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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 computationFri, 20 Nov 2009 12:01:46 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258744030tvtrkdmau1nwkad.htm/, Retrieved Thu, 25 Apr 2024 07:25:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58427, Retrieved Thu, 25 Apr 2024 07:25:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7] [2009-11-18 17:01:04] [8b1aef4e7013bd33fbc2a5833375c5f5]
-   PD        [Multiple Regression] [WS7(2)] [2009-11-20 19:01:46] [5edea6bc5a9a9483633d9320282a2734] [Current]
-   P           [Multiple Regression] [WS7(3)] [2009-11-21 10:22:47] [7d268329e554b8694908ba13e6e6f258]
-   PD            [Multiple Regression] [WS7(4)] [2009-11-21 10:55:20] [7d268329e554b8694908ba13e6e6f258]
-    D              [Multiple Regression] [WS 7] [2009-11-25 18:27:00] [9717cb857c153ca3061376906953b329]
- RMPD              [Univariate Data Series] [Niet-werkende wer...] [2009-11-25 19:16:52] [9717cb857c153ca3061376906953b329]
- RMP                 [Univariate Explorative Data Analysis] [Univariate EDA] [2009-12-17 13:35:10] [9717cb857c153ca3061376906953b329]
- RMP                   [Central Tendency] [Robustness of Cen...] [2009-12-17 22:54:55] [9717cb857c153ca3061376906953b329]
-    D                    [Central Tendency] [Robustness of Cen...] [2009-12-29 21:57:55] [9717cb857c153ca3061376906953b329]
-    D                  [Univariate Explorative Data Analysis] [Univariate EDA] [2009-12-29 21:52:56] [9717cb857c153ca3061376906953b329]
-    D                  [Univariate Explorative Data Analysis] [] [2010-12-16 18:32:59] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-16 18:42:27] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-16 18:44:05] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-16 18:45:46] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-16 18:49:45] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-16 18:50:41] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-16 18:52:04] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Variance Reduction Matrix] [] [2010-12-16 18:53:37] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Standard Deviation-Mean Plot] [] [2010-12-16 18:55:46] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-    D                    [Univariate Explorative Data Analysis] [] [2010-12-19 14:43:39] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-   PD                  [Univariate Explorative Data Analysis] [Paper tijdreeks] [2011-12-16 15:35:08] [fbaf17a8836493f6de0f4e0e997711e1]
-   PD                    [Univariate Explorative Data Analysis] [Paper wijn] [2011-12-17 10:19:24] [fbaf17a8836493f6de0f4e0e997711e1]
- R PD                      [Univariate Explorative Data Analysis] [paper lag] [2011-12-18 14:28:16] [fbaf17a8836493f6de0f4e0e997711e1]
- RMP                       [ARIMA Forecasting] [paper arima forec...] [2011-12-18 14:35:06] [fbaf17a8836493f6de0f4e0e997711e1]
- RMPD                        [Histogram] [frequency] [2011-12-18 21:24:53] [fbaf17a8836493f6de0f4e0e997711e1]
- RMPD                    [Central Tendency] [Paper wijn] [2011-12-17 10:31:45] [fbaf17a8836493f6de0f4e0e997711e1]
- RMPD                    [(Partial) Autocorrelation Function] [Paper wijn] [2011-12-17 10:49:14] [fbaf17a8836493f6de0f4e0e997711e1]
- RMPD                  [Central Tendency] [Paper tijdreeks mean] [2011-12-16 16:04:44] [fbaf17a8836493f6de0f4e0e997711e1]
-   PD                [Univariate Data Series] [] [2010-12-16 17:58:43] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-   PD                  [Univariate Data Series] [] [2010-12-19 14:40:10] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-19 14:45:15] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-   P                     [Univariate Data Series] [] [2010-12-19 15:24:57] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-19 16:23:25] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-19 16:24:34] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [(Partial) Autocorrelation Function] [] [2010-12-19 16:26:19] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-19 16:28:33] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-19 16:29:44] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Spectral Analysis] [] [2010-12-19 16:30:39] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Variance Reduction Matrix] [] [2010-12-19 16:33:11] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [Standard Deviation-Mean Plot] [] [2010-12-19 16:35:52] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [ARIMA Backward Selection] [] [2010-12-19 16:37:56] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                       [ARIMA Forecasting] [] [2010-12-27 23:50:02] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [ARIMA Forecasting] [] [2010-12-19 16:45:16] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-   PD                [Univariate Data Series] [Werloosheid bij V...] [2010-12-26 15:10:52] [e4afca2801c0b93eac84a600ed82fb9c]
