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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, 10 Dec 2012 16:59:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t13551767899mktesu9145i2m3.htm/, Retrieved Fri, 19 Apr 2024 13:20:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198350, Retrieved Fri, 19 Apr 2024 13:20:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-12-20 08:07:41] [ca5872d1d4d784184b94263c274137e3]
- R P   [Multiple Regression] [] [2011-12-20 08:15:19] [ca5872d1d4d784184b94263c274137e3]
- R P       [Multiple Regression] [t] [2012-12-10 21:59:39] [69fed4bf76000787e6433dea6d892b14] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	13	14	12	9
39	16	18	11	9
30	19	11	14	9
31	15	12	12	9
34	14	16	21	9
35	13	18	12	9
39	19	14	22	9
34	15	14	11	9
36	14	15	10	9
37	15	15	13	9
38	16	17	10	9
36	16	19	8	9
38	16	10	15	9
39	16	16	14	9
33	17	18	10	9
32	15	14	14	9
36	15	14	14	9
38	20	17	11	9
39	18	14	10	9
32	16	16	13	9
32	16	18	7	9
31	16	11	14	9
39	19	14	12	9
37	16	12	14	9
39	17	17	11	9
41	17	9	9	9
36	16	16	11	9
33	15	14	15	9
33	16	15	14	9
34	14	11	13	9
31	15	16	9	9
27	12	13	15	9
37	14	17	10	9
34	16	15	11	9
34	14	14	13	9
32	7	16	8	9
29	10	9	20	9
36	14	15	12	9
29	16	17	10	9
35	16	13	10	9
37	16	15	9	9
34	14	16	14	9
38	20	16	8	9
35	14	12	14	9
38	14	12	11	9
37	11	11	13	9
38	14	15	9	9
33	15	15	11	9
36	16	17	15	9
38	14	13	11	9
32	16	16	10	9
32	14	14	14	9
32	12	11	18	9
34	16	12	14	9
32	9	12	11	10
37	14	15	12	10
39	16	16	13	10
29	16	15	9	10
37	15	12	10	10
35	16	12	15	10
30	12	8	20	10
38	16	13	12	10
34	16	11	12	10
31	14	14	14	10
34	16	15	13	10
35	17	10	11	10
36	18	11	17	10
30	18	12	12	10
39	12	15	13	10
35	16	15	14	10
38	10	14	13	10
31	14	16	15	10
34	18	15	13	10
38	18	15	10	10
34	16	13	11	10
39	17	12	19	10
37	16	17	13	10
34	16	13	17	10
28	13	15	13	10
37	16	13	9	10
33	16	15	11	10
37	20	16	10	10
35	16	15	9	10
37	15	16	12	10
32	15	15	12	10
33	16	14	13	10
38	14	15	13	10
33	16	14	12	10
29	16	13	15	10
33	15	7	22	10
31	12	17	13	10
36	17	13	15	10
35	16	15	13	10
32	15	14	15	10
29	13	13	10	10
39	16	16	11	10
37	16	12	16	10
35	16	14	11	10
37	16	17	11	10
32	14	15	10	10
38	16	17	10	10
37	16	12	16	10
36	20	16	12	10
32	15	11	11	10
33	16	15	16	10
40	13	9	19	10
38	17	16	11	10
41	16	15	16	10
36	16	10	15	11
43	12	10	24	11
30	16	15	14	11
31	16	11	15	11
32	17	13	11	11
32	13	14	15	11
37	12	18	12	11
37	18	16	10	11
33	14	14	14	11
34	14	14	13	11
33	13	14	9	11
38	16	14	15	11
33	13	12	15	11
31	16	14	14	11
38	13	15	11	11
37	16	15	8	11
33	15	15	11	11
31	16	13	11	11
39	15	17	8	11
44	17	17	10	11
33	15	19	11	11
35	12	15	13	11
32	16	13	11	11
28	10	9	20	11
40	16	15	10	11
27	12	15	15	11
37	14	15	12	11
32	15	16	14	11
28	13	11	23	11
34	15	14	14	11
30	11	11	16	11
35	12	15	11	11
31	8	13	12	11
32	16	15	10	11
30	15	16	14	11
30	17	14	12	11
31	16	15	12	11
40	10	16	11	11
32	18	16	12	11
36	13	11	13	11
32	16	12	11	11
35	13	9	19	11
38	10	16	12	11
42	15	13	17	11
34	16	16	9	11
35	16	12	12	11
35	14	9	19	11
33	10	13	18	11
36	17	13	15	11
32	13	14	14	11
33	15	19	11	11
34	16	13	9	11
32	12	12	18	11
34	13	13	16	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
connected[t] = + 30.0077097493809 + 0.267094674463107learning[t] + 0.133484251054619happiness[t] -0.0201783941181385depression[t] -0.0656407988678097month[t] -0.00409937096057251t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
connected[t] =  +  30.0077097493809 +  0.267094674463107learning[t] +  0.133484251054619happiness[t] -0.0201783941181385depression[t] -0.0656407988678097month[t] -0.00409937096057251t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]connected[t] =  +  30.0077097493809 +  0.267094674463107learning[t] +  0.133484251054619happiness[t] -0.0201783941181385depression[t] -0.0656407988678097month[t] -0.00409937096057251t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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
connected[t] = + 30.0077097493809 + 0.267094674463107learning[t] + 0.133484251054619happiness[t] -0.0201783941181385depression[t] -0.0656407988678097month[t] -0.00409937096057251t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30.00770974938098.8828023.37820.0009220.000461
learning0.2670946744631070.1213182.20160.0291610.01458
happiness0.1334842510546190.1338080.99760.3200290.160015
depression-0.02017839411813850.100087-0.20160.8404850.420243
month-0.06564079886780970.959635-0.06840.9455530.472777
t-0.004099370960572510.016807-0.24390.8076180.403809

\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) & 30.0077097493809 & 8.882802 & 3.3782 & 0.000922 & 0.000461 \tabularnewline
learning & 0.267094674463107 & 0.121318 & 2.2016 & 0.029161 & 0.01458 \tabularnewline
happiness & 0.133484251054619 & 0.133808 & 0.9976 & 0.320029 & 0.160015 \tabularnewline
depression & -0.0201783941181385 & 0.100087 & -0.2016 & 0.840485 & 0.420243 \tabularnewline
month & -0.0656407988678097 & 0.959635 & -0.0684 & 0.945553 & 0.472777 \tabularnewline
t & -0.00409937096057251 & 0.016807 & -0.2439 & 0.807618 & 0.403809 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&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]30.0077097493809[/C][C]8.882802[/C][C]3.3782[/C][C]0.000922[/C][C]0.000461[/C][/ROW]
[ROW][C]learning[/C][C]0.267094674463107[/C][C]0.121318[/C][C]2.2016[/C][C]0.029161[/C][C]0.01458[/C][/ROW]
[ROW][C]happiness[/C][C]0.133484251054619[/C][C]0.133808[/C][C]0.9976[/C][C]0.320029[/C][C]0.160015[/C][/ROW]
[ROW][C]depression[/C][C]-0.0201783941181385[/C][C]0.100087[/C][C]-0.2016[/C][C]0.840485[/C][C]0.420243[/C][/ROW]
[ROW][C]month[/C][C]-0.0656407988678097[/C][C]0.959635[/C][C]-0.0684[/C][C]0.945553[/C][C]0.472777[/C][/ROW]
[ROW][C]t[/C][C]-0.00409937096057251[/C][C]0.016807[/C][C]-0.2439[/C][C]0.807618[/C][C]0.403809[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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)30.00770974938098.8828023.37820.0009220.000461
learning0.2670946744631070.1213182.20160.0291610.01458
happiness0.1334842510546190.1338080.99760.3200290.160015
depression-0.02017839411813850.100087-0.20160.8404850.420243
month-0.06564079886780970.959635-0.06840.9455530.472777
t-0.004099370960572510.016807-0.24390.8076180.403809






Multiple Linear Regression - Regression Statistics
Multiple R0.249308146172092
R-squared0.0621545517477652
Adjusted R-squared0.0320954027653217
F-TEST (value)2.06774156460876
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.0722890459834615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.32052426539283
Sum Squared Residuals1720.03749794177

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.249308146172092 \tabularnewline
R-squared & 0.0621545517477652 \tabularnewline
Adjusted R-squared & 0.0320954027653217 \tabularnewline
F-TEST (value) & 2.06774156460876 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.0722890459834615 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.32052426539283 \tabularnewline
Sum Squared Residuals & 1720.03749794177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.249308146172092[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0621545517477652[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0320954027653217[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.06774156460876[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.0722890459834615[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.32052426539283[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1720.03749794177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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 R0.249308146172092
R-squared0.0621545517477652
Adjusted R-squared0.0320954027653217
F-TEST (value)2.06774156460876
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.0722890459834615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.32052426539283
Sum Squared Residuals1720.03749794177






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14134.51171274197756.48828725802246
23935.86301279274283.13698720725721
33035.6652725054348-5.6652725054348
43134.7666354759127-3.76663547591269
53434.8477728876442-0.847772887644241
63535.025152891393-0.0251528913930451
73935.88790062181133.11209937818874
83435.0373848882978-1.03738488829778
93634.91985348804691.08014651195314
103735.1223136091951.87768639080502
113835.71281259716122.28718740283884
123636.0160385165461-0.0160385165461042
133834.6693321272673.33066787273301
143935.48631665675233.51368334324773
153336.0969940388366-3.0969940388366
163234.9440547382588-2.94405473825878
173634.93995536729821.06004463270179
183836.73231730417141.26768269582855
193935.81375422523893.18624577476106
203235.481898825107-3.48189882510698
213235.8658383209645-3.86583832096447
223134.7861004337946-3.7861004337946
233936.02409462762352.97590537237652
243734.91138594292812.08861405707193
253935.90233768405813.09766231594188
264134.87072109289696.12927890710313
273635.49356001661920.506439983380755
283334.8746838926138-1.87468389261377
293335.2913418412891-2.29134184128907
303434.2392945113019-0.239294511301944
313135.2504246465501-4.25042464655013
322733.9235181343275-6.92351813432754
333735.08843708710241.91156291289765
343435.3313801688406-1.33138016884062
353434.6192504096629-0.619250409662937
363233.1133487901605-1.11334879016054
372932.7340029557893-3.7340029557893
383634.7606149419541.23938505804602
392935.5980302102651-6.59803021026513
403535.0599938350861-0.0599938350860857
413735.34304136035291.65695863964711
423434.83734492093-0.837344920930028
433836.55688396145691.44311603854307
443534.29520917479040.704790825209592
453834.35164498618433.64835501381575
463733.37242055254353.62757944745654
473834.78425578566323.21574421433676
483335.0068943009295-2.0068943009295
493635.45614453006870.543855469931285
503834.4646323824363.53536761756399
513235.4153535076836-3.41535350768364
523234.5293827092151-2.52938270921507
533233.5099276596919-1.50992765969187
543434.7884048141109-0.788404814110898
553232.9095371053952-0.909537105395181
563734.62118546579592.37881453420414
573935.2645813006983.73541869930202
582935.2077112551553-6.20771125515535
593734.51588606244972.48411393755033
603534.67798939536150.322010604638485
613032.9706823517393-2.97068235173935
623834.86381008684943.1361899131506
633434.5927422137796-0.592742213779594
643134.4145494588204-3.41454945882039
653435.0983020819588-1.09830208195879
663534.73423291842450.265767081575496
673635.00964210827280.990357891727174
683035.2399189589576-5.23991895895756
693934.01352590026414.98647409973593
703535.0576268330378-0.0576268330377839
713833.33765355836214.66234644163791
723134.6285445991269-3.6285445991269
733435.5996964632004-1.59969646320042
743835.65613227459432.34386772540574
753434.8306966584801-0.8306966584801
763934.79878055798294.20121944201709
773735.31607813254121.68392186745885
783434.6973281808895-0.697328180889551
792834.2396268651215-6.23962686512145
803734.85055659191352.14944340808649
813335.0730689348259-2.0730689348259
823736.29101090689050.708989093109484
833535.105226981141-0.105226981141034
