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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Dec 2011 08:59:48 -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/Dec/19/t1324303460hsxdxcv1g898fp5.htm/, Retrieved Fri, 31 May 2024 19:30:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157380, Retrieved Fri, 31 May 2024 19:30:59 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper Multiple Re...] [2011-12-19 13:59:48] [3627de22d386f4cb93d383ef7c1ade7f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3,7	6,6	0,8564
3	6,5	0,8973
2,7	6,3	0,9383
2,5	6,2	0,9217
2,2	6,3	0,9095
2,9	6,5	0,892
3,1	6,6	0,8742
3	6,4	0,8532
2,8	6,2	0,8607
2,5	6,3	0,9005
1,9	6,6	0,9111
1,9	7,1	0,9059
1,8	7,2	0,8883
2	7,3	0,8924
2,6	7,3	0,8833
2,5	7,3	0,87
2,5	7,4	0,8758
1,6	7,3	0,8858
1,4	7,4	0,917
0,8	7,4	0,9554
1,1	7,6	0,9922
1,3	7,6	0,9778
1,2	7,7	0,9808
1,3	7,7	0,9811
1,1	7,8	1,0014
1,3	7,8	1,0183
1,2	8	1,0622
1,6	8,1	1,0773
1,7	8,1	1,0807
1,5	8,2	1,0848
0,9	8,1	1,1582
1,5	8,1	1,1663
1,4	8,1	1,1372
1,6	8	1,1139
1,7	8,2	1,1222
1,4	8,4	1,1692
1,8	8,4	1,1702
1,7	8,5	1,2286
1,4	8,6	1,2613
1,2	8,5	1,2646
1	8,3	1,2262
1,7	7,8	1,1985
2,4	7,8	1,2007
2	8	1,2138
2,1	8,6	1,2266
2	8,9	1,2176
1,8	8,9	1,2218
2,7	8,6	1,249
2,3	8,3	1,2991
1,9	8,3	1,3408
2	8,3	1,3119
2,3	8,4	1,3014
2,8	8,5	1,3201
2,4	8,4	1,2938
2,3	8,6	1,2694
2,7	8,5	1,2165
2,7	8,5	1,2037
2,9	8,5	1,2292
3	8,5	1,2256
2,2	8,5	1,2015
2,3	8,5	1,1786
2,8	8,5	1,1856
2,8	8,5	1,2103
2,8	8,6	1,1938
2,2	8,6	1,202
2,6	8,6	1,2271
2,8	8,6	1,277
2,5	8,4	1,265
2,4	8	1,2684
2,3	7,9	1,2811
1,9	8	1,2727
1,7	8	1,2611
2	8	1,2881
2,1	8	1,3213
1,7	7,9	1,2999
1,8	7,9	1,3074
1,8	7,9	1,3242
1,8	8	1,3516
1,3	7,9	1,3511
1,3	7,5	1,3419
1,3	7,2	1,3716
1,2	7	1,3622
1,4	6,9	1,3896
2,2	7,1	1,4227
2,9	7,1	1,4684
3,1	7,2	1,457
3,5	7,1	1,4718
3,6	6,9	1,4748
4,4	6,8	1,5527
4,1	6,7	1,575
5,1	6,7	1,5557
5,8	6,9	1,5553
5,9	7,3	1,577
5,4	7,4	1,4975
5,5	7,3	1,437
4,8	7,1	1,3322
3,2	7	1,2732
2,7	7,1	1,3449
2,1	7,5	1,3239
1,9	7,7	1,2785
0,6	7,8	1,305
0,7	7,7	1,319
-0,2	7,7	1,365
-1	7,8	1,4016
-1,7	8	1,4088
-0,7	8,1	1,4268
-1	8,1	1,4562
-0,9	8	1,4816
0	8,1	1,4914
0,3	8,2	1,4614
0,8	8,3	1,4272
0,8	8,4	1,3686
1,9	8,5	1,3569
2,1	8,5	1,3406
2,5	8,5	1,2565
2,7	8,5	1,2208
2,4	8,5	1,277
2,4	8,3	1,2894
2,9	8,2	1,3067
3,1	8,1	1,3898
3	7,9	1,3661
3,4	7,6	1,322
3,7	7,3	1,336
3,5	7,1	1,3649
3,5	7	1,3999
3,3	7	1,4442
3,1	7	1,4349
3,4	7	1,4388
4	6,9	1,4264
3,4	6,8	1,4343
3,4	6,7	1,377
3,4	6,6	1,3706

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 7.14906607319036 -0.222845689345414Inflatie[t] + 0.863654343067528Wisselkoers[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid[t] =  +  7.14906607319036 -0.222845689345414Inflatie[t] +  0.863654343067528Wisselkoers[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid[t] =  +  7.14906607319036 -0.222845689345414Inflatie[t] +  0.863654343067528Wisselkoers[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 7.14906607319036 -0.222845689345414Inflatie[t] + 0.863654343067528Wisselkoers[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.149066073190360.36315319.686100
Inflatie-0.2228456893454140.043993-5.06551e-061e-06
Wisselkoers0.8636543430675280.2921672.9560.0037060.001853

\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) & 7.14906607319036 & 0.363153 & 19.6861 & 0 & 0 \tabularnewline
Inflatie & -0.222845689345414 & 0.043993 & -5.0655 & 1e-06 & 1e-06 \tabularnewline
Wisselkoers & 0.863654343067528 & 0.292167 & 2.956 & 0.003706 & 0.001853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&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]7.14906607319036[/C][C]0.363153[/C][C]19.6861[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]-0.222845689345414[/C][C]0.043993[/C][C]-5.0655[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Wisselkoers[/C][C]0.863654343067528[/C][C]0.292167[/C][C]2.956[/C][C]0.003706[/C][C]0.001853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.149066073190360.36315319.686100
Inflatie-0.2228456893454140.043993-5.06551e-061e-06
Wisselkoers0.8636543430675280.2921672.9560.0037060.001853






Multiple Linear Regression - Regression Statistics
Multiple R0.437171971591209
R-squared0.191119332744945
Adjusted R-squared0.178578547206107
F-TEST (value)15.2398214731493
F-TEST (DF numerator)2
F-TEST (DF denominator)129
p-value1.14431973663454e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.629175091884246
Sum Squared Residuals51.0661072159339

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.437171971591209 \tabularnewline
R-squared & 0.191119332744945 \tabularnewline
Adjusted R-squared & 0.178578547206107 \tabularnewline
F-TEST (value) & 15.2398214731493 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value & 1.14431973663454e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.629175091884246 \tabularnewline
Sum Squared Residuals & 51.0661072159339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.437171971591209[/C][/ROW]
[ROW][C]R-squared[/C][C]0.191119332744945[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.178578547206107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.2398214731493[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C]1.14431973663454e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.629175091884246[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]51.0661072159339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.437171971591209
R-squared0.191119332744945
Adjusted R-squared0.178578547206107
F-TEST (value)15.2398214731493
F-TEST (DF numerator)2
F-TEST (DF denominator)129
p-value1.14431973663454e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.629175091884246
Sum Squared Residuals51.0661072159339






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.67.06417060201535-0.464170602015352
26.57.25548604718862-0.755486047188615
36.37.35774958205801-1.05774958205801
46.27.38798205783217-1.18798205783217
56.37.44429918165037-1.14429918165037
66.57.2731932481049-0.773193248104899
76.67.21325106292921-0.613251062929215
86.47.21739889065934-0.817398890659337
96.27.26844543610143-1.06844543610143
106.37.36967258575914-1.06967258575914
116.67.5125347354029-0.912534735402903
127.17.50804373281895-0.408043732818952
137.27.5151279853155-0.315127985315504
147.37.474099830253-0.174099830252998
157.37.33253316212384-0.0325331621238357
167.37.34333112829558-0.0433311282955789
177.47.348340323485370.0516596765146299
187.37.55753798732692-0.257537987326918
197.47.62905314069971-0.229053140699707
207.47.79592488108075-0.395924881080748
217.67.76085365410201-0.16085365410201
227.67.70384789369275-0.103847893692755
237.77.7287234256565-0.0287234256564983
247.77.70669795302488-0.00669795302487716
257.87.768799274058230.0312007259417689
267.87.738825894586990.0611741054130105
2787.79902488918220.200975110817805
288.17.722927794024350.37707220597565
298.17.703579649856240.396420350143762
308.27.75168977053190.448310229468102
318.17.94878941292030.151210587079698
328.17.82207759949190.277922400508099
338.17.819229827043180.280770172956822
3487.754537542980620.245462457019379
358.27.739421305093540.460578694906459
368.47.846866766021340.553133233978662
378.47.758592144626240.64140785537376
388.57.831314127195920.668685872804075
398.67.926409331017860.673590668982142
408.57.973828528219060.526171471780937
418.37.985233339314350.314766660685648
427.87.80531813146959-0.00531813146959258
437.87.651226188482550.148773811517448
4487.75167833611490.248321663885098
458.67.740448542771620.859551457228375
468.97.754960222618561.14503977738144
478.97.803156708728521.09684329127148
488.67.626086986449090.973913013550911
498.37.758494344774940.541505655225063
508.37.883647006619020.416352993380982
518.37.836402827169830.463597172830174
528.47.760480749763990.639519250236007
538.57.665208241306650.834791758693351
548.47.731632407822140.668367592177862
558.67.732843810785830.867156189214167
568.57.598018220299390.901981779700606
578.57.586963444708130.91303655529187
588.57.564417492587270.935582507412731
598.57.539023768017690.960976231982315
608.57.696486249826090.803513750173912
618.57.65442399643530.845576003564699
628.57.549046732164070.950953267835933
638.57.570378994437830.929621005562166
648.67.556128697777221.04387130222278
658.67.696918076997620.903081923002378
668.67.629457525270450.970542474729548
678.67.627984739120440.972015260879561
688.47.684474593807250.715525406192748
6987.709695587508220.290304412491777
707.97.742948566599720.157051433400278
7187.824832145856120.17516785414388
7287.859382893345620.14061710665438
7387.815847853804820.184152146195181
7487.822236609060120.177763390939881
757.97.892892681856640.00710731814336049
767.97.87708552049510.0229144795048955
777.97.891594913458640.00840508654136094
7887.915259042458690.0847409575413104
797.98.02625005995986-0.126250059959862
807.58.01830444000364-0.518304440003641
817.28.04395497399275-0.843954973992747
8278.05812119210245-1.05812119210245
836.98.03721618323342-1.13721618323342
