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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, 21 Nov 2011 14:39:55 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321904635uimfv9tx7fftrib.htm/, Retrieved Mon, 24 Jun 2024 12:38:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145942, Retrieved Mon, 24 Jun 2024 12:38:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [Tutorial 2-1] [2011-11-21 19:39:55] [5ae3d23a633522d14794d358c652ae9c] [Current]
-   PD      [Multiple Regression] [Tutorial2-1] [2011-11-21 20:30:04] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD        [Multiple Regression] [Statistiek Paper 3.1] [2011-12-15 07:45:19] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD          [Multiple Regression] [Paper statistiek 3.1] [2011-12-15 07:48:26] [9e469a83342941fcd5c6dffbf184cd3a]
-  M          [Multiple Regression] [] [2011-12-17 13:36:03] [4c0148be6b1ebc4ef8d5b4e23a77fcfa]
- R PD        [Multiple Regression] [] [2011-12-19 08:43:54] [d0c153a232569da05656a074c1bdec10]
- R PD        [Multiple Regression] [multiple regressi...] [2011-12-19 21:00:38] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP           [Kendall tau Correlation Matrix] [pearson correlatie] [2011-12-21 13:23:03] [2e8e2c135ae7a1d1ed044e87454acf31]
- R P             [Kendall tau Correlation Matrix] [kendall'tau] [2011-12-21 14:00:53] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP             [Recursive Partitioning (Regression Trees)] [Regression trees] [2011-12-21 14:21:32] [2e8e2c135ae7a1d1ed044e87454acf31]
- R P               [Recursive Partitioning (Regression Trees)] [regression trees ] [2011-12-21 14:30:40] [2e8e2c135ae7a1d1ed044e87454acf31]
- RM            [Multiple Regression] [] [2011-12-21 15:30:16] [2e8e2c135ae7a1d1ed044e87454acf31]
-    D        [Multiple Regression] [] [2011-12-21 17:36:12] [ff74c68cc78961a8924de2f2c00accbc]
-               [Multiple Regression] [] [2011-12-21 17:48:59] [ff74c68cc78961a8924de2f2c00accbc]
-    D        [Multiple Regression] [Multiple regression] [2011-12-22 18:36:40] [c035d973aa8488be257660c2dc4ec375]
- R  D        [Multiple Regression] [Workshop 7 Tutorial] [2012-11-03 14:50:24] [bc2c61a583a6186666a33616ccc196e4]
- R  D        [Multiple Regression] [] [2012-11-05 15:06:35] [8fcd082199f7dbedf65d69a953eb5ad7]
-  M          [Multiple Regression] [] [2012-11-05 16:12:46] [60d1ad8da4696c30bdea6b2c1b52db5e]
-  M          [Multiple Regression] [] [2012-11-05 16:13:54] [60d1ad8da4696c30bdea6b2c1b52db5e]
-   PD        [Multiple Regression] [multiple regression] [2012-12-17 13:56:18] [dbdfdab7c884aa7a69290945f2923e51]
-    D        [Multiple Regression] [] [2012-12-17 16:23:19] [edf0418499cd31d27dbea8ea1d30b3db]
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Dataseries X:
47.38555556	46	26	95556
24.06138889	48	20	54565
31.4825	37	24	63016
42.36388889	75	25	79774
23.94611111	31	15	31258
10.34916667	18	16	52491
85.01527778	79	20	91256
9.097222222	16	18	22807
32.36166667	38	19	77411
36.26083333	24	20	48821
44.96555556	65	30	52295
35.63166667	74	37	63262
28.43055556	43	23	50466
53.61777778	42	36	62932
39.32611111	55	29	38439
70.43305556	121	35	70817
50.30833333	42	24	105965
55.12	102	22	73795
31.62583333	36	19	82043
44.42777778	50	30	74349
46.33944444	48	27	82204
79.63194444	56	26	55709
25.46027778	19	15	37137
30.07722222	32	30	70780
40.65055556	77	28	55027
40.31722222	90	24	56699
44.92777778	81	21	65911
44.69583333	55	27	56316
29.69111111	34	21	26982
52.26388889	38	30	54628
52.61138889	53	30	96750
35.96777778	48	33	53009
56.675	63	30	64664
17.42527778	25	20	36990
67.67361111	56	27	85224
46.45972222	37	25	37048
73.48	83	30	59635
33.89555556	50	20	42051
22.49	26	8	26998
58.27638889	108	24	63717
62.27916667	55	25	55071
32.21416667	41	25	40001
38.38638889	49	21	54506
22.52944444	31	21	35838
25.86805556	49	21	50838
84.93222222	96	26	86997
21.88888889	42	26	33032
44.12083333	55	30	61704
61.59583333	70	34	117986
36.41888889	39	30	56733
35.75944444	53	18	55064
6.718888889	24	4	5950
71.57277778	209	31	84607
18.06361111	17	18	32551
27.24055556	58	14	31701
48.21861111	27	20	71170
50.01166667	58	36	101773
54.79611111	114	24	101653
58.90555556	75	26	81493
39.32833333	51	22	55901
68.08527778	86	31	109104
57.46638889	77	21	114425
40.47111111	62	31	36311
47.39861111	60	26	70027
39.46222222	39	24	73713
31.89444444	35	15	40671
31.51694444	86	19	89041
40.35694444	102	28	57231
41.94416667	49	24	68608
25.50333333	35	18	59155
33.00194444	33	25	55827
19.2975	28	20	22618
35.175	44	25	58425
40.53	37	24	65724
27.33138889	33	23	56979
53.035	45	25	72369
55.22138889	57	20	79194
29.49805556	58	23	202316
24.81055556	36	22	44970
33.43388889	42	25	49319
27.44194444	30	18	36252
76.37583333	67	30	75741
36.88833333	53	22	38417
37.56972222	59	25	64102
22.48694444	25	8	56622
30.34361111	39	21	15430
26.84277778	36	22	72571
62.83083333	114	24	67271
47.57944444	54	30	43460
32.72638889	70	27	99501
37.10027778	51	24	28340
42.27583333	49	25	76013
31.11222222	42	21	37361
47.11472222	51	24	48204
52.07861111	51	24	76168
36.25916667	27	20	85168
39.53861111	29	20	125410
52.71222222	54	24	123328
56.00083333	92	40	83038
68.565	72	22	120087
43.31861111	63	31	91939
50.71694444	41	26	103646
29.54194444	111	20	29467
12.02416667	14	19	43750
35.41472222	45	15	34497
35.53611111	91	21	66477
41.39055556	29	22	71181
52.12583333	64	24	74482
20.58666667	32	19	174949
26.11277778	65	24	46765
49.0625	42	23	90257
39.42583333	55	27	51370
6.371666667	10	1	1168
34.97972222	53	24	51360
17.1825	25	11	25162
25.35833333	33	27	21067
70.86111111	66	22	58233
5.848333333	16	0	855
46.97027778	35	17	85903
8.726111111	19	8	14116
52.41694444	76	24	57637
38.20666667	35	31	94137
21.435	46	24	62147
20.71305556	29	20	62832
10.615	34	8	8773
25.26694444	25	22	63785
53.95111111	48	33	65196
37.5725	38	33	73087
67.85333333	50	31	72631
56.04111111	65	33	86281
71.22277778	72	35	162365
38.65111111	23	21	56530
21.24166667	29	20	35606
52.63944444	194	24	70111
77.87055556	114	29	92046
14.16638889	15	20	63989
70.35388889	86	27	104911
28.6775	50	24	43448
46.68305556	33	26	60029
35.76888889	50	26	38650
21.04055556	72	12	47261
69.23111111	81	21	73586
42.32388889	54	24	83042
48.12777778	63	21	37238
54.77694444	69	30	63958
18.75194444	39	32	78956
38.72472222	49	24	99518
51.49055556	67	29	111436
0	0	0	0
4.08	10	0	6023
0.027222222	1	0	0
0.126388889	2	0	0
0	0	0	0
0	0	0	0
38.30138889	58	20	42564
51.46888889	72	27	38885
0	0	0	0
0.056388889	4	0	0
1.999722222	5	0	1644
12.96111111	20	5	6179
4.874166667	5	1	3926
20.43527778	27	23	23238
0.269166667	2	0	0
29.29916667	33	16	49288




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
otaal#peer_reviews[t] = + 8.11506537560171 + 0.223303383989131AantalurenRFC[t] + 0.0267960415866275`#logins`[t] + 6.6247085818747e-05`totaal#karakterscompendium`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
otaal#peer_reviews[t] =  +  8.11506537560171 +  0.223303383989131AantalurenRFC[t] +  0.0267960415866275`#logins`[t] +  6.6247085818747e-05`totaal#karakterscompendium`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]otaal#peer_reviews[t] =  +  8.11506537560171 +  0.223303383989131AantalurenRFC[t] +  0.0267960415866275`#logins`[t] +  6.6247085818747e-05`totaal#karakterscompendium`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
otaal#peer_reviews[t] = + 8.11506537560171 + 0.223303383989131AantalurenRFC[t] + 0.0267960415866275`#logins`[t] + 6.6247085818747e-05`totaal#karakterscompendium`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.115065375601711.0617567.643100
AantalurenRFC0.2233033839891310.0377125.921300
`#logins`0.02679604158662750.0206671.29660.1966420.098321
`totaal#karakterscompendium`6.6247085818747e-051.7e-053.80080.0002050.000102

\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) & 8.11506537560171 & 1.061756 & 7.6431 & 0 & 0 \tabularnewline
AantalurenRFC & 0.223303383989131 & 0.037712 & 5.9213 & 0 & 0 \tabularnewline
`#logins` & 0.0267960415866275 & 0.020667 & 1.2966 & 0.196642 & 0.098321 \tabularnewline
`totaal#karakterscompendium` & 6.6247085818747e-05 & 1.7e-05 & 3.8008 & 0.000205 & 0.000102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&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]8.11506537560171[/C][C]1.061756[/C][C]7.6431[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]AantalurenRFC[/C][C]0.223303383989131[/C][C]0.037712[/C][C]5.9213[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`#logins`[/C][C]0.0267960415866275[/C][C]0.020667[/C][C]1.2966[/C][C]0.196642[/C][C]0.098321[/C][/ROW]
[ROW][C]`totaal#karakterscompendium`[/C][C]6.6247085818747e-05[/C][C]1.7e-05[/C][C]3.8008[/C][C]0.000205[/C][C]0.000102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.115065375601711.0617567.643100
AantalurenRFC0.2233033839891310.0377125.921300
`#logins`0.02679604158662750.0206671.29660.1966420.098321
`totaal#karakterscompendium`6.6247085818747e-051.7e-053.80080.0002050.000102







Multiple Linear Regression - Regression Statistics
Multiple R0.749890420984178
R-squared0.562335643483828
Adjusted R-squared0.55412943679915
F-TEST (value)68.525649559103
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.81872189370463
Sum Squared Residuals5417.20391620442

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.749890420984178 \tabularnewline
R-squared & 0.562335643483828 \tabularnewline
Adjusted R-squared & 0.55412943679915 \tabularnewline
F-TEST (value) & 68.525649559103 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.81872189370463 \tabularnewline
Sum Squared Residuals & 5417.20391620442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.749890420984178[/C][/ROW]
[ROW][C]R-squared[/C][C]0.562335643483828[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.55412943679915[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]68.525649559103[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.81872189370463[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5417.20391620442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.749890420984178
R-squared0.562335643483828
Adjusted R-squared0.55412943679915
F-TEST (value)68.525649559103
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.81872189370463
Sum Squared Residuals5417.20391620442







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12626.2593447298357-0.259344729835695
22018.38903717207521.61096282792478
32420.31129406069893.68870593930111
42524.869563266780.130436733219978
51516.3637417175523-1.36374171755227
61614.38577384475141.61422615524863
72035.2615959434708-15.2615959434708
81812.08613983432965.91386016567037
91921.4880377951478-2.48803779514783
102020.0895861392927-0.0895861392926681
113023.36216015112316.63783984887686
123722.245567340661414.7544326593386
132318.95915986199464.04084013800543
143625.382591947236710.6174080527633
152920.91697308464868.08302691535142
163531.77674593525233.22325406474771
172427.4943926464658-3.49439264646575
182228.045447840913-6.04544784091303
191921.576988138613-2.57698813861299
203024.30114515986225.69885484013779
212725.19480557003241.80519442996759
222631.0882852754159-5.08828527541586
231516.7697743773757-1.7697743773757
243020.37785294334389.62214705665623
252822.90114558670645.09885441329362
262423.28582479200310.714175207996914
272124.6844812309036-3.68448123090364
282723.30034938063793.69965061936205
292117.44373524436873.55626475563125