843734.90698200441762.09301799558244
853234.7693983824024-2.76939838240237
863334.8787310407321-1.87873104073214
873834.47392657193.52607342810003
883334.8907106929291-1.89071069292914
892934.6925918885595-5.69259188855953
903333.4792435779812-0.47924357798117
913134.1903082412407-3.19030824124071
923634.94738845014091.05261154985908
933534.98351969506280.0164803049372452
943234.5384846103482-2.53848461034818
952933.9676036099975-4.96760360999747
963935.14506262147193.85493737852807
973734.50613427570222.49386572429781
983534.86989537744160.130104622558449
993735.26624875964481.73375124035517
1003234.4811699317669-2.48116993176695
1013835.27822841184182.72177158815817
1023734.48563742089932.51436257910067
1033636.1645673284822-0.164567328482216
1043234.1777517240511-2.17775172405115
1053334.8737920611815-1.87379206118147
1064033.20696797814956.79303202185055
1073835.36706421536872.63293578463126
1084134.86149394829986.13850605170025
1093634.14451091731641.85548908268358
1104332.890427301440210.1095726985598
1113034.8239118247865-4.8239118247865
1123134.2656970554893-3.26569705548932
1133234.8763744375736-2.87637443757364
1143233.8566670433427-1.8566670433427
1153734.17994518449192.82005481550808
1163735.5518021464371.44819785356297
1173334.1316419990422-1.13164199904223
1183434.1477210221998-0.147721022199799
1193333.9572405532487-0.957240553248674
1203834.63335484096863.36664515903141
1213333.5610029445095-0.561002944509461
1223134.6453344931656-3.64533449316558
1233834.03397053222473.96602946777527
1243734.89169036700792.10830963299211
1253334.5599611392298-1.55996113922979
1263134.5559879406231-3.55598794062309
1273934.87926608177234.1207339182277
1284435.36899927150178.63100072849833
1293335.077500659606-2.07750065960598
1303533.69782347280131.30217652719867
1313234.5354910858202-2.53549108582023
1322832.2132811167993-4.21328111679929
1334034.81443924012655.18556075987354
1342733.6410692007228-6.64106920072276
1353734.23169436104282.76830563895718
1363234.5878171273637-2.5878171273637
1372833.2005016051406-5.20050160514057
1383434.3126498833333-0.312649883333318
1393032.7993622731202-2.79936227312018
1403533.69718655143191.30281344856811
1413132.3375615863915-1.33756158639151
1423234.7775449014813-2.77754490148131
1433034.5591215306397-4.55912153063969
1443034.8625997947324-4.86259979473237
1453134.7248900003633-3.72489000036331
1464033.27188522779696.72811477220315
1473235.384364858423-3.384364858423
1483633.35719246575572.64280753424434
1493234.3282181574753-2.32821815747531
1503532.96095485701642.03904514298355
1513833.23120997887594.76879002112415
1524234.06123925647637.93876074352373
1533434.8861144660878-0.886114466087767
1543534.28754290855430.712457091445695
1553533.20755267667671.79244732332331
1563332.68919000620030.310809993799696
1573634.61528853883591.3847114611641
1583233.6964731151957-1.69647311519565
1593334.9545195307888-1.9545195307888
1603434.4569661161999-0.456966116199904
1613233.069398249269-1.06939824926904
1623433.50623459206250.493765407937532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.5117127419775 & 6.48828725802246 \tabularnewline
2 & 39 & 35.8630127927428 & 3.13698720725721 \tabularnewline
3 & 30 & 35.6652725054348 & -5.6652725054348 \tabularnewline
4 & 31 & 34.7666354759127 & -3.76663547591269 \tabularnewline
5 & 34 & 34.8477728876442 & -0.847772887644241 \tabularnewline
6 & 35 & 35.025152891393 & -0.0251528913930451 \tabularnewline
7 & 39 & 35.8879006218113 & 3.11209937818874 \tabularnewline
8 & 34 & 35.0373848882978 & -1.03738488829778 \tabularnewline
9 & 36 & 34.9198534880469 & 1.08014651195314 \tabularnewline
10 & 37 & 35.122313609195 & 1.87768639080502 \tabularnewline
11 & 38 & 35.7128125971612 & 2.28718740283884 \tabularnewline
12 & 36 & 36.0160385165461 & -0.0160385165461042 \tabularnewline
13 & 38 & 34.669332127267 & 3.33066787273301 \tabularnewline
14 & 39 & 35.4863166567523 & 3.51368334324773 \tabularnewline
15 & 33 & 36.0969940388366 & -3.0969940388366 \tabularnewline
16 & 32 & 34.9440547382588 & -2.94405473825878 \tabularnewline
17 & 36 & 34.9399553672982 & 1.06004463270179 \tabularnewline
18 & 38 & 36.7323173041714 & 1.26768269582855 \tabularnewline
19 & 39 & 35.8137542252389 & 3.18624577476106 \tabularnewline
20 & 32 & 35.481898825107 & -3.48189882510698 \tabularnewline
21 & 32 & 35.8658383209645 & -3.86583832096447 \tabularnewline
22 & 31 & 34.7861004337946 & -3.7861004337946 \tabularnewline
23 & 39 & 36.0240946276235 & 2.97590537237652 \tabularnewline
24 & 37 & 34.9113859429281 & 2.08861405707193 \tabularnewline
25 & 39 & 35.9023376840581 & 3.09766231594188 \tabularnewline
26 & 41 & 34.8707210928969 & 6.12927890710313 \tabularnewline
27 & 36 & 35.4935600166192 & 0.506439983380755 \tabularnewline
28 & 33 & 34.8746838926138 & -1.87468389261377 \tabularnewline
29 & 33 & 35.2913418412891 & -2.29134184128907 \tabularnewline
30 & 34 & 34.2392945113019 & -0.239294511301944 \tabularnewline
31 & 31 & 35.2504246465501 & -4.25042464655013 \tabularnewline
32 & 27 & 33.9235181343275 & -6.92351813432754 \tabularnewline
33 & 37 & 35.0884370871024 & 1.91156291289765 \tabularnewline
34 & 34 & 35.3313801688406 & -1.33138016884062 \tabularnewline
35 & 34 & 34.6192504096629 & -0.619250409662937 \tabularnewline
36 & 32 & 33.1133487901605 & -1.11334879016054 \tabularnewline
37 & 29 & 32.7340029557893 & -3.7340029557893 \tabularnewline
38 & 36 & 34.760614941954 & 1.23938505804602 \tabularnewline
39 & 29 & 35.5980302102651 & -6.59803021026513 \tabularnewline
40 & 35 & 35.0599938350861 & -0.0599938350860857 \tabularnewline
41 & 37 & 35.3430413603529 & 1.65695863964711 \tabularnewline
42 & 34 & 34.83734492093 & -0.837344920930028 \tabularnewline
43 & 38 & 36.5568839614569 & 1.44311603854307 \tabularnewline
44 & 35 & 34.2952091747904 & 0.704790825209592 \tabularnewline
45 & 38 & 34.3516449861843 & 3.64835501381575 \tabularnewline
46 & 37 & 33.3724205525435 & 3.62757944745654 \tabularnewline
47 & 38 & 34.7842557856632 & 3.21574421433676 \tabularnewline
48 & 33 & 35.0068943009295 & -2.0068943009295 \tabularnewline
49 & 36 & 35.4561445300687 & 0.543855469931285 \tabularnewline
50 & 38 & 34.464632382436 & 3.53536761756399 \tabularnewline
51 & 32 & 35.4153535076836 & -3.41535350768364 \tabularnewline
52 & 32 & 34.5293827092151 & -2.52938270921507 \tabularnewline
53 & 32 & 33.5099276596919 & -1.50992765969187 \tabularnewline
54 & 34 & 34.7884048141109 & -0.788404814110898 \tabularnewline
55 & 32 & 32.9095371053952 & -0.909537105395181 \tabularnewline
56 & 37 & 34.6211854657959 & 2.37881453420414 \tabularnewline
57 & 39 & 35.264581300698 & 3.73541869930202 \tabularnewline
58 & 29 & 35.2077112551553 & -6.20771125515535 \tabularnewline
59 & 37 & 34.5158860624497 & 2.48411393755033 \tabularnewline
60 & 35 & 34.6779893953615 & 0.322010604638485 \tabularnewline
61 & 30 & 32.9706823517393 & -2.97068235173935 \tabularnewline
62 & 38 & 34.8638100868494 & 3.1361899131506 \tabularnewline
63 & 34 & 34.5927422137796 & -0.592742213779594 \tabularnewline
64 & 31 & 34.4145494588204 & -3.41454945882039 \tabularnewline
65 & 34 & 35.0983020819588 & -1.09830208195879 \tabularnewline
66 & 35 & 34.7342329184245 & 0.265767081575496 \tabularnewline
67 & 36 & 35.0096421082728 & 0.990357891727174 \tabularnewline
68 & 30 & 35.2399189589576 & -5.23991895895756 \tabularnewline
69 & 39 & 34.0135259002641 & 4.98647409973593 \tabularnewline
70 & 35 & 35.0576268330378 & -0.0576268330377839 \tabularnewline
71 & 38 & 33.3376535583621 & 4.66234644163791 \tabularnewline
72 & 31 & 34.6285445991269 & -3.6285445991269 \tabularnewline
73 & 34 & 35.5996964632004 & -1.59969646320042 \tabularnewline
74 & 38 & 35.6561322745943 & 2.34386772540574 \tabularnewline
75 & 34 & 34.8306966584801 & -0.8306966584801 \tabularnewline
76 & 39 & 34.7987805579829 & 4.20121944201709 \tabularnewline
77 & 37 & 35.3160781325412 & 1.68392186745885 \tabularnewline
78 & 34 & 34.6973281808895 & -0.697328180889551 \tabularnewline
79 & 28 & 34.2396268651215 & -6.23962686512145 \tabularnewline
80 & 37 & 34.8505565919135 & 2.14944340808649 \tabularnewline
81 & 33 & 35.0730689348259 & -2.0730689348259 \tabularnewline
82 & 37 & 36.2910109068905 & 0.708989093109484 \tabularnewline
83 & 35 & 35.105226981141 & -0.105226981141034 \tabularnewline
84 & 37 & 34.9069820044176 & 2.09301799558244 \tabularnewline
85 & 32 & 34.7693983824024 & -2.76939838240237 \tabularnewline
86 & 33 & 34.8787310407321 & -1.87873104073214 \tabularnewline
87 & 38 & 34.4739265719 & 3.52607342810003 \tabularnewline
88 & 33 & 34.8907106929291 & -1.89071069292914 \tabularnewline
89 & 29 & 34.6925918885595 & -5.69259188855953 \tabularnewline
90 & 33 & 33.4792435779812 & -0.47924357798117 \tabularnewline
91 & 31 & 34.1903082412407 & -3.19030824124071 \tabularnewline
92 & 36 & 34.9473884501409 & 1.05261154985908 \tabularnewline
93 & 35 & 34.9835196950628 & 0.0164803049372452 \tabularnewline
94 & 32 & 34.5384846103482 & -2.53848461034818 \tabularnewline
95 & 29 & 33.9676036099975 & -4.96760360999747 \tabularnewline
96 & 39 & 35.1450626214719 & 3.85493737852807 \tabularnewline
97 & 37 & 34.5061342757022 & 2.49386572429781 \tabularnewline
98 & 35 & 34.8698953774416 & 0.130104622558449 \tabularnewline
99 & 37 & 35.2662487596448 & 1.73375124035517 \tabularnewline
100 & 32 & 34.4811699317669 & -2.48116993176695 \tabularnewline
101 & 38 & 35.2782284118418 & 2.72177158815817 \tabularnewline
102 & 37 & 34.4856374208993 & 2.51436257910067 \tabularnewline
103 & 36 & 36.1645673284822 & -0.164567328482216 \tabularnewline
104 & 32 & 34.1777517240511 & -2.17775172405115 \tabularnewline
105 & 33 & 34.8737920611815 & -1.87379206118147 \tabularnewline
106 & 40 & 33.2069679781495 & 6.79303202185055 \tabularnewline
107 & 38 & 35.3670642153687 & 2.63293578463126 \tabularnewline
108 & 41 & 34.8614939482998 & 6.13850605170025 \tabularnewline
109 & 36 & 34.1445109173164 & 1.85548908268358 \tabularnewline
110 & 43 & 32.8904273014402 & 10.1095726985598 \tabularnewline
111 & 30 & 34.8239118247865 & -4.8239118247865 \tabularnewline
112 & 31 & 34.2656970554893 & -3.26569705548932 \tabularnewline
113 & 32 & 34.8763744375736 & -2.87637443757364 \tabularnewline
114 & 32 & 33.8566670433427 & -1.8566670433427 \tabularnewline
115 & 37 & 34.1799451844919 & 2.82005481550808 \tabularnewline
116 & 37 & 35.551802146437 & 1.44819785356297 \tabularnewline
117 & 33 & 34.1316419990422 & -1.13164199904223 \tabularnewline
118 & 34 & 34.1477210221998 & -0.147721022199799 \tabularnewline
119 & 33 & 33.9572405532487 & -0.957240553248674 \tabularnewline
120 & 38 & 34.6333548409686 & 3.36664515903141 \tabularnewline
121 & 33 & 33.5610029445095 & -0.561002944509461 \tabularnewline
122 & 31 & 34.6453344931656 & -3.64533449316558 \tabularnewline
123 & 38 & 34.0339705322247 & 3.96602946777527 \tabularnewline
124 & 37 & 34.8916903670079 & 2.10830963299211 \tabularnewline
125 & 33 & 34.5599611392298 & -1.55996113922979 \tabularnewline
