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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Nov 2010 16:04:53 +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/18/t12900965061gj996g400gk7u2.htm/, Retrieved Fri, 19 Apr 2024 08:13:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97342, Retrieved Fri, 19 Apr 2024 08:13:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185

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] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    D      [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- 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]

[Truncated]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&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]8 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=97342&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.303577528406636 + 0.0945470254137575FindingFriends[t] + 0.2438215702385KnowingPeople[t] + 0.348903064267891Liked[t] + 0.627086448309147Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.303577528406636 +  0.0945470254137575FindingFriends[t] +  0.2438215702385KnowingPeople[t] +  0.348903064267891Liked[t] +  0.627086448309147Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.303577528406636 +  0.0945470254137575FindingFriends[t] +  0.2438215702385KnowingPeople[t] +  0.348903064267891Liked[t] +  0.627086448309147Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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] = + 0.303577528406636 + 0.0945470254137575FindingFriends[t] + 0.2438215702385KnowingPeople[t] + 0.348903064267891Liked[t] + 0.627086448309147Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.3035775284066361.4251180.2130.8315990.4158
FindingFriends0.09454702541375750.0959580.98530.3260540.163027
KnowingPeople0.24382157023850.0613743.97270.000115.5e-05
Liked0.3489030642678910.0964793.61640.0004070.000203
Celebrity0.6270864483091470.1560334.01899.2e-054.6e-05

\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) & 0.303577528406636 & 1.425118 & 0.213 & 0.831599 & 0.4158 \tabularnewline
FindingFriends & 0.0945470254137575 & 0.095958 & 0.9853 & 0.326054 & 0.163027 \tabularnewline
KnowingPeople & 0.2438215702385 & 0.061374 & 3.9727 & 0.00011 & 5.5e-05 \tabularnewline
Liked & 0.348903064267891 & 0.096479 & 3.6164 & 0.000407 & 0.000203 \tabularnewline
Celebrity & 0.627086448309147 & 0.156033 & 4.0189 & 9.2e-05 & 4.6e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&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]0.303577528406636[/C][C]1.425118[/C][C]0.213[/C][C]0.831599[/C][C]0.4158[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0945470254137575[/C][C]0.095958[/C][C]0.9853[/C][C]0.326054[/C][C]0.163027[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.2438215702385[/C][C]0.061374[/C][C]3.9727[/C][C]0.00011[/C][C]5.5e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.348903064267891[/C][C]0.096479[/C][C]3.6164[/C][C]0.000407[/C][C]0.000203[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.627086448309147[/C][C]0.156033[/C][C]4.0189[/C][C]9.2e-05[/C][C]4.6e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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)0.3035775284066361.4251180.2130.8315990.4158
FindingFriends0.09454702541375750.0959580.98530.3260540.163027
KnowingPeople0.24382157023850.0613743.97270.000115.5e-05
Liked0.3489030642678910.0964793.61640.0004070.000203
Celebrity0.6270864483091470.1560334.01899.2e-054.6e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.706541003093969
R-squared0.499200189053031
Adjusted R-squared0.485933968895496
F-TEST (value)37.6294214271329
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10551474685275
Sum Squared Residuals669.412044731376

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.706541003093969 \tabularnewline
R-squared & 0.499200189053031 \tabularnewline
Adjusted R-squared & 0.485933968895496 \tabularnewline
F-TEST (value) & 37.6294214271329 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10551474685275 \tabularnewline
Sum Squared Residuals & 669.412044731376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.706541003093969[/C][/ROW]
[ROW][C]R-squared[/C][C]0.499200189053031[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.485933968895496[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.6294214271329[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/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]2.10551474685275[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.412044731376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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 R0.706541003093969
R-squared0.499200189053031
Adjusted R-squared0.485933968895496
F-TEST (value)37.6294214271329
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10551474685275
Sum Squared Residuals669.412044731376






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.36319002253451.63680997746547
21211.0598864723080.940113527691951
31513.51987434354741.48012565645264
41210.81060720986951.18939279013047
51010.6606294334218-0.660629433421751
6129.212988941183372.78701105881664
71516.7307807258967-1.73078072589671
8910.4664586061786-1.46645860617862
91212.2446325096978-0.244632509697797
10117.98226247396783.01773752603221
111113.3246193688968-2.32461936889679
121112.0695345670648-1.06953456706481
131512.21810588026652.78189411973349
14711.2482772915125-4.24827729151253
151111.6322454012062-0.632245401206211
161110.95410174665560.0458982533443776
171012.0695345670648-2.06953456706481
181414.3458860796766-0.345886079676616
19108.698819171275081.30118082872492
2069.50608033863283-3.50608033863283
21118.651164480900842.34883551909916
221514.38024789347940.61975210652063
231111.6253812457601-0.625381245760121
24129.491172325224622.50882767477538
251413.39372391228670.606276087713301
261514.83352922015360.166470779846384
27914.6240694637179-5.62406946371787
281312.49353085635190.506469143648087
291313.2151643300748-0.215164330074817
301610.78170308826685.21829691173321
31138.499190651831934.50080934816807
321213.5878947395298-1.58789473952983
331414.4747949188931-0.474794918893128
341110.05461192234390.945388077656127
35910.4664586061786-1.46645860617862
361614.24080458564721.75919541435278
371212.87242218963-0.87242218962998
38109.19153908816770.808460911832304
391313.0553553166344-0.0553553166344411
401615.21679409822430.783205901775738
411412.90608077180971.0939192281903
42158.09280166019726.9071983398028
4359.74045158766314-4.74045158766314
44810.4769930747943-2.47699307479425
451111.2435228309355-0.243522830935547
461613.7875232589732.21247674102702
471713.02099350283173.97900649716831
4898.484282638423720.515717361576283
49911.9406257278483-2.94062572784829
501314.6842546753289-1.68425467532887
511010.9555082099017-0.955508209901694
52612.4122767075096-6.41227670750964
531212.3840758175299-0.384075817529943
54810.4328000239989-2.4328000239989
551412.37354134891431.62645865108569
561212.4282688683253-0.428268868325295
571110.77554216444370.224457835556259
581614.40061359908761.5993864009124
59810.2695293709796-2.26952937097964
601515.251155912027-0.251155912027017
6179.45681051142187-2.45681051142187
621613.98644854679312.01355145320691
631413.73646605273150.263533947268463
641613.75316144517022.24683855482978
6599.93969919132188-0.939699191321884
661412.27899432350061.72100567649945
671113.0448208480188-2.04482084801881
681310.46716183780172.53283816219834
691512.90608077180972.0939192281903
7055.60121038311099-0.601210383110992
711512.42826886832532.57173113167471
721312.27899432350060.721005676499448
731112.0457072218777-1.04570722187768
741113.9782324466372-2.97823244663722
751212.4731651507437-0.473165150743683
761213.3937239122867-1.3937239122867
771212.2684598548849-0.268459854884918
781211.90080622184550.0991937781544784
791410.78170308826683.21829691173321
8067.99317785836783-1.99317785836783
8179.84007538949251-2.84007538949251
821411.98989555505932.01010444494074
831413.85824293919960.141757060800385
841011.2533540679281-1.25335406792814
85138.725345800706364.27465419929364
861212.4079031627171-0.407903162717064
8799.29154380578147-0.291543805781471
881212.0164221844905-0.016422184490546
891615.00733434178850.992665658211484
901010.2331715045558-0.233171504555754
911413.12677822848410.873221771515887
921013.5093398749317-3.50933987493172
931615.3113411236380.68865887636198
941513.44845143169771.55154856830232
951211.34790109334190.652098906658098
96109.734993895463120.26500610453688
97810.2389515125944-2.23895151259441
9888.60972983806134-0.609729838061339
991112.8424339206198-1.8424339206198
1001312.41843763133270.581562368667303
1011615.46061566846280.539384331537238
1021614.71861648913161.28138351086837
1031415.8149764249307-1.81497642493067
104118.885154814146742.11484518585326
10546.96753760279542-2.96753760279542
1061414.5700451759299-0.570045175929921
107910.3480842355777-1.34808423557774
1081415.251155912027-1.25115591202702
109810.4531657296071-2.45316572960713
110810.9016925957044-2.9016925957044
1111112.1949817667024-1.19498176670243
1121213.6426222589408-1.64262225894082
1131111.4431513503787-0.443151350378695
1141413.60388690034550.396113099654519
1151514.3360548426840.663945157315982
1161613.40425838090232.59574161909767
1171613.49880540631612.50119459368391
1181112.7336821134209-1.73368211342087
1191413.74262697655460.25737302344541
1201410.96463621527133.03536378472874
1211211.40808630495290.591913695047095
1221412.56700894453441.4329910554656
123810.2331715045558-2.23317150455575
1241313.8371740019683-0.837174001968348
1251613.74262697655462.25737302344541
1261210.92114639609891.07885360390106
1271615.45078443147020.549215568529835
1281213.3937239122867-1.3937239122867
1291111.5182445753979-0.518244575397912
13046.42684120345585-2.42684120345585
1311615.45078443147020.549215568529835
1321512.56771217615742.43228782384256
1331011.4795092168026-1.47950921680258
1341313.2107907852822-0.210790785282238
1351513.25498383607761.74501616392241
1361210.67662159423741.3233784057626
1371413.64807995114080.351920048859167
138710.6759183626144-3.67591836261437
1391914.09153004082254.90846995917752
1401212.6826249071794-0.682624907179428
1411212.2888255604931-0.288825560493149
1421313.4988054063161-0.49880540631609