303024.4229640095695.57703599043101
313027.69296230816192.30703769183812
323320.944693636768812.0553063632312
333026.74273684052673.25726315947333
342015.12656961492754.87343038507255
352730.3732317138972-3.37323171389717
362521.93545414064093.06454585935913
373030.6981144456141-0.698114445614086
382019.80961591943680.190384080563198
39815.6223943857041-7.6223943857041
402428.2434182798742-4.24341827987416
412527.1442895954245-2.14428959542451
422519.057185190295.94281480971001
432121.6107456112428-0.610745611242845
442116.35080690920654.64919309079348
452118.57236510596682.42763489403324
462635.4164357243343-9.41643572433431
472616.3166358219049.683364178096
483023.52888923323566.47111076676439
493431.56157497629582.43842502370421
503021.05096804749638.9490319525037
511821.1682900662397-3.16829006623975
52410.652691159863-6.65269115986298
533135.3028487368496-4.30284873684964
541814.76067246098713.23932753901294
551417.8522428934581-3.85224289345813
562024.3207426282798-4.32074262827976
573627.57917485900498.42082514099515
582430.1401861715177-6.14018617151772
592628.6772521515337-2.67725215153367
602221.9670917601150.0329082398850018
613132.8510199313342-1.85101993133421
622130.5911224773545-9.59112247735453
633121.21925395180029.78074604819976
642624.94618280667661.05381719332345
652422.85543019589451.14456980410546
661518.8693994323732-3.86939943237324
671923.3560720668881-4.35607206688814
682823.65149084684374.34850915315625
692423.33942583321420.660574166785848
701818.6667538283334-0.666753828333429
712520.06715667963694.93284332036312
722014.67292817960595.32707182039406
732521.01927372619133.98072627380872
742422.51102853573771.48897146426229
752318.87721907908674.12278092091327
762525.9580175704804-0.958017570480386
772027.2199344680861-7.2199344680861
782329.6590968297791-6.6590968297791
792217.59913533718774.40086466281231
802519.97363967678845.02636032321156
811817.44841503499550.551584965004545
823031.9830027264823-1.9830027264823
832220.31755953789981.6824404621002
842522.33204863162372.66795136837632
85817.557419697524-9.55741969752405
862116.95813457497664.04186542502337
872219.88142325161482.11857674838517
882429.6566595280362-5.65665952803623
893023.06580092273726.93419907726284
902723.89035295759813.10964704240194
912419.64372348383374.35627651616625
922523.90404779123631.09595220876366
932118.66302100066212.33697899933791
942423.19591492876050.804085071239527
952426.156901623479-2.15690162347896
962022.5774849214886-2.57748492148859
972026.0293032732362-6.0293032732362
982429.5029898204471-5.50298982044706
994028.586502302588811.4134976974112
1002233.3105906877695-11.3105906877695
1013125.56709926922125.43290073077883
1022627.405213856464-1.40521385646401
1032019.63834503260930.36165496739074
1041914.0735570694454.92644293055501
1051519.5144402812503-4.51444028125031
1062122.8927465486344-1.89274654863439
1072222.8503355190163-0.850335519016259
1082426.4041024589402-2.40410245894019
1091925.159472455745-6.159472455745
1102418.78592469067645.21407530932363
1112326.1755846239494-3.17558462394943
1122721.79588246055575.20411753944429
11319.88321711609612-8.88321711609612
1142420.74879625006963.25120374993041
1151114.2887859840319-3.28878598403194
1162716.057563749817310.9424362501827
1172229.564896572895-7.564896572895
11809.9063959233181-9.9063959233181
1191725.232372219405-8.23237221940496
120811.5079041693165-3.50790416931651
1212425.6747288933428-1.67472889334276
1223123.82090670720887.17909329279122
1232418.25124896677135.74875103322875
1242017.67988287708032.32011712291971
125811.9776818944795-3.97768189447953
1262218.65273098073353.34726901926648
1273325.76778605963547.23221394036456
1283322.365182112059910.6348178879401
1293129.4183384925661.58166150743404
1303328.08684264363374.91315735636633
1313536.7048757541796-1.7048757541796
1322121.1072459992308-0.107245999230789
1332015.99428036825634.00571963174366
1342429.7127129520054-5.71271295200544
1352934.6563519474112-5.65635194741124
1362015.91949335189994.08050664810006
1372733.0798344383145-6.07983443831453
1382418.73695363393435.26304636606571
1392623.40056534407462.59943465592542
1402620.00263125249595.99736874750413
1411217.873711150278-5.87371115027801
1422130.6199441893673-9.61994418936735
1432424.5144097345569-0.514409734556948
1442123.0172206194286-2.01722061942863
1453026.4329004179113.56709958208898
1463218.578088555213313.4219114447867
1472424.6682104156216-0.668210415621594
1482928.7907257172320.209274282768019
14908.1150653756017-8.1150653756017
15009.69310979602995-9.69310979602995
15108.14794023148063-8.14794023148063
15208.19688052538729-8.19688052538729
15308.1150653756017-8.1150653756017
15408.1150653756017-8.1150653756017
1552021.0418064990359-1.04180649903594
1562724.11357536119842.88642463880157
15708.1150653756017-8.1150653756017
15808.2348413716813-8.2348413716813
15908.80450053183172-8.80450053183172
160511.9545869217304-6.95458692173041
16119.59754955332736-8.59754955332736
1622314.94127495972868.05872504027142
16308.22876328637314-8.22876328637314
1641618.8071241792674-2.80712417926736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 26.2593447298357 & -0.259344729835695 \tabularnewline
2 & 20 & 18.3890371720752 & 1.61096282792478 \tabularnewline
3 & 24 & 20.3112940606989 & 3.68870593930111 \tabularnewline
4 & 25 & 24.86956326678 & 0.130436733219978 \tabularnewline
5 & 15 & 16.3637417175523 & -1.36374171755227 \tabularnewline
6 & 16 & 14.3857738447514 & 1.61422615524863 \tabularnewline
7 & 20 & 35.2615959434708 & -15.2615959434708 \tabularnewline
8 & 18 & 12.0861398343296 & 5.91386016567037 \tabularnewline
9 & 19 & 21.4880377951478 & -2.48803779514783 \tabularnewline
10 & 20 & 20.0895861392927 & -0.0895861392926681 \tabularnewline
11 & 30 & 23.3621601511231 & 6.63783984887686 \tabularnewline
12 & 37 & 22.2455673406614 & 14.7544326593386 \tabularnewline
13 & 23 & 18.9591598619946 & 4.04084013800543 \tabularnewline
14 & 36 & 25.3825919472367 & 10.6174080527633 \tabularnewline
15 & 29 & 20.9169730846486 & 8.08302691535142 \tabularnewline
16 & 35 & 31.7767459352523 & 3.22325406474771 \tabularnewline
17 & 24 & 27.4943926464658 & -3.49439264646575 \tabularnewline
18 & 22 & 28.045447840913 & -6.04544784091303 \tabularnewline
19 & 19 & 21.576988138613 & -2.57698813861299 \tabularnewline
20 & 30 & 24.3011451598622 & 5.69885484013779 \tabularnewline
21 & 27 & 25.1948055700324 & 1.80519442996759 \tabularnewline
22 & 26 & 31.0882852754159 & -5.08828527541586 \tabularnewline
23 & 15 & 16.7697743773757 & -1.7697743773757 \tabularnewline
24 & 30 & 20.3778529433438 & 9.62214705665623 \tabularnewline
25 & 28 & 22.9011455867064 & 5.09885441329362 \tabularnewline
26 & 24 & 23.2858247920031 & 0.714175207996914 \tabularnewline
27 & 21 & 24.6844812309036 & -3.68448123090364 \tabularnewline
28 & 27 & 23.3003493806379 & 3.69965061936205 \tabularnewline
29 & 21 & 17.4437352443687 & 3.55626475563125 \tabularnewline
30 & 30 & 24.422964009569 & 5.57703599043101 \tabularnewline
31 & 30 & 27.6929623081619 & 2.30703769183812 \tabularnewline
32 & 33 & 20.9446936367688 & 12.0553063632312 \tabularnewline
33 & 30 & 26.7427368405267 & 3.25726315947333 \tabularnewline
34 & 20 & 15.1265696149275 & 4.87343038507255 \tabularnewline
35 & 27 & 30.3732317138972 & -3.37323171389717 \tabularnewline
36 & 25 & 21.9354541406409 & 3.06454585935913 \tabularnewline
37 & 30 & 30.6981144456141 & -0.698114445614086 \tabularnewline
38 & 20 & 19.8096159194368 & 0.190384080563198 \tabularnewline
39 & 8 & 15.6223943857041 & -7.6223943857041 \tabularnewline
40 & 24 & 28.2434182798742 & -4.24341827987416 \tabularnewline
41 & 25 & 27.1442895954245 & -2.14428959542451 \tabularnewline
42 & 25 & 19.05718519029 & 5.94281480971001 \tabularnewline
43 & 21 & 21.6107456112428 & -0.610745611242845 \tabularnewline
44 & 21 & 16.3508069092065 & 4.64919309079348 \tabularnewline
45 & 21 & 18.5723651059668 & 2.42763489403324 \tabularnewline
46 & 26 & 35.4164357243343 & -9.41643572433431 \tabularnewline
47 & 26 & 16.316635821904 & 9.683364178096 \tabularnewline
48 & 30 & 23.5288892332356 & 6.47111076676439 \tabularnewline
49 & 34 & 31.5615749762958 & 2.43842502370421 \tabularnewline
50 & 30 & 21.0509680474963 & 8.9490319525037 \tabularnewline
51 & 18 & 21.1682900662397 & -3.16829006623975 \tabularnewline
52 & 4 & 10.652691159863 & -6.65269115986298 \tabularnewline
53 & 31 & 35.3028487368496 & -4.30284873684964 \tabularnewline
54 & 18 & 14.7606724609871 & 3.23932753901294 \tabularnewline
55 & 14 & 17.8522428934581 & -3.85224289345813 \tabularnewline
56 & 20 & 24.3207426282798 & -4.32074262827976 \tabularnewline
57 & 36 & 27.5791748590049 & 8.42082514099515 \tabularnewline
58 & 24 & 30.1401861715177 & -6.14018617151772 \tabularnewline
59 & 26 & 28.6772521515337 & -2.67725215153367 \tabularnewline
60 & 22 & 21.967091760115 & 0.0329082398850018 \tabularnewline
61 & 31 & 32.8510199313342 & -1.85101993133421 \tabularnewline
62 & 21 & 30.5911224773545 & -9.59112247735453 \tabularnewline
63 & 31 & 21.2192539518002 & 9.78074604819976 \tabularnewline
64 & 26 & 24.9461828066766 & 1.05381719332345 \tabularnewline
65 & 24 & 22.8554301958945 & 1.14456980410546 \tabularnewline
66 & 15 & 18.8693994323732 & -3.86939943237324 \tabularnewline
67 & 19 & 23.3560720668881 & -4.35607206688814 \tabularnewline
68 & 28 & 23.6514908468437 & 4.34850915315625 \tabularnewline
69 & 24 & 23.3394258332142 & 0.660574166785848 \tabularnewline
70 & 18 & 18.6667538283334 & -0.666753828333429 \tabularnewline
71 & 25 & 20.0671566796369 & 4.93284332036312 \tabularnewline
72 & 20 & 14.6729281796059 & 5.32707182039406 \tabularnewline
73 & 25 & 21.0192737261913 & 3.98072627380872 \tabularnewline
74 & 24 & 22.5110285357377 & 1.48897146426229 \tabularnewline
75 & 23 & 18.8772190790867 & 4.12278092091327 \tabularnewline
76 & 25 & 25.9580175704804 & -0.958017570480386 \tabularnewline
77 & 20 & 27.2199344680861 & -7.2199344680861 \tabularnewline
78 & 23 & 29.6590968297791 & -6.6590968297791 \tabularnewline
79 & 22 & 17.5991353371877 & 4.40086466281231 \tabularnewline
80 & 25 & 19.9736396767884 & 5.02636032321156 \tabularnewline
81 & 18 & 17.4484150349955 & 0.551584965004545 \tabularnewline
82 & 30 & 31.9830027264823 & -1.9830027264823 \tabularnewline
83 & 22 & 20.3175595378998 & 1.6824404621002 \tabularnewline
84 & 25 & 22.3320486316237 & 2.66795136837632 \tabularnewline
85 & 8 & 17.557419697524 & -9.55741969752405 \tabularnewline
86 & 21 & 16.9581345749766 & 4.04186542502337 \tabularnewline
87 & 22 & 19.8814232516148 & 2.11857674838517 \tabularnewline
88 & 24 & 29.6566595280362 & -5.65665952803623 \tabularnewline
89 & 30 & 23.0658009227372 & 6.93419907726284 \tabularnewline
90 & 27 & 23.8903529575981 & 3.10964704240194 \tabularnewline
91 & 24 & 19.6437234838337 & 4.35627651616625 \tabularnewline
92 & 25 & 23.9040477912363 & 1.09595220876366 \tabularnewline
93 & 21 & 18.6630210006621 & 2.33697899933791 \tabularnewline
94 & 24 & 23.1959149287605 & 0.804085071239527 \tabularnewline
95 & 24 & 26.156901623479 & -2.15690162347896 \tabularnewline
96 & 20 & 22.5774849214886 & -2.57748492148859 \tabularnewline
97 & 20 & 26.0293032732362 & -6.0293032732362 \tabularnewline
98 & 24 & 29.5029898204471 & -5.50298982044706 \tabularnewline
99 & 40 & 28.5865023025888 & 11.4134976974112 \tabularnewline
100 & 22 & 33.3105906877695 & -11.3105906877695 \tabularnewline
