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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 computationSun, 19 Dec 2010 15:27:49 +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/19/t12927724910mthvhny74w8y6e.htm/, Retrieved Sat, 04 May 2024 23:31:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112500, Retrieved Sat, 04 May 2024 23:31:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paperMR4] [2010-12-19 15:27:49] [13dfa60174f50d862e8699db2153bfc5] [Current]
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Dataset

Dataseries X:
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Boxplots 
15	0	13,6	13,7	13	14,4	15
14,4	0	15,2	13,6	13,7	13	14,4
13	0	12,9	15,2	13,6	13,7	13
13,7	0	14	12,9	15,2	13,6	13,7
13,6	0	14,1	14	12,9	15,2	13,6
15,2	0	13,2	14,1	14	12,9	15,2
12,9	0	11,3	13,2	14,1	14	12,9
14	0	13,3	11,3	13,2	14,1	14
14,1	0	14,4	13,3	11,3	13,2	14,1
13,2	0	13,3	14,4	13,3	11,3	13,2
11,3	0	11,6	13,3	14,4	13,3	11,3
13,3	0	13,2	11,6	13,3	14,4	13,3
14,4	0	13,1	13,2	11,6	13,3	14,4
13,3	0	14,6	13,1	13,2	11,6	13,3
11,6	0	14	14,6	13,1	13,2	11,6
13,2	0	14,3	14	14,6	13,1	13,2
13,1	0	13,8	14,3	14	14,6	13,1
14,6	0	13,7	13,8	14,3	14	14,6
14	0	11	13,7	13,8	14,3	14
14,3	0	14,4	11	13,7	13,8	14,3
13,8	0	15,6	14,4	11	13,7	13,8
13,7	0	13,7	15,6	14,4	11	13,7
11	0	12,6	13,7	15,6	14,4	11
14,4	0	13,2	12,6	13,7	15,6	14,4
15,6	0	13,3	13,2	12,6	13,7	15,6
13,7	0	14,3	13,3	13,2	12,6	13,7
12,6	0	14	14,3	13,3	13,2	12,6
13,2	0	13,4	14	14,3	13,3	13,2
13,3	0	13,9	13,4	14	14,3	13,3
14,3	0	13,7	13,9	13,4	14	14,3
14	0	10,5	13,7	13,9	13,4	14
13,4	0	14,5	10,5	13,7	13,9	13,4
13,9	0	15	14,5	10,5	13,7	13,9
13,7	0	13,5	15	14,5	10,5	13,7
10,5	0	13,5	13,5	15	14,5	10,5
14,5	0	13,2	13,5	13,5	15	14,5
15	0	13,8	13,2	13,5	13,5	15
13,5	0	16,2	13,8	13,2	13,5	13,5
13,5	0	14,7	16,2	13,8	13,2	13,5
13,2	0	13,9	14,7	16,2	13,8	13,2
13,8	0	16	13,9	14,7	16,2	13,8
16,2	0	14,4	16	13,9	14,7	16,2
14,7	0	12,3	14,4	16	13,9	14,7
13,9	0	15,9	12,3	14,4	16	13,9
16	0	15,9	15,9	12,3	14,4	16
14,4	0	15,5	15,9	15,9	12,3	14,4
12,3	0	15,1	15,5	15,9	15,9	12,3
15,9	0	14,5	15,1	15,5	15,9	15,9
15,9	0	15,1	14,5	15,1	15,5	15,9
15,5	0	17,4	15,1	14,5	15,1	15,5
15,1	0	16,2	17,4	15,1	14,5	15,1
14,5	0	15,6	16,2	17,4	15,1	14,5
15,1	0	17,2	15,6	16,2	17,4	15,1
17,4	0	14,9	17,2	15,6	16,2	17,4
16,2	0	13,8	14,9	17,2	15,6	16,2
15,6	0	17,5	13,8	14,9	17,2	15,6
17,2	0	16,2	17,5	13,8	14,9	17,2
14,9	0	17,5	16,2	17,5	13,8	14,9
13,8	0	16,6	17,5	16,2	17,5	13,8
17,5	0	16,2	16,6	17,5	16,2	17,5
16,2	0	16,6	16,2	16,6	17,5	16,2
17,5	0	19,6	16,6	16,2	16,6	17,5
16,6	0	15,9	19,6	16,6	16,2	16,6
16,2	0	18	15,9	19,6	16,6	16,2
16,6	0	18,3	18	15,9	19,6	16,6
19,6	0	16,3	18,3	18	15,9	19,6
15,9	0	14,9	16,3	18,3	18	15,9
18	0	18,2	14,9	16,3	18,3	18
18,3	0	18,4	18,2	14,9	16,3	18,3
16,3	0	18,5	18,4	18,2	14,9	16,3
14,9	0	16	18,5	18,4	18,2	14,9
18,2	0	17,4	16	18,5	18,4	18,2
18,4	0	17,2	17,4	16	18,5	18,4
18,5	0	19,6	17,2	17,4	16	18,5
16	0	17,2	19,6	17,2	17,4	16
17,4	0	18,3	17,2	19,6	17,2	17,4
17,2	0	19,3	18,3	17,2	19,6	17,2
19,6	0	18,1	19,3	18,3	17,2	19,6
17,2	0	16,2	18,1	19,3	18,3	17,2
18,3	0	18,4	16,2	18,1	19,3	18,3
19,3	0	20,5	18,4	16,2	18,1	19,3
18,1	0	19	20,5	18,4	16,2	18,1
16,2	0	16,5	19	20,5	18,4	16,2
18,4	0	18,7	16,5	19	20,5	18,4
20,5	0	19	18,7	16,5	19	20,5
19	0	19,2	19	18,7	16,5	19
16,5	0	20,5	19,2	19	18,7	16,5
18,7	0	19,3	20,5	19,2	19	18,7
19	0	20,6	19,3	20,5	19,2	19
19,2	0	20,1	20,6	19,3	20,5	19,2
20,5	0	16,1	20,1	20,6	19,3	20,5
19,3	0	20,4	16,1	20,1	20,6	19,3
20,6	0	19,7	20,4	16,1	20,1	20,6
20,1	0	15,6	19,7	20,4	16,1	20,1
16,1	0	14,4	15,6	19,7	20,4	16,1
20,4	0	13,7	14,4	15,6	19,7	20,4
19,7	1	14,1	13,7	14,4	15,6	19,7
15,6	1	15	14,1	13,7	14,4	15,6
14,4	1	14,2	15	14,1	13,7	14,4
13,7	1	13,6	14,2	15	14,1	13,7
14,1	1	15,4	13,6	14,2	15	14,1
15	1	14,8	15,4	13,6	14,2	15
14,2	1	12,5	14,8	15,4	13,6	14,2
13,6	1	16,2	12,5	14,8	15,4	13,6
15,4	1	16,1	16,2	12,5	14,8	15,4
14,8	1	16	16,1	16,2	12,5	14,8
12,5	1	15,8	16	16,1	16,2	12,5
16,2	1	15,2	15,8	16	16,1	16,2
16,1	1	15,7	15,2	15,8	16	16,1
16	1	18,9	15,7	15,2	15,8	16
15,8	1	17,4	18,9	15,7	15,2	15,8
15,2	1	17	17,4	18,9	15,7	15,2
15,7	1	19,8	17	17,4	18,9	15,7
18,9	1	17,7	19,8	17	17,4	18,9
17,4	1	16	17,7	19,8	17	17,4
17	1	19,6	16	17,7	19,8	17
19,8	1	19,7	19,6	16	17,7	19,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
uitvoercijfer[t] = + 2.67868885112499e-16 + 1.25611255456158e-16X[t] + 2.21995911620994e-17Y1[t] + 5.45430166641358e-17Y2[t] -2.94857131153639e-17Y3[t] + 4.46932466310697e-17Y4[t] + 1Y5[t] -7.60702659134785e-17M1[t] -3.57828127270844e-18M2[t] + 4.1212859638548e-17M3[t] + 1.0850439386405e-16M4[t] -4.33776525511453e-17M5[t] + 5.98989216374346e-17M6[t] + 4.21435215809026e-16M7[t] -6.42465345341248e-17M8[t] -2.41337263452827e-17M9[t] + 1.78461801535162e-16M10[t] + 2.47939467126275e-17M11[t] -2.26079895601757e-18t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
uitvoercijfer[t] =  +  2.67868885112499e-16 +  1.25611255456158e-16X[t] +  2.21995911620994e-17Y1[t] +  5.45430166641358e-17Y2[t] -2.94857131153639e-17Y3[t] +  4.46932466310697e-17Y4[t] +  1Y5[t] -7.60702659134785e-17M1[t] -3.57828127270844e-18M2[t] +  4.1212859638548e-17M3[t] +  1.0850439386405e-16M4[t] -4.33776525511453e-17M5[t] +  5.98989216374346e-17M6[t] +  4.21435215809026e-16M7[t] -6.42465345341248e-17M8[t] -2.41337263452827e-17M9[t] +  1.78461801535162e-16M10[t] +  2.47939467126275e-17M11[t] -2.26079895601757e-18t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]uitvoercijfer[t] =  +  2.67868885112499e-16 +  1.25611255456158e-16X[t] +  2.21995911620994e-17Y1[t] +  5.45430166641358e-17Y2[t] -2.94857131153639e-17Y3[t] +  4.46932466310697e-17Y4[t] +  1Y5[t] -7.60702659134785e-17M1[t] -3.57828127270844e-18M2[t] +  4.1212859638548e-17M3[t] +  1.0850439386405e-16M4[t] -4.33776525511453e-17M5[t] +  5.98989216374346e-17M6[t] +  4.21435215809026e-16M7[t] -6.42465345341248e-17M8[t] -2.41337263452827e-17M9[t] +  1.78461801535162e-16M10[t] +  2.47939467126275e-17M11[t] -2.26079895601757e-18t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&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
uitvoercijfer[t] = + 2.67868885112499e-16 + 1.25611255456158e-16X[t] + 2.21995911620994e-17Y1[t] + 5.45430166641358e-17Y2[t] -2.94857131153639e-17Y3[t] + 4.46932466310697e-17Y4[t] + 1Y5[t] -7.60702659134785e-17M1[t] -3.57828127270844e-18M2[t] + 4.1212859638548e-17M3[t] + 1.0850439386405e-16M4[t] -4.33776525511453e-17M5[t] + 5.98989216374346e-17M6[t] + 4.21435215809026e-16M7[t] -6.42465345341248e-17M8[t] -2.41337263452827e-17M9[t] + 1.78461801535162e-16M10[t] + 2.47939467126275e-17M11[t] -2.26079895601757e-18t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.67868885112499e-1600.49080.6246460.312323
X1.25611255456158e-1600.59870.5507730.275387
Y12.21995911620994e-1700.51560.6073190.30366
Y25.45430166641358e-1701.25460.2126160.106308
Y3-2.94857131153639e-170-0.66770.5058990.252949
Y44.46932466310697e-1700.96840.3352420.167621
Y5102272589140773384800
M1-7.60702659134785e-170-0.44360.6583380.329169
M2-3.57828127270844e-180-0.01660.9868180.493409
M34.1212859638548e-1700.1880.8512880.425644
M41.0850439386405e-1600.55720.5786450.289323
M5-4.33776525511453e-170-0.25050.8027050.401353
M65.98989216374346e-1700.32190.7482150.374107
M74.21435215809026e-1602.10150.0381630.019081
M8-6.42465345341248e-170-0.35580.7227380.361369
