Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 13:31:22 -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/t13219003018vfw28suhto4byx.htm/, Retrieved Thu, 28 Mar 2024 12:08:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145897, Retrieved Thu, 28 Mar 2024 12:08:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
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] [] [2011-11-21 18:31:22] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P       [Multiple Regression] [] [2011-11-21 18:38:54] [74be16979710d4c4e7c6647856088456]
-    D        [Multiple Regression] [paper multiple re...] [2011-12-14 18:23:03] [c5b29addb16cc630d2ea8a9650ee6d6d]
-               [Multiple Regression] [] [2011-12-20 21:22:01] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
95556	114468	70	127
54565	88594	44	90
63016	74151	36	68
79774	77921	119	111
31258	53212	30	51
52491	34956	23	33
91256	149703	46	123
22807	6853	39	5
77411	58907	58	63
48821	67067	51	66
52295	110563	65	99
63262	58126	40	72
50466	57113	42	55
62932	77993	76	116
38439	68091	31	71
70817	124676	83	125
105965	109522	36	123
73795	75865	62	74
82043	79746	28	116
74349	77844	38	117
82204	98681	70	98
55709	105531	76	101
37137	51428	33	43
70780	65703	40	103
55027	72562	126	107
56699	81728	56	77
65911	95580	63	87
56316	98278	46	99
26982	46629	35	46
54628	115189	108	96
96750	124865	34	92
53009	59392	54	96
64664	127818	35	96
36990	17821	23	15
85224	154076	46	147
37048	64881	49	56
59635	136506	56	81
42051	66524	38	69
26998	45988	19	34
63717	107445	29	98
55071	102772	26	82
40001	46657	52	64
54506	97563	54	61
35838	36663	45	45
50838	55369	56	37
86997	77921	596	64
33032	56968	57	21
61704	77519	55	104
117986	129805	99	126
56733	72761	51	104
55064	81278	21	87
5950	15049	20	7
84607	113935	58	130
32551	25109	21	21
31701	45824	66	35
71170	89644	47	97
101773	109011	55	103
101653	134245	158	210
81493	136692	46	151
55901	50741	45	57
109104	149510	46	117
114425	147888	117	152
36311	54987	56	52
70027	74467	30	83
73713	100033	45	87
40671	85505	38	80
89041	62426	33	88
57231	82932	61	83
68608	72002	63	120
59155	65469	41	76
55827	63572	33	70
22618	23824	36	26
58425	73831	35	66
65724	63551	73	89
56979	56756	46	100
72369	81399	54	98
79194	117881	24	109
202316	70711	27	51
44970	50495	32	82
49319	53845	52	65
36252	51390	31	46
75741	104953	89	104
38417	65983	36	36
64102	76839	37	123
56622	55792	31	59
15430	25155	142	27
72571	55291	44	84
67271	84279	222	61
43460	99692	52	46
99501	59633	51	125
28340	63249	45	58
76013	82928	51	152
37361	50000	64	52
48204	69455	66	85
76168	84068	81	95
85168	76195	43	78
125410	114634	45	144
123328	139357	35	149
83038	110044	97	101
120087	155118	41	205
91939	83061	44	61
103646	127122	61	145
29467	45653	35	28
43750	19630	43	49
34497	67229	57	68
66477	86060	32	142
71181	88003	66	82
74482	95815	32	105
174949	85499	24	52
46765	27220	55	56
90257	109882	38	81
51370	72579	43	100
1168	5841	9	11
51360	68369	36	87
25162	24610	25	31
21067	30995	78	67
58233	150662	42	150
855	6622	2	4
85903	93694	46	75
14116	13155	22	39
57637	111908	131	88
94137	57550	51	67
62147	16356	67	24
62832	40174	38	58
8773	13983	52	16
63785	52316	64	49
65196	99585	75	109
73087	86271	37	124
72631	131012	107	115
86281	130274	84	128
162365	159051	68	159
56530	76506	30	75
35606	49145	31	30
70111	66398	109	83
92046	127546	108	135
63989	6802	33	8
104911	99509	106	115
43448	43106	50	60
60029	108303	52	99
38650	64167	134	98
47261	8579	39	36
73586	97811	78	93
83042	84365	40	158
37238	10901	37	16
63958	91346	41	100
78956	33660	95	49
99518	93634	37	89
111436	109348	38	153
0	0	0	0
6023	7953	0	5
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
42564	63538	36	80
38885	108281	65	122
0	0	0	0
0	0	0	0
1644	4245	0	6
6179	21509	7	13
3926	7670	3	3
23238	10641	53	18
0	0	0	0
49288	41243	25	49




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Review[t] = + 24.8712210910794 + 0.000181483338713392Grootte[t] + 0.000228445322752162Tijd[t] + 0.0183647958759403Hyperlinks[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Review[t] =  +  24.8712210910794 +  0.000181483338713392Grootte[t] +  0.000228445322752162Tijd[t] +  0.0183647958759403Hyperlinks[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Review[t] =  +  24.8712210910794 +  0.000181483338713392Grootte[t] +  0.000228445322752162Tijd[t] +  0.0183647958759403Hyperlinks[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Review[t] = + 24.8712210910794 + 0.000181483338713392Grootte[t] + 0.000228445322752162Tijd[t] + 0.0183647958759403Hyperlinks[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.87122109107948.8039112.8250.005330.002665
Grootte0.0001814833387133920.0001811.00510.3163840.158192
Tijd0.0002284453227521620.0002141.06890.2867260.143363
Hyperlinks0.01836479587594030.1893240.0970.9228460.461423

\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) & 24.8712210910794 & 8.803911 & 2.825 & 0.00533 & 0.002665 \tabularnewline
Grootte & 0.000181483338713392 & 0.000181 & 1.0051 & 0.316384 & 0.158192 \tabularnewline
Tijd & 0.000228445322752162 & 0.000214 & 1.0689 & 0.286726 & 0.143363 \tabularnewline
Hyperlinks & 0.0183647958759403 & 0.189324 & 0.097 & 0.922846 & 0.461423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&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]24.8712210910794[/C][C]8.803911[/C][C]2.825[/C][C]0.00533[/C][C]0.002665[/C][/ROW]
[ROW][C]Grootte[/C][C]0.000181483338713392[/C][C]0.000181[/C][C]1.0051[/C][C]0.316384[/C][C]0.158192[/C][/ROW]
[ROW][C]Tijd[/C][C]0.000228445322752162[/C][C]0.000214[/C][C]1.0689[/C][C]0.286726[/C][C]0.143363[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]0.0183647958759403[/C][C]0.189324[/C][C]0.097[/C][C]0.922846[/C][C]0.461423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.87122109107948.8039112.8250.005330.002665
Grootte0.0001814833387133920.0001811.00510.3163840.158192
Tijd0.0002284453227521620.0002141.06890.2867260.143363
Hyperlinks0.01836479587594030.1893240.0970.9228460.461423







Multiple Linear Regression - Regression Statistics
Multiple R0.27808746920463
R-squared0.0773326405286362
Adjusted R-squared0.0600326275385481
F-TEST (value)4.4700914717777
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0.00481798674128575
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation51.769892551807
Sum Squared Residuals428819.483972103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.27808746920463 \tabularnewline
R-squared & 0.0773326405286362 \tabularnewline
Adjusted R-squared & 0.0600326275385481 \tabularnewline
F-TEST (value) & 4.4700914717777 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0.00481798674128575 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 51.769892551807 \tabularnewline
Sum Squared Residuals & 428819.483972103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.27808746920463[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0773326405286362[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0600326275385481[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.4700914717777[/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]0.00481798674128575[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]51.769892551807[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]428819.483972103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.27808746920463
R-squared0.0773326405286362
Adjusted R-squared0.0600326275385481
F-TEST (value)4.4700914717777
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0.00481798674128575
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation51.769892551807
Sum Squared Residuals428819.483972103







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17070.695051286215-0.695051286215051
24456.6655760207152-12.6655760207152
33654.495830410402-18.495830410402
411959.188053290002159.8119467099979
53043.6366643965436-13.6366643965436
62342.9890359895146-19.9890359895146
74677.8904846934162-31.8904846934162
83930.6676713733168.33232862668401
95853.53403859176764.4659614082324
105150.26463815923720.735361840762817
116561.43760730026163.56239269973845
124050.9530981981258-10.9530981981258
134248.0872207541104-6.08722075411035
147656.23978294200919.760217057991
153148.7062301265927-17.7062301265927
168368.500575232686714.4994247673133
173671.3807616090469-35.3807616090469
186256.95378337684655.04621662315352
192860.1085756789452-32.1085756789452
203858.2961046628857-20.2961046628857
217064.13284035702335.86715964297673
227660.944384146292115.0556158537079
233344.1491401220422-11.1491401220422
244054.6177288212204-14.6177288212204
2512653.399187438729272.6008125612708
265655.24561353312610.754386466873932
276360.26551061887622.73448938112382
284659.3609010152178-13.3609010152178
293541.2649621011479-6.26496210114793
3010862.862701604903645.1372983950964
313472.6441205576352-38.6441205576352
325449.82231640592424.17768359407576
333567.5691043732682-32.5691043732682
342335.9308858249931-12.9308858249931
354678.2355236917148-32.2355236917148
364947.44500537826881.55499462173121
375668.3656856888103-12.3656856888103
383848.9670445335209-10.9670445335209
391940.9010548321719-21.9010548321719
402962.7798526828287-33.7798526828287
412659.8493860090769-33.8493860090769
425243.96465648266168.03534351733843
435458.171215523093-4.17121552309303
444540.57712766636974.42287233363025
455647.4257575874658.57424241253496
4659659.6357620393597536.36423796064
475744.265712595412.7342874046
485555.6882607685731-0.688260768573136
499978.251023692730520.7489763072695
505153.6991642461741-2.69916424617408
512155.0297358378506-34.0297358378506
522029.5174741896529-9.51747418965294
535868.6413232412431-10.6413232412431
542136.9003795719178-15.9003795719178
556641.735470737085624.2645292629144
564760.0475280200725-13.0475280200725
575570.1359519757152-15.1359519757152
5815877.843796408123280.1562035918768
594673.6605750477553-27.6605750477553
604547.6546586951928-2.65465869519275
614680.975320600226-34.975320600226
6211782.213222987673934.7867770123261
635644.977554950823411.0224450491766
643056.1158707582504-26.1158707582503
654562.6987106497334-17.6987106497334
663853.2547309518906-15.2547309518906
673356.9077088086677-23.9077088086677
686155.72739961317085.27260038682916
696355.97472562744187.02527437255824
704151.9586793145028-10.9586793145028
713350.8111532107482-17.8111532107482
723634.89597730812081.10402269187916
733553.5528083073362-18.5528083073362
747352.951427583859720.0485724161403
754650.0140825733455-4.01408257334547
765458.3999596529742-4.39995965297421
772468.1747384589728-44.1747384589728
782778.6784060530191-51.6784060530191
793246.0737866672181-14.0737866672181
805247.31614800861144.68385199138857
813144.0349408326441-13.0349408326441
828964.502911378475824.4970886215242
833647.5779068971215-11.5779068971215
843756.3170461169792-19.3170461169792
853148.9761150993781-17.9761150993781
8614233.913900589908108.086099410092
874452.2152616587177-8.21526165871772
8822257.4531826743298164.54681732567
895256.3774387176652-4.37743871766515
905158.8474741925728-7.8474741925728
914545.5285552897729-0.528555289772925
925160.4022768150346-9.40227681503464
936444.028855631907419.9711443680926
946651.04712149162614.952878508374
958159.644041035544121.3559589644559
964359.1666395280459-16.1666395280459
974576.4631783336326-31.4631783336326
983581.8250077162127-46.8250077162127
