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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 20 Dec 2009 02:23:55 -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/Dec/20/t1261301211sc9u39o20qobquu.htm/, Retrieved Sat, 27 Apr 2024 09:35:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69800, Retrieved Sat, 27 Apr 2024 09:35:53 +0000

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Estimated Impact

167

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [multiple regressi...] [2009-11-19 15:54:48] [77c4589624c8ef9dff4002b842437335]
- R P         [Multiple Regression] [] [2009-12-20 09:23:55] [8f072ead2c7c0b3cf3fdae49bab9dd9b] [Current]
- RMPD          [Kendall tau Correlation Matrix] [Kendall tau corre...] [2009-12-20 19:30:31] [77c4589624c8ef9dff4002b842437335] 

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Dataseries X:

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Boxplots

102.9	127.5	112.7	97	95.1
97.4	134.6	102.9	112.7	97
111.4	131.8	97.4	102.9	112.7
87.4	135.9	111.4	97.4	102.9
96.8	142.7	87.4	111.4	97.4
114.1	141.7	96.8	87.4	111.4
110.3	153.4	114.1	96.8	87.4
103.9	145	110.3	114.1	96.8
101.6	137.7	103.9	110.3	114.1
94.6	148.3	101.6	103.9	110.3
95.9	152.2	94.6	101.6	103.9
104.7	169.4	95.9	94.6	101.6
102.8	168.6	104.7	95.9	94.6
98.1	161.1	102.8	104.7	95.9
113.9	174.1	98.1	102.8	104.7
80.9	179	113.9	98.1	102.8
95.7	190.6	80.9	113.9	98.1
113.2	190	95.7	80.9	113.9
105.9	181.6	113.2	95.7	80.9
108.8	174.8	105.9	113.2	95.7
102.3	180.5	108.8	105.9	113.2
99	196.8	102.3	108.8	105.9
100.7	193.8	99	102.3	108.8
115.5	197	100.7	99	102.3
100.7	216.3	115.5	100.7	99
109.9	221.4	100.7	115.5	100.7
114.6	217.9	109.9	100.7	115.5
85.4	229.7	114.6	109.9	100.7
100.5	227.4	85.4	114.6	109.9
114.8	204.2	100.5	85.4	114.6
116.5	196.6	114.8	100.5	85.4
112.9	198.8	116.5	114.8	100.5
102	207.5	112.9	116.5	114.8
106	190.7	102	112.9	116.5
105.3	201.6	106	102	112.9
118.8	210.5	105.3	106	102
106.1	223.5	118.8	105.3	106
109.3	223.8	106.1	118.8	105.3
117.2	231.2	109.3	106.1	118.8
92.5	244	117.2	109.3	106.1
104.2	234.7	92.5	117.2	109.3
112.5	250.2	104.2	92.5	117.2
122.4	265.7	112.5	104.2	92.5
113.3	287.6	122.4	112.5	104.2
100	283.3	113.3	122.4	112.5
110.7	295.4	100	113.3	122.4
112.8	312.3	110.7	100	113.3
109.8	333.8	112.8	110.7	100
117.3	347.7	109.8	112.8	110.7
109.1	383.2	117.3	109.8	112.8
115.9	407.1	109.1	117.3	109.8
96	413.6	115.9	109.1	117.3
99.8	362.7	96	115.9	109.1
116.8	321.9	99.8	96	115.9
115.7	239.4	116.8	99.8	96
99.4	191	115.7	116.8	99.8
94.3	159.7	99.4	115.7	116.8
91	163.4	94.3	99.4	115.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