126 & 31 & 34.5559879406231 & -3.55598794062309 \tabularnewline
127 & 39 & 34.8792660817723 & 4.1207339182277 \tabularnewline
128 & 44 & 35.3689992715017 & 8.63100072849833 \tabularnewline
129 & 33 & 35.077500659606 & -2.07750065960598 \tabularnewline
130 & 35 & 33.6978234728013 & 1.30217652719867 \tabularnewline
131 & 32 & 34.5354910858202 & -2.53549108582023 \tabularnewline
132 & 28 & 32.2132811167993 & -4.21328111679929 \tabularnewline
133 & 40 & 34.8144392401265 & 5.18556075987354 \tabularnewline
134 & 27 & 33.6410692007228 & -6.64106920072276 \tabularnewline
135 & 37 & 34.2316943610428 & 2.76830563895718 \tabularnewline
136 & 32 & 34.5878171273637 & -2.5878171273637 \tabularnewline
137 & 28 & 33.2005016051406 & -5.20050160514057 \tabularnewline
138 & 34 & 34.3126498833333 & -0.312649883333318 \tabularnewline
139 & 30 & 32.7993622731202 & -2.79936227312018 \tabularnewline
140 & 35 & 33.6971865514319 & 1.30281344856811 \tabularnewline
141 & 31 & 32.3375615863915 & -1.33756158639151 \tabularnewline
142 & 32 & 34.7775449014813 & -2.77754490148131 \tabularnewline
143 & 30 & 34.5591215306397 & -4.55912153063969 \tabularnewline
144 & 30 & 34.8625997947324 & -4.86259979473237 \tabularnewline
145 & 31 & 34.7248900003633 & -3.72489000036331 \tabularnewline
146 & 40 & 33.2718852277969 & 6.72811477220315 \tabularnewline
147 & 32 & 35.384364858423 & -3.384364858423 \tabularnewline
148 & 36 & 33.3571924657557 & 2.64280753424434 \tabularnewline
149 & 32 & 34.3282181574753 & -2.32821815747531 \tabularnewline
150 & 35 & 32.9609548570164 & 2.03904514298355 \tabularnewline
151 & 38 & 33.2312099788759 & 4.76879002112415 \tabularnewline
152 & 42 & 34.0612392564763 & 7.93876074352373 \tabularnewline
153 & 34 & 34.8861144660878 & -0.886114466087767 \tabularnewline
154 & 35 & 34.2875429085543 & 0.712457091445695 \tabularnewline
155 & 35 & 33.2075526766767 & 1.79244732332331 \tabularnewline
156 & 33 & 32.6891900062003 & 0.310809993799696 \tabularnewline
157 & 36 & 34.6152885388359 & 1.3847114611641 \tabularnewline
158 & 32 & 33.6964731151957 & -1.69647311519565 \tabularnewline
159 & 33 & 34.9545195307888 & -1.9545195307888 \tabularnewline
160 & 34 & 34.4569661161999 & -0.456966116199904 \tabularnewline
161 & 32 & 33.069398249269 & -1.06939824926904 \tabularnewline
162 & 34 & 33.5062345920625 & 0.493765407937532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&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]41[/C][C]34.5117127419775[/C][C]6.48828725802246[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]35.8630127927428[/C][C]3.13698720725721[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.6652725054348[/C][C]-5.6652725054348[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.7666354759127[/C][C]-3.76663547591269[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.8477728876442[/C][C]-0.847772887644241[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]35.025152891393[/C][C]-0.0251528913930451[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]35.8879006218113[/C][C]3.11209937818874[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.0373848882978[/C][C]-1.03738488829778[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.9198534880469[/C][C]1.08014651195314[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]35.122313609195[/C][C]1.87768639080502[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]35.7128125971612[/C][C]2.28718740283884[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]36.0160385165461[/C][C]-0.0160385165461042[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.669332127267[/C][C]3.33066787273301[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.4863166567523[/C][C]3.51368334324773[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.0969940388366[/C][C]-3.0969940388366[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.9440547382588[/C][C]-2.94405473825878[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.9399553672982[/C][C]1.06004463270179[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.7323173041714[/C][C]1.26768269582855[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]35.8137542252389[/C][C]3.18624577476106[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]35.481898825107[/C][C]-3.48189882510698[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.8658383209645[/C][C]-3.86583832096447[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]34.7861004337946[/C][C]-3.7861004337946[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.0240946276235[/C][C]2.97590537237652[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]34.9113859429281[/C][C]2.08861405707193[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.9023376840581[/C][C]3.09766231594188[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.8707210928969[/C][C]6.12927890710313[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.4935600166192[/C][C]0.506439983380755[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]34.8746838926138[/C][C]-1.87468389261377[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]35.2913418412891[/C][C]-2.29134184128907[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.2392945113019[/C][C]-0.239294511301944[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]35.2504246465501[/C][C]-4.25042464655013[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.9235181343275[/C][C]-6.92351813432754[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]35.0884370871024[/C][C]1.91156291289765[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.3313801688406[/C][C]-1.33138016884062[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]34.6192504096629[/C][C]-0.619250409662937[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]33.1133487901605[/C][C]-1.11334879016054[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.7340029557893[/C][C]-3.7340029557893[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.760614941954[/C][C]1.23938505804602[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]35.5980302102651[/C][C]-6.59803021026513[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]35.0599938350861[/C][C]-0.0599938350860857[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]35.3430413603529[/C][C]1.65695863964711[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.83734492093[/C][C]-0.837344920930028[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]36.5568839614569[/C][C]1.44311603854307[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.2952091747904[/C][C]0.704790825209592[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]34.3516449861843[/C][C]3.64835501381575[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.3724205525435[/C][C]3.62757944745654[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]34.7842557856632[/C][C]3.21574421433676[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]35.0068943009295[/C][C]-2.0068943009295[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]35.4561445300687[/C][C]0.543855469931285[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.464632382436[/C][C]3.53536761756399[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.4153535076836[/C][C]-3.41535350768364[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]34.5293827092151[/C][C]-2.52938270921507[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.5099276596919[/C][C]-1.50992765969187[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]34.7884048141109[/C][C]-0.788404814110898[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.9095371053952[/C][C]-0.909537105395181[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.6211854657959[/C][C]2.37881453420414[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]35.264581300698[/C][C]3.73541869930202[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.2077112551553[/C][C]-6.20771125515535[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]34.5158860624497[/C][C]2.48411393755033[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.6779893953615[/C][C]0.322010604638485[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]32.9706823517393[/C][C]-2.97068235173935[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.8638100868494[/C][C]3.1361899131506[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.5927422137796[/C][C]-0.592742213779594[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.4145494588204[/C][C]-3.41454945882039[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]35.0983020819588[/C][C]-1.09830208195879[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]34.7342329184245[/C][C]0.265767081575496[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.0096421082728[/C][C]0.990357891727174[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]35.2399189589576[/C][C]-5.23991895895756[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]34.0135259002641[/C][C]4.98647409973593[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.0576268330378[/C][C]-0.0576268330377839[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.3376535583621[/C][C]4.66234644163791[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]34.6285445991269[/C][C]-3.6285445991269[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]35.5996964632004[/C][C]-1.59969646320042[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]35.6561322745943[/C][C]2.34386772540574[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]34.8306966584801[/C][C]-0.8306966584801[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.7987805579829[/C][C]4.20121944201709[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.3160781325412[/C][C]1.68392186745885[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]34.6973281808895[/C][C]-0.697328180889551[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]34.2396268651215[/C][C]-6.23962686512145[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]34.8505565919135[/C][C]2.14944340808649[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.0730689348259[/C][C]-2.0730689348259[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.2910109068905[/C][C]0.708989093109484[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.105226981141[/C][C]-0.105226981141034[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.9069820044176[/C][C]2.09301799558244[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.7693983824024[/C][C]-2.76939838240237[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.8787310407321[/C][C]-1.87873104073214[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]34.4739265719[/C][C]3.52607342810003[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.8907106929291[/C][C]-1.89071069292914[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.6925918885595[/C][C]-5.69259188855953[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.4792435779812[/C][C]-0.47924357798117[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.1903082412407[/C][C]-3.19030824124071[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.9473884501409[/C][C]1.05261154985908[