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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 29 Dec 2010 11:20:21 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t12936214822lqdet8brxvir1d.htm/, Retrieved Wed, 01 May 2024 23:44:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116711, Retrieved Wed, 01 May 2024 23:44:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-25 16:04:54] [1253bc7c4737195066123d9caa6dfc18]
- R  D  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2010-12-27 21:10:05] [1253bc7c4737195066123d9caa6dfc18]
- RMPD    [Multiple Regression] [] [2010-12-29 10:42:05] [b98453cac15ba1066b407e146608df68]
-             [Multiple Regression] [Interactie effect...] [2010-12-29 11:20:21] [2980b4453f2452156691660add27a53b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	26	14	28	13	26	3	6	2
12	12	12	8	8	13	13	5	5	1
15	10	0	12	0	16	0	6	0	0
12	9	27	7	21	12	36	6	18	3
10	10	30	10	30	11	33	5	15	3
12	12	12	7	7	12	12	3	3	1
15	13	39	16	48	18	54	8	24	3
9	12	12	11	11	11	11	4	4	1
12	12	48	14	56	14	56	4	16	4
11	6	0	6	0	9	0	4	0	0
11	5	15	16	48	14	42	6	18	3
11	12	24	11	22	12	24	6	12	2
15	11	44	16	64	11	44	5	20	4
7	14	42	12	36	12	36	4	12	3
11	14	14	7	7	13	13	6	6	1
11	12	12	13	13	11	11	4	4	1
10	12	24	11	22	12	24	6	12	2
14	11	33	15	45	16	48	6	18	3
10	11	11	7	7	9	9	4	4	1
6	7	7	9	9	11	11	4	4	1
11	9	18	7	14	13	26	2	4	2
15	11	33	14	42	15	45	7	21	3
11	11	44	15	60	10	40	5	20	4
12	12	24	7	14	11	22	4	8	2
14	12	12	15	15	13	13	6	6	1
15	11	22	17	34	16	32	6	12	2
9	11	22	15	30	15	30	7	14	2
13	8	32	14	56	14	56	5	20	4
13	9	18	14	28	14	28	6	12	2
16	12	36	8	24	14	42	4	12	3
13	10	30	8	24	8	24	4	12	3
12	10	30	14	42	13	39	7	21	3
14	12	48	14	56	15	60	7	28	4
11	8	16	8	16	13	26	4	8	2
9	12	24	11	22	11	22	4	8	2
16	11	44	16	64	15	60	6	24	4
12	12	36	10	30	15	45	6	18	3
10	7	28	8	32	9	36	5	20	4
13	11	22	14	28	13	26	6	12	2
16	11	55	16	80	16	80	7	35	5
14	12	36	13	39	13	39	6	18	3
15	9	9	5	5	11	11	3	3	1
5	15	15	8	8	12	12	3	3	1
8	11	11	10	10	12	12	4	4	1
11	11	22	8	16	12	24	6	12	2
16	11	33	13	39	14	42	7	21	3
17	11	99	15	135	14	126	5	45	9
9	15	0	6	0	8	0	4	0	0
9	11	0	12	0	13	0	5	0	0
13	12	24	16	32	16	32	6	12	2
10	12	24	5	10	13	26	6	12	2
6	9	27	15	45	11	33	6	18	3
12	12	12	12	12	14	14	5	5	1
8	12	24	8	16	13	26	4	8	2
14	13	0	13	0	13	0	5	0	0
12	11	55	14	70	13	65	5	25	5
11	9	18	12	24	12	24	4	8	2
16	9	36	16	64	16	64	6	24	4
8	11	33	10	30	15	45	2	6	3
15	11	0	15	0	15	0	8	0	0
7	12	0	8	0	12	0	3	0	0
16	12	48	16	64	14	56	6	24	4
14	9	9	19	19	12	12	6	6	1
16	11	11	14	14	15	15	6	6	1
9	9	36	6	24	12	48	5	20	4
14	12	24	13	26	13	26	5	10	2
11	12	48	15	60	12	48	6	24	4
13	12	12	7	7	12	12	5	5	1
15	12	48	13	52	13	52	6	24	4
5	14	28	4	8	5	10	2	4	2
15	11	55	14	70	13	65	5	25	5
13	12	48	13	52	13	52	5	20	4
11	11	44	11	44	14	56	5	20	4
11	6	24	14	56	17	68	6	24	4
12	10	40	12	48	13	52	6	24	4
12	12	36	15	45	13	39	6	18	3
12	13	39	14	42	12	36	5	15	3
12	8	24	13	39	13	39	5	15	3
14	12	24	8	16	14	28	4	8	2
6	12	12	6	6	11	11	2	2	1
7	12	12	7	7	12	12	4	4	1
14	6	30	13	65	12	60	6	30	5
14	11	44	13	52	16	64	6	24	4
10	10	20	11	22	12	24	5	10	2
13	12	36	5	15	12	36	3	9	3
12	13	26	12	24	12	24	6	12	2
9	11	22	8	16	10	20	4	8	2
12	7	14	11	22	15	30	5	10	2
16	11	22	14	28	15	30	8	16	2
10	11	33	9	27	12	36	4	12	3
14	11	22	10	20	16	32	6	12	2
10	11	33	13	39	15	45	6	18	3
16	12	48	16	64	16	64	7	28	4
15	10	30	16	48	13	39	6	18	3
12	11	33	11	33	12	36	5	15	3
10	12	0	8	0	11	0	4	0	0
8	7	7	4	4	13	13	6	6	1
8	13	26	7	14	10	20	3	6	2
11	8	16	14	28	15	30	5	10	2
13	12	36	11	33	13	39	6	18	3
16	11	44	17	68	16	64	7	28	4
16	12	48	15	60	15	60	7	28	4
14	14	14	17	17	18	18	6	6	1
11	10	20	5	10	13	26	3	6	2
4	10	20	4	8	10	20	2	4	2
14	13	39	10	30	16	48	8	24	3
9	10	30	11	33	13	39	3	9	3
14	11	33	15	45	15	45	8	24	3
8	10	10	10	10	14	14	3	3	1
8	7	7	9	9	15	15	4	4	1
11	10	10	12	12	14	14	5	5	1
12	8	8	15	15	13	13	7	7	1
11	12	0	7	0	13	0	6	0	0
14	12	12	13	13	15	15	6	6	1
15	12	36	12	36	16	48	7	21	3
16	11	33	14	42	14	42	6	18	3
16	12	0	14	0	14	0	6	0	0
11	12	24	8	16	16	32	6	12	2
14	12	60	15	75	14	70	6	30	5
14	11	22	12	24	12	24	4	8	2
12	12	36	12	36	13	39	4	12	3
14	11	33	16	48	12	36	5	15	3
8	11	55	9	45	12	60	4	20	5
13	13	52	15	60	14	56	6	24	4
16	12	48	15	60	14	56	6	24	4
12	12	0	6	0	14	0	5	0	0
16	12	36	14	42	16	48	8	24	3
12	12	0	15	0	13	0	6	0	0
11	8	16	10	20	14	28	5	10	2
4	8	0	6	0	4	0	4	0	0
16	12	72	14	84	16	96	8	48	6
15	11	33	12	36	13	39	6	18	3
10	12	12	8	8	16	16	4	4	1
13	13	78	11	66	15	90	6	36	6
15	12	24	13	26	14	28	6	12	2
12	12	12	9	9	13	13	4	4	1
14	11	33	15	45	14	42	6	18	3
7	12	12	13	13	12	12	3	3	1
19	12	24	15	30	15	30	6	12	2
12	10	40	14	56	14	56	5	20	4
12	11	11	16	16	13	13	4	4	1
13	12	24	14	28	14	28	6	12	2
15	12	0	14	0	16	0	4	0	0
8	10	50	10	50	6	30	4	20	5
12	12	24	10	20	13	26	4	8	2
10	13	13	4	4	13	13	6	6	1
8	12	12	8	8	14	14	5	5	1
10	15	60	15	60	15	60	6	24	4
15	11	33	16	48	14	42	6	18	3
16	12	0	12	0	15	0	8	0	0
13	11	33	12	36	13	39	7	21	3
16	12	36	15	45	16	48	7	21	3
9	11	0	9	0	12	0	4	0	0
14	10	20	12	24	15	30	6	12	2
14	11	55	14	70	12	60	6	30	5
12	11	22	11	22	14	28	2	4	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=116711&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=116711&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&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] = -0.956521145565252 + 0.122464404572676FindingFriends[t] -0.00611986826304842sum_friends[t] + 0.129901135405865KnowingPeople[t] + 0.0388655008006026sum_know[t] + 0.422882703355404Liked[t] -0.0255765571618555sum_liked[t] + 0.782432477255014Celebrity[t] -0.0824572683867577sum_celeb[t] + 0.579627939212371Sum[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -0.956521145565252 +  0.122464404572676FindingFriends[t] -0.00611986826304842sum_friends[t] +  0.129901135405865KnowingPeople[t] +  0.0388655008006026sum_know[t] +  0.422882703355404Liked[t] -0.0255765571618555sum_liked[t] +  0.782432477255014Celebrity[t] -0.0824572683867577sum_celeb[t] +  0.579627939212371Sum[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -0.956521145565252 +  0.122464404572676FindingFriends[t] -0.00611986826304842sum_friends[t] +  0.129901135405865KnowingPeople[t] +  0.0388655008006026sum_know[t] +  0.422882703355404Liked[t] -0.0255765571618555sum_liked[t] +  0.782432477255014Celebrity[t] -0.0824572683867577sum_celeb[t] +  0.579627939212371Sum[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&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] = -0.956521145565252 + 0.122464404572676FindingFriends[t] -0.00611986826304842sum_friends[t] + 0.129901135405865KnowingPeople[t] + 0.0388655008006026sum_know[t] + 0.422882703355404Liked[t] -0.0255765571618555sum_liked[t] + 0.782432477255014Celebrity[t] -0.0824572683867577sum_celeb[t] + 0.579627939212371Sum[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9565211455652522.451522-0.39020.6969760.348488
FindingFriends0.1224644045726760.1772490.69090.4907140.245357
sum_friends-0.006119868263048420.066175-0.09250.9264430.463221
KnowingPeople0.1299011354058650.1119011.16090.2475930.123797
sum_know0.03886550080060260.0452310.85930.3916010.1958
Liked0.4228827033554040.1696342.49290.0137870.006893
sum_liked-0.02557655716185550.063828-0.40070.6892190.34461
Celebrity0.7824324772550140.2911712.68720.0080420.004021
sum_celeb-0.08245726838675770.117131-0.7040.482570.241285
Sum0.5796279392123710.8875440.65310.5147380.257369

\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) & -0.956521145565252 & 2.451522 & -0.3902 & 0.696976 & 0.348488 \tabularnewline
FindingFriends & 0.122464404572676 & 0.177249 & 0.6909 & 0.490714 & 0.245357 \tabularnewline
sum_friends & -0.00611986826304842 & 0.066175 & -0.0925 & 0.926443 & 0.463221 \tabularnewline
KnowingPeople & 0.129901135405865 & 0.111901 & 1.1609 & 0.247593 & 0.123797 \tabularnewline
sum_know & 0.0388655008006026 & 0.045231 & 0.8593 & 0.391601 & 0.1958 \tabularnewline
Liked & 0.422882703355404 & 0.169634 & 2.4929 & 0.013787 & 0.006893 \tabularnewline
sum_liked & -0.0255765571618555 & 0.063828 & -0.4007 & 0.689219 & 0.34461 \tabularnewline
Celebrity & 0.782432477255014 & 0.291171 & 2.6872 & 0.008042 & 0.004021 \tabularnewline
sum_celeb & -0.0824572683867577 & 0.117131 & -0.704 & 0.48257 & 0.241285 \tabularnewline
Sum & 0.579627939212371 & 0.887544 & 0.6531 & 0.514738 & 0.257369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&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]-0.956521145565252[/C][C]2.451522[/C][C]-0.3902[/C][C]0.696976[/C][C]0.348488[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.122464404572676[/C][C]0.177249[/C][C]0.6909[/C][C]0.490714[/C][C]0.245357[/C][/ROW]
[ROW][C]sum_friends[/C][C]-0.00611986826304842[/C][C]0.066175[/C][C]-0.0925[/C][C]0.926443[/C][C]0.463221[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.129901135405865[/C][C]0.111901[/C][C]1.1609[/C][C]0.247593[/C][C]0.123797[/C][/ROW]
[ROW][C]sum_know[/C][C]0.0388655008006026[/C][C]0.045231[/C][C]0.8593[/C][C]0.391601[/C][C]0.1958[/C][/ROW]
[ROW][C]Liked[/C][C]0.422882703355404[/C][C]0.169634[/C][C]2.4929[/C][C]0.013787[/C][C]0.006893[/C][/ROW]
[ROW][C]sum_liked[/C][C]-0.0255765571618555[/C][C]0.063828[/C][C]-0.4007[/C][C]0.689219[/C][C]0.34461[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.782432477255014[/C][C]0.291171[/C][C]2.6872[/C][C]0.008042[/C][C]0.004021[/C][/ROW]
[ROW][C]sum_celeb[/C][C]-0.0824572683867577[/C][C]0.117131[/C][C]-0.704[/C][C]0.48257[/C][C]0.241285[/C][/ROW]
[ROW][C]Sum[/C][C]0.579627939212371[/C][C]0.887544[/C][C]0.6531[/C][C]0.514738[/C][C]0.257369[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&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)-0.9565211455652522.451522-0.39020.6969760.348488
FindingFriends0.1224644045726760.1772490.69090.4907140.245357
sum_friends-0.006119868263048420.066175-0.09250.9264430.463221
KnowingPeople0.1299011354058650.1119011.16090.2475930.123797
sum_know0.03886550080060260.0452310.85930.3916010.1958
Liked0.4228827033554040.1696342.49290.0137870.006893
sum_liked-0.02557655716185550.063828-0.40070.6892190.34461
Celebrity0.7824324772550140.2911712.68720.0080420.004021
sum_celeb-0.08245726838675770.117131-0.7040.482570.241285
Sum0.5796279392123710.8875440.65310.5147380.257369






Multiple Linear Regression - Regression Statistics
Multiple R0.716779838383906
R-squared0.513773336713658
Adjusted R-squared0.483800460209705
F-TEST (value)17.1412756011558
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10987944174862
Sum Squared Residuals649.932323772166

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.716779838383906 \tabularnewline
R-squared & 0.513773336713658 \tabularnewline
Adjusted R-squared & 0.483800460209705 \tabularnewline
F-TEST (value) & 17.1412756011558 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10987944174862 \tabularnewline
Sum Squared Residuals & 649.932323772166 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.716779838383906[/C][/ROW]