tot.ind.prod.index[t] = + 8.93968106591842 + 0.0278761866410181prijsindex.grondst.incl.energie[t] + 0.0209391269251189`y(t-1)`[t] + 0.327716059508625`y(t-2)`[t] + 0.645907122351861`y(t-3)`[t] -6.5190957391177M1[t] -11.7428285168030M2[t] -6.29787050803359M3[t] -28.2519629104457M4[t] -18.8007596663826M5[t] -1.50640187826471M6[t] + 11.9119058393715M7[t] -6.17371142112734M8[t] -22.9232796894441M9[t] -20.3332537513160M10[t] -13.2633906802557M11[t] -0.147205196270872t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot.ind.prod.index[t] =  +  8.93968106591842 +  0.0278761866410181prijsindex.grondst.incl.energie[t] +  0.0209391269251189`y(t-1)`[t] +  0.327716059508625`y(t-2)`[t] +  0.645907122351861`y(t-3)`[t] -6.5190957391177M1[t] -11.7428285168030M2[t] -6.29787050803359M3[t] -28.2519629104457M4[t] -18.8007596663826M5[t] -1.50640187826471M6[t] +  11.9119058393715M7[t] -6.17371142112734M8[t] -22.9232796894441M9[t] -20.3332537513160M10[t] -13.2633906802557M11[t] -0.147205196270872t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot.ind.prod.index[t] =  +  8.93968106591842 +  0.0278761866410181prijsindex.grondst.incl.energie[t] +  0.0209391269251189`y(t-1)`[t] +  0.327716059508625`y(t-2)`[t] +  0.645907122351861`y(t-3)`[t] -6.5190957391177M1[t] -11.7428285168030M2[t] -6.29787050803359M3[t] -28.2519629104457M4[t] -18.8007596663826M5[t] -1.50640187826471M6[t] +  11.9119058393715M7[t] -6.17371142112734M8[t] -22.9232796894441M9[t] -20.3332537513160M10[t] -13.2633906802557M11[t] -0.147205196270872t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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
tot.ind.prod.index[t] = + 8.93968106591842 + 0.0278761866410181prijsindex.grondst.incl.energie[t] + 0.0209391269251189`y(t-1)`[t] + 0.327716059508625`y(t-2)`[t] + 0.645907122351861`y(t-3)`[t] -6.5190957391177M1[t] -11.7428285168030M2[t] -6.29787050803359M3[t] -28.2519629104457M4[t] -18.8007596663826M5[t] -1.50640187826471M6[t] + 11.9119058393715M7[t] -6.17371142112734M8[t] -22.9232796894441M9[t] -20.3332537513160M10[t] -13.2633906802557M11[t] -0.147205196270872t + 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)	8.93968106591842	26.166019	0.3417	0.734358	0.367179
prijsindex.grondst.incl.energie	0.0278761866410181	0.015012	1.857	0.070511	0.035256
`y(t-1)`	0.0209391269251189	0.139073	0.1506	0.881059	0.44053
`y(t-2)`	0.327716059508625	0.146297	2.2401	0.030569	0.015284
`y(t-3)`	0.645907122351861	0.159049	4.0611	0.000215	0.000107
M1	-6.5190957391177	2.959739	-2.2026	0.033303	0.016651
M2	-11.7428285168030	3.114127	-3.7708	0.000514	0.000257
M3	-6.29787050803359	3.308044	-1.9038	0.063971	0.031985
M4	-28.2519629104457	3.155708	-8.9527	0	0
M5	-18.8007596663826	3.857053	-4.8744	1.7e-05	8e-06
M6	-1.50640187826471	3.817827	-0.3946	0.695205	0.347602
M7	11.9119058393715	3.812479	3.1245	0.003266	0.001633
M8	-6.17371142112734	3.68833	-1.6739	0.101774	0.050887
M9	-22.9232796894441	4.121535	-5.5618	2e-06	1e-06
M10	-20.3332537513160	3.579621	-5.6803	1e-06	1e-06
M11	-13.2633906802557	3.119782	-4.2514	0.00012	6e-05
t	-0.147205196270872	0.060013	-2.4529	0.018514	0.009257