-   PD                [Univariate Data Series] [Werkloosheid vrou...] [2010-12-26 15:13:10] [e4afca2801c0b93eac84a600ed82fb9c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.1	10.9
7.7	10
7.5	9.2
7.6	9.2
7.8	9.5
7.8	9.6
7.8	9.5
7.5	9.1
7.5	8.9
7.1	9
7.5	10.1
7.5	10.3
7.6	10.2
7.7	9.6
7.7	9.2
7.9	9.3
8.1	9.4
8.2	9.4
8.2	9.2
8.2	9
7.9	9
7.3	9
6.9	9.8
6.6	10
6.7	9.8
6.9	9.3
7	9
7.1	9
7.2	9.1
7.1	9.1
6.9	9.1
7	9.2
6.8	8.8
6.4	8.3
6.7	8.4
6.6	8.1
6.4	7.7
6.3	7.9
6.2	7.9
6.5	8
6.8	7.9
6.8	7.6
6.4	7.1
6.1	6.8
5.8	6.5
6.1	6.9
7.2	8.2
7.3	8.7
6.9	8.3
6.1	7.9
5.8	7.5
6.2	7.8
7.1	8.3
7.7	8.4
7.9	8.2
7.7	7.7
7.4	7.2
7.5	7.3
8	8.1
8.1	8.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Werkl_Vrouwen[t] = + 2.77768360598676 + 0.878437173686043Werkl_Mannen[t] + 0.330274973894885M1[t] + 0.0659624086320924M2[t] -0.226193873999304M3[t] -0.319450052210233M4[t] -0.438118691263488M5[t] -0.563531152105813M6[t] -0.69325617821093M7[t] -0.830274973894884M8[t] -0.917018795683954M9[t] -0.721331360946745M10[t] -0.235137486947442M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkl_Vrouwen[t] =  +  2.77768360598676 +  0.878437173686043Werkl_Mannen[t] +  0.330274973894885M1[t] +  0.0659624086320924M2[t] -0.226193873999304M3[t] -0.319450052210233M4[t] -0.438118691263488M5[t] -0.563531152105813M6[t] -0.69325617821093M7[t] -0.830274973894884M8[t] -0.917018795683954M9[t] -0.721331360946745M10[t] -0.235137486947442M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkl_Vrouwen[t] =  +  2.77768360598676 +  0.878437173686043Werkl_Mannen[t] +  0.330274973894885M1[t] +  0.0659624086320924M2[t] -0.226193873999304M3[t] -0.319450052210233M4[t] -0.438118691263488M5[t] -0.563531152105813M6[t] -0.69325617821093M7[t] -0.830274973894884M8[t] -0.917018795683954M9[t] -0.721331360946745M10[t] -0.235137486947442M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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
Werkl_Vrouwen[t] = + 2.77768360598676 + 0.878437173686043Werkl_Mannen[t] + 0.330274973894885M1[t] + 0.0659624086320924M2[t] -0.226193873999304M3[t] -0.319450052210233M4[t] -0.438118691263488M5[t] -0.563531152105813M6[t] -0.69325617821093M7[t] -0.830274973894884M8[t] -0.917018795683954M9[t] -0.721331360946745M10[t] -0.235137486947442M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.777683605986761.1975842.31940.0247680.012384
Werkl_Mannen0.8784371736860430.1590085.52451e-061e-06
M10.3302749738948850.4822940.68480.4968330.248417
M20.06596240863209240.4841770.13620.8922170.446108
M3-0.2261938739993040.485898-0.46550.6437110.321855
M4-0.3194500522102330.482797-0.66170.5114170.255708
M5-0.4381186912634880.482975-0.90710.3689680.184484
M6-0.5635311521058130.48448-1.16320.2506340.125317
M7-0.693256178210930.483394-1.43410.1581510.079076
M8-0.8302749738948840.482294-1.72150.0917350.045867
M9-0.9170187956839540.48264-1.90.0635770.031789
M10-0.7213313609467450.485148-1.48680.1437380.071869
M11-0.2351374869474420.482168-0.48770.6280530.314026

\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) & 2.77768360598676 & 1.197584 & 2.3194 & 0.024768 & 0.012384 \tabularnewline
Werkl_Mannen & 0.878437173686043 & 0.159008 & 5.5245 & 1e-06 & 1e-06 \tabularnewline
M1 & 0.330274973894885 & 0.482294 & 0.6848 & 0.496833 & 0.248417 \tabularnewline
M2 & 0.0659624086320924 & 0.484177 & 0.1362 & 0.892217 & 0.446108 \tabularnewline
M3 & -0.226193873999304 & 0.485898 & -0.4655 & 0.643711 & 0.321855 \tabularnewline
M4 & -0.319450052210233 & 0.482797 & -0.6617 & 0.511417 & 0.255708 \tabularnewline
M5 & -0.438118691263488 & 0.482975 & -0.9071 & 0.368968 & 0.184484 \tabularnewline
M6 & -0.563531152105813 & 0.48448 & -1.1632 & 0.250634 & 0.125317 \tabularnewline
M7 & -0.69325617821093 & 0.483394 & -1.4341 & 0.158151 & 0.079076 \tabularnewline
M8 & -0.830274973894884 & 0.482294 & -1.7215 & 0.091735 & 0.045867 \tabularnewline
M9 & -0.917018795683954 & 0.48264 & -1.9 & 0.063577 & 0.031789 \tabularnewline
M10 & -0.721331360946745 & 0.485148 & -1.4868 & 0.143738 & 0.071869 \tabularnewline
M11 & -0.235137486947442 & 0.482168 & -0.4877 & 0.628053 & 0.314026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&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]2.77768360598676[/C][C]1.197584[/C][C]2.3194[/C][C]0.024768[/C][C]0.012384[/C][/ROW]