847.17.88752659051263-0.787526590512626
857.17.77100361144902-0.671003611449022
867.27.71658881406897-0.516588814068969
877.17.6402326226082-0.540232622608204
886.97.62053901670286-0.720539016702864
896.87.50954113855149-0.709541138551494
906.77.59565433720552-0.895654337205524
916.77.35614011903891-0.656140119038908
926.97.19980267475989-0.299802674759891
937.37.196259405069910.103740594930085
947.47.239021729468750.160978270531247
957.37.164486072778630.135513927221373
967.17.22996708016694-0.12996708016694
9777.53556457687862-0.535564576878617
987.17.70891143794926-0.608911437949265
997.57.8244821103521-0.324482110352095
1007.77.82984134104591-0.129841341045912
1017.88.14242757728624-0.342427577286239
1027.78.13223416915464-0.432234169154643
1037.78.37252338934662-0.672523389346621
1047.88.58240968977922-0.782409689779224
10588.7446199835911-0.7446199835911
1068.18.5373200724209-0.437320072420902
1078.18.62956521691071-0.529565216910711
10888.62921746829008-0.629217468290085
1098.18.43712016044127-0.337120160441275
1108.28.34435682334562-0.144356823345625
1118.38.203397000140010.0966029998599924
1128.48.152786855636250.247213144363749
1138.57.897551841542410.602448158457594
1148.57.838905137881320.661094862118677
1158.57.677133531891180.822866468108822
1168.57.601731933974580.898268066025415
1178.57.71712301485860.782876985141396
1188.37.727832328712640.572167671287359
1198.27.6313507041750.568649295824997
1208.17.658551242214830.441448757785168
1217.97.660367203218670.239632796781328
1227.67.533141770951230.0668582290487703
1237.37.47837922495055-0.178379224950551
1247.17.54790797333428-0.447907973334285
12577.57813587534165-0.578135875341648
12677.66096490060862-0.660964900608623
12777.69750205308718-0.697502053087177
12877.63401659822152-0.634016598221517
1296.97.48959987076023-0.589599870760231
1306.87.63013015367771-0.830130153677713
1316.77.58064275981994-0.880642759819943
1326.67.57511537202431-0.975115372024312

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.6 & 7.06417060201535 & -0.464170602015352 \tabularnewline
2 & 6.5 & 7.25548604718862 & -0.755486047188615 \tabularnewline
3 & 6.3 & 7.35774958205801 & -1.05774958205801 \tabularnewline
4 & 6.2 & 7.38798205783217 & -1.18798205783217 \tabularnewline
5 & 6.3 & 7.44429918165037 & -1.14429918165037 \tabularnewline
6 & 6.5 & 7.2731932481049 & -0.773193248104899 \tabularnewline
7 & 6.6 & 7.21325106292921 & -0.613251062929215 \tabularnewline
8 & 6.4 & 7.21739889065934 & -0.817398890659337 \tabularnewline
9 & 6.2 & 7.26844543610143 & -1.06844543610143 \tabularnewline
10 & 6.3 & 7.36967258575914 & -1.06967258575914 \tabularnewline
11 & 6.6 & 7.5125347354029 & -0.912534735402903 \tabularnewline
12 & 7.1 & 7.50804373281895 & -0.408043732818952 \tabularnewline
13 & 7.2 & 7.5151279853155 & -0.315127985315504 \tabularnewline
14 & 7.3 & 7.474099830253 & -0.174099830252998 \tabularnewline
15 & 7.3 & 7.33253316212384 & -0.0325331621238357 \tabularnewline
16 & 7.3 & 7.34333112829558 & -0.0433311282955789 \tabularnewline
17 & 7.4 & 7.34834032348537 & 0.0516596765146299 \tabularnewline
18 & 7.3 & 7.55753798732692 & -0.257537987326918 \tabularnewline
19 & 7.4 & 7.62905314069971 & -0.229053140699707 \tabularnewline
20 & 7.4 & 7.79592488108075 & -0.395924881080748 \tabularnewline
21 & 7.6 & 7.76085365410201 & -0.16085365410201 \tabularnewline
22 & 7.6 & 7.70384789369275 & -0.103847893692755 \tabularnewline
23 & 7.7 & 7.7287234256565 & -0.0287234256564983 \tabularnewline
24 & 7.7 & 7.70669795302488 & -0.00669795302487716 \tabularnewline
25 & 7.8 & 7.76879927405823 & 0.0312007259417689 \tabularnewline
26 & 7.8 & 7.73882589458699 & 0.0611741054130105 \tabularnewline
27 & 8 & 7.7990248891822 & 0.200975110817805 \tabularnewline
28 & 8.1 & 7.72292779402435 & 0.37707220597565 \tabularnewline
29 & 8.1 & 7.70357964985624 & 0.396420350143762 \tabularnewline
30 & 8.2 & 7.7516897705319 & 0.448310229468102 \tabularnewline
31 & 8.1 & 7.9487894129203 & 0.151210587079698 \tabularnewline
32 & 8.1 & 7.8220775994919 & 0.277922400508099 \tabularnewline
33 & 8.1 & 7.81922982704318 & 0.280770172956822 \tabularnewline
34 & 8 & 7.75453754298062 & 0.245462457019379 \tabularnewline
35 & 8.2 & 7.73942130509354 & 0.460578694906459 \tabularnewline
36 & 8.4 & 7.84686676602134 & 0.553133233978662 \tabularnewline
37 & 8.4 & 7.75859214462624 & 0.64140785537376 \tabularnewline
38 & 8.5 & 7.83131412719592 & 0.668685872804075 \tabularnewline
39 & 8.6 & 7.92640933101786 & 0.673590668982142 \tabularnewline
40 & 8.5 & 7.97382852821906 & 0.526171471780937 \tabularnewline
41 & 8.3 & 7.98523333931435 & 0.314766660685648 \tabularnewline
42 & 7.8 & 7.80531813146959 & -0.00531813146959258 \tabularnewline
43 & 7.8 & 7.65122618848255 & 0.148773811517448 \tabularnewline
44 & 8 & 7.7516783361149 & 0.248321663885098 \tabularnewline
45 & 8.6 & 7.74044854277162 & 0.859551457228375 \tabularnewline
46 & 8.9 & 7.75496022261856 & 1.14503977738144 \tabularnewline
47 & 8.9 & 7.80315670872852 & 1.09684329127148 \tabularnewline
48 & 8.6 & 7.62608698644909 & 0.973913013550911 \tabularnewline
49 & 8.3 & 7.75849434477494 & 0.541505655225063 \tabularnewline
50 & 8.3 & 7.88364700661902 & 0.416352993380982 \tabularnewline
51 & 8.3 & 7.83640282716983 & 0.463597172830174 \tabularnewline
52 & 8.4 & 7.76048074976399 & 0.639519250236007 \tabularnewline
53 & 8.5 & 7.66520824130665 & 0.834791758693351 \tabularnewline
54 & 8.4 & 7.73163240782214 & 0.668367592177862 \tabularnewline
55 & 8.6 & 7.73284381078583 & 0.867156189214167 \tabularnewline
56 & 8.5 & 7.59801822029939 & 0.901981779700606 \tabularnewline
57 & 8.5 & 7.58696344470813 & 0.91303655529187 \tabularnewline
58 & 8.5 & 7.56441749258727 & 0.935582507412731 \tabularnewline
59 & 8.5 & 7.53902376801769 & 0.960976231982315 \tabularnewline
60 & 8.5 & 7.69648624982609 & 0.803513750173912 \tabularnewline
61 & 8.5 & 7.6544239964353 & 0.845576003564699 \tabularnewline
62 & 8.5 & 7.54904673216407 & 0.950953267835933 \tabularnewline
63 & 8.5 & 7.57037899443783 & 0.929621005562166 \tabularnewline
64 & 8.6 & 7.55612869777722 & 1.04387130222278 \tabularnewline
65 & 8.6 & 7.69691807699762 & 0.903081923002378 \tabularnewline
66 & 8.6 & 7.62945752527045 & 0.970542474729548 \tabularnewline
67 & 8.6 & 7.62798473912044 & 0.972015260879561 \tabularnewline
68 & 8.4 & 7.68447459380725 & 0.715525406192748 \tabularnewline
69 & 8 & 7.70969558750822 & 0.290304412491777 \tabularnewline
70 & 7.9 & 7.74294856659972 & 0.157051433400278 \tabularnewline
71 & 8 & 7.82483214585612 & 0.17516785414388 \tabularnewline
72 & 8 & 7.85938289334562 & 0.14061710665438 \tabularnewline
73 & 8 & 7.81584785380482 & 0.184152146195181 \tabularnewline
74 & 8 & 7.82223660906012 & 0.177763390939881 \tabularnewline
75 & 7.9 & 7.89289268185664 & 0.00710731814336049 \tabularnewline
76 & 7.9 & 7.8770855204951 & 0.0229144795048955 \tabularnewline
77 & 7.9 & 7.89159491345864 & 0.00840508654136094 \tabularnewline
78 & 8 & 7.91525904245869 & 0.0847409575413104 \tabularnewline
79 & 7.9 & 8.02625005995986 & -0.126250059959862 \tabularnewline
80 & 7.5 & 8.01830444000364 & -0.518304440003641 \tabularnewline
81 & 7.2 & 8.04395497399275 & -0.843954973992747 \tabularnewline
82 & 7 & 8.05812119210245 & -1.05812119210245 \tabularnewline
83 & 6.9 & 8.03721618323342 & -1.13721618323342 \tabularnewline
84 & 7.1 & 7.88752659051263 & -0.787526590512626 \tabularnewline
85 & 7.1 & 7.77100361144902 & -0.671003611449022 \tabularnewline
86 & 7.2 & 7.71658881406897 & -0.516588814068969 \tabularnewline
87 & 7.1 & 7.6402326226082 & -0.540232622608204 \tabularnewline
88 & 6.9 & 7.62053901670286 & -0.720539016702864 \tabularnewline
89 & 6.8 & 7.50954113855149 & -0.709541138551494 \tabularnewline
90 & 6.7 & 7.59565433720552 & -0.895654337205524 \tabularnewline
91 & 6.7 & 7.35614011903891 & -0.656140119038908 \tabularnewline
92 & 6.9 & 7.19980267475989 & -0.299802674759891 \tabularnewline
93 & 7.3 & 7.19625940506991 & 0.103740594930085 \tabularnewline
94 & 7.4 & 7.23902172946875 & 0.160978270531247 \tabularnewline
95 & 7.3 & 7.16448607277863 & 0.135513927221373 \tabularnewline
96 & 7.1 & 7.22996708016694 & -0.12996708016694 \tabularnewline
97 & 7 & 7.53556457687862 & -0.535564576878617 \tabularnewline
98 & 7.1 & 7.70891143794926 & -0.608911437949265 \tabularnewline
99 & 7.5 & 7.8244821103521 & -0.324482110352095 \tabularnewline
100 & 7.7 & 7.82984134104591 & -0.129841341045912 \tabularnewline
101 & 7.8 & 8.14242757728624 & -0.342427577286239 \tabularnewline
102 & 7.7 & 8.13223416915464 & -0.432234169154643 \tabularnewline
103 & 7.7 & 8.37252338934662 & -0.672523389346621 \tabularnewline
104 & 7.8 & 8.58240968977922 & -0.782409689779224 \tabularnewline
105 & 8 & 8.7446199835911 & -0.7446199835911 \tabularnewline
106 & 8.1 & 8.5373200724209 & -0.437320072420902 \tabularnewline
107 & 8.1 & 8.62956521691071 & -0.529565216910711 \tabularnewline
108 & 8 & 8.62921746829008 & -0.629217468290085 \tabularnewline
