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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 10:42:05 +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/t12936192095ghd0cgpll8uzjz.htm/, Retrieved Thu, 02 May 2024 05:58:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116687, Retrieved Thu, 02 May 2024 05:58:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-             [Multiple Regression] [Interactie effect...] [2010-12-29 11:20:21] [26b496433b0542586fba8728b2eb65c5]
- R           [Multiple Regression] [paper interactie ...] [2010-12-29 11:41:23] [60c32809ce17837c238062fc404bf26f]
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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 time17 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 17 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116687&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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 time17 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -0.956521145565202 + 0.122464404572674FindingFriends[t] -0.00611986826304754sum_friends[t] + 0.129901135405866KnowingPeople[t] + 0.0388655008006025sum_know[t] + 0.422882703355402Liked[t] -0.0255765571618549sum_liked[t] + 0.782432477255013Celebrity[t] -0.0824572683867579sum_celeb[t] + 0.579627939212355Sum[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -0.956521145565202 +  0.122464404572674FindingFriends[t] -0.00611986826304754sum_friends[t] +  0.129901135405866KnowingPeople[t] +  0.0388655008006025sum_know[t] +  0.422882703355402Liked[t] -0.0255765571618549sum_liked[t] +  0.782432477255013Celebrity[t] -0.0824572683867579sum_celeb[t] +  0.579627939212355Sum[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116687&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -0.956521145565202 +  0.122464404572674FindingFriends[t] -0.00611986826304754sum_friends[t] +  0.129901135405866KnowingPeople[t] +  0.0388655008006025sum_know[t] +  0.422882703355402Liked[t] -0.0255765571618549sum_liked[t] +  0.782432477255013Celebrity[t] -0.0824572683867579sum_celeb[t] +  0.579627939212355Sum[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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] = -0.956521145565202 + 0.122464404572674FindingFriends[t] -0.00611986826304754sum_friends[t] + 0.129901135405866KnowingPeople[t] + 0.0388655008006025sum_know[t] + 0.422882703355402Liked[t] -0.0255765571618549sum_liked[t] + 0.782432477255013Celebrity[t] -0.0824572683867579sum_celeb[t] + 0.579627939212355Sum[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9565211455652022.451522-0.39020.6969760.348488
FindingFriends0.1224644045726740.1772490.69090.4907140.245357
sum_friends-0.006119868263047540.066175-0.09250.9264430.463221
KnowingPeople0.1299011354058660.1119011.16090.2475930.123797
sum_know0.03886550080060250.0452310.85930.3916010.1958
Liked0.4228827033554020.1696342.49290.0137870.006893
sum_liked-0.02557655716185490.063828-0.40070.6892190.34461
Celebrity0.7824324772550130.2911712.68720.0080420.004021
sum_celeb-0.08245726838675790.117131-0.7040.482570.241285
Sum0.5796279392123550.8875440.65310.5147380.257369

\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) & -0.956521145565202 & 2.451522 & -0.3902 & 0.696976 & 0.348488 \tabularnewline
FindingFriends & 0.122464404572674 & 0.177249 & 0.6909 & 0.490714 & 0.245357 \tabularnewline
sum_friends & -0.00611986826304754 & 0.066175 & -0.0925 & 0.926443 & 0.463221 \tabularnewline
KnowingPeople & 0.129901135405866 & 0.111901 & 1.1609 & 0.247593 & 0.123797 \tabularnewline
sum_know & 0.0388655008006025 & 0.045231 & 0.8593 & 0.391601 & 0.1958 \tabularnewline
Liked & 0.422882703355402 & 0.169634 & 2.4929 & 0.013787 & 0.006893 \tabularnewline
sum_liked & -0.0255765571618549 & 0.063828 & -0.4007 & 0.689219 & 0.34461 \tabularnewline
Celebrity & 0.782432477255013 & 0.291171 & 2.6872 & 0.008042 & 0.004021 \tabularnewline
sum_celeb & -0.0824572683867579 & 0.117131 & -0.704 & 0.48257 & 0.241285 \tabularnewline
Sum & 0.579627939212355 & 0.887544 & 0.6531 & 0.514738 & 0.257369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116687&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]-0.956521145565202[/C][C]2.451522[/C][C]-0.3902[/C][C]0.696976[/C][C]0.348488[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.122464404572674[/C][C]0.177249[/C][C]0.6909[/C][C]0.490714[/C][C]0.245357[/C][/ROW]
[ROW][C]sum_friends[/C][C]-0.00611986826304754[/C][C]0.066175[/C][C]-0.0925[/C][C]0.926443[/C][C]0.463221[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.129901135405866[/C][C]0.111901[/C][C]1.1609[/C][C]0.247593[/C][C]0.123797[/C][/ROW]
[ROW][C]sum_know[/C][C]0.0388655008006025[/C][C]0.045231[/C][C]0.8593[/C][C]0.391601[/C][C]0.1958[/C][/ROW]
[ROW][C]Liked[/C][C]0.422882703355402[/C][C]0.169634[/C][C]2.4929[/C][C]0.013787[/C][C]0.006893[/C][/ROW]
[ROW][C]sum_liked[/C][C]-0.0255765571618549[/C][C]0.063828[/C][C]-0.4007[/C][C]0.689219[/C][C]0.34461[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.782432477255013[/C][C]0.291171[/C][C]2.6872[/C][C]0.008042[/C][C]0.004021[/C][/ROW]
[ROW][C]sum_celeb[/C][C]-0.0824572683867579[/C][C]0.117131[/C][C]-0.704[/C][C]0.48257[/C][C]0.241285[/C][/ROW]
[ROW][C]Sum[/C][C]0.579627939212355[/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=116687&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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)-0.9565211455652022.451522-0.39020.6969760.348488
FindingFriends0.1224644045726740.1772490.69090.4907140.245357
sum_friends-0.006119868263047540.066175-0.09250.9264430.463221
KnowingPeople0.1299011354058660.1119011.16090.2475930.123797
sum_know0.03886550080060250.0452310.85930.3916010.1958
Liked0.4228827033554020.1696342.49290.0137870.006893
sum_liked-0.02557655716185490.063828-0.40070.6892190.34461
Celebrity0.7824324772550130.2911712.68720.0080420.004021
sum_celeb-0.08245726838675790.117131-0.7040.482570.241285
Sum0.5796279392123550.8875440.65310.5147380.257369






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=116687&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=116687&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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
11311.22754381442041.77245618557957
21211.03423026387180.965769736128203
31513.28765464224841.71234535775158
41210.80898974978381.19101025021617
51010.7713737590258-0.771373759025812
6129.068207063735282.93179293626472
71516.5909272245955-1.59092722459553
8910.0459426712359-1.04594267123586
91212.8313779907896-0.831377990789564
10117.49334633352473.5066536664753
111113.3033553546585-2.30335535465845
121111.9752469677655-0.97524696776552
131512.79499331606972.20500668393034
14711.4918805935504-4.49188059355038
151111.7981279091528-0.798127909152836
161110.38347594364880.616524056351206
171011.9752469677655-1.97524696776552
181414.3737925792916-0.373792579291638
19108.459919297713261.54008070228674
2069.12668671727479-3.12668671727479
21118.71570224270322.28429775729680
221514.31620258170890.683797418291134
231112.1890537027534-1.18905370275341
24129.537952949742552.46204705025745
251412.91557192618531.08442807381466
261514.59773347788810.402266522111863
27914.4282575553238-5.4282575553238
281312.89204166841560.107958331584390
291313.0109285527104-0.0109285527103813
301610.98998650649235.0100134935077
31138.704858715706214.29514128429379
321213.5197917181857-1.51979171818566
331414.5097646766215-0.509764676621488
341110.04814559262670.95185440737331
35910.3684814977708-1.36848149777083
361614.52990261860961.47009738139042
371212.8992561581670-0.899256158166964
38109.478995531705560.521004468294444
391312.85964829977180.140351700228154
401615.45081410485700.549185895142972
411412.94644300785031.05355699214969
42157.984334036199917.01566596380009
4359.58600730887062-4.58600730887062
44810.1581376449133-2.15813764491331
451111.2421258886977-0.242125888697726
461613.72355191203142.27644808796865
471715.09602839222591.90397160777410
4898.172643271323360.827356728676637
49911.3590384594999-2.35903845949989
501314.5003260089276-1.50032600892764
511011.1011837347548-1.10118373475478
52612.4348178203754-6.43481782037539
531212.1066029548912-0.106602954891221
54810.489044264813-2.48904426481301
551411.73386840405112.26613159594890
561213.1770648027584-1.17706480275844
571110.61716921966990.382830780330139
581614.65450923027661.34549076972341
59810.6549086700045-2.65490867000448
601514.94180470419330.0581952958066707
6178.97415066458367-1.97415066458367
621614.30731107542211.69268892457792
631412.84429871588881.15570128411122
641613.42507304605632.57492695394367
65910.0659685416539-1.06596854165392
661412.14472289032991.85527710967014
671113.3807949873978-2.38079498739784
681310.46815748147182.53184251852822
691513.13064518488931.86935481511073
7055.67009245966134-0.670092459661344
711513.17706480275841.82293519724156
721312.67804178118130.321958218818715
731112.3299070471522-1.32990704715223
741114.1104046332066-3.11040463320657
751212.6493121832400-0.649312183240023
761213.4394382834657-1.43943828346566
771212.4158317414769-0.415831741476938
781211.99496313662140.00503686337855314