999766.935116051570930.0648839484291
1004185.8657755163921-44.8657755163921
1014461.6517672705996-17.6517672705996
1026175.3845649362792-14.3845649362792
1033541.1624192370777-6.16241923707768
1044338.19537384333634.8046261566637
1055747.73880854954439.26119145045571
1063259.2034944891641-27.2034944891641
1076659.39917362402296.60082637597707
1083262.2052552916023-30.2052552916023
1092477.1083657511846-53.1083657511846
1105540.604999680377714.3950003196223
1113867.8409402139382-29.8409402139382
1124352.6108328684095-9.6108328684095
113926.6195555155274-17.6195555155274
1143651.4085208798485-15.4085208798485
1152535.6290529248706-10.6290529248706
1167837.005634690145740.9943653098543
1174272.6122889522536-30.6122889522535
118226.6126134564479-24.6126134564479
1194663.2425000972125-17.2425000972125
1202231.154465160324-9.15446516032399
12113162.512337500134868.4876624998652
1225156.3329877956169-5.33298779561688
1236740.327072942057526.6729270579425
1243846.5169027861691-8.51690278616912
1255229.951562103670522.0484378963295
1266449.298356353936314.7016436460637
1277561.454699058589213.5453009414108
1283760.1207349953934-23.1207349953934
12910770.093567615311136.9064323846889
1308472.64096488694511.359035113055
1316893.5922229546078-25.5922229546078
1323053.9852717817198-23.9852717817198
1333143.1110061122416-12.1110061122416
13410954.287790049415154.7122099505849
13510873.192571065291434.8074289347086
1363338.1849619043784-5.18496190437837
13710668.75513678631837.244863213682
1385043.70556102660996.29443897339006
1395262.324913012451-10.324913012451
14013448.343953153232185.6560468467679
1413936.06927023743772.93072976256234
1427862.278245533817215.7217544661828
1434062.1163879069016-22.1163879069016
1443734.4134168554252.58658314457495
1454159.1825785082235-18.1825785082235
1469547.789764144292847.2102358557072
1473765.9567961766933-28.9567961766933
1483872.8848513452672-34.8848513452672
149024.8712210910794-24.8712210910794
150027.8729448713778-27.8729448713778
151024.8712210910794-24.8712210910794
152024.8712210910794-24.8712210910794
153024.8712210910794-24.8712210910794
154024.8712210910794-24.8712210910794
1553648.5800205071783-12.5800205071783
1566558.90499380674126.09500619325884
157024.8712210910794-24.8712210910794
158024.8712210910794-24.8712210910794
159026.2495188702628-26.2495188702628
160731.1449794344529-24.1449794344529
161327.3909946920051-24.3909946920051
1625331.849983921273921.1500160787261
163024.8712210910794-24.8712210910794
1642544.1378173337735-19.1378173337735

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 70 & 70.695051286215 & -0.695051286215051 \tabularnewline
2 & 44 & 56.6655760207152 & -12.6655760207152 \tabularnewline
3 & 36 & 54.495830410402 & -18.495830410402 \tabularnewline
4 & 119 & 59.1880532900021 & 59.8119467099979 \tabularnewline
5 & 30 & 43.6366643965436 & -13.6366643965436 \tabularnewline
6 & 23 & 42.9890359895146 & -19.9890359895146 \tabularnewline
7 & 46 & 77.8904846934162 & -31.8904846934162 \tabularnewline
8 & 39 & 30.667671373316 & 8.33232862668401 \tabularnewline
9 & 58 & 53.5340385917676 & 4.4659614082324 \tabularnewline
10 & 51 & 50.2646381592372 & 0.735361840762817 \tabularnewline
11 & 65 & 61.4376073002616 & 3.56239269973845 \tabularnewline
12 & 40 & 50.9530981981258 & -10.9530981981258 \tabularnewline
13 & 42 & 48.0872207541104 & -6.08722075411035 \tabularnewline
14 & 76 & 56.239782942009 & 19.760217057991 \tabularnewline
15 & 31 & 48.7062301265927 & -17.7062301265927 \tabularnewline
16 & 83 & 68.5005752326867 & 14.4994247673133 \tabularnewline
17 & 36 & 71.3807616090469 & -35.3807616090469 \tabularnewline
18 & 62 & 56.9537833768465 & 5.04621662315352 \tabularnewline
19 & 28 & 60.1085756789452 & -32.1085756789452 \tabularnewline
20 & 38 & 58.2961046628857 & -20.2961046628857 \tabularnewline
21 & 70 & 64.1328403570233 & 5.86715964297673 \tabularnewline
22 & 76 & 60.9443841462921 & 15.0556158537079 \tabularnewline
23 & 33 & 44.1491401220422 & -11.1491401220422 \tabularnewline
24 & 40 & 54.6177288212204 & -14.6177288212204 \tabularnewline
25 & 126 & 53.3991874387292 & 72.6008125612708 \tabularnewline
26 & 56 & 55.2456135331261 & 0.754386466873932 \tabularnewline
27 & 63 & 60.2655106188762 & 2.73448938112382 \tabularnewline
28 & 46 & 59.3609010152178 & -13.3609010152178 \tabularnewline
29 & 35 & 41.2649621011479 & -6.26496210114793 \tabularnewline
30 & 108 & 62.8627016049036 & 45.1372983950964 \tabularnewline
31 & 34 & 72.6441205576352 & -38.6441205576352 \tabularnewline
32 & 54 & 49.8223164059242 & 4.17768359407576 \tabularnewline
33 & 35 & 67.5691043732682 & -32.5691043732682 \tabularnewline
34 & 23 & 35.9308858249931 & -12.9308858249931 \tabularnewline
35 & 46 & 78.2355236917148 & -32.2355236917148 \tabularnewline
36 & 49 & 47.4450053782688 & 1.55499462173121 \tabularnewline
37 & 56 & 68.3656856888103 & -12.3656856888103 \tabularnewline
38 & 38 & 48.9670445335209 & -10.9670445335209 \tabularnewline
39 & 19 & 40.9010548321719 & -21.9010548321719 \tabularnewline
40 & 29 & 62.7798526828287 & -33.7798526828287 \tabularnewline
41 & 26 & 59.8493860090769 & -33.8493860090769 \tabularnewline
42 & 52 & 43.9646564826616 & 8.03534351733843 \tabularnewline
43 & 54 & 58.171215523093 & -4.17121552309303 \tabularnewline
44 & 45 & 40.5771276663697 & 4.42287233363025 \tabularnewline
45 & 56 & 47.425757587465 & 8.57424241253496 \tabularnewline
46 & 596 & 59.6357620393597 & 536.36423796064 \tabularnewline
47 & 57 & 44.2657125954 & 12.7342874046 \tabularnewline
48 & 55 & 55.6882607685731 & -0.688260768573136 \tabularnewline
49 & 99 & 78.2510236927305 & 20.7489763072695 \tabularnewline
50 & 51 & 53.6991642461741 & -2.69916424617408 \tabularnewline
51 & 21 & 55.0297358378506 & -34.0297358378506 \tabularnewline
52 & 20 & 29.5174741896529 & -9.51747418965294 \tabularnewline
53 & 58 & 68.6413232412431 & -10.6413232412431 \tabularnewline
54 & 21 & 36.9003795719178 & -15.9003795719178 \tabularnewline
55 & 66 & 41.7354707370856 & 24.2645292629144 \tabularnewline
56 & 47 & 60.0475280200725 & -13.0475280200725 \tabularnewline
57 & 55 & 70.1359519757152 & -15.1359519757152 \tabularnewline
58 & 158 & 77.8437964081232 & 80.1562035918768 \tabularnewline
59 & 46 & 73.6605750477553 & -27.6605750477553 \tabularnewline
60 & 45 & 47.6546586951928 & -2.65465869519275 \tabularnewline
61 & 46 & 80.975320600226 & -34.975320600226 \tabularnewline
62 & 117 & 82.2132229876739 & 34.7867770123261 \tabularnewline
63 & 56 & 44.9775549508234 & 11.0224450491766 \tabularnewline
64 & 30 & 56.1158707582504 & -26.1158707582503 \tabularnewline
65 & 45 & 62.6987106497334 & -17.6987106497334 \tabularnewline
66 & 38 & 53.2547309518906 & -15.2547309518906 \tabularnewline
67 & 33 & 56.9077088086677 & -23.9077088086677 \tabularnewline
68 & 61 & 55.7273996131708 & 5.27260038682916 \tabularnewline
69 & 63 & 55.9747256274418 & 7.02527437255824 \tabularnewline
70 & 41 & 51.9586793145028 & -10.9586793145028 \tabularnewline
71 & 33 & 50.8111532107482 & -17.8111532107482 \tabularnewline
72 & 36 & 34.8959773081208 & 1.10402269187916 \tabularnewline
73 & 35 & 53.5528083073362 & -18.5528083073362 \tabularnewline
74 & 73 & 52.9514275838597 & 20.0485724161403 \tabularnewline
75 & 46 & 50.0140825733455 & -4.01408257334547 \tabularnewline
76 & 54 & 58.3999596529742 & -4.39995965297421 \tabularnewline
77 & 24 & 68.1747384589728 & -44.1747384589728 \tabularnewline
78 & 27 & 78.6784060530191 & -51.6784060530191 \tabularnewline
79 & 32 & 46.0737866672181 & -14.0737866672181 \tabularnewline
80 & 52 & 47.3161480086114 & 4.68385199138857 \tabularnewline
81 & 31 & 44.0349408326441 & -13.0349408326441 \tabularnewline
82 & 89 & 64.5029113784758 & 24.4970886215242 \tabularnewline
83 & 36 & 47.5779068971215 & -11.5779068971215 \tabularnewline
84 & 37 & 56.3170461169792 & -19.3170461169792 \tabularnewline
85 & 31 & 48.9761150993781 & -17.9761150993781 \tabularnewline
86 & 142 & 33.913900589908 & 108.086099410092 \tabularnewline
87 & 44 & 52.2152616587177 & -8.21526165871772 \tabularnewline
88 & 222 & 57.4531826743298 & 164.54681732567 \tabularnewline
89 & 52 & 56.3774387176652 & -4.37743871766515 \tabularnewline
90 & 51 & 58.8474741925728 & -7.8474741925728 \tabularnewline
91 & 45 & 45.5285552897729 & -0.528555289772925 \tabularnewline
92 & 51 & 60.4022768150346 & -9.40227681503464 \tabularnewline
93 & 64 & 44.0288556319074 & 19.9711443680926 \tabularnewline
94 & 66 & 51.047121491626 & 14.952878508374 \tabularnewline
95 & 81 & 59.6440410355441 & 21.3559589644559 \tabularnewline
96 & 43 & 59.1666395280459 & -16.1666395280459 \tabularnewline
97 & 45 & 76.4631783336326 & -31.4631783336326 \tabularnewline
98 & 35 & 81.8250077162127 & -46.8250077162127 \tabularnewline
99 & 97 & 66.9351160515709 & 30.0648839484291 \tabularnewline
100 & 41 & 85.8657755163921 & -44.8657755163921 \tabularnewline
101 & 44 & 61.6517672705996 & -17.6517672705996 \tabularnewline
102 & 61 & 75.3845649362792 & -14.3845649362792 \tabularnewline
103 & 35 & 41.1624192370777 & -6.16241923707768 \tabularnewline
104 & 43 & 38.1953738433363 & 4.8046261566637 \tabularnewline
105 & 57 & 47.7388085495443 & 9.26119145045571 \tabularnewline
106 & 32 & 59.2034944891641 & -27.2034944891641 \tabularnewline
107 & 66 & 59.3991736240229 & 6.60082637597707 \tabularnewline
108 & 32 & 62.2052552916023 & -30.2052552916023 \tabularnewline
109 & 24 & 77.1083657511846 & -53.1083657511846 \tabularnewline
110 & 55 & 40.6049996803777 & 14.3950003196223 \tabularnewline
111 & 38 & 67.8409402139382 & -29.8409402139382 \tabularnewline
112 & 43 & 52.6108328684095 & -9.6108328684095 \tabularnewline
113 & 9 & 26.6195555155274 & -17.6195555155274 \tabularnewline
114 & 36 & 51.4085208798485 & -15.4085208798485 \tabularnewline
115 & 25 & 35.6290529248706 & -10.6290529248706 \tabularnewline
116 & 78 & 37.0056346901457 & 40.9943653098543 \tabularnewline
117 & 42 & 72.6122889522536 & -30.6122889522535 \tabularnewline
118 & 2 & 26.6126134564479 & -24.6126134564479 \tabularnewline
119 & 46 & 63.2425000972125 & -17.2425000972125 \tabularnewline
120 & 22 & 31.154465160324 & -9.15446516032399 \tabularnewline
121 & 131 & 62.5123375001348 & 68.4876624998652 \tabularnewline
122 & 51 & 56.3329877956169 & -5.33298779561688 \tabularnewline
123 & 67 & 40.3270729420575 & 26.6729270579425 \tabularnewline
124 & 38 & 46.5169027861691 & -8.51690278616912 \tabularnewline
125 & 52 & 29.9515621036705 & 22.0484378963295 \tabularnewline
126 & 64 & 49.2983563539363 & 14.7016436460637 \tabularnewline
127 & 75 & 61.4546990585892 & 13.5453009414108 \tabularnewline
128 & 37 & 60.1207349953934 & -23.1207349953934 \tabularnewline
129 & 107 & 70.0935676153111 & 36.9064323846889 \tabularnewline
130 & 84 & 72.640964886945 & 11.359035113055 \tabularnewline
131 & 68 & 93.5922229546078 & -25.5922229546078 \tabularnewline
132 & 30 & 53.9852717817198 & -23.9852717817198 \tabularnewline
133 & 31 & 43.1110061122416 & -12.1110061122416 \tabularnewline
134 & 109 & 54.2877900494151 & 54.7122099505849 \tabularnewline
135 & 108 & 73.1925710652914 & 34.8074289347086 \tabularnewline
136 & 33 & 38.1849619043784 & -5.18496190437837 \tabularnewline
137 & 106 & 68.755136786318 & 37.244863213682 \tabularnewline
138 & 50 & 43.7055610266099 & 6.29443897339006 \tabularnewline