[ROW][C]R-squared[/C][C]0.513773336713658[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.483800460209705[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.1412756011558[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.10987944174862[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]649.932323772166[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&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 R0.716779838383906
R-squared0.513773336713658
Adjusted R-squared0.483800460209705
F-TEST (value)17.1412756011558
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10987944174862
Sum Squared Residuals649.932323772166






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.22754381442051.77245618557947
21211.03423026387180.9657697361282
31513.28765464224841.71234535775157
41210.80898974978381.19101025021616
51010.7713737590258-0.771373759025813
6129.068207063735272.93179293626473
71516.5909272245955-1.59092722459553
8910.0459426712359-1.04594267123586
91212.8313779907896-0.831377990789559
10117.493346333524683.50665366647532
111113.3033553546585-2.30335535465845
121111.9752469677655-0.975246967765519
131512.79499331606972.20500668393034
14711.4918805935504-4.49188059355038
151111.7981279091528-0.798127909152844
161110.38347594364880.616524056351211
171011.9752469677655-1.97524696776552
181414.3737925792916-0.373792579291638
19108.459919297713261.54008070228674
2069.12668671727478-3.12668671727478
21118.71570224270322.2842977572968
221514.31620258170890.683797418291133
231112.1890537027534-1.18905370275341
24129.537952949742552.46204705025745
251412.91557192618531.08442807381467
261514.59773347788810.402266522111864
27914.4282575553238-5.4282575553238
281312.89204166841560.107958331584388
291313.0109285527104-0.0109285527103798
301610.98998650649235.0100134935077
31138.704858715706224.29514128429379
321213.5197917181857-1.51979171818566
331414.5097646766215-0.509764676621488
341110.04814559262670.951854407373312
35910.3684814977708-1.36848149777083
361614.52990261860961.47009738139043
371212.899256158167-0.899256158166965
38109.478995531705560.521004468294436
391312.85964829977180.140351700228155
401615.4508141048570.549185895142973
411412.94644300785031.05355699214969
42157.98433403619997.0156659638001
4359.58600730887062-4.58600730887062
44810.1581376449133-2.15813764491331
451111.2421258886977-0.242125888697728
461613.72355191203142.27644808796864
471715.09602839222591.90397160777411
4898.172643271323360.827356728676637
49911.3590384594999-2.35903845949989
501314.5003260089276-1.50032600892764
511011.1011837347548-1.10118373475479
52612.4348178203754-6.43481782037539
531212.1066029548912-0.106602954891223
54810.489044264813-2.48904426481301
551411.73386840405112.2661315959489
561213.1770648027584-1.17706480275844
571110.61716921966990.382830780330143
581614.65450923027661.34549076972341
59810.6549086700045-2.65490867000447
601514.94180470419330.058195295806669
6178.97415066458367-1.97415066458367
621614.30731107542211.69268892457793
631412.84429871588881.15570128411123
641613.42507304605632.57492695394366
65910.0659685416539-1.06596854165392
661412.14472289032991.85527710967015
671113.3807949873978-2.38079498739784
681310.46815748147182.53184251852821
691513.13064518488931.86935481511073
7055.67009245966134-0.67009245966134
711513.17706480275841.82293519724156
721312.67804178118130.321958218818717
731112.3299070471522-1.32990704715223
741114.1104046332066-3.11040463320658
751212.64931218324-0.649312183240025
761213.4394382834657-1.43943828346566
771212.4158317414769-0.415831741476936
781211.99496313662140.00503686337855331
791410.86077385384473.1392261461553
8067.802159072467-1.802159072467
8179.76818227260353-2.76818227260353
821412.46865706483751.53134293516251
831413.99438967749270.00561032250727054
841011.1372796911909-1.13727969119086
85139.023126857234863.97687314276514
861212.2931037728192-0.293103772819169
8799.26363082967135-0.263630829671345
881211.92179445414620.078205545853795
891614.83814335879821.16185664120177
901010.4400732807768-0.440073280776767
911413.14430851883860.855691481161359
921013.5346442718065-3.53464427180645
931615.4010674285460.598932571453979
941513.47772632170631.52227367829373
951211.46812922848690.531870771513146
96109.333700438483280.666299561516721
97810.477416246366-2.47741624636605
9888.65893008827593-0.658930088275935
991112.654915533214-1.654915533214
1001312.4534477322350.546552267765037
1011615.58844563563380.411554364366185
1021614.79512781522981.20487218477024
1031415.4723250021853-1.47232500218527
104119.028180577217141.97181942278286
10547.08784173263349-3.08784173263349
1061414.4196353340098-0.419635334009812
107910.6400561163837-1.64005611638368
1081415.0977608916113-1.09776089161128
109810.1364301921225-2.13643019212252
110810.715911302049-2.71591130204897
1111111.873913882272-0.873913882271967
1121213.1501689898151-1.15016898981508
1131111.6144296642982-0.614429664298249
1141413.37265094615950.627349053840505
1151514.27346513774690.726534862253111
1161613.43498887774432.56501112225571
1171612.94662031549473.05337968450529
1181112.8392689128711-1.83926891287108
1191414.2583045582921-0.258304558292105
1201410.8376185557633.16238144423698
1211211.62982402585320.370175974146844
1221412.70061741752521.29938258247478
123810.8907755528467-2.8907755528467
1241314.1199328683343-1.11993286833428
1251614.02194793681381.9780520631862
1261211.12497875499280.875021245007226
1271615.3015210854570.698478914543025
1281212.6536387475452-0.653638747545172
1291111.452657396154-0.452657396154021
13045.62386162589302-1.62386162589302
1311615.24579149419840.754208505801594
1321512.59584057025912.40415942974089
1331011.5261734935842-1.52617349358419
1341313.3974531743111-0.397453174311143
1351513.1339704198431.86602958015695
1361210.503021691211.49697830878999
1371413.6814865155520.318513484448037
138710.0808068809741-3.08080688097408
1391913.92096428288895.07903571711112
1401213.0880115314566-1.08801153145658
1411211.56804360834570.431956391654338
1421313.3416025568501-0.341602556850117
1431512.22752076769552.77247923230451
14489.35313962372138-1.35313962372138
1451210.90430853882711.09569146117285
1461011.1754834642238-1.17548346422381
147811.4315364100654-3.43153641006535
1481014.6364792060832-4.63647920608323
1491513.92798415335961.07201584664036
1501614.67456570254841.32543429745159
1511313.1309012423538-0.130901242353846
1521615.01295805116990.987041948830092
15399.76401987267187-0.764019872671873
1541413.07761853577450.92238146422549
1551413.25221102053350.74778897946646
1561210.13840971585631.86159028414367

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2275438144205 & 1.77245618557947 \tabularnewline
2 & 12 & 11.0342302638718 & 0.9657697361282 \tabularnewline
3 & 15 & 13.2876546422484 & 1.71234535775157 \tabularnewline
4 & 12 & 10.8089897497838 & 1.19101025021616 \tabularnewline
5 & 10 & 10.7713737590258 & -0.771373759025813 \tabularnewline
6 & 12 & 9.06820706373527 & 2.93179293626473 \tabularnewline
7 & 15 & 16.5909272245955 & -1.59092722459553 \tabularnewline
8 & 9 & 10.0459426712359 & -1.04594267123586 \tabularnewline
9 & 12 & 12.8313779907896 & -0.831377990789559 \tabularnewline
10 & 11 & 7.49334633352468 & 3.50665366647532 \tabularnewline
11 & 11 & 13.3033553546585 & -2.30335535465845 \tabularnewline
12 & 11 & 11.9752469677655 & -0.975246967765519 \tabularnewline
13 & 15 & 12.7949933160697 & 2.20500668393034 \tabularnewline
14 & 7 & 11.4918805935504 & -4.49188059355038 \tabularnewline
15 & 11 & 11.7981279091528 & -0.798127909152844 \tabularnewline
16 & 11 & 10.3834759436488 & 0.616524056351211 \tabularnewline
17 & 10 & 11.9752469677655 & -1.97524696776552 \tabularnewline
18 & 14 & 14.3737925792916 & -0.373792579291638 \tabularnewline
19 & 10 & 8.45991929771326 & 1.54008070228674 \tabularnewline
20 & 6 & 9.12668671727478 & -3.12668671727478 \tabularnewline
21 & 11 & 8.7157022427032 & 2.2842977572968 \tabularnewline
22 & 15 & 14.3162025817089 & 0.683797418291133 \tabularnewline
23 & 11 & 12.1890537027534 & -1.18905370275341 \tabularnewline
24 & 12 & 9.53795294974255 & 2.46204705025745 \tabularnewline
25 & 14 & 12.9155719261853 & 1.08442807381467 \tabularnewline
26 & 15 & 14.5977334778881 & 0.402266522111864 \tabularnewline
27 & 9 & 14.4282575553238 & -5.4282575553238 \tabularnewline
28 & 13 & 12.8920416684156 & 0.107958331584388 \tabularnewline
29 & 13 & 13.0109285527104 & -0.0109285527103798 \tabularnewline
30 & 16 & 10.9899865064923 & 5.0100134935077 \tabularnewline
31 & 13 & 8.70485871570622 & 4.29514128429379 \tabularnewline
32 & 12 & 13.5197917181857 & -1.51979171818566 \tabularnewline
33 & 14 & 14.5097646766215 & -0.509764676621488 \tabularnewline
34 & 11 & 10.0481455926267 & 0.951854407373312 \tabularnewline
35 & 9 & 10.3684814977708 & -1.36848149777083 \tabularnewline
36 & 16 & 14.5299026186096 & 1.47009738139043 \tabularnewline
37 & 12 & 12.899256158167 & -0.899256158166965 \tabularnewline
38 & 10 & 9.47899553170556 & 0.521004468294436 \tabularnewline
39 & 13 & 12.8596482997718 & 0.140351700228155 \tabularnewline
40 & 16 & 15.450814104857 & 0.549185895142973 \tabularnewline
41 & 14 & 12.9464430078503 & 1.05355699214969 \tabularnewline
42 & 15 & 7.9843340361999 & 7.0156659638001 \tabularnewline
43 & 5 & 9.58600730887062 & -4.58600730887062 \tabularnewline
44 & 8 & 10.1581376449133 & -2.15813764491331 \tabularnewline
45 & 11 & 11.2421258886977 & -0.242125888697728 \tabularnewline
46 & 16 & 13.7235519120314 & 2.27644808796864 \tabularnewline
47 & 17 & 15.0960283922259 & 1.90397160777411 \tabularnewline
48 & 9 & 8.17264327132336 & 0.827356728676637 \tabularnewline
49 & 9 & 11.3590384594999 & -2.35903845949989 \tabularnewline
50 & 13 & 14.5003260089276 & -1.50032600892764 \tabularnewline
51 & 10 & 11.1011837347548 & -1.10118373475479 \tabularnewline
52 & 6 & 12.4348178203754 & -6.43481782037539 \tabularnewline
53 & 12 & 12.1066029548912 & -0.106602954891223 \tabularnewline
54 & 8 & 10.489044264813 & -2.48904426481301 \tabularnewline
55 & 14 & 11.7338684040511 & 2.2661315959489 \tabularnewline
56 & 12 & 13.1770648027584 & -1.17706480275844 \tabularnewline
57 & 11 & 10.6171692196699 & 0.382830780330143 \tabularnewline
58 & 16 & 14.6545092302766 & 1.34549076972341 \tabularnewline
59 & 8 & 10.6549086700045 & -2.65490867000447 \tabularnewline
60 & 15 & 14.9418047041933 & 0.058195295806669 \tabularnewline
61 & 7 & 8.97415066458367 & -1.97415066458367 \tabularnewline
62 & 16 & 14.3073110754221 & 1.69268892457793 \tabularnewline
63 & 14 & 12.8442987158888 & 1.15570128411123 \tabularnewline
64 & 16 & 13.4250730460563 & 2.57492695394366 \tabularnewline
65 & 9 & 10.0659685416539 & -1.06596854165392 \tabularnewline