1431512.94243863823362.05756136176642
14488.28902766377315-0.289027663773149
1451210.92044316447591.0795568355241
1461010.806233665077-0.806233665076951
147811.4087895365759-3.40878953657594
1481014.3751711170638-4.37517111706375
1491513.89190152137931.10809847862067
1501614.61423822672531.38576177327473
1511313.1947986244666-0.194798624466587
1521615.06751955339950.932480446600481
153910.2331715045558-1.23317150455575
1541413.17097127927950.829028720720535
1551412.70645225236651.29354774763345
1561210.16444787695021.83555212304975

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3631900225345 & 1.63680997746547 \tabularnewline
2 & 12 & 11.059886472308 & 0.940113527691951 \tabularnewline
3 & 15 & 13.5198743435474 & 1.48012565645264 \tabularnewline
4 & 12 & 10.8106072098695 & 1.18939279013047 \tabularnewline
5 & 10 & 10.6606294334218 & -0.660629433421751 \tabularnewline
6 & 12 & 9.21298894118337 & 2.78701105881664 \tabularnewline
7 & 15 & 16.7307807258967 & -1.73078072589671 \tabularnewline
8 & 9 & 10.4664586061786 & -1.46645860617862 \tabularnewline
9 & 12 & 12.2446325096978 & -0.244632509697797 \tabularnewline
10 & 11 & 7.9822624739678 & 3.01773752603221 \tabularnewline
11 & 11 & 13.3246193688968 & -2.32461936889679 \tabularnewline
12 & 11 & 12.0695345670648 & -1.06953456706481 \tabularnewline
13 & 15 & 12.2181058802665 & 2.78189411973349 \tabularnewline
14 & 7 & 11.2482772915125 & -4.24827729151253 \tabularnewline
15 & 11 & 11.6322454012062 & -0.632245401206211 \tabularnewline
16 & 11 & 10.9541017466556 & 0.0458982533443776 \tabularnewline
17 & 10 & 12.0695345670648 & -2.06953456706481 \tabularnewline
18 & 14 & 14.3458860796766 & -0.345886079676616 \tabularnewline
19 & 10 & 8.69881917127508 & 1.30118082872492 \tabularnewline
20 & 6 & 9.50608033863283 & -3.50608033863283 \tabularnewline
21 & 11 & 8.65116448090084 & 2.34883551909916 \tabularnewline
22 & 15 & 14.3802478934794 & 0.61975210652063 \tabularnewline
23 & 11 & 11.6253812457601 & -0.625381245760121 \tabularnewline
24 & 12 & 9.49117232522462 & 2.50882767477538 \tabularnewline
25 & 14 & 13.3937239122867 & 0.606276087713301 \tabularnewline
26 & 15 & 14.8335292201536 & 0.166470779846384 \tabularnewline
27 & 9 & 14.6240694637179 & -5.62406946371787 \tabularnewline
28 & 13 & 12.4935308563519 & 0.506469143648087 \tabularnewline
29 & 13 & 13.2151643300748 & -0.215164330074817 \tabularnewline
30 & 16 & 10.7817030882668 & 5.21829691173321 \tabularnewline
31 & 13 & 8.49919065183193 & 4.50080934816807 \tabularnewline
32 & 12 & 13.5878947395298 & -1.58789473952983 \tabularnewline
33 & 14 & 14.4747949188931 & -0.474794918893128 \tabularnewline
34 & 11 & 10.0546119223439 & 0.945388077656127 \tabularnewline
35 & 9 & 10.4664586061786 & -1.46645860617862 \tabularnewline
36 & 16 & 14.2408045856472 & 1.75919541435278 \tabularnewline
37 & 12 & 12.87242218963 & -0.87242218962998 \tabularnewline
38 & 10 & 9.1915390881677 & 0.808460911832304 \tabularnewline
39 & 13 & 13.0553553166344 & -0.0553553166344411 \tabularnewline
40 & 16 & 15.2167940982243 & 0.783205901775738 \tabularnewline
41 & 14 & 12.9060807718097 & 1.0939192281903 \tabularnewline
42 & 15 & 8.0928016601972 & 6.9071983398028 \tabularnewline
43 & 5 & 9.74045158766314 & -4.74045158766314 \tabularnewline
44 & 8 & 10.4769930747943 & -2.47699307479425 \tabularnewline
45 & 11 & 11.2435228309355 & -0.243522830935547 \tabularnewline
46 & 16 & 13.787523258973 & 2.21247674102702 \tabularnewline
47 & 17 & 13.0209935028317 & 3.97900649716831 \tabularnewline
48 & 9 & 8.48428263842372 & 0.515717361576283 \tabularnewline
49 & 9 & 11.9406257278483 & -2.94062572784829 \tabularnewline
50 & 13 & 14.6842546753289 & -1.68425467532887 \tabularnewline
51 & 10 & 10.9555082099017 & -0.955508209901694 \tabularnewline
52 & 6 & 12.4122767075096 & -6.41227670750964 \tabularnewline
53 & 12 & 12.3840758175299 & -0.384075817529943 \tabularnewline
54 & 8 & 10.4328000239989 & -2.4328000239989 \tabularnewline
55 & 14 & 12.3735413489143 & 1.62645865108569 \tabularnewline
56 & 12 & 12.4282688683253 & -0.428268868325295 \tabularnewline
57 & 11 & 10.7755421644437 & 0.224457835556259 \tabularnewline
58 & 16 & 14.4006135990876 & 1.5993864009124 \tabularnewline
59 & 8 & 10.2695293709796 & -2.26952937097964 \tabularnewline
60 & 15 & 15.251155912027 & -0.251155912027017 \tabularnewline
61 & 7 & 9.45681051142187 & -2.45681051142187 \tabularnewline
62 & 16 & 13.9864485467931 & 2.01355145320691 \tabularnewline
63 & 14 & 13.7364660527315 & 0.263533947268463 \tabularnewline
64 & 16 & 13.7531614451702 & 2.24683855482978 \tabularnewline
65 & 9 & 9.93969919132188 & -0.939699191321884 \tabularnewline
66 & 14 & 12.2789943235006 & 1.72100567649945 \tabularnewline
67 & 11 & 13.0448208480188 & -2.04482084801881 \tabularnewline
68 & 13 & 10.4671618378017 & 2.53283816219834 \tabularnewline
69 & 15 & 12.9060807718097 & 2.0939192281903 \tabularnewline
70 & 5 & 5.60121038311099 & -0.601210383110992 \tabularnewline
71 & 15 & 12.4282688683253 & 2.57173113167471 \tabularnewline
72 & 13 & 12.2789943235006 & 0.721005676499448 \tabularnewline
73 & 11 & 12.0457072218777 & -1.04570722187768 \tabularnewline
74 & 11 & 13.9782324466372 & -2.97823244663722 \tabularnewline
75 & 12 & 12.4731651507437 & -0.473165150743683 \tabularnewline
76 & 12 & 13.3937239122867 & -1.3937239122867 \tabularnewline
77 & 12 & 12.2684598548849 & -0.268459854884918 \tabularnewline
78 & 12 & 11.9008062218455 & 0.0991937781544784 \tabularnewline
79 & 14 & 10.7817030882668 & 3.21829691173321 \tabularnewline
80 & 6 & 7.99317785836783 & -1.99317785836783 \tabularnewline
81 & 7 & 9.84007538949251 & -2.84007538949251 \tabularnewline
82 & 14 & 11.9898955550593 & 2.01010444494074 \tabularnewline
83 & 14 & 13.8582429391996 & 0.141757060800385 \tabularnewline
84 & 10 & 11.2533540679281 & -1.25335406792814 \tabularnewline
85 & 13 & 8.72534580070636 & 4.27465419929364 \tabularnewline
86 & 12 & 12.4079031627171 & -0.407903162717064 \tabularnewline
87 & 9 & 9.29154380578147 & -0.291543805781471 \tabularnewline
88 & 12 & 12.0164221844905 & -0.016422184490546 \tabularnewline
89 & 16 & 15.0073343417885 & 0.992665658211484 \tabularnewline
90 & 10 & 10.2331715045558 & -0.233171504555754 \tabularnewline
91 & 14 & 13.1267782284841 & 0.873221771515887 \tabularnewline
92 & 10 & 13.5093398749317 & -3.50933987493172 \tabularnewline
93 & 16 & 15.311341123638 & 0.68865887636198 \tabularnewline
94 & 15 & 13.4484514316977 & 1.55154856830232 \tabularnewline
95 & 12 & 11.3479010933419 & 0.652098906658098 \tabularnewline
96 & 10 & 9.73499389546312 & 0.26500610453688 \tabularnewline
97 & 8 & 10.2389515125944 & -2.23895151259441 \tabularnewline
98 & 8 & 8.60972983806134 & -0.609729838061339 \tabularnewline
99 & 11 & 12.8424339206198 & -1.8424339206198 \tabularnewline
100 & 13 & 12.4184376313327 & 0.581562368667303 \tabularnewline
101 & 16 & 15.4606156684628 & 0.539384331537238 \tabularnewline
102 & 16 & 14.7186164891316 & 1.28138351086837 \tabularnewline
103 & 14 & 15.8149764249307 & -1.81497642493067 \tabularnewline
104 & 11 & 8.88515481414674 & 2.11484518585326 \tabularnewline
105 & 4 & 6.96753760279542 & -2.96753760279542 \tabularnewline
106 & 14 & 14.5700451759299 & -0.570045175929921 \tabularnewline
107 & 9 & 10.3480842355777 & -1.34808423557774 \tabularnewline
108 & 14 & 15.251155912027 & -1.25115591202702 \tabularnewline
109 & 8 & 10.4531657296071 & -2.45316572960713 \tabularnewline
110 & 8 & 10.9016925957044 & -2.9016925957044 \tabularnewline
111 & 11 & 12.1949817667024 & -1.19498176670243 \tabularnewline
112 & 12 & 13.6426222589408 & -1.64262225894082 \tabularnewline
113 & 11 & 11.4431513503787 & -0.443151350378695 \tabularnewline
114 & 14 & 13.6038869003455 & 0.396113099654519 \tabularnewline
115 & 15 & 14.336054842684 & 0.663945157315982 \tabularnewline
116 & 16 & 13.4042583809023 & 2.59574161909767 \tabularnewline
117 & 16 & 13.4988054063161 & 2.50119459368391 \tabularnewline
118 & 11 & 12.7336821134209 & -1.73368211342087 \tabularnewline
119 & 14 & 13.7426269765546 & 0.25737302344541 \tabularnewline
120 & 14 & 10.9646362152713 & 3.03536378472874 \tabularnewline
121 & 12 & 11.4080863049529 & 0.591913695047095 \tabularnewline
122 & 14 & 12.5670089445344 & 1.4329910554656 \tabularnewline
123 & 8 & 10.2331715045558 & -2.23317150455575 \tabularnewline
124 & 13 & 13.8371740019683 & -0.837174001968348 \tabularnewline
125 & 16 & 13.7426269765546 & 2.25737302344541 \tabularnewline
126 & 12 & 10.9211463960989 & 1.07885360390106 \tabularnewline
127 & 16 & 15.4507844314702 & 0.549215568529835 \tabularnewline
128 & 12 & 13.3937239122867 & -1.3937239122867 \tabularnewline
129 & 11 & 11.5182445753979 & -0.518244575397912 \tabularnewline
130 & 4 & 6.42684120345585 & -2.42684120345585 \tabularnewline
131 & 16 & 15.4507844314702 & 0.549215568529835 \tabularnewline
132 & 15 & 12.5677121761574 & 2.43228782384256 \tabularnewline
133 & 10 & 11.4795092168026 & -1.47950921680258 \tabularnewline
134 & 13 & 13.2107907852822 & -0.210790785282238 \tabularnewline
135 & 15 & 13.2549838360776 & 1.74501616392241 \tabularnewline
136 & 12 & 10.6766215942374 & 1.3233784057626 \tabularnewline
137 & 14 & 13.6480799511408 & 0.351920048859167 \tabularnewline
138 & 7 & 10.6759183626144 & -3.67591836261437 \tabularnewline
139 & 19 & 14.0915300408225 & 4.90846995917752 \tabularnewline
140 & 12 & 12.6826249071794 & -0.682624907179428 \tabularnewline
141 & 12 & 12.2888255604931 & -0.288825560493149 \tabularnewline
142 & 13 & 13.4988054063161 & -0.49880540631609 \tabularnewline
143 & 15 & 12.9424386382336 & 2.05756136176642 \tabularnewline
144 & 8 & 8.28902766377315 & -0.289027663773149 \tabularnewline
145 & 12 & 10.9204431644759 & 1.0795568355241 \tabularnewline
146 & 10 & 10.806233665077 & -0.806233665076951 \tabularnewline
147 & 8 & 11.4087895365759 & -3.40878953657594 \tabularnewline
148 & 10 & 14.3751711170638 & -4.37517111706375 \tabularnewline
149 & 15 & 13.8919015213793 & 1.10809847862067 \tabularnewline
150 & 16 & 14.6142382267253 & 1.38576177327473 \tabularnewline
151 & 13 & 13.1947986244666 & -0.194798624466587 \tabularnewline
152 & 16 & 15.0675195533995 & 0.932480446600481 \tabularnewline
153 & 9 & 10.2331715045558 & -1.23317150455575 \tabularnewline
154 & 14 & 13.1709712792795 & 0.829028720720535 \tabularnewline
