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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 22 Nov 2010 10:41:15 +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/Nov/22/t1290422378uzkwkw0gfijorx9.htm/, Retrieved Fri, 09 May 2025 17:15:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98439, Retrieved Fri, 09 May 2025 17:15:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact261

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D      [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
- R  D        [Multiple Regression] [] [2010-12-02 15:03:12] [11b6443b23f19c2dbda3ee0ee9d024b2]
- RMPD        [Univariate Explorative Data Analysis] [] [2010-12-02 15:42:43] [11b6443b23f19c2dbda3ee0ee9d024b2]
- R  D          [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2010-12-02 19:58:40] [69c775ce4d55db2aa75a88e773e8d700]
- R PD        [Multiple Regression] [] [2010-12-02 16:12:01] [11b6443b23f19c2dbda3ee0ee9d024b2]
-             [Multiple Regression] [WS 4: Personality...] [2010-12-02 16:43:36] [4f1a20f787b3465111b61213cdeef1a9]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 17:36:13] [11b6443b23f19c2dbda3ee0ee9d024b2]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [Multiple regressi...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 18:02:49] [94f4aa1c01e87d8321fffb341ed4df07]
- R             [Multiple Regression] [] [2011-11-25 00:47:59] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 17:03:35] [3931071255a6f7f4a767409781cc5f7d]
- RMPD        [Univariate Explorative Data Analysis] [EDA - assignment ...] [2010-12-02 18:07:45] [74be16979710d4c4e7c6647856088456]
-             [Multiple Regression] [] [2010-12-02 18:37:01] [f47feae0308dca73181bb669fbad1c56]
- RM            [Multiple Regression] [] [2012-11-20 16:54:06] [74be16979710d4c4e7c6647856088456]
- R             [Multiple Regression] [W7 ] [2012-11-20 19:29:08] [783d8509970888a6ec44a5a7a0d2a339]
- RM            [Multiple Regression] [Workshop 7] [2012-11-20 19:35:01] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [] [2010-12-02 23:03:05] [d67ce207bd02ca41b9162077ae11c874]
-   PD        [Multiple Regression] [Tutorial1] [2010-12-03 19:28:18] [a7c91bc614e4e21e8b9c8593f39a36f1]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [f5fdea4413921432bb019d1f20c4f2ec]
- R  D          [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
-    D            [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:44:07] [f5fdea4413921432bb019d1f20c4f2ec]
-   P               [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:51:02] [f5fdea4413921432bb019d1f20c4f2ec]
- RMP             [Kendall tau Correlation Matrix] [workshop 10 a] [2012-12-07 13:55:33] [dbae308bdff61c0f4902cc85498d0d35]
- R P               [Kendall tau Correlation Matrix] [workshop 10 b] [2012-12-07 14:31:15] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Multiple Regression] [workshop 10 c] [2012-12-07 14:38:23] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Recursive Partitioning (Regression Trees)] [workshop 10 d] [2012-12-07 14:46:54] [dbae308bdff61c0f4902cc85498d0d35]
-   P                 [Recursive Partitioning (Regression Trees)] [WS 10 recursive p...] [2012-12-10 17:38:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
-                   [Kendall tau Correlation Matrix] [WS 10 Pearson cor...] [2012-12-10 15:35:41] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R P               [Kendall tau Correlation Matrix] [WS 10 Kendall] [2012-12-10 15:49:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
- RMP               [Multiple Regression] [WS 10 multiple re...] [2012-12-10 15:57:07] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R PD        [Multiple Regression] [] [2011-11-21 16:29:10] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-21 21:29:21] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD        [Multiple Regression] [] [2011-11-22 00:46:31] [bdca8f3e7c3554be8c1291e54f61d441]
- R             [Multiple Regression] [] [2011-12-19 20:06:55] [74be16979710d4c4e7c6647856088456]
- RM            [Multiple Regression] [WS7] [2012-11-20 16:38:51] [bdca8f3e7c3554be8c1291e54f61d441]
- RM            [Multiple Regression] [] [2012-11-20 18:17:58] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-11-20 18:19:47] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-11-20 18:21:00] [10b150b957285d7c50cf113330698f19]
- RM            [Multiple Regression] [] [2012-12-21 15:52:30] [74be16979710d4c4e7c6647856088456]
- R  D        [Multiple Regression] [ws7-1] [2011-11-22 10:24:02] [f7a862281046b7153543b12c78921b36]
-    D          [Multiple Regression] [ws7-1] [2011-11-22 10:38:43] [f7a862281046b7153543b12c78921b36]
- R  D            [Multiple Regression] [ws7-3] [2011-11-22 17:14:48] [f7a862281046b7153543b12c78921b36]
-   P               [Multiple Regression] [ws7-3] [2011-11-22 17:19:19] [f7a862281046b7153543b12c78921b36]
-    D                [Multiple Regression] [paper2-3] [2011-12-21 18:58:43] [f7a862281046b7153543b12c78921b36]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	1	1	0
12	12	8	13	5	1	0	0
15	10	12	16	6	0	0	0
12	9	7	12	6	2	0	1
10	10	10	11	5	0	1	2
12	12	7	12	3	0	0	1
15	13	16	18	8	1	1	1
9	12	11	11	4	1	0	0
12	12	14	14	4	4	0	0
11	6	6	9	4	0	0	0
11	5	16	14	6	0	2	1
11	12	11	12	6	2	0	0
15	11	16	11	5	0	2	2
7	14	12	12	4	1	1	1
11	14	7	13	6	0	1	0
11	12	13	11	4	0	0	1
10	12	11	12	6	1	1	0
14	11	15	16	6	2	0	1
10	11	7	9	4	1	0	0
6	7	9	11	4	1	0	0
11	9	7	13	2	0	1	1
15	11	14	15	7	1	2	0
11	11	15	10	5	1	2	1
12	12	7	11	4	2	0	0
14	12	15	13	6	1	0	0
15	11	17	16	6	1	1	0
9	11	15	15	7	1	1	0
13	8	14	14	5	2	2	0
13	9	14	14	6	0	0	2
16	12	8	14	4	1	1	1
13	10	8	8	4	0	1	2
12	10	14	13	7	1	1	1
14	12	14	15	7	1	2	1
11	8	8	13	4	0	2	0
9	12	11	11	4	1	1	0
16	11	16	15	6	2	2	0
12	12	10	15	6	1	1	1
10	7	8	9	5	1	1	2
13	11	14	13	6	1	0	1
16	11	16	16	7	1	3	1
14	12	13	13	6	0	1	2
15	9	5	11	3	1	0	0
5	15	8	12	3	1	0	0
8	11	10	12	4	1	0	0
11	11	8	12	6	0	1	1
16	11	13	14	7	2	0	1
17	11	15	14	5	1	4	4
9	15	6	8	4	0	0	0
9	11	12	13	5	0	0	0
13	12	16	16	6	1	0	1
10	12	5	13	6	1	1	0
6	9	15	11	6	0	2	1
12	12	12	14	5	0	1	0
8	12	8	13	4	0	1	1
14	13	13	13	5	0	0	0
12	11	14	13	5	1	2	2
11	9	12	12	4	0	0	2
16	9	16	16	6	0	3	1
8	11	10	15	2	1	2	0
15	11	15	15	8	0	0	0
7	12	8	12	3	0	0	0
16	12	16	14	6	2	2	0
14	9	19	12	6	0	1	0
16	11	14	15	6	0	0	1
9	9	6	12	5	1	2	1
14	12	13	13	5	2	0	0
11	12	15	12	6	3	1	0
13	12	7	12	5	1	0	0
15	12	13	13	6	1	2	1
5	14	4	5	2	2	0	0
15	11	14	13	5	1	2	2
13	12	13	13	5	1	3	0
11	11	11	14	5	2	0	2
11	6	14	17	6	1	2	1
12	10	12	13	6	0	3	1
12	12	15	13	6	1	1	1
12	13	14	12	5	1	0	2
12	8	13	13	5	0	1	2
14	12	8	14	4	2	0	0
6	12	6	11	2	1	0	0
7	12	7	12	4	0	1	0
14	6	13	12	6	3	1	1
14	11	13	16	6	1	2	1
10	10	11	12	5	1	1	0
13	12	5	12	3	3	0	0
12	13	12	12	6	2	0	0
9	11	8	10	4	1	1	0
12	7	11	15	5	0	0	2
16	11	14	15	8	1	0	1
10	11	9	12	4	2	0	1
14	11	10	16	6	1	1	0
10	11	13	15	6	1	1	1
16	12	16	16	7	0	3	1
15	10	16	13	6	2	1	0
12	11	11	12	5	1	1	1
10	12	8	11	4	0	0	0
8	7	4	13	6	0	0	1
8	13	7	10	3	1	1	0
11	8	14	15	5	1	1	0
13	12	11	13	6	1	0	2
16	11	17	16	7	1	1	2
16	12	15	15	7	1	1	2
14	14	17	18	6	0	0	1
11	10	5	13	3	0	1	1
4	10	4	10	2	1	0	1
14	13	10	16	8	2	1	0
9	10	11	13	3	1	1	1
14	11	15	15	8	1	1	1
8	10	10	14	3	0	1	0
8	7	9	15	4	0	1	0
11	10	12	14	5	1	0	0
12	8	15	13	7	1	0	0
11	12	7	13	6	0	0	0
14	12	13	15	6	0	1	0
15	12	12	16	7	2	1	0
16	11	14	14	6	2	1	0
16	12	14	14	6	0	0	0
11	12	8	16	6	1	1	0
14	12	15	14	6	0	4	1
14	11	12	12	4	2	0	0
12	12	12	13	4	1	1	1
14	11	16	12	5	0	0	3
8	11	9	12	4	1	2	2
13	13	15	14	6	1	1	2
16	12	15	14	6	2	0	2
12	12	6	14	5	0	0	0
16	12	14	16	8	2	0	1
12	12	15	13	6	0	0	0
11	8	10	14	5	1	1	0
4	8	6	4	4	0	0	0
16	12	14	16	8	3	2	1
15	11	12	13	6	1	0	2
10	12	8	16	4	0	1	0
13	13	11	15	6	0	2	4
15	12	13	14	6	0	2	0
12	12	9	13	4	0	1	0
14	11	15	14	6	0	3	0
7	12	13	12	3	1	0	0
19	12	15	15	6	1	1	0
12	10	14	14	5	2	1	1
12	11	16	13	4	1	0	0
13	12	14	14	6	0	1	1
15	12	14	16	4	0	0	0
8	10	10	6	4	2	1	2
12	12	10	13	4	1	0	1
10	13	4	13	6	0	1	0
8	12	8	14	5	1	0	0
10	15	15	15	6	2	2	0
15	11	16	14	6	2	0	1
16	12	12	15	8	0	0	0
13	11	12	13	7	1	1	1
16	12	15	16	7	2	1	0
9	11	9	12	4	0	0	0
14	10	12	15	6	1	0	1
14	11	14	12	6	2	1	2
12	11	11	14	2	1	1	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 9 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&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 time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -0.17879188700372 + 0.100252682453213FindingFriends[t] + 0.212183545436264KnowingPeople[t] + 0.382293973272379Liked[t] + 0.592277125614447Celebrity[t] + 0.310366099758153bestfriend[t] -0.0288664388538009secondbestfriend[t] + 0.408723247498028thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -0.17879188700372 +  0.100252682453213FindingFriends[t] +  0.212183545436264KnowingPeople[t] +  0.382293973272379Liked[t] +  0.592277125614447Celebrity[t] +  0.310366099758153bestfriend[t] -0.0288664388538009secondbestfriend[t] +  0.408723247498028thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -0.17879188700372 +  0.100252682453213FindingFriends[t] +  0.212183545436264KnowingPeople[t] +  0.382293973272379Liked[t] +  0.592277125614447Celebrity[t] +  0.310366099758153bestfriend[t] -0.0288664388538009secondbestfriend[t] +  0.408723247498028thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&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] = -0.17879188700372 + 0.100252682453213FindingFriends[t] + 0.212183545436264KnowingPeople[t] + 0.382293973272379Liked[t] + 0.592277125614447Celebrity[t] + 0.310366099758153bestfriend[t] -0.0288664388538009secondbestfriend[t] + 0.408723247498028thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.178791887003721.432707-0.12480.9008570.450428
FindingFriends0.1002526824532130.0966951.03680.3015210.150761
KnowingPeople0.2121835454362640.0635973.33640.0010730.000537
Liked0.3822939732723790.097263.93060.000136.5e-05
Celebrity0.5922771256144470.1555383.80790.0002050.000102
bestfriend0.3103660997581530.2094361.48190.140490.070245
secondbestfriend-0.02886643885380090.200708-0.14380.8858360.442918
thirdbestfriend0.4087232474980280.2130141.91880.056940.02847

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.17879188700372 & 1.432707 & -0.1248 & 0.900857 & 0.450428 \tabularnewline
FindingFriends & 0.100252682453213 & 0.096695 & 1.0368 & 0.301521 & 0.150761 \tabularnewline
KnowingPeople & 0.212183545436264 & 0.063597 & 3.3364 & 0.001073 & 0.000537 \tabularnewline
Liked & 0.382293973272379 & 0.09726 & 3.9306 & 0.00013 & 6.5e-05 \tabularnewline
Celebrity & 0.592277125614447 & 0.155538 & 3.8079 & 0.000205 & 0.000102 \tabularnewline
bestfriend & 0.310366099758153 & 0.209436 & 1.4819 & 0.14049 & 0.070245 \tabularnewline
secondbestfriend & -0.0288664388538009 & 0.200708 & -0.1438 & 0.885836 & 0.442918 \tabularnewline
thirdbestfriend & 0.408723247498028 & 0.213014 & 1.9188 & 0.05694 & 0.02847 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.17879188700372[/C][C]1.432707[/C][C]-0.1248[/C][C]0.900857[/C][C]0.450428[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.100252682453213[/C][C]0.096695[/C][C]1.0368[/C][C]0.301521[/C][C]0.150761[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.212183545436264[/C][C]0.063597[/C][C]3.3364[/C][C]0.001073[/C][C]0.000537[/C][/ROW]