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]34.9835196950628[/C][C]0.0164803049372452[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]34.5384846103482[/C][C]-2.53848461034818[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.9676036099975[/C][C]-4.96760360999747[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.1450626214719[/C][C]3.85493737852807[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.5061342757022[/C][C]2.49386572429781[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.8698953774416[/C][C]0.130104622558449[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.2662487596448[/C][C]1.73375124035517[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]34.4811699317669[/C][C]-2.48116993176695[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.2782284118418[/C][C]2.72177158815817[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.4856374208993[/C][C]2.51436257910067[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.1645673284822[/C][C]-0.164567328482216[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]34.1777517240511[/C][C]-2.17775172405115[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]34.8737920611815[/C][C]-1.87379206118147[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.2069679781495[/C][C]6.79303202185055[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.3670642153687[/C][C]2.63293578463126[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]34.8614939482998[/C][C]6.13850605170025[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.1445109173164[/C][C]1.85548908268358[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]32.8904273014402[/C][C]10.1095726985598[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.8239118247865[/C][C]-4.8239118247865[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.2656970554893[/C][C]-3.26569705548932[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]34.8763744375736[/C][C]-2.87637443757364[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.8566670433427[/C][C]-1.8566670433427[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.1799451844919[/C][C]2.82005481550808[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.551802146437[/C][C]1.44819785356297[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]34.1316419990422[/C][C]-1.13164199904223[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]34.1477210221998[/C][C]-0.147721022199799[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]33.9572405532487[/C][C]-0.957240553248674[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]34.6333548409686[/C][C]3.36664515903141[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]33.5610029445095[/C][C]-0.561002944509461[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]34.6453344931656[/C][C]-3.64533449316558[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]34.0339705322247[/C][C]3.96602946777527[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]34.8916903670079[/C][C]2.10830963299211[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]34.5599611392298[/C][C]-1.55996113922979[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.5559879406231[/C][C]-3.55598794062309[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.8792660817723[/C][C]4.1207339182277[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]35.3689992715017[/C][C]8.63100072849833[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.077500659606[/C][C]-2.07750065960598[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.6978234728013[/C][C]1.30217652719867[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.5354910858202[/C][C]-2.53549108582023[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]32.2132811167993[/C][C]-4.21328111679929[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]34.8144392401265[/C][C]5.18556075987354[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]33.6410692007228[/C][C]-6.64106920072276[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]34.2316943610428[/C][C]2.76830563895718[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]34.5878171273637[/C][C]-2.5878171273637[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]33.2005016051406[/C][C]-5.20050160514057[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.3126498833333[/C][C]-0.312649883333318[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]32.7993622731202[/C][C]-2.79936227312018[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.6971865514319[/C][C]1.30281344856811[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.3375615863915[/C][C]-1.33756158639151[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.7775449014813[/C][C]-2.77754490148131[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.5591215306397[/C][C]-4.55912153063969[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.8625997947324[/C][C]-4.86259979473237[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]34.7248900003633[/C][C]-3.72489000036331[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]33.2718852277969[/C][C]6.72811477220315[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]35.384364858423[/C][C]-3.384364858423[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]33.3571924657557[/C][C]2.64280753424434[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]34.3282181574753[/C][C]-2.32821815747531[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]32.9609548570164[/C][C]2.03904514298355[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]33.2312099788759[/C][C]4.76879002112415[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]34.0612392564763[/C][C]7.93876074352373[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]34.8861144660878[/C][C]-0.886114466087767[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]34.2875429085543[/C][C]0.712457091445695[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.2075526766767[/C][C]1.79244732332331[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.6891900062003[/C][C]0.310809993799696[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]34.6152885388359[/C][C]1.3847114611641[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]33.6964731151957[/C][C]-1.69647311519565[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]34.9545195307888[/C][C]-1.9545195307888[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.4569661161999[/C][C]-0.456966116199904[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.069398249269[/C][C]-1.06939824926904[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]33.5062345920625[/C][C]0.493765407937532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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
14134.51171274197756.48828725802246
23935.86301279274283.13698720725721
33035.6652725054348-5.6652725054348
43134.7666354759127-3.76663547591269
53434.8477728876442-0.847772887644241
63535.025152891393-0.0251528913930451
73935.88790062181133.11209937818874
83435.0373848882978-1.03738488829778
93634.91985348804691.08014651195314
103735.1223136091951.87768639080502
113835.71281259716122.28718740283884
123636.0160385165461-0.0160385165461042
133834.6693321272673.33066787273301
143935.48631665675233.51368334324773
153336.0969940388366-3.0969940388366
163234.9440547382588-2.94405473825878
173634.93995536729821.06004463270179
183836.73231730417141.26768269582855
193935.81375422523893.18624577476106
203235.481898825107-3.48189882510698
213235.8658383209645-3.86583832096447
223134.7861004337946-3.7861004337946
233936.02409462762352.97590537237652
243734.91138594292812.08861405707193
253935.90233768405813.09766231594188
264134.87072109289696.12927890710313
273635.49356001661920.506439983380755
283334.8746838926138-1.87468389261377
293335.2913418412891-2.29134184128907
303434.2392945113019-0.239294511301944
313135.2504246465501-4.25042464655013
322733.9235181343275-6.92351813432754
333735.08843708710241.91156291289765
343435.3313801688406-1.33138016884062
353434.6192504096629-0.619250409662937
363233.1133487901605-1.11334879016054
372932.7340029557893-3.7340029557893
383634.7606149419541.23938505804602
392935.5980302102651-6.59803021026513
403535.0599938350861-0.0599938350860857
413735.34304136035291.65695863964711
423434.83734492093-0.837344920930028
433836.55688396145691.44311603854307
443534.29520917479040.704790825209592
453834.35164498618433.64835501381575
463733.37242055254353.62757944745654
473834.78425578566323.21574421433676
483335.0068943009295-2.0068943009295
493635.45614453006870.543855469931285
503834.4646323824363.53536761756399
513235.4153535076836-3.41535350768364
523234.5293827092151-2.52938270921507
533233.5099276596919-1.50992765969187
543434.7884048141109-0.788404814110898
553232.9095371053952-0.909537105395181
563734.62118546579592.37881453420414
573935.2645813006983.73541869930202
582935.2077112551553-6.20771125515535
593734.51588606244972.48411393755033
603534.67798939536150.322010604638485
613032.9706823517393-2.97068235173935
623834.86381008684943.1361899131506
633434.5927422137796-0.592742213779594
643134.4145494588204-3.41454945882039
653435.0983020819588-1.09830208195879
663534.73423291842450.265767081575496
673635.00964210827280.990357891727174
683035.2399189589576-5.23991895895756
693934.01352590026414.98647409973593
703535.0576268330378-0.0576268330377839
713833.33765355836214.66234644163791
723134.6285445991269-3.6285445991269
733435.5996964632004-1.59969646320042
743835.65613227459432.34386772540574
753434.8306966584801-0.8306966584801
763934.79878055798294.20121944201709
773735.31607813254121.68392186745885
783434.6973281808895-0.697328180889551
792834.2396268651215-6.23962686512145
803734.85055659191352.14944340808649
813335.0730689348259-2.0730689348259
823736.29101090689050.708989093109484
833535.105226981141-0.105226981141034
843734.90698200441762.09301799558244
853234.7693983824024-2.76939838240237
863334.8787310407321-1.87873104073214
873834.47392657193.52607342810003
883334.8907106929291-1.89071069292914
892934.6925918885595-5.69259188855953
903333.4792435779812-0.47924357798117
913134.1903082412407-3.19030824124071
923634.94738845014091.05261154985908
933534.98351969506280.0164803049372452
943234.5384846103482-2.53848461034818
952933.9676036099975-4.96760360999747
963935.14506262147193.85493737852807
973734.50613427570222.49386572429781
983534.86989537744160.130104622558449
993735.26624875964481.73375124035517
1003234.4811699317669-2.48116993176695
1013835.27822841184182.72177158815817
1023734.48563742089932.51436257910067
1033636.1645673284822-0.164567328482216
1043234.1777517240511-2.17775172405115
1053334.8737920611815-1.87379206118147
1064033.20696797814956.79303202185055
1073835.36706421536872.63293578463126
1084134.86149394829986.13850605170025
1093634.14451091731641.85548908268358
1104332.890427301440210.1095726985598
1113034.8239118247865-4.8239118247865
1123134.2656970554893-3.26569705548932
1133234.8763744375736-2.87637443757364
1143233.8566670433427-1.8566670433427
1153734.17994518449192.82005481550808
