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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 26 Nov 2009 10:21:31 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/26/t12592561371ardc4hh0liqygx.htm/, Retrieved Sat, 27 Apr 2024 09:22:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60185, Retrieved Sat, 27 Apr 2024 09:22:05 +0000

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Estimated Impact

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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws 7 3] [2009-11-20 17:02:43] [55b7a497389226c9339ee8d75ebc3b97]
-   P         [Multiple Regression] [ws7verbetering] [2009-11-26 17:21:31] [5edea6bc5a9a9483633d9320282a2734] [Current]
-    D          [Multiple Regression] [ws7verbetering2] [2009-11-26 17:29:10] [7d268329e554b8694908ba13e6e6f258] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&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]3 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=60185&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
saldo_zichtrek[t] = + 32.8617038709678 -0.55318467741935crisis[t] -1.08223211917562M1[t] -1.72115552867384M2[t] -2.26926673387097M3[t] -0.965814874551973M4[t] -1.46756301523298M5[t] -0.99851115591398M6[t] -0.881859296594985M7[t] -0.751807437275987M8[t] -0.231355577956992M9[t] + 1.43949628136201M10[t] + 1.593148140681M11[t] + 0.0899481406810033t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
saldo_zichtrek[t] =  +  32.8617038709678 -0.55318467741935crisis[t] -1.08223211917562M1[t] -1.72115552867384M2[t] -2.26926673387097M3[t] -0.965814874551973M4[t] -1.46756301523298M5[t] -0.99851115591398M6[t] -0.881859296594985M7[t] -0.751807437275987M8[t] -0.231355577956992M9[t] +  1.43949628136201M10[t] +  1.593148140681M11[t] +  0.0899481406810033t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]saldo_zichtrek[t] =  +  32.8617038709678 -0.55318467741935crisis[t] -1.08223211917562M1[t] -1.72115552867384M2[t] -2.26926673387097M3[t] -0.965814874551973M4[t] -1.46756301523298M5[t] -0.99851115591398M6[t] -0.881859296594985M7[t] -0.751807437275987M8[t] -0.231355577956992M9[t] +  1.43949628136201M10[t] +  1.593148140681M11[t] +  0.0899481406810033t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
saldo_zichtrek[t] = + 32.8617038709678 -0.55318467741935crisis[t] -1.08223211917562M1[t] -1.72115552867384M2[t] -2.26926673387097M3[t] -0.965814874551973M4[t] -1.46756301523298M5[t] -0.99851115591398M6[t] -0.881859296594985M7[t] -0.751807437275987M8[t] -0.231355577956992M9[t] + 1.43949628136201M10[t] + 1.593148140681M11[t] + 0.0899481406810033t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	32.8617038709678	0.908805	36.1592	0	0
crisis	-0.55318467741935	0.761496	-0.7264	0.471169	0.235585
M1	-1.08223211917562	1.019167	-1.0619	0.293716	0.146858
M2	-1.72115552867384	1.07	-1.6086	0.114411	0.057206
M3	-2.26926673387097	1.072621	-2.1156	0.039704	0.019852
M4	-0.965814874551973	1.070408	-0.9023	0.371505	0.185753
M5	-1.46756301523298	1.068451	-1.3735	0.1761	0.08805
M6	-0.99851115591398	1.066752	-0.936	0.354043	0.177022
M7	-0.881859296594985	1.065313	-0.8278	0.411971	0.205986
M8	-0.751807437275987	1.064134	-0.7065	0.483367	0.241683
M9	-0.231355577956992	1.063216	-0.2176	0.828683	0.414341
M10	1.43949628136201	1.06256	1.3547	0.181976	0.090988
M11	1.593148140681	1.062166	1.4999	0.140327	0.070163
t	0.0899481406810033	0.016703	5.3852	2e-06	1e-06

\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) & 32.8617038709678 & 0.908805 & 36.1592 & 0 & 0 \tabularnewline
crisis & -0.55318467741935 & 0.761496 & -0.7264 & 0.471169 & 0.235585 \tabularnewline
M1 & -1.08223211917562 & 1.019167 & -1.0619 & 0.293716 & 0.146858 \tabularnewline
M2 & -1.72115552867384 & 1.07 & -1.6086 & 0.114411 & 0.057206 \tabularnewline
M3 & -2.26926673387097 & 1.072621 & -2.1156 & 0.039704 & 0.019852 \tabularnewline
M4 & -0.965814874551973 & 1.070408 & -0.9023 & 0.371505 & 0.185753 \tabularnewline
