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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 26 Nov 2009 10:29:10 -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/t1259256596isq1f3iiloj2hze.htm/, Retrieved Sat, 27 Apr 2024 10:26:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60191, Retrieved Sat, 27 Apr 2024 10:26:28 +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] [7d268329e554b8694908ba13e6e6f258]
-    D          [Multiple Regression] [ws7verbetering2] [2009-11-26 17:29:10] [5edea6bc5a9a9483633d9320282a2734] [Current]

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Dataseries X:

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Histogram

Boxplots

30.996	0	30.524	30.167	29.571	29.837
31.033	0	30.996	30.524	30.167	29.571
31.198	0	31.033	30.996	30.524	30.167
30.937	0	31.198	31.033	30.996	30.524
31.649	0	30.937	31.198	31.033	30.996
33.115	0	31.649	30.937	31.198	31.033
34.106	0	33.115	31.649	30.937	31.198
33.926	0	34.106	33.115	31.649	30.937
33.382	0	33.926	34.106	33.115	31.649
32.851	0	33.382	33.926	34.106	33.115
32.948	0	32.851	33.382	33.926	34.106
36.112	0	32.948	32.851	33.382	33.926
36.113	0	36.112	32.948	32.851	33.382
35.210	0	36.113	36.112	32.948	32.851
35.193	0	35.210	36.113	36.112	32.948
34.383	0	35.193	35.210	36.113	36.112
35.349	0	34.383	35.193	35.210	36.113
37.058	0	35.349	34.383	35.193	35.210
38.076	0	37.058	35.349	34.383	35.193
36.630	0	38.076	37.058	35.349	34.383
36.045	0	36.630	38.076	37.058	35.349
35.638	0	36.045	36.630	38.076	37.058
35.114	0	35.638	36.045	36.630	38.076
35.465	0	35.114	35.638	36.045	36.630
35.254	0	35.465	35.114	35.638	36.045
35.299	0	35.254	35.465	35.114	35.638
35.916	0	35.299	35.254	35.465	35.114
36.683	0	35.916	35.299	35.254	35.465
37.288	0	36.683	35.916	35.299	35.254
38.536	0	37.288	36.683	35.916	35.299
38.977	0	38.536	37.288	36.683	35.916
36.407	0	38.977	38.536	37.288	36.683
34.955	0	36.407	38.977	38.536	37.288
34.951	0	34.955	36.407	38.977	38.536
32.680	0	34.951	34.955	36.407	38.977
34.791	0	32.680	34.951	34.955	36.407
34.178	0	34.791	32.680	34.951	34.955
35.213	0	34.178	34.791	32.680	34.951
34.871	0	35.213	34.178	34.791	32.680
35.299	0	34.871	35.213	34.178	34.791
35.443	0	35.299	34.871	35.213	34.178
37.108	0	35.443	35.299	34.871	35.213
36.419	0	37.108	35.443	35.299	34.871
34.471	0	36.419	37.108	35.443	35.299
33.868	0	34.471	36.419	37.108	35.443
34.385	0	33.868	34.471	36.419	37.108
33.643	1	34.385	33.868	34.471	36.419
34.627	1	33.643	34.385	33.868	34.471
32.919	1	34.627	33.643	34.385	33.868
35.500	1	32.919	34.627	33.643	34.385
36.110	1	35.500	32.919	34.627	33.643
37.086	1	36.110	35.500	32.919	34.627
37.711	1	37.086	36.110	35.500	32.919
40.427	1	37.711	37.086	36.110	35.500
39.884	1	40.427	37.711	37.086	36.110
38.512	1	39.884	40.427	37.711	37.086
38.767	1	38.512	39.884	40.427	37.711

