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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 computationWed, 29 Dec 2010 11:10:13 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293620878w3v19bc33za40be.htm/, Retrieved Thu, 02 May 2024 12:47:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116705, Retrieved Thu, 02 May 2024 12:47:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-25 16:04:54] [1253bc7c4737195066123d9caa6dfc18]
- R  D  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2010-12-27 21:10:05] [1253bc7c4737195066123d9caa6dfc18]
- RMPD    [Multiple Regression] [] [2010-12-29 10:35:59] [b98453cac15ba1066b407e146608df68]
-             [Multiple Regression] [Interactie effecten] [2010-12-29 11:10:13] [2980b4453f2452156691660add27a53b] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
13	13	14	182	13	169	3	39	2	26
12	12	8	96	13	156	5	60	1	12
15	10	12	120	16	160	6	60	0	0
12	9	7	63	12	108	6	54	3	27
10	10	10	100	11	110	5	50	3	30
12	12	7	84	12	144	3	36	1	12
15	13	16	208	18	234	8	104	3	39
9	12	11	132	11	132	4	48	1	12
12	12	14	168	14	168	4	48	4	48
11	6	6	36	9	54	4	24	0	0
11	5	16	80	14	70	6	30	3	15
11	12	11	132	12	144	6	72	2	24
15	11	16	176	11	121	5	55	4	44
7	14	12	168	12	168	4	56	3	42
11	14	7	98	13	182	6	84	1	14
11	12	13	156	11	132	4	48	1	12
10	12	11	132	12	144	6	72	2	24
14	11	15	165	16	176	6	66	3	33
10	11	7	77	9	99	4	44	1	11
6	7	9	63	11	77	4	28	1	7
11	9	7	63	13	117	2	18	2	18
15	11	14	154	15	165	7	77	3	33
11	11	15	165	10	110	5	55	4	44
12	12	7	84	11	132	4	48	2	24
14	12	15	180	13	156	6	72	1	12
15	11	17	187	16	176	6	66	2	22
9	11	15	165	15	165	7	77	2	22
13	8	14	112	14	112	5	40	4	32
13	9	14	126	14	126	6	54	2	18
16	12	8	96	14	168	4	48	3	36
13	10	8	80	8	80	4	40	3	30
12	10	14	140	13	130	7	70	3	30
14	12	14	168	15	180	7	84	4	48
11	8	8	64	13	104	4	32	2	16
9	12	11	132	11	132	4	48	2	24
16	11	16	176	15	165	6	66	4	44
12	12	10	120	15	180	6	72	3	36
10	7	8	56	9	63	5	35	4	28
13	11	14	154	13	143	6	66	2	22
16	11	16	176	16	176	7	77	5	55
14	12	13	156	13	156	6	72	3	36
15	9	5	45	11	99	3	27	1	9
5	15	8	120	12	180	3	45	1	15
8	11	10	110	12	132	4	44	1	11
11	11	8	88	12	132	6	66	2	22
16	11	13	143	14	154	7	77	3	33
17	11	15	165	14	154	5	55	9	99
9	15	6	90	8	120	4	60	0	0
9	11	12	132	13	143	5	55	0	0
13	12	16	192	16	192	6	72	2	24
10	12	5	60	13	156	6	72	2	24
6	9	15	135	11	99	6	54	3	27
12	12	12	144	14	168	5	60	1	12
8	12	8	96	13	156	4	48	2	24
14	13	13	169	13	169	5	65	0	0
12	11	14	154	13	143	5	55	5	55
11	9	12	108	12	108	4	36	2	18
16	9	16	144	16	144	6	54	4	36
8	11	10	110	15	165	2	22	3	33
15	11	15	165	15	165	8	88	0	0
7	12	8	96	12	144	3	36	0	0
16	12	16	192	14	168	6	72	4	48
14	9	19	171	12	108	6	54	1	9
16	11	14	154	15	165	6	66	1	11
9	9	6	54	12	108	5	45	4	36
14	12	13	156	13	156	5	60	2	24
11	12	15	180	12	144	6	72	4	48
13	12	7	84	12	144	5	60	1	12
15	12	13	156	13	156	6	72	4	48
5	14	4	56	5	70	2	28	2	28
15	11	14	154	13	143	5	55	5	55
13	12	13	156	13	156	5	60	4	48
11	11	11	121	14	154	5	55	4	44
11	6	14	84	17	102	6	36	4	24
12	10	12	120	13	130	6	60	4	40
12	12	15	180	13	156	6	72	3	36
12	13	14	182	12	156	5	65	3	39
12	8	13	104	13	104	5	40	3	24
14	12	8	96	14	168	4	48	2	24
6	12	6	72	11	132	2	24	1	12
7	12	7	84	12	144	4	48	1	12
14	6	13	78	12	72	6	36	5	30
14	11	13	143	16	176	6	66	4	44
10	10	11	110	12	120	5	50	2	20
13	12	5	60	12	144	3	36	3	36
12	13	12	156	12	156	6	78	2	26
9	11	8	88	10	110	4	44	2	22
12	7	11	77	15	105	5	35	2	14
16	11	14	154	15	165	8	88	2	22
10	11	9	99	12	132	4	44	3	33
14	11	10	110	16	176	6	66	2	22
10	11	13	143	15	165	6	66	3	33
16	12	16	192	16	192	7	84	4	48
15	10	16	160	13	130	6	60	3	30
12	11	11	121	12	132	5	55	3	33
10	12	8	96	11	132	4	48	0	0
8	7	4	28	13	91	6	42	1	7
8	13	7	91	10	130	3	39	2	26
11	8	14	112	15	120	5	40	2	16
13	12	11	132	13	156	6	72	3	36
16	11	17	187	16	176	7	77	4	44
16	12	15	180	15	180	7	84	4	48
14	14	17	238	18	252	6	84	1	14
11	10	5	50	13	130	3	30	2	20
4	10	4	40	10	100	2	20	2	20
14	13	10	130	16	208	8	104	3	39
9	10	11	110	13	130	3	30	3	30
14	11	15	165	15	165	8	88	3	33
8	10	10	100	14	140	3	30	1	10
8	7	9	63	15	105	4	28	1	7
11	10	12	120	14	140	5	50	1	10
12	8	15	120	13	104	7	56	1	8
11	12	7	84	13	156	6	72	0	0
14	12	13	156	15	180	6	72	1	12
15	12	12	144	16	192	7	84	3	36
16	11	14	154	14	154	6	66	3	33
16	12	14	168	14	168	6	72	0	0
11	12	8	96	16	192	6	72	2	24
14	12	15	180	14	168	6	72	5	60
14	11	12	132	12	132	4	44	2	22
12	12	12	144	13	156	4	48	3	36
14	11	16	176	12	132	5	55	3	33
8	11	9	99	12	132	4	44	5	55
13	13	15	195	14	182	6	78	4	52
16	12	15	180	14	168	6	72	4	48
12	12	6	72	14	168	5	60	0	0
16	12	14	168	16	192	8	96	3	36
12	12	15	180	13	156	6	72	0	0
11	8	10	80	14	112	5	40	2	16
4	8	6	48	4	32	4	32	0	0
16	12	14	168	16	192	8	96	6	72
15	11	12	132	13	143	6	66	3	33
10	12	8	96	16	192	4	48	1	12
13	13	11	143	15	195	6	78	6	78
15	12	13	156	14	168	6	72	2	24
12	12	9	108	13	156	4	48	1	12
14	11	15	165	14	154	6	66	3	33
7	12	13	156	12	144	3	36	1	12
19	12	15	180	15	180	6	72	2	24
12	10	14	140	14	140	5	50	4	40
12	11	16	176	13	143	4	44	1	11
13	12	14	168	14	168	6	72	2	24
15	12	14	168	16	192	4	48	0	0
8	10	10	100	6	60	4	40	5	50
12	12	10	120	13	156	4	48	2	24
10	13	4	52	13	169	6	78	1	13
8	12	8	96	14	168	5	60	1	12
10	15	15	225	15	225	6	90	4	60
15	11	16	176	14	154	6	66	3	33
16	12	12	144	15	180	8	96	0	0
13	11	12	132	13	143	7	77	3	33
16	12	15	180	16	192	7	84	3	36
9	11	9	99	12	132	4	44	0	0
14	10	12	120	15	150	6	60	2	20
14	11	14	154	12	132	6	66	5	55
12	11	11	121	14	154	2	22	2	22

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 7.13071247390499 -0.501068230469585FindingFriends[t] + 0.0144399681786234KnowingPeople[t] + 0.0195920449423157friends_knowning[t] + 0.453370355865953Liked[t] -0.0119296940515746friends_liked[t] -1.04257015127670Celebrity[t] + 0.146732923963822friends_celeb[t] + 1.14069077304853Sum[t] -0.0845908632946572friends_sum[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  7.13071247390499 -0.501068230469585FindingFriends[t] +  0.0144399681786234KnowingPeople[t] +  0.0195920449423157friends_knowning[t] +  0.453370355865953Liked[t] -0.0119296940515746friends_liked[t] -1.04257015127670Celebrity[t] +  0.146732923963822friends_celeb[t] +  1.14069077304853Sum[t] -0.0845908632946572friends_sum[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  7.13071247390499 -0.501068230469585FindingFriends[t] +  0.0144399681786234KnowingPeople[t] +  0.0195920449423157friends_knowning[t] +  0.453370355865953Liked[t] -0.0119296940515746friends_liked[t] -1.04257015127670Celebrity[t] +  0.146732923963822friends_celeb[t] +  1.14069077304853Sum[t] -0.0845908632946572friends_sum[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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
Popularity[t] = + 7.13071247390499 -0.501068230469585FindingFriends[t] + 0.0144399681786234KnowingPeople[t] + 0.0195920449423157friends_knowning[t] + 0.453370355865953Liked[t] -0.0119296940515746friends_liked[t] -1.04257015127670Celebrity[t] + 0.146732923963822friends_celeb[t] + 1.14069077304853Sum[t] -0.0845908632946572friends_sum[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.130712473904995.6180271.26930.206370.103185
FindingFriends-0.5010682304695850.493294-1.01580.3114250.155712
KnowingPeople0.01443996817862340.399690.03610.971230.485615
friends_knowning0.01959204494231570.0357770.54760.5847940.292397
Liked0.4533703558659530.5020720.9030.3680140.184007
friends_liked-0.01192969405157460.047077-0.25340.8003080.400154
Celebrity-1.042570151276701.209949-0.86170.3902850.195143
friends_celeb0.1467329239638220.1071871.36890.1731210.086561
Sum1.140690773048530.8414241.35570.1772980.088649
friends_sum-0.08459086329465720.075323-1.1230.2632630.131631

\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) & 7.13071247390499 & 5.618027 & 1.2693 & 0.20637 & 0.103185 \tabularnewline
FindingFriends & -0.501068230469585 & 0.493294 & -1.0158 & 0.311425 & 0.155712 \tabularnewline
KnowingPeople & 0.0144399681786234 & 0.39969 & 0.0361 & 0.97123 & 0.485615 \tabularnewline
friends_knowning & 0.0195920449423157 & 0.035777 & 0.5476 & 0.584794 & 0.292397 \tabularnewline
Liked & 0.453370355865953 & 0.502072 & 0.903 & 0.368014 & 0.184007 \tabularnewline
friends_liked & -0.0119296940515746 & 0.047077 & -0.2534 & 0.800308 & 0.400154 \tabularnewline
Celebrity & -1.04257015127670 & 1.209949 & -0.8617 & 0.390285 & 0.195143 \tabularnewline
friends_celeb & 0.146732923963822 & 0.107187 & 1.3689 & 0.173121 & 0.086561 \tabularnewline
Sum & 1.14069077304853 & 0.841424 & 1.3557 & 0.177298 & 0.088649 \tabularnewline
friends_sum & -0.0845908632946572 & 0.075323 & -1.123 & 0.263263 & 0.131631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&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]7.13071247390499[/C][C]5.618027[/C][C]1.2693[/C][C]0.20637[/C][C]0.103185[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.501068230469585[/C][C]0.493294[/C][C]-1.0158[/C][C]0.311425[/C][C]0.155712[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.0144399681786234[/C][C]0.39969[/C][C]0.0361[/C][C]0.97123[/C][C]0.485615[/C][/ROW]
[ROW][C]friends_knowning[/C][C]0.0195920449423157[/C][C]0.035777[/C][C]0.5476[/C][C]0.584794[/C][C]0.292397[/C][/ROW]
[ROW][C]Liked[/C][C]0.453370355865953[/C][C]0.502072[/C][C]0.903[/C][C]0.368014[/C][C]0.184007[/C][/ROW]
[ROW][C]friends_liked[/C][C]-0.0119296940515746[/C][C]0.047077[/C][C]-0.2534[/C][C]0.800308[/C][C]0.400154[/C][/ROW]
[ROW][C]Celebrity[/C][C]-1.04257015127670[/C][C]1.209949[/C][C]-0.8617[/C][C]0.390285[/C][C]0.195143[/C][/ROW]
[ROW][C]friends_celeb[/C][C]0.146732923963822[/C][C]0.107187[/C][C]1.3689[/C][C]0.173121[/C][C]0.086561[/C][/ROW]
[ROW][C]Sum[/C][C]1.14069077304853[/C][C]0.841424[/C][C]1.3557[/C][C]0.177298[/C][C]0.088649[/C][/ROW]
[ROW][C]friends_sum[/C][C]-0.0845908632946572[/C][C]0.075323[/C][C]-1.123[/C][C]0.263263[/C][C]0.131631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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)7.130712473904995.6180271.26930.206370.103185
FindingFriends-0.5010682304695850.493294-1.01580.3114250.155712
KnowingPeople0.01443996817862340.399690.03610.971230.485615
friends_knowning0.01959204494231570.0357770.54760.5847940.292397
Liked0.4533703558659530.5020720.9030.3680140.184007
friends_liked-0.01192969405157460.047077-0.25340.8003080.400154
Celebrity-1.042570151276701.209949-0.86170.3902850.195143
friends_celeb0.1467329239638220.1071871.36890.1731210.086561
Sum1.140690773048530.8414241.35570.1772980.088649
friends_sum-0.08459086329465720.075323-1.1230.2632630.131631






Multiple Linear Regression - Regression Statistics
Multiple R0.722001202058945
R-squared0.521285735774561
Adjusted R-squared0.491775952363404
F-TEST (value)17.6648445199185
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09351674679716
Sum Squared Residuals639.890605891545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.722001202058945 \tabularnewline
R-squared & 0.521285735774561 \tabularnewline