[ROW][C]Liked[/C][C]0.382293973272379[/C][C]0.09726[/C][C]3.9306[/C][C]0.00013[/C][C]6.5e-05[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.592277125614447[/C][C]0.155538[/C][C]3.8079[/C][C]0.000205[/C][C]0.000102[/C][/ROW]
[ROW][C]bestfriend[/C][C]0.310366099758153[/C][C]0.209436[/C][C]1.4819[/C][C]0.14049[/C][C]0.070245[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]-0.0288664388538009[/C][C]0.200708[/C][C]-0.1438[/C][C]0.885836[/C][C]0.442918[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]0.408723247498028[/C][C]0.213014[/C][C]1.9188[/C][C]0.05694[/C][C]0.02847[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.178791887003721.432707-0.12480.9008570.450428
FindingFriends0.1002526824532130.0966951.03680.3015210.150761
KnowingPeople0.2121835454362640.0635973.33640.0010730.000537
Liked0.3822939732723790.097263.93060.000136.5e-05
Celebrity0.5922771256144470.1555383.80790.0002050.000102
bestfriend0.3103660997581530.2094361.48190.140490.070245
secondbestfriend-0.02886643885380090.200708-0.14380.8858360.442918
thirdbestfriend0.4087232474980280.2130141.91880.056940.02847






Multiple Linear Regression - Regression Statistics
Multiple R0.718894523522317
R-squared0.516809335950379
Adjusted R-squared0.493955723461546
F-TEST (value)22.6139012465928
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.08902237966842
Sum Squared Residuals645.874146407814

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.718894523522317 \tabularnewline
R-squared & 0.516809335950379 \tabularnewline
Adjusted R-squared & 0.493955723461546 \tabularnewline
F-TEST (value) & 22.6139012465928 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.08902237966842 \tabularnewline
Sum Squared Residuals & 645.874146407814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.718894523522317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.516809335950379[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.493955723461546[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.6139012465928[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/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]2.08902237966842[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]645.874146407814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&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 R0.718894523522317
R-squared0.516809335950379
Adjusted R-squared0.493955723461546
F-TEST (value)22.6139012465928
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.08902237966842
Sum Squared Residuals645.874146407814






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.12321531128441.87678468871563
21210.96328204629631.03671795370375
31513.04030380880831.95969619119169
41211.37941295309860.6205870469014
51010.9007697821017-0.9007697821017
6129.28260742409862.71739257590141
71516.829161144089-1.82916114408904
8910.2429676104458-1.24296761044584
91212.9574984658462-0.95749846584623
10117.505579742242343.49442025775766
111112.974177001533-1.97417700153297
121112.1201819347053-1.12018193470527
131512.24525729831872.7547427016813
14711.4178073027051-4.41780730270514
151111.2046484527689-0.204648452768911
161110.76569184905820.234308150941755
171011.7809493960933-1.78094939609331
181414.8065625745847-0.806562574584652
19108.529392799702821.47060720029718
2069.31733710730725-3.31733710730725
21118.742999785543092.25700021445691
221514.02753995652670.972460043473327
231111.5524226318702-0.552422631870179
24129.704599528458942.29540047154106
251413.04084398996450.959156010035451
261514.48297387934720.517026120652803
27914.2685899408167-5.26858994081674
281312.47029978442390.529700215576085
291313.4172767656789-0.417276765678926
301611.13315570259844.86684429740159
31138.73724364579764.26275635420241
321213.6002890138805-1.60028901388053
331414.5365158864779-0.536515886477914
34119.60189521340321.3981047865968
35910.214101171592-1.21410117159204
361614.16999602154291.83000397845709
371213.1243710179722-1.12437101797221
38109.721422797082930.27857720291707
391313.1371310095731-0.137131009573099
401615.21405782931580.785942170684194
411413.09469085547610.905309144523878
42158.076831164854176.92316883514583
4359.69719186915463-4.69719186915462
44810.3128253558287-2.31282535582874
451111.1425032250712-0.142503225071184
461614.20988466278181.79011533721819
471714.25003538974622.74996461025383
4898.025559911048870.974440088951125
49911.4013974458299-2.40139744582994
501314.808632702716-1.80863270271598
511010.8901420967481-0.89014209674811
52612.0161222660924-6.01612226609242
531211.85507766270170.144922337298265
54810.4404956295679-2.44049562956788
551411.81408635617262.18591364382737
561212.8958442537491-0.895844253749078
571111.0437674770327-0.0437674770327478
581614.11090923903681.88909076096322
59810.2174201467094-2.21742014670938
601514.57936740552680.420632594473168
6179.08606772203683-2.08606772203683
621613.88795473072372.11204526927626
631412.86729361246561.13270638753437
641613.59135285635972.4086471436403
65910.2068533045821-1.20685330458214
661412.33456587323571.66543412676427
671113.2504157773547-2.25041577735468
681310.36880452758762.63119547241239
691512.96746726888242.03253273111755
7055.79023616619341-0.790236166193413
711512.89584425374912.10415574625092
721311.93760045691621.06239954308383
731113.0096865681784-2.00968656817842
741114.1073106126889-3.10731061268895
751212.2155458199278-0.215545819927803
761213.4207007986088-1.42070079860878
771212.7717885230907-0.771788523090728
781212.1014030000488-0.101403000048823
791411.06366499371232.93633500628766
8067.99749563203563-1.99749563203563
8179.43729486336121-2.43729486336121
821412.63325583926091.3667441607391
831414.0140965062464-0.0140965062463691
841010.9881669055724-0.98816690557244
85139.38061538500253.6193846149975
861212.4326181625947-0.432618162594745
8799.09500387955766-0.095003879557659
881212.3702376121216-0.37023761212164
891615.08627320734670.91372679265325
901010.8197311576487-0.819731157648661
911412.99768906129341.00231093870665
921013.6606689718278-3.66066897182779
931615.00394441201090.996055587989134
941513.33402183139871.66597816860126
951211.49714283552370.502857164476318
96109.29605087437890.703949125621102
97810.3039187256395-2.30391872563946
9888.49104857341337-0.491048573413374
991112.5710940967919-1.57109409679194
1001313.0095563032155-0.00955630321554896
1011615.89269749995770.107302500042301
1021615.1862891182660.813710881733994
1031415.6755434598453-1.67554345984527
104119.011162502738221.98883749726178
10547.39905245048233-3.39905245048233
1061414.6931147771868-0.693114777186823
107910.594629875114-1.59462987511395
1081415.2695903139292-1.26959031392921
109810.0456509556939-2.04565095569389
110810.5072804617848-2.50728046178481
1111111.9938048364073-0.993804836407263
1121213.2321103857661-1.23211038576614
1131111.0330095267163-0.0330095267162853
1141413.04183230702480.958167692975176
1151514.42495205999170.575047940008309
1161613.39220139625182.6077986037482
1171612.90058831804253.09941168195749
1181112.673574652874-1.67357465287404
1191413.40602935556160.593970644438402
1201411.04755854645942.95244145354057
1211211.59959591107110.400404088928909
1221413.09400739679670.905992603203296
123810.8603554276809-2.86035542768093
1241314.3119707018324-1.31197070183239
1251614.55095055799111.44904944200886
1261210.6108428289381.38915717106205
1271615.87918596283050.120814037169506
1281212.7304778902064-0.730477890206395
1291111.3400659417745-0.340065941774508
13045.79461524078687-1.79461524078687
1311616.131819184881-0.131819184881045
1321513.12148716619861.8785128338014
1331011.178654301887-1.17865430188699
1341314.3237444497438-1.32374444974382
1351512.63067189489862.36932810510136
1361210.24395592750611.75604407249388
1371412.92591986446421.07408013553584
138710.4573515489763-3.4573515489763
1391913.77656549765555.22343450234449
1401213.1083948356822-1.10839483568217
1411211.96822060171870.0317793982812942
1421313.2804451266867-0.280445126686737
1431512.48062201335842.51937798664163
14489.01775498964167-1.01775498964167
1451211.20409525905240.795904740947636
1461010.4678451340069-0.467845134006906
147811.3455760195686-3.34557601956863
1481014.3588232059195-4.3588232059195
1491514.25415817347620.745841826523842
1501614.04306945167131.95693054832875
1511313.2761746054612-0.276174605461218
1521615.06150269630050.938497303699518
15399.79027571063433-0.790275710634327
1541413.37709918279210.622900817207884
1551413.44505994470310.554940055296899
1561210.07617615772711.92382384227293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.1232153112844 & 1.87678468871563 \tabularnewline
2 & 12 & 10.9632820462963 & 1.03671795370375 \tabularnewline
3 & 15 & 13.0403038088083 & 1.95969619119169 \tabularnewline
4 & 12 & 11.3794129530986 & 0.6205870469014 \tabularnewline
5 & 10 & 10.9007697821017 & -0.9007697821017 \tabularnewline
6 & 12 & 9.2826074240986 & 2.71739257590141 \tabularnewline
7 & 15 & 16.829161144089 & -1.82916114408904 \tabularnewline
8 & 9 & 10.2429676104458 & -1.24296761044584 \tabularnewline
9 & 12 & 12.9574984658462 & -0.95749846584623 \tabularnewline
10 & 11 & 7.50557974224234 & 3.49442025775766 \tabularnewline
11 & 11 & 12.974177001533 & -1.97417700153297 \tabularnewline
12 & 11 & 12.1201819347053 & -1.12018193470527 \tabularnewline
13 & 15 & 12.2452572983187 & 2.7547427016813 \tabularnewline
14 & 7 & 11.4178073027051 & -4.41780730270514 \tabularnewline
15 & 11 & 11.2046484527689 & -0.204648452768911 \tabularnewline
16 & 11 & 10.7656918490582 & 0.234308150941755 \tabularnewline
17 & 10 & 11.7809493960933 & -1.78094939609331 \tabularnewline
18 & 14 & 14.8065625745847 & -0.806562574584652 \tabularnewline
19 & 10 & 8.52939279970282 & 1.47060720029718 \tabularnewline
20 & 6 & 9.31733710730725 & -3.31733710730725 \tabularnewline
21 & 11 & 8.74299978554309 & 2.25700021445691 \tabularnewline
22 & 15 & 14.0275399565267 & 0.972460043473327 \tabularnewline
23 & 11 & 11.5524226318702 & -0.552422631870179 \tabularnewline
24 & 12 & 9.70459952845894 & 2.29540047154106 \tabularnewline
25 & 14 & 13.0408439899645 & 0.959156010035451 \tabularnewline
26 & 15 & 14.4829738793472 & 0.517026120652803 \tabularnewline
27 & 9 & 14.2685899408167 & -5.26858994081674 \tabularnewline
28 & 13 & 12.4702997844239 & 0.529700215576085 \tabularnewline
29 & 13 & 13.4172767656789 & -0.417276765678926 \tabularnewline
30 & 16 & 11.1331557025984 & 4.86684429740159 \tabularnewline
31 & 13 & 8.7372436457976 & 4.26275635420241 \tabularnewline
32 & 12 & 13.6002890138805 & -1.60028901388053 \tabularnewline
33 & 14 & 14.5365158864779 & -0.536515886477914 \tabularnewline
34 & 11 & 9.6018952134032 & 1.3981047865968 \tabularnewline
35 & 9 & 10.214101171592 & -1.21410117159204 \tabularnewline
36 & 16 & 14.1699960215429 & 1.83000397845709 \tabularnewline
37 & 12 & 13.1243710179722 & -1.12437101797221 \tabularnewline
38 & 10 & 9.72142279708293 & 0.27857720291707 \tabularnewline
39 & 13 & 13.1371310095731 & -0.137131009573099 \tabularnewline
40 & 16 & 15.2140578293158 & 0.785942170684194 \tabularnewline
41 & 14 & 13.0946908554761 & 0.905309144523878 \tabularnewline