155 & 14 & 12.7064522523665 & 1.29354774763345 \tabularnewline
156 & 12 & 10.1644478769502 & 1.83555212304975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&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]11.3631900225345[/C][C]1.63680997746547[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.059886472308[/C][C]0.940113527691951[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5198743435474[/C][C]1.48012565645264[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8106072098695[/C][C]1.18939279013047[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.6606294334218[/C][C]-0.660629433421751[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.21298894118337[/C][C]2.78701105881664[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7307807258967[/C][C]-1.73078072589671[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.4664586061786[/C][C]-1.46645860617862[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.2446325096978[/C][C]-0.244632509697797[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.9822624739678[/C][C]3.01773752603221[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3246193688968[/C][C]-2.32461936889679[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.0695345670648[/C][C]-1.06953456706481[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2181058802665[/C][C]2.78189411973349[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.2482772915125[/C][C]-4.24827729151253[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.6322454012062[/C][C]-0.632245401206211[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9541017466556[/C][C]0.0458982533443776[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.0695345670648[/C][C]-2.06953456706481[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3458860796766[/C][C]-0.345886079676616[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.69881917127508[/C][C]1.30118082872492[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.50608033863283[/C][C]-3.50608033863283[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.65116448090084[/C][C]2.34883551909916[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3802478934794[/C][C]0.61975210652063[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6253812457601[/C][C]-0.625381245760121[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.49117232522462[/C][C]2.50882767477538[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.3937239122867[/C][C]0.606276087713301[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.8335292201536[/C][C]0.166470779846384[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.6240694637179[/C][C]-5.62406946371787[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4935308563519[/C][C]0.506469143648087[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2151643300748[/C][C]-0.215164330074817[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.7817030882668[/C][C]5.21829691173321[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.49919065183193[/C][C]4.50080934816807[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5878947395298[/C][C]-1.58789473952983[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4747949188931[/C][C]-0.474794918893128[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0546119223439[/C][C]0.945388077656127[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4664586061786[/C][C]-1.46645860617862[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2408045856472[/C][C]1.75919541435278[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.87242218963[/C][C]-0.87242218962998[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.1915390881677[/C][C]0.808460911832304[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.0553553166344[/C][C]-0.0553553166344411[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2167940982243[/C][C]0.783205901775738[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9060807718097[/C][C]1.0939192281903[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.0928016601972[/C][C]6.9071983398028[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.74045158766314[/C][C]-4.74045158766314[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.4769930747943[/C][C]-2.47699307479425[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2435228309355[/C][C]-0.243522830935547[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.787523258973[/C][C]2.21247674102702[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.0209935028317[/C][C]3.97900649716831[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.48428263842372[/C][C]0.515717361576283[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.9406257278483[/C][C]-2.94062572784829[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6842546753289[/C][C]-1.68425467532887[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9555082099017[/C][C]-0.955508209901694[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4122767075096[/C][C]-6.41227670750964[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.3840758175299[/C][C]-0.384075817529943[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4328000239989[/C][C]-2.4328000239989[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.3735413489143[/C][C]1.62645865108569[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4282688683253[/C][C]-0.428268868325295[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7755421644437[/C][C]0.224457835556259[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4006135990876[/C][C]1.5993864009124[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2695293709796[/C][C]-2.26952937097964[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.251155912027[/C][C]-0.251155912027017[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.45681051142187[/C][C]-2.45681051142187[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.9864485467931[/C][C]2.01355145320691[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.7364660527315[/C][C]0.263533947268463[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7531614451702[/C][C]2.24683855482978[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.93969919132188[/C][C]-0.939699191321884[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2789943235006[/C][C]1.72100567649945[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.0448208480188[/C][C]-2.04482084801881[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4671618378017[/C][C]2.53283816219834[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9060807718097[/C][C]2.0939192281903[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.60121038311099[/C][C]-0.601210383110992[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4282688683253[/C][C]2.57173113167471[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.2789943235006[/C][C]0.721005676499448[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.0457072218777[/C][C]-1.04570722187768[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9782324466372[/C][C]-2.97823244663722[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4731651507437[/C][C]-0.473165150743683[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.3937239122867[/C][C]-1.3937239122867[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.2684598548849[/C][C]-0.268459854884918[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9008062218455[/C][C]0.0991937781544784[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7817030882668[/C][C]3.21829691173321[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.99317785836783[/C][C]-1.99317785836783[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.84007538949251[/C][C]-2.84007538949251[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.9898955550593[/C][C]2.01010444494074[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8582429391996[/C][C]0.141757060800385[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2533540679281[/C][C]-1.25335406792814[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.72534580070636[/C][C]4.27465419929364[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4079031627171[/C][C]-0.407903162717064[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.29154380578147[/C][C]-0.291543805781471[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0164221844905[/C][C]-0.016422184490546[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0073343417885[/C][C]0.992665658211484[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2331715045558[/C][C]-0.233171504555754[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1267782284841[/C][C]0.873221771515887[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5093398749317[/C][C]-3.50933987493172[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.311341123638[/C][C]0.68865887636198[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4484514316977[/C][C]1.55154856830232[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.3479010933419[/C][C]0.652098906658098[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.73499389546312[/C][C]0.26500610453688[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2389515125944[/C][C]-2.23895151259441[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.60972983806134[/C][C]-0.609729838061339[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.8424339206198[/C][C]-1.8424339206198[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.4184376313327[/C][C]0.581562368667303[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4606156684628[/C][C]0.539384331537238[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7186164891316[/C][C]1.28138351086837[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.8149764249307[/C][C]-1.81497642493067[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.88515481414674[/C][C]2.11484518585326[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.96753760279542[/C][C]-2.96753760279542[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5700451759299[/C][C]-0.570045175929921[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3480842355777[/C][C]-1.34808423557774[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.251155912027[/C][C]-1.25115591202702[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4531657296071[/C][C]-2.45316572960713[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.9016925957044[/C][C]-2.9016925957044[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1949