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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 computationThu, 24 Nov 2011 14:26:19 -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/24/t1322162815jvbldkyhuti9xi0.htm/, Retrieved Wed, 24 Apr 2024 21:09:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147165, Retrieved Wed, 24 Apr 2024 21:09:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multipele lineair...] [2011-11-24 19:26:19] [85205d4a623eda481b08e88585a15826] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	14	13	3	25	55	147
12	8	13	5	158	7	71
10	12	16	6	0	0	0
9	7	12	6	143	10	0
10	10	11	5	67	74	43
12	7	12	3	0	0	0
13	16	18	8	148	138	8
12	11	11	4	28		
12	14	14	4	114	113	34
6	6	9	4	0	0	0
5	16	14	6	123	115	103
12	11	12	6	145	9	
11	16	11	5	113	114	73
14	12	12	4	152	59	159
14	7	13	6	0	0	0
12	13	11	4	36	114	113
12	11	12	6	0	0	0
11	15	16	6	8	102	44
11	7	9	4	108	0	0
7	9	11	4	112	86	0
9	7	13	2	51	17	41
11	14	15	7	43	45	74
11	15	10	5	120	123	0
12	7	11	4	13	24	0
12	15	13	6	55	5	0
11	17	16	6	103	123	32
11	15	15	7	127	136	126
8	14	14	5	14	4	154
9	14	14	6	135	76	129
12	8	14	4	38	99	98
10	8	8	4	11	98	82
10	14	13	7	43	67	45
12	14	15	7	141	92	8
8	8	13	4	62	13	0
12	11	11	4	62	24	129
11	16	15	6	135	129	31
12	10	15	6	117	117	117
7	8	9	5	82	11	99
11	14	13	6	145	20	55
11	16	16	7	87	91	132
12	13	13	6	76	111	58
9	5	11	3	124		0
15	8	12	3	151	58	0
11	10	12	4	131		0
11	8	12	6	127	146	101
11	13	14	7	76	129	31
11	15	14	5	25	48	147
15	6	8	4			0
11	12	13	5	58	111	132
12	16	16	6	115	32	123
12	5	13	6	130	112	39
9	15	11	6	17	51	136
12	12	14	5	102	53	141
12	8	13	4	21	131	0
13	13	13	5	0	0	0
11	14	13	5	14	76	135
9	12	12	4	110	106	118
9	16	16	6	133	26	154
11	10	15	2	83	44	
11	15	15	8	56	63	116
12	8	12	3	0	0	0
12	16	14	6	44	116	88
9	19	12	6	70	119	25
11	14	15	6	36	18	113
9	6	12	5	5	134	157
12	13	13	5	118	138	26
12	15	12	6	17	41	38
12	7	12	5	79	0	0
12	13	13	6	122	57	53
14	4	5	2	119	101	0
11	14	13	5	36	114	106
12	13	13	5	36	113	106
11	11	14	5	141	122	102
6	14	17	6		14	138
10	12	13	6	37	10	142
12	15	13	6	110	27	73
13	14	12	5	10	39	130
8	13	13	5	14	133	86
12	8	14	4	157	42	78
12	6	11	2	59	0	0
12	7	12	4	77	58	0
6	13	12	6	129	133	4
11	13	16	6	125	151	91
10	11	12	5	87	111	132
12	5	12	3	61	139	0
13	12	12	6	146	126	0
11	8	10	4	96	139	0
7	11	15	5	133	138	14
11	14	15	8	47	52	97
11	9	12	4	74	67	45
11	10	16	6	109	97	0
11	13	15	6	30	137	149
12	16	16	7	116	56	57
10	16	13	6	149	3	105
11	11	12	5	19	78	0
12	8	11	4	96	0	0
7	4	13	6		0	0
13	7	10	3	21	0	0
8	14	15	5	26	118	128
12	11	13	6	156	39	29
11	17	16	7	53	63	148
12	15	15	7	72	78	93
14	17	18	6	27	26	4
10	5	13	3	66	50	0
10	4	10	2	71	104	158
13	10	16	8	66	54	144
10	11	13	3	40	104	0
11	15	15	8	57	148	122
10	10	14	3	3	30	149
7	9	15	4	12	38	17
10	12	14	5	107	132	91
8	15	13	7	80	132	111
12	7	13	6	98	84	99
12	13	15	6	155	71	40
12	12	16	7	111	125	132
11	14	14	6	81	25	123
12	14	14	6	50	66	54
12	8	16	6	49	86	90
12	15	14	6	96	61	86
11	12	12	4	2	60	152
12	12	13	4	1	144	152
11	16	12	5	22	120	123
11	9	12	4	64	139	100
13	15	14	6	56	131	116
12	15	14	6	144	159	59
12	6	14	5			
12	14	16	8	94	18	5
12	15	13	6	25	123	147
8	10	14	5	93	18	139
8	6	4	4	0	0	0
12	14	16	8	48	123	81
11	12	13	6	30	105	3
12	8	16	4	19	0	0
13	11	15	6	0	0	0
12	13	14	6	10	68	37
12	9	13	4	78	157	5
11	15	14	6	93	94	69
12	13	12	3	0	0	0
12	15	15	6	95	87	
10	14	14	5	50	156	142
11	16	13	4	86	139	17
12	14	14	6	33	145	100
12	14	16	4	152	55	70
10	10	6	4	51	41	
12	10	13	4	48	25	123
13	4	13	6	97	47	109
12	8	14	5	77	0	0
15	15	15	6	130	143	37
11	16	14	6	8	102	44
12	12	15	8	84	148	98
11	12	13	7	51	153	11
12	15	16	7	33	32	9
11	9	12	4	6	106	0
10	12	15	6	116	63	57
11	14	12	6	88	56	63
11	11	14	2	142	39	66

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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&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]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
SocialInteraction[t] = + 27.9420388975997 -0.0235186604478959FindingFriends[t] -0.103524019785357KnowingPeople[t] -0.0650164478230016Celebrity[t] -0.0586169113929113Firstbestfriend[t] -0.0830608601912293Secondbestfriend[t] + 0.0500508614619878Thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SocialInteraction[t] =  +  27.9420388975997 -0.0235186604478959FindingFriends[t] -0.103524019785357KnowingPeople[t] -0.0650164478230016Celebrity[t] -0.0586169113929113Firstbestfriend[t] -0.0830608601912293Secondbestfriend[t] +  0.0500508614619878Thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SocialInteraction[t] =  +  27.9420388975997 -0.0235186604478959FindingFriends[t] -0.103524019785357KnowingPeople[t] -0.0650164478230016Celebrity[t] -0.0586169113929113Firstbestfriend[t] -0.0830608601912293Secondbestfriend[t] +  0.0500508614619878Thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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
SocialInteraction[t] = + 27.9420388975997 -0.0235186604478959FindingFriends[t] -0.103524019785357KnowingPeople[t] -0.0650164478230016Celebrity[t] -0.0586169113929113Firstbestfriend[t] -0.0830608601912293Secondbestfriend[t] + 0.0500508614619878Thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)27.94203889759975.4130395.1621e-060
FindingFriends-0.02351866044789590.05548-0.42390.672240.33612
KnowingPeople-0.1035240197853570.05191-1.99430.0479430.023972
Celebrity-0.06501644782300160.044774-1.45210.1485750.074288
Firstbestfriend-0.05861691139291130.04904-1.19530.233870.116935
Secondbestfriend-0.08306086019122930.061998-1.33970.1823720.091186
Thirdbestfriend0.05005086146198780.0620920.80610.4214830.210742

\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) & 27.9420388975997 & 5.413039 & 5.162 & 1e-06 & 0 \tabularnewline
FindingFriends & -0.0235186604478959 & 0.05548 & -0.4239 & 0.67224 & 0.33612 \tabularnewline
KnowingPeople & -0.103524019785357 & 0.05191 & -1.9943 & 0.047943 & 0.023972 \tabularnewline
Celebrity & -0.0650164478230016 & 0.044774 & -1.4521 & 0.148575 & 0.074288 \tabularnewline
Firstbestfriend & -0.0586169113929113 & 0.04904 & -1.1953 & 0.23387 & 0.116935 \tabularnewline
Secondbestfriend & -0.0830608601912293 & 0.061998 & -1.3397 & 0.182372 & 0.091186 \tabularnewline
Thirdbestfriend & 0.0500508614619878 & 0.062092 & 0.8061 & 0.421483 & 0.210742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&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]27.9420388975997[/C][C]5.413039[/C][C]5.162[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0235186604478959[/C][C]0.05548[/C][C]-0.4239[/C][C]0.67224[/C][C]0.33612[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]-0.103524019785357[/C][C]0.05191[/C][C]-1.9943[/C][C]0.047943[/C][C]0.023972[/C][/ROW]
[ROW][C]Celebrity[/C][C]-0.0650164478230016[/C][C]0.044774[/C][C]-1.4521[/C][C]0.148575[/C][C]0.074288[/C][/ROW]
[ROW][C]Firstbestfriend[/C][C]-0.0586169113929113[/C][C]0.04904[/C][C]-1.1953[/C][C]0.23387[/C][C]0.116935[/C][/ROW]
[ROW][C]Secondbestfriend[/C][C]-0.0830608601912293[/C][C]0.061998[/C][C]-1.3397[/C][C]0.182372[/C][C]0.091186[/C][/ROW]
[ROW][C]Thirdbestfriend[/C][C]0.0500508614619878[/C][C]0.062092[/C][C]0.8061[/C][C]0.421483[/C][C]0.210742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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)27.94203889759975.4130395.1621e-060
FindingFriends-0.02351866044789590.05548-0.42390.672240.33612
KnowingPeople-0.1035240197853570.05191-1.99430.0479430.023972
Celebrity-0.06501644782300160.044774-1.45210.1485750.074288
Firstbestfriend-0.05861691139291130.04904-1.19530.233870.116935
Secondbestfriend-0.08306086019122930.061998-1.33970.1823720.091186
Thirdbestfriend0.05005086146198780.0620920.80610.4214830.210742






Multiple Linear Regression - Regression Statistics
Multiple R0.289478171476088
R-squared0.0837976117611396
Adjusted R-squared0.0469035558589037
F-TEST (value)2.27130386486082
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.0397729929742828
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26.2441374632755
Sum Squared Residuals102624.457927504

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.289478171476088 \tabularnewline
R-squared & 0.0837976117611396 \tabularnewline
Adjusted R-squared & 0.0469035558589037 \tabularnewline
F-TEST (value) & 2.27130386486082 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0.0397729929742828 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 26.2441374632755 \tabularnewline
Sum Squared Residuals & 102624.457927504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.289478171476088[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0837976117611396[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0469035558589037[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.27130386486082[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0.0397729929742828[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]26.2441374632755[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]102624.457927504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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 R0.289478171476088
R-squared0.0837976117611396
Adjusted R-squared0.0469035558589037
F-TEST (value)2.27130386486082
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0.0397729929742828
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26.2441374632755
Sum Squared Residuals102624.457927504






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11327.3956225902223-14.3956225902223
21219.7937082600744-7.79370826007442
31025.6133319687212-15.6133319687212
4916.9321944190035-7.93219441900351
51018.3213561617562-8.32135616175625
61226.3400706935711-14.3400706935711
7135.444881690868297.55511830913171
81224.3472118429135-12.3472118429135
9146.508332416543777.49166758345623
10928.2334737382438-19.2334737382438
11141.1068682819854412.8931317180146
121210.78159572332871.21840427667133
1357.51973457612595-2.51973457612595
1447.17026855649266-3.17026855649266
15626.7094042544806-20.7094042544806
1646.93030820067093-2.93030820067093
17626.852153752801-20.852153752801
18613.5579615981953-7.55796159819527
19424.7947169238373-20.7947169238373
20415.9465662010257-11.9465662010257
21221.2601293715934-19.2601293715934
22716.0707481555264-9.07074815552639
23511.6520757282816-6.65207572828156
24423.8530651963509-19.8530651963509
25624.8748856088575-18.8748856088575
2669.56570010127077-3.56570010127077
2771.752754630857455.24724536914255
28516.1964564226999-11.1964564226999
2967.84489470650244-1.84489470650244
3049.57759085141208-5.57759085141208
31411.1082710146212-7.10827101462121
32715.9533949632238-8.95339496322383
33714.0988053979422-7.0988053979422
34424.0715567698839-20.0715567698839
35414.3891868393625-10.3891868393625
3668.61366268561219-2.61366268561219
3764.846773406895561.15322659310444
38517.2811393197627-12.2811393197627
39617.7125021016927-11.7125021016927
4076.760525605333950.239474394666047