42 & 15 & 8.07683116485417 & 6.92316883514583 \tabularnewline
43 & 5 & 9.69719186915463 & -4.69719186915462 \tabularnewline
44 & 8 & 10.3128253558287 & -2.31282535582874 \tabularnewline
45 & 11 & 11.1425032250712 & -0.142503225071184 \tabularnewline
46 & 16 & 14.2098846627818 & 1.79011533721819 \tabularnewline
47 & 17 & 14.2500353897462 & 2.74996461025383 \tabularnewline
48 & 9 & 8.02555991104887 & 0.974440088951125 \tabularnewline
49 & 9 & 11.4013974458299 & -2.40139744582994 \tabularnewline
50 & 13 & 14.808632702716 & -1.80863270271598 \tabularnewline
51 & 10 & 10.8901420967481 & -0.89014209674811 \tabularnewline
52 & 6 & 12.0161222660924 & -6.01612226609242 \tabularnewline
53 & 12 & 11.8550776627017 & 0.144922337298265 \tabularnewline
54 & 8 & 10.4404956295679 & -2.44049562956788 \tabularnewline
55 & 14 & 11.8140863561726 & 2.18591364382737 \tabularnewline
56 & 12 & 12.8958442537491 & -0.895844253749078 \tabularnewline
57 & 11 & 11.0437674770327 & -0.0437674770327478 \tabularnewline
58 & 16 & 14.1109092390368 & 1.88909076096322 \tabularnewline
59 & 8 & 10.2174201467094 & -2.21742014670938 \tabularnewline
60 & 15 & 14.5793674055268 & 0.420632594473168 \tabularnewline
61 & 7 & 9.08606772203683 & -2.08606772203683 \tabularnewline
62 & 16 & 13.8879547307237 & 2.11204526927626 \tabularnewline
63 & 14 & 12.8672936124656 & 1.13270638753437 \tabularnewline
64 & 16 & 13.5913528563597 & 2.4086471436403 \tabularnewline
65 & 9 & 10.2068533045821 & -1.20685330458214 \tabularnewline
66 & 14 & 12.3345658732357 & 1.66543412676427 \tabularnewline
67 & 11 & 13.2504157773547 & -2.25041577735468 \tabularnewline
68 & 13 & 10.3688045275876 & 2.63119547241239 \tabularnewline
69 & 15 & 12.9674672688824 & 2.03253273111755 \tabularnewline
70 & 5 & 5.79023616619341 & -0.790236166193413 \tabularnewline
71 & 15 & 12.8958442537491 & 2.10415574625092 \tabularnewline
72 & 13 & 11.9376004569162 & 1.06239954308383 \tabularnewline
73 & 11 & 13.0096865681784 & -2.00968656817842 \tabularnewline
74 & 11 & 14.1073106126889 & -3.10731061268895 \tabularnewline
75 & 12 & 12.2155458199278 & -0.215545819927803 \tabularnewline
76 & 12 & 13.4207007986088 & -1.42070079860878 \tabularnewline
77 & 12 & 12.7717885230907 & -0.771788523090728 \tabularnewline
78 & 12 & 12.1014030000488 & -0.101403000048823 \tabularnewline
79 & 14 & 11.0636649937123 & 2.93633500628766 \tabularnewline
80 & 6 & 7.99749563203563 & -1.99749563203563 \tabularnewline
81 & 7 & 9.43729486336121 & -2.43729486336121 \tabularnewline
82 & 14 & 12.6332558392609 & 1.3667441607391 \tabularnewline
83 & 14 & 14.0140965062464 & -0.0140965062463691 \tabularnewline
84 & 10 & 10.9881669055724 & -0.98816690557244 \tabularnewline
85 & 13 & 9.3806153850025 & 3.6193846149975 \tabularnewline
86 & 12 & 12.4326181625947 & -0.432618162594745 \tabularnewline
87 & 9 & 9.09500387955766 & -0.095003879557659 \tabularnewline
88 & 12 & 12.3702376121216 & -0.37023761212164 \tabularnewline
89 & 16 & 15.0862732073467 & 0.91372679265325 \tabularnewline
90 & 10 & 10.8197311576487 & -0.819731157648661 \tabularnewline
91 & 14 & 12.9976890612934 & 1.00231093870665 \tabularnewline
92 & 10 & 13.6606689718278 & -3.66066897182779 \tabularnewline
93 & 16 & 15.0039444120109 & 0.996055587989134 \tabularnewline
94 & 15 & 13.3340218313987 & 1.66597816860126 \tabularnewline
95 & 12 & 11.4971428355237 & 0.502857164476318 \tabularnewline
96 & 10 & 9.2960508743789 & 0.703949125621102 \tabularnewline
97 & 8 & 10.3039187256395 & -2.30391872563946 \tabularnewline
98 & 8 & 8.49104857341337 & -0.491048573413374 \tabularnewline
99 & 11 & 12.5710940967919 & -1.57109409679194 \tabularnewline
100 & 13 & 13.0095563032155 & -0.00955630321554896 \tabularnewline
101 & 16 & 15.8926974999577 & 0.107302500042301 \tabularnewline
102 & 16 & 15.186289118266 & 0.813710881733994 \tabularnewline
103 & 14 & 15.6755434598453 & -1.67554345984527 \tabularnewline
104 & 11 & 9.01116250273822 & 1.98883749726178 \tabularnewline
105 & 4 & 7.39905245048233 & -3.39905245048233 \tabularnewline
106 & 14 & 14.6931147771868 & -0.693114777186823 \tabularnewline
107 & 9 & 10.594629875114 & -1.59462987511395 \tabularnewline
108 & 14 & 15.2695903139292 & -1.26959031392921 \tabularnewline
109 & 8 & 10.0456509556939 & -2.04565095569389 \tabularnewline
110 & 8 & 10.5072804617848 & -2.50728046178481 \tabularnewline
111 & 11 & 11.9938048364073 & -0.993804836407263 \tabularnewline
112 & 12 & 13.2321103857661 & -1.23211038576614 \tabularnewline
113 & 11 & 11.0330095267163 & -0.0330095267162853 \tabularnewline
114 & 14 & 13.0418323070248 & 0.958167692975176 \tabularnewline
115 & 15 & 14.4249520599917 & 0.575047940008309 \tabularnewline
116 & 16 & 13.3922013962518 & 2.6077986037482 \tabularnewline
117 & 16 & 12.9005883180425 & 3.09941168195749 \tabularnewline
118 & 11 & 12.673574652874 & -1.67357465287404 \tabularnewline
119 & 14 & 13.4060293555616 & 0.593970644438402 \tabularnewline
120 & 14 & 11.0475585464594 & 2.95244145354057 \tabularnewline
121 & 12 & 11.5995959110711 & 0.400404088928909 \tabularnewline
122 & 14 & 13.0940073967967 & 0.905992603203296 \tabularnewline
123 & 8 & 10.8603554276809 & -2.86035542768093 \tabularnewline
124 & 13 & 14.3119707018324 & -1.31197070183239 \tabularnewline
125 & 16 & 14.5509505579911 & 1.44904944200886 \tabularnewline
126 & 12 & 10.610842828938 & 1.38915717106205 \tabularnewline
127 & 16 & 15.8791859628305 & 0.120814037169506 \tabularnewline
128 & 12 & 12.7304778902064 & -0.730477890206395 \tabularnewline
129 & 11 & 11.3400659417745 & -0.340065941774508 \tabularnewline
130 & 4 & 5.79461524078687 & -1.79461524078687 \tabularnewline
131 & 16 & 16.131819184881 & -0.131819184881045 \tabularnewline
132 & 15 & 13.1214871661986 & 1.8785128338014 \tabularnewline
133 & 10 & 11.178654301887 & -1.17865430188699 \tabularnewline
134 & 13 & 14.3237444497438 & -1.32374444974382 \tabularnewline
135 & 15 & 12.6306718948986 & 2.36932810510136 \tabularnewline
136 & 12 & 10.2439559275061 & 1.75604407249388 \tabularnewline
137 & 14 & 12.9259198644642 & 1.07408013553584 \tabularnewline
138 & 7 & 10.4573515489763 & -3.4573515489763 \tabularnewline
139 & 19 & 13.7765654976555 & 5.22343450234449 \tabularnewline
140 & 12 & 13.1083948356822 & -1.10839483568217 \tabularnewline
141 & 12 & 11.9682206017187 & 0.0317793982812942 \tabularnewline
142 & 13 & 13.2804451266867 & -0.280445126686737 \tabularnewline
143 & 15 & 12.4806220133584 & 2.51937798664163 \tabularnewline
144 & 8 & 9.01775498964167 & -1.01775498964167 \tabularnewline
145 & 12 & 11.2040952590524 & 0.795904740947636 \tabularnewline
146 & 10 & 10.4678451340069 & -0.467845134006906 \tabularnewline
147 & 8 & 11.3455760195686 & -3.34557601956863 \tabularnewline
148 & 10 & 14.3588232059195 & -4.3588232059195 \tabularnewline
149 & 15 & 14.2541581734762 & 0.745841826523842 \tabularnewline
150 & 16 & 14.0430694516713 & 1.95693054832875 \tabularnewline
151 & 13 & 13.2761746054612 & -0.276174605461218 \tabularnewline
152 & 16 & 15.0615026963005 & 0.938497303699518 \tabularnewline
153 & 9 & 9.79027571063433 & -0.790275710634327 \tabularnewline
154 & 14 & 13.3770991827921 & 0.622900817207884 \tabularnewline
155 & 14 & 13.4450599447031 & 0.554940055296899 \tabularnewline
156 & 12 & 10.0761761577271 & 1.92382384227293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&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]11.1232153112844[/C][C]1.87678468871563[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.9632820462963[/C][C]1.03671795370375[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.0403038088083[/C][C]1.95969619119169[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.3794129530986[/C][C]0.6205870469014[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.9007697821017[/C][C]-0.9007697821017[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.2826074240986[/C][C]2.71739257590141[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.829161144089[/C][C]-1.82916114408904[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2429676104458[/C][C]-1.24296761044584[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.9574984658462[/C][C]-0.95749846584623[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.50557974224234[/C][C]3.49442025775766[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]12.974177001533[/C][C]-1.97417700153297[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1201819347053[/C][C]-1.12018193470527[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2452572983187[/C][C]2.7547427016813[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4178073027051[/C][C]-4.41780730270514[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.2046484527689[/C][C]-0.204648452768911[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.7656918490582[/C][C]0.234308150941755[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.7809493960933[/C][C]-1.78094939609331[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.8065625745847[/C][C]-0.806562574584652[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.52939279970282[/C][C]1.47060720029718[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.31733710730725[/C][C]-3.31733710730725[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.74299978554309[/C][C]2.25700021445691[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.0275399565267[/C][C]0.972460043473327[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.5524226318702[/C][C]-0.552422631870179[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.70459952845894[/C][C]2.29540047154106[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.0408439899645[/C][C]0.959156010035451[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.4829738793472[/C][C]0.517026120652803[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.2685899408167[/C][C]-5.26858994081674[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4702997844239[/C][C]0.529700215576085[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.4172767656789[/C][C]-0.417276765678926[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.1331557025984[/C][C]4.86684429740159[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.7372436457976[/C][C]4.26275635420241[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.6002890138805[/C][C]-1.60028901388053[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5365158864779[/C][C]-0.536515886477914[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]9.6018952134032[/C][C]1.3981047865968[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.214101171592[/C][C]-1.21410117159204[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.1699960215429[/C][C]1.83000397845709[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.1243710179722[/C][C]-1.12437101797221[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.72142279708293[/C][C]0.27857720291707[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.1371310095731[/C][C]-0.137131009573099[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2140578293158[/C][C]0.785942170684194[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.0946908554761[/C][C]0.905309144523878[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.07683116485417[/C][C]6.