\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) & 8.93968106591842 & 26.166019 & 0.3417 & 0.734358 & 0.367179 \tabularnewline
prijsindex.grondst.incl.energie & 0.0278761866410181 & 0.015012 & 1.857 & 0.070511 & 0.035256 \tabularnewline
`y(t-1)` & 0.0209391269251189 & 0.139073 & 0.1506 & 0.881059 & 0.44053 \tabularnewline
`y(t-2)` & 0.327716059508625 & 0.146297 & 2.2401 & 0.030569 & 0.015284 \tabularnewline
`y(t-3)` & 0.645907122351861 & 0.159049 & 4.0611 & 0.000215 & 0.000107 \tabularnewline
M1 & -6.5190957391177 & 2.959739 & -2.2026 & 0.033303 & 0.016651 \tabularnewline
M2 & -11.7428285168030 & 3.114127 & -3.7708 & 0.000514 & 0.000257 \tabularnewline
M3 & -6.29787050803359 & 3.308044 & -1.9038 & 0.063971 & 0.031985 \tabularnewline
M4 & -28.2519629104457 & 3.155708 & -8.9527 & 0 & 0 \tabularnewline
M5 & -18.8007596663826 & 3.857053 & -4.8744 & 1.7e-05 & 8e-06 \tabularnewline
M6 & -1.50640187826471 & 3.817827 & -0.3946 & 0.695205 & 0.347602 \tabularnewline
M7 & 11.9119058393715 & 3.812479 & 3.1245 & 0.003266 & 0.001633 \tabularnewline
M8 & -6.17371142112734 & 3.68833 & -1.6739 & 0.101774 & 0.050887 \tabularnewline
M9 & -22.9232796894441 & 4.121535 & -5.5618 & 2e-06 & 1e-06 \tabularnewline
M10 & -20.3332537513160 & 3.579621 & -5.6803 & 1e-06 & 1e-06 \tabularnewline
M11 & -13.2633906802557 & 3.119782 & -4.2514 & 0.00012 & 6e-05 \tabularnewline
t & -0.147205196270872 & 0.060013 & -2.4529 & 0.018514 & 0.009257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&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]8.93968106591842[/C][C]26.166019[/C][C]0.3417[/C][C]0.734358[/C][C]0.367179[/C][/ROW]
[ROW][C]prijsindex.grondst.incl.energie[/C][C]0.0278761866410181[/C][C]0.015012[/C][C]1.857[/C][C]0.070511[/C][C]0.035256[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]0.0209391269251189[/C][C]0.139073[/C][C]0.1506[/C][C]0.881059[/C][C]0.44053[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]0.327716059508625[/C][C]0.146297[/C][C]2.2401[/C][C]0.030569[/C][C]0.015284[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]0.645907122351861[/C][C]0.159049[/C][C]4.0611[/C][C]0.000215[/C][C]0.000107[/C][/ROW]
[ROW][C]M1[/C][C]-6.5190957391177[/C][C]2.959739[/C][C]-2.2026[/C][C]0.033303[/C][C]0.016651[/C][/ROW]
[ROW][C]M2[/C][C]-11.7428285168030[/C][C]3.114127[/C][C]-3.7708[/C][C]0.000514[/C][C]0.000257[/C][/ROW]
[ROW][C]M3[/C][C]-6.29787050803359[/C][C]3.308044[/C][C]-1.9038[/C][C]0.063971[/C][C]0.031985[/C][/ROW]
[ROW][C]M4[/C][C]-28.2519629104457[/C][C]3.155708[/C][C]-8.9527[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-18.8007596663826[/C][C]3.857053[/C][C]-4.8744[/C][C]1.7e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]M6[/C][C]-1.50640187826471[/C][C]3.817827[/C][C]-0.3946[/C][C]0.695205[/C][C]0.347602[/C][/ROW]
[ROW][C]M7[/C][C]11.9119058393715[/C][C]3.812479[/C][C]3.1245[/C][C]0.003266[/C][C]0.001633[/C][/ROW]
[ROW][C]M8[/C][C]-6.17371142112734[/C][C]3.68833[/C][C]-1.6739[/C][C]0.101774[/C][C]0.050887[/C][/ROW]
[ROW][C]M9[/C][C]-22.9232796894441[/C][C]4.121535[/C][C]-5.5618[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M10[/C][C]-20.3332537513160[/C][C]3.579621[/C][C]-5.6803[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M11[/C][C]-13.2633906802557[/C][C]3.119782[/C][C]-4.2514[/C][C]0.00012[/C][C]6e-05[/C][/ROW]
[ROW][C]t[/C][C]-0.147205196270872[/C][C]0.060013[/C][C]-2.4529[/C][C]0.018514[/C][C]0.009257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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)	8.93968106591842	26.166019	0.3417	0.734358	0.367179
prijsindex.grondst.incl.energie	0.0278761866410181	0.015012	1.857	0.070511	0.035256
`y(t-1)`	0.0209391269251189	0.139073	0.1506	0.881059	0.44053
`y(t-2)`	0.327716059508625	0.146297	2.2401	0.030569	0.015284
`y(t-3)`	0.645907122351861	0.159049	4.0611	0.000215	0.000107
M1	-6.5190957391177	2.959739	-2.2026	0.033303	0.016651
M2	-11.7428285168030	3.114127	-3.7708	0.000514	0.000257
M3	-6.29787050803359	3.308044	-1.9038	0.063971	0.031985
M4	-28.2519629104457	3.155708	-8.9527	0	0
M5	-18.8007596663826	3.857053	-4.8744	1.7e-05	8e-06
M6	-1.50640187826471	3.817827	-0.3946	0.695205	0.347602
M7	11.9119058393715	3.812479	3.1245	0.003266	0.001633
M8	-6.17371142112734	3.68833	-1.6739	0.101774	0.050887
M9	-22.9232796894441	4.121535	-5.5618	2e-06	1e-06
M10	-20.3332537513160	3.579621	-5.6803	1e-06	1e-06
M11	-13.2633906802557	3.119782	-4.2514	0.00012	6e-05
t	-0.147205196270872	0.060013	-2.4529	0.018514	0.009257