41610.5002035062404-4.50020350624037
42322.7349652556635-19.7349652556635
4315124.4800796751928126.519920324807
4413131.437834405186999.5621655948131
4514625.984478843296120.015521156704
4612925.1142799648238103.885720035176
474821.740658096132426.2593419038676
481119.9760978468674-8.97609784686737
491222.27662095045-10.27662095045
501211.11750342573390.882496574266075
51927.6347218817025-18.6347218817025
521222.5614173700431-10.5614173700431
531214.0360837412127-2.03608374121269
541325.9654018154524-12.9654018154524
551125.5654873183387-14.5654873183387
56916.8111511625327-7.81115116253266
57923.2714584118466-14.2714584118466
581118.0546370827505-7.0546370827505
591510.39283089211814.60716910788186
60827.9498532504127-19.9498532504127
611610.47245016315695.52754983684307
621913.98614502144745.01385497855263
631414.6369988970176-0.636998897017622
6466.52250179505387-0.522501795053874
65139.79862957008623.2013704299138
661520.9743955236895-5.97439552368954
67722.6065058328249-15.6065058328249
681312.04046807959540.959531920404565
69414.5106916902501-10.5106916902501
70149.891915349703854.10808465029615
71139.900481399634783.09951860036522
72112.604672648690498.39532735130951
731417.6917152451611-3.69171524516109
741314.7508049064657-1.75080490646574
75139.999727013601293.00027298639871
761216.6195397688071-4.61953976880711
771312.09054394751680.909456052483224
78143.5954580987710510.404541901229
791121.140710117307-10.140710117307
801216.25795679646-4.25795679645997
81125.301664913472696.69833508652731
8216-0.57124722211593616.5712472221159
83123.117121846831858.88287815316815
841212.0400506170099-0.0400506170098617
85123.981085050144348.01891494985566
86108.841505563759851.15849443624015
87154.0498870031970210.950112996803
881513.35835328325061.64164671674938
891212.7801629273859-0.780162927385908
90169.947205076379546.05279492362046
91156.858116653153658.14188334684635
92168.756742133358457.24325786664155
93135.662912961147737.33708703885227
941220.1898828586454-8.18988285864545
951117.5284357809232-6.52843578092315
961326.9579896800857-13.9579896800857
97326.5671226163032-23.5671226163032
9855.82620287012542-0.826202870125421
99617.0942072440508-11.0942072440508
10079.35254945192591-2.35254945192591
101710.7955362781183-3.79553627811829
102624.0045325439158-18.0045325439158
103320.7957023786965-17.7957023786965
10424.44130252545934-2.44130252545934
105810.5879643760902-2.58796437609024
106315.094858415746-12.094858415746
10782.631848034056985.36815196594302
108314.6712083975264-11.6712083975264
109421.7384352316343-17.7384352316343
11054.779687871108640.220312128891365
11174.544381982699982.45561801730002
11269.47213297998762-3.47213297998762
113613.4464637348775-7.44646373487751
11472.701867994545974.29813200545403
115614.2863609055327-8.28636090553268
116615.8555563520763-9.85555635207626
117610.7864747509264-4.78647475092638
118612.7369617648438-6.73696176484383
119410.7515882604824-6.75158826048237
12041.755411868126272.24458813187373
12156.21299948177468-1.21299948177468
12244.23814040596108-0.238140405961078
12364.272835594051991.72716440594801
12463.75800618827932.2419938117207
125517.7944748872588-12.7944748872588
126528.9897738404319-23.9897738404319
12714718.6915900982079128.308409901792
12813926.6382120584407112.361787941559
129026.8706149095641-26.8706149095641
1308128.007644752963952.9923552470361
131323.9787356595691-20.9787356595691
132025.1705839084356-25.1705839084356
133027.6249209546387-27.6249209546387
1343727.02765548150239.97234451849774
135521.8486625776555-16.8486625776555
1366925.357954606960643.6420453930394
137021.2436497187682-21.2436497187682
1381017.0572417033441-7.05724170334407
1391110.22421298041420.775787019585755
1401216.8002460299006-4.80024602990059
1411215.7217700035704-3.72177000357044
1421021.0312949717815-11.0312949717816
1431013.0701633477934-3.07016334779339
14449.50053849546677-5.50053849546677
145822.8396539919611-14.8396539919611
146157.611065974998967.38893402500104
1471617.4385094770858-1.4385094770858
148125.034977506656786.96502249334322
1491214.3143427666314-2.31434276663136
1501522.6227999835644-7.62279998356444
151921.1427362131168-12.1427362131168
1521211.54943195212930.450568047870696
1531413.352401691120.647598308879961
1541111.0559789029534-0.0559789029533709
1551410.86704681966893.13295318033105
156811.0389466181082-3.03894661810822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 27.3956225902223 & -14.3956225902223 \tabularnewline
2 & 12 & 19.7937082600744 & -7.79370826007442 \tabularnewline
3 & 10 & 25.6133319687212 & -15.6133319687212 \tabularnewline
4 & 9 & 16.9321944190035 & -7.93219441900351 \tabularnewline
5 & 10 & 18.3213561617562 & -8.32135616175625 \tabularnewline
6 & 12 & 26.3400706935711 & -14.3400706935711 \tabularnewline
7 & 13 & 5.44488169086829 & 7.55511830913171 \tabularnewline
8 & 12 & 24.3472118429135 & -12.3472118429135 \tabularnewline
9 & 14 & 6.50833241654377 & 7.49166758345623 \tabularnewline
10 & 9 & 28.2334737382438 & -19.2334737382438 \tabularnewline
11 & 14 & 1.10686828198544 & 12.8931317180146 \tabularnewline
12 & 12 & 10.7815957233287 & 1.21840427667133 \tabularnewline
13 & 5 & 7.51973457612595 & -2.51973457612595 \tabularnewline
14 & 4 & 7.17026855649266 & -3.17026855649266 \tabularnewline
15 & 6 & 26.7094042544806 & -20.7094042544806 \tabularnewline
16 & 4 & 6.93030820067093 & -2.93030820067093 \tabularnewline
17 & 6 & 26.852153752801 & -20.852153752801 \tabularnewline
18 & 6 & 13.5579615981953 & -7.55796159819527 \tabularnewline
19 & 4 & 24.7947169238373 & -20.7947169238373 \tabularnewline
20 & 4 & 15.9465662010257 & -11.9465662010257 \tabularnewline
21 & 2 & 21.2601293715934 & -19.2601293715934 \tabularnewline
22 & 7 & 16.0707481555264 & -9.07074815552639 \tabularnewline
23 & 5 & 11.6520757282816 & -6.65207572828156 \tabularnewline
24 & 4 & 23.8530651963509 & -19.8530651963509 \tabularnewline
25 & 6 & 24.8748856088575 & -18.8748856088575 \tabularnewline
26 & 6 & 9.56570010127077 & -3.56570010127077 \tabularnewline
27 & 7 & 1.75275463085745 & 5.24724536914255 \tabularnewline
28 & 5 & 16.1964564226999 & -11.1964564226999 \tabularnewline
29 & 6 & 7.84489470650244 & -1.84489470650244 \tabularnewline
30 & 4 & 9.57759085141208 & -5.57759085141208 \tabularnewline
31 & 4 & 11.1082710146212 & -7.10827101462121 \tabularnewline
32 & 7 & 15.9533949632238 & -8.95339496322383 \tabularnewline
33 & 7 & 14.0988053979422 & -7.0988053979422 \tabularnewline
34 & 4 & 24.0715567698839 & -20.0715567698839 \tabularnewline
35 & 4 & 14.3891868393625 & -10.3891868393625 \tabularnewline
36 & 6 & 8.61366268561219 & -2.61366268561219 \tabularnewline
37 & 6 & 4.84677340689556 & 1.15322659310444 \tabularnewline
38 & 5 & 17.2811393197627 & -12.2811393197627 \tabularnewline
39 & 6 & 17.7125021016927 & -11.7125021016927 \tabularnewline
40 & 7 & 6.76052560533395 & 0.239474394666047 \tabularnewline
41 & 6 & 10.5002035062404 & -4.50020350624037 \tabularnewline
42 & 3 & 22.7349652556635 & -19.7349652556635 \tabularnewline
43 & 151 & 24.4800796751928 & 126.519920324807 \tabularnewline
44 & 131 & 31.4378344051869 & 99.5621655948131 \tabularnewline
45 & 146 & 25.984478843296 & 120.015521156704 \tabularnewline
46 & 129 & 25.1142799648238 & 103.885720035176 \tabularnewline
47 & 48 & 21.7406580961324 & 26.2593419038676 \tabularnewline
48 & 11 & 19.9760978468674 & -8.97609784686737 \tabularnewline
49 & 12 & 22.27662095045 & -10.27662095045 \tabularnewline
50 & 12 & 11.1175034257339 & 0.882496574266075 \tabularnewline
51 & 9 & 27.6347218817025 & -18.6347218817025 \tabularnewline
52 & 12 & 22.5614173700431 & -10.5614173700431 \tabularnewline
53 & 12 & 14.0360837412127 & -2.03608374121269 \tabularnewline
54 & 13 & 25.9654018154524 & -12.9654018154524 \tabularnewline
55 & 11 & 25.5654873183387 & -14.5654873183387 \tabularnewline
56 & 9 & 16.8111511625327 & -7.81115116253266 \tabularnewline
57 & 9 & 23.2714584118466 & -14.2714584118466 \tabularnewline
58 & 11 & 18.0546370827505 & -7.0546370827505 \tabularnewline
59 & 15 & 10.3928308921181 & 4.60716910788186 \tabularnewline
60 & 8 & 27.9498532504127 & -19.9498532504127 \tabularnewline
61 & 16 & 10.4724501631569 & 5.52754983684307 \tabularnewline
62 & 19 & 13.9861450214474 & 5.01385497855263 \tabularnewline
63 & 14 & 14.6369988970176 & -0.636998897017622 \tabularnewline
64 & 6 & 6.52250179505387 & -0.522501795053874 \tabularnewline
65 & 13 & 9.7986295700862 & 3.2013704299138 \tabularnewline
66 & 15 & 20.9743955236895 & -5.97439552368954 \tabularnewline
67 & 7 & 22.6065058328249 & -15.6065058328249 \tabularnewline
68 & 13 & 12.0404680795954 & 0.959531920404565 \tabularnewline
69 & 4 & 14.5106916902501 & -10.5106916902501 \tabularnewline
70 & 14 & 9.89191534970385 & 4.10808465029615 \tabularnewline
71 & 13 & 9.90048139963478 & 3.09951860036522 \tabularnewline
72 & 11 & 2.60467264869049 & 8.39532735130951 \tabularnewline
73 & 14 & 17.6917152451611 & -3.69171524516109 \tabularnewline
74 & 13 & 14.7508049064657 & -1.75080490646574 \tabularnewline
75 & 13 & 9.99972701360129 & 3.00027298639871 \tabularnewline
76 & 12 & 16.6195397688071 & -4.61953976880711 \tabularnewline
77 & 13 & 12.0905439475168 & 0.909456052483224 \tabularnewline
78 & 14 & 3.59545809877105 & 10.404541901229 \tabularnewline
79 & 11 & 21.140710117307 & -10.140710117307 \tabularnewline
80 & 12 & 16.25795679646 & -4.25795679645997 \tabularnewline
81 & 12 & 5.30166491347269 & 6.69833508652731 \tabularnewline
82 & 16 & -0.571247222115936 & 16.5712472221159 \tabularnewline
83 & 12 & 3.11712184683185 & 8.88287815316815 \tabularnewline
84 & 12 & 12.0400506170099 & -0.0400506170098617 \tabularnewline
85 & 12 & 3.98108505014434 & 8.01891494985566 \tabularnewline
86 & 10 & 8.84150556375985 & 1.15849443624015 \tabularnewline
87 & 15 & 4.04988700319702 & 10.950112996803 \tabularnewline
88 & 15 & 13.3583532832506 & 1.64164671674938 \tabularnewline
89 & 12 & 12.7801629273859 & -0.780162927385908 \tabularnewline
90 & 16 & 9.94720507637954 & 6.05279492362046 \tabularnewline
91 & 15 & 6.85811665315365 & 8.14188334684635 \tabularnewline
92 & 16 & 8.75674213335845 & 7.24325786664155 \tabularnewline
93 & 13 & 5.66291296114773 & 7.33708703885227 \tabularnewline
94 & 12 & 20.1898828586454 & -8.18988285864545 \tabularnewline
95 & 11 & 17.5284357809232 & -6.52843578092315 \tabularnewline
96 & 13 & 26.9579896800857 & -13.9579896800857 \tabularnewline
97 & 3 & 26.5671226163032 & -23.5671226163032 \tabularnewline
98 & 5 & 5.82620287012542 & -0.826202870125421 \tabularnewline
99 & 6 & 17.0942072440508 & -11.0942072440508 \tabularnewline
100 & 7 & 9.35254945192591 & -2.35254945192591 \tabularnewline
101 & 7 & 10.7955362781183 & -3.79553627811829 \tabularnewline
102 & 6 & 24.0045325439158 & -18.0045325439158 \tabularnewline
103 & 3 & 20.7957023786965 & -17.7957023786965 \tabularnewline