Adjusted R-squared & 0.491775952363404 \tabularnewline
F-TEST (value) & 17.6648445199185 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09351674679716 \tabularnewline
Sum Squared Residuals & 639.890605891545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.722001202058945[/C][/ROW]
[ROW][C]R-squared[/C][C]0.521285735774561[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.491775952363404[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.6648445199185[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.09351674679716[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]639.890605891545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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 R0.722001202058945
R-squared0.521285735774561
Adjusted R-squared0.491775952363404
F-TEST (value)17.6648445199185
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09351674679716
Sum Squared Residuals639.890605891545






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11310.93932622453882.06067377546121
21210.86375721733151.13624278266855
31512.53808435620292.46191564379707
41210.91479031769241.08520968230764
51010.9065837761927-0.906583776192673
6128.867548810019663.13245118998034
71517.3348198468565-2.33481984685653
8910.2737377490074-1.27373774900741
91212.3298145937458-0.329814593745768
10117.703695812116043.29630418788396
111112.2356569872936-1.23565698729358
121112.1460020623454-1.14600206234543
131512.54000744258592.45999255741406
14710.9327752831474-3.93277528314742
151111.8860311300231-0.886031130023053
161110.77282676398020.227173236019762
171012.1460020623454-2.14600206234543
181414.2820743220530-0.282074322053038
19108.624081994344571.37591800565543
2069.54277687401176-3.54277687401176
21119.769315766106761.23068423389324
221514.30147015053560.698529849464355
231111.9879112587432-0.987911258743211
24129.40116013257442.59883986742559
251413.32879370602550.671206293974508
261514.53178797033390.468212029666071
27914.3212313362724-5.32123133627244
281313.0420161354698-0.04201613546983
291312.56280214171560.437197858284439
301610.70694713531465.29305286468536
31139.058883606576973.94111639342303
321213.2656904540232-1.26569045402323
331414.7947034298603-0.794703429860286
341110.59780439189720.402195608102839
35910.3993381625201-1.39933816252006
361614.40007434010581.59992565989420
371212.9527000501128-0.952700050112836
381010.2816921026549-0.281692102654904
391312.87549941880570.124500581194257
401615.50390135053710.496098649462928
411413.08090551807800.919094481922042
42158.594425962985346.40557403701466
4358.72145244464664-3.72145244464664
44810.2803705458728-2.28037054587275
451111.1736409222425-0.173640922242532
461613.74937396669292.25062603330708
471714.32744252797422.67255747202575
4898.293707264202540.706292735797464
49911.4237199277776-2.42371992777761
501314.6345807087657-1.63458070876572
511010.9589490446740-0.958949044674015
52612.0949341895663-6.0949341895663
531212.1721492745242-0.172149274524156
54810.2711326945549-2.27113269455494
551412.31808629218001.68191370781998
561212.9345812369023-0.934581236902313
571110.93320684879770.0667931512023159
581614.39509111263131.60490888736871
59810.5242002387326-2.52420023873258
601514.47234079498320.52765920501683
6178.99149290399343-1.99149290399343
621614.26535348129691.73464651870310
631412.44526478281271.55473521718729
641613.30959558459572.69040441540429
65910.8253687840318-1.82536878403182
661412.23708016827621.76291983172384
671113.3953809193164-2.39538091931637
681310.30399868259802.69600131740202
691513.20650593159061.79349406840940
7054.638663774814780.361336225185218
711512.93458123690232.06541876309769
721312.48828099530140.511719004698555
731112.3566762937614-1.35667629376135
741114.0222084053630-3.02220840536303
751212.7149926705601-0.714992670560117
761213.5799945330508-1.57999453305078
771212.4119671620672-0.411967162067232
781212.0489431376081-0.0489431376080512
791410.5813467218023.41865327819800
8067.58956533899704-1.58956533899704
8179.58577374630882-2.58577374630882
821412.61440080302861.38559919697143
831414.0323606737721-0.0323606737721461
841011.1562354690427-1.15623546904270
85138.619660622072134.38033937792787
861212.6976423672448-0.697642367244768
8799.3863694549946-0.386369454994597
881211.85851047603710.141489523962873
891614.66277088605371.33722911394632
901010.4708006369433-0.470800636943259
911412.92212073252531.07787926747475
921013.5000256756662-3.50002567566621
931615.60400647208020.395993527919832
941513.26165220086531.73834779913472
951211.50219757435680.497802425643212
96109.399503813035530.600496186964467
9789.49370108808755-1.49370108808755
9888.16051735795122-0.160517357951225
991112.4720212055406-1.47202120554064
1001312.58181650310510.418183496894866
1011615.52366253627390.476337463726132
1021615.04424793734670.955752062653301
1031416.2050902994530-2.20509029945303
104119.37862820205921.62137179794081
10547.74128845004528-3.74128845004528
1061415.1234318658932-1.12343186589319
107910.9355728477718-1.93557284777184
1081415.1029146254051-1.10291462540508
109810.4697215653163-2.46972156531635
110811.0222268640315-3.02222686403148
1111111.7399605772429-0.739960577242933
1121211.72595454052750.274045459472457
1131111.2068372326216-0.20683723262155
1141413.45013274554680.549867254453217
1151514.48022802862190.519771971378125
1161613.40783441691172.59216558308832
1171613.26386281227352.73613718772651
1181112.6382246488744-1.63822464887442
1191414.1414093873231-0.141409387323128
1201410.95046674776823.04953325223175
1211211.39491113801320.60508886198677
1221412.65195988707731.34804011292273
123810.8911831905579-2.89118319055786
1241314.1836397913579-1.18363979135789
1251614.01580897381051.98419102618952
1261210.54928181609301.45071818390696
1271615.69754197988390.302458020116144
1281213.2031932925128-1.20319329251285
1291111.4293830912187-0.429383091218686
13046.10612877200011-2.10612877200011
1311616.0743432204218-0.0743432204217903
1321512.62578577052492.37421422947515
1331011.0761743627835-1.07617436278347
1341313.4873970152744-0.487397015274438
1351513.26551913181241.73448086818763
1361210.39507678852871.60492321147130
1371413.63778687945580.362213120544226
138710.3648158549381-3.36481585493814
1391914.07482217403234.92517782596775
1401213.0450278327524-1.04502783275237
1411211.98222904243600.017770957564037
1421313.5150636392988-0.515063639298782
1431512.44784099418932.55215900581071
14489.40102189128404-1.40102189128404
1451210.77022170952781.22977829047224
1461010.8018247307924-0.80182473079244
147811.1739712445785-3.17397124457851
1481014.7937263715450-4.79372637154504
1491513.86773934199991.13226065800013
1501614.51143769712601.48856230287396
1511313.1972777828502-0.197277782850188
1521615.22886155108110.771138448918888
15399.84022680652135-0.840226806521354
1541412.79357522105661.20642477894339
1551413.18392952792900.816070472070984
1561210.22181770317071.77818229682926

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 10.9393262245388 & 2.06067377546121 \tabularnewline
2 & 12 & 10.8637572173315 & 1.13624278266855 \tabularnewline
3 & 15 & 12.5380843562029 & 2.46191564379707 \tabularnewline
4 & 12 & 10.9147903176924 & 1.08520968230764 \tabularnewline
5 & 10 & 10.9065837761927 & -0.906583776192673 \tabularnewline
6 & 12 & 8.86754881001966 & 3.13245118998034 \tabularnewline
7 & 15 & 17.3348198468565 & -2.33481984685653 \tabularnewline
8 & 9 & 10.2737377490074 & -1.27373774900741 \tabularnewline
9 & 12 & 12.3298145937458 & -0.329814593745768 \tabularnewline
10 & 11 & 7.70369581211604 & 3.29630418788396 \tabularnewline
11 & 11 & 12.2356569872936 & -1.23565698729358 \tabularnewline
12 & 11 & 12.1460020623454 & -1.14600206234543 \tabularnewline
13 & 15 & 12.5400074425859 & 2.45999255741406 \tabularnewline
14 & 7 & 10.9327752831474 & -3.93277528314742 \tabularnewline
15 & 11 & 11.8860311300231 & -0.886031130023053 \tabularnewline
16 & 11 & 10.7728267639802 & 0.227173236019762 \tabularnewline
17 & 10 & 12.1460020623454 & -2.14600206234543 \tabularnewline
18 & 14 & 14.2820743220530 & -0.282074322053038 \tabularnewline
19 & 10 & 8.62408199434457 & 1.37591800565543 \tabularnewline
20 & 6 & 9.54277687401176 & -3.54277687401176 \tabularnewline
21 & 11 & 9.76931576610676 & 1.23068423389324 \tabularnewline
22 & 15 & 14.3014701505356 & 0.698529849464355 \tabularnewline
23 & 11 & 11.9879112587432 & -0.987911258743211 \tabularnewline
24 & 12 & 9.4011601325744 & 2.59883986742559 \tabularnewline
25 & 14 & 13.3287937060255 & 0.671206293974508 \tabularnewline
26 & 15 & 14.5317879703339 & 0.468212029666071 \tabularnewline
27 & 9 & 14.3212313362724 & -5.32123133627244 \tabularnewline
28 & 13 & 13.0420161354698 & -0.04201613546983 \tabularnewline
29 & 13 & 12.5628021417156 & 0.437197858284439 \tabularnewline
30 & 16 & 10.7069471353146 & 5.29305286468536 \tabularnewline
31 & 13 & 9.05888360657697 & 3.94111639342303 \tabularnewline
32 & 12 & 13.2656904540232 & -1.26569045402323 \tabularnewline
33 & 14 & 14.7947034298603 & -0.794703429860286 \tabularnewline
34 & 11 & 10.5978043918972 & 0.402195608102839 \tabularnewline
35 & 9 & 10.3993381625201 & -1.39933816252006 \tabularnewline
36 & 16 & 14.4000743401058 & 1.59992565989420 \tabularnewline
37 & 12 & 12.9527000501128 & -0.952700050112836 \tabularnewline
38 & 10 & 10.2816921026549 & -0.281692102654904 \tabularnewline
39 & 13 & 12.8754994188057 & 0.124500581194257 \tabularnewline
40 & 16 & 15.5039013505371 & 0.496098649462928 \tabularnewline
41 & 14 & 13.0809055180780 & 0.919094481922042 \tabularnewline
42 & 15 & 8.59442596298534 & 6.40557403701466 \tabularnewline
43 & 5 & 8.72145244464664 & -3.72145244464664 \tabularnewline
44 & 8 & 10.2803705458728 & -2.28037054587275 \tabularnewline
45 & 11 & 11.1736409222425 & -0.173640922242532 \tabularnewline
46 & 16 & 13.7493739666929 & 2.25062603330708 \tabularnewline
47 & 17 & 14.3274425279742 & 2.67255747202575 \tabularnewline
48 & 9 & 8.29370726420254 & 0.706292735797464 \tabularnewline
49 & 9 & 11.4237199277776 & -2.42371992777761 \tabularnewline
50 & 13 & 14.6345807087657 & -1.63458070876572 \tabularnewline
51 & 10 & 10.9589490446740 & -0.958949044674015 \tabularnewline
52 & 6 & 12.0949341895663 & -6.0949341895663 \tabularnewline
53 & 12 & 12.1721492745242 & -0.172149274524156 \tabularnewline
54 & 8 & 10.2711326945549 & -2.27113269455494 \tabularnewline
55 & 14 & 12.3180862921800 & 1.68191370781998 \tabularnewline
56 & 12 & 12.9345812369023 & -0.934581236902313 \tabularnewline
57 & 11 & 10.9332068487977 & 0.0667931512023159 \tabularnewline
58 & 16 & 14.3950911126313 & 1.60490888736871 \tabularnewline
59 & 8 & 10.5242002387326 & -2.52420023873258 \tabularnewline
60 & 15 & 14.4723407949832 & 0.52765920501683 \tabularnewline
61 & 7 & 8.99149290399343 & -1.99149290399343 \tabularnewline