M5 & -1.46756301523298 & 1.068451 & -1.3735 & 0.1761 & 0.08805 \tabularnewline
M6 & -0.99851115591398 & 1.066752 & -0.936 & 0.354043 & 0.177022 \tabularnewline
M7 & -0.881859296594985 & 1.065313 & -0.8278 & 0.411971 & 0.205986 \tabularnewline
M8 & -0.751807437275987 & 1.064134 & -0.7065 & 0.483367 & 0.241683 \tabularnewline
M9 & -0.231355577956992 & 1.063216 & -0.2176 & 0.828683 & 0.414341 \tabularnewline
M10 & 1.43949628136201 & 1.06256 & 1.3547 & 0.181976 & 0.090988 \tabularnewline
M11 & 1.593148140681 & 1.062166 & 1.4999 & 0.140327 & 0.070163 \tabularnewline
t & 0.0899481406810033 & 0.016703 & 5.3852 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&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]32.8617038709678[/C][C]0.908805[/C][C]36.1592[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]crisis[/C][C]-0.55318467741935[/C][C]0.761496[/C][C]-0.7264[/C][C]0.471169[/C][C]0.235585[/C][/ROW]
[ROW][C]M1[/C][C]-1.08223211917562[/C][C]1.019167[/C][C]-1.0619[/C][C]0.293716[/C][C]0.146858[/C][/ROW]
[ROW][C]M2[/C][C]-1.72115552867384[/C][C]1.07[/C][C]-1.6086[/C][C]0.114411[/C][C]0.057206[/C][/ROW]
[ROW][C]M3[/C][C]-2.26926673387097[/C][C]1.072621[/C][C]-2.1156[/C][C]0.039704[/C][C]0.019852[/C][/ROW]
[ROW][C]M4[/C][C]-0.965814874551973[/C][C]1.070408[/C][C]-0.9023[/C][C]0.371505[/C][C]0.185753[/C][/ROW]
[ROW][C]M5[/C][C]-1.46756301523298[/C][C]1.068451[/C][C]-1.3735[/C][C]0.1761[/C][C]0.08805[/C][/ROW]
[ROW][C]M6[/C][C]-0.99851115591398[/C][C]1.066752[/C][C]-0.936[/C][C]0.354043[/C][C]0.177022[/C][/ROW]
[ROW][C]M7[/C][C]-0.881859296594985[/C][C]1.065313[/C][C]-0.8278[/C][C]0.411971[/C][C]0.205986[/C][/ROW]
[ROW][C]M8[/C][C]-0.751807437275987[/C][C]1.064134[/C][C]-0.7065[/C][C]0.483367[/C][C]0.241683[/C][/ROW]
[ROW][C]M9[/C][C]-0.231355577956992[/C][C]1.063216[/C][C]-0.2176[/C][C]0.828683[/C][C]0.414341[/C][/ROW]
[ROW][C]M10[/C][C]1.43949628136201[/C][C]1.06256[/C][C]1.3547[/C][C]0.181976[/C][C]0.090988[/C][/ROW]
[ROW][C]M11[/C][C]1.593148140681[/C][C]1.062166[/C][C]1.4999[/C][C]0.140327[/C][C]0.070163[/C][/ROW]
[ROW][C]t[/C][C]0.0899481406810033[/C][C]0.016703[/C][C]5.3852[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	32.8617038709678	0.908805	36.1592	0	0
crisis	-0.55318467741935	0.761496	-0.7264	0.471169	0.235585
M1	-1.08223211917562	1.019167	-1.0619	0.293716	0.146858
M2	-1.72115552867384	1.07	-1.6086	0.114411	0.057206
M3	-2.26926673387097	1.072621	-2.1156	0.039704	0.019852
M4	-0.965814874551973	1.070408	-0.9023	0.371505	0.185753
M5	-1.46756301523298	1.068451	-1.3735	0.1761	0.08805
M6	-0.99851115591398	1.066752	-0.936	0.354043	0.177022
M7	-0.881859296594985	1.065313	-0.8278	0.411971	0.205986
M8	-0.751807437275987	1.064134	-0.7065	0.483367	0.241683
M9	-0.231355577956992	1.063216	-0.2176	0.828683	0.414341
M10	1.43949628136201	1.06256	1.3547	0.181976	0.090988
M11	1.593148140681	1.062166	1.4999	0.140327	0.070163
t	0.0899481406810033	0.016703	5.3852	2e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.797953120470371
R-squared	0.636729182468403
Adjusted R-squared	0.53625002017243
F-TEST (value)	6.33692765663034
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	1.07600384402495e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.67922394172143
Sum Squared Residuals	132.530273183172

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.797953120470371 \tabularnewline
R-squared & 0.636729182468403 \tabularnewline
Adjusted R-squared & 0.53625002017243 \tabularnewline
F-TEST (value) & 6.33692765663034 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.07600384402495e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.67922394172143 \tabularnewline
Sum Squared Residuals & 132.530273183172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.797953120470371[/C][/ROW]
[ROW][C]R-squared[/C][C]0.636729182468403[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.53625002017243[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.33692765663034[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.07600384402495e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.67922394172143[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]132.530273183172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.797953120470371