139 & 52 & 62.324913012451 & -10.324913012451 \tabularnewline
140 & 134 & 48.3439531532321 & 85.6560468467679 \tabularnewline
141 & 39 & 36.0692702374377 & 2.93072976256234 \tabularnewline
142 & 78 & 62.2782455338172 & 15.7217544661828 \tabularnewline
143 & 40 & 62.1163879069016 & -22.1163879069016 \tabularnewline
144 & 37 & 34.413416855425 & 2.58658314457495 \tabularnewline
145 & 41 & 59.1825785082235 & -18.1825785082235 \tabularnewline
146 & 95 & 47.7897641442928 & 47.2102358557072 \tabularnewline
147 & 37 & 65.9567961766933 & -28.9567961766933 \tabularnewline
148 & 38 & 72.8848513452672 & -34.8848513452672 \tabularnewline
149 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
150 & 0 & 27.8729448713778 & -27.8729448713778 \tabularnewline
151 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
152 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
153 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
154 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
155 & 36 & 48.5800205071783 & -12.5800205071783 \tabularnewline
156 & 65 & 58.9049938067412 & 6.09500619325884 \tabularnewline
157 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
158 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
159 & 0 & 26.2495188702628 & -26.2495188702628 \tabularnewline
160 & 7 & 31.1449794344529 & -24.1449794344529 \tabularnewline
161 & 3 & 27.3909946920051 & -24.3909946920051 \tabularnewline
162 & 53 & 31.8499839212739 & 21.1500160787261 \tabularnewline
163 & 0 & 24.8712210910794 & -24.8712210910794 \tabularnewline
164 & 25 & 44.1378173337735 & -19.1378173337735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&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]70[/C][C]70.695051286215[/C][C]-0.695051286215051[/C][/ROW]
[ROW][C]2[/C][C]44[/C][C]56.6655760207152[/C][C]-12.6655760207152[/C][/ROW]
[ROW][C]3[/C][C]36[/C][C]54.495830410402[/C][C]-18.495830410402[/C][/ROW]
[ROW][C]4[/C][C]119[/C][C]59.1880532900021[/C][C]59.8119467099979[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]43.6366643965436[/C][C]-13.6366643965436[/C][/ROW]
[ROW][C]6[/C][C]23[/C][C]42.9890359895146[/C][C]-19.9890359895146[/C][/ROW]
[ROW][C]7[/C][C]46[/C][C]77.8904846934162[/C][C]-31.8904846934162[/C][/ROW]
[ROW][C]8[/C][C]39[/C][C]30.667671373316[/C][C]8.33232862668401[/C][/ROW]
[ROW][C]9[/C][C]58[/C][C]53.5340385917676[/C][C]4.4659614082324[/C][/ROW]
[ROW][C]10[/C][C]51[/C][C]50.2646381592372[/C][C]0.735361840762817[/C][/ROW]
[ROW][C]11[/C][C]65[/C][C]61.4376073002616[/C][C]3.56239269973845[/C][/ROW]
[ROW][C]12[/C][C]40[/C][C]50.9530981981258[/C][C]-10.9530981981258[/C][/ROW]
[ROW][C]13[/C][C]42[/C][C]48.0872207541104[/C][C]-6.08722075411035[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]56.239782942009[/C][C]19.760217057991[/C][/ROW]
[ROW][C]15[/C][C]31[/C][C]48.7062301265927[/C][C]-17.7062301265927[/C][/ROW]
[ROW][C]16[/C][C]83[/C][C]68.5005752326867[/C][C]14.4994247673133[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]71.3807616090469[/C][C]-35.3807616090469[/C][/ROW]
[ROW][C]18[/C][C]62[/C][C]56.9537833768465[/C][C]5.04621662315352[/C][/ROW]
[ROW][C]19[/C][C]28[/C][C]60.1085756789452[/C][C]-32.1085756789452[/C][/ROW]
[ROW][C]20[/C][C]38[/C][C]58.2961046628857[/C][C]-20.2961046628857[/C][/ROW]
[ROW][C]21[/C][C]70[/C][C]64.1328403570233[/C][C]5.86715964297673[/C][/ROW]
[ROW][C]22[/C][C]76[/C][C]60.9443841462921[/C][C]15.0556158537079[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]44.1491401220422[/C][C]-11.1491401220422[/C][/ROW]
[ROW][C]24[/C][C]40[/C][C]54.6177288212204[/C][C]-14.6177288212204[/C][/ROW]
[ROW][C]25[/C][C]126[/C][C]53.3991874387292[/C][C]72.6008125612708[/C][/ROW]
[ROW][C]26[/C][C]56[/C][C]55.2456135331261[/C][C]0.754386466873932[/C][/ROW]
[ROW][C]27[/C][C]63[/C][C]60.2655106188762[/C][C]2.73448938112382[/C][/ROW]
[ROW][C]28[/C][C]46[/C][C]59.3609010152178[/C][C]-13.3609010152178[/C][/ROW]
[ROW][C]29[/C][C]35[/C][C]41.2649621011479[/C][C]-6.26496210114793[/C][/ROW]
[ROW][C]30[/C][C]108[/C][C]62.8627016049036[/C][C]45.1372983950964[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]72.6441205576352[/C][C]-38.6441205576352[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]49.8223164059242[/C][C]4.17768359407576[/C][/ROW]
[ROW][C]33[/C][C]35[/C][C]67.5691043732682[/C][C]-32.5691043732682[/C][/ROW]
[ROW][C]34[/C][C]23[/C][C]35.9308858249931[/C][C]-12.9308858249931[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]78.2355236917148[/C][C]-32.2355236917148[/C][/ROW]
[ROW][C]36[/C][C]49[/C][C]47.4450053782688[/C][C]1.55499462173121[/C][/ROW]
[ROW][C]37[/C][C]56[/C][C]68.3656856888103[/C][C]-12.3656856888103[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]48.9670445335209[/C][C]-10.9670445335209[/C][/ROW]
[ROW][C]39[/C][C]19[/C][C]40.9010548321719[/C][C]-21.9010548321719[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]62.7798526828287[/C][C]-33.7798526828287[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]59.8493860090769[/C][C]-33.8493860090769[/C][/ROW]
[ROW][C]42[/C][C]52[/C][C]43.9646564826616[/C][C]8.03534351733843[/C][/ROW]
[ROW][C]43[/C][C]54[/C][C]58.171215523093[/C][C]-4.17121552309303[/C][/ROW]
[ROW][C]44[/C][C]45[/C][C]40.5771276663697[/C][C]4.42287233363025[/C][/ROW]
[ROW][C]45[/C][C]56[/C][C]47.425757587465[/C][C]8.57424241253496[/C][/ROW]
[ROW][C]46[/C][C]596[/C][C]59.6357620393597[/C][C]536.36423796064[/C][/ROW]
[ROW][C]47[/C][C]57[/C][C]44.2657125954[/C][C]12.7342874046[/C][/ROW]
[ROW][C]48[/C][C]55[/C][C]55.6882607685731[/C][C]-0.688260768573136[/C][/ROW]
[ROW][C]49[/C][C]99[/C][C]78.2510236927305[/C][C]20.7489763072695[/C][/ROW]
[ROW][C]50[/C][C]51[/C][C]53.6991642461741[/C][C]-2.69916424617408[/C][/ROW]
[ROW][C]51[/C][C]21[/C][C]55.0297358378506[/C][C]-34.0297358378506[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]29.5174741896529[/C][C]-9.51747418965294[/C][/ROW]
[ROW][C]53[/C][C]58[/C][C]68.6413232412431[/C][C]-10.6413232412431[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]36.9003795719178[/C][C]-15.9003795719178[/C][/ROW]
[ROW][C]55[/C][C]66[/C][C]41.7354707370856[/C][C]24.2645292629144[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]60.0475280200725[/C][C]-13.0475280200725[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]70.1359519757152[/C][C]-15.1359519757152[/C][/ROW]
[ROW][C]58[/C][C]158[/C][C]77.8437964081232[/C][C]80.1562035918768[/C][/ROW]
[ROW][C]59[/C][C]46[/C][C]73.6605750477553[/C][C]-27.6605750477553[/C][/ROW]
[ROW][C]60[/C][C]45[/C][C]47.6546586951928[/C][C]-2.65465869519275[/C][/ROW]
[ROW][C]61[/C][C]46[/C][C]80.975320600226[/C][C]-34.975320600226[/C][/ROW]
[ROW][C]62[/C][C]117[/C][C]82.2132229876739[/C][C]34.7867770123261[/C][/ROW]
[ROW][C]63[/C][C]56[/C][C]44.9775549508234[/C][C]11.0224450491766[/C][/ROW]
[ROW][C]64[/C][C]30[/C][C]56.1158707582504[/C][C]-26.1158707582503[/C][/ROW]
[ROW][C]65[/C][C]45[/C][C]62.6987106497334[/C][C]-17.6987106497334[/C][/ROW]
[ROW][C]66[/C][C]38[/C][C]53.2547309518906[/C][C]-15.2547309518906[/C][/ROW]
[ROW][C]67[/C][C]33[/C][C]56.9077088086677[/C][C]-23.9077088086677[/C][/ROW]
[ROW][C]68[/C][C]61[/C][C]55.7273996131708[/C][C]5.27260038682916[/C][/ROW]
[ROW][C]69[/C][C]63[/C][C]55.9747256274418[/C][C]7.02527437255824[/C][/ROW]
[ROW][C]70[/C][C]41[/C][C]51.9586793145028[/C][C]-10.9586793145028[/C][/ROW]
[ROW][C]71[/C][C]33[/C][C]50.8111532107482[/C][C]-17.8111532107482[/C][/ROW]
[ROW][C]72[/C][C]36[/C][C]34.8959773081208[/C][C]1.10402269187916[/C][/ROW]
[ROW][C]73[/C][C]35[/C][C]53.5528083073362[/C][C]-18.5528083073362[/C][/ROW]
[ROW][C]74[/C][C]73[/C][C]52.9514275838597[/C][C]20.0485724161403[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]50.0140825733455[/C][C]-4.01408257334547[/C][/ROW]
[ROW][C]76[/C][C]54[/C][C]58.3999596529742[/C][C]-4.39995965297421[/C][/ROW]
[ROW][C]77[/C][C]24[/C][C]68.1747384589728[/C][C]-44.1747384589728[/C][/ROW]
[ROW][C]78[/C][C]27[/C][C]78.6784060530191[/C][C]-51.6784060530191[/C][/ROW]
[ROW][C]79[/C][C]32[/C][C]46.0737866672181[/C][C]-14.0737866672181[/C][/ROW]
[ROW][C]80[/C][C]52[/C][C]47.3161480086114[/C][C]4.68385199138857[/C][/ROW]
[ROW][C]81[/C][C]31[/C][C]44.0349408326441[/C][C]-13.0349408326441[/C][/ROW]
[ROW][C]82[/C][C]89[/C][C]64.5029113784758[/C][C]24.4970886215242[/C][/ROW]
[ROW][C]83[/C][C]36[/C][C]47.5779068971215[/C][C]-11.5779068971215[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]56.3170461169792[/C][C]-19.3170461169792[/C][/ROW]
[ROW][C]85[/C][C]31[/C][C]48.9761150993781[/C][C]-17.9761150993781[/C][/ROW]
[ROW][C]86[/C][C]142[/C][C]33.913900589908[/C][C]108.086099410092[/C][/ROW]
[ROW][C]87[/C][C]44[/C][C]52.2152616587177[/C][C]-8.21526165871772[/C][/ROW]
[ROW][C]88[/C][C]222[/C][C]57.4531826743298[/C][C]164.54681732567[/C][/ROW]
[ROW][C]89[/C][C]52[/C][C]56.3774387176652[/C][C]-4.37743871766515[/C][/ROW]
[ROW][C]90[/C][C]51[/C][C]58.8474741925728[/C][C]-7.8474741925728[/C][/ROW]
[ROW][C]91[/C][C]45[/C][C]45.5285552897729[/C][C]-0.528555289772925[/C][/ROW]
[ROW][C]92[/C][C]51[/C][C]60.4022768150346[/C][C]-9.40227681503464[/C][/ROW]
[ROW][C]93[/C][C]64[/C][C]44.0288556319074[/C][C]19.9711443680926[/C][/ROW]
[ROW][C]94[/C][C]66[/C][C]51.047121491626[/C][C]14.952878508374[/C][/ROW]
[ROW][C]95[/C][C]81[/C][C]59.6440410355441[/C][C]21.3559589644559[/C][/ROW]
[ROW][C]96[/C][C]43[/C][C]59.1666395280459[/C][C]-16.1666395280459[/C][/ROW]
[ROW][C]97[/C][C]45[/C][C]76.4631783336326[/C][C]-31.4631783336326[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]81.8250077162127[/C][C]-46.8250077162127[/C][/ROW]
[ROW][C]99[/C][C]97[/C][C]66.9351160515709[/C][C]30.0648839484291[/C][/ROW]
[ROW][C]100[/C][C]41[/C][C]85.8657755163921[/C][C]-44.8657755163921[/C][/ROW]
[ROW][C]101[/C][C]44[/C][C]61.6517672705996[/C][C]-17.6517672705996[/C][/ROW]
[ROW][C]102[/C][C]61[/C][C]75.3845649362792[/C][C]-14.3845649362792[/C][/ROW]
[ROW][C]103[/C][C]35[/C][C]41.1624192370777[/C][C]-6.16241923707768[/C][/ROW]
[ROW][C]104[/C][C]43[/C][C]38.1953738433363[/C][C]4.8046261566637[/C][/ROW]
[ROW][C]105[/C][C]57[/C][C]47.7388085495443[/C][C]9.26119145045571[/C][/ROW]
[ROW][C]106[/C][C]32[/C][C]59.2034944891641[/C][C]-27.2034944891641[/C][/ROW]
[ROW][C]107[/C][C]66[/C][C]59.3991736240229[/C][C]6.60082637597707[/C][/ROW]
[ROW][C]108[/C][C]32[/C][C]62.2052552916023[/C][C]-30.2052552916023[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]77.1083657511846[/C][C]-53.1083657511846[/C][/ROW]
[ROW][C]110[/C][C]55[/C][C]40.6049996803777[/C][C]14.3950003196223[/C][/ROW]
[ROW][C]111[/C][C]38[/C][C]67.8409402139382[/C][C]-29.8409402139382[/C][/ROW]
[ROW][C]112[/C][C]43[/C][C]52.6108328684095[/C][C]-9.6108328684095[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]26.6195555155274[/C][C]-17.6195555155274[/C][/ROW]
[ROW][C]114[/C][C]36[/C][C]51.4085208798485[/C][C]-15.4085208798485[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]35.6290529248706[/C][C]-10.6290529248706[/C][/ROW]
[ROW][C]116[/C][C]78[/C][C]37.0056346901457[/C][C]40.9943653098543[/C][/ROW]
[ROW][C]117[/C][C]42[/C][C]72.6122889522536[/C][C]-30.6122889522535[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]26.6126134564479[/C][C]-24.6126134564479[/C][/ROW]
[ROW][C]119[/C][C]46[/C][C]63.2425000972125[/C][C]-17.2425000972125[/C][/ROW]
[ROW][C]120[/C][C]22[/C][C]31.154465160324[/C][C]-9.15446516032399[/C][/ROW]
[ROW][C]121[/C][C]131[/C][C]62.5123375001348[/C][C]68.4876624998652[/C][/ROW]