62 & 16 & 14.2653534812969 & 1.73464651870310 \tabularnewline
63 & 14 & 12.4452647828127 & 1.55473521718729 \tabularnewline
64 & 16 & 13.3095955845957 & 2.69040441540429 \tabularnewline
65 & 9 & 10.8253687840318 & -1.82536878403182 \tabularnewline
66 & 14 & 12.2370801682762 & 1.76291983172384 \tabularnewline
67 & 11 & 13.3953809193164 & -2.39538091931637 \tabularnewline
68 & 13 & 10.3039986825980 & 2.69600131740202 \tabularnewline
69 & 15 & 13.2065059315906 & 1.79349406840940 \tabularnewline
70 & 5 & 4.63866377481478 & 0.361336225185218 \tabularnewline
71 & 15 & 12.9345812369023 & 2.06541876309769 \tabularnewline
72 & 13 & 12.4882809953014 & 0.511719004698555 \tabularnewline
73 & 11 & 12.3566762937614 & -1.35667629376135 \tabularnewline
74 & 11 & 14.0222084053630 & -3.02220840536303 \tabularnewline
75 & 12 & 12.7149926705601 & -0.714992670560117 \tabularnewline
76 & 12 & 13.5799945330508 & -1.57999453305078 \tabularnewline
77 & 12 & 12.4119671620672 & -0.411967162067232 \tabularnewline
78 & 12 & 12.0489431376081 & -0.0489431376080512 \tabularnewline
79 & 14 & 10.581346721802 & 3.41865327819800 \tabularnewline
80 & 6 & 7.58956533899704 & -1.58956533899704 \tabularnewline
81 & 7 & 9.58577374630882 & -2.58577374630882 \tabularnewline
82 & 14 & 12.6144008030286 & 1.38559919697143 \tabularnewline
83 & 14 & 14.0323606737721 & -0.0323606737721461 \tabularnewline
84 & 10 & 11.1562354690427 & -1.15623546904270 \tabularnewline
85 & 13 & 8.61966062207213 & 4.38033937792787 \tabularnewline
86 & 12 & 12.6976423672448 & -0.697642367244768 \tabularnewline
87 & 9 & 9.3863694549946 & -0.386369454994597 \tabularnewline
88 & 12 & 11.8585104760371 & 0.141489523962873 \tabularnewline
89 & 16 & 14.6627708860537 & 1.33722911394632 \tabularnewline
90 & 10 & 10.4708006369433 & -0.470800636943259 \tabularnewline
91 & 14 & 12.9221207325253 & 1.07787926747475 \tabularnewline
92 & 10 & 13.5000256756662 & -3.50002567566621 \tabularnewline
93 & 16 & 15.6040064720802 & 0.395993527919832 \tabularnewline
94 & 15 & 13.2616522008653 & 1.73834779913472 \tabularnewline
95 & 12 & 11.5021975743568 & 0.497802425643212 \tabularnewline
96 & 10 & 9.39950381303553 & 0.600496186964467 \tabularnewline
97 & 8 & 9.49370108808755 & -1.49370108808755 \tabularnewline
98 & 8 & 8.16051735795122 & -0.160517357951225 \tabularnewline
99 & 11 & 12.4720212055406 & -1.47202120554064 \tabularnewline
100 & 13 & 12.5818165031051 & 0.418183496894866 \tabularnewline
101 & 16 & 15.5236625362739 & 0.476337463726132 \tabularnewline
102 & 16 & 15.0442479373467 & 0.955752062653301 \tabularnewline
103 & 14 & 16.2050902994530 & -2.20509029945303 \tabularnewline
104 & 11 & 9.3786282020592 & 1.62137179794081 \tabularnewline
105 & 4 & 7.74128845004528 & -3.74128845004528 \tabularnewline
106 & 14 & 15.1234318658932 & -1.12343186589319 \tabularnewline
107 & 9 & 10.9355728477718 & -1.93557284777184 \tabularnewline
108 & 14 & 15.1029146254051 & -1.10291462540508 \tabularnewline
109 & 8 & 10.4697215653163 & -2.46972156531635 \tabularnewline
110 & 8 & 11.0222268640315 & -3.02222686403148 \tabularnewline
111 & 11 & 11.7399605772429 & -0.739960577242933 \tabularnewline
112 & 12 & 11.7259545405275 & 0.274045459472457 \tabularnewline
113 & 11 & 11.2068372326216 & -0.20683723262155 \tabularnewline
114 & 14 & 13.4501327455468 & 0.549867254453217 \tabularnewline
115 & 15 & 14.4802280286219 & 0.519771971378125 \tabularnewline
116 & 16 & 13.4078344169117 & 2.59216558308832 \tabularnewline
117 & 16 & 13.2638628122735 & 2.73613718772651 \tabularnewline
118 & 11 & 12.6382246488744 & -1.63822464887442 \tabularnewline
119 & 14 & 14.1414093873231 & -0.141409387323128 \tabularnewline
120 & 14 & 10.9504667477682 & 3.04953325223175 \tabularnewline
121 & 12 & 11.3949111380132 & 0.60508886198677 \tabularnewline
122 & 14 & 12.6519598870773 & 1.34804011292273 \tabularnewline
123 & 8 & 10.8911831905579 & -2.89118319055786 \tabularnewline
124 & 13 & 14.1836397913579 & -1.18363979135789 \tabularnewline
125 & 16 & 14.0158089738105 & 1.98419102618952 \tabularnewline
126 & 12 & 10.5492818160930 & 1.45071818390696 \tabularnewline
127 & 16 & 15.6975419798839 & 0.302458020116144 \tabularnewline
128 & 12 & 13.2031932925128 & -1.20319329251285 \tabularnewline
129 & 11 & 11.4293830912187 & -0.429383091218686 \tabularnewline
130 & 4 & 6.10612877200011 & -2.10612877200011 \tabularnewline
131 & 16 & 16.0743432204218 & -0.0743432204217903 \tabularnewline
132 & 15 & 12.6257857705249 & 2.37421422947515 \tabularnewline
133 & 10 & 11.0761743627835 & -1.07617436278347 \tabularnewline
134 & 13 & 13.4873970152744 & -0.487397015274438 \tabularnewline
135 & 15 & 13.2655191318124 & 1.73448086818763 \tabularnewline
136 & 12 & 10.3950767885287 & 1.60492321147130 \tabularnewline
137 & 14 & 13.6377868794558 & 0.362213120544226 \tabularnewline
138 & 7 & 10.3648158549381 & -3.36481585493814 \tabularnewline
139 & 19 & 14.0748221740323 & 4.92517782596775 \tabularnewline
140 & 12 & 13.0450278327524 & -1.04502783275237 \tabularnewline
141 & 12 & 11.9822290424360 & 0.017770957564037 \tabularnewline
142 & 13 & 13.5150636392988 & -0.515063639298782 \tabularnewline
143 & 15 & 12.4478409941893 & 2.55215900581071 \tabularnewline
144 & 8 & 9.40102189128404 & -1.40102189128404 \tabularnewline
145 & 12 & 10.7702217095278 & 1.22977829047224 \tabularnewline
146 & 10 & 10.8018247307924 & -0.80182473079244 \tabularnewline
147 & 8 & 11.1739712445785 & -3.17397124457851 \tabularnewline
148 & 10 & 14.7937263715450 & -4.79372637154504 \tabularnewline
149 & 15 & 13.8677393419999 & 1.13226065800013 \tabularnewline
150 & 16 & 14.5114376971260 & 1.48856230287396 \tabularnewline
151 & 13 & 13.1972777828502 & -0.197277782850188 \tabularnewline
152 & 16 & 15.2288615510811 & 0.771138448918888 \tabularnewline
153 & 9 & 9.84022680652135 & -0.840226806521354 \tabularnewline
154 & 14 & 12.7935752210566 & 1.20642477894339 \tabularnewline
155 & 14 & 13.1839295279290 & 0.816070472070984 \tabularnewline
156 & 12 & 10.2218177031707 & 1.77818229682926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&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]10.9393262245388[/C][C]2.06067377546121[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.8637572173315[/C][C]1.13624278266855[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]12.5380843562029[/C][C]2.46191564379707[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.9147903176924[/C][C]1.08520968230764[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.9065837761927[/C][C]-0.906583776192673[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]8.86754881001966[/C][C]3.13245118998034[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]17.3348198468565[/C][C]-2.33481984685653[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2737377490074[/C][C]-1.27373774900741[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.3298145937458[/C][C]-0.329814593745768[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.70369581211604[/C][C]3.29630418788396[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]12.2356569872936[/C][C]-1.23565698729358[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1460020623454[/C][C]-1.14600206234543[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.5400074425859[/C][C]2.45999255741406[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]10.9327752831474[/C][C]-3.93277528314742[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.8860311300231[/C][C]-0.886031130023053[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.7728267639802[/C][C]0.227173236019762[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.1460020623454[/C][C]-2.14600206234543[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.2820743220530[/C][C]-0.282074322053038[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.62408199434457[/C][C]1.37591800565543[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.54277687401176[/C][C]-3.54277687401176[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]9.76931576610676[/C][C]1.23068423389324[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3014701505356[/C][C]0.698529849464355[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.9879112587432[/C][C]-0.987911258743211[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.4011601325744[/C][C]2.59883986742559[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.3287937060255[/C][C]0.671206293974508[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5317879703339[/C][C]0.468212029666071[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.3212313362724[/C][C]-5.32123133627244[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.0420161354698[/C][C]-0.04201613546983[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]12.5628021417156[/C][C]0.437197858284439[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.7069471353146[/C][C]5.29305286468536[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]9.05888360657697[/C][C]3.94111639342303[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.2656904540232[/C][C]-1.26569045402323[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7947034298603[/C][C]-0.794703429860286[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.5978043918972[/C][C]0.402195608102839[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.3993381625201[/C][C]-1.39933816252006[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.4000743401058[/C][C]1.59992565989420[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.9527000501128[/C][C]-0.952700050112836[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]10.2816921026549[/C][C]-0.281692102654904[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8754994188057[/C][C]0.124500581194257[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.5039013505371[/C][C]0.496098649462928[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.0809055180780[/C][C]0.919094481922042[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.59442596298534[/C][C]6.40557403701466[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]8.72145244464664[/C][C]-3.72145244464664[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.2803705458728[/C][C]-2.28037054587275[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.1736409222425[/C][C]-0.173640922242532[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7493739666929[/C][C]2.25062603330708[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.3274425279742[/C][C]2.67255747202575[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.29370726420254[/C][C]0.706292735797464[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4237199277776[/C][C]-2.42371992777761[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6345807087657[/C][C]-1.63458070876572[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9589490446740[/C][C]-0.958949044