M9-2.41337263452827e-170-0.09780.9222620.461131
M101.78461801535162e-1600.69770.4870220.243511
M112.47939467126275e-1700.0990.9213030.460651
t-2.26079895601757e-180-0.67460.5015340.250767

\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) & 2.67868885112499e-16 & 0 & 0.4908 & 0.624646 & 0.312323 \tabularnewline
X & 1.25611255456158e-16 & 0 & 0.5987 & 0.550773 & 0.275387 \tabularnewline
Y1 & 2.21995911620994e-17 & 0 & 0.5156 & 0.607319 & 0.30366 \tabularnewline
Y2 & 5.45430166641358e-17 & 0 & 1.2546 & 0.212616 & 0.106308 \tabularnewline
Y3 & -2.94857131153639e-17 & 0 & -0.6677 & 0.505899 & 0.252949 \tabularnewline
Y4 & 4.46932466310697e-17 & 0 & 0.9684 & 0.335242 & 0.167621 \tabularnewline
Y5 & 1 & 0 & 22725891407733848 & 0 & 0 \tabularnewline
M1 & -7.60702659134785e-17 & 0 & -0.4436 & 0.658338 & 0.329169 \tabularnewline
M2 & -3.57828127270844e-18 & 0 & -0.0166 & 0.986818 & 0.493409 \tabularnewline
M3 & 4.1212859638548e-17 & 0 & 0.188 & 0.851288 & 0.425644 \tabularnewline
M4 & 1.0850439386405e-16 & 0 & 0.5572 & 0.578645 & 0.289323 \tabularnewline
M5 & -4.33776525511453e-17 & 0 & -0.2505 & 0.802705 & 0.401353 \tabularnewline
M6 & 5.98989216374346e-17 & 0 & 0.3219 & 0.748215 & 0.374107 \tabularnewline
M7 & 4.21435215809026e-16 & 0 & 2.1015 & 0.038163 & 0.019081 \tabularnewline
M8 & -6.42465345341248e-17 & 0 & -0.3558 & 0.722738 & 0.361369 \tabularnewline
M9 & -2.41337263452827e-17 & 0 & -0.0978 & 0.922262 & 0.461131 \tabularnewline
M10 & 1.78461801535162e-16 & 0 & 0.6977 & 0.487022 & 0.243511 \tabularnewline
M11 & 2.47939467126275e-17 & 0 & 0.099 & 0.921303 & 0.460651 \tabularnewline
t & -2.26079895601757e-18 & 0 & -0.6746 & 0.501534 & 0.250767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&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]2.67868885112499e-16[/C][C]0[/C][C]0.4908[/C][C]0.624646[/C][C]0.312323[/C][/ROW]
[ROW][C]X[/C][C]1.25611255456158e-16[/C][C]0[/C][C]0.5987[/C][C]0.550773[/C][C]0.275387[/C][/ROW]
[ROW][C]Y1[/C][C]2.21995911620994e-17[/C][C]0[/C][C]0.5156[/C][C]0.607319[/C][C]0.30366[/C][/ROW]
[ROW][C]Y2[/C][C]5.45430166641358e-17[/C][C]0[/C][C]1.2546[/C][C]0.212616[/C][C]0.106308[/C][/ROW]
[ROW][C]Y3[/C][C]-2.94857131153639e-17[/C][C]0[/C][C]-0.6677[/C][C]0.505899[/C][C]0.252949[/C][/ROW]
[ROW][C]Y4[/C][C]4.46932466310697e-17[/C][C]0[/C][C]0.9684[/C][C]0.335242[/C][C]0.167621[/C][/ROW]
[ROW][C]Y5[/C][C]1[/C][C]0[/C][C]22725891407733848[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-7.60702659134785e-17[/C][C]0[/C][C]-0.4436[/C][C]0.658338[/C][C]0.329169[/C][/ROW]
[ROW][C]M2[/C][C]-3.57828127270844e-18[/C][C]0[/C][C]-0.0166[/C][C]0.986818[/C][C]0.493409[/C][/ROW]
[ROW][C]M3[/C][C]4.1212859638548e-17[/C][C]0[/C][C]0.188[/C][C]0.851288[/C][C]0.425644[/C][/ROW]
[ROW][C]M4[/C][C]1.0850439386405e-16[/C][C]0[/C][C]0.5572[/C][C]0.578645[/C][C]0.289323[/C][/ROW]
[ROW][C]M5[/C][C]-4.33776525511453e-17[/C][C]0[/C][C]-0.2505[/C][C]0.802705[/C][C]0.401353[/C][/ROW]
[ROW][C]M6[/C][C]5.98989216374346e-17[/C][C]0[/C][C]0.3219[/C][C]0.748215[/C][C]0.374107[/C][/ROW]
[ROW][C]M7[/C][C]4.21435215809026e-16[/C][C]0[/C][C]2.1015[/C][C]0.038163[/C][C]0.019081[/C][/ROW]
[ROW][C]M8[/C][C]-6.42465345341248e-17[/C][C]0[/C][C]-0.3558[/C][C]0.722738[/C][C]0.361369[/C][/ROW]
[ROW][C]M9[/C][C]-2.41337263452827e-17[/C][C]0[/C][C]-0.0978[/C][C]0.922262[/C][C]0.461131[/C][/ROW]
[ROW][C]M10[/C][C]1.78461801535162e-16[/C][C]0[/C][C]0.6977[/C][C]0.487022[/C][C]0.243511[/C][/ROW]
[ROW][C]M11[/C][C]2.47939467126275e-17[/C][C]0[/C][C]0.099[/C][C]0.921303[/C][C]0.460651[/C][/ROW]
[ROW][C]t[/C][C]-2.26079895601757e-18[/C][C]0[/C][C]-0.6746[/C][C]0.501534[/C][C]0.250767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&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)2.67868885112499e-1600.49080.6246460.312323
X1.25611255456158e-1600.59870.5507730.275387
Y12.21995911620994e-1700.51560.6073190.30366
Y25.45430166641358e-1701.25460.2126160.106308
Y3-2.94857131153639e-170-0.66770.5058990.252949
Y44.46932466310697e-1700.96840.3352420.167621
Y5102272589140773384800
M1-7.60702659134785e-170-0.44360.6583380.329169
M2-3.57828127270844e-180-0.01660.9868180.493409
M34.1212859638548e-1700.1880.8512880.425644
M41.0850439386405e-1600.55720.5786450.289323
M5-4.33776525511453e-170-0.25050.8027050.401353
M65.98989216374346e-1700.32190.7482150.374107
M74.21435215809026e-1602.10150.0381630.019081
M8-6.42465345341248e-170-0.35580.7227380.361369
M9-2.41337263452827e-170-0.09780.9222620.461131
M101.78461801535162e-1600.69770.4870220.243511
M112.47939467126275e-1700.0990.9213030.460651
t-2.26079895601757e-180-0.67460.5015340.250767






Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.19975962880079e+32
F-TEST (DF numerator)18
F-TEST (DF denominator)98
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.26900285120209e-16
Sum Squared Residuals1.04726520483441e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 3.19975962880079e+32 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 98 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.26900285120209e-16 \tabularnewline
Sum Squared Residuals & 1.04726520483441e-29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.19975962880079e+32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]98[/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]3.26900285120209e-16[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.04726520483441e-29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.19975962880079e+32
F-TEST (DF numerator)18
F-TEST (DF denominator)98
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.26900285120209e-16
Sum Squared Residuals1.04726520483441e-29






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11515-2.89296795448131e-16
214.414.4-2.57901354069519e-16
31313-6.74979900756436e-16
413.713.7-5.99532317973799e-17
513.613.61.19375966974017e-16
615.215.21.76246915351944e-16
712.912.92.86085812889703e-15
81414-6.18645742387088e-17
914.114.1-7.4461608657557e-17
1013.213.2-1.10493330503759e-16
1111.311.36.08080803806897e-18
1213.313.3-1.1815875102057e-16
1314.414.43.69914892012958e-18
1413.313.32.66069956729712e-17
1511.611.6-1.32121368677276e-16
1613.213.2-6.76248333803364e-17
1713.113.1-6.31738092303693e-17
1814.614.6-1.24684451230098e-16
191414-4.51966447946513e-16
2014.314.33.76756307634271e-17
2113.813.8-7.06613987846437e-17
2213.713.7-2.32681453430692e-17
231111-1.71997573888969e-17
2414.414.4-1.24266653161158e-16
2515.615.6-6.86351439679292e-17
2613.713.72.46877428755157e-17
2712.612.68.92254042244412e-17
2813.213.2-6.11380785941764e-17
2913.313.32.95146079605198e-17
3014.314.3-7.76536297158759e-17
311414-3.62775832681693e-16
3213.413.4-1.39314095149291e-17
3313.913.9-6.77557766024397e-17
3413.713.79.63564371041354e-18
3510.510.5-7.25107076860199e-17
3614.514.5-5.98920229497522e-17
3715151.739712445657e-17
3813.513.52.64018155009445e-18
3913.513.51.23390119246382e-16
4013.213.25.30304495003798e-17
4113.813.8-1.16322040956552e-16
4216.216.2-8.83220749074289e-17
4314.714.7-2.81157382641636e-16
4413.913.9-2.91859322944952e-17
4516162.37501759892062e-17
4614.414.4-1.30911500757679e-17
4712.312.32.58468368412539e-17
4815.915.9-1.06845734128434e-17
4915.915.94.83336180301154e-17
5015.515.5-1.23932134928787e-17
5115.115.11.05807742029812e-16
5214.514.56.04747476917268e-17