66 & 14 & 12.1447228903299 & 1.85527710967015 \tabularnewline
67 & 11 & 13.3807949873978 & -2.38079498739784 \tabularnewline
68 & 13 & 10.4681574814718 & 2.53184251852821 \tabularnewline
69 & 15 & 13.1306451848893 & 1.86935481511073 \tabularnewline
70 & 5 & 5.67009245966134 & -0.67009245966134 \tabularnewline
71 & 15 & 13.1770648027584 & 1.82293519724156 \tabularnewline
72 & 13 & 12.6780417811813 & 0.321958218818717 \tabularnewline
73 & 11 & 12.3299070471522 & -1.32990704715223 \tabularnewline
74 & 11 & 14.1104046332066 & -3.11040463320658 \tabularnewline
75 & 12 & 12.64931218324 & -0.649312183240025 \tabularnewline
76 & 12 & 13.4394382834657 & -1.43943828346566 \tabularnewline
77 & 12 & 12.4158317414769 & -0.415831741476936 \tabularnewline
78 & 12 & 11.9949631366214 & 0.00503686337855331 \tabularnewline
79 & 14 & 10.8607738538447 & 3.1392261461553 \tabularnewline
80 & 6 & 7.802159072467 & -1.802159072467 \tabularnewline
81 & 7 & 9.76818227260353 & -2.76818227260353 \tabularnewline
82 & 14 & 12.4686570648375 & 1.53134293516251 \tabularnewline
83 & 14 & 13.9943896774927 & 0.00561032250727054 \tabularnewline
84 & 10 & 11.1372796911909 & -1.13727969119086 \tabularnewline
85 & 13 & 9.02312685723486 & 3.97687314276514 \tabularnewline
86 & 12 & 12.2931037728192 & -0.293103772819169 \tabularnewline
87 & 9 & 9.26363082967135 & -0.263630829671345 \tabularnewline
88 & 12 & 11.9217944541462 & 0.078205545853795 \tabularnewline
89 & 16 & 14.8381433587982 & 1.16185664120177 \tabularnewline
90 & 10 & 10.4400732807768 & -0.440073280776767 \tabularnewline
91 & 14 & 13.1443085188386 & 0.855691481161359 \tabularnewline
92 & 10 & 13.5346442718065 & -3.53464427180645 \tabularnewline
93 & 16 & 15.401067428546 & 0.598932571453979 \tabularnewline
94 & 15 & 13.4777263217063 & 1.52227367829373 \tabularnewline
95 & 12 & 11.4681292284869 & 0.531870771513146 \tabularnewline
96 & 10 & 9.33370043848328 & 0.666299561516721 \tabularnewline
97 & 8 & 10.477416246366 & -2.47741624636605 \tabularnewline
98 & 8 & 8.65893008827593 & -0.658930088275935 \tabularnewline
99 & 11 & 12.654915533214 & -1.654915533214 \tabularnewline
100 & 13 & 12.453447732235 & 0.546552267765037 \tabularnewline
101 & 16 & 15.5884456356338 & 0.411554364366185 \tabularnewline
102 & 16 & 14.7951278152298 & 1.20487218477024 \tabularnewline
103 & 14 & 15.4723250021853 & -1.47232500218527 \tabularnewline
104 & 11 & 9.02818057721714 & 1.97181942278286 \tabularnewline
105 & 4 & 7.08784173263349 & -3.08784173263349 \tabularnewline
106 & 14 & 14.4196353340098 & -0.419635334009812 \tabularnewline
107 & 9 & 10.6400561163837 & -1.64005611638368 \tabularnewline
108 & 14 & 15.0977608916113 & -1.09776089161128 \tabularnewline
109 & 8 & 10.1364301921225 & -2.13643019212252 \tabularnewline
110 & 8 & 10.715911302049 & -2.71591130204897 \tabularnewline
111 & 11 & 11.873913882272 & -0.873913882271967 \tabularnewline
112 & 12 & 13.1501689898151 & -1.15016898981508 \tabularnewline
113 & 11 & 11.6144296642982 & -0.614429664298249 \tabularnewline
114 & 14 & 13.3726509461595 & 0.627349053840505 \tabularnewline
115 & 15 & 14.2734651377469 & 0.726534862253111 \tabularnewline
116 & 16 & 13.4349888777443 & 2.56501112225571 \tabularnewline
117 & 16 & 12.9466203154947 & 3.05337968450529 \tabularnewline
118 & 11 & 12.8392689128711 & -1.83926891287108 \tabularnewline
119 & 14 & 14.2583045582921 & -0.258304558292105 \tabularnewline
120 & 14 & 10.837618555763 & 3.16238144423698 \tabularnewline
121 & 12 & 11.6298240258532 & 0.370175974146844 \tabularnewline
122 & 14 & 12.7006174175252 & 1.29938258247478 \tabularnewline
123 & 8 & 10.8907755528467 & -2.8907755528467 \tabularnewline
124 & 13 & 14.1199328683343 & -1.11993286833428 \tabularnewline
125 & 16 & 14.0219479368138 & 1.9780520631862 \tabularnewline
126 & 12 & 11.1249787549928 & 0.875021245007226 \tabularnewline
127 & 16 & 15.301521085457 & 0.698478914543025 \tabularnewline
128 & 12 & 12.6536387475452 & -0.653638747545172 \tabularnewline
129 & 11 & 11.452657396154 & -0.452657396154021 \tabularnewline
130 & 4 & 5.62386162589302 & -1.62386162589302 \tabularnewline
131 & 16 & 15.2457914941984 & 0.754208505801594 \tabularnewline
132 & 15 & 12.5958405702591 & 2.40415942974089 \tabularnewline
133 & 10 & 11.5261734935842 & -1.52617349358419 \tabularnewline
134 & 13 & 13.3974531743111 & -0.397453174311143 \tabularnewline
135 & 15 & 13.133970419843 & 1.86602958015695 \tabularnewline
136 & 12 & 10.50302169121 & 1.49697830878999 \tabularnewline
137 & 14 & 13.681486515552 & 0.318513484448037 \tabularnewline
138 & 7 & 10.0808068809741 & -3.08080688097408 \tabularnewline
139 & 19 & 13.9209642828889 & 5.07903571711112 \tabularnewline
140 & 12 & 13.0880115314566 & -1.08801153145658 \tabularnewline
141 & 12 & 11.5680436083457 & 0.431956391654338 \tabularnewline
142 & 13 & 13.3416025568501 & -0.341602556850117 \tabularnewline
143 & 15 & 12.2275207676955 & 2.77247923230451 \tabularnewline
144 & 8 & 9.35313962372138 & -1.35313962372138 \tabularnewline
145 & 12 & 10.9043085388271 & 1.09569146117285 \tabularnewline
146 & 10 & 11.1754834642238 & -1.17548346422381 \tabularnewline
147 & 8 & 11.4315364100654 & -3.43153641006535 \tabularnewline
148 & 10 & 14.6364792060832 & -4.63647920608323 \tabularnewline
149 & 15 & 13.9279841533596 & 1.07201584664036 \tabularnewline
150 & 16 & 14.6745657025484 & 1.32543429745159 \tabularnewline
151 & 13 & 13.1309012423538 & -0.130901242353846 \tabularnewline
152 & 16 & 15.0129580511699 & 0.987041948830092 \tabularnewline
153 & 9 & 9.76401987267187 & -0.764019872671873 \tabularnewline
154 & 14 & 13.0776185357745 & 0.92238146422549 \tabularnewline
155 & 14 & 13.2522110205335 & 0.74778897946646 \tabularnewline
156 & 12 & 10.1384097158563 & 1.86159028414367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&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]11.2275438144205[/C][C]1.77245618557947[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0342302638718[/C][C]0.9657697361282[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.2876546422484[/C][C]1.71234535775157[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8089897497838[/C][C]1.19101025021616[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.7713737590258[/C][C]-0.771373759025813[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.06820706373527[/C][C]2.93179293626473[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.5909272245955[/C][C]-1.59092722459553[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.0459426712359[/C][C]-1.04594267123586[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.8313779907896[/C][C]-0.831377990789559[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.49334633352468[/C][C]3.50665366647532[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3033553546585[/C][C]-2.30335535465845[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.9752469677655[/C][C]-0.975246967765519[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.7949933160697[/C][C]2.20500668393034[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4918805935504[/C][C]-4.49188059355038[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.7981279091528[/C][C]-0.798127909152844[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.3834759436488[/C][C]0.616524056351211[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.9752469677655[/C][C]-1.97524696776552[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3737925792916[/C][C]-0.373792579291638[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.45991929771326[/C][C]1.54008070228674[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.12668671727478[/C][C]-3.12668671727478[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.7157022427032[/C][C]2.2842977572968[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3162025817089[/C][C]0.683797418291133[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]12.1890537027534[/C][C]-1.18905370275341[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.53795294974255[/C][C]2.46204705025745[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.9155719261853[/C][C]1.08442807381467[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5977334778881[/C][C]0.402266522111864[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.4282575553238[/C][C]-5.4282575553238[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.8920416684156[/C][C]0.107958331584388[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.0109285527104[/C][C]-0.0109285527103798[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.9899865064923[/C][C]5.0100134935077[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.70485871570622[/C][C]4.29514128429379[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5197917181857[/C][C]-1.51979171818566[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5097646766215[/C][C]-0.509764676621488[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0481455926267[/C][C]0.951854407373312[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.3684814977708[/C][C]-1.36848149777083[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.5299026186096[/C][C]1.47009738139043[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.899256158167[/C][C]-0.899256158166965[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.47899553170556[/C][C]0.521004468294436[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8596482997718[/C][C]0.140351700228155[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.450814104857[/C][C]0.549185895142973[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9464430078503[/C][C]1.05355699214969[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]7.9843340361999[/C][C]7.0156659638001[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.58600730887062[/C][C]-4.58600730887062[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.1581376449133[/C][C]-2.15813764491331[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2421258886977[/C][C]-0.242125888697728[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7235519120314[/C][C]2.27644808796864[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]15.0960283922259[/C][C]1.90397160777411[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.17264327132336[/C][C]0.827356728676637[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.3590384594999[/C][C]-2.35903845949989[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.5003260089276[/C][C]-1.50032600892764[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.1011837347548[/C][C]-1.10118373475479[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4348178203754[/C][C]-6.43481782037539[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.1066029548912[/C][C]-0.106602954891223[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