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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 computationSat, 21 Nov 2009 03:55:20 -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/21/t1258800966zkrb8i1kzkp5zkb.htm/, Retrieved Sun, 28 Apr 2024 22:08:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58525, Retrieved Sun, 28 Apr 2024 22:08:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact235

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] [7d268329e554b8694908ba13e6e6f258]
-   P         [Multiple Regression] [WS7(3)] [2009-11-21 10:22:47] [7d268329e554b8694908ba13e6e6f258]
-   PD            [Multiple Regression] [WS7(4)] [2009-11-21 10:55:20] [5edea6bc5a9a9483633d9320282a2734] [Current]
-    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 
9,5	7,8	9,2	9,2	10	10,9
9,6	7,8	9,5	9,2	9,2	10
9,5	7,8	9,6	9,5	9,2	9,2
9,1	7,5	9,5	9,6	9,5	9,2
8,9	7,5	9,1	9,5	9,6	9,5
9	7,1	8,9	9,1	9,5	9,6
10,1	7,5	9	8,9	9,1	9,5
10,3	7,5	10,1	9	8,9	9,1
10,2	7,6	10,3	10,1	9	8,9
9,6	7,7	10,2	10,3	10,1	9
9,2	7,7	9,6	10,2	10,3	10,1
9,3	7,9	9,2	9,6	10,2	10,3
9,4	8,1	9,3	9,2	9,6	10,2
9,4	8,2	9,4	9,3	9,2	9,6
9,2	8,2	9,4	9,4	9,3	9,2
9	8,2	9,2	9,4	9,4	9,3
9	7,9	9	9,2	9,4	9,4
9	7,3	9	9	9,2	9,4
9,8	6,9	9	9	9	9,2
10	6,6	9,8	9	9	9
9,8	6,7	10	9,8	9	9
9,3	6,9	9,8	10	9,8	9
9	7	9,3	9,8	10	9,8
9	7,1	9	9,3	9,8	10
9,1	7,2	9	9	9,3	9,8
9,1	7,1	9,1	9	9	9,3
9,1	6,9	9,1	9,1	9	9
9,2	7	9,1	9,1	9,1	9
8,8	6,8	9,2	9,1	9,1	9,1
8,3	6,4	8,8	9,2	9,1	9,1
8,4	6,7	8,3	8,8	9,2	9,1
8,1	6,6	8,4	8,3	8,8	9,2
7,7	6,4	8,1	8,4	8,3	8,8
7,9	6,3	7,7	8,1	8,4	8,3
7,9	6,2	7,9	7,7	8,1	8,4
8	6,5	7,9	7,9	7,7	8,1
7,9	6,8	8	7,9	7,9	7,7
7,6	6,8	7,9	8	7,9	7,9
7,1	6,4	7,6	7,9	8	7,9
6,8	6,1	7,1	7,6	7,9	8
6,5	5,8	6,8	7,1	7,6	7,9
6,9	6,1	6,5	6,8	7,1	7,6
8,2	7,2	6,9	6,5	6,8	7,1
8,7	7,3	8,2	6,9	6,5	6,8
8,3	6,9	8,7	8,2	6,9	6,5
7,9	6,1	8,3	8,7	8,2	6,9
7,5	5,8	7,9	8,3	8,7	8,2
7,8	6,2	7,5	7,9	8,3	8,7
8,3	7,1	7,8	7,5	7,9	8,3
8,4	7,7	8,3	7,8	7,5	7,9
8,2	7,9	8,4	8,3	7,8	7,5
7,7	7,7	8,2	8,4	8,3	7,8
7,2	7,4	7,7	8,2	8,4	8,3
7,3	7,5	7,2	7,7	8,2	8,4
8,1	8	7,3	7,2	7,7	8,2
8,5	8,1	8,1	7,3	7,2	7,7

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=58525&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=58525&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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
Y[t][t] = + 0.896550866003112 + 0.0673089460667498`X[t]`[t] + 1.40499954314054Y1[t] -0.52400110570065Y2[t] -0.374098836785457Y3[t] + 0.366078798454609Y4[t] -0.223823163888711M1[t] -0.429401490852397M2[t] -0.320995317768826M3[t] -0.266240827611457M4[t] -0.340227919439894M5[t] -0.113023296599354M6[t] + 0.486610505980998M7[t] -0.457802485721289M8[t] -0.434078057541609M9[t] + 0.0287063053531581M10[t] -0.170539719028066M11[t] -0.00408144041800594t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t][t] =  +  0.896550866003112 +  0.0673089460667498`X[t]`[t] +  1.40499954314054Y1[t] -0.52400110570065Y2[t] -0.374098836785457Y3[t] +  0.366078798454609Y4[t] -0.223823163888711M1[t] -0.429401490852397M2[t] -0.320995317768826M3[t] -0.266240827611457M4[t] -0.340227919439894M5[t] -0.113023296599354M6[t] +  0.486610505980998M7[t] -0.457802485721289M8[t] -0.434078057541609M9[t] +  0.0287063053531581M10[t] -0.170539719028066M11[t] -0.00408144041800594t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t][t] =  +  0.896550866003112 +  0.0673089460667498`X[t]`[t] +  1.40499954314054Y1[t] -0.52400110570065Y2[t] -0.374098836785457Y3[t] +  0.366078798454609Y4[t] -0.223823163888711M1[t] -0.429401490852397M2[t] -0.320995317768826M3[t] -0.266240827611457M4[t] -0.340227919439894M5[t] -0.113023296599354M6[t] +  0.486610505980998M7[t] -0.457802485721289M8[t] -0.434078057541609M9[t] +  0.0287063053531581M10[t] -0.170539719028066M11[t] -0.00408144041800594t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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