[ROW][C]122[/C][C]51[/C][C]56.3329877956169[/C][C]-5.33298779561688[/C][/ROW]
[ROW][C]123[/C][C]67[/C][C]40.3270729420575[/C][C]26.6729270579425[/C][/ROW]
[ROW][C]124[/C][C]38[/C][C]46.5169027861691[/C][C]-8.51690278616912[/C][/ROW]
[ROW][C]125[/C][C]52[/C][C]29.9515621036705[/C][C]22.0484378963295[/C][/ROW]
[ROW][C]126[/C][C]64[/C][C]49.2983563539363[/C][C]14.7016436460637[/C][/ROW]
[ROW][C]127[/C][C]75[/C][C]61.4546990585892[/C][C]13.5453009414108[/C][/ROW]
[ROW][C]128[/C][C]37[/C][C]60.1207349953934[/C][C]-23.1207349953934[/C][/ROW]
[ROW][C]129[/C][C]107[/C][C]70.0935676153111[/C][C]36.9064323846889[/C][/ROW]
[ROW][C]130[/C][C]84[/C][C]72.640964886945[/C][C]11.359035113055[/C][/ROW]
[ROW][C]131[/C][C]68[/C][C]93.5922229546078[/C][C]-25.5922229546078[/C][/ROW]
[ROW][C]132[/C][C]30[/C][C]53.9852717817198[/C][C]-23.9852717817198[/C][/ROW]
[ROW][C]133[/C][C]31[/C][C]43.1110061122416[/C][C]-12.1110061122416[/C][/ROW]
[ROW][C]134[/C][C]109[/C][C]54.2877900494151[/C][C]54.7122099505849[/C][/ROW]
[ROW][C]135[/C][C]108[/C][C]73.1925710652914[/C][C]34.8074289347086[/C][/ROW]
[ROW][C]136[/C][C]33[/C][C]38.1849619043784[/C][C]-5.18496190437837[/C][/ROW]
[ROW][C]137[/C][C]106[/C][C]68.755136786318[/C][C]37.244863213682[/C][/ROW]
[ROW][C]138[/C][C]50[/C][C]43.7055610266099[/C][C]6.29443897339006[/C][/ROW]
[ROW][C]139[/C][C]52[/C][C]62.324913012451[/C][C]-10.324913012451[/C][/ROW]
[ROW][C]140[/C][C]134[/C][C]48.3439531532321[/C][C]85.6560468467679[/C][/ROW]
[ROW][C]141[/C][C]39[/C][C]36.0692702374377[/C][C]2.93072976256234[/C][/ROW]
[ROW][C]142[/C][C]78[/C][C]62.2782455338172[/C][C]15.7217544661828[/C][/ROW]
[ROW][C]143[/C][C]40[/C][C]62.1163879069016[/C][C]-22.1163879069016[/C][/ROW]
[ROW][C]144[/C][C]37[/C][C]34.413416855425[/C][C]2.58658314457495[/C][/ROW]
[ROW][C]145[/C][C]41[/C][C]59.1825785082235[/C][C]-18.1825785082235[/C][/ROW]
[ROW][C]146[/C][C]95[/C][C]47.7897641442928[/C][C]47.2102358557072[/C][/ROW]
[ROW][C]147[/C][C]37[/C][C]65.9567961766933[/C][C]-28.9567961766933[/C][/ROW]
[ROW][C]148[/C][C]38[/C][C]72.8848513452672[/C][C]-34.8848513452672[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]27.8729448713778[/C][C]-27.8729448713778[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]155[/C][C]36[/C][C]48.5800205071783[/C][C]-12.5800205071783[/C][/ROW]
[ROW][C]156[/C][C]65[/C][C]58.9049938067412[/C][C]6.09500619325884[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]26.2495188702628[/C][C]-26.2495188702628[/C][/ROW]
[ROW][C]160[/C][C]7[/C][C]31.1449794344529[/C][C]-24.1449794344529[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]27.3909946920051[/C][C]-24.3909946920051[/C][/ROW]
[ROW][C]162[/C][C]53[/C][C]31.8499839212739[/C][C]21.1500160787261[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]24.8712210910794[/C][C]-24.8712210910794[/C][/ROW]
[ROW][C]164[/C][C]25[/C][C]44.1378173337735[/C][C]-19.1378173337735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17070.695051286215-0.695051286215051
24456.6655760207152-12.6655760207152
33654.495830410402-18.495830410402
411959.188053290002159.8119467099979
53043.6366643965436-13.6366643965436
62342.9890359895146-19.9890359895146
74677.8904846934162-31.8904846934162
83930.6676713733168.33232862668401
95853.53403859176764.4659614082324
105150.26463815923720.735361840762817
116561.43760730026163.56239269973845
124050.9530981981258-10.9530981981258
134248.0872207541104-6.08722075411035
147656.23978294200919.760217057991
153148.7062301265927-17.7062301265927
168368.500575232686714.4994247673133
173671.3807616090469-35.3807616090469
186256.95378337684655.04621662315352
192860.1085756789452-32.1085756789452
203858.2961046628857-20.2961046628857
217064.13284035702335.86715964297673
227660.944384146292115.0556158537079
233344.1491401220422-11.1491401220422
244054.6177288212204-14.6177288212204
2512653.399187438729272.6008125612708
265655.24561353312610.754386466873932
276360.26551061887622.73448938112382
284659.3609010152178-13.3609010152178
293541.2649621011479-6.26496210114793
3010862.862701604903645.1372983950964
313472.6441205576352-38.6441205576352
325449.82231640592424.17768359407576
333567.5691043732682-32.5691043732682
342335.9308858249931-12.9308858249931
354678.2355236917148-32.2355236917148
364947.44500537826881.55499462173121
375668.3656856888103-12.3656856888103
383848.9670445335209-10.9670445335209
391940.9010548321719-21.9010548321719
402962.7798526828287-33.7798526828287
412659.8493860090769-33.8493860090769
425243.96465648266168.03534351733843
435458.171215523093-4.17121552309303
444540.57712766636974.42287233363025
455647.4257575874658.57424241253496
4659659.6357620393597536.36423796064
475744.265712595412.7342874046
485555.6882607685731-0.688260768573136
499978.251023692730520.7489763072695
505153.6991642461741-2.69916424617408
512155.0297358378506-34.0297358378506
522029.5174741896529-9.51747418965294
535868.6413232412431-10.6413232412431
542136.9003795719178-15.9003795719178
556641.735470737085624.2645292629144
564760.0475280200725-13.0475280200725
575570.1359519757152-15.1359519757152
5815877.843796408123280.1562035918768
594673.6605750477553-27.6605750477553
604547.6546586951928-2.65465869519275
614680.975320600226-34.975320600226
6211782.213222987673934.7867770123261
635644.977554950823411.0224450491766
643056.1158707582504-26.1158707582503
654562.6987106497334-17.6987106497334
663853.2547309518906-15.2547309518906
673356.9077088086677-23.9077088086677
686155.72739961317085.27260038682916
696355.97472562744187.02527437255824
704151.9586793145028-10.9586793145028
713350.8111532107482-17.8111532107482
723634.89597730812081.10402269187916
733553.5528083073362-18.5528083073362
747352.951427583859720.0485724161403
754650.0140825733455-4.01408257334547
765458.3999596529742-4.39995965297421
772468.1747384589728-44.1747384589728
782778.6784060530191-51.6784060530191
793246.0737866672181-14.0737866672181
805247.31614800861144.68385199138857
813144.0349408326441-13.0349408326441
828964.502911378475824.4970886215242
833647.5779068971215-11.5779068971215
843756.3170461169792-19.3170461169792
853148.9761150993781-17.9761150993781
8614233.913900589908108.086099410092
874452.2152616587177-8.21526165871772
8822257.4531826743298164.54681732567
895256.3774387176652-4.37743871766515
905158.8474741925728-7.8474741925728
914545.5285552897729-0.528555289772925
925160.4022768150346-9.40227681503464
936444.028855631907419.9711443680926
946651.04712149162614.952878508374
958159.644041035544121.3559589644559
964359.1666395280459-16.1666395280459
974576.4631783336326-31.4631783336326
983581.8250077162127-46.8250077162127
999766.935116051570930.0648839484291
1004185.8657755163921-44.8657755163921
1014461.6517672705996-17.6517672705996
1026175.3845649362792-14.3845649362792
1033541.1624192370777-6.16241923707768
1044338.19537384333634.8046261566637
1055747.73880854954439.26119145045571
1063259.2034944891641-27.2034944891641
1076659.39917362402296.60082637597707
1083262.2052552916023-30.2052552916023
1092477.1083657511846-53.1083657511846
1105540.604999680377714.3950003196223
1113867.8409402139382-29.8409402139382
1124352.6108328684095-9.6108328684095
113926.6195555155274-17.6195555155274
1143651.4085208798485-15.4085208798485
1152535.6290529248706-10.6290529248706
1167837.005634690145740.9943653098543
1174272.6122889522536-30.6122889522535
118226.6126134564479-24.6126134564479
1194663.2425000972125-17.2425000972125
1202231.154465160324-9.15446516032399
12113162.512337500134868.4876624998652
1225156.3329877956169-5.33298779561688
1236740.327072942057526.6729270579425
1243846.5169027861691-8.51690278616912
1255229.951562103670522.0484378963295
1266449.298356353936314.7016436460637
1277561.454699058589213.5453009414108
1283760.1207349953934-23.1207349953934
12910770.093567615311136.9064323846889
1308472.64096488694511.359035113055
1316893.5922229546078-25.5922229546078
1323053.9852717817198-23.9852717817198
1333143.1110061122416-12.1110061122416
13410954.287790049415154.7122099505849
13510873.192571065291434.8074289347086
1363338.1849619043784-5.18496190437837
13710668.75513678631837.244863213682
1385043.70556102660996.29443897339006
1395262.324913012451-10.324913012451
14013448.343953153232185.6560468467679
1413936.06927023743772.93072976256234
1427862.278245533817215.7217544661828
1434062.1163879069016-22.1163879069016
1443734.4134168554252.58658314457495
1454159.1825785082235-18.1825785082235
1469547.789764144292847.2102358557072
1473765.9567961766933-28.9567961766933
1483872.8848513452672-34.8848513452672
149024.8712210910794-24.8712210910794
150027.8729448713778-27.8729448713778
151024.8712210910794-24.8712210910794
152024.8712210910794-24.8712210910794
153024.8712210910794-24.8712210910794
154024.8712210910794-24.8712210910794
1553648.5800205071783-12.5800205071783
1566558.90499380674126.09500619325884
157024.8712210910794-24.8712210910794
158024.8712210910794-24.8712210910794
159026.2495188702628-26.2495188702628
160731.1449794344529-24.1449794344529
161327.3909946920051-24.3909946920051
1625331.849983921273921.1500160787261
163024.8712210910794-24.8712210910794
1642544.1378173337735-19.1378173337735







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.03117074106566490.06234148213132980.968829258934335
80.01905605361223860.03811210722447710.980943946387761
90.005235784495837460.01047156899167490.994764215504162
100.001457474552875080.002914949105750170.998542525447125
110.0008673407622005930.001734681524401190.999132659237799
120.0007442422913254230.001488484582650850.999255757708675
130.0002106405485552660.0004212810971105320.999789359451445
140.000134201733695810.000268403467391620.999865798266304
155.99595726307378e-050.0001199191452614760.999940040427369
162.89137968572658e-055.78275937145317e-050.999971086203143
176.80389943214933e-050.0001360779886429870.999931961005678
183.36222984216537e-056.72445968433074e-050.999966377701578
190.0001356658052550020.0002713316105100040.999864334194745
200.0001113477302209180.0002226954604418360.999888652269779
215.5387585496253e-050.0001107751709925060.999944612414504
222.86207107816885e-055.7241421563377e-050.999971379289218
231.13076741899623e-052.26153483799246e-050.99998869232581
245.65194314703584e-061.13038862940717e-050.999994348056853
254.0832783632458e-058.16655672649159e-050.999959167216368
261.75758001468909e-053.51516002937818e-050.999982424199853
277.89914164514358e-061.57982832902872e-050.999992100858355
283.97595433620531e-067.95190867241062e-060.999996024045664
291.730464690075e-063.46092938015001e-060.99999826953531
303.51416914932139e-067.02833829864278e-060.999996485830851
311.62613457965919e-063.25226915931838e-060.99999837386542
327.32029107808501e-071.464058215617e-060.999999267970892
334.58938565849222e-079.17877131698443e-070.999999541061434
341.85017361255453e-073.70034722510906e-070.999999814982639
351.45785025312438e-072.91570050624875e-070.999999854214975
365.78137318154511e-081.15627463630902e-070.999999942186268
372.46082096138278e-084.92164192276556e-080.99999997539179
381.13044979635981e-082.26089959271961e-080.999999988695502
395.99920069338934e-091.19984013867787e-080.999999994000799
404.4319635509521e-098.8639271019042e-090.999999995568036
412.75064592346271e-095.50129184692542e-090.999999997249354