[ROW][C]Werkl_Mannen[/C][C]0.878437173686043[/C][C]0.159008[/C][C]5.5245[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]0.330274973894885[/C][C]0.482294[/C][C]0.6848[/C][C]0.496833[/C][C]0.248417[/C][/ROW]
[ROW][C]M2[/C][C]0.0659624086320924[/C][C]0.484177[/C][C]0.1362[/C][C]0.892217[/C][C]0.446108[/C][/ROW]
[ROW][C]M3[/C][C]-0.226193873999304[/C][C]0.485898[/C][C]-0.4655[/C][C]0.643711[/C][C]0.321855[/C][/ROW]
[ROW][C]M4[/C][C]-0.319450052210233[/C][C]0.482797[/C][C]-0.6617[/C][C]0.511417[/C][C]0.255708[/C][/ROW]
[ROW][C]M5[/C][C]-0.438118691263488[/C][C]0.482975[/C][C]-0.9071[/C][C]0.368968[/C][C]0.184484[/C][/ROW]
[ROW][C]M6[/C][C]-0.563531152105813[/C][C]0.48448[/C][C]-1.1632[/C][C]0.250634[/C][C]0.125317[/C][/ROW]
[ROW][C]M7[/C][C]-0.69325617821093[/C][C]0.483394[/C][C]-1.4341[/C][C]0.158151[/C][C]0.079076[/C][/ROW]
[ROW][C]M8[/C][C]-0.830274973894884[/C][C]0.482294[/C][C]-1.7215[/C][C]0.091735[/C][C]0.045867[/C][/ROW]
[ROW][C]M9[/C][C]-0.917018795683954[/C][C]0.48264[/C][C]-1.9[/C][C]0.063577[/C][C]0.031789[/C][/ROW]
[ROW][C]M10[/C][C]-0.721331360946745[/C][C]0.485148[/C][C]-1.4868[/C][C]0.143738[/C][C]0.071869[/C][/ROW]
[ROW][C]M11[/C][C]-0.235137486947442[/C][C]0.482168[/C][C]-0.4877[/C][C]0.628053[/C][C]0.314026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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)2.777683605986761.1975842.31940.0247680.012384
Werkl_Mannen0.8784371736860430.1590085.52451e-061e-06
M10.3302749738948850.4822940.68480.4968330.248417
M20.06596240863209240.4841770.13620.8922170.446108
M3-0.2261938739993040.485898-0.46550.6437110.321855
M4-0.3194500522102330.482797-0.66170.5114170.255708
M5-0.4381186912634880.482975-0.90710.3689680.184484
M6-0.5635311521058130.48448-1.16320.2506340.125317
M7-0.693256178210930.483394-1.43410.1581510.079076
M8-0.8302749738948840.482294-1.72150.0917350.045867
M9-0.9170187956839540.48264-1.90.0635770.031789
M10-0.7213313609467450.485148-1.48680.1437380.071869
M11-0.2351374869474420.482168-0.48770.6280530.314026






Multiple Linear Regression - Regression Statistics
Multiple R0.698814048315905
R-squared0.488341074123664
Adjusted R-squared0.357704752623323
F-TEST (value)3.73817226721581
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.000545406954329142
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.762308273255654
Sum Squared Residuals27.3123534632788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.698814048315905 \tabularnewline
R-squared & 0.488341074123664 \tabularnewline
Adjusted R-squared & 0.357704752623323 \tabularnewline
F-TEST (value) & 3.73817226721581 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.000545406954329142 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.762308273255654 \tabularnewline
Sum Squared Residuals & 27.3123534632788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.698814048315905[/C][/ROW]
[ROW][C]R-squared[/C][C]0.488341074123664[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.357704752623323[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.73817226721581[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.000545406954329142[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.762308273255654[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27.3123534632788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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 R0.698814048315905
R-squared0.488341074123664
Adjusted R-squared0.357704752623323
F-TEST (value)3.73817226721581
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.000545406954329142
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.762308273255654
Sum Squared Residuals27.3123534632788






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.910.22329968673860.676700313261408
2109.607612252001390.392387747998611
39.29.139768534632790.0602314653672115
49.29.134356073790460.0656439262095371
59.59.191374869474420.308625130525582
69.69.06596240863210.534037591367908
79.58.936237382526970.563762617473026
89.18.53568743473720.564312565262791
98.98.448943612948140.451056387051862
1098.293256178210930.706743821789071
1110.19.130824921684650.96917507831535
1210.39.36596240863210.934037591367908
1310.29.784081099895580.415918900104417
149.69.6076122520014-0.00761225200139373
159.29.31545596937-0.115455969369997
169.39.39788722589628-0.0978872258962753
179.49.45490602158023-0.0549060215802293
189.49.4173372781065-0.0173372781065079
199.29.28761225200139-0.0876122520013917
2099.15059345631744-0.150593456317437
2198.800318482422560.199681517577444
2298.468943612948140.531056387051862
