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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 12:19:34 -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/t1321896056zwtmq3sqkv0ozuk.htm/, Retrieved Fri, 26 Apr 2024 16:45:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145838, Retrieved Fri, 26 Apr 2024 16:45:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7] [2011-11-21 17:19:34] [84449ea5bbe6e767918d59f07903f9b5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	65	146455	1	95556	114468	127
11	54	84944	4	54565	88594	90
10	58	113337	9	63016	74151	68
9	75	128655	2	79774	77921	111
9	41	74398	1	31258	53212	51
10	0	35523	2	52491	34956	33
10	111	293403	0	91256	149703	123
10	1	32750	0	22807	6853	5
9	36	106539	5	77411	58907	63
9	60	130539	0	48821	67067	66
11	63	154991	0	52295	110563	99
11	71	126683	7	63262	58126	72
9	38	100672	6	50466	57113	55
9	76	179562	3	62932	77993	116
9	61	125971	4	38439	68091	71
9	125	234509	0	70817	124676	125
9	84	158980	4	105965	109522	123
9	69	184217	3	73795	75865	74
11	77	107342	0	82043	79746	116
9	95	141371	5	74349	77844	117
9	78	154730	0	82204	98681	98
9	76	264020	1	55709	105531	101
9	40	90938	3	37137	51428	43
9	81	101324	5	70780	65703	103
10	102	130232	0	55027	72562	107
9	70	137793	0	56699	81728	77
9	75	161678	4	65911	95580	87
10	93	151503	0	56316	98278	99
10	42	105324	0	26982	46629	46
10	95	175914	0	54628	115189	96
10	87	181853	3	96750	124865	92
11	44	114928	4	53009	59392	96
11	84	190410	1	64664	127818	96
11	28	61499	4	36990	17821	15
9	87	223004	1	85224	154076	147
9	71	167131	0	37048	64881	56
9	68	233482	0	59635	136506	81
10	50	121185	2	42051	66524	69
9	30	78776	1	26998	45988	34
10	86	188967	2	63717	107445	98
10	75	199512	8	55071	102772	82
9	46	102531	5	40001	46657	64
10	52	118958	3	54506	97563	61
10	31	68948	4	35838	36663	45
10	30	93125	1	50838	55369	37
11	70	277108	2	86997	77921	64
11	20	78800	2	33032	56968	21
10	84	157250	0	61704	77519	104
11	81	210554	6	117986	129805	126
11	79	127324	3	56733	72761	104
10	70	114397	0	55064	81278	87
9	8	24188	0	5950	15049	7
9	67	246209	6	84607	113935	130
10	21	65029	5	32551	25109	21
10	30	98030	3	31701	45824	35
9	70	173587	1	71170	89644	97
9	87	172684	5	101773	109011	103
9	87	191381	5	101653	134245	210
9	112	191276	0	81493	136692	151
11	54	134043	9	55901	50741	57
11	96	233406	6	109104	149510	117
11	93	195304	6	114425	147888	152
11	49	127619	5	36311	54987	52
9	49	162810	6	70027	74467	83
9	38	129100	2	73713	100033	87
9	64	108715	0	40671	85505	80
9	62	106469	3	89041	62426	88
9	66	142069	8	57231	82932	83
10	98	143937	2	78792	79169	140
10	97	84256	5	59155	65469	76
10	56	118807	11	55827	63572	70
10	22	69471	6	22618	23824	26
9	51	122433	5	58425	73831	66
10	56	131122	1	65724	63551	89
10	94	94763	0	56979	56756	100
10	98	188780	3	72369	81399	98
10	76	191467	3	79194	117881	109
10	57	105615	6	202316	70711	51
10	75	89318	1	44970	50495	82
11	48	107335	0	49319	53845	65
11	48	98599	1	36252	51390	46
11	109	260646	0	75741	104953	104
11	27	131876	5	38417	65983	36
11	83	119291	2	64102	76839	123
11	49	80953	0	56622	55792	59
11	24	99768	0	15430	25155	27
10	43	84572	5	72571	55291	84
10	44	202373	1	67271	84279	61
10	49	166790	0	43460	99692	46
10	106	99946	1	99501	59633	125
10	42	116900	1	28340	63249	58
9	108	142146	2	76013	82928	152
9	27	99246	4	37361	50000	52
11	79	156833	1	48204	69455	85
11	49	175078	4	76168	84068	95
10	64	130533	0	85168	76195	78
9	75	142339	2	125410	114634	144
9	115	176789	0	123328	139357	149
9	92	181379	7	83038	110044	101
9	106	228548	7	120087	155118	205
11	73	142141	6	91939	83061	61
10	105	167845	0	103646	127122	145
10	30	103012	0	29467	45653	28
10	13	43287	4	43750	19630	49
11	69	125366	4	34497	67229	68
10	72	118372	0	66477	86060	142
10	80	135171	0	71181	88003	82
10	106	175568	0	74482	95815	105
10	28	74112	0	174949	85499	52
11	70	88817	0	46765	27220	56
9	51	164767	4	90257	109882	81
9	90	141933	0	51370	72579	100
9	12	22938	0	1168	5841	11
9	84	115199	0	51360	68369	87
10	23	61857	4	25162	24610	31
10	57	91185	0	21067	30995	67
10	84	213765	1	58233	150662	150
11	4	21054	0	855	6622	4
10	56	167105	5	85903	93694	75
11	18	31414	0	14116	13155	39
11	86	178863	1	57637	111908	88
11	39	126681	7	94137	57550	67
10	16	64320	5	62147	16356	24
9	18	67746	2	62832	40174	58
9	16	38214	0	8773	13983	16
9	42	90961	1	63785	52316	49
9	75	181510	0	65196	99585	109
10	30	116775	0	73087	86271	124
10	104	223914	2	72631	131012	115
10	121	185139	0	86281	130274	128
10	106	242879	2	162365	159051	159
9	57	139144	0	56530	76506	75
10	28	75812	0	35606	49145	30
10	56	178218	4	70111	66398	83
10	81	246834	4	92046	127546	135
9	2	50999	8	63989	6802	8
11	88	223842	0	104911	99509	115
11	41	93577	4	43448	43106	60
11	83	155383	0	60029	108303	99
11	55	111664	1	38650	64167	98
11	3	75426	0	47261	8579	36
11	54	243551	9	73586	97811	93
10	89	136548	0	83042	84365	158
9	41	173260	3	37238	10901	16
9	94	185039	7	63958	91346	100
9	101	67507	5	78956	33660	49
10	70	139350	2	99518	93634	89
10	111	172964	1	111436	109348	153
11	0	0	9	0	0	0
11	4	14688	0	6023	7953	5
11	0	98	0	0	0	0
10	0	455	0	0	0	0
9	0	0	1	0	0	0
11	0	0	0	0	0	0
10	42	128066	2	42564	63538	80
9	97	176460	1	38885	108281	122
9	0	0	0	0	0	0
9	0	203	0	0	0	0
10	7	7199	0	1644	4245	6
9	12	46660	0	6179	21509	13
11	0	17547	0	3926	7670	3
11	37	73567	0	23238	10641	18
10	0	969	0	0	0	0
9	39	101060	2	49288	41243	49

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
Shared[t] = + 2.0476581669649 + 0.0242200811399153Month[t] -0.0240369650285742BloggedComputations[t] + 1.59695309402963e-05TotalTime[t] + 3.27622786814469e-05Characters[t] -2.80139775914687e-05Writing[t] + 0.00310851816386592Hyperlinks[t] -0.0088912411097865t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Shared[t] =  +  2.0476581669649 +  0.0242200811399153Month[t] -0.0240369650285742BloggedComputations[t] +  1.59695309402963e-05TotalTime[t] +  3.27622786814469e-05Characters[t] -2.80139775914687e-05Writing[t] +  0.00310851816386592Hyperlinks[t] -0.0088912411097865t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Shared[t] =  +  2.0476581669649 +  0.0242200811399153Month[t] -0.0240369650285742BloggedComputations[t] +  1.59695309402963e-05TotalTime[t] +  3.27622786814469e-05Characters[t] -2.80139775914687e-05Writing[t] +  0.00310851816386592Hyperlinks[t] -0.0088912411097865t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Shared[t] = + 2.0476581669649 + 0.0242200811399153Month[t] -0.0240369650285742BloggedComputations[t] + 1.59695309402963e-05TotalTime[t] + 3.27622786814469e-05Characters[t] -2.80139775914687e-05Writing[t] + 0.00310851816386592Hyperlinks[t] -0.0088912411097865t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.04765816696492.5297470.80940.41950.20975
Month0.02422008113991530.2483270.09750.9224280.461214
BloggedComputations-0.02403696502857420.012643-1.90120.0591140.029557
TotalTime1.59695309402963e-057e-062.43960.0158260.007913
Characters3.27622786814469e-059e-063.80170.0002060.000103
Writing-2.80139775914687e-051.4e-05-2.04020.0430150.021508
Hyperlinks0.003108518163865920.010680.29110.7713970.385698
t-0.00889124110978650.004322-2.05720.0413340.020667

\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) & 2.0476581669649 & 2.529747 & 0.8094 & 0.4195 & 0.20975 \tabularnewline
Month & 0.0242200811399153 & 0.248327 & 0.0975 & 0.922428 & 0.461214 \tabularnewline
BloggedComputations & -0.0240369650285742 & 0.012643 & -1.9012 & 0.059114 & 0.029557 \tabularnewline
TotalTime & 1.59695309402963e-05 & 7e-06 & 2.4396 & 0.015826 & 0.007913 \tabularnewline
Characters & 3.27622786814469e-05 & 9e-06 & 3.8017 & 0.000206 & 0.000103 \tabularnewline
Writing & -2.80139775914687e-05 & 1.4e-05 & -2.0402 & 0.043015 & 0.021508 \tabularnewline
Hyperlinks & 0.00310851816386592 & 0.01068 & 0.2911 & 0.771397 & 0.385698 \tabularnewline
t & -0.0088912411097865 & 0.004322 & -2.0572 & 0.041334 & 0.020667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&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]2.0476581669649[/C][C]2.529747[/C][C]0.8094[/C][C]0.4195[/C][C]0.20975[/C][/ROW]
[ROW][C]Month[/C][C]0.0242200811399153[/C][C]0.248327[/C][C]0.0975[/C][C]0.922428[/C][C]0.461214[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]-0.0240369650285742[/C][C]0.012643[/C][C]-1.9012[/C][C]0.059114[/C][C]0.029557[/C][/ROW]
[ROW][C]TotalTime[/C][C]1.59695309402963e-05[/C][C]7e-06[/C][C]2.4396[/C][C]0.015826[/C][C]0.007913[/C][/ROW]
[ROW][C]Characters[/C][C]3.27622786814469e-05[/C][C]9e-06[/C][C]3.8017[/C][C]0.000206[/C][C]0.000103[/C][/ROW]
[ROW][C]Writing[/C][C]-2.80139775914687e-05[/C][C]1.4e-05[/C][C]-2.0402[/C][C]0.043015[/C][C]0.021508[/C][/ROW]
[ROW][C]Hyperlinks[/C][C]0.00310851816386592[/C][C]0.01068[/C][C]0.2911[/C][C]0.771397[/C][C]0.385698[/C][/ROW]
[ROW][C]t[/C][C]-0.0088912411097865[/C][C]0.004322[/C][C]-2.0572[/C][C]0.041334[/C][C]0.020667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.04765816696492.5297470.80940.41950.20975
Month0.02422008113991530.2483270.09750.9224280.461214
BloggedComputations-0.02403696502857420.012643-1.90120.0591140.029557
TotalTime1.59695309402963e-057e-062.43960.0158260.007913
Characters3.27622786814469e-059e-063.80170.0002060.000103
Writing-2.80139775914687e-051.4e-05-2.04020.0430150.021508
Hyperlinks0.003108518163865920.010680.29110.7713970.385698
t-0.00889124110978650.004322-2.05720.0413340.020667






Multiple Linear Regression - Regression Statistics
Multiple R0.377406219328649
R-squared0.142435454387944
Adjusted R-squared0.103954994007916
F-TEST (value)3.70150078718575
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0.000989380131877216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.46803065495836
Sum Squared Residuals950.223348955017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.377406219328649 \tabularnewline
R-squared & 0.142435454387944 \tabularnewline
Adjusted R-squared & 0.103954994007916 \tabularnewline
F-TEST (value) & 3.70150078718575 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.000989380131877216 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.46803065495836 \tabularnewline
Sum Squared Residuals & 950.223348955017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.377406219328649[/C][/ROW]
[ROW][C]R-squared[/C][C]0.142435454387944[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.103954994007916[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.70150078718575[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.000989380131877216[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.46803065495836[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]950.223348955017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.377406219328649
R-squared0.142435454387944
Adjusted R-squared0.103954994007916
F-TEST (value)3.70150078718575
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0.000989380131877216
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.46803065495836
Sum Squared Residuals950.223348955017






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
113.40031286695302-2.40031286695302
241.940386342196422.05961365780358
392.877642547705696.12235745229431
423.25760794658377-1.25760794658377
512.11570624618442-1.11570624618442
623.6468464482855-1.6468464482855
703.42335120903354-3.42335120903354
803.28846631502464-3.28846631502464
954.1034428644080.896557135591997
1002.74499115502219-2.74499115502219