104 & 2 & 4.44130252545934 & -2.44130252545934 \tabularnewline
105 & 8 & 10.5879643760902 & -2.58796437609024 \tabularnewline
106 & 3 & 15.094858415746 & -12.094858415746 \tabularnewline
107 & 8 & 2.63184803405698 & 5.36815196594302 \tabularnewline
108 & 3 & 14.6712083975264 & -11.6712083975264 \tabularnewline
109 & 4 & 21.7384352316343 & -17.7384352316343 \tabularnewline
110 & 5 & 4.77968787110864 & 0.220312128891365 \tabularnewline
111 & 7 & 4.54438198269998 & 2.45561801730002 \tabularnewline
112 & 6 & 9.47213297998762 & -3.47213297998762 \tabularnewline
113 & 6 & 13.4464637348775 & -7.44646373487751 \tabularnewline
114 & 7 & 2.70186799454597 & 4.29813200545403 \tabularnewline
115 & 6 & 14.2863609055327 & -8.28636090553268 \tabularnewline
116 & 6 & 15.8555563520763 & -9.85555635207626 \tabularnewline
117 & 6 & 10.7864747509264 & -4.78647475092638 \tabularnewline
118 & 6 & 12.7369617648438 & -6.73696176484383 \tabularnewline
119 & 4 & 10.7515882604824 & -6.75158826048237 \tabularnewline
120 & 4 & 1.75541186812627 & 2.24458813187373 \tabularnewline
121 & 5 & 6.21299948177468 & -1.21299948177468 \tabularnewline
122 & 4 & 4.23814040596108 & -0.238140405961078 \tabularnewline
123 & 6 & 4.27283559405199 & 1.72716440594801 \tabularnewline
124 & 6 & 3.7580061882793 & 2.2419938117207 \tabularnewline
125 & 5 & 17.7944748872588 & -12.7944748872588 \tabularnewline
126 & 5 & 28.9897738404319 & -23.9897738404319 \tabularnewline
127 & 147 & 18.6915900982079 & 128.308409901792 \tabularnewline
128 & 139 & 26.6382120584407 & 112.361787941559 \tabularnewline
129 & 0 & 26.8706149095641 & -26.8706149095641 \tabularnewline
130 & 81 & 28.0076447529639 & 52.9923552470361 \tabularnewline
131 & 3 & 23.9787356595691 & -20.9787356595691 \tabularnewline
132 & 0 & 25.1705839084356 & -25.1705839084356 \tabularnewline
133 & 0 & 27.6249209546387 & -27.6249209546387 \tabularnewline
134 & 37 & 27.0276554815023 & 9.97234451849774 \tabularnewline
135 & 5 & 21.8486625776555 & -16.8486625776555 \tabularnewline
136 & 69 & 25.3579546069606 & 43.6420453930394 \tabularnewline
137 & 0 & 21.2436497187682 & -21.2436497187682 \tabularnewline
138 & 10 & 17.0572417033441 & -7.05724170334407 \tabularnewline
139 & 11 & 10.2242129804142 & 0.775787019585755 \tabularnewline
140 & 12 & 16.8002460299006 & -4.80024602990059 \tabularnewline
141 & 12 & 15.7217700035704 & -3.72177000357044 \tabularnewline
142 & 10 & 21.0312949717815 & -11.0312949717816 \tabularnewline
143 & 10 & 13.0701633477934 & -3.07016334779339 \tabularnewline
144 & 4 & 9.50053849546677 & -5.50053849546677 \tabularnewline
145 & 8 & 22.8396539919611 & -14.8396539919611 \tabularnewline
146 & 15 & 7.61106597499896 & 7.38893402500104 \tabularnewline
147 & 16 & 17.4385094770858 & -1.4385094770858 \tabularnewline
148 & 12 & 5.03497750665678 & 6.96502249334322 \tabularnewline
149 & 12 & 14.3143427666314 & -2.31434276663136 \tabularnewline
150 & 15 & 22.6227999835644 & -7.62279998356444 \tabularnewline
151 & 9 & 21.1427362131168 & -12.1427362131168 \tabularnewline
152 & 12 & 11.5494319521293 & 0.450568047870696 \tabularnewline
153 & 14 & 13.35240169112 & 0.647598308879961 \tabularnewline
154 & 11 & 11.0559789029534 & -0.0559789029533709 \tabularnewline
155 & 14 & 10.8670468196689 & 3.13295318033105 \tabularnewline
156 & 8 & 11.0389466181082 & -3.03894661810822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&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]13[/C][C]27.3956225902223[/C][C]-14.3956225902223[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]19.7937082600744[/C][C]-7.79370826007442[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]25.6133319687212[/C][C]-15.6133319687212[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]16.9321944190035[/C][C]-7.93219441900351[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]18.3213561617562[/C][C]-8.32135616175625[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]26.3400706935711[/C][C]-14.3400706935711[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]5.44488169086829[/C][C]7.55511830913171[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]24.3472118429135[/C][C]-12.3472118429135[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]6.50833241654377[/C][C]7.49166758345623[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]28.2334737382438[/C][C]-19.2334737382438[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]1.10686828198544[/C][C]12.8931317180146[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]10.7815957233287[/C][C]1.21840427667133[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]7.51973457612595[/C][C]-2.51973457612595[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]7.17026855649266[/C][C]-3.17026855649266[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]26.7094042544806[/C][C]-20.7094042544806[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]6.93030820067093[/C][C]-2.93030820067093[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]26.852153752801[/C][C]-20.852153752801[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]13.5579615981953[/C][C]-7.55796159819527[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]24.7947169238373[/C][C]-20.7947169238373[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]15.9465662010257[/C][C]-11.9465662010257[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]21.2601293715934[/C][C]-19.2601293715934[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]16.0707481555264[/C][C]-9.07074815552639[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]11.6520757282816[/C][C]-6.65207572828156[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]23.8530651963509[/C][C]-19.8530651963509[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]24.8748856088575[/C][C]-18.8748856088575[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]9.56570010127077[/C][C]-3.56570010127077[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]1.75275463085745[/C][C]5.24724536914255[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]16.1964564226999[/C][C]-11.1964564226999[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]7.84489470650244[/C][C]-1.84489470650244[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]9.57759085141208[/C][C]-5.57759085141208[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]11.1082710146212[/C][C]-7.10827101462121[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]15.9533949632238[/C][C]-8.95339496322383[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]14.0988053979422[/C][C]-7.0988053979422[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]24.0715567698839[/C][C]-20.0715567698839[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]14.3891868393625[/C][C]-10.3891868393625[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]8.61366268561219[/C][C]-2.61366268561219[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]4.84677340689556[/C][C]1.15322659310444[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]17.2811393197627[/C][C]-12.2811393197627[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]17.7125021016927[/C][C]-11.7125021016927[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.76052560533395[/C][C]0.239474394666047[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]10.5002035062404[/C][C]-4.50020350624037[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]22.7349652556635[/C][C]-19.7349652556635[/C][/ROW]
[ROW][C]43[/C][C]151[/C][C]24.4800796751928[/C][C]126.519920324807[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]31.4378344051869[/C][C]99.5621655948131[/C][/ROW]
[ROW][C]45[/C][C]146[/C][C]25.984478843296[/C][C]120.015521156704[/C][/ROW]
[ROW][C]46[/C][C]129[/C][C]25.1142799648238[/C][C]103.885720035176[/C][/ROW]
[ROW][C]47[/C][C]48[/C][C]21.7406580961324[/C][C]26.2593419038676[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]19.9760978468674[/C][C]-8.97609784686737[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]22.27662095045[/C][C]-10.27662095045[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]11.1175034257339[/C][C]0.882496574266075[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]27.6347218817025[/C][C]-18.6347218817025[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]22.5614173700431[/C][C]-10.5614173700431[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.0360837412127[/C][C]-2.03608374121269[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]25.9654018154524[/C][C]-12.9654018154524[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]25.5654873183387[/C][C]-14.5654873183387[/C][/ROW]
[ROW][C]56[/C][C]9[/C][C]16.8111511625327[/C][C]-7.81115116253266[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]23.2714584118466[/C][C]-14.2714584118466[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]18.0546370827505[/C][C]-7.0546370827505[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]10.3928308921181[/C][C]4.60716910788186[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]27.9498532504127[/C][C]-19.9498532504127[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]10.4724501631569[/C][C]5.52754983684307[/C][/ROW]
[ROW][C]62[/C][C]19[/C][C]13.9861450214474[/C][C]5.01385497855263[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]14.6369988970176[/C][C]-0.636998897017622[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]6.52250179505387[/C][C]-0.522501795053874[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]9.7986295700862[/C][C]3.2013704299138[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]20.9743955236895[/C][C]-5.97439552368954[/C][/ROW]
[ROW][C]67[/C][C]7[/C][C]22.6065058328249[/C][C]-15.6065058328249[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.0404680795954[/C][C]0.959531920404565[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]14.5106916902501[/C][C]-10.5106916902501[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]9.89191534970385[/C][C]4.10808465029615[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]9.90048139963478[/C][C]3.09951860036522[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]2.60467264869049[/C][C]8.39532735130951[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]17.6917152451611[/C][C]-3.69171524516109[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]14.7508049064657[/C][C]-1.75080490646574[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]9.99972701360129[/C][C]3.00027298639871[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]16.6195397688071[/C][C]-4.61953976880711[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.0905439475168[/C][C]0.909456052483224[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]3.59545809877105[/C][C]10.404541901229[/C][/ROW]