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=60191&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=60191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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] = + 4.25594081528187 + 0.720234616005331crisis[t] + 0.856141359397383`Yt-1`[t] + 0.0821221882076306`Yt-2`[t] -0.250361502036348`Yt-3`[t] + 0.228921383100153`Yt-4`[t] -1.70906219387124M1[t] -1.01532730724768M2[t] -0.767054591316031M3[t] -1.18454400203984M4[t] -0.537866020905931M5[t] + 0.613409664166027M6[t] -0.685453433357246M7[t] -2.43197864185542M8[t] -1.44235537582931M9[t] -1.1955274997821M10[t] -2.55721615948579M11[t] -0.00737361908607257t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
saldo_zichtrek[t] =  +  4.25594081528187 +  0.720234616005331crisis[t] +  0.856141359397383`Yt-1`[t] +  0.0821221882076306`Yt-2`[t] -0.250361502036348`Yt-3`[t] +  0.228921383100153`Yt-4`[t] -1.70906219387124M1[t] -1.01532730724768M2[t] -0.767054591316031M3[t] -1.18454400203984M4[t] -0.537866020905931M5[t] +  0.613409664166027M6[t] -0.685453433357246M7[t] -2.43197864185542M8[t] -1.44235537582931M9[t] -1.1955274997821M10[t] -2.55721615948579M11[t] -0.00737361908607257t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]saldo_zichtrek[t] =  +  4.25594081528187 +  0.720234616005331crisis[t] +  0.856141359397383`Yt-1`[t] +  0.0821221882076306`Yt-2`[t] -0.250361502036348`Yt-3`[t] +  0.228921383100153`Yt-4`[t] -1.70906219387124M1[t] -1.01532730724768M2[t] -0.767054591316031M3[t] -1.18454400203984M4[t] -0.537866020905931M5[t] +  0.613409664166027M6[t] -0.685453433357246M7[t] -2.43197864185542M8[t] -1.44235537582931M9[t] -1.1955274997821M10[t] -2.55721615948579M11[t] -0.00737361908607257t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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] = + 4.25594081528187 + 0.720234616005331crisis[t] + 0.856141359397383`Yt-1`[t] + 0.0821221882076306`Yt-2`[t] -0.250361502036348`Yt-3`[t] + 0.228921383100153`Yt-4`[t] -1.70906219387124M1[t] -1.01532730724768M2[t] -0.767054591316031M3[t] -1.18454400203984M4[t] -0.537866020905931M5[t] + 0.613409664166027M6[t] -0.685453433357246M7[t] -2.43197864185542M8[t] -1.44235537582931M9[t] -1.1955274997821M10[t] -2.55721615948579M11[t] -0.00737361908607257t + 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)	4.25594081528187	2.781155	1.5303	0.13402	0.06701
crisis	0.720234616005331	0.45011	1.6001	0.117641	0.05882
`Yt-1`	0.856141359397383	0.15499	5.5239	2e-06	1e-06
`Yt-2`	0.0821221882076306	0.194523	0.4222	0.675218	0.337609
`Yt-3`	-0.250361502036348	0.192694	-1.2993	0.201479	0.10074
`Yt-4`	0.228921383100153	0.160764	1.424	0.162412	0.081206
M1	-1.70906219387124	0.635516	-2.6893	0.010482	0.005241
M2	-1.01532730724768	0.596114	-1.7032	0.09648	0.04824
M3	-0.767054591316031	0.660432	-1.1614	0.252521	0.12626
M4	-1.18454400203984	0.57887	-2.0463	0.04751	0.023755
M5	-0.537866020905931	0.604839	-0.8893	0.37931	0.189655
M6	0.613409664166027	0.608885	1.0074	0.319937	0.159969
M7	-0.685453433357246	0.714932	-0.9588	0.343582	0.171791
M8	-2.43197864185542	0.705807	-3.4457	0.001377	0.000689
M9	-1.44235537582931	0.686764	-2.1002	0.042227	0.021113
M10	-1.1955274997821	0.627557	-1.9051	0.064166	0.032083
M11	-2.55721615948579	0.609255	-4.1973	0.000151	7.6e-05
t	-0.00737361908607257	0.013092	-0.5632	0.576502	0.288251