1102.10081743788704-2.10081743788704
1273.191907857529643.80809214247036
1363.068720063301572.93127993669843
1433.41936276891481-0.419362768914809
1542.25026746760431.7497325323957
1602.07977753184703-2.07977753184703
1743.420074505708030.579925494291972
1833.9113473409516-0.911347340951596
1902.82300164127222-2.82300164127222
2052.68075016710342.3192498328966
2102.90838289816667-2.90838289816667
2213.64226885790411-2.64226885790411
2332.461555140069820.538444859930183
2452.521840782528062.47815921747194
2501.79822258166696-1.79822258166696
2602.35978663020722-2.35978663020722
2742.556984485718661.44301551428134
2801.62452442039306-1.62452442039306
2902.42515520954393-2.42515520954393
3001.41012757194471-1.41012757194471
3132.784890478107330.215109521892675
3243.178586351883340.821413648116658
3311.87858857142446-0.878588571424456
3443.080119390495580.919880609504425
3512.35730177575827-1.35730177575827
3602.47821241348037-2.47821241348037
3702.41223981256144-2.41223981256144
3822.41398366060867-0.413983660608667
3912.1576861283747-1.1576861283747
4022.26693176319943-0.266931763199433
4182.488756206352675.51124379364733
4252.626299275677032.37370072432297
4331.799549564803441.20045043519656
4442.541507073242841.45849292675716
4512.88528471678993-1.88528471678993
4625.51406716928963-3.51406716928963
4722.22543266028381-0.225432660283814
4802.52841708694465-2.52841708694465
4963.914671835308762.08532816469124
5032.147564546143290.852435453856709
5101.77822768363395-1.77822768363395
5201.79238249019819-1.79238249019819
5364.100021641590141.89997835840986
5452.771759171808822.22824082819118
5532.508907507610210.491092492389794
5612.97917743885306-1.97917743885306
5753.025965725275961.97403427472404
5852.937432063705652.06256793629435
5901.4134995830804-1.4134995830804
6093.210384756147115.78961524385289
6162.941371536447073.05862846355293
6262.724685014788483.27531498521152
6352.425004612157912.57499538784209
6463.584921739709952.41507826029005
6522.71909470668889-0.719094706688885
6601.06239971372766-1.06239971372766
6733.32182899014913-0.321828990149135
6882.153139890232785.84686010976722
6922.41810655681274-0.418106556812743
7051.021668168731843.97833183126816
71112.475514300367748.52448569963226
7262.384729361122443.61527063887756
7352.396889012588692.60311098741131
7413.02940376132128-2.02940376132128
7501.46451422413474-1.46451422413474
7632.668528496117670.331471503882335
7732.467150936584530.532849063415473
7866.7188284060338-0.718828406033796
7911.5246974813337-0.524697481333696
8002.47253893235512-2.47253893235512
8111.90574564329404-0.90574564329404
8202.99194511034746-2.99194511034746
8352.555194684489132.44480531551087
8421.807077322351880.192922677648121
8502.14880619436754-2.14880619436754
8602.45055367039374-2.45055367039374
8752.92309269321282.0769073067872
8813.7141860241702-2.7141860241702
8901.75835631170975-1.75835631170975
9012.51570646066709-1.51570646066709
9111.67496263574762-0.674962635747622
9221.761368153692250.238631846307755
9342.359643044708261.64035695529174
9411.98171771575977-0.98171771575977
9543.523180805655420.476819194344577
9602.88072199716655-2.88072199716655
9723.21849287475914-1.21849287475914
9802.05301533201026-2.05301533201026
9972.022247078771314.97775292122869
10072.704498658137444.29550134186256
10162.806172112678653.19382788732135
10201.82469838843469-1.82469838843469
10302.07152796993174-2.07152796993174
10442.77971514560961.2202848543904
10541.182212235185852.81778776481415
10601.7155359227878-1.7155359227878
10701.6957926223408-1.6957926223408
10801.66786043863506-1.66786043863506
10905.32941632111299-5.32941632111299
11002.01548532462613-2.01548532462613
11142.814660694539081.18533930546093
11201.33372006794675-1.33372006794675
11301.24762457037457-1.24762457037457
11401.11043042248376-1.11043042248376
11541.933647861191672.06635213880833
11601.37473008830449-1.37473008830449
11710.7976870947219570.202312905278043
11801.35992451416926-1.35992451416926
11952.977094983884722.02290501611528
12001.53131225981602-1.53131225981602
12111.05429895581331-0.0542989558133068
12273.995151091868653.00484890813135
12353.491290263145581.50870973685442
12422.92571148403482-0.925711484034819
12501.3253422862288-1.3253422862288
12612.36487457410146-1.36487457410146
12701.91733450244195-1.91733450244195
12802.65867219452394-2.65867219452394
12921.285715482738630.714284517261372
13000.757267429527603-0.757267429527603
13123.81390242146849-1.81390242146849
13201.88590558379941-1.88590558379941
13301.52801328052296-1.52801328052296
13443.293351537287680.706648462712318
13542.946586331098351.05341366890165
13683.753528804941064.24647119505894
13703.56233796113088-3.56233796113088
13841.99834907290762.0016509270924
13900.804954384070507-0.804954384070507
14011.30381788146453-0.30381788146453
14103.61277380154696-3.61277380154696
14293.602784006504715.39721599349529
14301.90812091960833-1.90812091960833
14433.73102319639604-0.731023196396044
14571.519217098504875.48078290149513
14651.413984732368813.58601526763119
14722.43964690836025-0.439646908360247
14812.1312342700466-1.1312342700466
14990.9892841341457828.01071586585422
15001.1088801349055-1.1088801349055
15100.973066665958356-0.973066665958356
15200.94565646625434-0.94565646625434
15310.9052790074268040.0947209925731961
15400.944827928596848-0.944827928596848
15521.810540999246650.189459000753349
1561-0.01517797901169331.01517797901169
15700.869714042987657-0.869714042987657
15800.864064616658751-0.864064616658751
15900.776450500206883-0.776450500206883
16000.940031265077647-0.940031265077647
16101.08588965270611-1.08588965270611
16201.67834719974432-1.67834719974432
16300.856061152950001-0.856061152950001
16422.09563861980674-0.0956386198067398

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 3.40031286695302 & -2.40031286695302 \tabularnewline
2 & 4 & 1.94038634219642 & 2.05961365780358 \tabularnewline
3 & 9 & 2.87764254770569 & 6.12235745229431 \tabularnewline
4 & 2 & 3.25760794658377 & -1.25760794658377 \tabularnewline
5 & 1 & 2.11570624618442 & -1.11570624618442 \tabularnewline
6 & 2 & 3.6468464482855 & -1.6468464482855 \tabularnewline
7 & 0 & 3.42335120903354 & -3.42335120903354 \tabularnewline
8 & 0 & 3.28846631502464 & -3.28846631502464 \tabularnewline
9 & 5 & 4.103442864408 & 0.896557135591997 \tabularnewline
10 & 0 & 2.74499115502219 & -2.74499115502219 \tabularnewline
11 & 0 & 2.10081743788704 & -2.10081743788704 \tabularnewline
12 & 7 & 3.19190785752964 & 3.80809214247036 \tabularnewline
13 & 6 & 3.06872006330157 & 2.93127993669843 \tabularnewline
14 & 3 & 3.41936276891481 & -0.419362768914809 \tabularnewline
15 & 4 & 2.2502674676043 & 1.7497325323957 \tabularnewline
16 & 0 & 2.07977753184703 & -2.07977753184703 \tabularnewline
17 & 4 & 3.42007450570803 & 0.579925494291972 \tabularnewline
18 & 3 & 3.9113473409516 & -0.911347340951596 \tabularnewline
19 & 0 & 2.82300164127222 & -2.82300164127222 \tabularnewline
20 & 5 & 2.6807501671034 & 2.3192498328966 \tabularnewline
21 & 0 & 2.90838289816667 & -2.90838289816667 \tabularnewline
22 & 1 & 3.64226885790411 & -2.64226885790411 \tabularnewline
23 & 3 & 2.46155514006982 & 0.538444859930183 \tabularnewline
24 & 5 & 2.52184078252806 & 2.47815921747194 \tabularnewline
25 & 0 & 1.79822258166696 & -1.79822258166696 \tabularnewline
26 & 0 & 2.35978663020722 & -2.35978663020722 \tabularnewline
27 & 4 & 2.55698448571866 & 1.44301551428134 \tabularnewline
28 & 0 & 1.62452442039306 & -1.62452442039306 \tabularnewline
29 & 0 & 2.42515520954393 & -2.42515520954393 \tabularnewline
30 & 0 & 1.41012757194471 & -1.41012757194471 \tabularnewline
31 & 3 & 2.78489047810733 & 0.215109521892675 \tabularnewline
32 & 4 & 3.17858635188334 & 0.821413648116658 \tabularnewline
33 & 1 & 1.87858857142446 & -0.878588571424456 \tabularnewline
34 & 4 & 3.08011939049558 & 0.919880609504425 \tabularnewline
35 & 1 & 2.35730177575827 & -1.35730177575827 \tabularnewline
36 & 0 & 2.47821241348037 & -2.47821241348037 \tabularnewline
37 & 0 & 2.41223981256144 & -2.41223981256144 \tabularnewline
38 & 2 & 2.41398366060867 & -0.413983660608667 \tabularnewline
39 & 1 & 2.1576861283747 & -1.1576861283747 \tabularnewline
40 & 2 & 2.26693176319943 & -0.266931763199433 \tabularnewline
41 & 8 & 2.48875620635267 & 5.51124379364733 \tabularnewline
42 & 5 & 2.62629927567703 & 2.37370072432297 \tabularnewline
43 & 3 & 1.79954956480344 & 1.20045043519656 \tabularnewline
44 & 4 & 2.54150707324284 & 1.45849292675716 \tabularnewline
45 & 1 & 2.88528471678993 & -1.88528471678993 \tabularnewline
46 & 2 & 5.51406716928963 & -3.51406716928963 \tabularnewline
47 & 2 & 2.22543266028381 & -0.225432660283814 \tabularnewline
48 & 0 & 2.52841708694465 & -2.52841708694465 \tabularnewline
49 & 6 & 3.91467183530876 & 2.08532816469124 \tabularnewline
50 & 3 & 2.14756454614329 & 0.852435453856709 \tabularnewline
51 & 0 & 1.77822768363395 & -1.77822768363395 \tabularnewline
52 & 0 & 1.79238249019819 & -1.79238249019819 \tabularnewline
53 & 6 & 4.10002164159014 & 1.89997835840986 \tabularnewline
54 & 5 & 2.77175917180882 & 2.22824082819118 \tabularnewline
55 & 3 & 2.50890750761021 & 0.491092492389794 \tabularnewline
56 & 1 & 2.97917743885306 & -1.97917743885306 \tabularnewline
57 & 5 & 3.02596572527596 & 1.97403427472404 \tabularnewline
58 & 5 & 2.93743206370565 & 2.06256793629435 \tabularnewline
59 & 0 & 1.4134995830804 & -1.4134995830804 \tabularnewline
60 & 9 & 3.21038475614711 & 5.78961524385289 \tabularnewline
61 & 6 & 2.94137153644707 & 3.05862846355293 \tabularnewline
62 & 6 & 2.72468501478848 & 3.27531498521152 \tabularnewline
63 & 5 & 2.42500461215791 & 2.57499538784209 \tabularnewline
64 & 6 & 3.58492173970995 & 2.41507826029005 \tabularnewline
65 & 2 & 2.71909470668889 & -0.719094706688885 \tabularnewline
66 & 0 & 1.06239971372766 & -1.06239971372766 \tabularnewline
67 & 3 & 3.32182899014913 & -0.321828990149135 \tabularnewline
68 & 8 & 2.15313989023278 & 5.84686010976722 \tabularnewline
69 & 2 & 2.41810655681274 & -0.418106556812743 \tabularnewline
70 & 5 & 1.02166816873184 & 3.97833183126816 \tabularnewline
71 & 11 & 2.47551430036774 & 8.52448569963226 \tabularnewline
72 & 6 & 2.38472936112244 & 3.61527063887756 \tabularnewline
73 & 5 & 2.39688901258869 & 2.60311098741131 \tabularnewline
74 & 1 & 3.02940376132128 & -2.02940376132128 \tabularnewline
75 & 0 & 1.46451422413474 & -1.46451422413474 \tabularnewline
76 & 3 & 2.66852849611767 & 0.331471503882335 \tabularnewline
77 & 3 & 2.46715093658453 & 0.532849063415473 \tabularnewline
78 & 6 & 6.7188284060338 & -0.718828406033796 \tabularnewline
79 & 1 & 1.5246974813337 & -0.524697481333696 \tabularnewline
80 & 0 & 2.47253893235512 & -2.47253893235512 \tabularnewline
81 & 1 & 1.90574564329404 & -0.90574564329404 \tabularnewline
82 & 0 & 2.99194511034746 & -2.99194511034746 \tabularnewline
83 & 5 & 2.55519468448913 & 2.44480531551087 \tabularnewline
84 & 2 & 1.80707732235188 & 0.192922677648121 \tabularnewline
85 & 0 & 2.14880619436754 & -2.14880619436754 \tabularnewline
86 & 0 & 2.45055367039374 & -2.45055367039374 \tabularnewline
87 & 5 & 2.9230926932128 & 2.0769073067872 \tabularnewline
88 & 1 & 3.7141860241702 & -2.7141860241702 \tabularnewline
89 & 0 & 1.75835631170975 & -1.75835631170975 \tabularnewline