Y[t][t] = + 0.896550866003112 + 0.0673089460667498`X[t]`[t] + 1.40499954314054Y1[t] -0.52400110570065Y2[t] -0.374098836785457Y3[t] + 0.366078798454609Y4[t] -0.223823163888711M1[t] -0.429401490852397M2[t] -0.320995317768826M3[t] -0.266240827611457M4[t] -0.340227919439894M5[t] -0.113023296599354M6[t] + 0.486610505980998M7[t] -0.457802485721289M8[t] -0.434078057541609M9[t] + 0.0287063053531581M10[t] -0.170539719028066M11[t] -0.00408144041800594t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8965508660031120.6965651.28710.2058430.102922
`X[t]`0.06730894606674980.0519761.2950.2031360.101568
Y11.404999543140540.1561828.995900
Y2-0.524001105700650.275272-1.90360.0645590.03228
Y3-0.3740988367854570.272264-1.3740.1774830.088742
Y40.3660787984546090.1451392.52230.0159690.007985
M1-0.2238231638887110.136939-1.63450.1104170.055209
M2-0.4294014908523970.141424-3.03630.0043110.002155
M3-0.3209953177688260.140445-2.28560.0279480.013974
M4-0.2662408276114570.139002-1.91540.0629930.031497
M5-0.3402279194398940.131747-2.58240.0137880.006894
M6-0.1130232965993540.129688-0.87150.3889520.194476
M70.4866105059809980.1359153.58030.0009590.00048
M8-0.4578024857212890.179057-2.55670.0146830.007342
M9-0.4340780575416090.191835-2.26280.0294480.014724
M100.02870630535315810.178020.16130.8727480.436374
M11-0.1705397190280660.142509-1.19670.2388380.119419
t-0.004081440418005940.003494-1.16820.2500080.125004

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.896550866003112 & 0.696565 & 1.2871 & 0.205843 & 0.102922 \tabularnewline
`X[t]` & 0.0673089460667498 & 0.051976 & 1.295 & 0.203136 & 0.101568 \tabularnewline
Y1 & 1.40499954314054 & 0.156182 & 8.9959 & 0 & 0 \tabularnewline
Y2 & -0.52400110570065 & 0.275272 & -1.9036 & 0.064559 & 0.03228 \tabularnewline
Y3 & -0.374098836785457 & 0.272264 & -1.374 & 0.177483 & 0.088742 \tabularnewline
Y4 & 0.366078798454609 & 0.145139 & 2.5223 & 0.015969 & 0.007985 \tabularnewline
M1 & -0.223823163888711 & 0.136939 & -1.6345 & 0.110417 & 0.055209 \tabularnewline
M2 & -0.429401490852397 & 0.141424 & -3.0363 & 0.004311 & 0.002155 \tabularnewline
M3 & -0.320995317768826 & 0.140445 & -2.2856 & 0.027948 & 0.013974 \tabularnewline
M4 & -0.266240827611457 & 0.139002 & -1.9154 & 0.062993 & 0.031497 \tabularnewline
M5 & -0.340227919439894 & 0.131747 & -2.5824 & 0.013788 & 0.006894 \tabularnewline
M6 & -0.113023296599354 & 0.129688 & -0.8715 & 0.388952 & 0.194476 \tabularnewline
M7 & 0.486610505980998 & 0.135915 & 3.5803 & 0.000959 & 0.00048 \tabularnewline
M8 & -0.457802485721289 & 0.179057 & -2.5567 & 0.014683 & 0.007342 \tabularnewline
M9 & -0.434078057541609 & 0.191835 & -2.2628 & 0.029448 & 0.014724 \tabularnewline
M10 & 0.0287063053531581 & 0.17802 & 0.1613 & 0.872748 & 0.436374 \tabularnewline
M11 & -0.170539719028066 & 0.142509 & -1.1967 & 0.238838 & 0.119419 \tabularnewline
t & -0.00408144041800594 & 0.003494 & -1.1682 & 0.250008 & 0.125004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.896550866003112[/C][C]0.696565[/C][C]1.2871[/C][C]0.205843[/C][C]0.102922[/C][/ROW]
[ROW][C]`X[t]`[/C][C]0.0673089460667498[/C][C]0.051976[/C][C]1.295[/C][C]0.203136[/C][C]0.101568[/C][/ROW]
[ROW][C]Y1[/C][C]1.40499954314054[/C][C]0.156182[/C][C]8.9959[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.52400110570065[/C][C]0.275272[/C][C]-1.9036[/C][C]0.064559[/C][C]0.03228[/C][/ROW]
[ROW][C]Y3[/C][C]-0.374098836785457[/C][C]0.272264[/C][C]-1.374[/C][C]0.177483[/C][C]0.088742[/C][/ROW]
[ROW][C]Y4[/C][C]0.366078798454609[/C][C]0.145139[/C][C]2.5223[/C][C]0.015969[/C][C]0.007985[/C][/ROW]