92316883514583[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.69719186915463[/C][C]-4.69719186915462[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.3128253558287[/C][C]-2.31282535582874[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.1425032250712[/C][C]-0.142503225071184[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]14.2098846627818[/C][C]1.79011533721819[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.2500353897462[/C][C]2.74996461025383[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.02555991104887[/C][C]0.974440088951125[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4013974458299[/C][C]-2.40139744582994[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.808632702716[/C][C]-1.80863270271598[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.8901420967481[/C][C]-0.89014209674811[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.0161222660924[/C][C]-6.01612226609242[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.8550776627017[/C][C]0.144922337298265[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4404956295679[/C][C]-2.44049562956788[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8140863561726[/C][C]2.18591364382737[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.8958442537491[/C][C]-0.895844253749078[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]11.0437674770327[/C][C]-0.0437674770327478[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.1109092390368[/C][C]1.88909076096322[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2174201467094[/C][C]-2.21742014670938[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5793674055268[/C][C]0.420632594473168[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.08606772203683[/C][C]-2.08606772203683[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.8879547307237[/C][C]2.11204526927626[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.8672936124656[/C][C]1.13270638753437[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.5913528563597[/C][C]2.4086471436403[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.2068533045821[/C][C]-1.20685330458214[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.3345658732357[/C][C]1.66543412676427[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2504157773547[/C][C]-2.25041577735468[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3688045275876[/C][C]2.63119547241239[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9674672688824[/C][C]2.03253273111755[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.79023616619341[/C][C]-0.790236166193413[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.8958442537491[/C][C]2.10415574625092[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.9376004569162[/C][C]1.06239954308383[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]13.0096865681784[/C][C]-2.00968656817842[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1073106126889[/C][C]-3.10731061268895[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.2155458199278[/C][C]-0.215545819927803[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4207007986088[/C][C]-1.42070079860878[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.7717885230907[/C][C]-0.771788523090728[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.1014030000488[/C][C]-0.101403000048823[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.0636649937123[/C][C]2.93633500628766[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.99749563203563[/C][C]-1.99749563203563[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.43729486336121[/C][C]-2.43729486336121[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.6332558392609[/C][C]1.3667441607391[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.0140965062464[/C][C]-0.0140965062463691[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.9881669055724[/C][C]-0.98816690557244[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.3806153850025[/C][C]3.6193846149975[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4326181625947[/C][C]-0.432618162594745[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.09500387955766[/C][C]-0.095003879557659[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.3702376121216[/C][C]-0.37023761212164[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0862732073467[/C][C]0.91372679265325[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.8197311576487[/C][C]-0.819731157648661[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]12.9976890612934[/C][C]1.00231093870665[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.6606689718278[/C][C]-3.66066897182779[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0039444120109[/C][C]0.996055587989134[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.3340218313987[/C][C]1.66597816860126[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4971428355237[/C][C]0.502857164476318[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.2960508743789[/C][C]0.703949125621102[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.3039187256395[/C][C]-2.30391872563946[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.49104857341337[/C][C]-0.491048573413374[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.5710940967919[/C][C]-1.57109409679194[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]13.0095563032155[/C][C]-0.00955630321554896[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.8926974999577[/C][C]0.107302500042301[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.186289118266[/C][C]0.813710881733994[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.6755434598453[/C][C]-1.67554345984527[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.01116250273822[/C][C]1.98883749726178[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.39905245048233[/C][C]-3.39905245048233[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.6931147771868[/C][C]-0.693114777186823[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.594629875114[/C][C]-1.59462987511395[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2695903139292[/C][C]-1.26959031392921[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.0456509556939[/C][C]-2.04565095569389[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.5072804617848[/C][C]-2.50728046178481[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.9938048364073[/C][C]-0.993804836407263[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.2321103857661[/C][C]-1.23211038576614[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0330095267163[/C][C]-0.0330095267162853[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.0418323070248[/C][C]0.958167692975176[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4249520599917[/C][C]0.575047940008309[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3922013962518[/C][C]2.6077986037482[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.9005883180425[/C][C]3.09941168195749[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.673574652874[/C][C]-1.67357465287404[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.4060293555616[/C][C]0.593970644438402[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]11.0475585464594[/C][C]2.95244145354057[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.5995959110711[/C][C]0.400404088928909[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.0940073967967[/C][C]0.905992603203296[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8603554276809[/C][C]-2.86035542768093[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.3119707018324[/C][C]-1.31197070183239[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.5509505579911[/C][C]1.44904944200886[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.610842828938[/C][C]1.38915717106205[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.8791859628305[/C][C]0.120814037169506[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.7304778902064[/C][C]-0.730477890206395[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.3400659417745[/C][C]-0.340065941774508[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.79461524078687[/C][C]-1.79461524078687[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.131819184881[/C][C]-0.131819184881045[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.1214871661986[/C][C]1.8785128338014[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.178654301887[/C][C]-1.17865430188699[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]14.3237444497438[/C][C]-1.32374444974382[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]12.6306718948986[/C][C]2.36932810510136[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.2439559275061[/C][C]1.75604407249388[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]12.9259198644642[/C][C]1.07408013553584[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.4573515489763[/C][C]-3.4573515489763[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.7765654976555[/C][C]5.22343450234449[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1083948356822[/C][C]-1.10839483568217[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.9682206017187[/C][C]0.0317793982812942[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.2804451266867[/C][C]-0.280445126686737[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4806220133584[/C][C]2.51937798664163[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]9.01775498964167[/C][C]-1.01775498964167[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.2040952590524[/C][C]0.795904740947636[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.4678451340069[/C][C]-0.467845134006906[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.3455760195686[/C][C]-3.34557601956863[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3588232059195[/C][C]-4.3588232059195[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]14.2541581734762[/C][C]0.745841826523842[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.0430694516713[/C][C]1.95693054832875[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.2761746054612[/C][C]-0.276174605461218[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0615026963005[/C][C]0.938497303699518[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.79027571063433[/C][C]-0.790275710634327[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.3770991827921[/C][C]0.622900817207884[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.4450599447031[/C][C]0.554940055296899[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.0761761577271[/C][C]1.92382384227293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&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
11311.12321531128441.87678468871563
21210.96328204629631.03671795370375
31513.04030380880831.95969619119169
41211.37941295309860.6205870469014
51010.9007697821017-0.9007697821017
6129.28260742409862.71739257590141
71516.829161144089-1.82916114408904
8910.2429676104458-1.24296761044584
91212.9574984658462-0.95749846584623
10117.505579742242343.49442025775766
111112.974177001533-1.97417700153297
121112.1201819347053-1.12018193470527
131512.24525729831872.7547427016813
14711.4178073027051-4.41780730270514
