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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 15:04:04 -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/t1321905887nsu3b3f81uf1iqs.htm/, Retrieved Tue, 16 Apr 2024 08:25:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145957, Retrieved Tue, 16 Apr 2024 08:25:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact196

Tree of Dependent Computations

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] [WS7] [2011-11-21 20:04:04] [5ecdd7f9023ba8f0fbc3191d3a9c3da8] [Current]
-    D      [Multiple Regression] [WS7 (2)] [2011-11-21 20:07:43] [8501ca4b76170905b8a207a77f626994]
-             [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:16:12] [f722e8e78b9e5c5ebaa2263f273aa636]
-    D          [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:27:04] [f722e8e78b9e5c5ebaa2263f273aa636]
- R P             [Multiple Regression] [Paper: Multiple R...] [2011-12-21 16:51:41] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 21:45:13] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 23:01:32] [f722e8e78b9e5c5ebaa2263f273aa636]
-   PD        [Multiple Regression] [WS7 (2)] [2011-11-21 20:25:04] [8501ca4b76170905b8a207a77f626994]
-               [Multiple Regression] [Paper Deel 1 Mult...] [2011-12-21 20:30:44] [8501ca4b76170905b8a207a77f626994]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
47,38555556	46	26	99
24,06138889	48	20	77
31,4825	37	24	90
42,36388889	75	25	96
23,94611111	31	15	41
10,34916667	18	16	64
85,01527778	79	20	76
9,097222222	16	18	67
32,36166667	38	19	72
36,26083333	24	20	75
44,96555556	65	30	113
35,63166667	74	37	139
28,43055556	43	23	76
53,61777778	42	36	123
39,32611111	55	29	110
70,43305556	121	35	133
50,30833333	42	24	92
55,12	102	22	83
31,62583333	36	19	72
44,42777778	50	30	115
46,33944444	48	27	99
79,63194444	56	26	92
25,46027778	19	15	56
30,07722222	32	30	120
40,65055556	77	28	107
40,31722222	90	24	90
44,92777778	81	21	78
44,69583333	55	27	103
29,69111111	34	21	81
52,26388889	38	30	114
52,61138889	53	30	115
35,96777778	48	33	118
56,675	63	30	113
17,42527778	25	20	75
67,67361111	56	27	103
46,45972222	37	25	93
73,48	83	30	114
33,89555556	50	20	76
22,49	26	8	27
58,27638889	108	24	92
62,27916667	55	25	96
32,21416667	41	25	92
38,38638889	49	21	76
22,52944444	31	21	79
25,86805556	49	21	57
84,93222222	96	26	99
21,88888889	42	26	82
44,12083333	55	30	113
61,59583333	70	34	129
36,41888889	39	30	110
35,75944444	53	18	78
6,718888889	24	4	12
71,57277778	209	31	114
18,06361111	17	18	67
27,24055556	58	14	52
48,21861111	27	20	76
50,01166667	58	36	138
54,79611111	114	24	92
58,90555556	75	26	93
39,32833333	51	22	83
68,08527778	86	31	118
57,46638889	77	21	77
40,47111111	62	31	122
47,39861111	60	26	99
39,46222222	39	24	92
31,89444444	35	15	58
31,51694444	86	19	73
40,35694444	102	28	103
41,94416667	49	24	92
25,50333333	35	18	69
33,00194444	33	25	95
19,2975	28	20	76
35,175	44	25	95
40,53	37	24	92
27,33138889	33	23	88
53,035	45	25	95
55,22138889	57	20	76
29,49805556	58	23	87
24,81055556	36	22	84
33,43388889	42	25	95
27,44194444	30	18	69
76,37583333	67	30	115
36,88833333	53	22	83
37,56972222	59	25	47
22,48694444	25	8	28
30,34361111	39	21	79
26,84277778	36	22	83
62,83083333	114	24	92
47,57944444	54	30	98
32,72638889	70	27	103
37,10027778	51	24	89
42,27583333	49	25	95
31,11222222	42	21	78
47,11472222	51	24	92
52,07861111	51	24	92
36,25916667	27	20	76
39,53861111	29	20	67
52,71222222	54	24	92
56,00083333	92	40	151
68,565	72	22	83
43,31861111	63	31	118
50,71694444	41	26	98
29,54194444	111	20	76
12,02416667	14	19	71
35,41472222	45	15	57
35,53611111	91	21	79
41,39055556	29	22	83
52,12583333	64	24	92
20,58666667	32	19	75
26,11277778	65	24	95
49,0625	42	23	88
39,42583333	55	27	99
6,371666667	10	1	0
34,97972222	53	24	91
17,1825	25	11	32
25,35833333	33	27	101
70,86111111	66	22	84
5,848333333	16	0	0
46,97027778	35	17	60
8,726111111	19	8	25
52,41694444	76	24	90
38,20666667	35	31	115
21,435	46	24	92
20,71305556	29	20	71
10,615	34	8	27
25,26694444	25	22	83
53,95111111	48	33	126
37,5725	38	33	125
67,85333333	50	31	119
56,04111111	65	33	127
71,22277778	72	35	133
38,65111111	23	21	79
21,24166667	29	20	76
52,63944444	194	24	92
77,87055556	114	29	109
14,16638889	15	20	76
70,35388889	86	27	100
28,6775	50	24	87
46,68305556	33	26	97
35,76888889	50	26	95
21,04055556	72	12	48
69,23111111	81	21	80
42,32388889	54	24	91
48,12777778	63	21	79
54,77694444	69	30	114
18,75194444	39	32	120
38,72472222	49	24	89
51,49055556	67	29	111
0	0	0	0
4,08	10	0	0
0,027222222	1	0	0
0,126388889	2	0	0
0	0	0	0
0	0	0	0
38,30138889	58	20	74
51,46888889	72	27	107
0	0	0	0
0,056388889	4	0	0
1,999722222	5	0	0
12,96111111	20	5	15
4,874166667	5	1	4
20,43527778	27	23	82
0,269166667	2	0	0
29,29916667	33	16	54

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.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 & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Reviewed_compendiums[t] = + 0.843449171232774 + 0.00528823940014578Total_time[t] + 0.000916445389594762Logins[t] + 0.254727511090191long_feedback[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Reviewed_compendiums[t] =  +  0.843449171232774 +  0.00528823940014578Total_time[t] +  0.000916445389594762Logins[t] +  0.254727511090191long_feedback[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Reviewed_compendiums[t] =  +  0.843449171232774 +  0.00528823940014578Total_time[t] +  0.000916445389594762Logins[t] +  0.254727511090191long_feedback[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Reviewed_compendiums[t] = + 0.843449171232774 + 0.00528823940014578Total_time[t] + 0.000916445389594762Logins[t] + 0.254727511090191long_feedback[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8434491712327740.2872852.93590.0038160.001908
Total_time0.005288239400145780.0094510.55950.576580.28829
Logins0.0009164453895947620.0049370.18560.8529580.426479
long_feedback0.2547275110901910.00470754.115600

\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) & 0.843449171232774 & 0.287285 & 2.9359 & 0.003816 & 0.001908 \tabularnewline
Total_time & 0.00528823940014578 & 0.009451 & 0.5595 & 0.57658 & 0.28829 \tabularnewline
Logins & 0.000916445389594762 & 0.004937 & 0.1856 & 0.852958 & 0.426479 \tabularnewline
long_feedback & 0.254727511090191 & 0.004707 & 54.1156 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&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]0.843449171232774[/C][C]0.287285[/C][C]2.9359[/C][C]0.003816[/C][C]0.001908[/C][/ROW]
[ROW][C]Total_time[/C][C]0.00528823940014578[/C][C]0.009451[/C][C]0.5595[/C][C]0.57658[/C][C]0.28829[/C][/ROW]
[ROW][C]Logins[/C][C]0.000916445389594762[/C][C]0.004937[/C][C]0.1856[/C][C]0.852958[/C][C]0.426479[/C][/ROW]
[ROW][C]long_feedback[/C][C]0.254727511090191[/C][C]0.004707[/C][C]54.1156[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8434491712327740.2872852.93590.0038160.001908
Total_time0.005288239400145780.0094510.55950.576580.28829
Logins0.0009164453895947620.0049370.18560.8529580.426479
long_feedback0.2547275110901910.00470754.115600






Multiple Linear Regression - Regression Statistics
Multiple R0.987562453991805
R-squared0.975279600534316
Adjusted R-squared0.974816093044334
F-TEST (value)2104.12910603253
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.38288082019299
Sum Squared Residuals305.97749805722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.987562453991805 \tabularnewline
R-squared & 0.975279600534316 \tabularnewline
Adjusted R-squared & 0.974816093044334 \tabularnewline
F-TEST (value) & 2104.12910603253 \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 & 1.38288082019299 \tabularnewline
Sum Squared Residuals & 305.97749805722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.987562453991805[/C][/ROW]
[ROW][C]R-squared[/C][C]0.975279600534316[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.974816093044334[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2104.12910603253[/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]1.38288082019299[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]305.97749805722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.987562453991805
R-squared0.975279600534316
Adjusted R-squared0.974816093044334
F-TEST (value)2104.12910603253
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.38288082019299
Sum Squared Residuals305.97749805722






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12626.3542154189932-0.354215418993183
22020.6286992886283-0.628699288628332
32423.969320645680.0306793543199644
42525.5900540264822-0.590054026482182
51511.44231970126023.5576802987398
61617.2172347689607-1.21723476896066
72020.7247203414358-0.724720341435792
81817.97296382949530.0270361705046694
91919.3897911352698-0.389791135269786
102020.1617631598452-0.161763159845179
113029.92501549730980.0749845026901775
123736.50681895517650.493181044823547
132320.39249474992032.60750525007973
143632.49696738669373.50303261330633
152929.1218457778079-0.12184577780788
163535.2055648978542-0.205564897854158
172424.5829134083647-0.582913408364675
182222.3707977771933-0.370797777193308
191919.3840669816301-0.384066981630068
203030.4178799410016-0.417879941001565
212726.35051622373070.64948377626932
222624.75081391944551.24918608055454
231515.2602422987806-0.260242298780607
243031.5991323061134-1.59913230611344
252828.3848290224322-0.384829022432186
262424.0646123774617-0.0646123774617056
272121.024015957442-0.0240159574420171
282727.3671495767882-0.367149576788186
292121.6645504163905-0.664550416390454
303030.1935943167521-0.193594316752058
313030.4639061718777-0.46390617187772
323331.13549107816771.86450892183229
333029.98510495197210.0148950480279407
342020.0630726782516-0.0630726782516288
352727.4895780119618-0.489578011961772
362524.81270631559920.187293684400849
373030.3470302339736-0.347030233973596
382020.4278100959692-0.42781009596923
3987.863852054906670.136147945093333
402424.6855357894329-0.685535789432872
412525.6770418753113-0.677041875311337
422524.48631067793090.513689322069137
432120.45064225233480.54935774766517
442121.1144734501863-0.114473450186282
452115.54461959808135.45538040191866
462626.5985934504485-0.598593450448496
472621.88534947164494.11465052835509
483029.9113839500350.0886160499649904
493434.0931827918395-0.093182791839532
503029.09180856448560.90819143551442
511820.9498711449311-2.94987114493111
5243.957705086613350.0422949133866516
533130.45241650537390.547583494626101
541818.0212966858795-0.0212966858794766
551414.2864881597134-0.286488159713442
562020.4824755986985-0.482475598698537
573636.3134732004268-0.313473200426845
582424.6726299196908-0.672629919690793
592624.913347786641.08665221336001
602222.2405489484457-0.24054894844571
613131.3401610319065-0.340161031906497
622120.83192984208850.168070157911545
633132.1910460627305-1.1910460627305
642626.3671146953214-0.367114695321432
652424.5228072400856-0.52280724008563
661515.818385860833-0.818385860833022
671919.6840409316817-0.68404093168166
682827.38727742691820.612722573081812
692424.545096810411-0.54509681041104
701818.5865907572425-0.586590757242508
712525.2473276055266-0.247327605526551
722020.3304502848202-0.330450284820236
732525.2689001428432-0.268900142843191
742424.5266210138332-0.526621013833236
752323.434247772615-0.434247772614989
762525.3642645439194-0.36426454391939
772020.547001325753-0.547001325753042
782323.2138892483159-0.213889248315945
792222.4047562942861-0.404756294286105
802525.2578598396921-0.257859839692068