\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) & 4.25594081528187 & 2.781155 & 1.5303 & 0.13402 & 0.06701 \tabularnewline
crisis & 0.720234616005331 & 0.45011 & 1.6001 & 0.117641 & 0.05882 \tabularnewline
`Yt-1` & 0.856141359397383 & 0.15499 & 5.5239 & 2e-06 & 1e-06 \tabularnewline
`Yt-2` & 0.0821221882076306 & 0.194523 & 0.4222 & 0.675218 & 0.337609 \tabularnewline
`Yt-3` & -0.250361502036348 & 0.192694 & -1.2993 & 0.201479 & 0.10074 \tabularnewline
`Yt-4` & 0.228921383100153 & 0.160764 & 1.424 & 0.162412 & 0.081206 \tabularnewline
M1 & -1.70906219387124 & 0.635516 & -2.6893 & 0.010482 & 0.005241 \tabularnewline
M2 & -1.01532730724768 & 0.596114 & -1.7032 & 0.09648 & 0.04824 \tabularnewline
M3 & -0.767054591316031 & 0.660432 & -1.1614 & 0.252521 & 0.12626 \tabularnewline
M4 & -1.18454400203984 & 0.57887 & -2.0463 & 0.04751 & 0.023755 \tabularnewline
M5 & -0.537866020905931 & 0.604839 & -0.8893 & 0.37931 & 0.189655 \tabularnewline
M6 & 0.613409664166027 & 0.608885 & 1.0074 & 0.319937 & 0.159969 \tabularnewline
M7 & -0.685453433357246 & 0.714932 & -0.9588 & 0.343582 & 0.171791 \tabularnewline
M8 & -2.43197864185542 & 0.705807 & -3.4457 & 0.001377 & 0.000689 \tabularnewline
M9 & -1.44235537582931 & 0.686764 & -2.1002 & 0.042227 & 0.021113 \tabularnewline
M10 & -1.1955274997821 & 0.627557 & -1.9051 & 0.064166 & 0.032083 \tabularnewline
M11 & -2.55721615948579 & 0.609255 & -4.1973 & 0.000151 & 7.6e-05 \tabularnewline
t & -0.00737361908607257 & 0.013092 & -0.5632 & 0.576502 & 0.288251 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&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]4.25594081528187[/C][C]2.781155[/C][C]1.5303[/C][C]0.13402[/C][C]0.06701[/C][/ROW]
[ROW][C]crisis[/C][C]0.720234616005331[/C][C]0.45011[/C][C]1.6001[/C][C]0.117641[/C][C]0.05882[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.856141359397383[/C][C]0.15499[/C][C]5.5239[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.0821221882076306[/C][C]0.194523[/C][C]0.4222[/C][C]0.675218[/C][C]0.337609[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-0.250361502036348[/C][C]0.192694[/C][C]-1.2993[/C][C]0.201479[/C][C]0.10074[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]0.228921383100153[/C][C]0.160764[/C][C]1.424[/C][C]0.162412[/C][C]0.081206[/C][/ROW]
[ROW][C]M1[/C][C]-1.70906219387124[/C][C]0.635516[/C][C]-2.6893[/C][C]0.010482[/C][C]0.005241[/C][/ROW]
[ROW][C]M2[/C][C]-1.01532730724768[/C][C]0.596114[/C][C]-1.7032[/C][C]0.09648[/C][C]0.04824[/C][/ROW]
[ROW][C]M3[/C][C]-0.767054591316031[/C][C]0.660432[/C][C]-1.1614[/C][C]0.252521[/C][C]0.12626[/C][/ROW]
[ROW][C]M4[/C][C]-1.18454400203984[/C][C]0.57887[/C][C]-2.0463[/C][C]0.04751[/C][C]0.023755[/C][/ROW]
[ROW][C]M5[/C][C]-0.537866020905931[/C][C]0.604839[/C][C]-0.8893[/C][C]0.37931[/C][C]0.189655[/C][/ROW]
[ROW][C]M6[/C][C]0.613409664166027[/C][C]0.608885[/C][C]1.0074[/C][C]0.319937[/C][C]0.159969[/C][/ROW]
[ROW][C]M7[/C][C]-0.685453433357246[/C][C]0.714932[/C][C]-0.9588[/C][C]0.343582[/C][C]0.171791[/C][/ROW]
[ROW][C]M8[/C][C]-2.43197864185542[/C][C]0.705807[/C][C]-3.4457[/C][C]0.001377[/C][C]0.000689[/C][/ROW]
[ROW][C]M9[/C][C]-1.44235537582931[/C][C]0.686764[/C][C]-2.1002[/C][C]0.042227[/C][C]0.021113[/C][/ROW]
[ROW][C]M10[/C][C]-1.1955274997821[/C][C]0.627557[/C][C]-1.9051[/C][C]0.064166[/C][C]0.032083[/C][/ROW]
[ROW][C]M11[/C][C]-2.55721615948579[/C][C]0.609255[/C][C]-4.1973[/C][C]0.000151[/C][C]7.6e-05[/C][/ROW]
[ROW][C]t[/C][C]-0.00737361908607257[/C][C]0.013092[/C][C]-0.5632[/C][C]0.576502[/C][C]0.288251[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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)	4.25594081528187	2.781155	1.5303	0.13402	0.06701
crisis	0.720234616005331	0.45011	1.6001	0.117641	0.05882
`Yt-1`	0.856141359397383	0.15499	5.5239	2e-06	1e-06
`Yt-2`	0.0821221882076306	0.194523	0.4222	0.675218	0.337609
`Yt-3`	-0.250361502036348	0.192694	-1.2993	0.201479	0.10074
`Yt-4`	0.228921383100153	0.160764	1.424	0.162412	0.081206
M1	-1.70906219387124	0.635516	-2.6893	0.010482	0.005241
M2	-1.01532730724768	0.596114	-1.7032	0.09648	0.04824
M3	-0.767054591316031	0.660432	-1.1614	0.252521	0.12626
M4	-1.18454400203984	0.57887	-2.0463	0.04751	0.023755
M5	-0.537866020905931	0.604839	-0.8893	0.37931	0.189655
M6	0.613409664166027	0.608885	1.0074	0.319937	0.159969
M7	-0.685453433357246	0.714932	-0.9588	0.343582	0.171791
M8	-2.43197864185542	0.705807	-3.4457	0.001377	0.000689
M9	-1.44235537582931	0.686764	-2.1002	0.042227	0.021113
M10	-1.1955274997821	0.627557	-1.9051	0.064166	0.032083
M11	-2.55721615948579	0.609255	-4.1973	0.000151	7.6e-05
t	-0.00737361908607257	0.013092	-0.5632	0.576502	0.288251

Multiple Linear Regression - Regression Statistics
Multiple R	0.951284299126547
R-squared	0.904941817764685
Adjusted R-squared	0.86350619986724
F-TEST (value)	21.8397085329934
F-TEST (DF numerator)	17
F-TEST (DF denominator)	39
p-value	8.32667268468867e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.797997887006061
Sum Squared Residuals	24.8352244789794