817667024[/C][C]-1.19498176670243[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6426222589408[/C][C]-1.64262225894082[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4431513503787[/C][C]-0.443151350378695[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.6038869003455[/C][C]0.396113099654519[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.336054842684[/C][C]0.663945157315982[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4042583809023[/C][C]2.59574161909767[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4988054063161[/C][C]2.50119459368391[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7336821134209[/C][C]-1.73368211342087[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7426269765546[/C][C]0.25737302344541[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9646362152713[/C][C]3.03536378472874[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.4080863049529[/C][C]0.591913695047095[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5670089445344[/C][C]1.4329910554656[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.2331715045558[/C][C]-2.23317150455575[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.8371740019683[/C][C]-0.837174001968348[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.7426269765546[/C][C]2.25737302344541[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.9211463960989[/C][C]1.07885360390106[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.4507844314702[/C][C]0.549215568529835[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.3937239122867[/C][C]-1.3937239122867[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.5182445753979[/C][C]-0.518244575397912[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.42684120345585[/C][C]-2.42684120345585[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.4507844314702[/C][C]0.549215568529835[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5677121761574[/C][C]2.43228782384256[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4795092168026[/C][C]-1.47950921680258[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.2107907852822[/C][C]-0.210790785282238[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.2549838360776[/C][C]1.74501616392241[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.6766215942374[/C][C]1.3233784057626[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6480799511408[/C][C]0.351920048859167[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.6759183626144[/C][C]-3.67591836261437[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0915300408225[/C][C]4.90846995917752[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.6826249071794[/C][C]-0.682624907179428[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2888255604931[/C][C]-0.288825560493149[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.4988054063161[/C][C]-0.49880540631609[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.9424386382336[/C][C]2.05756136176642[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.28902766377315[/C][C]-0.289027663773149[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9204431644759[/C][C]1.0795568355241[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.806233665077[/C][C]-0.806233665076951[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4087895365759[/C][C]-3.40878953657594[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.3751711170638[/C][C]-4.37517111706375[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8919015213793[/C][C]1.10809847862067[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.6142382267253[/C][C]1.38576177327473[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1947986244666[/C][C]-0.194798624466587[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0675195533995[/C][C]0.932480446600481[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.2331715045558[/C][C]-1.23317150455575[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.1709712792795[/C][C]0.829028720720535[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.7064522523665[/C][C]1.29354774763345[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1644478769502[/C][C]1.83555212304975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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
11311.36319002253451.63680997746547
21211.0598864723080.940113527691951
31513.51987434354741.48012565645264
41210.81060720986951.18939279013047
51010.6606294334218-0.660629433421751
6129.212988941183372.78701105881664
71516.7307807258967-1.73078072589671
8910.4664586061786-1.46645860617862
91212.2446325096978-0.244632509697797
10117.98226247396783.01773752603221
111113.3246193688968-2.32461936889679
121112.0695345670648-1.06953456706481
131512.21810588026652.78189411973349
14711.2482772915125-4.24827729151253
151111.6322454012062-0.632245401206211
161110.95410174665560.0458982533443776
171012.0695345670648-2.06953456706481
181414.3458860796766-0.345886079676616
19108.698819171275081.30118082872492
2069.50608033863283-3.50608033863283
21118.651164480900842.34883551909916
221514.38024789347940.61975210652063
231111.6253812457601-0.625381245760121
24129.491172325224622.50882767477538
251413.39372391228670.606276087713301
261514.83352922015360.166470779846384
27914.6240694637179-5.62406946371787
281312.49353085635190.506469143648087
291313.2151643300748-0.215164330074817
301610.78170308826685.21829691173321
31138.499190651831934.50080934816807
321213.5878947395298-1.58789473952983
331414.4747949188931-0.474794918893128
341110.05461192234390.945388077656127
35910.4664586061786-1.46645860617862
361614.24080458564721.75919541435278
371212.87242218963-0.87242218962998
38109.19153908816770.808460911832304
391313.0553553166344-0.0553553166344411
401615.21679409822430.783205901775738
411412.90608077180971.0939192281903
42158.09280166019726.9071983398028
4359.74045158766314-4.74045158766314
44810.4769930747943-2.47699307479425
451111.2435228309355-0.243522830935547
461613.7875232589732.21247674102702
471713.02099350283173.97900649716831
4898.484282638423720.515717361576283
49911.9406257278483-2.94062572784829
501314.6842546753289-1.68425467532887
511010.9555082099017-0.955508209901694
52612.4122767075096-6.41227670750964
531212.3840758175299-0.384075817529943
54810.4328000239989-2.4328000239989
551412.37354134891431.62645865108569
561212.4282688683253-0.428268868325295
571110.77554216444370.224457835556259
581614.40061359908761.5993864009124
59810.2695293709796-2.26952937097964
601515.251155912027-0.251155912027017
6179.45681051142187-2.45681051142187
621613.98644854679312.01355145320691
631413.73646605273150.263533947268463
641613.75316144517022.24683855482978
6599.93969919132188-0.939699191321884
661412.27899432350061.72100567649945
671113.0448208480188-2.04482084801881
681310.46716183780172.53283816219834
691512.90608077180972.0939192281903
7055.60121038311099-0.601210383110992
711512.42826886832532.57173113167471
721312.27899432350060.721005676499448
731112.0457072218777-1.04570722187768
741113.9782324466372-2.97823244663722
751212.4731651507437-0.473165150743683
761213.3937239122867-1.3937239122867
771212.2684598548849-0.268459854884918
781211.90080622184550.0991937781544784
791410.78170308826683.21829691173321
8067.99317785836783-1.99317785836783
8179.84007538949251-2.84007538949251
821411.98989555505932.01010444494074
831413.85824293919960.141757060800385
841011.2533540679281-1.25335406792814
85138.725345800706364.27465419929364
861212.4079031627171-0.407903162717064
8799.29154380578147-0.291543805781471
881212.0164221844905-0.016422184490546
891615.00733434178850.992665658211484
901010.2331715045558-0.233171504555754
911413.12677822848410.873221771515887
921013.5093398749317-3.50933987493172
931615.3113411236380.68865887636198
941513.44845143169771.55154856830232
951211.34790109334190.652098906658098
96109.734993895463120.26500610453688
97810.2389515125944-2.23895151259441
9888.60972983806134-0.609729838061339
991112.8424339206198-1.8424339206198
1001312.41843763133270.581562368667303
1011615.46061566846280.539384331537238
1021614.71861648913161.28138351086837
1031415.8149764249307-1.81497642493067
104118.885154814146742.11484518585326
10546.96753760279542-2.96753760279542
1061414.5700451759299-0.570045175929921
107910.3480842355777-1.34808423557774
1081415.251155912027-1.25115591202702
109810.4531657296071-2.45316572960713
110810.9016925957044-2.9016925957044
1111112.1949817667024-1.19498176670243
1121213.6426222589408-1.64262225894082
1131111.4431513503787-0.443151350378695
1141413.60388690034550.396113099654519
1151514.3360548426840.663945157315982
1161613.40425838090232.59574161909767
1171613.49880540631612.50119459368391
1181112.7336821134209-1.73368211342087
1191413.74262697655460.25737302344541
1201410.96463621527133.03536378472874
1211211.40808630495290.591913695047095
1221412.56700894453441.4329910554656
123810.2331715045558-2.23317150455575
1241313.8371740019683-0.837174001968348
1251613.74262697655462.25737302344541
1261210.92114639609891.07885360390106
1271615.45078443147020.549215568529835
1281213.3937239122867-1.3937239122867
1291111.5182445753979-0.518244575397912
13046.42684120345585-2.42684120345585
1311615.45078443147020.549215568529835
1321512.56771217615742.43228782384256
1331011.4795092168026-1.47950921680258
1341313.2107907852822-0.210790785282238
1351513.25498383607761.74501616392241
1361210.67662159423741.3233784057626
1371413.64807995114080.351920048859167
138710.6759183626144-3.67591836261437
1391914.09153004082254.90846995917752
1401212.6826249071794-0.682624907179428
1411212.2888255604931-0.288825560493149
1421313.4988054063161-0.49880540631609
1431512.94243863823362.05756136176642
14488.28902766377315-0.289027663773149
1451210.92044316447591.0795568355241
1461010.806233665077-0.806233665076951
147811.4087895365759-3.40878953657594
1481014.3751711170638-4.37517111706375
1491513.89190152137931.10809847862067
1501614.61423822672531.38576177327473
1511313.1947986244666-0.194798624466587
1521615.06751955339950.932480446600481
153910.2331715045558-1.23317150455575
1541413.17097127927950.829028720720535
1551412.70645225236651.29354774763345
1561210.16444787695021.83555212304975






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.06025482791299530.1205096558259910.939745172087005