674015[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.0949341895663[/C][C]-6.0949341895663[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.1721492745242[/C][C]-0.172149274524156[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.2711326945549[/C][C]-2.27113269455494[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3180862921800[/C][C]1.68191370781998[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9345812369023[/C][C]-0.934581236902313[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.9332068487977[/C][C]0.0667931512023159[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.3950911126313[/C][C]1.60490888736871[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.5242002387326[/C][C]-2.52420023873258[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.4723407949832[/C][C]0.52765920501683[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]8.99149290399343[/C][C]-1.99149290399343[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.2653534812969[/C][C]1.73464651870310[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.4452647828127[/C][C]1.55473521718729[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.3095955845957[/C][C]2.69040441540429[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.8253687840318[/C][C]-1.82536878403182[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2370801682762[/C][C]1.76291983172384[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.3953809193164[/C][C]-2.39538091931637[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3039986825980[/C][C]2.69600131740202[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.2065059315906[/C][C]1.79349406840940[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]4.63866377481478[/C][C]0.361336225185218[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.9345812369023[/C][C]2.06541876309769[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.4882809953014[/C][C]0.511719004698555[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.3566762937614[/C][C]-1.35667629376135[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.0222084053630[/C][C]-3.02220840536303[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.7149926705601[/C][C]-0.714992670560117[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.5799945330508[/C][C]-1.57999453305078[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.4119671620672[/C][C]-0.411967162067232[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.0489431376081[/C][C]-0.0489431376080512[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.581346721802[/C][C]3.41865327819800[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.58956533899704[/C][C]-1.58956533899704[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.58577374630882[/C][C]-2.58577374630882[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.6144008030286[/C][C]1.38559919697143[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.0323606737721[/C][C]-0.0323606737721461[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1562354690427[/C][C]-1.15623546904270[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.61966062207213[/C][C]4.38033937792787[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.6976423672448[/C][C]-0.697642367244768[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.3863694549946[/C][C]-0.386369454994597[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.8585104760371[/C][C]0.141489523962873[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6627708860537[/C][C]1.33722911394632[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4708006369433[/C][C]-0.470800636943259[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.9221207325253[/C][C]1.07787926747475[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5000256756662[/C][C]-3.50002567566621[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.6040064720802[/C][C]0.395993527919832[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.2616522008653[/C][C]1.73834779913472[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.5021975743568[/C][C]0.497802425643212[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.39950381303553[/C][C]0.600496186964467[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]9.49370108808755[/C][C]-1.49370108808755[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.16051735795122[/C][C]-0.160517357951225[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.4720212055406[/C][C]-1.47202120554064[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.5818165031051[/C][C]0.418183496894866[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5236625362739[/C][C]0.476337463726132[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.0442479373467[/C][C]0.955752062653301[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]16.2050902994530[/C][C]-2.20509029945303[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.3786282020592[/C][C]1.62137179794081[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.74128845004528[/C][C]-3.74128845004528[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]15.1234318658932[/C][C]-1.12343186589319[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.9355728477718[/C][C]-1.93557284777184[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.1029146254051[/C][C]-1.10291462540508[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4697215653163[/C][C]-2.46972156531635[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]11.0222268640315[/C][C]-3.02222686403148[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.7399605772429[/C][C]-0.739960577242933[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.7259545405275[/C][C]0.274045459472457[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.2068372326216[/C][C]-0.20683723262155[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.4501327455468[/C][C]0.549867254453217[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4802280286219[/C][C]0.519771971378125[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4078344169117[/C][C]2.59216558308832[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.2638628122735[/C][C]2.73613718772651[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.6382246488744[/C][C]-1.63822464887442[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.1414093873231[/C][C]-0.141409387323128[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9504667477682[/C][C]3.04953325223175[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.3949111380132[/C][C]0.60508886198677[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.6519598870773[/C][C]1.34804011292273[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8911831905579[/C][C]-2.89118319055786[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.1836397913579[/C][C]-1.18363979135789[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.0158089738105[/C][C]1.98419102618952[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.5492818160930[/C][C]1.45071818390696[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.6975419798839[/C][C]0.302458020116144[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.2031932925128[/C][C]-1.20319329251285[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4293830912187[/C][C]-0.429383091218686[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.10612877200011[/C][C]-2.10612877200011[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.0743432204218[/C][C]-0.0743432204217903[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.6257857705249[/C][C]2.37421422947515[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.0761743627835[/C][C]-1.07617436278347[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.4873970152744[/C][C]-0.487397015274438[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.2655191318124[/C][C]1.73448086818763[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.3950767885287[/C][C]1.60492321147130[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6377868794558[/C][C]0.362213120544226[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.3648158549381[/C][C]-3.36481585493814[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0748221740323[/C][C]4.92517782596775[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.0450278327524[/C][C]-1.04502783275237[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.9822290424360[/C][C]0.017770957564037[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.5150636392988[/C][C]-0.515063639298782[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4478409941893[/C][C]2.55215900581071[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]9.40102189128404[/C][C]-1.40102189128404[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.7702217095278[/C][C]1.22977829047224[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.8018247307924[/C][C]-0.80182473079244[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.1739712445785[/C][C]-3.17397124457851[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.7937263715450[/C][C]-4.79372637154504[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8677393419999[/C][C]1.13226065800013[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.5114376971260[/C][C]1.48856230287396[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1972777828502[/C][C]-0.197277782850188[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.2288615510811[/C][C]0.771138448918888[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.84022680652135[/C][C]-0.840226806521354[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.7935752210566[/C][C]1.20642477894339[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.1839295279290[/C][C]0.816070472070984[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.2218177031707[/C][C]1.77818229682926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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
11310.93932622453882.06067377546121
21210.86375721733151.13624278266855
31512.53808435620292.46191564379707
41210.91479031769241.08520968230764
51010.9065837761927-0.906583776192673
6128.867548810019663.13245118998034
71517.3348198468565-2.33481984685653
8910.2737377490074-1.27373774900741
91212.3298145937458-0.329814593745768
10117.703695812116043.29630418788396
111112.2356569872936-1.23565698729358
121112.1460020623454-1.14600206234543
131512.54000744258592.45999255741406
14710.9327752831474-3.93277528314742
151111.8860311300231-0.886031130023053
161110.77282676398020.227173236019762
171012.1460020623454-2.14600206234543
181414.2820743220530-0.282074322053038
19108.624081994344571.37591800565543
2069.54277687401176-3.54277687401176
21119.769315766106761.23068423389324
221514.30147015053560.698529849464355
231111.9879112587432-0.987911258743211
24129.40116013257442.59883986742559
251413.32879370602550.671206293974508
261514.53178797033390.468212029666071
27914.3212313362724-5.32123133627244