1163735.5518021464371.44819785356297
1173334.1316419990422-1.13164199904223
1183434.1477210221998-0.147721022199799
1193333.9572405532487-0.957240553248674
1203834.63335484096863.36664515903141
1213333.5610029445095-0.561002944509461
1223134.6453344931656-3.64533449316558
1233834.03397053222473.96602946777527
1243734.89169036700792.10830963299211
1253334.5599611392298-1.55996113922979
1263134.5559879406231-3.55598794062309
1273934.87926608177234.1207339182277
1284435.36899927150178.63100072849833
1293335.077500659606-2.07750065960598
1303533.69782347280131.30217652719867
1313234.5354910858202-2.53549108582023
1322832.2132811167993-4.21328111679929
1334034.81443924012655.18556075987354
1342733.6410692007228-6.64106920072276
1353734.23169436104282.76830563895718
1363234.5878171273637-2.5878171273637
1372833.2005016051406-5.20050160514057
1383434.3126498833333-0.312649883333318
1393032.7993622731202-2.79936227312018
1403533.69718655143191.30281344856811
1413132.3375615863915-1.33756158639151
1423234.7775449014813-2.77754490148131
1433034.5591215306397-4.55912153063969
1443034.8625997947324-4.86259979473237
1453134.7248900003633-3.72489000036331
1464033.27188522779696.72811477220315
1473235.384364858423-3.384364858423
1483633.35719246575572.64280753424434
1493234.3282181574753-2.32821815747531
1503532.96095485701642.03904514298355
1513833.23120997887594.76879002112415
1524234.06123925647637.93876074352373
1533434.8861144660878-0.886114466087767
1543534.28754290855430.712457091445695
1553533.20755267667671.79244732332331
1563332.68919000620030.310809993799696
1573634.61528853883591.3847114611641
1583233.6964731151957-1.69647311519565
1593334.9545195307888-1.9545195307888
1603434.4569661161999-0.456966116199904
1613233.069398249269-1.06939824926904
1623433.50623459206250.493765407937532






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9262335988939120.1475328022121760.0737664011060882
100.8827528510096370.2344942979807260.117247148990363
110.8135969309974230.3728061380051530.186403069002577
120.747950368796160.5040992624076810.252049631203841
130.7776710032477140.4446579935045720.222328996752286
140.712640182531080.5747196349378390.28735981746892
150.7285498887240360.5429002225519280.271450111275964
160.7450418998971340.5099162002057310.254958100102866
170.6704549721793860.6590900556412280.329545027820614
180.6251632692248270.7496734615503470.374836730775173
190.6222242741058480.7555514517883030.377775725894152
200.6591584925945740.6816830148108520.340841507405426
210.6636407385769760.6727185228460480.336359261423024
220.6345016766373440.7309966467253110.365498323362656
230.6581754564529360.6836490870941280.341824543547064
240.6307850984179530.7384298031640930.369214901582047
250.6141777053806220.7716445892387570.385822294619378
260.7292935293749990.5414129412500030.270706470625001
270.6727351275029370.6545297449941270.327264872497063
280.6425994729192730.7148010541614540.357400527080727
290.6094826610483420.7810346779033150.390517338951658
300.5479798464441840.9040403071116320.452020153555816
310.5648807339119550.8702385321760890.435119266088045
320.6455828947752270.7088342104495470.354417105224773
330.6479411515768420.7041176968463160.352058848423158
340.5930150775807380.8139698448385240.406984922419262
350.5397920642197690.9204158715604610.460207935780231
360.4953498789400560.9906997578801130.504650121059944
370.458908601294010.917817202588020.54109139870599
380.4325326453010270.8650652906020540.567467354698973
390.5406405051631890.9187189896736220.459359494836811
400.4894765574701730.9789531149403460.510523442529827
410.4652924628104180.9305849256208360.534707537189582
420.4204555852348640.8409111704697280.579544414765136
430.377752200778210.755504401556420.62224779922179
440.3446559729159820.6893119458319640.655344027084018
450.3748999579642630.7497999159285250.625100042035737
460.4092335285215850.8184670570431690.590766471478415
470.4113639276547980.8227278553095960.588636072345202
480.3742922866476220.7485845732952450.625707713352378
490.3364329874096530.6728659748193060.663567012590347
500.3439692131310440.6879384262620890.656030786868956
510.3425838893164960.6851677786329920.657416110683504
520.3141182777255710.6282365554511420.685881722274429
530.2755124380299360.5510248760598720.724487561970064
540.2366882842700710.4733765685401430.763311715729929
550.2005517370810170.4011034741620350.799448262918983
560.1841898607917740.3683797215835480.815810139208226
570.1768368145282480.3536736290564960.823163185471752
580.3105752060979730.6211504121959460.689424793902027
590.2872236973798370.5744473947596740.712776302620163
600.2475562799570550.4951125599141090.752443720042945
610.2349636217074050.469927243414810.765036378292595
620.2290161260440930.4580322520881860.770983873955907
630.1977499582561010.3954999165122020.802250041743899
640.1934680889116790.3869361778233580.806531911088321
650.1640318594008750.328063718801750.835968140599125
660.1374230641579550.2748461283159090.862576935842045
670.1161420338983930.2322840677967870.883857966101607
680.1530978007344760.3061956014689530.846902199265524
690.2080744010618140.4161488021236270.791925598938186
700.1763378948388520.3526757896777030.823662105161148
710.213102520113390.4262050402267810.78689747988661
720.2133378377107280.4266756754214560.786662162289272
730.185871646116470.371743292232940.81412835388353
740.1723558380743230.3447116761486460.827644161925677
750.1453451883493270.2906903766986540.854654811650673
760.1696943625873130.3393887251746260.830305637412687
770.1496131244597650.2992262489195310.850386875540235
780.1247019344898720.2494038689797440.875298065510128
790.1885320107552180.3770640215104360.811467989244782
800.1722592678926710.3445185357853430.827740732107329
810.1531233247148080.3062466494296170.846876675285192
820.1289417824726140.2578835649452280.871058217527386
830.1061190901002210.2122381802004420.893880909899779
840.09507426229358450.1901485245871690.904925737706415
850.08770976946884950.1754195389376990.912290230531151
860.07486744874107790.1497348974821560.925132551258922
870.07831139027169610.1566227805433920.921688609728304
880.06665566663850230.1333113332770050.933344333361498
890.09670521380104390.1934104276020880.903294786198956
900.07971273559487790.1594254711897560.920287264405122
910.08211669267980350.1642333853596070.917883307320197
920.06822717806082670.1364543561216530.931772821939173
930.05517119695170320.1103423939034060.944828803048297
940.05273109693931320.1054621938786260.947268903060687
950.07768651324202930.1553730264840590.922313486757971
960.08077398428682160.1615479685736430.919226015713178
970.07246633747755280.1449326749551060.927533662522447
980.0587110545319920.1174221090639840.941288945468008
990.04882170722626390.09764341445252780.951178292773736
1000.05136363320775580.1027272664155120.948636366792244
1010.04459105607569510.08918211215139030.955408943924305
1020.03815546335331630.07631092670663260.961844536646684
1030.02997148170219050.05994296340438090.97002851829781
1040.03321170169587570.06642340339175140.966788298304124
1050.04100817783697420.08201635567394840.958991822163026
1060.05585796975559720.1117159395111940.944142030244403
1070.05031522306061290.1006304461212260.949684776939387
1080.05473511119540210.1094702223908040.945264888804598
1090.04728886646834960.09457773293669920.95271113353165
1100.3102852020542760.6205704041085520.689714797945724
1110.3358696868233180.6717393736466350.664130313176682
1120.3151386079843430.6302772159686860.684861392015657
1130.2936868755443810.5873737510887620.706313124455619
1140.2589678025951930.5179356051903860.741032197404807
1150.2419560948892810.4839121897785620.758043905110719
1160.2136028220550170.4272056441100350.786397177944983
1170.1795014144866470.3590028289732930.820498585513353
1180.1474911994759630.2949823989519260.852508800524037
1190.1260196726654060.2520393453308120.873980327334594
1200.1482881655210720.2965763310421440.851711834478928
1210.1203101406306430.2406202812612870.879689859369357
1220.1099784132545190.2199568265090380.890021586745481
1230.115466562169340.230933124338680.88453343783066
1240.09857471486247840.1971494297249570.901425285137522
1250.07904647734974960.1580929546994990.92095352265025
1260.07633040606271540.1526608121254310.923669593937285
1270.07900872420249750.1580174484049950.920991275797503
1280.3514994552375630.7029989104751260.648500544762437
1290.3049629628442120.6099259256884240.695037037155788
1300.2805848559859330.5611697119718660.719415144014067
1310.2396055171145070.4792110342290130.760394482885493
1320.2386793744755420.4773587489510840.761320625524458
1330.4211807730125970.8423615460251940.578819226987403
1340.5195312967247170.9609374065505670.480468703275283
1350.5751031458203980.8497937083592050.424896854179602
1360.5161832192299710.9676335615400580.483816780770029
1370.5346768329489230.9306463341021540.465323167051077
1380.4795031337649620.9590062675299230.520496866235038
1390.4837532331558710.9675064663117430.516246766844129
1400.4248721057945750.8497442115891510.575127894205425
1410.4862435006431920.9724870012863830.513756499356808
1420.4235673326765230.8471346653530470.576432667323477
1430.4514521278915430.9029042557830870.548547872108456
1440.5045610864072040.9908778271855930.495438913592796
1450.5942082502052630.8115834995894740.405791749794737
1460.6137113231571820.7725773536856360.386288676842818
1470.7438683715177530.5122632569644950.256131628482247
1480.6593194641004650.681361071799070.340680535899535
1490.7458680296078140.5082639407843720.254131970392186
1500.740289847836110.5194203043277810.25971015216389
1510.7812276933880970.4375446132238060.218772306611903
1520.9884911303480590.02301773930388250.0115088696519412
1530.9565262266274360.08694754674512780.0434737733725639

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.926233598893912 & 0.147532802212176 & 0.0737664011060882 \tabularnewline
10 & 0.882752851009637 & 0.234494297980726 & 0.117247148990363 \tabularnewline
11 & 0.813596930997423 & 0.372806138005153 & 0.186403069002577 \tabularnewline
12 & 0.74795036879616 & 0.504099262407681 & 0.252049631203841 \tabularnewline
13 & 0.777671003247714 & 0.444657993504572 & 0.222328996752286 \tabularnewline
14 & 0.71264018253108 & 0.574719634937839 & 0.28735981746892 \tabularnewline
15 & 0.728549888724036 & 0.542900222551928 & 0.271450111275964 \tabularnewline
16 & 0.745041899897134 & 0.509916200205731 & 0.254958100102866 \tabularnewline
17 & 0.670454972179386 & 0.659090055641228 & 0.329545027820614 \tabularnewline
18 & 0.625163269224827 & 0.749673461550347 & 0.374836730775173 \tabularnewline
19 & 0.622224274105848 & 0.755551451788303 & 0.377775725894152 \tabularnewline
20 & 0.659158492594574 & 0.681683014810852 & 0.340841507405426 \tabularnewline
21 & 0.663640738576976 & 0.672718522846048 & 0.336359261423024 \tabularnewline
22 & 0.634501676637344 & 0.730996646725311 & 0.365498323362656 \tabularnewline