101 & 31 & 25.5670992692212 & 5.43290073077883 \tabularnewline
102 & 26 & 27.405213856464 & -1.40521385646401 \tabularnewline
103 & 20 & 19.6383450326093 & 0.36165496739074 \tabularnewline
104 & 19 & 14.073557069445 & 4.92644293055501 \tabularnewline
105 & 15 & 19.5144402812503 & -4.51444028125031 \tabularnewline
106 & 21 & 22.8927465486344 & -1.89274654863439 \tabularnewline
107 & 22 & 22.8503355190163 & -0.850335519016259 \tabularnewline
108 & 24 & 26.4041024589402 & -2.40410245894019 \tabularnewline
109 & 19 & 25.159472455745 & -6.159472455745 \tabularnewline
110 & 24 & 18.7859246906764 & 5.21407530932363 \tabularnewline
111 & 23 & 26.1755846239494 & -3.17558462394943 \tabularnewline
112 & 27 & 21.7958824605557 & 5.20411753944429 \tabularnewline
113 & 1 & 9.88321711609612 & -8.88321711609612 \tabularnewline
114 & 24 & 20.7487962500696 & 3.25120374993041 \tabularnewline
115 & 11 & 14.2887859840319 & -3.28878598403194 \tabularnewline
116 & 27 & 16.0575637498173 & 10.9424362501827 \tabularnewline
117 & 22 & 29.564896572895 & -7.564896572895 \tabularnewline
118 & 0 & 9.9063959233181 & -9.9063959233181 \tabularnewline
119 & 17 & 25.232372219405 & -8.23237221940496 \tabularnewline
120 & 8 & 11.5079041693165 & -3.50790416931651 \tabularnewline
121 & 24 & 25.6747288933428 & -1.67472889334276 \tabularnewline
122 & 31 & 23.8209067072088 & 7.17909329279122 \tabularnewline
123 & 24 & 18.2512489667713 & 5.74875103322875 \tabularnewline
124 & 20 & 17.6798828770803 & 2.32011712291971 \tabularnewline
125 & 8 & 11.9776818944795 & -3.97768189447953 \tabularnewline
126 & 22 & 18.6527309807335 & 3.34726901926648 \tabularnewline
127 & 33 & 25.7677860596354 & 7.23221394036456 \tabularnewline
128 & 33 & 22.3651821120599 & 10.6348178879401 \tabularnewline
129 & 31 & 29.418338492566 & 1.58166150743404 \tabularnewline
130 & 33 & 28.0868426436337 & 4.91315735636633 \tabularnewline
131 & 35 & 36.7048757541796 & -1.7048757541796 \tabularnewline
132 & 21 & 21.1072459992308 & -0.107245999230789 \tabularnewline
133 & 20 & 15.9942803682563 & 4.00571963174366 \tabularnewline
134 & 24 & 29.7127129520054 & -5.71271295200544 \tabularnewline
135 & 29 & 34.6563519474112 & -5.65635194741124 \tabularnewline
136 & 20 & 15.9194933518999 & 4.08050664810006 \tabularnewline
137 & 27 & 33.0798344383145 & -6.07983443831453 \tabularnewline
138 & 24 & 18.7369536339343 & 5.26304636606571 \tabularnewline
139 & 26 & 23.4005653440746 & 2.59943465592542 \tabularnewline
140 & 26 & 20.0026312524959 & 5.99736874750413 \tabularnewline
141 & 12 & 17.873711150278 & -5.87371115027801 \tabularnewline
142 & 21 & 30.6199441893673 & -9.61994418936735 \tabularnewline
143 & 24 & 24.5144097345569 & -0.514409734556948 \tabularnewline
144 & 21 & 23.0172206194286 & -2.01722061942863 \tabularnewline
145 & 30 & 26.432900417911 & 3.56709958208898 \tabularnewline
146 & 32 & 18.5780885552133 & 13.4219114447867 \tabularnewline
147 & 24 & 24.6682104156216 & -0.668210415621594 \tabularnewline
148 & 29 & 28.790725717232 & 0.209274282768019 \tabularnewline
149 & 0 & 8.1150653756017 & -8.1150653756017 \tabularnewline
150 & 0 & 9.69310979602995 & -9.69310979602995 \tabularnewline
151 & 0 & 8.14794023148063 & -8.14794023148063 \tabularnewline
152 & 0 & 8.19688052538729 & -8.19688052538729 \tabularnewline
153 & 0 & 8.1150653756017 & -8.1150653756017 \tabularnewline
154 & 0 & 8.1150653756017 & -8.1150653756017 \tabularnewline
155 & 20 & 21.0418064990359 & -1.04180649903594 \tabularnewline
156 & 27 & 24.1135753611984 & 2.88642463880157 \tabularnewline
157 & 0 & 8.1150653756017 & -8.1150653756017 \tabularnewline
158 & 0 & 8.2348413716813 & -8.2348413716813 \tabularnewline
159 & 0 & 8.80450053183172 & -8.80450053183172 \tabularnewline
160 & 5 & 11.9545869217304 & -6.95458692173041 \tabularnewline
161 & 1 & 9.59754955332736 & -8.59754955332736 \tabularnewline
162 & 23 & 14.9412749597286 & 8.05872504027142 \tabularnewline
163 & 0 & 8.22876328637314 & -8.22876328637314 \tabularnewline
164 & 16 & 18.8071241792674 & -2.80712417926736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&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]26[/C][C]26.2593447298357[/C][C]-0.259344729835695[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]18.3890371720752[/C][C]1.61096282792478[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]20.3112940606989[/C][C]3.68870593930111[/C][/ROW]
[ROW][C]4[/C][C]25[/C][C]24.86956326678[/C][C]0.130436733219978[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]16.3637417175523[/C][C]-1.36374171755227[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]14.3857738447514[/C][C]1.61422615524863[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]35.2615959434708[/C][C]-15.2615959434708[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]12.0861398343296[/C][C]5.91386016567037[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.4880377951478[/C][C]-2.48803779514783[/C][/ROW]
[ROW][C]10[/C][C]20[/C][C]20.0895861392927[/C][C]-0.0895861392926681[/C][/ROW]
[ROW][C]11[/C][C]30[/C][C]23.3621601511231[/C][C]6.63783984887686[/C][/ROW]
[ROW][C]12[/C][C]37[/C][C]22.2455673406614[/C][C]14.7544326593386[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]18.9591598619946[/C][C]4.04084013800543[/C][/ROW]
[ROW][C]14[/C][C]36[/C][C]25.3825919472367[/C][C]10.6174080527633[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]20.9169730846486[/C][C]8.08302691535142[/C][/ROW]
[ROW][C]16[/C][C]35[/C][C]31.7767459352523[/C][C]3.22325406474771[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]27.4943926464658[/C][C]-3.49439264646575[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]28.045447840913[/C][C]-6.04544784091303[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]21.576988138613[/C][C]-2.57698813861299[/C][/ROW]
[ROW][C]20[/C][C]30[/C][C]24.3011451598622[/C][C]5.69885484013779[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.1948055700324[/C][C]1.80519442996759[/C][/ROW]
[ROW][C]22[/C][C]26[/C][C]31.0882852754159[/C][C]-5.08828527541586[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]16.7697743773757[/C][C]-1.7697743773757[/C][/ROW]
[ROW][C]24[/C][C]30[/C][C]20.3778529433438[/C][C]9.62214705665623[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]22.9011455867064[/C][C]5.09885441329362[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]23.2858247920031[/C][C]0.714175207996914[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]24.6844812309036[/C][C]-3.68448123090364[/C][/ROW]
[ROW][C]28[/C][C]27[/C][C]23.3003493806379[/C][C]3.69965061936205[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]17.4437352443687[/C][C]3.55626475563125[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]24.422964009569[/C][C]5.57703599043101[/C][/ROW]
[ROW][C]31[/C][C]30[/C][C]27.6929623081619[/C][C]2.30703769183812[/C][/ROW]
[ROW][C]32[/C][C]33[/C][C]20.9446936367688[/C][C]12.0553063632312[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]26.7427368405267[/C][C]3.25726315947333[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]15.1265696149275[/C][C]4.87343038507255[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]30.3732317138972[/C][C]-3.37323171389717[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]21.9354541406409[/C][C]3.06454585935913[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]30.6981144456141[/C][C]-0.698114445614086[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]19.8096159194368[/C][C]0.190384080563198[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]15.6223943857041[/C][C]-7.6223943857041[/C][/ROW]
[ROW][C]40[/C][C]24[/C][C]28.2434182798742[/C][C]-4.24341827987416[/C][/ROW]
[ROW][C]41[/C][C]25[/C][C]27.1442895954245[/C][C]-2.14428959542451[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]19.05718519029[/C][C]5.94281480971001[/C][/ROW]
[ROW][C]43[/C][C]21[/C][C]21.6107456112428[/C][C]-0.610745611242845[/C][/ROW]
[ROW][C]44[/C][C]21[/C][C]16.3508069092065[/C][C]4.64919309079348[/C][/ROW]
[ROW][C]45[/C][C]21[/C][C]18.5723651059668[/C][C]2.42763489403324[/C][/ROW]
[ROW][C]46[/C][C]26[/C][C]35.4164357243343[/C][C]-9.41643572433431[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]16.316635821904[/C][C]9.683364178096[/C][/ROW]
[ROW][C]48[/C][C]30[/C][C]23.5288892332356[/C][C]6.47111076676439[/C][/ROW]
[ROW][C]49[/C][C]34[/C][C]31.5615749762958[/C][C]2.43842502370421[/C][/ROW]
[ROW][C]50[/C][C]30[/C][C]21.0509680474963[/C][C]8.9490319525037[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]21.1682900662397[/C][C]-3.16829006623975[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]10.652691159863[/C][C]-6.65269115986298[/C][/ROW]
[ROW][C]53[/C][C]31[/C][C]35.3028487368496[/C][C]-4.30284873684964[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]14.7606724609871[/C][C]3.23932753901294[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]17.8522428934581[/C][C]-3.85224289345813[/C][/ROW]
[ROW][C]56[/C][C]20[/C][C]24.3207426282798[/C][C]-4.32074262827976[/C][/ROW]
[ROW][C]57[/C][C]36[/C][C]27.5791748590049[/C][C]8.42082514099515[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]30.1401861715177[/C][C]-6.14018617151772[/C][/ROW]
[ROW][C]59[/C][C]26[/C][C]28.6772521515337[/C][C]-2.67725215153367[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]21.967091760115[/C][C]0.0329082398850018[/C][/ROW]
[ROW][C]61[/C][C]31[/C][C]32.8510199313342[/C][C]-1.85101993133421[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]30.5911224773545[/C][C]-9.59112247735453[/C][/ROW]
[ROW][C]63[/C][C]31[/C][C]21.2192539518002[/C][C]9.78074604819976[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]24.9461828066766[/C][C]1.05381719332345[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]22.8554301958945[/C][C]1.14456980410546[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]18.8693994323732[/C][C]-3.86939943237324[/C][/ROW]
[ROW][C]67[/C][C]19[/C][C]23.3560720668881[/C][C]-4.35607206688814[/C][/ROW]
[ROW][C]68[/C][C]28[/C][C]23.6514908468437[/C][C]4.34850915315625[/C][/ROW]
[ROW][C]69[/C][C]24[/C][C]23.3394258332142[/C][C]0.660574166785848[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]18.6667538283334[/C][C]-0.666753828333429[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]20.0671566796369[/C][C]4.93284332036312[/C][/ROW]
[ROW][C]72[/C][C]20[/C][C]14.6729281796059[/C][C]5.32707182039406[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]21.0192737261913[/C][C]3.98072627380872[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.5110285357377[/C][C]1.48897146426229[/C][/ROW]
[ROW][C]75[/C][C]23[/C][C]18.8772190790867[/C][C]4.12278092091327[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]25.9580175704804[/C][C]-0.958017570480386[/C][/ROW]
[ROW][C]77[/C][C]20[/C][C]27.2199344680861[/C][C]-7.2199344680861[/C][/ROW]
[ROW][C]78[/C][C]23[/C][C]29.6590968297791[/C][C]-6.6590968297791[/C][/ROW]
[ROW][C]79[/C][C]22[/C][C]17.5991353371877[/C][C]4.40086466281231[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]19.9736396767884[/C][C]5.02636032321156[/C][/ROW]
[ROW][C]81[/C][C]18[/C][C]17.4484150349955[/C][C]0.551584965004545[/C][/ROW]