421.04250515119602e-092.08501030239203e-090.999999998957495
434.91474490004878e-109.82948980009756e-100.999999999508525
441.82197923325826e-103.64395846651652e-100.999999999817802
451.0920326782271e-102.18406535645421e-100.999999999890797
4615.46227729081711e-162.73113864540856e-16
470.9999999999999991.33300442127444e-156.66502210637218e-16
480.9999999999999983.43005088934515e-151.71502544467257e-15
490.9999999999999975.60816104513648e-152.80408052256824e-15
500.9999999999999931.38853492353074e-146.94267461765368e-15
510.9999999999999892.25094843828566e-141.12547421914283e-14
520.9999999999999735.42844692949118e-142.71422346474559e-14
530.9999999999999371.25672192910828e-136.28360964554139e-14
540.9999999999998752.49156681507787e-131.24578340753893e-13
550.9999999999997714.58538623176201e-132.292693115881e-13
560.9999999999995119.77679888003552e-134.88839944001776e-13
570.9999999999992861.42797313665274e-127.1398656832637e-13
580.9999999999999051.90108445068085e-139.50542225340425e-14
590.9999999999998293.42120374866348e-131.71060187433174e-13
600.9999999999996237.53377958515257e-133.76688979257628e-13
610.9999999999995798.41379150830567e-134.20689575415283e-13
620.9999999999993721.25684525132116e-126.28422625660582e-13
630.9999999999986422.71568895910949e-121.35784447955474e-12
640.9999999999978194.36109220553618e-122.18054610276809e-12
650.9999999999957928.41573300891623e-124.20786650445811e-12
660.9999999999916561.66876625710526e-118.34383128552629e-12
670.999999999988552.29002706552325e-111.14501353276163e-11
680.9999999999751754.96500390484977e-112.48250195242488e-11
690.9999999999484971.03006768787825e-105.15033843939123e-11
700.9999999998956722.08656055879129e-101.04328027939565e-10
710.9999999998046613.90678822972152e-101.95339411486076e-10
720.9999999995926448.14711148351121e-104.0735557417556e-10
730.9999999992675221.46495601870156e-097.32478009350778e-10
740.9999999987364982.52700476769789e-091.26350238384894e-09
750.9999999974608785.07824474438323e-092.53912237219161e-09
760.999999995020449.95911997997305e-094.97955998998653e-09
770.9999999951101589.77968345274091e-094.88984172637046e-09
780.9999999975528464.89430826144917e-092.44715413072459e-09
790.9999999953560479.28790615465869e-094.64395307732935e-09
800.9999999908915471.82169050988505e-089.10845254942527e-09
810.9999999832299653.35400699275627e-081.67700349637814e-08
820.9999999725748285.48503430983317e-082.74251715491659e-08
830.9999999509683129.80633760619039e-084.9031688030952e-08
840.9999999150302311.69939538689379e-078.49697693446896e-08
850.9999998538078832.92384234286178e-071.46192117143089e-07
860.9999999931960891.36078226535046e-086.80391132675229e-09
870.9999999866907412.66185181111773e-081.33092590555887e-08
880.9999999999998562.87717925918111e-131.43858962959055e-13
890.9999999999996447.12979953810361e-133.5648997690518e-13
900.9999999999991441.71221042105044e-128.5610521052522e-13
910.9999999999979254.15070927883687e-122.07535463941844e-12
920.9999999999952049.5912132749736e-124.7956066374868e-12
930.9999999999914371.71269349766101e-118.56346748830505e-12
940.9999999999827493.45009930405985e-111.72504965202993e-11
950.9999999999716995.66016345145808e-112.83008172572904e-11
960.9999999999385211.22959045413721e-106.14795227068606e-11
970.9999999999003711.99257791119163e-109.96288955595814e-11
980.9999999999143231.71353352843424e-108.5676676421712e-11
990.9999999998901792.19643069894238e-101.09821534947119e-10
1000.9999999999337461.3250707596561e-106.62535379828049e-11
1010.9999999998550962.89808062755107e-101.44904031377553e-10
1020.9999999997129645.74071354940282e-102.87035677470141e-10
1030.9999999993549531.29009335203121e-096.45046676015604e-10
1040.9999999986027192.79456201977515e-091.39728100988757e-09
1050.9999999970884465.8231078529484e-092.9115539264742e-09
1060.999999996740766.5184797697372e-093.2592398848686e-09
1070.9999999932032971.35934069208151e-086.79670346040755e-09
1080.9999999908680091.82639822826886e-089.13199114134428e-09
1090.9999999923234591.53530813920977e-087.67654069604884e-09
1100.9999999849203913.01592174650366e-081.50796087325183e-08
1110.9999999791741994.16516024375478e-082.08258012187739e-08
1120.9999999573767168.52465682752318e-084.26232841376159e-08
1130.9999999146230241.70753951592177e-078.53769757960886e-08
1140.9999998406474863.18705026935626e-071.59352513467813e-07
1150.9999996744620616.51075878857519e-073.25537939428759e-07
1160.9999997355658075.28868385593727e-072.64434192796864e-07
1170.9999997321141055.35771789994652e-072.67885894997326e-07
1180.9999995093132099.81373581410893e-074.90686790705446e-07
1190.9999992154322941.56913541175951e-067.84567705879753e-07
1200.9999983945526843.21089463280256e-061.60544731640128e-06
1210.9999995415009819.16998038631286e-074.58499019315643e-07
1220.999999048550861.9028982808201e-069.51449140410048e-07
1230.9999987540751172.49184976597741e-061.2459248829887e-06
1240.999997417502865.16499427915024e-062.58249713957512e-06
1250.9999967601878586.47962428370322e-063.23981214185161e-06
1260.9999943193845081.13612309838261e-055.68061549191307e-06
1270.9999889431997932.21136004137326e-051.10568002068663e-05
1280.9999851568579282.96862841444658e-051.48431420722329e-05
1290.9999849229287363.01541425286819e-051.50770712643409e-05
1300.9999711861837555.76276324900758e-052.88138162450379e-05
1310.999973668796455.26624070991506e-052.63312035495753e-05
1320.9999578548281018.42903437971362e-054.21451718985681e-05
1330.9999151318606490.0001697362787028518.48681393514255e-05
1340.999952040885479.59182290604379e-054.7959114530219e-05
1350.9999359113192910.0001281773614188766.4088680709438e-05
1360.999866863997660.000266272004680810.000133136002340405
1370.9998444496036970.0003111007926053740.000155550396302687
1380.9997046477797850.0005907044404298030.000295352220214902
1390.9994055805354460.001188838929108070.000594419464554035
1400.9999985191110332.96177793307858e-061.48088896653929e-06
1410.9999962827432727.43451345691275e-063.71725672845638e-06
1420.9999949282790041.01434419916316e-055.07172099581582e-06
1430.999988721823732.25563525390434e-051.12781762695217e-05
1440.9999724634365475.50731269069798e-052.75365634534899e-05
1450.9999243886463280.0001512227073439087.56113536719538e-05
1460.9999993550240471.28995190681613e-066.44975953408065e-07
1470.9999974959861115.0080277782605e-062.50401388913025e-06
1480.9999994329026361.13419472868737e-065.67097364343683e-07
1490.9999973671949065.26561018714726e-062.63280509357363e-06
1500.9999885289788362.29420423279173e-051.14710211639587e-05
1510.999950933020569.81339588798337e-054.90669794399169e-05
1520.9998002350043170.0003995299913652360.000199764995682618
1530.9992304722718990.001539055456201160.000769527728100582
1540.9972157116436910.005568576712617070.00278428835630853
1550.9939629759698950.01207404806021040.00603702403010518
1560.9879331238476920.02413375230461650.0120668761523082
1570.9578349785013470.08433004299730640.0421650214986532

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0311707410656649 & 0.0623414821313298 & 0.968829258934335 \tabularnewline
8 & 0.0190560536122386 & 0.0381121072244771 & 0.980943946387761 \tabularnewline
9 & 0.00523578449583746 & 0.0104715689916749 & 0.994764215504162 \tabularnewline
10 & 0.00145747455287508 & 0.00291494910575017 & 0.998542525447125 \tabularnewline
11 & 0.000867340762200593 & 0.00173468152440119 & 0.999132659237799 \tabularnewline
12 & 0.000744242291325423 & 0.00148848458265085 & 0.999255757708675 \tabularnewline
13 & 0.000210640548555266 & 0.000421281097110532 & 0.999789359451445 \tabularnewline
14 & 0.00013420173369581 & 0.00026840346739162 & 0.999865798266304 \tabularnewline
15 & 5.99595726307378e-05 & 0.000119919145261476 & 0.999940040427369 \tabularnewline
16 & 2.89137968572658e-05 & 5.78275937145317e-05 & 0.999971086203143 \tabularnewline
17 & 6.80389943214933e-05 & 0.000136077988642987 & 0.999931961005678 \tabularnewline
18 & 3.36222984216537e-05 & 6.72445968433074e-05 & 0.999966377701578 \tabularnewline
19 & 0.000135665805255002 & 0.000271331610510004 & 0.999864334194745 \tabularnewline
20 & 0.000111347730220918 & 0.000222695460441836 & 0.999888652269779 \tabularnewline
21 & 5.5387585496253e-05 & 0.000110775170992506 & 0.999944612414504 \tabularnewline
22 & 2.86207107816885e-05 & 5.7241421563377e-05 & 0.999971379289218 \tabularnewline
23 & 1.13076741899623e-05 & 2.26153483799246e-05 & 0.99998869232581 \tabularnewline
24 & 5.65194314703584e-06 & 1.13038862940717e-05 & 0.999994348056853 \tabularnewline
25 & 4.0832783632458e-05 & 8.16655672649159e-05 & 0.999959167216368 \tabularnewline
26 & 1.75758001468909e-05 & 3.51516002937818e-05 & 0.999982424199853 \tabularnewline
27 & 7.89914164514358e-06 & 1.57982832902872e-05 & 0.999992100858355 \tabularnewline
28 & 3.97595433620531e-06 & 7.95190867241062e-06 & 0.999996024045664 \tabularnewline
29 & 1.730464690075e-06 & 3.46092938015001e-06 & 0.99999826953531 \tabularnewline
30 & 3.51416914932139e-06 & 7.02833829864278e-06 & 0.999996485830851 \tabularnewline
31 & 1.62613457965919e-06 & 3.25226915931838e-06 & 0.99999837386542 \tabularnewline
32 & 7.32029107808501e-07 & 1.464058215617e-06 & 0.999999267970892 \tabularnewline
33 & 4.58938565849222e-07 & 9.17877131698443e-07 & 0.999999541061434 \tabularnewline
34 & 1.85017361255453e-07 & 3.70034722510906e-07 & 0.999999814982639 \tabularnewline
35 & 1.45785025312438e-07 & 2.91570050624875e-07 & 0.999999854214975 \tabularnewline
36 & 5.78137318154511e-08 & 1.15627463630902e-07 & 0.999999942186268 \tabularnewline
37 & 2.46082096138278e-08 & 4.92164192276556e-08 & 0.99999997539179 \tabularnewline
38 & 1.13044979635981e-08 & 2.26089959271961e-08 & 0.999999988695502 \tabularnewline
39 & 5.99920069338934e-09 & 1.19984013867787e-08 & 0.999999994000799 \tabularnewline
40 & 4.4319635509521e-09 & 8.8639271019042e-09 & 0.999999995568036 \tabularnewline
41 & 2.75064592346271e-09 & 5.50129184692542e-09 & 0.999999997249354 \tabularnewline
42 & 1.04250515119602e-09 & 2.08501030239203e-09 & 0.999999998957495 \tabularnewline
43 & 4.91474490004878e-10 & 9.82948980009756e-10 & 0.999999999508525 \tabularnewline
44 & 1.82197923325826e-10 & 3.64395846651652e-10 & 0.999999999817802 \tabularnewline
45 & 1.0920326782271e-10 & 2.18406535645421e-10 & 0.999999999890797 \tabularnewline
46 & 1 & 5.46227729081711e-16 & 2.73113864540856e-16 \tabularnewline
47 & 0.999999999999999 & 1.33300442127444e-15 & 6.66502210637218e-16 \tabularnewline
48 & 0.999999999999998 & 3.43005088934515e-15 & 1.71502544467257e-15 \tabularnewline
49 & 0.999999999999997 & 5.60816104513648e-15 & 2.80408052256824e-15 \tabularnewline
50 & 0.999999999999993 & 1.38853492353074e-14 & 6.94267461765368e-15 \tabularnewline
51 & 0.999999999999989 & 2.25094843828566e-14 & 1.12547421914283e-14 \tabularnewline
52 & 0.999999999999973 & 5.42844692949118e-14 & 2.71422346474559e-14 \tabularnewline
53 & 0.999999999999937 & 1.25672192910828e-13 & 6.28360964554139e-14 \tabularnewline
54 & 0.999999999999875 & 2.49156681507787e-13 & 1.24578340753893e-13 \tabularnewline
55 & 0.999999999999771 & 4.58538623176201e-13 & 2.292693115881e-13 \tabularnewline
56 & 0.999999999999511 & 9.77679888003552e-13 & 4.88839944001776e-13 \tabularnewline
57 & 0.999999999999286 & 1.42797313665274e-12 & 7.1398656832637e-13 \tabularnewline
