676.8466.93257638470152-0.086576384701525
1686.9926.909813226806790.0821867731932114
1696.7746.87691848996468-0.102918489964683
1706.756.84862901628047-0.0986290162804717
1716.4856.88392633588882-0.398926335888821
1726.276.81590651649582-0.545906516495823
1736.476.79314335860109-0.323143358601086
1746.786.760248621758980.0197513782410201
1756.716.73195914807477-0.0219591480747694
1766.1416.76725646768312-0.62625646768312
1776.726.699236648290120.02076335170988
1786.686.676473490395380.0035265096046167
1796.3716.64357875355328-0.272578753553277
1806.0976.61528927986907-0.518289279869067
1816.276.65058659947742-0.380586599477418
1826.4476.58256678008442-0.135566780084417
1836.376.55980362218968-0.18980362218968
1846.4466.52690888534758-0.0809088853475753
1856.546.498619411663360.0413805883366355
1866.3746.53391673127171-0.159916731271715
1876.336.46589691187871-0.135896911878715
1886.636.443133753983980.186866246016022
1896.4986.410239017141870.0877609828581275
1906.4856.381949543457660.103050456542338
1916.366.41724686306601-0.0572468630660119







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.009800496080879970.01960099216175990.99019950391912
100.5626126856036790.8747746287926410.437387314396321
110.7518702163654350.4962595672691310.248129783634565
120.7162723362298930.5674553275402150.283727663770107
130.6591247576536010.6817504846927970.340875242346399
140.5658550541262670.8682898917474670.434144945873733
150.6294974044414560.7410051911170880.370502595558544
160.5554981399980170.8890037200039650.444501860001983
170.4781039632936110.9562079265872210.521896036706389
180.4022980048831270.8045960097662530.597701995116873
190.342601680645250.68520336129050.65739831935475
200.4405882334640310.8811764669280620.559411766535969
210.4149674645466060.8299349290932120.585032535453394
220.3986776656204190.7973553312408370.601322334379581
230.387614527385920.775229054771840.61238547261408
240.3462353863533440.6924707727066870.653764613646656
250.3438633004504110.6877266009008220.656136699549589
260.2898860085037580.5797720170075170.710113991496242
270.2393840239014390.4787680478028770.760615976098561
280.2046000808991250.409200161798250.795399919100875
290.1811058175886180.3622116351772350.818894182411382
300.1776699554554880.3553399109109760.822330044544512
310.144971243382840.2899424867656790.85502875661716
320.1192135814261470.2384271628522940.880786418573853
330.09859388758730590.1971877751746120.901406112412694
340.07818475633402690.1563695126680540.921815243665973
350.07389309623580620.1477861924716120.926106903764194
360.05545848950767890.1109169790153580.944541510492321
370.04307138873566480.08614277747132950.956928611264335
380.03179523633707780.06359047267415570.968204763662922
390.02475399071660410.04950798143320830.975246009283396
400.01845512738481480.03691025476962950.981544872615185
410.02545206769799470.05090413539598930.974547932302005
420.0242819750206690.0485639500413380.97571802497933
430.01964695900311280.03929391800622560.980353040996887
440.01454231971222130.02908463942444260.985457680287779
450.01132144530138720.02264289060277450.988678554698613
460.008814769194186630.01762953838837330.991185230805813
470.006677537705268980.0133550754105380.99332246229473
480.005065952901623230.01013190580324650.994934047098377
490.003663185098870840.007326370197741680.99633681490113
500.002850588763307530.005701177526615060.997149411236692
510.002083871048338730.004167742096677450.997916128951661
520.001520118610275770.003040237220551530.998479881389724
530.001163741633303340.002327483266606670.998836258366697
540.0008498973166343620.001699794633268720.999150102683366
550.0008507987173938630.001701597434787730.999149201282606
560.0006994642969864880.001398928593972980.999300535703013
570.000522663431341820.001045326862683640.999477336568658
580.0003883859083752640.0007767718167505270.999611614091625
590.0002865753361094810.0005731506722189620.99971342466389
600.0002030610913021840.0004061221826043680.999796938908698
610.0001383558484395130.0002767116968790250.99986164415156
629.52151324557899e-050.000190430264911580.999904784867544
636.59238146849357e-050.0001318476293698710.999934076185315
644.59408602636177e-059.18817205272354e-050.999954059139736
653.29227910012516e-056.58455820025033e-050.999967077208999
662.22973808462597e-054.45947616925194e-050.999977702619154
671.50957341631349e-053.01914683262698e-050.999984904265837
681.03658276897281e-052.07316553794562e-050.99998963417231
696.73206556311419e-061.34641311262284e-050.999993267934437
704.672061320517e-069.344122641034e-060.99999532793868
713.05156862036547e-066.10313724073095e-060.99999694843138
722.02086413093841e-064.04172826187682e-060.999997979135869
731.39863098740406e-062.79726197480812e-060.999998601369013
741.03595166874245e-062.07190333748489e-060.999998964048331
751.09878769382288e-062.19757538764577e-060.999998901212306
761.79617167535311e-063.59234335070622e-060.999998203828325
772.51754178775245e-065.0350835755049e-060.999997482458212
784.26905523215714e-068.53811046431427e-060.999995730944768
797.46063140274517e-061.49212628054903e-050.999992539368597
807.5352075041219e-061.50704150082438e-050.999992464792496