151111.2046484527689-0.204648452768911
161110.76569184905820.234308150941755
171011.7809493960933-1.78094939609331
181414.8065625745847-0.806562574584652
19108.529392799702821.47060720029718
2069.31733710730725-3.31733710730725
21118.742999785543092.25700021445691
221514.02753995652670.972460043473327
231111.5524226318702-0.552422631870179
24129.704599528458942.29540047154106
251413.04084398996450.959156010035451
261514.48297387934720.517026120652803
27914.2685899408167-5.26858994081674
281312.47029978442390.529700215576085
291313.4172767656789-0.417276765678926
301611.13315570259844.86684429740159
31138.73724364579764.26275635420241
321213.6002890138805-1.60028901388053
331414.5365158864779-0.536515886477914
34119.60189521340321.3981047865968
35910.214101171592-1.21410117159204
361614.16999602154291.83000397845709
371213.1243710179722-1.12437101797221
38109.721422797082930.27857720291707
391313.1371310095731-0.137131009573099
401615.21405782931580.785942170684194
411413.09469085547610.905309144523878
42158.076831164854176.92316883514583
4359.69719186915463-4.69719186915462
44810.3128253558287-2.31282535582874
451111.1425032250712-0.142503225071184
461614.20988466278181.79011533721819
471714.25003538974622.74996461025383
4898.025559911048870.974440088951125
49911.4013974458299-2.40139744582994
501314.808632702716-1.80863270271598
511010.8901420967481-0.89014209674811
52612.0161222660924-6.01612226609242
531211.85507766270170.144922337298265
54810.4404956295679-2.44049562956788
551411.81408635617262.18591364382737
561212.8958442537491-0.895844253749078
571111.0437674770327-0.0437674770327478
581614.11090923903681.88909076096322
59810.2174201467094-2.21742014670938
601514.57936740552680.420632594473168
6179.08606772203683-2.08606772203683
621613.88795473072372.11204526927626
631412.86729361246561.13270638753437
641613.59135285635972.4086471436403
65910.2068533045821-1.20685330458214
661412.33456587323571.66543412676427
671113.2504157773547-2.25041577735468
681310.36880452758762.63119547241239
691512.96746726888242.03253273111755
7055.79023616619341-0.790236166193413
711512.89584425374912.10415574625092
721311.93760045691621.06239954308383
731113.0096865681784-2.00968656817842
741114.1073106126889-3.10731061268895
751212.2155458199278-0.215545819927803
761213.4207007986088-1.42070079860878
771212.7717885230907-0.771788523090728
781212.1014030000488-0.101403000048823
791411.06366499371232.93633500628766
8067.99749563203563-1.99749563203563
8179.43729486336121-2.43729486336121
821412.63325583926091.3667441607391
831414.0140965062464-0.0140965062463691
841010.9881669055724-0.98816690557244
85139.38061538500253.6193846149975
861212.4326181625947-0.432618162594745
8799.09500387955766-0.095003879557659
881212.3702376121216-0.37023761212164
891615.08627320734670.91372679265325
901010.8197311576487-0.819731157648661
911412.99768906129341.00231093870665
921013.6606689718278-3.66066897182779
931615.00394441201090.996055587989134
941513.33402183139871.66597816860126
951211.49714283552370.502857164476318
96109.29605087437890.703949125621102
97810.3039187256395-2.30391872563946
9888.49104857341337-0.491048573413374
991112.5710940967919-1.57109409679194
1001313.0095563032155-0.00955630321554896
1011615.89269749995770.107302500042301
1021615.1862891182660.813710881733994
1031415.6755434598453-1.67554345984527
104119.011162502738221.98883749726178
10547.39905245048233-3.39905245048233
1061414.6931147771868-0.693114777186823
107910.594629875114-1.59462987511395
1081415.2695903139292-1.26959031392921
109810.0456509556939-2.04565095569389
110810.5072804617848-2.50728046178481
1111111.9938048364073-0.993804836407263
1121213.2321103857661-1.23211038576614
1131111.0330095267163-0.0330095267162853
1141413.04183230702480.958167692975176
1151514.42495205999170.575047940008309
1161613.39220139625182.6077986037482
1171612.90058831804253.09941168195749
1181112.673574652874-1.67357465287404
1191413.40602935556160.593970644438402
1201411.04755854645942.95244145354057
1211211.59959591107110.400404088928909
1221413.09400739679670.905992603203296
123810.8603554276809-2.86035542768093
1241314.3119707018324-1.31197070183239
1251614.55095055799111.44904944200886
1261210.6108428289381.38915717106205
1271615.87918596283050.120814037169506
1281212.7304778902064-0.730477890206395
1291111.3400659417745-0.340065941774508
13045.79461524078687-1.79461524078687
1311616.131819184881-0.131819184881045
1321513.12148716619861.8785128338014
1331011.178654301887-1.17865430188699
1341314.3237444497438-1.32374444974382
1351512.63067189489862.36932810510136
1361210.24395592750611.75604407249388
1371412.92591986446421.07408013553584
138710.4573515489763-3.4573515489763
1391913.77656549765555.22343450234449
1401213.1083948356822-1.10839483568217
1411211.96822060171870.0317793982812942
1421313.2804451266867-0.280445126686737
1431512.48062201335842.51937798664163
14489.01775498964167-1.01775498964167
1451211.20409525905240.795904740947636
1461010.4678451340069-0.467845134006906
147811.3455760195686-3.34557601956863
1481014.3588232059195-4.3588232059195
1491514.25415817347620.745841826523842
1501614.04306945167131.95693054832875
1511313.2761746054612-0.276174605461218
1521615.06150269630050.938497303699518
15399.79027571063433-0.790275710634327
1541413.37709918279210.622900817207884
1551413.44505994470310.554940055296899
1561210.07617615772711.92382384227293






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2614611292221170.5229222584442340.738538870777883
120.138882989021840.277765978043680.86111701097816
130.6133159687199050.773368062560190.386684031280095
140.8647090486143670.2705819027712670.135290951385633
150.8059182230822240.3881635538355520.194081776917776
160.7401202663768420.5197594672463160.259879733623158
170.6636766582465220.6726466835069570.336323341753478
180.5733743347615890.8532513304768230.426625665238411
190.4940433790442870.9880867580885740.505956620955713
200.7440425860324480.5119148279351030.255957413967552
210.6837722217914430.6324555564171140.316227778208557
220.6851569506316470.6296860987367050.314843049368353
230.6158860580678330.7682278838643340.384113941932167
240.6129406307656220.7741187384687570.387059369234378
250.5772857075038330.8454285849923350.422714292496167
260.5164351808880590.9671296382238830.483564819111942
270.7425610496384540.5148779007230910.257438950361546
280.699798351442880.600403297114240.30020164855712
290.6388799441910440.7222401116179120.361120055808956
300.7566081187445440.4867837625109120.243391881255456
310.8207686914043230.3584626171913550.179231308595677
320.784607512721310.430784974557380.21539248727869
330.7378282682535840.5243434634928320.262171731746416
340.6987410736495310.6025178527009380.301258926350469
350.6735364491920640.6529271016158720.326463550807936
360.7045437008211580.5909125983576840.295456299178842
370.6820521807676150.635895638464770.317947819232385
380.6343645407259740.7312709185480520.365635459274026
390.5849390359063390.8301219281873230.415060964093661
400.5418095622940460.9163808754119070.458190437705954
410.4933899109254220.9867798218508440.506610089074578
420.8201510223441170.3596979553117660.179848977655883
430.9575031925767030.08499361484659450.0424968074232972
440.9615093951465030.07698120970699450.0384906048534972
450.9506264293537040.09874714129259160.0493735706462958
460.9543590487179660.09128190256406880.0456409512820344
470.9552881372414160.0894237255171680.044711862758584
480.9460721450457430.1078557099085130.0539278549542566
490.9467459202982580.1065081594034840.0532540797017418
500.9410485458479360.1179029083041280.0589514541520638
510.9330309021706910.1339381956586190.0669690978293093
520.9891537196909740.02169256061805210.010846280309026
530.9852450027051370.02950999458972560.0147549972948628
540.9901900545393240.01961989092135290.00980994546067645
550.9924166407996260.01516671840074840.00758335920037418
560.9903106726310110.01937865473797740.00968932736898872
570.9872026825522080.02559463489558360.0127973174477918
580.9866799714961810.02664005700763750.0133200285038188
590.9904728722107740.01905425557845170.00952712778922585
600.9885697721692750.02286045566145090.0114302278307254
610.9889988800572660.02200223988546820.0110011199427341
620.990163650696620.01967269860676030.00983634930338016
630.9889802764548350.02203944709033090.0110197235451655
640.9902364952937780.01952700941244420.00976350470622212
650.9888000983379820.02239980332403620.0111999016620181
660.9873683120012950.0252633759974090.0126316879987045
670.9886330851756110.02273382964877790.011366914824389
680.990603640393710.01879271921258010.00939635960629004
690.990669929918730.01866014016254110.00933007008127055
700.9877281825817030.02454363483659360.0122718174182968
710.9883790416521390.02324191669572270.0116209583478613
720.985664684435910.02867063112817820.0143353155640891
730.9855911407760010.02881771844799750.0144088592239987
740.9900150228679390.0199699542641230.00998497713206149
750.9866018267727140.02679634645457190.0133981732272859
760.9843432741033830.03131345179323510.0156567258966175
770.97976320393560.04047359212880050.0202367960644003
780.97366793426680.05266413146640090.0263320657332004
790.9800619984841350.0398760030317290.0199380015158645
800.9796433634994480.04071327300110450.0203566365005523
810.981264145287460.03747170942507850.0187358547125393
820.979187754149380.0416244917012390.0208122458506195
830.972349988027680.05530002394464150.0276500119723208
840.9654917701951120.06901645960977590.034508229804888
850.9855484778169030.02890304436619360.0144515221830968
860.9809439746324210.03811205073515750.0190560253675788
870.974780944195140.05043811160971990.02521905580486
880.9676507754066320.06469844918673560.0323492245933678
890.9594274145294980.08114517094100320.0405725854705016
900.9492633563191270.1014732873617450.0507366436808727
910.9402979916274520.1194040167450970.0597020083725485
920.9662563646341640.06748727073167180.0337436353658359
930.957709641148740.08458071770252040.0422903588512602
940.953160767572930.09367846485414020.0468392324270701
950.9415400023334980.1169199953330030.0584599976665017
960.9276961486576470.1446077026847060.0723038513423532
970.9241705364247580.1516589271504840.075829463575242
980.905781518326750.18843696334650.0942184816732498
990.8973089073544350.2053821852911310.102691092645565
1000.8726997813408020.2546004373183950.127300218659198
1010.845252117712840.309495764574320.15474788228716
1020.8161083499707650.367783300058470.183891650029235
1030.844015972810250.3119680543795010.155984027189751
1040.8844484521370340.2311030957259310.115551547862966
1050.8918024226861690.2163951546276620.108197577313831
1060.8671749344729170.2656501310541670.132825065527083
1070.8456960875737550.3086078248524890.154303912426245
1080.8408456295907870.3183087408184260.159154370409213
1090.8326763205528640.3346473588942730.167323679447136
1100.8545954674456510.2908090651086980.145404532554349
1110.842200233462480.3155995330750390.157799766537519
1120.886160387958570.227679224082860.11383961204143
1130.8564507370826780.2870985258346440.143549262917322
1140.8256501668851590.3486996662296820.174349833114841