[ROW][C]M1[/C][C]-0.223823163888711[/C][C]0.136939[/C][C]-1.6345[/C][C]0.110417[/C][C]0.055209[/C][/ROW]
[ROW][C]M2[/C][C]-0.429401490852397[/C][C]0.141424[/C][C]-3.0363[/C][C]0.004311[/C][C]0.002155[/C][/ROW]
[ROW][C]M3[/C][C]-0.320995317768826[/C][C]0.140445[/C][C]-2.2856[/C][C]0.027948[/C][C]0.013974[/C][/ROW]
[ROW][C]M4[/C][C]-0.266240827611457[/C][C]0.139002[/C][C]-1.9154[/C][C]0.062993[/C][C]0.031497[/C][/ROW]
[ROW][C]M5[/C][C]-0.340227919439894[/C][C]0.131747[/C][C]-2.5824[/C][C]0.013788[/C][C]0.006894[/C][/ROW]
[ROW][C]M6[/C][C]-0.113023296599354[/C][C]0.129688[/C][C]-0.8715[/C][C]0.388952[/C][C]0.194476[/C][/ROW]
[ROW][C]M7[/C][C]0.486610505980998[/C][C]0.135915[/C][C]3.5803[/C][C]0.000959[/C][C]0.00048[/C][/ROW]
[ROW][C]M8[/C][C]-0.457802485721289[/C][C]0.179057[/C][C]-2.5567[/C][C]0.014683[/C][C]0.007342[/C][/ROW]
[ROW][C]M9[/C][C]-0.434078057541609[/C][C]0.191835[/C][C]-2.2628[/C][C]0.029448[/C][C]0.014724[/C][/ROW]
[ROW][C]M10[/C][C]0.0287063053531581[/C][C]0.17802[/C][C]0.1613[/C][C]0.872748[/C][C]0.436374[/C][/ROW]
[ROW][C]M11[/C][C]-0.170539719028066[/C][C]0.142509[/C][C]-1.1967[/C][C]0.238838[/C][C]0.119419[/C][/ROW]
[ROW][C]t[/C][C]-0.00408144041800594[/C][C]0.003494[/C][C]-1.1682[/C][C]0.250008[/C][C]0.125004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8965508660031120.6965651.28710.2058430.102922
`X[t]`0.06730894606674980.0519761.2950.2031360.101568
Y11.404999543140540.1561828.995900
Y2-0.524001105700650.275272-1.90360.0645590.03228
Y3-0.3740988367854570.272264-1.3740.1774830.088742
Y40.3660787984546090.1451392.52230.0159690.007985
M1-0.2238231638887110.136939-1.63450.1104170.055209
M2-0.4294014908523970.141424-3.03630.0043110.002155
M3-0.3209953177688260.140445-2.28560.0279480.013974
M4-0.2662408276114570.139002-1.91540.0629930.031497
M5-0.3402279194398940.131747-2.58240.0137880.006894
M6-0.1130232965993540.129688-0.87150.3889520.194476
M70.4866105059809980.1359153.58030.0009590.00048
M8-0.4578024857212890.179057-2.55670.0146830.007342
M9-0.4340780575416090.191835-2.26280.0294480.014724
M100.02870630535315810.178020.16130.8727480.436374
M11-0.1705397190280660.142509-1.19670.2388380.119419
t-0.004081440418005940.003494-1.16820.2500080.125004






Multiple Linear Regression - Regression Statistics
Multiple R0.985302965025289
R-squared0.970821932887626
Adjusted R-squared0.957768587074195
F-TEST (value)74.3734171118595
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.187914623193336
Sum Squared Residuals1.34185241317596

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985302965025289 \tabularnewline
R-squared & 0.970821932887626 \tabularnewline
Adjusted R-squared & 0.957768587074195 \tabularnewline
F-TEST (value) & 74.3734171118595 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.187914623193336 \tabularnewline
Sum Squared Residuals & 1.34185241317596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985302965025289[/C][/ROW]
[ROW][C]R-squared[/C][C]0.970821932887626[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.957768587074195[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]74.3734171118595[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]0.187914623193336[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.34185241317596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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 R0.985302965025289
R-squared0.970821932887626
Adjusted R-squared0.957768587074195
F-TEST (value)74.3734171118595
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.187914623193336
Sum Squared Residuals1.34185241317596






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.59.54811220076473-0.0481122007647298