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.951284299126547 \tabularnewline
R-squared & 0.904941817764685 \tabularnewline
Adjusted R-squared & 0.86350619986724 \tabularnewline
F-TEST (value) & 21.8397085329934 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 8.32667268468867e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.797997887006061 \tabularnewline
Sum Squared Residuals & 24.8352244789794 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.951284299126547[/C][/ROW]
[ROW][C]R-squared[/C][C]0.904941817764685[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.86350619986724[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.8397085329934[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]8.32667268468867e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.797997887006061[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24.8352244789794[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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	0.951284299126547
R-squared	0.904941817764685
Adjusted R-squared	0.86350619986724
F-TEST (value)	21.8397085329934
F-TEST (DF numerator)	17
F-TEST (DF denominator)	39
p-value	8.32667268468867e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.797997887006061
Sum Squared Residuals	24.8352244789794

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	30.996	30.5766312390722	0.419368760927771
2	31.033	31.4863003063172	-0.453300306317151
3	31.198	31.8446963943952	-0.64669639439516
4	30.937	31.5276895146551	-0.590689514655115
5	31.649	32.0558786602024	-0.406878660202423
6	33.115	33.7550799262958	-0.640079926295762
7	34.106	33.8655338208098	0.240466179190174
8	33.926	32.8424563378618	1.08354366213825
9	33.382	33.5479456914060	-0.16594569140605
10	32.851	33.3943675540844	-0.543367554084434
11	32.948	31.7979459040885	1.15005409591149
12	36.112	34.4822180825613	1.62978191743874
13	36.113	35.4909881081682	0.622011891831774
14	35.21	36.2921978004303	-1.08219780043035
15	35.193	34.990144953646	0.202855046353993
16	34.383	35.2006280794017	-0.817628079401719
17	35.349	35.3713672208601	-0.0223672208600803
18	37.058	37.0733230041708	-0.0153230041708290
19	38.076	37.5084630577169	0.56753694228308
20	36.63	36.3391874223678	0.290812577632183
21	36.045	35.9603273003092	0.0846726996907642
22	35.638	35.7165488125198	-0.0785488125198321
23	35.114	34.5460602202944	0.567939779705613
24	35.465	36.4293021164978	-0.964302116497802
25	35.254	34.9383180162834	0.315681983716615
26	35.299	35.5108767691942	-0.211876769194184
27	35.916	35.5651427535416	0.350857246458405
28	36.683	35.8053921233471	0.87760787665294
29	37.288	37.0924576187510	0.195542381248963
30	38.536	38.6731413410107	-0.137141341010666
31	38.977	38.4342701861058	0.542729813894208
32	36.407	37.1845321810047	-0.777532181004741
33	34.955	35.8287607015273	-0.873760701527295
34	34.951	34.7893281446608	0.161671855339216
35	32.68	34.0419832733365	-1.36198327333654
36	34.791	34.4223972441813	0.368602755818647
37	34.178	33.9953839492391	0.182616050760897
38	35.213	34.8979457883645	0.315054211635549
39	34.871	34.9262166989959	-0.0552166989958623
40	35.299	34.9302744295397	0.368725570460320
41	35.443	35.5084685425946	-0.0654685425945736
42	37.108	37.1333605260916	-0.0253605260916374
43	36.419	37.078978932089	-0.659978932089023
44	34.471	34.9338584469193	-0.462858446919319
45	33.868	33.8078753163541	0.0601246836459072
46	34.385	33.9247554887349	0.46024451126505
47	33.643	33.9990106022806	-0.356010602280565
48	34.627	35.6610825567596	-1.03408255675958
49	32.919	34.4586786872371	-1.53967868723706
50	35.5	34.0676793356939	1.43232066430613
51	36.11	35.9617991994214	0.148200800578625
52	37.086	36.9240158530564	0.161984146943574
53	37.711	37.4118279575919	0.299172042408114
54	40.427	39.6090952024311	0.817904797568894
55	39.884	40.5747540032784	-0.690754003278439
56	38.512	38.6459656118464	-0.133965611846371
57	38.767	37.8720909904033	0.894909009596673