239.88.603762617473021.19623738252698
24108.575368952314651.42463104768535
259.88.993487643578140.806512356421857
269.38.904862513052560.395137486947441
2798.700549947789770.299450052210234
2898.695137486947440.304862513052559
299.18.66431256526280.435687434737208
309.18.451056387051860.648943612948138
319.18.145643926209540.954356073790463
329.28.096468847894191.10353115210581
338.87.83403759136790.965962408632092
348.37.67835015663070.6216498433693
358.48.42807518273582-0.0280751827358157
368.18.57536895231465-0.475368952314654
377.78.72995649147233-1.02995649147233
387.98.37780020884093-0.477800208840933
397.97.99780020884093-0.0978002088409323
4088.16807518273582-0.168075182735816
417.98.31293769578837-0.412937695788374
427.68.18752523494605-0.58752523494605
437.17.70642533936652-0.606425339366516
446.87.30587539157675-0.505875391576749
456.56.95560041768187-0.455600417681866
466.97.41481900452489-0.514819004524887
478.28.86729376957884-0.667293769578838
488.79.19027497389488-0.490274973894884
498.39.16917507831535-0.869175078315351
507.98.20211277410372-0.302112774103725
517.57.64642533936652-0.146425339366515
527.87.90454403063-0.104544030630004
538.38.57646884789419-0.276468847894187
548.48.97811869126349-0.578118691263488
558.29.02408109989558-0.82408109989558
567.78.71137486947442-1.01137486947442
577.28.36109989557953-1.16109989557953
587.38.64463104768535-1.34463104768535
598.19.57004350852767-1.47004350852767
608.59.89302471284372-1.39302471284372

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10.9 & 10.2232996867386 & 0.676700313261408 \tabularnewline
2 & 10 & 9.60761225200139 & 0.392387747998611 \tabularnewline
3 & 9.2 & 9.13976853463279 & 0.0602314653672115 \tabularnewline
4 & 9.2 & 9.13435607379046 & 0.0656439262095371 \tabularnewline
5 & 9.5 & 9.19137486947442 & 0.308625130525582 \tabularnewline
6 & 9.6 & 9.0659624086321 & 0.534037591367908 \tabularnewline
7 & 9.5 & 8.93623738252697 & 0.563762617473026 \tabularnewline
8 & 9.1 & 8.5356874347372 & 0.564312565262791 \tabularnewline
9 & 8.9 & 8.44894361294814 & 0.451056387051862 \tabularnewline
10 & 9 & 8.29325617821093 & 0.706743821789071 \tabularnewline
11 & 10.1 & 9.13082492168465 & 0.96917507831535 \tabularnewline
12 & 10.3 & 9.3659624086321 & 0.934037591367908 \tabularnewline
13 & 10.2 & 9.78408109989558 & 0.415918900104417 \tabularnewline
14 & 9.6 & 9.6076122520014 & -0.00761225200139373 \tabularnewline
15 & 9.2 & 9.31545596937 & -0.115455969369997 \tabularnewline
16 & 9.3 & 9.39788722589628 & -0.0978872258962753 \tabularnewline
17 & 9.4 & 9.45490602158023 & -0.0549060215802293 \tabularnewline
18 & 9.4 & 9.4173372781065 & -0.0173372781065079 \tabularnewline
19 & 9.2 & 9.28761225200139 & -0.0876122520013917 \tabularnewline
20 & 9 & 9.15059345631744 & -0.150593456317437 \tabularnewline
21 & 9 & 8.80031848242256 & 0.199681517577444 \tabularnewline
22 & 9 & 8.46894361294814 & 0.531056387051862 \tabularnewline
23 & 9.8 & 8.60376261747302 & 1.19623738252698 \tabularnewline
24 & 10 & 8.57536895231465 & 1.42463104768535 \tabularnewline
25 & 9.8 & 8.99348764357814 & 0.806512356421857 \tabularnewline
26 & 9.3 & 8.90486251305256 & 0.395137486947441 \tabularnewline
27 & 9 & 8.70054994778977 & 0.299450052210234 \tabularnewline
28 & 9 & 8.69513748694744 & 0.304862513052559 \tabularnewline
29 & 9.1 & 8.6643125652628 & 0.435687434737208 \tabularnewline
30 & 9.1 & 8.45105638705186 & 0.648943612948138 \tabularnewline
31 & 9.1 & 8.14564392620954 & 0.954356073790463 \tabularnewline
32 & 9.2 & 8.09646884789419 & 1.10353115210581 \tabularnewline
33 & 8.8 & 7.8340375913679 & 0.965962408632092 \tabularnewline
34 & 8.3 & 7.6783501566307 & 0.6216498433693 \tabularnewline
35 & 8.4 & 8.42807518273582 & -0.0280751827358157 \tabularnewline
36 & 8.1 & 8.57536895231465 & -0.475368952314654 \tabularnewline
37 & 7.7 & 8.72995649147233 & -1.02995649147233 \tabularnewline
38 & 7.9 & 8.37780020884093 & -0.477800208840933 \tabularnewline
39 & 7.9 & 7.99780020884093 & -0.0978002088409323 \tabularnewline
40 & 8 & 8.16807518273582 & -0.168075182735816 \tabularnewline
41 & 7.9 & 8.31293769578837 & -0.412937695788374 \tabularnewline
42 & 7.6 & 8.18752523494605 & -0.58752523494605 \tabularnewline
43 & 7.1 & 7.70642533936652 & -0.606425339366516 \tabularnewline
44 & 6.8 & 7.30587539157675 & -0.505875391576749 \tabularnewline
45 & 6.5 & 6.95560041768187 & -0.455600417681866 \tabularnewline
46 & 6.9 & 7.41481900452489 & -0.514819004524887 \tabularnewline
47 & 8.2 & 8.86729376957884 & -0.667293769578838 \tabularnewline
48 & 8.7 & 9.19027497389488 & -0.490274973894884 \tabularnewline