[ROW][C]79[/C][C]11[/C][C]21.140710117307[/C][C]-10.140710117307[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]16.25795679646[/C][C]-4.25795679645997[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]5.30166491347269[/C][C]6.69833508652731[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]-0.571247222115936[/C][C]16.5712472221159[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]3.11712184683185[/C][C]8.88287815316815[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.0400506170099[/C][C]-0.0400506170098617[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]3.98108505014434[/C][C]8.01891494985566[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]8.84150556375985[/C][C]1.15849443624015[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]4.04988700319702[/C][C]10.950112996803[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]13.3583532832506[/C][C]1.64164671674938[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.7801629273859[/C][C]-0.780162927385908[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]9.94720507637954[/C][C]6.05279492362046[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]6.85811665315365[/C][C]8.14188334684635[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]8.75674213335845[/C][C]7.24325786664155[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]5.66291296114773[/C][C]7.33708703885227[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]20.1898828586454[/C][C]-8.18988285864545[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]17.5284357809232[/C][C]-6.52843578092315[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]26.9579896800857[/C][C]-13.9579896800857[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]26.5671226163032[/C][C]-23.5671226163032[/C][/ROW]
[ROW][C]98[/C][C]5[/C][C]5.82620287012542[/C][C]-0.826202870125421[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]17.0942072440508[/C][C]-11.0942072440508[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]9.35254945192591[/C][C]-2.35254945192591[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]10.7955362781183[/C][C]-3.79553627811829[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]24.0045325439158[/C][C]-18.0045325439158[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]20.7957023786965[/C][C]-17.7957023786965[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]4.44130252545934[/C][C]-2.44130252545934[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]10.5879643760902[/C][C]-2.58796437609024[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]15.094858415746[/C][C]-12.094858415746[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]2.63184803405698[/C][C]5.36815196594302[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]14.6712083975264[/C][C]-11.6712083975264[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]21.7384352316343[/C][C]-17.7384352316343[/C][/ROW]
[ROW][C]110[/C][C]5[/C][C]4.77968787110864[/C][C]0.220312128891365[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]4.54438198269998[/C][C]2.45561801730002[/C][/ROW]
[ROW][C]112[/C][C]6[/C][C]9.47213297998762[/C][C]-3.47213297998762[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]13.4464637348775[/C][C]-7.44646373487751[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]2.70186799454597[/C][C]4.29813200545403[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]14.2863609055327[/C][C]-8.28636090553268[/C][/ROW]
[ROW][C]116[/C][C]6[/C][C]15.8555563520763[/C][C]-9.85555635207626[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]10.7864747509264[/C][C]-4.78647475092638[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]12.7369617648438[/C][C]-6.73696176484383[/C][/ROW]
[ROW][C]119[/C][C]4[/C][C]10.7515882604824[/C][C]-6.75158826048237[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]1.75541186812627[/C][C]2.24458813187373[/C][/ROW]
[ROW][C]121[/C][C]5[/C][C]6.21299948177468[/C][C]-1.21299948177468[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]4.23814040596108[/C][C]-0.238140405961078[/C][/ROW]
[ROW][C]123[/C][C]6[/C][C]4.27283559405199[/C][C]1.72716440594801[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]3.7580061882793[/C][C]2.2419938117207[/C][/ROW]
[ROW][C]125[/C][C]5[/C][C]17.7944748872588[/C][C]-12.7944748872588[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]28.9897738404319[/C][C]-23.9897738404319[/C][/ROW]
[ROW][C]127[/C][C]147[/C][C]18.6915900982079[/C][C]128.308409901792[/C][/ROW]
[ROW][C]128[/C][C]139[/C][C]26.6382120584407[/C][C]112.361787941559[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]26.8706149095641[/C][C]-26.8706149095641[/C][/ROW]
[ROW][C]130[/C][C]81[/C][C]28.0076447529639[/C][C]52.9923552470361[/C][/ROW]
[ROW][C]131[/C][C]3[/C][C]23.9787356595691[/C][C]-20.9787356595691[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]25.1705839084356[/C][C]-25.1705839084356[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]27.6249209546387[/C][C]-27.6249209546387[/C][/ROW]
[ROW][C]134[/C][C]37[/C][C]27.0276554815023[/C][C]9.97234451849774[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]21.8486625776555[/C][C]-16.8486625776555[/C][/ROW]
[ROW][C]136[/C][C]69[/C][C]25.3579546069606[/C][C]43.6420453930394[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]21.2436497187682[/C][C]-21.2436497187682[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]17.0572417033441[/C][C]-7.05724170334407[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]10.2242129804142[/C][C]0.775787019585755[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]16.8002460299006[/C][C]-4.80024602990059[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]15.7217700035704[/C][C]-3.72177000357044[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]21.0312949717815[/C][C]-11.0312949717816[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]13.0701633477934[/C][C]-3.07016334779339[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]9.50053849546677[/C][C]-5.50053849546677[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]22.8396539919611[/C][C]-14.8396539919611[/C][/ROW]
[ROW][C]146[/C][C]15[/C][C]7.61106597499896[/C][C]7.38893402500104[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]17.4385094770858[/C][C]-1.4385094770858[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]5.03497750665678[/C][C]6.96502249334322[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.3143427666314[/C][C]-2.31434276663136[/C][/ROW]
[ROW][C]150[/C][C]15[/C][C]22.6227999835644[/C][C]-7.62279998356444[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]21.1427362131168[/C][C]-12.1427362131168[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]11.5494319521293[/C][C]0.450568047870696[/C][/ROW]
[ROW][C]153[/C][C]14[/C][C]13.35240169112[/C][C]0.647598308879961[/C][/ROW]
[ROW][C]154[/C][C]11[/C][C]11.0559789029534[/C][C]-0.0559789029533709[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]10.8670468196689[/C][C]3.13295318033105[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]11.0389466181082[/C][C]-3.03894661810822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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
11327.3956225902223-14.3956225902223
21219.7937082600744-7.79370826007442
31025.6133319687212-15.6133319687212
4916.9321944190035-7.93219441900351
51018.3213561617562-8.32135616175625
61226.3400706935711-14.3400706935711
7135.444881690868297.55511830913171
81224.3472118429135-12.3472118429135
9146.508332416543777.49166758345623
10928.2334737382438-19.2334737382438
11141.1068682819854412.8931317180146
121210.78159572332871.21840427667133
1357.51973457612595-2.51973457612595
1447.17026855649266-3.17026855649266
15626.7094042544806-20.7094042544806
1646.93030820067093-2.93030820067093
17626.852153752801-20.852153752801
18613.5579615981953-7.55796159819527
19424.7947169238373-20.7947169238373
20415.9465662010257-11.9465662010257
21221.2601293715934-19.2601293715934
22716.0707481555264-9.07074815552639
23511.6520757282816-6.65207572828156
24423.8530651963509-19.8530651963509
25624.8748856088575-18.8748856088575
2669.56570010127077-3.56570010127077
2771.752754630857455.24724536914255
28516.1964564226999-11.1964564226999
2967.84489470650244-1.84489470650244
3049.57759085141208-5.57759085141208
31411.1082710146212-7.10827101462121
32715.9533949632238-8.95339496322383
33714.0988053979422-7.0988053979422
34424.0715567698839-20.0715567698839
35414.3891868393625-10.3891868393625
3668.61366268561219-2.61366268561219
3764.846773406895561.15322659310444
38517.2811393197627-12.2811393197627
39617.7125021016927-11.7125021016927
4076.760525605333950.239474394666047
41610.5002035062404-4.50020350624037
42322.7349652556635-19.7349652556635
4315124.4800796751928126.519920324807
4413131.437834405186999.5621655948131
4514625.984478843296120.015521156704
4612925.1142799648238103.885720035176
474821.740658096132426.2593419038676
481119.9760978468674-8.97609784686737
491222.27662095045-10.27662095045
501211.11750342573390.882496574266075
51927.6347218817025-18.6347218817025
521222.5614173700431-10.5614173700431
531214.0360837412127-2.03608374121269
541325.9654018154524-12.9654018154524
551125.5654873183387-14.5654873183387
56916.8111511625327-7.81115116253266
57923.2714584118466-14.2714584118466
581118.0546370827505-7.0546370827505
591510.39283089211814.60716910788186
60827.9498532504127-19.9498532504127
611610.47245016315695.52754983684307
621913.98614502144745.01385497855263
631414.6369988970176-0.636998897017622
6466.52250179505387-0.522501795053874
65139.79862957008623.2013704299138
661520.9743955236895-5.97439552368954
67722.6065058328249-15.6065058328249
681312.04046807959540.959531920404565
69414.5106916902501-10.5106916902501
70149.891915349703854.10808465029615
71139.900481399634783.09951860036522
72112.604672648690498.39532735130951
731417.6917152451611-3.69171524516109
741314.7508049064657-1.75080490646574
75139.999727013601293.00027298639871
761216.6195397688071-4.61953976880711
771312.09054394751680.909456052483224
78143.5954580987710510.404541901229
791121.140710117307-10.140710117307
801216.25795679646-4.25795679645997
81125.301664913472696.69833508652731
8216-0.57124722211593616.5712472221159
83123.117121846831858.88287815316815
841212.0400506170099-0.0400506170098617
85123.981085050144348.01891494985566
86108.841505563759851.15849443624015
87154.0498870031970210.950112996803
881513.35835328325061.64164671674938
891212.7801629273859-0.780162927385908
90169.947205076379546.05279492362046
91156.858116653153658.14188334684635
92168.756742133358457.24325786664155
93135.662912961147737.33708703885227
941220.1898828586454-8.18988285864545
951117.5284357809232-6.52843578092315
961326.9579896800857-13.9579896800857
97326.5671226163032-23.5671226163032
9855.82620287012542-0.826202870125421
99617.0942072440508-11.0942072440508
10079.35254945192591-2.35254945192591
101710.7955362781183-3.79553627811829
