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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 11:37:37 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258742319oog40ytrzm0dk8h.htm/, Retrieved Fri, 29 Mar 2024 06:56:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58403, Retrieved Fri, 29 Mar 2024 06:56:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
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] [Workshop 7 - mult...] [2009-11-20 10:14:52] [74be16979710d4c4e7c6647856088456]
-    D    [Multiple Regression] [Workshop 7 - auto...] [2009-11-20 14:38:49] [1646a2766cb8c4a6f9d3b2fffef409b3]
-    D        [Multiple Regression] [Workshop 7 - mode...] [2009-11-20 18:37:37] [d904c6aa144b8c40108ebe5ec22fe1a0] [Current]
-   P           [Multiple Regression] [] [2009-11-21 19:58:43] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2009-11-22 16:14:27] [74be16979710d4c4e7c6647856088456]
-                 [Multiple Regression] [] [2009-11-25 12:35:32] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2009-11-22 17:08:38] [3af9fa3d2c04a43d660a9a466bdfbaa0]
-               [Multiple Regression] [workshop 7-model ...] [2009-11-22 19:52:12] [24c4941ee50deadff4640c9c09cc70cb]
-               [Multiple Regression] [] [2009-12-17 09:17:03] [68cb6e9d2b1cb3475e83bcdfaf88b501]
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Dataseries X:
267366	0	267413	262813	258113	267037	269645
264777	0	267366	267413	262813	258113	267037
258863	0	264777	267366	267413	262813	258113
254844	0	258863	264777	267366	267413	262813
254868	0	254844	258863	264777	267366	267413
277267	0	254868	254844	258863	264777	267366
285351	0	277267	254868	254844	258863	264777
286602	0	285351	277267	254868	254844	258863
283042	0	286602	285351	277267	254868	254844
276687	0	283042	286602	285351	277267	254868
277915	0	276687	283042	286602	285351	277267
277128	0	277915	276687	283042	286602	285351
277103	0	277128	277915	276687	283042	286602
275037	0	277103	277128	277915	276687	283042
270150	0	275037	277103	277128	277915	276687
267140	0	270150	275037	277103	277128	277915
264993	0	267140	270150	275037	277103	277128
287259	0	264993	267140	270150	275037	277103
291186	0	287259	264993	267140	270150	275037
292300	0	291186	287259	264993	267140	270150
288186	0	292300	291186	287259	264993	267140
281477	0	288186	292300	291186	287259	264993
282656	0	281477	288186	292300	291186	287259
280190	0	282656	281477	288186	292300	291186
280408	0	280190	282656	281477	288186	292300
276836	0	280408	280190	282656	281477	288186
275216	0	276836	280408	280190	282656	281477
274352	0	275216	276836	280408	280190	282656
271311	0	274352	275216	276836	280408	280190
289802	0	271311	274352	275216	276836	280408
290726	0	289802	271311	274352	275216	276836
292300	0	290726	289802	271311	274352	275216
278506	0	292300	290726	289802	271311	274352
269826	0	278506	292300	290726	289802	271311
265861	0	269826	278506	292300	290726	289802
269034	0	265861	269826	278506	292300	290726
264176	0	269034	265861	269826	278506	292300
255198	0	264176	269034	265861	269826	278506
253353	0	255198	264176	269034	265861	269826
246057	0	253353	255198	264176	269034	265861
235372	0	246057	253353	255198	264176	269034
258556	0	235372	246057	253353	255198	264176
260993	0	258556	235372	246057	253353	255198
254663	0	260993	258556	235372	246057	253353
250643	0	254663	260993	258556	235372	246057
243422	0	250643	254663	260993	258556	235372
247105	0	243422	250643	254663	260993	258556
248541	0	247105	243422	250643	254663	260993
245039	0	248541	247105	243422	250643	254663
237080	0	245039	248541	247105	243422	250643
237085	0	237080	245039	248541	247105	243422
225554	0	237085	237080	245039	248541	247105
226839	0	225554	237085	237080	245039	248541
247934	0	226839	225554	237085	237080	245039
248333	1	247934	226839	225554	237085	237080