5315.115.11.01605082441038e-17
5417.417.4-2.7030544884209e-17
5516.216.2-2.82677379293975e-16
5615.615.6-5.63846947730492e-17
5717.217.21.45383358439572e-17
5814.914.98.39707768018205e-17
5913.813.8-1.71215932246123e-16
6017.517.56.17088890378218e-17
6116.216.26.99292671256961e-17
6217.517.54.399592581324e-19
6316.616.61.37195859457235e-16
6416.216.22.88417670813949e-17
6516.616.6-1.24676138789622e-16
6619.619.61.11401944620687e-16
6715.915.9-3.29726925643704e-16
6818181.24086179939321e-17
6918.318.31.09831892180842e-16
7016.316.3-4.53693916462838e-18
7114.914.9-8.63377341698488e-17
7218.218.2-1.25917464527822e-17
7318.418.41.50250330807415e-17
7418.518.59.10776006556092e-18
7516161.80952817728626e-16
7617.417.41.54663357875496e-17
7717.217.21.02360655488643e-16
7819.619.6-1.77785889214598e-17
7917.217.2-2.91943580338292e-16
8018.318.36.70206189704102e-17
8119.319.36.12348762080334e-17
8218.118.12.77652331965105e-17
8316.216.22.94629547582709e-16
8418.418.4-4.18321427506925e-17
8520.520.51.20604404388515e-17
8619191.62458117465963e-16
8716.516.5-6.6699792415586e-17
8818.718.72.48556162729576e-17
8919191.3161757279558e-16
9019.219.24.53570097117462e-19
9120.520.5-3.39057005183814e-16
9219.319.38.49741910060189e-17
9320.620.6-2.7239555401431e-18
9420.120.1-1.74652269959715e-17
9516.116.1-2.87837186831201e-17
9620.420.42.84299234820946e-16
9719.719.72.90363891613402e-17
9815.615.69.01680099153656e-18
9914.414.46.10915174221326e-17
10013.713.7-1.3540638913855e-17
10114.114.13.48896599531176e-17
1021515-1.48632837906236e-17
10314.214.2-2.86291300456655e-16
10413.613.62.32717049038207e-17
10515.415.44.74745275543349e-17
10614.814.84.74831383744517e-17
10712.512.54.9490657711978e-17
10816.216.22.14177658890305e-17
10916.116.11.62450918202615e-16
11016163.53370096826234e-17
11115.815.81.76137601740669e-16
11215.215.21.9587866351739e-17
11315.715.7-1.23746982439437e-16
11418.918.96.22301433799458e-17
11517.417.4-2.35262274710747e-16
1161717-6.39841528164268e-17
11719.819.8-4.1227068191591e-17

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 15 & -2.89296795448131e-16 \tabularnewline
2 & 14.4 & 14.4 & -2.57901354069519e-16 \tabularnewline
3 & 13 & 13 & -6.74979900756436e-16 \tabularnewline
4 & 13.7 & 13.7 & -5.99532317973799e-17 \tabularnewline
5 & 13.6 & 13.6 & 1.19375966974017e-16 \tabularnewline
6 & 15.2 & 15.2 & 1.76246915351944e-16 \tabularnewline
7 & 12.9 & 12.9 & 2.86085812889703e-15 \tabularnewline
8 & 14 & 14 & -6.18645742387088e-17 \tabularnewline
9 & 14.1 & 14.1 & -7.4461608657557e-17 \tabularnewline
10 & 13.2 & 13.2 & -1.10493330503759e-16 \tabularnewline
11 & 11.3 & 11.3 & 6.08080803806897e-18 \tabularnewline
12 & 13.3 & 13.3 & -1.1815875102057e-16 \tabularnewline
13 & 14.4 & 14.4 & 3.69914892012958e-18 \tabularnewline
14 & 13.3 & 13.3 & 2.66069956729712e-17 \tabularnewline
15 & 11.6 & 11.6 & -1.32121368677276e-16 \tabularnewline
16 & 13.2 & 13.2 & -6.76248333803364e-17 \tabularnewline
17 & 13.1 & 13.1 & -6.31738092303693e-17 \tabularnewline
18 & 14.6 & 14.6 & -1.24684451230098e-16 \tabularnewline
19 & 14 & 14 & -4.51966447946513e-16 \tabularnewline
20 & 14.3 & 14.3 & 3.76756307634271e-17 \tabularnewline
21 & 13.8 & 13.8 & -7.06613987846437e-17 \tabularnewline
22 & 13.7 & 13.7 & -2.32681453430692e-17 \tabularnewline
23 & 11 & 11 & -1.71997573888969e-17 \tabularnewline
24 & 14.4 & 14.4 & -1.24266653161158e-16 \tabularnewline
25 & 15.6 & 15.6 & -6.86351439679292e-17 \tabularnewline
26 & 13.7 & 13.7 & 2.46877428755157e-17 \tabularnewline
27 & 12.6 & 12.6 & 8.92254042244412e-17 \tabularnewline
28 & 13.2 & 13.2 & -6.11380785941764e-17 \tabularnewline
29 & 13.3 & 13.3 & 2.95146079605198e-17 \tabularnewline
30 & 14.3 & 14.3 & -7.76536297158759e-17 \tabularnewline
31 & 14 & 14 & -3.62775832681693e-16 \tabularnewline
32 & 13.4 & 13.4 & -1.39314095149291e-17 \tabularnewline
33 & 13.9 & 13.9 & -6.77557766024397e-17 \tabularnewline
34 & 13.7 & 13.7 & 9.63564371041354e-18 \tabularnewline
35 & 10.5 & 10.5 & -7.25107076860199e-17 \tabularnewline
36 & 14.5 & 14.5 & -5.98920229497522e-17 \tabularnewline
37 & 15 & 15 & 1.739712445657e-17 \tabularnewline
38 & 13.5 & 13.5 & 2.64018155009445e-18 \tabularnewline
39 & 13.5 & 13.5 & 1.23390119246382e-16 \tabularnewline
40 & 13.2 & 13.2 & 5.30304495003798e-17 \tabularnewline
41 & 13.8 & 13.8 & -1.16322040956552e-16 \tabularnewline
42 & 16.2 & 16.2 & -8.83220749074289e-17 \tabularnewline
43 & 14.7 & 14.7 & -2.81157382641636e-16 \tabularnewline
44 & 13.9 & 13.9 & -2.91859322944952e-17 \tabularnewline
45 & 16 & 16 & 2.37501759892062e-17 \tabularnewline
46 & 14.4 & 14.4 & -1.30911500757679e-17 \tabularnewline
47 & 12.3 & 12.3 & 2.58468368412539e-17 \tabularnewline
48 & 15.9 & 15.9 & -1.06845734128434e-17 \tabularnewline
49 & 15.9 & 15.9 & 4.83336180301154e-17 \tabularnewline
50 & 15.5 & 15.5 & -1.23932134928787e-17 \tabularnewline
51 & 15.1 & 15.1 & 1.05807742029812e-16 \tabularnewline
52 & 14.5 & 14.5 & 6.04747476917268e-17 \tabularnewline
53 & 15.1 & 15.1 & 1.01605082441038e-17 \tabularnewline
54 & 17.4 & 17.4 & -2.7030544884209e-17 \tabularnewline
55 & 16.2 & 16.2 & -2.82677379293975e-16 \tabularnewline
56 & 15.6 & 15.6 & -5.63846947730492e-17 \tabularnewline
57 & 17.2 & 17.2 & 1.45383358439572e-17 \tabularnewline
58 & 14.9 & 14.9 & 8.39707768018205e-17 \tabularnewline
59 & 13.8 & 13.8 & -1.71215932246123e-16 \tabularnewline
60 & 17.5 & 17.5 & 6.17088890378218e-17 \tabularnewline
61 & 16.2 & 16.2 & 6.99292671256961e-17 \tabularnewline
62 & 17.5 & 17.5 & 4.399592581324e-19 \tabularnewline
63 & 16.6 & 16.6 & 1.37195859457235e-16 \tabularnewline
64 & 16.2 & 16.2 & 2.88417670813949e-17 \tabularnewline
65 & 16.6 & 16.6 & -1.24676138789622e-16 \tabularnewline
66 & 19.6 & 19.6 & 1.11401944620687e-16 \tabularnewline
67 & 15.9 & 15.9 & -3.29726925643704e-16 \tabularnewline
68 & 18 & 18 & 1.24086179939321e-17 \tabularnewline
69 & 18.3 & 18.3 & 1.09831892180842e-16 \tabularnewline
70 & 16.3 & 16.3 & -4.53693916462838e-18 \tabularnewline
71 & 14.9 & 14.9 & -8.63377341698488e-17 \tabularnewline
72 & 18.2 & 18.2 & -1.25917464527822e-17 \tabularnewline
73 & 18.4 & 18.4 & 1.50250330807415e-17 \tabularnewline
74 & 18.5 & 18.5 & 9.10776006556092e-18 \tabularnewline
75 & 16 & 16 & 1.80952817728626e-16 \tabularnewline
76 & 17.4 & 17.4 & 1.54663357875496e-17 \tabularnewline
77 & 17.2 & 17.2 & 1.02360655488643e-16 \tabularnewline
78 & 19.6 & 19.6 & -1.77785889214598e-17 \tabularnewline
79 & 17.2 & 17.2 & -2.91943580338292e-16 \tabularnewline
80 & 18.3 & 18.3 & 6.70206189704102e-17 \tabularnewline
81 & 19.3 & 19.3 & 6.12348762080334e-17 \tabularnewline
82 & 18.1 & 18.1 & 2.77652331965105e-17 \tabularnewline
83 & 16.2 & 16.2 & 2.94629547582709e-16 \tabularnewline
84 & 18.4 & 18.4 & -4.18321427506925e-17 \tabularnewline
85 & 20.5 & 20.5 & 1.20604404388515e-17 \tabularnewline
86 & 19 & 19 & 1.62458117465963e-16 \tabularnewline
87 & 16.5 & 16.5 & -6.6699792415586e-17 \tabularnewline
88 & 18.7 & 18.7 & 2.48556162729576e-17 \tabularnewline
89 & 19 & 19 & 1.3161757279558e-16 \tabularnewline
90 & 19.2 & 19.2 & 4.53570097117462e-19 \tabularnewline
91 & 20.5 & 20.5 & -3.39057005183814e-16 \tabularnewline
92 & 19.3 & 19.3 & 8.49741910060189e-17 \tabularnewline
93 & 20.6 & 20.6 & -2.7239555401431e-18 \tabularnewline