109 & 8.1 & 8.43712016044127 & -0.337120160441275 \tabularnewline
110 & 8.2 & 8.34435682334562 & -0.144356823345625 \tabularnewline
111 & 8.3 & 8.20339700014001 & 0.0966029998599924 \tabularnewline
112 & 8.4 & 8.15278685563625 & 0.247213144363749 \tabularnewline
113 & 8.5 & 7.89755184154241 & 0.602448158457594 \tabularnewline
114 & 8.5 & 7.83890513788132 & 0.661094862118677 \tabularnewline
115 & 8.5 & 7.67713353189118 & 0.822866468108822 \tabularnewline
116 & 8.5 & 7.60173193397458 & 0.898268066025415 \tabularnewline
117 & 8.5 & 7.7171230148586 & 0.782876985141396 \tabularnewline
118 & 8.3 & 7.72783232871264 & 0.572167671287359 \tabularnewline
119 & 8.2 & 7.631350704175 & 0.568649295824997 \tabularnewline
120 & 8.1 & 7.65855124221483 & 0.441448757785168 \tabularnewline
121 & 7.9 & 7.66036720321867 & 0.239632796781328 \tabularnewline
122 & 7.6 & 7.53314177095123 & 0.0668582290487703 \tabularnewline
123 & 7.3 & 7.47837922495055 & -0.178379224950551 \tabularnewline
124 & 7.1 & 7.54790797333428 & -0.447907973334285 \tabularnewline
125 & 7 & 7.57813587534165 & -0.578135875341648 \tabularnewline
126 & 7 & 7.66096490060862 & -0.660964900608623 \tabularnewline
127 & 7 & 7.69750205308718 & -0.697502053087177 \tabularnewline
128 & 7 & 7.63401659822152 & -0.634016598221517 \tabularnewline
129 & 6.9 & 7.48959987076023 & -0.589599870760231 \tabularnewline
130 & 6.8 & 7.63013015367771 & -0.830130153677713 \tabularnewline
131 & 6.7 & 7.58064275981994 & -0.880642759819943 \tabularnewline
132 & 6.6 & 7.57511537202431 & -0.975115372024312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&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]6.6[/C][C]7.06417060201535[/C][C]-0.464170602015352[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]7.25548604718862[/C][C]-0.755486047188615[/C][/ROW]
[ROW][C]3[/C][C]6.3[/C][C]7.35774958205801[/C][C]-1.05774958205801[/C][/ROW]
[ROW][C]4[/C][C]6.2[/C][C]7.38798205783217[/C][C]-1.18798205783217[/C][/ROW]
[ROW][C]5[/C][C]6.3[/C][C]7.44429918165037[/C][C]-1.14429918165037[/C][/ROW]
[ROW][C]6[/C][C]6.5[/C][C]7.2731932481049[/C][C]-0.773193248104899[/C][/ROW]
[ROW][C]7[/C][C]6.6[/C][C]7.21325106292921[/C][C]-0.613251062929215[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]7.21739889065934[/C][C]-0.817398890659337[/C][/ROW]
[ROW][C]9[/C][C]6.2[/C][C]7.26844543610143[/C][C]-1.06844543610143[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]7.36967258575914[/C][C]-1.06967258575914[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]7.5125347354029[/C][C]-0.912534735402903[/C][/ROW]
[ROW][C]12[/C][C]7.1[/C][C]7.50804373281895[/C][C]-0.408043732818952[/C][/ROW]
[ROW][C]13[/C][C]7.2[/C][C]7.5151279853155[/C][C]-0.315127985315504[/C][/ROW]
[ROW][C]14[/C][C]7.3[/C][C]7.474099830253[/C][C]-0.174099830252998[/C][/ROW]
[ROW][C]15[/C][C]7.3[/C][C]7.33253316212384[/C][C]-0.0325331621238357[/C][/ROW]
[ROW][C]16[/C][C]7.3[/C][C]7.34333112829558[/C][C]-0.0433311282955789[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]7.34834032348537[/C][C]0.0516596765146299[/C][/ROW]
[ROW][C]18[/C][C]7.3[/C][C]7.55753798732692[/C][C]-0.257537987326918[/C][/ROW]
[ROW][C]19[/C][C]7.4[/C][C]7.62905314069971[/C][C]-0.229053140699707[/C][/ROW]
[ROW][C]20[/C][C]7.4[/C][C]7.79592488108075[/C][C]-0.395924881080748[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]7.76085365410201[/C][C]-0.16085365410201[/C][/ROW]
[ROW][C]22[/C][C]7.6[/C][C]7.70384789369275[/C][C]-0.103847893692755[/C][/ROW]
[ROW][C]23[/C][C]7.7[/C][C]7.7287234256565[/C][C]-0.0287234256564983[/C][/ROW]
[ROW][C]24[/C][C]7.7[/C][C]7.70669795302488[/C][C]-0.00669795302487716[/C][/ROW]
[ROW][C]25[/C][C]7.8[/C][C]7.76879927405823[/C][C]0.0312007259417689[/C][/ROW]
[ROW][C]26[/C][C]7.8[/C][C]7.73882589458699[/C][C]0.0611741054130105[/C][/ROW]
[ROW][C]27[/C][C]8[/C][C]7.7990248891822[/C][C]0.200975110817805[/C][/ROW]
[ROW][C]28[/C][C]8.1[/C][C]7.72292779402435[/C][C]0.37707220597565[/C][/ROW]
[ROW][C]29[/C][C]8.1[/C][C]7.70357964985624[/C][C]0.396420350143762[/C][/ROW]
[ROW][C]30[/C][C]8.2[/C][C]7.7516897705319[/C][C]0.448310229468102[/C][/ROW]
[ROW][C]31[/C][C]8.1[/C][C]7.9487894129203[/C][C]0.151210587079698[/C][/ROW]
[ROW][C]32[/C][C]8.1[/C][C]7.8220775994919[/C][C]0.277922400508099[/C][/ROW]
[ROW][C]33[/C][C]8.1[/C][C]7.81922982704318[/C][C]0.280770172956822[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.75453754298062[/C][C]0.245462457019379[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]7.73942130509354[/C][C]0.460578694906459[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]7.84686676602134[/C][C]0.553133233978662[/C][/ROW]
[ROW][C]37[/C][C]8.4[/C][C]7.75859214462624[/C][C]0.64140785537376[/C][/ROW]
[ROW][C]38[/C][C]8.5[/C][C]7.83131412719592[/C][C]0.668685872804075[/C][/ROW]
[ROW][C]39[/C][C]8.6[/C][C]7.92640933101786[/C][C]0.673590668982142[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]7.97382852821906[/C][C]0.526171471780937[/C][/ROW]
[ROW][C]41[/C][C]8.3[/C][C]7.98523333931435[/C][C]0.314766660685648[/C][/ROW]
[ROW][C]42[/C][C]7.8[/C][C]7.80531813146959[/C][C]-0.00531813146959258[/C][/ROW]
[ROW][C]43[/C][C]7.8[/C][C]7.65122618848255[/C][C]0.148773811517448[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]7.7516783361149[/C][C]0.248321663885098[/C][/ROW]
[ROW][C]45[/C][C]8.6[/C][C]7.74044854277162[/C][C]0.859551457228375[/C][/ROW]
[ROW][C]46[/C][C]8.9[/C][C]7.75496022261856[/C][C]1.14503977738144[/C][/ROW]
[ROW][C]47[/C][C]8.9[/C][C]7.80315670872852[/C][C]1.09684329127148[/C][/ROW]
[ROW][C]48[/C][C]8.6[/C][C]7.62608698644909[/C][C]0.973913013550911[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]7.75849434477494[/C][C]0.541505655225063[/C][/ROW]
[ROW][C]50[/C][C]8.3[/C][C]7.88364700661902[/C][C]0.416352993380982[/C][/ROW]
[ROW][C]51[/C][C]8.3[/C][C]7.83640282716983[/C][C]0.463597172830174[/C][/ROW]
[ROW][C]52[/C][C]8.4[/C][C]7.76048074976399[/C][C]0.639519250236007[/C][/ROW]
[ROW][C]53[/C][C]8.5[/C][C]7.66520824130665[/C][C]0.834791758693351[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]7.73163240782214[/C][C]0.668367592177862[/C][/ROW]
[ROW][C]55[/C][C]8.6[/C][C]7.73284381078583[/C][C]0.867156189214167[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]7.59801822029939[/C][C]0.901981779700606[/C][/ROW]
[ROW][C]57[/C][C]8.5[/C][C]7.58696344470813[/C][C]0.91303655529187[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]7.56441749258727[/C][C]0.935582507412731[/C][/ROW]
[ROW][C]59[/C][C]8.5[/C][C]7.53902376801769[/C][C]0.960976231982315[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]7.69648624982609[/C][C]0.803513750173912[/C][/ROW]
[ROW][C]61[/C][C]8.5[/C][C]7.6544239964353[/C][C]0.845576003564699[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]7.54904673216407[/C][C]0.950953267835933[/C][/ROW]
[ROW][C]63[/C][C]8.5[/C][C]7.57037899443783[/C][C]0.929621005562166[/C][/ROW]
[ROW][C]64[/C][C]8.6[/C][C]7.55612869777722[/C][C]1.04387130222278[/C][/ROW]
[ROW][C]65[/C][C]8.6[/C][C]7.69691807699762[/C][C]0.903081923002378[/C][/ROW]
[ROW][C]66[/C][C]8.6[/C][C]7.62945752527045[/C][C]0.970542474729548[/C][/ROW]
[ROW][C]67[/C][C]8.6[/C][C]7.62798473912044[/C][C]0.972015260879561[/C][/ROW]
[ROW][C]68[/C][C]8.4[/C][C]7.68447459380725[/C][C]0.715525406192748[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]7.70969558750822[/C][C]0.290304412491777[/C][/ROW]
[ROW][C]70[/C][C]7.9[/C][C]7.74294856659972[/C][C]0.157051433400278[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]7.82483214585612[/C][C]0.17516785414388[/C][/ROW]
[ROW][C]72[/C][C]8[/C][C]7.85938289334562[/C][C]0.14061710665438[/C][/ROW]
[ROW][C]73[/C][C]8[/C][C]7.81584785380482[/C][C]0.184152146195181[/C][/ROW]
[ROW][C]74[/C][C]8[/C][C]7.82223660906012[/C][C]0.177763390939881[/C][/ROW]
[ROW][C]75[/C][C]7.9[/C][C]7.89289268185664[/C][C]0.00710731814336049[/C][/ROW]
[ROW][C]76[/C][C]7.9[/C][C]7.8770855204951[/C][C]0.0229144795048955[/C][/ROW]
[ROW][C]77[/C][C]7.9[/C][C]7.89159491345864[/C][C]0.00840508654136094[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]7.91525904245869[/C][C]0.0847409575413104[/C][/ROW]
[ROW][C]79[/C][C]7.9[/C][C]8.02625005995986[/C][C]-0.126250059959862[/C][/ROW]
[ROW][C]80[/C][C]7.5[/C][C]8.01830444000364[/C][C]-0.518304440003641[/C][/ROW]
[ROW][C]81[/C][C]7.2[/C][C]8.04395497399275[/C][C]-0.843954973992747[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]8.05812119210245[/C][C]-1.05812119210245[/C][/ROW]
[ROW][C]83[/C][C]6.9[/C][C]8.03721618323342[/C][C]-1.13721618323342[/C][/ROW]
[ROW][C]84[/C][C]7.1[/C][C]7.88752659051263[/C][C]-0.787526590512626[/C][/ROW]
[ROW][C]85[/C][C]7.1[/C][C]7.77100361144902[/C][C]-0.671003611449022[/C][/ROW]
[ROW][C]86[/C][C]7.2[/C][C]7.71658881406897[/C][C]-0.516588814068969[/C][/ROW]
[ROW][C]87[/C][C]7.1[/C][C]7.6402326226082[/C][C]-0.540232622608204[/C][/ROW]
[ROW][C]88[/C][C]6.9[/C][C]7.62053901670286[/C][C]-0.720539016702864[/C][/ROW]
[ROW][C]89[/C][C]6.8[/C][C]7.50954113855149[/C][C]-0.709541138551494[/C][/ROW]