90 & 1 & 2.51570646066709 & -1.51570646066709 \tabularnewline
91 & 1 & 1.67496263574762 & -0.674962635747622 \tabularnewline
92 & 2 & 1.76136815369225 & 0.238631846307755 \tabularnewline
93 & 4 & 2.35964304470826 & 1.64035695529174 \tabularnewline
94 & 1 & 1.98171771575977 & -0.98171771575977 \tabularnewline
95 & 4 & 3.52318080565542 & 0.476819194344577 \tabularnewline
96 & 0 & 2.88072199716655 & -2.88072199716655 \tabularnewline
97 & 2 & 3.21849287475914 & -1.21849287475914 \tabularnewline
98 & 0 & 2.05301533201026 & -2.05301533201026 \tabularnewline
99 & 7 & 2.02224707877131 & 4.97775292122869 \tabularnewline
100 & 7 & 2.70449865813744 & 4.29550134186256 \tabularnewline
101 & 6 & 2.80617211267865 & 3.19382788732135 \tabularnewline
102 & 0 & 1.82469838843469 & -1.82469838843469 \tabularnewline
103 & 0 & 2.07152796993174 & -2.07152796993174 \tabularnewline
104 & 4 & 2.7797151456096 & 1.2202848543904 \tabularnewline
105 & 4 & 1.18221223518585 & 2.81778776481415 \tabularnewline
106 & 0 & 1.7155359227878 & -1.7155359227878 \tabularnewline
107 & 0 & 1.6957926223408 & -1.6957926223408 \tabularnewline
108 & 0 & 1.66786043863506 & -1.66786043863506 \tabularnewline
109 & 0 & 5.32941632111299 & -5.32941632111299 \tabularnewline
110 & 0 & 2.01548532462613 & -2.01548532462613 \tabularnewline
111 & 4 & 2.81466069453908 & 1.18533930546093 \tabularnewline
112 & 0 & 1.33372006794675 & -1.33372006794675 \tabularnewline
113 & 0 & 1.24762457037457 & -1.24762457037457 \tabularnewline
114 & 0 & 1.11043042248376 & -1.11043042248376 \tabularnewline
115 & 4 & 1.93364786119167 & 2.06635213880833 \tabularnewline
116 & 0 & 1.37473008830449 & -1.37473008830449 \tabularnewline
117 & 1 & 0.797687094721957 & 0.202312905278043 \tabularnewline
118 & 0 & 1.35992451416926 & -1.35992451416926 \tabularnewline
119 & 5 & 2.97709498388472 & 2.02290501611528 \tabularnewline
120 & 0 & 1.53131225981602 & -1.53131225981602 \tabularnewline
121 & 1 & 1.05429895581331 & -0.0542989558133068 \tabularnewline
122 & 7 & 3.99515109186865 & 3.00484890813135 \tabularnewline
123 & 5 & 3.49129026314558 & 1.50870973685442 \tabularnewline
124 & 2 & 2.92571148403482 & -0.925711484034819 \tabularnewline
125 & 0 & 1.3253422862288 & -1.3253422862288 \tabularnewline
126 & 1 & 2.36487457410146 & -1.36487457410146 \tabularnewline
127 & 0 & 1.91733450244195 & -1.91733450244195 \tabularnewline
128 & 0 & 2.65867219452394 & -2.65867219452394 \tabularnewline
129 & 2 & 1.28571548273863 & 0.714284517261372 \tabularnewline
130 & 0 & 0.757267429527603 & -0.757267429527603 \tabularnewline
131 & 2 & 3.81390242146849 & -1.81390242146849 \tabularnewline
132 & 0 & 1.88590558379941 & -1.88590558379941 \tabularnewline
133 & 0 & 1.52801328052296 & -1.52801328052296 \tabularnewline
134 & 4 & 3.29335153728768 & 0.706648462712318 \tabularnewline
135 & 4 & 2.94658633109835 & 1.05341366890165 \tabularnewline
136 & 8 & 3.75352880494106 & 4.24647119505894 \tabularnewline
137 & 0 & 3.56233796113088 & -3.56233796113088 \tabularnewline
138 & 4 & 1.9983490729076 & 2.0016509270924 \tabularnewline
139 & 0 & 0.804954384070507 & -0.804954384070507 \tabularnewline
140 & 1 & 1.30381788146453 & -0.30381788146453 \tabularnewline
141 & 0 & 3.61277380154696 & -3.61277380154696 \tabularnewline
142 & 9 & 3.60278400650471 & 5.39721599349529 \tabularnewline
143 & 0 & 1.90812091960833 & -1.90812091960833 \tabularnewline
144 & 3 & 3.73102319639604 & -0.731023196396044 \tabularnewline
145 & 7 & 1.51921709850487 & 5.48078290149513 \tabularnewline
146 & 5 & 1.41398473236881 & 3.58601526763119 \tabularnewline
147 & 2 & 2.43964690836025 & -0.439646908360247 \tabularnewline
148 & 1 & 2.1312342700466 & -1.1312342700466 \tabularnewline
149 & 9 & 0.989284134145782 & 8.01071586585422 \tabularnewline
150 & 0 & 1.1088801349055 & -1.1088801349055 \tabularnewline
151 & 0 & 0.973066665958356 & -0.973066665958356 \tabularnewline
152 & 0 & 0.94565646625434 & -0.94565646625434 \tabularnewline
153 & 1 & 0.905279007426804 & 0.0947209925731961 \tabularnewline
154 & 0 & 0.944827928596848 & -0.944827928596848 \tabularnewline
155 & 2 & 1.81054099924665 & 0.189459000753349 \tabularnewline
156 & 1 & -0.0151779790116933 & 1.01517797901169 \tabularnewline
157 & 0 & 0.869714042987657 & -0.869714042987657 \tabularnewline
158 & 0 & 0.864064616658751 & -0.864064616658751 \tabularnewline
159 & 0 & 0.776450500206883 & -0.776450500206883 \tabularnewline
160 & 0 & 0.940031265077647 & -0.940031265077647 \tabularnewline
161 & 0 & 1.08588965270611 & -1.08588965270611 \tabularnewline
162 & 0 & 1.67834719974432 & -1.67834719974432 \tabularnewline
163 & 0 & 0.856061152950001 & -0.856061152950001 \tabularnewline
164 & 2 & 2.09563861980674 & -0.0956386198067398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&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]1[/C][C]3.40031286695302[/C][C]-2.40031286695302[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]1.94038634219642[/C][C]2.05961365780358[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]2.87764254770569[/C][C]6.12235745229431[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]3.25760794658377[/C][C]-1.25760794658377[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]2.11570624618442[/C][C]-1.11570624618442[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]3.6468464482855[/C][C]-1.6468464482855[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]3.42335120903354[/C][C]-3.42335120903354[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]3.28846631502464[/C][C]-3.28846631502464[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]4.103442864408[/C][C]0.896557135591997[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]2.74499115502219[/C][C]-2.74499115502219[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.10081743788704[/C][C]-2.10081743788704[/C][/ROW]
[ROW][C]12[/C][C]7[/C][C]3.19190785752964[/C][C]3.80809214247036[/C][/ROW]
[ROW][C]13[/C][C]6[/C][C]3.06872006330157[/C][C]2.93127993669843[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]3.41936276891481[/C][C]-0.419362768914809[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]2.2502674676043[/C][C]1.7497325323957[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]2.07977753184703[/C][C]-2.07977753184703[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.42007450570803[/C][C]0.579925494291972[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]3.9113473409516[/C][C]-0.911347340951596[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]2.82300164127222[/C][C]-2.82300164127222[/C][/ROW]
[ROW][C]20[/C][C]5[/C][C]2.6807501671034[/C][C]2.3192498328966[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]2.90838289816667[/C][C]-2.90838289816667[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]3.64226885790411[/C][C]-2.64226885790411[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]2.46155514006982[/C][C]0.538444859930183[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]2.52184078252806[/C][C]2.47815921747194[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]1.79822258166696[/C][C]-1.79822258166696[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]2.35978663020722[/C][C]-2.35978663020722[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]2.55698448571866[/C][C]1.44301551428134[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]1.62452442039306[/C][C]-1.62452442039306[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]2.42515520954393[/C][C]-2.42515520954393[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]1.41012757194471[/C][C]-1.41012757194471[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.78489047810733[/C][C]0.215109521892675[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.17858635188334[/C][C]0.821413648116658[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.87858857142446[/C][C]-0.878588571424456[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.08011939049558[/C][C]0.919880609504425[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]2.35730177575827[/C][C]-1.35730177575827[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]2.47821241348037[/C][C]-2.47821241348037[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]2.41223981256144[/C][C]-2.41223981256144[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]2.41398366060867[/C][C]-0.413983660608667[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]2.1576861283747[/C][C]-1.1576861283747[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]2.26693176319943[/C][C]-0.266931763199433[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]2.48875620635267[/C][C]5.51124379364733[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]2.62629927567703[/C][C]2.37370072432297[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]1.79954956480344[/C][C]1.20045043519656[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]2.54150707324284[/C][C]1.45849292675716[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]2.88528471678993[/C][C]-1.88528471678993[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]5.51406716928963[/C][C]-3.51406716928963[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]2.22543266028381[/C][C]-0.225432660283814[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]2.52841708694465[/C][C]-2.52841708694465[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]3.91467183530876[/C][C]2.08532816469124[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.14756454614329[/C][C]0.852435453856709[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]1.77822768363395[/C][C]-1.77822768363395[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]1.79238249019819[/C][C]-1.79238249019819[/C][/ROW]
[ROW][C]53[/C][C]6[/C][C]4.10002164159014[/C][C]1.89997835840986[/C][/ROW]
[ROW][C]54[/C][C]5[/C][C]2.77175917180882[/C][C]2.22824082819118[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.50890750761021[/C][C]0.491092492389794[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]2.97917743885306[/C][C]-1.97917743885306[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]3.02596572527596[/C][C]1.97403427472404[/C][/ROW]
[ROW][C]58[/C][C]5[/C][C]2.93743206370565[/C][C]2.06256793629435[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]1.4134995830804[/C][C]-1.4134995830804[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]3.21038475614711[/C][C]5.78961524385289[/C][/ROW]