.489044264813[/C][C]-2.48904426481301[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.7338684040511[/C][C]2.2661315959489[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.1770648027584[/C][C]-1.17706480275844[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.6171692196699[/C][C]0.382830780330143[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.6545092302766[/C][C]1.34549076972341[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.6549086700045[/C][C]-2.65490867000447[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.9418047041933[/C][C]0.058195295806669[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]8.97415066458367[/C][C]-1.97415066458367[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.3073110754221[/C][C]1.69268892457793[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.8442987158888[/C][C]1.15570128411123[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.4250730460563[/C][C]2.57492695394366[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.0659685416539[/C][C]-1.06596854165392[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1447228903299[/C][C]1.85527710967015[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.3807949873978[/C][C]-2.38079498739784[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4681574814718[/C][C]2.53184251852821[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.1306451848893[/C][C]1.86935481511073[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.67009245966134[/C][C]-0.67009245966134[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]13.1770648027584[/C][C]1.82293519724156[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.6780417811813[/C][C]0.321958218818717[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.3299070471522[/C][C]-1.32990704715223[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1104046332066[/C][C]-3.11040463320658[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.64931218324[/C][C]-0.649312183240025[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4394382834657[/C][C]-1.43943828346566[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.4158317414769[/C][C]-0.415831741476936[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9949631366214[/C][C]0.00503686337855331[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.8607738538447[/C][C]3.1392261461553[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.802159072467[/C][C]-1.802159072467[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.76818227260353[/C][C]-2.76818227260353[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.4686570648375[/C][C]1.53134293516251[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.9943896774927[/C][C]0.00561032250727054[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1372796911909[/C][C]-1.13727969119086[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.02312685723486[/C][C]3.97687314276514[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.2931037728192[/C][C]-0.293103772819169[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.26363082967135[/C][C]-0.263630829671345[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9217944541462[/C][C]0.078205545853795[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8381433587982[/C][C]1.16185664120177[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4400732807768[/C][C]-0.440073280776767[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1443085188386[/C][C]0.855691481161359[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5346442718065[/C][C]-3.53464427180645[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.401067428546[/C][C]0.598932571453979[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4777263217063[/C][C]1.52227367829373[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4681292284869[/C][C]0.531870771513146[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.33370043848328[/C][C]0.666299561516721[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.477416246366[/C][C]-2.47741624636605[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.65893008827593[/C][C]-0.658930088275935[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.654915533214[/C][C]-1.654915533214[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.453447732235[/C][C]0.546552267765037[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5884456356338[/C][C]0.411554364366185[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7951278152298[/C][C]1.20487218477024[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.4723250021853[/C][C]-1.47232500218527[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.02818057721714[/C][C]1.97181942278286[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.08784173263349[/C][C]-3.08784173263349[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.4196353340098[/C][C]-0.419635334009812[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.6400561163837[/C][C]-1.64005611638368[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.0977608916113[/C][C]-1.09776089161128[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.1364301921225[/C][C]-2.13643019212252[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.715911302049[/C][C]-2.71591130204897[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.873913882272[/C][C]-0.873913882271967[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.1501689898151[/C][C]-1.15016898981508[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.6144296642982[/C][C]-0.614429664298249[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.3726509461595[/C][C]0.627349053840505[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.2734651377469[/C][C]0.726534862253111[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4349888777443[/C][C]2.56501112225571[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.9466203154947[/C][C]3.05337968450529[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.8392689128711[/C][C]-1.83926891287108[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.2583045582921[/C][C]-0.258304558292105[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.837618555763[/C][C]3.16238144423698[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.6298240258532[/C][C]0.370175974146844[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.7006174175252[/C][C]1.29938258247478[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8907755528467[/C][C]-2.8907755528467[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.1199328683343[/C][C]-1.11993286833428[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.0219479368138[/C][C]1.9780520631862[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.1249787549928[/C][C]0.875021245007226[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.301521085457[/C][C]0.698478914543025[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.6536387475452[/C][C]-0.653638747545172[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.452657396154[/C][C]-0.452657396154021[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.62386162589302[/C][C]-1.62386162589302[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.2457914941984[/C][C]0.754208505801594[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5958405702591[/C][C]2.40415942974089[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.5261734935842[/C][C]-1.52617349358419[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.3974531743111[/C][C]-0.397453174311143[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.133970419843[/C][C]1.86602958015695[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.50302169121[/C][C]1.49697830878999[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.681486515552[/C][C]0.318513484448037[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.0808068809741[/C][C]-3.08080688097408[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.9209642828889[/C][C]5.07903571711112[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.0880115314566[/C][C]-1.08801153145658[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.5680436083457[/C][C]0.431956391654338[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.3416025568501[/C][C]-0.341602556850117[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.2275207676955[/C][C]2.77247923230451[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]9.35313962372138[/C][C]-1.35313962372138[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9043085388271[/C][C]1.09569146117285[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]11.1754834642238[/C][C]-1.17548346422381[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4315364100654[/C][C]-3.43153641006535[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.6364792060832[/C][C]-4.63647920608323[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.9279841533596[/C][C]1.07201584664036[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.6745657025484[/C][C]1.32543429745159[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1309012423538[/C][C]-0.130901242353846[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0129580511699[/C][C]0.987041948830092[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.76401987267187[/C][C]-0.764019872671873[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.0776185357745[/C][C]0.92238146422549[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.2522110205335[/C][C]0.74778897946646[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1384097158563[/C][C]1.86159028414367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&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
11311.22754381442051.77245618557947
21211.03423026387180.9657697361282
31513.28765464224841.71234535775157
41210.80898974978381.19101025021616
51010.7713737590258-0.771373759025813
6129.068207063735272.93179293626473
71516.5909272245955-1.59092722459553
8910.0459426712359-1.04594267123586
91212.8313779907896-0.831377990789559
10117.493346333524683.50665366647532
111113.3033553546585-2.30335535465845
121111.9752469677655-0.975246967765519
131512.79499331606972.20500668393034
14711.4918805935504-4.49188059355038
151111.7981279091528-0.798127909152844
161110.38347594364880.616524056351211
171011.9752469677655-1.97524696776552
181414.3737925792916-0.373792579291638
19108.459919297713261.54008070228674
2069.12668671727478-3.12668671727478
21118.71570224270322.2842977572968
221514.31620258170890.683797418291133
231112.1890537027534-1.18905370275341
24129.537952949742552.46204705025745
251412.91557192618531.08442807381467
261514.59773347788810.402266522111864
27914.4282575553238-5.4282575553238
281312.89204166841560.107958331584388
291313.0109285527104-0.0109285527103798
301610.98998650649235.0100134935077
31138.704858715706224.29514128429379
321213.5197917181857-1.51979171818566
331414.5097646766215-0.509764676621488