791410.86077385384473.13922614615530
8067.802159072467-1.80215907246700
8179.76818227260353-2.76818227260353
821412.46865706483751.53134293516252
831413.99438967749270.00561032250726964
841011.1372796911909-1.13727969119086
85139.023126857234873.97687314276513
861212.2931037728192-0.293103772819169
8799.26363082967135-0.263630829671349
881211.92179445414620.078205545853794
891614.83814335879821.16185664120178
901010.4400732807768-0.440073280776769
911413.14430851883860.855691481161362
921013.5346442718065-3.53464427180645
931615.40106742854600.598932571453977
941513.47772632170631.52227367829373
951211.46812922848690.531870771513145
96109.333700438483280.666299561516719
97810.4774162463660-2.47741624636605
9888.65893008827594-0.658930088275938
991112.654915533214-1.654915533214
1001312.45344773223500.546552267765038
1011615.58844563563380.411554364366184
1021614.79512781522981.20487218477023
1031415.4723250021853-1.47232500218526
104119.028180577217141.97181942278286
10547.0878417326335-3.08784173263349
1061414.4196353340098-0.419635334009809
107910.6400561163837-1.64005611638368
1081415.0977608916113-1.09776089161128
109810.1364301921225-2.13643019212252
110810.7159113020490-2.71591130204898
1111111.8739138822720-0.873913882271969
1121213.1501689898151-1.15016898981508
1131111.6144296642982-0.614429664298243
1141413.37265094615950.627349053840509
1151514.27346513774690.726534862253112
1161613.43498887774432.56501112225571
1171612.94662031549473.05337968450529
1181112.8392689128711-1.83926891287107
1191414.2583045582921-0.258304558292107
1201410.83761855576303.16238144423698
1211211.62982402585320.370175974146841
1221412.70061741752521.29938258247478
123810.8907755528467-2.8907755528467
1241314.1199328683343-1.11993286833428
1251614.02194793681381.9780520631862
1261211.12497875499280.875021245007234
1271615.30152108545700.698478914543027
1281212.6536387475452-0.653638747545174
1291111.4526573961540-0.452657396154021
13045.62386162589304-1.62386162589304
1311615.24579149419840.754208505801591
1321512.59584057025912.40415942974090
1331011.5261734935842-1.52617349358419
1341313.3974531743112-0.397453174311151
1351513.13397041984301.86602958015695
1361210.50302169121001.49697830878999
1371413.68148651555200.318513484448036
138710.0808068809741-3.08080688097409
1391913.92096428288895.07903571711112
1401213.0880115314566-1.08801153145658
1411211.56804360834570.431956391654331
1421313.3416025568501-0.341602556850117
1431512.22752076769552.77247923230451
14489.35313962372137-1.35313962372137
1451210.90430853882711.09569146117285
1461011.1754834642238-1.17548346422380
147811.4315364100653-3.43153641006535
1481014.6364792060832-4.63647920608323
1491513.92798415335961.07201584664036
1501614.67456570254841.32543429745160
1511313.1309012423538-0.130901242353844
1521615.01295805116990.987041948830091
15399.76401987267188-0.764019872671875
1541413.07761853577450.92238146422549
1551413.25221102053350.747788979466463
1561210.13840971585631.86159028414366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2275438144204 & 1.77245618557957 \tabularnewline
2 & 12 & 11.0342302638718 & 0.965769736128203 \tabularnewline
3 & 15 & 13.2876546422484 & 1.71234535775158 \tabularnewline
4 & 12 & 10.8089897497838 & 1.19101025021617 \tabularnewline
5 & 10 & 10.7713737590258 & -0.771373759025812 \tabularnewline
6 & 12 & 9.06820706373528 & 2.93179293626472 \tabularnewline
7 & 15 & 16.5909272245955 & -1.59092722459553 \tabularnewline
8 & 9 & 10.0459426712359 & -1.04594267123586 \tabularnewline
9 & 12 & 12.8313779907896 & -0.831377990789564 \tabularnewline
10 & 11 & 7.4933463335247 & 3.5066536664753 \tabularnewline
11 & 11 & 13.3033553546585 & -2.30335535465845 \tabularnewline
12 & 11 & 11.9752469677655 & -0.97524696776552 \tabularnewline
13 & 15 & 12.7949933160697 & 2.20500668393034 \tabularnewline
14 & 7 & 11.4918805935504 & -4.49188059355038 \tabularnewline
15 & 11 & 11.7981279091528 & -0.798127909152836 \tabularnewline
16 & 11 & 10.3834759436488 & 0.616524056351206 \tabularnewline
17 & 10 & 11.9752469677655 & -1.97524696776552 \tabularnewline
18 & 14 & 14.3737925792916 & -0.373792579291638 \tabularnewline
19 & 10 & 8.45991929771326 & 1.54008070228674 \tabularnewline
20 & 6 & 9.12668671727479 & -3.12668671727479 \tabularnewline
21 & 11 & 8.7157022427032 & 2.28429775729680 \tabularnewline
22 & 15 & 14.3162025817089 & 0.683797418291134 \tabularnewline
23 & 11 & 12.1890537027534 & -1.18905370275341 \tabularnewline
24 & 12 & 9.53795294974255 & 2.46204705025745 \tabularnewline
25 & 14 & 12.9155719261853 & 1.08442807381466 \tabularnewline
26 & 15 & 14.5977334778881 & 0.402266522111863 \tabularnewline
27 & 9 & 14.4282575553238 & -5.4282575553238 \tabularnewline
28 & 13 & 12.8920416684156 & 0.107958331584390 \tabularnewline
29 & 13 & 13.0109285527104 & -0.0109285527103813 \tabularnewline
30 & 16 & 10.9899865064923 & 5.0100134935077 \tabularnewline
31 & 13 & 8.70485871570621 & 4.29514128429379 \tabularnewline
32 & 12 & 13.5197917181857 & -1.51979171818566 \tabularnewline
33 & 14 & 14.5097646766215 & -0.509764676621488 \tabularnewline
34 & 11 & 10.0481455926267 & 0.95185440737331 \tabularnewline
35 & 9 & 10.3684814977708 & -1.36848149777083 \tabularnewline
36 & 16 & 14.5299026186096 & 1.47009738139042 \tabularnewline
37 & 12 & 12.8992561581670 & -0.899256158166964 \tabularnewline
38 & 10 & 9.47899553170556 & 0.521004468294444 \tabularnewline
39 & 13 & 12.8596482997718 & 0.140351700228154 \tabularnewline
40 & 16 & 15.4508141048570 & 0.549185895142972 \tabularnewline
41 & 14 & 12.9464430078503 & 1.05355699214969 \tabularnewline
42 & 15 & 7.98433403619991 & 7.01566596380009 \tabularnewline
43 & 5 & 9.58600730887062 & -4.58600730887062 \tabularnewline
44 & 8 & 10.1581376449133 & -2.15813764491331 \tabularnewline
45 & 11 & 11.2421258886977 & -0.242125888697726 \tabularnewline
46 & 16 & 13.7235519120314 & 2.27644808796865 \tabularnewline
47 & 17 & 15.0960283922259 & 1.90397160777410 \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 & -1.10118373475478 \tabularnewline
52 & 6 & 12.4348178203754 & -6.43481782037539 \tabularnewline
53 & 12 & 12.1066029548912 & -0.106602954891221 \tabularnewline
54 & 8 & 10.489044264813 & -2.48904426481301 \tabularnewline
55 & 14 & 11.7338684040511 & 2.26613159594890 \tabularnewline
56 & 12 & 13.1770648027584 & -1.17706480275844 \tabularnewline
57 & 11 & 10.6171692196699 & 0.382830780330139 \tabularnewline
58 & 16 & 14.6545092302766 & 1.34549076972341 \tabularnewline
59 & 8 & 10.6549086700045 & -2.65490867000448 \tabularnewline
60 & 15 & 14.9418047041933 & 0.0581952958066707 \tabularnewline
61 & 7 & 8.97415066458367 & -1.97415066458367 \tabularnewline
62 & 16 & 14.3073110754221 & 1.69268892457792 \tabularnewline
63 & 14 & 12.8442987158888 & 1.15570128411122 \tabularnewline
64 & 16 & 13.4250730460563 & 2.57492695394367 \tabularnewline
65 & 9 & 10.0659685416539 & -1.06596854165392 \tabularnewline
66 & 14 & 12.1447228903299 & 1.85527710967014 \tabularnewline
67 & 11 & 13.3807949873978 & -2.38079498739784 \tabularnewline
68 & 13 & 10.4681574814718 & 2.53184251852822 \tabularnewline
69 & 15 & 13.1306451848893 & 1.86935481511073 \tabularnewline
70 & 5 & 5.67009245966134 & -0.670092459661344 \tabularnewline
71 & 15 & 13.1770648027584 & 1.82293519724156 \tabularnewline
72 & 13 & 12.6780417811813 & 0.321958218818715 \tabularnewline
73 & 11 & 12.3299070471522 & -1.32990704715223 \tabularnewline
74 & 11 & 14.1104046332066 & -3.11040463320657 \tabularnewline
75 & 12 & 12.6493121832400 & -0.649312183240023 \tabularnewline
76 & 12 & 13.4394382834657 & -1.43943828346566 \tabularnewline
77 & 12 & 12.4158317414769 & -0.415831741476938 \tabularnewline
78 & 12 & 11.9949631366214 & 0.00503686337855314 \tabularnewline
79 & 14 & 10.8607738538447 & 3.13922614615530 \tabularnewline
80 & 6 & 7.802159072467 & -1.80215907246700 \tabularnewline
81 & 7 & 9.76818227260353 & -2.76818227260353 \tabularnewline
82 & 14 & 12.4686570648375 & 1.53134293516252 \tabularnewline
83 & 14 & 13.9943896774927 & 0.00561032250726964 \tabularnewline
84 & 10 & 11.1372796911909 & -1.13727969119086 \tabularnewline
85 & 13 & 9.02312685723487 & 3.97687314276513 \tabularnewline
86 & 12 & 12.2931037728192 & -0.293103772819169 \tabularnewline
87 & 9 & 9.26363082967135 & -0.263630829671349 \tabularnewline
88 & 12 & 11.9217944541462 & 0.078205545853794 \tabularnewline
89 & 16 & 14.8381433587982 & 1.16185664120178 \tabularnewline
90 & 10 & 10.4400732807768 & -0.440073280776769 \tabularnewline
91 & 14 & 13.1443085188386 & 0.855691481161362 \tabularnewline
92 & 10 & 13.5346442718065 & -3.53464427180645 \tabularnewline
93 & 16 & 15.4010674285460 & 0.598932571453977 \tabularnewline
94 & 15 & 13.4777263217063 & 1.52227367829373 \tabularnewline
95 & 12 & 11.4681292284869 & 0.531870771513145 \tabularnewline
96 & 10 & 9.33370043848328 & 0.666299561516719 \tabularnewline
97 & 8 & 10.4774162463660 & -2.47741624636605 \tabularnewline
98 & 8 & 8.65893008827594 & -0.658930088275938 \tabularnewline
99 & 11 & 12.654915533214 & -1.654915533214 \tabularnewline
100 & 13 & 12.4534477322350 & 0.546552267765038 \tabularnewline
101 & 16 & 15.5884456356338 & 0.411554364366184 \tabularnewline