49 & 8.3 & 9.16917507831535 & -0.869175078315351 \tabularnewline
50 & 7.9 & 8.20211277410372 & -0.302112774103725 \tabularnewline
51 & 7.5 & 7.64642533936652 & -0.146425339366515 \tabularnewline
52 & 7.8 & 7.90454403063 & -0.104544030630004 \tabularnewline
53 & 8.3 & 8.57646884789419 & -0.276468847894187 \tabularnewline
54 & 8.4 & 8.97811869126349 & -0.578118691263488 \tabularnewline
55 & 8.2 & 9.02408109989558 & -0.82408109989558 \tabularnewline
56 & 7.7 & 8.71137486947442 & -1.01137486947442 \tabularnewline
57 & 7.2 & 8.36109989557953 & -1.16109989557953 \tabularnewline
58 & 7.3 & 8.64463104768535 & -1.34463104768535 \tabularnewline
59 & 8.1 & 9.57004350852767 & -1.47004350852767 \tabularnewline
60 & 8.5 & 9.89302471284372 & -1.39302471284372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&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]10.9[/C][C]10.2232996867386[/C][C]0.676700313261408[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]9.60761225200139[/C][C]0.392387747998611[/C][/ROW]
[ROW][C]3[/C][C]9.2[/C][C]9.13976853463279[/C][C]0.0602314653672115[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]9.13435607379046[/C][C]0.0656439262095371[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]9.19137486947442[/C][C]0.308625130525582[/C][/ROW]
[ROW][C]6[/C][C]9.6[/C][C]9.0659624086321[/C][C]0.534037591367908[/C][/ROW]
[ROW][C]7[/C][C]9.5[/C][C]8.93623738252697[/C][C]0.563762617473026[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]8.5356874347372[/C][C]0.564312565262791[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.44894361294814[/C][C]0.451056387051862[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.29325617821093[/C][C]0.706743821789071[/C][/ROW]
[ROW][C]11[/C][C]10.1[/C][C]9.13082492168465[/C][C]0.96917507831535[/C][/ROW]
[ROW][C]12[/C][C]10.3[/C][C]9.3659624086321[/C][C]0.934037591367908[/C][/ROW]
[ROW][C]13[/C][C]10.2[/C][C]9.78408109989558[/C][C]0.415918900104417[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]9.6076122520014[/C][C]-0.00761225200139373[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]9.31545596937[/C][C]-0.115455969369997[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]9.39788722589628[/C][C]-0.0978872258962753[/C][/ROW]
[ROW][C]17[/C][C]9.4[/C][C]9.45490602158023[/C][C]-0.0549060215802293[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.4173372781065[/C][C]-0.0173372781065079[/C][/ROW]
[ROW][C]19[/C][C]9.2[/C][C]9.28761225200139[/C][C]-0.0876122520013917[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.15059345631744[/C][C]-0.150593456317437[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.80031848242256[/C][C]0.199681517577444[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]8.46894361294814[/C][C]0.531056387051862[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]8.60376261747302[/C][C]1.19623738252698[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]8.57536895231465[/C][C]1.42463104768535[/C][/ROW]
[ROW][C]25[/C][C]9.8[/C][C]8.99348764357814[/C][C]0.806512356421857[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]8.90486251305256[/C][C]0.395137486947441[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.70054994778977[/C][C]0.299450052210234[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.69513748694744[/C][C]0.304862513052559[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]8.6643125652628[/C][C]0.435687434737208[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]8.45105638705186[/C][C]0.648943612948138[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]8.14564392620954[/C][C]0.954356073790463[/C][/ROW]
[ROW][C]32[/C][C]9.2[/C][C]8.09646884789419[/C][C]1.10353115210581[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]7.8340375913679[/C][C]0.965962408632092[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]7.6783501566307[/C][C]0.6216498433693[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.42807518273582[/C][C]-0.0280751827358157[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.57536895231465[/C][C]-0.475368952314654[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]8.72995649147233[/C][C]-1.02995649147233[/C][/ROW]
[ROW][C]38[/C][C]7.9[/C][C]8.37780020884093[/C][C]-0.477800208840933[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.99780020884093[/C][C]-0.0978002088409323[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.16807518273582[/C][C]-0.168075182735816[/C][/ROW]