811818.592260369948-0.592260369947996
823030.6024084787422-0.602408478742232
832222.2294785350885-0.229478535088544
842513.068390155754211.9316098442458
8588.11764696207448-0.11764696207448
862121.1631281973666-0.163128197366641
872222.1607756608096-0.16077566080957
882424.7151194543038-0.715119454303823
893026.10784480183423.89215519816576
902727.3175989699466-0.317598969946644
912423.75713152384160.242868476158369
922525.3110332763807-0.31103327638074
932120.91521462200050.0847853779994737
942424.5742728367704-0.574272836770382
952424.6005230695764-0.600523069576427
962020.4192311934071-0.419231193407075
972018.14585897167271.85414102832725
982424.6066230929815-0.606623092981483
994039.6877621349510.312237865049005
1002222.4144047942404-0.414404794240424
1013131.1881107254512-0.188110725451247
1022626.1125228628875-0.112522862887464
1032020.4606903268768-0.460690326876814
1041919.0055193660289-0.00551936602885669
1051515.5914388752944-0.591438875294432
1062121.2382425407108-0.238242540710826
1072222.2312926747232-0.231292674723168
1082424.6126865820455-0.612686582045525
1091920.0862059772661-1.08620597726607
1102425.240222295428-1.240222295428
1112323.5574150991022-0.557415099102191
1122726.32037051078870.679629489211345
11310.8863085238417440.113691476158256
1142424.2572054313386-0.257205431338613
115119.108505834351751.89149416564825
1162726.73527142663640.264728573363599
1172222.6757760182321-0.675776018232061
11800.889039684223047-0.889039684223047
1191716.4075654988720.592434501127978
12087.275195175477080.724804824522917
1212424.115768369782-0.115768369782002
1223130.37123453527310.628765464726945
1232424.4338900909938-0.433890090993805
1242019.06521497144440.934785028555636
12587.808385775146690.191614224853307
1262222.1423613775674-0.142361377567377
1273333.2684113387509-0.268411338750902
1283332.91790535717320.0820946428268075
1293131.5606699311921-0.560669931192141
1303333.5497708418105-0.549770841810507
1313535.164835313923-0.164835313922989
1322121.1923971199498-0.192397119949836
1332020.3416479489946-0.341647948994575
1342424.7345405812011-0.7345405812011
1352929.125020794501-0.125020794501
1362020.2914019508171-0.291401950817075
1372726.76706279093860.232937209061426
1382423.20221839095680.797781609043214
1392625.82913161856950.170868381430511
1402625.27753944180820.722460558191833
1411213.2476212665261-1.2476212665261
1422121.661992824493-0.661992824492982
1432424.2969595882737-0.296959588273738
1442121.2791698176-0.279169817599969
1453030.2352937632038-0.235293763203761
1463231.54565664366680.454343356333192
1472423.76388908415340.236110915846603
1482929.4518991279946-0.451899127994581
14900.843449171232773-0.843449171232773
15000.874189641881316-0.874189641881316
15100.844509574249306-0.844509574249306
15200.845950436714513-0.845950436714513
15300.843449171232773-0.843449171232773
15400.843449171232773-0.843449171232773
1552019.94898573831180.0510142616882116
1562728.4374567320438-1.43745673204383
15700.843449171232773-0.843449171232773
15800.847413150735691-0.847413150735691
15900.858606408024472-0.858606408024472
16054.75123220381910.248767796180904
16111.89271720275282-0.892717202752816
1622321.86391574725661.13608425274341
16300.846705479785597-0.846705479785597
1641614.78391847553541.21608152446457

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 26.3542154189932 & -0.354215418993183 \tabularnewline
2 & 20 & 20.6286992886283 & -0.628699288628332 \tabularnewline
3 & 24 & 23.96932064568 & 0.0306793543199644 \tabularnewline
4 & 25 & 25.5900540264822 & -0.590054026482182 \tabularnewline
5 & 15 & 11.4423197012602 & 3.5576802987398 \tabularnewline
6 & 16 & 17.2172347689607 & -1.21723476896066 \tabularnewline
7 & 20 & 20.7247203414358 & -0.724720341435792 \tabularnewline
8 & 18 & 17.9729638294953 & 0.0270361705046694 \tabularnewline
9 & 19 & 19.3897911352698 & -0.389791135269786 \tabularnewline
10 & 20 & 20.1617631598452 & -0.161763159845179 \tabularnewline
11 & 30 & 29.9250154973098 & 0.0749845026901775 \tabularnewline
12 & 37 & 36.5068189551765 & 0.493181044823547 \tabularnewline
13 & 23 & 20.3924947499203 & 2.60750525007973 \tabularnewline
14 & 36 & 32.4969673866937 & 3.50303261330633 \tabularnewline
15 & 29 & 29.1218457778079 & -0.12184577780788 \tabularnewline
16 & 35 & 35.2055648978542 & -0.205564897854158 \tabularnewline
17 & 24 & 24.5829134083647 & -0.582913408364675 \tabularnewline
18 & 22 & 22.3707977771933 & -0.370797777193308 \tabularnewline
19 & 19 & 19.3840669816301 & -0.384066981630068 \tabularnewline
20 & 30 & 30.4178799410016 & -0.417879941001565 \tabularnewline
21 & 27 & 26.3505162237307 & 0.64948377626932 \tabularnewline
22 & 26 & 24.7508139194455 & 1.24918608055454 \tabularnewline
23 & 15 & 15.2602422987806 & -0.260242298780607 \tabularnewline
24 & 30 & 31.5991323061134 & -1.59913230611344 \tabularnewline
25 & 28 & 28.3848290224322 & -0.384829022432186 \tabularnewline
26 & 24 & 24.0646123774617 & -0.0646123774617056 \tabularnewline
27 & 21 & 21.024015957442 & -0.0240159574420171 \tabularnewline
28 & 27 & 27.3671495767882 & -0.367149576788186 \tabularnewline
29 & 21 & 21.6645504163905 & -0.664550416390454 \tabularnewline
30 & 30 & 30.1935943167521 & -0.193594316752058 \tabularnewline
31 & 30 & 30.4639061718777 & -0.46390617187772 \tabularnewline
32 & 33 & 31.1354910781677 & 1.86450892183229 \tabularnewline
33 & 30 & 29.9851049519721 & 0.0148950480279407 \tabularnewline
34 & 20 & 20.0630726782516 & -0.0630726782516288 \tabularnewline
35 & 27 & 27.4895780119618 & -0.489578011961772 \tabularnewline
36 & 25 & 24.8127063155992 & 0.187293684400849 \tabularnewline
37 & 30 & 30.3470302339736 & -0.347030233973596 \tabularnewline
38 & 20 & 20.4278100959692 & -0.42781009596923 \tabularnewline
39 & 8 & 7.86385205490667 & 0.136147945093333 \tabularnewline
40 & 24 & 24.6855357894329 & -0.685535789432872 \tabularnewline
41 & 25 & 25.6770418753113 & -0.677041875311337 \tabularnewline
42 & 25 & 24.4863106779309 & 0.513689322069137 \tabularnewline
43 & 21 & 20.4506422523348 & 0.54935774766517 \tabularnewline
44 & 21 & 21.1144734501863 & -0.114473450186282 \tabularnewline
45 & 21 & 15.5446195980813 & 5.45538040191866 \tabularnewline
46 & 26 & 26.5985934504485 & -0.598593450448496 \tabularnewline
47 & 26 & 21.8853494716449 & 4.11465052835509 \tabularnewline
48 & 30 & 29.911383950035 & 0.0886160499649904 \tabularnewline
49 & 34 & 34.0931827918395 & -0.093182791839532 \tabularnewline
50 & 30 & 29.0918085644856 & 0.90819143551442 \tabularnewline
51 & 18 & 20.9498711449311 & -2.94987114493111 \tabularnewline
52 & 4 & 3.95770508661335 & 0.0422949133866516 \tabularnewline
53 & 31 & 30.4524165053739 & 0.547583494626101 \tabularnewline
54 & 18 & 18.0212966858795 & -0.0212966858794766 \tabularnewline
55 & 14 & 14.2864881597134 & -0.286488159713442 \tabularnewline
56 & 20 & 20.4824755986985 & -0.482475598698537 \tabularnewline
57 & 36 & 36.3134732004268 & -0.313473200426845 \tabularnewline
58 & 24 & 24.6726299196908 & -0.672629919690793 \tabularnewline
59 & 26 & 24.91334778664 & 1.08665221336001 \tabularnewline
60 & 22 & 22.2405489484457 & -0.24054894844571 \tabularnewline
61 & 31 & 31.3401610319065 & -0.340161031906497 \tabularnewline
62 & 21 & 20.8319298420885 & 0.168070157911545 \tabularnewline
63 & 31 & 32.1910460627305 & -1.1910460627305 \tabularnewline
64 & 26 & 26.3671146953214 & -0.367114695321432 \tabularnewline
65 & 24 & 24.5228072400856 & -0.52280724008563 \tabularnewline
66 & 15 & 15.818385860833 & -0.818385860833022 \tabularnewline
67 & 19 & 19.6840409316817 & -0.68404093168166 \tabularnewline
68 & 28 & 27.3872774269182 & 0.612722573081812 \tabularnewline
69 & 24 & 24.545096810411 & -0.54509681041104 \tabularnewline
70 & 18 & 18.5865907572425 & -0.586590757242508 \tabularnewline
71 & 25 & 25.2473276055266 & -0.247327605526551 \tabularnewline
72 & 20 & 20.3304502848202 & -0.330450284820236 \tabularnewline
73 & 25 & 25.2689001428432 & -0.268900142843191 \tabularnewline
74 & 24 & 24.5266210138332 & -0.526621013833236 \tabularnewline
75 & 23 & 23.434247772615 & -0.434247772614989 \tabularnewline
76 & 25 & 25.3642645439194 & -0.36426454391939 \tabularnewline
77 & 20 & 20.547001325753 & -0.547001325753042 \tabularnewline
78 & 23 & 23.2138892483159 & -0.213889248315945 \tabularnewline
79 & 22 & 22.4047562942861 & -0.404756294286105 \tabularnewline
80 & 25 & 25.2578598396921 & -0.257859839692068 \tabularnewline
81 & 18 & 18.592260369948 & -0.592260369947996 \tabularnewline
82 & 30 & 30.6024084787422 & -0.602408478742232 \tabularnewline
83 & 22 & 22.2294785350885 & -0.229478535088544 \tabularnewline
84 & 25 & 13.0683901557542 & 11.9316098442458 \tabularnewline
85 & 8 & 8.11764696207448 & -0.11764696207448 \tabularnewline
86 & 21 & 21.1631281973666 & -0.163128197366641 \tabularnewline
87 & 22 & 22.1607756608096 & -0.16077566080957 \tabularnewline
88 & 24 & 24.7151194543038 & -0.715119454303823 \tabularnewline
89 & 30 & 26.1078448018342 & 3.89215519816576 \tabularnewline
90 & 27 & 27.3175989699466 & -0.317598969946644 \tabularnewline
91 & 24 & 23.7571315238416 & 0.242868476158369 \tabularnewline
92 & 25 & 25.3110332763807 & -0.31103327638074 \tabularnewline
93 & 21 & 20.9152146220005 & 0.0847853779994737 \tabularnewline
94 & 24 & 24.5742728367704 & -0.574272836770382 \tabularnewline
95 & 24 & 24.6005230695764 & -0.600523069576427 \tabularnewline
96 & 20 & 20.4192311934071 & -0.419231193407075 \tabularnewline
97 & 20 & 18.1458589716727 & 1.85414102832725 \tabularnewline
98 & 24 & 24.6066230929815 & -0.606623092981483 \tabularnewline
99 & 40 & 39.687762134951 & 0.312237865049005 \tabularnewline
100 & 22 & 22.4144047942404 & -0.414404794240424 \tabularnewline
101 & 31 & 31.1881107254512 & -0.188110725451247 \tabularnewline
102 & 26 & 26.1125228628875 & -0.112522862887464 \tabularnewline
103 & 20 & 20.4606903268768 & -0.460690326876814 \tabularnewline
104 & 19 & 19.0055193660289 & -0.00551936602885669 \tabularnewline
105 & 15 & 15.5914388752944 & -0.591438875294432 \tabularnewline
106 & 21 & 21.2382425407108 & -0.238242540710826 \tabularnewline
107 & 22 & 22.2312926747232 & -0.231292674723168 \tabularnewline
108 & 24 & 24.6126865820455 & -0.612686582045525 \tabularnewline
109 & 19 & 20.0862059772661 & -1.08620597726607 \tabularnewline
110 & 24 & 25.240222295428 & -1.240222295428 \tabularnewline
111 & 23 & 23.5574150991022 & -0.557415099102191 \tabularnewline
112 & 27 & 26.3203705107887 & 0.679629489211345 \tabularnewline
113 & 1 & 0.886308523841744 & 0.113691476158256 \tabularnewline
114 & 24 & 24.2572054313386 & -0.257205431338613 \tabularnewline
115 & 11 & 9.10850583435175 & 1.89149416564825 \tabularnewline
116 & 27 & 26.7352714266364 & 0.264728573363599 \tabularnewline
117 & 22 & 22.6757760182321 & -0.675776018232061 \tabularnewline
118 & 0 & 0.889039684223047 & -0.889039684223047 \tabularnewline
119 & 17 & 16.407565498872 & 0.592434501127978 \tabularnewline
120 & 8 & 7.27519517547708 & 0.724804824522917 \tabularnewline
121 & 24 & 24.115768369782 & -0.115768369782002 \tabularnewline
122 & 31 & 30.3712345352731 & 0.628765464726945 \tabularnewline
123 & 24 & 24.4338900909938 & -0.433890090993805 \tabularnewline
124 & 20 & 19.0652149714444 & 0.934785028555636 \tabularnewline
125 & 8 & 7.80838577514669 & 0.191614224853307 \tabularnewline
126 & 22 & 22.1423613775674 & -0.142361377567377 \tabularnewline
127 & 33 & 33.2684113387509 & -0.268411338750902 \tabularnewline
128 & 33 & 32.9179053571732 & 0.0820946428268075 \tabularnewline