341110.04814559262670.951854407373312
35910.3684814977708-1.36848149777083
361614.52990261860961.47009738139043
371212.899256158167-0.899256158166965
38109.478995531705560.521004468294436
391312.85964829977180.140351700228155
401615.4508141048570.549185895142973
411412.94644300785031.05355699214969
42157.98433403619997.0156659638001
4359.58600730887062-4.58600730887062
44810.1581376449133-2.15813764491331
451111.2421258886977-0.242125888697728
461613.72355191203142.27644808796864
471715.09602839222591.90397160777411
4898.172643271323360.827356728676637
49911.3590384594999-2.35903845949989
501314.5003260089276-1.50032600892764
511011.1011837347548-1.10118373475479
52612.4348178203754-6.43481782037539
531212.1066029548912-0.106602954891223
54810.489044264813-2.48904426481301
551411.73386840405112.2661315959489
561213.1770648027584-1.17706480275844
571110.61716921966990.382830780330143
581614.65450923027661.34549076972341
59810.6549086700045-2.65490867000447
601514.94180470419330.058195295806669
6178.97415066458367-1.97415066458367
621614.30731107542211.69268892457793
631412.84429871588881.15570128411123
641613.42507304605632.57492695394366
65910.0659685416539-1.06596854165392
661412.14472289032991.85527710967015
671113.3807949873978-2.38079498739784
681310.46815748147182.53184251852821
691513.13064518488931.86935481511073
7055.67009245966134-0.67009245966134
711513.17706480275841.82293519724156
721312.67804178118130.321958218818717
731112.3299070471522-1.32990704715223
741114.1104046332066-3.11040463320658
751212.64931218324-0.649312183240025
761213.4394382834657-1.43943828346566
771212.4158317414769-0.415831741476936
781211.99496313662140.00503686337855331
791410.86077385384473.1392261461553
8067.802159072467-1.802159072467
8179.76818227260353-2.76818227260353
821412.46865706483751.53134293516251
831413.99438967749270.00561032250727054
841011.1372796911909-1.13727969119086
85139.023126857234863.97687314276514
861212.2931037728192-0.293103772819169
8799.26363082967135-0.263630829671345
881211.92179445414620.078205545853795
891614.83814335879821.16185664120177
901010.4400732807768-0.440073280776767
911413.14430851883860.855691481161359
921013.5346442718065-3.53464427180645
931615.4010674285460.598932571453979
941513.47772632170631.52227367829373
951211.46812922848690.531870771513146
96109.333700438483280.666299561516721
97810.477416246366-2.47741624636605
9888.65893008827593-0.658930088275935
991112.654915533214-1.654915533214
1001312.4534477322350.546552267765037
1011615.58844563563380.411554364366185
1021614.79512781522981.20487218477024
1031415.4723250021853-1.47232500218527
104119.028180577217141.97181942278286
10547.08784173263349-3.08784173263349
1061414.4196353340098-0.419635334009812
107910.6400561163837-1.64005611638368
1081415.0977608916113-1.09776089161128
109810.1364301921225-2.13643019212252
110810.715911302049-2.71591130204897
1111111.873913882272-0.873913882271967
1121213.1501689898151-1.15016898981508
1131111.6144296642982-0.614429664298249
1141413.37265094615950.627349053840505
1151514.27346513774690.726534862253111
1161613.43498887774432.56501112225571
1171612.94662031549473.05337968450529
1181112.8392689128711-1.83926891287108
1191414.2583045582921-0.258304558292105
1201410.8376185557633.16238144423698
1211211.62982402585320.370175974146844
1221412.70061741752521.29938258247478
123810.8907755528467-2.8907755528467
1241314.1199328683343-1.11993286833428
1251614.02194793681381.9780520631862
1261211.12497875499280.875021245007226
1271615.3015210854570.698478914543025
1281212.6536387475452-0.653638747545172
1291111.452657396154-0.452657396154021
13045.62386162589302-1.62386162589302
1311615.24579149419840.754208505801594
1321512.59584057025912.40415942974089
1331011.5261734935842-1.52617349358419
1341313.3974531743111-0.397453174311143
1351513.1339704198431.86602958015695
1361210.503021691211.49697830878999
1371413.6814865155520.318513484448037
138710.0808068809741-3.08080688097408
1391913.92096428288895.07903571711112
1401213.0880115314566-1.08801153145658
1411211.56804360834570.431956391654338
1421313.3416025568501-0.341602556850117
1431512.22752076769552.77247923230451
14489.35313962372138-1.35313962372138
1451210.90430853882711.09569146117285
1461011.1754834642238-1.17548346422381
147811.4315364100654-3.43153641006535
1481014.6364792060832-4.63647920608323
1491513.92798415335961.07201584664036
1501614.67456570254841.32543429745159
1511313.1309012423538-0.130901242353846
1521615.01295805116990.987041948830092
15399.76401987267187-0.764019872671873
1541413.07761853577450.92238146422549
1551413.25221102053350.74778897946646
1561210.13840971585631.86159028414367






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.185718828596560.371437657193120.81428117140344
140.7760120014895150.447975997020970.223987998510485
150.6607512066527440.6784975866945130.339248793347256
160.6146978123321980.7706043753356040.385302187667802
170.5122675172013890.9754649655972220.487732482798611
180.4162445663921520.8324891327843040.583755433607848
190.3469405663617140.6938811327234280.653059433638286
200.6806131657987040.6387736684025920.319386834201296
210.6108025123210980.7783949753578050.389197487678902
220.5579587582997720.8840824834004560.442041241700228
230.5300091370696590.9399817258606820.469990862930341
240.5241010233676540.9517979532646920.475898976632346
250.5849372562665620.8301254874668770.415062743733438
260.5224489880027320.9551020239945370.477551011997268
270.7371402686081540.5257194627836920.262859731391846
280.683883548694180.632232902611640.31611645130582
290.6283807574194770.7432384851610470.371619242580523
300.8194045550826940.3611908898346120.180595444917306
310.8840705675466120.2318588649067760.115929432453388
320.8552166371249410.2895667257501180.144783362875059
330.81740435246080.3651912950784010.182595647539201
340.7842950994893870.4314098010212270.215704900510613
350.7693022783549450.461395443290110.230697721645055
360.7685743747122610.4628512505754780.231425625287739
370.7341112505739570.5317774988520870.265888749426043
380.6866217059458660.6267565881082690.313378294054134
390.6443349871585070.7113300256829870.355665012841493
400.5954738914237020.8090522171525970.404526108576298
410.5601628132880160.8796743734239680.439837186711984
420.8170260861402380.3659478277195240.182973913859762
430.9611000445245620.07779991095087640.0388999554754382
440.96343124701720.07313750596559860.0365687529827993
450.951966749862720.09606650027456080.0480332501372804
460.9574978528868650.085004294226270.042502147113135
470.9499752555751030.1000494888497930.0500247444248967
480.9445517399054780.1108965201890440.0554482600945219
490.9428904563331120.1142190873337770.0571095436668883
500.933376903455980.133246193088040.0666230965440198
510.9290429230589210.1419141538821570.0709570769410787
520.9919177029708740.01616459405825260.00808229702912631
530.9888733299138770.02225334017224650.0111266700861232
540.9923466949673780.01530661006524420.0076533050326221
550.9947338689136350.0105322621727290.0052661310863645
560.993273511690110.0134529766197790.00672648830988948
570.9907204861819070.0185590276361850.0092795138180925
580.9887276970818750.022544605836250.011272302918125
590.9922382604035220.01552347919295530.00776173959647766
600.9913993778089660.01720124438206740.00860062219103369
610.9913591353364040.01728172932719260.00864086466359628
620.99081393128150.01837213743699930.00918606871849963
630.9892560564113040.02148788717739260.0107439435886963
640.9913372657873160.01732546842536890.00866273421268446
650.9892090224489770.0215819551020460.010790977551023
660.988401552160470.02319689567905980.0115984478395299
670.988858211346510.02228357730698130.0111417886534906
680.9900785657008640.01984286859827220.00992143429913608
690.9896728274045840.02065434519083140.0103271725954157
700.9866114841021720.02677703179565490.0133885158978275
710.9862372399554730.02752552008905330.0137627600445267
720.9816028104803180.03679437903936430.0183971895196822
730.9781144551958210.04377108960835770.0218855448041788
740.9876114960706630.02477700785867390.0123885039293369
750.9839452139048820.03210957219023550.0160547860951177
760.9811482519723440.03770349605531150.0188517480276557
770.9748979562271230.05020408754575420.0251020437728771
780.9669834339911530.06603313201769410.0330165660088471
790.9778840268452320.04423194630953520.0221159731547676
800.9758805335952920.04823893280941660.0241194664047083
810.9812934879876660.03741302402466790.0187065120123339
820.981948902557110.03610219488578110.0180510974428905
830.9762812733953850.04743745320922920.0237187266046146
840.9714319579320850.05713608413582960.0285680420679148
850.9918752214871540.01624955702569190.00812477851284593
860.9888869612938480.02222607741230340.0111130387061517
870.9849343657535680.03013126849286440.0150656342464322
880.9798891612749920.04022167745001570.0201108387250078
890.9745932030637460.05081359387250740.0254067969362537
900.9668617072900540.06627658541989270.0331382927099464
910.9593457546060740.08130849078785140.0406542453939257
920.9808522651709150.03829546965817040.0191477348290852
930.9747493995132170.05050120097356520.0252506004867826
940.9691450396364850.06170992072703030.0308549603635151
950.9600236245646880.07995275087062310.0399763754353115
960.9480110014039370.1039779971921270.0519889985960634
970.9511565755469170.09768684890616530.0488434244530827
980.9376781923724080.1246436152551830.0623218076275916
990.9349795788342140.1300408423315720.065020421165786
1000.9192020670838820.1615958658322350.0807979329161176
1010.9054332731017150.189133453796570.094566726898285
1020.8858673521150190.2282652957699630.114132647884981
1030.9112457164125420.1775085671749150.0887542835874576
1040.9411389070219390.1177221859561230.0588610929780614
1050.9418202883484250.116359423303150.0581797116515749
1060.924314930224090.151370139551820.0756850697759099
1070.9182159677559640.1635680644880720.0817840322440362
1080.9250448879845050.149910224030990.0749551120154951
1090.9165666257495960.1668667485008080.083433374250404
1100.9052093887736410.1895812224527170.0947906112263587
1110.8845545052820020.2308909894359960.115445494717998
1120.8830947170461930.2338105659076140.116905282953807
1130.8528017073331060.2943965853337890.147198292666894
1140.8202025407975020.3595949184049950.179797459202498
1150.7803039923531670.4393920152936660.219696007646833
1160.7739979863640790.4520040272718420.226002013635921
1170.7838540939065390.4322918121869230.216145906093461
1180.7663660445027060.4672679109945880.233633955497294
1190.7170656460593480.5658687078813030.282934353940652
1200.7904971266629220.4190057466741570.209502873337078
1210.7565319982311440.4869360035377110.243468001768856