[ROW][C]41[/C][C]7.9[/C][C]8.31293769578837[/C][C]-0.412937695788374[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]8.18752523494605[/C][C]-0.58752523494605[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]7.70642533936652[/C][C]-0.606425339366516[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]7.30587539157675[/C][C]-0.505875391576749[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]6.95560041768187[/C][C]-0.455600417681866[/C][/ROW]
[ROW][C]46[/C][C]6.9[/C][C]7.41481900452489[/C][C]-0.514819004524887[/C][/ROW]
[ROW][C]47[/C][C]8.2[/C][C]8.86729376957884[/C][C]-0.667293769578838[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]9.19027497389488[/C][C]-0.490274973894884[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]9.16917507831535[/C][C]-0.869175078315351[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]8.20211277410372[/C][C]-0.302112774103725[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.64642533936652[/C][C]-0.146425339366515[/C][/ROW]
[ROW][C]52[/C][C]7.8[/C][C]7.90454403063[/C][C]-0.104544030630004[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]8.57646884789419[/C][C]-0.276468847894187[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]8.97811869126349[/C][C]-0.578118691263488[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]9.02408109989558[/C][C]-0.82408109989558[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]8.71137486947442[/C][C]-1.01137486947442[/C][/ROW]
[ROW][C]57[/C][C]7.2[/C][C]8.36109989557953[/C][C]-1.16109989557953[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]8.64463104768535[/C][C]-1.34463104768535[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]9.57004350852767[/C][C]-1.47004350852767[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]9.89302471284372[/C][C]-1.39302471284372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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
110.910.22329968673860.676700313261408
2109.607612252001390.392387747998611
39.29.139768534632790.0602314653672115
49.29.134356073790460.0656439262095371
59.59.191374869474420.308625130525582
69.69.06596240863210.534037591367908
79.58.936237382526970.563762617473026
89.18.53568743473720.564312565262791
98.98.448943612948140.451056387051862
1098.293256178210930.706743821789071
1110.19.130824921684650.96917507831535
1210.39.36596240863210.934037591367908
1310.29.784081099895580.415918900104417
149.69.6076122520014-0.00761225200139373
159.29.31545596937-0.115455969369997
169.39.39788722589628-0.0978872258962753
179.49.45490602158023-0.0549060215802293
189.49.4173372781065-0.0173372781065079
199.29.28761225200139-0.0876122520013917
2099.15059345631744-0.150593456317437
2198.800318482422560.199681517577444
2298.468943612948140.531056387051862
239.88.603762617473021.19623738252698
24108.575368952314651.42463104768535
259.88.993487643578140.806512356421857
269.38.904862513052560.395137486947441
2798.700549947789770.299450052210234
2898.695137486947440.304862513052559
299.18.66431256526280.435687434737208
309.18.451056387051860.648943612948138
319.18.145643926209540.954356073790463
329.28.096468847894191.10353115210581
338.87.83403759136790.965962408632092
348.37.67835015663070.6216498433693
358.48.42807518273582-0.0280751827358157
368.18.57536895231465-0.475368952314654
377.78.72995649147233-1.02995649147233
387.98.37780020884093-0.477800208840933
397.97.99780020884093-0.0978002088409323
4088.16807518273582-0.168075182735816
417.98.31293769578837-0.412937695788374
427.68.18752523494605-0.58752523494605
437.17.70642533936652-0.606425339366516
446.87.30587539157675-0.505875391576749
456.56.95560041768187-0.455600417681866
466.97.41481900452489-0.514819004524887
478.28.86729376957884-0.667293769578838
488.79.19027497389488-0.490274973894884
498.39.16917507831535-0.869175078315351
507.98.20211277410372-0.302112774103725
517.57.64642533936652-0.146425339366515
527.87.90454403063-0.104544030630004
538.38.57646884789419-0.276468847894187
548.48.97811869126349-0.578118691263488
558.29.02408109989558-0.82408109989558
567.78.71137486947442-1.01137486947442
577.28.36109989557953-1.16109989557953
587.38.64463104768535-1.34463104768535
598.19.57004350852767-1.47004350852767
608.59.89302471284372-1.39302471284372






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.02423215710371210.04846431420742420.975767842896288
170.0104566236070150.020913247214030.989543376392985
180.005721768082267370.01144353616453470.994278231917733
190.002948110095857290.005896220191714570.997051889904143
200.0008800513495481170.001760102699096230.999119948650452
210.000241995811402740.000483991622805480.999758004188597
227.07054821847148e-050.0001414109643694300.999929294517815
233.76748107288849e-057.53496214577698e-050.999962325189271
242.45846793086990e-054.91693586173979e-050.99997541532069