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 30.996 & 30.5766312390722 & 0.419368760927771 \tabularnewline
2 & 31.033 & 31.4863003063172 & -0.453300306317151 \tabularnewline
3 & 31.198 & 31.8446963943952 & -0.64669639439516 \tabularnewline
4 & 30.937 & 31.5276895146551 & -0.590689514655115 \tabularnewline
5 & 31.649 & 32.0558786602024 & -0.406878660202423 \tabularnewline
6 & 33.115 & 33.7550799262958 & -0.640079926295762 \tabularnewline
7 & 34.106 & 33.8655338208098 & 0.240466179190174 \tabularnewline
8 & 33.926 & 32.8424563378618 & 1.08354366213825 \tabularnewline
9 & 33.382 & 33.5479456914060 & -0.16594569140605 \tabularnewline
10 & 32.851 & 33.3943675540844 & -0.543367554084434 \tabularnewline
11 & 32.948 & 31.7979459040885 & 1.15005409591149 \tabularnewline
12 & 36.112 & 34.4822180825613 & 1.62978191743874 \tabularnewline
13 & 36.113 & 35.4909881081682 & 0.622011891831774 \tabularnewline
14 & 35.21 & 36.2921978004303 & -1.08219780043035 \tabularnewline
15 & 35.193 & 34.990144953646 & 0.202855046353993 \tabularnewline
16 & 34.383 & 35.2006280794017 & -0.817628079401719 \tabularnewline
17 & 35.349 & 35.3713672208601 & -0.0223672208600803 \tabularnewline
18 & 37.058 & 37.0733230041708 & -0.0153230041708290 \tabularnewline
19 & 38.076 & 37.5084630577169 & 0.56753694228308 \tabularnewline
20 & 36.63 & 36.3391874223678 & 0.290812577632183 \tabularnewline
21 & 36.045 & 35.9603273003092 & 0.0846726996907642 \tabularnewline
22 & 35.638 & 35.7165488125198 & -0.0785488125198321 \tabularnewline
23 & 35.114 & 34.5460602202944 & 0.567939779705613 \tabularnewline
24 & 35.465 & 36.4293021164978 & -0.964302116497802 \tabularnewline
25 & 35.254 & 34.9383180162834 & 0.315681983716615 \tabularnewline
26 & 35.299 & 35.5108767691942 & -0.211876769194184 \tabularnewline
27 & 35.916 & 35.5651427535416 & 0.350857246458405 \tabularnewline
28 & 36.683 & 35.8053921233471 & 0.87760787665294 \tabularnewline
29 & 37.288 & 37.0924576187510 & 0.195542381248963 \tabularnewline
30 & 38.536 & 38.6731413410107 & -0.137141341010666 \tabularnewline
31 & 38.977 & 38.4342701861058 & 0.542729813894208 \tabularnewline
32 & 36.407 & 37.1845321810047 & -0.777532181004741 \tabularnewline
33 & 34.955 & 35.8287607015273 & -0.873760701527295 \tabularnewline
34 & 34.951 & 34.7893281446608 & 0.161671855339216 \tabularnewline
35 & 32.68 & 34.0419832733365 & -1.36198327333654 \tabularnewline
36 & 34.791 & 34.4223972441813 & 0.368602755818647 \tabularnewline
37 & 34.178 & 33.9953839492391 & 0.182616050760897 \tabularnewline
38 & 35.213 & 34.8979457883645 & 0.315054211635549 \tabularnewline
39 & 34.871 & 34.9262166989959 & -0.0552166989958623 \tabularnewline
40 & 35.299 & 34.9302744295397 & 0.368725570460320 \tabularnewline
41 & 35.443 & 35.5084685425946 & -0.0654685425945736 \tabularnewline
42 & 37.108 & 37.1333605260916 & -0.0253605260916374 \tabularnewline
43 & 36.419 & 37.078978932089 & -0.659978932089023 \tabularnewline
44 & 34.471 & 34.9338584469193 & -0.462858446919319 \tabularnewline
45 & 33.868 & 33.8078753163541 & 0.0601246836459072 \tabularnewline
46 & 34.385 & 33.9247554887349 & 0.46024451126505 \tabularnewline
47 & 33.643 & 33.9990106022806 & -0.356010602280565 \tabularnewline
48 & 34.627 & 35.6610825567596 & -1.03408255675958 \tabularnewline
49 & 32.919 & 34.4586786872371 & -1.53967868723706 \tabularnewline
50 & 35.5 & 34.0676793356939 & 1.43232066430613 \tabularnewline
51 & 36.11 & 35.9617991994214 & 0.148200800578625 \tabularnewline
52 & 37.086 & 36.9240158530564 & 0.161984146943574 \tabularnewline
53 & 37.711 & 37.4118279575919 & 0.299172042408114 \tabularnewline
54 & 40.427 & 39.6090952024311 & 0.817904797568894 \tabularnewline
55 & 39.884 & 40.5747540032784 & -0.690754003278439 \tabularnewline