94 & 20.1 & 20.1 & -1.74652269959715e-17 \tabularnewline
95 & 16.1 & 16.1 & -2.87837186831201e-17 \tabularnewline
96 & 20.4 & 20.4 & 2.84299234820946e-16 \tabularnewline
97 & 19.7 & 19.7 & 2.90363891613402e-17 \tabularnewline
98 & 15.6 & 15.6 & 9.01680099153656e-18 \tabularnewline
99 & 14.4 & 14.4 & 6.10915174221326e-17 \tabularnewline
100 & 13.7 & 13.7 & -1.3540638913855e-17 \tabularnewline
101 & 14.1 & 14.1 & 3.48896599531176e-17 \tabularnewline
102 & 15 & 15 & -1.48632837906236e-17 \tabularnewline
103 & 14.2 & 14.2 & -2.86291300456655e-16 \tabularnewline
104 & 13.6 & 13.6 & 2.32717049038207e-17 \tabularnewline
105 & 15.4 & 15.4 & 4.74745275543349e-17 \tabularnewline
106 & 14.8 & 14.8 & 4.74831383744517e-17 \tabularnewline
107 & 12.5 & 12.5 & 4.9490657711978e-17 \tabularnewline
108 & 16.2 & 16.2 & 2.14177658890305e-17 \tabularnewline
109 & 16.1 & 16.1 & 1.62450918202615e-16 \tabularnewline
110 & 16 & 16 & 3.53370096826234e-17 \tabularnewline
111 & 15.8 & 15.8 & 1.76137601740669e-16 \tabularnewline
112 & 15.2 & 15.2 & 1.9587866351739e-17 \tabularnewline
113 & 15.7 & 15.7 & -1.23746982439437e-16 \tabularnewline
114 & 18.9 & 18.9 & 6.22301433799458e-17 \tabularnewline
115 & 17.4 & 17.4 & -2.35262274710747e-16 \tabularnewline
116 & 17 & 17 & -6.39841528164268e-17 \tabularnewline
117 & 19.8 & 19.8 & -4.1227068191591e-17 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&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]15[/C][C]15[/C][C]-2.89296795448131e-16[/C][/ROW]
[ROW][C]2[/C][C]14.4[/C][C]14.4[/C][C]-2.57901354069519e-16[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]13[/C][C]-6.74979900756436e-16[/C][/ROW]
[ROW][C]4[/C][C]13.7[/C][C]13.7[/C][C]-5.99532317973799e-17[/C][/ROW]
[ROW][C]5[/C][C]13.6[/C][C]13.6[/C][C]1.19375966974017e-16[/C][/ROW]
[ROW][C]6[/C][C]15.2[/C][C]15.2[/C][C]1.76246915351944e-16[/C][/ROW]
[ROW][C]7[/C][C]12.9[/C][C]12.9[/C][C]2.86085812889703e-15[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14[/C][C]-6.18645742387088e-17[/C][/ROW]
[ROW][C]9[/C][C]14.1[/C][C]14.1[/C][C]-7.4461608657557e-17[/C][/ROW]
[ROW][C]10[/C][C]13.2[/C][C]13.2[/C][C]-1.10493330503759e-16[/C][/ROW]
[ROW][C]11[/C][C]11.3[/C][C]11.3[/C][C]6.08080803806897e-18[/C][/ROW]
[ROW][C]12[/C][C]13.3[/C][C]13.3[/C][C]-1.1815875102057e-16[/C][/ROW]
[ROW][C]13[/C][C]14.4[/C][C]14.4[/C][C]3.69914892012958e-18[/C][/ROW]
[ROW][C]14[/C][C]13.3[/C][C]13.3[/C][C]2.66069956729712e-17[/C][/ROW]
[ROW][C]15[/C][C]11.6[/C][C]11.6[/C][C]-1.32121368677276e-16[/C][/ROW]
[ROW][C]16[/C][C]13.2[/C][C]13.2[/C][C]-6.76248333803364e-17[/C][/ROW]
[ROW][C]17[/C][C]13.1[/C][C]13.1[/C][C]-6.31738092303693e-17[/C][/ROW]
[ROW][C]18[/C][C]14.6[/C][C]14.6[/C][C]-1.24684451230098e-16[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14[/C][C]-4.51966447946513e-16[/C][/ROW]
[ROW][C]20[/C][C]14.3[/C][C]14.3[/C][C]3.76756307634271e-17[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]13.8[/C][C]-7.06613987846437e-17[/C][/ROW]
[ROW][C]22[/C][C]13.7[/C][C]13.7[/C][C]-2.32681453430692e-17[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11[/C][C]-1.71997573888969e-17[/C][/ROW]
[ROW][C]24[/C][C]14.4[/C][C]14.4[/C][C]-1.24266653161158e-16[/C][/ROW]
[ROW][C]25[/C][C]15.6[/C][C]15.6[/C][C]-6.86351439679292e-17[/C][/ROW]
[ROW][C]26[/C][C]13.7[/C][C]13.7[/C][C]2.46877428755157e-17[/C][/ROW]
[ROW][C]27[/C][C]12.6[/C][C]12.6[/C][C]8.92254042244412e-17[/C][/ROW]
[ROW][C]28[/C][C]13.2[/C][C]13.2[/C][C]-6.11380785941764e-17[/C][/ROW]
[ROW][C]29[/C][C]13.3[/C][C]13.3[/C][C]2.95146079605198e-17[/C][/ROW]
[ROW][C]30[/C][C]14.3[/C][C]14.3[/C][C]-7.76536297158759e-17[/C][/ROW]
[ROW][C]31[/C][C]14[/C][C]14[/C][C]-3.62775832681693e-16[/C][/ROW]
[ROW][C]32[/C][C]13.4[/C][C]13.4[/C][C]-1.39314095149291e-17[/C][/ROW]
[ROW][C]33[/C][C]13.9[/C][C]13.9[/C][C]-6.77557766024397e-17[/C][/ROW]
[ROW][C]34[/C][C]13.7[/C][C]13.7[/C][C]9.63564371041354e-18[/C][/ROW]
[ROW][C]35[/C][C]10.5[/C][C]10.5[/C][C]-7.25107076860199e-17[/C][/ROW]
[ROW][C]36[/C][C]14.5[/C][C]14.5[/C][C]-5.98920229497522e-17[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]15[/C][C]1.739712445657e-17[/C][/ROW]
[ROW][C]38[/C][C]13.5[/C][C]13.5[/C][C]2.64018155009445e-18[/C][/ROW]
[ROW][C]39[/C][C]13.5[/C][C]13.5[/C][C]1.23390119246382e-16[/C][/ROW]
[ROW][C]40[/C][C]13.2[/C][C]13.2[/C][C]5.30304495003798e-17[/C][/ROW]
[ROW][C]41[/C][C]13.8[/C][C]13.8[/C][C]-1.16322040956552e-16[/C][/ROW]
[ROW][C]42[/C][C]16.2[/C][C]16.2[/C][C]-8.83220749074289e-17[/C][/ROW]
[ROW][C]43[/C][C]14.7[/C][C]14.7[/C][C]-2.81157382641636e-16[/C][/ROW]
[ROW][C]44[/C][C]13.9[/C][C]13.9[/C][C]-2.91859322944952e-17[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]16[/C][C]2.37501759892062e-17[/C][/ROW]
[ROW][C]46[/C][C]14.4[/C][C]14.4[/C][C]-1.30911500757679e-17[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]12.3[/C][C]2.58468368412539e-17[/C][/ROW]
[ROW][C]48[/C][C]15.9[/C][C]15.9[/C][C]-1.06845734128434e-17[/C][/ROW]
[ROW][C]49[/C][C]15.9[/C][C]15.9[/C][C]4.83336180301154e-17[/C][/ROW]
[ROW][C]50[/C][C]15.5[/C][C]15.5[/C][C]-1.23932134928787e-17[/C][/ROW]
[ROW][C]51[/C][C]15.1[/C][C]15.1[/C][C]1.05807742029812e-16[/C][/ROW]
[ROW][C]52[/C][C]14.5[/C][C]14.5[/C][C]6.04747476917268e-17[/C][/ROW]
[ROW][C]53[/C][C]15.1[/C][C]15.1[/C][C]1.01605082441038e-17[/C][/ROW]
[ROW][C]54[/C][C]17.4[/C][C]17.4[/C][C]-2.7030544884209e-17[/C][/ROW]
[ROW][C]55[/C][C]16.2[/C][C]16.2[/C][C]-2.82677379293975e-16[/C][/ROW]
[ROW][C]56[/C][C]15.6[/C][C]15.6[/C][C]-5.63846947730492e-17[/C][/ROW]
[ROW][C]57[/C][C]17.2[/C][C]17.2[/C][C]1.45383358439572e-17[/C][/ROW]
[ROW][C]58[/C][C]14.9[/C][C]14.9[/C][C]8.39707768018205e-17[/C][/ROW]
[ROW][C]59[/C][C]13.8[/C][C]13.8[/C][C]-1.71215932246123e-16[/C][/ROW]
[ROW][C]60[/C][C]17.5[/C][C]17.5[/C][C]6.17088890378218e-17[/C][/ROW]
[ROW][C]61[/C][C]16.2[/C][C]16.2[/C][C]6.99292671256961e-17[/C][/ROW]
[ROW][C]62[/C][C]17.5[/C][C]17.5[/C][C]4.399592581324e-19[/C][/ROW]
[ROW][C]63[/C][C]16.6[/C][C]16.6[/C][C]1.37195859457235e-16[/C][/ROW]
[ROW][C]64[/C][C]16.2[/C][C]16.2[/C][C]2.88417670813949e-17[/C][/ROW]
[ROW][C]65[/C][C]16.6[/C][C]16.6[/C][C]-1.24676138789622e-16[/C][/ROW]
[ROW][C]66[/C][C]19.6[/C][C]19.6[/C][C]1.11401944620687e-16[/C][/ROW]
[ROW][C]67[/C][C]15.9[/C][C]15.9[/C][C]-3.29726925643704e-16[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]18[/C][C]1.24086179939321e-17[/C][/ROW]
[ROW][C]69[/C][C]18.3[/C][C]18.3[/C][C]1.09831892180842e-16[/C][/ROW]
[ROW][C]70[/C][C]16.3[/C][C]16.3[/C][C]-4.53693916462838e-18[/C][/ROW]
[ROW][C]71[/C][C]14.9[/C][C]14.9[/C][C]-8.63377341698488e-17[/C][/ROW]
[ROW][C]72[/C][C]18.2[/C][C]18.2[/C][C]-1.25917464527822e-17[/C][/ROW]
[ROW][C]73[/C][C]18.4[/C][C]18.4[/C][C]1.50250330807415e-17[/C][/ROW]
[ROW][C]74[/C][C]18.5[/C][C]18.5[/C][C]9.10776006556092e-18[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]16[/C][C]1.80952817728626e-16[/C][/ROW]
[ROW][C]76[/C][C]17.4[/C][C]17.4[/C][C]1.54663357875496e-17[/C][/ROW]
[ROW][C]77[/C][C]17.2[/C][C]17.2[/C][C]1.02360655488643e-16[/C][/ROW]
[ROW][C]78[/C][C]19.6[/C][C]19.6[/C][C]-1.77785889214598e-17[/C][/ROW]
[ROW][C]79[/C][C]17.2[/C][C]17.2[/C][C]-2.91943580338292e-16[/C][/ROW]
[ROW][C]80[/C][C]18.3[/C][C]18.3[/C][C]6.70206189704102e-17[/C][/ROW]
[ROW][C]81[/C][C]19.3[/C][C]19.3[/C][C]6.12348762080334e-17[/C][/ROW]
[ROW][C]82[/C][C]18.1[/C][C]18.1[/C][C]2.77652331965105e-17[/C][/ROW]