254.45039281119642e-058.90078562239284e-050.999955496071888
262.08899626572351e-054.17799253144702e-050.999979110037343
277.28157948620034e-061.45631589724007e-050.999992718420514
282.41898386373480e-064.83796772746961e-060.999997581016136
298.90327494515459e-071.78065498903092e-060.999999109672505
304.51573473905689e-079.03146947811378e-070.999999548426526
318.02995025736654e-071.60599005147331e-060.999999197004974
323.33353053533942e-056.66706107067884e-050.999966664694647
330.002237867397325540.004475734794651080.997762132602674
340.1447429769894700.2894859539789400.85525702301053
350.8727822052807550.2544355894384900.127217794719245
360.981481713786110.03703657242777980.0185182862138899
370.996187916708140.007624166583719810.00381208329185991
380.99290872655250.01418254689500060.0070912734475003
390.985251848657270.02949630268545870.0147481513427294
400.9669642870918560.06607142581628820.0330357129081441
410.9443644213525090.1112711572949820.0556355786474911
420.9300007783275050.1399984433449900.0699992216724952
430.9320093895116830.1359812209766330.0679906104883167
440.9080666037755980.1838667924488040.0919333962244018

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0242321571037121 & 0.0484643142074242 & 0.975767842896288 \tabularnewline
17 & 0.010456623607015 & 0.02091324721403 & 0.989543376392985 \tabularnewline
18 & 0.00572176808226737 & 0.0114435361645347 & 0.994278231917733 \tabularnewline
19 & 0.00294811009585729 & 0.00589622019171457 & 0.997051889904143 \tabularnewline
20 & 0.000880051349548117 & 0.00176010269909623 & 0.999119948650452 \tabularnewline
21 & 0.00024199581140274 & 0.00048399162280548 & 0.999758004188597 \tabularnewline
22 & 7.07054821847148e-05 & 0.000141410964369430 & 0.999929294517815 \tabularnewline
23 & 3.76748107288849e-05 & 7.53496214577698e-05 & 0.999962325189271 \tabularnewline
24 & 2.45846793086990e-05 & 4.91693586173979e-05 & 0.99997541532069 \tabularnewline
25 & 4.45039281119642e-05 & 8.90078562239284e-05 & 0.999955496071888 \tabularnewline
26 & 2.08899626572351e-05 & 4.17799253144702e-05 & 0.999979110037343 \tabularnewline
27 & 7.28157948620034e-06 & 1.45631589724007e-05 & 0.999992718420514 \tabularnewline
28 & 2.41898386373480e-06 & 4.83796772746961e-06 & 0.999997581016136 \tabularnewline
29 & 8.90327494515459e-07 & 1.78065498903092e-06 & 0.999999109672505 \tabularnewline
30 & 4.51573473905689e-07 & 9.03146947811378e-07 & 0.999999548426526 \tabularnewline
31 & 8.02995025736654e-07 & 1.60599005147331e-06 & 0.999999197004974 \tabularnewline
32 & 3.33353053533942e-05 & 6.66706107067884e-05 & 0.999966664694647 \tabularnewline
33 & 0.00223786739732554 & 0.00447573479465108 & 0.997762132602674 \tabularnewline
34 & 0.144742976989470 & 0.289485953978940 & 0.85525702301053 \tabularnewline
35 & 0.872782205280755 & 0.254435589438490 & 0.127217794719245 \tabularnewline
36 & 0.98148171378611 & 0.0370365724277798 & 0.0185182862138899 \tabularnewline
37 & 0.99618791670814 & 0.00762416658371981 & 0.00381208329185991 \tabularnewline
38 & 0.9929087265525 & 0.0141825468950006 & 0.0070912734475003 \tabularnewline
39 & 0.98525184865727 & 0.0294963026854587 & 0.0147481513427294 \tabularnewline
40 & 0.966964287091856 & 0.0660714258162882 & 0.0330357129081441 \tabularnewline
41 & 0.944364421352509 & 0.111271157294982 & 0.0556355786474911 \tabularnewline
42 & 0.930000778327505 & 0.139998443344990 & 0.0699992216724952 \tabularnewline
43 & 0.932009389511683 & 0.135981220976633 & 0.0679906104883167 \tabularnewline
44 & 0.908066603775598 & 0.183866792448804 & 0.0919333962244018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&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]16[/C][C]0.0242321571037121[/C][C]0.0484643142074242[/C][C]0.975767842896288[/C][/ROW]
[ROW][C]17[/C][C]0.010456623607015[/C][C]0.02091324721403[/C][C]0.989543376392985[/C][/ROW]
[ROW][C]18[/C][C]0.00572176808226737[/C][C]0.0114435361645347[/C][C]0.994278231917733[/C][/ROW]
[ROW][C]19[/C][C]0.00294811009585729[/C][C]0.00589622019171457[/C][C]0.997051889904143[/C][/ROW]
[ROW][C]20[/C][C]0.000880051349548117[/C][C]0.00176010269909623[/C][C]0.999119948650452[/C][/ROW]
[ROW][C]21[/C][C]0.00024199581140274[/C][C]0.00048399162280548[/C][C]0.999758004188597[/C][/ROW]
[ROW][C]22[/C][C]7.07054821847148e-05[/C][C]0.000141410964369430[/C][C]0.999929294517815[/C][/ROW]
[ROW][C]23[/C][C]3.76748107288849e-05[/C][C]7.53496214577698e-05[/C][C]0.999962325189271[/C][/ROW]
[ROW][C]24[/C][C]2.45846793086990e-05[/C][C]4.91693586173979e-05[/C][C]0.99997541532069[/C][/ROW]
[ROW][C]25[/C][C]4.45039281119642e-05[/C][C]8.90078562239284e-05[/C][C]0.999955496071888[/C][/ROW]