Multiple Linear Regression - Regression Statistics
Multiple R	0.929546668821959
R-squared	0.864057009518
Adjusted R-squared	0.811006086403074
F-TEST (value)	16.2873133733443
F-TEST (DF numerator)	16
F-TEST (DF denominator)	41
p-value	6.66466881682481e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.97108867293895
Sum Squared Residuals	646.551355182106

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.929546668821959 \tabularnewline
R-squared & 0.864057009518 \tabularnewline
Adjusted R-squared & 0.811006086403074 \tabularnewline
F-TEST (value) & 16.2873133733443 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 6.66466881682481e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.97108867293895 \tabularnewline
Sum Squared Residuals & 646.551355182106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.929546668821959[/C][/ROW]
[ROW][C]R-squared[/C][C]0.864057009518[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.811006086403074[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.2873133733443[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C]6.66466881682481e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.97108867293895[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]646.551355182106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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	0.929546668821959
R-squared	0.864057009518
Adjusted R-squared	0.811006086403074
F-TEST (value)	16.2873133733443
F-TEST (DF numerator)	16
F-TEST (DF denominator)	41
p-value	6.66466881682481e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.97108867293895
Sum Squared Residuals	646.551355182106

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	102.9	101.401658639719	1.49834136028072
2	97.4	102.395803813802	-4.99580381380193
3	111.4	114.429462543357	-3.02946254335714
4	87.4	84.6032769605083	2.79672303949167
5	96.8	94.6298296914422	2.17017030855783
6	114.1	113.123448174463	0.976551825536647
7	110.3	114.661708998270	-4.36170899826953
8	103.9	107.856172671007	-3.95617267100652
9	101.6	100.550764822173	1.04923517782681
10	94.6	98.6890833047051	-4.08908330470515
11	95.9	100.686331898997	-4.78633189899693
12	104.7	110.529609860240	-5.82960986024025
13	102.8	99.9299533133781	2.87004668662187
14	98.1	98.0337401811899	0.0662598188101135
15	113.9	108.656791687104	5.24320831289639
16	80.9	84.2554365962194	-3.35543659621941
17	95.7	95.333957485701	0.36604251429894
18	113.2	112.16498601343	1.03501398656999
19	105.9	109.103625931317	-3.20362593131658
20	108.8	105.822846231043	2.97715376895690
21	102.3	98.0567379051368	4.24326209486323
22	99	97.0530907436357	1.94690925636427
23	100.7	103.565997207664	-2.86599720766351
24	115.5	111.527123713007	3.97287628699327
25	100.7	104.134356055685	-3.43435605568510
26	109.9	104.543927343832	5.35607265616787
27	114.6	114.645981200878	-0.0459812008781376
28	85.4	86.427598837779	-1.02759883777902
29	100.5	102.538670155411	-2.03867015541112
30	114.8	112.821730571158	1.97826942884230
31	116.5	112.268428114986	4.23157188501364
32	112.9	108.572066983086	4.32793301691394
33	102	101.636024636141	0.363975363858816
34	106	103.300553252713	2.69944674728739
35	105.3	104.713447380479	0.586552619521113
36	118.8	112.333550141120	6.46644985887962
37	106.1	108.666545093306	-2.56654509330558
38	109.3	107.010074881113	2.28992511888722
39	117.2	117.138868876906	0.0611311230941532
40	92.5	88.4054765064953	4.09452349350471
41	104.2	101.588889245120	2.61111075488027
42	112.5	116.421190111643	-3.92119011164308
43	122.4	118.178540253583	4.22145974641741
44	113.3	111.040660266248	2.25933973375173
45	100	102.438891248742	-2.43889124874154
46	110.7	108.352787830606	2.34721216939407
47	112.8	105.734223512861	7.06577648713933
48	109.8	114.409716285633	-4.60971628563265
49	117.3	115.667486897912	1.63251310208809
50	109.1	111.816453780063	-2.71645378006328
51	115.9	118.128895691755	-2.22889569175527
52	96	98.508211098998	-2.50821109899796
53	99.8	102.908653422326	-3.10865342232592
54	116.8	116.868645129306	-0.068645129305859
55	115.7	116.587696701845	-0.887696701844936
56	99.4	105.008253848616	-5.60825384861605
57	94.3	97.5175813878073	-3.21758138780731
58	91	93.9044848683406	-2.90448486834056