[ROW][C]83[/C][C]16.2[/C][C]16.2[/C][C]2.94629547582709e-16[/C][/ROW]
[ROW][C]84[/C][C]18.4[/C][C]18.4[/C][C]-4.18321427506925e-17[/C][/ROW]
[ROW][C]85[/C][C]20.5[/C][C]20.5[/C][C]1.20604404388515e-17[/C][/ROW]
[ROW][C]86[/C][C]19[/C][C]19[/C][C]1.62458117465963e-16[/C][/ROW]
[ROW][C]87[/C][C]16.5[/C][C]16.5[/C][C]-6.6699792415586e-17[/C][/ROW]
[ROW][C]88[/C][C]18.7[/C][C]18.7[/C][C]2.48556162729576e-17[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]19[/C][C]1.3161757279558e-16[/C][/ROW]
[ROW][C]90[/C][C]19.2[/C][C]19.2[/C][C]4.53570097117462e-19[/C][/ROW]
[ROW][C]91[/C][C]20.5[/C][C]20.5[/C][C]-3.39057005183814e-16[/C][/ROW]
[ROW][C]92[/C][C]19.3[/C][C]19.3[/C][C]8.49741910060189e-17[/C][/ROW]
[ROW][C]93[/C][C]20.6[/C][C]20.6[/C][C]-2.7239555401431e-18[/C][/ROW]
[ROW][C]94[/C][C]20.1[/C][C]20.1[/C][C]-1.74652269959715e-17[/C][/ROW]
[ROW][C]95[/C][C]16.1[/C][C]16.1[/C][C]-2.87837186831201e-17[/C][/ROW]
[ROW][C]96[/C][C]20.4[/C][C]20.4[/C][C]2.84299234820946e-16[/C][/ROW]
[ROW][C]97[/C][C]19.7[/C][C]19.7[/C][C]2.90363891613402e-17[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]15.6[/C][C]9.01680099153656e-18[/C][/ROW]
[ROW][C]99[/C][C]14.4[/C][C]14.4[/C][C]6.10915174221326e-17[/C][/ROW]
[ROW][C]100[/C][C]13.7[/C][C]13.7[/C][C]-1.3540638913855e-17[/C][/ROW]
[ROW][C]101[/C][C]14.1[/C][C]14.1[/C][C]3.48896599531176e-17[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]15[/C][C]-1.48632837906236e-17[/C][/ROW]
[ROW][C]103[/C][C]14.2[/C][C]14.2[/C][C]-2.86291300456655e-16[/C][/ROW]
[ROW][C]104[/C][C]13.6[/C][C]13.6[/C][C]2.32717049038207e-17[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]15.4[/C][C]4.74745275543349e-17[/C][/ROW]
[ROW][C]106[/C][C]14.8[/C][C]14.8[/C][C]4.74831383744517e-17[/C][/ROW]
[ROW][C]107[/C][C]12.5[/C][C]12.5[/C][C]4.9490657711978e-17[/C][/ROW]
[ROW][C]108[/C][C]16.2[/C][C]16.2[/C][C]2.14177658890305e-17[/C][/ROW]
[ROW][C]109[/C][C]16.1[/C][C]16.1[/C][C]1.62450918202615e-16[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]16[/C][C]3.53370096826234e-17[/C][/ROW]
[ROW][C]111[/C][C]15.8[/C][C]15.8[/C][C]1.76137601740669e-16[/C][/ROW]
[ROW][C]112[/C][C]15.2[/C][C]15.2[/C][C]1.9587866351739e-17[/C][/ROW]
[ROW][C]113[/C][C]15.7[/C][C]15.7[/C][C]-1.23746982439437e-16[/C][/ROW]
[ROW][C]114[/C][C]18.9[/C][C]18.9[/C][C]6.22301433799458e-17[/C][/ROW]
[ROW][C]115[/C][C]17.4[/C][C]17.4[/C][C]-2.35262274710747e-16[/C][/ROW]
[ROW][C]116[/C][C]17[/C][C]17[/C][C]-6.39841528164268e-17[/C][/ROW]
[ROW][C]117[/C][C]19.8[/C][C]19.8[/C][C]-4.1227068191591e-17[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&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
11515-2.89296795448131e-16
214.414.4-2.57901354069519e-16
31313-6.74979900756436e-16
413.713.7-5.99532317973799e-17
513.613.61.19375966974017e-16
615.215.21.76246915351944e-16
712.912.92.86085812889703e-15
81414-6.18645742387088e-17
914.114.1-7.4461608657557e-17
1013.213.2-1.10493330503759e-16
1111.311.36.08080803806897e-18
1213.313.3-1.1815875102057e-16
1314.414.43.69914892012958e-18
1413.313.32.66069956729712e-17
1511.611.6-1.32121368677276e-16
1613.213.2-6.76248333803364e-17
1713.113.1-6.31738092303693e-17
1814.614.6-1.24684451230098e-16
191414-4.51966447946513e-16
2014.314.33.76756307634271e-17
2113.813.8-7.06613987846437e-17
2213.713.7-2.32681453430692e-17
231111-1.71997573888969e-17
2414.414.4-1.24266653161158e-16
2515.615.6-6.86351439679292e-17
2613.713.72.46877428755157e-17
2712.612.68.92254042244412e-17
2813.213.2-6.11380785941764e-17
2913.313.32.95146079605198e-17
3014.314.3-7.76536297158759e-17
311414-3.62775832681693e-16
3213.413.4-1.39314095149291e-17
3313.913.9-6.77557766024397e-17
3413.713.79.63564371041354e-18
3510.510.5-7.25107076860199e-17
3614.514.5-5.98920229497522e-17
3715151.739712445657e-17
3813.513.52.64018155009445e-18
3913.513.51.23390119246382e-16
4013.213.25.30304495003798e-17
4113.813.8-1.16322040956552e-16
4216.216.2-8.83220749074289e-17
4314.714.7-2.81157382641636e-16
4413.913.9-2.91859322944952e-17
4516162.37501759892062e-17
4614.414.4-1.30911500757679e-17
4712.312.32.58468368412539e-17
4815.915.9-1.06845734128434e-17
4915.915.94.83336180301154e-17
5015.515.5-1.23932134928787e-17
5115.115.11.05807742029812e-16
5214.514.56.04747476917268e-17
5315.115.11.01605082441038e-17
5417.417.4-2.7030544884209e-17
5516.216.2-2.82677379293975e-16
5615.615.6-5.63846947730492e-17
5717.217.21.45383358439572e-17
5814.914.98.39707768018205e-17
5913.813.8-1.71215932246123e-16
6017.517.56.17088890378218e-17
6116.216.26.99292671256961e-17
6217.517.54.399592581324e-19
6316.616.61.37195859457235e-16
6416.216.22.88417670813949e-17
6516.616.6-1.24676138789622e-16
6619.619.61.11401944620687e-16
6715.915.9-3.29726925643704e-16
6818181.24086179939321e-17
6918.318.31.09831892180842e-16
7016.316.3-4.53693916462838e-18
7114.914.9-8.63377341698488e-17
7218.218.2-1.25917464527822e-17
7318.418.41.50250330807415e-17
7418.518.59.10776006556092e-18
7516161.80952817728626e-16
7617.417.41.54663357875496e-17
7717.217.21.02360655488643e-16
7819.619.6-1.77785889214598e-17
7917.217.2-2.91943580338292e-16
8018.318.36.70206189704102e-17
8119.319.36.12348762080334e-17
8218.118.12.77652331965105e-17
8316.216.22.94629547582709e-16
8418.418.4-4.18321427506925e-17
8520.520.51.20604404388515e-17
8619191.62458117465963e-16
8716.516.5-6.6699792415586e-17
8818.718.72.48556162729576e-17
8919191.3161757279558e-16
9019.219.24.53570097117462e-19
9120.520.5-3.39057005183814e-16
9219.319.38.49741910060189e-17
9320.620.6-2.7239555401431e-18
9420.120.1-1.74652269959715e-17
9516.116.1-2.87837186831201e-17
9620.420.42.84299234820946e-16
9719.719.72.90363891613402e-17
9815.615.69.01680099153656e-18
9914.414.46.10915174221326e-17
10013.713.7-1.3540638913855e-17
10114.114.13.48896599531176e-17
1021515-1.48632837906236e-17
10314.214.2-2.86291300456655e-16
10413.613.62.32717049038207e-17
10515.415.44.74745275543349e-17
10614.814.84.74831383744517e-17
10712.512.54.9490657711978e-17
10816.216.22.14177658890305e-17
10916.116.11.62450918202615e-16
11016163.53370096826234e-17
11115.815.81.76137601740669e-16
11215.215.21.9587866351739e-17
11315.715.7-1.23746982439437e-16
11418.918.96.22301433799458e-17
11517.417.4-2.35262274710747e-16
1161717-6.39841528164268e-17
11719.819.8-4.1227068191591e-17






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.812773766730760.3744524665384780.187226233269239
230.004580353652448240.009160707304896470.995419646347552
240.2501455515008650.500291103001730.749854448499135
250.005148721687474040.01029744337494810.994851278312526
261.48026337390134e-072.96052674780269e-070.999999851973663
274.18991909481381e-068.37983818962763e-060.999995810080905
280.001261989552302740.002523979104605480.998738010447697
290.1800704160609790.3601408321219570.819929583939021
300.001068054243313870.002136108486627730.998931945756686
312.44811334985993e-104.89622669971986e-100.999999999755189
320.9758536839372950.04829263212540910.0241463160627045
330.3430614811606880.6861229623213770.656938518839312
347.26122304504278e-081.45224460900856e-070.99999992738777
350.9999999997927484.14503633384166e-102.07251816692083e-10
362.91457208265004e-155.82914416530008e-150.999999999999997
370.9999993515913141.29681737251484e-066.48408686257421e-07