281313.0420161354698-0.04201613546983
291312.56280214171560.437197858284439
301610.70694713531465.29305286468536
31139.058883606576973.94111639342303
321213.2656904540232-1.26569045402323
331414.7947034298603-0.794703429860286
341110.59780439189720.402195608102839
35910.3993381625201-1.39933816252006
361614.40007434010581.59992565989420
371212.9527000501128-0.952700050112836
381010.2816921026549-0.281692102654904
391312.87549941880570.124500581194257
401615.50390135053710.496098649462928
411413.08090551807800.919094481922042
42158.594425962985346.40557403701466
4358.72145244464664-3.72145244464664
44810.2803705458728-2.28037054587275
451111.1736409222425-0.173640922242532
461613.74937396669292.25062603330708
471714.32744252797422.67255747202575
4898.293707264202540.706292735797464
49911.4237199277776-2.42371992777761
501314.6345807087657-1.63458070876572
511010.9589490446740-0.958949044674015
52612.0949341895663-6.0949341895663
531212.1721492745242-0.172149274524156
54810.2711326945549-2.27113269455494
551412.31808629218001.68191370781998
561212.9345812369023-0.934581236902313
571110.93320684879770.0667931512023159
581614.39509111263131.60490888736871
59810.5242002387326-2.52420023873258
601514.47234079498320.52765920501683
6178.99149290399343-1.99149290399343
621614.26535348129691.73464651870310
631412.44526478281271.55473521718729
641613.30959558459572.69040441540429
65910.8253687840318-1.82536878403182
661412.23708016827621.76291983172384
671113.3953809193164-2.39538091931637
681310.30399868259802.69600131740202
691513.20650593159061.79349406840940
7054.638663774814780.361336225185218
711512.93458123690232.06541876309769
721312.48828099530140.511719004698555
731112.3566762937614-1.35667629376135
741114.0222084053630-3.02220840536303
751212.7149926705601-0.714992670560117
761213.5799945330508-1.57999453305078
771212.4119671620672-0.411967162067232
781212.0489431376081-0.0489431376080512
791410.5813467218023.41865327819800
8067.58956533899704-1.58956533899704
8179.58577374630882-2.58577374630882
821412.61440080302861.38559919697143
831414.0323606737721-0.0323606737721461
841011.1562354690427-1.15623546904270
85138.619660622072134.38033937792787
861212.6976423672448-0.697642367244768
8799.3863694549946-0.386369454994597
881211.85851047603710.141489523962873
891614.66277088605371.33722911394632
901010.4708006369433-0.470800636943259
911412.92212073252531.07787926747475
921013.5000256756662-3.50002567566621
931615.60400647208020.395993527919832
941513.26165220086531.73834779913472
951211.50219757435680.497802425643212
96109.399503813035530.600496186964467
9789.49370108808755-1.49370108808755
9888.16051735795122-0.160517357951225
991112.4720212055406-1.47202120554064
1001312.58181650310510.418183496894866
1011615.52366253627390.476337463726132
1021615.04424793734670.955752062653301
1031416.2050902994530-2.20509029945303
104119.37862820205921.62137179794081
10547.74128845004528-3.74128845004528
1061415.1234318658932-1.12343186589319
107910.9355728477718-1.93557284777184
1081415.1029146254051-1.10291462540508
109810.4697215653163-2.46972156531635
110811.0222268640315-3.02222686403148
1111111.7399605772429-0.739960577242933
1121211.72595454052750.274045459472457
1131111.2068372326216-0.20683723262155
1141413.45013274554680.549867254453217
1151514.48022802862190.519771971378125
1161613.40783441691172.59216558308832
1171613.26386281227352.73613718772651
1181112.6382246488744-1.63822464887442
1191414.1414093873231-0.141409387323128
1201410.95046674776823.04953325223175
1211211.39491113801320.60508886198677
1221412.65195988707731.34804011292273
123810.8911831905579-2.89118319055786
1241314.1836397913579-1.18363979135789
1251614.01580897381051.98419102618952
1261210.54928181609301.45071818390696
1271615.69754197988390.302458020116144
1281213.2031932925128-1.20319329251285
1291111.4293830912187-0.429383091218686
13046.10612877200011-2.10612877200011
1311616.0743432204218-0.0743432204217903
1321512.62578577052492.37421422947515
1331011.0761743627835-1.07617436278347
1341313.4873970152744-0.487397015274438
1351513.26551913181241.73448086818763
1361210.39507678852871.60492321147130
1371413.63778687945580.362213120544226
138710.3648158549381-3.36481585493814
1391914.07482217403234.92517782596775
1401213.0450278327524-1.04502783275237
1411211.98222904243600.017770957564037
1421313.5150636392988-0.515063639298782
1431512.44784099418932.55215900581071
14489.40102189128404-1.40102189128404
1451210.77022170952781.22977829047224
1461010.8018247307924-0.80182473079244
147811.1739712445785-3.17397124457851
1481014.7937263715450-4.79372637154504
1491513.86773934199991.13226065800013
1501614.51143769712601.48856230287396
1511313.1972777828502-0.197277782850188
1521615.22886155108110.771138448918888
15399.84022680652135-0.840226806521354
1541412.79357522105661.20642477894339
1551413.18392952792900.816070472070984
1561210.22181770317071.77818229682926






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7898567546380460.4202864907239070.210143245361953
140.7942902572015040.4114194855969920.205709742798496
150.7202810796803460.5594378406393080.279718920319654
160.6523604345969220.6952791308061560.347639565403078
170.5596470212249760.8807059575500480.440352978775024
180.4616115289743340.9232230579486680.538388471025666
190.3668002944528070.7336005889056150.633199705547193
200.78748591030770.4250281793846010.212514089692301
210.7466492526422190.5067014947155630.253350747357781
220.7155048730724590.5689902538550820.284495126927541
230.6495913161694570.7008173676610860.350408683830543
240.6776320019475830.6447359961048330.322367998052417
250.612063670996890.7758726580062220.387936329003111
260.5424017602106310.9151964795787380.457598239789369
270.7794511569164080.4410976861671840.220548843083592
280.7229350643796290.5541298712407420.277064935620371
290.6736303987855840.6527392024288330.326369601214416
300.820414569955330.3591708600893390.179585430044670
310.863602288904170.2727954221916600.136397711095830
320.8295368824971780.3409262350056430.170463117502821
330.7938871419278080.4122257161443830.206112858072192
340.748011208619610.5039775827607810.251988791380391
350.7267026399417640.5465947201164720.273297360058236
360.7295595217307140.5408809565385720.270440478269286
370.6969631452702830.6060737094594350.303036854729717
380.7141915196329030.5716169607341940.285808480367097
390.6703875633683320.6592248732633350.329612436631668
400.6315972753847310.7368054492305390.368402724615269
410.5998466211228150.800306757754370.400153378877185
420.8589058849516620.2821882300966760.141094115048338
430.9440001586580840.1119996826838310.0559998413419157
440.9491198464297450.1017603071405110.0508801535702555
450.9338427767558450.1323144464883090.0661572232441546
460.939947278879070.1201054422418600.0600527211209302
470.9347969007239570.1304061985520870.0652030992760433
480.9207704035557920.1584591928884150.0792295964442076
490.9190128067438670.1619743865122670.0809871932561333
500.9059598217031280.1880803565937430.0940401782968716
510.8948115087000920.2103769825998160.105188491299908
520.9781574702131620.04368505957367680.0218425297868384
530.9711648381510990.0576703236978020.028835161848901
540.976673377976080.04665324404783820.0233266220239191
550.9785811898977020.04283762020459610.0214188101022980
560.9738781980005930.05224360399881480.0261218019994074
570.9658585222950670.06828295540986620.0341414777049331
580.9594751766150940.08104964676981170.0405248233849058
590.9722119773976140.05557604520477270.0277880226023863
600.9669889602787060.06602207944258880.0330110397212944
610.9664756164496830.06704876710063310.0335243835503165
620.9637195857009560.07256082859808860.0362804142990443
630.975166711134010.0496665777319810.0248332888659905
640.979524992909630.04095001418074130.0204750070903706
650.9799913370157180.04001732596856350.0200086629842817
660.9780335338043730.04393293239125350.0219664661956268
670.9810279010737370.03794419785252670.0189720989262633
680.9846393529409020.03072129411819550.0153606470590977
690.9832587411856710.03348251762865860.0167412588143293
700.9784648100384530.04307037992309340.0215351899615467
710.978438755695840.0431224886083180.021561244304159
720.9717362325257350.05652753494853010.0282637674742651
730.967229320697420.06554135860515780.0327706793025789
740.9831713469439960.03365730611200820.0168286530560041
750.9786314960716220.04273700785675510.0213685039283776
760.976356828459680.04728634308063910.0236431715403196
770.9693359646131150.06132807077376940.0306640353868847
780.9599077610062030.0801844779875930.0400922389937965
790.9748721372236590.05025572555268250.0251278627763412
800.9735242855972290.05295142880554290.0264757144027715
810.9797943457412670.04041130851746530.0202056542587327
820.979387216776990.04122556644601850.0206127832230093
830.9726502437570040.05469951248599160.0273497562429958
840.9671765957549930.06564680849001350.0328234042450067
850.9897074137121950.02058517257561050.0102925862878052
860.986493814682750.02701237063449950.0135061853172497
870.9818999273584570.03620014528308630.0181000726415431
880.9757554052950730.04848918940985360.0242445947049268
890.9703204393328260.05935912133434820.0296795606671741
900.9616352920621860.07672941587562890.0383647079378144
910.953494781228670.09301043754265840.0465052187713292
920.9774566890846160.04508662183076770.0225433109153838
930.9699903255022870.06001934899542560.0300096744977128
940.9644198627730510.07116027445389710.0355801372269485
950.9537786927207020.09244261455859540.0462213072792977
960.9406497936862640.1187004126274710.0593502063137357
970.9277892354035960.1444215291928070.0722107645964036
980.9101136706320.1797726587359990.0898863293679996
990.9071066109921920.1857867780156170.0928933890078084
1000.8846070787330350.230785842533930.115392921266965
1010.8606153615192670.2787692769614660.139384638480733
1020.835871566962960.328256866074080.16412843303704
1030.8757964797092010.2484070405815980.124203520290799
1040.9143595963501020.1712808072997970.0856404036498984
1050.9250435429538490.1499129140923020.0749564570461511
1060.9094357696655770.1811284606688450.0905642303344224
1070.9058587229002430.1882825541995130.0941412770997566
1080.9101648604873390.1796702790253220.089835139512661
1090.9088652677780520.1822694644438950.0911347322219477
1100.9012211640393770.1975576719212460.0987788359606229
1110.8860252928460510.2279494143078980.113974707153949
1120.8999150570776160.2001698858447680.100084942922384
1130.8717775715931920.2564448568136170.128222428406808
1140.8410652168344670.3178695663310670.158934783165533
1150.8028144007972310.3943711984055380.197185599202769
1160.7922881920239450.4154236159521110.207711807976055
1170.8010632139116210.3978735721767570.198936786088379
1180.7928508623057720.4142982753884560.207149137694228