58 & 0.999999999999905 & 1.90108445068085e-13 & 9.50542225340425e-14 \tabularnewline
59 & 0.999999999999829 & 3.42120374866348e-13 & 1.71060187433174e-13 \tabularnewline
60 & 0.999999999999623 & 7.53377958515257e-13 & 3.76688979257628e-13 \tabularnewline
61 & 0.999999999999579 & 8.41379150830567e-13 & 4.20689575415283e-13 \tabularnewline
62 & 0.999999999999372 & 1.25684525132116e-12 & 6.28422625660582e-13 \tabularnewline
63 & 0.999999999998642 & 2.71568895910949e-12 & 1.35784447955474e-12 \tabularnewline
64 & 0.999999999997819 & 4.36109220553618e-12 & 2.18054610276809e-12 \tabularnewline
65 & 0.999999999995792 & 8.41573300891623e-12 & 4.20786650445811e-12 \tabularnewline
66 & 0.999999999991656 & 1.66876625710526e-11 & 8.34383128552629e-12 \tabularnewline
67 & 0.99999999998855 & 2.29002706552325e-11 & 1.14501353276163e-11 \tabularnewline
68 & 0.999999999975175 & 4.96500390484977e-11 & 2.48250195242488e-11 \tabularnewline
69 & 0.999999999948497 & 1.03006768787825e-10 & 5.15033843939123e-11 \tabularnewline
70 & 0.999999999895672 & 2.08656055879129e-10 & 1.04328027939565e-10 \tabularnewline
71 & 0.999999999804661 & 3.90678822972152e-10 & 1.95339411486076e-10 \tabularnewline
72 & 0.999999999592644 & 8.14711148351121e-10 & 4.0735557417556e-10 \tabularnewline
73 & 0.999999999267522 & 1.46495601870156e-09 & 7.32478009350778e-10 \tabularnewline
74 & 0.999999998736498 & 2.52700476769789e-09 & 1.26350238384894e-09 \tabularnewline
75 & 0.999999997460878 & 5.07824474438323e-09 & 2.53912237219161e-09 \tabularnewline
76 & 0.99999999502044 & 9.95911997997305e-09 & 4.97955998998653e-09 \tabularnewline
77 & 0.999999995110158 & 9.77968345274091e-09 & 4.88984172637046e-09 \tabularnewline
78 & 0.999999997552846 & 4.89430826144917e-09 & 2.44715413072459e-09 \tabularnewline
79 & 0.999999995356047 & 9.28790615465869e-09 & 4.64395307732935e-09 \tabularnewline
80 & 0.999999990891547 & 1.82169050988505e-08 & 9.10845254942527e-09 \tabularnewline
81 & 0.999999983229965 & 3.35400699275627e-08 & 1.67700349637814e-08 \tabularnewline
82 & 0.999999972574828 & 5.48503430983317e-08 & 2.74251715491659e-08 \tabularnewline
83 & 0.999999950968312 & 9.80633760619039e-08 & 4.9031688030952e-08 \tabularnewline
84 & 0.999999915030231 & 1.69939538689379e-07 & 8.49697693446896e-08 \tabularnewline
85 & 0.999999853807883 & 2.92384234286178e-07 & 1.46192117143089e-07 \tabularnewline
86 & 0.999999993196089 & 1.36078226535046e-08 & 6.80391132675229e-09 \tabularnewline
87 & 0.999999986690741 & 2.66185181111773e-08 & 1.33092590555887e-08 \tabularnewline
88 & 0.999999999999856 & 2.87717925918111e-13 & 1.43858962959055e-13 \tabularnewline
89 & 0.999999999999644 & 7.12979953810361e-13 & 3.5648997690518e-13 \tabularnewline
90 & 0.999999999999144 & 1.71221042105044e-12 & 8.5610521052522e-13 \tabularnewline
91 & 0.999999999997925 & 4.15070927883687e-12 & 2.07535463941844e-12 \tabularnewline
92 & 0.999999999995204 & 9.5912132749736e-12 & 4.7956066374868e-12 \tabularnewline
93 & 0.999999999991437 & 1.71269349766101e-11 & 8.56346748830505e-12 \tabularnewline
94 & 0.999999999982749 & 3.45009930405985e-11 & 1.72504965202993e-11 \tabularnewline
95 & 0.999999999971699 & 5.66016345145808e-11 & 2.83008172572904e-11 \tabularnewline
96 & 0.999999999938521 & 1.22959045413721e-10 & 6.14795227068606e-11 \tabularnewline
97 & 0.999999999900371 & 1.99257791119163e-10 & 9.96288955595814e-11 \tabularnewline
98 & 0.999999999914323 & 1.71353352843424e-10 & 8.5676676421712e-11 \tabularnewline
99 & 0.999999999890179 & 2.19643069894238e-10 & 1.09821534947119e-10 \tabularnewline
100 & 0.999999999933746 & 1.3250707596561e-10 & 6.62535379828049e-11 \tabularnewline
101 & 0.999999999855096 & 2.89808062755107e-10 & 1.44904031377553e-10 \tabularnewline
102 & 0.999999999712964 & 5.74071354940282e-10 & 2.87035677470141e-10 \tabularnewline
103 & 0.999999999354953 & 1.29009335203121e-09 & 6.45046676015604e-10 \tabularnewline
104 & 0.999999998602719 & 2.79456201977515e-09 & 1.39728100988757e-09 \tabularnewline
105 & 0.999999997088446 & 5.8231078529484e-09 & 2.9115539264742e-09 \tabularnewline
106 & 0.99999999674076 & 6.5184797697372e-09 & 3.2592398848686e-09 \tabularnewline
107 & 0.999999993203297 & 1.35934069208151e-08 & 6.79670346040755e-09 \tabularnewline
108 & 0.999999990868009 & 1.82639822826886e-08 & 9.13199114134428e-09 \tabularnewline
109 & 0.999999992323459 & 1.53530813920977e-08 & 7.67654069604884e-09 \tabularnewline
110 & 0.999999984920391 & 3.01592174650366e-08 & 1.50796087325183e-08 \tabularnewline
111 & 0.999999979174199 & 4.16516024375478e-08 & 2.08258012187739e-08 \tabularnewline
112 & 0.999999957376716 & 8.52465682752318e-08 & 4.26232841376159e-08 \tabularnewline
113 & 0.999999914623024 & 1.70753951592177e-07 & 8.53769757960886e-08 \tabularnewline
114 & 0.999999840647486 & 3.18705026935626e-07 & 1.59352513467813e-07 \tabularnewline
115 & 0.999999674462061 & 6.51075878857519e-07 & 3.25537939428759e-07 \tabularnewline
116 & 0.999999735565807 & 5.28868385593727e-07 & 2.64434192796864e-07 \tabularnewline
117 & 0.999999732114105 & 5.35771789994652e-07 & 2.67885894997326e-07 \tabularnewline
118 & 0.999999509313209 & 9.81373581410893e-07 & 4.90686790705446e-07 \tabularnewline
119 & 0.999999215432294 & 1.56913541175951e-06 & 7.84567705879753e-07 \tabularnewline
120 & 0.999998394552684 & 3.21089463280256e-06 & 1.60544731640128e-06 \tabularnewline
121 & 0.999999541500981 & 9.16998038631286e-07 & 4.58499019315643e-07 \tabularnewline
122 & 0.99999904855086 & 1.9028982808201e-06 & 9.51449140410048e-07 \tabularnewline
123 & 0.999998754075117 & 2.49184976597741e-06 & 1.2459248829887e-06 \tabularnewline
124 & 0.99999741750286 & 5.16499427915024e-06 & 2.58249713957512e-06 \tabularnewline
125 & 0.999996760187858 & 6.47962428370322e-06 & 3.23981214185161e-06 \tabularnewline
126 & 0.999994319384508 & 1.13612309838261e-05 & 5.68061549191307e-06 \tabularnewline
127 & 0.999988943199793 & 2.21136004137326e-05 & 1.10568002068663e-05 \tabularnewline
128 & 0.999985156857928 & 2.96862841444658e-05 & 1.48431420722329e-05 \tabularnewline
129 & 0.999984922928736 & 3.01541425286819e-05 & 1.50770712643409e-05 \tabularnewline
130 & 0.999971186183755 & 5.76276324900758e-05 & 2.88138162450379e-05 \tabularnewline
131 & 0.99997366879645 & 5.26624070991506e-05 & 2.63312035495753e-05 \tabularnewline
132 & 0.999957854828101 & 8.42903437971362e-05 & 4.21451718985681e-05 \tabularnewline
133 & 0.999915131860649 & 0.000169736278702851 & 8.48681393514255e-05 \tabularnewline
134 & 0.99995204088547 & 9.59182290604379e-05 & 4.7959114530219e-05 \tabularnewline
135 & 0.999935911319291 & 0.000128177361418876 & 6.4088680709438e-05 \tabularnewline
136 & 0.99986686399766 & 0.00026627200468081 & 0.000133136002340405 \tabularnewline
137 & 0.999844449603697 & 0.000311100792605374 & 0.000155550396302687 \tabularnewline
138 & 0.999704647779785 & 0.000590704440429803 & 0.000295352220214902 \tabularnewline
139 & 0.999405580535446 & 0.00118883892910807 & 0.000594419464554035 \tabularnewline
140 & 0.999998519111033 & 2.96177793307858e-06 & 1.48088896653929e-06 \tabularnewline
141 & 0.999996282743272 & 7.43451345691275e-06 & 3.71725672845638e-06 \tabularnewline
142 & 0.999994928279004 & 1.01434419916316e-05 & 5.07172099581582e-06 \tabularnewline
143 & 0.99998872182373 & 2.25563525390434e-05 & 1.12781762695217e-05 \tabularnewline
144 & 0.999972463436547 & 5.50731269069798e-05 & 2.75365634534899e-05 \tabularnewline
145 & 0.999924388646328 & 0.000151222707343908 & 7.56113536719538e-05 \tabularnewline
146 & 0.999999355024047 & 1.28995190681613e-06 & 6.44975953408065e-07 \tabularnewline
147 & 0.999997495986111 & 5.0080277782605e-06 & 2.50401388913025e-06 \tabularnewline
148 & 0.999999432902636 & 1.13419472868737e-06 & 5.67097364343683e-07 \tabularnewline
149 & 0.999997367194906 & 5.26561018714726e-06 & 2.63280509357363e-06 \tabularnewline
150 & 0.999988528978836 & 2.29420423279173e-05 & 1.14710211639587e-05 \tabularnewline
151 & 0.99995093302056 & 9.81339588798337e-05 & 4.90669794399169e-05 \tabularnewline
152 & 0.999800235004317 & 0.000399529991365236 & 0.000199764995682618 \tabularnewline
153 & 0.999230472271899 & 0.00153905545620116 & 0.000769527728100582 \tabularnewline
154 & 0.997215711643691 & 0.00556857671261707 & 0.00278428835630853 \tabularnewline
155 & 0.993962975969895 & 0.0120740480602104 & 0.00603702403010518 \tabularnewline
156 & 0.987933123847692 & 0.0241337523046165 & 0.0120668761523082 \tabularnewline
157 & 0.957834978501347 & 0.0843300429973064 & 0.0421650214986532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&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]0.0311707410656649[/C][C]0.0623414821313298[/C][C]0.968829258934335[/C][/ROW]
[ROW][C]8[/C][C]0.0190560536122386[/C][C]0.0381121072244771[/C][C]0.980943946387761[/C][/ROW]
[ROW][C]9[/C][C]0.00523578449583746[/C][C]0.0104715689916749[/C][C]0.994764215504162[/C][/ROW]
[ROW][C]10[/C][C]0.00145747455287508[/C][C]0.00291494910575017[/C][C]0.998542525447125[/C][/ROW]
[ROW][C]11[/C][C]0.000867340762200593[/C][C]0.00173468152440119[/C][C]0.999132659237799[/C][/ROW]
[ROW][C]12[/C][C]0.000744242291325423[/C][C]0.00148848458265085[/C][C]0.999255757708675[/C][/ROW]
[ROW][C]13[/C][C]0.000210640548555266[/C][C]0.000421281097110532[/C][C]0.999789359451445[/C][/ROW]
[ROW][C]14[/C][C]0.00013420173369581[/C][C]0.00026840346739162[/C][C]0.999865798266304[/C][/ROW]
[ROW][C]15[/C][C]5.99595726307378e-05[/C][C]0.000119919145261476[/C][C]0.999940040427369[/C][/ROW]
[ROW][C]16[/C][C]2.89137968572658e-05[/C][C]5.78275937145317e-05[/C][C]0.999971086203143[/C][/ROW]
[ROW][C]17[/C][C]6.80389943214933e-05[/C][C]0.000136077988642987[/C][C]0.999931961005678[/C][/ROW]
[ROW][C]18[/C][C]3.36222984216537e-05[/C][C]6.72445968433074e-05[/C][C]0.999966377701578[/C][/ROW]
[ROW][C]19[/C][C]0.000135665805255002[/C][C]0.000271331610510004[/C][C]0.999864334194745[/C][/ROW]
[ROW][C]20[/C][C]0.000111347730220918[/C][C]0.000222695460441836[/C][C]0.999888652269779[/C][/ROW]
[ROW][C]21[/C][C]5.5387585496253e-05[/C][C]0.000110775170992506[/C][C]0.999944612414504[/C][/ROW]
[ROW][C]22[/C][C]2.86207107816885e-05[/C][C]5.7241421563377e-05[/C][C]0.999971379289218[/C][/ROW]
[ROW][C]23[/C][C]1.13076741899623e-05[/C][C]2.26153483799246e-05[/C][C]0.99998869232581[/C][/ROW]
[ROW][C]24[/C][C]5.65194314703584e-06[/C][C]1.13038862940717e-05[/C][C]0.999994348056853[/C][/ROW]
[ROW][C]25[/C][C]4.0832783632458e-05[/C][C]8.16655672649159e-05[/C][C]0.999959167216368[/C][/ROW]
[ROW][C]26[/C][C]1.75758001468909e-05[/C][C]3.51516002937818e-05[/C][C]0.999982424199853[/C][/ROW]
[ROW][C]27[/C][C]7.89914164514358e-06[/C][C]1.57982832902872e-05[/C][C]0.999992100858355[/C][/ROW]
[ROW][C]28[/C][C]3.97595433620531e-06[/C][C]7.95190867241062e-06[/C][C]0.999996024045664[/C][/ROW]
[ROW][C]29[/C][C]1.730464690075e-06[/C][C]3.46092938015001e-06[/C][C]0.99999826953531[/C][/ROW]
[ROW][C]30[/C][C]3.51416914932139e-06[/C][C]7.02833829864278e-06[/C][C]0.999996485830851[/C][/ROW]
[ROW][C]31[/C][C]1.62613457965919e-06[/C][C]3.25226915931838e-06[/C][C]0.99999837386542[/C][/ROW]
[ROW][C]32[/C][C]7.32029107808501e-07[/C][C]1.464058215617e-06[/C][C]0.999999267970892[/C][/ROW]
[ROW][C]33[/C][C]4.58938565849222e-07[/C][C]9.17877131698443e-07[/C][C]0.999999541061434[/C][/ROW]