102 & 16 & 14.7951278152298 & 1.20487218477023 \tabularnewline
103 & 14 & 15.4723250021853 & -1.47232500218526 \tabularnewline
104 & 11 & 9.02818057721714 & 1.97181942278286 \tabularnewline
105 & 4 & 7.0878417326335 & -3.08784173263349 \tabularnewline
106 & 14 & 14.4196353340098 & -0.419635334009809 \tabularnewline
107 & 9 & 10.6400561163837 & -1.64005611638368 \tabularnewline
108 & 14 & 15.0977608916113 & -1.09776089161128 \tabularnewline
109 & 8 & 10.1364301921225 & -2.13643019212252 \tabularnewline
110 & 8 & 10.7159113020490 & -2.71591130204898 \tabularnewline
111 & 11 & 11.8739138822720 & -0.873913882271969 \tabularnewline
112 & 12 & 13.1501689898151 & -1.15016898981508 \tabularnewline
113 & 11 & 11.6144296642982 & -0.614429664298243 \tabularnewline
114 & 14 & 13.3726509461595 & 0.627349053840509 \tabularnewline
115 & 15 & 14.2734651377469 & 0.726534862253112 \tabularnewline
116 & 16 & 13.4349888777443 & 2.56501112225571 \tabularnewline
117 & 16 & 12.9466203154947 & 3.05337968450529 \tabularnewline
118 & 11 & 12.8392689128711 & -1.83926891287107 \tabularnewline
119 & 14 & 14.2583045582921 & -0.258304558292107 \tabularnewline
120 & 14 & 10.8376185557630 & 3.16238144423698 \tabularnewline
121 & 12 & 11.6298240258532 & 0.370175974146841 \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 & 0.875021245007234 \tabularnewline
127 & 16 & 15.3015210854570 & 0.698478914543027 \tabularnewline
128 & 12 & 12.6536387475452 & -0.653638747545174 \tabularnewline
129 & 11 & 11.4526573961540 & -0.452657396154021 \tabularnewline
130 & 4 & 5.62386162589304 & -1.62386162589304 \tabularnewline
131 & 16 & 15.2457914941984 & 0.754208505801591 \tabularnewline
132 & 15 & 12.5958405702591 & 2.40415942974090 \tabularnewline
133 & 10 & 11.5261734935842 & -1.52617349358419 \tabularnewline
134 & 13 & 13.3974531743112 & -0.397453174311151 \tabularnewline
135 & 15 & 13.1339704198430 & 1.86602958015695 \tabularnewline
136 & 12 & 10.5030216912100 & 1.49697830878999 \tabularnewline
137 & 14 & 13.6814865155520 & 0.318513484448036 \tabularnewline
138 & 7 & 10.0808068809741 & -3.08080688097409 \tabularnewline
139 & 19 & 13.9209642828889 & 5.07903571711112 \tabularnewline
140 & 12 & 13.0880115314566 & -1.08801153145658 \tabularnewline
141 & 12 & 11.5680436083457 & 0.431956391654331 \tabularnewline
142 & 13 & 13.3416025568501 & -0.341602556850117 \tabularnewline
143 & 15 & 12.2275207676955 & 2.77247923230451 \tabularnewline
144 & 8 & 9.35313962372137 & -1.35313962372137 \tabularnewline
145 & 12 & 10.9043085388271 & 1.09569146117285 \tabularnewline
146 & 10 & 11.1754834642238 & -1.17548346422380 \tabularnewline
147 & 8 & 11.4315364100653 & -3.43153641006535 \tabularnewline
148 & 10 & 14.6364792060832 & -4.63647920608323 \tabularnewline
149 & 15 & 13.9279841533596 & 1.07201584664036 \tabularnewline
150 & 16 & 14.6745657025484 & 1.32543429745160 \tabularnewline
151 & 13 & 13.1309012423538 & -0.130901242353844 \tabularnewline
152 & 16 & 15.0129580511699 & 0.987041948830091 \tabularnewline
153 & 9 & 9.76401987267188 & -0.764019872671875 \tabularnewline
154 & 14 & 13.0776185357745 & 0.92238146422549 \tabularnewline
155 & 14 & 13.2522110205335 & 0.747788979466463 \tabularnewline
156 & 12 & 10.1384097158563 & 1.86159028414366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116687&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]11.2275438144204[/C][C]1.77245618557957[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0342302638718[/C][C]0.965769736128203[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.2876546422484[/C][C]1.71234535775158[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8089897497838[/C][C]1.19101025021617[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.7713737590258[/C][C]-0.771373759025812[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.06820706373528[/C][C]2.93179293626472[/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]-0.831377990789564[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.4933463335247[/C][C]3.5066536664753[/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]-0.97524696776552[/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]-0.798127909152836[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.3834759436488[/C][C]0.616524056351206[/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]9.12668671727479[/C][C]-3.12668671727479[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.7157022427032[/C][C]2.28429775729680[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.3162025817089[/C][C]0.683797418291134[/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]1.08442807381466[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5977334778881[/C][C]0.402266522111863[/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]0.107958331584390[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.0109285527104[/C][C]-0.0109285527103813[/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]8.70485871570621[/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]0.95185440737331[/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]1.47009738139042[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.8992561581670[/C][C]-0.899256158166964[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.47899553170556[/C][C]0.521004468294444[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8596482997718[/C][C]0.140351700228154[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.4508141048570[/C][C]0.549185895142972[/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]7.98433403619991[/C][C]7.01566596380009[/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]-0.242125888697726[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.7235519120314[/C][C]2.27644808796865[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]15.0960283922259[/C][C]1.90397160777410[/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]-1.10118373475478[/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]-0.106602954891221[/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]2.26613159594890[/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]0.382830780330139[/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]-2.65490867000448[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.9418047041933[/C][C]0.0581952958066707[/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]1.69268892457792[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.8442987158888[/C][C]1.15570128411122[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.4250730460563[/C][C]2.57492695394367[/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]1.85527710967014[/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]2.53184251852822[/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]-0.670092459661344[/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]0.321958218818715[/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]-3.11040463320657[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.6493121832400[/C][C]-0.649312183240023[/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]-0.415831741476938[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9949631366214[/C][C]0.00503686337855314[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.8607738538447[/C][C]3.13922614615530[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.802159072467[/C][C]-1.80215907246700[/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]1.53134293516252[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.9943896774927[/C][C]0.00561032250726964[/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]9.02312685723487[/C][C]3.97687314276513[/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]-0.263630829671349[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9217944541462[/C][C]0.078205545853794[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8381433587982[/C][C]1.16185664120178[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4400732807768[/C][C]-0.440073280776769[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1443085188386[/C][C]0.855691481161362[/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]15.4010674285460[/C][C]0.598932571453977[/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]0.531870771513145[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.33370043848328[/C][C]0.666299561516719[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.4774162463660[/C][C]-2.47741624636605[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.65893008827594[/C][C]-0.658930088275938[/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]12.4534477322350[/C][C]0.546552267765038[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5884456356338[/C][C]0.411554364366184[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7951278152298[/C][C]1.20487218477023[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.4723250021853[/C][C]-1.47232500218526[/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]7.0878417326335[/C][C]-3.08784173263349[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.4196353340098[/C][C]-0.419635334009809[/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]10.7159113020490[/C][C]-2.71591130204898[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