1190.7457259070626520.5085481858746960.254274092937348
1200.815871346278010.3682573074439790.184128653721989
1210.783472604704190.4330547905916220.216527395295811
1220.7396006690943880.5207986618112240.260399330905612
1230.7317012568237240.5365974863525530.268298743176276
1240.6771872465931990.6456255068136030.322812753406801
1250.6825765944907030.6348468110185940.317423405509297
1260.65229204134690.69541591730620.3477079586531
1270.5889594662349450.822081067530110.411040533765055
1280.5521664216091310.8956671567817380.447833578390869
1290.499035241259990.998070482519980.50096475874001
1300.444943808642560.889887617285120.55505619135744
1310.3749814216961040.7499628433922080.625018578303896
1320.3753661108333560.7507322216667120.624633889166644
1330.375471583932360.750943167864720.62452841606764
1340.3350051002977210.6700102005954430.664994899702279
1350.2998499717172930.5996999434345860.700150028282707
1360.3010401747229910.6020803494459820.698959825277009
1370.2265019924438760.4530039848877520.773498007556124
1380.3514909112215090.7029818224430190.64850908877849
1390.6180004764806840.7639990470386320.381999523519316
1400.8133173246534120.3733653506931750.186682675346588
1410.746032629348770.5079347413024590.253967370651229
1420.7195342488723190.5609315022553630.280465751127681
1430.6100577640657470.7798844718685060.389942235934253

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.789856754638046 & 0.420286490723907 & 0.210143245361953 \tabularnewline
14 & 0.794290257201504 & 0.411419485596992 & 0.205709742798496 \tabularnewline
15 & 0.720281079680346 & 0.559437840639308 & 0.279718920319654 \tabularnewline
16 & 0.652360434596922 & 0.695279130806156 & 0.347639565403078 \tabularnewline
17 & 0.559647021224976 & 0.880705957550048 & 0.440352978775024 \tabularnewline
18 & 0.461611528974334 & 0.923223057948668 & 0.538388471025666 \tabularnewline
19 & 0.366800294452807 & 0.733600588905615 & 0.633199705547193 \tabularnewline
20 & 0.7874859103077 & 0.425028179384601 & 0.212514089692301 \tabularnewline
21 & 0.746649252642219 & 0.506701494715563 & 0.253350747357781 \tabularnewline
22 & 0.715504873072459 & 0.568990253855082 & 0.284495126927541 \tabularnewline
23 & 0.649591316169457 & 0.700817367661086 & 0.350408683830543 \tabularnewline
24 & 0.677632001947583 & 0.644735996104833 & 0.322367998052417 \tabularnewline
25 & 0.61206367099689 & 0.775872658006222 & 0.387936329003111 \tabularnewline
26 & 0.542401760210631 & 0.915196479578738 & 0.457598239789369 \tabularnewline
27 & 0.779451156916408 & 0.441097686167184 & 0.220548843083592 \tabularnewline
28 & 0.722935064379629 & 0.554129871240742 & 0.277064935620371 \tabularnewline
29 & 0.673630398785584 & 0.652739202428833 & 0.326369601214416 \tabularnewline
30 & 0.82041456995533 & 0.359170860089339 & 0.179585430044670 \tabularnewline
31 & 0.86360228890417 & 0.272795422191660 & 0.136397711095830 \tabularnewline
32 & 0.829536882497178 & 0.340926235005643 & 0.170463117502821 \tabularnewline
33 & 0.793887141927808 & 0.412225716144383 & 0.206112858072192 \tabularnewline
34 & 0.74801120861961 & 0.503977582760781 & 0.251988791380391 \tabularnewline
35 & 0.726702639941764 & 0.546594720116472 & 0.273297360058236 \tabularnewline
36 & 0.729559521730714 & 0.540880956538572 & 0.270440478269286 \tabularnewline
37 & 0.696963145270283 & 0.606073709459435 & 0.303036854729717 \tabularnewline
38 & 0.714191519632903 & 0.571616960734194 & 0.285808480367097 \tabularnewline
39 & 0.670387563368332 & 0.659224873263335 & 0.329612436631668 \tabularnewline
40 & 0.631597275384731 & 0.736805449230539 & 0.368402724615269 \tabularnewline
41 & 0.599846621122815 & 0.80030675775437 & 0.400153378877185 \tabularnewline
42 & 0.858905884951662 & 0.282188230096676 & 0.141094115048338 \tabularnewline
43 & 0.944000158658084 & 0.111999682683831 & 0.0559998413419157 \tabularnewline
44 & 0.949119846429745 & 0.101760307140511 & 0.0508801535702555 \tabularnewline
45 & 0.933842776755845 & 0.132314446488309 & 0.0661572232441546 \tabularnewline
46 & 0.93994727887907 & 0.120105442241860 & 0.0600527211209302 \tabularnewline
47 & 0.934796900723957 & 0.130406198552087 & 0.0652030992760433 \tabularnewline
48 & 0.920770403555792 & 0.158459192888415 & 0.0792295964442076 \tabularnewline
49 & 0.919012806743867 & 0.161974386512267 & 0.0809871932561333 \tabularnewline
50 & 0.905959821703128 & 0.188080356593743 & 0.0940401782968716 \tabularnewline
51 & 0.894811508700092 & 0.210376982599816 & 0.105188491299908 \tabularnewline
52 & 0.978157470213162 & 0.0436850595736768 & 0.0218425297868384 \tabularnewline
53 & 0.971164838151099 & 0.057670323697802 & 0.028835161848901 \tabularnewline
54 & 0.97667337797608 & 0.0466532440478382 & 0.0233266220239191 \tabularnewline
55 & 0.978581189897702 & 0.0428376202045961 & 0.0214188101022980 \tabularnewline
56 & 0.973878198000593 & 0.0522436039988148 & 0.0261218019994074 \tabularnewline
57 & 0.965858522295067 & 0.0682829554098662 & 0.0341414777049331 \tabularnewline
58 & 0.959475176615094 & 0.0810496467698117 & 0.0405248233849058 \tabularnewline
59 & 0.972211977397614 & 0.0555760452047727 & 0.0277880226023863 \tabularnewline
60 & 0.966988960278706 & 0.0660220794425888 & 0.0330110397212944 \tabularnewline
61 & 0.966475616449683 & 0.0670487671006331 & 0.0335243835503165 \tabularnewline
62 & 0.963719585700956 & 0.0725608285980886 & 0.0362804142990443 \tabularnewline
63 & 0.97516671113401 & 0.049666577731981 & 0.0248332888659905 \tabularnewline
64 & 0.97952499290963 & 0.0409500141807413 & 0.0204750070903706 \tabularnewline
65 & 0.979991337015718 & 0.0400173259685635 & 0.0200086629842817 \tabularnewline
66 & 0.978033533804373 & 0.0439329323912535 & 0.0219664661956268 \tabularnewline
67 & 0.981027901073737 & 0.0379441978525267 & 0.0189720989262633 \tabularnewline
68 & 0.984639352940902 & 0.0307212941181955 & 0.0153606470590977 \tabularnewline
69 & 0.983258741185671 & 0.0334825176286586 & 0.0167412588143293 \tabularnewline
70 & 0.978464810038453 & 0.0430703799230934 & 0.0215351899615467 \tabularnewline
71 & 0.97843875569584 & 0.043122488608318 & 0.021561244304159 \tabularnewline
72 & 0.971736232525735 & 0.0565275349485301 & 0.0282637674742651 \tabularnewline
73 & 0.96722932069742 & 0.0655413586051578 & 0.0327706793025789 \tabularnewline
74 & 0.983171346943996 & 0.0336573061120082 & 0.0168286530560041 \tabularnewline
75 & 0.978631496071622 & 0.0427370078567551 & 0.0213685039283776 \tabularnewline
76 & 0.97635682845968 & 0.0472863430806391 & 0.0236431715403196 \tabularnewline
77 & 0.969335964613115 & 0.0613280707737694 & 0.0306640353868847 \tabularnewline
78 & 0.959907761006203 & 0.080184477987593 & 0.0400922389937965 \tabularnewline
79 & 0.974872137223659 & 0.0502557255526825 & 0.0251278627763412 \tabularnewline
80 & 0.973524285597229 & 0.0529514288055429 & 0.0264757144027715 \tabularnewline
81 & 0.979794345741267 & 0.0404113085174653 & 0.0202056542587327 \tabularnewline
82 & 0.97938721677699 & 0.0412255664460185 & 0.0206127832230093 \tabularnewline
83 & 0.972650243757004 & 0.0546995124859916 & 0.0273497562429958 \tabularnewline
84 & 0.967176595754993 & 0.0656468084900135 & 0.0328234042450067 \tabularnewline
85 & 0.989707413712195 & 0.0205851725756105 & 0.0102925862878052 \tabularnewline
86 & 0.98649381468275 & 0.0270123706344995 & 0.0135061853172497 \tabularnewline
87 & 0.981899927358457 & 0.0362001452830863 & 0.0181000726415431 \tabularnewline
88 & 0.975755405295073 & 0.0484891894098536 & 0.0242445947049268 \tabularnewline
89 & 0.970320439332826 & 0.0593591213343482 & 0.0296795606671741 \tabularnewline
90 & 0.961635292062186 & 0.0767294158756289 & 0.0383647079378144 \tabularnewline
91 & 0.95349478122867 & 0.0930104375426584 & 0.0465052187713292 \tabularnewline
92 & 0.977456689084616 & 0.0450866218307677 & 0.0225433109153838 \tabularnewline
93 & 0.969990325502287 & 0.0600193489954256 & 0.0300096744977128 \tabularnewline
94 & 0.964419862773051 & 0.0711602744538971 & 0.0355801372269485 \tabularnewline
95 & 0.953778692720702 & 0.0924426145585954 & 0.0462213072792977 \tabularnewline
96 & 0.940649793686264 & 0.118700412627471 & 0.0593502063137357 \tabularnewline
97 & 0.927789235403596 & 0.144421529192807 & 0.0722107645964036 \tabularnewline
98 & 0.910113670632 & 0.179772658735999 & 0.0898863293679996 \tabularnewline
99 & 0.907106610992192 & 0.185786778015617 & 0.0928933890078084 \tabularnewline
100 & 0.884607078733035 & 0.23078584253393 & 0.115392921266965 \tabularnewline
101 & 0.860615361519267 & 0.278769276961466 & 0.139384638480733 \tabularnewline
102 & 0.83587156696296 & 0.32825686607408 & 0.16412843303704 \tabularnewline
103 & 0.875796479709201 & 0.248407040581598 & 0.124203520290799 \tabularnewline
104 & 0.914359596350102 & 0.171280807299797 & 0.0856404036498984 \tabularnewline
105 & 0.925043542953849 & 0.149912914092302 & 0.0749564570461511 \tabularnewline
106 & 0.909435769665577 & 0.181128460668845 & 0.0905642303344224 \tabularnewline
107 & 0.905858722900243 & 0.188282554199513 & 0.0941412770997566 \tabularnewline
108 & 0.910164860487339 & 0.179670279025322 & 0.089835139512661 \tabularnewline
109 & 0.908865267778052 & 0.182269464443895 & 0.0911347322219477 \tabularnewline
110 & 0.901221164039377 & 0.197557671921246 & 0.0987788359606229 \tabularnewline
111 & 0.886025292846051 & 0.227949414307898 & 0.113974707153949 \tabularnewline
112 & 0.899915057077616 & 0.200169885844768 & 0.100084942922384 \tabularnewline
113 & 0.871777571593192 & 0.256444856813617 & 0.128222428406808 \tabularnewline
114 & 0.841065216834467 & 0.317869566331067 & 0.158934783165533 \tabularnewline
115 & 0.802814400797231 & 0.394371198405538 & 0.197185599202769 \tabularnewline
116 & 0.792288192023945 & 0.415423615952111 & 0.207711807976055 \tabularnewline
117 & 0.801063213911621 & 0.397873572176757 & 0.198936786088379 \tabularnewline
118 & 0.792850862305772 & 0.414298275388456 & 0.207149137694228 \tabularnewline
119 & 0.745725907062652 & 0.508548185874696 & 0.254274092937348 \tabularnewline
120 & 0.81587134627801 & 0.368257307443979 & 0.184128653721989 \tabularnewline
121 & 0.78347260470419 & 0.433054790591622 & 0.216527395295811 \tabularnewline
122 & 0.739600669094388 & 0.520798661811224 & 0.260399330905612 \tabularnewline
123 & 0.731701256823724 & 0.536597486352553 & 0.268298743176276 \tabularnewline
124 & 0.677187246593199 & 0.645625506813603 & 0.322812753406801 \tabularnewline
125 & 0.682576594490703 & 0.634846811018594 & 0.317423405509297 \tabularnewline
126 & 0.6522920413469 & 0.6954159173062 & 0.3477079586531 \tabularnewline
127 & 0.588959466234945 & 0.82208106753011 & 0.411040533765055 \tabularnewline
128 & 0.552166421609131 & 0.895667156781738 & 0.447833578390869 \tabularnewline
129 & 0.49903524125999 & 0.99807048251998 & 0.50096475874001 \tabularnewline