129 & 31 & 31.5606699311921 & -0.560669931192141 \tabularnewline
130 & 33 & 33.5497708418105 & -0.549770841810507 \tabularnewline
131 & 35 & 35.164835313923 & -0.164835313922989 \tabularnewline
132 & 21 & 21.1923971199498 & -0.192397119949836 \tabularnewline
133 & 20 & 20.3416479489946 & -0.341647948994575 \tabularnewline
134 & 24 & 24.7345405812011 & -0.7345405812011 \tabularnewline
135 & 29 & 29.125020794501 & -0.125020794501 \tabularnewline
136 & 20 & 20.2914019508171 & -0.291401950817075 \tabularnewline
137 & 27 & 26.7670627909386 & 0.232937209061426 \tabularnewline
138 & 24 & 23.2022183909568 & 0.797781609043214 \tabularnewline
139 & 26 & 25.8291316185695 & 0.170868381430511 \tabularnewline
140 & 26 & 25.2775394418082 & 0.722460558191833 \tabularnewline
141 & 12 & 13.2476212665261 & -1.2476212665261 \tabularnewline
142 & 21 & 21.661992824493 & -0.661992824492982 \tabularnewline
143 & 24 & 24.2969595882737 & -0.296959588273738 \tabularnewline
144 & 21 & 21.2791698176 & -0.279169817599969 \tabularnewline
145 & 30 & 30.2352937632038 & -0.235293763203761 \tabularnewline
146 & 32 & 31.5456566436668 & 0.454343356333192 \tabularnewline
147 & 24 & 23.7638890841534 & 0.236110915846603 \tabularnewline
148 & 29 & 29.4518991279946 & -0.451899127994581 \tabularnewline
149 & 0 & 0.843449171232773 & -0.843449171232773 \tabularnewline
150 & 0 & 0.874189641881316 & -0.874189641881316 \tabularnewline
151 & 0 & 0.844509574249306 & -0.844509574249306 \tabularnewline
152 & 0 & 0.845950436714513 & -0.845950436714513 \tabularnewline
153 & 0 & 0.843449171232773 & -0.843449171232773 \tabularnewline
154 & 0 & 0.843449171232773 & -0.843449171232773 \tabularnewline
155 & 20 & 19.9489857383118 & 0.0510142616882116 \tabularnewline
156 & 27 & 28.4374567320438 & -1.43745673204383 \tabularnewline
157 & 0 & 0.843449171232773 & -0.843449171232773 \tabularnewline
158 & 0 & 0.847413150735691 & -0.847413150735691 \tabularnewline
159 & 0 & 0.858606408024472 & -0.858606408024472 \tabularnewline
160 & 5 & 4.7512322038191 & 0.248767796180904 \tabularnewline
161 & 1 & 1.89271720275282 & -0.892717202752816 \tabularnewline
162 & 23 & 21.8639157472566 & 1.13608425274341 \tabularnewline
163 & 0 & 0.846705479785597 & -0.846705479785597 \tabularnewline
164 & 16 & 14.7839184755354 & 1.21608152446457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&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]26.3542154189932[/C][C]-0.354215418993183[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.6286992886283[/C][C]-0.628699288628332[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]23.96932064568[/C][C]0.0306793543199644[/C][/ROW]
[ROW][C]4[/C][C]25[/C][C]25.5900540264822[/C][C]-0.590054026482182[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]11.4423197012602[/C][C]3.5576802987398[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.2172347689607[/C][C]-1.21723476896066[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.7247203414358[/C][C]-0.724720341435792[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]17.9729638294953[/C][C]0.0270361705046694[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]19.3897911352698[/C][C]-0.389791135269786[/C][/ROW]
[ROW][C]10[/C][C]20[/C][C]20.1617631598452[/C][C]-0.161763159845179[/C][/ROW]
[ROW][C]11[/C][C]30[/C][C]29.9250154973098[/C][C]0.0749845026901775[/C][/ROW]
[ROW][C]12[/C][C]37[/C][C]36.5068189551765[/C][C]0.493181044823547[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]20.3924947499203[/C][C]2.60750525007973[/C][/ROW]
[ROW][C]14[/C][C]36[/C][C]32.4969673866937[/C][C]3.50303261330633[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]29.1218457778079[/C][C]-0.12184577780788[/C][/ROW]
[ROW][C]16[/C][C]35[/C][C]35.2055648978542[/C][C]-0.205564897854158[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]24.5829134083647[/C][C]-0.582913408364675[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]22.3707977771933[/C][C]-0.370797777193308[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]19.3840669816301[/C][C]-0.384066981630068[/C][/ROW]
[ROW][C]20[/C][C]30[/C][C]30.4178799410016[/C][C]-0.417879941001565[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]26.3505162237307[/C][C]0.64948377626932[/C][/ROW]
[ROW][C]22[/C][C]26[/C][C]24.7508139194455[/C][C]1.24918608055454[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]15.2602422987806[/C][C]-0.260242298780607[/C][/ROW]
[ROW][C]24[/C][C]30[/C][C]31.5991323061134[/C][C]-1.59913230611344[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]28.3848290224322[/C][C]-0.384829022432186[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]24.0646123774617[/C][C]-0.0646123774617056[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]21.024015957442[/C][C]-0.0240159574420171[/C][/ROW]
[ROW][C]28[/C][C]27[/C][C]27.3671495767882[/C][C]-0.367149576788186[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]21.6645504163905[/C][C]-0.664550416390454[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]30.1935943167521[/C][C]-0.193594316752058[/C][/ROW]
[ROW][C]31[/C][C]30[/C][C]30.4639061718777[/C][C]-0.46390617187772[/C][/ROW]
[ROW][C]32[/C][C]33[/C][C]31.1354910781677[/C][C]1.86450892183229[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]29.9851049519721[/C][C]0.0148950480279407[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]20.0630726782516[/C][C]-0.0630726782516288[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]27.4895780119618[/C][C]-0.489578011961772[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]24.8127063155992[/C][C]0.187293684400849[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]30.3470302339736[/C][C]-0.347030233973596[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]20.4278100959692[/C][C]-0.42781009596923[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]7.86385205490667[/C][C]0.136147945093333[/C][/ROW]
[ROW][C]40[/C][C]24[/C][C]24.6855357894329[/C][C]-0.685535789432872[/C][/ROW]
[ROW][C]41[/C][C]25[/C][C]25.6770418753113[/C][C]-0.677041875311337[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]24.4863106779309[/C][C]0.513689322069137[/C][/ROW]
[ROW][C]43[/C][C]21[/C][C]20.4506422523348[/C][C]0.54935774766517[/C][/ROW]
[ROW][C]44[/C][C]21[/C][C]21.1144734501863[/C][C]-0.114473450186282[/C][/ROW]
[ROW][C]45[/C][C]21[/C][C]15.5446195980813[/C][C]5.45538040191866[/C][/ROW]
[ROW][C]46[/C][C]26[/C][C]26.5985934504485[/C][C]-0.598593450448496[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]21.8853494716449[/C][C]4.11465052835509[/C][/ROW]
[ROW][C]48[/C][C]30[/C][C]29.911383950035[/C][C]0.0886160499649904[/C][/ROW]
[ROW][C]49[/C][C]34[/C][C]34.0931827918395[/C][C]-0.093182791839532[/C][/ROW]
[ROW][C]50[/C][C]30[/C][C]29.0918085644856[/C][C]0.90819143551442[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.9498711449311[/C][C]-2.94987114493111[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.95770508661335[/C][C]0.0422949133866516[/C][/ROW]
[ROW][C]53[/C][C]31[/C][C]30.4524165053739[/C][C]0.547583494626101[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]18.0212966858795[/C][C]-0.0212966858794766[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]14.2864881597134[/C][C]-0.286488159713442[/C][/ROW]
[ROW][C]56[/C][C]20[/C][C]20.4824755986985[/C][C]-0.482475598698537[/C][/ROW]
[ROW][C]57[/C][C]36[/C][C]36.3134732004268[/C][C]-0.313473200426845[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]24.6726299196908[/C][C]-0.672629919690793[/C][/ROW]
[ROW][C]59[/C][C]26[/C][C]24.91334778664[/C][C]1.08665221336001[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]22.2405489484457[/C][C]-0.24054894844571[/C][/ROW]
[ROW][C]61[/C][C]31[/C][C]31.3401610319065[/C][C]-0.340161031906497[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]20.8319298420885[/C][C]0.168070157911545[/C][/ROW]
[ROW][C]63[/C][C]31[/C][C]32.1910460627305[/C][C]-1.1910460627305[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]26.3671146953214[/C][C]-0.367114695321432[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]24.5228072400856[/C][C]-0.52280724008563[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]15.818385860833[/C][C]-0.818385860833022[/C][/ROW]
[ROW][C]67[/C][C]19[/C][C]19.6840409316817[/C][C]-0.68404093168166[/C][/ROW]
[ROW][C]68[/C][C]28[/C][C]27.3872774269182[/C][C]0.612722573081812[/C][/ROW]
[ROW][C]69[/C][C]24[/C][C]24.545096810411[/C][C]-0.54509681041104[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]18.5865907572425[/C][C]-0.586590757242508[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]25.2473276055266[/C][C]-0.247327605526551[/C][/ROW]
[ROW][C]72[/C][C]20[/C][C]20.3304502848202[/C][C]-0.330450284820236[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]25.2689001428432[/C][C]-0.268900142843191[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]24.5266210138332[/C][C]-0.526621013833236[/C][/ROW]
[ROW][C]75[/C][C]23[/C][C]23.434247772615[/C][C]-0.434247772614989[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]25.3642645439194[/C][C]-0.36426454391939[/C][/ROW]
[ROW][C]77[/C][C]20[/C][C]20.547001325753[/C][C]-0.547001325753042[/C][/ROW]
[ROW][C]78[/C][C]23[/C][C]23.2138892483159[/C][C]-0.213889248315945[/C][/ROW]
[ROW][C]79[/C][C]22[/C][C]22.4047562942861[/C][C]-0.404756294286105[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]25.2578598396921[/C][C]-0.257859839692068[/C][/ROW]
[ROW][C]81[/C][C]18[/C][C]18.592260369948[/C][C]-0.592260369947996[/C][/ROW]
[ROW][C]82[/C][C]30[/C][C]30.6024084787422[/C][C]-0.602408478742232[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]22.2294785350885[/C][C]-0.229478535088544[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]13.0683901557542[/C][C]11.9316098442458[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]8.11764696207448[/C][C]-0.11764696207448[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]21.1631281973666[/C][C]-0.163128197366641[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]22.1607756608096[/C][C]-0.16077566080957[/C][/ROW]
[ROW][C]88[/C][C]24[/C][C]24.7151194543038[/C][C]-0.715119454303823[/C][/ROW]
[ROW][C]89[/C][C]30[/C][C]26.1078448018342[/C][C]3.89215519816576[/C][/ROW]
[ROW][C]90[/C][C]27[/C][C]27.3175989699466[/C][C]-0.317598969946644[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]23.7571315238416[/C][C]0.242868476158369[/C][/ROW]
[ROW][C]92[/C][C]25[/C][C]25.3110332763807[/C][C]-0.31103327638074[/C][/ROW]
[ROW][C]93[/C][C]21[/C][C]20.9152146220005[/C][C]0.0847853779994737[/C][/ROW]
[ROW][C]94[/C][C]24[/C][C]24.5742728367704[/C][C]-0.574272836770382[/C][/ROW]
[ROW][C]95[/C][C]24[/C][C]24.6005230695764[/C][C]-0.600523069576427[/C][/ROW]
[ROW][C]96[/C][C]20[/C][C]20.4192311934071[/C][C]-0.419231193407075[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]18.1458589716727[/C][C]1.85414102832725[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]24.6066230929815[/C][C]-0.606623092981483[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]39.687762134951[/C][C]0.312237865049005[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]22.4144047942404[/C][C]-0.414404794240424[/C][/ROW]
[ROW][C]101[/C][C]31[/C][C]31.1881107254512[/C][C]-0.188110725451247[/C][/ROW]
[ROW][C]102[/C][C]26[/C][C]26.1125228628875[/C][C]-0.112522862887464[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.4606903268768[/C][C]-0.460690326876814[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]19.0055193660289[/C][C]-0.00551936602885669[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]15.5914388752944[/C][C]-0.591438875294432[/C][/ROW]