90.04069540981256390.08139081962512790.959304590187436
100.01701945403569130.03403890807138270.982980545964309
110.0332446241085250.06648924821705010.966755375891475
120.01685239051616050.03370478103232110.98314760948384
130.3400622488499650.680124497699930.659937751150035
140.6873835015880520.6252329968238970.312616498411948
150.6012092341853720.7975815316292560.398790765814628
160.5105821672358630.9788356655282750.489417832764137
170.4535987466287940.9071974932575880.546401253371206
180.3707235097170030.7414470194340050.629276490282997
190.3013179996571590.6026359993143180.698682000342841
200.597546286096090.804907427807820.40245371390391
210.534862766631690.930274466736620.46513723336831
220.5029403951166370.9941192097667270.497059604883363
230.4328018044117460.8656036088234920.567198195588254
240.4185839827894390.8371679655788780.581416017210561
250.3835480252706620.7670960505413240.616451974729338
260.3290064505902190.6580129011804380.670993549409781
270.5899525224805920.8200949550388160.410047477519408
280.5317834451220430.9364331097559140.468216554877957
290.4704994501200940.9409989002401880.529500549879906
300.645914844697550.70817031060490.35408515530245
310.7776194645504570.4447610708990860.222380535449543
320.7381322898998420.5237354202003170.261867710100158
330.6945479331478840.6109041337042330.305452066852116
340.6502482964694070.6995034070611860.349751703530593
350.6428795252159040.7142409495681920.357120474784096
360.6645268017999450.6709463964001090.335473198200054
370.623649875754510.752700248490980.37635012424549
380.5742704823215980.8514590353568030.425729517678402
390.5234171683315460.9531656633369090.476582831668454
400.50655459359110.98689081281780.4934454064089
410.4782864758028280.9565729516056550.521713524197172
420.784717916131570.4305641677368590.21528208386843
430.9463041867797530.1073916264404930.0536958132202466
440.9574280168490020.08514396630199550.0425719831509978
450.9450185032535570.1099629934928860.0549814967464428
460.9519147494393270.09617050112134540.0480852505606727
470.9774773956164270.04504520876714690.0225226043835734
480.9704046328422150.05919073431557060.0295953671577853
490.9780464314618140.04390713707637240.0219535685381862
500.9749737380156960.05005252396860730.0250262619843037
510.969720419682740.0605591606345180.030279580317259
520.9971950944675510.005609811064897280.00280490553244864
530.9960151860001930.007969627999614830.00398481399980742
540.9970664581600680.005867083679864110.00293354183993206
550.9967770542126450.00644589157470940.0032229457873547
560.9954521634667750.009095673066450140.00454783653322507
570.9936983097251510.0126033805496980.006301690274849
580.9928244923345930.01435101533081330.00717550766540665
590.9948755413886040.01024891722279230.00512445861139614
600.9931078366989610.01378432660207710.00689216330103856
610.9943035599736130.01139288005277460.0056964400263873
620.994618210572370.01076357885525860.0053817894276293
630.9927116967501240.0145766064997510.00728830324987549
640.993145262741360.01370947451727920.00685473725863961
650.9915210335169250.01695793296615010.00847896648307504
660.9906400312074340.01871993758513190.00935996879256594
670.9905792676869370.01884146462612530.00942073231306266
680.9920527826438850.01589443471222940.00794721735611471
690.9921794112651230.01564117746975430.00782058873487717
700.9895339729612780.02093205407744420.0104660270387221
710.9911188159670930.01776236806581420.0088811840329071
720.988299003951780.02340199209643860.0117009960482193
730.9853847296478660.02923054070426820.0146152703521341
740.9896747351764060.0206505296471870.0103252648235935
750.9861518732867260.02769625342654810.013848126713274
760.9839573921778190.0320852156443630.0160426078221815
770.978848235528330.04230352894333890.0211517644716695
780.9721454368926180.05570912621476390.0278545631073819
790.9822727781780780.03545444364384410.017727221821922
800.981837554459240.03632489108151890.0181624455407594
810.9854206847977940.0291586304044130.0145793152022065
820.9860931986057190.02781360278856260.0139068013942813
830.9813073644520550.03738527109588950.0186926355479448
840.9772777699119120.04544446017617620.0227222300880881
850.9937818370323720.01243632593525510.00621816296762753
860.991538283792010.016923432415980.00846171620799002
870.98850468802550.02299062394899840.0114953119744992
880.9849102225631930.03017955487361370.0150897774368069
890.9808971934403340.03820561311933260.0191028065596663
900.9747753662833420.05044926743331570.0252246337166578
910.969338911513750.06132217697249790.030661088486249
920.9831730816758790.03365383664824280.0168269183241214
930.9780670412970310.04386591740593710.0219329587029686
940.9745862171231950.050827565753610.025413782876805
950.9676912335440760.06461753291184880.0323087664559244
960.9588710609814570.08225787803708510.0411289390185426
970.9560434051392860.08791318972142890.0439565948607145
980.9440023909730540.1119952180538910.0559976090269456
990.9415310416885330.1169379166229340.058468958311467
1000.927235535914430.1455289281711390.0727644640855697
1010.9096423520111910.1807152959776170.0903576479888085
1020.893626100634780.212747798730440.10637389936522
1030.9046921340887070.1906157318225860.0953078659112928
1040.9332530481912390.1334939036175230.0667469518087613
1050.9330786262369960.1338427475260070.0669213737630037
1060.9163290922377540.1673418155244920.0836709077622459
1070.9003312966006640.1993374067986710.0996687033993356
1080.8968412946739830.2063174106520330.103158705326017
1090.897519008070580.2049619838588390.102480991929419
1100.9195621391629540.1608757216740910.0804378608370456
1110.91161627005250.1767674598950010.0883837299475005
1120.9383769188566260.1232461622867480.061623081143374
1130.9207273068043110.1585453863913770.0792726931956885
1140.8986139378332840.2027721243334330.101386062166716
1150.8725093718426540.2549812563146910.127490628157346
1160.8711970883278750.2576058233442510.128802911672125
1170.8779524793149640.2440950413700730.122047520685036
1180.8697217026380880.2605565947238240.130278297361912
1190.8368168781945840.3263662436108330.163183121805416
1200.8844673073692340.2310653852615310.115532692630766
1210.860662772722780.2786744545544390.139337227277219
1220.8387255467957450.3225489064085110.161274453204255
1230.8311604577216010.3376790845567970.168839542278399
1240.7948497716061760.4103004567876480.205150228393824
1250.79485241066910.41029517866180.2051475893309
1260.7785030718169870.4429938563660250.221496928183013
1270.7300541982644710.5398916034710580.269945801735529
1280.7050093286279450.589981342744110.294990671372055
1290.7229483003899970.5541033992200060.277051699610003
1300.7186493105007460.5627013789985080.281350689499254
1310.6625361731803110.6749276536393780.337463826819689
1320.6512050199096170.6975899601807660.348794980090383
1330.6250240742971170.7499518514057660.374975925702883
1340.5538353589992710.8923292820014590.446164641000729
1350.5290316728198130.9419366543603750.470968327180187
1360.524968980108930.950062039782140.47503101989107
1370.4518375920058140.9036751840116280.548162407994186
1380.5218511957828360.9562976084343290.478148804217164
1390.8500554446455760.2998891107088480.149944555354424
1400.877750977115010.2444980457699810.12224902288499
1410.844213121210480.3115737575790410.155786878789521
1420.7772333154367750.445533369126450.222766684563225
1430.7493126862805940.5013746274388120.250687313719406
1440.6522203170475290.6955593659049420.347779682952471
1450.6428770048645420.7142459902709160.357122995135458
1460.7639182410131730.4721635179736540.236081758986827
1470.7316741929776480.5366516140447040.268325807022352
1480.8524624487800220.2950751024399550.147537551219978

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0602548279129953 & 0.120509655825991 & 0.939745172087005 \tabularnewline
9 & 0.0406954098125639 & 0.0813908196251279 & 0.959304590187436 \tabularnewline
10 & 0.0170194540356913 & 0.0340389080713827 & 0.982980545964309 \tabularnewline
11 & 0.033244624108525 & 0.0664892482170501 & 0.966755375891475 \tabularnewline
12 & 0.0168523905161605 & 0.0337047810323211 & 0.98314760948384 \tabularnewline
13 & 0.340062248849965 & 0.68012449769993 & 0.659937751150035 \tabularnewline
14 & 0.687383501588052 & 0.625232996823897 & 0.312616498411948 \tabularnewline
15 & 0.601209234185372 & 0.797581531629256 & 0.398790765814628 \tabularnewline
16 & 0.510582167235863 & 0.978835665528275 & 0.489417832764137 \tabularnewline
17 & 0.453598746628794 & 0.907197493257588 & 0.546401253371206 \tabularnewline
18 & 0.370723509717003 & 0.741447019434005 & 0.629276490282997 \tabularnewline
19 & 0.301317999657159 & 0.602635999314318 & 0.698682000342841 \tabularnewline
20 & 0.59754628609609 & 0.80490742780782 & 0.40245371390391 \tabularnewline
21 & 0.53486276663169 & 0.93027446673662 & 0.46513723336831 \tabularnewline
22 & 0.502940395116637 & 0.994119209766727 & 0.497059604883363 \tabularnewline
23 & 0.432801804411746 & 0.865603608823492 & 0.567198195588254 \tabularnewline
24 & 0.418583982789439 & 0.837167965578878 & 0.581416017210561 \tabularnewline
25 & 0.383548025270662 & 0.767096050541324 & 0.616451974729338 \tabularnewline
26 & 0.329006450590219 & 0.658012901180438 & 0.670993549409781 \tabularnewline
27 & 0.589952522480592 & 0.820094955038816 & 0.410047477519408 \tabularnewline
28 & 0.531783445122043 & 0.936433109755914 & 0.468216554877957 \tabularnewline
29 & 0.470499450120094 & 0.940998900240188 & 0.529500549879906 \tabularnewline
30 & 0.64591484469755 & 0.7081703106049 & 0.35408515530245 \tabularnewline
31 & 0.777619464550457 & 0.444761070899086 & 0.222380535449543 \tabularnewline
32 & 0.738132289899842 & 0.523735420200317 & 0.261867710100158 \tabularnewline
33 & 0.694547933147884 & 0.610904133704233 & 0.305452066852116 \tabularnewline
34 & 0.650248296469407 & 0.699503407061186 & 0.349751703530593 \tabularnewline
35 & 0.642879525215904 & 0.714240949568192 & 0.357120474784096 \tabularnewline
36 & 0.664526801799945 & 0.670946396400109 & 0.335473198200054 \tabularnewline
37 & 0.62364987575451 & 0.75270024849098 & 0.37635012424549 \tabularnewline
38 & 0.574270482321598 & 0.851459035356803 & 0.425729517678402 \tabularnewline
39 & 0.523417168331546 & 0.953165663336909 & 0.476582831668454 \tabularnewline
40 & 0.5065545935911 & 0.9868908128178 & 0.4934454064089 \tabularnewline