1220.7152155086409210.5695689827181580.284784491359079
1230.7137470136486390.5725059727027220.286252986351361
1240.658871174476170.682257651047660.34112882552383
1250.6584475076237920.6831049847524160.341552492376208
1260.6380857816234850.723828436753030.361914218376515
1270.573311410424490.853377179151020.42668858957551
1280.6079598199070740.7840803601858520.392040180092926
1290.6017893647925530.7964212704148940.398210635207447
1300.5734541189458760.8530917621082470.426545881054124
1310.4969080032710270.9938160065420540.503091996728973
1320.4759302549179350.951860509835870.524069745082065
1330.4725165606904270.9450331213808530.527483439309573
1340.3974249292629390.7948498585258780.602575070737061
1350.3589823513791240.7179647027582480.641017648620876
1360.360248443401390.720496886802780.63975155659861
1370.2762863127323840.5525726254647680.723713687267616
1380.3388029722779920.6776059445559830.661197027722008
1390.6299649576913660.7400700846172680.370035042308634
1400.7911030588471750.4177938823056510.208896941152825
1410.7237013836275380.5525972327449250.276298616372462
1420.6841626069634010.6316747860731980.315837393036599
1430.7313374463696910.5373251072606180.268662553630309

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.18571882859656 & 0.37143765719312 & 0.81428117140344 \tabularnewline
14 & 0.776012001489515 & 0.44797599702097 & 0.223987998510485 \tabularnewline
15 & 0.660751206652744 & 0.678497586694513 & 0.339248793347256 \tabularnewline
16 & 0.614697812332198 & 0.770604375335604 & 0.385302187667802 \tabularnewline
17 & 0.512267517201389 & 0.975464965597222 & 0.487732482798611 \tabularnewline
18 & 0.416244566392152 & 0.832489132784304 & 0.583755433607848 \tabularnewline
19 & 0.346940566361714 & 0.693881132723428 & 0.653059433638286 \tabularnewline
20 & 0.680613165798704 & 0.638773668402592 & 0.319386834201296 \tabularnewline
21 & 0.610802512321098 & 0.778394975357805 & 0.389197487678902 \tabularnewline
22 & 0.557958758299772 & 0.884082483400456 & 0.442041241700228 \tabularnewline
23 & 0.530009137069659 & 0.939981725860682 & 0.469990862930341 \tabularnewline
24 & 0.524101023367654 & 0.951797953264692 & 0.475898976632346 \tabularnewline
25 & 0.584937256266562 & 0.830125487466877 & 0.415062743733438 \tabularnewline
26 & 0.522448988002732 & 0.955102023994537 & 0.477551011997268 \tabularnewline
27 & 0.737140268608154 & 0.525719462783692 & 0.262859731391846 \tabularnewline
28 & 0.68388354869418 & 0.63223290261164 & 0.31611645130582 \tabularnewline
29 & 0.628380757419477 & 0.743238485161047 & 0.371619242580523 \tabularnewline
30 & 0.819404555082694 & 0.361190889834612 & 0.180595444917306 \tabularnewline
31 & 0.884070567546612 & 0.231858864906776 & 0.115929432453388 \tabularnewline
32 & 0.855216637124941 & 0.289566725750118 & 0.144783362875059 \tabularnewline
33 & 0.8174043524608 & 0.365191295078401 & 0.182595647539201 \tabularnewline
34 & 0.784295099489387 & 0.431409801021227 & 0.215704900510613 \tabularnewline
35 & 0.769302278354945 & 0.46139544329011 & 0.230697721645055 \tabularnewline
36 & 0.768574374712261 & 0.462851250575478 & 0.231425625287739 \tabularnewline
37 & 0.734111250573957 & 0.531777498852087 & 0.265888749426043 \tabularnewline
38 & 0.686621705945866 & 0.626756588108269 & 0.313378294054134 \tabularnewline
39 & 0.644334987158507 & 0.711330025682987 & 0.355665012841493 \tabularnewline
40 & 0.595473891423702 & 0.809052217152597 & 0.404526108576298 \tabularnewline
41 & 0.560162813288016 & 0.879674373423968 & 0.439837186711984 \tabularnewline
42 & 0.817026086140238 & 0.365947827719524 & 0.182973913859762 \tabularnewline
43 & 0.961100044524562 & 0.0777999109508764 & 0.0388999554754382 \tabularnewline
44 & 0.9634312470172 & 0.0731375059655986 & 0.0365687529827993 \tabularnewline
45 & 0.95196674986272 & 0.0960665002745608 & 0.0480332501372804 \tabularnewline
46 & 0.957497852886865 & 0.08500429422627 & 0.042502147113135 \tabularnewline
47 & 0.949975255575103 & 0.100049488849793 & 0.0500247444248967 \tabularnewline
48 & 0.944551739905478 & 0.110896520189044 & 0.0554482600945219 \tabularnewline
49 & 0.942890456333112 & 0.114219087333777 & 0.0571095436668883 \tabularnewline
50 & 0.93337690345598 & 0.13324619308804 & 0.0666230965440198 \tabularnewline
51 & 0.929042923058921 & 0.141914153882157 & 0.0709570769410787 \tabularnewline
52 & 0.991917702970874 & 0.0161645940582526 & 0.00808229702912631 \tabularnewline
53 & 0.988873329913877 & 0.0222533401722465 & 0.0111266700861232 \tabularnewline
54 & 0.992346694967378 & 0.0153066100652442 & 0.0076533050326221 \tabularnewline
55 & 0.994733868913635 & 0.010532262172729 & 0.0052661310863645 \tabularnewline
56 & 0.99327351169011 & 0.013452976619779 & 0.00672648830988948 \tabularnewline
57 & 0.990720486181907 & 0.018559027636185 & 0.0092795138180925 \tabularnewline
58 & 0.988727697081875 & 0.02254460583625 & 0.011272302918125 \tabularnewline
59 & 0.992238260403522 & 0.0155234791929553 & 0.00776173959647766 \tabularnewline
60 & 0.991399377808966 & 0.0172012443820674 & 0.00860062219103369 \tabularnewline
61 & 0.991359135336404 & 0.0172817293271926 & 0.00864086466359628 \tabularnewline
62 & 0.9908139312815 & 0.0183721374369993 & 0.00918606871849963 \tabularnewline
63 & 0.989256056411304 & 0.0214878871773926 & 0.0107439435886963 \tabularnewline
64 & 0.991337265787316 & 0.0173254684253689 & 0.00866273421268446 \tabularnewline
65 & 0.989209022448977 & 0.021581955102046 & 0.010790977551023 \tabularnewline
66 & 0.98840155216047 & 0.0231968956790598 & 0.0115984478395299 \tabularnewline
67 & 0.98885821134651 & 0.0222835773069813 & 0.0111417886534906 \tabularnewline
68 & 0.990078565700864 & 0.0198428685982722 & 0.00992143429913608 \tabularnewline
69 & 0.989672827404584 & 0.0206543451908314 & 0.0103271725954157 \tabularnewline
70 & 0.986611484102172 & 0.0267770317956549 & 0.0133885158978275 \tabularnewline
71 & 0.986237239955473 & 0.0275255200890533 & 0.0137627600445267 \tabularnewline
72 & 0.981602810480318 & 0.0367943790393643 & 0.0183971895196822 \tabularnewline
73 & 0.978114455195821 & 0.0437710896083577 & 0.0218855448041788 \tabularnewline
74 & 0.987611496070663 & 0.0247770078586739 & 0.0123885039293369 \tabularnewline
75 & 0.983945213904882 & 0.0321095721902355 & 0.0160547860951177 \tabularnewline
76 & 0.981148251972344 & 0.0377034960553115 & 0.0188517480276557 \tabularnewline
77 & 0.974897956227123 & 0.0502040875457542 & 0.0251020437728771 \tabularnewline
78 & 0.966983433991153 & 0.0660331320176941 & 0.0330165660088471 \tabularnewline
79 & 0.977884026845232 & 0.0442319463095352 & 0.0221159731547676 \tabularnewline
80 & 0.975880533595292 & 0.0482389328094166 & 0.0241194664047083 \tabularnewline
81 & 0.981293487987666 & 0.0374130240246679 & 0.0187065120123339 \tabularnewline
82 & 0.98194890255711 & 0.0361021948857811 & 0.0180510974428905 \tabularnewline
83 & 0.976281273395385 & 0.0474374532092292 & 0.0237187266046146 \tabularnewline
84 & 0.971431957932085 & 0.0571360841358296 & 0.0285680420679148 \tabularnewline
85 & 0.991875221487154 & 0.0162495570256919 & 0.00812477851284593 \tabularnewline
86 & 0.988886961293848 & 0.0222260774123034 & 0.0111130387061517 \tabularnewline
87 & 0.984934365753568 & 0.0301312684928644 & 0.0150656342464322 \tabularnewline
88 & 0.979889161274992 & 0.0402216774500157 & 0.0201108387250078 \tabularnewline
89 & 0.974593203063746 & 0.0508135938725074 & 0.0254067969362537 \tabularnewline
90 & 0.966861707290054 & 0.0662765854198927 & 0.0331382927099464 \tabularnewline
91 & 0.959345754606074 & 0.0813084907878514 & 0.0406542453939257 \tabularnewline
92 & 0.980852265170915 & 0.0382954696581704 & 0.0191477348290852 \tabularnewline
93 & 0.974749399513217 & 0.0505012009735652 & 0.0252506004867826 \tabularnewline
94 & 0.969145039636485 & 0.0617099207270303 & 0.0308549603635151 \tabularnewline
95 & 0.960023624564688 & 0.0799527508706231 & 0.0399763754353115 \tabularnewline
96 & 0.948011001403937 & 0.103977997192127 & 0.0519889985960634 \tabularnewline
97 & 0.951156575546917 & 0.0976868489061653 & 0.0488434244530827 \tabularnewline
98 & 0.937678192372408 & 0.124643615255183 & 0.0623218076275916 \tabularnewline
99 & 0.934979578834214 & 0.130040842331572 & 0.065020421165786 \tabularnewline
100 & 0.919202067083882 & 0.161595865832235 & 0.0807979329161176 \tabularnewline
101 & 0.905433273101715 & 0.18913345379657 & 0.094566726898285 \tabularnewline
102 & 0.885867352115019 & 0.228265295769963 & 0.114132647884981 \tabularnewline
103 & 0.911245716412542 & 0.177508567174915 & 0.0887542835874576 \tabularnewline
104 & 0.941138907021939 & 0.117722185956123 & 0.0588610929780614 \tabularnewline
105 & 0.941820288348425 & 0.11635942330315 & 0.0581797116515749 \tabularnewline
106 & 0.92431493022409 & 0.15137013955182 & 0.0756850697759099 \tabularnewline
107 & 0.918215967755964 & 0.163568064488072 & 0.0817840322440362 \tabularnewline
108 & 0.925044887984505 & 0.14991022403099 & 0.0749551120154951 \tabularnewline
109 & 0.916566625749596 & 0.166866748500808 & 0.083433374250404 \tabularnewline
110 & 0.905209388773641 & 0.189581222452717 & 0.0947906112263587 \tabularnewline
111 & 0.884554505282002 & 0.230890989435996 & 0.115445494717998 \tabularnewline
112 & 0.883094717046193 & 0.233810565907614 & 0.116905282953807 \tabularnewline
113 & 0.852801707333106 & 0.294396585333789 & 0.147198292666894 \tabularnewline
114 & 0.820202540797502 & 0.359594918404995 & 0.179797459202498 \tabularnewline
115 & 0.780303992353167 & 0.439392015293666 & 0.219696007646833 \tabularnewline
116 & 0.773997986364079 & 0.452004027271842 & 0.226002013635921 \tabularnewline
117 & 0.783854093906539 & 0.432291812186923 & 0.216145906093461 \tabularnewline
118 & 0.766366044502706 & 0.467267910994588 & 0.233633955497294 \tabularnewline
119 & 0.717065646059348 & 0.565868707881303 & 0.282934353940652 \tabularnewline
120 & 0.790497126662922 & 0.419005746674157 & 0.209502873337078 \tabularnewline
121 & 0.756531998231144 & 0.486936003537711 & 0.243468001768856 \tabularnewline
122 & 0.715215508640921 & 0.569568982718158 & 0.284784491359079 \tabularnewline
123 & 0.713747013648639 & 0.572505972702722 & 0.286252986351361 \tabularnewline
124 & 0.65887117447617 & 0.68225765104766 & 0.34112882552383 \tabularnewline
125 & 0.658447507623792 & 0.683104984752416 & 0.341552492376208 \tabularnewline
126 & 0.638085781623485 & 0.72382843675303 & 0.361914218376515 \tabularnewline
127 & 0.57331141042449 & 0.85337717915102 & 0.42668858957551 \tabularnewline
128 & 0.607959819907074 & 0.784080360185852 & 0.392040180092926 \tabularnewline
129 & 0.601789364792553 & 0.796421270414894 & 0.398210635207447 \tabularnewline
130 & 0.573454118945876 & 0.853091762108247 & 0.426545881054124 \tabularnewline