102624.0045325439158-18.0045325439158
103320.7957023786965-17.7957023786965
10424.44130252545934-2.44130252545934
105810.5879643760902-2.58796437609024
106315.094858415746-12.094858415746
10782.631848034056985.36815196594302
108314.6712083975264-11.6712083975264
109421.7384352316343-17.7384352316343
11054.779687871108640.220312128891365
11174.544381982699982.45561801730002
11269.47213297998762-3.47213297998762
113613.4464637348775-7.44646373487751
11472.701867994545974.29813200545403
115614.2863609055327-8.28636090553268
116615.8555563520763-9.85555635207626
117610.7864747509264-4.78647475092638
118612.7369617648438-6.73696176484383
119410.7515882604824-6.75158826048237
12041.755411868126272.24458813187373
12156.21299948177468-1.21299948177468
12244.23814040596108-0.238140405961078
12364.272835594051991.72716440594801
12463.75800618827932.2419938117207
125517.7944748872588-12.7944748872588
126528.9897738404319-23.9897738404319
12714718.6915900982079128.308409901792
12813926.6382120584407112.361787941559
129026.8706149095641-26.8706149095641
1308128.007644752963952.9923552470361
131323.9787356595691-20.9787356595691
132025.1705839084356-25.1705839084356
133027.6249209546387-27.6249209546387
1343727.02765548150239.97234451849774
135521.8486625776555-16.8486625776555
1366925.357954606960643.6420453930394
137021.2436497187682-21.2436497187682
1381017.0572417033441-7.05724170334407
1391110.22421298041420.775787019585755
1401216.8002460299006-4.80024602990059
1411215.7217700035704-3.72177000357044
1421021.0312949717815-11.0312949717816
1431013.0701633477934-3.07016334779339
14449.50053849546677-5.50053849546677
145822.8396539919611-14.8396539919611
146157.611065974998967.38893402500104
1471617.4385094770858-1.4385094770858
148125.034977506656786.96502249334322
1491214.3143427666314-2.31434276663136
1501522.6227999835644-7.62279998356444
151921.1427362131168-12.1427362131168
1521211.54943195212930.450568047870696
1531413.352401691120.647598308879961
1541111.0559789029534-0.0559789029533709
1551410.86704681966893.13295318033105
156811.0389466181082-3.03894661810822






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0003241670820769450.0006483341641538910.999675832917923
112.18228267352062e-054.36456534704124e-050.999978177173265
121.35215895810369e-062.70431791620738e-060.999998647841042
131.90159205465295e-073.8031841093059e-070.999999809840795
141.00327754604483e-082.00655509208965e-080.999999989967225
151.45236881512174e-082.90473763024347e-080.999999985476312
163.63168147300776e-087.26336294601553e-080.999999963683185
177.89876467533151e-091.5797529350663e-080.999999992101235
182.06710643541581e-094.13421287083162e-090.999999997932894
192.29058635629455e-104.5811727125891e-100.999999999770941
202.41507641564842e-114.83015283129685e-110.999999999975849
219.23882355752122e-121.84776471150424e-110.999999999990761
229.85170117582147e-131.97034023516429e-120.999999999999015
231.01443093491334e-132.02886186982668e-130.999999999999899
242.99913454520019e-145.99826909040038e-140.99999999999997
253.24043668978395e-156.48087337956791e-150.999999999999997
263.23205029881246e-166.46410059762493e-161
273.82621570387844e-177.65243140775688e-171
285.46773565533404e-181.09354713106681e-171
297.0865742235845e-191.4173148447169e-181
301.83532484552691e-193.67064969105383e-191
316.76169049034053e-201.35233809806811e-191
326.76553232457267e-211.35310646491453e-201
339.26902980171902e-221.8538059603438e-211
341.22709515679458e-222.45419031358916e-221
351.29226372908024e-232.58452745816049e-231
361.20480350795778e-242.40960701591556e-241
371.1548735301731e-252.3097470603462e-251
381.0985604631266e-262.19712092625321e-261
391.603549965199e-273.207099930398e-271
401.53618159280871e-283.07236318561742e-281
411.38972727105505e-292.77945454211009e-291
421.51089038389108e-303.02178076778217e-301
430.1478582055274610.2957164110549210.852141794472539
440.7506559214574110.4986881570851780.249344078542589
450.996099607298180.007800785403640540.00390039270182027
460.9999949350069471.01299861054401e-055.06499305272004e-06
470.999995808865148.38226971932794e-064.19113485966397e-06
480.9999947050168441.0589966312559e-055.29498315627951e-06
490.9999952625913979.47481720561002e-064.73740860280501e-06
500.9999925964662021.48070675963345e-057.40353379816723e-06
510.9999928378849391.43242301229753e-057.16211506148767e-06
520.9999903612104171.92775791661849e-059.63878958309244e-06
530.9999862520650652.74958698692384e-051.37479349346192e-05
540.99997915175254.16964950007113e-052.08482475003556e-05
550.9999703942677365.92114645285324e-052.96057322642662e-05
560.9999514938083769.70123832484994e-054.85061916242497e-05
570.9999358257768970.0001283484462061076.41742231030537e-05
580.9998983377734780.0002033244530446470.000101662226522324
590.9998513275765360.0002973448469275350.000148672423463768
600.9998169084321090.0003661831357810810.00018309156789054
610.9997282116851280.0005435766297444410.000271788314872221
620.9995892300836660.0008215398326672720.000410769916333636
630.9993899924156670.001220015168666250.000610007584333127
640.9991352409419130.001729518116174010.000864759058087004
650.9987253157127120.002549368574576180.00127468428728809
660.9981551723497040.003689655300592170.00184482765029608
670.9975958829379850.004808234124030180.00240411706201509
680.996542361630930.006915276738140450.00345763836907022
690.995248455843960.009503088312080390.0047515441560402
700.9934482873997890.01310342520042240.00655171260021118
710.9910322465221840.01793550695563240.0089677534778162
720.9880899848070330.02382003038593440.0119100151929672
730.9838881942421230.03222361151575360.0161118057578768
740.9784366677043060.04312666459138890.0215633322956944
750.971929432484460.05614113503108070.0280705675155403
760.9634849345211670.07303013095766640.0365150654788332
770.9529128728334330.09417425433313310.0470871271665666
780.9427472049396310.1145055901207380.0572527950603691
790.9310198326610630.1379603346778730.0689801673389366
800.9146577953511110.1706844092977770.0853422046488887
810.8962035369093960.2075929261812080.103796463090604
820.8821138530375190.2357722939249610.117886146962481
830.8596109647247150.280778070550570.140389035275285
840.8311216894711020.3377566210577960.168878310528898
850.8013438141615390.3973123716769220.198656185838461
860.7661517142714750.4676965714570510.233848285728526
870.7326260245632130.5347479508735740.267373975436787
880.6916056815164020.6167886369671970.308394318483598
890.6481092442890560.7037815114218880.351890755710944
900.6036197776245080.7927604447509850.396380222375492
910.5657536875733770.8684926248532450.434246312426623
920.5203511985777360.9592976028445280.479648801422264
930.4746405732885870.9492811465771750.525359426711412
940.4316041478242790.8632082956485590.568395852175721
950.3920237232682440.7840474465364890.607976276731756
960.3614957258711310.7229914517422620.638504274128869
970.357843037917860.715686075835720.64215696208214
980.3129840619384970.6259681238769950.687015938061503
990.2753851959806630.5507703919613260.724614804019337
1000.2360202055564120.4720404111128230.763979794443588
1010.1999133579631270.3998267159262540.800086642036873
1020.1869855971878480.3739711943756970.813014402812152
1030.1744693306839160.3489386613678310.825530669316084
1040.1450553390906770.2901106781813530.854944660909323
1050.1185607955458860.2371215910917720.881439204454114
1060.1071504296120170.2143008592240330.892849570387983
1070.08613820236412820.1722764047282560.913861797635872
1080.06980126191444570.1396025238288910.930198738085554
1090.0680617668013790.1361235336027580.931938233198621
1100.05267134694380270.1053426938876050.947328653056197
1110.04029763564240070.08059527128480140.959702364357599
1120.0303104088571890.06062081771437790.969689591142811
1130.02262141514186650.04524283028373290.977378584858134
1140.01746834114345860.03493668228691710.982531658856541
1150.01265629790250950.02531259580501910.98734370209749
1160.009536781449554650.01907356289910930.990463218550445
1170.006684424290414110.01336884858082820.993315575709586
1180.004569696013303070.009139392026606140.995430303986697
1190.003091371047766620.006182742095533240.996908628952233
1200.002021426325497930.004042852650995870.997978573674502
1210.001331968591566120.002663937183132250.998668031408434
1220.0008561678471206170.001712335694241230.999143832152879
1230.001037739448975720.002075478897951430.998962260551024
1240.02749171455906480.05498342911812970.972508285440935
1250.02809123052051380.05618246104102760.971908769479486
1260.02369008701090590.04738017402181180.976309912989094
1270.3196551227222070.6393102454444140.680344877277793
1280.9579704282434090.0840591435131820.042029571756591
1290.9617338636793210.07653227264135720.0382661363206786
1300.9982680137005950.003463972598809880.00173198629940494
1310.9975214766305780.004957046738843270.00247852336942163
1320.9977476120632920.004504775873416480.00225238793670824
1330.9984793727063250.00304125458734990.00152062729367495
1340.9997344994042990.0005310011914029080.000265500595701454
1350.9995327588595560.0009344822808890020.000467241140444501
1360.9999999999372271.25545747435188e-106.27728737175941e-11
1370.999999999875862.48280976009983e-101.24140488004992e-10
1380.999999999026831.94634087544683e-099.73170437723413e-10
1390.9999999917643951.6471210304821e-088.23560515241048e-09
1400.9999999298981551.40203690003848e-077.0101845001924e-08
1410.9999994609563591.0780872823732e-065.390436411866e-07
1420.9999961990900097.60181998104707e-063.80090999052353e-06
1430.9999690135102246.197297955258e-053.098648977629e-05
1440.9999497715437780.0001004569124444895.02284562222447e-05
1450.9998043859844030.0003912280311940080.000195614015597004
1460.9998502256743590.000299548651282690.000149774325641345

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.000324167082076945 & 0.000648334164153891 & 0.999675832917923 \tabularnewline
11 & 2.18228267352062e-05 & 4.36456534704124e-05 & 0.999978177173265 \tabularnewline
12 & 1.35215895810369e-06 & 2.70431791620738e-06 & 0.999998647841042 \tabularnewline
13 & 1.90159205465295e-07 & 3.8031841093059e-07 & 0.999999809840795 \tabularnewline
14 & 1.00327754604483e-08 & 2.00655509208965e-08 & 0.999999989967225 \tabularnewline
15 & 1.45236881512174e-08 & 2.90473763024347e-08 & 0.999999985476312 \tabularnewline
16 & 3.63168147300776e-08 & 7.26336294601553e-08 & 0.999999963683185 \tabularnewline
17 & 7.89876467533151e-09 & 1.5797529350663e-08 & 0.999999992101235 \tabularnewline
18 & 2.06710643541581e-09 & 4.13421287083162e-09 & 0.999999997932894 \tabularnewline
19 & 2.29058635629455e-10 & 4.5811727125891e-10 & 0.999999999770941 \tabularnewline
20 & 2.41507641564842e-11 & 4.83015283129685e-11 & 0.999999999975849 \tabularnewline