.8739138822720[/C][C]-0.873913882271969[/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]-0.614429664298243[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.3726509461595[/C][C]0.627349053840509[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.2734651377469[/C][C]0.726534862253112[/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]-1.83926891287107[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.2583045582921[/C][C]-0.258304558292107[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8376185557630[/C][C]3.16238144423698[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.6298240258532[/C][C]0.370175974146841[/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]0.875021245007234[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3015210854570[/C][C]0.698478914543027[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.6536387475452[/C][C]-0.653638747545174[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4526573961540[/C][C]-0.452657396154021[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.62386162589304[/C][C]-1.62386162589304[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.2457914941984[/C][C]0.754208505801591[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5958405702591[/C][C]2.40415942974090[/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]13.3974531743112[/C][C]-0.397453174311151[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.1339704198430[/C][C]1.86602958015695[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.5030216912100[/C][C]1.49697830878999[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6814865155520[/C][C]0.318513484448036[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.0808068809741[/C][C]-3.08080688097409[/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]0.431956391654331[/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]9.35313962372137[/C][C]-1.35313962372137[/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]-1.17548346422380[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4315364100653[/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]1.32543429745160[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1309012423538[/C][C]-0.130901242353844[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0129580511699[/C][C]0.987041948830091[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.76401987267188[/C][C]-0.764019872671875[/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]0.747788979466463[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.1384097158563[/C][C]1.86159028414366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116687&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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
11311.22754381442041.77245618557957
21211.03423026387180.965769736128203
31513.28765464224841.71234535775158
41210.80898974978381.19101025021617
51010.7713737590258-0.771373759025812
6129.068207063735282.93179293626472
71516.5909272245955-1.59092722459553
8910.0459426712359-1.04594267123586
91212.8313779907896-0.831377990789564
10117.49334633352473.5066536664753
111113.3033553546585-2.30335535465845
121111.9752469677655-0.97524696776552
131512.79499331606972.20500668393034
14711.4918805935504-4.49188059355038
151111.7981279091528-0.798127909152836
161110.38347594364880.616524056351206
171011.9752469677655-1.97524696776552
181414.3737925792916-0.373792579291638
19108.459919297713261.54008070228674
2069.12668671727479-3.12668671727479
21118.71570224270322.28429775729680
221514.31620258170890.683797418291134
231112.1890537027534-1.18905370275341
24129.537952949742552.46204705025745
251412.91557192618531.08442807381466
261514.59773347788810.402266522111863
27914.4282575553238-5.4282575553238
281312.89204166841560.107958331584390
291313.0109285527104-0.0109285527103813
301610.98998650649235.0100134935077
31138.704858715706214.29514128429379
321213.5197917181857-1.51979171818566
331414.5097646766215-0.509764676621488
341110.04814559262670.95185440737331
35910.3684814977708-1.36848149777083
361614.52990261860961.47009738139042
371212.8992561581670-0.899256158166964
38109.478995531705560.521004468294444
391312.85964829977180.140351700228154
401615.45081410485700.549185895142972
411412.94644300785031.05355699214969
42157.984334036199917.01566596380009
4359.58600730887062-4.58600730887062
44810.1581376449133-2.15813764491331
451111.2421258886977-0.242125888697726
461613.72355191203142.27644808796865
471715.09602839222591.90397160777410
4898.172643271323360.827356728676637
49911.3590384594999-2.35903845949989
501314.5003260089276-1.50032600892764
511011.1011837347548-1.10118373475478
52612.4348178203754-6.43481782037539
531212.1066029548912-0.106602954891221
54810.489044264813-2.48904426481301
551411.73386840405112.26613159594890
561213.1770648027584-1.17706480275844
571110.61716921966990.382830780330139
581614.65450923027661.34549076972341
59810.6549086700045-2.65490867000448
601514.94180470419330.0581952958066707
6178.97415066458367-1.97415066458367
621614.30731107542211.69268892457792
631412.84429871588881.15570128411122
641613.42507304605632.57492695394367
65910.0659685416539-1.06596854165392
661412.14472289032991.85527710967014
671113.3807949873978-2.38079498739784
681310.46815748147182.53184251852822
691513.13064518488931.86935481511073
7055.67009245966134-0.670092459661344
711513.17706480275841.82293519724156
721312.67804178118130.321958218818715
731112.3299070471522-1.32990704715223
741114.1104046332066-3.11040463320657
751212.6493121832400-0.649312183240023
761213.4394382834657-1.43943828346566
771212.4158317414769-0.415831741476938
781211.99496313662140.00503686337855314
791410.86077385384473.13922614615530
8067.802159072467-1.80215907246700
8179.76818227260353-2.76818227260353
821412.46865706483751.53134293516252
831413.99438967749270.00561032250726964
841011.1372796911909-1.13727969119086
85139.023126857234873.97687314276513
861212.2931037728192-0.293103772819169
8799.26363082967135-0.263630829671349
881211.92179445414620.078205545853794
891614.83814335879821.16185664120178
901010.4400732807768-0.440073280776769
911413.14430851883860.855691481161362
921013.5346442718065-3.53464427180645
931615.40106742854600.598932571453977
941513.47772632170631.52227367829373
951211.46812922848690.531870771513145
96109.333700438483280.666299561516719
97810.4774162463660-2.47741624636605
9888.65893008827594-0.658930088275938
991112.654915533214-1.654915533214
1001312.45344773223500.546552267765038
1011615.58844563563380.411554364366184
1021614.79512781522981.20487218477023
1031415.4723250021853-1.47232500218526
104119.028180577217141.97181942278286
10547.0878417326335-3.08784173263349
1061414.4196353340098-0.419635334009809
107910.6400561163837-1.64005611638368
1081415.0977608916113-1.09776089161128
109810.1364301921225-2.13643019212252
110810.7159113020490-2.71591130204898
1111111.8739138822720-0.873913882271969
1121213.1501689898151-1.15016898981508
1131111.6144296642982-0.614429664298243
1141413.37265094615950.627349053840509
1151514.27346513774690.726534862253112
1161613.43498887774432.56501112225571
1171612.94662031549473.05337968450529
1181112.8392689128711-1.83926891287107
1191414.2583045582921-0.258304558292107
1201410.83761855576303.16238144423698
1211211.62982402585320.370175974146841
1221412.70061741752521.29938258247478
123810.8907755528467-2.8907755528467
1241314.1199328683343-1.11993286833428
1251614.02194793681381.9780520631862
1261211.12497875499280.875021245007234
1271615.30152108545700.698478914543027
1281212.6536387475452-0.653638747545174
1291111.4526573961540-0.452657396154021
13045.62386162589304-1.62386162589304
1311615.24579149419840.754208505801591
1321512.59584057025912.40415942974090
1331011.5261734935842-1.52617349358419
1341313.3974531743112-0.397453174311151
1351513.13397041984301.86602958015695
1361210.50302169121001.49697830878999
1371413.68148651555200.318513484448036
138710.0808068809741-3.08080688097409
1391913.92096428288895.07903571711112
1401213.0880115314566-1.08801153145658
1411211.56804360834570.431956391654331
1421313.3416025568501-0.341602556850117
1431512.22752076769552.77247923230451
14489.35313962372137-1.35313962372137
1451210.90430853882711.09569146117285
1461011.1754834642238-1.17548346422380
147811.4315364100653-3.43153641006535
1481014.6364792060832-4.63647920608323
1491513.92798415335961.07201584664036
1501614.67456570254841.32543429745160
1511313.1309012423538-0.130901242353844
1521615.01295805116990.987041948830091
15399.76401987267188-0.764019872671875
1541413.07761853577450.92238146422549
1551413.25221102053350.747788979466463
1561210.13840971585631.86159028414366






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.1857188285965610.3714376571931220.814281171403439
140.7760120014895160.4479759970209690.223987998510484
150.6607512066527450.678497586694510.339248793347255
160.6146978123321990.7706043753356030.385302187667801
170.5122675172013890.9754649655972230.487732482798611
180.4162445663921510.8324891327843020.583755433607849
190.3469405663617110.6938811327234220.653059433638289
200.6806131657987030.6387736684025940.319386834201297