816.67822927194775e-061.33564585438955e-050.999993321770728
826.8877413208542e-061.37754826417084e-050.999993112258679
839.63224732744167e-061.92644946548833e-050.999990367752673
841.24924962581717e-052.49849925163434e-050.999987507503742
854.49489449570013e-058.98978899140026e-050.999955051055043
868.74716993785133e-050.0001749433987570270.999912528300621
877.17149936097983e-050.0001434299872195970.99992828500639
887.86947507736898e-050.000157389501547380.999921305249226
890.0001116982981736480.0002233965963472960.999888301701826
900.0001531673617750330.0003063347235500650.999846832638225
910.0002648013364759690.0005296026729519380.999735198663524
920.00049143132881940.00098286265763880.99950856867118
930.001337155474133840.002674310948267680.998662844525866
940.003918137028276410.007836274056552820.996081862971724
950.009279035682705220.01855807136541040.990720964317295
960.01027513878080190.02055027756160390.989724861219198
970.0161042741425840.0322085482851680.983895725857416
980.02017853831085750.0403570766217150.979821461689143
990.0193444099260260.03868881985205190.980655590073974
1000.01992615870098230.03985231740196470.980073841299018
1010.02580077641808650.05160155283617290.974199223581914
1020.02563640084048480.05127280168096960.974363599159515
1030.02380054223324190.04760108446648380.976199457766758
1040.02081495835803030.04162991671606060.97918504164197
1050.02162896636063370.04325793272126730.978371033639366
1060.02612747723016690.05225495446033390.973872522769833
1070.05132873398109270.1026574679621850.948671266018907
1080.1079806281673610.2159612563347210.89201937183264
1090.2087714513315190.4175429026630380.79122854866848
1100.3930392965929610.7860785931859220.606960703407039
1110.6294053036004450.7411893927991110.370594696399555
1120.8234204030880110.3531591938239780.176579596911989
1130.9328344895169720.1343310209660550.0671655104830277
1140.9844557767893630.03108844642127490.0155442232106375
1150.9933276104955690.01334477900886260.00667238950443128
1160.9964125432818680.00717491343626470.00358745671813235
1170.998298732272190.003402535455620930.00170126772781047
1180.9993350991918470.001329801616306360.000664900808153179
1190.9999462457452630.0001075085094739015.37542547369506e-05
1200.9999809001873143.81996253717745e-051.90998126858873e-05
1210.9999958022885328.39542293644765e-064.19771146822383e-06
1220.9999968772695636.24546087331351e-063.12273043665675e-06
1230.9999964966393257.00672135018853e-063.50336067509427e-06
1240.9999965034570956.99308581067558e-063.49654290533779e-06
1250.9999959801617478.03967650664838e-064.01983825332419e-06
1260.999995271946979.45610605883736e-064.72805302941868e-06
1270.9999985519662282.89606754306076e-061.44803377153038e-06
1280.9999998276370973.44725806084521e-071.7236290304226e-07
1290.9999999631028237.37943532316414e-083.68971766158207e-08
1300.9999999963985967.20280832000984e-093.60140416000492e-09
1310.9999999989864672.02706588427827e-091.01353294213913e-09
1320.9999999995632338.73534346471606e-104.36767173235803e-10
1330.9999999997114755.77049804071214e-102.88524902035607e-10
1340.999999999470321.05935874438496e-095.29679372192478e-10
1350.9999999988888242.22235215806398e-091.11117607903199e-09
1360.999999998180433.63914084604054e-091.81957042302027e-09
1370.9999999968196256.3607497451228e-093.1803748725614e-09
1380.9999999932408191.3518361802544e-086.75918090127202e-09
1390.999999988063892.38722179146925e-081.19361089573462e-08
1400.9999999796512174.06975668908285e-082.03487834454143e-08
1410.9999999686745496.26509027705035e-083.13254513852518e-08
1420.9999999654269526.91460952194351e-083.45730476097175e-08
1430.9999999296262161.40747568383003e-077.03737841915017e-08
1440.9999998543431742.9131365306343e-071.45656826531715e-07
1450.9999997000371875.99925627054954e-072.99962813527477e-07
1460.9999997349186255.30162750616439e-072.65081375308219e-07
1470.9999998850359232.29928153694554e-071.14964076847277e-07
1480.9999999786756674.26486654761524e-082.13243327380762e-08
1490.999999981426173.71476587916551e-081.85738293958275e-08
1500.9999999929329091.41341829847502e-087.06709149237508e-09
1510.9999999974799045.04019198681444e-092.52009599340722e-09
1520.999999994121991.17560200708876e-085.87801003544382e-09
1530.9999999858306562.8338688467322e-081.4169344233661e-08
1540.9999999680117766.39764469665358e-083.19882234832679e-08
1550.999999992233721.55325614395581e-087.76628071977904e-09
1560.9999999934179921.31640153625566e-086.58200768127828e-09
1570.9999999893122352.13755301654036e-081.06877650827018e-08
1580.99999997167245.66552001178202e-082.83276000589101e-08
1590.9999999582493628.35012755558817e-084.17506377779409e-08
1600.999999997346385.30723880385314e-092.65361940192657e-09
1610.999999997368535.26293963808536e-092.63146981904268e-09
1620.9999999923063341.53873313228747e-087.69366566143734e-09
1630.9999999959553838.08923435531434e-094.04461717765717e-09
1640.9999999870280062.59439881062583e-081.29719940531291e-08
1650.9999999547000029.05999952096426e-084.52999976048213e-08
1660.9999999572491518.55016975920611e-084.27508487960305e-08
1670.9999999144188171.71162366142917e-078.55811830714584e-08