[ROW][C]106[/C][C]21[/C][C]21.2382425407108[/C][C]-0.238242540710826[/C][/ROW]
[ROW][C]107[/C][C]22[/C][C]22.2312926747232[/C][C]-0.231292674723168[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]24.6126865820455[/C][C]-0.612686582045525[/C][/ROW]
[ROW][C]109[/C][C]19[/C][C]20.0862059772661[/C][C]-1.08620597726607[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]25.240222295428[/C][C]-1.240222295428[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]23.5574150991022[/C][C]-0.557415099102191[/C][/ROW]
[ROW][C]112[/C][C]27[/C][C]26.3203705107887[/C][C]0.679629489211345[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0.886308523841744[/C][C]0.113691476158256[/C][/ROW]
[ROW][C]114[/C][C]24[/C][C]24.2572054313386[/C][C]-0.257205431338613[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]9.10850583435175[/C][C]1.89149416564825[/C][/ROW]
[ROW][C]116[/C][C]27[/C][C]26.7352714266364[/C][C]0.264728573363599[/C][/ROW]
[ROW][C]117[/C][C]22[/C][C]22.6757760182321[/C][C]-0.675776018232061[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.889039684223047[/C][C]-0.889039684223047[/C][/ROW]
[ROW][C]119[/C][C]17[/C][C]16.407565498872[/C][C]0.592434501127978[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.27519517547708[/C][C]0.724804824522917[/C][/ROW]
[ROW][C]121[/C][C]24[/C][C]24.115768369782[/C][C]-0.115768369782002[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]30.3712345352731[/C][C]0.628765464726945[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]24.4338900909938[/C][C]-0.433890090993805[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]19.0652149714444[/C][C]0.934785028555636[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]7.80838577514669[/C][C]0.191614224853307[/C][/ROW]
[ROW][C]126[/C][C]22[/C][C]22.1423613775674[/C][C]-0.142361377567377[/C][/ROW]
[ROW][C]127[/C][C]33[/C][C]33.2684113387509[/C][C]-0.268411338750902[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]32.9179053571732[/C][C]0.0820946428268075[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]31.5606699311921[/C][C]-0.560669931192141[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]33.5497708418105[/C][C]-0.549770841810507[/C][/ROW]
[ROW][C]131[/C][C]35[/C][C]35.164835313923[/C][C]-0.164835313922989[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]21.1923971199498[/C][C]-0.192397119949836[/C][/ROW]
[ROW][C]133[/C][C]20[/C][C]20.3416479489946[/C][C]-0.341647948994575[/C][/ROW]
[ROW][C]134[/C][C]24[/C][C]24.7345405812011[/C][C]-0.7345405812011[/C][/ROW]
[ROW][C]135[/C][C]29[/C][C]29.125020794501[/C][C]-0.125020794501[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]20.2914019508171[/C][C]-0.291401950817075[/C][/ROW]
[ROW][C]137[/C][C]27[/C][C]26.7670627909386[/C][C]0.232937209061426[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]23.2022183909568[/C][C]0.797781609043214[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]25.8291316185695[/C][C]0.170868381430511[/C][/ROW]
[ROW][C]140[/C][C]26[/C][C]25.2775394418082[/C][C]0.722460558191833[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.2476212665261[/C][C]-1.2476212665261[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]21.661992824493[/C][C]-0.661992824492982[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.2969595882737[/C][C]-0.296959588273738[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.2791698176[/C][C]-0.279169817599969[/C][/ROW]
[ROW][C]145[/C][C]30[/C][C]30.2352937632038[/C][C]-0.235293763203761[/C][/ROW]
[ROW][C]146[/C][C]32[/C][C]31.5456566436668[/C][C]0.454343356333192[/C][/ROW]
[ROW][C]147[/C][C]24[/C][C]23.7638890841534[/C][C]0.236110915846603[/C][/ROW]
[ROW][C]148[/C][C]29[/C][C]29.4518991279946[/C][C]-0.451899127994581[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.843449171232773[/C][C]-0.843449171232773[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.874189641881316[/C][C]-0.874189641881316[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.844509574249306[/C][C]-0.844509574249306[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.845950436714513[/C][C]-0.845950436714513[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.843449171232773[/C][C]-0.843449171232773[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.843449171232773[/C][C]-0.843449171232773[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.9489857383118[/C][C]0.0510142616882116[/C][/ROW]
[ROW][C]156[/C][C]27[/C][C]28.4374567320438[/C][C]-1.43745673204383[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]0.843449171232773[/C][C]-0.843449171232773[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]0.847413150735691[/C][C]-0.847413150735691[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]0.858606408024472[/C][C]-0.858606408024472[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]4.7512322038191[/C][C]0.248767796180904[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.89271720275282[/C][C]-0.892717202752816[/C][/ROW]
[ROW][C]162[/C][C]23[/C][C]21.8639157472566[/C][C]1.13608425274341[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]0.846705479785597[/C][C]-0.846705479785597[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]14.7839184755354[/C][C]1.21608152446457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12626.3542154189932-0.354215418993183
22020.6286992886283-0.628699288628332
32423.969320645680.0306793543199644
42525.5900540264822-0.590054026482182
51511.44231970126023.5576802987398
61617.2172347689607-1.21723476896066
72020.7247203414358-0.724720341435792
81817.97296382949530.0270361705046694
91919.3897911352698-0.389791135269786
102020.1617631598452-0.161763159845179
113029.92501549730980.0749845026901775
123736.50681895517650.493181044823547
132320.39249474992032.60750525007973
143632.49696738669373.50303261330633
152929.1218457778079-0.12184577780788
163535.2055648978542-0.205564897854158
172424.5829134083647-0.582913408364675
182222.3707977771933-0.370797777193308
191919.3840669816301-0.384066981630068
203030.4178799410016-0.417879941001565
212726.35051622373070.64948377626932
222624.75081391944551.24918608055454
231515.2602422987806-0.260242298780607
243031.5991323061134-1.59913230611344
252828.3848290224322-0.384829022432186
262424.0646123774617-0.0646123774617056
272121.024015957442-0.0240159574420171
282727.3671495767882-0.367149576788186
292121.6645504163905-0.664550416390454
303030.1935943167521-0.193594316752058
313030.4639061718777-0.46390617187772
323331.13549107816771.86450892183229
333029.98510495197210.0148950480279407
342020.0630726782516-0.0630726782516288
352727.4895780119618-0.489578011961772
362524.81270631559920.187293684400849
373030.3470302339736-0.347030233973596
382020.4278100959692-0.42781009596923
3987.863852054906670.136147945093333
402424.6855357894329-0.685535789432872
412525.6770418753113-0.677041875311337
422524.48631067793090.513689322069137
432120.45064225233480.54935774766517
442121.1144734501863-0.114473450186282
452115.54461959808135.45538040191866
462626.5985934504485-0.598593450448496
472621.88534947164494.11465052835509
483029.9113839500350.0886160499649904
493434.0931827918395-0.093182791839532
503029.09180856448560.90819143551442
511820.9498711449311-2.94987114493111
5243.957705086613350.0422949133866516
533130.45241650537390.547583494626101
541818.0212966858795-0.0212966858794766
551414.2864881597134-0.286488159713442
562020.4824755986985-0.482475598698537
573636.3134732004268-0.313473200426845
582424.6726299196908-0.672629919690793
592624.913347786641.08665221336001
602222.2405489484457-0.24054894844571
613131.3401610319065-0.340161031906497
622120.83192984208850.168070157911545
633132.1910460627305-1.1910460627305
642626.3671146953214-0.367114695321432
652424.5228072400856-0.52280724008563
661515.818385860833-0.818385860833022
671919.6840409316817-0.68404093168166
682827.38727742691820.612722573081812
692424.545096810411-0.54509681041104
701818.5865907572425-0.586590757242508
712525.2473276055266-0.247327605526551
722020.3304502848202-0.330450284820236
732525.2689001428432-0.268900142843191
742424.5266210138332-0.526621013833236
752323.434247772615-0.434247772614989
762525.3642645439194-0.36426454391939
772020.547001325753-0.547001325753042
782323.2138892483159-0.213889248315945
792222.4047562942861-0.404756294286105
802525.2578598396921-0.257859839692068
811818.592260369948-0.592260369947996
823030.6024084787422-0.602408478742232
832222.2294785350885-0.229478535088544
842513.068390155754211.9316098442458
8588.11764696207448-0.11764696207448
862121.1631281973666-0.163128197366641
872222.1607756608096-0.16077566080957
882424.7151194543038-0.715119454303823
893026.10784480183423.89215519816576
902727.3175989699466-0.317598969946644
912423.75713152384160.242868476158369
922525.3110332763807-0.31103327638074
932120.91521462200050.0847853779994737
942424.5742728367704-0.574272836770382
952424.6005230695764-0.600523069576427
962020.4192311934071-0.419231193407075
972018.14585897167271.85414102832725
982424.6066230929815-0.606623092981483
994039.6877621349510.312237865049005
1002222.4144047942404-0.414404794240424
1013131.1881107254512-0.188110725451247
1022626.1125228628875-0.112522862887464
1032020.4606903268768-0.460690326876814
1041919.0055193660289-0.00551936602885669
1051515.5914388752944-0.591438875294432
1062121.2382425407108-0.238242540710826
1072222.2312926747232-0.231292674723168
1082424.6126865820455-0.612686582045525
1091920.0862059772661-1.08620597726607
1102425.240222295428-1.240222295428
1112323.5574150991022-0.557415099102191
1122726.32037051078870.679629489211345
11310.8863085238417440.113691476158256
1142424.2572054313386-0.257205431338613
115119.108505834351751.89149416564825
1162726.73527142663640.264728573363599
1172222.6757760182321-0.675776018232061
11800.889039684223047-0.889039684223047
1191716.4075654988720.592434501127978
12087.275195175477080.724804824522917
1212424.115768369782-0.115768369782002
1223130.37123453527310.628765464726945
1232424.4338900909938-0.433890090993805
1242019.06521497144440.934785028555636
12587.808385775146690.191614224853307
1262222.1423613775674-0.142361377567377
1273333.2684113387509-0.268411338750902
1283332.91790535717320.0820946428268075
1293131.5606699311921-0.560669931192141
1303333.5497708418105-0.549770841810507
1313535.164835313923-0.164835313922989
1322121.1923971199498-0.192397119949836
1332020.3416479489946-0.341647948994575
1342424.7345405812011-0.7345405812011
1352929.125020794501-0.125020794501
1362020.2914019508171-0.291401950817075
1372726.76706279093860.232937209061426
1382423.20221839095680.797781609043214
1392625.82913161856950.170868381430511
1402625.27753944180820.722460558191833
1411213.2476212665261-1.2476212665261
1422121.661992824493-0.661992824492982
1432424.2969595882737-0.296959588273738
1442121.2791698176-0.279169817599969
1453030.2352937632038-0.235293763203761
1463231.54565664366680.454343356333192
1472423.76388908415340.236110915846603
1482929.4518991279946-0.451899127994581
14900.843449171232773-0.843449171232773
15000.874189641881316-0.874189641881316
15100.844509574249306-0.844509574249306
15200.845950436714513-0.845950436714513
15300.843449171232773-0.843449171232773
15400.843449171232773-0.843449171232773
1552019.94898573831180.0510142616882116
1562728.4374567320438-1.43745673204383
15700.843449171232773-0.843449171232773
15800.847413150735691-0.847413150735691