41 & 0.478286475802828 & 0.956572951605655 & 0.521713524197172 \tabularnewline
42 & 0.78471791613157 & 0.430564167736859 & 0.21528208386843 \tabularnewline
43 & 0.946304186779753 & 0.107391626440493 & 0.0536958132202466 \tabularnewline
44 & 0.957428016849002 & 0.0851439663019955 & 0.0425719831509978 \tabularnewline
45 & 0.945018503253557 & 0.109962993492886 & 0.0549814967464428 \tabularnewline
46 & 0.951914749439327 & 0.0961705011213454 & 0.0480852505606727 \tabularnewline
47 & 0.977477395616427 & 0.0450452087671469 & 0.0225226043835734 \tabularnewline
48 & 0.970404632842215 & 0.0591907343155706 & 0.0295953671577853 \tabularnewline
49 & 0.978046431461814 & 0.0439071370763724 & 0.0219535685381862 \tabularnewline
50 & 0.974973738015696 & 0.0500525239686073 & 0.0250262619843037 \tabularnewline
51 & 0.96972041968274 & 0.060559160634518 & 0.030279580317259 \tabularnewline
52 & 0.997195094467551 & 0.00560981106489728 & 0.00280490553244864 \tabularnewline
53 & 0.996015186000193 & 0.00796962799961483 & 0.00398481399980742 \tabularnewline
54 & 0.997066458160068 & 0.00586708367986411 & 0.00293354183993206 \tabularnewline
55 & 0.996777054212645 & 0.0064458915747094 & 0.0032229457873547 \tabularnewline
56 & 0.995452163466775 & 0.00909567306645014 & 0.00454783653322507 \tabularnewline
57 & 0.993698309725151 & 0.012603380549698 & 0.006301690274849 \tabularnewline
58 & 0.992824492334593 & 0.0143510153308133 & 0.00717550766540665 \tabularnewline
59 & 0.994875541388604 & 0.0102489172227923 & 0.00512445861139614 \tabularnewline
60 & 0.993107836698961 & 0.0137843266020771 & 0.00689216330103856 \tabularnewline
61 & 0.994303559973613 & 0.0113928800527746 & 0.0056964400263873 \tabularnewline
62 & 0.99461821057237 & 0.0107635788552586 & 0.0053817894276293 \tabularnewline
63 & 0.992711696750124 & 0.014576606499751 & 0.00728830324987549 \tabularnewline
64 & 0.99314526274136 & 0.0137094745172792 & 0.00685473725863961 \tabularnewline
65 & 0.991521033516925 & 0.0169579329661501 & 0.00847896648307504 \tabularnewline
66 & 0.990640031207434 & 0.0187199375851319 & 0.00935996879256594 \tabularnewline
67 & 0.990579267686937 & 0.0188414646261253 & 0.00942073231306266 \tabularnewline
68 & 0.992052782643885 & 0.0158944347122294 & 0.00794721735611471 \tabularnewline
69 & 0.992179411265123 & 0.0156411774697543 & 0.00782058873487717 \tabularnewline
70 & 0.989533972961278 & 0.0209320540774442 & 0.0104660270387221 \tabularnewline
71 & 0.991118815967093 & 0.0177623680658142 & 0.0088811840329071 \tabularnewline
72 & 0.98829900395178 & 0.0234019920964386 & 0.0117009960482193 \tabularnewline
73 & 0.985384729647866 & 0.0292305407042682 & 0.0146152703521341 \tabularnewline
74 & 0.989674735176406 & 0.020650529647187 & 0.0103252648235935 \tabularnewline
75 & 0.986151873286726 & 0.0276962534265481 & 0.013848126713274 \tabularnewline
76 & 0.983957392177819 & 0.032085215644363 & 0.0160426078221815 \tabularnewline
77 & 0.97884823552833 & 0.0423035289433389 & 0.0211517644716695 \tabularnewline
78 & 0.972145436892618 & 0.0557091262147639 & 0.0278545631073819 \tabularnewline
79 & 0.982272778178078 & 0.0354544436438441 & 0.017727221821922 \tabularnewline
80 & 0.98183755445924 & 0.0363248910815189 & 0.0181624455407594 \tabularnewline
81 & 0.985420684797794 & 0.029158630404413 & 0.0145793152022065 \tabularnewline
82 & 0.986093198605719 & 0.0278136027885626 & 0.0139068013942813 \tabularnewline
83 & 0.981307364452055 & 0.0373852710958895 & 0.0186926355479448 \tabularnewline
84 & 0.977277769911912 & 0.0454444601761762 & 0.0227222300880881 \tabularnewline
85 & 0.993781837032372 & 0.0124363259352551 & 0.00621816296762753 \tabularnewline
86 & 0.99153828379201 & 0.01692343241598 & 0.00846171620799002 \tabularnewline
87 & 0.9885046880255 & 0.0229906239489984 & 0.0114953119744992 \tabularnewline
88 & 0.984910222563193 & 0.0301795548736137 & 0.0150897774368069 \tabularnewline
89 & 0.980897193440334 & 0.0382056131193326 & 0.0191028065596663 \tabularnewline
90 & 0.974775366283342 & 0.0504492674333157 & 0.0252246337166578 \tabularnewline
91 & 0.96933891151375 & 0.0613221769724979 & 0.030661088486249 \tabularnewline
92 & 0.983173081675879 & 0.0336538366482428 & 0.0168269183241214 \tabularnewline
93 & 0.978067041297031 & 0.0438659174059371 & 0.0219329587029686 \tabularnewline
94 & 0.974586217123195 & 0.05082756575361 & 0.025413782876805 \tabularnewline
95 & 0.967691233544076 & 0.0646175329118488 & 0.0323087664559244 \tabularnewline
96 & 0.958871060981457 & 0.0822578780370851 & 0.0411289390185426 \tabularnewline
97 & 0.956043405139286 & 0.0879131897214289 & 0.0439565948607145 \tabularnewline
98 & 0.944002390973054 & 0.111995218053891 & 0.0559976090269456 \tabularnewline
99 & 0.941531041688533 & 0.116937916622934 & 0.058468958311467 \tabularnewline
100 & 0.92723553591443 & 0.145528928171139 & 0.0727644640855697 \tabularnewline
101 & 0.909642352011191 & 0.180715295977617 & 0.0903576479888085 \tabularnewline
102 & 0.89362610063478 & 0.21274779873044 & 0.10637389936522 \tabularnewline
103 & 0.904692134088707 & 0.190615731822586 & 0.0953078659112928 \tabularnewline
104 & 0.933253048191239 & 0.133493903617523 & 0.0667469518087613 \tabularnewline
105 & 0.933078626236996 & 0.133842747526007 & 0.0669213737630037 \tabularnewline
106 & 0.916329092237754 & 0.167341815524492 & 0.0836709077622459 \tabularnewline
107 & 0.900331296600664 & 0.199337406798671 & 0.0996687033993356 \tabularnewline
108 & 0.896841294673983 & 0.206317410652033 & 0.103158705326017 \tabularnewline
109 & 0.89751900807058 & 0.204961983858839 & 0.102480991929419 \tabularnewline
110 & 0.919562139162954 & 0.160875721674091 & 0.0804378608370456 \tabularnewline
111 & 0.9116162700525 & 0.176767459895001 & 0.0883837299475005 \tabularnewline
112 & 0.938376918856626 & 0.123246162286748 & 0.061623081143374 \tabularnewline
113 & 0.920727306804311 & 0.158545386391377 & 0.0792726931956885 \tabularnewline
114 & 0.898613937833284 & 0.202772124333433 & 0.101386062166716 \tabularnewline
115 & 0.872509371842654 & 0.254981256314691 & 0.127490628157346 \tabularnewline
116 & 0.871197088327875 & 0.257605823344251 & 0.128802911672125 \tabularnewline
117 & 0.877952479314964 & 0.244095041370073 & 0.122047520685036 \tabularnewline
118 & 0.869721702638088 & 0.260556594723824 & 0.130278297361912 \tabularnewline
119 & 0.836816878194584 & 0.326366243610833 & 0.163183121805416 \tabularnewline
120 & 0.884467307369234 & 0.231065385261531 & 0.115532692630766 \tabularnewline
121 & 0.86066277272278 & 0.278674454554439 & 0.139337227277219 \tabularnewline
122 & 0.838725546795745 & 0.322548906408511 & 0.161274453204255 \tabularnewline
123 & 0.831160457721601 & 0.337679084556797 & 0.168839542278399 \tabularnewline
124 & 0.794849771606176 & 0.410300456787648 & 0.205150228393824 \tabularnewline
125 & 0.7948524106691 & 0.4102951786618 & 0.2051475893309 \tabularnewline
126 & 0.778503071816987 & 0.442993856366025 & 0.221496928183013 \tabularnewline
127 & 0.730054198264471 & 0.539891603471058 & 0.269945801735529 \tabularnewline
128 & 0.705009328627945 & 0.58998134274411 & 0.294990671372055 \tabularnewline
129 & 0.722948300389997 & 0.554103399220006 & 0.277051699610003 \tabularnewline
130 & 0.718649310500746 & 0.562701378998508 & 0.281350689499254 \tabularnewline
131 & 0.662536173180311 & 0.674927653639378 & 0.337463826819689 \tabularnewline
132 & 0.651205019909617 & 0.697589960180766 & 0.348794980090383 \tabularnewline
133 & 0.625024074297117 & 0.749951851405766 & 0.374975925702883 \tabularnewline
134 & 0.553835358999271 & 0.892329282001459 & 0.446164641000729 \tabularnewline
135 & 0.529031672819813 & 0.941936654360375 & 0.470968327180187 \tabularnewline
136 & 0.52496898010893 & 0.95006203978214 & 0.47503101989107 \tabularnewline
137 & 0.451837592005814 & 0.903675184011628 & 0.548162407994186 \tabularnewline
138 & 0.521851195782836 & 0.956297608434329 & 0.478148804217164 \tabularnewline
139 & 0.850055444645576 & 0.299889110708848 & 0.149944555354424 \tabularnewline
140 & 0.87775097711501 & 0.244498045769981 & 0.12224902288499 \tabularnewline
141 & 0.84421312121048 & 0.311573757579041 & 0.155786878789521 \tabularnewline
142 & 0.777233315436775 & 0.44553336912645 & 0.222766684563225 \tabularnewline
143 & 0.749312686280594 & 0.501374627438812 & 0.250687313719406 \tabularnewline
144 & 0.652220317047529 & 0.695559365904942 & 0.347779682952471 \tabularnewline
145 & 0.642877004864542 & 0.714245990270916 & 0.357122995135458 \tabularnewline
146 & 0.763918241013173 & 0.472163517973654 & 0.236081758986827 \tabularnewline
147 & 0.731674192977648 & 0.536651614044704 & 0.268325807022352 \tabularnewline
148 & 0.852462448780022 & 0.295075102439955 & 0.147537551219978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&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]8[/C][C]0.0602548279129953[/C][C]0.120509655825991[/C][C]0.939745172087005[/C][/ROW]
[ROW][C]9[/C][C]0.0406954098125639[/C][C]0.0813908196251279[/C][C]0.959304590187436[/C][/ROW]
[ROW][C]10[/C][C]0.0170194540356913[/C][C]0.0340389080713827[/C][C]0.982980545964309[/C][/ROW]
[ROW][C]11[/C][C]0.033244624108525[/C][C]0.0664892482170501[/C][C]0.966755375891475[/C][/ROW]
[ROW][C]12[/C][C]0.0168523905161605[/C][C]0.0337047810323211[/C][C]0.98314760948384[/C][/ROW]
[ROW][C]13[/C][C]0.340062248849965[/C][C]0.68012449769993[/C][C]0.659937751150035[/C][/ROW]
[ROW][C]14[/C][C]0.687383501588052[/C][C]0.625232996823897[/C][C]0.312616498411948[/C][/ROW]
[ROW][C]15[/C][C]0.601209234185372[/C][C]0.797581531629256[/C][C]0.398790765814628[/C][/ROW]
[ROW][C]16[/C][C]0.510582167235863[/C][C]0.978835665528275[/C][C]0.489417832764137[/C][/ROW]
[ROW][C]17[/C][C]0.453598746628794[/C][C]0.907197493257588[/C][C]0.546401253371206[/C][/ROW]
[ROW][C]18[/C][C]0.370723509717003[/C][C]0.741447019434005[/C][C]0.629276490282997[/C][/ROW]
[ROW][C]19[/C][C]0.301317999657159[/C][C]0.602635999314318[/C][C]0.698682000342841[/C][/ROW]
[ROW][C]20[/C][C]0.59754628609609[/C][C]0.80490742780782[/C][C]0.40245371390391[/C][/ROW]
[ROW][C]21[/C][C]0.53486276663169[/C][C]0.93027446673662[/C][C]0.46513723336831[/C][/ROW]
[ROW][C]22[/C][C]0.502940395116637[/C][C]0.994119209766727[/C][C]0.497059604883363[/C][/ROW]
[ROW][C]23[/C][C]0.432801804411746[/C][C]0.865603608823492[/C][C]0.567198195588254[/C][/ROW]