R-squared	0.636729182468403
Adjusted R-squared	0.53625002017243
F-TEST (value)	6.33692765663034
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	1.07600384402495e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.67922394172143
Sum Squared Residuals	132.530273183172

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	29.837	31.8694198924731	-2.03241989247307
2	29.571	31.3204446236559	-1.74944462365591
3	30.167	30.8622815591398	-0.695281559139789
4	30.524	32.2556815591398	-1.73168155913979
5	30.996	31.8438815591398	-0.847881559139785
6	31.033	32.4028815591398	-1.36988155913979
7	31.198	32.6094815591398	-1.41148155913979
8	30.937	32.8294815591398	-1.89248155913979
9	31.649	33.4398815591398	-1.79088155913979
10	33.115	35.2006815591398	-2.08568155913979
11	34.106	35.4442815591398	-1.33828155913979
12	33.926	33.9410815591398	-0.0150815591397881
13	33.382	32.9487975806452	0.433202419354821
14	32.851	32.3998223118280	0.451177688172041
15	32.948	31.9416592473118	1.00634075268817
16	36.112	33.3350592473118	2.77694075268817
17	36.113	32.9232592473118	3.18974075268817
18	35.21	33.4822592473118	1.72774075268817
19	35.193	33.6888592473118	1.50414075268817
20	34.383	33.9088592473118	0.474140752688173
21	35.349	34.5192592473118	0.829740752688169
22	37.058	36.2800592473118	0.77794075268817
23	38.076	36.5236592473118	1.55234075268817
24	36.63	35.0204592473118	1.60954075268817
25	36.045	34.0281752688172	2.01682473118279
26	35.638	33.4792	2.15880000000000
27	35.114	33.0210369354839	2.09296306451613
28	35.465	34.4144369354839	1.05056306451613
29	35.254	34.0026369354839	1.25136306451613
30	35.299	34.5616369354839	0.73736306451613
31	35.916	34.7682369354839	1.14776306451613
32	36.683	34.9882369354839	1.69476306451613
33	37.288	35.5986369354839	1.68936306451613
34	38.536	37.3594369354839	1.17656306451613
35	38.977	37.6030369354839	1.37396306451613
36	36.407	36.0998369354839	0.307163064516126
37	34.955	35.1075529569893	-0.152552956989258
38	34.951	34.5585776881720	0.392422311827960
39	32.68	34.1004146236559	-1.42041462365591
40	34.791	35.4938146236559	-0.702814623655914
41	34.178	35.0820146236559	-0.904014623655912
42	35.213	35.6410146236559	-0.42801462365591
43	34.871	35.8476146236559	-0.976614623655907
44	35.299	36.0676146236559	-0.76861462365591
45	35.443	36.6780146236559	-1.23501462365591
46	37.108	38.4388146236559	-1.33081462365591
47	36.419	38.6824146236559	-2.26341462365591
48	34.471	37.1792146236559	-2.70821462365591
49	33.868	36.1869306451613	-2.31893064516129
50	34.385	35.6379553763441	-1.25295537634408
51	33.643	34.6266076344086	-0.9836076344086
52	34.627	36.0200076344086	-1.3930076344086
53	32.919	35.6082076344086	-2.6892076344086
54	35.5	36.1672076344086	-0.667207634408602
55	36.11	36.3738076344086	-0.263807634408602
56	37.086	36.5938076344086	0.492192365591397
57	37.711	37.2042076344086	0.506792365591399
58	40.427	38.9650076344086	1.46199236559140
59	39.884	39.2086076344086	0.675392365591399
60	38.512	37.7054076344086	0.806592365591398
61	38.767	36.713123655914	2.05387634408602

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 29.837 & 31.8694198924731 & -2.03241989247307 \tabularnewline
2 & 29.571 & 31.3204446236559 & -1.74944462365591 \tabularnewline
3 & 30.167 & 30.8622815591398 & -0.695281559139789 \tabularnewline
4 & 30.524 & 32.2556815591398 & -1.73168155913979 \tabularnewline
5 & 30.996 & 31.8438815591398 & -0.847881559139785 \tabularnewline
6 & 31.033 & 32.4028815591398 & -1.36988155913979 \tabularnewline
7 & 31.198 & 32.6094815591398 & -1.41148155913979 \tabularnewline
8 & 30.937 & 32.8294815591398 & -1.89248155913979 \tabularnewline
9 & 31.649 & 33.4398815591398 & -1.79088155913979 \tabularnewline
10 & 33.115 & 35.2006815591398 & -2.08568155913979 \tabularnewline