56 & 38.512 & 38.6459656118464 & -0.133965611846371 \tabularnewline
57 & 38.767 & 37.8720909904033 & 0.894909009596673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&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]30.996[/C][C]30.5766312390722[/C][C]0.419368760927771[/C][/ROW]
[ROW][C]2[/C][C]31.033[/C][C]31.4863003063172[/C][C]-0.453300306317151[/C][/ROW]
[ROW][C]3[/C][C]31.198[/C][C]31.8446963943952[/C][C]-0.64669639439516[/C][/ROW]
[ROW][C]4[/C][C]30.937[/C][C]31.5276895146551[/C][C]-0.590689514655115[/C][/ROW]
[ROW][C]5[/C][C]31.649[/C][C]32.0558786602024[/C][C]-0.406878660202423[/C][/ROW]
[ROW][C]6[/C][C]33.115[/C][C]33.7550799262958[/C][C]-0.640079926295762[/C][/ROW]
[ROW][C]7[/C][C]34.106[/C][C]33.8655338208098[/C][C]0.240466179190174[/C][/ROW]
[ROW][C]8[/C][C]33.926[/C][C]32.8424563378618[/C][C]1.08354366213825[/C][/ROW]
[ROW][C]9[/C][C]33.382[/C][C]33.5479456914060[/C][C]-0.16594569140605[/C][/ROW]
[ROW][C]10[/C][C]32.851[/C][C]33.3943675540844[/C][C]-0.543367554084434[/C][/ROW]
[ROW][C]11[/C][C]32.948[/C][C]31.7979459040885[/C][C]1.15005409591149[/C][/ROW]
[ROW][C]12[/C][C]36.112[/C][C]34.4822180825613[/C][C]1.62978191743874[/C][/ROW]
[ROW][C]13[/C][C]36.113[/C][C]35.4909881081682[/C][C]0.622011891831774[/C][/ROW]
[ROW][C]14[/C][C]35.21[/C][C]36.2921978004303[/C][C]-1.08219780043035[/C][/ROW]
[ROW][C]15[/C][C]35.193[/C][C]34.990144953646[/C][C]0.202855046353993[/C][/ROW]
[ROW][C]16[/C][C]34.383[/C][C]35.2006280794017[/C][C]-0.817628079401719[/C][/ROW]
[ROW][C]17[/C][C]35.349[/C][C]35.3713672208601[/C][C]-0.0223672208600803[/C][/ROW]
[ROW][C]18[/C][C]37.058[/C][C]37.0733230041708[/C][C]-0.0153230041708290[/C][/ROW]
[ROW][C]19[/C][C]38.076[/C][C]37.5084630577169[/C][C]0.56753694228308[/C][/ROW]
[ROW][C]20[/C][C]36.63[/C][C]36.3391874223678[/C][C]0.290812577632183[/C][/ROW]
[ROW][C]21[/C][C]36.045[/C][C]35.9603273003092[/C][C]0.0846726996907642[/C][/ROW]
[ROW][C]22[/C][C]35.638[/C][C]35.7165488125198[/C][C]-0.0785488125198321[/C][/ROW]
[ROW][C]23[/C][C]35.114[/C][C]34.5460602202944[/C][C]0.567939779705613[/C][/ROW]
[ROW][C]24[/C][C]35.465[/C][C]36.4293021164978[/C][C]-0.964302116497802[/C][/ROW]
[ROW][C]25[/C][C]35.254[/C][C]34.9383180162834[/C][C]0.315681983716615[/C][/ROW]
[ROW][C]26[/C][C]35.299[/C][C]35.5108767691942[/C][C]-0.211876769194184[/C][/ROW]
[ROW][C]27[/C][C]35.916[/C][C]35.5651427535416[/C][C]0.350857246458405[/C][/ROW]
[ROW][C]28[/C][C]36.683[/C][C]35.8053921233471[/C][C]0.87760787665294[/C][/ROW]
[ROW][C]29[/C][C]37.288[/C][C]37.0924576187510[/C][C]0.195542381248963[/C][/ROW]
[ROW][C]30[/C][C]38.536[/C][C]38.6731413410107[/C][C]-0.137141341010666[/C][/ROW]
[ROW][C]31[/C][C]38.977[/C][C]38.4342701861058[/C][C]0.542729813894208[/C][/ROW]
[ROW][C]32[/C][C]36.407[/C][C]37.1845321810047[/C][C]-0.777532181004741[/C][/ROW]
[ROW][C]33[/C][C]34.955[/C][C]35.8287607015273[/C][C]-0.873760701527295[/C][/ROW]
[ROW][C]34[/C][C]34.951[/C][C]34.7893281446608[/C][C]0.161671855339216[/C][/ROW]
[ROW][C]35[/C][C]32.68[/C][C]34.0419832733365[/C][C]-1.36198327333654[/C][/ROW]
[ROW][C]36[/C][C]34.791[/C][C]34.4223972441813[/C][C]0.368602755818647[/C][/ROW]
[ROW][C]37[/C][C]34.178[/C][C]33.9953839492391[/C][C]0.182616050760897[/C][/ROW]
[ROW][C]38[/C][C]35.213[/C][C]34.8979457883645[/C][C]0.315054211635549[/C][/ROW]
[ROW][C]39[/C][C]34.871[/C][C]34.9262166989959[/C][C]-0.0552166989958623[/C][/ROW]
[ROW][C]40[/C][C]35.299[/C][C]34.9302744295397[/C][C]0.368725570460320[/C][/ROW]
[ROW][C]41[/C][C]35.443[/C][C]35.5084685425946[/C][C]-0.0654685425945736[/C][/ROW]
[ROW][C]42[/C][C]37.108[/C][C]37.1333605260916[/C][C]-0.0253605260916374[/C][/ROW]
[ROW][C]43[/C][C]36.419[/C][C]37.078978932089[/C][C]-0.659978932089023[/C][/ROW]
[ROW][C]44[/C][C]34.471[/C][C]34.9338584469193[/C][C]-0.462858446919319[/C][/ROW]