[ROW][C]61[/C][C]6[/C][C]2.94137153644707[/C][C]3.05862846355293[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]2.72468501478848[/C][C]3.27531498521152[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]2.42500461215791[/C][C]2.57499538784209[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]3.58492173970995[/C][C]2.41507826029005[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]2.71909470668889[/C][C]-0.719094706688885[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]1.06239971372766[/C][C]-1.06239971372766[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]3.32182899014913[/C][C]-0.321828990149135[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]2.15313989023278[/C][C]5.84686010976722[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]2.41810655681274[/C][C]-0.418106556812743[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]1.02166816873184[/C][C]3.97833183126816[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]2.47551430036774[/C][C]8.52448569963226[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]2.38472936112244[/C][C]3.61527063887756[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]2.39688901258869[/C][C]2.60311098741131[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]3.02940376132128[/C][C]-2.02940376132128[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]1.46451422413474[/C][C]-1.46451422413474[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.66852849611767[/C][C]0.331471503882335[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.46715093658453[/C][C]0.532849063415473[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]6.7188284060338[/C][C]-0.718828406033796[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.5246974813337[/C][C]-0.524697481333696[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]2.47253893235512[/C][C]-2.47253893235512[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.90574564329404[/C][C]-0.90574564329404[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]2.99194511034746[/C][C]-2.99194511034746[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]2.55519468448913[/C][C]2.44480531551087[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.80707732235188[/C][C]0.192922677648121[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]2.14880619436754[/C][C]-2.14880619436754[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]2.45055367039374[/C][C]-2.45055367039374[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]2.9230926932128[/C][C]2.0769073067872[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]3.7141860241702[/C][C]-2.7141860241702[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]1.75835631170975[/C][C]-1.75835631170975[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]2.51570646066709[/C][C]-1.51570646066709[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.67496263574762[/C][C]-0.674962635747622[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.76136815369225[/C][C]0.238631846307755[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]2.35964304470826[/C][C]1.64035695529174[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.98171771575977[/C][C]-0.98171771575977[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]3.52318080565542[/C][C]0.476819194344577[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]2.88072199716655[/C][C]-2.88072199716655[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]3.21849287475914[/C][C]-1.21849287475914[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]2.05301533201026[/C][C]-2.05301533201026[/C][/ROW]
[ROW][C]99[/C][C]7[/C][C]2.02224707877131[/C][C]4.97775292122869[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]2.70449865813744[/C][C]4.29550134186256[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]2.80617211267865[/C][C]3.19382788732135[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]1.82469838843469[/C][C]-1.82469838843469[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]2.07152796993174[/C][C]-2.07152796993174[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]2.7797151456096[/C][C]1.2202848543904[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]1.18221223518585[/C][C]2.81778776481415[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]1.7155359227878[/C][C]-1.7155359227878[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]1.6957926223408[/C][C]-1.6957926223408[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]1.66786043863506[/C][C]-1.66786043863506[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]5.32941632111299[/C][C]-5.32941632111299[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]2.01548532462613[/C][C]-2.01548532462613[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]2.81466069453908[/C][C]1.18533930546093[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]1.33372006794675[/C][C]-1.33372006794675[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]1.24762457037457[/C][C]-1.24762457037457[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]1.11043042248376[/C][C]-1.11043042248376[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]1.93364786119167[/C][C]2.06635213880833[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1.37473008830449[/C][C]-1.37473008830449[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0.797687094721957[/C][C]0.202312905278043[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]1.35992451416926[/C][C]-1.35992451416926[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]2.97709498388472[/C][C]2.02290501611528[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]1.53131225981602[/C][C]-1.53131225981602[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.05429895581331[/C][C]-0.0542989558133068[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]3.99515109186865[/C][C]3.00484890813135[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]3.49129026314558[/C][C]1.50870973685442[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]2.92571148403482[/C][C]-0.925711484034819[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]1.3253422862288[/C][C]-1.3253422862288[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]2.36487457410146[/C][C]-1.36487457410146[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]1.91733450244195[/C][C]-1.91733450244195[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]2.65867219452394[/C][C]-2.65867219452394[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.28571548273863[/C][C]0.714284517261372[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.757267429527603[/C][C]-0.757267429527603[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]3.81390242146849[/C][C]-1.81390242146849[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]1.88590558379941[/C][C]-1.88590558379941[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]1.52801328052296[/C][C]-1.52801328052296[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]3.29335153728768[/C][C]0.706648462712318[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]2.94658633109835[/C][C]1.05341366890165[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]3.75352880494106[/C][C]4.24647119505894[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]3.56233796113088[/C][C]-3.56233796113088[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]1.9983490729076[/C][C]2.0016509270924[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.804954384070507[/C][C]-0.804954384070507[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.30381788146453[/C][C]-0.30381788146453[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]3.61277380154696[/C][C]-3.61277380154696[/C][/ROW]
[ROW][C]142[/C][C]9[/C][C]3.60278400650471[/C][C]5.39721599349529[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]1.90812091960833[/C][C]-1.90812091960833[/C][/ROW]
[ROW][C]144[/C][C]3[/C][C]3.73102319639604[/C][C]-0.731023196396044[/C][/ROW]
[ROW][C]145[/C][C]7[/C][C]1.51921709850487[/C][C]5.48078290149513[/C][/ROW]
[ROW][C]146[/C][C]5[/C][C]1.41398473236881[/C][C]3.58601526763119[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.43964690836025[/C][C]-0.439646908360247[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]2.1312342700466[/C][C]-1.1312342700466[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]0.989284134145782[/C][C]8.01071586585422[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]1.1088801349055[/C][C]-1.1088801349055[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.973066665958356[/C][C]-0.973066665958356[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.94565646625434[/C][C]-0.94565646625434[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.905279007426804[/C][C]0.0947209925731961[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.944827928596848[/C][C]-0.944827928596848[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.81054099924665[/C][C]0.189459000753349[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]-0.0151779790116933[/C][C]1.01517797901169[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]0.869714042987657[/C][C]-0.869714042987657[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]0.864064616658751[/C][C]-0.864064616658751[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]0.776450500206883[/C][C]-0.776450500206883[/C][/ROW]
[ROW][C]160[/C][C]0[/C][C]0.940031265077647[/C][C]-0.940031265077647[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]1.08588965270611[/C][C]-1.08588965270611[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]1.67834719974432[/C][C]-1.67834719974432[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]0.856061152950001[/C][C]-0.856061152950001[/C][/ROW]
[ROW][C]164[/C][C]2[/C][C]2.09563861980674[/C][C]-0.0956386198067398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
113.40031286695302-2.40031286695302
241.940386342196422.05961365780358
392.877642547705696.12235745229431
423.25760794658377-1.25760794658377
512.11570624618442-1.11570624618442
623.6468464482855-1.6468464482855
703.42335120903354-3.42335120903354
803.28846631502464-3.28846631502464
954.1034428644080.896557135591997
1002.74499115502219-2.74499115502219
1102.10081743788704-2.10081743788704
1273.191907857529643.80809214247036
1363.068720063301572.93127993669843
1433.41936276891481-0.419362768914809
1542.25026746760431.7497325323957
1602.07977753184703-2.07977753184703
1743.420074505708030.579925494291972
1833.9113473409516-0.911347340951596
1902.82300164127222-2.82300164127222
2052.68075016710342.3192498328966
2102.90838289816667-2.90838289816667
2213.64226885790411-2.64226885790411
2332.461555140069820.538444859930183
2452.521840782528062.47815921747194
2501.79822258166696-1.79822258166696
2602.35978663020722-2.35978663020722
2742.556984485718661.44301551428134
2801.62452442039306-1.62452442039306
2902.42515520954393-2.42515520954393
3001.41012757194471-1.41012757194471
3132.784890478107330.215109521892675
3243.178586351883340.821413648116658
3311.87858857142446-0.878588571424456
3443.080119390495580.919880609504425
3512.35730177575827-1.35730177575827
3602.47821241348037-2.47821241348037
3702.41223981256144-2.41223981256144
3822.41398366060867-0.413983660608667
3912.1576861283747-1.1576861283747
4022.26693176319943-0.266931763199433
4182.488756206352675.51124379364733
4252.626299275677032.37370072432297
4331.799549564803441.20045043519656
4442.541507073242841.45849292675716
4512.88528471678993-1.88528471678993
4625.51406716928963-3.51406716928963
4722.22543266028381-0.225432660283814
4802.52841708694465-2.52841708694465
4963.914671835308762.08532816469124
5032.147564546143290.852435453856709
5101.77822768363395-1.77822768363395
5201.79238249019819-1.79238249019819
5364.100021641590141.89997835840986
5452.771759171808822.22824082819118
5532.508907507610210.491092492389794
5612.97917743885306-1.97917743885306
5753.025965725275961.97403427472404
5852.937432063705652.06256793629435
5901.4134995830804-1.4134995830804
6093.210384756147115.78961524385289
6162.941371536447073.05862846355293
6262.724685014788483.27531498521152
6352.425004612157912.57499538784209
6463.584921739709952.41507826029005
6522.71909470668889-0.719094706688885
6601.06239971372766-1.06239971372766
6733.32182899014913-0.321828990149135