1680.9999998960451312.07909737499034e-071.03954868749517e-07
1690.9999996982437186.03512563534575e-073.01756281767288e-07
1700.999999344271991.31145601860315e-066.55728009301574e-07
1710.9999982055289583.58894208479392e-061.79447104239696e-06
1720.9999963774350267.24512994741222e-063.62256497370611e-06
1730.9999886377250442.2724549912262e-051.1362274956131e-05
1740.9999826350165133.47299669739253e-051.73649834869626e-05
1750.9999843254496553.13491006890438e-051.56745503445219e-05
1760.9999484205131880.0001031589736239535.15794868119764e-05
1770.9999646944022397.0611195522148e-053.5305597761074e-05
1780.9999597238155548.05523688922496e-054.02761844461248e-05
1790.9997778633322560.0004442733354881030.000222136667744052
1800.9998608712089480.0002782575821048320.000139128791052416
1810.9989986769325460.002002646134908140.00100132306745407
1820.9958043689373680.008391262125264530.00419563106263227

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00980049608087997 & 0.0196009921617599 & 0.99019950391912 \tabularnewline
10 & 0.562612685603679 & 0.874774628792641 & 0.437387314396321 \tabularnewline
11 & 0.751870216365435 & 0.496259567269131 & 0.248129783634565 \tabularnewline
12 & 0.716272336229893 & 0.567455327540215 & 0.283727663770107 \tabularnewline
13 & 0.659124757653601 & 0.681750484692797 & 0.340875242346399 \tabularnewline
14 & 0.565855054126267 & 0.868289891747467 & 0.434144945873733 \tabularnewline
15 & 0.629497404441456 & 0.741005191117088 & 0.370502595558544 \tabularnewline
16 & 0.555498139998017 & 0.889003720003965 & 0.444501860001983 \tabularnewline
17 & 0.478103963293611 & 0.956207926587221 & 0.521896036706389 \tabularnewline
18 & 0.402298004883127 & 0.804596009766253 & 0.597701995116873 \tabularnewline
19 & 0.34260168064525 & 0.6852033612905 & 0.65739831935475 \tabularnewline
20 & 0.440588233464031 & 0.881176466928062 & 0.559411766535969 \tabularnewline
21 & 0.414967464546606 & 0.829934929093212 & 0.585032535453394 \tabularnewline
22 & 0.398677665620419 & 0.797355331240837 & 0.601322334379581 \tabularnewline
23 & 0.38761452738592 & 0.77522905477184 & 0.61238547261408 \tabularnewline
24 & 0.346235386353344 & 0.692470772706687 & 0.653764613646656 \tabularnewline
25 & 0.343863300450411 & 0.687726600900822 & 0.656136699549589 \tabularnewline
26 & 0.289886008503758 & 0.579772017007517 & 0.710113991496242 \tabularnewline
27 & 0.239384023901439 & 0.478768047802877 & 0.760615976098561 \tabularnewline
28 & 0.204600080899125 & 0.40920016179825 & 0.795399919100875 \tabularnewline
29 & 0.181105817588618 & 0.362211635177235 & 0.818894182411382 \tabularnewline
30 & 0.177669955455488 & 0.355339910910976 & 0.822330044544512 \tabularnewline
31 & 0.14497124338284 & 0.289942486765679 & 0.85502875661716 \tabularnewline
32 & 0.119213581426147 & 0.238427162852294 & 0.880786418573853 \tabularnewline
33 & 0.0985938875873059 & 0.197187775174612 & 0.901406112412694 \tabularnewline
34 & 0.0781847563340269 & 0.156369512668054 & 0.921815243665973 \tabularnewline
35 & 0.0738930962358062 & 0.147786192471612 & 0.926106903764194 \tabularnewline
36 & 0.0554584895076789 & 0.110916979015358 & 0.944541510492321 \tabularnewline
37 & 0.0430713887356648 & 0.0861427774713295 & 0.956928611264335 \tabularnewline
38 & 0.0317952363370778 & 0.0635904726741557 & 0.968204763662922 \tabularnewline
39 & 0.0247539907166041 & 0.0495079814332083 & 0.975246009283396 \tabularnewline
40 & 0.0184551273848148 & 0.0369102547696295 & 0.981544872615185 \tabularnewline
41 & 0.0254520676979947 & 0.0509041353959893 & 0.974547932302005 \tabularnewline
42 & 0.024281975020669 & 0.048563950041338 & 0.97571802497933 \tabularnewline
43 & 0.0196469590031128 & 0.0392939180062256 & 0.980353040996887 \tabularnewline
44 & 0.0145423197122213 & 0.0290846394244426 & 0.985457680287779 \tabularnewline
45 & 0.0113214453013872 & 0.0226428906027745 & 0.988678554698613 \tabularnewline
46 & 0.00881476919418663 & 0.0176295383883733 & 0.991185230805813 \tabularnewline
47 & 0.00667753770526898 & 0.013355075410538 & 0.99332246229473 \tabularnewline
48 & 0.00506595290162323 & 0.0101319058032465 & 0.994934047098377 \tabularnewline
49 & 0.00366318509887084 & 0.00732637019774168 & 0.99633681490113 \tabularnewline
50 & 0.00285058876330753 & 0.00570117752661506 & 0.997149411236692 \tabularnewline
51 & 0.00208387104833873 & 0.00416774209667745 & 0.997916128951661 \tabularnewline
52 & 0.00152011861027577 & 0.00304023722055153 & 0.998479881389724 \tabularnewline
53 & 0.00116374163330334 & 0.00232748326660667 & 0.998836258366697 \tabularnewline
54 & 0.000849897316634362 & 0.00169979463326872 & 0.999150102683366 \tabularnewline
55 & 0.000850798717393863 & 0.00170159743478773 & 0.999149201282606 \tabularnewline
56 & 0.000699464296986488 & 0.00139892859397298 & 0.999300535703013 \tabularnewline
57 & 0.00052266343134182 & 0.00104532686268364 & 0.999477336568658 \tabularnewline
58 & 0.000388385908375264 & 0.000776771816750527 & 0.999611614091625 \tabularnewline
59 & 0.000286575336109481 & 0.000573150672218962 & 0.99971342466389 \tabularnewline
60 & 0.000203061091302184 & 0.000406122182604368 & 0.999796938908698 \tabularnewline
61 & 0.000138355848439513 & 0.000276711696879025 & 0.99986164415156 \tabularnewline