1150.7878216948411710.4243566103176580.212178305158829
1160.7993282437061750.4013435125876510.200671756293825
1170.8083614870413760.3832770259172490.191638512958624
1180.7908367067637820.4183265864724360.209163293236218
1190.7486765439383780.5026469121232440.251323456061622
1200.8295178872995930.3409642254008140.170482112700407
1210.7989138047382250.402172390523550.201086195261775
1220.7531780658401930.4936438683196140.246821934159807
1230.7535117969913280.4929764060173440.246488203008672
1240.7221348368826990.5557303262346030.277865163117301
1250.6979772209867370.6040455580265270.302022779013263
1260.6816893253331960.6366213493336070.318310674666804
1270.6190850667373380.7618298665253240.380914933262662
1280.604098320408820.791803359182360.39590167959118
1290.595087127880760.809825744238480.40491287211924
1300.583916776722240.832166446555520.41608322327776
1310.5170328696803710.9659342606392580.482967130319629
1320.4881460029228370.9762920058456740.511853997077163
1330.4536106142182480.9072212284364960.546389385781752
1340.4094417839599710.8188835679199420.590558216040029
1350.3609712769556650.721942553911330.639028723044335
1360.3372528720262520.6745057440525050.662747127973748
1370.3042138989317730.6084277978635470.695786101068227
1380.3352764335017480.6705528670034970.664723566498252
1390.7298268530738040.5403462938523920.270173146926196
1400.7275974199120.5448051601759990.272402580088
1410.6359699823534190.7280600352931610.364030017646581
1420.6347135128653790.7305729742692420.365286487134621
1430.5128198137942240.9743603724115510.487180186205776
1440.3956039295584320.7912078591168640.604396070441568
1450.3507749987437050.701549997487410.649225001256295

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.261461129222117 & 0.522922258444234 & 0.738538870777883 \tabularnewline
12 & 0.13888298902184 & 0.27776597804368 & 0.86111701097816 \tabularnewline
13 & 0.613315968719905 & 0.77336806256019 & 0.386684031280095 \tabularnewline
14 & 0.864709048614367 & 0.270581902771267 & 0.135290951385633 \tabularnewline
15 & 0.805918223082224 & 0.388163553835552 & 0.194081776917776 \tabularnewline
16 & 0.740120266376842 & 0.519759467246316 & 0.259879733623158 \tabularnewline
17 & 0.663676658246522 & 0.672646683506957 & 0.336323341753478 \tabularnewline
18 & 0.573374334761589 & 0.853251330476823 & 0.426625665238411 \tabularnewline
19 & 0.494043379044287 & 0.988086758088574 & 0.505956620955713 \tabularnewline
20 & 0.744042586032448 & 0.511914827935103 & 0.255957413967552 \tabularnewline
21 & 0.683772221791443 & 0.632455556417114 & 0.316227778208557 \tabularnewline
22 & 0.685156950631647 & 0.629686098736705 & 0.314843049368353 \tabularnewline
23 & 0.615886058067833 & 0.768227883864334 & 0.384113941932167 \tabularnewline
24 & 0.612940630765622 & 0.774118738468757 & 0.387059369234378 \tabularnewline
25 & 0.577285707503833 & 0.845428584992335 & 0.422714292496167 \tabularnewline
26 & 0.516435180888059 & 0.967129638223883 & 0.483564819111942 \tabularnewline
27 & 0.742561049638454 & 0.514877900723091 & 0.257438950361546 \tabularnewline
28 & 0.69979835144288 & 0.60040329711424 & 0.30020164855712 \tabularnewline
29 & 0.638879944191044 & 0.722240111617912 & 0.361120055808956 \tabularnewline
30 & 0.756608118744544 & 0.486783762510912 & 0.243391881255456 \tabularnewline
31 & 0.820768691404323 & 0.358462617191355 & 0.179231308595677 \tabularnewline
32 & 0.78460751272131 & 0.43078497455738 & 0.21539248727869 \tabularnewline
33 & 0.737828268253584 & 0.524343463492832 & 0.262171731746416 \tabularnewline
34 & 0.698741073649531 & 0.602517852700938 & 0.301258926350469 \tabularnewline
35 & 0.673536449192064 & 0.652927101615872 & 0.326463550807936 \tabularnewline
36 & 0.704543700821158 & 0.590912598357684 & 0.295456299178842 \tabularnewline
37 & 0.682052180767615 & 0.63589563846477 & 0.317947819232385 \tabularnewline
38 & 0.634364540725974 & 0.731270918548052 & 0.365635459274026 \tabularnewline
39 & 0.584939035906339 & 0.830121928187323 & 0.415060964093661 \tabularnewline
40 & 0.541809562294046 & 0.916380875411907 & 0.458190437705954 \tabularnewline
41 & 0.493389910925422 & 0.986779821850844 & 0.506610089074578 \tabularnewline
42 & 0.820151022344117 & 0.359697955311766 & 0.179848977655883 \tabularnewline
43 & 0.957503192576703 & 0.0849936148465945 & 0.0424968074232972 \tabularnewline
44 & 0.961509395146503 & 0.0769812097069945 & 0.0384906048534972 \tabularnewline
45 & 0.950626429353704 & 0.0987471412925916 & 0.0493735706462958 \tabularnewline
46 & 0.954359048717966 & 0.0912819025640688 & 0.0456409512820344 \tabularnewline
47 & 0.955288137241416 & 0.089423725517168 & 0.044711862758584 \tabularnewline
48 & 0.946072145045743 & 0.107855709908513 & 0.0539278549542566 \tabularnewline
49 & 0.946745920298258 & 0.106508159403484 & 0.0532540797017418 \tabularnewline
50 & 0.941048545847936 & 0.117902908304128 & 0.0589514541520638 \tabularnewline
51 & 0.933030902170691 & 0.133938195658619 & 0.0669690978293093 \tabularnewline
52 & 0.989153719690974 & 0.0216925606180521 & 0.010846280309026 \tabularnewline
53 & 0.985245002705137 & 0.0295099945897256 & 0.0147549972948628 \tabularnewline
54 & 0.990190054539324 & 0.0196198909213529 & 0.00980994546067645 \tabularnewline
55 & 0.992416640799626 & 0.0151667184007484 & 0.00758335920037418 \tabularnewline
56 & 0.990310672631011 & 0.0193786547379774 & 0.00968932736898872 \tabularnewline
57 & 0.987202682552208 & 0.0255946348955836 & 0.0127973174477918 \tabularnewline
58 & 0.986679971496181 & 0.0266400570076375 & 0.0133200285038188 \tabularnewline
59 & 0.990472872210774 & 0.0190542555784517 & 0.00952712778922585 \tabularnewline
60 & 0.988569772169275 & 0.0228604556614509 & 0.0114302278307254 \tabularnewline
61 & 0.988998880057266 & 0.0220022398854682 & 0.0110011199427341 \tabularnewline
62 & 0.99016365069662 & 0.0196726986067603 & 0.00983634930338016 \tabularnewline
63 & 0.988980276454835 & 0.0220394470903309 & 0.0110197235451655 \tabularnewline
64 & 0.990236495293778 & 0.0195270094124442 & 0.00976350470622212 \tabularnewline
65 & 0.988800098337982 & 0.0223998033240362 & 0.0111999016620181 \tabularnewline
66 & 0.987368312001295 & 0.025263375997409 & 0.0126316879987045 \tabularnewline
67 & 0.988633085175611 & 0.0227338296487779 & 0.011366914824389 \tabularnewline
68 & 0.99060364039371 & 0.0187927192125801 & 0.00939635960629004 \tabularnewline
69 & 0.99066992991873 & 0.0186601401625411 & 0.00933007008127055 \tabularnewline
70 & 0.987728182581703 & 0.0245436348365936 & 0.0122718174182968 \tabularnewline
71 & 0.988379041652139 & 0.0232419166957227 & 0.0116209583478613 \tabularnewline
72 & 0.98566468443591 & 0.0286706311281782 & 0.0143353155640891 \tabularnewline
73 & 0.985591140776001 & 0.0288177184479975 & 0.0144088592239987 \tabularnewline
74 & 0.990015022867939 & 0.019969954264123 & 0.00998497713206149 \tabularnewline
75 & 0.986601826772714 & 0.0267963464545719 & 0.0133981732272859 \tabularnewline
76 & 0.984343274103383 & 0.0313134517932351 & 0.0156567258966175 \tabularnewline
77 & 0.9797632039356 & 0.0404735921288005 & 0.0202367960644003 \tabularnewline
78 & 0.9736679342668 & 0.0526641314664009 & 0.0263320657332004 \tabularnewline
79 & 0.980061998484135 & 0.039876003031729 & 0.0199380015158645 \tabularnewline
80 & 0.979643363499448 & 0.0407132730011045 & 0.0203566365005523 \tabularnewline
81 & 0.98126414528746 & 0.0374717094250785 & 0.0187358547125393 \tabularnewline
82 & 0.97918775414938 & 0.041624491701239 & 0.0208122458506195 \tabularnewline
83 & 0.97234998802768 & 0.0553000239446415 & 0.0276500119723208 \tabularnewline
84 & 0.965491770195112 & 0.0690164596097759 & 0.034508229804888 \tabularnewline
85 & 0.985548477816903 & 0.0289030443661936 & 0.0144515221830968 \tabularnewline
86 & 0.980943974632421 & 0.0381120507351575 & 0.0190560253675788 \tabularnewline
87 & 0.97478094419514 & 0.0504381116097199 & 0.02521905580486 \tabularnewline
88 & 0.967650775406632 & 0.0646984491867356 & 0.0323492245933678 \tabularnewline
89 & 0.959427414529498 & 0.0811451709410032 & 0.0405725854705016 \tabularnewline
90 & 0.949263356319127 & 0.101473287361745 & 0.0507366436808727 \tabularnewline
91 & 0.940297991627452 & 0.119404016745097 & 0.0597020083725485 \tabularnewline
92 & 0.966256364634164 & 0.0674872707316718 & 0.0337436353658359 \tabularnewline
93 & 0.95770964114874 & 0.0845807177025204 & 0.0422903588512602 \tabularnewline
94 & 0.95316076757293 & 0.0936784648541402 & 0.0468392324270701 \tabularnewline
95 & 0.941540002333498 & 0.116919995333003 & 0.0584599976665017 \tabularnewline
96 & 0.927696148657647 & 0.144607702684706 & 0.0723038513423532 \tabularnewline
97 & 0.924170536424758 & 0.151658927150484 & 0.075829463575242 \tabularnewline
98 & 0.90578151832675 & 0.1884369633465 & 0.0942184816732498 \tabularnewline
99 & 0.897308907354435 & 0.205382185291131 & 0.102691092645565 \tabularnewline
100 & 0.872699781340802 & 0.254600437318395 & 0.127300218659198 \tabularnewline
101 & 0.84525211771284 & 0.30949576457432 & 0.15474788228716 \tabularnewline
102 & 0.816108349970765 & 0.36778330005847 & 0.183891650029235 \tabularnewline
103 & 0.84401597281025 & 0.311968054379501 & 0.155984027189751 \tabularnewline
104 & 0.884448452137034 & 0.231103095725931 & 0.115551547862966 \tabularnewline
105 & 0.891802422686169 & 0.216395154627662 & 0.108197577313831 \tabularnewline
106 & 0.867174934472917 & 0.265650131054167 & 0.132825065527083 \tabularnewline
107 & 0.845696087573755 & 0.308607824852489 & 0.154303912426245 \tabularnewline
108 & 0.840845629590787 & 0.318308740818426 & 0.159154370409213 \tabularnewline
109 & 0.832676320552864 & 0.334647358894273 & 0.167323679447136 \tabularnewline
110 & 0.854595467445651 & 0.290809065108698 & 0.145404532554349 \tabularnewline
111 & 0.84220023346248 & 0.315599533075039 & 0.157799766537519 \tabularnewline
112 & 0.88616038795857 & 0.22767922408286 & 0.11383961204143 \tabularnewline
113 & 0.856450737082678 & 0.287098525834644 & 0.143549262917322 \tabularnewline
114 & 0.825650166885159 & 0.348699666229682 & 0.174349833114841 \tabularnewline
115 & 0.787821694841171 & 0.424356610317658 & 0.212178305158829 \tabularnewline
116 & 0.799328243706175 & 0.401343512587651 & 0.200671756293825 \tabularnewline
117 & 0.808361487041376 & 0.383277025917249 & 0.191638512958624 \tabularnewline
118 & 0.790836706763782 & 0.418326586472436 & 0.209163293236218 \tabularnewline
119 & 0.748676543938378 & 0.502646912123244 & 0.251323456061622 \tabularnewline
120 & 0.829517887299593 & 0.340964225400814 & 0.170482112700407 \tabularnewline
121 & 0.798913804738225 & 0.40217239052355 & 0.201086195261775 \tabularnewline
122 & 0.753178065840193 & 0.493643868319614 & 0.246821934159807 \tabularnewline
123 & 0.753511796991328 & 0.492976406017344 & 0.246488203008672 \tabularnewline
124 & 0.722134836882699 & 0.555730326234603 & 0.277865163117301 \tabularnewline
125 & 0.697977220986737 & 0.604045558026527 & 0.302022779013263 \tabularnewline
126 & 0.681689325333196 & 0.636621349333607 & 0.318310674666804 \tabularnewline
127 & 0.619085066737338 & 0.761829866525324 & 0.380914933262662 \tabularnewline
128 & 0.60409832040882 & 0.79180335918236 & 0.39590167959118 \tabularnewline