11 & 34.106 & 35.4442815591398 & -1.33828155913979 \tabularnewline
12 & 33.926 & 33.9410815591398 & -0.0150815591397881 \tabularnewline
13 & 33.382 & 32.9487975806452 & 0.433202419354821 \tabularnewline
14 & 32.851 & 32.3998223118280 & 0.451177688172041 \tabularnewline
15 & 32.948 & 31.9416592473118 & 1.00634075268817 \tabularnewline
16 & 36.112 & 33.3350592473118 & 2.77694075268817 \tabularnewline
17 & 36.113 & 32.9232592473118 & 3.18974075268817 \tabularnewline
18 & 35.21 & 33.4822592473118 & 1.72774075268817 \tabularnewline
19 & 35.193 & 33.6888592473118 & 1.50414075268817 \tabularnewline
20 & 34.383 & 33.9088592473118 & 0.474140752688173 \tabularnewline
21 & 35.349 & 34.5192592473118 & 0.829740752688169 \tabularnewline
22 & 37.058 & 36.2800592473118 & 0.77794075268817 \tabularnewline
23 & 38.076 & 36.5236592473118 & 1.55234075268817 \tabularnewline
24 & 36.63 & 35.0204592473118 & 1.60954075268817 \tabularnewline
25 & 36.045 & 34.0281752688172 & 2.01682473118279 \tabularnewline
26 & 35.638 & 33.4792 & 2.15880000000000 \tabularnewline
27 & 35.114 & 33.0210369354839 & 2.09296306451613 \tabularnewline
28 & 35.465 & 34.4144369354839 & 1.05056306451613 \tabularnewline
29 & 35.254 & 34.0026369354839 & 1.25136306451613 \tabularnewline
30 & 35.299 & 34.5616369354839 & 0.73736306451613 \tabularnewline
31 & 35.916 & 34.7682369354839 & 1.14776306451613 \tabularnewline
32 & 36.683 & 34.9882369354839 & 1.69476306451613 \tabularnewline
33 & 37.288 & 35.5986369354839 & 1.68936306451613 \tabularnewline
34 & 38.536 & 37.3594369354839 & 1.17656306451613 \tabularnewline
35 & 38.977 & 37.6030369354839 & 1.37396306451613 \tabularnewline
36 & 36.407 & 36.0998369354839 & 0.307163064516126 \tabularnewline
37 & 34.955 & 35.1075529569893 & -0.152552956989258 \tabularnewline
38 & 34.951 & 34.5585776881720 & 0.392422311827960 \tabularnewline
39 & 32.68 & 34.1004146236559 & -1.42041462365591 \tabularnewline
40 & 34.791 & 35.4938146236559 & -0.702814623655914 \tabularnewline
41 & 34.178 & 35.0820146236559 & -0.904014623655912 \tabularnewline
42 & 35.213 & 35.6410146236559 & -0.42801462365591 \tabularnewline
43 & 34.871 & 35.8476146236559 & -0.976614623655907 \tabularnewline
44 & 35.299 & 36.0676146236559 & -0.76861462365591 \tabularnewline
45 & 35.443 & 36.6780146236559 & -1.23501462365591 \tabularnewline
46 & 37.108 & 38.4388146236559 & -1.33081462365591 \tabularnewline
47 & 36.419 & 38.6824146236559 & -2.26341462365591 \tabularnewline
48 & 34.471 & 37.1792146236559 & -2.70821462365591 \tabularnewline
49 & 33.868 & 36.1869306451613 & -2.31893064516129 \tabularnewline
50 & 34.385 & 35.6379553763441 & -1.25295537634408 \tabularnewline
51 & 33.643 & 34.6266076344086 & -0.9836076344086 \tabularnewline
52 & 34.627 & 36.0200076344086 & -1.3930076344086 \tabularnewline
53 & 32.919 & 35.6082076344086 & -2.6892076344086 \tabularnewline
54 & 35.5 & 36.1672076344086 & -0.667207634408602 \tabularnewline
55 & 36.11 & 36.3738076344086 & -0.263807634408602 \tabularnewline
56 & 37.086 & 36.5938076344086 & 0.492192365591397 \tabularnewline
57 & 37.711 & 37.2042076344086 & 0.506792365591399 \tabularnewline
58 & 40.427 & 38.9650076344086 & 1.46199236559140 \tabularnewline
59 & 39.884 & 39.2086076344086 & 0.675392365591399 \tabularnewline
60 & 38.512 & 37.7054076344086 & 0.806592365591398 \tabularnewline
61 & 38.767 & 36.713123655914 & 2.05387634408602 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&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]29.837[/C][C]31.8694198924731[/C][C]-2.03241989247307[/C][/ROW]
[ROW][C]2[/C][C]29.571[/C][C]31.3204446236559[/C][C]-1.74944462365591[/C][/ROW]
[ROW][C]3[/C][C]30.167[/C][C]30.8622815591398[/C][C]-0.695281559139789[/C][/ROW]
[ROW][C]4[/C][C]30.524[/C][C]32.2556815591398[/C][C]-1.73168155913979[/C][/ROW]
[ROW][C]5[/C][C]30.996[/C][C]31.8438815591398[/C][C]-0.847881559139785[/C][/ROW]
[ROW][C]6[/C][C]31.033[/C][C]32.4028815591398[/C][C]-1.36988155913979[/C][/ROW]