6882.153139890232785.84686010976722
6922.41810655681274-0.418106556812743
7051.021668168731843.97833183126816
71112.475514300367748.52448569963226
7262.384729361122443.61527063887756
7352.396889012588692.60311098741131
7413.02940376132128-2.02940376132128
7501.46451422413474-1.46451422413474
7632.668528496117670.331471503882335
7732.467150936584530.532849063415473
7866.7188284060338-0.718828406033796
7911.5246974813337-0.524697481333696
8002.47253893235512-2.47253893235512
8111.90574564329404-0.90574564329404
8202.99194511034746-2.99194511034746
8352.555194684489132.44480531551087
8421.807077322351880.192922677648121
8502.14880619436754-2.14880619436754
8602.45055367039374-2.45055367039374
8752.92309269321282.0769073067872
8813.7141860241702-2.7141860241702
8901.75835631170975-1.75835631170975
9012.51570646066709-1.51570646066709
9111.67496263574762-0.674962635747622
9221.761368153692250.238631846307755
9342.359643044708261.64035695529174
9411.98171771575977-0.98171771575977
9543.523180805655420.476819194344577
9602.88072199716655-2.88072199716655
9723.21849287475914-1.21849287475914
9802.05301533201026-2.05301533201026
9972.022247078771314.97775292122869
10072.704498658137444.29550134186256
10162.806172112678653.19382788732135
10201.82469838843469-1.82469838843469
10302.07152796993174-2.07152796993174
10442.77971514560961.2202848543904
10541.182212235185852.81778776481415
10601.7155359227878-1.7155359227878
10701.6957926223408-1.6957926223408
10801.66786043863506-1.66786043863506
10905.32941632111299-5.32941632111299
11002.01548532462613-2.01548532462613
11142.814660694539081.18533930546093
11201.33372006794675-1.33372006794675
11301.24762457037457-1.24762457037457
11401.11043042248376-1.11043042248376
11541.933647861191672.06635213880833
11601.37473008830449-1.37473008830449
11710.7976870947219570.202312905278043
11801.35992451416926-1.35992451416926
11952.977094983884722.02290501611528
12001.53131225981602-1.53131225981602
12111.05429895581331-0.0542989558133068
12273.995151091868653.00484890813135
12353.491290263145581.50870973685442
12422.92571148403482-0.925711484034819
12501.3253422862288-1.3253422862288
12612.36487457410146-1.36487457410146
12701.91733450244195-1.91733450244195
12802.65867219452394-2.65867219452394
12921.285715482738630.714284517261372
13000.757267429527603-0.757267429527603
13123.81390242146849-1.81390242146849
13201.88590558379941-1.88590558379941
13301.52801328052296-1.52801328052296
13443.293351537287680.706648462712318
13542.946586331098351.05341366890165
13683.753528804941064.24647119505894
13703.56233796113088-3.56233796113088
13841.99834907290762.0016509270924
13900.804954384070507-0.804954384070507
14011.30381788146453-0.30381788146453
14103.61277380154696-3.61277380154696
14293.602784006504715.39721599349529
14301.90812091960833-1.90812091960833
14433.73102319639604-0.731023196396044
14571.519217098504875.48078290149513
14651.413984732368813.58601526763119
14722.43964690836025-0.439646908360247
14812.1312342700466-1.1312342700466
14990.9892841341457828.01071586585422
15001.1088801349055-1.1088801349055
15100.973066665958356-0.973066665958356
15200.94565646625434-0.94565646625434
15310.9052790074268040.0947209925731961
15400.944827928596848-0.944827928596848
15521.810540999246650.189459000753349
1561-0.01517797901169331.01517797901169
15700.869714042987657-0.869714042987657
15800.864064616658751-0.864064616658751
15900.776450500206883-0.776450500206883
16000.940031265077647-0.940031265077647
16101.08588965270611-1.08588965270611
16201.67834719974432-1.67834719974432
16300.856061152950001-0.856061152950001
16422.09563861980674-0.0956386198067398






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2799629006054610.5599258012109230.720037099394539
120.1997905243933190.3995810487866370.800209475606681
130.3098248984398530.6196497968797050.690175101560147
140.8301279413560460.3397441172879080.169872058643954
150.7521655411233620.4956689177532760.247834458876638
160.8888370509676070.2223258980647870.111162949032393
170.8614995042776240.2770009914447520.138500495722376
180.8063032421235640.3873935157528710.193696757876436
190.8818603421110830.2362793157778340.118139657888917
200.8532488616427070.2935022767145860.146751138357293
210.8501683749951450.2996632500097090.149831625004855
220.83375545910530.3324890817893990.1662445408947
230.7879671717735940.4240656564528120.212032828226406
240.7501029715277760.4997940569444480.249897028472224
250.7569779148021550.4860441703956910.243022085197845
260.7250642045087310.5498715909825380.274935795491269
270.7205544428640920.5588911142718150.279445557135908
280.6718810869759290.6562378260481430.328118913024071
290.6257321296172690.7485357407654610.374267870382731
300.567372414798720.8652551704025610.43262758520128
310.5085831973469410.9828336053061180.491416802653059
320.529109969389160.941780061221680.47089003061084
330.4768532997252370.9537065994504740.523146700274763
340.4159957691947790.8319915383895590.584004230805221
350.3734417013383480.7468834026766970.626558298661652
360.3496922598925010.6993845197850030.650307740107499
370.3192860840090950.6385721680181890.680713915990905
380.2722540568585760.5445081137171510.727745943141424
390.2340336215972890.4680672431945780.765966378402711
400.1993810742539470.3987621485078950.800618925746053
410.498826633702260.997653267404520.50117336629774
420.4830293906088590.9660587812177170.516970609391141
430.4342258931154570.8684517862309140.565774106884543
440.3844718871355240.7689437742710480.615528112864476
450.3926802739415960.7853605478831920.607319726058404
460.416648852519690.8332977050393810.58335114748031
470.367879398415640.735758796831280.63212060158436
480.3810712271128780.7621424542257550.618928772887122
490.3682102969290210.7364205938580430.631789703070979
500.3215837973445850.643167594689170.678416202655415
510.3191185775807620.6382371551615240.680881422419238
520.3119621738207260.6239243476414520.688037826179274
530.3159390980234330.6318781960468670.684060901976567
540.2941488663489370.5882977326978740.705851133651063
550.2540820529953750.508164105990750.745917947004625
560.2577657117868940.5155314235737880.742234288213106
570.2250024873156280.4500049746312550.774997512684372
580.1986418512423740.3972837024847480.801358148757626
590.1876710241377840.3753420482755690.812328975862216
600.3239183853542520.6478367707085030.676081614645748
610.3165604157308420.6331208314616840.683439584269158
620.3117382592541460.6234765185082920.688261740745854
630.2964767264263730.5929534528527470.703523273573627
640.2694916830266490.5389833660532990.730508316973351
650.2507612840671550.5015225681343090.749238715932845
660.2310628032580350.462125606516070.768937196741965
670.2226660636436240.4453321272872470.777333936356376
680.3613254988856540.7226509977713080.638674501114346
690.3421307155703510.6842614311407010.657869284429649
700.3629098921880640.7258197843761280.637090107811936
710.7589731578411850.4820536843176290.241026842158815
720.7788963556186510.4422072887626990.221103644381349
730.7704507021211330.4590985957577330.229549297878867
740.7976116298466990.4047767403066030.202388370153301
750.8057801390580760.3884397218838490.194219860941924
760.7764172974848250.447165405030350.223582702515175
770.7449230339404540.5101539321190930.255076966059546
780.7660249775326520.4679500449346970.233975022467348
790.7457929469266790.5084141061466420.254207053073321
800.7701786753876530.4596426492246930.229821324612347
810.7492012415645740.5015975168708520.250798758435426
820.7767909636350710.4464180727298580.223209036364929
830.7703868837014290.4592262325971410.229613116298571
840.7424234517337070.5151530965325860.257576548266293
850.744918408467850.5101631830643010.25508159153215
860.7521242449776630.4957515100446750.247875755022337
870.7485432783947720.5029134432104550.251456721605228
880.7735892863531040.4528214272937920.226410713646896
890.7700980287985520.4598039424028970.229901971201448
900.7519985869650450.496002826069910.248001413034955
910.721240870017020.557518259965960.27875912998298
920.684777268300170.630445463399660.31522273169983
930.6531264714752510.6937470570494980.346873528524749
940.6194729459049470.7610541081901060.380527054095053
950.5738093074070150.8523813851859710.426190692592986
960.5989783388968330.8020433222063330.401021661103167
970.5693060365478670.8613879269042660.430693963452133
980.5494069566448520.9011860867102950.450593043355148
990.6686725117655390.6626549764689220.331327488234461
1000.7944506717526230.4110986564947540.205549328247377
1010.814032719577780.3719345608444390.18596728042222
1020.7943216874161510.4113566251676980.205678312583849
1030.7946602035668380.4106795928663250.205339796433162
1040.7879832225899550.424033554820090.212016777410045
1050.8000539707984880.3998920584030240.199946029201512
1060.7801688495803310.4396623008393390.219831150419669
1070.7561962056359880.4876075887280240.243803794364012
1080.7323993340160620.5352013319678750.267600665983938
1090.8469543419810870.3060913160378260.153045658018913
1100.8336874505335460.3326250989329070.166312549466454
1110.8032615642817240.3934768714365530.196738435718276
1120.772165789049440.4556684219011190.22783421095056
1130.7396353920792680.5207292158414630.260364607920732
1140.7024053359920060.5951893280159870.297594664007994
1150.6908271122735820.6183457754528360.309172887726418
1160.6509695692831240.6980608614337520.349030430716876
1170.6120779397929320.7758441204141350.387922060207068
1180.575339287956870.849321424086260.42466071204313
1190.545850402286590.908299195426820.45414959771341
1200.5048447502015260.9903104995969480.495155249798474
1210.4549848957424810.9099697914849610.545015104257519
1220.4747624667599130.9495249335198260.525237533240087
1230.4437107165069280.8874214330138570.556289283493072
1240.3939017713450.787803542690.606098228655
1250.352220307733730.7044406154674610.64777969226627
1260.3152524379724380.6305048759448770.684747562027562
1270.299714326406370.599428652812740.70028567359363
1280.2717116820811780.5434233641623560.728288317918822
1290.2278721112921920.4557442225843840.772127888707808
1300.2049283649453830.4098567298907650.795071635054617
1310.182099402680940.364198805361880.81790059731906
1320.208248060558090.416496121116180.79175193944191
1330.2775166494838030.5550332989676060.722483350516197
1340.2306863464700860.4613726929401730.769313653529914
1350.1893493653027280.3786987306054550.810650634697272
1360.2271182407506450.454236481501290.772881759249355
1370.3280151299061340.6560302598122690.671984870093866
1380.2823997738303070.5647995476606130.717600226169693
1390.4962912534533740.9925825069067470.503708746546626
1400.4585540963739250.9171081927478510.541445903626075
1410.4425420427869660.8850840855739310.557457957213034
1420.5123926086593610.9752147826812770.487607391340639
1430.4426685575700150.885337115140030.557331442429985
1440.5169824000455020.9660351999089950.483017599954498
1450.4803398371595620.9606796743191240.519660162840438
1460.4435604937343420.8871209874686850.556439506265658
1470.3511623565662780.7023247131325550.648837643433722
1480.3069373516398310.6138747032796620.693062648360169
1490.9999977911845234.41763095450299e-062.2088154772515e-06
1500.9999849704915353.00590169296111e-051.50295084648056e-05