15900.858606408024472-0.858606408024472
16054.75123220381910.248767796180904
16111.89271720275282-0.892717202752816
1622321.86391574725661.13608425274341
16300.846705479785597-0.846705479785597
1641614.78391847553541.21608152446457






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7894962878958540.4210074242082920.210503712104146
80.666956870217910.6660862595641790.33304312978209
90.5500119691752520.8999760616494970.449988030824748
100.4188465173239910.8376930346479820.581153482676009
110.4419028059119860.8838056118239730.558097194088014
120.4669387731104130.9338775462208270.533061226889587
130.6197503699533970.7604992600932050.380249630046603
140.9040891755971740.1918216488056520.095910824402826
150.866777367795010.2664452644099790.133222632204989
160.818912101060840.362175797878320.18108789893916
170.7961400891102850.4077198217794310.203859910889715
180.7359635628275370.5280728743449260.264036437172463
190.6855357980543450.628928403891310.314464201945655
200.6360112731339860.7279774537320280.363988726866014
210.5695282884378780.8609434231242440.430471711562122
220.5198562625590830.9602874748818330.480143737440916
230.4651165101517390.9302330203034790.534883489848261
240.5193845641584630.9612308716830740.480615435841537
250.4535835057015540.9071670114031070.546416494298446
260.3887800346140010.7775600692280020.611219965386
270.3268728050757110.6537456101514210.673127194924289
280.2766395106503040.5532790213006080.723360489349696
290.2431669835949440.4863339671898880.756833016405056
300.2004820226641160.4009640453282320.799517977335884
310.1654909083277950.3309818166555890.834509091672205
320.2073002222842270.4146004445684550.792699777715772
330.1667112366070330.3334224732140660.833288763392967
340.1322936419990640.2645872839981280.867706358000936
350.1110761059147880.2221522118295770.888923894085212
360.0856230683616870.1712461367233740.914376931638313
370.06641992620726840.1328398524145370.933580073792732
380.05177216072848370.1035443214569670.948227839271516
390.03823633821997610.07647267643995210.961763661780024
400.02905110894276120.05810221788552250.970948891057239
410.02363366522226750.04726733044453490.976366334777732
420.01751417407961590.03502834815923190.982485825920384
430.01297539834547980.02595079669095960.98702460165452
440.009121185200085060.01824237040017010.990878814799915
450.3485008825538070.6970017651076140.651499117446193
460.3081332646281550.6162665292563110.691866735371845
470.639209255128940.721581489742120.36079074487106
480.5911855496782010.8176289006435980.408814450321799
490.5421267275379360.9157465449241280.457873272462064
500.5104625816105940.9790748367788130.489537418389406
510.7069264852987030.5861470294025930.293073514701297
520.6731979786300030.6536040427399930.326802021369997
530.6387523659067310.7224952681865370.361247634093269
540.596123334988940.807753330022120.40387666501106
550.5571822518468050.8856354963063890.442817748153194
560.516295803492210.9674083930155810.48370419650779
570.470033086533540.940066173067080.52996691346646
580.4354616306801370.8709232613602740.564538369319863
590.4170639061557960.8341278123115920.582936093844204
600.3743133775674140.7486267551348270.625686622432586
610.331889114979530.663778229959060.66811088502047
620.2902947976220630.5805895952441250.709705202377937
630.2803469181119460.5606938362238910.719653081888054
640.245028407588180.490056815176360.75497159241182
650.2157656067933770.4315312135867550.784234393206623
660.1996895974120540.3993791948241080.800310402587946
670.1795209027510720.3590418055021440.820479097248928
680.1555374265314430.3110748530628860.844462573468557
690.1338243521220270.2676487042440530.866175647877973
700.1165737468334750.233147493666950.883426253166525
710.0963158029112110.1926316058224220.903684197088789
720.0799992671445530.1599985342891060.920000732855447
730.06487092180576160.1297418436115230.935129078194238
740.05340375203121290.1068075040624260.946596247968787
750.043329637247450.08665927449490.95667036275255
760.03429701706417740.06859403412835490.965702982935823
770.02765570457188740.05531140914377480.972344295428113
780.02136906671239060.04273813342478130.97863093328761
790.01668298996709730.03336597993419460.983317010032903
800.01261439843257770.02522879686515550.987385601567422
810.01001346391398180.02002692782796350.989986536086018
820.00776186840915740.01552373681831480.992238131590843
830.005699706039987790.01139941207997560.994300293960012
840.9999999999912221.75559208350561e-118.77796041752803e-12
850.9999999999831773.36463099042285e-111.68231549521143e-11
860.9999999999629897.40225576517483e-113.70112788258742e-11
870.9999999999196591.60682429473428e-108.03412147367142e-11
880.9999999998484623.03075432087786e-101.51537716043893e-10
890.9999999999999992.61726581306073e-151.30863290653037e-15
900.9999999999999976.74203980680771e-153.37101990340385e-15
910.9999999999999921.61307727135695e-148.06538635678474e-15
920.999999999999984.04248161740396e-142.02124080870198e-14
930.9999999999999491.01659582400991e-135.08297912004956e-14
940.9999999999998942.12133585959745e-131.06066792979873e-13
950.9999999999997854.30143532975456e-132.15071766487728e-13
960.9999999999995249.51416636640106e-134.75708318320053e-13
970.9999999999999774.58713765892047e-142.29356882946024e-14
980.999999999999959.85558622936485e-144.92779311468243e-14
990.9999999999998842.31835651176547e-131.15917825588274e-13
1000.9999999999997045.91444494590033e-132.95722247295017e-13
1010.9999999999992551.49029719722803e-127.45148598614017e-13
1020.9999999999980783.84452509557775e-121.92226254778887e-12
1030.9999999999954979.00632999128322e-124.50316499564161e-12
1040.9999999999887062.25888234161409e-111.12944117080704e-11
1050.9999999999760844.78321084759603e-112.39160542379802e-11
1060.9999999999434581.13084403303142e-105.65422016515708e-11
1070.9999999998667272.66546238049894e-101.33273119024947e-10
1080.9999999997328735.34254000832913e-102.67127000416457e-10
1090.999999999765344.69319733269317e-102.34659866634658e-10
1100.9999999998633932.73213317392261e-101.36606658696131e-10
1110.999999999729325.41360853635326e-102.70680426817663e-10
1120.9999999995673278.65346745890074e-104.32673372945037e-10
1130.999999999275661.44867804389409e-097.24339021947047e-10
1140.9999999983369033.32619392704599e-091.663096963523e-09
1150.9999999999796074.07852795492651e-112.03926397746325e-11
1160.9999999999454031.09193682551349e-105.45968412756743e-11
1170.9999999998724352.55129991398824e-101.27564995699412e-10
1180.9999999997487625.02476836660985e-102.51238418330492e-10
1190.9999999997764124.47176830084345e-102.23588415042172e-10
1200.9999999998819692.36062677024017e-101.18031338512009e-10
1210.9999999996893666.21267666061037e-103.10633833030518e-10
1220.999999999346171.30765886055297e-096.53829430276483e-10
1230.9999999987492352.50152915912835e-091.25076457956417e-09
1240.9999999991461641.70767139831622e-098.5383569915811e-10
1250.999999998829372.34125925850234e-091.17062962925117e-09
1260.999999996819896.36021982829593e-093.18010991414796e-09
1270.9999999931441871.37116267718053e-086.85581338590264e-09
1280.9999999831829843.36340317298718e-081.68170158649359e-08
1290.9999999721730795.5653842563464e-082.7826921281732e-08
1300.9999999658912766.82174475669725e-083.41087237834863e-08
1310.999999927935691.44128618422923e-077.20643092114614e-08
1320.9999998222632273.55473545041109e-071.77736772520554e-07
1330.9999996161512977.67697405588156e-073.83848702794078e-07
1340.9999992717652541.45646949115968e-067.28234745579838e-07
1350.9999983953672653.20926546940118e-061.60463273470059e-06
1360.9999974957750165.00844996768482e-062.50422498384241e-06
1370.999996049898867.90020227863696e-063.95010113931848e-06
1380.9999957677836628.46443267645796e-064.23221633822898e-06
1390.9999911365344811.7726931037629e-058.86346551881451e-06
1400.9999867397491152.65205017698107e-051.32602508849054e-05
1410.9999793075808274.1384838345998e-052.0692419172999e-05
1420.9999484508010080.0001030983979849015.15491989924507e-05
1430.9998865024959170.0002269950081666480.000113497504083324
1440.9997424908460820.0005150183078369010.00025750915391845
1450.9995104612610430.0009790774779139380.000489538738956969
1460.9988983160429350.00220336791412910.00110168395706455
1470.9975362898480990.004927420303801690.00246371015190084
1480.9969945111743430.00601097765131460.0030054888256573
1490.9940718763721510.01185624725569720.00592812362784858
1500.988229723151860.02354055369628140.0117702768481407
1510.9769248827830330.04615023443393470.0230751172169673
1520.9555966895191620.08880662096167680.0444033104808384
1530.9228508958436750.1542982083126490.0771491041563246
1540.8728625998339760.2542748003320480.127137400166024
1550.8827472588970890.2345054822058230.117252741102911
1560.9997764587649850.0004470824700304370.000223541235015219
1570.9995081832974540.0009836334050926650.000491816702546332

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.789496287895854 & 0.421007424208292 & 0.210503712104146 \tabularnewline
8 & 0.66695687021791 & 0.666086259564179 & 0.33304312978209 \tabularnewline
9 & 0.550011969175252 & 0.899976061649497 & 0.449988030824748 \tabularnewline
10 & 0.418846517323991 & 0.837693034647982 & 0.581153482676009 \tabularnewline
11 & 0.441902805911986 & 0.883805611823973 & 0.558097194088014 \tabularnewline
12 & 0.466938773110413 & 0.933877546220827 & 0.533061226889587 \tabularnewline
13 & 0.619750369953397 & 0.760499260093205 & 0.380249630046603 \tabularnewline
14 & 0.904089175597174 & 0.191821648805652 & 0.095910824402826 \tabularnewline
15 & 0.86677736779501 & 0.266445264409979 & 0.133222632204989 \tabularnewline
16 & 0.81891210106084 & 0.36217579787832 & 0.18108789893916 \tabularnewline
17 & 0.796140089110285 & 0.407719821779431 & 0.203859910889715 \tabularnewline
18 & 0.735963562827537 & 0.528072874344926 & 0.264036437172463 \tabularnewline
19 & 0.685535798054345 & 0.62892840389131 & 0.314464201945655 \tabularnewline
20 & 0.636011273133986 & 0.727977453732028 & 0.363988726866014 \tabularnewline
21 & 0.569528288437878 & 0.860943423124244 & 0.430471711562122 \tabularnewline
22 & 0.519856262559083 & 0.960287474881833 & 0.480143737440916 \tabularnewline
23 & 0.465116510151739 & 0.930233020303479 & 0.534883489848261 \tabularnewline
24 & 0.519384564158463 & 0.961230871683074 & 0.480615435841537 \tabularnewline
25 & 0.453583505701554 & 0.907167011403107 & 0.546416494298446 \tabularnewline
26 & 0.388780034614001 & 0.777560069228002 & 0.611219965386 \tabularnewline
27 & 0.326872805075711 & 0.653745610151421 & 0.673127194924289 \tabularnewline
28 & 0.276639510650304 & 0.553279021300608 & 0.723360489349696 \tabularnewline
29 & 0.243166983594944 & 0.486333967189888 & 0.756833016405056 \tabularnewline
30 & 0.200482022664116 & 0.400964045328232 & 0.799517977335884 \tabularnewline
31 & 0.165490908327795 & 0.330981816655589 & 0.834509091672205 \tabularnewline
32 & 0.207300222284227 & 0.414600444568455 & 0.792699777715772 \tabularnewline
33 & 0.166711236607033 & 0.333422473214066 & 0.833288763392967 \tabularnewline
34 & 0.132293641999064 & 0.264587283998128 & 0.867706358000936 \tabularnewline
35 & 0.111076105914788 & 0.222152211829577 & 0.888923894085212 \tabularnewline
36 & 0.085623068361687 & 0.171246136723374 & 0.914376931638313 \tabularnewline
37 & 0.0664199262072684 & 0.132839852414537 & 0.933580073792732 \tabularnewline
38 & 0.0517721607284837 & 0.103544321456967 & 0.948227839271516 \tabularnewline