210.6108025123210980.7783949753578040.389197487678902
220.5579587582997720.8840824834004560.442041241700228
230.5300091370696620.9399817258606750.469990862930338
240.5241010233676530.9517979532646930.475898976632347
250.5849372562665620.8301254874668770.415062743733438
260.5224489880027310.9551020239945380.477551011997269
270.7371402686081540.5257194627836910.262859731391846
280.6838835486941810.6322329026116380.316116451305819
290.628380757419480.7432384851610410.371619242580520
300.8194045550826960.3611908898346070.180595444917304
310.8840705675466120.2318588649067760.115929432453388
320.8552166371249420.2895667257501170.144783362875058
330.81740435246080.3651912950784010.182595647539201
340.7842950994893870.4314098010212260.215704900510613
350.7693022783549460.4613954432901070.230697721645054
360.768574374712260.4628512505754790.231425625287740
370.7341112505739550.531777498852090.265888749426045
380.6866217059458650.6267565881082690.313378294054135
390.6443349871585070.7113300256829860.355665012841493
400.5954738914237010.8090522171525970.404526108576299
410.5601628132880130.8796743734239730.439837186711987
420.817026086140240.3659478277195190.182973913859760
430.9611000445245630.07779991095087490.0388999554754375
440.963431247017200.07313750596559780.0365687529827989
450.951966749862720.09606650027456070.0480332501372804
460.9574978528868640.08500429422627140.0425021471131357
470.9499752555751030.1000494888497940.050024744424897
480.944551739905480.1108965201890410.0554482600945207
490.9428904563331120.1142190873337760.057109543666888
500.933376903455980.1332461930880390.0666230965440194
510.9290429230589190.1419141538821620.070957076941081
520.9919177029708740.01616459405825240.0080822970291262
530.9888733299138770.02225334017224690.0111266700861234
540.9923466949673780.01530661006524410.00765330503262204
550.9947338689136360.0105322621727290.0052661310863645
560.993273511690110.01345297661977900.00672648830988949
570.9907204861819070.01855902763618500.00927951381809248
580.9887276970818750.022544605836250.011272302918125
590.9922382604035220.01552347919295570.00776173959647783
600.9913993778089660.01720124438206730.00860062219103365
610.9913591353364040.01728172932719220.0086408646635961
620.99081393128150.01837213743699920.00918606871849959
630.9892560564113040.02148788717739230.0107439435886962
640.9913372657873150.01732546842536910.00866273421268455
650.9892090224489770.02158195510204580.0107909775510229
660.988401552160470.02319689567905970.0115984478395299
670.9888582113465090.02228357730698210.0111417886534910
680.9900785657008640.01984286859827220.0099214342991361
690.9896728274045840.02065434519083130.0103271725954156
700.9866114841021730.02677703179565450.0133885158978273
710.9862372399554730.02752552008905350.0137627600445268
720.9816028104803180.03679437903936410.0183971895196820
730.9781144551958210.04377108960835720.0218855448041786
740.9876114960706630.02477700785867410.0123885039293370
750.9839452139048820.03210957219023570.0160547860951178
760.9811482519723440.03770349605531190.0188517480276559
770.9748979562271220.0502040875457550.0251020437728775
780.9669834339911520.06603313201769570.0330165660088478
790.9778840268452330.04423194630953450.0221159731547673
800.9758805335952920.04823893280941670.0241194664047083
810.9812934879876660.03741302402466820.0187065120123341
820.981948902557110.03610219488578080.0180510974428904
830.9762812733953850.047437453209230.023718726604615
840.9714319579320840.05713608413583120.0285680420679156
850.9918752214871540.01624955702569200.00812477851284599
860.9888869612938480.02222607741230350.0111130387061517
870.9849343657535680.03013126849286440.0150656342464322
880.9798891612749920.04022167745001520.0201108387250076
890.9745932030637460.05081359387250740.0254067969362537
900.9668617072900540.06627658541989180.0331382927099459
910.9593457546060740.08130849078785140.0406542453939257
920.9808522651709150.03829546965817070.0191477348290854
930.9747493995132170.05050120097356510.0252506004867826
940.9691450396364850.06170992072702980.0308549603635149
950.9600236245646880.07995275087062350.0399763754353118
960.9480110014039370.1039779971921260.0519889985960632
970.9511565755469180.09768684890616450.0488434244530823
980.937678192372410.1246436152551830.0623218076275913
990.9349795788342140.1300408423315730.0650204211657863
1000.9192020670838820.1615958658322360.080797932916118
1010.9054332731017150.1891334537965710.0945667268982853
1020.8858673521150160.2282652957699670.114132647884984
1030.9112457164125420.1775085671749150.0887542835874575
1040.941138907021940.1177221859561220.058861092978061
1050.9418202883484240.1163594233031520.0581797116515760
1060.924314930224090.1513701395518180.0756850697759092
1070.9182159677559640.1635680644880720.0817840322440358
1080.9250448879845050.1499102240309900.0749551120154952
1090.9165666257495960.1668667485008080.0834333742504041
1100.9052093887736410.1895812224527170.0947906112263585
1110.8845545052820010.2308909894359970.115445494717999
1120.8830947170461940.2338105659076130.116905282953806
1130.8528017073331060.2943965853337880.147198292666894
1140.8202025407975020.3595949184049970.179797459202498
1150.7803039923531680.4393920152936640.219696007646832
1160.7739979863640780.4520040272718430.226002013635922
1170.7838540939065390.4322918121869220.216145906093461
1180.7663660445027060.4672679109945880.233633955497294
1190.717065646059350.5658687078813010.282934353940651
1200.7904971266629210.4190057466741570.209502873337079
1210.7565319982311440.4869360035377120.243468001768856
1220.7152155086409220.5695689827181570.284784491359078
1230.7137470136486390.5725059727027230.286252986351361
1240.6588711744761720.6822576510476560.341128825523828
1250.6584475076237910.6831049847524170.341552492376209
1260.6380857816234840.7238284367530330.361914218376517
1270.573311410424490.853377179151020.42668858957551
1280.6079598199070750.784080360185850.392040180092925
1290.6017893647925520.7964212704148960.398210635207448
1300.5734541189458770.8530917621082460.426545881054123
1310.4969080032710290.9938160065420580.503091996728971
1320.4759302549179330.9518605098358650.524069745082067
1330.4725165606904250.945033121380850.527483439309575
1340.397424929262940.794849858525880.60257507073706
1350.3589823513791260.7179647027582520.641017648620874
1360.3602484434013890.7204968868027780.639751556598611
1370.2762863127323830.5525726254647660.723713687267617
1380.3388029722779890.6776059445559770.661197027722011
1390.6299649576913650.740070084617270.370035042308635
1400.7911030588471740.4177938823056520.208896941152826
1410.723701383627540.552597232744920.27629861637246
1420.6841626069634020.6316747860731970.315837393036598
1430.731337446369690.5373251072606210.268662553630311

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.185718828596561 & 0.371437657193122 & 0.814281171403439 \tabularnewline
14 & 0.776012001489516 & 0.447975997020969 & 0.223987998510484 \tabularnewline
15 & 0.660751206652745 & 0.67849758669451 & 0.339248793347255 \tabularnewline
16 & 0.614697812332199 & 0.770604375335603 & 0.385302187667801 \tabularnewline
17 & 0.512267517201389 & 0.975464965597223 & 0.487732482798611 \tabularnewline
18 & 0.416244566392151 & 0.832489132784302 & 0.583755433607849 \tabularnewline
19 & 0.346940566361711 & 0.693881132723422 & 0.653059433638289 \tabularnewline
20 & 0.680613165798703 & 0.638773668402594 & 0.319386834201297 \tabularnewline
21 & 0.610802512321098 & 0.778394975357804 & 0.389197487678902 \tabularnewline
22 & 0.557958758299772 & 0.884082483400456 & 0.442041241700228 \tabularnewline
23 & 0.530009137069662 & 0.939981725860675 & 0.469990862930338 \tabularnewline
24 & 0.524101023367653 & 0.951797953264693 & 0.475898976632347 \tabularnewline
25 & 0.584937256266562 & 0.830125487466877 & 0.415062743733438 \tabularnewline
26 & 0.522448988002731 & 0.955102023994538 & 0.477551011997269 \tabularnewline
27 & 0.737140268608154 & 0.525719462783691 & 0.262859731391846 \tabularnewline
28 & 0.683883548694181 & 0.632232902611638 & 0.316116451305819 \tabularnewline
29 & 0.62838075741948 & 0.743238485161041 & 0.371619242580520 \tabularnewline
30 & 0.819404555082696 & 0.361190889834607 & 0.180595444917304 \tabularnewline
31 & 0.884070567546612 & 0.231858864906776 & 0.115929432453388 \tabularnewline
32 & 0.855216637124942 & 0.289566725750117 & 0.144783362875058 \tabularnewline
33 & 0.8174043524608 & 0.365191295078401 & 0.182595647539201 \tabularnewline
34 & 0.784295099489387 & 0.431409801021226 & 0.215704900510613 \tabularnewline
35 & 0.769302278354946 & 0.461395443290107 & 0.230697721645054 \tabularnewline
36 & 0.76857437471226 & 0.462851250575479 & 0.231425625287740 \tabularnewline
37 & 0.734111250573955 & 0.53177749885209 & 0.265888749426045 \tabularnewline
38 & 0.686621705945865 & 0.626756588108269 & 0.313378294054135 \tabularnewline
39 & 0.644334987158507 & 0.711330025682986 & 0.355665012841493 \tabularnewline
40 & 0.595473891423701 & 0.809052217152597 & 0.404526108576299 \tabularnewline
41 & 0.560162813288013 & 0.879674373423973 & 0.439837186711987 \tabularnewline
42 & 0.81702608614024 & 0.365947827719519 & 0.182973913859760 \tabularnewline