[ROW][C]90[/C][C]6.7[/C][C]7.59565433720552[/C][C]-0.895654337205524[/C][/ROW]
[ROW][C]91[/C][C]6.7[/C][C]7.35614011903891[/C][C]-0.656140119038908[/C][/ROW]
[ROW][C]92[/C][C]6.9[/C][C]7.19980267475989[/C][C]-0.299802674759891[/C][/ROW]
[ROW][C]93[/C][C]7.3[/C][C]7.19625940506991[/C][C]0.103740594930085[/C][/ROW]
[ROW][C]94[/C][C]7.4[/C][C]7.23902172946875[/C][C]0.160978270531247[/C][/ROW]
[ROW][C]95[/C][C]7.3[/C][C]7.16448607277863[/C][C]0.135513927221373[/C][/ROW]
[ROW][C]96[/C][C]7.1[/C][C]7.22996708016694[/C][C]-0.12996708016694[/C][/ROW]
[ROW][C]97[/C][C]7[/C][C]7.53556457687862[/C][C]-0.535564576878617[/C][/ROW]
[ROW][C]98[/C][C]7.1[/C][C]7.70891143794926[/C][C]-0.608911437949265[/C][/ROW]
[ROW][C]99[/C][C]7.5[/C][C]7.8244821103521[/C][C]-0.324482110352095[/C][/ROW]
[ROW][C]100[/C][C]7.7[/C][C]7.82984134104591[/C][C]-0.129841341045912[/C][/ROW]
[ROW][C]101[/C][C]7.8[/C][C]8.14242757728624[/C][C]-0.342427577286239[/C][/ROW]
[ROW][C]102[/C][C]7.7[/C][C]8.13223416915464[/C][C]-0.432234169154643[/C][/ROW]
[ROW][C]103[/C][C]7.7[/C][C]8.37252338934662[/C][C]-0.672523389346621[/C][/ROW]
[ROW][C]104[/C][C]7.8[/C][C]8.58240968977922[/C][C]-0.782409689779224[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]8.7446199835911[/C][C]-0.7446199835911[/C][/ROW]
[ROW][C]106[/C][C]8.1[/C][C]8.5373200724209[/C][C]-0.437320072420902[/C][/ROW]
[ROW][C]107[/C][C]8.1[/C][C]8.62956521691071[/C][C]-0.529565216910711[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]8.62921746829008[/C][C]-0.629217468290085[/C][/ROW]
[ROW][C]109[/C][C]8.1[/C][C]8.43712016044127[/C][C]-0.337120160441275[/C][/ROW]
[ROW][C]110[/C][C]8.2[/C][C]8.34435682334562[/C][C]-0.144356823345625[/C][/ROW]
[ROW][C]111[/C][C]8.3[/C][C]8.20339700014001[/C][C]0.0966029998599924[/C][/ROW]
[ROW][C]112[/C][C]8.4[/C][C]8.15278685563625[/C][C]0.247213144363749[/C][/ROW]
[ROW][C]113[/C][C]8.5[/C][C]7.89755184154241[/C][C]0.602448158457594[/C][/ROW]
[ROW][C]114[/C][C]8.5[/C][C]7.83890513788132[/C][C]0.661094862118677[/C][/ROW]
[ROW][C]115[/C][C]8.5[/C][C]7.67713353189118[/C][C]0.822866468108822[/C][/ROW]
[ROW][C]116[/C][C]8.5[/C][C]7.60173193397458[/C][C]0.898268066025415[/C][/ROW]
[ROW][C]117[/C][C]8.5[/C][C]7.7171230148586[/C][C]0.782876985141396[/C][/ROW]
[ROW][C]118[/C][C]8.3[/C][C]7.72783232871264[/C][C]0.572167671287359[/C][/ROW]
[ROW][C]119[/C][C]8.2[/C][C]7.631350704175[/C][C]0.568649295824997[/C][/ROW]
[ROW][C]120[/C][C]8.1[/C][C]7.65855124221483[/C][C]0.441448757785168[/C][/ROW]
[ROW][C]121[/C][C]7.9[/C][C]7.66036720321867[/C][C]0.239632796781328[/C][/ROW]
[ROW][C]122[/C][C]7.6[/C][C]7.53314177095123[/C][C]0.0668582290487703[/C][/ROW]
[ROW][C]123[/C][C]7.3[/C][C]7.47837922495055[/C][C]-0.178379224950551[/C][/ROW]
[ROW][C]124[/C][C]7.1[/C][C]7.54790797333428[/C][C]-0.447907973334285[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]7.57813587534165[/C][C]-0.578135875341648[/C][/ROW]
[ROW][C]126[/C][C]7[/C][C]7.66096490060862[/C][C]-0.660964900608623[/C][/ROW]
[ROW][C]127[/C][C]7[/C][C]7.69750205308718[/C][C]-0.697502053087177[/C][/ROW]
[ROW][C]128[/C][C]7[/C][C]7.63401659822152[/C][C]-0.634016598221517[/C][/ROW]
[ROW][C]129[/C][C]6.9[/C][C]7.48959987076023[/C][C]-0.589599870760231[/C][/ROW]
[ROW][C]130[/C][C]6.8[/C][C]7.63013015367771[/C][C]-0.830130153677713[/C][/ROW]
[ROW][C]131[/C][C]6.7[/C][C]7.58064275981994[/C][C]-0.880642759819943[/C][/ROW]
[ROW][C]132[/C][C]6.6[/C][C]7.57511537202431[/C][C]-0.975115372024312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.67.06417060201535-0.464170602015352
26.57.25548604718862-0.755486047188615
36.37.35774958205801-1.05774958205801
46.27.38798205783217-1.18798205783217
56.37.44429918165037-1.14429918165037
66.57.2731932481049-0.773193248104899
76.67.21325106292921-0.613251062929215
86.47.21739889065934-0.817398890659337
96.27.26844543610143-1.06844543610143
106.37.36967258575914-1.06967258575914
116.67.5125347354029-0.912534735402903
127.17.50804373281895-0.408043732818952
137.27.5151279853155-0.315127985315504
147.37.474099830253-0.174099830252998
157.37.33253316212384-0.0325331621238357
167.37.34333112829558-0.0433311282955789
177.47.348340323485370.0516596765146299
187.37.55753798732692-0.257537987326918
197.47.62905314069971-0.229053140699707
207.47.79592488108075-0.395924881080748
217.67.76085365410201-0.16085365410201
227.67.70384789369275-0.103847893692755
237.77.7287234256565-0.0287234256564983
247.77.70669795302488-0.00669795302487716
257.87.768799274058230.0312007259417689
267.87.738825894586990.0611741054130105
2787.79902488918220.200975110817805
288.17.722927794024350.37707220597565
298.17.703579649856240.396420350143762
308.27.75168977053190.448310229468102
318.17.94878941292030.151210587079698
328.17.82207759949190.277922400508099
338.17.819229827043180.280770172956822
3487.754537542980620.245462457019379
358.27.739421305093540.460578694906459
368.47.846866766021340.553133233978662
378.47.758592144626240.64140785537376
388.57.831314127195920.668685872804075
398.67.926409331017860.673590668982142
408.57.973828528219060.526171471780937
418.37.985233339314350.314766660685648
427.87.80531813146959-0.00531813146959258
437.87.651226188482550.148773811517448
4487.75167833611490.248321663885098
458.67.740448542771620.859551457228375
468.97.754960222618561.14503977738144
478.97.803156708728521.09684329127148
488.67.626086986449090.973913013550911
498.37.758494344774940.541505655225063
508.37.883647006619020.416352993380982
518.37.836402827169830.463597172830174
528.47.760480749763990.639519250236007
538.57.665208241306650.834791758693351
548.47.731632407822140.668367592177862
558.67.732843810785830.867156189214167
568.57.598018220299390.901981779700606
578.57.586963444708130.91303655529187
588.57.564417492587270.935582507412731
598.57.539023768017690.960976231982315
608.57.696486249826090.803513750173912
618.57.65442399643530.845576003564699
628.57.549046732164070.950953267835933
638.57.570378994437830.929621005562166
648.67.556128697777221.04387130222278
658.67.696918076997620.903081923002378
668.67.629457525270450.970542474729548
678.67.627984739120440.972015260879561
688.47.684474593807250.715525406192748
6987.709695587508220.290304412491777
707.97.742948566599720.157051433400278
7187.824832145856120.17516785414388
7287.859382893345620.14061710665438
7387.815847853804820.184152146195181
7487.822236609060120.177763390939881
757.97.892892681856640.00710731814336049
767.97.87708552049510.0229144795048955
777.97.891594913458640.00840508654136094
7887.915259042458690.0847409575413104
797.98.02625005995986-0.126250059959862
807.58.01830444000364-0.518304440003641
817.28.04395497399275-0.843954973992747
8278.05812119210245-1.05812119210245
836.98.03721618323342-1.13721618323342
847.17.88752659051263-0.787526590512626
857.17.77100361144902-0.671003611449022
867.27.71658881406897-0.516588814068969
877.17.6402326226082-0.540232622608204
886.97.62053901670286-0.720539016702864
896.87.50954113855149-0.709541138551494
906.77.59565433720552-0.895654337205524
916.77.35614011903891-0.656140119038908
926.97.19980267475989-0.299802674759891
937.37.196259405069910.103740594930085
947.47.239021729468750.160978270531247
957.37.164486072778630.135513927221373
967.17.22996708016694-0.12996708016694
9777.53556457687862-0.535564576878617
987.17.70891143794926-0.608911437949265
997.57.8244821103521-0.324482110352095
1007.77.82984134104591-0.129841341045912
1017.88.14242757728624-0.342427577286239
1027.78.13223416915464-0.432234169154643
1037.78.37252338934662-0.672523389346621
1047.88.58240968977922-0.782409689779224
10588.7446199835911-0.7446199835911
1068.18.5373200724209-0.437320072420902
1078.18.62956521691071-0.529565216910711
10888.62921746829008-0.629217468290085
1098.18.43712016044127-0.337120160441275
1108.28.34435682334562-0.144356823345625
1118.38.203397000140010.0966029998599924
1128.48.152786855636250.247213144363749
1138.57.897551841542410.602448158457594
1148.57.838905137881320.661094862118677
1158.57.677133531891180.822866468108822
1168.57.601731933974580.898268066025415
1178.57.71712301485860.782876985141396
1188.37.727832328712640.572167671287359
1198.27.6313507041750.568649295824997
1208.17.658551242214830.441448757785168
1217.97.660367203218670.239632796781328
1227.67.533141770951230.0668582290487703
1237.37.47837922495055-0.178379224950551
1247.17.54790797333428-0.447907973334285
12577.57813587534165-0.578135875341648
12677.66096490060862-0.660964900608623
12777.69750205308718-0.697502053087177
12877.63401659822152-0.634016598221517
1296.97.48959987076023-0.589599870760231
1306.87.63013015367771-0.830130153677713
1316.77.58064275981994-0.880642759819943
1326.67.57511537202431-0.975115372024312






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.003529178109492640.007058356218985290.996470821890507
70.0006260686258358620.001252137251671720.999373931374164
80.0003103991913746150.0006207983827492310.999689600808625