1510.9998787994390430.0002424011219145570.000121200560957279
1520.9994044741374720.001191051725056520.00059552586252826
1530.9999759003958984.8199208203556e-052.4099604101778e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.279962900605461 & 0.559925801210923 & 0.720037099394539 \tabularnewline
12 & 0.199790524393319 & 0.399581048786637 & 0.800209475606681 \tabularnewline
13 & 0.309824898439853 & 0.619649796879705 & 0.690175101560147 \tabularnewline
14 & 0.830127941356046 & 0.339744117287908 & 0.169872058643954 \tabularnewline
15 & 0.752165541123362 & 0.495668917753276 & 0.247834458876638 \tabularnewline
16 & 0.888837050967607 & 0.222325898064787 & 0.111162949032393 \tabularnewline
17 & 0.861499504277624 & 0.277000991444752 & 0.138500495722376 \tabularnewline
18 & 0.806303242123564 & 0.387393515752871 & 0.193696757876436 \tabularnewline
19 & 0.881860342111083 & 0.236279315777834 & 0.118139657888917 \tabularnewline
20 & 0.853248861642707 & 0.293502276714586 & 0.146751138357293 \tabularnewline
21 & 0.850168374995145 & 0.299663250009709 & 0.149831625004855 \tabularnewline
22 & 0.8337554591053 & 0.332489081789399 & 0.1662445408947 \tabularnewline
23 & 0.787967171773594 & 0.424065656452812 & 0.212032828226406 \tabularnewline
24 & 0.750102971527776 & 0.499794056944448 & 0.249897028472224 \tabularnewline
25 & 0.756977914802155 & 0.486044170395691 & 0.243022085197845 \tabularnewline
26 & 0.725064204508731 & 0.549871590982538 & 0.274935795491269 \tabularnewline
27 & 0.720554442864092 & 0.558891114271815 & 0.279445557135908 \tabularnewline
28 & 0.671881086975929 & 0.656237826048143 & 0.328118913024071 \tabularnewline
29 & 0.625732129617269 & 0.748535740765461 & 0.374267870382731 \tabularnewline
30 & 0.56737241479872 & 0.865255170402561 & 0.43262758520128 \tabularnewline
31 & 0.508583197346941 & 0.982833605306118 & 0.491416802653059 \tabularnewline
32 & 0.52910996938916 & 0.94178006122168 & 0.47089003061084 \tabularnewline
33 & 0.476853299725237 & 0.953706599450474 & 0.523146700274763 \tabularnewline
34 & 0.415995769194779 & 0.831991538389559 & 0.584004230805221 \tabularnewline
35 & 0.373441701338348 & 0.746883402676697 & 0.626558298661652 \tabularnewline
36 & 0.349692259892501 & 0.699384519785003 & 0.650307740107499 \tabularnewline
37 & 0.319286084009095 & 0.638572168018189 & 0.680713915990905 \tabularnewline
38 & 0.272254056858576 & 0.544508113717151 & 0.727745943141424 \tabularnewline
39 & 0.234033621597289 & 0.468067243194578 & 0.765966378402711 \tabularnewline
40 & 0.199381074253947 & 0.398762148507895 & 0.800618925746053 \tabularnewline
41 & 0.49882663370226 & 0.99765326740452 & 0.50117336629774 \tabularnewline
42 & 0.483029390608859 & 0.966058781217717 & 0.516970609391141 \tabularnewline
43 & 0.434225893115457 & 0.868451786230914 & 0.565774106884543 \tabularnewline
44 & 0.384471887135524 & 0.768943774271048 & 0.615528112864476 \tabularnewline
45 & 0.392680273941596 & 0.785360547883192 & 0.607319726058404 \tabularnewline
46 & 0.41664885251969 & 0.833297705039381 & 0.58335114748031 \tabularnewline
47 & 0.36787939841564 & 0.73575879683128 & 0.63212060158436 \tabularnewline
48 & 0.381071227112878 & 0.762142454225755 & 0.618928772887122 \tabularnewline
49 & 0.368210296929021 & 0.736420593858043 & 0.631789703070979 \tabularnewline
50 & 0.321583797344585 & 0.64316759468917 & 0.678416202655415 \tabularnewline
51 & 0.319118577580762 & 0.638237155161524 & 0.680881422419238 \tabularnewline
52 & 0.311962173820726 & 0.623924347641452 & 0.688037826179274 \tabularnewline
53 & 0.315939098023433 & 0.631878196046867 & 0.684060901976567 \tabularnewline
54 & 0.294148866348937 & 0.588297732697874 & 0.705851133651063 \tabularnewline
55 & 0.254082052995375 & 0.50816410599075 & 0.745917947004625 \tabularnewline
56 & 0.257765711786894 & 0.515531423573788 & 0.742234288213106 \tabularnewline
57 & 0.225002487315628 & 0.450004974631255 & 0.774997512684372 \tabularnewline
58 & 0.198641851242374 & 0.397283702484748 & 0.801358148757626 \tabularnewline
59 & 0.187671024137784 & 0.375342048275569 & 0.812328975862216 \tabularnewline
60 & 0.323918385354252 & 0.647836770708503 & 0.676081614645748 \tabularnewline
61 & 0.316560415730842 & 0.633120831461684 & 0.683439584269158 \tabularnewline
62 & 0.311738259254146 & 0.623476518508292 & 0.688261740745854 \tabularnewline
63 & 0.296476726426373 & 0.592953452852747 & 0.703523273573627 \tabularnewline
64 & 0.269491683026649 & 0.538983366053299 & 0.730508316973351 \tabularnewline
65 & 0.250761284067155 & 0.501522568134309 & 0.749238715932845 \tabularnewline
66 & 0.231062803258035 & 0.46212560651607 & 0.768937196741965 \tabularnewline
67 & 0.222666063643624 & 0.445332127287247 & 0.777333936356376 \tabularnewline
68 & 0.361325498885654 & 0.722650997771308 & 0.638674501114346 \tabularnewline
69 & 0.342130715570351 & 0.684261431140701 & 0.657869284429649 \tabularnewline
70 & 0.362909892188064 & 0.725819784376128 & 0.637090107811936 \tabularnewline
71 & 0.758973157841185 & 0.482053684317629 & 0.241026842158815 \tabularnewline
72 & 0.778896355618651 & 0.442207288762699 & 0.221103644381349 \tabularnewline
73 & 0.770450702121133 & 0.459098595757733 & 0.229549297878867 \tabularnewline
74 & 0.797611629846699 & 0.404776740306603 & 0.202388370153301 \tabularnewline
75 & 0.805780139058076 & 0.388439721883849 & 0.194219860941924 \tabularnewline
76 & 0.776417297484825 & 0.44716540503035 & 0.223582702515175 \tabularnewline
77 & 0.744923033940454 & 0.510153932119093 & 0.255076966059546 \tabularnewline
78 & 0.766024977532652 & 0.467950044934697 & 0.233975022467348 \tabularnewline
79 & 0.745792946926679 & 0.508414106146642 & 0.254207053073321 \tabularnewline
80 & 0.770178675387653 & 0.459642649224693 & 0.229821324612347 \tabularnewline
81 & 0.749201241564574 & 0.501597516870852 & 0.250798758435426 \tabularnewline
82 & 0.776790963635071 & 0.446418072729858 & 0.223209036364929 \tabularnewline
83 & 0.770386883701429 & 0.459226232597141 & 0.229613116298571 \tabularnewline
84 & 0.742423451733707 & 0.515153096532586 & 0.257576548266293 \tabularnewline
85 & 0.74491840846785 & 0.510163183064301 & 0.25508159153215 \tabularnewline
86 & 0.752124244977663 & 0.495751510044675 & 0.247875755022337 \tabularnewline
87 & 0.748543278394772 & 0.502913443210455 & 0.251456721605228 \tabularnewline
88 & 0.773589286353104 & 0.452821427293792 & 0.226410713646896 \tabularnewline
89 & 0.770098028798552 & 0.459803942402897 & 0.229901971201448 \tabularnewline
90 & 0.751998586965045 & 0.49600282606991 & 0.248001413034955 \tabularnewline
91 & 0.72124087001702 & 0.55751825996596 & 0.27875912998298 \tabularnewline
92 & 0.68477726830017 & 0.63044546339966 & 0.31522273169983 \tabularnewline
93 & 0.653126471475251 & 0.693747057049498 & 0.346873528524749 \tabularnewline
94 & 0.619472945904947 & 0.761054108190106 & 0.380527054095053 \tabularnewline
95 & 0.573809307407015 & 0.852381385185971 & 0.426190692592986 \tabularnewline
96 & 0.598978338896833 & 0.802043322206333 & 0.401021661103167 \tabularnewline
97 & 0.569306036547867 & 0.861387926904266 & 0.430693963452133 \tabularnewline
98 & 0.549406956644852 & 0.901186086710295 & 0.450593043355148 \tabularnewline
99 & 0.668672511765539 & 0.662654976468922 & 0.331327488234461 \tabularnewline
100 & 0.794450671752623 & 0.411098656494754 & 0.205549328247377 \tabularnewline
101 & 0.81403271957778 & 0.371934560844439 & 0.18596728042222 \tabularnewline
102 & 0.794321687416151 & 0.411356625167698 & 0.205678312583849 \tabularnewline
103 & 0.794660203566838 & 0.410679592866325 & 0.205339796433162 \tabularnewline
104 & 0.787983222589955 & 0.42403355482009 & 0.212016777410045 \tabularnewline
105 & 0.800053970798488 & 0.399892058403024 & 0.199946029201512 \tabularnewline
106 & 0.780168849580331 & 0.439662300839339 & 0.219831150419669 \tabularnewline
107 & 0.756196205635988 & 0.487607588728024 & 0.243803794364012 \tabularnewline
108 & 0.732399334016062 & 0.535201331967875 & 0.267600665983938 \tabularnewline
109 & 0.846954341981087 & 0.306091316037826 & 0.153045658018913 \tabularnewline
110 & 0.833687450533546 & 0.332625098932907 & 0.166312549466454 \tabularnewline
111 & 0.803261564281724 & 0.393476871436553 & 0.196738435718276 \tabularnewline
112 & 0.77216578904944 & 0.455668421901119 & 0.22783421095056 \tabularnewline
113 & 0.739635392079268 & 0.520729215841463 & 0.260364607920732 \tabularnewline
114 & 0.702405335992006 & 0.595189328015987 & 0.297594664007994 \tabularnewline
115 & 0.690827112273582 & 0.618345775452836 & 0.309172887726418 \tabularnewline
116 & 0.650969569283124 & 0.698060861433752 & 0.349030430716876 \tabularnewline
117 & 0.612077939792932 & 0.775844120414135 & 0.387922060207068 \tabularnewline
118 & 0.57533928795687 & 0.84932142408626 & 0.42466071204313 \tabularnewline
119 & 0.54585040228659 & 0.90829919542682 & 0.45414959771341 \tabularnewline
120 & 0.504844750201526 & 0.990310499596948 & 0.495155249798474 \tabularnewline
121 & 0.454984895742481 & 0.909969791484961 & 0.545015104257519 \tabularnewline
122 & 0.474762466759913 & 0.949524933519826 & 0.525237533240087 \tabularnewline
123 & 0.443710716506928 & 0.887421433013857 & 0.556289283493072 \tabularnewline
124 & 0.393901771345 & 0.78780354269 & 0.606098228655 \tabularnewline
125 & 0.35222030773373 & 0.704440615467461 & 0.64777969226627 \tabularnewline
126 & 0.315252437972438 & 0.630504875944877 & 0.684747562027562 \tabularnewline
127 & 0.29971432640637 & 0.59942865281274 & 0.70028567359363 \tabularnewline
128 & 0.271711682081178 & 0.543423364162356 & 0.728288317918822 \tabularnewline
129 & 0.227872111292192 & 0.455744222584384 & 0.772127888707808 \tabularnewline
130 & 0.204928364945383 & 0.409856729890765 & 0.795071635054617 \tabularnewline
131 & 0.18209940268094 & 0.36419880536188 & 0.81790059731906 \tabularnewline
132 & 0.20824806055809 & 0.41649612111618 & 0.79175193944191 \tabularnewline
133 & 0.277516649483803 & 0.555033298967606 & 0.722483350516197 \tabularnewline
134 & 0.230686346470086 & 0.461372692940173 & 0.769313653529914 \tabularnewline
135 & 0.189349365302728 & 0.378698730605455 & 0.810650634697272 \tabularnewline
136 & 0.227118240750645 & 0.45423648150129 & 0.772881759249355 \tabularnewline
137 & 0.328015129906134 & 0.656030259812269 & 0.671984870093866 \tabularnewline
138 & 0.282399773830307 & 0.564799547660613 & 0.717600226169693 \tabularnewline
139 & 0.496291253453374 & 0.992582506906747 & 0.503708746546626 \tabularnewline
140 & 0.458554096373925 & 0.917108192747851 & 0.541445903626075 \tabularnewline
141 & 0.442542042786966 & 0.885084085573931 & 0.557457957213034 \tabularnewline
142 & 0.512392608659361 & 0.975214782681277 & 0.487607391340639 \tabularnewline
143 & 0.442668557570015 & 0.88533711514003 & 0.557331442429985 \tabularnewline
144 & 0.516982400045502 & 0.966035199908995 & 0.483017599954498 \tabularnewline
145 & 0.480339837159562 & 0.960679674319124 & 0.519660162840438 \tabularnewline
146 & 0.443560493734342 & 0.887120987468685 & 0.556439506265658 \tabularnewline
147 & 0.351162356566278 & 0.702324713132555 & 0.648837643433722 \tabularnewline
148 & 0.306937351639831 & 0.613874703279662 & 0.693062648360169 \tabularnewline
149 & 0.999997791184523 & 4.41763095450299e-06 & 2.2088154772515e-06 \tabularnewline
150 & 0.999984970491535 & 3.00590169296111e-05 & 1.50295084648056e-05 \tabularnewline