43 & 0.961100044524563 & 0.0777999109508749 & 0.0388999554754375 \tabularnewline
44 & 0.96343124701720 & 0.0731375059655978 & 0.0365687529827989 \tabularnewline
45 & 0.95196674986272 & 0.0960665002745607 & 0.0480332501372804 \tabularnewline
46 & 0.957497852886864 & 0.0850042942262714 & 0.0425021471131357 \tabularnewline
47 & 0.949975255575103 & 0.100049488849794 & 0.050024744424897 \tabularnewline
48 & 0.94455173990548 & 0.110896520189041 & 0.0554482600945207 \tabularnewline
49 & 0.942890456333112 & 0.114219087333776 & 0.057109543666888 \tabularnewline
50 & 0.93337690345598 & 0.133246193088039 & 0.0666230965440194 \tabularnewline
51 & 0.929042923058919 & 0.141914153882162 & 0.070957076941081 \tabularnewline
52 & 0.991917702970874 & 0.0161645940582524 & 0.0080822970291262 \tabularnewline
53 & 0.988873329913877 & 0.0222533401722469 & 0.0111266700861234 \tabularnewline
54 & 0.992346694967378 & 0.0153066100652441 & 0.00765330503262204 \tabularnewline
55 & 0.994733868913636 & 0.010532262172729 & 0.0052661310863645 \tabularnewline
56 & 0.99327351169011 & 0.0134529766197790 & 0.00672648830988949 \tabularnewline
57 & 0.990720486181907 & 0.0185590276361850 & 0.00927951381809248 \tabularnewline
58 & 0.988727697081875 & 0.02254460583625 & 0.011272302918125 \tabularnewline
59 & 0.992238260403522 & 0.0155234791929557 & 0.00776173959647783 \tabularnewline
60 & 0.991399377808966 & 0.0172012443820673 & 0.00860062219103365 \tabularnewline
61 & 0.991359135336404 & 0.0172817293271922 & 0.0086408646635961 \tabularnewline
62 & 0.9908139312815 & 0.0183721374369992 & 0.00918606871849959 \tabularnewline
63 & 0.989256056411304 & 0.0214878871773923 & 0.0107439435886962 \tabularnewline
64 & 0.991337265787315 & 0.0173254684253691 & 0.00866273421268455 \tabularnewline
65 & 0.989209022448977 & 0.0215819551020458 & 0.0107909775510229 \tabularnewline
66 & 0.98840155216047 & 0.0231968956790597 & 0.0115984478395299 \tabularnewline
67 & 0.988858211346509 & 0.0222835773069821 & 0.0111417886534910 \tabularnewline
68 & 0.990078565700864 & 0.0198428685982722 & 0.0099214342991361 \tabularnewline
69 & 0.989672827404584 & 0.0206543451908313 & 0.0103271725954156 \tabularnewline
70 & 0.986611484102173 & 0.0267770317956545 & 0.0133885158978273 \tabularnewline
71 & 0.986237239955473 & 0.0275255200890535 & 0.0137627600445268 \tabularnewline
72 & 0.981602810480318 & 0.0367943790393641 & 0.0183971895196820 \tabularnewline
73 & 0.978114455195821 & 0.0437710896083572 & 0.0218855448041786 \tabularnewline
74 & 0.987611496070663 & 0.0247770078586741 & 0.0123885039293370 \tabularnewline
75 & 0.983945213904882 & 0.0321095721902357 & 0.0160547860951178 \tabularnewline
76 & 0.981148251972344 & 0.0377034960553119 & 0.0188517480276559 \tabularnewline
77 & 0.974897956227122 & 0.050204087545755 & 0.0251020437728775 \tabularnewline
78 & 0.966983433991152 & 0.0660331320176957 & 0.0330165660088478 \tabularnewline
79 & 0.977884026845233 & 0.0442319463095345 & 0.0221159731547673 \tabularnewline
80 & 0.975880533595292 & 0.0482389328094167 & 0.0241194664047083 \tabularnewline
81 & 0.981293487987666 & 0.0374130240246682 & 0.0187065120123341 \tabularnewline
82 & 0.98194890255711 & 0.0361021948857808 & 0.0180510974428904 \tabularnewline
83 & 0.976281273395385 & 0.04743745320923 & 0.023718726604615 \tabularnewline
84 & 0.971431957932084 & 0.0571360841358312 & 0.0285680420679156 \tabularnewline
85 & 0.991875221487154 & 0.0162495570256920 & 0.00812477851284599 \tabularnewline
86 & 0.988886961293848 & 0.0222260774123035 & 0.0111130387061517 \tabularnewline
87 & 0.984934365753568 & 0.0301312684928644 & 0.0150656342464322 \tabularnewline
88 & 0.979889161274992 & 0.0402216774500152 & 0.0201108387250076 \tabularnewline
89 & 0.974593203063746 & 0.0508135938725074 & 0.0254067969362537 \tabularnewline
90 & 0.966861707290054 & 0.0662765854198918 & 0.0331382927099459 \tabularnewline
91 & 0.959345754606074 & 0.0813084907878514 & 0.0406542453939257 \tabularnewline
92 & 0.980852265170915 & 0.0382954696581707 & 0.0191477348290854 \tabularnewline
93 & 0.974749399513217 & 0.0505012009735651 & 0.0252506004867826 \tabularnewline
94 & 0.969145039636485 & 0.0617099207270298 & 0.0308549603635149 \tabularnewline
95 & 0.960023624564688 & 0.0799527508706235 & 0.0399763754353118 \tabularnewline
96 & 0.948011001403937 & 0.103977997192126 & 0.0519889985960632 \tabularnewline
97 & 0.951156575546918 & 0.0976868489061645 & 0.0488434244530823 \tabularnewline
98 & 0.93767819237241 & 0.124643615255183 & 0.0623218076275913 \tabularnewline
99 & 0.934979578834214 & 0.130040842331573 & 0.0650204211657863 \tabularnewline
100 & 0.919202067083882 & 0.161595865832236 & 0.080797932916118 \tabularnewline
101 & 0.905433273101715 & 0.189133453796571 & 0.0945667268982853 \tabularnewline
102 & 0.885867352115016 & 0.228265295769967 & 0.114132647884984 \tabularnewline
103 & 0.911245716412542 & 0.177508567174915 & 0.0887542835874575 \tabularnewline
104 & 0.94113890702194 & 0.117722185956122 & 0.058861092978061 \tabularnewline
105 & 0.941820288348424 & 0.116359423303152 & 0.0581797116515760 \tabularnewline
106 & 0.92431493022409 & 0.151370139551818 & 0.0756850697759092 \tabularnewline
107 & 0.918215967755964 & 0.163568064488072 & 0.0817840322440358 \tabularnewline
108 & 0.925044887984505 & 0.149910224030990 & 0.0749551120154952 \tabularnewline
109 & 0.916566625749596 & 0.166866748500808 & 0.0834333742504041 \tabularnewline
110 & 0.905209388773641 & 0.189581222452717 & 0.0947906112263585 \tabularnewline
111 & 0.884554505282001 & 0.230890989435997 & 0.115445494717999 \tabularnewline
112 & 0.883094717046194 & 0.233810565907613 & 0.116905282953806 \tabularnewline
113 & 0.852801707333106 & 0.294396585333788 & 0.147198292666894 \tabularnewline
114 & 0.820202540797502 & 0.359594918404997 & 0.179797459202498 \tabularnewline
115 & 0.780303992353168 & 0.439392015293664 & 0.219696007646832 \tabularnewline
116 & 0.773997986364078 & 0.452004027271843 & 0.226002013635922 \tabularnewline
117 & 0.783854093906539 & 0.432291812186922 & 0.216145906093461 \tabularnewline
118 & 0.766366044502706 & 0.467267910994588 & 0.233633955497294 \tabularnewline
119 & 0.71706564605935 & 0.565868707881301 & 0.282934353940651 \tabularnewline
120 & 0.790497126662921 & 0.419005746674157 & 0.209502873337079 \tabularnewline
121 & 0.756531998231144 & 0.486936003537712 & 0.243468001768856 \tabularnewline
122 & 0.715215508640922 & 0.569568982718157 & 0.284784491359078 \tabularnewline
123 & 0.713747013648639 & 0.572505972702723 & 0.286252986351361 \tabularnewline
124 & 0.658871174476172 & 0.682257651047656 & 0.341128825523828 \tabularnewline
125 & 0.658447507623791 & 0.683104984752417 & 0.341552492376209 \tabularnewline
126 & 0.638085781623484 & 0.723828436753033 & 0.361914218376517 \tabularnewline
127 & 0.57331141042449 & 0.85337717915102 & 0.42668858957551 \tabularnewline
128 & 0.607959819907075 & 0.78408036018585 & 0.392040180092925 \tabularnewline
129 & 0.601789364792552 & 0.796421270414896 & 0.398210635207448 \tabularnewline
130 & 0.573454118945877 & 0.853091762108246 & 0.426545881054123 \tabularnewline
131 & 0.496908003271029 & 0.993816006542058 & 0.503091996728971 \tabularnewline
132 & 0.475930254917933 & 0.951860509835865 & 0.524069745082067 \tabularnewline
133 & 0.472516560690425 & 0.94503312138085 & 0.527483439309575 \tabularnewline
134 & 0.39742492926294 & 0.79484985852588 & 0.60257507073706 \tabularnewline
135 & 0.358982351379126 & 0.717964702758252 & 0.641017648620874 \tabularnewline
136 & 0.360248443401389 & 0.720496886802778 & 0.639751556598611 \tabularnewline
137 & 0.276286312732383 & 0.552572625464766 & 0.723713687267617 \tabularnewline
138 & 0.338802972277989 & 0.677605944555977 & 0.661197027722011 \tabularnewline
139 & 0.629964957691365 & 0.74007008461727 & 0.370035042308635 \tabularnewline
140 & 0.791103058847174 & 0.417793882305652 & 0.208896941152826 \tabularnewline
141 & 0.72370138362754 & 0.55259723274492 & 0.27629861637246 \tabularnewline
142 & 0.684162606963402 & 0.631674786073197 & 0.315837393036598 \tabularnewline
143 & 0.73133744636969 & 0.537325107260621 & 0.268662553630311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116687&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]0.185718828596561[/C][C]0.371437657193122[/C][C]0.814281171403439[/C][/ROW]
[ROW][C]14[/C][C]0.776012001489516[/C][C]0.447975997020969[/C][C]0.223987998510484[/C][/ROW]
[ROW][C]15[/C][C]0.660751206652745[/C][C]0.67849758669451[/C][C]0.339248793347255[/C][/ROW]
[ROW][C]16[/C][C]0.614697812332199[/C][C]0.770604375335603[/C][C]0.385302187667801[/C][/ROW]
[ROW][C]17[/C][C]0.512267517201389[/C][C]0.975464965597223[/C][C]0.487732482798611[/C][/ROW]
[ROW][C]18[/C][C]0.416244566392151[/C][C]0.832489132784302[/C][C]0.583755433607849[/C][/ROW]
[ROW][C]19[/C][C]0.346940566361711[/C][C]0.693881132723422[/C][C]0.653059433638289[/C][/ROW]
[ROW][C]20[/C][C]0.680613165798703[/C][C]0.638773668402594[/C][C]0.319386834201297[/C][/ROW]
[ROW][C]21[/C][C]0.610802512321098[/C][C]0.778394975357804[/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]0.530009137069662[/C][C]0.939981725860675[/C][C]0.469990862930338[/C][/ROW]