[ROW][C]82[/C][C]30[/C][C]31.9830027264823[/C][C]-1.9830027264823[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]20.3175595378998[/C][C]1.6824404621002[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]22.3320486316237[/C][C]2.66795136837632[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]17.557419697524[/C][C]-9.55741969752405[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]16.9581345749766[/C][C]4.04186542502337[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]19.8814232516148[/C][C]2.11857674838517[/C][/ROW]
[ROW][C]88[/C][C]24[/C][C]29.6566595280362[/C][C]-5.65665952803623[/C][/ROW]
[ROW][C]89[/C][C]30[/C][C]23.0658009227372[/C][C]6.93419907726284[/C][/ROW]
[ROW][C]90[/C][C]27[/C][C]23.8903529575981[/C][C]3.10964704240194[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]19.6437234838337[/C][C]4.35627651616625[/C][/ROW]
[ROW][C]92[/C][C]25[/C][C]23.9040477912363[/C][C]1.09595220876366[/C][/ROW]
[ROW][C]93[/C][C]21[/C][C]18.6630210006621[/C][C]2.33697899933791[/C][/ROW]
[ROW][C]94[/C][C]24[/C][C]23.1959149287605[/C][C]0.804085071239527[/C][/ROW]
[ROW][C]95[/C][C]24[/C][C]26.156901623479[/C][C]-2.15690162347896[/C][/ROW]
[ROW][C]96[/C][C]20[/C][C]22.5774849214886[/C][C]-2.57748492148859[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]26.0293032732362[/C][C]-6.0293032732362[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]29.5029898204471[/C][C]-5.50298982044706[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]28.5865023025888[/C][C]11.4134976974112[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]33.3105906877695[/C][C]-11.3105906877695[/C][/ROW]
[ROW][C]101[/C][C]31[/C][C]25.5670992692212[/C][C]5.43290073077883[/C][/ROW]
[ROW][C]102[/C][C]26[/C][C]27.405213856464[/C][C]-1.40521385646401[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]19.6383450326093[/C][C]0.36165496739074[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]14.073557069445[/C][C]4.92644293055501[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]19.5144402812503[/C][C]-4.51444028125031[/C][/ROW]
[ROW][C]106[/C][C]21[/C][C]22.8927465486344[/C][C]-1.89274654863439[/C][/ROW]
[ROW][C]107[/C][C]22[/C][C]22.8503355190163[/C][C]-0.850335519016259[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]26.4041024589402[/C][C]-2.40410245894019[/C][/ROW]
[ROW][C]109[/C][C]19[/C][C]25.159472455745[/C][C]-6.159472455745[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]18.7859246906764[/C][C]5.21407530932363[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]26.1755846239494[/C][C]-3.17558462394943[/C][/ROW]
[ROW][C]112[/C][C]27[/C][C]21.7958824605557[/C][C]5.20411753944429[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]9.88321711609612[/C][C]-8.88321711609612[/C][/ROW]
[ROW][C]114[/C][C]24[/C][C]20.7487962500696[/C][C]3.25120374993041[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]14.2887859840319[/C][C]-3.28878598403194[/C][/ROW]
[ROW][C]116[/C][C]27[/C][C]16.0575637498173[/C][C]10.9424362501827[/C][/ROW]
[ROW][C]117[/C][C]22[/C][C]29.564896572895[/C][C]-7.564896572895[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]9.9063959233181[/C][C]-9.9063959233181[/C][/ROW]
[ROW][C]119[/C][C]17[/C][C]25.232372219405[/C][C]-8.23237221940496[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]11.5079041693165[/C][C]-3.50790416931651[/C][/ROW]
[ROW][C]121[/C][C]24[/C][C]25.6747288933428[/C][C]-1.67472889334276[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]23.8209067072088[/C][C]7.17909329279122[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]18.2512489667713[/C][C]5.74875103322875[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]17.6798828770803[/C][C]2.32011712291971[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]11.9776818944795[/C][C]-3.97768189447953[/C][/ROW]
[ROW][C]126[/C][C]22[/C][C]18.6527309807335[/C][C]3.34726901926648[/C][/ROW]
[ROW][C]127[/C][C]33[/C][C]25.7677860596354[/C][C]7.23221394036456[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]22.3651821120599[/C][C]10.6348178879401[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]29.418338492566[/C][C]1.58166150743404[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]28.0868426436337[/C][C]4.91315735636633[/C][/ROW]
[ROW][C]131[/C][C]35[/C][C]36.7048757541796[/C][C]-1.7048757541796[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]21.1072459992308[/C][C]-0.107245999230789[/C][/ROW]
[ROW][C]133[/C][C]20[/C][C]15.9942803682563[/C][C]4.00571963174366[/C][/ROW]
[ROW][C]134[/C][C]24[/C][C]29.7127129520054[/C][C]-5.71271295200544[/C][/ROW]
[ROW][C]135[/C][C]29[/C][C]34.6563519474112[/C][C]-5.65635194741124[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]15.9194933518999[/C][C]4.08050664810006[/C][/ROW]
[ROW][C]137[/C][C]27[/C][C]33.0798344383145[/C][C]-6.07983443831453[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]18.7369536339343[/C][C]5.26304636606571[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]23.4005653440746[/C][C]2.59943465592542[/C][/ROW]
[ROW][C]140[/C][C]26[/C][C]20.0026312524959[/C][C]5.99736874750413[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]17.873711150278[/C][C]-5.87371115027801[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]30.6199441893673[/C][C]-9.61994418936735[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.5144097345569[/C][C]-0.514409734556948[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]23.0172206194286[/C][C]-2.01722061942863[/C][/ROW]
[ROW][C]145[/C][C]30[/C][C]26.432900417911[/C][C]3.56709958208898[/C][/ROW]
[ROW][C]146[/C][C]32[/C][C]18.5780885552133[/C][C]13.4219114447867[/C][/ROW]
[ROW][C]147[/C][C]24[/C][C]24.6682104156216[/C][C]-0.668210415621594[/C][/ROW]
[ROW][C]148[/C][C]29[/C][C]28.790725717232[/C][C]0.209274282768019[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]8.1150653756017[/C][C]-8.1150653756017[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]9.69310979602995[/C][C]-9.69310979602995[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]8.14794023148063[/C][C]-8.14794023148063[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]8.19688052538729[/C][C]-8.19688052538729[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]8.1150653756017[/C][C]-8.1150653756017[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]8.1150653756017[/C][C]-8.1150653756017[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]21.0418064990359[/C][C]-1.04180649903594[/C][/ROW]
[ROW][C]156[/C][C]27[/C][C]24.1135753611984[/C][C]2.88642463880157[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]8.1150653756017[/C][C]-8.1150653756017[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]8.2348413716813[/C][C]-8.2348413716813[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]8.80450053183172[/C][C]-8.80450053183172[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]11.9545869217304[/C][C]-6.95458692173041[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]9.59754955332736[/C][C]-8.59754955332736[/C][/ROW]
[ROW][C]162[/C][C]23[/C][C]14.9412749597286[/C][C]8.05872504027142[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]8.22876328637314[/C][C]-8.22876328637314[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]18.8071241792674[/C][C]-2.80712417926736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12626.2593447298357-0.259344729835695
22018.38903717207521.61096282792478
32420.31129406069893.68870593930111
42524.869563266780.130436733219978
51516.3637417175523-1.36374171755227
61614.38577384475141.61422615524863
72035.2615959434708-15.2615959434708
81812.08613983432965.91386016567037
91921.4880377951478-2.48803779514783
102020.0895861392927-0.0895861392926681
113023.36216015112316.63783984887686
123722.245567340661414.7544326593386
132318.95915986199464.04084013800543
143625.382591947236710.6174080527633
152920.91697308464868.08302691535142
163531.77674593525233.22325406474771
172427.4943926464658-3.49439264646575
182228.045447840913-6.04544784091303
191921.576988138613-2.57698813861299
203024.30114515986225.69885484013779
212725.19480557003241.80519442996759
222631.0882852754159-5.08828527541586
231516.7697743773757-1.7697743773757
243020.37785294334389.62214705665623
252822.90114558670645.09885441329362
262423.28582479200310.714175207996914
272124.6844812309036-3.68448123090364
282723.30034938063793.69965061936205
292117.44373524436873.55626475563125
303024.4229640095695.57703599043101
313027.69296230816192.30703769183812
323320.944693636768812.0553063632312
333026.74273684052673.25726315947333
342015.12656961492754.87343038507255
352730.3732317138972-3.37323171389717
362521.93545414064093.06454585935913
373030.6981144456141-0.698114445614086
382019.80961591943680.190384080563198
39815.6223943857041-7.6223943857041
402428.2434182798742-4.24341827987416
412527.1442895954245-2.14428959542451
422519.057185190295.94281480971001
432121.6107456112428-0.610745611242845
442116.35080690920654.64919309079348
452118.57236510596682.42763489403324
462635.4164357243343-9.41643572433431
472616.3166358219049.683364178096
483023.52888923323566.47111076676439
493431.56157497629582.43842502370421
503021.05096804749638.9490319525037
511821.1682900662397-3.16829006623975
52410.652691159863-6.65269115986298
533135.3028487368496-4.30284873684964
541814.76067246098713.23932753901294
551417.8522428934581-3.85224289345813
562024.3207426282798-4.32074262827976
573627.57917485900498.42082514099515
582430.1401861715177-6.14018617151772
592628.6772521515337-2.67725215153367
602221.9670917601150.0329082398850018
613132.8510199313342-1.85101993133421
622130.5911224773545-9.59112247735453
633121.21925395180029.78074604819976
642624.94618280667661.05381719332345
652422.85543019589451.14456980410546
661518.8693994323732-3.86939943237324
671923.3560720668881-4.35607206688814
682823.65149084684374.34850915315625
692423.33942583321420.660574166785848
701818.6667538283334-0.666753828333429
712520.06715667963694.93284332036312
722014.67292817960595.32707182039406
732521.01927372619133.98072627380872
742422.51102853573771.48897146426229
752318.87721907908674.12278092091327
762525.9580175704804-0.958017570480386
772027.2199344680861-7.2199344680861
782329.6590968297791-6.6590968297791
792217.59913533718774.40086466281231
802519.97363967678845.02636032321156
811817.44841503499550.551584965004545
823031.9830027264823-1.9830027264823
832220.31755953789981.6824404621002
842522.33204863162372.66795136837632
85817.557419697524-9.55741969752405
862116.95813457497664.04186542502337
872219.88142325161482.11857674838517
882429.6566595280362-5.65665952803623
893023.06580092273726.93419907726284
902723.89035295759813.10964704240194
912419.64372348383374.35627651616625
922523.90404779123631.09595220876366
932118.66302100066212.33697899933791
942423.19591492876050.804085071239527
952426.156901623479-2.15690162347896
962022.5774849214886-2.57748492148859
972026.0293032732362-6.0293032732362
982429.5029898204471-5.50298982044706
994028.586502302588811.4134976974112
1002233.3105906877695-11.3105906877695
1013125.56709926922125.43290073077883
1022627.405213856464-1.40521385646401
1032019.63834503260930.36165496739074
1041914.0735570694454.92644293055501
1051519.5144402812503-4.51444028125031
1062122.8927465486344-1.89274654863439
1072222.8503355190163-0.850335519016259
1082426.4041024589402-2.40410245894019
1091925.159472455745-6.159472455745
1102418.78592469067645.21407530932363
1112326.1755846239494-3.17558462394943
1122721.79588246055575.20411753944429
11319.88321711609612-8.88321711609612
1142420.74879625006963.25120374993041