62 & 9.52151324557899e-05 & 0.00019043026491158 & 0.999904784867544 \tabularnewline
63 & 6.59238146849357e-05 & 0.000131847629369871 & 0.999934076185315 \tabularnewline
64 & 4.59408602636177e-05 & 9.18817205272354e-05 & 0.999954059139736 \tabularnewline
65 & 3.29227910012516e-05 & 6.58455820025033e-05 & 0.999967077208999 \tabularnewline
66 & 2.22973808462597e-05 & 4.45947616925194e-05 & 0.999977702619154 \tabularnewline
67 & 1.50957341631349e-05 & 3.01914683262698e-05 & 0.999984904265837 \tabularnewline
68 & 1.03658276897281e-05 & 2.07316553794562e-05 & 0.99998963417231 \tabularnewline
69 & 6.73206556311419e-06 & 1.34641311262284e-05 & 0.999993267934437 \tabularnewline
70 & 4.672061320517e-06 & 9.344122641034e-06 & 0.99999532793868 \tabularnewline
71 & 3.05156862036547e-06 & 6.10313724073095e-06 & 0.99999694843138 \tabularnewline
72 & 2.02086413093841e-06 & 4.04172826187682e-06 & 0.999997979135869 \tabularnewline
73 & 1.39863098740406e-06 & 2.79726197480812e-06 & 0.999998601369013 \tabularnewline
74 & 1.03595166874245e-06 & 2.07190333748489e-06 & 0.999998964048331 \tabularnewline
75 & 1.09878769382288e-06 & 2.19757538764577e-06 & 0.999998901212306 \tabularnewline
76 & 1.79617167535311e-06 & 3.59234335070622e-06 & 0.999998203828325 \tabularnewline
77 & 2.51754178775245e-06 & 5.0350835755049e-06 & 0.999997482458212 \tabularnewline
78 & 4.26905523215714e-06 & 8.53811046431427e-06 & 0.999995730944768 \tabularnewline
79 & 7.46063140274517e-06 & 1.49212628054903e-05 & 0.999992539368597 \tabularnewline
80 & 7.5352075041219e-06 & 1.50704150082438e-05 & 0.999992464792496 \tabularnewline
81 & 6.67822927194775e-06 & 1.33564585438955e-05 & 0.999993321770728 \tabularnewline
82 & 6.8877413208542e-06 & 1.37754826417084e-05 & 0.999993112258679 \tabularnewline
83 & 9.63224732744167e-06 & 1.92644946548833e-05 & 0.999990367752673 \tabularnewline
84 & 1.24924962581717e-05 & 2.49849925163434e-05 & 0.999987507503742 \tabularnewline
85 & 4.49489449570013e-05 & 8.98978899140026e-05 & 0.999955051055043 \tabularnewline
86 & 8.74716993785133e-05 & 0.000174943398757027 & 0.999912528300621 \tabularnewline
87 & 7.17149936097983e-05 & 0.000143429987219597 & 0.99992828500639 \tabularnewline
88 & 7.86947507736898e-05 & 0.00015738950154738 & 0.999921305249226 \tabularnewline
89 & 0.000111698298173648 & 0.000223396596347296 & 0.999888301701826 \tabularnewline
90 & 0.000153167361775033 & 0.000306334723550065 & 0.999846832638225 \tabularnewline
91 & 0.000264801336475969 & 0.000529602672951938 & 0.999735198663524 \tabularnewline
92 & 0.0004914313288194 & 0.0009828626576388 & 0.99950856867118 \tabularnewline
93 & 0.00133715547413384 & 0.00267431094826768 & 0.998662844525866 \tabularnewline
94 & 0.00391813702827641 & 0.00783627405655282 & 0.996081862971724 \tabularnewline
95 & 0.00927903568270522 & 0.0185580713654104 & 0.990720964317295 \tabularnewline
96 & 0.0102751387808019 & 0.0205502775616039 & 0.989724861219198 \tabularnewline
97 & 0.016104274142584 & 0.032208548285168 & 0.983895725857416 \tabularnewline
98 & 0.0201785383108575 & 0.040357076621715 & 0.979821461689143 \tabularnewline
99 & 0.019344409926026 & 0.0386888198520519 & 0.980655590073974 \tabularnewline
100 & 0.0199261587009823 & 0.0398523174019647 & 0.980073841299018 \tabularnewline
101 & 0.0258007764180865 & 0.0516015528361729 & 0.974199223581914 \tabularnewline
102 & 0.0256364008404848 & 0.0512728016809696 & 0.974363599159515 \tabularnewline
103 & 0.0238005422332419 & 0.0476010844664838 & 0.976199457766758 \tabularnewline
104 & 0.0208149583580303 & 0.0416299167160606 & 0.97918504164197 \tabularnewline
105 & 0.0216289663606337 & 0.0432579327212673 & 0.978371033639366 \tabularnewline
106 & 0.0261274772301669 & 0.0522549544603339 & 0.973872522769833 \tabularnewline
107 & 0.0513287339810927 & 0.102657467962185 & 0.948671266018907 \tabularnewline
108 & 0.107980628167361 & 0.215961256334721 & 0.89201937183264 \tabularnewline
109 & 0.208771451331519 & 0.417542902663038 & 0.79122854866848 \tabularnewline
110 & 0.393039296592961 & 0.786078593185922 & 0.606960703407039 \tabularnewline
111 & 0.629405303600445 & 0.741189392799111 & 0.370594696399555 \tabularnewline
112 & 0.823420403088011 & 0.353159193823978 & 0.176579596911989 \tabularnewline
113 & 0.932834489516972 & 0.134331020966055 & 0.0671655104830277 \tabularnewline
114 & 0.984455776789363 & 0.0310884464212749 & 0.0155442232106375 \tabularnewline
115 & 0.993327610495569 & 0.0133447790088626 & 0.00667238950443128 \tabularnewline
116 & 0.996412543281868 & 0.0071749134362647 & 0.00358745671813235 \tabularnewline
117 & 0.99829873227219 & 0.00340253545562093 & 0.00170126772781047 \tabularnewline
118 & 0.999335099191847 & 0.00132980161630636 & 0.000664900808153179 \tabularnewline
119 & 0.999946245745263 & 0.000107508509473901 & 5.37542547369506e-05 \tabularnewline
120 & 0.999980900187314 & 3.81996253717745e-05 & 1.90998126858873e-05 \tabularnewline
121 & 0.999995802288532 & 8.39542293644765e-06 & 4.19771146822383e-06 \tabularnewline
122 & 0.999996877269563 & 6.24546087331351e-06 & 3.12273043665675e-06 \tabularnewline
123 & 0.999996496639325 & 7.00672135018853e-06 & 3.50336067509427e-06 \tabularnewline