[ROW][C]45[/C][C]33.868[/C][C]33.8078753163541[/C][C]0.0601246836459072[/C][/ROW]
[ROW][C]46[/C][C]34.385[/C][C]33.9247554887349[/C][C]0.46024451126505[/C][/ROW]
[ROW][C]47[/C][C]33.643[/C][C]33.9990106022806[/C][C]-0.356010602280565[/C][/ROW]
[ROW][C]48[/C][C]34.627[/C][C]35.6610825567596[/C][C]-1.03408255675958[/C][/ROW]
[ROW][C]49[/C][C]32.919[/C][C]34.4586786872371[/C][C]-1.53967868723706[/C][/ROW]
[ROW][C]50[/C][C]35.5[/C][C]34.0676793356939[/C][C]1.43232066430613[/C][/ROW]
[ROW][C]51[/C][C]36.11[/C][C]35.9617991994214[/C][C]0.148200800578625[/C][/ROW]
[ROW][C]52[/C][C]37.086[/C][C]36.9240158530564[/C][C]0.161984146943574[/C][/ROW]
[ROW][C]53[/C][C]37.711[/C][C]37.4118279575919[/C][C]0.299172042408114[/C][/ROW]
[ROW][C]54[/C][C]40.427[/C][C]39.6090952024311[/C][C]0.817904797568894[/C][/ROW]
[ROW][C]55[/C][C]39.884[/C][C]40.5747540032784[/C][C]-0.690754003278439[/C][/ROW]
[ROW][C]56[/C][C]38.512[/C][C]38.6459656118464[/C][C]-0.133965611846371[/C][/ROW]
[ROW][C]57[/C][C]38.767[/C][C]37.8720909904033[/C][C]0.894909009596673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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	30.996	30.5766312390722	0.419368760927771
2	31.033	31.4863003063172	-0.453300306317151
3	31.198	31.8446963943952	-0.64669639439516
4	30.937	31.5276895146551	-0.590689514655115
5	31.649	32.0558786602024	-0.406878660202423
6	33.115	33.7550799262958	-0.640079926295762
7	34.106	33.8655338208098	0.240466179190174
8	33.926	32.8424563378618	1.08354366213825
9	33.382	33.5479456914060	-0.16594569140605
10	32.851	33.3943675540844	-0.543367554084434
11	32.948	31.7979459040885	1.15005409591149
12	36.112	34.4822180825613	1.62978191743874
13	36.113	35.4909881081682	0.622011891831774
14	35.21	36.2921978004303	-1.08219780043035
15	35.193	34.990144953646	0.202855046353993
16	34.383	35.2006280794017	-0.817628079401719
17	35.349	35.3713672208601	-0.0223672208600803
18	37.058	37.0733230041708	-0.0153230041708290
19	38.076	37.5084630577169	0.56753694228308
20	36.63	36.3391874223678	0.290812577632183
21	36.045	35.9603273003092	0.0846726996907642
22	35.638	35.7165488125198	-0.0785488125198321
23	35.114	34.5460602202944	0.567939779705613
24	35.465	36.4293021164978	-0.964302116497802
25	35.254	34.9383180162834	0.315681983716615
26	35.299	35.5108767691942	-0.211876769194184
27	35.916	35.5651427535416	0.350857246458405
28	36.683	35.8053921233471	0.87760787665294
29	37.288	37.0924576187510	0.195542381248963
30	38.536	38.6731413410107	-0.137141341010666
31	38.977	38.4342701861058	0.542729813894208
32	36.407	37.1845321810047	-0.777532181004741
33	34.955	35.8287607015273	-0.873760701527295
34	34.951	34.7893281446608	0.161671855339216
35	32.68	34.0419832733365	-1.36198327333654
36	34.791	34.4223972441813	0.368602755818647
37	34.178	33.9953839492391	0.182616050760897
38	35.213	34.8979457883645	0.315054211635549
39	34.871	34.9262166989959	-0.0552166989958623
40	35.299	34.9302744295397	0.368725570460320
41	35.443	35.5084685425946	-0.0654685425945736
42	37.108	37.1333605260916	-0.0253605260916374
43	36.419	37.078978932089	-0.659978932089023
44	34.471	34.9338584469193	-0.462858446919319
45	33.868	33.8078753163541	0.0601246836459072
46	34.385	33.9247554887349	0.46024451126505
47	33.643	33.9990106022806	-0.356010602280565
48	34.627	35.6610825567596	-1.03408255675958
49	32.919	34.4586786872371	-1.53967868723706
50	35.5	34.0676793356939	1.43232066430613
51	36.11	35.9617991994214	0.148200800578625
52	37.086	36.9240158530564	0.161984146943574
53	37.711	37.4118279575919	0.299172042408114
54	40.427	39.6090952024311	0.817904797568894
55	39.884	40.5747540032784	-0.690754003278439
56	38.512	38.6459656118464	-0.133965611846371
57	38.767	37.8720909904033	0.894909009596673