130 & 0.44494380864256 & 0.88988761728512 & 0.55505619135744 \tabularnewline
131 & 0.374981421696104 & 0.749962843392208 & 0.625018578303896 \tabularnewline
132 & 0.375366110833356 & 0.750732221666712 & 0.624633889166644 \tabularnewline
133 & 0.37547158393236 & 0.75094316786472 & 0.62452841606764 \tabularnewline
134 & 0.335005100297721 & 0.670010200595443 & 0.664994899702279 \tabularnewline
135 & 0.299849971717293 & 0.599699943434586 & 0.700150028282707 \tabularnewline
136 & 0.301040174722991 & 0.602080349445982 & 0.698959825277009 \tabularnewline
137 & 0.226501992443876 & 0.453003984887752 & 0.773498007556124 \tabularnewline
138 & 0.351490911221509 & 0.702981822443019 & 0.64850908877849 \tabularnewline
139 & 0.618000476480684 & 0.763999047038632 & 0.381999523519316 \tabularnewline
140 & 0.813317324653412 & 0.373365350693175 & 0.186682675346588 \tabularnewline
141 & 0.74603262934877 & 0.507934741302459 & 0.253967370651229 \tabularnewline
142 & 0.719534248872319 & 0.560931502255363 & 0.280465751127681 \tabularnewline
143 & 0.610057764065747 & 0.779884471868506 & 0.389942235934253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&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]13[/C][C]0.789856754638046[/C][C]0.420286490723907[/C][C]0.210143245361953[/C][/ROW]
[ROW][C]14[/C][C]0.794290257201504[/C][C]0.411419485596992[/C][C]0.205709742798496[/C][/ROW]
[ROW][C]15[/C][C]0.720281079680346[/C][C]0.559437840639308[/C][C]0.279718920319654[/C][/ROW]
[ROW][C]16[/C][C]0.652360434596922[/C][C]0.695279130806156[/C][C]0.347639565403078[/C][/ROW]
[ROW][C]17[/C][C]0.559647021224976[/C][C]0.880705957550048[/C][C]0.440352978775024[/C][/ROW]
[ROW][C]18[/C][C]0.461611528974334[/C][C]0.923223057948668[/C][C]0.538388471025666[/C][/ROW]
[ROW][C]19[/C][C]0.366800294452807[/C][C]0.733600588905615[/C][C]0.633199705547193[/C][/ROW]
[ROW][C]20[/C][C]0.7874859103077[/C][C]0.425028179384601[/C][C]0.212514089692301[/C][/ROW]
[ROW][C]21[/C][C]0.746649252642219[/C][C]0.506701494715563[/C][C]0.253350747357781[/C][/ROW]
[ROW][C]22[/C][C]0.715504873072459[/C][C]0.568990253855082[/C][C]0.284495126927541[/C][/ROW]
[ROW][C]23[/C][C]0.649591316169457[/C][C]0.700817367661086[/C][C]0.350408683830543[/C][/ROW]
[ROW][C]24[/C][C]0.677632001947583[/C][C]0.644735996104833[/C][C]0.322367998052417[/C][/ROW]
[ROW][C]25[/C][C]0.61206367099689[/C][C]0.775872658006222[/C][C]0.387936329003111[/C][/ROW]
[ROW][C]26[/C][C]0.542401760210631[/C][C]0.915196479578738[/C][C]0.457598239789369[/C][/ROW]
[ROW][C]27[/C][C]0.779451156916408[/C][C]0.441097686167184[/C][C]0.220548843083592[/C][/ROW]
[ROW][C]28[/C][C]0.722935064379629[/C][C]0.554129871240742[/C][C]0.277064935620371[/C][/ROW]
[ROW][C]29[/C][C]0.673630398785584[/C][C]0.652739202428833[/C][C]0.326369601214416[/C][/ROW]
[ROW][C]30[/C][C]0.82041456995533[/C][C]0.359170860089339[/C][C]0.179585430044670[/C][/ROW]
[ROW][C]31[/C][C]0.86360228890417[/C][C]0.272795422191660[/C][C]0.136397711095830[/C][/ROW]
[ROW][C]32[/C][C]0.829536882497178[/C][C]0.340926235005643[/C][C]0.170463117502821[/C][/ROW]
[ROW][C]33[/C][C]0.793887141927808[/C][C]0.412225716144383[/C][C]0.206112858072192[/C][/ROW]
[ROW][C]34[/C][C]0.74801120861961[/C][C]0.503977582760781[/C][C]0.251988791380391[/C][/ROW]
[ROW][C]35[/C][C]0.726702639941764[/C][C]0.546594720116472[/C][C]0.273297360058236[/C][/ROW]
[ROW][C]36[/C][C]0.729559521730714[/C][C]0.540880956538572[/C][C]0.270440478269286[/C][/ROW]
[ROW][C]37[/C][C]0.696963145270283[/C][C]0.606073709459435[/C][C]0.303036854729717[/C][/ROW]
[ROW][C]38[/C][C]0.714191519632903[/C][C]0.571616960734194[/C][C]0.285808480367097[/C][/ROW]
[ROW][C]39[/C][C]0.670387563368332[/C][C]0.659224873263335[/C][C]0.329612436631668[/C][/ROW]
[ROW][C]40[/C][C]0.631597275384731[/C][C]0.736805449230539[/C][C]0.368402724615269[/C][/ROW]
[ROW][C]41[/C][C]0.599846621122815[/C][C]0.80030675775437[/C][C]0.400153378877185[/C][/ROW]
[ROW][C]42[/C][C]0.858905884951662[/C][C]0.282188230096676[/C][C]0.141094115048338[/C][/ROW]
[ROW][C]43[/C][C]0.944000158658084[/C][C]0.111999682683831[/C][C]0.0559998413419157[/C][/ROW]
[ROW][C]44[/C][C]0.949119846429745[/C][C]0.101760307140511[/C][C]0.0508801535702555[/C][/ROW]
[ROW][C]45[/C][C]0.933842776755845[/C][C]0.132314446488309[/C][C]0.0661572232441546[/C][/ROW]
[ROW][C]46[/C][C]0.93994727887907[/C][C]0.120105442241860[/C][C]0.0600527211209302[/C][/ROW]
[ROW][C]47[/C][C]0.934796900723957[/C][C]0.130406198552087[/C][C]0.0652030992760433[/C][/ROW]
[ROW][C]48[/C][C]0.920770403555792[/C][C]0.158459192888415[/C][C]0.0792295964442076[/C][/ROW]
[ROW][C]49[/C][C]0.919012806743867[/C][C]0.161974386512267[/C][C]0.0809871932561333[/C][/ROW]
[ROW][C]50[/C][C]0.905959821703128[/C][C]0.188080356593743[/C][C]0.0940401782968716[/C][/ROW]
[ROW][C]51[/C][C]0.894811508700092[/C][C]0.210376982599816[/C][C]0.105188491299908[/C][/ROW]
[ROW][C]52[/C][C]0.978157470213162[/C][C]0.0436850595736768[/C][C]0.0218425297868384[/C][/ROW]
[ROW][C]53[/C][C]0.971164838151099[/C][C]0.057670323697802[/C][C]0.028835161848901[/C][/ROW]
[ROW][C]54[/C][C]0.97667337797608[/C][C]0.0466532440478382[/C][C]0.0233266220239191[/C][/ROW]
[ROW][C]55[/C][C]0.978581189897702[/C][C]0.0428376202045961[/C][C]0.0214188101022980[/C][/ROW]
[ROW][C]56[/C][C]0.973878198000593[/C][C]0.0522436039988148[/C][C]0.0261218019994074[/C][/ROW]
[ROW][C]57[/C][C]0.965858522295067[/C][C]0.0682829554098662[/C][C]0.0341414777049331[/C][/ROW]
[ROW][C]58[/C][C]0.959475176615094[/C][C]0.0810496467698117[/C][C]0.0405248233849058[/C][/ROW]
[ROW][C]59[/C][C]0.972211977397614[/C][C]0.0555760452047727[/C][C]0.0277880226023863[/C][/ROW]
[ROW][C]60[/C][C]0.966988960278706[/C][C]0.0660220794425888[/C][C]0.0330110397212944[/C][/ROW]
[ROW][C]61[/C][C]0.966475616449683[/C][C]0.0670487671006331[/C][C]0.0335243835503165[/C][/ROW]
[ROW][C]62[/C][C]0.963719585700956[/C][C]0.0725608285980886[/C][C]0.0362804142990443[/C][/ROW]
[ROW][C]63[/C][C]0.97516671113401[/C][C]0.049666577731981[/C][C]0.0248332888659905[/C][/ROW]
[ROW][C]64[/C][C]0.97952499290963[/C][C]0.0409500141807413[/C][C]0.0204750070903706[/C][/ROW]
[ROW][C]65[/C][C]0.979991337015718[/C][C]0.0400173259685635[/C][C]0.0200086629842817[/C][/ROW]
[ROW][C]66[/C][C]0.978033533804373[/C][C]0.0439329323912535[/C][C]0.0219664661956268[/C][/ROW]
[ROW][C]67[/C][C]0.981027901073737[/C][C]0.0379441978525267[/C][C]0.0189720989262633[/C][/ROW]
[ROW][C]68[/C][C]0.984639352940902[/C][C]0.0307212941181955[/C][C]0.0153606470590977[/C][/ROW]
[ROW][C]69[/C][C]0.983258741185671[/C][C]0.0334825176286586[/C][C]0.0167412588143293[/C][/ROW]
[ROW][C]70[/C][C]0.978464810038453[/C][C]0.0430703799230934[/C][C]0.0215351899615467[/C][/ROW]
[ROW][C]71[/C][C]0.97843875569584[/C][C]0.043122488608318[/C][C]0.021561244304159[/C][/ROW]
[ROW][C]72[/C][C]0.971736232525735[/C][C]0.0565275349485301[/C][C]0.0282637674742651[/C][/ROW]
[ROW][C]73[/C][C]0.96722932069742[/C][C]0.0655413586051578[/C][C]0.0327706793025789[/C][/ROW]
[ROW][C]74[/C][C]0.983171346943996[/C][C]0.0336573061120082[/C][C]0.0168286530560041[/C][/ROW]
[ROW][C]75[/C][C]0.978631496071622[/C][C]0.0427370078567551[/C][C]0.0213685039283776[/C][/ROW]
[ROW][C]76[/C][C]0.97635682845968[/C][C]0.0472863430806391[/C][C]0.0236431715403196[/C][/ROW]
[ROW][C]77[/C][C]0.969335964613115[/C][C]0.0613280707737694[/C][C]0.0306640353868847[/C][/ROW]
[ROW][C]78[/C][C]0.959907761006203[/C][C]0.080184477987593[/C][C]0.0400922389937965[/C][/ROW]
[ROW][C]79[/C][C]0.974872137223659[/C][C]0.0502557255526825[/C][C]0.0251278627763412[/C][/ROW]
[ROW][C]80[/C][C]0.973524285597229[/C][C]0.0529514288055429[/C][C]0.0264757144027715[/C][/ROW]
[ROW][C]81[/C][C]0.979794345741267[/C][C]0.0404113085174653[/C][C]0.0202056542587327[/C][/ROW]
[ROW][C]82[/C][C]0.97938721677699[/C][C]0.0412255664460185[/C][C]0.0206127832230093[/C][/ROW]
[ROW][C]83[/C][C]0.972650243757004[/C][C]0.0546995124859916[/C][C]0.0273497562429958[/C][/ROW]
[ROW][C]84[/C][C]0.967176595754993[/C][C]0.0656468084900135[/C][C]0.0328234042450067[/C][/ROW]
[ROW][C]85[/C][C]0.989707413712195[/C][C]0.0205851725756105[/C][C]0.0102925862878052[/C][/ROW]
[ROW][C]86[/C][C]0.98649381468275[/C][C]0.0270123706344995[/C][C]0.0135061853172497[/C][/ROW]
[ROW][C]87[/C][C]0.981899927358457[/C][C]0.0362001452830863[/C][C]0.0181000726415431[/C][/ROW]
[ROW][C]88[/C][C]0.975755405295073[/C][C]0.0484891894098536[/C][C]0.0242445947049268[/C][/ROW]
[ROW][C]89[/C][C]0.970320439332826[/C][C]0.0593591213343482[/C][C]0.0296795606671741[/C][/ROW]
[ROW][C]90[/C][C]0.961635292062186[/C][C]0.0767294158756289[/C][C]0.0383647079378144[/C][/ROW]
[ROW][C]91[/C][C]0.95349478122867[/C][C]0.0930104375426584[/C][C]0.0465052187713292[/C][/ROW]
[ROW][C]92[/C][C]0.977456689084616[/C][C]0.0450866218307677[/C][C]0.0225433109153838[/C][/ROW]
[ROW][C]93[/C][C]0.969990325502287[/C][C]0.0600193489954256[/C][C]0.0300096744977128[/C][/ROW]
[ROW][C]94[/C][C]0.964419862773051[/C][C]0.0711602744538971[/C][C]0.0355801372269485[/C][/ROW]
[ROW][C]95[/C][C]0.953778692720702[/C][C]0.0924426145585954[/C][C]0.0462213072792977[/C][/ROW]
[ROW][C]96[/C][C]0.940649793686264[/C][C]0.118700412627471[/C][C]0.0593502063137357[/C][/ROW]
[ROW][C]97[/C][C]0.927789235403596[/C][C]0.144421529192807[/C][C]0.0722107645964036[/C][/ROW]
[ROW][C]98[/C][C]0.910113670632[/C][C]0.179772658735999[/C][C]0.0898863293679996[/C][/ROW]
[ROW][C]99[/C][C]0.907106610992192[/C][C]0.185786778015617[/C][C]0.0928933890078084[/C][/ROW]
[ROW][C]100[/C][C]0.884607078733035[/C][C]0.23078584253393[/C][C]0.115392921266965[/C][/ROW]
[ROW][C]101[/C][C]0.860615361519267[/C][C]0.278769276961466[/C][C]0.139384638480733[/C][/ROW]
[ROW][C]102[/C][C]0.83587156696296[/C][C]0.32825686607408[/C][C]0.16412843303704[/C][/ROW]
[ROW][C]103[/C][C]0.875796479709201[/C][C]0.248407040581598[/C][C]0.124203520290799[/C][/ROW]
[ROW][C]104[/C][C]0.914359596350102[/C][C]0.171280807299797[/C][C]0.0856404036498984[/C][/ROW]
[ROW][C]105[/C][C]0.925043542953849[/C][C]0.149912914092302[/C][C]0.0749564570461511[/C][/ROW]
[ROW][C]106[/C][C]0.909435769665577[/C][C]0.181128460668845[/C][C]0.0905642303344224[/C][/ROW]
[ROW][C]107[/C][C]0.905858722900243[/C][C]0.188282554199513[/C][C]0.0941412770997566[/C][/ROW]
[ROW][C]108[/C][C]0.910164860487339[/C][C]0.179670279025322[/C][C]0.089835139512661[/C][/ROW]
[ROW][C]109[/C][C]0.908865267778052[/C][C]0.182269464443895[/C][C]0.0911347322219477[/C][/ROW]
[ROW][C]110[/C][C]0.901221164039377[/C][C]0.197557671921246[/C][C]0.0987788359606229[/C][/ROW]
[ROW][C]111[/C][C]0.886025292846051[/C][C]0.227949414307898[/C][C]0.113974707153949[/C][/ROW]
[ROW][C]112[/C][C]0.899915057077616[/C][C]0.200169885844768[/C][C]0.100084942922384[/C][/ROW]
[ROW][C]113[/C][C]0.871777571593192[/C][C]0.256444856813617[/C][C]0.128222428406808[/C][/ROW]
[ROW][C]114[/C][C]0.841065216834467[/C][C]0.317869566331067[/C][C]0.158934783165533[/C][/ROW]