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102.9 & 101.401658639719 & 1.49834136028072 \tabularnewline
2 & 97.4 & 102.395803813802 & -4.99580381380193 \tabularnewline
3 & 111.4 & 114.429462543357 & -3.02946254335714 \tabularnewline
4 & 87.4 & 84.6032769605083 & 2.79672303949167 \tabularnewline
5 & 96.8 & 94.6298296914422 & 2.17017030855783 \tabularnewline
6 & 114.1 & 113.123448174463 & 0.976551825536647 \tabularnewline
7 & 110.3 & 114.661708998270 & -4.36170899826953 \tabularnewline
8 & 103.9 & 107.856172671007 & -3.95617267100652 \tabularnewline
9 & 101.6 & 100.550764822173 & 1.04923517782681 \tabularnewline
10 & 94.6 & 98.6890833047051 & -4.08908330470515 \tabularnewline
11 & 95.9 & 100.686331898997 & -4.78633189899693 \tabularnewline
12 & 104.7 & 110.529609860240 & -5.82960986024025 \tabularnewline
13 & 102.8 & 99.9299533133781 & 2.87004668662187 \tabularnewline
14 & 98.1 & 98.0337401811899 & 0.0662598188101135 \tabularnewline
15 & 113.9 & 108.656791687104 & 5.24320831289639 \tabularnewline
16 & 80.9 & 84.2554365962194 & -3.35543659621941 \tabularnewline
17 & 95.7 & 95.333957485701 & 0.36604251429894 \tabularnewline
18 & 113.2 & 112.16498601343 & 1.03501398656999 \tabularnewline
19 & 105.9 & 109.103625931317 & -3.20362593131658 \tabularnewline
20 & 108.8 & 105.822846231043 & 2.97715376895690 \tabularnewline
21 & 102.3 & 98.0567379051368 & 4.24326209486323 \tabularnewline
22 & 99 & 97.0530907436357 & 1.94690925636427 \tabularnewline
23 & 100.7 & 103.565997207664 & -2.86599720766351 \tabularnewline
24 & 115.5 & 111.527123713007 & 3.97287628699327 \tabularnewline
25 & 100.7 & 104.134356055685 & -3.43435605568510 \tabularnewline
26 & 109.9 & 104.543927343832 & 5.35607265616787 \tabularnewline
27 & 114.6 & 114.645981200878 & -0.0459812008781376 \tabularnewline
28 & 85.4 & 86.427598837779 & -1.02759883777902 \tabularnewline
29 & 100.5 & 102.538670155411 & -2.03867015541112 \tabularnewline
30 & 114.8 & 112.821730571158 & 1.97826942884230 \tabularnewline
31 & 116.5 & 112.268428114986 & 4.23157188501364 \tabularnewline
32 & 112.9 & 108.572066983086 & 4.32793301691394 \tabularnewline
33 & 102 & 101.636024636141 & 0.363975363858816 \tabularnewline
34 & 106 & 103.300553252713 & 2.69944674728739 \tabularnewline
35 & 105.3 & 104.713447380479 & 0.586552619521113 \tabularnewline
36 & 118.8 & 112.333550141120 & 6.46644985887962 \tabularnewline
37 & 106.1 & 108.666545093306 & -2.56654509330558 \tabularnewline
38 & 109.3 & 107.010074881113 & 2.28992511888722 \tabularnewline
39 & 117.2 & 117.138868876906 & 0.0611311230941532 \tabularnewline
40 & 92.5 & 88.4054765064953 & 4.09452349350471 \tabularnewline
41 & 104.2 & 101.588889245120 & 2.61111075488027 \tabularnewline
42 & 112.5 & 116.421190111643 & -3.92119011164308 \tabularnewline
43 & 122.4 & 118.178540253583 & 4.22145974641741 \tabularnewline
44 & 113.3 & 111.040660266248 & 2.25933973375173 \tabularnewline
45 & 100 & 102.438891248742 & -2.43889124874154 \tabularnewline
46 & 110.7 & 108.352787830606 & 2.34721216939407 \tabularnewline
47 & 112.8 & 105.734223512861 & 7.06577648713933 \tabularnewline
48 & 109.8 & 114.409716285633 & -4.60971628563265 \tabularnewline
49 & 117.3 & 115.667486897912 & 1.63251310208809 \tabularnewline
50 & 109.1 & 111.816453780063 & -2.71645378006328 \tabularnewline
51 & 115.9 & 118.128895691755 & -2.22889569175527 \tabularnewline
52 & 96 & 98.508211098998 & -2.50821109899796 \tabularnewline
53 & 99.8 & 102.908653422326 & -3.10865342232592 \tabularnewline
54 & 116.8 & 116.868645129306 & -0.068645129305859 \tabularnewline
55 & 115.7 & 116.587696701845 & -0.887696701844936 \tabularnewline
56 & 99.4 & 105.008253848616 & -5.60825384861605 \tabularnewline