90.0003073173116448240.0006146346232896470.999692682688355
106.81411071884088e-050.0001362822143768180.999931858892812
110.0005581834809577020.00111636696191540.999441816519042
120.01028411808832130.02056823617664270.989715881911679
130.01422430450261380.02844860900522770.985775695497386
140.02160547554204930.04321095108409870.978394524457951
150.04827554695732430.09655109391464860.951724453042676
160.05189192716961770.1037838543392350.948108072830382
170.06728024449463970.1345604889892790.93271975550536
180.05207591598300540.1041518319660110.947924084016995
190.04738278356704950.09476556713409890.95261721643295
200.0427602314801960.0855204629603920.957239768519804
210.0857001059625170.1714002119250340.914299894037483
220.1079891157433120.2159782314866240.892010884256688
230.123660286563090.2473205731261790.87633971343691
240.1428423993740550.2856847987481090.857157600625945
250.1548947472239550.3097894944479110.845105252776045
260.1770956707238440.3541913414476880.822904329276156
270.1851029077178530.3702058154357060.814897092282147
280.2020178585125830.4040357170251670.797982141487417
290.1986858461650890.3973716923301790.801314153834911
300.1836762564272280.3673525128544550.816323743572772
310.1798813042402990.3597626084805980.820118695759701
320.1506290770349760.3012581540699520.849370922965024
330.1292509950574190.2585019901148380.870749004942581
340.1205337153054830.2410674306109660.879466284694517
350.112089194957970.2241783899159390.88791080504203
360.08977509139731010.179550182794620.91022490860269
370.07519048801957510.150380976039150.924809511980425
380.05681967558083690.1136393511616740.943180324419163
390.04444258492012610.08888516984025210.955557415079874
400.03698725576156150.07397451152312290.963012744238439
410.03319496049420790.06638992098841570.966805039505792
420.04188157883446780.08376315766893550.958118421165532
430.0382653073534550.07653061470691010.961734692646545
440.0316575419723320.0633150839446640.968342458027668
450.03316139155459960.06632278310919920.9668386084454
460.05605598769753890.1121119753950780.943944012302461
470.07091354842173830.1418270968434770.929086451578262
480.0711138851169090.1422277702338180.928886114883091
490.06218947831939380.1243789566387880.937810521680606
500.07185142557943910.1437028511588780.928148574420561
510.06585156905426260.1317031381085250.934148430945737
520.05537487568443030.1107497513688610.94462512431557
530.0543227854202620.1086455708405240.945677214579738
540.04543671484554810.09087342969109630.954563285154452
550.04328449912354890.08656899824709780.956715500876451
560.04486622555730640.08973245111461280.955133774442694
570.04678670552667790.09357341105335570.953213294473322
580.04696129558101640.09392259116203280.953038704418984
590.0478095400272480.0956190800544960.952190459972752
600.04116567074984930.08233134149969870.958834329250151
610.03813473489821950.07626946979643910.96186526510178
620.03904566369358320.07809132738716650.960954336306417
630.03688904794433280.07377809588866570.963110952055667
640.04164312159411810.08328624318823620.958356878405882
650.03957565268303690.07915130536607380.960424347316963
660.0410432530272480.08208650605449610.958956746972752
670.0482547483956280.09650949679125590.951745251604372
680.045816101974990.091632203949980.95418389802501
690.04739839518155340.09479679036310670.952601604818447
700.05715538671018170.1143107734203630.942844613289818
710.06189981067351790.1237996213470360.938100189326482
720.06413206247243150.1282641249448630.935867937527568
730.06876220478319320.1375244095663860.931237795216807
740.08161256904437940.1632251380887590.918387430955621
750.09715521708234280.1943104341646860.902844782917657
760.1109901455331370.2219802910662730.889009854466863
770.128477027258050.25695405451610.87152297274195
780.1475153564097950.295030712819590.852484643590205
790.1778053816474040.3556107632948080.822194618352596
800.2709739213399590.5419478426799190.729026078660041
810.4784309685535640.9568619371071280.521569031446436
820.7278488256102860.5443023487794280.272151174389714
830.896538881450130.2069222370997410.10346111854987
840.9402207508141310.1195584983717370.0597792491858687
850.9600836033463730.07983279330725380.0399163966536269
860.9650736145203480.06985277095930360.0349263854796518
870.9677141311345740.06457173773085280.0322858688654264
880.9724033339319150.05519333213617090.0275966660680855
890.9723294572746250.05534108545074920.0276705427253746
900.9735959828021960.05280803439560860.0264040171978043
910.9673840273525690.06523194529486230.0326159726474312
920.9562674303572340.08746513928553170.0437325696427658
930.9629622094550750.0740755810898490.0370377905449245
940.9703804090422750.0592391819154510.0296195909577255
950.9729136718239380.05417265635212320.0270863281760616
960.9621286185339470.07574276293210550.0378713814660527
970.9742660404788860.05146791904222730.0257339595211137
980.9767205399613060.04655892007738730.0232794600386936
990.9737608378307240.0524783243385530.0262391621692765
1000.9722252474497120.05554950510057610.0277747525502881
1010.977344797682230.04531040463554010.02265520231777
1020.9844140913512530.03117181729749460.0155859086487473
1030.9917290662722690.01654186745546260.0082709337277313
1040.9961914416164720.007617116767056930.00380855838352846
1050.9991779640546940.001644071890612770.000822035945306385
1060.9992444632877180.001511073424564340.000755536712282172
1070.999412591846650.001174816306699580.000587408153349792
1080.9996961854516530.0006076290966940830.000303814548347042
1090.9994481606464360.001103678707127350.000551839353563674
1100.9990246163460220.001950767307955370.000975383653977683
1110.998153148847360.003693702305280750.00184685115264037
1120.9990633153953950.001873369209211040.000936684604605519
1130.9981411262063080.003717747587383280.00185887379369164
1140.9964504547772870.007099090445426670.00354954522271334
1150.9934792895963620.01304142080727640.0065207104036382
1160.9884332976552810.02313340468943870.0115667023447194
1170.9794688373176580.04106232536468420.0205311626823421
1180.9674170983455780.06516580330884420.0325829016544221
1190.9474131923943770.1051736152112460.052586807605623
1200.9806371703843310.03872565923133760.0193628296156688
1210.9926105113294540.01477897734109250.00738948867054623
1220.9960082061358450.007983587728310890.00399179386415545
1230.9976150880822950.004769823835409580.00238491191770479
1240.9992328008053710.001534398389257960.000767199194628981
1250.9994203365884960.001159326823008170.000579663411504085
1260.9954090166342970.009181966731406660.00459098336570333

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.00352917810949264 & 0.00705835621898529 & 0.996470821890507 \tabularnewline
7 & 0.000626068625835862 & 0.00125213725167172 & 0.999373931374164 \tabularnewline
8 & 0.000310399191374615 & 0.000620798382749231 & 0.999689600808625 \tabularnewline
9 & 0.000307317311644824 & 0.000614634623289647 & 0.999692682688355 \tabularnewline
10 & 6.81411071884088e-05 & 0.000136282214376818 & 0.999931858892812 \tabularnewline
11 & 0.000558183480957702 & 0.0011163669619154 & 0.999441816519042 \tabularnewline
12 & 0.0102841180883213 & 0.0205682361766427 & 0.989715881911679 \tabularnewline
13 & 0.0142243045026138 & 0.0284486090052277 & 0.985775695497386 \tabularnewline
14 & 0.0216054755420493 & 0.0432109510840987 & 0.978394524457951 \tabularnewline
15 & 0.0482755469573243 & 0.0965510939146486 & 0.951724453042676 \tabularnewline
16 & 0.0518919271696177 & 0.103783854339235 & 0.948108072830382 \tabularnewline
17 & 0.0672802444946397 & 0.134560488989279 & 0.93271975550536 \tabularnewline
18 & 0.0520759159830054 & 0.104151831966011 & 0.947924084016995 \tabularnewline
19 & 0.0473827835670495 & 0.0947655671340989 & 0.95261721643295 \tabularnewline
20 & 0.042760231480196 & 0.085520462960392 & 0.957239768519804 \tabularnewline
21 & 0.085700105962517 & 0.171400211925034 & 0.914299894037483 \tabularnewline
22 & 0.107989115743312 & 0.215978231486624 & 0.892010884256688 \tabularnewline
23 & 0.12366028656309 & 0.247320573126179 & 0.87633971343691 \tabularnewline
24 & 0.142842399374055 & 0.285684798748109 & 0.857157600625945 \tabularnewline
25 & 0.154894747223955 & 0.309789494447911 & 0.845105252776045 \tabularnewline
26 & 0.177095670723844 & 0.354191341447688 & 0.822904329276156 \tabularnewline
27 & 0.185102907717853 & 0.370205815435706 & 0.814897092282147 \tabularnewline
28 & 0.202017858512583 & 0.404035717025167 & 0.797982141487417 \tabularnewline
29 & 0.198685846165089 & 0.397371692330179 & 0.801314153834911 \tabularnewline
30 & 0.183676256427228 & 0.367352512854455 & 0.816323743572772 \tabularnewline
31 & 0.179881304240299 & 0.359762608480598 & 0.820118695759701 \tabularnewline
32 & 0.150629077034976 & 0.301258154069952 & 0.849370922965024 \tabularnewline
33 & 0.129250995057419 & 0.258501990114838 & 0.870749004942581 \tabularnewline
34 & 0.120533715305483 & 0.241067430610966 & 0.879466284694517 \tabularnewline