[ROW][C]7[/C][C]31.198[/C][C]32.6094815591398[/C][C]-1.41148155913979[/C][/ROW]
[ROW][C]8[/C][C]30.937[/C][C]32.8294815591398[/C][C]-1.89248155913979[/C][/ROW]
[ROW][C]9[/C][C]31.649[/C][C]33.4398815591398[/C][C]-1.79088155913979[/C][/ROW]
[ROW][C]10[/C][C]33.115[/C][C]35.2006815591398[/C][C]-2.08568155913979[/C][/ROW]
[ROW][C]11[/C][C]34.106[/C][C]35.4442815591398[/C][C]-1.33828155913979[/C][/ROW]
[ROW][C]12[/C][C]33.926[/C][C]33.9410815591398[/C][C]-0.0150815591397881[/C][/ROW]
[ROW][C]13[/C][C]33.382[/C][C]32.9487975806452[/C][C]0.433202419354821[/C][/ROW]
[ROW][C]14[/C][C]32.851[/C][C]32.3998223118280[/C][C]0.451177688172041[/C][/ROW]
[ROW][C]15[/C][C]32.948[/C][C]31.9416592473118[/C][C]1.00634075268817[/C][/ROW]
[ROW][C]16[/C][C]36.112[/C][C]33.3350592473118[/C][C]2.77694075268817[/C][/ROW]
[ROW][C]17[/C][C]36.113[/C][C]32.9232592473118[/C][C]3.18974075268817[/C][/ROW]
[ROW][C]18[/C][C]35.21[/C][C]33.4822592473118[/C][C]1.72774075268817[/C][/ROW]
[ROW][C]19[/C][C]35.193[/C][C]33.6888592473118[/C][C]1.50414075268817[/C][/ROW]
[ROW][C]20[/C][C]34.383[/C][C]33.9088592473118[/C][C]0.474140752688173[/C][/ROW]
[ROW][C]21[/C][C]35.349[/C][C]34.5192592473118[/C][C]0.829740752688169[/C][/ROW]
[ROW][C]22[/C][C]37.058[/C][C]36.2800592473118[/C][C]0.77794075268817[/C][/ROW]
[ROW][C]23[/C][C]38.076[/C][C]36.5236592473118[/C][C]1.55234075268817[/C][/ROW]
[ROW][C]24[/C][C]36.63[/C][C]35.0204592473118[/C][C]1.60954075268817[/C][/ROW]
[ROW][C]25[/C][C]36.045[/C][C]34.0281752688172[/C][C]2.01682473118279[/C][/ROW]
[ROW][C]26[/C][C]35.638[/C][C]33.4792[/C][C]2.15880000000000[/C][/ROW]
[ROW][C]27[/C][C]35.114[/C][C]33.0210369354839[/C][C]2.09296306451613[/C][/ROW]
[ROW][C]28[/C][C]35.465[/C][C]34.4144369354839[/C][C]1.05056306451613[/C][/ROW]
[ROW][C]29[/C][C]35.254[/C][C]34.0026369354839[/C][C]1.25136306451613[/C][/ROW]
[ROW][C]30[/C][C]35.299[/C][C]34.5616369354839[/C][C]0.73736306451613[/C][/ROW]
[ROW][C]31[/C][C]35.916[/C][C]34.7682369354839[/C][C]1.14776306451613[/C][/ROW]
[ROW][C]32[/C][C]36.683[/C][C]34.9882369354839[/C][C]1.69476306451613[/C][/ROW]
[ROW][C]33[/C][C]37.288[/C][C]35.5986369354839[/C][C]1.68936306451613[/C][/ROW]
[ROW][C]34[/C][C]38.536[/C][C]37.3594369354839[/C][C]1.17656306451613[/C][/ROW]
[ROW][C]35[/C][C]38.977[/C][C]37.6030369354839[/C][C]1.37396306451613[/C][/ROW]
[ROW][C]36[/C][C]36.407[/C][C]36.0998369354839[/C][C]0.307163064516126[/C][/ROW]
[ROW][C]37[/C][C]34.955[/C][C]35.1075529569893[/C][C]-0.152552956989258[/C][/ROW]
[ROW][C]38[/C][C]34.951[/C][C]34.5585776881720[/C][C]0.392422311827960[/C][/ROW]
[ROW][C]39[/C][C]32.68[/C][C]34.1004146236559[/C][C]-1.42041462365591[/C][/ROW]
[ROW][C]40[/C][C]34.791[/C][C]35.4938146236559[/C][C]-0.702814623655914[/C][/ROW]
[ROW][C]41[/C][C]34.178[/C][C]35.0820146236559[/C][C]-0.904014623655912[/C][/ROW]
[ROW][C]42[/C][C]35.213[/C][C]35.6410146236559[/C][C]-0.42801462365591[/C][/ROW]
[ROW][C]43[/C][C]34.871[/C][C]35.8476146236559[/C][C]-0.976614623655907[/C][/ROW]
[ROW][C]44[/C][C]35.299[/C][C]36.0676146236559[/C][C]-0.76861462365591[/C][/ROW]
[ROW][C]45[/C][C]35.443[/C][C]36.6780146236559[/C][C]-1.23501462365591[/C][/ROW]
[ROW][C]46[/C][C]37.108[/C][C]38.4388146236559[/C][C]-1.33081462365591[/C][/ROW]
[ROW][C]47[/C][C]36.419[/C][C]38.6824146236559[/C][C]-2.26341462365591[/C][/ROW]
[ROW][C]48[/C][C]34.471[/C][C]37.1792146236559[/C][C]-2.70821462365591[/C][/ROW]
[ROW][C]49[/C][C]33.868[/C][C]36.1869306451613[/C][C]-2.31893064516129[/C][/ROW]
[ROW][C]50[/C][C]34.385[/C][C]35.6379553763441[/C][C]-1.25295537634408[/C][/ROW]
[ROW][C]51[/C][C]33.643[/C][C]34.6266076344086[/C][C]-0.9836076344086[/C][/ROW]
[ROW][C]52[/C][C]34.627[/C][C]36.0200076344086[/C][C]-1.3930076344086[/C][/ROW]
[ROW][C]53[/C][C]32.919[/C][C]35.6082076344086[/C][C]-2.6892076344086[/C][/ROW]
[ROW][C]54[/C][C]35.5[/C][C]36.1672076344086[/C][C]-0.667207634408602[/C][/ROW]
[ROW][C]55[/C][C]36.11[/C][C]36.3738076344086[/C][C]-0.263807634408602[/C][/ROW]
[ROW][C]56[/C][C]37.086[/C][C]36.5938076344086[/C][C]0.492192365591397[/C][/ROW]