39 & 0.0382363382199761 & 0.0764726764399521 & 0.961763661780024 \tabularnewline
40 & 0.0290511089427612 & 0.0581022178855225 & 0.970948891057239 \tabularnewline
41 & 0.0236336652222675 & 0.0472673304445349 & 0.976366334777732 \tabularnewline
42 & 0.0175141740796159 & 0.0350283481592319 & 0.982485825920384 \tabularnewline
43 & 0.0129753983454798 & 0.0259507966909596 & 0.98702460165452 \tabularnewline
44 & 0.00912118520008506 & 0.0182423704001701 & 0.990878814799915 \tabularnewline
45 & 0.348500882553807 & 0.697001765107614 & 0.651499117446193 \tabularnewline
46 & 0.308133264628155 & 0.616266529256311 & 0.691866735371845 \tabularnewline
47 & 0.63920925512894 & 0.72158148974212 & 0.36079074487106 \tabularnewline
48 & 0.591185549678201 & 0.817628900643598 & 0.408814450321799 \tabularnewline
49 & 0.542126727537936 & 0.915746544924128 & 0.457873272462064 \tabularnewline
50 & 0.510462581610594 & 0.979074836778813 & 0.489537418389406 \tabularnewline
51 & 0.706926485298703 & 0.586147029402593 & 0.293073514701297 \tabularnewline
52 & 0.673197978630003 & 0.653604042739993 & 0.326802021369997 \tabularnewline
53 & 0.638752365906731 & 0.722495268186537 & 0.361247634093269 \tabularnewline
54 & 0.59612333498894 & 0.80775333002212 & 0.40387666501106 \tabularnewline
55 & 0.557182251846805 & 0.885635496306389 & 0.442817748153194 \tabularnewline
56 & 0.51629580349221 & 0.967408393015581 & 0.48370419650779 \tabularnewline
57 & 0.47003308653354 & 0.94006617306708 & 0.52996691346646 \tabularnewline
58 & 0.435461630680137 & 0.870923261360274 & 0.564538369319863 \tabularnewline
59 & 0.417063906155796 & 0.834127812311592 & 0.582936093844204 \tabularnewline
60 & 0.374313377567414 & 0.748626755134827 & 0.625686622432586 \tabularnewline
61 & 0.33188911497953 & 0.66377822995906 & 0.66811088502047 \tabularnewline
62 & 0.290294797622063 & 0.580589595244125 & 0.709705202377937 \tabularnewline
63 & 0.280346918111946 & 0.560693836223891 & 0.719653081888054 \tabularnewline
64 & 0.24502840758818 & 0.49005681517636 & 0.75497159241182 \tabularnewline
65 & 0.215765606793377 & 0.431531213586755 & 0.784234393206623 \tabularnewline
66 & 0.199689597412054 & 0.399379194824108 & 0.800310402587946 \tabularnewline
67 & 0.179520902751072 & 0.359041805502144 & 0.820479097248928 \tabularnewline
68 & 0.155537426531443 & 0.311074853062886 & 0.844462573468557 \tabularnewline
69 & 0.133824352122027 & 0.267648704244053 & 0.866175647877973 \tabularnewline
70 & 0.116573746833475 & 0.23314749366695 & 0.883426253166525 \tabularnewline
71 & 0.096315802911211 & 0.192631605822422 & 0.903684197088789 \tabularnewline
72 & 0.079999267144553 & 0.159998534289106 & 0.920000732855447 \tabularnewline
73 & 0.0648709218057616 & 0.129741843611523 & 0.935129078194238 \tabularnewline
74 & 0.0534037520312129 & 0.106807504062426 & 0.946596247968787 \tabularnewline
75 & 0.04332963724745 & 0.0866592744949 & 0.95667036275255 \tabularnewline
76 & 0.0342970170641774 & 0.0685940341283549 & 0.965702982935823 \tabularnewline
77 & 0.0276557045718874 & 0.0553114091437748 & 0.972344295428113 \tabularnewline
78 & 0.0213690667123906 & 0.0427381334247813 & 0.97863093328761 \tabularnewline
79 & 0.0166829899670973 & 0.0333659799341946 & 0.983317010032903 \tabularnewline
80 & 0.0126143984325777 & 0.0252287968651555 & 0.987385601567422 \tabularnewline
81 & 0.0100134639139818 & 0.0200269278279635 & 0.989986536086018 \tabularnewline
82 & 0.0077618684091574 & 0.0155237368183148 & 0.992238131590843 \tabularnewline
83 & 0.00569970603998779 & 0.0113994120799756 & 0.994300293960012 \tabularnewline
84 & 0.999999999991222 & 1.75559208350561e-11 & 8.77796041752803e-12 \tabularnewline
85 & 0.999999999983177 & 3.36463099042285e-11 & 1.68231549521143e-11 \tabularnewline
86 & 0.999999999962989 & 7.40225576517483e-11 & 3.70112788258742e-11 \tabularnewline
87 & 0.999999999919659 & 1.60682429473428e-10 & 8.03412147367142e-11 \tabularnewline
88 & 0.999999999848462 & 3.03075432087786e-10 & 1.51537716043893e-10 \tabularnewline
89 & 0.999999999999999 & 2.61726581306073e-15 & 1.30863290653037e-15 \tabularnewline
90 & 0.999999999999997 & 6.74203980680771e-15 & 3.37101990340385e-15 \tabularnewline
91 & 0.999999999999992 & 1.61307727135695e-14 & 8.06538635678474e-15 \tabularnewline
92 & 0.99999999999998 & 4.04248161740396e-14 & 2.02124080870198e-14 \tabularnewline
93 & 0.999999999999949 & 1.01659582400991e-13 & 5.08297912004956e-14 \tabularnewline
94 & 0.999999999999894 & 2.12133585959745e-13 & 1.06066792979873e-13 \tabularnewline
95 & 0.999999999999785 & 4.30143532975456e-13 & 2.15071766487728e-13 \tabularnewline
96 & 0.999999999999524 & 9.51416636640106e-13 & 4.75708318320053e-13 \tabularnewline
97 & 0.999999999999977 & 4.58713765892047e-14 & 2.29356882946024e-14 \tabularnewline
98 & 0.99999999999995 & 9.85558622936485e-14 & 4.92779311468243e-14 \tabularnewline
99 & 0.999999999999884 & 2.31835651176547e-13 & 1.15917825588274e-13 \tabularnewline
100 & 0.999999999999704 & 5.91444494590033e-13 & 2.95722247295017e-13 \tabularnewline
101 & 0.999999999999255 & 1.49029719722803e-12 & 7.45148598614017e-13 \tabularnewline
102 & 0.999999999998078 & 3.84452509557775e-12 & 1.92226254778887e-12 \tabularnewline
103 & 0.999999999995497 & 9.00632999128322e-12 & 4.50316499564161e-12 \tabularnewline
104 & 0.999999999988706 & 2.25888234161409e-11 & 1.12944117080704e-11 \tabularnewline
105 & 0.999999999976084 & 4.78321084759603e-11 & 2.39160542379802e-11 \tabularnewline
106 & 0.999999999943458 & 1.13084403303142e-10 & 5.65422016515708e-11 \tabularnewline
107 & 0.999999999866727 & 2.66546238049894e-10 & 1.33273119024947e-10 \tabularnewline
108 & 0.999999999732873 & 5.34254000832913e-10 & 2.67127000416457e-10 \tabularnewline
109 & 0.99999999976534 & 4.69319733269317e-10 & 2.34659866634658e-10 \tabularnewline
110 & 0.999999999863393 & 2.73213317392261e-10 & 1.36606658696131e-10 \tabularnewline
111 & 0.99999999972932 & 5.41360853635326e-10 & 2.70680426817663e-10 \tabularnewline
112 & 0.999999999567327 & 8.65346745890074e-10 & 4.32673372945037e-10 \tabularnewline
113 & 0.99999999927566 & 1.44867804389409e-09 & 7.24339021947047e-10 \tabularnewline
114 & 0.999999998336903 & 3.32619392704599e-09 & 1.663096963523e-09 \tabularnewline
115 & 0.999999999979607 & 4.07852795492651e-11 & 2.03926397746325e-11 \tabularnewline
116 & 0.999999999945403 & 1.09193682551349e-10 & 5.45968412756743e-11 \tabularnewline
117 & 0.999999999872435 & 2.55129991398824e-10 & 1.27564995699412e-10 \tabularnewline
118 & 0.999999999748762 & 5.02476836660985e-10 & 2.51238418330492e-10 \tabularnewline
119 & 0.999999999776412 & 4.47176830084345e-10 & 2.23588415042172e-10 \tabularnewline
120 & 0.999999999881969 & 2.36062677024017e-10 & 1.18031338512009e-10 \tabularnewline
121 & 0.999999999689366 & 6.21267666061037e-10 & 3.10633833030518e-10 \tabularnewline
122 & 0.99999999934617 & 1.30765886055297e-09 & 6.53829430276483e-10 \tabularnewline
123 & 0.999999998749235 & 2.50152915912835e-09 & 1.25076457956417e-09 \tabularnewline
124 & 0.999999999146164 & 1.70767139831622e-09 & 8.5383569915811e-10 \tabularnewline
125 & 0.99999999882937 & 2.34125925850234e-09 & 1.17062962925117e-09 \tabularnewline
126 & 0.99999999681989 & 6.36021982829593e-09 & 3.18010991414796e-09 \tabularnewline
127 & 0.999999993144187 & 1.37116267718053e-08 & 6.85581338590264e-09 \tabularnewline
128 & 0.999999983182984 & 3.36340317298718e-08 & 1.68170158649359e-08 \tabularnewline
129 & 0.999999972173079 & 5.5653842563464e-08 & 2.7826921281732e-08 \tabularnewline
130 & 0.999999965891276 & 6.82174475669725e-08 & 3.41087237834863e-08 \tabularnewline
131 & 0.99999992793569 & 1.44128618422923e-07 & 7.20643092114614e-08 \tabularnewline
132 & 0.999999822263227 & 3.55473545041109e-07 & 1.77736772520554e-07 \tabularnewline
133 & 0.999999616151297 & 7.67697405588156e-07 & 3.83848702794078e-07 \tabularnewline
134 & 0.999999271765254 & 1.45646949115968e-06 & 7.28234745579838e-07 \tabularnewline
135 & 0.999998395367265 & 3.20926546940118e-06 & 1.60463273470059e-06 \tabularnewline
136 & 0.999997495775016 & 5.00844996768482e-06 & 2.50422498384241e-06 \tabularnewline
137 & 0.99999604989886 & 7.90020227863696e-06 & 3.95010113931848e-06 \tabularnewline
138 & 0.999995767783662 & 8.46443267645796e-06 & 4.23221633822898e-06 \tabularnewline
139 & 0.999991136534481 & 1.7726931037629e-05 & 8.86346551881451e-06 \tabularnewline
140 & 0.999986739749115 & 2.65205017698107e-05 & 1.32602508849054e-05 \tabularnewline
141 & 0.999979307580827 & 4.1384838345998e-05 & 2.0692419172999e-05 \tabularnewline
142 & 0.999948450801008 & 0.000103098397984901 & 5.15491989924507e-05 \tabularnewline
143 & 0.999886502495917 & 0.000226995008166648 & 0.000113497504083324 \tabularnewline
144 & 0.999742490846082 & 0.000515018307836901 & 0.00025750915391845 \tabularnewline
145 & 0.999510461261043 & 0.000979077477913938 & 0.000489538738956969 \tabularnewline
146 & 0.998898316042935 & 0.0022033679141291 & 0.00110168395706455 \tabularnewline
147 & 0.997536289848099 & 0.00492742030380169 & 0.00246371015190084 \tabularnewline
148 & 0.996994511174343 & 0.0060109776513146 & 0.0030054888256573 \tabularnewline
149 & 0.994071876372151 & 0.0118562472556972 & 0.00592812362784858 \tabularnewline
150 & 0.98822972315186 & 0.0235405536962814 & 0.0117702768481407 \tabularnewline
151 & 0.976924882783033 & 0.0461502344339347 & 0.0230751172169673 \tabularnewline
152 & 0.955596689519162 & 0.0888066209616768 & 0.0444033104808384 \tabularnewline
153 & 0.922850895843675 & 0.154298208312649 & 0.0771491041563246 \tabularnewline
154 & 0.872862599833976 & 0.254274800332048 & 0.127137400166024 \tabularnewline
155 & 0.882747258897089 & 0.234505482205823 & 0.117252741102911 \tabularnewline
156 & 0.999776458764985 & 0.000447082470030437 & 0.000223541235015219 \tabularnewline
157 & 0.999508183297454 & 0.000983633405092665 & 0.000491816702546332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&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]0.789496287895854[/C][C]0.421007424208292[/C][C]0.210503712104146[/C][/ROW]
[ROW][C]8[/C][C]0.66695687021791[/C][C]0.666086259564179[/C][C]0.33304312978209[/C][/ROW]
[ROW][C]9[/C][C]0.550011969175252[/C][C]0.899976061649497[/C][C]0.449988030824748[/C][/ROW]
[ROW][C]10[/C][C]0.418846517323991[/C][C]0.837693034647982[/C][C]0.581153482676009[/C][/ROW]
[ROW][C]11[/C][C]0.441902805911986[/C][C]0.883805611823973[/C][C]0.558097194088014[/C][/ROW]
[ROW][C]12[/C][C]0.466938773110413[/C][C]0.933877546220827[/C][C]0.533061226889587[/C][/ROW]
[ROW][C]13[/C][C]0.619750369953397[/C][C]0.760499260093205[/C][C]0.380249630046603[/C][/ROW]
[ROW][C]14[/C][C]0.904089175597174[/C][C]0.191821648805652[/C][C]0.095910824402826[/C][/ROW]
[ROW][C]15[/C][C]0.86677736779501[/C][C]0.266445264409979[/C][C]0.133222632204989[/C][/ROW]
[ROW][C]16[/C][C]0.81891210106084[/C][C]0.36217579787832[/C][C]0.18108789893916[/C][/ROW]
[ROW][C]17[/C][C]0.796140089110285[/C][C]0.407719821779431[/C][C]0.203859910889715[/C][/ROW]
[ROW][C]18[/C][C]0.735963562827537[/C][C]0.528072874344926[/C][C]0.264036437172463[/C][/ROW]
[ROW][C]19[/C][C]0.685535798054345[/C][C]0.62892840389131[/C][C]0.314464201945655[/C][/ROW]
[ROW][C]20[/C][C]0.636011273133986[/C][C]0.727977453732028[/C][C]0.363988726866014[/C][/ROW]
[ROW][C]21[/C][C]0.569528288437878[/C][C]0.860943423124244[/C][C]0.430471711562122[/C][/ROW]
[ROW][C]22[/C][C]0.519856262559083[/C][C]0.960287474881833[/C][C]0.480143737440916[/C][/ROW]