29.69.72976044714444-0.129760447144436
39.59.52452176365017-0.0245217636501714
49.19.24987241364975-0.149872413649753
58.98.7346179305750.165382069425006
698.933435831746990.0665641682530137
710.19.914243602658940.185756397341062
810.310.3872368053984-0.0872368053984253
910.210.00758373675470.192416263245295
109.69.85281653776541-0.252816537765412
119.29.18675636859490.0132436314050998
129.39.229702925951950.0702970740480514
139.49.55321192967871-0.153211929678711
149.49.36837515628910.0316248437109006
159.29.23645837532421-0.0364583753242111
1699.00532951260238-0.00532951260237865
1798.76747648889340.232523511106605
1899.1298342921731-0.1298342921731
199.89.700067083574910.0999329164250862
20109.782163842456110.217836157543888
219.89.670336748892050.129663251107952
229.39.45742226138556-0.157422261385556
2398.881169412169460.118830587830545
2499.04289480234236-0.0428948023423637
259.19.092755083054320.00724491694567558
269.18.946054627188340.153945372811657
279.18.874693820534110.225306179465889
289.28.89468788120160.305312118798396
298.88.98026539390133-0.180265393901325
308.38.56206507007088-0.262065070070878
318.48.64750090308469-0.24750090308469
328.18.28102349808174-0.181023498081745
337.77.85392262212873-0.153922622128725
347.97.680645881546940.219354118453061
357.98.1100254039305-0.210025403930501
3688.23169204039826-0.231692040398255
377.97.94322878748668-0.0432287874866822
387.67.6138847149118-0.0138847149117937
397.17.28477623310001-0.184776233100014
406.86.84397492268328-0.0439749226832812
416.56.66183616771515-0.161836167715152
426.96.718078281582090.181921718417913
438.28.03606088519260.163939114807396
448.78.71360232298069-0.0136023229806874
458.38.46815689222452-0.168156892224523
467.97.70911531930210.190884680697906
477.57.422048815305140.0779511846948565
487.87.595710231307430.204289768692568
498.38.062691999015550.237308000984447
508.48.44192505446633-0.0419250544663281
518.28.17954980739150.0204501926085077
527.77.80613526986298-0.106135269862983
537.27.25580401891513-0.0558040189151345
547.37.156586524426950.143413475573052
558.18.30212752548885-0.202127525488854
568.58.435973531083030.0640264689169696

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.5 & 9.54811220076473 & -0.0481122007647298 \tabularnewline
2 & 9.6 & 9.72976044714444 & -0.129760447144436 \tabularnewline
3 & 9.5 & 9.52452176365017 & -0.0245217636501714 \tabularnewline
4 & 9.1 & 9.24987241364975 & -0.149872413649753 \tabularnewline
5 & 8.9 & 8.734617930575 & 0.165382069425006 \tabularnewline
6 & 9 & 8.93343583174699 & 0.0665641682530137 \tabularnewline
7 & 10.1 & 9.91424360265894 & 0.185756397341062 \tabularnewline
8 & 10.3 & 10.3872368053984 & -0.0872368053984253 \tabularnewline
9 & 10.2 & 10.0075837367547 & 0.192416263245295 \tabularnewline
10 & 9.6 & 9.85281653776541 & -0.252816537765412 \tabularnewline
11 & 9.2 & 9.1867563685949 & 0.0132436314050998 \tabularnewline
12 & 9.3 & 9.22970292595195 & 0.0702970740480514 \tabularnewline
13 & 9.4 & 9.55321192967871 & -0.153211929678711 \tabularnewline
14 & 9.4 & 9.3683751562891 & 0.0316248437109006 \tabularnewline
15 & 9.2 & 9.23645837532421 & -0.0364583753242111 \tabularnewline
16 & 9 & 9.00532951260238 & -0.00532951260237865 \tabularnewline
17 & 9 & 8.7674764888934 & 0.232523511106605 \tabularnewline
18 & 9 & 9.1298342921731 & -0.1298342921731 \tabularnewline
19 & 9.8 & 9.70006708357491 & 0.0999329164250862 \tabularnewline
20 & 10 & 9.78216384245611 & 0.217836157543888 \tabularnewline
21 & 9.8 & 9.67033674889205 & 0.129663251107952 \tabularnewline
22 & 9.3 & 9.45742226138556 & -0.157422261385556 \tabularnewline
23 & 9 & 8.88116941216946 & 0.118830587830545 \tabularnewline