[ROW][C]34[/C][C]1.85017361255453e-07[/C][C]3.70034722510906e-07[/C][C]0.999999814982639[/C][/ROW]
[ROW][C]35[/C][C]1.45785025312438e-07[/C][C]2.91570050624875e-07[/C][C]0.999999854214975[/C][/ROW]
[ROW][C]36[/C][C]5.78137318154511e-08[/C][C]1.15627463630902e-07[/C][C]0.999999942186268[/C][/ROW]
[ROW][C]37[/C][C]2.46082096138278e-08[/C][C]4.92164192276556e-08[/C][C]0.99999997539179[/C][/ROW]
[ROW][C]38[/C][C]1.13044979635981e-08[/C][C]2.26089959271961e-08[/C][C]0.999999988695502[/C][/ROW]
[ROW][C]39[/C][C]5.99920069338934e-09[/C][C]1.19984013867787e-08[/C][C]0.999999994000799[/C][/ROW]
[ROW][C]40[/C][C]4.4319635509521e-09[/C][C]8.8639271019042e-09[/C][C]0.999999995568036[/C][/ROW]
[ROW][C]41[/C][C]2.75064592346271e-09[/C][C]5.50129184692542e-09[/C][C]0.999999997249354[/C][/ROW]
[ROW][C]42[/C][C]1.04250515119602e-09[/C][C]2.08501030239203e-09[/C][C]0.999999998957495[/C][/ROW]
[ROW][C]43[/C][C]4.91474490004878e-10[/C][C]9.82948980009756e-10[/C][C]0.999999999508525[/C][/ROW]
[ROW][C]44[/C][C]1.82197923325826e-10[/C][C]3.64395846651652e-10[/C][C]0.999999999817802[/C][/ROW]
[ROW][C]45[/C][C]1.0920326782271e-10[/C][C]2.18406535645421e-10[/C][C]0.999999999890797[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]5.46227729081711e-16[/C][C]2.73113864540856e-16[/C][/ROW]
[ROW][C]47[/C][C]0.999999999999999[/C][C]1.33300442127444e-15[/C][C]6.66502210637218e-16[/C][/ROW]
[ROW][C]48[/C][C]0.999999999999998[/C][C]3.43005088934515e-15[/C][C]1.71502544467257e-15[/C][/ROW]
[ROW][C]49[/C][C]0.999999999999997[/C][C]5.60816104513648e-15[/C][C]2.80408052256824e-15[/C][/ROW]
[ROW][C]50[/C][C]0.999999999999993[/C][C]1.38853492353074e-14[/C][C]6.94267461765368e-15[/C][/ROW]
[ROW][C]51[/C][C]0.999999999999989[/C][C]2.25094843828566e-14[/C][C]1.12547421914283e-14[/C][/ROW]
[ROW][C]52[/C][C]0.999999999999973[/C][C]5.42844692949118e-14[/C][C]2.71422346474559e-14[/C][/ROW]
[ROW][C]53[/C][C]0.999999999999937[/C][C]1.25672192910828e-13[/C][C]6.28360964554139e-14[/C][/ROW]
[ROW][C]54[/C][C]0.999999999999875[/C][C]2.49156681507787e-13[/C][C]1.24578340753893e-13[/C][/ROW]
[ROW][C]55[/C][C]0.999999999999771[/C][C]4.58538623176201e-13[/C][C]2.292693115881e-13[/C][/ROW]
[ROW][C]56[/C][C]0.999999999999511[/C][C]9.77679888003552e-13[/C][C]4.88839944001776e-13[/C][/ROW]
[ROW][C]57[/C][C]0.999999999999286[/C][C]1.42797313665274e-12[/C][C]7.1398656832637e-13[/C][/ROW]
[ROW][C]58[/C][C]0.999999999999905[/C][C]1.90108445068085e-13[/C][C]9.50542225340425e-14[/C][/ROW]
[ROW][C]59[/C][C]0.999999999999829[/C][C]3.42120374866348e-13[/C][C]1.71060187433174e-13[/C][/ROW]
[ROW][C]60[/C][C]0.999999999999623[/C][C]7.53377958515257e-13[/C][C]3.76688979257628e-13[/C][/ROW]
[ROW][C]61[/C][C]0.999999999999579[/C][C]8.41379150830567e-13[/C][C]4.20689575415283e-13[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999372[/C][C]1.25684525132116e-12[/C][C]6.28422625660582e-13[/C][/ROW]
[ROW][C]63[/C][C]0.999999999998642[/C][C]2.71568895910949e-12[/C][C]1.35784447955474e-12[/C][/ROW]
[ROW][C]64[/C][C]0.999999999997819[/C][C]4.36109220553618e-12[/C][C]2.18054610276809e-12[/C][/ROW]
[ROW][C]65[/C][C]0.999999999995792[/C][C]8.41573300891623e-12[/C][C]4.20786650445811e-12[/C][/ROW]
[ROW][C]66[/C][C]0.999999999991656[/C][C]1.66876625710526e-11[/C][C]8.34383128552629e-12[/C][/ROW]
[ROW][C]67[/C][C]0.99999999998855[/C][C]2.29002706552325e-11[/C][C]1.14501353276163e-11[/C][/ROW]
[ROW][C]68[/C][C]0.999999999975175[/C][C]4.96500390484977e-11[/C][C]2.48250195242488e-11[/C][/ROW]
[ROW][C]69[/C][C]0.999999999948497[/C][C]1.03006768787825e-10[/C][C]5.15033843939123e-11[/C][/ROW]
[ROW][C]70[/C][C]0.999999999895672[/C][C]2.08656055879129e-10[/C][C]1.04328027939565e-10[/C][/ROW]
[ROW][C]71[/C][C]0.999999999804661[/C][C]3.90678822972152e-10[/C][C]1.95339411486076e-10[/C][/ROW]
[ROW][C]72[/C][C]0.999999999592644[/C][C]8.14711148351121e-10[/C][C]4.0735557417556e-10[/C][/ROW]
[ROW][C]73[/C][C]0.999999999267522[/C][C]1.46495601870156e-09[/C][C]7.32478009350778e-10[/C][/ROW]
[ROW][C]74[/C][C]0.999999998736498[/C][C]2.52700476769789e-09[/C][C]1.26350238384894e-09[/C][/ROW]
[ROW][C]75[/C][C]0.999999997460878[/C][C]5.07824474438323e-09[/C][C]2.53912237219161e-09[/C][/ROW]
[ROW][C]76[/C][C]0.99999999502044[/C][C]9.95911997997305e-09[/C][C]4.97955998998653e-09[/C][/ROW]
[ROW][C]77[/C][C]0.999999995110158[/C][C]9.77968345274091e-09[/C][C]4.88984172637046e-09[/C][/ROW]
[ROW][C]78[/C][C]0.999999997552846[/C][C]4.89430826144917e-09[/C][C]2.44715413072459e-09[/C][/ROW]
[ROW][C]79[/C][C]0.999999995356047[/C][C]9.28790615465869e-09[/C][C]4.64395307732935e-09[/C][/ROW]
[ROW][C]80[/C][C]0.999999990891547[/C][C]1.82169050988505e-08[/C][C]9.10845254942527e-09[/C][/ROW]
[ROW][C]81[/C][C]0.999999983229965[/C][C]3.35400699275627e-08[/C][C]1.67700349637814e-08[/C][/ROW]
[ROW][C]82[/C][C]0.999999972574828[/C][C]5.48503430983317e-08[/C][C]2.74251715491659e-08[/C][/ROW]
[ROW][C]83[/C][C]0.999999950968312[/C][C]9.80633760619039e-08[/C][C]4.9031688030952e-08[/C][/ROW]
[ROW][C]84[/C][C]0.999999915030231[/C][C]1.69939538689379e-07[/C][C]8.49697693446896e-08[/C][/ROW]
[ROW][C]85[/C][C]0.999999853807883[/C][C]2.92384234286178e-07[/C][C]1.46192117143089e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999993196089[/C][C]1.36078226535046e-08[/C][C]6.80391132675229e-09[/C][/ROW]
[ROW][C]87[/C][C]0.999999986690741[/C][C]2.66185181111773e-08[/C][C]1.33092590555887e-08[/C][/ROW]
[ROW][C]88[/C][C]0.999999999999856[/C][C]2.87717925918111e-13[/C][C]1.43858962959055e-13[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999644[/C][C]7.12979953810361e-13[/C][C]3.5648997690518e-13[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999144[/C][C]1.71221042105044e-12[/C][C]8.5610521052522e-13[/C][/ROW]
[ROW][C]91[/C][C]0.999999999997925[/C][C]4.15070927883687e-12[/C][C]2.07535463941844e-12[/C][/ROW]
[ROW][C]92[/C][C]0.999999999995204[/C][C]9.5912132749736e-12[/C][C]4.7956066374868e-12[/C][/ROW]
[ROW][C]93[/C][C]0.999999999991437[/C][C]1.71269349766101e-11[/C][C]8.56346748830505e-12[/C][/ROW]
[ROW][C]94[/C][C]0.999999999982749[/C][C]3.45009930405985e-11[/C][C]1.72504965202993e-11[/C][/ROW]
[ROW][C]95[/C][C]0.999999999971699[/C][C]5.66016345145808e-11[/C][C]2.83008172572904e-11[/C][/ROW]
[ROW][C]96[/C][C]0.999999999938521[/C][C]1.22959045413721e-10[/C][C]6.14795227068606e-11[/C][/ROW]
[ROW][C]97[/C][C]0.999999999900371[/C][C]1.99257791119163e-10[/C][C]9.96288955595814e-11[/C][/ROW]
[ROW][C]98[/C][C]0.999999999914323[/C][C]1.71353352843424e-10[/C][C]8.5676676421712e-11[/C][/ROW]
[ROW][C]99[/C][C]0.999999999890179[/C][C]2.19643069894238e-10[/C][C]1.09821534947119e-10[/C][/ROW]
[ROW][C]100[/C][C]0.999999999933746[/C][C]1.3250707596561e-10[/C][C]6.62535379828049e-11[/C][/ROW]
[ROW][C]101[/C][C]0.999999999855096[/C][C]2.89808062755107e-10[/C][C]1.44904031377553e-10[/C][/ROW]
[ROW][C]102[/C][C]0.999999999712964[/C][C]5.74071354940282e-10[/C][C]2.87035677470141e-10[/C][/ROW]
[ROW][C]103[/C][C]0.999999999354953[/C][C]1.29009335203121e-09[/C][C]6.45046676015604e-10[/C][/ROW]
[ROW][C]104[/C][C]0.999999998602719[/C][C]2.79456201977515e-09[/C][C]1.39728100988757e-09[/C][/ROW]
[ROW][C]105[/C][C]0.999999997088446[/C][C]5.8231078529484e-09[/C][C]2.9115539264742e-09[/C][/ROW]
[ROW][C]106[/C][C]0.99999999674076[/C][C]6.5184797697372e-09[/C][C]3.2592398848686e-09[/C][/ROW]
[ROW][C]107[/C][C]0.999999993203297[/C][C]1.35934069208151e-08[/C][C]6.79670346040755e-09[/C][/ROW]
[ROW][C]108[/C][C]0.999999990868009[/C][C]1.82639822826886e-08[/C][C]9.13199114134428e-09[/C][/ROW]
[ROW][C]109[/C][C]0.999999992323459[/C][C]1.53530813920977e-08[/C][C]7.67654069604884e-09[/C][/ROW]
[ROW][C]110[/C][C]0.999999984920391[/C][C]3.01592174650366e-08[/C][C]1.50796087325183e-08[/C][/ROW]
[ROW][C]111[/C][C]0.999999979174199[/C][C]4.16516024375478e-08[/C][C]2.08258012187739e-08[/C][/ROW]
[ROW][C]112[/C][C]0.999999957376716[/C][C]8.52465682752318e-08[/C][C]4.26232841376159e-08[/C][/ROW]
[ROW][C]113[/C][C]0.999999914623024[/C][C]1.70753951592177e-07[/C][C]8.53769757960886e-08[/C][/ROW]
[ROW][C]114[/C][C]0.999999840647486[/C][C]3.18705026935626e-07[/C][C]1.59352513467813e-07[/C][/ROW]
[ROW][C]115[/C][C]0.999999674462061[/C][C]6.51075878857519e-07[/C][C]3.25537939428759e-07[/C][/ROW]
[ROW][C]116[/C][C]0.999999735565807[/C][C]5.28868385593727e-07[/C][C]2.64434192796864e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999999732114105[/C][C]5.35771789994652e-07[/C][C]2.67885894997326e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999999509313209[/C][C]9.81373581410893e-07[/C][C]4.90686790705446e-07[/C][/ROW]
[ROW][C]119[/C][C]0.999999215432294[/C][C]1.56913541175951e-06[/C][C]7.84567705879753e-07[/C][/ROW]
[ROW][C]120[/C][C]0.999998394552684[/C][C]3.21089463280256e-06[/C][C]1.60544731640128e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999999541500981[/C][C]9.16998038631286e-07[/C][C]4.58499019315643e-07[/C][/ROW]
[ROW][C]122[/C][C]0.99999904855086[/C][C]1.9028982808201e-06[/C][C]9.51449140410048e-07[/C][/ROW]
[ROW][C]123[/C][C]0.999998754075117[/C][C]2.49184976597741e-06[/C][C]1.2459248829887e-06[/C][/ROW]
[ROW][C]124[/C][C]0.99999741750286[/C][C]5.16499427915024e-06[/C][C]2.58249713957512e-06[/C][/ROW]
[ROW][C]125[/C][C]0.999996760187858[/C][C]6.47962428370322e-06[/C][C]3.23981214185161e-06[/C][/ROW]
[ROW][C]126[/C][C]0.999994319384508[/C][C]1.13612309838261e-05[/C][C]5.68061549191307e-06[/C][/ROW]
[ROW][C]127[/C][C]0.999988943199793[/C][C]2.21136004137326e-05[/C][C]1.10568002068663e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999985156857928[/C][C]2.96862841444658e-05[/C][C]1.48431420722329e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999984922928736[/C][C]3.01541425286819e-05[/C][C]1.50770712643409e-05[/C][/ROW]
[ROW][C]130[/C][C]0.999971186183755[/C][C]5.76276324900758e-05[/C][C]2.88138162450379e-05[/C][/ROW]
[ROW][C]131[/C][C]0.99997366879645[/C][C]5.26624070991506e-05[/C][C]2.63312035495753e-05[/C][/ROW]
[ROW][C]132[/C][C]0.999957854828101[/C][C]8.42903437971362e-05[/C][C]4.21451718985681e-05[/C][/ROW]
[ROW][C]133[/C][C]0.999915131860649[/C][C]0.000169736278702851[/C][C]8.48681393514255e-05[/C][/ROW]
[ROW][C]134[/C][C]0.99995204088547[/C][C]9.59182290604379e-05[/C][C]4.7959114530219e-05[/C][/ROW]
[ROW][C]135[/C][C]0.999935911319291[/C][C]0.000128177361418876[/C][C]6.4088680709438e-05[/C][/ROW]
[ROW][C]136[/C][C]0.99986686399766[/C][C]0.00026627200468081[/C][C]0.000133136002340405[/C][/ROW]
[ROW][C]137[/C][C]0.999844449603697[/C][C]0.000311100792605374[/C][C]0.000155550396302687[/C][/ROW]
[ROW][C]138[/C][C]0.999704647779785[/C][C]0.000590704440429803[/C][C]0.000295352220214902[/C][/ROW]
[ROW][C]139[/C][C]0.999405580535446[/C][C]0.00118883892910807[/C][C]0.000594419464554035[/C][/ROW]
[ROW][C]140[/C][C]0.999998519111033[/C][C]2.96177793307858e-06[/C][C]1.48088896653929e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999996282743272[/C][C]7.43451345691275e-06[/C][C]3.71725672845638e-06[/C][/ROW]
[ROW][C]142[/C][C]0.999994928279004[/C][C]1.01434419916316e-05[/C][C]5.07172099581582e-06[/C][/ROW]
[ROW][C]143[/C][C]0.99998872182373[/C][C]2.25563525390434e-05[/C][C]1.12781762695217e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999972463436547[/C][C]5.50731269069798e-05[/C][C]2.75365634534899e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999924388646328[/C][C]0.000151222707343908[/C][C]7.56113536719538e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999999355024047[/C][C]1.28995190681613e-06[/C][C]6.44975953408065e-07[/C][/ROW]