[ROW][C]23[/C][C]0.465116510151739[/C][C]0.930233020303479[/C][C]0.534883489848261[/C][/ROW]
[ROW][C]24[/C][C]0.519384564158463[/C][C]0.961230871683074[/C][C]0.480615435841537[/C][/ROW]
[ROW][C]25[/C][C]0.453583505701554[/C][C]0.907167011403107[/C][C]0.546416494298446[/C][/ROW]
[ROW][C]26[/C][C]0.388780034614001[/C][C]0.777560069228002[/C][C]0.611219965386[/C][/ROW]
[ROW][C]27[/C][C]0.326872805075711[/C][C]0.653745610151421[/C][C]0.673127194924289[/C][/ROW]
[ROW][C]28[/C][C]0.276639510650304[/C][C]0.553279021300608[/C][C]0.723360489349696[/C][/ROW]
[ROW][C]29[/C][C]0.243166983594944[/C][C]0.486333967189888[/C][C]0.756833016405056[/C][/ROW]
[ROW][C]30[/C][C]0.200482022664116[/C][C]0.400964045328232[/C][C]0.799517977335884[/C][/ROW]
[ROW][C]31[/C][C]0.165490908327795[/C][C]0.330981816655589[/C][C]0.834509091672205[/C][/ROW]
[ROW][C]32[/C][C]0.207300222284227[/C][C]0.414600444568455[/C][C]0.792699777715772[/C][/ROW]
[ROW][C]33[/C][C]0.166711236607033[/C][C]0.333422473214066[/C][C]0.833288763392967[/C][/ROW]
[ROW][C]34[/C][C]0.132293641999064[/C][C]0.264587283998128[/C][C]0.867706358000936[/C][/ROW]
[ROW][C]35[/C][C]0.111076105914788[/C][C]0.222152211829577[/C][C]0.888923894085212[/C][/ROW]
[ROW][C]36[/C][C]0.085623068361687[/C][C]0.171246136723374[/C][C]0.914376931638313[/C][/ROW]
[ROW][C]37[/C][C]0.0664199262072684[/C][C]0.132839852414537[/C][C]0.933580073792732[/C][/ROW]
[ROW][C]38[/C][C]0.0517721607284837[/C][C]0.103544321456967[/C][C]0.948227839271516[/C][/ROW]
[ROW][C]39[/C][C]0.0382363382199761[/C][C]0.0764726764399521[/C][C]0.961763661780024[/C][/ROW]
[ROW][C]40[/C][C]0.0290511089427612[/C][C]0.0581022178855225[/C][C]0.970948891057239[/C][/ROW]
[ROW][C]41[/C][C]0.0236336652222675[/C][C]0.0472673304445349[/C][C]0.976366334777732[/C][/ROW]
[ROW][C]42[/C][C]0.0175141740796159[/C][C]0.0350283481592319[/C][C]0.982485825920384[/C][/ROW]
[ROW][C]43[/C][C]0.0129753983454798[/C][C]0.0259507966909596[/C][C]0.98702460165452[/C][/ROW]
[ROW][C]44[/C][C]0.00912118520008506[/C][C]0.0182423704001701[/C][C]0.990878814799915[/C][/ROW]
[ROW][C]45[/C][C]0.348500882553807[/C][C]0.697001765107614[/C][C]0.651499117446193[/C][/ROW]
[ROW][C]46[/C][C]0.308133264628155[/C][C]0.616266529256311[/C][C]0.691866735371845[/C][/ROW]
[ROW][C]47[/C][C]0.63920925512894[/C][C]0.72158148974212[/C][C]0.36079074487106[/C][/ROW]
[ROW][C]48[/C][C]0.591185549678201[/C][C]0.817628900643598[/C][C]0.408814450321799[/C][/ROW]
[ROW][C]49[/C][C]0.542126727537936[/C][C]0.915746544924128[/C][C]0.457873272462064[/C][/ROW]
[ROW][C]50[/C][C]0.510462581610594[/C][C]0.979074836778813[/C][C]0.489537418389406[/C][/ROW]
[ROW][C]51[/C][C]0.706926485298703[/C][C]0.586147029402593[/C][C]0.293073514701297[/C][/ROW]
[ROW][C]52[/C][C]0.673197978630003[/C][C]0.653604042739993[/C][C]0.326802021369997[/C][/ROW]
[ROW][C]53[/C][C]0.638752365906731[/C][C]0.722495268186537[/C][C]0.361247634093269[/C][/ROW]
[ROW][C]54[/C][C]0.59612333498894[/C][C]0.80775333002212[/C][C]0.40387666501106[/C][/ROW]
[ROW][C]55[/C][C]0.557182251846805[/C][C]0.885635496306389[/C][C]0.442817748153194[/C][/ROW]
[ROW][C]56[/C][C]0.51629580349221[/C][C]0.967408393015581[/C][C]0.48370419650779[/C][/ROW]
[ROW][C]57[/C][C]0.47003308653354[/C][C]0.94006617306708[/C][C]0.52996691346646[/C][/ROW]
[ROW][C]58[/C][C]0.435461630680137[/C][C]0.870923261360274[/C][C]0.564538369319863[/C][/ROW]
[ROW][C]59[/C][C]0.417063906155796[/C][C]0.834127812311592[/C][C]0.582936093844204[/C][/ROW]
[ROW][C]60[/C][C]0.374313377567414[/C][C]0.748626755134827[/C][C]0.625686622432586[/C][/ROW]
[ROW][C]61[/C][C]0.33188911497953[/C][C]0.66377822995906[/C][C]0.66811088502047[/C][/ROW]
[ROW][C]62[/C][C]0.290294797622063[/C][C]0.580589595244125[/C][C]0.709705202377937[/C][/ROW]
[ROW][C]63[/C][C]0.280346918111946[/C][C]0.560693836223891[/C][C]0.719653081888054[/C][/ROW]
[ROW][C]64[/C][C]0.24502840758818[/C][C]0.49005681517636[/C][C]0.75497159241182[/C][/ROW]
[ROW][C]65[/C][C]0.215765606793377[/C][C]0.431531213586755[/C][C]0.784234393206623[/C][/ROW]
[ROW][C]66[/C][C]0.199689597412054[/C][C]0.399379194824108[/C][C]0.800310402587946[/C][/ROW]
[ROW][C]67[/C][C]0.179520902751072[/C][C]0.359041805502144[/C][C]0.820479097248928[/C][/ROW]
[ROW][C]68[/C][C]0.155537426531443[/C][C]0.311074853062886[/C][C]0.844462573468557[/C][/ROW]
[ROW][C]69[/C][C]0.133824352122027[/C][C]0.267648704244053[/C][C]0.866175647877973[/C][/ROW]
[ROW][C]70[/C][C]0.116573746833475[/C][C]0.23314749366695[/C][C]0.883426253166525[/C][/ROW]
[ROW][C]71[/C][C]0.096315802911211[/C][C]0.192631605822422[/C][C]0.903684197088789[/C][/ROW]
[ROW][C]72[/C][C]0.079999267144553[/C][C]0.159998534289106[/C][C]0.920000732855447[/C][/ROW]
[ROW][C]73[/C][C]0.0648709218057616[/C][C]0.129741843611523[/C][C]0.935129078194238[/C][/ROW]
[ROW][C]74[/C][C]0.0534037520312129[/C][C]0.106807504062426[/C][C]0.946596247968787[/C][/ROW]
[ROW][C]75[/C][C]0.04332963724745[/C][C]0.0866592744949[/C][C]0.95667036275255[/C][/ROW]
[ROW][C]76[/C][C]0.0342970170641774[/C][C]0.0685940341283549[/C][C]0.965702982935823[/C][/ROW]
[ROW][C]77[/C][C]0.0276557045718874[/C][C]0.0553114091437748[/C][C]0.972344295428113[/C][/ROW]
[ROW][C]78[/C][C]0.0213690667123906[/C][C]0.0427381334247813[/C][C]0.97863093328761[/C][/ROW]
[ROW][C]79[/C][C]0.0166829899670973[/C][C]0.0333659799341946[/C][C]0.983317010032903[/C][/ROW]
[ROW][C]80[/C][C]0.0126143984325777[/C][C]0.0252287968651555[/C][C]0.987385601567422[/C][/ROW]
[ROW][C]81[/C][C]0.0100134639139818[/C][C]0.0200269278279635[/C][C]0.989986536086018[/C][/ROW]
[ROW][C]82[/C][C]0.0077618684091574[/C][C]0.0155237368183148[/C][C]0.992238131590843[/C][/ROW]
[ROW][C]83[/C][C]0.00569970603998779[/C][C]0.0113994120799756[/C][C]0.994300293960012[/C][/ROW]
[ROW][C]84[/C][C]0.999999999991222[/C][C]1.75559208350561e-11[/C][C]8.77796041752803e-12[/C][/ROW]
[ROW][C]85[/C][C]0.999999999983177[/C][C]3.36463099042285e-11[/C][C]1.68231549521143e-11[/C][/ROW]
[ROW][C]86[/C][C]0.999999999962989[/C][C]7.40225576517483e-11[/C][C]3.70112788258742e-11[/C][/ROW]
[ROW][C]87[/C][C]0.999999999919659[/C][C]1.60682429473428e-10[/C][C]8.03412147367142e-11[/C][/ROW]
[ROW][C]88[/C][C]0.999999999848462[/C][C]3.03075432087786e-10[/C][C]1.51537716043893e-10[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999999[/C][C]2.61726581306073e-15[/C][C]1.30863290653037e-15[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999997[/C][C]6.74203980680771e-15[/C][C]3.37101990340385e-15[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999992[/C][C]1.61307727135695e-14[/C][C]8.06538635678474e-15[/C][/ROW]
[ROW][C]92[/C][C]0.99999999999998[/C][C]4.04248161740396e-14[/C][C]2.02124080870198e-14[/C][/ROW]
[ROW][C]93[/C][C]0.999999999999949[/C][C]1.01659582400991e-13[/C][C]5.08297912004956e-14[/C][/ROW]
[ROW][C]94[/C][C]0.999999999999894[/C][C]2.12133585959745e-13[/C][C]1.06066792979873e-13[/C][/ROW]
[ROW][C]95[/C][C]0.999999999999785[/C][C]4.30143532975456e-13[/C][C]2.15071766487728e-13[/C][/ROW]
[ROW][C]96[/C][C]0.999999999999524[/C][C]9.51416636640106e-13[/C][C]4.75708318320053e-13[/C][/ROW]
[ROW][C]97[/C][C]0.999999999999977[/C][C]4.58713765892047e-14[/C][C]2.29356882946024e-14[/C][/ROW]
[ROW][C]98[/C][C]0.99999999999995[/C][C]9.85558622936485e-14[/C][C]4.92779311468243e-14[/C][/ROW]
[ROW][C]99[/C][C]0.999999999999884[/C][C]2.31835651176547e-13[/C][C]1.15917825588274e-13[/C][/ROW]
[ROW][C]100[/C][C]0.999999999999704[/C][C]5.91444494590033e-13[/C][C]2.95722247295017e-13[/C][/ROW]
[ROW][C]101[/C][C]0.999999999999255[/C][C]1.49029719722803e-12[/C][C]7.45148598614017e-13[/C][/ROW]
[ROW][C]102[/C][C]0.999999999998078[/C][C]3.84452509557775e-12[/C][C]1.92226254778887e-12[/C][/ROW]
[ROW][C]103[/C][C]0.999999999995497[/C][C]9.00632999128322e-12[/C][C]4.50316499564161e-12[/C][/ROW]
[ROW][C]104[/C][C]0.999999999988706[/C][C]2.25888234161409e-11[/C][C]1.12944117080704e-11[/C][/ROW]
[ROW][C]105[/C][C]0.999999999976084[/C][C]4.78321084759603e-11[/C][C]2.39160542379802e-11[/C][/ROW]
[ROW][C]106[/C][C]0.999999999943458[/C][C]1.13084403303142e-10[/C][C]5.65422016515708e-11[/C][/ROW]
[ROW][C]107[/C][C]0.999999999866727[/C][C]2.66546238049894e-10[/C][C]1.33273119024947e-10[/C][/ROW]
[ROW][C]108[/C][C]0.999999999732873[/C][C]5.34254000832913e-10[/C][C]2.67127000416457e-10[/C][/ROW]
[ROW][C]109[/C][C]0.99999999976534[/C][C]4.69319733269317e-10[/C][C]2.34659866634658e-10[/C][/ROW]
[ROW][C]110[/C][C]0.999999999863393[/C][C]2.73213317392261e-10[/C][C]1.36606658696131e-10[/C][/ROW]
[ROW][C]111[/C][C]0.99999999972932[/C][C]5.41360853635326e-10[/C][C]2.70680426817663e-10[/C][/ROW]
[ROW][C]112[/C][C]0.999999999567327[/C][C]8.65346745890074e-10[/C][C]4.32673372945037e-10[/C][/ROW]
[ROW][C]113[/C][C]0.99999999927566[/C][C]1.44867804389409e-09[/C][C]7.24339021947047e-10[/C][/ROW]
[ROW][C]114[/C][C]0.999999998336903[/C][C]3.32619392704599e-09[/C][C]1.663096963523e-09[/C][/ROW]
[ROW][C]115[/C][C]0.999999999979607[/C][C]4.07852795492651e-11[/C][C]2.03926397746325e-11[/C][/ROW]
[ROW][C]116[/C][C]0.999999999945403[/C][C]1.09193682551349e-10[/C][C]5.45968412756743e-11[/C][/ROW]
[ROW][C]117[/C][C]0.999999999872435[/C][C]2.55129991398824e-10[/C][C]1.27564995699412e-10[/C][/ROW]
[ROW][C]118[/C][C]0.999999999748762[/C][C]5.02476836660985e-10[/C][C]2.51238418330492e-10[/C][/ROW]
[ROW][C]119[/C][C]0.999999999776412[/C][C]4.47176830084345e-10[/C][C]2.23588415042172e-10[/C][/ROW]
[ROW][C]120[/C][C]0.999999999881969[/C][C]2.36062677024017e-10[/C][C]1.18031338512009e-10[/C][/ROW]
[ROW][C]121[/C][C]0.999999999689366[/C][C]6.21267666061037e-10[/C][C]3.10633833030518e-10[/C][/ROW]
[ROW][C]122[/C][C]0.99999999934617[/C][C]1.30765886055297e-09[/C][C]6.53829430276483e-10[/C][/ROW]
[ROW][C]123[/C][C]0.999999998749235[/C][C]2.50152915912835e-09[/C][C]1.25076457956417e-09[/C][/ROW]
[ROW][C]124[/C][C]0.999999999146164[/C][C]1.70767139831622e-09[/C][C]8.5383569915811e-10[/C][/ROW]
[ROW][C]125[/C][C]0.99999999882937[/C][C]2.34125925850234e-09[/C][C]1.17062962925117e-09[/C][/ROW]
[ROW][C]126[/C][C]0.99999999681989[/C][C]6.36021982829593e-09[/C][C]3.18010991414796e-09[/C][/ROW]
[ROW][C]127[/C][C]0.999999993144187[/C][C]1.37116267718053e-08[/C][C]6.85581338590264e-09[/C][/ROW]
[ROW][C]128[/C][C]0.999999983182984[/C][C]3.36340317298718e-08[/C][C]1.68170158649359e-08[/C][/ROW]
[ROW][C]129[/C][C]0.999999972173079[/C][C]5.5653842563464e-08[/C][C]2.7826921281732e-08[/C][/ROW]
[ROW][C]130[/C][C]0.999999965891276[/C][C]6.82174475669725e-08[/C][C]3.41087237834863e-08[/C][/ROW]
[ROW][C]131[/C][C]0.99999992793569[/C][C]1.44128618422923e-07[/C][C]7.20643092114614e-08[/C][/ROW]
[ROW][C]132[/C][C]0.999999822263227[/C][C]3.55473545041109e-07[/C][C]1.77736772520554e-07[/C][/ROW]
[ROW][C]133[/C][C]0.999999616151297[/C][C]7.67697405588156e-07[/C][C]3.83848702794078e-07[/C][/ROW]
[ROW][C]134[/C][C]0.999999271765254[/C][C]1.45646949115968e-06[/C][C]7.28234745579838e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999998395367265[/C][C]3.20926546940118e-06[/C][C]1.60463273470059e-06[/C][/ROW]
[ROW][C]136[/C][C]0.999997495775016[/C][C]5.00844996768482e-06[/C][C]2.50422498384241e-06[/C][/ROW]
[ROW][C]137[/C][C]0.99999604989886[/C][C]7.90020227863696e-06[/C][C]3.95010113931848e-06[/C][/ROW]
[ROW][C]138[/C][C]0.999995767783662[/C][C]8.46443267645796e-06[/C][C]4.23221633822898e-06[/C][/ROW]
[ROW][C]139[/C][C]0.999991136534481[/C][C]1.7726931037629e-05[/C][C]8.86346551881451e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999986739749115[/C][C]2.65205017698107e-05[/C][C]1.32602508849054e-05[/C][/ROW]
[ROW][C]141[/C][C]0.999979307580827[/C][C]4.1384838345998e-05[/C][C]2.0692419172999e-05[/C][/ROW]
