## Free Statistics

of Irreproducible Research!

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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 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-05totaal#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-05totaal#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-05totaal#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-05totaal#karakterscompendium[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 8.11506537560171 1.061756 7.6431 0 0 AantalurenRFC 0.223303383989131 0.037712 5.9213 0 0 #logins 0.0267960415866275 0.020667 1.2966 0.196642 0.098321 totaal#karakterscompendium 6.6247085818747e-05 1.7e-05 3.8008 0.000205 0.000102

\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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 8.11506537560171 1.061756 7.6431 0 0 AantalurenRFC 0.223303383989131 0.037712 5.9213 0 0 #logins 0.0267960415866275 0.020667 1.2966 0.196642 0.098321 totaal#karakterscompendium 6.6247085818747e-05 1.7e-05 3.8008 0.000205 0.000102

 Multiple Linear Regression - Regression Statistics Multiple R 0.749890420984178 R-squared 0.562335643483828 Adjusted R-squared 0.55412943679915 F-TEST (value) 68.525649559103 F-TEST (DF numerator) 3 F-TEST (DF denominator) 160 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.81872189370463 Sum Squared Residuals 5417.20391620442

\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]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 R 0.749890420984178 R-squared 0.562335643483828 Adjusted R-squared 0.55412943679915 F-TEST (value) 68.525649559103 F-TEST (DF numerator) 3 F-TEST (DF denominator) 160 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.81872189370463 Sum Squared Residuals 5417.20391620442

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

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

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 5 0.033112582781457 OK 10% type I error level 14 0.0927152317880795 OK

\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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 5 0.033112582781457 OK 10% type I error level 14 0.0927152317880795 OK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}