124 & 0.999996503457095 & 6.99308581067558e-06 & 3.49654290533779e-06 \tabularnewline
125 & 0.999995980161747 & 8.03967650664838e-06 & 4.01983825332419e-06 \tabularnewline
126 & 0.99999527194697 & 9.45610605883736e-06 & 4.72805302941868e-06 \tabularnewline
127 & 0.999998551966228 & 2.89606754306076e-06 & 1.44803377153038e-06 \tabularnewline
128 & 0.999999827637097 & 3.44725806084521e-07 & 1.7236290304226e-07 \tabularnewline
129 & 0.999999963102823 & 7.37943532316414e-08 & 3.68971766158207e-08 \tabularnewline
130 & 0.999999996398596 & 7.20280832000984e-09 & 3.60140416000492e-09 \tabularnewline
131 & 0.999999998986467 & 2.02706588427827e-09 & 1.01353294213913e-09 \tabularnewline
132 & 0.999999999563233 & 8.73534346471606e-10 & 4.36767173235803e-10 \tabularnewline
133 & 0.999999999711475 & 5.77049804071214e-10 & 2.88524902035607e-10 \tabularnewline
134 & 0.99999999947032 & 1.05935874438496e-09 & 5.29679372192478e-10 \tabularnewline
135 & 0.999999998888824 & 2.22235215806398e-09 & 1.11117607903199e-09 \tabularnewline
136 & 0.99999999818043 & 3.63914084604054e-09 & 1.81957042302027e-09 \tabularnewline
137 & 0.999999996819625 & 6.3607497451228e-09 & 3.1803748725614e-09 \tabularnewline
138 & 0.999999993240819 & 1.3518361802544e-08 & 6.75918090127202e-09 \tabularnewline
139 & 0.99999998806389 & 2.38722179146925e-08 & 1.19361089573462e-08 \tabularnewline
140 & 0.999999979651217 & 4.06975668908285e-08 & 2.03487834454143e-08 \tabularnewline
141 & 0.999999968674549 & 6.26509027705035e-08 & 3.13254513852518e-08 \tabularnewline
142 & 0.999999965426952 & 6.91460952194351e-08 & 3.45730476097175e-08 \tabularnewline
143 & 0.999999929626216 & 1.40747568383003e-07 & 7.03737841915017e-08 \tabularnewline
144 & 0.999999854343174 & 2.9131365306343e-07 & 1.45656826531715e-07 \tabularnewline
145 & 0.999999700037187 & 5.99925627054954e-07 & 2.99962813527477e-07 \tabularnewline
146 & 0.999999734918625 & 5.30162750616439e-07 & 2.65081375308219e-07 \tabularnewline
147 & 0.999999885035923 & 2.29928153694554e-07 & 1.14964076847277e-07 \tabularnewline
148 & 0.999999978675667 & 4.26486654761524e-08 & 2.13243327380762e-08 \tabularnewline
149 & 0.99999998142617 & 3.71476587916551e-08 & 1.85738293958275e-08 \tabularnewline
150 & 0.999999992932909 & 1.41341829847502e-08 & 7.06709149237508e-09 \tabularnewline
151 & 0.999999997479904 & 5.04019198681444e-09 & 2.52009599340722e-09 \tabularnewline
152 & 0.99999999412199 & 1.17560200708876e-08 & 5.87801003544382e-09 \tabularnewline
153 & 0.999999985830656 & 2.8338688467322e-08 & 1.4169344233661e-08 \tabularnewline
154 & 0.999999968011776 & 6.39764469665358e-08 & 3.19882234832679e-08 \tabularnewline
155 & 0.99999999223372 & 1.55325614395581e-08 & 7.76628071977904e-09 \tabularnewline
156 & 0.999999993417992 & 1.31640153625566e-08 & 6.58200768127828e-09 \tabularnewline
157 & 0.999999989312235 & 2.13755301654036e-08 & 1.06877650827018e-08 \tabularnewline
158 & 0.9999999716724 & 5.66552001178202e-08 & 2.83276000589101e-08 \tabularnewline
159 & 0.999999958249362 & 8.35012755558817e-08 & 4.17506377779409e-08 \tabularnewline
160 & 0.99999999734638 & 5.30723880385314e-09 & 2.65361940192657e-09 \tabularnewline
161 & 0.99999999736853 & 5.26293963808536e-09 & 2.63146981904268e-09 \tabularnewline
162 & 0.999999992306334 & 1.53873313228747e-08 & 7.69366566143734e-09 \tabularnewline
163 & 0.999999995955383 & 8.08923435531434e-09 & 4.04461717765717e-09 \tabularnewline
164 & 0.999999987028006 & 2.59439881062583e-08 & 1.29719940531291e-08 \tabularnewline
165 & 0.999999954700002 & 9.05999952096426e-08 & 4.52999976048213e-08 \tabularnewline
166 & 0.999999957249151 & 8.55016975920611e-08 & 4.27508487960305e-08 \tabularnewline
167 & 0.999999914418817 & 1.71162366142917e-07 & 8.55811830714584e-08 \tabularnewline
168 & 0.999999896045131 & 2.07909737499034e-07 & 1.03954868749517e-07 \tabularnewline
169 & 0.999999698243718 & 6.03512563534575e-07 & 3.01756281767288e-07 \tabularnewline
170 & 0.99999934427199 & 1.31145601860315e-06 & 6.55728009301574e-07 \tabularnewline
171 & 0.999998205528958 & 3.58894208479392e-06 & 1.79447104239696e-06 \tabularnewline
172 & 0.999996377435026 & 7.24512994741222e-06 & 3.62256497370611e-06 \tabularnewline
173 & 0.999988637725044 & 2.2724549912262e-05 & 1.1362274956131e-05 \tabularnewline
174 & 0.999982635016513 & 3.47299669739253e-05 & 1.73649834869626e-05 \tabularnewline
175 & 0.999984325449655 & 3.13491006890438e-05 & 1.56745503445219e-05 \tabularnewline
176 & 0.999948420513188 & 0.000103158973623953 & 5.15794868119764e-05 \tabularnewline
177 & 0.999964694402239 & 7.0611195522148e-05 & 3.5305597761074e-05 \tabularnewline
178 & 0.999959723815554 & 8.05523688922496e-05 & 4.02761844461248e-05 \tabularnewline
179 & 0.999777863332256 & 0.000444273335488103 & 0.000222136667744052 \tabularnewline
180 & 0.999860871208948 & 0.000278257582104832 & 0.000139128791052416 \tabularnewline
181 & 0.998998676932546 & 0.00200264613490814 & 0.00100132306745407 \tabularnewline
182 & 0.995804368937368 & 0.00839126212526453 & 0.00419563106263227 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.00980049608087997[/C][C]0.0196009921617599[/C][C]0.99019950391912[/C][/ROW]