[ROW][C]24[/C][C]0.524101023367653[/C][C]0.951797953264693[/C][C]0.475898976632347[/C][/ROW]
[ROW][C]25[/C][C]0.584937256266562[/C][C]0.830125487466877[/C][C]0.415062743733438[/C][/ROW]
[ROW][C]26[/C][C]0.522448988002731[/C][C]0.955102023994538[/C][C]0.477551011997269[/C][/ROW]
[ROW][C]27[/C][C]0.737140268608154[/C][C]0.525719462783691[/C][C]0.262859731391846[/C][/ROW]
[ROW][C]28[/C][C]0.683883548694181[/C][C]0.632232902611638[/C][C]0.316116451305819[/C][/ROW]
[ROW][C]29[/C][C]0.62838075741948[/C][C]0.743238485161041[/C][C]0.371619242580520[/C][/ROW]
[ROW][C]30[/C][C]0.819404555082696[/C][C]0.361190889834607[/C][C]0.180595444917304[/C][/ROW]
[ROW][C]31[/C][C]0.884070567546612[/C][C]0.231858864906776[/C][C]0.115929432453388[/C][/ROW]
[ROW][C]32[/C][C]0.855216637124942[/C][C]0.289566725750117[/C][C]0.144783362875058[/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]0.431409801021226[/C][C]0.215704900510613[/C][/ROW]
[ROW][C]35[/C][C]0.769302278354946[/C][C]0.461395443290107[/C][C]0.230697721645054[/C][/ROW]
[ROW][C]36[/C][C]0.76857437471226[/C][C]0.462851250575479[/C][C]0.231425625287740[/C][/ROW]
[ROW][C]37[/C][C]0.734111250573955[/C][C]0.53177749885209[/C][C]0.265888749426045[/C][/ROW]
[ROW][C]38[/C][C]0.686621705945865[/C][C]0.626756588108269[/C][C]0.313378294054135[/C][/ROW]
[ROW][C]39[/C][C]0.644334987158507[/C][C]0.711330025682986[/C][C]0.355665012841493[/C][/ROW]
[ROW][C]40[/C][C]0.595473891423701[/C][C]0.809052217152597[/C][C]0.404526108576299[/C][/ROW]
[ROW][C]41[/C][C]0.560162813288013[/C][C]0.879674373423973[/C][C]0.439837186711987[/C][/ROW]
[ROW][C]42[/C][C]0.81702608614024[/C][C]0.365947827719519[/C][C]0.182973913859760[/C][/ROW]
[ROW][C]43[/C][C]0.961100044524563[/C][C]0.0777999109508749[/C][C]0.0388999554754375[/C][/ROW]
[ROW][C]44[/C][C]0.96343124701720[/C][C]0.0731375059655978[/C][C]0.0365687529827989[/C][/ROW]
[ROW][C]45[/C][C]0.95196674986272[/C][C]0.0960665002745607[/C][C]0.0480332501372804[/C][/ROW]
[ROW][C]46[/C][C]0.957497852886864[/C][C]0.0850042942262714[/C][C]0.0425021471131357[/C][/ROW]
[ROW][C]47[/C][C]0.949975255575103[/C][C]0.100049488849794[/C][C]0.050024744424897[/C][/ROW]
[ROW][C]48[/C][C]0.94455173990548[/C][C]0.110896520189041[/C][C]0.0554482600945207[/C][/ROW]
[ROW][C]49[/C][C]0.942890456333112[/C][C]0.114219087333776[/C][C]0.057109543666888[/C][/ROW]
[ROW][C]50[/C][C]0.93337690345598[/C][C]0.133246193088039[/C][C]0.0666230965440194[/C][/ROW]
[ROW][C]51[/C][C]0.929042923058919[/C][C]0.141914153882162[/C][C]0.070957076941081[/C][/ROW]
[ROW][C]52[/C][C]0.991917702970874[/C][C]0.0161645940582524[/C][C]0.0080822970291262[/C][/ROW]
[ROW][C]53[/C][C]0.988873329913877[/C][C]0.0222533401722469[/C][C]0.0111266700861234[/C][/ROW]
[ROW][C]54[/C][C]0.992346694967378[/C][C]0.0153066100652441[/C][C]0.00765330503262204[/C][/ROW]
[ROW][C]55[/C][C]0.994733868913636[/C][C]0.010532262172729[/C][C]0.0052661310863645[/C][/ROW]
[ROW][C]56[/C][C]0.99327351169011[/C][C]0.0134529766197790[/C][C]0.00672648830988949[/C][/ROW]
[ROW][C]57[/C][C]0.990720486181907[/C][C]0.0185590276361850[/C][C]0.00927951381809248[/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]0.0155234791929557[/C][C]0.00776173959647783[/C][/ROW]
[ROW][C]60[/C][C]0.991399377808966[/C][C]0.0172012443820673[/C][C]0.00860062219103365[/C][/ROW]
[ROW][C]61[/C][C]0.991359135336404[/C][C]0.0172817293271922[/C][C]0.0086408646635961[/C][/ROW]
[ROW][C]62[/C][C]0.9908139312815[/C][C]0.0183721374369992[/C][C]0.00918606871849959[/C][/ROW]
[ROW][C]63[/C][C]0.989256056411304[/C][C]0.0214878871773923[/C][C]0.0107439435886962[/C][/ROW]
[ROW][C]64[/C][C]0.991337265787315[/C][C]0.0173254684253691[/C][C]0.00866273421268455[/C][/ROW]
[ROW][C]65[/C][C]0.989209022448977[/C][C]0.0215819551020458[/C][C]0.0107909775510229[/C][/ROW]
[ROW][C]66[/C][C]0.98840155216047[/C][C]0.0231968956790597[/C][C]0.0115984478395299[/C][/ROW]
[ROW][C]67[/C][C]0.988858211346509[/C][C]0.0222835773069821[/C][C]0.0111417886534910[/C][/ROW]
[ROW][C]68[/C][C]0.990078565700864[/C][C]0.0198428685982722[/C][C]0.0099214342991361[/C][/ROW]
[ROW][C]69[/C][C]0.989672827404584[/C][C]0.0206543451908313[/C][C]0.0103271725954156[/C][/ROW]
[ROW][C]70[/C][C]0.986611484102173[/C][C]0.0267770317956545[/C][C]0.0133885158978273[/C][/ROW]
[ROW][C]71[/C][C]0.986237239955473[/C][C]0.0275255200890535[/C][C]0.0137627600445268[/C][/ROW]
[ROW][C]72[/C][C]0.981602810480318[/C][C]0.0367943790393641[/C][C]0.0183971895196820[/C][/ROW]
[ROW][C]73[/C][C]0.978114455195821[/C][C]0.0437710896083572[/C][C]0.0218855448041786[/C][/ROW]
[ROW][C]74[/C][C]0.987611496070663[/C][C]0.0247770078586741[/C][C]0.0123885039293370[/C][/ROW]
[ROW][C]75[/C][C]0.983945213904882[/C][C]0.0321095721902357[/C][C]0.0160547860951178[/C][/ROW]
[ROW][C]76[/C][C]0.981148251972344[/C][C]0.0377034960553119[/C][C]0.0188517480276559[/C][/ROW]
[ROW][C]77[/C][C]0.974897956227122[/C][C]0.050204087545755[/C][C]0.0251020437728775[/C][/ROW]
[ROW][C]78[/C][C]0.966983433991152[/C][C]0.0660331320176957[/C][C]0.0330165660088478[/C][/ROW]
[ROW][C]79[/C][C]0.977884026845233[/C][C]0.0442319463095345[/C][C]0.0221159731547673[/C][/ROW]
[ROW][C]80[/C][C]0.975880533595292[/C][C]0.0482389328094167[/C][C]0.0241194664047083[/C][/ROW]
[ROW][C]81[/C][C]0.981293487987666[/C][C]0.0374130240246682[/C][C]0.0187065120123341[/C][/ROW]
[ROW][C]82[/C][C]0.98194890255711[/C][C]0.0361021948857808[/C][C]0.0180510974428904[/C][/ROW]
[ROW][C]83[/C][C]0.976281273395385[/C][C]0.04743745320923[/C][C]0.023718726604615[/C][/ROW]
[ROW][C]84[/C][C]0.971431957932084[/C][C]0.0571360841358312[/C][C]0.0285680420679156[/C][/ROW]
[ROW][C]85[/C][C]0.991875221487154[/C][C]0.0162495570256920[/C][C]0.00812477851284599[/C][/ROW]
[ROW][C]86[/C][C]0.988886961293848[/C][C]0.0222260774123035[/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]0.0402216774500152[/C][C]0.0201108387250076[/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]0.0662765854198918[/C][C]0.0331382927099459[/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]0.0382954696581707[/C][C]0.0191477348290854[/C][/ROW]
[ROW][C]93[/C][C]0.974749399513217[/C][C]0.0505012009735651[/C][C]0.0252506004867826[/C][/ROW]
[ROW][C]94[/C][C]0.969145039636485[/C][C]0.0617099207270298[/C][C]0.0308549603635149[/C][/ROW]
[ROW][C]95[/C][C]0.960023624564688[/C][C]0.0799527508706235[/C][C]0.0399763754353118[/C][/ROW]
[ROW][C]96[/C][C]0.948011001403937[/C][C]0.103977997192126[/C][C]0.0519889985960632[/C][/ROW]
[ROW][C]97[/C][C]0.951156575546918[/C][C]0.0976868489061645[/C][C]0.0488434244530823[/C][/ROW]
[ROW][C]98[/C][C]0.93767819237241[/C][C]0.124643615255183[/C][C]0.0623218076275913[/C][/ROW]
[ROW][C]99[/C][C]0.934979578834214[/C][C]0.130040842331573[/C][C]0.0650204211657863[/C][/ROW]
[ROW][C]100[/C][C]0.919202067083882[/C][C]0.161595865832236[/C][C]0.080797932916118[/C][/ROW]
[ROW][C]101[/C][C]0.905433273101715[/C][C]0.189133453796571[/C][C]0.0945667268982853[/C][/ROW]
[ROW][C]102[/C][C]0.885867352115016[/C][C]0.228265295769967[/C][C]0.114132647884984[/C][/ROW]
[ROW][C]103[/C][C]0.911245716412542[/C][C]0.177508567174915[/C][C]0.0887542835874575[/C][/ROW]
[ROW][C]104[/C][C]0.94113890702194[/C][C]0.117722185956122[/C][C]0.058861092978061[/C][/ROW]
[ROW][C]105[/C][C]0.941820288348424[/C][C]0.116359423303152[/C][C]0.0581797116515760[/C][/ROW]
[ROW][C]106[/C][C]0.92431493022409[/C][C]0.151370139551818[/C][C]0.0756850697759092[/C][/ROW]
[ROW][C]107[/C][C]0.918215967755964[/C][C]0.163568064488072[/C][C]0.0817840322440358[/C][/ROW]
[ROW][C]108[/C][C]0.925044887984505[/C][C]0.149910224030990[/C][C]0.0749551120154952[/C][/ROW]
[ROW][C]109[/C][C]0.916566625749596[/C][C]0.166866748500808[/C][C]0.0834333742504041[/C][/ROW]
[ROW][C]110[/C][C]0.905209388773641[/C][C]0.189581222452717[/C][C]0.0947906112263585[/C][/ROW]
[ROW][C]111[/C][C]0.884554505282001[/C][C]0.230890989435997[/C][C]0.115445494717999[/C][/ROW]
[ROW][C]112[/C][C]0.883094717046194[/C][C]0.233810565907613[/C][C]0.116905282953806[/C][/ROW]
[ROW][C]113[/C][C]0.852801707333106[/C][C]0.294396585333788[/C][C]0.147198292666894[/C][/ROW]
[ROW][C]114[/C][C]0.820202540797502[/C][C]0.359594918404997[/C][C]0.179797459202498[/C][/ROW]
[ROW][C]115[/C][C]0.780303992353168[/C][C]0.439392015293664[/C][C]0.219696007646832[/C][/ROW]
[ROW][C]116[/C][C]0.773997986364078[/C][C]0.452004027271843[/C][C]0.226002013635922[/C][/ROW]
[ROW][C]117[/C][C]0.783854093906539[/C][C]0.432291812186922[/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]0.71706564605935[/C][C]0.565868707881301[/C][C]0.282934353940651[/C][/ROW]
[ROW][C]120[/C][C]0.790497126662921[/C][C]0.419005746674157[/C][C]0.209502873337079[/C][/ROW]
[ROW][C]121[/C][C]0.756531998231144[/C][C]0.486936003537712[/C][C]0.243468001768856[/C][/ROW]
[ROW][C]122[/C][C]0.715215508640922[/C][C]0.569568982718157[/C][C]0.284784491359078[/C][/ROW]
[ROW][C]123[/C][C]0.713747013648639[/C][C]0.572505972702723[/C][C]0.286252986351361[/C][/ROW]
[ROW][C]124[/C][C]0.658871174476172[/C][C]0.682257651047656[/C][C]0.341128825523828[/C][/ROW]
[ROW][C]125[/C][C]0.658447507623791[/C][C]0.683104984752417[/C][C]0.341552492376209[/C][/ROW]
[ROW][C]126[/C][C]0.638085781623484[/C][C]0.723828436753033[/C][C]0.361914218376517[/C][/ROW]