151 & 0.999878799439043 & 0.000242401121914557 & 0.000121200560957279 \tabularnewline
152 & 0.999404474137472 & 0.00119105172505652 & 0.00059552586252826 \tabularnewline
153 & 0.999975900395898 & 4.8199208203556e-05 & 2.4099604101778e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&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]11[/C][C]0.279962900605461[/C][C]0.559925801210923[/C][C]0.720037099394539[/C][/ROW]
[ROW][C]12[/C][C]0.199790524393319[/C][C]0.399581048786637[/C][C]0.800209475606681[/C][/ROW]
[ROW][C]13[/C][C]0.309824898439853[/C][C]0.619649796879705[/C][C]0.690175101560147[/C][/ROW]
[ROW][C]14[/C][C]0.830127941356046[/C][C]0.339744117287908[/C][C]0.169872058643954[/C][/ROW]
[ROW][C]15[/C][C]0.752165541123362[/C][C]0.495668917753276[/C][C]0.247834458876638[/C][/ROW]
[ROW][C]16[/C][C]0.888837050967607[/C][C]0.222325898064787[/C][C]0.111162949032393[/C][/ROW]
[ROW][C]17[/C][C]0.861499504277624[/C][C]0.277000991444752[/C][C]0.138500495722376[/C][/ROW]
[ROW][C]18[/C][C]0.806303242123564[/C][C]0.387393515752871[/C][C]0.193696757876436[/C][/ROW]
[ROW][C]19[/C][C]0.881860342111083[/C][C]0.236279315777834[/C][C]0.118139657888917[/C][/ROW]
[ROW][C]20[/C][C]0.853248861642707[/C][C]0.293502276714586[/C][C]0.146751138357293[/C][/ROW]
[ROW][C]21[/C][C]0.850168374995145[/C][C]0.299663250009709[/C][C]0.149831625004855[/C][/ROW]
[ROW][C]22[/C][C]0.8337554591053[/C][C]0.332489081789399[/C][C]0.1662445408947[/C][/ROW]
[ROW][C]23[/C][C]0.787967171773594[/C][C]0.424065656452812[/C][C]0.212032828226406[/C][/ROW]
[ROW][C]24[/C][C]0.750102971527776[/C][C]0.499794056944448[/C][C]0.249897028472224[/C][/ROW]
[ROW][C]25[/C][C]0.756977914802155[/C][C]0.486044170395691[/C][C]0.243022085197845[/C][/ROW]
[ROW][C]26[/C][C]0.725064204508731[/C][C]0.549871590982538[/C][C]0.274935795491269[/C][/ROW]
[ROW][C]27[/C][C]0.720554442864092[/C][C]0.558891114271815[/C][C]0.279445557135908[/C][/ROW]
[ROW][C]28[/C][C]0.671881086975929[/C][C]0.656237826048143[/C][C]0.328118913024071[/C][/ROW]
[ROW][C]29[/C][C]0.625732129617269[/C][C]0.748535740765461[/C][C]0.374267870382731[/C][/ROW]
[ROW][C]30[/C][C]0.56737241479872[/C][C]0.865255170402561[/C][C]0.43262758520128[/C][/ROW]
[ROW][C]31[/C][C]0.508583197346941[/C][C]0.982833605306118[/C][C]0.491416802653059[/C][/ROW]
[ROW][C]32[/C][C]0.52910996938916[/C][C]0.94178006122168[/C][C]0.47089003061084[/C][/ROW]
[ROW][C]33[/C][C]0.476853299725237[/C][C]0.953706599450474[/C][C]0.523146700274763[/C][/ROW]
[ROW][C]34[/C][C]0.415995769194779[/C][C]0.831991538389559[/C][C]0.584004230805221[/C][/ROW]
[ROW][C]35[/C][C]0.373441701338348[/C][C]0.746883402676697[/C][C]0.626558298661652[/C][/ROW]
[ROW][C]36[/C][C]0.349692259892501[/C][C]0.699384519785003[/C][C]0.650307740107499[/C][/ROW]
[ROW][C]37[/C][C]0.319286084009095[/C][C]0.638572168018189[/C][C]0.680713915990905[/C][/ROW]
[ROW][C]38[/C][C]0.272254056858576[/C][C]0.544508113717151[/C][C]0.727745943141424[/C][/ROW]
[ROW][C]39[/C][C]0.234033621597289[/C][C]0.468067243194578[/C][C]0.765966378402711[/C][/ROW]
[ROW][C]40[/C][C]0.199381074253947[/C][C]0.398762148507895[/C][C]0.800618925746053[/C][/ROW]
[ROW][C]41[/C][C]0.49882663370226[/C][C]0.99765326740452[/C][C]0.50117336629774[/C][/ROW]
[ROW][C]42[/C][C]0.483029390608859[/C][C]0.966058781217717[/C][C]0.516970609391141[/C][/ROW]
[ROW][C]43[/C][C]0.434225893115457[/C][C]0.868451786230914[/C][C]0.565774106884543[/C][/ROW]
[ROW][C]44[/C][C]0.384471887135524[/C][C]0.768943774271048[/C][C]0.615528112864476[/C][/ROW]
[ROW][C]45[/C][C]0.392680273941596[/C][C]0.785360547883192[/C][C]0.607319726058404[/C][/ROW]
[ROW][C]46[/C][C]0.41664885251969[/C][C]0.833297705039381[/C][C]0.58335114748031[/C][/ROW]
[ROW][C]47[/C][C]0.36787939841564[/C][C]0.73575879683128[/C][C]0.63212060158436[/C][/ROW]
[ROW][C]48[/C][C]0.381071227112878[/C][C]0.762142454225755[/C][C]0.618928772887122[/C][/ROW]
[ROW][C]49[/C][C]0.368210296929021[/C][C]0.736420593858043[/C][C]0.631789703070979[/C][/ROW]
[ROW][C]50[/C][C]0.321583797344585[/C][C]0.64316759468917[/C][C]0.678416202655415[/C][/ROW]
[ROW][C]51[/C][C]0.319118577580762[/C][C]0.638237155161524[/C][C]0.680881422419238[/C][/ROW]
[ROW][C]52[/C][C]0.311962173820726[/C][C]0.623924347641452[/C][C]0.688037826179274[/C][/ROW]
[ROW][C]53[/C][C]0.315939098023433[/C][C]0.631878196046867[/C][C]0.684060901976567[/C][/ROW]
[ROW][C]54[/C][C]0.294148866348937[/C][C]0.588297732697874[/C][C]0.705851133651063[/C][/ROW]
[ROW][C]55[/C][C]0.254082052995375[/C][C]0.50816410599075[/C][C]0.745917947004625[/C][/ROW]
[ROW][C]56[/C][C]0.257765711786894[/C][C]0.515531423573788[/C][C]0.742234288213106[/C][/ROW]
[ROW][C]57[/C][C]0.225002487315628[/C][C]0.450004974631255[/C][C]0.774997512684372[/C][/ROW]
[ROW][C]58[/C][C]0.198641851242374[/C][C]0.397283702484748[/C][C]0.801358148757626[/C][/ROW]
[ROW][C]59[/C][C]0.187671024137784[/C][C]0.375342048275569[/C][C]0.812328975862216[/C][/ROW]
[ROW][C]60[/C][C]0.323918385354252[/C][C]0.647836770708503[/C][C]0.676081614645748[/C][/ROW]
[ROW][C]61[/C][C]0.316560415730842[/C][C]0.633120831461684[/C][C]0.683439584269158[/C][/ROW]
[ROW][C]62[/C][C]0.311738259254146[/C][C]0.623476518508292[/C][C]0.688261740745854[/C][/ROW]
[ROW][C]63[/C][C]0.296476726426373[/C][C]0.592953452852747[/C][C]0.703523273573627[/C][/ROW]
[ROW][C]64[/C][C]0.269491683026649[/C][C]0.538983366053299[/C][C]0.730508316973351[/C][/ROW]
[ROW][C]65[/C][C]0.250761284067155[/C][C]0.501522568134309[/C][C]0.749238715932845[/C][/ROW]
[ROW][C]66[/C][C]0.231062803258035[/C][C]0.46212560651607[/C][C]0.768937196741965[/C][/ROW]
[ROW][C]67[/C][C]0.222666063643624[/C][C]0.445332127287247[/C][C]0.777333936356376[/C][/ROW]
[ROW][C]68[/C][C]0.361325498885654[/C][C]0.722650997771308[/C][C]0.638674501114346[/C][/ROW]
[ROW][C]69[/C][C]0.342130715570351[/C][C]0.684261431140701[/C][C]0.657869284429649[/C][/ROW]
[ROW][C]70[/C][C]0.362909892188064[/C][C]0.725819784376128[/C][C]0.637090107811936[/C][/ROW]
[ROW][C]71[/C][C]0.758973157841185[/C][C]0.482053684317629[/C][C]0.241026842158815[/C][/ROW]
[ROW][C]72[/C][C]0.778896355618651[/C][C]0.442207288762699[/C][C]0.221103644381349[/C][/ROW]
[ROW][C]73[/C][C]0.770450702121133[/C][C]0.459098595757733[/C][C]0.229549297878867[/C][/ROW]
[ROW][C]74[/C][C]0.797611629846699[/C][C]0.404776740306603[/C][C]0.202388370153301[/C][/ROW]
[ROW][C]75[/C][C]0.805780139058076[/C][C]0.388439721883849[/C][C]0.194219860941924[/C][/ROW]
[ROW][C]76[/C][C]0.776417297484825[/C][C]0.44716540503035[/C][C]0.223582702515175[/C][/ROW]
[ROW][C]77[/C][C]0.744923033940454[/C][C]0.510153932119093[/C][C]0.255076966059546[/C][/ROW]
[ROW][C]78[/C][C]0.766024977532652[/C][C]0.467950044934697[/C][C]0.233975022467348[/C][/ROW]
[ROW][C]79[/C][C]0.745792946926679[/C][C]0.508414106146642[/C][C]0.254207053073321[/C][/ROW]
[ROW][C]80[/C][C]0.770178675387653[/C][C]0.459642649224693[/C][C]0.229821324612347[/C][/ROW]
[ROW][C]81[/C][C]0.749201241564574[/C][C]0.501597516870852[/C][C]0.250798758435426[/C][/ROW]
[ROW][C]82[/C][C]0.776790963635071[/C][C]0.446418072729858[/C][C]0.223209036364929[/C][/ROW]
[ROW][C]83[/C][C]0.770386883701429[/C][C]0.459226232597141[/C][C]0.229613116298571[/C][/ROW]
[ROW][C]84[/C][C]0.742423451733707[/C][C]0.515153096532586[/C][C]0.257576548266293[/C][/ROW]
[ROW][C]85[/C][C]0.74491840846785[/C][C]0.510163183064301[/C][C]0.25508159153215[/C][/ROW]
[ROW][C]86[/C][C]0.752124244977663[/C][C]0.495751510044675[/C][C]0.247875755022337[/C][/ROW]
[ROW][C]87[/C][C]0.748543278394772[/C][C]0.502913443210455[/C][C]0.251456721605228[/C][/ROW]
[ROW][C]88[/C][C]0.773589286353104[/C][C]0.452821427293792[/C][C]0.226410713646896[/C][/ROW]
[ROW][C]89[/C][C]0.770098028798552[/C][C]0.459803942402897[/C][C]0.229901971201448[/C][/ROW]
[ROW][C]90[/C][C]0.751998586965045[/C][C]0.49600282606991[/C][C]0.248001413034955[/C][/ROW]
[ROW][C]91[/C][C]0.72124087001702[/C][C]0.55751825996596[/C][C]0.27875912998298[/C][/ROW]
[ROW][C]92[/C][C]0.68477726830017[/C][C]0.63044546339966[/C][C]0.31522273169983[/C][/ROW]
[ROW][C]93[/C][C]0.653126471475251[/C][C]0.693747057049498[/C][C]0.346873528524749[/C][/ROW]
[ROW][C]94[/C][C]0.619472945904947[/C][C]0.761054108190106[/C][C]0.380527054095053[/C][/ROW]
[ROW][C]95[/C][C]0.573809307407015[/C][C]0.852381385185971[/C][C]0.426190692592986[/C][/ROW]
[ROW][C]96[/C][C]0.598978338896833[/C][C]0.802043322206333[/C][C]0.401021661103167[/C][/ROW]
[ROW][C]97[/C][C]0.569306036547867[/C][C]0.861387926904266[/C][C]0.430693963452133[/C][/ROW]
[ROW][C]98[/C][C]0.549406956644852[/C][C]0.901186086710295[/C][C]0.450593043355148[/C][/ROW]
[ROW][C]99[/C][C]0.668672511765539[/C][C]0.662654976468922[/C][C]0.331327488234461[/C][/ROW]
[ROW][C]100[/C][C]0.794450671752623[/C][C]0.411098656494754[/C][C]0.205549328247377[/C][/ROW]
[ROW][C]101[/C][C]0.81403271957778[/C][C]0.371934560844439[/C][C]0.18596728042222[/C][/ROW]
[ROW][C]102[/C][C]0.794321687416151[/C][C]0.411356625167698[/C][C]0.205678312583849[/C][/ROW]
[ROW][C]103[/C][C]0.794660203566838[/C][C]0.410679592866325[/C][C]0.205339796433162[/C][/ROW]
[ROW][C]104[/C][C]0.787983222589955[/C][C]0.42403355482009[/C][C]0.212016777410045[/C][/ROW]
[ROW][C]105[/C][C]0.800053970798488[/C][C]0.399892058403024[/C][C]0.199946029201512[/C][/ROW]
[ROW][C]106[/C][C]0.780168849580331[/C][C]0.439662300839339[/C][C]0.219831150419669[/C][/ROW]
[ROW][C]107[/C][C]0.756196205635988[/C][C]0.487607588728024[/C][C]0.243803794364012[/C][/ROW]
[ROW][C]108[/C][C]0.732399334016062[/C][C]0.535201331967875[/C][C]0.267600665983938[/C][/ROW]
[ROW][C]109[/C][C]0.846954341981087[/C][C]0.306091316037826[/C][C]0.153045658018913[/C][/ROW]
[ROW][C]110[/C][C]0.833687450533546[/C][C]0.332625098932907[/C][C]0.166312549466454[/C][/ROW]
[ROW][C]111[/C][C]0.803261564281724[/C][C]0.393476871436553[/C][C]0.196738435718276[/C][/ROW]
[ROW][C]112[/C][C]0.77216578904944[/C][C]0.455668421901119[/C][C]0.22783421095056[/C][/ROW]
[ROW][C]113[/C][C]0.739635392079268[/C][C]0.520729215841463[/C][C]0.260364607920732[/C][/ROW]
[ROW][C]114[/C][C]0.702405335992006[/C][C]0.595189328015987[/C][C]0.297594664007994[/C][/ROW]
[ROW][C]115[/C][C]0.690827112273582[/C][C]0.618345775452836[/C][C]0.309172887726418[/C][/ROW]
[ROW][C]116[/C][C]0.650969569283124[/C][C]0.698060861433752[/C][C]0.349030430716876[/C][/ROW]
[ROW][C]117[/C][C]0.612077939792932[/C][C]0.775844120414135[/C][C]0.387922060207068[/C][/ROW]
[ROW][C]118[/C][C]0.57533928795687[/C][C]0.84932142408626[/C][C]0.42466071204313[/C][/ROW]
[ROW][C]119[/C][C]0.54585040228659[/C][C]0.90829919542682[/C][C]0.45414959771341[/C][/ROW]
[ROW][C]120[/C][C]0.504844750201526[/C][C]0.990310499596948[/C][C]0.495155249798474[/C][/ROW]
[ROW][C]121[/C][C]0.454984895742481[/C][C]0.909969791484961[/C][C]0.545015104257519[/C][/ROW]
[ROW][C]122[/C][C]0.474762466759913[/C][C]0.949524933519826[/C][C]0.525237533240087[/C][/ROW]
[ROW][C]123[/C][C]0.443710716506928[/C][C]0.887421433013857[/C][C]0.556289283493072[/C][/ROW]
[ROW][C]124[/C][C]0.393901771345[/C][C]0.78780354269[/C][C]0.606098228655[/C][/ROW]
[ROW][C]125[/C][C]0.35222030773373[/C][C]0.704440615467461[/C][C]0.64777969226627[/C][/ROW]
[ROW][C]126[/C][C]0.315252437972438[/C][C]0.630504875944877[/C][C]0.684747562027562[/C][/ROW]
[ROW][C]127[/C][C]0.29971432640637[/C][C]0.59942865281274[/C][C]0.70028567359363[/C][/ROW]