23 & 0.658175456452936 & 0.683649087094128 & 0.341824543547064 \tabularnewline
24 & 0.630785098417953 & 0.738429803164093 & 0.369214901582047 \tabularnewline
25 & 0.614177705380622 & 0.771644589238757 & 0.385822294619378 \tabularnewline
26 & 0.729293529374999 & 0.541412941250003 & 0.270706470625001 \tabularnewline
27 & 0.672735127502937 & 0.654529744994127 & 0.327264872497063 \tabularnewline
28 & 0.642599472919273 & 0.714801054161454 & 0.357400527080727 \tabularnewline
29 & 0.609482661048342 & 0.781034677903315 & 0.390517338951658 \tabularnewline
30 & 0.547979846444184 & 0.904040307111632 & 0.452020153555816 \tabularnewline
31 & 0.564880733911955 & 0.870238532176089 & 0.435119266088045 \tabularnewline
32 & 0.645582894775227 & 0.708834210449547 & 0.354417105224773 \tabularnewline
33 & 0.647941151576842 & 0.704117696846316 & 0.352058848423158 \tabularnewline
34 & 0.593015077580738 & 0.813969844838524 & 0.406984922419262 \tabularnewline
35 & 0.539792064219769 & 0.920415871560461 & 0.460207935780231 \tabularnewline
36 & 0.495349878940056 & 0.990699757880113 & 0.504650121059944 \tabularnewline
37 & 0.45890860129401 & 0.91781720258802 & 0.54109139870599 \tabularnewline
38 & 0.432532645301027 & 0.865065290602054 & 0.567467354698973 \tabularnewline
39 & 0.540640505163189 & 0.918718989673622 & 0.459359494836811 \tabularnewline
40 & 0.489476557470173 & 0.978953114940346 & 0.510523442529827 \tabularnewline
41 & 0.465292462810418 & 0.930584925620836 & 0.534707537189582 \tabularnewline
42 & 0.420455585234864 & 0.840911170469728 & 0.579544414765136 \tabularnewline
43 & 0.37775220077821 & 0.75550440155642 & 0.62224779922179 \tabularnewline
44 & 0.344655972915982 & 0.689311945831964 & 0.655344027084018 \tabularnewline
45 & 0.374899957964263 & 0.749799915928525 & 0.625100042035737 \tabularnewline
46 & 0.409233528521585 & 0.818467057043169 & 0.590766471478415 \tabularnewline
47 & 0.411363927654798 & 0.822727855309596 & 0.588636072345202 \tabularnewline
48 & 0.374292286647622 & 0.748584573295245 & 0.625707713352378 \tabularnewline
49 & 0.336432987409653 & 0.672865974819306 & 0.663567012590347 \tabularnewline
50 & 0.343969213131044 & 0.687938426262089 & 0.656030786868956 \tabularnewline
51 & 0.342583889316496 & 0.685167778632992 & 0.657416110683504 \tabularnewline
52 & 0.314118277725571 & 0.628236555451142 & 0.685881722274429 \tabularnewline
53 & 0.275512438029936 & 0.551024876059872 & 0.724487561970064 \tabularnewline
54 & 0.236688284270071 & 0.473376568540143 & 0.763311715729929 \tabularnewline
55 & 0.200551737081017 & 0.401103474162035 & 0.799448262918983 \tabularnewline
56 & 0.184189860791774 & 0.368379721583548 & 0.815810139208226 \tabularnewline
57 & 0.176836814528248 & 0.353673629056496 & 0.823163185471752 \tabularnewline
58 & 0.310575206097973 & 0.621150412195946 & 0.689424793902027 \tabularnewline
59 & 0.287223697379837 & 0.574447394759674 & 0.712776302620163 \tabularnewline
60 & 0.247556279957055 & 0.495112559914109 & 0.752443720042945 \tabularnewline
61 & 0.234963621707405 & 0.46992724341481 & 0.765036378292595 \tabularnewline
62 & 0.229016126044093 & 0.458032252088186 & 0.770983873955907 \tabularnewline
63 & 0.197749958256101 & 0.395499916512202 & 0.802250041743899 \tabularnewline
64 & 0.193468088911679 & 0.386936177823358 & 0.806531911088321 \tabularnewline
65 & 0.164031859400875 & 0.32806371880175 & 0.835968140599125 \tabularnewline
66 & 0.137423064157955 & 0.274846128315909 & 0.862576935842045 \tabularnewline
67 & 0.116142033898393 & 0.232284067796787 & 0.883857966101607 \tabularnewline
68 & 0.153097800734476 & 0.306195601468953 & 0.846902199265524 \tabularnewline
69 & 0.208074401061814 & 0.416148802123627 & 0.791925598938186 \tabularnewline
70 & 0.176337894838852 & 0.352675789677703 & 0.823662105161148 \tabularnewline
71 & 0.21310252011339 & 0.426205040226781 & 0.78689747988661 \tabularnewline
72 & 0.213337837710728 & 0.426675675421456 & 0.786662162289272 \tabularnewline
73 & 0.18587164611647 & 0.37174329223294 & 0.81412835388353 \tabularnewline
74 & 0.172355838074323 & 0.344711676148646 & 0.827644161925677 \tabularnewline
75 & 0.145345188349327 & 0.290690376698654 & 0.854654811650673 \tabularnewline
76 & 0.169694362587313 & 0.339388725174626 & 0.830305637412687 \tabularnewline
77 & 0.149613124459765 & 0.299226248919531 & 0.850386875540235 \tabularnewline
78 & 0.124701934489872 & 0.249403868979744 & 0.875298065510128 \tabularnewline
79 & 0.188532010755218 & 0.377064021510436 & 0.811467989244782 \tabularnewline
80 & 0.172259267892671 & 0.344518535785343 & 0.827740732107329 \tabularnewline
81 & 0.153123324714808 & 0.306246649429617 & 0.846876675285192 \tabularnewline
82 & 0.128941782472614 & 0.257883564945228 & 0.871058217527386 \tabularnewline
83 & 0.106119090100221 & 0.212238180200442 & 0.893880909899779 \tabularnewline
84 & 0.0950742622935845 & 0.190148524587169 & 0.904925737706415 \tabularnewline
85 & 0.0877097694688495 & 0.175419538937699 & 0.912290230531151 \tabularnewline
86 & 0.0748674487410779 & 0.149734897482156 & 0.925132551258922 \tabularnewline
87 & 0.0783113902716961 & 0.156622780543392 & 0.921688609728304 \tabularnewline
88 & 0.0666556666385023 & 0.133311333277005 & 0.933344333361498 \tabularnewline
89 & 0.0967052138010439 & 0.193410427602088 & 0.903294786198956 \tabularnewline
90 & 0.0797127355948779 & 0.159425471189756 & 0.920287264405122 \tabularnewline
91 & 0.0821166926798035 & 0.164233385359607 & 0.917883307320197 \tabularnewline
92 & 0.0682271780608267 & 0.136454356121653 & 0.931772821939173 \tabularnewline
93 & 0.0551711969517032 & 0.110342393903406 & 0.944828803048297 \tabularnewline
94 & 0.0527310969393132 & 0.105462193878626 & 0.947268903060687 \tabularnewline
95 & 0.0776865132420293 & 0.155373026484059 & 0.922313486757971 \tabularnewline
96 & 0.0807739842868216 & 0.161547968573643 & 0.919226015713178 \tabularnewline
97 & 0.0724663374775528 & 0.144932674955106 & 0.927533662522447 \tabularnewline
98 & 0.058711054531992 & 0.117422109063984 & 0.941288945468008 \tabularnewline
99 & 0.0488217072262639 & 0.0976434144525278 & 0.951178292773736 \tabularnewline
100 & 0.0513636332077558 & 0.102727266415512 & 0.948636366792244 \tabularnewline
101 & 0.0445910560756951 & 0.0891821121513903 & 0.955408943924305 \tabularnewline
102 & 0.0381554633533163 & 0.0763109267066326 & 0.961844536646684 \tabularnewline
103 & 0.0299714817021905 & 0.0599429634043809 & 0.97002851829781 \tabularnewline
104 & 0.0332117016958757 & 0.0664234033917514 & 0.966788298304124 \tabularnewline
105 & 0.0410081778369742 & 0.0820163556739484 & 0.958991822163026 \tabularnewline
106 & 0.0558579697555972 & 0.111715939511194 & 0.944142030244403 \tabularnewline
107 & 0.0503152230606129 & 0.100630446121226 & 0.949684776939387 \tabularnewline
108 & 0.0547351111954021 & 0.109470222390804 & 0.945264888804598 \tabularnewline
109 & 0.0472888664683496 & 0.0945777329366992 & 0.95271113353165 \tabularnewline
110 & 0.310285202054276 & 0.620570404108552 & 0.689714797945724 \tabularnewline
111 & 0.335869686823318 & 0.671739373646635 & 0.664130313176682 \tabularnewline
112 & 0.315138607984343 & 0.630277215968686 & 0.684861392015657 \tabularnewline
113 & 0.293686875544381 & 0.587373751088762 & 0.706313124455619 \tabularnewline
114 & 0.258967802595193 & 0.517935605190386 & 0.741032197404807 \tabularnewline
115 & 0.241956094889281 & 0.483912189778562 & 0.758043905110719 \tabularnewline
116 & 0.213602822055017 & 0.427205644110035 & 0.786397177944983 \tabularnewline
117 & 0.179501414486647 & 0.359002828973293 & 0.820498585513353 \tabularnewline
118 & 0.147491199475963 & 0.294982398951926 & 0.852508800524037 \tabularnewline
119 & 0.126019672665406 & 0.252039345330812 & 0.873980327334594 \tabularnewline
120 & 0.148288165521072 & 0.296576331042144 & 0.851711834478928 \tabularnewline
121 & 0.120310140630643 & 0.240620281261287 & 0.879689859369357 \tabularnewline
122 & 0.109978413254519 & 0.219956826509038 & 0.890021586745481 \tabularnewline
123 & 0.11546656216934 & 0.23093312433868 & 0.88453343783066 \tabularnewline
124 & 0.0985747148624784 & 0.197149429724957 & 0.901425285137522 \tabularnewline
125 & 0.0790464773497496 & 0.158092954699499 & 0.92095352265025 \tabularnewline
126 & 0.0763304060627154 & 0.152660812125431 & 0.923669593937285 \tabularnewline
127 & 0.0790087242024975 & 0.158017448404995 & 0.920991275797503 \tabularnewline
128 & 0.351499455237563 & 0.702998910475126 & 0.648500544762437 \tabularnewline
129 & 0.304962962844212 & 0.609925925688424 & 0.695037037155788 \tabularnewline
130 & 0.280584855985933 & 0.561169711971866 & 0.719415144014067 \tabularnewline
131 & 0.239605517114507 & 0.479211034229013 & 0.760394482885493 \tabularnewline
132 & 0.238679374475542 & 0.477358748951084 & 0.761320625524458 \tabularnewline
133 & 0.421180773012597 & 0.842361546025194 & 0.578819226987403 \tabularnewline
134 & 0.519531296724717 & 0.960937406550567 & 0.480468703275283 \tabularnewline
135 & 0.575103145820398 & 0.849793708359205 & 0.424896854179602 \tabularnewline
136 & 0.516183219229971 & 0.967633561540058 & 0.483816780770029 \tabularnewline
137 & 0.534676832948923 & 0.930646334102154 & 0.465323167051077 \tabularnewline
138 & 0.479503133764962 & 0.959006267529923 & 0.520496866235038 \tabularnewline
139 & 0.483753233155871 & 0.967506466311743 & 0.516246766844129 \tabularnewline
140 & 0.424872105794575 & 0.849744211589151 & 0.575127894205425 \tabularnewline
141 & 0.486243500643192 & 0.972487001286383 & 0.513756499356808 \tabularnewline
142 & 0.423567332676523 & 0.847134665353047 & 0.576432667323477 \tabularnewline
143 & 0.451452127891543 & 0.902904255783087 & 0.548547872108456 \tabularnewline
144 & 0.504561086407204 & 0.990877827185593 & 0.495438913592796 \tabularnewline
145 & 0.594208250205263 & 0.811583499589474 & 0.405791749794737 \tabularnewline
146 & 0.613711323157182 & 0.772577353685636 & 0.386288676842818 \tabularnewline
147 & 0.743868371517753 & 0.512263256964495 & 0.256131628482247 \tabularnewline
148 & 0.659319464100465 & 0.68136107179907 & 0.340680535899535 \tabularnewline
149 & 0.745868029607814 & 0.508263940784372 & 0.254131970392186 \tabularnewline
150 & 0.74028984783611 & 0.519420304327781 & 0.25971015216389 \tabularnewline
151 & 0.781227693388097 & 0.437544613223806 & 0.218772306611903 \tabularnewline
152 & 0.988491130348059 & 0.0230177393038825 & 0.0115088696519412 \tabularnewline
153 & 0.956526226627436 & 0.0869475467451278 & 0.0434737733725639 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&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]0.926233598893912[/C][C]0.147532802212176[/C][C]0.0737664011060882[/C][/ROW]
[ROW][C]10[/C][C]0.882752851009637[/C][C]0.234494297980726[/C][C]0.117247148990363[/C][/ROW]
[ROW][C]11[/C][C]0.813596930997423[/C][C]0.372806138005153[/C][C]0.186403069002577[/C][/ROW]
[ROW][C]12[/C][C]0.74795036879616[/C][C]0.504099262407681[/C][C]0.252049631203841[/C][/ROW]
[ROW][C]13[/C][C]0.777671003247714[/C][C]0.444657993504572[/C][C]0.222328996752286[/C][/ROW]