[ROW][C]127[/C][C]0.57331141042449[/C][C]0.85337717915102[/C][C]0.42668858957551[/C][/ROW]
[ROW][C]128[/C][C]0.607959819907075[/C][C]0.78408036018585[/C][C]0.392040180092925[/C][/ROW]
[ROW][C]129[/C][C]0.601789364792552[/C][C]0.796421270414896[/C][C]0.398210635207448[/C][/ROW]
[ROW][C]130[/C][C]0.573454118945877[/C][C]0.853091762108246[/C][C]0.426545881054123[/C][/ROW]
[ROW][C]131[/C][C]0.496908003271029[/C][C]0.993816006542058[/C][C]0.503091996728971[/C][/ROW]
[ROW][C]132[/C][C]0.475930254917933[/C][C]0.951860509835865[/C][C]0.524069745082067[/C][/ROW]
[ROW][C]133[/C][C]0.472516560690425[/C][C]0.94503312138085[/C][C]0.527483439309575[/C][/ROW]
[ROW][C]134[/C][C]0.39742492926294[/C][C]0.79484985852588[/C][C]0.60257507073706[/C][/ROW]
[ROW][C]135[/C][C]0.358982351379126[/C][C]0.717964702758252[/C][C]0.641017648620874[/C][/ROW]
[ROW][C]136[/C][C]0.360248443401389[/C][C]0.720496886802778[/C][C]0.639751556598611[/C][/ROW]
[ROW][C]137[/C][C]0.276286312732383[/C][C]0.552572625464766[/C][C]0.723713687267617[/C][/ROW]
[ROW][C]138[/C][C]0.338802972277989[/C][C]0.677605944555977[/C][C]0.661197027722011[/C][/ROW]
[ROW][C]139[/C][C]0.629964957691365[/C][C]0.74007008461727[/C][C]0.370035042308635[/C][/ROW]
[ROW][C]140[/C][C]0.791103058847174[/C][C]0.417793882305652[/C][C]0.208896941152826[/C][/ROW]
[ROW][C]141[/C][C]0.72370138362754[/C][C]0.55259723274492[/C][C]0.27629861637246[/C][/ROW]
[ROW][C]142[/C][C]0.684162606963402[/C][C]0.631674786073197[/C][C]0.315837393036598[/C][/ROW]
[ROW][C]143[/C][C]0.73133744636969[/C][C]0.537325107260621[/C][C]0.268662553630311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116687&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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
130.1857188285965610.3714376571931220.814281171403439
140.7760120014895160.4479759970209690.223987998510484
150.6607512066527450.678497586694510.339248793347255
160.6146978123321990.7706043753356030.385302187667801
170.5122675172013890.9754649655972230.487732482798611
180.4162445663921510.8324891327843020.583755433607849
190.3469405663617110.6938811327234220.653059433638289
200.6806131657987030.6387736684025940.319386834201297
210.6108025123210980.7783949753578040.389197487678902
220.5579587582997720.8840824834004560.442041241700228
230.5300091370696620.9399817258606750.469990862930338
240.5241010233676530.9517979532646930.475898976632347
250.5849372562665620.8301254874668770.415062743733438
260.5224489880027310.9551020239945380.477551011997269
270.7371402686081540.5257194627836910.262859731391846
280.6838835486941810.6322329026116380.316116451305819
290.628380757419480.7432384851610410.371619242580520
300.8194045550826960.3611908898346070.180595444917304
310.8840705675466120.2318588649067760.115929432453388
320.8552166371249420.2895667257501170.144783362875058
330.81740435246080.3651912950784010.182595647539201
340.7842950994893870.4314098010212260.215704900510613
350.7693022783549460.4613954432901070.230697721645054
360.768574374712260.4628512505754790.231425625287740
370.7341112505739550.531777498852090.265888749426045
380.6866217059458650.6267565881082690.313378294054135
390.6443349871585070.7113300256829860.355665012841493
400.5954738914237010.8090522171525970.404526108576299
410.5601628132880130.8796743734239730.439837186711987
420.817026086140240.3659478277195190.182973913859760
430.9611000445245630.07779991095087490.0388999554754375
440.963431247017200.07313750596559780.0365687529827989
450.951966749862720.09606650027456070.0480332501372804
460.9574978528868640.08500429422627140.0425021471131357
470.9499752555751030.1000494888497940.050024744424897
480.944551739905480.1108965201890410.0554482600945207
490.9428904563331120.1142190873337760.057109543666888
500.933376903455980.1332461930880390.0666230965440194
510.9290429230589190.1419141538821620.070957076941081
520.9919177029708740.01616459405825240.0080822970291262
530.9888733299138770.02225334017224690.0111266700861234
540.9923466949673780.01530661006524410.00765330503262204
550.9947338689136360.0105322621727290.0052661310863645
560.993273511690110.01345297661977900.00672648830988949
570.9907204861819070.01855902763618500.00927951381809248
580.9887276970818750.022544605836250.011272302918125
590.9922382604035220.01552347919295570.00776173959647783
600.9913993778089660.01720124438206730.00860062219103365
610.9913591353364040.01728172932719220.0086408646635961
620.99081393128150.01837213743699920.00918606871849959
630.9892560564113040.02148788717739230.0107439435886962
640.9913372657873150.01732546842536910.00866273421268455
650.9892090224489770.02158195510204580.0107909775510229
660.988401552160470.02319689567905970.0115984478395299
670.9888582113465090.02228357730698210.0111417886534910
680.9900785657008640.01984286859827220.0099214342991361
690.9896728274045840.02065434519083130.0103271725954156
700.9866114841021730.02677703179565450.0133885158978273
710.9862372399554730.02752552008905350.0137627600445268
720.9816028104803180.03679437903936410.0183971895196820
730.9781144551958210.04377108960835720.0218855448041786
740.9876114960706630.02477700785867410.0123885039293370
750.9839452139048820.03210957219023570.0160547860951178
760.9811482519723440.03770349605531190.0188517480276559
770.9748979562271220.0502040875457550.0251020437728775
780.9669834339911520.06603313201769570.0330165660088478
790.9778840268452330.04423194630953450.0221159731547673
800.9758805335952920.04823893280941670.0241194664047083
810.9812934879876660.03741302402466820.0187065120123341
820.981948902557110.03610219488578080.0180510974428904
830.9762812733953850.047437453209230.023718726604615
840.9714319579320840.05713608413583120.0285680420679156
850.9918752214871540.01624955702569200.00812477851284599
860.9888869612938480.02222607741230350.0111130387061517
870.9849343657535680.03013126849286440.0150656342464322
880.9798891612749920.04022167745001520.0201108387250076
890.9745932030637460.05081359387250740.0254067969362537
900.9668617072900540.06627658541989180.0331382927099459
910.9593457546060740.08130849078785140.0406542453939257
920.9808522651709150.03829546965817070.0191477348290854
930.9747493995132170.05050120097356510.0252506004867826
940.9691450396364850.06170992072702980.0308549603635149
950.9600236245646880.07995275087062350.0399763754353118
960.9480110014039370.1039779971921260.0519889985960632
970.9511565755469180.09768684890616450.0488434244530823
980.937678192372410.1246436152551830.0623218076275913
990.9349795788342140.1300408423315730.0650204211657863
1000.9192020670838820.1615958658322360.080797932916118
1010.9054332731017150.1891334537965710.0945667268982853
1020.8858673521150160.2282652957699670.114132647884984
1030.9112457164125420.1775085671749150.0887542835874575
1040.941138907021940.1177221859561220.058861092978061
1050.9418202883484240.1163594233031520.0581797116515760
1060.924314930224090.1513701395518180.0756850697759092
1070.9182159677559640.1635680644880720.0817840322440358
1080.9250448879845050.1499102240309900.0749551120154952
1090.9165666257495960.1668667485008080.0834333742504041
1100.9052093887736410.1895812224527170.0947906112263585
1110.8845545052820010.2308909894359970.115445494717999
1120.8830947170461940.2338105659076130.116905282953806
1130.8528017073331060.2943965853337880.147198292666894
1140.8202025407975020.3595949184049970.179797459202498
1150.7803039923531680.4393920152936640.219696007646832
1160.7739979863640780.4520040272718430.226002013635922
1170.7838540939065390.4322918121869220.216145906093461
1180.7663660445027060.4672679109945880.233633955497294
1190.717065646059350.5658687078813010.282934353940651
1200.7904971266629210.4190057466741570.209502873337079
1210.7565319982311440.4869360035377120.243468001768856
1220.7152155086409220.5695689827181570.284784491359078
1230.7137470136486390.5725059727027230.286252986351361
1240.6588711744761720.6822576510476560.341128825523828
1250.6584475076237910.6831049847524170.341552492376209
1260.6380857816234840.7238284367530330.361914218376517
1270.573311410424490.853377179151020.42668858957551
1280.6079598199070750.784080360185850.392040180092925
1290.6017893647925520.7964212704148960.398210635207448
1300.5734541189458770.8530917621082460.426545881054123
1310.4969080032710290.9938160065420580.503091996728971
1320.4759302549179330.9518605098358650.524069745082067
1330.4725165606904250.945033121380850.527483439309575
1340.397424929262940.794849858525880.60257507073706
1350.3589823513791260.7179647027582520.641017648620874
1360.3602484434013890.7204968868027780.639751556598611
1370.2762863127323830.5525726254647660.723713687267617
1380.3388029722779890.6776059445559770.661197027722011
1390.6299649576913650.740070084617270.370035042308635
1400.7911030588471740.4177938823056520.208896941152826
1410.723701383627540.552597232744920.27629861637246
1420.6841626069634020.6316747860731970.315837393036598
1430.731337446369690.5373251072606210.268662553630311






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=116687&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=116687&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116687&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')
}