[ROW][C]57[/C][C]37.711[/C][C]37.2042076344086[/C][C]0.506792365591399[/C][/ROW]
[ROW][C]58[/C][C]40.427[/C][C]38.9650076344086[/C][C]1.46199236559140[/C][/ROW]
[ROW][C]59[/C][C]39.884[/C][C]39.2086076344086[/C][C]0.675392365591399[/C][/ROW]
[ROW][C]60[/C][C]38.512[/C][C]37.7054076344086[/C][C]0.806592365591398[/C][/ROW]
[ROW][C]61[/C][C]38.767[/C][C]36.713123655914[/C][C]2.05387634408602[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	29.837	31.8694198924731	-2.03241989247307
2	29.571	31.3204446236559	-1.74944462365591
3	30.167	30.8622815591398	-0.695281559139789
4	30.524	32.2556815591398	-1.73168155913979
5	30.996	31.8438815591398	-0.847881559139785
6	31.033	32.4028815591398	-1.36988155913979
7	31.198	32.6094815591398	-1.41148155913979
8	30.937	32.8294815591398	-1.89248155913979
9	31.649	33.4398815591398	-1.79088155913979
10	33.115	35.2006815591398	-2.08568155913979
11	34.106	35.4442815591398	-1.33828155913979
12	33.926	33.9410815591398	-0.0150815591397881
13	33.382	32.9487975806452	0.433202419354821
14	32.851	32.3998223118280	0.451177688172041
15	32.948	31.9416592473118	1.00634075268817
16	36.112	33.3350592473118	2.77694075268817
17	36.113	32.9232592473118	3.18974075268817
18	35.21	33.4822592473118	1.72774075268817
19	35.193	33.6888592473118	1.50414075268817
20	34.383	33.9088592473118	0.474140752688173
21	35.349	34.5192592473118	0.829740752688169
22	37.058	36.2800592473118	0.77794075268817
23	38.076	36.5236592473118	1.55234075268817
24	36.63	35.0204592473118	1.60954075268817
25	36.045	34.0281752688172	2.01682473118279
26	35.638	33.4792	2.15880000000000
27	35.114	33.0210369354839	2.09296306451613
28	35.465	34.4144369354839	1.05056306451613
29	35.254	34.0026369354839	1.25136306451613
30	35.299	34.5616369354839	0.73736306451613
31	35.916	34.7682369354839	1.14776306451613
32	36.683	34.9882369354839	1.69476306451613
33	37.288	35.5986369354839	1.68936306451613
34	38.536	37.3594369354839	1.17656306451613
35	38.977	37.6030369354839	1.37396306451613
36	36.407	36.0998369354839	0.307163064516126
37	34.955	35.1075529569893	-0.152552956989258
38	34.951	34.5585776881720	0.392422311827960
39	32.68	34.1004146236559	-1.42041462365591
40	34.791	35.4938146236559	-0.702814623655914
41	34.178	35.0820146236559	-0.904014623655912
42	35.213	35.6410146236559	-0.42801462365591
43	34.871	35.8476146236559	-0.976614623655907
44	35.299	36.0676146236559	-0.76861462365591
45	35.443	36.6780146236559	-1.23501462365591
46	37.108	38.4388146236559	-1.33081462365591
47	36.419	38.6824146236559	-2.26341462365591
48	34.471	37.1792146236559	-2.70821462365591
49	33.868	36.1869306451613	-2.31893064516129
50	34.385	35.6379553763441	-1.25295537634408
51	33.643	34.6266076344086	-0.9836076344086
52	34.627	36.0200076344086	-1.3930076344086
53	32.919	35.6082076344086	-2.6892076344086
54	35.5	36.1672076344086	-0.667207634408602
55	36.11	36.3738076344086	-0.263807634408602
56	37.086	36.5938076344086	0.492192365591397
57	37.711	37.2042076344086	0.506792365591399
58	40.427	38.9650076344086	1.46199236559140
59	39.884	39.2086076344086	0.675392365591399
60	38.512	37.7054076344086	0.806592365591398
61	38.767	36.713123655914	2.05387634408602

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.489899798911477	0.979799597822953	0.510100201088523
18	0.315387349780485	0.63077469956097	0.684612650219515
19	0.186831139465138	0.373662278930276	0.813168860534862
20	0.138858373743986	0.277716747487972	0.861141626256014
21	0.0930179676372175	0.186035935274435	0.906982032362782
22	0.0701561253674618	0.140312250734924	0.929843874632538
23	0.041612937670937	0.083225875341874	0.958387062329063
24	0.0446804160789222	0.0893608321578443	0.955319583921078
25	0.0566661668457133	0.113332333691427	0.943333833154287
26	0.0531180056332661	0.106236011266532	0.946881994366734
27	0.103689006871131	0.207378013742262	0.896310993128869
28	0.298718316038996	0.597436632077991	0.701281683961004
29	0.531941376633085	0.93611724673383	0.468058623366915