[ROW][C]24[/C][C]0.418583982789439[/C][C]0.837167965578878[/C][C]0.581416017210561[/C][/ROW]
[ROW][C]25[/C][C]0.383548025270662[/C][C]0.767096050541324[/C][C]0.616451974729338[/C][/ROW]
[ROW][C]26[/C][C]0.329006450590219[/C][C]0.658012901180438[/C][C]0.670993549409781[/C][/ROW]
[ROW][C]27[/C][C]0.589952522480592[/C][C]0.820094955038816[/C][C]0.410047477519408[/C][/ROW]
[ROW][C]28[/C][C]0.531783445122043[/C][C]0.936433109755914[/C][C]0.468216554877957[/C][/ROW]
[ROW][C]29[/C][C]0.470499450120094[/C][C]0.940998900240188[/C][C]0.529500549879906[/C][/ROW]
[ROW][C]30[/C][C]0.64591484469755[/C][C]0.7081703106049[/C][C]0.35408515530245[/C][/ROW]
[ROW][C]31[/C][C]0.777619464550457[/C][C]0.444761070899086[/C][C]0.222380535449543[/C][/ROW]
[ROW][C]32[/C][C]0.738132289899842[/C][C]0.523735420200317[/C][C]0.261867710100158[/C][/ROW]
[ROW][C]33[/C][C]0.694547933147884[/C][C]0.610904133704233[/C][C]0.305452066852116[/C][/ROW]
[ROW][C]34[/C][C]0.650248296469407[/C][C]0.699503407061186[/C][C]0.349751703530593[/C][/ROW]
[ROW][C]35[/C][C]0.642879525215904[/C][C]0.714240949568192[/C][C]0.357120474784096[/C][/ROW]
[ROW][C]36[/C][C]0.664526801799945[/C][C]0.670946396400109[/C][C]0.335473198200054[/C][/ROW]
[ROW][C]37[/C][C]0.62364987575451[/C][C]0.75270024849098[/C][C]0.37635012424549[/C][/ROW]
[ROW][C]38[/C][C]0.574270482321598[/C][C]0.851459035356803[/C][C]0.425729517678402[/C][/ROW]
[ROW][C]39[/C][C]0.523417168331546[/C][C]0.953165663336909[/C][C]0.476582831668454[/C][/ROW]
[ROW][C]40[/C][C]0.5065545935911[/C][C]0.9868908128178[/C][C]0.4934454064089[/C][/ROW]
[ROW][C]41[/C][C]0.478286475802828[/C][C]0.956572951605655[/C][C]0.521713524197172[/C][/ROW]
[ROW][C]42[/C][C]0.78471791613157[/C][C]0.430564167736859[/C][C]0.21528208386843[/C][/ROW]
[ROW][C]43[/C][C]0.946304186779753[/C][C]0.107391626440493[/C][C]0.0536958132202466[/C][/ROW]
[ROW][C]44[/C][C]0.957428016849002[/C][C]0.0851439663019955[/C][C]0.0425719831509978[/C][/ROW]
[ROW][C]45[/C][C]0.945018503253557[/C][C]0.109962993492886[/C][C]0.0549814967464428[/C][/ROW]
[ROW][C]46[/C][C]0.951914749439327[/C][C]0.0961705011213454[/C][C]0.0480852505606727[/C][/ROW]
[ROW][C]47[/C][C]0.977477395616427[/C][C]0.0450452087671469[/C][C]0.0225226043835734[/C][/ROW]
[ROW][C]48[/C][C]0.970404632842215[/C][C]0.0591907343155706[/C][C]0.0295953671577853[/C][/ROW]
[ROW][C]49[/C][C]0.978046431461814[/C][C]0.0439071370763724[/C][C]0.0219535685381862[/C][/ROW]
[ROW][C]50[/C][C]0.974973738015696[/C][C]0.0500525239686073[/C][C]0.0250262619843037[/C][/ROW]
[ROW][C]51[/C][C]0.96972041968274[/C][C]0.060559160634518[/C][C]0.030279580317259[/C][/ROW]
[ROW][C]52[/C][C]0.997195094467551[/C][C]0.00560981106489728[/C][C]0.00280490553244864[/C][/ROW]
[ROW][C]53[/C][C]0.996015186000193[/C][C]0.00796962799961483[/C][C]0.00398481399980742[/C][/ROW]
[ROW][C]54[/C][C]0.997066458160068[/C][C]0.00586708367986411[/C][C]0.00293354183993206[/C][/ROW]
[ROW][C]55[/C][C]0.996777054212645[/C][C]0.0064458915747094[/C][C]0.0032229457873547[/C][/ROW]
[ROW][C]56[/C][C]0.995452163466775[/C][C]0.00909567306645014[/C][C]0.00454783653322507[/C][/ROW]
[ROW][C]57[/C][C]0.993698309725151[/C][C]0.012603380549698[/C][C]0.006301690274849[/C][/ROW]
[ROW][C]58[/C][C]0.992824492334593[/C][C]0.0143510153308133[/C][C]0.00717550766540665[/C][/ROW]
[ROW][C]59[/C][C]0.994875541388604[/C][C]0.0102489172227923[/C][C]0.00512445861139614[/C][/ROW]
[ROW][C]60[/C][C]0.993107836698961[/C][C]0.0137843266020771[/C][C]0.00689216330103856[/C][/ROW]
[ROW][C]61[/C][C]0.994303559973613[/C][C]0.0113928800527746[/C][C]0.0056964400263873[/C][/ROW]
[ROW][C]62[/C][C]0.99461821057237[/C][C]0.0107635788552586[/C][C]0.0053817894276293[/C][/ROW]
[ROW][C]63[/C][C]0.992711696750124[/C][C]0.014576606499751[/C][C]0.00728830324987549[/C][/ROW]
[ROW][C]64[/C][C]0.99314526274136[/C][C]0.0137094745172792[/C][C]0.00685473725863961[/C][/ROW]
[ROW][C]65[/C][C]0.991521033516925[/C][C]0.0169579329661501[/C][C]0.00847896648307504[/C][/ROW]
[ROW][C]66[/C][C]0.990640031207434[/C][C]0.0187199375851319[/C][C]0.00935996879256594[/C][/ROW]
[ROW][C]67[/C][C]0.990579267686937[/C][C]0.0188414646261253[/C][C]0.00942073231306266[/C][/ROW]
[ROW][C]68[/C][C]0.992052782643885[/C][C]0.0158944347122294[/C][C]0.00794721735611471[/C][/ROW]
[ROW][C]69[/C][C]0.992179411265123[/C][C]0.0156411774697543[/C][C]0.00782058873487717[/C][/ROW]
[ROW][C]70[/C][C]0.989533972961278[/C][C]0.0209320540774442[/C][C]0.0104660270387221[/C][/ROW]
[ROW][C]71[/C][C]0.991118815967093[/C][C]0.0177623680658142[/C][C]0.0088811840329071[/C][/ROW]
[ROW][C]72[/C][C]0.98829900395178[/C][C]0.0234019920964386[/C][C]0.0117009960482193[/C][/ROW]
[ROW][C]73[/C][C]0.985384729647866[/C][C]0.0292305407042682[/C][C]0.0146152703521341[/C][/ROW]
[ROW][C]74[/C][C]0.989674735176406[/C][C]0.020650529647187[/C][C]0.0103252648235935[/C][/ROW]
[ROW][C]75[/C][C]0.986151873286726[/C][C]0.0276962534265481[/C][C]0.013848126713274[/C][/ROW]
[ROW][C]76[/C][C]0.983957392177819[/C][C]0.032085215644363[/C][C]0.0160426078221815[/C][/ROW]
[ROW][C]77[/C][C]0.97884823552833[/C][C]0.0423035289433389[/C][C]0.0211517644716695[/C][/ROW]
[ROW][C]78[/C][C]0.972145436892618[/C][C]0.0557091262147639[/C][C]0.0278545631073819[/C][/ROW]
[ROW][C]79[/C][C]0.982272778178078[/C][C]0.0354544436438441[/C][C]0.017727221821922[/C][/ROW]
[ROW][C]80[/C][C]0.98183755445924[/C][C]0.0363248910815189[/C][C]0.0181624455407594[/C][/ROW]
[ROW][C]81[/C][C]0.985420684797794[/C][C]0.029158630404413[/C][C]0.0145793152022065[/C][/ROW]
[ROW][C]82[/C][C]0.986093198605719[/C][C]0.0278136027885626[/C][C]0.0139068013942813[/C][/ROW]
[ROW][C]83[/C][C]0.981307364452055[/C][C]0.0373852710958895[/C][C]0.0186926355479448[/C][/ROW]
[ROW][C]84[/C][C]0.977277769911912[/C][C]0.0454444601761762[/C][C]0.0227222300880881[/C][/ROW]
[ROW][C]85[/C][C]0.993781837032372[/C][C]0.0124363259352551[/C][C]0.00621816296762753[/C][/ROW]
[ROW][C]86[/C][C]0.99153828379201[/C][C]0.01692343241598[/C][C]0.00846171620799002[/C][/ROW]
[ROW][C]87[/C][C]0.9885046880255[/C][C]0.0229906239489984[/C][C]0.0114953119744992[/C][/ROW]
[ROW][C]88[/C][C]0.984910222563193[/C][C]0.0301795548736137[/C][C]0.0150897774368069[/C][/ROW]
[ROW][C]89[/C][C]0.980897193440334[/C][C]0.0382056131193326[/C][C]0.0191028065596663[/C][/ROW]
[ROW][C]90[/C][C]0.974775366283342[/C][C]0.0504492674333157[/C][C]0.0252246337166578[/C][/ROW]
[ROW][C]91[/C][C]0.96933891151375[/C][C]0.0613221769724979[/C][C]0.030661088486249[/C][/ROW]
[ROW][C]92[/C][C]0.983173081675879[/C][C]0.0336538366482428[/C][C]0.0168269183241214[/C][/ROW]
[ROW][C]93[/C][C]0.978067041297031[/C][C]0.0438659174059371[/C][C]0.0219329587029686[/C][/ROW]
[ROW][C]94[/C][C]0.974586217123195[/C][C]0.05082756575361[/C][C]0.025413782876805[/C][/ROW]
[ROW][C]95[/C][C]0.967691233544076[/C][C]0.0646175329118488[/C][C]0.0323087664559244[/C][/ROW]
[ROW][C]96[/C][C]0.958871060981457[/C][C]0.0822578780370851[/C][C]0.0411289390185426[/C][/ROW]
[ROW][C]97[/C][C]0.956043405139286[/C][C]0.0879131897214289[/C][C]0.0439565948607145[/C][/ROW]
[ROW][C]98[/C][C]0.944002390973054[/C][C]0.111995218053891[/C][C]0.0559976090269456[/C][/ROW]
[ROW][C]99[/C][C]0.941531041688533[/C][C]0.116937916622934[/C][C]0.058468958311467[/C][/ROW]
[ROW][C]100[/C][C]0.92723553591443[/C][C]0.145528928171139[/C][C]0.0727644640855697[/C][/ROW]
[ROW][C]101[/C][C]0.909642352011191[/C][C]0.180715295977617[/C][C]0.0903576479888085[/C][/ROW]
[ROW][C]102[/C][C]0.89362610063478[/C][C]0.21274779873044[/C][C]0.10637389936522[/C][/ROW]
[ROW][C]103[/C][C]0.904692134088707[/C][C]0.190615731822586[/C][C]0.0953078659112928[/C][/ROW]
[ROW][C]104[/C][C]0.933253048191239[/C][C]0.133493903617523[/C][C]0.0667469518087613[/C][/ROW]
[ROW][C]105[/C][C]0.933078626236996[/C][C]0.133842747526007[/C][C]0.0669213737630037[/C][/ROW]
[ROW][C]106[/C][C]0.916329092237754[/C][C]0.167341815524492[/C][C]0.0836709077622459[/C][/ROW]
[ROW][C]107[/C][C]0.900331296600664[/C][C]0.199337406798671[/C][C]0.0996687033993356[/C][/ROW]
[ROW][C]108[/C][C]0.896841294673983[/C][C]0.206317410652033[/C][C]0.103158705326017[/C][/ROW]
[ROW][C]109[/C][C]0.89751900807058[/C][C]0.204961983858839[/C][C]0.102480991929419[/C][/ROW]
[ROW][C]110[/C][C]0.919562139162954[/C][C]0.160875721674091[/C][C]0.0804378608370456[/C][/ROW]
[ROW][C]111[/C][C]0.9116162700525[/C][C]0.176767459895001[/C][C]0.0883837299475005[/C][/ROW]
[ROW][C]112[/C][C]0.938376918856626[/C][C]0.123246162286748[/C][C]0.061623081143374[/C][/ROW]
[ROW][C]113[/C][C]0.920727306804311[/C][C]0.158545386391377[/C][C]0.0792726931956885[/C][/ROW]
[ROW][C]114[/C][C]0.898613937833284[/C][C]0.202772124333433[/C][C]0.101386062166716[/C][/ROW]
[ROW][C]115[/C][C]0.872509371842654[/C][C]0.254981256314691[/C][C]0.127490628157346[/C][/ROW]
[ROW][C]116[/C][C]0.871197088327875[/C][C]0.257605823344251[/C][C]0.128802911672125[/C][/ROW]
[ROW][C]117[/C][C]0.877952479314964[/C][C]0.244095041370073[/C][C]0.122047520685036[/C][/ROW]
[ROW][C]118[/C][C]0.869721702638088[/C][C]0.260556594723824[/C][C]0.130278297361912[/C][/ROW]
[ROW][C]119[/C][C]0.836816878194584[/C][C]0.326366243610833[/C][C]0.163183121805416[/C][/ROW]
[ROW][C]120[/C][C]0.884467307369234[/C][C]0.231065385261531[/C][C]0.115532692630766[/C][/ROW]
[ROW][C]121[/C][C]0.86066277272278[/C][C]0.278674454554439[/C][C]0.139337227277219[/C][/ROW]
[ROW][C]122[/C][C]0.838725546795745[/C][C]0.322548906408511[/C][C]0.161274453204255[/C][/ROW]
[ROW][C]123[/C][C]0.831160457721601[/C][C]0.337679084556797[/C][C]0.168839542278399[/C][/ROW]
[ROW][C]124[/C][C]0.794849771606176[/C][C]0.410300456787648[/C][C]0.205150228393824[/C][/ROW]
[ROW][C]125[/C][C]0.7948524106691[/C][C]0.4102951786618[/C][C]0.2051475893309[/C][/ROW]
[ROW][C]126[/C][C]0.778503071816987[/C][C]0.442993856366025[/C][C]0.221496928183013[/C][/ROW]
[ROW][C]127[/C][C]0.730054198264471[/C][C]0.539891603471058[/C][C]0.269945801735529[/C][/ROW]
[ROW][C]128[/C][C]0.705009328627945[/C][C]0.58998134274411[/C][C]0.294990671372055[/C][/ROW]
[ROW][C]129[/C][C]0.722948300389997[/C][C]0.554103399220006[/C][C]0.277051699610003[/C][/ROW]
[ROW][C]130[/C][C]0.718649310500746[/C][C]0.562701378998508[/C][C]0.281350689499254[/C][/ROW]
[ROW][C]131[/C][C]0.662536173180311[/C][C]0.674927653639378[/C][C]0.337463826819689[/C][/ROW]
[ROW][C]132[/C][C]0.651205019909617[/C][C]0.697589960180766[/C][C]0.348794980090383[/C][/ROW]