[ROW][C]10[/C][C]0.562612685603679[/C][C]0.874774628792641[/C][C]0.437387314396321[/C][/ROW]
[ROW][C]11[/C][C]0.751870216365435[/C][C]0.496259567269131[/C][C]0.248129783634565[/C][/ROW]
[ROW][C]12[/C][C]0.716272336229893[/C][C]0.567455327540215[/C][C]0.283727663770107[/C][/ROW]
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[ROW][C]78[/C][C]4.26905523215714e-06[/C][C]8.53811046431427e-06[/C][C]0.999995730944768[/C][/ROW]
[ROW][C]79[/C][C]7.46063140274517e-06[/C][C]1.49212628054903e-05[/C][C]0.999992539368597[/C][/ROW]
[ROW][C]80[/C][C]7.5352075041219e-06[/C][C]1.50704150082438e-05[/C][C]0.999992464792496[/C][/ROW]
[ROW][C]81[/C][C]6.67822927194775e-06[/C][C]1.33564585438955e-05[/C][C]0.999993321770728[/C][/ROW]
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[ROW][C]83[/C][C]9.63224732744167e-06[/C][C]1.92644946548833e-05[/C][C]0.999990367752673[/C][/ROW]
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[ROW][C]121[/C][C]0.999995802288532[/C][C]8.39542293644765e-06[/C][C]4.19771146822383e-06[/C][/ROW]
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[ROW][C]130[/C][C]0.999999996398596[/C][C]7.20280832000984e-09[/C][C]3.60140416000492e-09[/C][/ROW]
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[ROW][C]154[/C][C]0.999999968011776[/C][C]6.39764469665358e-08[/C][C]3.19882234832679e-08[/C][/ROW]
[ROW][C]155[/C][C]0.99999999223372[/C][C]1.55325614395581e-08[/C][C]7.76628071977904e-09[/C][/ROW]
[ROW][C]156[/C][C]0.999999993417992[/C][C]1.31640153625566e-08[/C][C]6.58200768127828e-09[/C][/ROW]
[ROW][C]157[/C][C]0.999999989312235[/C][C]2.13755301654036e-08[/C][C]1.06877650827018e-08[/C][/ROW]
[ROW][C]158[/C][C]0.9999999716724[/C][C]5.66552001178202e-08[/C][C]2.83276000589101e-08[/C][/ROW]
[ROW][C]159[/C][C]0.999999958249362[/C][C]8.35012755558817e-08[/C][C]4.17506377779409e-08[/C][/ROW]
[ROW][C]160[/C][C]0.99999999734638[/C][C]5.30723880385314e-09[/C][C]2.65361940192657e-09[/C][/ROW]
[ROW][C]161[/C][C]0.99999999736853[/C][C]5.26293963808536e-09[/C][C]2.63146981904268e-09[/C][/ROW]
[ROW][C]162[/C][C]0.999999992306334[/C][C]1.53873313228747e-08[/C][C]7.69366566143734e-09[/C][/ROW]
[ROW][C]163[/C][C]0.999999995955383[/C][C]8.08923435531434e-09[/C][C]4.04461717765717e-09[/C][/ROW]
[ROW][C]164[/C][C]0.999999987028006[/C][C]2.59439881062583e-08[/C][C]1.29719940531291e-08[/C][/ROW]
[ROW][C]165[/C][C]0.999999954700002[/C][C]9.05999952096426e-08[/C][C]4.52999976048213e-08[/C][/ROW]
[ROW][C]166[/C][C]0.999999957249151[/C][C]8.55016975920611e-08[/C][C]4.27508487960305e-08[/C][/ROW]
[ROW][C]167[/C][C]0.999999914418817[/C][C]1.71162366142917e-07[/C][C]8.55811830714584e-08[/C][/ROW]
[ROW][C]168[/C][C]0.999999896045131[/C][C]2.07909737499034e-07[/C][C]1.03954868749517e-07[/C][/ROW]
[ROW][C]169[/C][C]0.999999698243718[/C][C]6.03512563534575e-07[/C][C]3.01756281767288e-07[/C][/ROW]
[ROW][C]170[/C][C]0.99999934427199[/C][C]1.31145601860315e-06[/C][C]6.55728009301574e-07[/C][/ROW]
[ROW][C]171[/C][C]0.999998205528958[/C][C]3.58894208479392e-06[/C][C]1.79447104239696e-06[/C][/ROW]
[ROW][C]172[/C][C]0.999996377435026[/C][C]7.24512994741222e-06[/C][C]3.62256497370611e-06[/C][/ROW]
[ROW][C]173[/C][C]0.999988637725044[/C][C]2.2724549912262e-05[/C][C]1.1362274956131e-05[/C][/ROW]
[ROW][C]174[/C][C]0.999982635016513[/C][C]3.47299669739253e-05[/C][C]1.73649834869626e-05[/C][/ROW]
[ROW][C]175[/C][C]0.999984325449655[/C][C]3.13491006890438e-05[/C][C]1.56745503445219e-05[/C][/ROW]
[ROW][C]176[/C][C]0.999948420513188[/C][C]0.000103158973623953[/C][C]5.15794868119764e-05[/C][/ROW]
[ROW][C]177[/C][C]0.999964694402239[/C][C]7.0611195522148e-05[/C][C]3.5305597761074e-05[/C][/ROW]
[ROW][C]178[/C][C]0.999959723815554[/C][C]8.05523688922496e-05[/C][C]4.02761844461248e-05[/C][/ROW]
[ROW][C]179[/C][C]0.999777863332256[/C][C]0.000444273335488103[/C][C]0.000222136667744052[/C][/ROW]
[ROW][C]180[/C][C]0.999860871208948[/C][C]0.000278257582104832[/C][C]0.000139128791052416[/C][/ROW]
[ROW][C]181[/C][C]0.998998676932546[/C][C]0.00200264613490814[/C][C]0.00100132306745407[/C][/ROW]
[ROW][C]182[/C][C]0.995804368937368[/C][C]0.00839126212526453[/C][C]0.00419563106263227[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.009800496080879970.01960099216175990.99019950391912
100.5626126856036790.8747746287926410.437387314396321
110.7518702163654350.4962595672691310.248129783634565
120.7162723362298930.5674553275402150.283727663770107
130.6591247576536010.6817504846927970.340875242346399
140.5658550541262670.8682898917474670.434144945873733
150.6294974044414560.7410051911170880.370502595558544
160.5554981399980170.8890037200039650.444501860001983
170.4781039632936110.9562079265872210.521896036706389
180.4022980048831270.8045960097662530.597701995116873
190.342601680645250.68520336129050.65739831935475
200.4405882334640310.8811764669280620.559411766535969
210.4149674645466060.8299349290932120.585032535453394
220.3986776656204190.7973553312408370.601322334379581
230.387614527385920.775229054771840.61238547261408
240.3462353863533440.6924707727066870.653764613646656
250.3438633004504110.6877266009008220.656136699549589
260.2898860085037580.5797720170075170.710113991496242
270.2393840239014390.4787680478028770.760615976098561