[ROW][C]147[/C][C]0.999997495986111[/C][C]5.0080277782605e-06[/C][C]2.50401388913025e-06[/C][/ROW]
[ROW][C]148[/C][C]0.999999432902636[/C][C]1.13419472868737e-06[/C][C]5.67097364343683e-07[/C][/ROW]
[ROW][C]149[/C][C]0.999997367194906[/C][C]5.26561018714726e-06[/C][C]2.63280509357363e-06[/C][/ROW]
[ROW][C]150[/C][C]0.999988528978836[/C][C]2.29420423279173e-05[/C][C]1.14710211639587e-05[/C][/ROW]
[ROW][C]151[/C][C]0.99995093302056[/C][C]9.81339588798337e-05[/C][C]4.90669794399169e-05[/C][/ROW]
[ROW][C]152[/C][C]0.999800235004317[/C][C]0.000399529991365236[/C][C]0.000199764995682618[/C][/ROW]
[ROW][C]153[/C][C]0.999230472271899[/C][C]0.00153905545620116[/C][C]0.000769527728100582[/C][/ROW]
[ROW][C]154[/C][C]0.997215711643691[/C][C]0.00556857671261707[/C][C]0.00278428835630853[/C][/ROW]
[ROW][C]155[/C][C]0.993962975969895[/C][C]0.0120740480602104[/C][C]0.00603702403010518[/C][/ROW]
[ROW][C]156[/C][C]0.987933123847692[/C][C]0.0241337523046165[/C][C]0.0120668761523082[/C][/ROW]
[ROW][C]157[/C][C]0.957834978501347[/C][C]0.0843300429973064[/C][C]0.0421650214986532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.03117074106566490.06234148213132980.968829258934335
80.01905605361223860.03811210722447710.980943946387761
90.005235784495837460.01047156899167490.994764215504162
100.001457474552875080.002914949105750170.998542525447125
110.0008673407622005930.001734681524401190.999132659237799
120.0007442422913254230.001488484582650850.999255757708675
130.0002106405485552660.0004212810971105320.999789359451445
140.000134201733695810.000268403467391620.999865798266304
155.99595726307378e-050.0001199191452614760.999940040427369
162.89137968572658e-055.78275937145317e-050.999971086203143
176.80389943214933e-050.0001360779886429870.999931961005678
183.36222984216537e-056.72445968433074e-050.999966377701578
190.0001356658052550020.0002713316105100040.999864334194745
200.0001113477302209180.0002226954604418360.999888652269779
215.5387585496253e-050.0001107751709925060.999944612414504
222.86207107816885e-055.7241421563377e-050.999971379289218
231.13076741899623e-052.26153483799246e-050.99998869232581
245.65194314703584e-061.13038862940717e-050.999994348056853
254.0832783632458e-058.16655672649159e-050.999959167216368
261.75758001468909e-053.51516002937818e-050.999982424199853
277.89914164514358e-061.57982832902872e-050.999992100858355
283.97595433620531e-067.95190867241062e-060.999996024045664
291.730464690075e-063.46092938015001e-060.99999826953531
303.51416914932139e-067.02833829864278e-060.999996485830851
311.62613457965919e-063.25226915931838e-060.99999837386542
327.32029107808501e-071.464058215617e-060.999999267970892
334.58938565849222e-079.17877131698443e-070.999999541061434
341.85017361255453e-073.70034722510906e-070.999999814982639
351.45785025312438e-072.91570050624875e-070.999999854214975
365.78137318154511e-081.15627463630902e-070.999999942186268
372.46082096138278e-084.92164192276556e-080.99999997539179
381.13044979635981e-082.26089959271961e-080.999999988695502
395.99920069338934e-091.19984013867787e-080.999999994000799
404.4319635509521e-098.8639271019042e-090.999999995568036
412.75064592346271e-095.50129184692542e-090.999999997249354
421.04250515119602e-092.08501030239203e-090.999999998957495
434.91474490004878e-109.82948980009756e-100.999999999508525
441.82197923325826e-103.64395846651652e-100.999999999817802
451.0920326782271e-102.18406535645421e-100.999999999890797
4615.46227729081711e-162.73113864540856e-16
470.9999999999999991.33300442127444e-156.66502210637218e-16
480.9999999999999983.43005088934515e-151.71502544467257e-15
490.9999999999999975.60816104513648e-152.80408052256824e-15
500.9999999999999931.38853492353074e-146.94267461765368e-15
510.9999999999999892.25094843828566e-141.12547421914283e-14
520.9999999999999735.42844692949118e-142.71422346474559e-14
530.9999999999999371.25672192910828e-136.28360964554139e-14
540.9999999999998752.49156681507787e-131.24578340753893e-13
550.9999999999997714.58538623176201e-132.292693115881e-13
560.9999999999995119.77679888003552e-134.88839944001776e-13
570.9999999999992861.42797313665274e-127.1398656832637e-13
580.9999999999999051.90108445068085e-139.50542225340425e-14
590.9999999999998293.42120374866348e-131.71060187433174e-13
600.9999999999996237.53377958515257e-133.76688979257628e-13
610.9999999999995798.41379150830567e-134.20689575415283e-13
620.9999999999993721.25684525132116e-126.28422625660582e-13
630.9999999999986422.71568895910949e-121.35784447955474e-12
640.9999999999978194.36109220553618e-122.18054610276809e-12
650.9999999999957928.41573300891623e-124.20786650445811e-12
660.9999999999916561.66876625710526e-118.34383128552629e-12
670.999999999988552.29002706552325e-111.14501353276163e-11
680.9999999999751754.96500390484977e-112.48250195242488e-11
690.9999999999484971.03006768787825e-105.15033843939123e-11
700.9999999998956722.08656055879129e-101.04328027939565e-10
710.9999999998046613.90678822972152e-101.95339411486076e-10
720.9999999995926448.14711148351121e-104.0735557417556e-10
730.9999999992675221.46495601870156e-097.32478009350778e-10
740.9999999987364982.52700476769789e-091.26350238384894e-09
750.9999999974608785.07824474438323e-092.53912237219161e-09
760.999999995020449.95911997997305e-094.97955998998653e-09
770.9999999951101589.77968345274091e-094.88984172637046e-09
780.9999999975528464.89430826144917e-092.44715413072459e-09
790.9999999953560479.28790615465869e-094.64395307732935e-09
800.9999999908915471.82169050988505e-089.10845254942527e-09
810.9999999832299653.35400699275627e-081.67700349637814e-08
820.9999999725748285.48503430983317e-082.74251715491659e-08
830.9999999509683129.80633760619039e-084.9031688030952e-08
840.9999999150302311.69939538689379e-078.49697693446896e-08
850.9999998538078832.92384234286178e-071.46192117143089e-07
860.9999999931960891.36078226535046e-086.80391132675229e-09
870.9999999866907412.66185181111773e-081.33092590555887e-08
880.9999999999998562.87717925918111e-131.43858962959055e-13
890.9999999999996447.12979953810361e-133.5648997690518e-13
900.9999999999991441.71221042105044e-128.5610521052522e-13
910.9999999999979254.15070927883687e-122.07535463941844e-12
920.9999999999952049.5912132749736e-124.7956066374868e-12
930.9999999999914371.71269349766101e-118.56346748830505e-12
940.9999999999827493.45009930405985e-111.72504965202993e-11
950.9999999999716995.66016345145808e-112.83008172572904e-11
960.9999999999385211.22959045413721e-106.14795227068606e-11
970.9999999999003711.99257791119163e-109.96288955595814e-11
980.9999999999143231.71353352843424e-108.5676676421712e-11
990.9999999998901792.19643069894238e-101.09821534947119e-10
1000.9999999999337461.3250707596561e-106.62535379828049e-11
1010.9999999998550962.89808062755107e-101.44904031377553e-10
1020.9999999997129645.74071354940282e-102.87035677470141e-10
1030.9999999993549531.29009335203121e-096.45046676015604e-10
1040.9999999986027192.79456201977515e-091.39728100988757e-09
1050.9999999970884465.8231078529484e-092.9115539264742e-09
1060.999999996740766.5184797697372e-093.2592398848686e-09
1070.9999999932032971.35934069208151e-086.79670346040755e-09
1080.9999999908680091.82639822826886e-089.13199114134428e-09
1090.9999999923234591.53530813920977e-087.67654069604884e-09
1100.9999999849203913.01592174650366e-081.50796087325183e-08
1110.9999999791741994.16516024375478e-082.08258012187739e-08
1120.9999999573767168.52465682752318e-084.26232841376159e-08
1130.9999999146230241.70753951592177e-078.53769757960886e-08
1140.9999998406474863.18705026935626e-071.59352513467813e-07
1150.9999996744620616.51075878857519e-073.25537939428759e-07
1160.9999997355658075.28868385593727e-072.64434192796864e-07
1170.9999997321141055.35771789994652e-072.67885894997326e-07
1180.9999995093132099.81373581410893e-074.90686790705446e-07
1190.9999992154322941.56913541175951e-067.84567705879753e-07
1200.9999983945526843.21089463280256e-061.60544731640128e-06
1210.9999995415009819.16998038631286e-074.58499019315643e-07
1220.999999048550861.9028982808201e-069.51449140410048e-07
1230.9999987540751172.49184976597741e-061.2459248829887e-06
1240.999997417502865.16499427915024e-062.58249713957512e-06
1250.9999967601878586.47962428370322e-063.23981214185161e-06
1260.9999943193845081.13612309838261e-055.68061549191307e-06
1270.9999889431997932.21136004137326e-051.10568002068663e-05
1280.9999851568579282.96862841444658e-051.48431420722329e-05
1290.9999849229287363.01541425286819e-051.50770712643409e-05
1300.9999711861837555.76276324900758e-052.88138162450379e-05
1310.999973668796455.26624070991506e-052.63312035495753e-05
1320.9999578548281018.42903437971362e-054.21451718985681e-05
1330.9999151318606490.0001697362787028518.48681393514255e-05
1340.999952040885479.59182290604379e-054.7959114530219e-05
1350.9999359113192910.0001281773614188766.4088680709438e-05
1360.999866863997660.000266272004680810.000133136002340405
1370.9998444496036970.0003111007926053740.000155550396302687
1380.9997046477797850.0005907044404298030.000295352220214902
1390.9994055805354460.001188838929108070.000594419464554035
1400.9999985191110332.96177793307858e-061.48088896653929e-06
1410.9999962827432727.43451345691275e-063.71725672845638e-06
1420.9999949282790041.01434419916316e-055.07172099581582e-06
1430.999988721823732.25563525390434e-051.12781762695217e-05
1440.9999724634365475.50731269069798e-052.75365634534899e-05
1450.9999243886463280.0001512227073439087.56113536719538e-05
1460.9999993550240471.28995190681613e-066.44975953408065e-07
1470.9999974959861115.0080277782605e-062.50401388913025e-06
1480.9999994329026361.13419472868737e-065.67097364343683e-07
1490.9999973671949065.26561018714726e-062.63280509357363e-06
1500.9999885289788362.29420423279173e-051.14710211639587e-05
1510.999950933020569.81339588798337e-054.90669794399169e-05
1520.9998002350043170.0003995299913652360.000199764995682618
1530.9992304722718990.001539055456201160.000769527728100582
1540.9972157116436910.005568576712617070.00278428835630853
1550.9939629759698950.01207404806021040.00603702403010518
1560.9879331238476920.02413375230461650.0120668761523082
1570.9578349785013470.08433004299730640.0421650214986532







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1450.960264900662252NOK
5% type I error level1490.986754966887417NOK
10% type I error level1511NOK

\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 & 145 & 0.960264900662252 & NOK \tabularnewline
5% type I error level & 149 & 0.986754966887417 & NOK \tabularnewline
10% type I error level & 151 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145897&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]145[/C][C]0.960264900662252[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]149[/C][C]0.986754966887417[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]151[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145897&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145897&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1450.960264900662252NOK
5% type I error level1490.986754966887417NOK
10% type I error level1511NOK



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')
}