[ROW][C]14[/C][C]0.71264018253108[/C][C]0.574719634937839[/C][C]0.28735981746892[/C][/ROW]
[ROW][C]15[/C][C]0.728549888724036[/C][C]0.542900222551928[/C][C]0.271450111275964[/C][/ROW]
[ROW][C]16[/C][C]0.745041899897134[/C][C]0.509916200205731[/C][C]0.254958100102866[/C][/ROW]
[ROW][C]17[/C][C]0.670454972179386[/C][C]0.659090055641228[/C][C]0.329545027820614[/C][/ROW]
[ROW][C]18[/C][C]0.625163269224827[/C][C]0.749673461550347[/C][C]0.374836730775173[/C][/ROW]
[ROW][C]19[/C][C]0.622224274105848[/C][C]0.755551451788303[/C][C]0.377775725894152[/C][/ROW]
[ROW][C]20[/C][C]0.659158492594574[/C][C]0.681683014810852[/C][C]0.340841507405426[/C][/ROW]
[ROW][C]21[/C][C]0.663640738576976[/C][C]0.672718522846048[/C][C]0.336359261423024[/C][/ROW]
[ROW][C]22[/C][C]0.634501676637344[/C][C]0.730996646725311[/C][C]0.365498323362656[/C][/ROW]
[ROW][C]23[/C][C]0.658175456452936[/C][C]0.683649087094128[/C][C]0.341824543547064[/C][/ROW]
[ROW][C]24[/C][C]0.630785098417953[/C][C]0.738429803164093[/C][C]0.369214901582047[/C][/ROW]
[ROW][C]25[/C][C]0.614177705380622[/C][C]0.771644589238757[/C][C]0.385822294619378[/C][/ROW]
[ROW][C]26[/C][C]0.729293529374999[/C][C]0.541412941250003[/C][C]0.270706470625001[/C][/ROW]
[ROW][C]27[/C][C]0.672735127502937[/C][C]0.654529744994127[/C][C]0.327264872497063[/C][/ROW]
[ROW][C]28[/C][C]0.642599472919273[/C][C]0.714801054161454[/C][C]0.357400527080727[/C][/ROW]
[ROW][C]29[/C][C]0.609482661048342[/C][C]0.781034677903315[/C][C]0.390517338951658[/C][/ROW]
[ROW][C]30[/C][C]0.547979846444184[/C][C]0.904040307111632[/C][C]0.452020153555816[/C][/ROW]
[ROW][C]31[/C][C]0.564880733911955[/C][C]0.870238532176089[/C][C]0.435119266088045[/C][/ROW]
[ROW][C]32[/C][C]0.645582894775227[/C][C]0.708834210449547[/C][C]0.354417105224773[/C][/ROW]
[ROW][C]33[/C][C]0.647941151576842[/C][C]0.704117696846316[/C][C]0.352058848423158[/C][/ROW]
[ROW][C]34[/C][C]0.593015077580738[/C][C]0.813969844838524[/C][C]0.406984922419262[/C][/ROW]
[ROW][C]35[/C][C]0.539792064219769[/C][C]0.920415871560461[/C][C]0.460207935780231[/C][/ROW]
[ROW][C]36[/C][C]0.495349878940056[/C][C]0.990699757880113[/C][C]0.504650121059944[/C][/ROW]
[ROW][C]37[/C][C]0.45890860129401[/C][C]0.91781720258802[/C][C]0.54109139870599[/C][/ROW]
[ROW][C]38[/C][C]0.432532645301027[/C][C]0.865065290602054[/C][C]0.567467354698973[/C][/ROW]
[ROW][C]39[/C][C]0.540640505163189[/C][C]0.918718989673622[/C][C]0.459359494836811[/C][/ROW]
[ROW][C]40[/C][C]0.489476557470173[/C][C]0.978953114940346[/C][C]0.510523442529827[/C][/ROW]
[ROW][C]41[/C][C]0.465292462810418[/C][C]0.930584925620836[/C][C]0.534707537189582[/C][/ROW]
[ROW][C]42[/C][C]0.420455585234864[/C][C]0.840911170469728[/C][C]0.579544414765136[/C][/ROW]
[ROW][C]43[/C][C]0.37775220077821[/C][C]0.75550440155642[/C][C]0.62224779922179[/C][/ROW]
[ROW][C]44[/C][C]0.344655972915982[/C][C]0.689311945831964[/C][C]0.655344027084018[/C][/ROW]
[ROW][C]45[/C][C]0.374899957964263[/C][C]0.749799915928525[/C][C]0.625100042035737[/C][/ROW]
[ROW][C]46[/C][C]0.409233528521585[/C][C]0.818467057043169[/C][C]0.590766471478415[/C][/ROW]
[ROW][C]47[/C][C]0.411363927654798[/C][C]0.822727855309596[/C][C]0.588636072345202[/C][/ROW]
[ROW][C]48[/C][C]0.374292286647622[/C][C]0.748584573295245[/C][C]0.625707713352378[/C][/ROW]
[ROW][C]49[/C][C]0.336432987409653[/C][C]0.672865974819306[/C][C]0.663567012590347[/C][/ROW]
[ROW][C]50[/C][C]0.343969213131044[/C][C]0.687938426262089[/C][C]0.656030786868956[/C][/ROW]
[ROW][C]51[/C][C]0.342583889316496[/C][C]0.685167778632992[/C][C]0.657416110683504[/C][/ROW]
[ROW][C]52[/C][C]0.314118277725571[/C][C]0.628236555451142[/C][C]0.685881722274429[/C][/ROW]
[ROW][C]53[/C][C]0.275512438029936[/C][C]0.551024876059872[/C][C]0.724487561970064[/C][/ROW]
[ROW][C]54[/C][C]0.236688284270071[/C][C]0.473376568540143[/C][C]0.763311715729929[/C][/ROW]
[ROW][C]55[/C][C]0.200551737081017[/C][C]0.401103474162035[/C][C]0.799448262918983[/C][/ROW]
[ROW][C]56[/C][C]0.184189860791774[/C][C]0.368379721583548[/C][C]0.815810139208226[/C][/ROW]
[ROW][C]57[/C][C]0.176836814528248[/C][C]0.353673629056496[/C][C]0.823163185471752[/C][/ROW]
[ROW][C]58[/C][C]0.310575206097973[/C][C]0.621150412195946[/C][C]0.689424793902027[/C][/ROW]
[ROW][C]59[/C][C]0.287223697379837[/C][C]0.574447394759674[/C][C]0.712776302620163[/C][/ROW]
[ROW][C]60[/C][C]0.247556279957055[/C][C]0.495112559914109[/C][C]0.752443720042945[/C][/ROW]
[ROW][C]61[/C][C]0.234963621707405[/C][C]0.46992724341481[/C][C]0.765036378292595[/C][/ROW]
[ROW][C]62[/C][C]0.229016126044093[/C][C]0.458032252088186[/C][C]0.770983873955907[/C][/ROW]
[ROW][C]63[/C][C]0.197749958256101[/C][C]0.395499916512202[/C][C]0.802250041743899[/C][/ROW]
[ROW][C]64[/C][C]0.193468088911679[/C][C]0.386936177823358[/C][C]0.806531911088321[/C][/ROW]
[ROW][C]65[/C][C]0.164031859400875[/C][C]0.32806371880175[/C][C]0.835968140599125[/C][/ROW]
[ROW][C]66[/C][C]0.137423064157955[/C][C]0.274846128315909[/C][C]0.862576935842045[/C][/ROW]
[ROW][C]67[/C][C]0.116142033898393[/C][C]0.232284067796787[/C][C]0.883857966101607[/C][/ROW]
[ROW][C]68[/C][C]0.153097800734476[/C][C]0.306195601468953[/C][C]0.846902199265524[/C][/ROW]
[ROW][C]69[/C][C]0.208074401061814[/C][C]0.416148802123627[/C][C]0.791925598938186[/C][/ROW]
[ROW][C]70[/C][C]0.176337894838852[/C][C]0.352675789677703[/C][C]0.823662105161148[/C][/ROW]
[ROW][C]71[/C][C]0.21310252011339[/C][C]0.426205040226781[/C][C]0.78689747988661[/C][/ROW]
[ROW][C]72[/C][C]0.213337837710728[/C][C]0.426675675421456[/C][C]0.786662162289272[/C][/ROW]
[ROW][C]73[/C][C]0.18587164611647[/C][C]0.37174329223294[/C][C]0.81412835388353[/C][/ROW]
[ROW][C]74[/C][C]0.172355838074323[/C][C]0.344711676148646[/C][C]0.827644161925677[/C][/ROW]
[ROW][C]75[/C][C]0.145345188349327[/C][C]0.290690376698654[/C][C]0.854654811650673[/C][/ROW]
[ROW][C]76[/C][C]0.169694362587313[/C][C]0.339388725174626[/C][C]0.830305637412687[/C][/ROW]
[ROW][C]77[/C][C]0.149613124459765[/C][C]0.299226248919531[/C][C]0.850386875540235[/C][/ROW]
[ROW][C]78[/C][C]0.124701934489872[/C][C]0.249403868979744[/C][C]0.875298065510128[/C][/ROW]
[ROW][C]79[/C][C]0.188532010755218[/C][C]0.377064021510436[/C][C]0.811467989244782[/C][/ROW]
[ROW][C]80[/C][C]0.172259267892671[/C][C]0.344518535785343[/C][C]0.827740732107329[/C][/ROW]
[ROW][C]81[/C][C]0.153123324714808[/C][C]0.306246649429617[/C][C]0.846876675285192[/C][/ROW]
[ROW][C]82[/C][C]0.128941782472614[/C][C]0.257883564945228[/C][C]0.871058217527386[/C][/ROW]
[ROW][C]83[/C][C]0.106119090100221[/C][C]0.212238180200442[/C][C]0.893880909899779[/C][/ROW]
[ROW][C]84[/C][C]0.0950742622935845[/C][C]0.190148524587169[/C][C]0.904925737706415[/C][/ROW]
[ROW][C]85[/C][C]0.0877097694688495[/C][C]0.175419538937699[/C][C]0.912290230531151[/C][/ROW]
[ROW][C]86[/C][C]0.0748674487410779[/C][C]0.149734897482156[/C][C]0.925132551258922[/C][/ROW]
[ROW][C]87[/C][C]0.0783113902716961[/C][C]0.156622780543392[/C][C]0.921688609728304[/C][/ROW]
[ROW][C]88[/C][C]0.0666556666385023[/C][C]0.133311333277005[/C][C]0.933344333361498[/C][/ROW]
[ROW][C]89[/C][C]0.0967052138010439[/C][C]0.193410427602088[/C][C]0.903294786198956[/C][/ROW]
[ROW][C]90[/C][C]0.0797127355948779[/C][C]0.159425471189756[/C][C]0.920287264405122[/C][/ROW]
[ROW][C]91[/C][C]0.0821166926798035[/C][C]0.164233385359607[/C][C]0.917883307320197[/C][/ROW]
[ROW][C]92[/C][C]0.0682271780608267[/C][C]0.136454356121653[/C][C]0.931772821939173[/C][/ROW]
[ROW][C]93[/C][C]0.0551711969517032[/C][C]0.110342393903406[/C][C]0.944828803048297[/C][/ROW]
[ROW][C]94[/C][C]0.0527310969393132[/C][C]0.105462193878626[/C][C]0.947268903060687[/C][/ROW]
[ROW][C]95[/C][C]0.0776865132420293[/C][C]0.155373026484059[/C][C]0.922313486757971[/C][/ROW]
[ROW][C]96[/C][C]0.0807739842868216[/C][C]0.161547968573643[/C][C]0.919226015713178[/C][/ROW]
[ROW][C]97[/C][C]0.0724663374775528[/C][C]0.144932674955106[/C][C]0.927533662522447[/C][/ROW]
[ROW][C]98[/C][C]0.058711054531992[/C][C]0.117422109063984[/C][C]0.941288945468008[/C][/ROW]
[ROW][C]99[/C][C]0.0488217072262639[/C][C]0.0976434144525278[/C][C]0.951178292773736[/C][/ROW]
[ROW][C]100[/C][C]0.0513636332077558[/C][C]0.102727266415512[/C][C]0.948636366792244[/C][/ROW]
[ROW][C]101[/C][C]0.0445910560756951[/C][C]0.0891821121513903[/C][C]0.955408943924305[/C][/ROW]
[ROW][C]102[/C][C]0.0381554633533163[/C][C]0.0763109267066326[/C][C]0.961844536646684[/C][/ROW]
[ROW][C]103[/C][C]0.0299714817021905[/C][C]0.0599429634043809[/C][C]0.97002851829781[/C][/ROW]
[ROW][C]104[/C][C]0.0332117016958757[/C][C]0.0664234033917514[/C][C]0.966788298304124[/C][/ROW]
[ROW][C]105[/C][C]0.0410081778369742[/C][C]0.0820163556739484[/C][C]0.958991822163026[/C][/ROW]
[ROW][C]106[/C][C]0.0558579697555972[/C][C]0.111715939511194[/C][C]0.944142030244403[/C][/ROW]
[ROW][C]107[/C][C]0.0503152230606129[/C][C]0.100630446121226[/C][C]0.949684776939387[/C][/ROW]
[ROW][C]108[/C][C]0.0547351111954021[/C][C]0.109470222390804[/C][C]0.945264888804598[/C][/ROW]
[ROW][C]109[/C][C]0.0472888664683496[/C][C]0.0945777329366992[/C][C]0.95271113353165[/C][/ROW]
[ROW][C]110[/C][C]0.310285202054276[/C][C]0.620570404108552[/C][C]0.689714797945724[/C][/ROW]
[ROW][C]111[/C][C]0.335869686823318[/C][C]0.671739373646635[/C][C]0.664130313176682[/C][/ROW]
[ROW][C]112[/C][C]0.315138607984343[/C][C]0.630277215968686[/C][C]0.684861392015657[/C][/ROW]
[ROW][C]113[/C][C]0.293686875544381[/C][C]0.587373751088762[/C][C]0.706313124455619[/C][/ROW]
[ROW][C]114[/C][C]0.258967802595193[/C][C]0.517935605190386[/C][C]0.741032197404807[/C][/ROW]
[ROW][C]115[/C][C]0.241956094889281[/C][C]0.483912189778562[/C][C]0.758043905110719[/C][/ROW]
[ROW][C]116[/C][C]0.213602822055017[/C][C]0.427205644110035[/C][C]0.786397177944983[/C][/ROW]
[ROW][C]117[/C][C]0.179501414486647[/C][C]0.359002828973293[/C][C]0.820498585513353[/C][/ROW]
[ROW][C]118[/C][C]0.147491199475963[/C][C]0.294982398951926[/C][C]0.852508800524037[/C][/ROW]
[ROW][C]119[/C][C]0.126019672665406[/C][C]0.252039345330812[/C][C]0.873980327334594[/C][/ROW]
[ROW][C]120[/C][C]0.148288165521072[/C][C]0.296576331042144[/C][C]0.851711834478928[/C][/ROW]
[ROW][C]121[/C][C]0.120310140630643[/C][C]0.240620281261287[/C][C]0.879689859369357[/C][/ROW]
[ROW][C]122[/C][C]0.109978413254519[/C][C]0.219956826509038[/C][C]0.890021586745481[/C][/ROW]
[ROW][C]123[/C][C]0.11546656216934[/C][C]0.23093312433868[/C][C]0.88453343783066[/C][/ROW]
[ROW][C]124[/C][C]0.0985747148624784[/C][C]0.197149429724957[/C][C]0.901425285137522[/C][/ROW]
[ROW][C]125[/C][C]0.0790464773497496[/C][C]0.158092954699499[/C][C]0.92095352265025[/C][/ROW]
[ROW][C]126[/C][C]0.0763304060627154[/C][C]0.152660812125431[/C][C]0.923669593937285[/C][/ROW]
[ROW][C]127[/C][C]0.0790087242024975[/C][C]0.158017448404995[/C][C]0.920991275797503[/C][/ROW]
[ROW][C]128[/C][C]0.351499455237563[/C][C]0.702998910475126[/C][C]0.648500544762437[/C][/ROW]
[ROW][C]129[/C][C]0.304962962844212[/C][C]0.609925925688424[/C][C]0.695037037155788[/C][/ROW]
[ROW][C]130[/C][C]0.280584855985933[/C][C]0.561169711971866[/C][C]0.719415144014067[/C][/ROW]