380.6603429857135570.6793140285728850.339657014286443
394.44929285073193e-088.89858570146385e-080.999999955507072
400.7659578849398890.4680842301202220.234042115060111
412.79616392277038e-105.59232784554075e-100.999999999720384
420.05858847050188240.1171769410037650.941411529498118
431.65912474188311e-083.31824948376622e-080.999999983408753
440.7698331242100530.4603337515798940.230166875789947
450.0007868096382084840.001573619276416970.999213190361792
460.3669565535547970.7339131071095940.633043446445203
473.47880202971822e-076.95760405943644e-070.999999652119797
481.36800381641299e-092.73600763282597e-090.999999998631996
490.997341911182180.005316177635638780.00265808881781939
500.009812582441124080.01962516488224820.990187417558876
510.9999995962704638.07459074687847e-074.03729537343924e-07
520.9999993744447061.25111058751734e-066.25555293758668e-07
530.08492830948625960.1698566189725190.91507169051374
542.19921422398892e-114.39842844797783e-110.999999999978008
552.13694899955465e-184.2738979991093e-181
560.2739213642433670.5478427284867340.726078635756633
570.0260042772420140.05200855448402810.973995722757986
580.999997188409455.62318110184393e-062.81159055092197e-06
590.0005675924182014860.001135184836402970.999432407581799
6019.6122802739859e-174.80614013699295e-17
610.9555300804032780.08893983919344410.0444699195967221
623.42277750858199e-106.84555501716398e-100.999999999657722
630.9999998164081293.67183742671529e-071.83591871335765e-07
641.17916167748806e-172.35832335497612e-171
650.999999999999991.9206155399927e-149.6030776999635e-15
660.03175462526150250.0635092505230050.968245374738497
676.07443792874851e-141.2148875857497e-130.99999999999994
680.9999708925067765.82149864490032e-052.91074932245016e-05
690.9892413416967060.02151731660658890.0107586583032944
704.63389607470153e-189.26779214940306e-181
710.9999999999973375.32609489661068e-122.66304744830534e-12
720.8517591808311660.2964816383376690.148240819168834
731.55138417772829e-063.10276835545658e-060.999998448615822
748.25515699881246e-231.65103139976249e-221
750.999999532328749.35342518458306e-074.67671259229153e-07
760.5198221532909420.9603556934181150.480177846709058
770.09122738685298860.1824547737059770.908772613147011
780.9999995376325369.24734928059614e-074.62367464029807e-07
790.9999997122444895.75511022645216e-072.87755511322608e-07
800.009747423209079660.01949484641815930.99025257679092
810.9999999567724538.64550946735569e-084.32275473367784e-08
820.9970383549030.005923290194000290.00296164509700015
830.9999998432390893.13521822631798e-071.56760911315899e-07
840.647677614820530.704644770358940.35232238517947
850.6199599094535480.7600801810929040.380040090546452
860.6407414085972670.7185171828054650.359258591402733
870.2422135471010140.4844270942020290.757786452898985
880.9897664703323160.02046705933536890.0102335296676844
898.17553436501637e-081.63510687300327e-070.999999918244656
900.8504548436336810.2990903127326380.149545156366319
912.18650069460637e-154.37300138921275e-150.999999999999998
921.54004132081724e-253.08008264163447e-251
939.55374942309373e-050.0001910749884618750.99990446250577
946.86923087307166e-091.37384617461433e-080.99999999313077
954.34987564184505e-068.6997512836901e-060.999995650124358

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.81277376673076 & 0.374452466538478 & 0.187226233269239 \tabularnewline
23 & 0.00458035365244824 & 0.00916070730489647 & 0.995419646347552 \tabularnewline
24 & 0.250145551500865 & 0.50029110300173 & 0.749854448499135 \tabularnewline
25 & 0.00514872168747404 & 0.0102974433749481 & 0.994851278312526 \tabularnewline
26 & 1.48026337390134e-07 & 2.96052674780269e-07 & 0.999999851973663 \tabularnewline
27 & 4.18991909481381e-06 & 8.37983818962763e-06 & 0.999995810080905 \tabularnewline
28 & 0.00126198955230274 & 0.00252397910460548 & 0.998738010447697 \tabularnewline
29 & 0.180070416060979 & 0.360140832121957 & 0.819929583939021 \tabularnewline
30 & 0.00106805424331387 & 0.00213610848662773 & 0.998931945756686 \tabularnewline
31 & 2.44811334985993e-10 & 4.89622669971986e-10 & 0.999999999755189 \tabularnewline
32 & 0.975853683937295 & 0.0482926321254091 & 0.0241463160627045 \tabularnewline
33 & 0.343061481160688 & 0.686122962321377 & 0.656938518839312 \tabularnewline
34 & 7.26122304504278e-08 & 1.45224460900856e-07 & 0.99999992738777 \tabularnewline
35 & 0.999999999792748 & 4.14503633384166e-10 & 2.07251816692083e-10 \tabularnewline
36 & 2.91457208265004e-15 & 5.82914416530008e-15 & 0.999999999999997 \tabularnewline
37 & 0.999999351591314 & 1.29681737251484e-06 & 6.48408686257421e-07 \tabularnewline
38 & 0.660342985713557 & 0.679314028572885 & 0.339657014286443 \tabularnewline
39 & 4.44929285073193e-08 & 8.89858570146385e-08 & 0.999999955507072 \tabularnewline
40 & 0.765957884939889 & 0.468084230120222 & 0.234042115060111 \tabularnewline
41 & 2.79616392277038e-10 & 5.59232784554075e-10 & 0.999999999720384 \tabularnewline
42 & 0.0585884705018824 & 0.117176941003765 & 0.941411529498118 \tabularnewline
43 & 1.65912474188311e-08 & 3.31824948376622e-08 & 0.999999983408753 \tabularnewline
44 & 0.769833124210053 & 0.460333751579894 & 0.230166875789947 \tabularnewline
45 & 0.000786809638208484 & 0.00157361927641697 & 0.999213190361792 \tabularnewline
46 & 0.366956553554797 & 0.733913107109594 & 0.633043446445203 \tabularnewline
47 & 3.47880202971822e-07 & 6.95760405943644e-07 & 0.999999652119797 \tabularnewline
48 & 1.36800381641299e-09 & 2.73600763282597e-09 & 0.999999998631996 \tabularnewline
49 & 0.99734191118218 & 0.00531617763563878 & 0.00265808881781939 \tabularnewline
50 & 0.00981258244112408 & 0.0196251648822482 & 0.990187417558876 \tabularnewline
51 & 0.999999596270463 & 8.07459074687847e-07 & 4.03729537343924e-07 \tabularnewline
52 & 0.999999374444706 & 1.25111058751734e-06 & 6.25555293758668e-07 \tabularnewline
53 & 0.0849283094862596 & 0.169856618972519 & 0.91507169051374 \tabularnewline
54 & 2.19921422398892e-11 & 4.39842844797783e-11 & 0.999999999978008 \tabularnewline
55 & 2.13694899955465e-18 & 4.2738979991093e-18 & 1 \tabularnewline
56 & 0.273921364243367 & 0.547842728486734 & 0.726078635756633 \tabularnewline
57 & 0.026004277242014 & 0.0520085544840281 & 0.973995722757986 \tabularnewline
58 & 0.99999718840945 & 5.62318110184393e-06 & 2.81159055092197e-06 \tabularnewline
59 & 0.000567592418201486 & 0.00113518483640297 & 0.999432407581799 \tabularnewline
60 & 1 & 9.6122802739859e-17 & 4.80614013699295e-17 \tabularnewline
61 & 0.955530080403278 & 0.0889398391934441 & 0.0444699195967221 \tabularnewline
62 & 3.42277750858199e-10 & 6.84555501716398e-10 & 0.999999999657722 \tabularnewline
63 & 0.999999816408129 & 3.67183742671529e-07 & 1.83591871335765e-07 \tabularnewline
64 & 1.17916167748806e-17 & 2.35832335497612e-17 & 1 \tabularnewline
65 & 0.99999999999999 & 1.9206155399927e-14 & 9.6030776999635e-15 \tabularnewline
66 & 0.0317546252615025 & 0.063509250523005 & 0.968245374738497 \tabularnewline
67 & 6.07443792874851e-14 & 1.2148875857497e-13 & 0.99999999999994 \tabularnewline
68 & 0.999970892506776 & 5.82149864490032e-05 & 2.91074932245016e-05 \tabularnewline
69 & 0.989241341696706 & 0.0215173166065889 & 0.0107586583032944 \tabularnewline
70 & 4.63389607470153e-18 & 9.26779214940306e-18 & 1 \tabularnewline