[ROW][C]128[/C][C]0.271711682081178[/C][C]0.543423364162356[/C][C]0.728288317918822[/C][/ROW]
[ROW][C]129[/C][C]0.227872111292192[/C][C]0.455744222584384[/C][C]0.772127888707808[/C][/ROW]
[ROW][C]130[/C][C]0.204928364945383[/C][C]0.409856729890765[/C][C]0.795071635054617[/C][/ROW]
[ROW][C]131[/C][C]0.18209940268094[/C][C]0.36419880536188[/C][C]0.81790059731906[/C][/ROW]
[ROW][C]132[/C][C]0.20824806055809[/C][C]0.41649612111618[/C][C]0.79175193944191[/C][/ROW]
[ROW][C]133[/C][C]0.277516649483803[/C][C]0.555033298967606[/C][C]0.722483350516197[/C][/ROW]
[ROW][C]134[/C][C]0.230686346470086[/C][C]0.461372692940173[/C][C]0.769313653529914[/C][/ROW]
[ROW][C]135[/C][C]0.189349365302728[/C][C]0.378698730605455[/C][C]0.810650634697272[/C][/ROW]
[ROW][C]136[/C][C]0.227118240750645[/C][C]0.45423648150129[/C][C]0.772881759249355[/C][/ROW]
[ROW][C]137[/C][C]0.328015129906134[/C][C]0.656030259812269[/C][C]0.671984870093866[/C][/ROW]
[ROW][C]138[/C][C]0.282399773830307[/C][C]0.564799547660613[/C][C]0.717600226169693[/C][/ROW]
[ROW][C]139[/C][C]0.496291253453374[/C][C]0.992582506906747[/C][C]0.503708746546626[/C][/ROW]
[ROW][C]140[/C][C]0.458554096373925[/C][C]0.917108192747851[/C][C]0.541445903626075[/C][/ROW]
[ROW][C]141[/C][C]0.442542042786966[/C][C]0.885084085573931[/C][C]0.557457957213034[/C][/ROW]
[ROW][C]142[/C][C]0.512392608659361[/C][C]0.975214782681277[/C][C]0.487607391340639[/C][/ROW]
[ROW][C]143[/C][C]0.442668557570015[/C][C]0.88533711514003[/C][C]0.557331442429985[/C][/ROW]
[ROW][C]144[/C][C]0.516982400045502[/C][C]0.966035199908995[/C][C]0.483017599954498[/C][/ROW]
[ROW][C]145[/C][C]0.480339837159562[/C][C]0.960679674319124[/C][C]0.519660162840438[/C][/ROW]
[ROW][C]146[/C][C]0.443560493734342[/C][C]0.887120987468685[/C][C]0.556439506265658[/C][/ROW]
[ROW][C]147[/C][C]0.351162356566278[/C][C]0.702324713132555[/C][C]0.648837643433722[/C][/ROW]
[ROW][C]148[/C][C]0.306937351639831[/C][C]0.613874703279662[/C][C]0.693062648360169[/C][/ROW]
[ROW][C]149[/C][C]0.999997791184523[/C][C]4.41763095450299e-06[/C][C]2.2088154772515e-06[/C][/ROW]
[ROW][C]150[/C][C]0.999984970491535[/C][C]3.00590169296111e-05[/C][C]1.50295084648056e-05[/C][/ROW]
[ROW][C]151[/C][C]0.999878799439043[/C][C]0.000242401121914557[/C][C]0.000121200560957279[/C][/ROW]
[ROW][C]152[/C][C]0.999404474137472[/C][C]0.00119105172505652[/C][C]0.00059552586252826[/C][/ROW]
[ROW][C]153[/C][C]0.999975900395898[/C][C]4.8199208203556e-05[/C][C]2.4099604101778e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2799629006054610.5599258012109230.720037099394539
120.1997905243933190.3995810487866370.800209475606681
130.3098248984398530.6196497968797050.690175101560147
140.8301279413560460.3397441172879080.169872058643954
150.7521655411233620.4956689177532760.247834458876638
160.8888370509676070.2223258980647870.111162949032393
170.8614995042776240.2770009914447520.138500495722376
180.8063032421235640.3873935157528710.193696757876436
190.8818603421110830.2362793157778340.118139657888917
200.8532488616427070.2935022767145860.146751138357293
210.8501683749951450.2996632500097090.149831625004855
220.83375545910530.3324890817893990.1662445408947
230.7879671717735940.4240656564528120.212032828226406
240.7501029715277760.4997940569444480.249897028472224
250.7569779148021550.4860441703956910.243022085197845
260.7250642045087310.5498715909825380.274935795491269
270.7205544428640920.5588911142718150.279445557135908
280.6718810869759290.6562378260481430.328118913024071
290.6257321296172690.7485357407654610.374267870382731
300.567372414798720.8652551704025610.43262758520128
310.5085831973469410.9828336053061180.491416802653059
320.529109969389160.941780061221680.47089003061084
330.4768532997252370.9537065994504740.523146700274763
340.4159957691947790.8319915383895590.584004230805221
350.3734417013383480.7468834026766970.626558298661652
360.3496922598925010.6993845197850030.650307740107499
370.3192860840090950.6385721680181890.680713915990905
380.2722540568585760.5445081137171510.727745943141424
390.2340336215972890.4680672431945780.765966378402711
400.1993810742539470.3987621485078950.800618925746053
410.498826633702260.997653267404520.50117336629774
420.4830293906088590.9660587812177170.516970609391141
430.4342258931154570.8684517862309140.565774106884543
440.3844718871355240.7689437742710480.615528112864476
450.3926802739415960.7853605478831920.607319726058404
460.416648852519690.8332977050393810.58335114748031
470.367879398415640.735758796831280.63212060158436
480.3810712271128780.7621424542257550.618928772887122
490.3682102969290210.7364205938580430.631789703070979
500.3215837973445850.643167594689170.678416202655415
510.3191185775807620.6382371551615240.680881422419238
520.3119621738207260.6239243476414520.688037826179274
530.3159390980234330.6318781960468670.684060901976567
540.2941488663489370.5882977326978740.705851133651063
550.2540820529953750.508164105990750.745917947004625
560.2577657117868940.5155314235737880.742234288213106
570.2250024873156280.4500049746312550.774997512684372
580.1986418512423740.3972837024847480.801358148757626
590.1876710241377840.3753420482755690.812328975862216
600.3239183853542520.6478367707085030.676081614645748
610.3165604157308420.6331208314616840.683439584269158
620.3117382592541460.6234765185082920.688261740745854
630.2964767264263730.5929534528527470.703523273573627
640.2694916830266490.5389833660532990.730508316973351
650.2507612840671550.5015225681343090.749238715932845
660.2310628032580350.462125606516070.768937196741965
670.2226660636436240.4453321272872470.777333936356376
680.3613254988856540.7226509977713080.638674501114346
690.3421307155703510.6842614311407010.657869284429649
700.3629098921880640.7258197843761280.637090107811936
710.7589731578411850.4820536843176290.241026842158815
720.7788963556186510.4422072887626990.221103644381349
730.7704507021211330.4590985957577330.229549297878867
740.7976116298466990.4047767403066030.202388370153301
750.8057801390580760.3884397218838490.194219860941924
760.7764172974848250.447165405030350.223582702515175
770.7449230339404540.5101539321190930.255076966059546
780.7660249775326520.4679500449346970.233975022467348
790.7457929469266790.5084141061466420.254207053073321
800.7701786753876530.4596426492246930.229821324612347
810.7492012415645740.5015975168708520.250798758435426
820.7767909636350710.4464180727298580.223209036364929
830.7703868837014290.4592262325971410.229613116298571
840.7424234517337070.5151530965325860.257576548266293
850.744918408467850.5101631830643010.25508159153215
860.7521242449776630.4957515100446750.247875755022337
870.7485432783947720.5029134432104550.251456721605228
880.7735892863531040.4528214272937920.226410713646896
890.7700980287985520.4598039424028970.229901971201448
900.7519985869650450.496002826069910.248001413034955
910.721240870017020.557518259965960.27875912998298
920.684777268300170.630445463399660.31522273169983
930.6531264714752510.6937470570494980.346873528524749
940.6194729459049470.7610541081901060.380527054095053
950.5738093074070150.8523813851859710.426190692592986
960.5989783388968330.8020433222063330.401021661103167
970.5693060365478670.8613879269042660.430693963452133
980.5494069566448520.9011860867102950.450593043355148
990.6686725117655390.6626549764689220.331327488234461
1000.7944506717526230.4110986564947540.205549328247377
1010.814032719577780.3719345608444390.18596728042222
1020.7943216874161510.4113566251676980.205678312583849
1030.7946602035668380.4106795928663250.205339796433162
1040.7879832225899550.424033554820090.212016777410045
1050.8000539707984880.3998920584030240.199946029201512
1060.7801688495803310.4396623008393390.219831150419669
1070.7561962056359880.4876075887280240.243803794364012
1080.7323993340160620.5352013319678750.267600665983938
1090.8469543419810870.3060913160378260.153045658018913
1100.8336874505335460.3326250989329070.166312549466454
1110.8032615642817240.3934768714365530.196738435718276
1120.772165789049440.4556684219011190.22783421095056
1130.7396353920792680.5207292158414630.260364607920732
1140.7024053359920060.5951893280159870.297594664007994
1150.6908271122735820.6183457754528360.309172887726418
1160.6509695692831240.6980608614337520.349030430716876
1170.6120779397929320.7758441204141350.387922060207068
1180.575339287956870.849321424086260.42466071204313
1190.545850402286590.908299195426820.45414959771341
1200.5048447502015260.9903104995969480.495155249798474
1210.4549848957424810.9099697914849610.545015104257519
1220.4747624667599130.9495249335198260.525237533240087
1230.4437107165069280.8874214330138570.556289283493072
1240.3939017713450.787803542690.606098228655
1250.352220307733730.7044406154674610.64777969226627
1260.3152524379724380.6305048759448770.684747562027562
1270.299714326406370.599428652812740.70028567359363
1280.2717116820811780.5434233641623560.728288317918822
1290.2278721112921920.4557442225843840.772127888707808
1300.2049283649453830.4098567298907650.795071635054617
1310.182099402680940.364198805361880.81790059731906
1320.208248060558090.416496121116180.79175193944191
1330.2775166494838030.5550332989676060.722483350516197
1340.2306863464700860.4613726929401730.769313653529914
1350.1893493653027280.3786987306054550.810650634697272
1360.2271182407506450.454236481501290.772881759249355
1370.3280151299061340.6560302598122690.671984870093866
1380.2823997738303070.5647995476606130.717600226169693
1390.4962912534533740.9925825069067470.503708746546626
1400.4585540963739250.9171081927478510.541445903626075
1410.4425420427869660.8850840855739310.557457957213034
1420.5123926086593610.9752147826812770.487607391340639
1430.4426685575700150.885337115140030.557331442429985
1440.5169824000455020.9660351999089950.483017599954498
1450.4803398371595620.9606796743191240.519660162840438
1460.4435604937343420.8871209874686850.556439506265658
1470.3511623565662780.7023247131325550.648837643433722
1480.3069373516398310.6138747032796620.693062648360169
1490.9999977911845234.41763095450299e-062.2088154772515e-06
1500.9999849704915353.00590169296111e-051.50295084648056e-05
1510.9998787994390430.0002424011219145570.000121200560957279
1520.9994044741374720.001191051725056520.00059552586252826
1530.9999759003958984.8199208203556e-052.4099604101778e-05






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.034965034965035NOK
5% type I error level50.034965034965035OK
10% type I error level50.034965034965035OK

\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 & 5 & 0.034965034965035 & NOK \tabularnewline
5% type I error level & 5 & 0.034965034965035 & OK \tabularnewline
10% type I error level & 5 & 0.034965034965035 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145838&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]5[/C][C]0.034965034965035[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.034965034965035[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.034965034965035[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145838&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145838&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.034965034965035NOK
5% type I error level50.034965034965035OK
10% type I error level50.034965034965035OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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