30	0.552113505662196	0.89577298867561	0.447886494337804
31	0.502359413908198	0.995281172183604	0.497640586091802
32	0.409320843457601	0.818641686915201	0.5906791565424
33	0.333403638696861	0.666807277393721	0.66659636130314
34	0.25190679485651	0.50381358971302	0.74809320514349
35	0.224930816438897	0.449861632877793	0.775069183561103
36	0.270129221339302	0.540258442678603	0.729870778660698
37	0.324613667354081	0.649227334708161	0.67538633264592
38	0.295645457340919	0.591290914681838	0.70435454265908
39	0.408630463586316	0.817260927172633	0.591369536413684
40	0.478383060735909	0.956766121471817	0.521616939264091
41	0.76456317278457	0.47087365443086	0.23543682721543
42	0.843714599731485	0.312570800537029	0.156285400268515
43	0.866565821229026	0.266868357541947	0.133434178770974
44	0.865850339329847	0.268299321340306	0.134149660670153

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.489899798911477 & 0.979799597822953 & 0.510100201088523 \tabularnewline
18 & 0.315387349780485 & 0.63077469956097 & 0.684612650219515 \tabularnewline
19 & 0.186831139465138 & 0.373662278930276 & 0.813168860534862 \tabularnewline
20 & 0.138858373743986 & 0.277716747487972 & 0.861141626256014 \tabularnewline
21 & 0.0930179676372175 & 0.186035935274435 & 0.906982032362782 \tabularnewline
22 & 0.0701561253674618 & 0.140312250734924 & 0.929843874632538 \tabularnewline
23 & 0.041612937670937 & 0.083225875341874 & 0.958387062329063 \tabularnewline
24 & 0.0446804160789222 & 0.0893608321578443 & 0.955319583921078 \tabularnewline
25 & 0.0566661668457133 & 0.113332333691427 & 0.943333833154287 \tabularnewline
26 & 0.0531180056332661 & 0.106236011266532 & 0.946881994366734 \tabularnewline
27 & 0.103689006871131 & 0.207378013742262 & 0.896310993128869 \tabularnewline
28 & 0.298718316038996 & 0.597436632077991 & 0.701281683961004 \tabularnewline
29 & 0.531941376633085 & 0.93611724673383 & 0.468058623366915 \tabularnewline
30 & 0.552113505662196 & 0.89577298867561 & 0.447886494337804 \tabularnewline
31 & 0.502359413908198 & 0.995281172183604 & 0.497640586091802 \tabularnewline
32 & 0.409320843457601 & 0.818641686915201 & 0.5906791565424 \tabularnewline
33 & 0.333403638696861 & 0.666807277393721 & 0.66659636130314 \tabularnewline
34 & 0.25190679485651 & 0.50381358971302 & 0.74809320514349 \tabularnewline
35 & 0.224930816438897 & 0.449861632877793 & 0.775069183561103 \tabularnewline
36 & 0.270129221339302 & 0.540258442678603 & 0.729870778660698 \tabularnewline
37 & 0.324613667354081 & 0.649227334708161 & 0.67538633264592 \tabularnewline
38 & 0.295645457340919 & 0.591290914681838 & 0.70435454265908 \tabularnewline
39 & 0.408630463586316 & 0.817260927172633 & 0.591369536413684 \tabularnewline
40 & 0.478383060735909 & 0.956766121471817 & 0.521616939264091 \tabularnewline
41 & 0.76456317278457 & 0.47087365443086 & 0.23543682721543 \tabularnewline
42 & 0.843714599731485 & 0.312570800537029 & 0.156285400268515 \tabularnewline
43 & 0.866565821229026 & 0.266868357541947 & 0.133434178770974 \tabularnewline
44 & 0.865850339329847 & 0.268299321340306 & 0.134149660670153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&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]17[/C][C]0.489899798911477[/C][C]0.979799597822953[/C][C]0.510100201088523[/C][/ROW]
[ROW][C]18[/C][C]0.315387349780485[/C][C]0.63077469956097[/C][C]0.684612650219515[/C][/ROW]
[ROW][C]19[/C][C]0.186831139465138[/C][C]0.373662278930276[/C][C]0.813168860534862[/C][/ROW]
[ROW][C]20[/C][C]0.138858373743986[/C][C]0.277716747487972[/C][C]0.861141626256014[/C][/ROW]
[ROW][C]21[/C][C]0.0930179676372175[/C][C]0.186035935274435[/C][C]0.906982032362782[/C][/ROW]
[ROW][C]22[/C][C]0.0701561253674618[/C][C]0.140312250734924[/C][C]0.929843874632538[/C][/ROW]
[ROW][C]23[/C][C]0.041612937670937[/C][C]0.083225875341874[/C][C]0.958387062329063[/C][/ROW]
[ROW][C]24[/C][C]0.0446804160789222[/C][C]0.0893608321578443[/C][C]0.955319583921078[/C][/ROW]