280.2046000808991250.409200161798250.795399919100875
290.1811058175886180.3622116351772350.818894182411382
300.1776699554554880.3553399109109760.822330044544512
310.144971243382840.2899424867656790.85502875661716
320.1192135814261470.2384271628522940.880786418573853
330.09859388758730590.1971877751746120.901406112412694
340.07818475633402690.1563695126680540.921815243665973
350.07389309623580620.1477861924716120.926106903764194
360.05545848950767890.1109169790153580.944541510492321
370.04307138873566480.08614277747132950.956928611264335
380.03179523633707780.06359047267415570.968204763662922
390.02475399071660410.04950798143320830.975246009283396
400.01845512738481480.03691025476962950.981544872615185
410.02545206769799470.05090413539598930.974547932302005
420.0242819750206690.0485639500413380.97571802497933
430.01964695900311280.03929391800622560.980353040996887
440.01454231971222130.02908463942444260.985457680287779
450.01132144530138720.02264289060277450.988678554698613
460.008814769194186630.01762953838837330.991185230805813
470.006677537705268980.0133550754105380.99332246229473
480.005065952901623230.01013190580324650.994934047098377
490.003663185098870840.007326370197741680.99633681490113
500.002850588763307530.005701177526615060.997149411236692
510.002083871048338730.004167742096677450.997916128951661
520.001520118610275770.003040237220551530.998479881389724
530.001163741633303340.002327483266606670.998836258366697
540.0008498973166343620.001699794633268720.999150102683366
550.0008507987173938630.001701597434787730.999149201282606
560.0006994642969864880.001398928593972980.999300535703013
570.000522663431341820.001045326862683640.999477336568658
580.0003883859083752640.0007767718167505270.999611614091625
590.0002865753361094810.0005731506722189620.99971342466389
600.0002030610913021840.0004061221826043680.999796938908698
610.0001383558484395130.0002767116968790250.99986164415156
629.52151324557899e-050.000190430264911580.999904784867544
636.59238146849357e-050.0001318476293698710.999934076185315
644.59408602636177e-059.18817205272354e-050.999954059139736
653.29227910012516e-056.58455820025033e-050.999967077208999
662.22973808462597e-054.45947616925194e-050.999977702619154
671.50957341631349e-053.01914683262698e-050.999984904265837
681.03658276897281e-052.07316553794562e-050.99998963417231
696.73206556311419e-061.34641311262284e-050.999993267934437
704.672061320517e-069.344122641034e-060.99999532793868
713.05156862036547e-066.10313724073095e-060.99999694843138
722.02086413093841e-064.04172826187682e-060.999997979135869
731.39863098740406e-062.79726197480812e-060.999998601369013
741.03595166874245e-062.07190333748489e-060.999998964048331
751.09878769382288e-062.19757538764577e-060.999998901212306
761.79617167535311e-063.59234335070622e-060.999998203828325
772.51754178775245e-065.0350835755049e-060.999997482458212
784.26905523215714e-068.53811046431427e-060.999995730944768
797.46063140274517e-061.49212628054903e-050.999992539368597
807.5352075041219e-061.50704150082438e-050.999992464792496
816.67822927194775e-061.33564585438955e-050.999993321770728
826.8877413208542e-061.37754826417084e-050.999993112258679
839.63224732744167e-061.92644946548833e-050.999990367752673
841.24924962581717e-052.49849925163434e-050.999987507503742
854.49489449570013e-058.98978899140026e-050.999955051055043
868.74716993785133e-050.0001749433987570270.999912528300621
877.17149936097983e-050.0001434299872195970.99992828500639
887.86947507736898e-050.000157389501547380.999921305249226
890.0001116982981736480.0002233965963472960.999888301701826
900.0001531673617750330.0003063347235500650.999846832638225
910.0002648013364759690.0005296026729519380.999735198663524
920.00049143132881940.00098286265763880.99950856867118
930.001337155474133840.002674310948267680.998662844525866
940.003918137028276410.007836274056552820.996081862971724
950.009279035682705220.01855807136541040.990720964317295
960.01027513878080190.02055027756160390.989724861219198
970.0161042741425840.0322085482851680.983895725857416
980.02017853831085750.0403570766217150.979821461689143
990.0193444099260260.03868881985205190.980655590073974
1000.01992615870098230.03985231740196470.980073841299018
1010.02580077641808650.05160155283617290.974199223581914
1020.02563640084048480.05127280168096960.974363599159515
1030.02380054223324190.04760108446648380.976199457766758
1040.02081495835803030.04162991671606060.97918504164197
1050.02162896636063370.04325793272126730.978371033639366
1060.02612747723016690.05225495446033390.973872522769833
1070.05132873398109270.1026574679621850.948671266018907
1080.1079806281673610.2159612563347210.89201937183264
1090.2087714513315190.4175429026630380.79122854866848
1100.3930392965929610.7860785931859220.606960703407039
1110.6294053036004450.7411893927991110.370594696399555
1120.8234204030880110.3531591938239780.176579596911989
1130.9328344895169720.1343310209660550.0671655104830277
1140.9844557767893630.03108844642127490.0155442232106375
1150.9933276104955690.01334477900886260.00667238950443128
1160.9964125432818680.00717491343626470.00358745671813235
1170.998298732272190.003402535455620930.00170126772781047