57 & 94.3 & 97.5175813878073 & -3.21758138780731 \tabularnewline
58 & 91 & 93.9044848683406 & -2.90448486834056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&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]102.9[/C][C]101.401658639719[/C][C]1.49834136028072[/C][/ROW]
[ROW][C]2[/C][C]97.4[/C][C]102.395803813802[/C][C]-4.99580381380193[/C][/ROW]
[ROW][C]3[/C][C]111.4[/C][C]114.429462543357[/C][C]-3.02946254335714[/C][/ROW]
[ROW][C]4[/C][C]87.4[/C][C]84.6032769605083[/C][C]2.79672303949167[/C][/ROW]
[ROW][C]5[/C][C]96.8[/C][C]94.6298296914422[/C][C]2.17017030855783[/C][/ROW]
[ROW][C]6[/C][C]114.1[/C][C]113.123448174463[/C][C]0.976551825536647[/C][/ROW]
[ROW][C]7[/C][C]110.3[/C][C]114.661708998270[/C][C]-4.36170899826953[/C][/ROW]
[ROW][C]8[/C][C]103.9[/C][C]107.856172671007[/C][C]-3.95617267100652[/C][/ROW]
[ROW][C]9[/C][C]101.6[/C][C]100.550764822173[/C][C]1.04923517782681[/C][/ROW]
[ROW][C]10[/C][C]94.6[/C][C]98.6890833047051[/C][C]-4.08908330470515[/C][/ROW]
[ROW][C]11[/C][C]95.9[/C][C]100.686331898997[/C][C]-4.78633189899693[/C][/ROW]
[ROW][C]12[/C][C]104.7[/C][C]110.529609860240[/C][C]-5.82960986024025[/C][/ROW]
[ROW][C]13[/C][C]102.8[/C][C]99.9299533133781[/C][C]2.87004668662187[/C][/ROW]
[ROW][C]14[/C][C]98.1[/C][C]98.0337401811899[/C][C]0.0662598188101135[/C][/ROW]
[ROW][C]15[/C][C]113.9[/C][C]108.656791687104[/C][C]5.24320831289639[/C][/ROW]
[ROW][C]16[/C][C]80.9[/C][C]84.2554365962194[/C][C]-3.35543659621941[/C][/ROW]
[ROW][C]17[/C][C]95.7[/C][C]95.333957485701[/C][C]0.36604251429894[/C][/ROW]
[ROW][C]18[/C][C]113.2[/C][C]112.16498601343[/C][C]1.03501398656999[/C][/ROW]
[ROW][C]19[/C][C]105.9[/C][C]109.103625931317[/C][C]-3.20362593131658[/C][/ROW]
[ROW][C]20[/C][C]108.8[/C][C]105.822846231043[/C][C]2.97715376895690[/C][/ROW]
[ROW][C]21[/C][C]102.3[/C][C]98.0567379051368[/C][C]4.24326209486323[/C][/ROW]
[ROW][C]22[/C][C]99[/C][C]97.0530907436357[/C][C]1.94690925636427[/C][/ROW]
[ROW][C]23[/C][C]100.7[/C][C]103.565997207664[/C][C]-2.86599720766351[/C][/ROW]
[ROW][C]24[/C][C]115.5[/C][C]111.527123713007[/C][C]3.97287628699327[/C][/ROW]
[ROW][C]25[/C][C]100.7[/C][C]104.134356055685[/C][C]-3.43435605568510[/C][/ROW]
[ROW][C]26[/C][C]109.9[/C][C]104.543927343832[/C][C]5.35607265616787[/C][/ROW]
[ROW][C]27[/C][C]114.6[/C][C]114.645981200878[/C][C]-0.0459812008781376[/C][/ROW]
[ROW][C]28[/C][C]85.4[/C][C]86.427598837779[/C][C]-1.02759883777902[/C][/ROW]
[ROW][C]29[/C][C]100.5[/C][C]102.538670155411[/C][C]-2.03867015541112[/C][/ROW]
[ROW][C]30[/C][C]114.8[/C][C]112.821730571158[/C][C]1.97826942884230[/C][/ROW]
[ROW][C]31[/C][C]116.5[/C][C]112.268428114986[/C][C]4.23157188501364[/C][/ROW]
[ROW][C]32[/C][C]112.9[/C][C]108.572066983086[/C][C]4.32793301691394[/C][/ROW]
[ROW][C]33[/C][C]102[/C][C]101.636024636141[/C][C]0.363975363858816[/C][/ROW]
[ROW][C]34[/C][C]106[/C][C]103.300553252713[/C][C]2.69944674728739[/C][/ROW]
[ROW][C]35[/C][C]105.3[/C][C]104.713447380479[/C][C]0.586552619521113[/C][/ROW]
[ROW][C]36[/C][C]118.8[/C][C]112.333550141120[/C][C]6.46644985887962[/C][/ROW]
[ROW][C]37[/C][C]106.1[/C][C]108.666545093306[/C][C]-2.56654509330558[/C][/ROW]
[ROW][C]38[/C][C]109.3[/C][C]107.010074881113[/C][C]2.28992511888722[/C][/ROW]
[ROW][C]39[/C][C]117.2[/C][C]117.138868876906[/C][C]0.0611311230941532[/C][/ROW]
[ROW][C]40[/C][C]92.5[/C][C]88.4054765064953[/C][C]4.09452349350471[/C][/ROW]
[ROW][C]41[/C][C]104.2[/C][C]101.588889245120[/C][C]2.61111075488027[/C][/ROW]
[ROW][C]42[/C][C]112.5[/C][C]116.421190111643[/C][C]-3.92119011164308[/C][/ROW]
[ROW][C]43[/C][C]122.4[/C][C]118.178540253583[/C][C]4.22145974641741[/C][/ROW]
[ROW][C]44[/C][C]113.3[/C][C]111.040660266248[/C][C]2.25933973375173[/C][/ROW]