35 & 0.11208919495797 & 0.224178389915939 & 0.88791080504203 \tabularnewline
36 & 0.0897750913973101 & 0.17955018279462 & 0.91022490860269 \tabularnewline
37 & 0.0751904880195751 & 0.15038097603915 & 0.924809511980425 \tabularnewline
38 & 0.0568196755808369 & 0.113639351161674 & 0.943180324419163 \tabularnewline
39 & 0.0444425849201261 & 0.0888851698402521 & 0.955557415079874 \tabularnewline
40 & 0.0369872557615615 & 0.0739745115231229 & 0.963012744238439 \tabularnewline
41 & 0.0331949604942079 & 0.0663899209884157 & 0.966805039505792 \tabularnewline
42 & 0.0418815788344678 & 0.0837631576689355 & 0.958118421165532 \tabularnewline
43 & 0.038265307353455 & 0.0765306147069101 & 0.961734692646545 \tabularnewline
44 & 0.031657541972332 & 0.063315083944664 & 0.968342458027668 \tabularnewline
45 & 0.0331613915545996 & 0.0663227831091992 & 0.9668386084454 \tabularnewline
46 & 0.0560559876975389 & 0.112111975395078 & 0.943944012302461 \tabularnewline
47 & 0.0709135484217383 & 0.141827096843477 & 0.929086451578262 \tabularnewline
48 & 0.071113885116909 & 0.142227770233818 & 0.928886114883091 \tabularnewline
49 & 0.0621894783193938 & 0.124378956638788 & 0.937810521680606 \tabularnewline
50 & 0.0718514255794391 & 0.143702851158878 & 0.928148574420561 \tabularnewline
51 & 0.0658515690542626 & 0.131703138108525 & 0.934148430945737 \tabularnewline
52 & 0.0553748756844303 & 0.110749751368861 & 0.94462512431557 \tabularnewline
53 & 0.054322785420262 & 0.108645570840524 & 0.945677214579738 \tabularnewline
54 & 0.0454367148455481 & 0.0908734296910963 & 0.954563285154452 \tabularnewline
55 & 0.0432844991235489 & 0.0865689982470978 & 0.956715500876451 \tabularnewline
56 & 0.0448662255573064 & 0.0897324511146128 & 0.955133774442694 \tabularnewline
57 & 0.0467867055266779 & 0.0935734110533557 & 0.953213294473322 \tabularnewline
58 & 0.0469612955810164 & 0.0939225911620328 & 0.953038704418984 \tabularnewline
59 & 0.047809540027248 & 0.095619080054496 & 0.952190459972752 \tabularnewline
60 & 0.0411656707498493 & 0.0823313414996987 & 0.958834329250151 \tabularnewline
61 & 0.0381347348982195 & 0.0762694697964391 & 0.96186526510178 \tabularnewline
62 & 0.0390456636935832 & 0.0780913273871665 & 0.960954336306417 \tabularnewline
63 & 0.0368890479443328 & 0.0737780958886657 & 0.963110952055667 \tabularnewline
64 & 0.0416431215941181 & 0.0832862431882362 & 0.958356878405882 \tabularnewline
65 & 0.0395756526830369 & 0.0791513053660738 & 0.960424347316963 \tabularnewline
66 & 0.041043253027248 & 0.0820865060544961 & 0.958956746972752 \tabularnewline
67 & 0.048254748395628 & 0.0965094967912559 & 0.951745251604372 \tabularnewline
68 & 0.04581610197499 & 0.09163220394998 & 0.95418389802501 \tabularnewline
69 & 0.0473983951815534 & 0.0947967903631067 & 0.952601604818447 \tabularnewline
70 & 0.0571553867101817 & 0.114310773420363 & 0.942844613289818 \tabularnewline
71 & 0.0618998106735179 & 0.123799621347036 & 0.938100189326482 \tabularnewline
72 & 0.0641320624724315 & 0.128264124944863 & 0.935867937527568 \tabularnewline
73 & 0.0687622047831932 & 0.137524409566386 & 0.931237795216807 \tabularnewline
74 & 0.0816125690443794 & 0.163225138088759 & 0.918387430955621 \tabularnewline
75 & 0.0971552170823428 & 0.194310434164686 & 0.902844782917657 \tabularnewline
76 & 0.110990145533137 & 0.221980291066273 & 0.889009854466863 \tabularnewline
77 & 0.12847702725805 & 0.2569540545161 & 0.87152297274195 \tabularnewline
78 & 0.147515356409795 & 0.29503071281959 & 0.852484643590205 \tabularnewline
79 & 0.177805381647404 & 0.355610763294808 & 0.822194618352596 \tabularnewline
80 & 0.270973921339959 & 0.541947842679919 & 0.729026078660041 \tabularnewline
81 & 0.478430968553564 & 0.956861937107128 & 0.521569031446436 \tabularnewline
82 & 0.727848825610286 & 0.544302348779428 & 0.272151174389714 \tabularnewline
83 & 0.89653888145013 & 0.206922237099741 & 0.10346111854987 \tabularnewline
84 & 0.940220750814131 & 0.119558498371737 & 0.0597792491858687 \tabularnewline
85 & 0.960083603346373 & 0.0798327933072538 & 0.0399163966536269 \tabularnewline
86 & 0.965073614520348 & 0.0698527709593036 & 0.0349263854796518 \tabularnewline
87 & 0.967714131134574 & 0.0645717377308528 & 0.0322858688654264 \tabularnewline
88 & 0.972403333931915 & 0.0551933321361709 & 0.0275966660680855 \tabularnewline
89 & 0.972329457274625 & 0.0553410854507492 & 0.0276705427253746 \tabularnewline
90 & 0.973595982802196 & 0.0528080343956086 & 0.0264040171978043 \tabularnewline
91 & 0.967384027352569 & 0.0652319452948623 & 0.0326159726474312 \tabularnewline
92 & 0.956267430357234 & 0.0874651392855317 & 0.0437325696427658 \tabularnewline
93 & 0.962962209455075 & 0.074075581089849 & 0.0370377905449245 \tabularnewline
94 & 0.970380409042275 & 0.059239181915451 & 0.0296195909577255 \tabularnewline
95 & 0.972913671823938 & 0.0541726563521232 & 0.0270863281760616 \tabularnewline
96 & 0.962128618533947 & 0.0757427629321055 & 0.0378713814660527 \tabularnewline
97 & 0.974266040478886 & 0.0514679190422273 & 0.0257339595211137 \tabularnewline
98 & 0.976720539961306 & 0.0465589200773873 & 0.0232794600386936 \tabularnewline
99 & 0.973760837830724 & 0.052478324338553 & 0.0262391621692765 \tabularnewline
100 & 0.972225247449712 & 0.0555495051005761 & 0.0277747525502881 \tabularnewline
101 & 0.97734479768223 & 0.0453104046355401 & 0.02265520231777 \tabularnewline
102 & 0.984414091351253 & 0.0311718172974946 & 0.0155859086487473 \tabularnewline
103 & 0.991729066272269 & 0.0165418674554626 & 0.0082709337277313 \tabularnewline
104 & 0.996191441616472 & 0.00761711676705693 & 0.00380855838352846 \tabularnewline
105 & 0.999177964054694 & 0.00164407189061277 & 0.000822035945306385 \tabularnewline
106 & 0.999244463287718 & 0.00151107342456434 & 0.000755536712282172 \tabularnewline
107 & 0.99941259184665 & 0.00117481630669958 & 0.000587408153349792 \tabularnewline
108 & 0.999696185451653 & 0.000607629096694083 & 0.000303814548347042 \tabularnewline
109 & 0.999448160646436 & 0.00110367870712735 & 0.000551839353563674 \tabularnewline
110 & 0.999024616346022 & 0.00195076730795537 & 0.000975383653977683 \tabularnewline
111 & 0.99815314884736 & 0.00369370230528075 & 0.00184685115264037 \tabularnewline
112 & 0.999063315395395 & 0.00187336920921104 & 0.000936684604605519 \tabularnewline
113 & 0.998141126206308 & 0.00371774758738328 & 0.00185887379369164 \tabularnewline
114 & 0.996450454777287 & 0.00709909044542667 & 0.00354954522271334 \tabularnewline
115 & 0.993479289596362 & 0.0130414208072764 & 0.0065207104036382 \tabularnewline
116 & 0.988433297655281 & 0.0231334046894387 & 0.0115667023447194 \tabularnewline
117 & 0.979468837317658 & 0.0410623253646842 & 0.0205311626823421 \tabularnewline
118 & 0.967417098345578 & 0.0651658033088442 & 0.0325829016544221 \tabularnewline
119 & 0.947413192394377 & 0.105173615211246 & 0.052586807605623 \tabularnewline
120 & 0.980637170384331 & 0.0387256592313376 & 0.0193628296156688 \tabularnewline
121 & 0.992610511329454 & 0.0147789773410925 & 0.00738948867054623 \tabularnewline
122 & 0.996008206135845 & 0.00798358772831089 & 0.00399179386415545 \tabularnewline
123 & 0.997615088082295 & 0.00476982383540958 & 0.00238491191770479 \tabularnewline
124 & 0.999232800805371 & 0.00153439838925796 & 0.000767199194628981 \tabularnewline
125 & 0.999420336588496 & 0.00115932682300817 & 0.000579663411504085 \tabularnewline
126 & 0.995409016634297 & 0.00918196673140666 & 0.00459098336570333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&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]6[/C][C]0.00352917810949264[/C][C]0.00705835621898529[/C][C]0.996470821890507[/C][/ROW]
[ROW][C]7[/C][C]0.000626068625835862[/C][C]0.00125213725167172[/C][C]0.999373931374164[/C][/ROW]
[ROW][C]8[/C][C]0.000310399191374615[/C][C]0.000620798382749231[/C][C]0.999689600808625[/C][/ROW]
[ROW][C]9[/C][C]0.000307317311644824[/C][C]0.000614634623289647[/C][C]0.999692682688355[/C][/ROW]
[ROW][C]10[/C][C]6.81411071884088e-05[/C][C]0.000136282214376818[/C][C]0.999931858892812[/C][/ROW]
[ROW][C]11[/C][C]0.000558183480957702[/C][C]0.0011163669619154[/C][C]0.999441816519042[/C][/ROW]
[ROW][C]12[/C][C]0.0102841180883213[/C][C]0.0205682361766427[/C][C]0.989715881911679[/C][/ROW]
[ROW][C]13[/C][C]0.0142243045026138[/C][C]0.0284486090052277[/C][C]0.985775695497386[/C][/ROW]
[ROW][C]14[/C][C]0.0216054755420493[/C][C]0.0432109510840987[/C][C]0.978394524457951[/C][/ROW]
[ROW][C]15[/C][C]0.0482755469573243[/C][C]0.0965510939146486[/C][C]0.951724453042676[/C][/ROW]
[ROW][C]16[/C][C]0.0518919271696177[/C][C]0.103783854339235[/C][C]0.948108072830382[/C][/ROW]
[ROW][C]17[/C][C]0.0672802444946397[/C][C]0.134560488989279[/C][C]0.93271975550536[/C][/ROW]
[ROW][C]18[/C][C]0.0520759159830054[/C][C]0.104151831966011[/C][C]0.947924084016995[/C][/ROW]
[ROW][C]19[/C][C]0.0473827835670495[/C][C]0.0947655671340989[/C][C]0.95261721643295[/C][/ROW]
[ROW][C]20[/C][C]0.042760231480196[/C][C]0.085520462960392[/C][C]0.957239768519804[/C][/ROW]