24 & 9 & 9.04289480234236 & -0.0428948023423637 \tabularnewline
25 & 9.1 & 9.09275508305432 & 0.00724491694567558 \tabularnewline
26 & 9.1 & 8.94605462718834 & 0.153945372811657 \tabularnewline
27 & 9.1 & 8.87469382053411 & 0.225306179465889 \tabularnewline
28 & 9.2 & 8.8946878812016 & 0.305312118798396 \tabularnewline
29 & 8.8 & 8.98026539390133 & -0.180265393901325 \tabularnewline
30 & 8.3 & 8.56206507007088 & -0.262065070070878 \tabularnewline
31 & 8.4 & 8.64750090308469 & -0.24750090308469 \tabularnewline
32 & 8.1 & 8.28102349808174 & -0.181023498081745 \tabularnewline
33 & 7.7 & 7.85392262212873 & -0.153922622128725 \tabularnewline
34 & 7.9 & 7.68064588154694 & 0.219354118453061 \tabularnewline
35 & 7.9 & 8.1100254039305 & -0.210025403930501 \tabularnewline
36 & 8 & 8.23169204039826 & -0.231692040398255 \tabularnewline
37 & 7.9 & 7.94322878748668 & -0.0432287874866822 \tabularnewline
38 & 7.6 & 7.6138847149118 & -0.0138847149117937 \tabularnewline
39 & 7.1 & 7.28477623310001 & -0.184776233100014 \tabularnewline
40 & 6.8 & 6.84397492268328 & -0.0439749226832812 \tabularnewline
41 & 6.5 & 6.66183616771515 & -0.161836167715152 \tabularnewline
42 & 6.9 & 6.71807828158209 & 0.181921718417913 \tabularnewline
43 & 8.2 & 8.0360608851926 & 0.163939114807396 \tabularnewline
44 & 8.7 & 8.71360232298069 & -0.0136023229806874 \tabularnewline
45 & 8.3 & 8.46815689222452 & -0.168156892224523 \tabularnewline
46 & 7.9 & 7.7091153193021 & 0.190884680697906 \tabularnewline
47 & 7.5 & 7.42204881530514 & 0.0779511846948565 \tabularnewline
48 & 7.8 & 7.59571023130743 & 0.204289768692568 \tabularnewline
49 & 8.3 & 8.06269199901555 & 0.237308000984447 \tabularnewline
50 & 8.4 & 8.44192505446633 & -0.0419250544663281 \tabularnewline
51 & 8.2 & 8.1795498073915 & 0.0204501926085077 \tabularnewline
52 & 7.7 & 7.80613526986298 & -0.106135269862983 \tabularnewline
53 & 7.2 & 7.25580401891513 & -0.0558040189151345 \tabularnewline
54 & 7.3 & 7.15658652442695 & 0.143413475573052 \tabularnewline
55 & 8.1 & 8.30212752548885 & -0.202127525488854 \tabularnewline
56 & 8.5 & 8.43597353108303 & 0.0640264689169696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&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]9.5[/C][C]9.54811220076473[/C][C]-0.0481122007647298[/C][/ROW]
[ROW][C]2[/C][C]9.6[/C][C]9.72976044714444[/C][C]-0.129760447144436[/C][/ROW]
[ROW][C]3[/C][C]9.5[/C][C]9.52452176365017[/C][C]-0.0245217636501714[/C][/ROW]
[ROW][C]4[/C][C]9.1[/C][C]9.24987241364975[/C][C]-0.149872413649753[/C][/ROW]
[ROW][C]5[/C][C]8.9[/C][C]8.734617930575[/C][C]0.165382069425006[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]8.93343583174699[/C][C]0.0665641682530137[/C][/ROW]
[ROW][C]7[/C][C]10.1[/C][C]9.91424360265894[/C][C]0.185756397341062[/C][/ROW]
[ROW][C]8[/C][C]10.3[/C][C]10.3872368053984[/C][C]-0.0872368053984253[/C][/ROW]
[ROW][C]9[/C][C]10.2[/C][C]10.0075837367547[/C][C]0.192416263245295[/C][/ROW]
[ROW][C]10[/C][C]9.6[/C][C]9.85281653776541[/C][C]-0.252816537765412[/C][/ROW]
[ROW][C]11[/C][C]9.2[/C][C]9.1867563685949[/C][C]0.0132436314050998[/C][/ROW]
[ROW][C]12[/C][C]9.3[/C][C]9.22970292595195[/C][C]0.0702970740480514[/C][/ROW]
[ROW][C]13[/C][C]9.4[/C][C]9.55321192967871[/C][C]-0.153211929678711[/C][/ROW]
[ROW][C]14[/C][C]9.4[/C][C]9.3683751562891[/C][C]0.0316248437109006[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]9.23645837532421[/C][C]-0.0364583753242111[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]9.00532951260238[/C][C]-0.00532951260237865[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]8.7674764888934[/C][C]0.232523511106605[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]9.1298342921731[/C][C]-0.1298342921731[/C][/ROW]