[ROW][C]26[/C][C]2.08899626572351e-05[/C][C]4.17799253144702e-05[/C][C]0.999979110037343[/C][/ROW]
[ROW][C]27[/C][C]7.28157948620034e-06[/C][C]1.45631589724007e-05[/C][C]0.999992718420514[/C][/ROW]
[ROW][C]28[/C][C]2.41898386373480e-06[/C][C]4.83796772746961e-06[/C][C]0.999997581016136[/C][/ROW]
[ROW][C]29[/C][C]8.90327494515459e-07[/C][C]1.78065498903092e-06[/C][C]0.999999109672505[/C][/ROW]
[ROW][C]30[/C][C]4.51573473905689e-07[/C][C]9.03146947811378e-07[/C][C]0.999999548426526[/C][/ROW]
[ROW][C]31[/C][C]8.02995025736654e-07[/C][C]1.60599005147331e-06[/C][C]0.999999197004974[/C][/ROW]
[ROW][C]32[/C][C]3.33353053533942e-05[/C][C]6.66706107067884e-05[/C][C]0.999966664694647[/C][/ROW]
[ROW][C]33[/C][C]0.00223786739732554[/C][C]0.00447573479465108[/C][C]0.997762132602674[/C][/ROW]
[ROW][C]34[/C][C]0.144742976989470[/C][C]0.289485953978940[/C][C]0.85525702301053[/C][/ROW]
[ROW][C]35[/C][C]0.872782205280755[/C][C]0.254435589438490[/C][C]0.127217794719245[/C][/ROW]
[ROW][C]36[/C][C]0.98148171378611[/C][C]0.0370365724277798[/C][C]0.0185182862138899[/C][/ROW]
[ROW][C]37[/C][C]0.99618791670814[/C][C]0.00762416658371981[/C][C]0.00381208329185991[/C][/ROW]
[ROW][C]38[/C][C]0.9929087265525[/C][C]0.0141825468950006[/C][C]0.0070912734475003[/C][/ROW]
[ROW][C]39[/C][C]0.98525184865727[/C][C]0.0294963026854587[/C][C]0.0147481513427294[/C][/ROW]
[ROW][C]40[/C][C]0.966964287091856[/C][C]0.0660714258162882[/C][C]0.0330357129081441[/C][/ROW]
[ROW][C]41[/C][C]0.944364421352509[/C][C]0.111271157294982[/C][C]0.0556355786474911[/C][/ROW]
[ROW][C]42[/C][C]0.930000778327505[/C][C]0.139998443344990[/C][C]0.0699992216724952[/C][/ROW]
[ROW][C]43[/C][C]0.932009389511683[/C][C]0.135981220976633[/C][C]0.0679906104883167[/C][/ROW]
[ROW][C]44[/C][C]0.908066603775598[/C][C]0.183866792448804[/C][C]0.0919333962244018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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
160.02423215710371210.04846431420742420.975767842896288
170.0104566236070150.020913247214030.989543376392985
180.005721768082267370.01144353616453470.994278231917733
190.002948110095857290.005896220191714570.997051889904143
200.0008800513495481170.001760102699096230.999119948650452
210.000241995811402740.000483991622805480.999758004188597
227.07054821847148e-050.0001414109643694300.999929294517815
233.76748107288849e-057.53496214577698e-050.999962325189271
242.45846793086990e-054.91693586173979e-050.99997541532069
254.45039281119642e-058.90078562239284e-050.999955496071888
262.08899626572351e-054.17799253144702e-050.999979110037343
277.28157948620034e-061.45631589724007e-050.999992718420514
282.41898386373480e-064.83796772746961e-060.999997581016136
298.90327494515459e-071.78065498903092e-060.999999109672505
304.51573473905689e-079.03146947811378e-070.999999548426526
318.02995025736654e-071.60599005147331e-060.999999197004974
323.33353053533942e-056.66706107067884e-050.999966664694647
330.002237867397325540.004475734794651080.997762132602674
340.1447429769894700.2894859539789400.85525702301053
350.8727822052807550.2544355894384900.127217794719245
360.981481713786110.03703657242777980.0185182862138899
370.996187916708140.007624166583719810.00381208329185991
380.99290872655250.01418254689500060.0070912734475003
390.985251848657270.02949630268545870.0147481513427294
400.9669642870918560.06607142581628820.0330357129081441
410.9443644213525090.1112711572949820.0556355786474911
420.9300007783275050.1399984433449900.0699992216724952
430.9320093895116830.1359812209766330.0679906104883167
440.9080666037755980.1838667924488040.0919333962244018






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.551724137931034NOK
5% type I error level220.758620689655172NOK
10% type I error level230.793103448275862NOK

\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 & 16 & 0.551724137931034 & NOK \tabularnewline
5% type I error level & 22 & 0.758620689655172 & NOK \tabularnewline
10% type I error level & 23 & 0.793103448275862 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58427&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]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.758620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.793103448275862[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58427&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58427&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 level160.551724137931034NOK
5% type I error level220.758620689655172NOK
10% type I error level230.793103448275862NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Include Monthly 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')
}