21 & 9.23882355752122e-12 & 1.84776471150424e-11 & 0.999999999990761 \tabularnewline
22 & 9.85170117582147e-13 & 1.97034023516429e-12 & 0.999999999999015 \tabularnewline
23 & 1.01443093491334e-13 & 2.02886186982668e-13 & 0.999999999999899 \tabularnewline
24 & 2.99913454520019e-14 & 5.99826909040038e-14 & 0.99999999999997 \tabularnewline
25 & 3.24043668978395e-15 & 6.48087337956791e-15 & 0.999999999999997 \tabularnewline
26 & 3.23205029881246e-16 & 6.46410059762493e-16 & 1 \tabularnewline
27 & 3.82621570387844e-17 & 7.65243140775688e-17 & 1 \tabularnewline
28 & 5.46773565533404e-18 & 1.09354713106681e-17 & 1 \tabularnewline
29 & 7.0865742235845e-19 & 1.4173148447169e-18 & 1 \tabularnewline
30 & 1.83532484552691e-19 & 3.67064969105383e-19 & 1 \tabularnewline
31 & 6.76169049034053e-20 & 1.35233809806811e-19 & 1 \tabularnewline
32 & 6.76553232457267e-21 & 1.35310646491453e-20 & 1 \tabularnewline
33 & 9.26902980171902e-22 & 1.8538059603438e-21 & 1 \tabularnewline
34 & 1.22709515679458e-22 & 2.45419031358916e-22 & 1 \tabularnewline
35 & 1.29226372908024e-23 & 2.58452745816049e-23 & 1 \tabularnewline
36 & 1.20480350795778e-24 & 2.40960701591556e-24 & 1 \tabularnewline
37 & 1.1548735301731e-25 & 2.3097470603462e-25 & 1 \tabularnewline
38 & 1.0985604631266e-26 & 2.19712092625321e-26 & 1 \tabularnewline
39 & 1.603549965199e-27 & 3.207099930398e-27 & 1 \tabularnewline
40 & 1.53618159280871e-28 & 3.07236318561742e-28 & 1 \tabularnewline
41 & 1.38972727105505e-29 & 2.77945454211009e-29 & 1 \tabularnewline
42 & 1.51089038389108e-30 & 3.02178076778217e-30 & 1 \tabularnewline
43 & 0.147858205527461 & 0.295716411054921 & 0.852141794472539 \tabularnewline
44 & 0.750655921457411 & 0.498688157085178 & 0.249344078542589 \tabularnewline
45 & 0.99609960729818 & 0.00780078540364054 & 0.00390039270182027 \tabularnewline
46 & 0.999994935006947 & 1.01299861054401e-05 & 5.06499305272004e-06 \tabularnewline
47 & 0.99999580886514 & 8.38226971932794e-06 & 4.19113485966397e-06 \tabularnewline
48 & 0.999994705016844 & 1.0589966312559e-05 & 5.29498315627951e-06 \tabularnewline
49 & 0.999995262591397 & 9.47481720561002e-06 & 4.73740860280501e-06 \tabularnewline
50 & 0.999992596466202 & 1.48070675963345e-05 & 7.40353379816723e-06 \tabularnewline
51 & 0.999992837884939 & 1.43242301229753e-05 & 7.16211506148767e-06 \tabularnewline
52 & 0.999990361210417 & 1.92775791661849e-05 & 9.63878958309244e-06 \tabularnewline
53 & 0.999986252065065 & 2.74958698692384e-05 & 1.37479349346192e-05 \tabularnewline
54 & 0.9999791517525 & 4.16964950007113e-05 & 2.08482475003556e-05 \tabularnewline
55 & 0.999970394267736 & 5.92114645285324e-05 & 2.96057322642662e-05 \tabularnewline
56 & 0.999951493808376 & 9.70123832484994e-05 & 4.85061916242497e-05 \tabularnewline
57 & 0.999935825776897 & 0.000128348446206107 & 6.41742231030537e-05 \tabularnewline
58 & 0.999898337773478 & 0.000203324453044647 & 0.000101662226522324 \tabularnewline
59 & 0.999851327576536 & 0.000297344846927535 & 0.000148672423463768 \tabularnewline
60 & 0.999816908432109 & 0.000366183135781081 & 0.00018309156789054 \tabularnewline
61 & 0.999728211685128 & 0.000543576629744441 & 0.000271788314872221 \tabularnewline
62 & 0.999589230083666 & 0.000821539832667272 & 0.000410769916333636 \tabularnewline
63 & 0.999389992415667 & 0.00122001516866625 & 0.000610007584333127 \tabularnewline
64 & 0.999135240941913 & 0.00172951811617401 & 0.000864759058087004 \tabularnewline
65 & 0.998725315712712 & 0.00254936857457618 & 0.00127468428728809 \tabularnewline
66 & 0.998155172349704 & 0.00368965530059217 & 0.00184482765029608 \tabularnewline
67 & 0.997595882937985 & 0.00480823412403018 & 0.00240411706201509 \tabularnewline
68 & 0.99654236163093 & 0.00691527673814045 & 0.00345763836907022 \tabularnewline
69 & 0.99524845584396 & 0.00950308831208039 & 0.0047515441560402 \tabularnewline
70 & 0.993448287399789 & 0.0131034252004224 & 0.00655171260021118 \tabularnewline
71 & 0.991032246522184 & 0.0179355069556324 & 0.0089677534778162 \tabularnewline
72 & 0.988089984807033 & 0.0238200303859344 & 0.0119100151929672 \tabularnewline
73 & 0.983888194242123 & 0.0322236115157536 & 0.0161118057578768 \tabularnewline
74 & 0.978436667704306 & 0.0431266645913889 & 0.0215633322956944 \tabularnewline
75 & 0.97192943248446 & 0.0561411350310807 & 0.0280705675155403 \tabularnewline
76 & 0.963484934521167 & 0.0730301309576664 & 0.0365150654788332 \tabularnewline
77 & 0.952912872833433 & 0.0941742543331331 & 0.0470871271665666 \tabularnewline
78 & 0.942747204939631 & 0.114505590120738 & 0.0572527950603691 \tabularnewline
79 & 0.931019832661063 & 0.137960334677873 & 0.0689801673389366 \tabularnewline
80 & 0.914657795351111 & 0.170684409297777 & 0.0853422046488887 \tabularnewline
81 & 0.896203536909396 & 0.207592926181208 & 0.103796463090604 \tabularnewline
82 & 0.882113853037519 & 0.235772293924961 & 0.117886146962481 \tabularnewline
83 & 0.859610964724715 & 0.28077807055057 & 0.140389035275285 \tabularnewline
84 & 0.831121689471102 & 0.337756621057796 & 0.168878310528898 \tabularnewline
85 & 0.801343814161539 & 0.397312371676922 & 0.198656185838461 \tabularnewline
86 & 0.766151714271475 & 0.467696571457051 & 0.233848285728526 \tabularnewline
87 & 0.732626024563213 & 0.534747950873574 & 0.267373975436787 \tabularnewline
88 & 0.691605681516402 & 0.616788636967197 & 0.308394318483598 \tabularnewline
89 & 0.648109244289056 & 0.703781511421888 & 0.351890755710944 \tabularnewline
90 & 0.603619777624508 & 0.792760444750985 & 0.396380222375492 \tabularnewline
91 & 0.565753687573377 & 0.868492624853245 & 0.434246312426623 \tabularnewline
92 & 0.520351198577736 & 0.959297602844528 & 0.479648801422264 \tabularnewline
93 & 0.474640573288587 & 0.949281146577175 & 0.525359426711412 \tabularnewline
94 & 0.431604147824279 & 0.863208295648559 & 0.568395852175721 \tabularnewline
95 & 0.392023723268244 & 0.784047446536489 & 0.607976276731756 \tabularnewline
96 & 0.361495725871131 & 0.722991451742262 & 0.638504274128869 \tabularnewline
97 & 0.35784303791786 & 0.71568607583572 & 0.64215696208214 \tabularnewline
98 & 0.312984061938497 & 0.625968123876995 & 0.687015938061503 \tabularnewline
99 & 0.275385195980663 & 0.550770391961326 & 0.724614804019337 \tabularnewline
100 & 0.236020205556412 & 0.472040411112823 & 0.763979794443588 \tabularnewline
101 & 0.199913357963127 & 0.399826715926254 & 0.800086642036873 \tabularnewline
102 & 0.186985597187848 & 0.373971194375697 & 0.813014402812152 \tabularnewline
103 & 0.174469330683916 & 0.348938661367831 & 0.825530669316084 \tabularnewline
104 & 0.145055339090677 & 0.290110678181353 & 0.854944660909323 \tabularnewline
105 & 0.118560795545886 & 0.237121591091772 & 0.881439204454114 \tabularnewline
106 & 0.107150429612017 & 0.214300859224033 & 0.892849570387983 \tabularnewline
107 & 0.0861382023641282 & 0.172276404728256 & 0.913861797635872 \tabularnewline
108 & 0.0698012619144457 & 0.139602523828891 & 0.930198738085554 \tabularnewline
109 & 0.068061766801379 & 0.136123533602758 & 0.931938233198621 \tabularnewline
110 & 0.0526713469438027 & 0.105342693887605 & 0.947328653056197 \tabularnewline
111 & 0.0402976356424007 & 0.0805952712848014 & 0.959702364357599 \tabularnewline
112 & 0.030310408857189 & 0.0606208177143779 & 0.969689591142811 \tabularnewline
113 & 0.0226214151418665 & 0.0452428302837329 & 0.977378584858134 \tabularnewline
114 & 0.0174683411434586 & 0.0349366822869171 & 0.982531658856541 \tabularnewline
115 & 0.0126562979025095 & 0.0253125958050191 & 0.98734370209749 \tabularnewline
116 & 0.00953678144955465 & 0.0190735628991093 & 0.990463218550445 \tabularnewline
117 & 0.00668442429041411 & 0.0133688485808282 & 0.993315575709586 \tabularnewline
118 & 0.00456969601330307 & 0.00913939202660614 & 0.995430303986697 \tabularnewline
119 & 0.00309137104776662 & 0.00618274209553324 & 0.996908628952233 \tabularnewline
120 & 0.00202142632549793 & 0.00404285265099587 & 0.997978573674502 \tabularnewline
121 & 0.00133196859156612 & 0.00266393718313225 & 0.998668031408434 \tabularnewline
122 & 0.000856167847120617 & 0.00171233569424123 & 0.999143832152879 \tabularnewline
123 & 0.00103773944897572 & 0.00207547889795143 & 0.998962260551024 \tabularnewline
124 & 0.0274917145590648 & 0.0549834291181297 & 0.972508285440935 \tabularnewline
125 & 0.0280912305205138 & 0.0561824610410276 & 0.971908769479486 \tabularnewline
126 & 0.0236900870109059 & 0.0473801740218118 & 0.976309912989094 \tabularnewline
127 & 0.319655122722207 & 0.639310245444414 & 0.680344877277793 \tabularnewline
128 & 0.957970428243409 & 0.084059143513182 & 0.042029571756591 \tabularnewline
129 & 0.961733863679321 & 0.0765322726413572 & 0.0382661363206786 \tabularnewline
130 & 0.998268013700595 & 0.00346397259880988 & 0.00173198629940494 \tabularnewline
131 & 0.997521476630578 & 0.00495704673884327 & 0.00247852336942163 \tabularnewline
132 & 0.997747612063292 & 0.00450477587341648 & 0.00225238793670824 \tabularnewline
133 & 0.998479372706325 & 0.0030412545873499 & 0.00152062729367495 \tabularnewline
134 & 0.999734499404299 & 0.000531001191402908 & 0.000265500595701454 \tabularnewline
135 & 0.999532758859556 & 0.000934482280889002 & 0.000467241140444501 \tabularnewline
136 & 0.999999999937227 & 1.25545747435188e-10 & 6.27728737175941e-11 \tabularnewline
137 & 0.99999999987586 & 2.48280976009983e-10 & 1.24140488004992e-10 \tabularnewline
138 & 0.99999999902683 & 1.94634087544683e-09 & 9.73170437723413e-10 \tabularnewline
139 & 0.999999991764395 & 1.6471210304821e-08 & 8.23560515241048e-09 \tabularnewline
140 & 0.999999929898155 & 1.40203690003848e-07 & 7.0101845001924e-08 \tabularnewline
141 & 0.999999460956359 & 1.0780872823732e-06 & 5.390436411866e-07 \tabularnewline
142 & 0.999996199090009 & 7.60181998104707e-06 & 3.80090999052353e-06 \tabularnewline
143 & 0.999969013510224 & 6.197297955258e-05 & 3.098648977629e-05 \tabularnewline
144 & 0.999949771543778 & 0.000100456912444489 & 5.02284562222447e-05 \tabularnewline
145 & 0.999804385984403 & 0.000391228031194008 & 0.000195614015597004 \tabularnewline
146 & 0.999850225674359 & 0.00029954865128269 & 0.000149774325641345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&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]10[/C][C]0.000324167082076945[/C][C]0.000648334164153891[/C][C]0.999675832917923[/C][/ROW]
[ROW][C]11[/C][C]2.18228267352062e-05[/C][C]4.36456534704124e-05[/C][C]0.999978177173265[/C][/ROW]
[ROW][C]12[/C][C]1.35215895810369e-06[/C][C]2.70431791620738e-06[/C][C]0.999998647841042[/C][/ROW]
[ROW][C]13[/C][C]1.90159205465295e-07[/C][C]3.8031841093059e-07[/C][C]0.999999809840795[/C][/ROW]
[ROW][C]14[/C][C]1.00327754604483e-08[/C][C]2.00655509208965e-08[/C][C]0.999999989967225[/C][/ROW]
[ROW][C]15[/C][C]1.45236881512174e-08[/C][C]2.90473763024347e-08[/C][C]0.999999985476312[/C][/ROW]