[ROW][C]19[/C][C]9.8[/C][C]9.70006708357491[/C][C]0.0999329164250862[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.78216384245611[/C][C]0.217836157543888[/C][/ROW]
[ROW][C]21[/C][C]9.8[/C][C]9.67033674889205[/C][C]0.129663251107952[/C][/ROW]
[ROW][C]22[/C][C]9.3[/C][C]9.45742226138556[/C][C]-0.157422261385556[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.88116941216946[/C][C]0.118830587830545[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]9.04289480234236[/C][C]-0.0428948023423637[/C][/ROW]
[ROW][C]25[/C][C]9.1[/C][C]9.09275508305432[/C][C]0.00724491694567558[/C][/ROW]
[ROW][C]26[/C][C]9.1[/C][C]8.94605462718834[/C][C]0.153945372811657[/C][/ROW]
[ROW][C]27[/C][C]9.1[/C][C]8.87469382053411[/C][C]0.225306179465889[/C][/ROW]
[ROW][C]28[/C][C]9.2[/C][C]8.8946878812016[/C][C]0.305312118798396[/C][/ROW]
[ROW][C]29[/C][C]8.8[/C][C]8.98026539390133[/C][C]-0.180265393901325[/C][/ROW]
[ROW][C]30[/C][C]8.3[/C][C]8.56206507007088[/C][C]-0.262065070070878[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.64750090308469[/C][C]-0.24750090308469[/C][/ROW]
[ROW][C]32[/C][C]8.1[/C][C]8.28102349808174[/C][C]-0.181023498081745[/C][/ROW]
[ROW][C]33[/C][C]7.7[/C][C]7.85392262212873[/C][C]-0.153922622128725[/C][/ROW]
[ROW][C]34[/C][C]7.9[/C][C]7.68064588154694[/C][C]0.219354118453061[/C][/ROW]
[ROW][C]35[/C][C]7.9[/C][C]8.1100254039305[/C][C]-0.210025403930501[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]8.23169204039826[/C][C]-0.231692040398255[/C][/ROW]
[ROW][C]37[/C][C]7.9[/C][C]7.94322878748668[/C][C]-0.0432287874866822[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]7.6138847149118[/C][C]-0.0138847149117937[/C][/ROW]
[ROW][C]39[/C][C]7.1[/C][C]7.28477623310001[/C][C]-0.184776233100014[/C][/ROW]
[ROW][C]40[/C][C]6.8[/C][C]6.84397492268328[/C][C]-0.0439749226832812[/C][/ROW]
[ROW][C]41[/C][C]6.5[/C][C]6.66183616771515[/C][C]-0.161836167715152[/C][/ROW]
[ROW][C]42[/C][C]6.9[/C][C]6.71807828158209[/C][C]0.181921718417913[/C][/ROW]
[ROW][C]43[/C][C]8.2[/C][C]8.0360608851926[/C][C]0.163939114807396[/C][/ROW]
[ROW][C]44[/C][C]8.7[/C][C]8.71360232298069[/C][C]-0.0136023229806874[/C][/ROW]
[ROW][C]45[/C][C]8.3[/C][C]8.46815689222452[/C][C]-0.168156892224523[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]7.7091153193021[/C][C]0.190884680697906[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]7.42204881530514[/C][C]0.0779511846948565[/C][/ROW]
[ROW][C]48[/C][C]7.8[/C][C]7.59571023130743[/C][C]0.204289768692568[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]8.06269199901555[/C][C]0.237308000984447[/C][/ROW]
[ROW][C]50[/C][C]8.4[/C][C]8.44192505446633[/C][C]-0.0419250544663281[/C][/ROW]
[ROW][C]51[/C][C]8.2[/C][C]8.1795498073915[/C][C]0.0204501926085077[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]7.80613526986298[/C][C]-0.106135269862983[/C][/ROW]
[ROW][C]53[/C][C]7.2[/C][C]7.25580401891513[/C][C]-0.0558040189151345[/C][/ROW]
[ROW][C]54[/C][C]7.3[/C][C]7.15658652442695[/C][C]0.143413475573052[/C][/ROW]
[ROW][C]55[/C][C]8.1[/C][C]8.30212752548885[/C][C]-0.202127525488854[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]8.43597353108303[/C][C]0.0640264689169696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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
19.59.54811220076473-0.0481122007647298
29.69.72976044714444-0.129760447144436
39.59.52452176365017-0.0245217636501714
49.19.24987241364975-0.149872413649753
58.98.7346179305750.165382069425006
698.933435831746990.0665641682530137
710.19.914243602658940.185756397341062
810.310.3872368053984-0.0872368053984253
910.210.00758373675470.192416263245295
109.69.85281653776541-0.252816537765412