1151114.2887859840319-3.28878598403194
1162716.057563749817310.9424362501827
1172229.564896572895-7.564896572895
11809.9063959233181-9.9063959233181
1191725.232372219405-8.23237221940496
120811.5079041693165-3.50790416931651
1212425.6747288933428-1.67472889334276
1223123.82090670720887.17909329279122
1232418.25124896677135.74875103322875
1242017.67988287708032.32011712291971
125811.9776818944795-3.97768189447953
1262218.65273098073353.34726901926648
1273325.76778605963547.23221394036456
1283322.365182112059910.6348178879401
1293129.4183384925661.58166150743404
1303328.08684264363374.91315735636633
1313536.7048757541796-1.7048757541796
1322121.1072459992308-0.107245999230789
1332015.99428036825634.00571963174366
1342429.7127129520054-5.71271295200544
1352934.6563519474112-5.65635194741124
1362015.91949335189994.08050664810006
1372733.0798344383145-6.07983443831453
1382418.73695363393435.26304636606571
1392623.40056534407462.59943465592542
1402620.00263125249595.99736874750413
1411217.873711150278-5.87371115027801
1422130.6199441893673-9.61994418936735
1432424.5144097345569-0.514409734556948
1442123.0172206194286-2.01722061942863
1453026.4329004179113.56709958208898
1463218.578088555213313.4219114447867
1472424.6682104156216-0.668210415621594
1482928.7907257172320.209274282768019
14908.1150653756017-8.1150653756017
15009.69310979602995-9.69310979602995
15108.14794023148063-8.14794023148063
15208.19688052538729-8.19688052538729
15308.1150653756017-8.1150653756017
15408.1150653756017-8.1150653756017
1552021.0418064990359-1.04180649903594
1562724.11357536119842.88642463880157
15708.1150653756017-8.1150653756017
15808.2348413716813-8.2348413716813
15908.80450053183172-8.80450053183172
160511.9545869217304-6.95458692173041
16119.59754955332736-8.59754955332736
1622314.94127495972868.05872504027142
16308.22876328637314-8.22876328637314
1641618.8071241792674-2.80712417926736







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1795067390148940.3590134780297870.820493260985106
80.1308248367592140.2616496735184280.869175163240786
90.0981547871529740.1963095743059480.901845212847026
100.06244460228333920.1248892045666780.937555397716661
110.2001626090136980.4003252180273960.799837390986302
120.3869822315357320.7739644630714650.613017768464268
130.2919936065225910.5839872130451820.708006393477409
140.7848319342420130.4303361315159750.215168065757987
150.7392043485707450.521591302858510.260795651429255
160.676952865772920.6460942684541610.32304713422708
170.6078100199092330.7843799601815340.392189980090767
180.7048411879497450.590317624100510.295158812050255
190.645041247810840.7099175043783190.35495875218916
200.6452570375235110.7094859249529780.354742962476489
210.5906530002895760.8186939994208470.409346999710424
220.535163541940530.929672916118940.46483645805947
230.5173393159610440.9653213680779130.482660684038956
240.5933322326631980.8133355346736050.406667767336802
250.5360518532578130.9278962934843740.463948146742187
260.500873205392770.9982535892144590.499126794607229
270.5012669850553890.9974660298892210.498733014944611
280.4499130139835120.8998260279670240.550086986016488
290.393243835949830.786487671899660.60675616405017
300.393213303164210.786426606328420.60678669683579
310.3600248356593740.7200496713187490.639975164340626
320.4736025611770350.947205122354070.526397438822965
330.4305814271700330.8611628543400660.569418572829967
340.385172831353560.7703456627071190.61482716864644
350.3361865535589330.6723731071178650.663813446441067
360.2886522096628810.5773044193257620.711347790337119
370.2419367344695720.4838734689391450.758063265530428
380.2174354950648930.4348709901297870.782564504935107
390.3758430641249840.7516861282499680.624156935875016
400.3688242610373060.7376485220746120.631175738962694
410.3233106962365320.6466213924730650.676689303763468
420.2988228039040890.5976456078081780.701177196095911
430.2622793510162720.5245587020325440.737720648983728
440.2294858405822750.458971681164550.770514159417725
450.1958718371961410.3917436743922820.804128162803859
460.2185213695308420.4370427390616850.781478630469158
470.2377663476655570.4755326953311150.762233652334443
480.2393994433129970.4787988866259930.760600556687004
490.2224649678869370.4449299357738750.777535032113063
500.2555936887874290.5111873775748580.744406311212571
510.2530166221636530.5060332443273060.746983377836347
520.3891013195644660.7782026391289320.610898680435534
530.3602215786779510.7204431573559020.639778421322049
540.3249739334950740.6499478669901480.675026066504926
550.327526204744610.6550524094892210.67247379525539
560.3171741233594070.6343482467188130.682825876640593
570.3564446458152110.7128892916304210.643555354184789
580.3700346537236030.7400693074472070.629965346276397
590.334239182249760.668478364499520.66576081775024
600.2939509839416760.5879019678833520.706049016058324
610.2570665668248670.5141331336497330.742933433175133
620.3320553580122850.664110716024570.667944641987715
630.400783115850950.80156623170190.59921688414905
640.3569816927709320.7139633855418640.643018307229068
650.3158214018953660.6316428037907320.684178598104634
660.31348076428810.62696152857620.6865192357119
670.3059898013879630.6119796027759270.694010198612037
680.2849379178226380.5698758356452770.715062082177361
690.2474634983693450.494926996738690.752536501630655
700.2198432722002610.4396865444005230.780156727799739
710.2054544905624330.4109089811248660.794545509437567
720.1931911931149890.3863823862299770.806808806885011
730.1743755685479560.3487511370959120.825624431452044
740.1481158832941690.2962317665883370.851884116705831
750.1333408413058320.2666816826116630.866659158694168
760.1107713503028920.2215427006057850.889228649697108
770.1238214560020340.2476429120040680.876178543997966
780.1217437411168630.2434874822337270.878256258883137
790.1101801681342910.2203603362685810.889819831865709
800.1030891043804350.2061782087608710.896910895619565
810.08799691075226110.1759938215045220.912003089247739
820.07257426044586580.1451485208917320.927425739554134
830.05965945219941410.1193189043988280.940340547800586
840.04955049775095130.09910099550190260.950449502249049
850.09065623675335960.1813124735067190.90934376324664
860.08194548510055280.1638909702011060.918054514899447
870.06819570459772490.136391409195450.931804295402275
880.06775836460858280.1355167292171660.932241635391417
890.07530576617843440.1506115323568690.924694233821566
900.06507230250349610.1301446050069920.934927697496504
910.05941116273393940.1188223254678790.940588837266061
920.04769874478579020.09539748957158030.95230125521421
930.0399546891758990.0799093783517980.960045310824101
940.03162971206209860.06325942412419710.968370287937901
950.02497048152853880.04994096305707770.975029518471461
960.02012910352860770.04025820705721530.979870896471392
970.0200690868728080.0401381737456160.979930913127192
980.01939793119072050.03879586238144090.98060206880928
990.04973967122511450.0994793424502290.950260328774886
1000.09478641762212870.1895728352442570.905213582377871
1010.09484305304476010.189686106089520.90515694695524
1020.0790010354087710.1580020708175420.920998964591229
1030.06875621513844140.1375124302768830.931243784861559
1040.06722453466887670.1344490693377530.932775465331123
1050.06545050213451570.1309010042690310.934549497865484
1060.05354839482234260.1070967896446850.946451605177657
1070.0422440801263390.0844881602526780.957755919873661
1080.03413096605741430.06826193211482850.965869033942586
1090.06015248295121310.1203049659024260.939847517048787
1100.06047377448099960.1209475489619990.939526225519
1110.0555103318670130.1110206637340260.944489668132987
1120.05715279385511120.1143055877102220.942847206144889
1130.0947250070998130.1894500141996260.905274992900187
1140.08464879739888520.169297594797770.915351202601115
1150.07691294571312040.1538258914262410.92308705428688
1160.1925138464842880.3850276929685750.807486153515712
1170.1972149708580430.3944299417160850.802785029141957
1180.2697147861629340.5394295723258680.730285213837066
1190.3699018215063510.7398036430127020.630098178493649
1200.346058375749060.6921167514981210.65394162425094
1210.3009553172417430.6019106344834860.699044682758257
1220.2958154716487910.5916309432975820.704184528351209
1230.2984800640160080.5969601280320160.701519935983992
1240.2597757459542840.5195514919085680.740224254045716
1250.2402604420924050.4805208841848090.759739557907595
1260.2110525244917580.4221050489835160.788947475508242
1270.2409810163310160.4819620326620320.759018983668984
1280.3605187187260170.7210374374520340.639481281273983
1290.3161486835561410.6322973671122830.683851316443859
1300.3072722351355490.6145444702710990.692727764864451
1310.3631271925552460.7262543851104910.636872807444754
1320.3104831597122880.6209663194245760.689516840287712
1330.3246664511182210.6493329022364420.675333548881779
1340.2897943609899050.579588721979810.710205639010095
1350.3071035361086710.6142070722173420.692896463891329
1360.2869362077659590.5738724155319170.713063792234041
1370.3876237963386080.7752475926772160.612376203661392
1380.4153287289967570.8306574579935130.584671271003243
1390.386835919850420.7736718397008410.613164080149579
1400.4857851582459970.9715703164919950.514214841754002
1410.8799178615493630.2401642769012740.120082138450637
1420.9093973898963720.1812052202072550.0906026101036275
1430.8780321118624880.2439357762750240.121967888137512
1440.8389641759676670.3220716480646660.161035824032333
1450.812885125257240.3742297494855190.18711487474276
1460.9820762829539460.03584743409210870.0179237170460544
1470.9702634068684690.05947318626306140.0297365931315307
1480.9510856981626930.09782860367461340.0489143018373067
1490.9304552485999090.1390895028001820.0695447514000911
1500.9080083399335410.1839833201329170.0919916600664585
1510.8665348298590950.266930340281810.133465170140905
1520.8084748229979650.383050354004070.191525177002035
1530.7305922911291990.5388154177416010.269407708870801
1540.6311790790338350.737641841932330.368820920966165
1550.5222395426579540.9555209146840930.477760457342046
1560.4966843599180790.9933687198361580.503315640081921
1570.3635008734609530.7270017469219070.636499126539047

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.179506739014894 & 0.359013478029787 & 0.820493260985106 \tabularnewline
8 & 0.130824836759214 & 0.261649673518428 & 0.869175163240786 \tabularnewline
9 & 0.098154787152974 & 0.196309574305948 & 0.901845212847026 \tabularnewline
10 & 0.0624446022833392 & 0.124889204566678 & 0.937555397716661 \tabularnewline
11 & 0.200162609013698 & 0.400325218027396 & 0.799837390986302 \tabularnewline
12 & 0.386982231535732 & 0.773964463071465 & 0.613017768464268 \tabularnewline
13 & 0.291993606522591 & 0.583987213045182 & 0.708006393477409 \tabularnewline
14 & 0.784831934242013 & 0.430336131515975 & 0.215168065757987 \tabularnewline
15 & 0.739204348570745 & 0.52159130285851 & 0.260795651429255 \tabularnewline
16 & 0.67695286577292 & 0.646094268454161 & 0.32304713422708 \tabularnewline
17 & 0.607810019909233 & 0.784379960181534 & 0.392189980090767 \tabularnewline
18 & 0.704841187949745 & 0.59031762410051 & 0.295158812050255 \tabularnewline
19 & 0.64504124781084 & 0.709917504378319 & 0.35495875218916 \tabularnewline
20 & 0.645257037523511 & 0.709485924952978 & 0.354742962476489 \tabularnewline