[ROW][C]131[/C][C]0.239605517114507[/C][C]0.479211034229013[/C][C]0.760394482885493[/C][/ROW]
[ROW][C]132[/C][C]0.238679374475542[/C][C]0.477358748951084[/C][C]0.761320625524458[/C][/ROW]
[ROW][C]133[/C][C]0.421180773012597[/C][C]0.842361546025194[/C][C]0.578819226987403[/C][/ROW]
[ROW][C]134[/C][C]0.519531296724717[/C][C]0.960937406550567[/C][C]0.480468703275283[/C][/ROW]
[ROW][C]135[/C][C]0.575103145820398[/C][C]0.849793708359205[/C][C]0.424896854179602[/C][/ROW]
[ROW][C]136[/C][C]0.516183219229971[/C][C]0.967633561540058[/C][C]0.483816780770029[/C][/ROW]
[ROW][C]137[/C][C]0.534676832948923[/C][C]0.930646334102154[/C][C]0.465323167051077[/C][/ROW]
[ROW][C]138[/C][C]0.479503133764962[/C][C]0.959006267529923[/C][C]0.520496866235038[/C][/ROW]
[ROW][C]139[/C][C]0.483753233155871[/C][C]0.967506466311743[/C][C]0.516246766844129[/C][/ROW]
[ROW][C]140[/C][C]0.424872105794575[/C][C]0.849744211589151[/C][C]0.575127894205425[/C][/ROW]
[ROW][C]141[/C][C]0.486243500643192[/C][C]0.972487001286383[/C][C]0.513756499356808[/C][/ROW]
[ROW][C]142[/C][C]0.423567332676523[/C][C]0.847134665353047[/C][C]0.576432667323477[/C][/ROW]
[ROW][C]143[/C][C]0.451452127891543[/C][C]0.902904255783087[/C][C]0.548547872108456[/C][/ROW]
[ROW][C]144[/C][C]0.504561086407204[/C][C]0.990877827185593[/C][C]0.495438913592796[/C][/ROW]
[ROW][C]145[/C][C]0.594208250205263[/C][C]0.811583499589474[/C][C]0.405791749794737[/C][/ROW]
[ROW][C]146[/C][C]0.613711323157182[/C][C]0.772577353685636[/C][C]0.386288676842818[/C][/ROW]
[ROW][C]147[/C][C]0.743868371517753[/C][C]0.512263256964495[/C][C]0.256131628482247[/C][/ROW]
[ROW][C]148[/C][C]0.659319464100465[/C][C]0.68136107179907[/C][C]0.340680535899535[/C][/ROW]
[ROW][C]149[/C][C]0.745868029607814[/C][C]0.508263940784372[/C][C]0.254131970392186[/C][/ROW]
[ROW][C]150[/C][C]0.74028984783611[/C][C]0.519420304327781[/C][C]0.25971015216389[/C][/ROW]
[ROW][C]151[/C][C]0.781227693388097[/C][C]0.437544613223806[/C][C]0.218772306611903[/C][/ROW]
[ROW][C]152[/C][C]0.988491130348059[/C][C]0.0230177393038825[/C][C]0.0115088696519412[/C][/ROW]
[ROW][C]153[/C][C]0.956526226627436[/C][C]0.0869475467451278[/C][C]0.0434737733725639[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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
90.9262335988939120.1475328022121760.0737664011060882
100.8827528510096370.2344942979807260.117247148990363
110.8135969309974230.3728061380051530.186403069002577
120.747950368796160.5040992624076810.252049631203841
130.7776710032477140.4446579935045720.222328996752286
140.712640182531080.5747196349378390.28735981746892
150.7285498887240360.5429002225519280.271450111275964
160.7450418998971340.5099162002057310.254958100102866
170.6704549721793860.6590900556412280.329545027820614
180.6251632692248270.7496734615503470.374836730775173
190.6222242741058480.7555514517883030.377775725894152
200.6591584925945740.6816830148108520.340841507405426
210.6636407385769760.6727185228460480.336359261423024
220.6345016766373440.7309966467253110.365498323362656
230.6581754564529360.6836490870941280.341824543547064
240.6307850984179530.7384298031640930.369214901582047
250.6141777053806220.7716445892387570.385822294619378
260.7292935293749990.5414129412500030.270706470625001
270.6727351275029370.6545297449941270.327264872497063
280.6425994729192730.7148010541614540.357400527080727
290.6094826610483420.7810346779033150.390517338951658
300.5479798464441840.9040403071116320.452020153555816
310.5648807339119550.8702385321760890.435119266088045
320.6455828947752270.7088342104495470.354417105224773
330.6479411515768420.7041176968463160.352058848423158
340.5930150775807380.8139698448385240.406984922419262
350.5397920642197690.9204158715604610.460207935780231
360.4953498789400560.9906997578801130.504650121059944
370.458908601294010.917817202588020.54109139870599
380.4325326453010270.8650652906020540.567467354698973
390.5406405051631890.9187189896736220.459359494836811
400.4894765574701730.9789531149403460.510523442529827
410.4652924628104180.9305849256208360.534707537189582
420.4204555852348640.8409111704697280.579544414765136
430.377752200778210.755504401556420.62224779922179
440.3446559729159820.6893119458319640.655344027084018
450.3748999579642630.7497999159285250.625100042035737
460.4092335285215850.8184670570431690.590766471478415
470.4113639276547980.8227278553095960.588636072345202
480.3742922866476220.7485845732952450.625707713352378
490.3364329874096530.6728659748193060.663567012590347
500.3439692131310440.6879384262620890.656030786868956
510.3425838893164960.6851677786329920.657416110683504
520.3141182777255710.6282365554511420.685881722274429
530.2755124380299360.5510248760598720.724487561970064
540.2366882842700710.4733765685401430.763311715729929
550.2005517370810170.4011034741620350.799448262918983
560.1841898607917740.3683797215835480.815810139208226
570.1768368145282480.3536736290564960.823163185471752
580.3105752060979730.6211504121959460.689424793902027
590.2872236973798370.5744473947596740.712776302620163
600.2475562799570550.4951125599141090.752443720042945
610.2349636217074050.469927243414810.765036378292595
620.2290161260440930.4580322520881860.770983873955907
630.1977499582561010.3954999165122020.802250041743899
640.1934680889116790.3869361778233580.806531911088321
650.1640318594008750.328063718801750.835968140599125
660.1374230641579550.2748461283159090.862576935842045
670.1161420338983930.2322840677967870.883857966101607
680.1530978007344760.3061956014689530.846902199265524
690.2080744010618140.4161488021236270.791925598938186
700.1763378948388520.3526757896777030.823662105161148
710.213102520113390.4262050402267810.78689747988661
720.2133378377107280.4266756754214560.786662162289272
730.185871646116470.371743292232940.81412835388353
740.1723558380743230.3447116761486460.827644161925677
750.1453451883493270.2906903766986540.854654811650673
760.1696943625873130.3393887251746260.830305637412687
770.1496131244597650.2992262489195310.850386875540235
780.1247019344898720.2494038689797440.875298065510128
790.1885320107552180.3770640215104360.811467989244782
800.1722592678926710.3445185357853430.827740732107329
810.1531233247148080.3062466494296170.846876675285192
820.1289417824726140.2578835649452280.871058217527386
830.1061190901002210.2122381802004420.893880909899779
840.09507426229358450.1901485245871690.904925737706415
850.08770976946884950.1754195389376990.912290230531151
860.07486744874107790.1497348974821560.925132551258922
870.07831139027169610.1566227805433920.921688609728304
880.06665566663850230.1333113332770050.933344333361498
890.09670521380104390.1934104276020880.903294786198956
900.07971273559487790.1594254711897560.920287264405122
910.08211669267980350.1642333853596070.917883307320197
920.06822717806082670.1364543561216530.931772821939173
930.05517119695170320.1103423939034060.944828803048297
940.05273109693931320.1054621938786260.947268903060687
950.07768651324202930.1553730264840590.922313486757971
960.08077398428682160.1615479685736430.919226015713178
970.07246633747755280.1449326749551060.927533662522447
980.0587110545319920.1174221090639840.941288945468008
990.04882170722626390.09764341445252780.951178292773736
1000.05136363320775580.1027272664155120.948636366792244
1010.04459105607569510.08918211215139030.955408943924305
1020.03815546335331630.07631092670663260.961844536646684
1030.02997148170219050.05994296340438090.97002851829781
1040.03321170169587570.06642340339175140.966788298304124
1050.04100817783697420.08201635567394840.958991822163026
1060.05585796975559720.1117159395111940.944142030244403
1070.05031522306061290.1006304461212260.949684776939387
1080.05473511119540210.1094702223908040.945264888804598
1090.04728886646834960.09457773293669920.95271113353165
1100.3102852020542760.6205704041085520.689714797945724
1110.3358696868233180.6717393736466350.664130313176682
1120.3151386079843430.6302772159686860.684861392015657
1130.2936868755443810.5873737510887620.706313124455619
1140.2589678025951930.5179356051903860.741032197404807
1150.2419560948892810.4839121897785620.758043905110719
1160.2136028220550170.4272056441100350.786397177944983
1170.1795014144866470.3590028289732930.820498585513353
1180.1474911994759630.2949823989519260.852508800524037
1190.1260196726654060.2520393453308120.873980327334594
1200.1482881655210720.2965763310421440.851711834478928
1210.1203101406306430.2406202812612870.879689859369357
1220.1099784132545190.2199568265090380.890021586745481
1230.115466562169340.230933124338680.88453343783066
1240.09857471486247840.1971494297249570.901425285137522
1250.07904647734974960.1580929546994990.92095352265025
1260.07633040606271540.1526608121254310.923669593937285
1270.07900872420249750.1580174484049950.920991275797503
1280.3514994552375630.7029989104751260.648500544762437
1290.3049629628442120.6099259256884240.695037037155788
1300.2805848559859330.5611697119718660.719415144014067
1310.2396055171145070.4792110342290130.760394482885493
1320.2386793744755420.4773587489510840.761320625524458
1330.4211807730125970.8423615460251940.578819226987403
1340.5195312967247170.9609374065505670.480468703275283
1350.5751031458203980.8497937083592050.424896854179602
1360.5161832192299710.9676335615400580.483816780770029
1370.5346768329489230.9306463341021540.465323167051077
1380.4795031337649620.9590062675299230.520496866235038
1390.4837532331558710.9675064663117430.516246766844129
1400.4248721057945750.8497442115891510.575127894205425
1410.4862435006431920.9724870012863830.513756499356808
1420.4235673326765230.8471346653530470.576432667323477
1430.4514521278915430.9029042557830870.548547872108456
1440.5045610864072040.9908778271855930.495438913592796
1450.5942082502052630.8115834995894740.405791749794737
1460.6137113231571820.7725773536856360.386288676842818
1470.7438683715177530.5122632569644950.256131628482247
1480.6593194641004650.681361071799070.340680535899535
1490.7458680296078140.5082639407843720.254131970392186
1500.740289847836110.5194203043277810.25971015216389
1510.7812276933880970.4375446132238060.218772306611903
1520.9884911303480590.02301773930388250.0115088696519412
1530.9565262266274360.08694754674512780.0434737733725639






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00689655172413793OK
10% type I error level90.0620689655172414OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00689655172413793 & OK \tabularnewline
10% type I error level & 9 & 0.0620689655172414 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198350&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00689655172413793[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0620689655172414[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198350&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198350&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 level00OK
5% type I error level10.00689655172413793OK
10% type I error level90.0620689655172414OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}