129 & 0.59508712788076 & 0.80982574423848 & 0.40491287211924 \tabularnewline
130 & 0.58391677672224 & 0.83216644655552 & 0.41608322327776 \tabularnewline
131 & 0.517032869680371 & 0.965934260639258 & 0.482967130319629 \tabularnewline
132 & 0.488146002922837 & 0.976292005845674 & 0.511853997077163 \tabularnewline
133 & 0.453610614218248 & 0.907221228436496 & 0.546389385781752 \tabularnewline
134 & 0.409441783959971 & 0.818883567919942 & 0.590558216040029 \tabularnewline
135 & 0.360971276955665 & 0.72194255391133 & 0.639028723044335 \tabularnewline
136 & 0.337252872026252 & 0.674505744052505 & 0.662747127973748 \tabularnewline
137 & 0.304213898931773 & 0.608427797863547 & 0.695786101068227 \tabularnewline
138 & 0.335276433501748 & 0.670552867003497 & 0.664723566498252 \tabularnewline
139 & 0.729826853073804 & 0.540346293852392 & 0.270173146926196 \tabularnewline
140 & 0.727597419912 & 0.544805160175999 & 0.272402580088 \tabularnewline
141 & 0.635969982353419 & 0.728060035293161 & 0.364030017646581 \tabularnewline
142 & 0.634713512865379 & 0.730572974269242 & 0.365286487134621 \tabularnewline
143 & 0.512819813794224 & 0.974360372411551 & 0.487180186205776 \tabularnewline
144 & 0.395603929558432 & 0.791207859116864 & 0.604396070441568 \tabularnewline
145 & 0.350774998743705 & 0.70154999748741 & 0.649225001256295 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.261461129222117[/C][C]0.522922258444234[/C][C]0.738538870777883[/C][/ROW]
[ROW][C]12[/C][C]0.13888298902184[/C][C]0.27776597804368[/C][C]0.86111701097816[/C][/ROW]
[ROW][C]13[/C][C]0.613315968719905[/C][C]0.77336806256019[/C][C]0.386684031280095[/C][/ROW]
[ROW][C]14[/C][C]0.864709048614367[/C][C]0.270581902771267[/C][C]0.135290951385633[/C][/ROW]
[ROW][C]15[/C][C]0.805918223082224[/C][C]0.388163553835552[/C][C]0.194081776917776[/C][/ROW]
[ROW][C]16[/C][C]0.740120266376842[/C][C]0.519759467246316[/C][C]0.259879733623158[/C][/ROW]
[ROW][C]17[/C][C]0.663676658246522[/C][C]0.672646683506957[/C][C]0.336323341753478[/C][/ROW]
[ROW][C]18[/C][C]0.573374334761589[/C][C]0.853251330476823[/C][C]0.426625665238411[/C][/ROW]
[ROW][C]19[/C][C]0.494043379044287[/C][C]0.988086758088574[/C][C]0.505956620955713[/C][/ROW]
[ROW][C]20[/C][C]0.744042586032448[/C][C]0.511914827935103[/C][C]0.255957413967552[/C][/ROW]
[ROW][C]21[/C][C]0.683772221791443[/C][C]0.632455556417114[/C][C]0.316227778208557[/C][/ROW]
[ROW][C]22[/C][C]0.685156950631647[/C][C]0.629686098736705[/C][C]0.314843049368353[/C][/ROW]
[ROW][C]23[/C][C]0.615886058067833[/C][C]0.768227883864334[/C][C]0.384113941932167[/C][/ROW]
[ROW][C]24[/C][C]0.612940630765622[/C][C]0.774118738468757[/C][C]0.387059369234378[/C][/ROW]
[ROW][C]25[/C][C]0.577285707503833[/C][C]0.845428584992335[/C][C]0.422714292496167[/C][/ROW]
[ROW][C]26[/C][C]0.516435180888059[/C][C]0.967129638223883[/C][C]0.483564819111942[/C][/ROW]
[ROW][C]27[/C][C]0.742561049638454[/C][C]0.514877900723091[/C][C]0.257438950361546[/C][/ROW]
[ROW][C]28[/C][C]0.69979835144288[/C][C]0.60040329711424[/C][C]0.30020164855712[/C][/ROW]
[ROW][C]29[/C][C]0.638879944191044[/C][C]0.722240111617912[/C][C]0.361120055808956[/C][/ROW]
[ROW][C]30[/C][C]0.756608118744544[/C][C]0.486783762510912[/C][C]0.243391881255456[/C][/ROW]
[ROW][C]31[/C][C]0.820768691404323[/C][C]0.358462617191355[/C][C]0.179231308595677[/C][/ROW]
[ROW][C]32[/C][C]0.78460751272131[/C][C]0.43078497455738[/C][C]0.21539248727869[/C][/ROW]
[ROW][C]33[/C][C]0.737828268253584[/C][C]0.524343463492832[/C][C]0.262171731746416[/C][/ROW]
[ROW][C]34[/C][C]0.698741073649531[/C][C]0.602517852700938[/C][C]0.301258926350469[/C][/ROW]
[ROW][C]35[/C][C]0.673536449192064[/C][C]0.652927101615872[/C][C]0.326463550807936[/C][/ROW]
[ROW][C]36[/C][C]0.704543700821158[/C][C]0.590912598357684[/C][C]0.295456299178842[/C][/ROW]
[ROW][C]37[/C][C]0.682052180767615[/C][C]0.63589563846477[/C][C]0.317947819232385[/C][/ROW]
[ROW][C]38[/C][C]0.634364540725974[/C][C]0.731270918548052[/C][C]0.365635459274026[/C][/ROW]
[ROW][C]39[/C][C]0.584939035906339[/C][C]0.830121928187323[/C][C]0.415060964093661[/C][/ROW]
[ROW][C]40[/C][C]0.541809562294046[/C][C]0.916380875411907[/C][C]0.458190437705954[/C][/ROW]
[ROW][C]41[/C][C]0.493389910925422[/C][C]0.986779821850844[/C][C]0.506610089074578[/C][/ROW]
[ROW][C]42[/C][C]0.820151022344117[/C][C]0.359697955311766[/C][C]0.179848977655883[/C][/ROW]
[ROW][C]43[/C][C]0.957503192576703[/C][C]0.0849936148465945[/C][C]0.0424968074232972[/C][/ROW]
[ROW][C]44[/C][C]0.961509395146503[/C][C]0.0769812097069945[/C][C]0.0384906048534972[/C][/ROW]
[ROW][C]45[/C][C]0.950626429353704[/C][C]0.0987471412925916[/C][C]0.0493735706462958[/C][/ROW]
[ROW][C]46[/C][C]0.954359048717966[/C][C]0.0912819025640688[/C][C]0.0456409512820344[/C][/ROW]
[ROW][C]47[/C][C]0.955288137241416[/C][C]0.089423725517168[/C][C]0.044711862758584[/C][/ROW]
[ROW][C]48[/C][C]0.946072145045743[/C][C]0.107855709908513[/C][C]0.0539278549542566[/C][/ROW]
[ROW][C]49[/C][C]0.946745920298258[/C][C]0.106508159403484[/C][C]0.0532540797017418[/C][/ROW]
[ROW][C]50[/C][C]0.941048545847936[/C][C]0.117902908304128[/C][C]0.0589514541520638[/C][/ROW]
[ROW][C]51[/C][C]0.933030902170691[/C][C]0.133938195658619[/C][C]0.0669690978293093[/C][/ROW]
[ROW][C]52[/C][C]0.989153719690974[/C][C]0.0216925606180521[/C][C]0.010846280309026[/C][/ROW]
[ROW][C]53[/C][C]0.985245002705137[/C][C]0.0295099945897256[/C][C]0.0147549972948628[/C][/ROW]
[ROW][C]54[/C][C]0.990190054539324[/C][C]0.0196198909213529[/C][C]0.00980994546067645[/C][/ROW]
[ROW][C]55[/C][C]0.992416640799626[/C][C]0.0151667184007484[/C][C]0.00758335920037418[/C][/ROW]
[ROW][C]56[/C][C]0.990310672631011[/C][C]0.0193786547379774[/C][C]0.00968932736898872[/C][/ROW]
[ROW][C]57[/C][C]0.987202682552208[/C][C]0.0255946348955836[/C][C]0.0127973174477918[/C][/ROW]
[ROW][C]58[/C][C]0.986679971496181[/C][C]0.0266400570076375[/C][C]0.0133200285038188[/C][/ROW]
[ROW][C]59[/C][C]0.990472872210774[/C][C]0.0190542555784517[/C][C]0.00952712778922585[/C][/ROW]
[ROW][C]60[/C][C]0.988569772169275[/C][C]0.0228604556614509[/C][C]0.0114302278307254[/C][/ROW]
[ROW][C]61[/C][C]0.988998880057266[/C][C]0.0220022398854682[/C][C]0.0110011199427341[/C][/ROW]
[ROW][C]62[/C][C]0.99016365069662[/C][C]0.0196726986067603[/C][C]0.00983634930338016[/C][/ROW]
[ROW][C]63[/C][C]0.988980276454835[/C][C]0.0220394470903309[/C][C]0.0110197235451655[/C][/ROW]
[ROW][C]64[/C][C]0.990236495293778[/C][C]0.0195270094124442[/C][C]0.00976350470622212[/C][/ROW]
[ROW][C]65[/C][C]0.988800098337982[/C][C]0.0223998033240362[/C][C]0.0111999016620181[/C][/ROW]
[ROW][C]66[/C][C]0.987368312001295[/C][C]0.025263375997409[/C][C]0.0126316879987045[/C][/ROW]
[ROW][C]67[/C][C]0.988633085175611[/C][C]0.0227338296487779[/C][C]0.011366914824389[/C][/ROW]
[ROW][C]68[/C][C]0.99060364039371[/C][C]0.0187927192125801[/C][C]0.00939635960629004[/C][/ROW]
[ROW][C]69[/C][C]0.99066992991873[/C][C]0.0186601401625411[/C][C]0.00933007008127055[/C][/ROW]
[ROW][C]70[/C][C]0.987728182581703[/C][C]0.0245436348365936[/C][C]0.0122718174182968[/C][/ROW]
[ROW][C]71[/C][C]0.988379041652139[/C][C]0.0232419166957227[/C][C]0.0116209583478613[/C][/ROW]
[ROW][C]72[/C][C]0.98566468443591[/C][C]0.0286706311281782[/C][C]0.0143353155640891[/C][/ROW]
[ROW][C]73[/C][C]0.985591140776001[/C][C]0.0288177184479975[/C][C]0.0144088592239987[/C][/ROW]
[ROW][C]74[/C][C]0.990015022867939[/C][C]0.019969954264123[/C][C]0.00998497713206149[/C][/ROW]
[ROW][C]75[/C][C]0.986601826772714[/C][C]0.0267963464545719[/C][C]0.0133981732272859[/C][/ROW]
[ROW][C]76[/C][C]0.984343274103383[/C][C]0.0313134517932351[/C][C]0.0156567258966175[/C][/ROW]
[ROW][C]77[/C][C]0.9797632039356[/C][C]0.0404735921288005[/C][C]0.0202367960644003[/C][/ROW]
[ROW][C]78[/C][C]0.9736679342668[/C][C]0.0526641314664009[/C][C]0.0263320657332004[/C][/ROW]
[ROW][C]79[/C][C]0.980061998484135[/C][C]0.039876003031729[/C][C]0.0199380015158645[/C][/ROW]
[ROW][C]80[/C][C]0.979643363499448[/C][C]0.0407132730011045[/C][C]0.0203566365005523[/C][/ROW]
[ROW][C]81[/C][C]0.98126414528746[/C][C]0.0374717094250785[/C][C]0.0187358547125393[/C][/ROW]
[ROW][C]82[/C][C]0.97918775414938[/C][C]0.041624491701239[/C][C]0.0208122458506195[/C][/ROW]
[ROW][C]83[/C][C]0.97234998802768[/C][C]0.0553000239446415[/C][C]0.0276500119723208[/C][/ROW]
[ROW][C]84[/C][C]0.965491770195112[/C][C]0.0690164596097759[/C][C]0.034508229804888[/C][/ROW]
[ROW][C]85[/C][C]0.985548477816903[/C][C]0.0289030443661936[/C][C]0.0144515221830968[/C][/ROW]
[ROW][C]86[/C][C]0.980943974632421[/C][C]0.0381120507351575[/C][C]0.0190560253675788[/C][/ROW]
[ROW][C]87[/C][C]0.97478094419514[/C][C]0.0504381116097199[/C][C]0.02521905580486[/C][/ROW]
[ROW][C]88[/C][C]0.967650775406632[/C][C]0.0646984491867356[/C][C]0.0323492245933678[/C][/ROW]
[ROW][C]89[/C][C]0.959427414529498[/C][C]0.0811451709410032[/C][C]0.0405725854705016[/C][/ROW]
[ROW][C]90[/C][C]0.949263356319127[/C][C]0.101473287361745[/C][C]0.0507366436808727[/C][/ROW]
[ROW][C]91[/C][C]0.940297991627452[/C][C]0.119404016745097[/C][C]0.0597020083725485[/C][/ROW]
[ROW][C]92[/C][C]0.966256364634164[/C][C]0.0674872707316718[/C][C]0.0337436353658359[/C][/ROW]
[ROW][C]93[/C][C]0.95770964114874[/C][C]0.0845807177025204[/C][C]0.0422903588512602[/C][/ROW]
[ROW][C]94[/C][C]0.95316076757293[/C][C]0.0936784648541402[/C][C]0.0468392324270701[/C][/ROW]
[ROW][C]95[/C][C]0.941540002333498[/C][C]0.116919995333003[/C][C]0.0584599976665017[/C][/ROW]
[ROW][C]96[/C][C]0.927696148657647[/C][C]0.144607702684706[/C][C]0.0723038513423532[/C][/ROW]
[ROW][C]97[/C][C]0.924170536424758[/C][C]0.151658927150484[/C][C]0.075829463575242[/C][/ROW]
[ROW][C]98[/C][C]0.90578151832675[/C][C]0.1884369633465[/C][C]0.0942184816732498[/C][/ROW]
[ROW][C]99[/C][C]0.897308907354435[/C][C]0.205382185291131[/C][C]0.102691092645565[/C][/ROW]
[ROW][C]100[/C][C]0.872699781340802[/C][C]0.254600437318395[/C][C]0.127300218659198[/C][/ROW]
[ROW][C]101[/C][C]0.84525211771284[/C][C]0.30949576457432[/C][C]0.15474788228716[/C][/ROW]
[ROW][C]102[/C][C]0.816108349970765[/C][C]0.36778330005847[/C][C]0.183891650029235[/C][/ROW]
[ROW][C]103[/C][C]0.84401597281025[/C][C]0.311968054379501[/C][C]0.155984027189751[/C][/ROW]
[ROW][C]104[/C][C]0.884448452137034[/C][C]0.231103095725931[/C][C]0.115551547862966[/C][/ROW]
[ROW][C]105[/C][C]0.891802422686169[/C][C]0.216395154627662[/C][C]0.108197577313831[/C][/ROW]
[ROW][C]106[/C][C]0.867174934472917[/C][C]0.265650131054167[/C][C]0.132825065527083[/C][/ROW]
[ROW][C]107[/C][C]0.845696087573755[/C][C]0.308607824852489[/C][C]0.154303912426245[/C][/ROW]
[ROW][C]108[/C][C]0.840845629590787[/C][C]0.318308740818426[/C][C]0.159154370409213[/C][/ROW]
[ROW][C]109[/C][C]0.832676320552864[/C][C]0.334647358894273[/C][C]0.167323679447136[/C][/ROW]
[ROW][C]110[/C][C]0.854595467445651[/C][C]0.290809065108698[/C][C]0.145404532554349[/C][/ROW]
[ROW][C]111[/C][C]0.84220023346248[/C][C]0.315599533075039[/C][C]0.157799766537519[/C][/ROW]
[ROW][C]112[/C][C]0.88616038795857[/C][C]0.22767922408286[/C][C]0.11383961204143[/C][/ROW]
[ROW][C]113[/C][C]0.856450737082678[/C][C]0.287098525834644[/C][C]0.143549262917322[/C][/ROW]
[ROW][C]114[/C][C]0.825650166885159[/C][C]0.348699666229682[/C][C]0.174349833114841[/C][/ROW]