119.29.18675636859490.0132436314050998
129.39.229702925951950.0702970740480514
139.49.55321192967871-0.153211929678711
149.49.36837515628910.0316248437109006
159.29.23645837532421-0.0364583753242111
1699.00532951260238-0.00532951260237865
1798.76747648889340.232523511106605
1899.1298342921731-0.1298342921731
199.89.700067083574910.0999329164250862
20109.782163842456110.217836157543888
219.89.670336748892050.129663251107952
229.39.45742226138556-0.157422261385556
2398.881169412169460.118830587830545
2499.04289480234236-0.0428948023423637
259.19.092755083054320.00724491694567558
269.18.946054627188340.153945372811657
279.18.874693820534110.225306179465889
289.28.89468788120160.305312118798396
298.88.98026539390133-0.180265393901325
308.38.56206507007088-0.262065070070878
318.48.64750090308469-0.24750090308469
328.18.28102349808174-0.181023498081745
337.77.85392262212873-0.153922622128725
347.97.680645881546940.219354118453061
357.98.1100254039305-0.210025403930501
3688.23169204039826-0.231692040398255
377.97.94322878748668-0.0432287874866822
387.67.6138847149118-0.0138847149117937
397.17.28477623310001-0.184776233100014
406.86.84397492268328-0.0439749226832812
416.56.66183616771515-0.161836167715152
426.96.718078281582090.181921718417913
438.28.03606088519260.163939114807396
448.78.71360232298069-0.0136023229806874
458.38.46815689222452-0.168156892224523
467.97.70911531930210.190884680697906
477.57.422048815305140.0779511846948565
487.87.595710231307430.204289768692568
498.38.062691999015550.237308000984447
508.48.44192505446633-0.0419250544663281
518.28.17954980739150.0204501926085077
527.77.80613526986298-0.106135269862983
537.27.25580401891513-0.0558040189151345
547.37.156586524426950.143413475573052
558.18.30212752548885-0.202127525488854
568.58.435973531083030.0640264689169696






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1289722099230090.2579444198460190.87102779007699
220.06774859368522140.1354971873704430.932251406314779
230.03669329969670690.07338659939341380.963306700303293
240.02086116406943610.04172232813887210.979138835930564
250.007653725613802470.01530745122760490.992346274386197
260.002951050543712470.005902101087424950.997048949456288
270.006215278023862440.01243055604772490.993784721976138
280.2221768224733490.4443536449466990.77782317752665
290.2442669374991250.4885338749982490.755733062500875
300.1892973284495880.3785946568991760.810702671550412
310.7264183596966510.5471632806066980.273581640303349
320.7684784720757140.4630430558485720.231521527924286
330.8634948426316060.2730103147367880.136505157368394
340.8583238295904470.2833523408191070.141676170409553
350.7969548914274270.4060902171451460.203045108572573

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.128972209923009 & 0.257944419846019 & 0.87102779007699 \tabularnewline
22 & 0.0677485936852214 & 0.135497187370443 & 0.932251406314779 \tabularnewline
23 & 0.0366932996967069 & 0.0733865993934138 & 0.963306700303293 \tabularnewline
24 & 0.0208611640694361 & 0.0417223281388721 & 0.979138835930564 \tabularnewline
25 & 0.00765372561380247 & 0.0153074512276049 & 0.992346274386197 \tabularnewline
26 & 0.00295105054371247 & 0.00590210108742495 & 0.997048949456288 \tabularnewline
27 & 0.00621527802386244 & 0.0124305560477249 & 0.993784721976138 \tabularnewline
28 & 0.222176822473349 & 0.444353644946699 & 0.77782317752665 \tabularnewline
29 & 0.244266937499125 & 0.488533874998249 & 0.755733062500875 \tabularnewline
30 & 0.189297328449588 & 0.378594656899176 & 0.810702671550412 \tabularnewline
31 & 0.726418359696651 & 0.547163280606698 & 0.273581640303349 \tabularnewline
32 & 0.768478472075714 & 0.463043055848572 & 0.231521527924286 \tabularnewline