[ROW][C]45[/C][C]100[/C][C]102.438891248742[/C][C]-2.43889124874154[/C][/ROW]
[ROW][C]46[/C][C]110.7[/C][C]108.352787830606[/C][C]2.34721216939407[/C][/ROW]
[ROW][C]47[/C][C]112.8[/C][C]105.734223512861[/C][C]7.06577648713933[/C][/ROW]
[ROW][C]48[/C][C]109.8[/C][C]114.409716285633[/C][C]-4.60971628563265[/C][/ROW]
[ROW][C]49[/C][C]117.3[/C][C]115.667486897912[/C][C]1.63251310208809[/C][/ROW]
[ROW][C]50[/C][C]109.1[/C][C]111.816453780063[/C][C]-2.71645378006328[/C][/ROW]
[ROW][C]51[/C][C]115.9[/C][C]118.128895691755[/C][C]-2.22889569175527[/C][/ROW]
[ROW][C]52[/C][C]96[/C][C]98.508211098998[/C][C]-2.50821109899796[/C][/ROW]
[ROW][C]53[/C][C]99.8[/C][C]102.908653422326[/C][C]-3.10865342232592[/C][/ROW]
[ROW][C]54[/C][C]116.8[/C][C]116.868645129306[/C][C]-0.068645129305859[/C][/ROW]
[ROW][C]55[/C][C]115.7[/C][C]116.587696701845[/C][C]-0.887696701844936[/C][/ROW]
[ROW][C]56[/C][C]99.4[/C][C]105.008253848616[/C][C]-5.60825384861605[/C][/ROW]
[ROW][C]57[/C][C]94.3[/C][C]97.5175813878073[/C][C]-3.21758138780731[/C][/ROW]
[ROW][C]58[/C][C]91[/C][C]93.9044848683406[/C][C]-2.90448486834056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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	102.9	101.401658639719	1.49834136028072
2	97.4	102.395803813802	-4.99580381380193
3	111.4	114.429462543357	-3.02946254335714
4	87.4	84.6032769605083	2.79672303949167
5	96.8	94.6298296914422	2.17017030855783
6	114.1	113.123448174463	0.976551825536647
7	110.3	114.661708998270	-4.36170899826953
8	103.9	107.856172671007	-3.95617267100652
9	101.6	100.550764822173	1.04923517782681
10	94.6	98.6890833047051	-4.08908330470515
11	95.9	100.686331898997	-4.78633189899693
12	104.7	110.529609860240	-5.82960986024025
13	102.8	99.9299533133781	2.87004668662187
14	98.1	98.0337401811899	0.0662598188101135
15	113.9	108.656791687104	5.24320831289639
16	80.9	84.2554365962194	-3.35543659621941
17	95.7	95.333957485701	0.36604251429894
18	113.2	112.16498601343	1.03501398656999
19	105.9	109.103625931317	-3.20362593131658
20	108.8	105.822846231043	2.97715376895690
21	102.3	98.0567379051368	4.24326209486323
22	99	97.0530907436357	1.94690925636427
23	100.7	103.565997207664	-2.86599720766351
24	115.5	111.527123713007	3.97287628699327
25	100.7	104.134356055685	-3.43435605568510
26	109.9	104.543927343832	5.35607265616787
27	114.6	114.645981200878	-0.0459812008781376
28	85.4	86.427598837779	-1.02759883777902
29	100.5	102.538670155411	-2.03867015541112
30	114.8	112.821730571158	1.97826942884230
31	116.5	112.268428114986	4.23157188501364
32	112.9	108.572066983086	4.32793301691394
33	102	101.636024636141	0.363975363858816
34	106	103.300553252713	2.69944674728739
35	105.3	104.713447380479	0.586552619521113
36	118.8	112.333550141120	6.46644985887962
37	106.1	108.666545093306	-2.56654509330558
38	109.3	107.010074881113	2.28992511888722
39	117.2	117.138868876906	0.0611311230941532
40	92.5	88.4054765064953	4.09452349350471
41	104.2	101.588889245120	2.61111075488027
42	112.5	116.421190111643	-3.92119011164308
43	122.4	118.178540253583	4.22145974641741
44	113.3	111.040660266248	2.25933973375173
45	100	102.438891248742	-2.43889124874154
46	110.7	108.352787830606	2.34721216939407
47	112.8	105.734223512861	7.06577648713933
48	109.8	114.409716285633	-4.60971628563265
49	117.3	115.667486897912	1.63251310208809
50	109.1	111.816453780063	-2.71645378006328
51	115.9	118.128895691755	-2.22889569175527
52	96	98.508211098998	-2.50821109899796
53	99.8	102.908653422326	-3.10865342232592
54	116.8	116.868645129306	-0.068645129305859
55	115.7	116.587696701845	-0.887696701844936
56	99.4	105.008253848616	-5.60825384861605
57	94.3	97.5175813878073	-3.21758138780731
58	91	93.9044848683406	-2.90448486834056