131 & 0.496908003271027 & 0.993816006542054 & 0.503091996728973 \tabularnewline
132 & 0.475930254917935 & 0.95186050983587 & 0.524069745082065 \tabularnewline
133 & 0.472516560690427 & 0.945033121380853 & 0.527483439309573 \tabularnewline
134 & 0.397424929262939 & 0.794849858525878 & 0.602575070737061 \tabularnewline
135 & 0.358982351379124 & 0.717964702758248 & 0.641017648620876 \tabularnewline
136 & 0.36024844340139 & 0.72049688680278 & 0.63975155659861 \tabularnewline
137 & 0.276286312732384 & 0.552572625464768 & 0.723713687267616 \tabularnewline
138 & 0.338802972277992 & 0.677605944555983 & 0.661197027722008 \tabularnewline
139 & 0.629964957691366 & 0.740070084617268 & 0.370035042308634 \tabularnewline
140 & 0.791103058847175 & 0.417793882305651 & 0.208896941152825 \tabularnewline
141 & 0.723701383627538 & 0.552597232744925 & 0.276298616372462 \tabularnewline
142 & 0.684162606963401 & 0.631674786073198 & 0.315837393036599 \tabularnewline
143 & 0.731337446369691 & 0.537325107260618 & 0.268662553630309 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]13[/C][C]0.18571882859656[/C][C]0.37143765719312[/C][C]0.81428117140344[/C][/ROW]
[ROW][C]14[/C][C]0.776012001489515[/C][C]0.44797599702097[/C][C]0.223987998510485[/C][/ROW]
[ROW][C]15[/C][C]0.660751206652744[/C][C]0.678497586694513[/C][C]0.339248793347256[/C][/ROW]
[ROW][C]16[/C][C]0.614697812332198[/C][C]0.770604375335604[/C][C]0.385302187667802[/C][/ROW]
[ROW][C]17[/C][C]0.512267517201389[/C][C]0.975464965597222[/C][C]0.487732482798611[/C][/ROW]
[ROW][C]18[/C][C]0.416244566392152[/C][C]0.832489132784304[/C][C]0.583755433607848[/C][/ROW]
[ROW][C]19[/C][C]0.346940566361714[/C][C]0.693881132723428[/C][C]0.653059433638286[/C][/ROW]
[ROW][C]20[/C][C]0.680613165798704[/C][C]0.638773668402592[/C][C]0.319386834201296[/C][/ROW]
[ROW][C]21[/C][C]0.610802512321098[/C][C]0.778394975357805[/C][C]0.389197487678902[/C][/ROW]
[ROW][C]22[/C][C]0.557958758299772[/C][C]0.884082483400456[/C][C]0.442041241700228[/C][/ROW]
[ROW][C]23[/C][C]0.530009137069659[/C][C]0.939981725860682[/C][C]0.469990862930341[/C][/ROW]
[ROW][C]24[/C][C]0.524101023367654[/C][C]0.951797953264692[/C][C]0.475898976632346[/C][/ROW]
[ROW][C]25[/C][C]0.584937256266562[/C][C]0.830125487466877[/C][C]0.415062743733438[/C][/ROW]
[ROW][C]26[/C][C]0.522448988002732[/C][C]0.955102023994537[/C][C]0.477551011997268[/C][/ROW]
[ROW][C]27[/C][C]0.737140268608154[/C][C]0.525719462783692[/C][C]0.262859731391846[/C][/ROW]
[ROW][C]28[/C][C]0.68388354869418[/C][C]0.63223290261164[/C][C]0.31611645130582[/C][/ROW]
[ROW][C]29[/C][C]0.628380757419477[/C][C]0.743238485161047[/C][C]0.371619242580523[/C][/ROW]
[ROW][C]30[/C][C]0.819404555082694[/C][C]0.361190889834612[/C][C]0.180595444917306[/C][/ROW]
[ROW][C]31[/C][C]0.884070567546612[/C][C]0.231858864906776[/C][C]0.115929432453388[/C][/ROW]
[ROW][C]32[/C][C]0.855216637124941[/C][C]0.289566725750118[/C][C]0.144783362875059[/C][/ROW]
[ROW][C]33[/C][C]0.8174043524608[/C][C]0.365191295078401[/C][C]0.182595647539201[/C][/ROW]
[ROW][C]34[/C][C]0.784295099489387[/C][C]0.431409801021227[/C][C]0.215704900510613[/C][/ROW]
[ROW][C]35[/C][C]0.769302278354945[/C][C]0.46139544329011[/C][C]0.230697721645055[/C][/ROW]
[ROW][C]36[/C][C]0.768574374712261[/C][C]0.462851250575478[/C][C]0.231425625287739[/C][/ROW]
[ROW][C]37[/C][C]0.734111250573957[/C][C]0.531777498852087[/C][C]0.265888749426043[/C][/ROW]
[ROW][C]38[/C][C]0.686621705945866[/C][C]0.626756588108269[/C][C]0.313378294054134[/C][/ROW]
[ROW][C]39[/C][C]0.644334987158507[/C][C]0.711330025682987[/C][C]0.355665012841493[/C][/ROW]
[ROW][C]40[/C][C]0.595473891423702[/C][C]0.809052217152597[/C][C]0.404526108576298[/C][/ROW]
[ROW][C]41[/C][C]0.560162813288016[/C][C]0.879674373423968[/C][C]0.439837186711984[/C][/ROW]
[ROW][C]42[/C][C]0.817026086140238[/C][C]0.365947827719524[/C][C]0.182973913859762[/C][/ROW]
[ROW][C]43[/C][C]0.961100044524562[/C][C]0.0777999109508764[/C][C]0.0388999554754382[/C][/ROW]
[ROW][C]44[/C][C]0.9634312470172[/C][C]0.0731375059655986[/C][C]0.0365687529827993[/C][/ROW]
[ROW][C]45[/C][C]0.95196674986272[/C][C]0.0960665002745608[/C][C]0.0480332501372804[/C][/ROW]
[ROW][C]46[/C][C]0.957497852886865[/C][C]0.08500429422627[/C][C]0.042502147113135[/C][/ROW]
[ROW][C]47[/C][C]0.949975255575103[/C][C]0.100049488849793[/C][C]0.0500247444248967[/C][/ROW]
[ROW][C]48[/C][C]0.944551739905478[/C][C]0.110896520189044[/C][C]0.0554482600945219[/C][/ROW]
[ROW][C]49[/C][C]0.942890456333112[/C][C]0.114219087333777[/C][C]0.0571095436668883[/C][/ROW]
[ROW][C]50[/C][C]0.93337690345598[/C][C]0.13324619308804[/C][C]0.0666230965440198[/C][/ROW]
[ROW][C]51[/C][C]0.929042923058921[/C][C]0.141914153882157[/C][C]0.0709570769410787[/C][/ROW]
[ROW][C]52[/C][C]0.991917702970874[/C][C]0.0161645940582526[/C][C]0.00808229702912631[/C][/ROW]
[ROW][C]53[/C][C]0.988873329913877[/C][C]0.0222533401722465[/C][C]0.0111266700861232[/C][/ROW]
[ROW][C]54[/C][C]0.992346694967378[/C][C]0.0153066100652442[/C][C]0.0076533050326221[/C][/ROW]
[ROW][C]55[/C][C]0.994733868913635[/C][C]0.010532262172729[/C][C]0.0052661310863645[/C][/ROW]
[ROW][C]56[/C][C]0.99327351169011[/C][C]0.013452976619779[/C][C]0.00672648830988948[/C][/ROW]
[ROW][C]57[/C][C]0.990720486181907[/C][C]0.018559027636185[/C][C]0.0092795138180925[/C][/ROW]
[ROW][C]58[/C][C]0.988727697081875[/C][C]0.02254460583625[/C][C]0.011272302918125[/C][/ROW]
[ROW][C]59[/C][C]0.992238260403522[/C][C]0.0155234791929553[/C][C]0.00776173959647766[/C][/ROW]
[ROW][C]60[/C][C]0.991399377808966[/C][C]0.0172012443820674[/C][C]0.00860062219103369[/C][/ROW]
[ROW][C]61[/C][C]0.991359135336404[/C][C]0.0172817293271926[/C][C]0.00864086466359628[/C][/ROW]
[ROW][C]62[/C][C]0.9908139312815[/C][C]0.0183721374369993[/C][C]0.00918606871849963[/C][/ROW]
[ROW][C]63[/C][C]0.989256056411304[/C][C]0.0214878871773926[/C][C]0.0107439435886963[/C][/ROW]
[ROW][C]64[/C][C]0.991337265787316[/C][C]0.0173254684253689[/C][C]0.00866273421268446[/C][/ROW]
[ROW][C]65[/C][C]0.989209022448977[/C][C]0.021581955102046[/C][C]0.010790977551023[/C][/ROW]
[ROW][C]66[/C][C]0.98840155216047[/C][C]0.0231968956790598[/C][C]0.0115984478395299[/C][/ROW]
[ROW][C]67[/C][C]0.98885821134651[/C][C]0.0222835773069813[/C][C]0.0111417886534906[/C][/ROW]
[ROW][C]68[/C][C]0.990078565700864[/C][C]0.0198428685982722[/C][C]0.00992143429913608[/C][/ROW]
[ROW][C]69[/C][C]0.989672827404584[/C][C]0.0206543451908314[/C][C]0.0103271725954157[/C][/ROW]
[ROW][C]70[/C][C]0.986611484102172[/C][C]0.0267770317956549[/C][C]0.0133885158978275[/C][/ROW]
[ROW][C]71[/C][C]0.986237239955473[/C][C]0.0275255200890533[/C][C]0.0137627600445267[/C][/ROW]
[ROW][C]72[/C][C]0.981602810480318[/C][C]0.0367943790393643[/C][C]0.0183971895196822[/C][/ROW]
[ROW][C]73[/C][C]0.978114455195821[/C][C]0.0437710896083577[/C][C]0.0218855448041788[/C][/ROW]
[ROW][C]74[/C][C]0.987611496070663[/C][C]0.0247770078586739[/C][C]0.0123885039293369[/C][/ROW]
[ROW][C]75[/C][C]0.983945213904882[/C][C]0.0321095721902355[/C][C]0.0160547860951177[/C][/ROW]
[ROW][C]76[/C][C]0.981148251972344[/C][C]0.0377034960553115[/C][C]0.0188517480276557[/C][/ROW]
[ROW][C]77[/C][C]0.974897956227123[/C][C]0.0502040875457542[/C][C]0.0251020437728771[/C][/ROW]
[ROW][C]78[/C][C]0.966983433991153[/C][C]0.0660331320176941[/C][C]0.0330165660088471[/C][/ROW]
[ROW][C]79[/C][C]0.977884026845232[/C][C]0.0442319463095352[/C][C]0.0221159731547676[/C][/ROW]
[ROW][C]80[/C][C]0.975880533595292[/C][C]0.0482389328094166[/C][C]0.0241194664047083[/C][/ROW]
[ROW][C]81[/C][C]0.981293487987666[/C][C]0.0374130240246679[/C][C]0.0187065120123339[/C][/ROW]
[ROW][C]82[/C][C]0.98194890255711[/C][C]0.0361021948857811[/C][C]0.0180510974428905[/C][/ROW]
[ROW][C]83[/C][C]0.976281273395385[/C][C]0.0474374532092292[/C][C]0.0237187266046146[/C][/ROW]
[ROW][C]84[/C][C]0.971431957932085[/C][C]0.0571360841358296[/C][C]0.0285680420679148[/C][/ROW]
[ROW][C]85[/C][C]0.991875221487154[/C][C]0.0162495570256919[/C][C]0.00812477851284593[/C][/ROW]
[ROW][C]86[/C][C]0.988886961293848[/C][C]0.0222260774123034[/C][C]0.0111130387061517[/C][/ROW]
[ROW][C]87[/C][C]0.984934365753568[/C][C]0.0301312684928644[/C][C]0.0150656342464322[/C][/ROW]
[ROW][C]88[/C][C]0.979889161274992[/C][C]0.0402216774500157[/C][C]0.0201108387250078[/C][/ROW]
[ROW][C]89[/C][C]0.974593203063746[/C][C]0.0508135938725074[/C][C]0.0254067969362537[/C][/ROW]
[ROW][C]90[/C][C]0.966861707290054[/C][C]0.0662765854198927[/C][C]0.0331382927099464[/C][/ROW]
[ROW][C]91[/C][C]0.959345754606074[/C][C]0.0813084907878514[/C][C]0.0406542453939257[/C][/ROW]
[ROW][C]92[/C][C]0.980852265170915[/C][C]0.0382954696581704[/C][C]0.0191477348290852[/C][/ROW]
[ROW][C]93[/C][C]0.974749399513217[/C][C]0.0505012009735652[/C][C]0.0252506004867826[/C][/ROW]
[ROW][C]94[/C][C]0.969145039636485[/C][C]0.0617099207270303[/C][C]0.0308549603635151[/C][/ROW]
[ROW][C]95[/C][C]0.960023624564688[/C][C]0.0799527508706231[/C][C]0.0399763754353115[/C][/ROW]
[ROW][C]96[/C][C]0.948011001403937[/C][C]0.103977997192127[/C][C]0.0519889985960634[/C][/ROW]
[ROW][C]97[/C][C]0.951156575546917[/C][C]0.0976868489061653[/C][C]0.0488434244530827[/C][/ROW]
[ROW][C]98[/C][C]0.937678192372408[/C][C]0.124643615255183[/C][C]0.0623218076275916[/C][/ROW]
[ROW][C]99[/C][C]0.934979578834214[/C][C]0.130040842331572[/C][C]0.065020421165786[/C][/ROW]
[ROW][C]100[/C][C]0.919202067083882[/C][C]0.161595865832235[/C][C]0.0807979329161176[/C][/ROW]
[ROW][C]101[/C][C]0.905433273101715[/C][C]0.18913345379657[/C][C]0.094566726898285[/C][/ROW]
[ROW][C]102[/C][C]0.885867352115019[/C][C]0.228265295769963[/C][C]0.114132647884981[/C][/ROW]
[ROW][C]103[/C][C]0.911245716412542[/C][C]0.177508567174915[/C][C]0.0887542835874576[/C][/ROW]
[ROW][C]104[/C][C]0.941138907021939[/C][C]0.117722185956123[/C][C]0.0588610929780614[/C][/ROW]
[ROW][C]105[/C][C]0.941820288348425[/C][C]0.11635942330315[/C][C]0.0581797116515749[/C][/ROW]
[ROW][C]106[/C][C]0.92431493022409[/C][C]0.15137013955182[/C][C]0.0756850697759099[/C][/ROW]
[ROW][C]107[/C][C]0.918215967755964[/C][C]0.163568064488072[/C][C]0.0817840322440362[/C][/ROW]
[ROW][C]108[/C][C]0.925044887984505[/C][C]0.14991022403099[/C][C]0.0749551120154951[/C][/ROW]
[ROW][C]109[/C][C]0.916566625749596[/C][C]0.166866748500808[/C][C]0.083433374250404[/C][/ROW]
[ROW][C]110[/C][C]0.905209388773641[/C][C]0.189581222452717[/C][C]0.0947906112263587[/C][/ROW]
[ROW][C]111[/C][C]0.884554505282002[/C][C]0.230890989435996[/C][C]0.115445494717998[/C][/ROW]
[ROW][C]112[/C][C]0.883094717046193[/C][C]0.233810565907614[/C][C]0.116905282953807[/C][/ROW]
[ROW][C]113[/C][C]0.852801707333106[/C][C]0.294396585333789[/C][C]0.147198292666894[/C][/ROW]
[ROW][C]114[/C][C]0.820202540797502[/C][C]0.359594918404995[/C][C]0.179797459202498[/C][/ROW]
[ROW][C]115[/C][C]0.780303992353167[/C][C]0.439392015293666[/C][C]0.219696007646833[/C][/ROW]