[ROW][C]16[/C][C]3.63168147300776e-08[/C][C]7.26336294601553e-08[/C][C]0.999999963683185[/C][/ROW]
[ROW][C]17[/C][C]7.89876467533151e-09[/C][C]1.5797529350663e-08[/C][C]0.999999992101235[/C][/ROW]
[ROW][C]18[/C][C]2.06710643541581e-09[/C][C]4.13421287083162e-09[/C][C]0.999999997932894[/C][/ROW]
[ROW][C]19[/C][C]2.29058635629455e-10[/C][C]4.5811727125891e-10[/C][C]0.999999999770941[/C][/ROW]
[ROW][C]20[/C][C]2.41507641564842e-11[/C][C]4.83015283129685e-11[/C][C]0.999999999975849[/C][/ROW]
[ROW][C]21[/C][C]9.23882355752122e-12[/C][C]1.84776471150424e-11[/C][C]0.999999999990761[/C][/ROW]
[ROW][C]22[/C][C]9.85170117582147e-13[/C][C]1.97034023516429e-12[/C][C]0.999999999999015[/C][/ROW]
[ROW][C]23[/C][C]1.01443093491334e-13[/C][C]2.02886186982668e-13[/C][C]0.999999999999899[/C][/ROW]
[ROW][C]24[/C][C]2.99913454520019e-14[/C][C]5.99826909040038e-14[/C][C]0.99999999999997[/C][/ROW]
[ROW][C]25[/C][C]3.24043668978395e-15[/C][C]6.48087337956791e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]26[/C][C]3.23205029881246e-16[/C][C]6.46410059762493e-16[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]3.82621570387844e-17[/C][C]7.65243140775688e-17[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.46773565533404e-18[/C][C]1.09354713106681e-17[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]7.0865742235845e-19[/C][C]1.4173148447169e-18[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.83532484552691e-19[/C][C]3.67064969105383e-19[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]6.76169049034053e-20[/C][C]1.35233809806811e-19[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]6.76553232457267e-21[/C][C]1.35310646491453e-20[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]9.26902980171902e-22[/C][C]1.8538059603438e-21[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.22709515679458e-22[/C][C]2.45419031358916e-22[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.29226372908024e-23[/C][C]2.58452745816049e-23[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.20480350795778e-24[/C][C]2.40960701591556e-24[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.1548735301731e-25[/C][C]2.3097470603462e-25[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.0985604631266e-26[/C][C]2.19712092625321e-26[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.603549965199e-27[/C][C]3.207099930398e-27[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]1.53618159280871e-28[/C][C]3.07236318561742e-28[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]1.38972727105505e-29[/C][C]2.77945454211009e-29[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1.51089038389108e-30[/C][C]3.02178076778217e-30[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0.147858205527461[/C][C]0.295716411054921[/C][C]0.852141794472539[/C][/ROW]
[ROW][C]44[/C][C]0.750655921457411[/C][C]0.498688157085178[/C][C]0.249344078542589[/C][/ROW]
[ROW][C]45[/C][C]0.99609960729818[/C][C]0.00780078540364054[/C][C]0.00390039270182027[/C][/ROW]
[ROW][C]46[/C][C]0.999994935006947[/C][C]1.01299861054401e-05[/C][C]5.06499305272004e-06[/C][/ROW]
[ROW][C]47[/C][C]0.99999580886514[/C][C]8.38226971932794e-06[/C][C]4.19113485966397e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999994705016844[/C][C]1.0589966312559e-05[/C][C]5.29498315627951e-06[/C][/ROW]
[ROW][C]49[/C][C]0.999995262591397[/C][C]9.47481720561002e-06[/C][C]4.73740860280501e-06[/C][/ROW]
[ROW][C]50[/C][C]0.999992596466202[/C][C]1.48070675963345e-05[/C][C]7.40353379816723e-06[/C][/ROW]
[ROW][C]51[/C][C]0.999992837884939[/C][C]1.43242301229753e-05[/C][C]7.16211506148767e-06[/C][/ROW]
[ROW][C]52[/C][C]0.999990361210417[/C][C]1.92775791661849e-05[/C][C]9.63878958309244e-06[/C][/ROW]
[ROW][C]53[/C][C]0.999986252065065[/C][C]2.74958698692384e-05[/C][C]1.37479349346192e-05[/C][/ROW]
[ROW][C]54[/C][C]0.9999791517525[/C][C]4.16964950007113e-05[/C][C]2.08482475003556e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999970394267736[/C][C]5.92114645285324e-05[/C][C]2.96057322642662e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999951493808376[/C][C]9.70123832484994e-05[/C][C]4.85061916242497e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999935825776897[/C][C]0.000128348446206107[/C][C]6.41742231030537e-05[/C][/ROW]
[ROW][C]58[/C][C]0.999898337773478[/C][C]0.000203324453044647[/C][C]0.000101662226522324[/C][/ROW]
[ROW][C]59[/C][C]0.999851327576536[/C][C]0.000297344846927535[/C][C]0.000148672423463768[/C][/ROW]
[ROW][C]60[/C][C]0.999816908432109[/C][C]0.000366183135781081[/C][C]0.00018309156789054[/C][/ROW]
[ROW][C]61[/C][C]0.999728211685128[/C][C]0.000543576629744441[/C][C]0.000271788314872221[/C][/ROW]
[ROW][C]62[/C][C]0.999589230083666[/C][C]0.000821539832667272[/C][C]0.000410769916333636[/C][/ROW]
[ROW][C]63[/C][C]0.999389992415667[/C][C]0.00122001516866625[/C][C]0.000610007584333127[/C][/ROW]
[ROW][C]64[/C][C]0.999135240941913[/C][C]0.00172951811617401[/C][C]0.000864759058087004[/C][/ROW]
[ROW][C]65[/C][C]0.998725315712712[/C][C]0.00254936857457618[/C][C]0.00127468428728809[/C][/ROW]
[ROW][C]66[/C][C]0.998155172349704[/C][C]0.00368965530059217[/C][C]0.00184482765029608[/C][/ROW]
[ROW][C]67[/C][C]0.997595882937985[/C][C]0.00480823412403018[/C][C]0.00240411706201509[/C][/ROW]
[ROW][C]68[/C][C]0.99654236163093[/C][C]0.00691527673814045[/C][C]0.00345763836907022[/C][/ROW]
[ROW][C]69[/C][C]0.99524845584396[/C][C]0.00950308831208039[/C][C]0.0047515441560402[/C][/ROW]
[ROW][C]70[/C][C]0.993448287399789[/C][C]0.0131034252004224[/C][C]0.00655171260021118[/C][/ROW]
[ROW][C]71[/C][C]0.991032246522184[/C][C]0.0179355069556324[/C][C]0.0089677534778162[/C][/ROW]
[ROW][C]72[/C][C]0.988089984807033[/C][C]0.0238200303859344[/C][C]0.0119100151929672[/C][/ROW]
[ROW][C]73[/C][C]0.983888194242123[/C][C]0.0322236115157536[/C][C]0.0161118057578768[/C][/ROW]
[ROW][C]74[/C][C]0.978436667704306[/C][C]0.0431266645913889[/C][C]0.0215633322956944[/C][/ROW]
[ROW][C]75[/C][C]0.97192943248446[/C][C]0.0561411350310807[/C][C]0.0280705675155403[/C][/ROW]
[ROW][C]76[/C][C]0.963484934521167[/C][C]0.0730301309576664[/C][C]0.0365150654788332[/C][/ROW]
[ROW][C]77[/C][C]0.952912872833433[/C][C]0.0941742543331331[/C][C]0.0470871271665666[/C][/ROW]
[ROW][C]78[/C][C]0.942747204939631[/C][C]0.114505590120738[/C][C]0.0572527950603691[/C][/ROW]
[ROW][C]79[/C][C]0.931019832661063[/C][C]0.137960334677873[/C][C]0.0689801673389366[/C][/ROW]
[ROW][C]80[/C][C]0.914657795351111[/C][C]0.170684409297777[/C][C]0.0853422046488887[/C][/ROW]
[ROW][C]81[/C][C]0.896203536909396[/C][C]0.207592926181208[/C][C]0.103796463090604[/C][/ROW]
[ROW][C]82[/C][C]0.882113853037519[/C][C]0.235772293924961[/C][C]0.117886146962481[/C][/ROW]
[ROW][C]83[/C][C]0.859610964724715[/C][C]0.28077807055057[/C][C]0.140389035275285[/C][/ROW]
[ROW][C]84[/C][C]0.831121689471102[/C][C]0.337756621057796[/C][C]0.168878310528898[/C][/ROW]
[ROW][C]85[/C][C]0.801343814161539[/C][C]0.397312371676922[/C][C]0.198656185838461[/C][/ROW]
[ROW][C]86[/C][C]0.766151714271475[/C][C]0.467696571457051[/C][C]0.233848285728526[/C][/ROW]
[ROW][C]87[/C][C]0.732626024563213[/C][C]0.534747950873574[/C][C]0.267373975436787[/C][/ROW]
[ROW][C]88[/C][C]0.691605681516402[/C][C]0.616788636967197[/C][C]0.308394318483598[/C][/ROW]
[ROW][C]89[/C][C]0.648109244289056[/C][C]0.703781511421888[/C][C]0.351890755710944[/C][/ROW]
[ROW][C]90[/C][C]0.603619777624508[/C][C]0.792760444750985[/C][C]0.396380222375492[/C][/ROW]
[ROW][C]91[/C][C]0.565753687573377[/C][C]0.868492624853245[/C][C]0.434246312426623[/C][/ROW]
[ROW][C]92[/C][C]0.520351198577736[/C][C]0.959297602844528[/C][C]0.479648801422264[/C][/ROW]
[ROW][C]93[/C][C]0.474640573288587[/C][C]0.949281146577175[/C][C]0.525359426711412[/C][/ROW]
[ROW][C]94[/C][C]0.431604147824279[/C][C]0.863208295648559[/C][C]0.568395852175721[/C][/ROW]
[ROW][C]95[/C][C]0.392023723268244[/C][C]0.784047446536489[/C][C]0.607976276731756[/C][/ROW]
[ROW][C]96[/C][C]0.361495725871131[/C][C]0.722991451742262[/C][C]0.638504274128869[/C][/ROW]
[ROW][C]97[/C][C]0.35784303791786[/C][C]0.71568607583572[/C][C]0.64215696208214[/C][/ROW]
[ROW][C]98[/C][C]0.312984061938497[/C][C]0.625968123876995[/C][C]0.687015938061503[/C][/ROW]
[ROW][C]99[/C][C]0.275385195980663[/C][C]0.550770391961326[/C][C]0.724614804019337[/C][/ROW]
[ROW][C]100[/C][C]0.236020205556412[/C][C]0.472040411112823[/C][C]0.763979794443588[/C][/ROW]
[ROW][C]101[/C][C]0.199913357963127[/C][C]0.399826715926254[/C][C]0.800086642036873[/C][/ROW]
[ROW][C]102[/C][C]0.186985597187848[/C][C]0.373971194375697[/C][C]0.813014402812152[/C][/ROW]
[ROW][C]103[/C][C]0.174469330683916[/C][C]0.348938661367831[/C][C]0.825530669316084[/C][/ROW]
[ROW][C]104[/C][C]0.145055339090677[/C][C]0.290110678181353[/C][C]0.854944660909323[/C][/ROW]
[ROW][C]105[/C][C]0.118560795545886[/C][C]0.237121591091772[/C][C]0.881439204454114[/C][/ROW]
[ROW][C]106[/C][C]0.107150429612017[/C][C]0.214300859224033[/C][C]0.892849570387983[/C][/ROW]
[ROW][C]107[/C][C]0.0861382023641282[/C][C]0.172276404728256[/C][C]0.913861797635872[/C][/ROW]
[ROW][C]108[/C][C]0.0698012619144457[/C][C]0.139602523828891[/C][C]0.930198738085554[/C][/ROW]
[ROW][C]109[/C][C]0.068061766801379[/C][C]0.136123533602758[/C][C]0.931938233198621[/C][/ROW]
[ROW][C]110[/C][C]0.0526713469438027[/C][C]0.105342693887605[/C][C]0.947328653056197[/C][/ROW]
[ROW][C]111[/C][C]0.0402976356424007[/C][C]0.0805952712848014[/C][C]0.959702364357599[/C][/ROW]
[ROW][C]112[/C][C]0.030310408857189[/C][C]0.0606208177143779[/C][C]0.969689591142811[/C][/ROW]
[ROW][C]113[/C][C]0.0226214151418665[/C][C]0.0452428302837329[/C][C]0.977378584858134[/C][/ROW]
[ROW][C]114[/C][C]0.0174683411434586[/C][C]0.0349366822869171[/C][C]0.982531658856541[/C][/ROW]
[ROW][C]115[/C][C]0.0126562979025095[/C][C]0.0253125958050191[/C][C]0.98734370209749[/C][/ROW]
[ROW][C]116[/C][C]0.00953678144955465[/C][C]0.0190735628991093[/C][C]0.990463218550445[/C][/ROW]
[ROW][C]117[/C][C]0.00668442429041411[/C][C]0.0133688485808282[/C][C]0.993315575709586[/C][/ROW]
[ROW][C]118[/C][C]0.00456969601330307[/C][C]0.00913939202660614[/C][C]0.995430303986697[/C][/ROW]
[ROW][C]119[/C][C]0.00309137104776662[/C][C]0.00618274209553324[/C][C]0.996908628952233[/C][/ROW]
[ROW][C]120[/C][C]0.00202142632549793[/C][C]0.00404285265099587[/C][C]0.997978573674502[/C][/ROW]
[ROW][C]121[/C][C]0.00133196859156612[/C][C]0.00266393718313225[/C][C]0.998668031408434[/C][/ROW]
[ROW][C]122[/C][C]0.000856167847120617[/C][C]0.00171233569424123[/C][C]0.999143832152879[/C][/ROW]
[ROW][C]123[/C][C]0.00103773944897572[/C][C]0.00207547889795143[/C][C]0.998962260551024[/C][/ROW]
[ROW][C]124[/C][C]0.0274917145590648[/C][C]0.0549834291181297[/C][C]0.972508285440935[/C][/ROW]
[ROW][C]125[/C][C]0.0280912305205138[/C][C]0.0561824610410276[/C][C]0.971908769479486[/C][/ROW]
[ROW][C]126[/C][C]0.0236900870109059[/C][C]0.0473801740218118[/C][C]0.976309912989094[/C][/ROW]