[ROW][C]21[/C][C]0.085700105962517[/C][C]0.171400211925034[/C][C]0.914299894037483[/C][/ROW]
[ROW][C]22[/C][C]0.107989115743312[/C][C]0.215978231486624[/C][C]0.892010884256688[/C][/ROW]
[ROW][C]23[/C][C]0.12366028656309[/C][C]0.247320573126179[/C][C]0.87633971343691[/C][/ROW]
[ROW][C]24[/C][C]0.142842399374055[/C][C]0.285684798748109[/C][C]0.857157600625945[/C][/ROW]
[ROW][C]25[/C][C]0.154894747223955[/C][C]0.309789494447911[/C][C]0.845105252776045[/C][/ROW]
[ROW][C]26[/C][C]0.177095670723844[/C][C]0.354191341447688[/C][C]0.822904329276156[/C][/ROW]
[ROW][C]27[/C][C]0.185102907717853[/C][C]0.370205815435706[/C][C]0.814897092282147[/C][/ROW]
[ROW][C]28[/C][C]0.202017858512583[/C][C]0.404035717025167[/C][C]0.797982141487417[/C][/ROW]
[ROW][C]29[/C][C]0.198685846165089[/C][C]0.397371692330179[/C][C]0.801314153834911[/C][/ROW]
[ROW][C]30[/C][C]0.183676256427228[/C][C]0.367352512854455[/C][C]0.816323743572772[/C][/ROW]
[ROW][C]31[/C][C]0.179881304240299[/C][C]0.359762608480598[/C][C]0.820118695759701[/C][/ROW]
[ROW][C]32[/C][C]0.150629077034976[/C][C]0.301258154069952[/C][C]0.849370922965024[/C][/ROW]
[ROW][C]33[/C][C]0.129250995057419[/C][C]0.258501990114838[/C][C]0.870749004942581[/C][/ROW]
[ROW][C]34[/C][C]0.120533715305483[/C][C]0.241067430610966[/C][C]0.879466284694517[/C][/ROW]
[ROW][C]35[/C][C]0.11208919495797[/C][C]0.224178389915939[/C][C]0.88791080504203[/C][/ROW]
[ROW][C]36[/C][C]0.0897750913973101[/C][C]0.17955018279462[/C][C]0.91022490860269[/C][/ROW]
[ROW][C]37[/C][C]0.0751904880195751[/C][C]0.15038097603915[/C][C]0.924809511980425[/C][/ROW]
[ROW][C]38[/C][C]0.0568196755808369[/C][C]0.113639351161674[/C][C]0.943180324419163[/C][/ROW]
[ROW][C]39[/C][C]0.0444425849201261[/C][C]0.0888851698402521[/C][C]0.955557415079874[/C][/ROW]
[ROW][C]40[/C][C]0.0369872557615615[/C][C]0.0739745115231229[/C][C]0.963012744238439[/C][/ROW]
[ROW][C]41[/C][C]0.0331949604942079[/C][C]0.0663899209884157[/C][C]0.966805039505792[/C][/ROW]
[ROW][C]42[/C][C]0.0418815788344678[/C][C]0.0837631576689355[/C][C]0.958118421165532[/C][/ROW]
[ROW][C]43[/C][C]0.038265307353455[/C][C]0.0765306147069101[/C][C]0.961734692646545[/C][/ROW]
[ROW][C]44[/C][C]0.031657541972332[/C][C]0.063315083944664[/C][C]0.968342458027668[/C][/ROW]
[ROW][C]45[/C][C]0.0331613915545996[/C][C]0.0663227831091992[/C][C]0.9668386084454[/C][/ROW]
[ROW][C]46[/C][C]0.0560559876975389[/C][C]0.112111975395078[/C][C]0.943944012302461[/C][/ROW]
[ROW][C]47[/C][C]0.0709135484217383[/C][C]0.141827096843477[/C][C]0.929086451578262[/C][/ROW]
[ROW][C]48[/C][C]0.071113885116909[/C][C]0.142227770233818[/C][C]0.928886114883091[/C][/ROW]
[ROW][C]49[/C][C]0.0621894783193938[/C][C]0.124378956638788[/C][C]0.937810521680606[/C][/ROW]
[ROW][C]50[/C][C]0.0718514255794391[/C][C]0.143702851158878[/C][C]0.928148574420561[/C][/ROW]
[ROW][C]51[/C][C]0.0658515690542626[/C][C]0.131703138108525[/C][C]0.934148430945737[/C][/ROW]
[ROW][C]52[/C][C]0.0553748756844303[/C][C]0.110749751368861[/C][C]0.94462512431557[/C][/ROW]
[ROW][C]53[/C][C]0.054322785420262[/C][C]0.108645570840524[/C][C]0.945677214579738[/C][/ROW]
[ROW][C]54[/C][C]0.0454367148455481[/C][C]0.0908734296910963[/C][C]0.954563285154452[/C][/ROW]
[ROW][C]55[/C][C]0.0432844991235489[/C][C]0.0865689982470978[/C][C]0.956715500876451[/C][/ROW]
[ROW][C]56[/C][C]0.0448662255573064[/C][C]0.0897324511146128[/C][C]0.955133774442694[/C][/ROW]
[ROW][C]57[/C][C]0.0467867055266779[/C][C]0.0935734110533557[/C][C]0.953213294473322[/C][/ROW]
[ROW][C]58[/C][C]0.0469612955810164[/C][C]0.0939225911620328[/C][C]0.953038704418984[/C][/ROW]
[ROW][C]59[/C][C]0.047809540027248[/C][C]0.095619080054496[/C][C]0.952190459972752[/C][/ROW]
[ROW][C]60[/C][C]0.0411656707498493[/C][C]0.0823313414996987[/C][C]0.958834329250151[/C][/ROW]
[ROW][C]61[/C][C]0.0381347348982195[/C][C]0.0762694697964391[/C][C]0.96186526510178[/C][/ROW]
[ROW][C]62[/C][C]0.0390456636935832[/C][C]0.0780913273871665[/C][C]0.960954336306417[/C][/ROW]
[ROW][C]63[/C][C]0.0368890479443328[/C][C]0.0737780958886657[/C][C]0.963110952055667[/C][/ROW]
[ROW][C]64[/C][C]0.0416431215941181[/C][C]0.0832862431882362[/C][C]0.958356878405882[/C][/ROW]
[ROW][C]65[/C][C]0.0395756526830369[/C][C]0.0791513053660738[/C][C]0.960424347316963[/C][/ROW]
[ROW][C]66[/C][C]0.041043253027248[/C][C]0.0820865060544961[/C][C]0.958956746972752[/C][/ROW]
[ROW][C]67[/C][C]0.048254748395628[/C][C]0.0965094967912559[/C][C]0.951745251604372[/C][/ROW]
[ROW][C]68[/C][C]0.04581610197499[/C][C]0.09163220394998[/C][C]0.95418389802501[/C][/ROW]
[ROW][C]69[/C][C]0.0473983951815534[/C][C]0.0947967903631067[/C][C]0.952601604818447[/C][/ROW]
[ROW][C]70[/C][C]0.0571553867101817[/C][C]0.114310773420363[/C][C]0.942844613289818[/C][/ROW]
[ROW][C]71[/C][C]0.0618998106735179[/C][C]0.123799621347036[/C][C]0.938100189326482[/C][/ROW]
[ROW][C]72[/C][C]0.0641320624724315[/C][C]0.128264124944863[/C][C]0.935867937527568[/C][/ROW]
[ROW][C]73[/C][C]0.0687622047831932[/C][C]0.137524409566386[/C][C]0.931237795216807[/C][/ROW]
[ROW][C]74[/C][C]0.0816125690443794[/C][C]0.163225138088759[/C][C]0.918387430955621[/C][/ROW]
[ROW][C]75[/C][C]0.0971552170823428[/C][C]0.194310434164686[/C][C]0.902844782917657[/C][/ROW]
[ROW][C]76[/C][C]0.110990145533137[/C][C]0.221980291066273[/C][C]0.889009854466863[/C][/ROW]
[ROW][C]77[/C][C]0.12847702725805[/C][C]0.2569540545161[/C][C]0.87152297274195[/C][/ROW]
[ROW][C]78[/C][C]0.147515356409795[/C][C]0.29503071281959[/C][C]0.852484643590205[/C][/ROW]
[ROW][C]79[/C][C]0.177805381647404[/C][C]0.355610763294808[/C][C]0.822194618352596[/C][/ROW]
[ROW][C]80[/C][C]0.270973921339959[/C][C]0.541947842679919[/C][C]0.729026078660041[/C][/ROW]
[ROW][C]81[/C][C]0.478430968553564[/C][C]0.956861937107128[/C][C]0.521569031446436[/C][/ROW]
[ROW][C]82[/C][C]0.727848825610286[/C][C]0.544302348779428[/C][C]0.272151174389714[/C][/ROW]
[ROW][C]83[/C][C]0.89653888145013[/C][C]0.206922237099741[/C][C]0.10346111854987[/C][/ROW]
[ROW][C]84[/C][C]0.940220750814131[/C][C]0.119558498371737[/C][C]0.0597792491858687[/C][/ROW]
[ROW][C]85[/C][C]0.960083603346373[/C][C]0.0798327933072538[/C][C]0.0399163966536269[/C][/ROW]
[ROW][C]86[/C][C]0.965073614520348[/C][C]0.0698527709593036[/C][C]0.0349263854796518[/C][/ROW]
[ROW][C]87[/C][C]0.967714131134574[/C][C]0.0645717377308528[/C][C]0.0322858688654264[/C][/ROW]
[ROW][C]88[/C][C]0.972403333931915[/C][C]0.0551933321361709[/C][C]0.0275966660680855[/C][/ROW]
[ROW][C]89[/C][C]0.972329457274625[/C][C]0.0553410854507492[/C][C]0.0276705427253746[/C][/ROW]
[ROW][C]90[/C][C]0.973595982802196[/C][C]0.0528080343956086[/C][C]0.0264040171978043[/C][/ROW]
[ROW][C]91[/C][C]0.967384027352569[/C][C]0.0652319452948623[/C][C]0.0326159726474312[/C][/ROW]
[ROW][C]92[/C][C]0.956267430357234[/C][C]0.0874651392855317[/C][C]0.0437325696427658[/C][/ROW]
[ROW][C]93[/C][C]0.962962209455075[/C][C]0.074075581089849[/C][C]0.0370377905449245[/C][/ROW]
[ROW][C]94[/C][C]0.970380409042275[/C][C]0.059239181915451[/C][C]0.0296195909577255[/C][/ROW]
[ROW][C]95[/C][C]0.972913671823938[/C][C]0.0541726563521232[/C][C]0.0270863281760616[/C][/ROW]
[ROW][C]96[/C][C]0.962128618533947[/C][C]0.0757427629321055[/C][C]0.0378713814660527[/C][/ROW]
[ROW][C]97[/C][C]0.974266040478886[/C][C]0.0514679190422273[/C][C]0.0257339595211137[/C][/ROW]
[ROW][C]98[/C][C]0.976720539961306[/C][C]0.0465589200773873[/C][C]0.0232794600386936[/C][/ROW]
[ROW][C]99[/C][C]0.973760837830724[/C][C]0.052478324338553[/C][C]0.0262391621692765[/C][/ROW]
[ROW][C]100[/C][C]0.972225247449712[/C][C]0.0555495051005761[/C][C]0.0277747525502881[/C][/ROW]
[ROW][C]101[/C][C]0.97734479768223[/C][C]0.0453104046355401[/C][C]0.02265520231777[/C][/ROW]
[ROW][C]102[/C][C]0.984414091351253[/C][C]0.0311718172974946[/C][C]0.0155859086487473[/C][/ROW]
[ROW][C]103[/C][C]0.991729066272269[/C][C]0.0165418674554626[/C][C]0.0082709337277313[/C][/ROW]
[ROW][C]104[/C][C]0.996191441616472[/C][C]0.00761711676705693[/C][C]0.00380855838352846[/C][/ROW]
[ROW][C]105[/C][C]0.999177964054694[/C][C]0.00164407189061277[/C][C]0.000822035945306385[/C][/ROW]
[ROW][C]106[/C][C]0.999244463287718[/C][C]0.00151107342456434[/C][C]0.000755536712282172[/C][/ROW]
[ROW][C]107[/C][C]0.99941259184665[/C][C]0.00117481630669958[/C][C]0.000587408153349792[/C][/ROW]
[ROW][C]108[/C][C]0.999696185451653[/C][C]0.000607629096694083[/C][C]0.000303814548347042[/C][/ROW]
[ROW][C]109[/C][C]0.999448160646436[/C][C]0.00110367870712735[/C][C]0.000551839353563674[/C][/ROW]
[ROW][C]110[/C][C]0.999024616346022[/C][C]0.00195076730795537[/C][C]0.000975383653977683[/C][/ROW]
[ROW][C]111[/C][C]0.99815314884736[/C][C]0.00369370230528075[/C][C]0.00184685115264037[/C][/ROW]
[ROW][C]112[/C][C]0.999063315395395[/C][C]0.00187336920921104[/C][C]0.000936684604605519[/C][/ROW]
[ROW][C]113[/C][C]0.998141126206308[/C][C]0.00371774758738328[/C][C]0.00185887379369164[/C][/ROW]
[ROW][C]114[/C][C]0.996450454777287[/C][C]0.00709909044542667[/C][C]0.00354954522271334[/C][/ROW]