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.309358967795888	0.618717935591775	0.690641032204112
22	0.161614061327992	0.323228122655983	0.838385938672008
23	0.13988710029793	0.27977420059586	0.86011289970207
24	0.449071255069568	0.898142510139137	0.550928744930432
25	0.498216936491179	0.996433872982359	0.501783063508821
26	0.571828988376596	0.856342023246807	0.428171011623404
27	0.538646435073217	0.922707129853567	0.461353564926783
28	0.554832542794564	0.890334914410872	0.445167457205436
29	0.474816950012249	0.949633900024497	0.525183049987751
30	0.352333913567286	0.704667827134572	0.647666086432714
31	0.555047418014722	0.889905163970557	0.444952581985278
32	0.565303876646763	0.869392246706474	0.434696123353237
33	0.442962996934555	0.88592599386911	0.557037003065445
34	0.309141875570677	0.618283751141354	0.690858124429323
35	0.615149794255518	0.769700411488964	0.384850205744482
36	0.669753866055279	0.660492267889443	0.330246133944721

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.309358967795888 & 0.618717935591775 & 0.690641032204112 \tabularnewline
22 & 0.161614061327992 & 0.323228122655983 & 0.838385938672008 \tabularnewline
23 & 0.13988710029793 & 0.27977420059586 & 0.86011289970207 \tabularnewline
24 & 0.449071255069568 & 0.898142510139137 & 0.550928744930432 \tabularnewline
25 & 0.498216936491179 & 0.996433872982359 & 0.501783063508821 \tabularnewline
26 & 0.571828988376596 & 0.856342023246807 & 0.428171011623404 \tabularnewline
27 & 0.538646435073217 & 0.922707129853567 & 0.461353564926783 \tabularnewline
28 & 0.554832542794564 & 0.890334914410872 & 0.445167457205436 \tabularnewline
29 & 0.474816950012249 & 0.949633900024497 & 0.525183049987751 \tabularnewline
30 & 0.352333913567286 & 0.704667827134572 & 0.647666086432714 \tabularnewline
31 & 0.555047418014722 & 0.889905163970557 & 0.444952581985278 \tabularnewline
32 & 0.565303876646763 & 0.869392246706474 & 0.434696123353237 \tabularnewline
33 & 0.442962996934555 & 0.88592599386911 & 0.557037003065445 \tabularnewline
34 & 0.309141875570677 & 0.618283751141354 & 0.690858124429323 \tabularnewline
35 & 0.615149794255518 & 0.769700411488964 & 0.384850205744482 \tabularnewline
36 & 0.669753866055279 & 0.660492267889443 & 0.330246133944721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&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]0.309358967795888[/C][C]0.618717935591775[/C][C]0.690641032204112[/C][/ROW]
[ROW][C]22[/C][C]0.161614061327992[/C][C]0.323228122655983[/C][C]0.838385938672008[/C][/ROW]
[ROW][C]23[/C][C]0.13988710029793[/C][C]0.27977420059586[/C][C]0.86011289970207[/C][/ROW]
[ROW][C]24[/C][C]0.449071255069568[/C][C]0.898142510139137[/C][C]0.550928744930432[/C][/ROW]
[ROW][C]25[/C][C]0.498216936491179[/C][C]0.996433872982359[/C][C]0.501783063508821[/C][/ROW]
[ROW][C]26[/C][C]0.571828988376596[/C][C]0.856342023246807[/C][C]0.428171011623404[/C][/ROW]
[ROW][C]27[/C][C]0.538646435073217[/C][C]0.922707129853567[/C][C]0.461353564926783[/C][/ROW]
[ROW][C]28[/C][C]0.554832542794564[/C][C]0.890334914410872[/C][C]0.445167457205436[/C][/ROW]
[ROW][C]29[/C][C]0.474816950012249[/C][C]0.949633900024497[/C][C]0.525183049987751[/C][/ROW]
[ROW][C]30[/C][C]0.352333913567286[/C][C]0.704667827134572[/C][C]0.647666086432714[/C][/ROW]
[ROW][C]31[/C][C]0.555047418014722[/C][C]0.889905163970557[/C][C]0.444952581985278[/C][/ROW]
[ROW][C]32[/C][C]0.565303876646763[/C][C]0.869392246706474[/C][C]0.434696123353237[/C][/ROW]
[ROW][C]33[/C][C]0.442962996934555[/C][C]0.88592599386911[/C][C]0.557037003065445[/C][/ROW]
[ROW][C]34[/C][C]0.309141875570677[/C][C]0.618283751141354[/C][C]0.690858124429323[/C][/ROW]
[ROW][C]35[/C][C]0.615149794255518[/C][C]0.769700411488964[/C][C]0.384850205744482[/C][/ROW]
[ROW][C]36[/C][C]0.669753866055279[/C][C]0.660492267889443[/C][C]0.330246133944721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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
21	0.309358967795888	0.618717935591775	0.690641032204112
22	0.161614061327992	0.323228122655983	0.838385938672008
23	0.13988710029793	0.27977420059586	0.86011289970207
24	0.449071255069568	0.898142510139137	0.550928744930432
25	0.498216936491179	0.996433872982359	0.501783063508821
26	0.571828988376596	0.856342023246807	0.428171011623404
27	0.538646435073217	0.922707129853567	0.461353564926783
28	0.554832542794564	0.890334914410872	0.445167457205436
29	0.474816950012249	0.949633900024497	0.525183049987751
30	0.352333913567286	0.704667827134572	0.647666086432714
31	0.555047418014722	0.889905163970557	0.444952581985278
32	0.565303876646763	0.869392246706474	0.434696123353237
33	0.442962996934555	0.88592599386911	0.557037003065445
34	0.309141875570677	0.618283751141354	0.690858124429323
35	0.615149794255518	0.769700411488964	0.384850205744482
36	0.669753866055279	0.660492267889443	0.330246133944721

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60191&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60191&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60191&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	0	0	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

PNG link

Postscript link

PDF link

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