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.477011089876752	0.954022179753505	0.522988910123248
21	0.391852030106857	0.783704060213714	0.608147969893143
22	0.403529733471464	0.80705946694293	0.596470266528536
23	0.529361960994027	0.941276078011945	0.470638039005973
24	0.493239014134886	0.986478028269773	0.506760985865114
25	0.550895480505404	0.898209038989192	0.449104519494596
26	0.662803395840522	0.674393208318956	0.337196604159478
27	0.551917527646938	0.896164944706124	0.448082472353062
28	0.534424508801414	0.931150982397173	0.465575491198586
29	0.641793178816782	0.716413642366436	0.358206821183218
30	0.566883170184653	0.866233659630695	0.433116829815347
31	0.488791013906447	0.977582027812893	0.511208986093553
32	0.384157120656949	0.768314241313899	0.615842879343051
33	0.336507634533565	0.67301526906713	0.663492365466435
34	0.230738459258926	0.461476918517851	0.769261540741074
35	0.525969182863191	0.948061634273618	0.474030817136809
36	0.479927943045405	0.95985588609081	0.520072056954595
37	0.454302885873803	0.908605771747605	0.545697114126197
38	0.306101719851925	0.612203439703850	0.693898280148075

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.477011089876752 & 0.954022179753505 & 0.522988910123248 \tabularnewline
21 & 0.391852030106857 & 0.783704060213714 & 0.608147969893143 \tabularnewline
22 & 0.403529733471464 & 0.80705946694293 & 0.596470266528536 \tabularnewline
23 & 0.529361960994027 & 0.941276078011945 & 0.470638039005973 \tabularnewline
24 & 0.493239014134886 & 0.986478028269773 & 0.506760985865114 \tabularnewline
25 & 0.550895480505404 & 0.898209038989192 & 0.449104519494596 \tabularnewline
26 & 0.662803395840522 & 0.674393208318956 & 0.337196604159478 \tabularnewline
27 & 0.551917527646938 & 0.896164944706124 & 0.448082472353062 \tabularnewline
28 & 0.534424508801414 & 0.931150982397173 & 0.465575491198586 \tabularnewline
29 & 0.641793178816782 & 0.716413642366436 & 0.358206821183218 \tabularnewline
30 & 0.566883170184653 & 0.866233659630695 & 0.433116829815347 \tabularnewline
31 & 0.488791013906447 & 0.977582027812893 & 0.511208986093553 \tabularnewline
32 & 0.384157120656949 & 0.768314241313899 & 0.615842879343051 \tabularnewline
33 & 0.336507634533565 & 0.67301526906713 & 0.663492365466435 \tabularnewline
34 & 0.230738459258926 & 0.461476918517851 & 0.769261540741074 \tabularnewline
35 & 0.525969182863191 & 0.948061634273618 & 0.474030817136809 \tabularnewline
36 & 0.479927943045405 & 0.95985588609081 & 0.520072056954595 \tabularnewline
37 & 0.454302885873803 & 0.908605771747605 & 0.545697114126197 \tabularnewline
38 & 0.306101719851925 & 0.612203439703850 & 0.693898280148075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69800&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]20[/C][C]0.477011089876752[/C][C]0.954022179753505[/C][C]0.522988910123248[/C][/ROW]
[ROW][C]21[/C][C]0.391852030106857[/C][C]0.783704060213714[/C][C]0.608147969893143[/C][/ROW]
[ROW][C]22[/C][C]0.403529733471464[/C][C]0.80705946694293[/C][C]0.596470266528536[/C][/ROW]
[ROW][C]23[/C][C]0.529361960994027[/C][C]0.941276078011945[/C][C]0.470638039005973[/C][/ROW]
[ROW][C]24[/C][C]0.493239014134886[/C][C]0.986478028269773[/C][C]0.506760985865114[/C][/ROW]
[ROW][C]25[/C][C]0.550895480505404[/C][C]0.898209038989192[/C][C]0.449104519494596[/C][/ROW]
[ROW][C]26[/C][C]0.662803395840522[/C][C]0.674393208318956[/C][C]0.337196604159478[/C][/ROW]
[ROW][C]27[/C][C]0.551917527646938[/C][C]0.896164944706124[/C][C]0.448082472353062[/C][/ROW]
[ROW][C]28[/C][C]0.534424508801414[/C][C]0.931150982397173[/C][C]0.465575491198586[/C][/ROW]
[ROW][C]29[/C][C]0.641793178816782[/C][C]0.716413642366436[/C][C]0.358206821183218[/C][/ROW]
[ROW][C]30[/C][C]0.566883170184653[/C][C]0.866233659630695[/C][C]0.433116829815347[/C][/ROW]
[ROW][C]31[/C][C]0.488791013906447[/C][C]0.977582027812893[/C][C]0.511208986093553[/C][/ROW]
[ROW][C]32[/C][C]0.384157120656949[/C][C]0.768314241313899[/C][C]0.615842879343051[/C][/ROW]
[ROW][C]33[/C][C]0.336507634533565[/C][C]0.67301526906713[/C][C]0.663492365466435[/C][/ROW]
[ROW][C]34[/C][C]0.230738459258926[/C][C]0.461476918517851[/C][C]0.769261540741074[/C][/ROW]
[ROW][C]35[/C][C]0.525969182863191[/C][C]0.948061634273618[/C][C]0.474030817136809[/C][/ROW]
[ROW][C]36[/C][C]0.479927943045405[/C][C]0.95985588609081[/C][C]0.520072056954595[/C][/ROW]
[ROW][C]37[/C][C]0.454302885873803[/C][C]0.908605771747605[/C][C]0.545697114126197[/C][/ROW]
[ROW][C]38[/C][C]0.306101719851925[/C][C]0.612203439703850[/C][C]0.693898280148075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69800&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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
20	0.477011089876752	0.954022179753505	0.522988910123248
21	0.391852030106857	0.783704060213714	0.608147969893143
22	0.403529733471464	0.80705946694293	0.596470266528536
23	0.529361960994027	0.941276078011945	0.470638039005973
24	0.493239014134886	0.986478028269773	0.506760985865114
25	0.550895480505404	0.898209038989192	0.449104519494596
26	0.662803395840522	0.674393208318956	0.337196604159478
27	0.551917527646938	0.896164944706124	0.448082472353062
28	0.534424508801414	0.931150982397173	0.465575491198586
29	0.641793178816782	0.716413642366436	0.358206821183218
30	0.566883170184653	0.866233659630695	0.433116829815347
31	0.488791013906447	0.977582027812893	0.511208986093553
32	0.384157120656949	0.768314241313899	0.615842879343051
33	0.336507634533565	0.67301526906713	0.663492365466435
34	0.230738459258926	0.461476918517851	0.769261540741074
35	0.525969182863191	0.948061634273618	0.474030817136809
36	0.479927943045405	0.95985588609081	0.520072056954595
37	0.454302885873803	0.908605771747605	0.545697114126197
38	0.306101719851925	0.612203439703850	0.693898280148075

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=69800&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=69800&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69800&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

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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