[ROW][C]142[/C][C]0.999948450801008[/C][C]0.000103098397984901[/C][C]5.15491989924507e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999886502495917[/C][C]0.000226995008166648[/C][C]0.000113497504083324[/C][/ROW]
[ROW][C]144[/C][C]0.999742490846082[/C][C]0.000515018307836901[/C][C]0.00025750915391845[/C][/ROW]
[ROW][C]145[/C][C]0.999510461261043[/C][C]0.000979077477913938[/C][C]0.000489538738956969[/C][/ROW]
[ROW][C]146[/C][C]0.998898316042935[/C][C]0.0022033679141291[/C][C]0.00110168395706455[/C][/ROW]
[ROW][C]147[/C][C]0.997536289848099[/C][C]0.00492742030380169[/C][C]0.00246371015190084[/C][/ROW]
[ROW][C]148[/C][C]0.996994511174343[/C][C]0.0060109776513146[/C][C]0.0030054888256573[/C][/ROW]
[ROW][C]149[/C][C]0.994071876372151[/C][C]0.0118562472556972[/C][C]0.00592812362784858[/C][/ROW]
[ROW][C]150[/C][C]0.98822972315186[/C][C]0.0235405536962814[/C][C]0.0117702768481407[/C][/ROW]
[ROW][C]151[/C][C]0.976924882783033[/C][C]0.0461502344339347[/C][C]0.0230751172169673[/C][/ROW]
[ROW][C]152[/C][C]0.955596689519162[/C][C]0.0888066209616768[/C][C]0.0444033104808384[/C][/ROW]
[ROW][C]153[/C][C]0.922850895843675[/C][C]0.154298208312649[/C][C]0.0771491041563246[/C][/ROW]
[ROW][C]154[/C][C]0.872862599833976[/C][C]0.254274800332048[/C][C]0.127137400166024[/C][/ROW]
[ROW][C]155[/C][C]0.882747258897089[/C][C]0.234505482205823[/C][C]0.117252741102911[/C][/ROW]
[ROW][C]156[/C][C]0.999776458764985[/C][C]0.000447082470030437[/C][C]0.000223541235015219[/C][/ROW]
[ROW][C]157[/C][C]0.999508183297454[/C][C]0.000983633405092665[/C][C]0.000491816702546332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7894962878958540.4210074242082920.210503712104146
80.666956870217910.6660862595641790.33304312978209
90.5500119691752520.8999760616494970.449988030824748
100.4188465173239910.8376930346479820.581153482676009
110.4419028059119860.8838056118239730.558097194088014
120.4669387731104130.9338775462208270.533061226889587
130.6197503699533970.7604992600932050.380249630046603
140.9040891755971740.1918216488056520.095910824402826
150.866777367795010.2664452644099790.133222632204989
160.818912101060840.362175797878320.18108789893916
170.7961400891102850.4077198217794310.203859910889715
180.7359635628275370.5280728743449260.264036437172463
190.6855357980543450.628928403891310.314464201945655
200.6360112731339860.7279774537320280.363988726866014
210.5695282884378780.8609434231242440.430471711562122
220.5198562625590830.9602874748818330.480143737440916
230.4651165101517390.9302330203034790.534883489848261
240.5193845641584630.9612308716830740.480615435841537
250.4535835057015540.9071670114031070.546416494298446
260.3887800346140010.7775600692280020.611219965386
270.3268728050757110.6537456101514210.673127194924289
280.2766395106503040.5532790213006080.723360489349696
290.2431669835949440.4863339671898880.756833016405056
300.2004820226641160.4009640453282320.799517977335884
310.1654909083277950.3309818166555890.834509091672205
320.2073002222842270.4146004445684550.792699777715772
330.1667112366070330.3334224732140660.833288763392967
340.1322936419990640.2645872839981280.867706358000936
350.1110761059147880.2221522118295770.888923894085212
360.0856230683616870.1712461367233740.914376931638313
370.06641992620726840.1328398524145370.933580073792732
380.05177216072848370.1035443214569670.948227839271516
390.03823633821997610.07647267643995210.961763661780024
400.02905110894276120.05810221788552250.970948891057239
410.02363366522226750.04726733044453490.976366334777732
420.01751417407961590.03502834815923190.982485825920384
430.01297539834547980.02595079669095960.98702460165452
440.009121185200085060.01824237040017010.990878814799915
450.3485008825538070.6970017651076140.651499117446193
460.3081332646281550.6162665292563110.691866735371845
470.639209255128940.721581489742120.36079074487106
480.5911855496782010.8176289006435980.408814450321799
490.5421267275379360.9157465449241280.457873272462064
500.5104625816105940.9790748367788130.489537418389406
510.7069264852987030.5861470294025930.293073514701297
520.6731979786300030.6536040427399930.326802021369997
530.6387523659067310.7224952681865370.361247634093269
540.596123334988940.807753330022120.40387666501106
550.5571822518468050.8856354963063890.442817748153194
560.516295803492210.9674083930155810.48370419650779
570.470033086533540.940066173067080.52996691346646
580.4354616306801370.8709232613602740.564538369319863
590.4170639061557960.8341278123115920.582936093844204
600.3743133775674140.7486267551348270.625686622432586
610.331889114979530.663778229959060.66811088502047
620.2902947976220630.5805895952441250.709705202377937
630.2803469181119460.5606938362238910.719653081888054
640.245028407588180.490056815176360.75497159241182
650.2157656067933770.4315312135867550.784234393206623
660.1996895974120540.3993791948241080.800310402587946
670.1795209027510720.3590418055021440.820479097248928
680.1555374265314430.3110748530628860.844462573468557
690.1338243521220270.2676487042440530.866175647877973
700.1165737468334750.233147493666950.883426253166525
710.0963158029112110.1926316058224220.903684197088789
720.0799992671445530.1599985342891060.920000732855447
730.06487092180576160.1297418436115230.935129078194238
740.05340375203121290.1068075040624260.946596247968787
750.043329637247450.08665927449490.95667036275255
760.03429701706417740.06859403412835490.965702982935823
770.02765570457188740.05531140914377480.972344295428113
780.02136906671239060.04273813342478130.97863093328761
790.01668298996709730.03336597993419460.983317010032903
800.01261439843257770.02522879686515550.987385601567422
810.01001346391398180.02002692782796350.989986536086018
820.00776186840915740.01552373681831480.992238131590843
830.005699706039987790.01139941207997560.994300293960012
840.9999999999912221.75559208350561e-118.77796041752803e-12
850.9999999999831773.36463099042285e-111.68231549521143e-11
860.9999999999629897.40225576517483e-113.70112788258742e-11
870.9999999999196591.60682429473428e-108.03412147367142e-11
880.9999999998484623.03075432087786e-101.51537716043893e-10
890.9999999999999992.61726581306073e-151.30863290653037e-15
900.9999999999999976.74203980680771e-153.37101990340385e-15
910.9999999999999921.61307727135695e-148.06538635678474e-15
920.999999999999984.04248161740396e-142.02124080870198e-14
930.9999999999999491.01659582400991e-135.08297912004956e-14
940.9999999999998942.12133585959745e-131.06066792979873e-13
950.9999999999997854.30143532975456e-132.15071766487728e-13
960.9999999999995249.51416636640106e-134.75708318320053e-13
970.9999999999999774.58713765892047e-142.29356882946024e-14
980.999999999999959.85558622936485e-144.92779311468243e-14
990.9999999999998842.31835651176547e-131.15917825588274e-13
1000.9999999999997045.91444494590033e-132.95722247295017e-13
1010.9999999999992551.49029719722803e-127.45148598614017e-13
1020.9999999999980783.84452509557775e-121.92226254778887e-12
1030.9999999999954979.00632999128322e-124.50316499564161e-12
1040.9999999999887062.25888234161409e-111.12944117080704e-11
1050.9999999999760844.78321084759603e-112.39160542379802e-11
1060.9999999999434581.13084403303142e-105.65422016515708e-11
1070.9999999998667272.66546238049894e-101.33273119024947e-10
1080.9999999997328735.34254000832913e-102.67127000416457e-10
1090.999999999765344.69319733269317e-102.34659866634658e-10
1100.9999999998633932.73213317392261e-101.36606658696131e-10
1110.999999999729325.41360853635326e-102.70680426817663e-10
1120.9999999995673278.65346745890074e-104.32673372945037e-10
1130.999999999275661.44867804389409e-097.24339021947047e-10
1140.9999999983369033.32619392704599e-091.663096963523e-09
1150.9999999999796074.07852795492651e-112.03926397746325e-11
1160.9999999999454031.09193682551349e-105.45968412756743e-11
1170.9999999998724352.55129991398824e-101.27564995699412e-10
1180.9999999997487625.02476836660985e-102.51238418330492e-10
1190.9999999997764124.47176830084345e-102.23588415042172e-10
1200.9999999998819692.36062677024017e-101.18031338512009e-10
1210.9999999996893666.21267666061037e-103.10633833030518e-10
1220.999999999346171.30765886055297e-096.53829430276483e-10
1230.9999999987492352.50152915912835e-091.25076457956417e-09
1240.9999999991461641.70767139831622e-098.5383569915811e-10
1250.999999998829372.34125925850234e-091.17062962925117e-09
1260.999999996819896.36021982829593e-093.18010991414796e-09
1270.9999999931441871.37116267718053e-086.85581338590264e-09
1280.9999999831829843.36340317298718e-081.68170158649359e-08
1290.9999999721730795.5653842563464e-082.7826921281732e-08
1300.9999999658912766.82174475669725e-083.41087237834863e-08
1310.999999927935691.44128618422923e-077.20643092114614e-08
1320.9999998222632273.55473545041109e-071.77736772520554e-07
1330.9999996161512977.67697405588156e-073.83848702794078e-07
1340.9999992717652541.45646949115968e-067.28234745579838e-07
1350.9999983953672653.20926546940118e-061.60463273470059e-06
1360.9999974957750165.00844996768482e-062.50422498384241e-06
1370.999996049898867.90020227863696e-063.95010113931848e-06
1380.9999957677836628.46443267645796e-064.23221633822898e-06
1390.9999911365344811.7726931037629e-058.86346551881451e-06
1400.9999867397491152.65205017698107e-051.32602508849054e-05
1410.9999793075808274.1384838345998e-052.0692419172999e-05
1420.9999484508010080.0001030983979849015.15491989924507e-05
1430.9998865024959170.0002269950081666480.000113497504083324
1440.9997424908460820.0005150183078369010.00025750915391845
1450.9995104612610430.0009790774779139380.000489538738956969
1460.9988983160429350.00220336791412910.00110168395706455
1470.9975362898480990.004927420303801690.00246371015190084
1480.9969945111743430.00601097765131460.0030054888256573
1490.9940718763721510.01185624725569720.00592812362784858
1500.988229723151860.02354055369628140.0117702768481407
1510.9769248827830330.04615023443393470.0230751172169673
1520.9555966895191620.08880662096167680.0444033104808384
1530.9228508958436750.1542982083126490.0771491041563246
1540.8728625998339760.2542748003320480.127137400166024
1550.8827472588970890.2345054822058230.117252741102911
1560.9997764587649850.0004470824700304370.000223541235015219
1570.9995081832974540.0009836334050926650.000491816702546332






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.443708609271523NOK
5% type I error level800.529801324503311NOK
10% type I error level860.56953642384106NOK

\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 & 67 & 0.443708609271523 & NOK \tabularnewline
5% type I error level & 80 & 0.529801324503311 & NOK \tabularnewline
10% type I error level & 86 & 0.56953642384106 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145957&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]67[/C][C]0.443708609271523[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]80[/C][C]0.529801324503311[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.56953642384106[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145957&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145957&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.443708609271523NOK
5% type I error level800.529801324503311NOK
10% type I error level860.56953642384106NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
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Figure 3
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Input Parameters & R Code

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