[ROW][C]133[/C][C]0.625024074297117[/C][C]0.749951851405766[/C][C]0.374975925702883[/C][/ROW]
[ROW][C]134[/C][C]0.553835358999271[/C][C]0.892329282001459[/C][C]0.446164641000729[/C][/ROW]
[ROW][C]135[/C][C]0.529031672819813[/C][C]0.941936654360375[/C][C]0.470968327180187[/C][/ROW]
[ROW][C]136[/C][C]0.52496898010893[/C][C]0.95006203978214[/C][C]0.47503101989107[/C][/ROW]
[ROW][C]137[/C][C]0.451837592005814[/C][C]0.903675184011628[/C][C]0.548162407994186[/C][/ROW]
[ROW][C]138[/C][C]0.521851195782836[/C][C]0.956297608434329[/C][C]0.478148804217164[/C][/ROW]
[ROW][C]139[/C][C]0.850055444645576[/C][C]0.299889110708848[/C][C]0.149944555354424[/C][/ROW]
[ROW][C]140[/C][C]0.87775097711501[/C][C]0.244498045769981[/C][C]0.12224902288499[/C][/ROW]
[ROW][C]141[/C][C]0.84421312121048[/C][C]0.311573757579041[/C][C]0.155786878789521[/C][/ROW]
[ROW][C]142[/C][C]0.777233315436775[/C][C]0.44553336912645[/C][C]0.222766684563225[/C][/ROW]
[ROW][C]143[/C][C]0.749312686280594[/C][C]0.501374627438812[/C][C]0.250687313719406[/C][/ROW]
[ROW][C]144[/C][C]0.652220317047529[/C][C]0.695559365904942[/C][C]0.347779682952471[/C][/ROW]
[ROW][C]145[/C][C]0.642877004864542[/C][C]0.714245990270916[/C][C]0.357122995135458[/C][/ROW]
[ROW][C]146[/C][C]0.763918241013173[/C][C]0.472163517973654[/C][C]0.236081758986827[/C][/ROW]
[ROW][C]147[/C][C]0.731674192977648[/C][C]0.536651614044704[/C][C]0.268325807022352[/C][/ROW]
[ROW][C]148[/C][C]0.852462448780022[/C][C]0.295075102439955[/C][C]0.147537551219978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&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
80.06025482791299530.1205096558259910.939745172087005
90.04069540981256390.08139081962512790.959304590187436
100.01701945403569130.03403890807138270.982980545964309
110.0332446241085250.06648924821705010.966755375891475
120.01685239051616050.03370478103232110.98314760948384
130.3400622488499650.680124497699930.659937751150035
140.6873835015880520.6252329968238970.312616498411948
150.6012092341853720.7975815316292560.398790765814628
160.5105821672358630.9788356655282750.489417832764137
170.4535987466287940.9071974932575880.546401253371206
180.3707235097170030.7414470194340050.629276490282997
190.3013179996571590.6026359993143180.698682000342841
200.597546286096090.804907427807820.40245371390391
210.534862766631690.930274466736620.46513723336831
220.5029403951166370.9941192097667270.497059604883363
230.4328018044117460.8656036088234920.567198195588254
240.4185839827894390.8371679655788780.581416017210561
250.3835480252706620.7670960505413240.616451974729338
260.3290064505902190.6580129011804380.670993549409781
270.5899525224805920.8200949550388160.410047477519408
280.5317834451220430.9364331097559140.468216554877957
290.4704994501200940.9409989002401880.529500549879906
300.645914844697550.70817031060490.35408515530245
310.7776194645504570.4447610708990860.222380535449543
320.7381322898998420.5237354202003170.261867710100158
330.6945479331478840.6109041337042330.305452066852116
340.6502482964694070.6995034070611860.349751703530593
350.6428795252159040.7142409495681920.357120474784096
360.6645268017999450.6709463964001090.335473198200054
370.623649875754510.752700248490980.37635012424549
380.5742704823215980.8514590353568030.425729517678402
390.5234171683315460.9531656633369090.476582831668454
400.50655459359110.98689081281780.4934454064089
410.4782864758028280.9565729516056550.521713524197172
420.784717916131570.4305641677368590.21528208386843
430.9463041867797530.1073916264404930.0536958132202466
440.9574280168490020.08514396630199550.0425719831509978
450.9450185032535570.1099629934928860.0549814967464428
460.9519147494393270.09617050112134540.0480852505606727
470.9774773956164270.04504520876714690.0225226043835734
480.9704046328422150.05919073431557060.0295953671577853
490.9780464314618140.04390713707637240.0219535685381862
500.9749737380156960.05005252396860730.0250262619843037
510.969720419682740.0605591606345180.030279580317259
520.9971950944675510.005609811064897280.00280490553244864
530.9960151860001930.007969627999614830.00398481399980742
540.9970664581600680.005867083679864110.00293354183993206
550.9967770542126450.00644589157470940.0032229457873547
560.9954521634667750.009095673066450140.00454783653322507
570.9936983097251510.0126033805496980.006301690274849
580.9928244923345930.01435101533081330.00717550766540665
590.9948755413886040.01024891722279230.00512445861139614
600.9931078366989610.01378432660207710.00689216330103856
610.9943035599736130.01139288005277460.0056964400263873
620.994618210572370.01076357885525860.0053817894276293
630.9927116967501240.0145766064997510.00728830324987549
640.993145262741360.01370947451727920.00685473725863961
650.9915210335169250.01695793296615010.00847896648307504
660.9906400312074340.01871993758513190.00935996879256594
670.9905792676869370.01884146462612530.00942073231306266
680.9920527826438850.01589443471222940.00794721735611471
690.9921794112651230.01564117746975430.00782058873487717
700.9895339729612780.02093205407744420.0104660270387221
710.9911188159670930.01776236806581420.0088811840329071
720.988299003951780.02340199209643860.0117009960482193
730.9853847296478660.02923054070426820.0146152703521341
740.9896747351764060.0206505296471870.0103252648235935
750.9861518732867260.02769625342654810.013848126713274
760.9839573921778190.0320852156443630.0160426078221815
770.978848235528330.04230352894333890.0211517644716695
780.9721454368926180.05570912621476390.0278545631073819
790.9822727781780780.03545444364384410.017727221821922
800.981837554459240.03632489108151890.0181624455407594
810.9854206847977940.0291586304044130.0145793152022065
820.9860931986057190.02781360278856260.0139068013942813
830.9813073644520550.03738527109588950.0186926355479448
840.9772777699119120.04544446017617620.0227222300880881
850.9937818370323720.01243632593525510.00621816296762753
860.991538283792010.016923432415980.00846171620799002
870.98850468802550.02299062394899840.0114953119744992
880.9849102225631930.03017955487361370.0150897774368069
890.9808971934403340.03820561311933260.0191028065596663
900.9747753662833420.05044926743331570.0252246337166578
910.969338911513750.06132217697249790.030661088486249
920.9831730816758790.03365383664824280.0168269183241214
930.9780670412970310.04386591740593710.0219329587029686
940.9745862171231950.050827565753610.025413782876805
950.9676912335440760.06461753291184880.0323087664559244
960.9588710609814570.08225787803708510.0411289390185426
970.9560434051392860.08791318972142890.0439565948607145
980.9440023909730540.1119952180538910.0559976090269456
990.9415310416885330.1169379166229340.058468958311467
1000.927235535914430.1455289281711390.0727644640855697
1010.9096423520111910.1807152959776170.0903576479888085
1020.893626100634780.212747798730440.10637389936522
1030.9046921340887070.1906157318225860.0953078659112928
1040.9332530481912390.1334939036175230.0667469518087613
1050.9330786262369960.1338427475260070.0669213737630037
1060.9163290922377540.1673418155244920.0836709077622459
1070.9003312966006640.1993374067986710.0996687033993356
1080.8968412946739830.2063174106520330.103158705326017
1090.897519008070580.2049619838588390.102480991929419
1100.9195621391629540.1608757216740910.0804378608370456
1110.91161627005250.1767674598950010.0883837299475005
1120.9383769188566260.1232461622867480.061623081143374
1130.9207273068043110.1585453863913770.0792726931956885
1140.8986139378332840.2027721243334330.101386062166716
1150.8725093718426540.2549812563146910.127490628157346
1160.8711970883278750.2576058233442510.128802911672125
1170.8779524793149640.2440950413700730.122047520685036
1180.8697217026380880.2605565947238240.130278297361912
1190.8368168781945840.3263662436108330.163183121805416
1200.8844673073692340.2310653852615310.115532692630766
1210.860662772722780.2786744545544390.139337227277219
1220.8387255467957450.3225489064085110.161274453204255
1230.8311604577216010.3376790845567970.168839542278399
1240.7948497716061760.4103004567876480.205150228393824
1250.79485241066910.41029517866180.2051475893309
1260.7785030718169870.4429938563660250.221496928183013
1270.7300541982644710.5398916034710580.269945801735529
1280.7050093286279450.589981342744110.294990671372055
1290.7229483003899970.5541033992200060.277051699610003
1300.7186493105007460.5627013789985080.281350689499254
1310.6625361731803110.6749276536393780.337463826819689
1320.6512050199096170.6975899601807660.348794980090383
1330.6250240742971170.7499518514057660.374975925702883
1340.5538353589992710.8923292820014590.446164641000729
1350.5290316728198130.9419366543603750.470968327180187
1360.524968980108930.950062039782140.47503101989107
1370.4518375920058140.9036751840116280.548162407994186
1380.5218511957828360.9562976084343290.478148804217164
1390.8500554446455760.2998891107088480.149944555354424
1400.877750977115010.2444980457699810.12224902288499
1410.844213121210480.3115737575790410.155786878789521
1420.7772333154367750.445533369126450.222766684563225
1430.7493126862805940.5013746274388120.250687313719406
1440.6522203170475290.6955593659049420.347779682952471
1450.6428770048645420.7142459902709160.357122995135458
1460.7639182410131730.4721635179736540.236081758986827
1470.7316741929776480.5366516140447040.268325807022352
1480.8524624487800220.2950751024399550.147537551219978






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0354609929078014NOK
5% type I error level430.304964539007092NOK
10% type I error level570.404255319148936NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 & 0.0354609929078014 & NOK \tabularnewline
5% type I error level & 43 & 0.304964539007092 & NOK \tabularnewline
10% type I error level & 57 & 0.404255319148936 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97342&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]5[/C][C]0.0354609929078014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.304964539007092[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.404255319148936[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97342&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97342&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0354609929078014NOK
5% type I error level430.304964539007092NOK
10% type I error level570.404255319148936NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}