21 & 0.590653000289576 & 0.818693999420847 & 0.409346999710424 \tabularnewline
22 & 0.53516354194053 & 0.92967291611894 & 0.46483645805947 \tabularnewline
23 & 0.517339315961044 & 0.965321368077913 & 0.482660684038956 \tabularnewline
24 & 0.593332232663198 & 0.813335534673605 & 0.406667767336802 \tabularnewline
25 & 0.536051853257813 & 0.927896293484374 & 0.463948146742187 \tabularnewline
26 & 0.50087320539277 & 0.998253589214459 & 0.499126794607229 \tabularnewline
27 & 0.501266985055389 & 0.997466029889221 & 0.498733014944611 \tabularnewline
28 & 0.449913013983512 & 0.899826027967024 & 0.550086986016488 \tabularnewline
29 & 0.39324383594983 & 0.78648767189966 & 0.60675616405017 \tabularnewline
30 & 0.39321330316421 & 0.78642660632842 & 0.60678669683579 \tabularnewline
31 & 0.360024835659374 & 0.720049671318749 & 0.639975164340626 \tabularnewline
32 & 0.473602561177035 & 0.94720512235407 & 0.526397438822965 \tabularnewline
33 & 0.430581427170033 & 0.861162854340066 & 0.569418572829967 \tabularnewline
34 & 0.38517283135356 & 0.770345662707119 & 0.61482716864644 \tabularnewline
35 & 0.336186553558933 & 0.672373107117865 & 0.663813446441067 \tabularnewline
36 & 0.288652209662881 & 0.577304419325762 & 0.711347790337119 \tabularnewline
37 & 0.241936734469572 & 0.483873468939145 & 0.758063265530428 \tabularnewline
38 & 0.217435495064893 & 0.434870990129787 & 0.782564504935107 \tabularnewline
39 & 0.375843064124984 & 0.751686128249968 & 0.624156935875016 \tabularnewline
40 & 0.368824261037306 & 0.737648522074612 & 0.631175738962694 \tabularnewline
41 & 0.323310696236532 & 0.646621392473065 & 0.676689303763468 \tabularnewline
42 & 0.298822803904089 & 0.597645607808178 & 0.701177196095911 \tabularnewline
43 & 0.262279351016272 & 0.524558702032544 & 0.737720648983728 \tabularnewline
44 & 0.229485840582275 & 0.45897168116455 & 0.770514159417725 \tabularnewline
45 & 0.195871837196141 & 0.391743674392282 & 0.804128162803859 \tabularnewline
46 & 0.218521369530842 & 0.437042739061685 & 0.781478630469158 \tabularnewline
47 & 0.237766347665557 & 0.475532695331115 & 0.762233652334443 \tabularnewline
48 & 0.239399443312997 & 0.478798886625993 & 0.760600556687004 \tabularnewline
49 & 0.222464967886937 & 0.444929935773875 & 0.777535032113063 \tabularnewline
50 & 0.255593688787429 & 0.511187377574858 & 0.744406311212571 \tabularnewline
51 & 0.253016622163653 & 0.506033244327306 & 0.746983377836347 \tabularnewline
52 & 0.389101319564466 & 0.778202639128932 & 0.610898680435534 \tabularnewline
53 & 0.360221578677951 & 0.720443157355902 & 0.639778421322049 \tabularnewline
54 & 0.324973933495074 & 0.649947866990148 & 0.675026066504926 \tabularnewline
55 & 0.32752620474461 & 0.655052409489221 & 0.67247379525539 \tabularnewline
56 & 0.317174123359407 & 0.634348246718813 & 0.682825876640593 \tabularnewline
57 & 0.356444645815211 & 0.712889291630421 & 0.643555354184789 \tabularnewline
58 & 0.370034653723603 & 0.740069307447207 & 0.629965346276397 \tabularnewline
59 & 0.33423918224976 & 0.66847836449952 & 0.66576081775024 \tabularnewline
60 & 0.293950983941676 & 0.587901967883352 & 0.706049016058324 \tabularnewline
61 & 0.257066566824867 & 0.514133133649733 & 0.742933433175133 \tabularnewline
62 & 0.332055358012285 & 0.66411071602457 & 0.667944641987715 \tabularnewline
63 & 0.40078311585095 & 0.8015662317019 & 0.59921688414905 \tabularnewline
64 & 0.356981692770932 & 0.713963385541864 & 0.643018307229068 \tabularnewline
65 & 0.315821401895366 & 0.631642803790732 & 0.684178598104634 \tabularnewline
66 & 0.3134807642881 & 0.6269615285762 & 0.6865192357119 \tabularnewline
67 & 0.305989801387963 & 0.611979602775927 & 0.694010198612037 \tabularnewline
68 & 0.284937917822638 & 0.569875835645277 & 0.715062082177361 \tabularnewline
69 & 0.247463498369345 & 0.49492699673869 & 0.752536501630655 \tabularnewline
70 & 0.219843272200261 & 0.439686544400523 & 0.780156727799739 \tabularnewline
71 & 0.205454490562433 & 0.410908981124866 & 0.794545509437567 \tabularnewline
72 & 0.193191193114989 & 0.386382386229977 & 0.806808806885011 \tabularnewline
73 & 0.174375568547956 & 0.348751137095912 & 0.825624431452044 \tabularnewline
74 & 0.148115883294169 & 0.296231766588337 & 0.851884116705831 \tabularnewline
75 & 0.133340841305832 & 0.266681682611663 & 0.866659158694168 \tabularnewline
76 & 0.110771350302892 & 0.221542700605785 & 0.889228649697108 \tabularnewline
77 & 0.123821456002034 & 0.247642912004068 & 0.876178543997966 \tabularnewline
78 & 0.121743741116863 & 0.243487482233727 & 0.878256258883137 \tabularnewline
79 & 0.110180168134291 & 0.220360336268581 & 0.889819831865709 \tabularnewline
80 & 0.103089104380435 & 0.206178208760871 & 0.896910895619565 \tabularnewline
81 & 0.0879969107522611 & 0.175993821504522 & 0.912003089247739 \tabularnewline
82 & 0.0725742604458658 & 0.145148520891732 & 0.927425739554134 \tabularnewline
83 & 0.0596594521994141 & 0.119318904398828 & 0.940340547800586 \tabularnewline
84 & 0.0495504977509513 & 0.0991009955019026 & 0.950449502249049 \tabularnewline
85 & 0.0906562367533596 & 0.181312473506719 & 0.90934376324664 \tabularnewline
86 & 0.0819454851005528 & 0.163890970201106 & 0.918054514899447 \tabularnewline
87 & 0.0681957045977249 & 0.13639140919545 & 0.931804295402275 \tabularnewline
88 & 0.0677583646085828 & 0.135516729217166 & 0.932241635391417 \tabularnewline
89 & 0.0753057661784344 & 0.150611532356869 & 0.924694233821566 \tabularnewline
90 & 0.0650723025034961 & 0.130144605006992 & 0.934927697496504 \tabularnewline
91 & 0.0594111627339394 & 0.118822325467879 & 0.940588837266061 \tabularnewline
92 & 0.0476987447857902 & 0.0953974895715803 & 0.95230125521421 \tabularnewline
93 & 0.039954689175899 & 0.079909378351798 & 0.960045310824101 \tabularnewline
94 & 0.0316297120620986 & 0.0632594241241971 & 0.968370287937901 \tabularnewline
95 & 0.0249704815285388 & 0.0499409630570777 & 0.975029518471461 \tabularnewline
96 & 0.0201291035286077 & 0.0402582070572153 & 0.979870896471392 \tabularnewline
97 & 0.020069086872808 & 0.040138173745616 & 0.979930913127192 \tabularnewline
98 & 0.0193979311907205 & 0.0387958623814409 & 0.98060206880928 \tabularnewline
99 & 0.0497396712251145 & 0.099479342450229 & 0.950260328774886 \tabularnewline
100 & 0.0947864176221287 & 0.189572835244257 & 0.905213582377871 \tabularnewline
101 & 0.0948430530447601 & 0.18968610608952 & 0.90515694695524 \tabularnewline
102 & 0.079001035408771 & 0.158002070817542 & 0.920998964591229 \tabularnewline
103 & 0.0687562151384414 & 0.137512430276883 & 0.931243784861559 \tabularnewline
104 & 0.0672245346688767 & 0.134449069337753 & 0.932775465331123 \tabularnewline
105 & 0.0654505021345157 & 0.130901004269031 & 0.934549497865484 \tabularnewline
106 & 0.0535483948223426 & 0.107096789644685 & 0.946451605177657 \tabularnewline
107 & 0.042244080126339 & 0.084488160252678 & 0.957755919873661 \tabularnewline
108 & 0.0341309660574143 & 0.0682619321148285 & 0.965869033942586 \tabularnewline
109 & 0.0601524829512131 & 0.120304965902426 & 0.939847517048787 \tabularnewline
110 & 0.0604737744809996 & 0.120947548961999 & 0.939526225519 \tabularnewline
111 & 0.055510331867013 & 0.111020663734026 & 0.944489668132987 \tabularnewline
112 & 0.0571527938551112 & 0.114305587710222 & 0.942847206144889 \tabularnewline
113 & 0.094725007099813 & 0.189450014199626 & 0.905274992900187 \tabularnewline
114 & 0.0846487973988852 & 0.16929759479777 & 0.915351202601115 \tabularnewline
115 & 0.0769129457131204 & 0.153825891426241 & 0.92308705428688 \tabularnewline
116 & 0.192513846484288 & 0.385027692968575 & 0.807486153515712 \tabularnewline
117 & 0.197214970858043 & 0.394429941716085 & 0.802785029141957 \tabularnewline
118 & 0.269714786162934 & 0.539429572325868 & 0.730285213837066 \tabularnewline
119 & 0.369901821506351 & 0.739803643012702 & 0.630098178493649 \tabularnewline
120 & 0.34605837574906 & 0.692116751498121 & 0.65394162425094 \tabularnewline
121 & 0.300955317241743 & 0.601910634483486 & 0.699044682758257 \tabularnewline
122 & 0.295815471648791 & 0.591630943297582 & 0.704184528351209 \tabularnewline
123 & 0.298480064016008 & 0.596960128032016 & 0.701519935983992 \tabularnewline
124 & 0.259775745954284 & 0.519551491908568 & 0.740224254045716 \tabularnewline
125 & 0.240260442092405 & 0.480520884184809 & 0.759739557907595 \tabularnewline
126 & 0.211052524491758 & 0.422105048983516 & 0.788947475508242 \tabularnewline
127 & 0.240981016331016 & 0.481962032662032 & 0.759018983668984 \tabularnewline
128 & 0.360518718726017 & 0.721037437452034 & 0.639481281273983 \tabularnewline
129 & 0.316148683556141 & 0.632297367112283 & 0.683851316443859 \tabularnewline
130 & 0.307272235135549 & 0.614544470271099 & 0.692727764864451 \tabularnewline
131 & 0.363127192555246 & 0.726254385110491 & 0.636872807444754 \tabularnewline
132 & 0.310483159712288 & 0.620966319424576 & 0.689516840287712 \tabularnewline
133 & 0.324666451118221 & 0.649332902236442 & 0.675333548881779 \tabularnewline
134 & 0.289794360989905 & 0.57958872197981 & 0.710205639010095 \tabularnewline
135 & 0.307103536108671 & 0.614207072217342 & 0.692896463891329 \tabularnewline
136 & 0.286936207765959 & 0.573872415531917 & 0.713063792234041 \tabularnewline
137 & 0.387623796338608 & 0.775247592677216 & 0.612376203661392 \tabularnewline
138 & 0.415328728996757 & 0.830657457993513 & 0.584671271003243 \tabularnewline
139 & 0.38683591985042 & 0.773671839700841 & 0.613164080149579 \tabularnewline
140 & 0.485785158245997 & 0.971570316491995 & 0.514214841754002 \tabularnewline
141 & 0.879917861549363 & 0.240164276901274 & 0.120082138450637 \tabularnewline
142 & 0.909397389896372 & 0.181205220207255 & 0.0906026101036275 \tabularnewline
143 & 0.878032111862488 & 0.243935776275024 & 0.121967888137512 \tabularnewline
144 & 0.838964175967667 & 0.322071648064666 & 0.161035824032333 \tabularnewline
145 & 0.81288512525724 & 0.374229749485519 & 0.18711487474276 \tabularnewline
146 & 0.982076282953946 & 0.0358474340921087 & 0.0179237170460544 \tabularnewline
147 & 0.970263406868469 & 0.0594731862630614 & 0.0297365931315307 \tabularnewline
148 & 0.951085698162693 & 0.0978286036746134 & 0.0489143018373067 \tabularnewline
149 & 0.930455248599909 & 0.139089502800182 & 0.0695447514000911 \tabularnewline
150 & 0.908008339933541 & 0.183983320132917 & 0.0919916600664585 \tabularnewline
151 & 0.866534829859095 & 0.26693034028181 & 0.133465170140905 \tabularnewline
152 & 0.808474822997965 & 0.38305035400407 & 0.191525177002035 \tabularnewline
153 & 0.730592291129199 & 0.538815417741601 & 0.269407708870801 \tabularnewline
154 & 0.631179079033835 & 0.73764184193233 & 0.368820920966165 \tabularnewline
155 & 0.522239542657954 & 0.955520914684093 & 0.477760457342046 \tabularnewline
156 & 0.496684359918079 & 0.993368719836158 & 0.503315640081921 \tabularnewline
157 & 0.363500873460953 & 0.727001746921907 & 0.636499126539047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.179506739014894[/C][C]0.359013478029787[/C][C]0.820493260985106[/C][/ROW]
[ROW][C]8[/C][C]0.130824836759214[/C][C]0.261649673518428[/C][C]0.869175163240786[/C][/ROW]
[ROW][C]9[/C][C]0.098154787152974[/C][C]0.196309574305948[/C][C]0.901845212847026[/C][/ROW]
[ROW][C]10[/C][C]0.0624446022833392[/C][C]0.124889204566678[/C][C]0.937555397716661[/C][/ROW]
[ROW][C]11[/C][C]0.200162609013698[/C][C]0.400325218027396[/C][C]0.799837390986302[/C][/ROW]