[ROW][C]115[/C][C]0.802814400797231[/C][C]0.394371198405538[/C][C]0.197185599202769[/C][/ROW]
[ROW][C]116[/C][C]0.792288192023945[/C][C]0.415423615952111[/C][C]0.207711807976055[/C][/ROW]
[ROW][C]117[/C][C]0.801063213911621[/C][C]0.397873572176757[/C][C]0.198936786088379[/C][/ROW]
[ROW][C]118[/C][C]0.792850862305772[/C][C]0.414298275388456[/C][C]0.207149137694228[/C][/ROW]
[ROW][C]119[/C][C]0.745725907062652[/C][C]0.508548185874696[/C][C]0.254274092937348[/C][/ROW]
[ROW][C]120[/C][C]0.81587134627801[/C][C]0.368257307443979[/C][C]0.184128653721989[/C][/ROW]
[ROW][C]121[/C][C]0.78347260470419[/C][C]0.433054790591622[/C][C]0.216527395295811[/C][/ROW]
[ROW][C]122[/C][C]0.739600669094388[/C][C]0.520798661811224[/C][C]0.260399330905612[/C][/ROW]
[ROW][C]123[/C][C]0.731701256823724[/C][C]0.536597486352553[/C][C]0.268298743176276[/C][/ROW]
[ROW][C]124[/C][C]0.677187246593199[/C][C]0.645625506813603[/C][C]0.322812753406801[/C][/ROW]
[ROW][C]125[/C][C]0.682576594490703[/C][C]0.634846811018594[/C][C]0.317423405509297[/C][/ROW]
[ROW][C]126[/C][C]0.6522920413469[/C][C]0.6954159173062[/C][C]0.3477079586531[/C][/ROW]
[ROW][C]127[/C][C]0.588959466234945[/C][C]0.82208106753011[/C][C]0.411040533765055[/C][/ROW]
[ROW][C]128[/C][C]0.552166421609131[/C][C]0.895667156781738[/C][C]0.447833578390869[/C][/ROW]
[ROW][C]129[/C][C]0.49903524125999[/C][C]0.99807048251998[/C][C]0.50096475874001[/C][/ROW]
[ROW][C]130[/C][C]0.44494380864256[/C][C]0.88988761728512[/C][C]0.55505619135744[/C][/ROW]
[ROW][C]131[/C][C]0.374981421696104[/C][C]0.749962843392208[/C][C]0.625018578303896[/C][/ROW]
[ROW][C]132[/C][C]0.375366110833356[/C][C]0.750732221666712[/C][C]0.624633889166644[/C][/ROW]
[ROW][C]133[/C][C]0.37547158393236[/C][C]0.75094316786472[/C][C]0.62452841606764[/C][/ROW]
[ROW][C]134[/C][C]0.335005100297721[/C][C]0.670010200595443[/C][C]0.664994899702279[/C][/ROW]
[ROW][C]135[/C][C]0.299849971717293[/C][C]0.599699943434586[/C][C]0.700150028282707[/C][/ROW]
[ROW][C]136[/C][C]0.301040174722991[/C][C]0.602080349445982[/C][C]0.698959825277009[/C][/ROW]
[ROW][C]137[/C][C]0.226501992443876[/C][C]0.453003984887752[/C][C]0.773498007556124[/C][/ROW]
[ROW][C]138[/C][C]0.351490911221509[/C][C]0.702981822443019[/C][C]0.64850908877849[/C][/ROW]
[ROW][C]139[/C][C]0.618000476480684[/C][C]0.763999047038632[/C][C]0.381999523519316[/C][/ROW]
[ROW][C]140[/C][C]0.813317324653412[/C][C]0.373365350693175[/C][C]0.186682675346588[/C][/ROW]
[ROW][C]141[/C][C]0.74603262934877[/C][C]0.507934741302459[/C][C]0.253967370651229[/C][/ROW]
[ROW][C]142[/C][C]0.719534248872319[/C][C]0.560931502255363[/C][C]0.280465751127681[/C][/ROW]
[ROW][C]143[/C][C]0.610057764065747[/C][C]0.779884471868506[/C][C]0.389942235934253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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
130.7898567546380460.4202864907239070.210143245361953
140.7942902572015040.4114194855969920.205709742798496
150.7202810796803460.5594378406393080.279718920319654
160.6523604345969220.6952791308061560.347639565403078
170.5596470212249760.8807059575500480.440352978775024
180.4616115289743340.9232230579486680.538388471025666
190.3668002944528070.7336005889056150.633199705547193
200.78748591030770.4250281793846010.212514089692301
210.7466492526422190.5067014947155630.253350747357781
220.7155048730724590.5689902538550820.284495126927541
230.6495913161694570.7008173676610860.350408683830543
240.6776320019475830.6447359961048330.322367998052417
250.612063670996890.7758726580062220.387936329003111
260.5424017602106310.9151964795787380.457598239789369
270.7794511569164080.4410976861671840.220548843083592
280.7229350643796290.5541298712407420.277064935620371
290.6736303987855840.6527392024288330.326369601214416
300.820414569955330.3591708600893390.179585430044670
310.863602288904170.2727954221916600.136397711095830
320.8295368824971780.3409262350056430.170463117502821
330.7938871419278080.4122257161443830.206112858072192
340.748011208619610.5039775827607810.251988791380391
350.7267026399417640.5465947201164720.273297360058236
360.7295595217307140.5408809565385720.270440478269286
370.6969631452702830.6060737094594350.303036854729717
380.7141915196329030.5716169607341940.285808480367097
390.6703875633683320.6592248732633350.329612436631668
400.6315972753847310.7368054492305390.368402724615269
410.5998466211228150.800306757754370.400153378877185
420.8589058849516620.2821882300966760.141094115048338
430.9440001586580840.1119996826838310.0559998413419157
440.9491198464297450.1017603071405110.0508801535702555
450.9338427767558450.1323144464883090.0661572232441546
460.939947278879070.1201054422418600.0600527211209302
470.9347969007239570.1304061985520870.0652030992760433
480.9207704035557920.1584591928884150.0792295964442076
490.9190128067438670.1619743865122670.0809871932561333
500.9059598217031280.1880803565937430.0940401782968716
510.8948115087000920.2103769825998160.105188491299908
520.9781574702131620.04368505957367680.0218425297868384
530.9711648381510990.0576703236978020.028835161848901
540.976673377976080.04665324404783820.0233266220239191
550.9785811898977020.04283762020459610.0214188101022980
560.9738781980005930.05224360399881480.0261218019994074
570.9658585222950670.06828295540986620.0341414777049331
580.9594751766150940.08104964676981170.0405248233849058
590.9722119773976140.05557604520477270.0277880226023863
600.9669889602787060.06602207944258880.0330110397212944
610.9664756164496830.06704876710063310.0335243835503165
620.9637195857009560.07256082859808860.0362804142990443
630.975166711134010.0496665777319810.0248332888659905
640.979524992909630.04095001418074130.0204750070903706
650.9799913370157180.04001732596856350.0200086629842817
660.9780335338043730.04393293239125350.0219664661956268
670.9810279010737370.03794419785252670.0189720989262633
680.9846393529409020.03072129411819550.0153606470590977
690.9832587411856710.03348251762865860.0167412588143293
700.9784648100384530.04307037992309340.0215351899615467
710.978438755695840.0431224886083180.021561244304159
720.9717362325257350.05652753494853010.0282637674742651
730.967229320697420.06554135860515780.0327706793025789
740.9831713469439960.03365730611200820.0168286530560041
750.9786314960716220.04273700785675510.0213685039283776
760.976356828459680.04728634308063910.0236431715403196
770.9693359646131150.06132807077376940.0306640353868847
780.9599077610062030.0801844779875930.0400922389937965
790.9748721372236590.05025572555268250.0251278627763412
800.9735242855972290.05295142880554290.0264757144027715
810.9797943457412670.04041130851746530.0202056542587327
820.979387216776990.04122556644601850.0206127832230093
830.9726502437570040.05469951248599160.0273497562429958
840.9671765957549930.06564680849001350.0328234042450067
850.9897074137121950.02058517257561050.0102925862878052
860.986493814682750.02701237063449950.0135061853172497
870.9818999273584570.03620014528308630.0181000726415431
880.9757554052950730.04848918940985360.0242445947049268
890.9703204393328260.05935912133434820.0296795606671741
900.9616352920621860.07672941587562890.0383647079378144
910.953494781228670.09301043754265840.0465052187713292
920.9774566890846160.04508662183076770.0225433109153838
930.9699903255022870.06001934899542560.0300096744977128
940.9644198627730510.07116027445389710.0355801372269485
950.9537786927207020.09244261455859540.0462213072792977
960.9406497936862640.1187004126274710.0593502063137357
970.9277892354035960.1444215291928070.0722107645964036
980.9101136706320.1797726587359990.0898863293679996
990.9071066109921920.1857867780156170.0928933890078084
1000.8846070787330350.230785842533930.115392921266965
1010.8606153615192670.2787692769614660.139384638480733
1020.835871566962960.328256866074080.16412843303704
1030.8757964797092010.2484070405815980.124203520290799
1040.9143595963501020.1712808072997970.0856404036498984
1050.9250435429538490.1499129140923020.0749564570461511
1060.9094357696655770.1811284606688450.0905642303344224
1070.9058587229002430.1882825541995130.0941412770997566
1080.9101648604873390.1796702790253220.089835139512661
1090.9088652677780520.1822694644438950.0911347322219477
1100.9012211640393770.1975576719212460.0987788359606229
1110.8860252928460510.2279494143078980.113974707153949
1120.8999150570776160.2001698858447680.100084942922384
1130.8717775715931920.2564448568136170.128222428406808
1140.8410652168344670.3178695663310670.158934783165533
1150.8028144007972310.3943711984055380.197185599202769
1160.7922881920239450.4154236159521110.207711807976055
1170.8010632139116210.3978735721767570.198936786088379
1180.7928508623057720.4142982753884560.207149137694228
1190.7457259070626520.5085481858746960.254274092937348
1200.815871346278010.3682573074439790.184128653721989
1210.783472604704190.4330547905916220.216527395295811
1220.7396006690943880.5207986618112240.260399330905612
1230.7317012568237240.5365974863525530.268298743176276
1240.6771872465931990.6456255068136030.322812753406801
1250.6825765944907030.6348468110185940.317423405509297
1260.65229204134690.69541591730620.3477079586531
1270.5889594662349450.822081067530110.411040533765055
1280.5521664216091310.8956671567817380.447833578390869
1290.499035241259990.998070482519980.50096475874001
1300.444943808642560.889887617285120.55505619135744
1310.3749814216961040.7499628433922080.625018578303896
1320.3753661108333560.7507322216667120.624633889166644
1330.375471583932360.750943167864720.62452841606764
1340.3350051002977210.6700102005954430.664994899702279
1350.2998499717172930.5996999434345860.700150028282707
1360.3010401747229910.6020803494459820.698959825277009
1370.2265019924438760.4530039848877520.773498007556124
1380.3514909112215090.7029818224430190.64850908877849
1390.6180004764806840.7639990470386320.381999523519316
1400.8133173246534120.3733653506931750.186682675346588
1410.746032629348770.5079347413024590.253967370651229
1420.7195342488723190.5609315022553630.280465751127681
1430.6100577640657470.7798844718685060.389942235934253






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level220.16793893129771NOK
10% type I error level440.33587786259542NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 22 & 0.16793893129771 & NOK \tabularnewline
10% type I error level & 44 & 0.33587786259542 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116705&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.16793893129771[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.33587786259542[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116705&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116705&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 level00OK
5% type I error level220.16793893129771NOK
10% type I error level440.33587786259542NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}