[ROW][C]115[/C][C]0.787821694841171[/C][C]0.424356610317658[/C][C]0.212178305158829[/C][/ROW]
[ROW][C]116[/C][C]0.799328243706175[/C][C]0.401343512587651[/C][C]0.200671756293825[/C][/ROW]
[ROW][C]117[/C][C]0.808361487041376[/C][C]0.383277025917249[/C][C]0.191638512958624[/C][/ROW]
[ROW][C]118[/C][C]0.790836706763782[/C][C]0.418326586472436[/C][C]0.209163293236218[/C][/ROW]
[ROW][C]119[/C][C]0.748676543938378[/C][C]0.502646912123244[/C][C]0.251323456061622[/C][/ROW]
[ROW][C]120[/C][C]0.829517887299593[/C][C]0.340964225400814[/C][C]0.170482112700407[/C][/ROW]
[ROW][C]121[/C][C]0.798913804738225[/C][C]0.40217239052355[/C][C]0.201086195261775[/C][/ROW]
[ROW][C]122[/C][C]0.753178065840193[/C][C]0.493643868319614[/C][C]0.246821934159807[/C][/ROW]
[ROW][C]123[/C][C]0.753511796991328[/C][C]0.492976406017344[/C][C]0.246488203008672[/C][/ROW]
[ROW][C]124[/C][C]0.722134836882699[/C][C]0.555730326234603[/C][C]0.277865163117301[/C][/ROW]
[ROW][C]125[/C][C]0.697977220986737[/C][C]0.604045558026527[/C][C]0.302022779013263[/C][/ROW]
[ROW][C]126[/C][C]0.681689325333196[/C][C]0.636621349333607[/C][C]0.318310674666804[/C][/ROW]
[ROW][C]127[/C][C]0.619085066737338[/C][C]0.761829866525324[/C][C]0.380914933262662[/C][/ROW]
[ROW][C]128[/C][C]0.60409832040882[/C][C]0.79180335918236[/C][C]0.39590167959118[/C][/ROW]
[ROW][C]129[/C][C]0.59508712788076[/C][C]0.80982574423848[/C][C]0.40491287211924[/C][/ROW]
[ROW][C]130[/C][C]0.58391677672224[/C][C]0.83216644655552[/C][C]0.41608322327776[/C][/ROW]
[ROW][C]131[/C][C]0.517032869680371[/C][C]0.965934260639258[/C][C]0.482967130319629[/C][/ROW]
[ROW][C]132[/C][C]0.488146002922837[/C][C]0.976292005845674[/C][C]0.511853997077163[/C][/ROW]
[ROW][C]133[/C][C]0.453610614218248[/C][C]0.907221228436496[/C][C]0.546389385781752[/C][/ROW]
[ROW][C]134[/C][C]0.409441783959971[/C][C]0.818883567919942[/C][C]0.590558216040029[/C][/ROW]
[ROW][C]135[/C][C]0.360971276955665[/C][C]0.72194255391133[/C][C]0.639028723044335[/C][/ROW]
[ROW][C]136[/C][C]0.337252872026252[/C][C]0.674505744052505[/C][C]0.662747127973748[/C][/ROW]
[ROW][C]137[/C][C]0.304213898931773[/C][C]0.608427797863547[/C][C]0.695786101068227[/C][/ROW]
[ROW][C]138[/C][C]0.335276433501748[/C][C]0.670552867003497[/C][C]0.664723566498252[/C][/ROW]
[ROW][C]139[/C][C]0.729826853073804[/C][C]0.540346293852392[/C][C]0.270173146926196[/C][/ROW]
[ROW][C]140[/C][C]0.727597419912[/C][C]0.544805160175999[/C][C]0.272402580088[/C][/ROW]
[ROW][C]141[/C][C]0.635969982353419[/C][C]0.728060035293161[/C][C]0.364030017646581[/C][/ROW]
[ROW][C]142[/C][C]0.634713512865379[/C][C]0.730572974269242[/C][C]0.365286487134621[/C][/ROW]
[ROW][C]143[/C][C]0.512819813794224[/C][C]0.974360372411551[/C][C]0.487180186205776[/C][/ROW]
[ROW][C]144[/C][C]0.395603929558432[/C][C]0.791207859116864[/C][C]0.604396070441568[/C][/ROW]
[ROW][C]145[/C][C]0.350774998743705[/C][C]0.70154999748741[/C][C]0.649225001256295[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2614611292221170.5229222584442340.738538870777883
120.138882989021840.277765978043680.86111701097816
130.6133159687199050.773368062560190.386684031280095
140.8647090486143670.2705819027712670.135290951385633
150.8059182230822240.3881635538355520.194081776917776
160.7401202663768420.5197594672463160.259879733623158
170.6636766582465220.6726466835069570.336323341753478
180.5733743347615890.8532513304768230.426625665238411
190.4940433790442870.9880867580885740.505956620955713
200.7440425860324480.5119148279351030.255957413967552
210.6837722217914430.6324555564171140.316227778208557
220.6851569506316470.6296860987367050.314843049368353
230.6158860580678330.7682278838643340.384113941932167
240.6129406307656220.7741187384687570.387059369234378
250.5772857075038330.8454285849923350.422714292496167
260.5164351808880590.9671296382238830.483564819111942
270.7425610496384540.5148779007230910.257438950361546
280.699798351442880.600403297114240.30020164855712
290.6388799441910440.7222401116179120.361120055808956
300.7566081187445440.4867837625109120.243391881255456
310.8207686914043230.3584626171913550.179231308595677
320.784607512721310.430784974557380.21539248727869
330.7378282682535840.5243434634928320.262171731746416
340.6987410736495310.6025178527009380.301258926350469
350.6735364491920640.6529271016158720.326463550807936
360.7045437008211580.5909125983576840.295456299178842
370.6820521807676150.635895638464770.317947819232385
380.6343645407259740.7312709185480520.365635459274026
390.5849390359063390.8301219281873230.415060964093661
400.5418095622940460.9163808754119070.458190437705954
410.4933899109254220.9867798218508440.506610089074578
420.8201510223441170.3596979553117660.179848977655883
430.9575031925767030.08499361484659450.0424968074232972
440.9615093951465030.07698120970699450.0384906048534972
450.9506264293537040.09874714129259160.0493735706462958
460.9543590487179660.09128190256406880.0456409512820344
470.9552881372414160.0894237255171680.044711862758584
480.9460721450457430.1078557099085130.0539278549542566
490.9467459202982580.1065081594034840.0532540797017418
500.9410485458479360.1179029083041280.0589514541520638
510.9330309021706910.1339381956586190.0669690978293093
520.9891537196909740.02169256061805210.010846280309026
530.9852450027051370.02950999458972560.0147549972948628
540.9901900545393240.01961989092135290.00980994546067645
550.9924166407996260.01516671840074840.00758335920037418
560.9903106726310110.01937865473797740.00968932736898872
570.9872026825522080.02559463489558360.0127973174477918
580.9866799714961810.02664005700763750.0133200285038188
590.9904728722107740.01905425557845170.00952712778922585
600.9885697721692750.02286045566145090.0114302278307254
610.9889988800572660.02200223988546820.0110011199427341
620.990163650696620.01967269860676030.00983634930338016
630.9889802764548350.02203944709033090.0110197235451655
640.9902364952937780.01952700941244420.00976350470622212
650.9888000983379820.02239980332403620.0111999016620181
660.9873683120012950.0252633759974090.0126316879987045
670.9886330851756110.02273382964877790.011366914824389
680.990603640393710.01879271921258010.00939635960629004
690.990669929918730.01866014016254110.00933007008127055
700.9877281825817030.02454363483659360.0122718174182968
710.9883790416521390.02324191669572270.0116209583478613
720.985664684435910.02867063112817820.0143353155640891
730.9855911407760010.02881771844799750.0144088592239987
740.9900150228679390.0199699542641230.00998497713206149
750.9866018267727140.02679634645457190.0133981732272859
760.9843432741033830.03131345179323510.0156567258966175
770.97976320393560.04047359212880050.0202367960644003
780.97366793426680.05266413146640090.0263320657332004
790.9800619984841350.0398760030317290.0199380015158645
800.9796433634994480.04071327300110450.0203566365005523
810.981264145287460.03747170942507850.0187358547125393
820.979187754149380.0416244917012390.0208122458506195
830.972349988027680.05530002394464150.0276500119723208
840.9654917701951120.06901645960977590.034508229804888
850.9855484778169030.02890304436619360.0144515221830968
860.9809439746324210.03811205073515750.0190560253675788
870.974780944195140.05043811160971990.02521905580486
880.9676507754066320.06469844918673560.0323492245933678
890.9594274145294980.08114517094100320.0405725854705016
900.9492633563191270.1014732873617450.0507366436808727
910.9402979916274520.1194040167450970.0597020083725485
920.9662563646341640.06748727073167180.0337436353658359
930.957709641148740.08458071770252040.0422903588512602
940.953160767572930.09367846485414020.0468392324270701
950.9415400023334980.1169199953330030.0584599976665017
960.9276961486576470.1446077026847060.0723038513423532
970.9241705364247580.1516589271504840.075829463575242
980.905781518326750.18843696334650.0942184816732498
990.8973089073544350.2053821852911310.102691092645565
1000.8726997813408020.2546004373183950.127300218659198
1010.845252117712840.309495764574320.15474788228716
1020.8161083499707650.367783300058470.183891650029235
1030.844015972810250.3119680543795010.155984027189751
1040.8844484521370340.2311030957259310.115551547862966
1050.8918024226861690.2163951546276620.108197577313831
1060.8671749344729170.2656501310541670.132825065527083
1070.8456960875737550.3086078248524890.154303912426245
1080.8408456295907870.3183087408184260.159154370409213
1090.8326763205528640.3346473588942730.167323679447136
1100.8545954674456510.2908090651086980.145404532554349
1110.842200233462480.3155995330750390.157799766537519
1120.886160387958570.227679224082860.11383961204143
1130.8564507370826780.2870985258346440.143549262917322
1140.8256501668851590.3486996662296820.174349833114841
1150.7878216948411710.4243566103176580.212178305158829
1160.7993282437061750.4013435125876510.200671756293825
1170.8083614870413760.3832770259172490.191638512958624
1180.7908367067637820.4183265864724360.209163293236218
1190.7486765439383780.5026469121232440.251323456061622
1200.8295178872995930.3409642254008140.170482112700407
1210.7989138047382250.402172390523550.201086195261775
1220.7531780658401930.4936438683196140.246821934159807
1230.7535117969913280.4929764060173440.246488203008672
1240.7221348368826990.5557303262346030.277865163117301
1250.6979772209867370.6040455580265270.302022779013263
1260.6816893253331960.6366213493336070.318310674666804
1270.6190850667373380.7618298665253240.380914933262662
1280.604098320408820.791803359182360.39590167959118
1290.595087127880760.809825744238480.40491287211924
1300.583916776722240.832166446555520.41608322327776
1310.5170328696803710.9659342606392580.482967130319629
1320.4881460029228370.9762920058456740.511853997077163
1330.4536106142182480.9072212284364960.546389385781752
1340.4094417839599710.8188835679199420.590558216040029
1350.3609712769556650.721942553911330.639028723044335
1360.3372528720262520.6745057440525050.662747127973748
1370.3042138989317730.6084277978635470.695786101068227
1380.3352764335017480.6705528670034970.664723566498252
1390.7298268530738040.5403462938523920.270173146926196
1400.7275974199120.5448051601759990.272402580088
1410.6359699823534190.7280600352931610.364030017646581
1420.6347135128653790.7305729742692420.365286487134621
1430.5128198137942240.9743603724115510.487180186205776
1440.3956039295584320.7912078591168640.604396070441568
1450.3507749987437050.701549997487410.649225001256295






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level320.237037037037037NOK
10% type I error level460.340740740740741NOK

\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 & 32 & 0.237037037037037 & NOK \tabularnewline
10% type I error level & 46 & 0.340740740740741 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98439&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]32[/C][C]0.237037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.340740740740741[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98439&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98439&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 level320.237037037037037NOK
10% type I error level460.340740740740741NOK


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')
}