71 & 0.999999999997337 & 5.32609489661068e-12 & 2.66304744830534e-12 \tabularnewline
72 & 0.851759180831166 & 0.296481638337669 & 0.148240819168834 \tabularnewline
73 & 1.55138417772829e-06 & 3.10276835545658e-06 & 0.999998448615822 \tabularnewline
74 & 8.25515699881246e-23 & 1.65103139976249e-22 & 1 \tabularnewline
75 & 0.99999953232874 & 9.35342518458306e-07 & 4.67671259229153e-07 \tabularnewline
76 & 0.519822153290942 & 0.960355693418115 & 0.480177846709058 \tabularnewline
77 & 0.0912273868529886 & 0.182454773705977 & 0.908772613147011 \tabularnewline
78 & 0.999999537632536 & 9.24734928059614e-07 & 4.62367464029807e-07 \tabularnewline
79 & 0.999999712244489 & 5.75511022645216e-07 & 2.87755511322608e-07 \tabularnewline
80 & 0.00974742320907966 & 0.0194948464181593 & 0.99025257679092 \tabularnewline
81 & 0.999999956772453 & 8.64550946735569e-08 & 4.32275473367784e-08 \tabularnewline
82 & 0.997038354903 & 0.00592329019400029 & 0.00296164509700015 \tabularnewline
83 & 0.999999843239089 & 3.13521822631798e-07 & 1.56760911315899e-07 \tabularnewline
84 & 0.64767761482053 & 0.70464477035894 & 0.35232238517947 \tabularnewline
85 & 0.619959909453548 & 0.760080181092904 & 0.380040090546452 \tabularnewline
86 & 0.640741408597267 & 0.718517182805465 & 0.359258591402733 \tabularnewline
87 & 0.242213547101014 & 0.484427094202029 & 0.757786452898985 \tabularnewline
88 & 0.989766470332316 & 0.0204670593353689 & 0.0102335296676844 \tabularnewline
89 & 8.17553436501637e-08 & 1.63510687300327e-07 & 0.999999918244656 \tabularnewline
90 & 0.850454843633681 & 0.299090312732638 & 0.149545156366319 \tabularnewline
91 & 2.18650069460637e-15 & 4.37300138921275e-15 & 0.999999999999998 \tabularnewline
92 & 1.54004132081724e-25 & 3.08008264163447e-25 & 1 \tabularnewline
93 & 9.55374942309373e-05 & 0.000191074988461875 & 0.99990446250577 \tabularnewline
94 & 6.86923087307166e-09 & 1.37384617461433e-08 & 0.99999999313077 \tabularnewline
95 & 4.34987564184505e-06 & 8.6997512836901e-06 & 0.999995650124358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&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]22[/C][C]0.81277376673076[/C][C]0.374452466538478[/C][C]0.187226233269239[/C][/ROW]
[ROW][C]23[/C][C]0.00458035365244824[/C][C]0.00916070730489647[/C][C]0.995419646347552[/C][/ROW]
[ROW][C]24[/C][C]0.250145551500865[/C][C]0.50029110300173[/C][C]0.749854448499135[/C][/ROW]
[ROW][C]25[/C][C]0.00514872168747404[/C][C]0.0102974433749481[/C][C]0.994851278312526[/C][/ROW]
[ROW][C]26[/C][C]1.48026337390134e-07[/C][C]2.96052674780269e-07[/C][C]0.999999851973663[/C][/ROW]
[ROW][C]27[/C][C]4.18991909481381e-06[/C][C]8.37983818962763e-06[/C][C]0.999995810080905[/C][/ROW]
[ROW][C]28[/C][C]0.00126198955230274[/C][C]0.00252397910460548[/C][C]0.998738010447697[/C][/ROW]
[ROW][C]29[/C][C]0.180070416060979[/C][C]0.360140832121957[/C][C]0.819929583939021[/C][/ROW]
[ROW][C]30[/C][C]0.00106805424331387[/C][C]0.00213610848662773[/C][C]0.998931945756686[/C][/ROW]
[ROW][C]31[/C][C]2.44811334985993e-10[/C][C]4.89622669971986e-10[/C][C]0.999999999755189[/C][/ROW]
[ROW][C]32[/C][C]0.975853683937295[/C][C]0.0482926321254091[/C][C]0.0241463160627045[/C][/ROW]
[ROW][C]33[/C][C]0.343061481160688[/C][C]0.686122962321377[/C][C]0.656938518839312[/C][/ROW]
[ROW][C]34[/C][C]7.26122304504278e-08[/C][C]1.45224460900856e-07[/C][C]0.99999992738777[/C][/ROW]
[ROW][C]35[/C][C]0.999999999792748[/C][C]4.14503633384166e-10[/C][C]2.07251816692083e-10[/C][/ROW]
[ROW][C]36[/C][C]2.91457208265004e-15[/C][C]5.82914416530008e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]37[/C][C]0.999999351591314[/C][C]1.29681737251484e-06[/C][C]6.48408686257421e-07[/C][/ROW]
[ROW][C]38[/C][C]0.660342985713557[/C][C]0.679314028572885[/C][C]0.339657014286443[/C][/ROW]
[ROW][C]39[/C][C]4.44929285073193e-08[/C][C]8.89858570146385e-08[/C][C]0.999999955507072[/C][/ROW]
[ROW][C]40[/C][C]0.765957884939889[/C][C]0.468084230120222[/C][C]0.234042115060111[/C][/ROW]
[ROW][C]41[/C][C]2.79616392277038e-10[/C][C]5.59232784554075e-10[/C][C]0.999999999720384[/C][/ROW]
[ROW][C]42[/C][C]0.0585884705018824[/C][C]0.117176941003765[/C][C]0.941411529498118[/C][/ROW]
[ROW][C]43[/C][C]1.65912474188311e-08[/C][C]3.31824948376622e-08[/C][C]0.999999983408753[/C][/ROW]
[ROW][C]44[/C][C]0.769833124210053[/C][C]0.460333751579894[/C][C]0.230166875789947[/C][/ROW]
[ROW][C]45[/C][C]0.000786809638208484[/C][C]0.00157361927641697[/C][C]0.999213190361792[/C][/ROW]
[ROW][C]46[/C][C]0.366956553554797[/C][C]0.733913107109594[/C][C]0.633043446445203[/C][/ROW]
[ROW][C]47[/C][C]3.47880202971822e-07[/C][C]6.95760405943644e-07[/C][C]0.999999652119797[/C][/ROW]
[ROW][C]48[/C][C]1.36800381641299e-09[/C][C]2.73600763282597e-09[/C][C]0.999999998631996[/C][/ROW]
[ROW][C]49[/C][C]0.99734191118218[/C][C]0.00531617763563878[/C][C]0.00265808881781939[/C][/ROW]
[ROW][C]50[/C][C]0.00981258244112408[/C][C]0.0196251648822482[/C][C]0.990187417558876[/C][/ROW]
[ROW][C]51[/C][C]0.999999596270463[/C][C]8.07459074687847e-07[/C][C]4.03729537343924e-07[/C][/ROW]
[ROW][C]52[/C][C]0.999999374444706[/C][C]1.25111058751734e-06[/C][C]6.25555293758668e-07[/C][/ROW]
[ROW][C]53[/C][C]0.0849283094862596[/C][C]0.169856618972519[/C][C]0.91507169051374[/C][/ROW]
[ROW][C]54[/C][C]2.19921422398892e-11[/C][C]4.39842844797783e-11[/C][C]0.999999999978008[/C][/ROW]
[ROW][C]55[/C][C]2.13694899955465e-18[/C][C]4.2738979991093e-18[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0.273921364243367[/C][C]0.547842728486734[/C][C]0.726078635756633[/C][/ROW]
[ROW][C]57[/C][C]0.026004277242014[/C][C]0.0520085544840281[/C][C]0.973995722757986[/C][/ROW]
[ROW][C]58[/C][C]0.99999718840945[/C][C]5.62318110184393e-06[/C][C]2.81159055092197e-06[/C][/ROW]
[ROW][C]59[/C][C]0.000567592418201486[/C][C]0.00113518483640297[/C][C]0.999432407581799[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]9.6122802739859e-17[/C][C]4.80614013699295e-17[/C][/ROW]
[ROW][C]61[/C][C]0.955530080403278[/C][C]0.0889398391934441[/C][C]0.0444699195967221[/C][/ROW]
[ROW][C]62[/C][C]3.42277750858199e-10[/C][C]6.84555501716398e-10[/C][C]0.999999999657722[/C][/ROW]
[ROW][C]63[/C][C]0.999999816408129[/C][C]3.67183742671529e-07[/C][C]1.83591871335765e-07[/C][/ROW]
[ROW][C]64[/C][C]1.17916167748806e-17[/C][C]2.35832335497612e-17[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0.99999999999999[/C][C]1.9206155399927e-14[/C][C]9.6030776999635e-15[/C][/ROW]
[ROW][C]66[/C][C]0.0317546252615025[/C][C]0.063509250523005[/C][C]0.968245374738497[/C][/ROW]
[ROW][C]67[/C][C]6.07443792874851e-14[/C][C]1.2148875857497e-13[/C][C]0.99999999999994[/C][/ROW]
[ROW][C]68[/C][C]0.999970892506776[/C][C]5.82149864490032e-05[/C][C]2.91074932245016e-05[/C][/ROW]
[ROW][C]69[/C][C]0.989241341696706[/C][C]0.0215173166065889[/C][C]0.0107586583032944[/C][/ROW]
[ROW][C]70[/C][C]4.63389607470153e-18[/C][C]9.26779214940306e-18[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0.999999999997337[/C][C]5.32609489661068e-12[/C][C]2.66304744830534e-12[/C][/ROW]
[ROW][C]72[/C][C]0.851759180831166[/C][C]0.296481638337669[/C][C]0.148240819168834[/C][/ROW]
[ROW][C]73[/C][C]1.55138417772829e-06[/C][C]3.10276835545658e-06[/C][C]0.999998448615822[/C][/ROW]
[ROW][C]74[/C][C]8.25515699881246e-23[/C][C]1.65103139976249e-22[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0.99999953232874[/C][C]9.35342518458306e-07[/C][C]4.67671259229153e-07[/C][/ROW]