[ROW][C]12[/C][C]0.386982231535732[/C][C]0.773964463071465[/C][C]0.613017768464268[/C][/ROW]
[ROW][C]13[/C][C]0.291993606522591[/C][C]0.583987213045182[/C][C]0.708006393477409[/C][/ROW]
[ROW][C]14[/C][C]0.784831934242013[/C][C]0.430336131515975[/C][C]0.215168065757987[/C][/ROW]
[ROW][C]15[/C][C]0.739204348570745[/C][C]0.52159130285851[/C][C]0.260795651429255[/C][/ROW]
[ROW][C]16[/C][C]0.67695286577292[/C][C]0.646094268454161[/C][C]0.32304713422708[/C][/ROW]
[ROW][C]17[/C][C]0.607810019909233[/C][C]0.784379960181534[/C][C]0.392189980090767[/C][/ROW]
[ROW][C]18[/C][C]0.704841187949745[/C][C]0.59031762410051[/C][C]0.295158812050255[/C][/ROW]
[ROW][C]19[/C][C]0.64504124781084[/C][C]0.709917504378319[/C][C]0.35495875218916[/C][/ROW]
[ROW][C]20[/C][C]0.645257037523511[/C][C]0.709485924952978[/C][C]0.354742962476489[/C][/ROW]
[ROW][C]21[/C][C]0.590653000289576[/C][C]0.818693999420847[/C][C]0.409346999710424[/C][/ROW]
[ROW][C]22[/C][C]0.53516354194053[/C][C]0.92967291611894[/C][C]0.46483645805947[/C][/ROW]
[ROW][C]23[/C][C]0.517339315961044[/C][C]0.965321368077913[/C][C]0.482660684038956[/C][/ROW]
[ROW][C]24[/C][C]0.593332232663198[/C][C]0.813335534673605[/C][C]0.406667767336802[/C][/ROW]
[ROW][C]25[/C][C]0.536051853257813[/C][C]0.927896293484374[/C][C]0.463948146742187[/C][/ROW]
[ROW][C]26[/C][C]0.50087320539277[/C][C]0.998253589214459[/C][C]0.499126794607229[/C][/ROW]
[ROW][C]27[/C][C]0.501266985055389[/C][C]0.997466029889221[/C][C]0.498733014944611[/C][/ROW]
[ROW][C]28[/C][C]0.449913013983512[/C][C]0.899826027967024[/C][C]0.550086986016488[/C][/ROW]
[ROW][C]29[/C][C]0.39324383594983[/C][C]0.78648767189966[/C][C]0.60675616405017[/C][/ROW]
[ROW][C]30[/C][C]0.39321330316421[/C][C]0.78642660632842[/C][C]0.60678669683579[/C][/ROW]
[ROW][C]31[/C][C]0.360024835659374[/C][C]0.720049671318749[/C][C]0.639975164340626[/C][/ROW]
[ROW][C]32[/C][C]0.473602561177035[/C][C]0.94720512235407[/C][C]0.526397438822965[/C][/ROW]
[ROW][C]33[/C][C]0.430581427170033[/C][C]0.861162854340066[/C][C]0.569418572829967[/C][/ROW]
[ROW][C]34[/C][C]0.38517283135356[/C][C]0.770345662707119[/C][C]0.61482716864644[/C][/ROW]
[ROW][C]35[/C][C]0.336186553558933[/C][C]0.672373107117865[/C][C]0.663813446441067[/C][/ROW]
[ROW][C]36[/C][C]0.288652209662881[/C][C]0.577304419325762[/C][C]0.711347790337119[/C][/ROW]
[ROW][C]37[/C][C]0.241936734469572[/C][C]0.483873468939145[/C][C]0.758063265530428[/C][/ROW]
[ROW][C]38[/C][C]0.217435495064893[/C][C]0.434870990129787[/C][C]0.782564504935107[/C][/ROW]
[ROW][C]39[/C][C]0.375843064124984[/C][C]0.751686128249968[/C][C]0.624156935875016[/C][/ROW]
[ROW][C]40[/C][C]0.368824261037306[/C][C]0.737648522074612[/C][C]0.631175738962694[/C][/ROW]
[ROW][C]41[/C][C]0.323310696236532[/C][C]0.646621392473065[/C][C]0.676689303763468[/C][/ROW]
[ROW][C]42[/C][C]0.298822803904089[/C][C]0.597645607808178[/C][C]0.701177196095911[/C][/ROW]
[ROW][C]43[/C][C]0.262279351016272[/C][C]0.524558702032544[/C][C]0.737720648983728[/C][/ROW]
[ROW][C]44[/C][C]0.229485840582275[/C][C]0.45897168116455[/C][C]0.770514159417725[/C][/ROW]
[ROW][C]45[/C][C]0.195871837196141[/C][C]0.391743674392282[/C][C]0.804128162803859[/C][/ROW]
[ROW][C]46[/C][C]0.218521369530842[/C][C]0.437042739061685[/C][C]0.781478630469158[/C][/ROW]
[ROW][C]47[/C][C]0.237766347665557[/C][C]0.475532695331115[/C][C]0.762233652334443[/C][/ROW]
[ROW][C]48[/C][C]0.239399443312997[/C][C]0.478798886625993[/C][C]0.760600556687004[/C][/ROW]
[ROW][C]49[/C][C]0.222464967886937[/C][C]0.444929935773875[/C][C]0.777535032113063[/C][/ROW]
[ROW][C]50[/C][C]0.255593688787429[/C][C]0.511187377574858[/C][C]0.744406311212571[/C][/ROW]
[ROW][C]51[/C][C]0.253016622163653[/C][C]0.506033244327306[/C][C]0.746983377836347[/C][/ROW]
[ROW][C]52[/C][C]0.389101319564466[/C][C]0.778202639128932[/C][C]0.610898680435534[/C][/ROW]
[ROW][C]53[/C][C]0.360221578677951[/C][C]0.720443157355902[/C][C]0.639778421322049[/C][/ROW]
[ROW][C]54[/C][C]0.324973933495074[/C][C]0.649947866990148[/C][C]0.675026066504926[/C][/ROW]
[ROW][C]55[/C][C]0.32752620474461[/C][C]0.655052409489221[/C][C]0.67247379525539[/C][/ROW]
[ROW][C]56[/C][C]0.317174123359407[/C][C]0.634348246718813[/C][C]0.682825876640593[/C][/ROW]
[ROW][C]57[/C][C]0.356444645815211[/C][C]0.712889291630421[/C][C]0.643555354184789[/C][/ROW]
[ROW][C]58[/C][C]0.370034653723603[/C][C]0.740069307447207[/C][C]0.629965346276397[/C][/ROW]
[ROW][C]59[/C][C]0.33423918224976[/C][C]0.66847836449952[/C][C]0.66576081775024[/C][/ROW]
[ROW][C]60[/C][C]0.293950983941676[/C][C]0.587901967883352[/C][C]0.706049016058324[/C][/ROW]
[ROW][C]61[/C][C]0.257066566824867[/C][C]0.514133133649733[/C][C]0.742933433175133[/C][/ROW]
[ROW][C]62[/C][C]0.332055358012285[/C][C]0.66411071602457[/C][C]0.667944641987715[/C][/ROW]
[ROW][C]63[/C][C]0.40078311585095[/C][C]0.8015662317019[/C][C]0.59921688414905[/C][/ROW]
[ROW][C]64[/C][C]0.356981692770932[/C][C]0.713963385541864[/C][C]0.643018307229068[/C][/ROW]
[ROW][C]65[/C][C]0.315821401895366[/C][C]0.631642803790732[/C][C]0.684178598104634[/C][/ROW]
[ROW][C]66[/C][C]0.3134807642881[/C][C]0.6269615285762[/C][C]0.6865192357119[/C][/ROW]
[ROW][C]67[/C][C]0.305989801387963[/C][C]0.611979602775927[/C][C]0.694010198612037[/C][/ROW]
[ROW][C]68[/C][C]0.284937917822638[/C][C]0.569875835645277[/C][C]0.715062082177361[/C][/ROW]
[ROW][C]69[/C][C]0.247463498369345[/C][C]0.49492699673869[/C][C]0.752536501630655[/C][/ROW]
[ROW][C]70[/C][C]0.219843272200261[/C][C]0.439686544400523[/C][C]0.780156727799739[/C][/ROW]
[ROW][C]71[/C][C]0.205454490562433[/C][C]0.410908981124866[/C][C]0.794545509437567[/C][/ROW]
[ROW][C]72[/C][C]0.193191193114989[/C][C]0.386382386229977[/C][C]0.806808806885011[/C][/ROW]
[ROW][C]73[/C][C]0.174375568547956[/C][C]0.348751137095912[/C][C]0.825624431452044[/C][/ROW]
[ROW][C]74[/C][C]0.148115883294169[/C][C]0.296231766588337[/C][C]0.851884116705831[/C][/ROW]
[ROW][C]75[/C][C]0.133340841305832[/C][C]0.266681682611663[/C][C]0.866659158694168[/C][/ROW]
[ROW][C]76[/C][C]0.110771350302892[/C][C]0.221542700605785[/C][C]0.889228649697108[/C][/ROW]
[ROW][C]77[/C][C]0.123821456002034[/C][C]0.247642912004068[/C][C]0.876178543997966[/C][/ROW]
[ROW][C]78[/C][C]0.121743741116863[/C][C]0.243487482233727[/C][C]0.878256258883137[/C][/ROW]
[ROW][C]79[/C][C]0.110180168134291[/C][C]0.220360336268581[/C][C]0.889819831865709[/C][/ROW]
[ROW][C]80[/C][C]0.103089104380435[/C][C]0.206178208760871[/C][C]0.896910895619565[/C][/ROW]
[ROW][C]81[/C][C]0.0879969107522611[/C][C]0.175993821504522[/C][C]0.912003089247739[/C][/ROW]
[ROW][C]82[/C][C]0.0725742604458658[/C][C]0.145148520891732[/C][C]0.927425739554134[/C][/ROW]
[ROW][C]83[/C][C]0.0596594521994141[/C][C]0.119318904398828[/C][C]0.940340547800586[/C][/ROW]
[ROW][C]84[/C][C]0.0495504977509513[/C][C]0.0991009955019026[/C][C]0.950449502249049[/C][/ROW]
[ROW][C]85[/C][C]0.0906562367533596[/C][C]0.181312473506719[/C][C]0.90934376324664[/C][/ROW]
[ROW][C]86[/C][C]0.0819454851005528[/C][C]0.163890970201106[/C][C]0.918054514899447[/C][/ROW]
[ROW][C]87[/C][C]0.0681957045977249[/C][C]0.13639140919545[/C][C]0.931804295402275[/C][/ROW]
[ROW][C]88[/C][C]0.0677583646085828[/C][C]0.135516729217166[/C][C]0.932241635391417[/C][/ROW]
[ROW][C]89[/C][C]0.0753057661784344[/C][C]0.150611532356869[/C][C]0.924694233821566[/C][/ROW]
[ROW][C]90[/C][C]0.0650723025034961[/C][C]0.130144605006992[/C][C]0.934927697496504[/C][/ROW]
[ROW][C]91[/C][C]0.0594111627339394[/C][C]0.118822325467879[/C][C]0.940588837266061[/C][/ROW]
[ROW][C]92[/C][C]0.0476987447857902[/C][C]0.0953974895715803[/C][C]0.95230125521421[/C][/ROW]
[ROW][C]93[/C][C]0.039954689175899[/C][C]0.079909378351798[/C][C]0.960045310824101[/C][/ROW]
[ROW][C]94[/C][C]0.0316297120620986[/C][C]0.0632594241241971[/C][C]0.968370287937901[/C][/ROW]
[ROW][C]95[/C][C]0.0249704815285388[/C][C]0.0499409630570777[/C][C]0.975029518471461[/C][/ROW]
[ROW][C]96[/C][C]0.0201291035286077[/C][C]0.0402582070572153[/C][C]0.979870896471392[/C][/ROW]
[ROW][C]97[/C][C]0.020069086872808[/C][C]0.040138173745616[/C][C]0.979930913127192[/C][/ROW]
[ROW][C]98[/C][C]0.0193979311907205[/C][C]0.0387958623814409[/C][C]0.98060206880928[/C][/ROW]
[ROW][C]99[/C][C]0.0497396712251145[/C][C]0.099479342450229[/C][C]0.950260328774886[/C][/ROW]
[ROW][C]100[/C][C]0.0947864176221287[/C][C]0.189572835244257[/C][C]0.905213582377871[/C][/ROW]
[ROW][C]101[/C][C]0.0948430530447601[/C][C]0.18968610608952[/C][C]0.90515694695524[/C][/ROW]
[ROW][C]102[/C][C]0.079001035408771[/C][C]0.158002070817542[/C][C]0.920998964591229[/C][/ROW]
[ROW][C]103[/C][C]0.0687562151384414[/C][C]0.137512430276883[/C][C]0.931243784861559[/C][/ROW]
[ROW][C]104[/C][C]0.0672245346688767[/C][C]0.134449069337753[/C][C]0.932775465331123[/C][/ROW]
[ROW][C]105[/C][C]0.0654505021345157[/C][C]0.130901004269031[/C][C]0.934549497865484[/C][/ROW]
[ROW][C]106[/C][C]0.0535483948223426[/C][C]0.107096789644685[/C][C]0.946451605177657[/C][/ROW]
[ROW][C]107[/C][C]0.042244080126339[/C][C]0.084488160252678[/C][C]0.957755919873661[/C][/ROW]
[ROW][C]108[/C][C]0.0341309660574143[/C][C]0.0682619321148285[/C][C]0.965869033942586[/C][/ROW]
[ROW][C]109[/C][C]0.0601524829512131[/C][C]0.120304965902426[/C][C]0.939847517048787[/C][/ROW]
[ROW][C]110[/C][C]0.0604737744809996[/C][C]0.120947548961999[/C][C]0.939526225519[/C][/ROW]
[ROW][C]111[/C][C]0.055510331867013[/C][C]0.111020663734026[/C][C]0.944489668132987[/C][/ROW]
[ROW][C]112[/C][C]0.0571527938551112[/C][C]0.114305587710222[/C][C]0.942847206144889[/C][/ROW]
[ROW][C]113[/C][C]0.094725007099813[/C][C]0.189450014199626[/C][C]0.905274992900187[/C][/ROW]
[ROW][C]114[/C][C]0.0846487973988852[/C][C]0.16929759479777[/C][C]0.915351202601115[/C][/ROW]
[ROW][C]115[/C][C]0.0769129457131204[/C][C]0.153825891426241[/C][C]0.92308705428688[/C][/ROW]
[ROW][C]116[/C][C]0.192513846484288[/C][C]0.385027692968575[/C][C]0.807486153515712[/C][/ROW]
[ROW][C]117[/C][C]0.197214970858043[/C][C]0.394429941716085[/C][C]0.802785029141957[/C][/ROW]
[ROW][C]118[/C][C]0.269714786162934[/C][C]0.539429572325868[/C][C]0.730285213837066[/C][/ROW]
[ROW][C]119[/C][C]0.369901821506351[/C][C]0.739803643012702[/C][C]0.630098178493649[/C][/ROW]
[ROW][C]120[/C][C]0.34605837574906[/C][C]0.692116751498121[/C][C]0.65394162425094[/C][/ROW]
[ROW][C]121[/C][C]0.300955317241743[/C][C]0.601910634483486[/C][C]0.699044682758257[/C][/ROW]
[ROW][C]122[/C][C]0.295815471648791[/C][C]0.591630943297582[/C][C]0.704184528351209[/C][/ROW]
[ROW][C]123[/C][C]0.298480064016008[/C][C]0.596960128032016[/C][C]0.701519935983992[/C][/ROW]
[ROW][C]124[/C][C]0.259775745954284[/C][C]0.519551491908568[/C][C]0.740224254045716[/C][/ROW]
[ROW][C]125[/C][C]0.240260442092405[/C][C]0.480520884184809[/C][C]0.759739557907595[/C][/ROW]
[ROW][C]126[/C][C]0.211052524491758[/C][C]0.422105048983516[/C][C]0.788947475508242[/C][/ROW]
[ROW][C]127[/C][C]0.240981016331016[/C][C]0.481962032662032[/C][C]0.759018983668984[/C][/ROW]
[ROW][C]128[/C][C]0.360518718726017[/C][C]0.721037437452034[/C][C]0.639481281273983[/C][/ROW]
[ROW][C]129[/C][C]0.316148683556141[/C][C]0.632297367112283[/C][C]0.683851316443859[/C][/ROW]
[ROW][C]130[/C][C]0.307272235135549[/C][C]0.614544470271099[/C][C]0.692727764864451[/C][/ROW]
[ROW][C]131[/C][C]0.363127192555246[/C][C]0.726254385110491[/C][C]0.636872807444754[/C][/ROW]
[ROW][C]132[/C][C]0.310483159712288[/C][C]0.620966319424576[/C][C]0.689516840287712[/C][/ROW]