[ROW][C]115[/C][C]0.993479289596362[/C][C]0.0130414208072764[/C][C]0.0065207104036382[/C][/ROW]
[ROW][C]116[/C][C]0.988433297655281[/C][C]0.0231334046894387[/C][C]0.0115667023447194[/C][/ROW]
[ROW][C]117[/C][C]0.979468837317658[/C][C]0.0410623253646842[/C][C]0.0205311626823421[/C][/ROW]
[ROW][C]118[/C][C]0.967417098345578[/C][C]0.0651658033088442[/C][C]0.0325829016544221[/C][/ROW]
[ROW][C]119[/C][C]0.947413192394377[/C][C]0.105173615211246[/C][C]0.052586807605623[/C][/ROW]
[ROW][C]120[/C][C]0.980637170384331[/C][C]0.0387256592313376[/C][C]0.0193628296156688[/C][/ROW]
[ROW][C]121[/C][C]0.992610511329454[/C][C]0.0147789773410925[/C][C]0.00738948867054623[/C][/ROW]
[ROW][C]122[/C][C]0.996008206135845[/C][C]0.00798358772831089[/C][C]0.00399179386415545[/C][/ROW]
[ROW][C]123[/C][C]0.997615088082295[/C][C]0.00476982383540958[/C][C]0.00238491191770479[/C][/ROW]
[ROW][C]124[/C][C]0.999232800805371[/C][C]0.00153439838925796[/C][C]0.000767199194628981[/C][/ROW]
[ROW][C]125[/C][C]0.999420336588496[/C][C]0.00115932682300817[/C][C]0.000579663411504085[/C][/ROW]
[ROW][C]126[/C][C]0.995409016634297[/C][C]0.00918196673140666[/C][C]0.00459098336570333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.003529178109492640.007058356218985290.996470821890507
70.0006260686258358620.001252137251671720.999373931374164
80.0003103991913746150.0006207983827492310.999689600808625
90.0003073173116448240.0006146346232896470.999692682688355
106.81411071884088e-050.0001362822143768180.999931858892812
110.0005581834809577020.00111636696191540.999441816519042
120.01028411808832130.02056823617664270.989715881911679
130.01422430450261380.02844860900522770.985775695497386
140.02160547554204930.04321095108409870.978394524457951
150.04827554695732430.09655109391464860.951724453042676
160.05189192716961770.1037838543392350.948108072830382
170.06728024449463970.1345604889892790.93271975550536
180.05207591598300540.1041518319660110.947924084016995
190.04738278356704950.09476556713409890.95261721643295
200.0427602314801960.0855204629603920.957239768519804
210.0857001059625170.1714002119250340.914299894037483
220.1079891157433120.2159782314866240.892010884256688
230.123660286563090.2473205731261790.87633971343691
240.1428423993740550.2856847987481090.857157600625945
250.1548947472239550.3097894944479110.845105252776045
260.1770956707238440.3541913414476880.822904329276156
270.1851029077178530.3702058154357060.814897092282147
280.2020178585125830.4040357170251670.797982141487417
290.1986858461650890.3973716923301790.801314153834911
300.1836762564272280.3673525128544550.816323743572772
310.1798813042402990.3597626084805980.820118695759701
320.1506290770349760.3012581540699520.849370922965024
330.1292509950574190.2585019901148380.870749004942581
340.1205337153054830.2410674306109660.879466284694517
350.112089194957970.2241783899159390.88791080504203
360.08977509139731010.179550182794620.91022490860269
370.07519048801957510.150380976039150.924809511980425
380.05681967558083690.1136393511616740.943180324419163
390.04444258492012610.08888516984025210.955557415079874
400.03698725576156150.07397451152312290.963012744238439
410.03319496049420790.06638992098841570.966805039505792
420.04188157883446780.08376315766893550.958118421165532
430.0382653073534550.07653061470691010.961734692646545
440.0316575419723320.0633150839446640.968342458027668
450.03316139155459960.06632278310919920.9668386084454
460.05605598769753890.1121119753950780.943944012302461
470.07091354842173830.1418270968434770.929086451578262
480.0711138851169090.1422277702338180.928886114883091
490.06218947831939380.1243789566387880.937810521680606
500.07185142557943910.1437028511588780.928148574420561
510.06585156905426260.1317031381085250.934148430945737
520.05537487568443030.1107497513688610.94462512431557
530.0543227854202620.1086455708405240.945677214579738
540.04543671484554810.09087342969109630.954563285154452
550.04328449912354890.08656899824709780.956715500876451
560.04486622555730640.08973245111461280.955133774442694
570.04678670552667790.09357341105335570.953213294473322
580.04696129558101640.09392259116203280.953038704418984
590.0478095400272480.0956190800544960.952190459972752
600.04116567074984930.08233134149969870.958834329250151
610.03813473489821950.07626946979643910.96186526510178
620.03904566369358320.07809132738716650.960954336306417
630.03688904794433280.07377809588866570.963110952055667
640.04164312159411810.08328624318823620.958356878405882
650.03957565268303690.07915130536607380.960424347316963
660.0410432530272480.08208650605449610.958956746972752
670.0482547483956280.09650949679125590.951745251604372
680.045816101974990.091632203949980.95418389802501
690.04739839518155340.09479679036310670.952601604818447
700.05715538671018170.1143107734203630.942844613289818
710.06189981067351790.1237996213470360.938100189326482
720.06413206247243150.1282641249448630.935867937527568
730.06876220478319320.1375244095663860.931237795216807
740.08161256904437940.1632251380887590.918387430955621
750.09715521708234280.1943104341646860.902844782917657
760.1109901455331370.2219802910662730.889009854466863
770.128477027258050.25695405451610.87152297274195
780.1475153564097950.295030712819590.852484643590205
790.1778053816474040.3556107632948080.822194618352596
800.2709739213399590.5419478426799190.729026078660041
810.4784309685535640.9568619371071280.521569031446436
820.7278488256102860.5443023487794280.272151174389714
830.896538881450130.2069222370997410.10346111854987
840.9402207508141310.1195584983717370.0597792491858687
850.9600836033463730.07983279330725380.0399163966536269
860.9650736145203480.06985277095930360.0349263854796518
870.9677141311345740.06457173773085280.0322858688654264
880.9724033339319150.05519333213617090.0275966660680855
890.9723294572746250.05534108545074920.0276705427253746
900.9735959828021960.05280803439560860.0264040171978043
910.9673840273525690.06523194529486230.0326159726474312
920.9562674303572340.08746513928553170.0437325696427658
930.9629622094550750.0740755810898490.0370377905449245
940.9703804090422750.0592391819154510.0296195909577255
950.9729136718239380.05417265635212320.0270863281760616
960.9621286185339470.07574276293210550.0378713814660527
970.9742660404788860.05146791904222730.0257339595211137
980.9767205399613060.04655892007738730.0232794600386936
990.9737608378307240.0524783243385530.0262391621692765
1000.9722252474497120.05554950510057610.0277747525502881
1010.977344797682230.04531040463554010.02265520231777
1020.9844140913512530.03117181729749460.0155859086487473
1030.9917290662722690.01654186745546260.0082709337277313
1040.9961914416164720.007617116767056930.00380855838352846
1050.9991779640546940.001644071890612770.000822035945306385
1060.9992444632877180.001511073424564340.000755536712282172
1070.999412591846650.001174816306699580.000587408153349792
1080.9996961854516530.0006076290966940830.000303814548347042
1090.9994481606464360.001103678707127350.000551839353563674
1100.9990246163460220.001950767307955370.000975383653977683
1110.998153148847360.003693702305280750.00184685115264037
1120.9990633153953950.001873369209211040.000936684604605519
1130.9981411262063080.003717747587383280.00185887379369164
1140.9964504547772870.007099090445426670.00354954522271334
1150.9934792895963620.01304142080727640.0065207104036382
1160.9884332976552810.02313340468943870.0115667023447194
1170.9794688373176580.04106232536468420.0205311626823421
1180.9674170983455780.06516580330884420.0325829016544221
1190.9474131923943770.1051736152112460.052586807605623
1200.9806371703843310.03872565923133760.0193628296156688
1210.9926105113294540.01477897734109250.00738948867054623
1220.9960082061358450.007983587728310890.00399179386415545
1230.9976150880822950.004769823835409580.00238491191770479
1240.9992328008053710.001534398389257960.000767199194628981
1250.9994203365884960.001159326823008170.000579663411504085
1260.9954090166342970.009181966731406660.00459098336570333






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.181818181818182NOK
5% type I error level340.28099173553719NOK
10% type I error level760.628099173553719NOK

\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 & 22 & 0.181818181818182 & NOK \tabularnewline
5% type I error level & 34 & 0.28099173553719 & NOK \tabularnewline
10% type I error level & 76 & 0.628099173553719 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157380&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]22[/C][C]0.181818181818182[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.28099173553719[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]76[/C][C]0.628099173553719[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157380&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157380&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.181818181818182NOK
5% type I error level340.28099173553719NOK
10% type I error level760.628099173553719NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}