33 & 0.863494842631606 & 0.273010314736788 & 0.136505157368394 \tabularnewline
34 & 0.858323829590447 & 0.283352340819107 & 0.141676170409553 \tabularnewline
35 & 0.796954891427427 & 0.406090217145146 & 0.203045108572573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&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]21[/C][C]0.128972209923009[/C][C]0.257944419846019[/C][C]0.87102779007699[/C][/ROW]
[ROW][C]22[/C][C]0.0677485936852214[/C][C]0.135497187370443[/C][C]0.932251406314779[/C][/ROW]
[ROW][C]23[/C][C]0.0366932996967069[/C][C]0.0733865993934138[/C][C]0.963306700303293[/C][/ROW]
[ROW][C]24[/C][C]0.0208611640694361[/C][C]0.0417223281388721[/C][C]0.979138835930564[/C][/ROW]
[ROW][C]25[/C][C]0.00765372561380247[/C][C]0.0153074512276049[/C][C]0.992346274386197[/C][/ROW]
[ROW][C]26[/C][C]0.00295105054371247[/C][C]0.00590210108742495[/C][C]0.997048949456288[/C][/ROW]
[ROW][C]27[/C][C]0.00621527802386244[/C][C]0.0124305560477249[/C][C]0.993784721976138[/C][/ROW]
[ROW][C]28[/C][C]0.222176822473349[/C][C]0.444353644946699[/C][C]0.77782317752665[/C][/ROW]
[ROW][C]29[/C][C]0.244266937499125[/C][C]0.488533874998249[/C][C]0.755733062500875[/C][/ROW]
[ROW][C]30[/C][C]0.189297328449588[/C][C]0.378594656899176[/C][C]0.810702671550412[/C][/ROW]
[ROW][C]31[/C][C]0.726418359696651[/C][C]0.547163280606698[/C][C]0.273581640303349[/C][/ROW]
[ROW][C]32[/C][C]0.768478472075714[/C][C]0.463043055848572[/C][C]0.231521527924286[/C][/ROW]
[ROW][C]33[/C][C]0.863494842631606[/C][C]0.273010314736788[/C][C]0.136505157368394[/C][/ROW]
[ROW][C]34[/C][C]0.858323829590447[/C][C]0.283352340819107[/C][C]0.141676170409553[/C][/ROW]
[ROW][C]35[/C][C]0.796954891427427[/C][C]0.406090217145146[/C][C]0.203045108572573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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
210.1289722099230090.2579444198460190.87102779007699
220.06774859368522140.1354971873704430.932251406314779
230.03669329969670690.07338659939341380.963306700303293
240.02086116406943610.04172232813887210.979138835930564
250.007653725613802470.01530745122760490.992346274386197
260.002951050543712470.005902101087424950.997048949456288
270.006215278023862440.01243055604772490.993784721976138
280.2221768224733490.4443536449466990.77782317752665
290.2442669374991250.4885338749982490.755733062500875
300.1892973284495880.3785946568991760.810702671550412
310.7264183596966510.5471632806066980.273581640303349
320.7684784720757140.4630430558485720.231521527924286
330.8634948426316060.2730103147367880.136505157368394
340.8583238295904470.2833523408191070.141676170409553
350.7969548914274270.4060902171451460.203045108572573






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0666666666666667NOK
5% type I error level40.266666666666667NOK
10% type I error level50.333333333333333NOK

\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 & 1 & 0.0666666666666667 & NOK \tabularnewline
5% type I error level & 4 & 0.266666666666667 & NOK \tabularnewline
10% type I error level & 5 & 0.333333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58525&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]1[/C][C]0.0666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.266666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58525&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58525&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 level10.0666666666666667NOK
5% type I error level40.266666666666667NOK
10% type I error level50.333333333333333NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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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')
}