[ROW][C]127[/C][C]0.319655122722207[/C][C]0.639310245444414[/C][C]0.680344877277793[/C][/ROW]
[ROW][C]128[/C][C]0.957970428243409[/C][C]0.084059143513182[/C][C]0.042029571756591[/C][/ROW]
[ROW][C]129[/C][C]0.961733863679321[/C][C]0.0765322726413572[/C][C]0.0382661363206786[/C][/ROW]
[ROW][C]130[/C][C]0.998268013700595[/C][C]0.00346397259880988[/C][C]0.00173198629940494[/C][/ROW]
[ROW][C]131[/C][C]0.997521476630578[/C][C]0.00495704673884327[/C][C]0.00247852336942163[/C][/ROW]
[ROW][C]132[/C][C]0.997747612063292[/C][C]0.00450477587341648[/C][C]0.00225238793670824[/C][/ROW]
[ROW][C]133[/C][C]0.998479372706325[/C][C]0.0030412545873499[/C][C]0.00152062729367495[/C][/ROW]
[ROW][C]134[/C][C]0.999734499404299[/C][C]0.000531001191402908[/C][C]0.000265500595701454[/C][/ROW]
[ROW][C]135[/C][C]0.999532758859556[/C][C]0.000934482280889002[/C][C]0.000467241140444501[/C][/ROW]
[ROW][C]136[/C][C]0.999999999937227[/C][C]1.25545747435188e-10[/C][C]6.27728737175941e-11[/C][/ROW]
[ROW][C]137[/C][C]0.99999999987586[/C][C]2.48280976009983e-10[/C][C]1.24140488004992e-10[/C][/ROW]
[ROW][C]138[/C][C]0.99999999902683[/C][C]1.94634087544683e-09[/C][C]9.73170437723413e-10[/C][/ROW]
[ROW][C]139[/C][C]0.999999991764395[/C][C]1.6471210304821e-08[/C][C]8.23560515241048e-09[/C][/ROW]
[ROW][C]140[/C][C]0.999999929898155[/C][C]1.40203690003848e-07[/C][C]7.0101845001924e-08[/C][/ROW]
[ROW][C]141[/C][C]0.999999460956359[/C][C]1.0780872823732e-06[/C][C]5.390436411866e-07[/C][/ROW]
[ROW][C]142[/C][C]0.999996199090009[/C][C]7.60181998104707e-06[/C][C]3.80090999052353e-06[/C][/ROW]
[ROW][C]143[/C][C]0.999969013510224[/C][C]6.197297955258e-05[/C][C]3.098648977629e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999949771543778[/C][C]0.000100456912444489[/C][C]5.02284562222447e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999804385984403[/C][C]0.000391228031194008[/C][C]0.000195614015597004[/C][/ROW]
[ROW][C]146[/C][C]0.999850225674359[/C][C]0.00029954865128269[/C][C]0.000149774325641345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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
100.0003241670820769450.0006483341641538910.999675832917923
112.18228267352062e-054.36456534704124e-050.999978177173265
121.35215895810369e-062.70431791620738e-060.999998647841042
131.90159205465295e-073.8031841093059e-070.999999809840795
141.00327754604483e-082.00655509208965e-080.999999989967225
151.45236881512174e-082.90473763024347e-080.999999985476312
163.63168147300776e-087.26336294601553e-080.999999963683185
177.89876467533151e-091.5797529350663e-080.999999992101235
182.06710643541581e-094.13421287083162e-090.999999997932894
192.29058635629455e-104.5811727125891e-100.999999999770941
202.41507641564842e-114.83015283129685e-110.999999999975849
219.23882355752122e-121.84776471150424e-110.999999999990761
229.85170117582147e-131.97034023516429e-120.999999999999015
231.01443093491334e-132.02886186982668e-130.999999999999899
242.99913454520019e-145.99826909040038e-140.99999999999997
253.24043668978395e-156.48087337956791e-150.999999999999997
263.23205029881246e-166.46410059762493e-161
273.82621570387844e-177.65243140775688e-171
285.46773565533404e-181.09354713106681e-171
297.0865742235845e-191.4173148447169e-181
301.83532484552691e-193.67064969105383e-191
316.76169049034053e-201.35233809806811e-191
326.76553232457267e-211.35310646491453e-201
339.26902980171902e-221.8538059603438e-211
341.22709515679458e-222.45419031358916e-221
351.29226372908024e-232.58452745816049e-231
361.20480350795778e-242.40960701591556e-241
371.1548735301731e-252.3097470603462e-251
381.0985604631266e-262.19712092625321e-261
391.603549965199e-273.207099930398e-271
401.53618159280871e-283.07236318561742e-281
411.38972727105505e-292.77945454211009e-291
421.51089038389108e-303.02178076778217e-301
430.1478582055274610.2957164110549210.852141794472539
440.7506559214574110.4986881570851780.249344078542589
450.996099607298180.007800785403640540.00390039270182027
460.9999949350069471.01299861054401e-055.06499305272004e-06
470.999995808865148.38226971932794e-064.19113485966397e-06
480.9999947050168441.0589966312559e-055.29498315627951e-06
490.9999952625913979.47481720561002e-064.73740860280501e-06
500.9999925964662021.48070675963345e-057.40353379816723e-06
510.9999928378849391.43242301229753e-057.16211506148767e-06
520.9999903612104171.92775791661849e-059.63878958309244e-06
530.9999862520650652.74958698692384e-051.37479349346192e-05
540.99997915175254.16964950007113e-052.08482475003556e-05
550.9999703942677365.92114645285324e-052.96057322642662e-05
560.9999514938083769.70123832484994e-054.85061916242497e-05
570.9999358257768970.0001283484462061076.41742231030537e-05
580.9998983377734780.0002033244530446470.000101662226522324
590.9998513275765360.0002973448469275350.000148672423463768
600.9998169084321090.0003661831357810810.00018309156789054
610.9997282116851280.0005435766297444410.000271788314872221
620.9995892300836660.0008215398326672720.000410769916333636
630.9993899924156670.001220015168666250.000610007584333127
640.9991352409419130.001729518116174010.000864759058087004
650.9987253157127120.002549368574576180.00127468428728809
660.9981551723497040.003689655300592170.00184482765029608
670.9975958829379850.004808234124030180.00240411706201509
680.996542361630930.006915276738140450.00345763836907022
690.995248455843960.009503088312080390.0047515441560402
700.9934482873997890.01310342520042240.00655171260021118
710.9910322465221840.01793550695563240.0089677534778162
720.9880899848070330.02382003038593440.0119100151929672
730.9838881942421230.03222361151575360.0161118057578768
740.9784366677043060.04312666459138890.0215633322956944
750.971929432484460.05614113503108070.0280705675155403
760.9634849345211670.07303013095766640.0365150654788332
770.9529128728334330.09417425433313310.0470871271665666
780.9427472049396310.1145055901207380.0572527950603691
790.9310198326610630.1379603346778730.0689801673389366
800.9146577953511110.1706844092977770.0853422046488887
810.8962035369093960.2075929261812080.103796463090604
820.8821138530375190.2357722939249610.117886146962481
830.8596109647247150.280778070550570.140389035275285
840.8311216894711020.3377566210577960.168878310528898
850.8013438141615390.3973123716769220.198656185838461
860.7661517142714750.4676965714570510.233848285728526
870.7326260245632130.5347479508735740.267373975436787
880.6916056815164020.6167886369671970.308394318483598
890.6481092442890560.7037815114218880.351890755710944
900.6036197776245080.7927604447509850.396380222375492
910.5657536875733770.8684926248532450.434246312426623
920.5203511985777360.9592976028445280.479648801422264
930.4746405732885870.9492811465771750.525359426711412
940.4316041478242790.8632082956485590.568395852175721
950.3920237232682440.7840474465364890.607976276731756
960.3614957258711310.7229914517422620.638504274128869
970.357843037917860.715686075835720.64215696208214
980.3129840619384970.6259681238769950.687015938061503
990.2753851959806630.5507703919613260.724614804019337
1000.2360202055564120.4720404111128230.763979794443588
1010.1999133579631270.3998267159262540.800086642036873
1020.1869855971878480.3739711943756970.813014402812152
1030.1744693306839160.3489386613678310.825530669316084
1040.1450553390906770.2901106781813530.854944660909323
1050.1185607955458860.2371215910917720.881439204454114
1060.1071504296120170.2143008592240330.892849570387983
1070.08613820236412820.1722764047282560.913861797635872
1080.06980126191444570.1396025238288910.930198738085554
1090.0680617668013790.1361235336027580.931938233198621
1100.05267134694380270.1053426938876050.947328653056197
1110.04029763564240070.08059527128480140.959702364357599
1120.0303104088571890.06062081771437790.969689591142811
1130.02262141514186650.04524283028373290.977378584858134
1140.01746834114345860.03493668228691710.982531658856541
1150.01265629790250950.02531259580501910.98734370209749
1160.009536781449554650.01907356289910930.990463218550445
1170.006684424290414110.01336884858082820.993315575709586
1180.004569696013303070.009139392026606140.995430303986697
1190.003091371047766620.006182742095533240.996908628952233
1200.002021426325497930.004042852650995870.997978573674502
1210.001331968591566120.002663937183132250.998668031408434
1220.0008561678471206170.001712335694241230.999143832152879
1230.001037739448975720.002075478897951430.998962260551024
1240.02749171455906480.05498342911812970.972508285440935
1250.02809123052051380.05618246104102760.971908769479486
1260.02369008701090590.04738017402181180.976309912989094
1270.3196551227222070.6393102454444140.680344877277793
1280.9579704282434090.0840591435131820.042029571756591
1290.9617338636793210.07653227264135720.0382661363206786
1300.9982680137005950.003463972598809880.00173198629940494
1310.9975214766305780.004957046738843270.00247852336942163
1320.9977476120632920.004504775873416480.00225238793670824
1330.9984793727063250.00304125458734990.00152062729367495
1340.9997344994042990.0005310011914029080.000265500595701454
1350.9995327588595560.0009344822808890020.000467241140444501
1360.9999999999372271.25545747435188e-106.27728737175941e-11
1370.999999999875862.48280976009983e-101.24140488004992e-10
1380.999999999026831.94634087544683e-099.73170437723413e-10
1390.9999999917643951.6471210304821e-088.23560515241048e-09
1400.9999999298981551.40203690003848e-077.0101845001924e-08
1410.9999994609563591.0780872823732e-065.390436411866e-07
1420.9999961990900097.60181998104707e-063.80090999052353e-06
1430.9999690135102246.197297955258e-053.098648977629e-05
1440.9999497715437780.0001004569124444895.02284562222447e-05
1450.9998043859844030.0003912280311940080.000195614015597004
1460.9998502256743590.000299548651282690.000149774325641345






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level810.591240875912409NOK
5% type I error level920.671532846715328NOK
10% type I error level1010.737226277372263NOK

\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 & 81 & 0.591240875912409 & NOK \tabularnewline
5% type I error level & 92 & 0.671532846715328 & NOK \tabularnewline
10% type I error level & 101 & 0.737226277372263 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147165&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]81[/C][C]0.591240875912409[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]92[/C][C]0.671532846715328[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.737226277372263[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147165&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147165&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 level810.591240875912409NOK
5% type I error level920.671532846715328NOK
10% type I error level1010.737226277372263NOK


Figures (Output of Computation)

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