[ROW][C]25[/C][C]0.0566661668457133[/C][C]0.113332333691427[/C][C]0.943333833154287[/C][/ROW]
[ROW][C]26[/C][C]0.0531180056332661[/C][C]0.106236011266532[/C][C]0.946881994366734[/C][/ROW]
[ROW][C]27[/C][C]0.103689006871131[/C][C]0.207378013742262[/C][C]0.896310993128869[/C][/ROW]
[ROW][C]28[/C][C]0.298718316038996[/C][C]0.597436632077991[/C][C]0.701281683961004[/C][/ROW]
[ROW][C]29[/C][C]0.531941376633085[/C][C]0.93611724673383[/C][C]0.468058623366915[/C][/ROW]
[ROW][C]30[/C][C]0.552113505662196[/C][C]0.89577298867561[/C][C]0.447886494337804[/C][/ROW]
[ROW][C]31[/C][C]0.502359413908198[/C][C]0.995281172183604[/C][C]0.497640586091802[/C][/ROW]
[ROW][C]32[/C][C]0.409320843457601[/C][C]0.818641686915201[/C][C]0.5906791565424[/C][/ROW]
[ROW][C]33[/C][C]0.333403638696861[/C][C]0.666807277393721[/C][C]0.66659636130314[/C][/ROW]
[ROW][C]34[/C][C]0.25190679485651[/C][C]0.50381358971302[/C][C]0.74809320514349[/C][/ROW]
[ROW][C]35[/C][C]0.224930816438897[/C][C]0.449861632877793[/C][C]0.775069183561103[/C][/ROW]
[ROW][C]36[/C][C]0.270129221339302[/C][C]0.540258442678603[/C][C]0.729870778660698[/C][/ROW]
[ROW][C]37[/C][C]0.324613667354081[/C][C]0.649227334708161[/C][C]0.67538633264592[/C][/ROW]
[ROW][C]38[/C][C]0.295645457340919[/C][C]0.591290914681838[/C][C]0.70435454265908[/C][/ROW]
[ROW][C]39[/C][C]0.408630463586316[/C][C]0.817260927172633[/C][C]0.591369536413684[/C][/ROW]
[ROW][C]40[/C][C]0.478383060735909[/C][C]0.956766121471817[/C][C]0.521616939264091[/C][/ROW]
[ROW][C]41[/C][C]0.76456317278457[/C][C]0.47087365443086[/C][C]0.23543682721543[/C][/ROW]
[ROW][C]42[/C][C]0.843714599731485[/C][C]0.312570800537029[/C][C]0.156285400268515[/C][/ROW]
[ROW][C]43[/C][C]0.866565821229026[/C][C]0.266868357541947[/C][C]0.133434178770974[/C][/ROW]
[ROW][C]44[/C][C]0.865850339329847[/C][C]0.268299321340306[/C][C]0.134149660670153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.489899798911477	0.979799597822953	0.510100201088523
18	0.315387349780485	0.63077469956097	0.684612650219515
19	0.186831139465138	0.373662278930276	0.813168860534862
20	0.138858373743986	0.277716747487972	0.861141626256014
21	0.0930179676372175	0.186035935274435	0.906982032362782
22	0.0701561253674618	0.140312250734924	0.929843874632538
23	0.041612937670937	0.083225875341874	0.958387062329063
24	0.0446804160789222	0.0893608321578443	0.955319583921078
25	0.0566661668457133	0.113332333691427	0.943333833154287
26	0.0531180056332661	0.106236011266532	0.946881994366734
27	0.103689006871131	0.207378013742262	0.896310993128869
28	0.298718316038996	0.597436632077991	0.701281683961004
29	0.531941376633085	0.93611724673383	0.468058623366915
30	0.552113505662196	0.89577298867561	0.447886494337804
31	0.502359413908198	0.995281172183604	0.497640586091802
32	0.409320843457601	0.818641686915201	0.5906791565424
33	0.333403638696861	0.666807277393721	0.66659636130314
34	0.25190679485651	0.50381358971302	0.74809320514349
35	0.224930816438897	0.449861632877793	0.775069183561103
36	0.270129221339302	0.540258442678603	0.729870778660698
37	0.324613667354081	0.649227334708161	0.67538633264592
38	0.295645457340919	0.591290914681838	0.70435454265908
39	0.408630463586316	0.817260927172633	0.591369536413684
40	0.478383060735909	0.956766121471817	0.521616939264091
41	0.76456317278457	0.47087365443086	0.23543682721543
42	0.843714599731485	0.312570800537029	0.156285400268515
43	0.866565821229026	0.266868357541947	0.133434178770974
44	0.865850339329847	0.268299321340306	0.134149660670153

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0714285714285714	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0714285714285714 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60185&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0714285714285714[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60185&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60185&T=6

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0714285714285714	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code