[ROW][C]116[/C][C]0.773997986364079[/C][C]0.452004027271842[/C][C]0.226002013635921[/C][/ROW]
[ROW][C]117[/C][C]0.783854093906539[/C][C]0.432291812186923[/C][C]0.216145906093461[/C][/ROW]
[ROW][C]118[/C][C]0.766366044502706[/C][C]0.467267910994588[/C][C]0.233633955497294[/C][/ROW]
[ROW][C]119[/C][C]0.717065646059348[/C][C]0.565868707881303[/C][C]0.282934353940652[/C][/ROW]
[ROW][C]120[/C][C]0.790497126662922[/C][C]0.419005746674157[/C][C]0.209502873337078[/C][/ROW]
[ROW][C]121[/C][C]0.756531998231144[/C][C]0.486936003537711[/C][C]0.243468001768856[/C][/ROW]
[ROW][C]122[/C][C]0.715215508640921[/C][C]0.569568982718158[/C][C]0.284784491359079[/C][/ROW]
[ROW][C]123[/C][C]0.713747013648639[/C][C]0.572505972702722[/C][C]0.286252986351361[/C][/ROW]
[ROW][C]124[/C][C]0.65887117447617[/C][C]0.68225765104766[/C][C]0.34112882552383[/C][/ROW]
[ROW][C]125[/C][C]0.658447507623792[/C][C]0.683104984752416[/C][C]0.341552492376208[/C][/ROW]
[ROW][C]126[/C][C]0.638085781623485[/C][C]0.72382843675303[/C][C]0.361914218376515[/C][/ROW]
[ROW][C]127[/C][C]0.57331141042449[/C][C]0.85337717915102[/C][C]0.42668858957551[/C][/ROW]
[ROW][C]128[/C][C]0.607959819907074[/C][C]0.784080360185852[/C][C]0.392040180092926[/C][/ROW]
[ROW][C]129[/C][C]0.601789364792553[/C][C]0.796421270414894[/C][C]0.398210635207447[/C][/ROW]
[ROW][C]130[/C][C]0.573454118945876[/C][C]0.853091762108247[/C][C]0.426545881054124[/C][/ROW]
[ROW][C]131[/C][C]0.496908003271027[/C][C]0.993816006542054[/C][C]0.503091996728973[/C][/ROW]
[ROW][C]132[/C][C]0.475930254917935[/C][C]0.95186050983587[/C][C]0.524069745082065[/C][/ROW]
[ROW][C]133[/C][C]0.472516560690427[/C][C]0.945033121380853[/C][C]0.527483439309573[/C][/ROW]
[ROW][C]134[/C][C]0.397424929262939[/C][C]0.794849858525878[/C][C]0.602575070737061[/C][/ROW]
[ROW][C]135[/C][C]0.358982351379124[/C][C]0.717964702758248[/C][C]0.641017648620876[/C][/ROW]
[ROW][C]136[/C][C]0.36024844340139[/C][C]0.72049688680278[/C][C]0.63975155659861[/C][/ROW]
[ROW][C]137[/C][C]0.276286312732384[/C][C]0.552572625464768[/C][C]0.723713687267616[/C][/ROW]
[ROW][C]138[/C][C]0.338802972277992[/C][C]0.677605944555983[/C][C]0.661197027722008[/C][/ROW]
[ROW][C]139[/C][C]0.629964957691366[/C][C]0.740070084617268[/C][C]0.370035042308634[/C][/ROW]
[ROW][C]140[/C][C]0.791103058847175[/C][C]0.417793882305651[/C][C]0.208896941152825[/C][/ROW]
[ROW][C]141[/C][C]0.723701383627538[/C][C]0.552597232744925[/C][C]0.276298616372462[/C][/ROW]
[ROW][C]142[/C][C]0.684162606963401[/C][C]0.631674786073198[/C][C]0.315837393036599[/C][/ROW]
[ROW][C]143[/C][C]0.731337446369691[/C][C]0.537325107260618[/C][C]0.268662553630309[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.185718828596560.371437657193120.81428117140344
140.7760120014895150.447975997020970.223987998510485
150.6607512066527440.6784975866945130.339248793347256
160.6146978123321980.7706043753356040.385302187667802
170.5122675172013890.9754649655972220.487732482798611
180.4162445663921520.8324891327843040.583755433607848
190.3469405663617140.6938811327234280.653059433638286
200.6806131657987040.6387736684025920.319386834201296
210.6108025123210980.7783949753578050.389197487678902
220.5579587582997720.8840824834004560.442041241700228
230.5300091370696590.9399817258606820.469990862930341
240.5241010233676540.9517979532646920.475898976632346
250.5849372562665620.8301254874668770.415062743733438
260.5224489880027320.9551020239945370.477551011997268
270.7371402686081540.5257194627836920.262859731391846
280.683883548694180.632232902611640.31611645130582
290.6283807574194770.7432384851610470.371619242580523
300.8194045550826940.3611908898346120.180595444917306
310.8840705675466120.2318588649067760.115929432453388
320.8552166371249410.2895667257501180.144783362875059
330.81740435246080.3651912950784010.182595647539201
340.7842950994893870.4314098010212270.215704900510613
350.7693022783549450.461395443290110.230697721645055
360.7685743747122610.4628512505754780.231425625287739
370.7341112505739570.5317774988520870.265888749426043
380.6866217059458660.6267565881082690.313378294054134
390.6443349871585070.7113300256829870.355665012841493
400.5954738914237020.8090522171525970.404526108576298
410.5601628132880160.8796743734239680.439837186711984
420.8170260861402380.3659478277195240.182973913859762
430.9611000445245620.07779991095087640.0388999554754382
440.96343124701720.07313750596559860.0365687529827993
450.951966749862720.09606650027456080.0480332501372804
460.9574978528868650.085004294226270.042502147113135
470.9499752555751030.1000494888497930.0500247444248967
480.9445517399054780.1108965201890440.0554482600945219
490.9428904563331120.1142190873337770.0571095436668883
500.933376903455980.133246193088040.0666230965440198
510.9290429230589210.1419141538821570.0709570769410787
520.9919177029708740.01616459405825260.00808229702912631
530.9888733299138770.02225334017224650.0111266700861232
540.9923466949673780.01530661006524420.0076533050326221
550.9947338689136350.0105322621727290.0052661310863645
560.993273511690110.0134529766197790.00672648830988948
570.9907204861819070.0185590276361850.0092795138180925
580.9887276970818750.022544605836250.011272302918125
590.9922382604035220.01552347919295530.00776173959647766
600.9913993778089660.01720124438206740.00860062219103369
610.9913591353364040.01728172932719260.00864086466359628
620.99081393128150.01837213743699930.00918606871849963
630.9892560564113040.02148788717739260.0107439435886963
640.9913372657873160.01732546842536890.00866273421268446
650.9892090224489770.0215819551020460.010790977551023
660.988401552160470.02319689567905980.0115984478395299
670.988858211346510.02228357730698130.0111417886534906
680.9900785657008640.01984286859827220.00992143429913608
690.9896728274045840.02065434519083140.0103271725954157
700.9866114841021720.02677703179565490.0133885158978275
710.9862372399554730.02752552008905330.0137627600445267
720.9816028104803180.03679437903936430.0183971895196822
730.9781144551958210.04377108960835770.0218855448041788
740.9876114960706630.02477700785867390.0123885039293369
750.9839452139048820.03210957219023550.0160547860951177
760.9811482519723440.03770349605531150.0188517480276557
770.9748979562271230.05020408754575420.0251020437728771
780.9669834339911530.06603313201769410.0330165660088471
790.9778840268452320.04423194630953520.0221159731547676
800.9758805335952920.04823893280941660.0241194664047083
810.9812934879876660.03741302402466790.0187065120123339
820.981948902557110.03610219488578110.0180510974428905
830.9762812733953850.04743745320922920.0237187266046146
840.9714319579320850.05713608413582960.0285680420679148
850.9918752214871540.01624955702569190.00812477851284593
860.9888869612938480.02222607741230340.0111130387061517
870.9849343657535680.03013126849286440.0150656342464322
880.9798891612749920.04022167745001570.0201108387250078
890.9745932030637460.05081359387250740.0254067969362537
900.9668617072900540.06627658541989270.0331382927099464
910.9593457546060740.08130849078785140.0406542453939257
920.9808522651709150.03829546965817040.0191477348290852
930.9747493995132170.05050120097356520.0252506004867826
940.9691450396364850.06170992072703030.0308549603635151
950.9600236245646880.07995275087062310.0399763754353115
960.9480110014039370.1039779971921270.0519889985960634
970.9511565755469170.09768684890616530.0488434244530827
980.9376781923724080.1246436152551830.0623218076275916
990.9349795788342140.1300408423315720.065020421165786
1000.9192020670838820.1615958658322350.0807979329161176
1010.9054332731017150.189133453796570.094566726898285
1020.8858673521150190.2282652957699630.114132647884981
1030.9112457164125420.1775085671749150.0887542835874576
1040.9411389070219390.1177221859561230.0588610929780614
1050.9418202883484250.116359423303150.0581797116515749
1060.924314930224090.151370139551820.0756850697759099
1070.9182159677559640.1635680644880720.0817840322440362
1080.9250448879845050.149910224030990.0749551120154951
1090.9165666257495960.1668667485008080.083433374250404
1100.9052093887736410.1895812224527170.0947906112263587
1110.8845545052820020.2308909894359960.115445494717998
1120.8830947170461930.2338105659076140.116905282953807
1130.8528017073331060.2943965853337890.147198292666894
1140.8202025407975020.3595949184049950.179797459202498
1150.7803039923531670.4393920152936660.219696007646833
1160.7739979863640790.4520040272718420.226002013635921
1170.7838540939065390.4322918121869230.216145906093461
1180.7663660445027060.4672679109945880.233633955497294
1190.7170656460593480.5658687078813030.282934353940652
1200.7904971266629220.4190057466741570.209502873337078
1210.7565319982311440.4869360035377110.243468001768856
1220.7152155086409210.5695689827181580.284784491359079
1230.7137470136486390.5725059727027220.286252986351361
1240.658871174476170.682257651047660.34112882552383
1250.6584475076237920.6831049847524160.341552492376208
1260.6380857816234850.723828436753030.361914218376515
1270.573311410424490.853377179151020.42668858957551
1280.6079598199070740.7840803601858520.392040180092926
1290.6017893647925530.7964212704148940.398210635207447
1300.5734541189458760.8530917621082470.426545881054124
1310.4969080032710270.9938160065420540.503091996728973
1320.4759302549179350.951860509835870.524069745082065
1330.4725165606904270.9450331213808530.527483439309573
1340.3974249292629390.7948498585258780.602575070737061
1350.3589823513791240.7179647027582480.641017648620876
1360.360248443401390.720496886802780.63975155659861
1370.2762863127323840.5525726254647680.723713687267616
1380.3388029722779920.6776059445559830.661197027722008
1390.6299649576913660.7400700846172680.370035042308634
1400.7911030588471750.4177938823056510.208896941152825
1410.7237013836275380.5525972327449250.276298616372462
1420.6841626069634010.6316747860731980.315837393036599
1430.7313374463696910.5373251072606180.268662553630309






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level350.267175572519084NOK
10% type I error level490.374045801526718NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 35 & 0.267175572519084 & NOK \tabularnewline
10% type I error level & 49 & 0.374045801526718 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116711&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.267175572519084[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.374045801526718[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116711&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116711&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level350.267175572519084NOK
10% type I error level490.374045801526718NOK


Figures (Output of Computation)

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

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