[ROW][C]133[/C][C]0.324666451118221[/C][C]0.649332902236442[/C][C]0.675333548881779[/C][/ROW]
[ROW][C]134[/C][C]0.289794360989905[/C][C]0.57958872197981[/C][C]0.710205639010095[/C][/ROW]
[ROW][C]135[/C][C]0.307103536108671[/C][C]0.614207072217342[/C][C]0.692896463891329[/C][/ROW]
[ROW][C]136[/C][C]0.286936207765959[/C][C]0.573872415531917[/C][C]0.713063792234041[/C][/ROW]
[ROW][C]137[/C][C]0.387623796338608[/C][C]0.775247592677216[/C][C]0.612376203661392[/C][/ROW]
[ROW][C]138[/C][C]0.415328728996757[/C][C]0.830657457993513[/C][C]0.584671271003243[/C][/ROW]
[ROW][C]139[/C][C]0.38683591985042[/C][C]0.773671839700841[/C][C]0.613164080149579[/C][/ROW]
[ROW][C]140[/C][C]0.485785158245997[/C][C]0.971570316491995[/C][C]0.514214841754002[/C][/ROW]
[ROW][C]141[/C][C]0.879917861549363[/C][C]0.240164276901274[/C][C]0.120082138450637[/C][/ROW]
[ROW][C]142[/C][C]0.909397389896372[/C][C]0.181205220207255[/C][C]0.0906026101036275[/C][/ROW]
[ROW][C]143[/C][C]0.878032111862488[/C][C]0.243935776275024[/C][C]0.121967888137512[/C][/ROW]
[ROW][C]144[/C][C]0.838964175967667[/C][C]0.322071648064666[/C][C]0.161035824032333[/C][/ROW]
[ROW][C]145[/C][C]0.81288512525724[/C][C]0.374229749485519[/C][C]0.18711487474276[/C][/ROW]
[ROW][C]146[/C][C]0.982076282953946[/C][C]0.0358474340921087[/C][C]0.0179237170460544[/C][/ROW]
[ROW][C]147[/C][C]0.970263406868469[/C][C]0.0594731862630614[/C][C]0.0297365931315307[/C][/ROW]
[ROW][C]148[/C][C]0.951085698162693[/C][C]0.0978286036746134[/C][C]0.0489143018373067[/C][/ROW]
[ROW][C]149[/C][C]0.930455248599909[/C][C]0.139089502800182[/C][C]0.0695447514000911[/C][/ROW]
[ROW][C]150[/C][C]0.908008339933541[/C][C]0.183983320132917[/C][C]0.0919916600664585[/C][/ROW]
[ROW][C]151[/C][C]0.866534829859095[/C][C]0.26693034028181[/C][C]0.133465170140905[/C][/ROW]
[ROW][C]152[/C][C]0.808474822997965[/C][C]0.38305035400407[/C][C]0.191525177002035[/C][/ROW]
[ROW][C]153[/C][C]0.730592291129199[/C][C]0.538815417741601[/C][C]0.269407708870801[/C][/ROW]
[ROW][C]154[/C][C]0.631179079033835[/C][C]0.73764184193233[/C][C]0.368820920966165[/C][/ROW]
[ROW][C]155[/C][C]0.522239542657954[/C][C]0.955520914684093[/C][C]0.477760457342046[/C][/ROW]
[ROW][C]156[/C][C]0.496684359918079[/C][C]0.993368719836158[/C][C]0.503315640081921[/C][/ROW]
[ROW][C]157[/C][C]0.363500873460953[/C][C]0.727001746921907[/C][C]0.636499126539047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1795067390148940.3590134780297870.820493260985106
80.1308248367592140.2616496735184280.869175163240786
90.0981547871529740.1963095743059480.901845212847026
100.06244460228333920.1248892045666780.937555397716661
110.2001626090136980.4003252180273960.799837390986302
120.3869822315357320.7739644630714650.613017768464268
130.2919936065225910.5839872130451820.708006393477409
140.7848319342420130.4303361315159750.215168065757987
150.7392043485707450.521591302858510.260795651429255
160.676952865772920.6460942684541610.32304713422708
170.6078100199092330.7843799601815340.392189980090767
180.7048411879497450.590317624100510.295158812050255
190.645041247810840.7099175043783190.35495875218916
200.6452570375235110.7094859249529780.354742962476489
210.5906530002895760.8186939994208470.409346999710424
220.535163541940530.929672916118940.46483645805947
230.5173393159610440.9653213680779130.482660684038956
240.5933322326631980.8133355346736050.406667767336802
250.5360518532578130.9278962934843740.463948146742187
260.500873205392770.9982535892144590.499126794607229
270.5012669850553890.9974660298892210.498733014944611
280.4499130139835120.8998260279670240.550086986016488
290.393243835949830.786487671899660.60675616405017
300.393213303164210.786426606328420.60678669683579
310.3600248356593740.7200496713187490.639975164340626
320.4736025611770350.947205122354070.526397438822965
330.4305814271700330.8611628543400660.569418572829967
340.385172831353560.7703456627071190.61482716864644
350.3361865535589330.6723731071178650.663813446441067
360.2886522096628810.5773044193257620.711347790337119
370.2419367344695720.4838734689391450.758063265530428
380.2174354950648930.4348709901297870.782564504935107
390.3758430641249840.7516861282499680.624156935875016
400.3688242610373060.7376485220746120.631175738962694
410.3233106962365320.6466213924730650.676689303763468
420.2988228039040890.5976456078081780.701177196095911
430.2622793510162720.5245587020325440.737720648983728
440.2294858405822750.458971681164550.770514159417725
450.1958718371961410.3917436743922820.804128162803859
460.2185213695308420.4370427390616850.781478630469158
470.2377663476655570.4755326953311150.762233652334443
480.2393994433129970.4787988866259930.760600556687004
490.2224649678869370.4449299357738750.777535032113063
500.2555936887874290.5111873775748580.744406311212571
510.2530166221636530.5060332443273060.746983377836347
520.3891013195644660.7782026391289320.610898680435534
530.3602215786779510.7204431573559020.639778421322049
540.3249739334950740.6499478669901480.675026066504926
550.327526204744610.6550524094892210.67247379525539
560.3171741233594070.6343482467188130.682825876640593
570.3564446458152110.7128892916304210.643555354184789
580.3700346537236030.7400693074472070.629965346276397
590.334239182249760.668478364499520.66576081775024
600.2939509839416760.5879019678833520.706049016058324
610.2570665668248670.5141331336497330.742933433175133
620.3320553580122850.664110716024570.667944641987715
630.400783115850950.80156623170190.59921688414905
640.3569816927709320.7139633855418640.643018307229068
650.3158214018953660.6316428037907320.684178598104634
660.31348076428810.62696152857620.6865192357119
670.3059898013879630.6119796027759270.694010198612037
680.2849379178226380.5698758356452770.715062082177361
690.2474634983693450.494926996738690.752536501630655
700.2198432722002610.4396865444005230.780156727799739
710.2054544905624330.4109089811248660.794545509437567
720.1931911931149890.3863823862299770.806808806885011
730.1743755685479560.3487511370959120.825624431452044
740.1481158832941690.2962317665883370.851884116705831
750.1333408413058320.2666816826116630.866659158694168
760.1107713503028920.2215427006057850.889228649697108
770.1238214560020340.2476429120040680.876178543997966
780.1217437411168630.2434874822337270.878256258883137
790.1101801681342910.2203603362685810.889819831865709
800.1030891043804350.2061782087608710.896910895619565
810.08799691075226110.1759938215045220.912003089247739
820.07257426044586580.1451485208917320.927425739554134
830.05965945219941410.1193189043988280.940340547800586
840.04955049775095130.09910099550190260.950449502249049
850.09065623675335960.1813124735067190.90934376324664
860.08194548510055280.1638909702011060.918054514899447
870.06819570459772490.136391409195450.931804295402275
880.06775836460858280.1355167292171660.932241635391417
890.07530576617843440.1506115323568690.924694233821566
900.06507230250349610.1301446050069920.934927697496504
910.05941116273393940.1188223254678790.940588837266061
920.04769874478579020.09539748957158030.95230125521421
930.0399546891758990.0799093783517980.960045310824101
940.03162971206209860.06325942412419710.968370287937901
950.02497048152853880.04994096305707770.975029518471461
960.02012910352860770.04025820705721530.979870896471392
970.0200690868728080.0401381737456160.979930913127192
980.01939793119072050.03879586238144090.98060206880928
990.04973967122511450.0994793424502290.950260328774886
1000.09478641762212870.1895728352442570.905213582377871
1010.09484305304476010.189686106089520.90515694695524
1020.0790010354087710.1580020708175420.920998964591229
1030.06875621513844140.1375124302768830.931243784861559
1040.06722453466887670.1344490693377530.932775465331123
1050.06545050213451570.1309010042690310.934549497865484
1060.05354839482234260.1070967896446850.946451605177657
1070.0422440801263390.0844881602526780.957755919873661
1080.03413096605741430.06826193211482850.965869033942586
1090.06015248295121310.1203049659024260.939847517048787
1100.06047377448099960.1209475489619990.939526225519
1110.0555103318670130.1110206637340260.944489668132987
1120.05715279385511120.1143055877102220.942847206144889
1130.0947250070998130.1894500141996260.905274992900187
1140.08464879739888520.169297594797770.915351202601115
1150.07691294571312040.1538258914262410.92308705428688
1160.1925138464842880.3850276929685750.807486153515712
1170.1972149708580430.3944299417160850.802785029141957
1180.2697147861629340.5394295723258680.730285213837066
1190.3699018215063510.7398036430127020.630098178493649
1200.346058375749060.6921167514981210.65394162425094
1210.3009553172417430.6019106344834860.699044682758257
1220.2958154716487910.5916309432975820.704184528351209
1230.2984800640160080.5969601280320160.701519935983992
1240.2597757459542840.5195514919085680.740224254045716
1250.2402604420924050.4805208841848090.759739557907595
1260.2110525244917580.4221050489835160.788947475508242
1270.2409810163310160.4819620326620320.759018983668984
1280.3605187187260170.7210374374520340.639481281273983
1290.3161486835561410.6322973671122830.683851316443859
1300.3072722351355490.6145444702710990.692727764864451
1310.3631271925552460.7262543851104910.636872807444754
1320.3104831597122880.6209663194245760.689516840287712
1330.3246664511182210.6493329022364420.675333548881779
1340.2897943609899050.579588721979810.710205639010095
1350.3071035361086710.6142070722173420.692896463891329
1360.2869362077659590.5738724155319170.713063792234041
1370.3876237963386080.7752475926772160.612376203661392
1380.4153287289967570.8306574579935130.584671271003243
1390.386835919850420.7736718397008410.613164080149579
1400.4857851582459970.9715703164919950.514214841754002
1410.8799178615493630.2401642769012740.120082138450637
1420.9093973898963720.1812052202072550.0906026101036275
1430.8780321118624880.2439357762750240.121967888137512
1440.8389641759676670.3220716480646660.161035824032333
1450.812885125257240.3742297494855190.18711487474276
1460.9820762829539460.03584743409210870.0179237170460544
1470.9702634068684690.05947318626306140.0297365931315307
1480.9510856981626930.09782860367461340.0489143018373067
1490.9304552485999090.1390895028001820.0695447514000911
1500.9080083399335410.1839833201329170.0919916600664585
1510.8665348298590950.266930340281810.133465170140905
1520.8084748229979650.383050354004070.191525177002035
1530.7305922911291990.5388154177416010.269407708870801
1540.6311790790338350.737641841932330.368820920966165
1550.5222395426579540.9555209146840930.477760457342046
1560.4966843599180790.9933687198361580.503315640081921
1570.3635008734609530.7270017469219070.636499126539047







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.033112582781457OK
10% type I error level140.0927152317880795OK

\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 & 5 & 0.033112582781457 & OK \tabularnewline
10% type I error level & 14 & 0.0927152317880795 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145942&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]5[/C][C]0.033112582781457[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.0927152317880795[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145942&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145942&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.033112582781457OK
10% type I error level140.0927152317880795OK



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