1180.9993350991918470.001329801616306360.000664900808153179
1190.9999462457452630.0001075085094739015.37542547369506e-05
1200.9999809001873143.81996253717745e-051.90998126858873e-05
1210.9999958022885328.39542293644765e-064.19771146822383e-06
1220.9999968772695636.24546087331351e-063.12273043665675e-06
1230.9999964966393257.00672135018853e-063.50336067509427e-06
1240.9999965034570956.99308581067558e-063.49654290533779e-06
1250.9999959801617478.03967650664838e-064.01983825332419e-06
1260.999995271946979.45610605883736e-064.72805302941868e-06
1270.9999985519662282.89606754306076e-061.44803377153038e-06
1280.9999998276370973.44725806084521e-071.7236290304226e-07
1290.9999999631028237.37943532316414e-083.68971766158207e-08
1300.9999999963985967.20280832000984e-093.60140416000492e-09
1310.9999999989864672.02706588427827e-091.01353294213913e-09
1320.9999999995632338.73534346471606e-104.36767173235803e-10
1330.9999999997114755.77049804071214e-102.88524902035607e-10
1340.999999999470321.05935874438496e-095.29679372192478e-10
1350.9999999988888242.22235215806398e-091.11117607903199e-09
1360.999999998180433.63914084604054e-091.81957042302027e-09
1370.9999999968196256.3607497451228e-093.1803748725614e-09
1380.9999999932408191.3518361802544e-086.75918090127202e-09
1390.999999988063892.38722179146925e-081.19361089573462e-08
1400.9999999796512174.06975668908285e-082.03487834454143e-08
1410.9999999686745496.26509027705035e-083.13254513852518e-08
1420.9999999654269526.91460952194351e-083.45730476097175e-08
1430.9999999296262161.40747568383003e-077.03737841915017e-08
1440.9999998543431742.9131365306343e-071.45656826531715e-07
1450.9999997000371875.99925627054954e-072.99962813527477e-07
1460.9999997349186255.30162750616439e-072.65081375308219e-07
1470.9999998850359232.29928153694554e-071.14964076847277e-07
1480.9999999786756674.26486654761524e-082.13243327380762e-08
1490.999999981426173.71476587916551e-081.85738293958275e-08
1500.9999999929329091.41341829847502e-087.06709149237508e-09
1510.9999999974799045.04019198681444e-092.52009599340722e-09
1520.999999994121991.17560200708876e-085.87801003544382e-09
1530.9999999858306562.8338688467322e-081.4169344233661e-08
1540.9999999680117766.39764469665358e-083.19882234832679e-08
1550.999999992233721.55325614395581e-087.76628071977904e-09
1560.9999999934179921.31640153625566e-086.58200768127828e-09
1570.9999999893122352.13755301654036e-081.06877650827018e-08
1580.99999997167245.66552001178202e-082.83276000589101e-08
1590.9999999582493628.35012755558817e-084.17506377779409e-08
1600.999999997346385.30723880385314e-092.65361940192657e-09
1610.999999997368535.26293963808536e-092.63146981904268e-09
1620.9999999923063341.53873313228747e-087.69366566143734e-09
1630.9999999959553838.08923435531434e-094.04461717765717e-09
1640.9999999870280062.59439881062583e-081.29719940531291e-08
1650.9999999547000029.05999952096426e-084.52999976048213e-08
1660.9999999572491518.55016975920611e-084.27508487960305e-08
1670.9999999144188171.71162366142917e-078.55811830714584e-08
1680.9999998960451312.07909737499034e-071.03954868749517e-07
1690.9999996982437186.03512563534575e-073.01756281767288e-07
1700.999999344271991.31145601860315e-066.55728009301574e-07
1710.9999982055289583.58894208479392e-061.79447104239696e-06
1720.9999963774350267.24512994741222e-063.62256497370611e-06
1730.9999886377250442.2724549912262e-051.1362274956131e-05
1740.9999826350165133.47299669739253e-051.73649834869626e-05
1750.9999843254496553.13491006890438e-051.56745503445219e-05
1760.9999484205131880.0001031589736239535.15794868119764e-05
1770.9999646944022397.0611195522148e-053.5305597761074e-05
1780.9999597238155548.05523688922496e-054.02761844461248e-05
1790.9997778633322560.0004442733354881030.000222136667744052
1800.9998608712089480.0002782575821048320.000139128791052416
1810.9989986769325460.002002646134908140.00100132306745407
1820.9958043689373680.008391262125264530.00419563106263227







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1130.649425287356322NOK
5% type I error level1340.770114942528736NOK
10% type I error level1400.804597701149425NOK

\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 & 113 & 0.649425287356322 & NOK \tabularnewline
5% type I error level & 134 & 0.770114942528736 & NOK \tabularnewline
10% type I error level & 140 & 0.804597701149425 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147310&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]113[/C][C]0.649425287356322[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]134[/C][C]0.770114942528736[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]140[/C][C]0.804597701149425[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147310&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147310&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1130.649425287356322NOK
5% type I error level1340.770114942528736NOK
10% type I error level1400.804597701149425NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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,4), dimnames=list(1:n, paste('D', seq(1:4), sep ='')))
for (i in 1:4){
x2[seq(i,n,5),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')
}