[ROW][C]76[/C][C]0.519822153290942[/C][C]0.960355693418115[/C][C]0.480177846709058[/C][/ROW]
[ROW][C]77[/C][C]0.0912273868529886[/C][C]0.182454773705977[/C][C]0.908772613147011[/C][/ROW]
[ROW][C]78[/C][C]0.999999537632536[/C][C]9.24734928059614e-07[/C][C]4.62367464029807e-07[/C][/ROW]
[ROW][C]79[/C][C]0.999999712244489[/C][C]5.75511022645216e-07[/C][C]2.87755511322608e-07[/C][/ROW]
[ROW][C]80[/C][C]0.00974742320907966[/C][C]0.0194948464181593[/C][C]0.99025257679092[/C][/ROW]
[ROW][C]81[/C][C]0.999999956772453[/C][C]8.64550946735569e-08[/C][C]4.32275473367784e-08[/C][/ROW]
[ROW][C]82[/C][C]0.997038354903[/C][C]0.00592329019400029[/C][C]0.00296164509700015[/C][/ROW]
[ROW][C]83[/C][C]0.999999843239089[/C][C]3.13521822631798e-07[/C][C]1.56760911315899e-07[/C][/ROW]
[ROW][C]84[/C][C]0.64767761482053[/C][C]0.70464477035894[/C][C]0.35232238517947[/C][/ROW]
[ROW][C]85[/C][C]0.619959909453548[/C][C]0.760080181092904[/C][C]0.380040090546452[/C][/ROW]
[ROW][C]86[/C][C]0.640741408597267[/C][C]0.718517182805465[/C][C]0.359258591402733[/C][/ROW]
[ROW][C]87[/C][C]0.242213547101014[/C][C]0.484427094202029[/C][C]0.757786452898985[/C][/ROW]
[ROW][C]88[/C][C]0.989766470332316[/C][C]0.0204670593353689[/C][C]0.0102335296676844[/C][/ROW]
[ROW][C]89[/C][C]8.17553436501637e-08[/C][C]1.63510687300327e-07[/C][C]0.999999918244656[/C][/ROW]
[ROW][C]90[/C][C]0.850454843633681[/C][C]0.299090312732638[/C][C]0.149545156366319[/C][/ROW]
[ROW][C]91[/C][C]2.18650069460637e-15[/C][C]4.37300138921275e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]92[/C][C]1.54004132081724e-25[/C][C]3.08008264163447e-25[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]9.55374942309373e-05[/C][C]0.000191074988461875[/C][C]0.99990446250577[/C][/ROW]
[ROW][C]94[/C][C]6.86923087307166e-09[/C][C]1.37384617461433e-08[/C][C]0.99999999313077[/C][/ROW]
[ROW][C]95[/C][C]4.34987564184505e-06[/C][C]8.6997512836901e-06[/C][C]0.999995650124358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.812773766730760.3744524665384780.187226233269239
230.004580353652448240.009160707304896470.995419646347552
240.2501455515008650.500291103001730.749854448499135
250.005148721687474040.01029744337494810.994851278312526
261.48026337390134e-072.96052674780269e-070.999999851973663
274.18991909481381e-068.37983818962763e-060.999995810080905
280.001261989552302740.002523979104605480.998738010447697
290.1800704160609790.3601408321219570.819929583939021
300.001068054243313870.002136108486627730.998931945756686
312.44811334985993e-104.89622669971986e-100.999999999755189
320.9758536839372950.04829263212540910.0241463160627045
330.3430614811606880.6861229623213770.656938518839312
347.26122304504278e-081.45224460900856e-070.99999992738777
350.9999999997927484.14503633384166e-102.07251816692083e-10
362.91457208265004e-155.82914416530008e-150.999999999999997
370.9999993515913141.29681737251484e-066.48408686257421e-07
380.6603429857135570.6793140285728850.339657014286443
394.44929285073193e-088.89858570146385e-080.999999955507072
400.7659578849398890.4680842301202220.234042115060111
412.79616392277038e-105.59232784554075e-100.999999999720384
420.05858847050188240.1171769410037650.941411529498118
431.65912474188311e-083.31824948376622e-080.999999983408753
440.7698331242100530.4603337515798940.230166875789947
450.0007868096382084840.001573619276416970.999213190361792
460.3669565535547970.7339131071095940.633043446445203
473.47880202971822e-076.95760405943644e-070.999999652119797
481.36800381641299e-092.73600763282597e-090.999999998631996
490.997341911182180.005316177635638780.00265808881781939
500.009812582441124080.01962516488224820.990187417558876
510.9999995962704638.07459074687847e-074.03729537343924e-07
520.9999993744447061.25111058751734e-066.25555293758668e-07
530.08492830948625960.1698566189725190.91507169051374
542.19921422398892e-114.39842844797783e-110.999999999978008
552.13694899955465e-184.2738979991093e-181
560.2739213642433670.5478427284867340.726078635756633
570.0260042772420140.05200855448402810.973995722757986
580.999997188409455.62318110184393e-062.81159055092197e-06
590.0005675924182014860.001135184836402970.999432407581799
6019.6122802739859e-174.80614013699295e-17
610.9555300804032780.08893983919344410.0444699195967221
623.42277750858199e-106.84555501716398e-100.999999999657722
630.9999998164081293.67183742671529e-071.83591871335765e-07
641.17916167748806e-172.35832335497612e-171
650.999999999999991.9206155399927e-149.6030776999635e-15
660.03175462526150250.0635092505230050.968245374738497
676.07443792874851e-141.2148875857497e-130.99999999999994
680.9999708925067765.82149864490032e-052.91074932245016e-05
690.9892413416967060.02151731660658890.0107586583032944
704.63389607470153e-189.26779214940306e-181
710.9999999999973375.32609489661068e-122.66304744830534e-12
720.8517591808311660.2964816383376690.148240819168834
731.55138417772829e-063.10276835545658e-060.999998448615822
748.25515699881246e-231.65103139976249e-221
750.999999532328749.35342518458306e-074.67671259229153e-07
760.5198221532909420.9603556934181150.480177846709058
770.09122738685298860.1824547737059770.908772613147011
780.9999995376325369.24734928059614e-074.62367464029807e-07
790.9999997122444895.75511022645216e-072.87755511322608e-07
800.009747423209079660.01949484641815930.99025257679092
810.9999999567724538.64550946735569e-084.32275473367784e-08
820.9970383549030.005923290194000290.00296164509700015
830.9999998432390893.13521822631798e-071.56760911315899e-07
840.647677614820530.704644770358940.35232238517947
850.6199599094535480.7600801810929040.380040090546452
860.6407414085972670.7185171828054650.359258591402733
870.2422135471010140.4844270942020290.757786452898985
880.9897664703323160.02046705933536890.0102335296676844
898.17553436501637e-081.63510687300327e-070.999999918244656
900.8504548436336810.2990903127326380.149545156366319
912.18650069460637e-154.37300138921275e-150.999999999999998
921.54004132081724e-253.08008264163447e-251
939.55374942309373e-050.0001910749884618750.99990446250577
946.86923087307166e-091.37384617461433e-080.99999999313077
954.34987564184505e-068.6997512836901e-060.999995650124358






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.621621621621622NOK
5% type I error level520.702702702702703NOK
10% type I error level550.743243243243243NOK

\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 & 46 & 0.621621621621622 & NOK \tabularnewline
5% type I error level & 52 & 0.702702702702703 & NOK \tabularnewline
10% type I error level & 55 & 0.743243243243243 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112500&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]46[/C][C]0.621621621621622[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]52[/C][C]0.702702702702703[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.743243243243243[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112500&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112500&T=6

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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 level460.621621621621622NOK
5% type I error level520.702702702702703NOK
10% type I error level550.743243243243243NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}