246969	1	248333	247934	226839	225554	237085
245098	1	246969	248333	247934	226839	225554
246263	1	245098	246969	248333	247934	226839
255765	1	246263	245098	246969	248333	247934
264319	1	255765	246263	245098	246969	248333
268347	1	264319	255765	246263	245098	246969
273046	1	268347	264319	255765	246263	245098
273963	1	273046	268347	264319	255765	246263
267430	1	273963	273046	268347	264319	255765
271993	1	267430	273963	273046	268347	264319
292710	1	271993	267430	273963	273046	268347
295881	1	292710	271993	267430	273963	273046




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&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]6 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=58403&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 21386.0345240392 + 4570.5834593585x[t] + 0.937414189322683y1[t] + 0.134008529853577y2[t] + 0.126857011464927y3[t] -0.259803480419487y4[t] + 0.00246372856114373y5[t] -3682.13734938526M1[t] -8617.31854672714M2[t] -7066.4047264724M3[t] -9287.79908724293M4[t] -5147.02671990541M5[t] + 17983.3445346842M6[t] + 782.48771753359M7[t] -6645.70370737072M8[t] -15236.7700945788M9[t] -10308.5541016025M10[t] -1107.26843820283M11[t] -68.7912791567189t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  21386.0345240392 +  4570.5834593585x[t] +  0.937414189322683y1[t] +  0.134008529853577y2[t] +  0.126857011464927y3[t] -0.259803480419487y4[t] +  0.00246372856114373y5[t] -3682.13734938526M1[t] -8617.31854672714M2[t] -7066.4047264724M3[t] -9287.79908724293M4[t] -5147.02671990541M5[t] +  17983.3445346842M6[t] +  782.48771753359M7[t] -6645.70370737072M8[t] -15236.7700945788M9[t] -10308.5541016025M10[t] -1107.26843820283M11[t] -68.7912791567189t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  21386.0345240392 +  4570.5834593585x[t] +  0.937414189322683y1[t] +  0.134008529853577y2[t] +  0.126857011464927y3[t] -0.259803480419487y4[t] +  0.00246372856114373y5[t] -3682.13734938526M1[t] -8617.31854672714M2[t] -7066.4047264724M3[t] -9287.79908724293M4[t] -5147.02671990541M5[t] +  17983.3445346842M6[t] +  782.48771753359M7[t] -6645.70370737072M8[t] -15236.7700945788M9[t] -10308.5541016025M10[t] -1107.26843820283M11[t] -68.7912791567189t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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
y[t] = + 21386.0345240392 + 4570.5834593585x[t] + 0.937414189322683y1[t] + 0.134008529853577y2[t] + 0.126857011464927y3[t] -0.259803480419487y4[t] + 0.00246372856114373y5[t] -3682.13734938526M1[t] -8617.31854672714M2[t] -7066.4047264724M3[t] -9287.79908724293M4[t] -5147.02671990541M5[t] + 17983.3445346842M6[t] + 782.48771753359M7[t] -6645.70370737072M8[t] -15236.7700945788M9[t] -10308.5541016025M10[t] -1107.26843820283M11[t] -68.7912791567189t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21386.034524039210753.8160641.98870.0524520.026226
x4570.58345935852098.7417182.17780.0343690.017184
y10.9374141893226830.1429286.558600
y20.1340085298535770.1948820.68760.4949890.247494
y30.1268570114649270.1985560.63890.5259270.262963
y4-0.2598034804194870.198715-1.30740.1972980.098649
y50.002463728561143730.1491280.01650.9868870.493444
M1-3682.137349385262465.403429-1.49350.1418450.070923
M2-8617.318546727142763.500072-3.11830.0030720.001536
M3-7066.40472647242705.339604-2.6120.0119790.00599
M4-9287.799087242932398.918476-3.87170.0003260.000163
M5-5147.026719905412443.087351-2.10680.0403890.020195
M617983.34453468422229.7909858.06500
M7782.487717533593408.613340.22960.8194080.409704
M8-6645.703707370724500.77405-1.47660.1463220.073161
M9-15236.77009457884515.96289-3.3740.0014740.000737
M10-10308.55410160254463.807728-2.30940.0252710.012636
M11-1107.268438202832654.333191-0.41720.6784250.339212
t-68.791279156718936.037059-1.90890.0622630.031132

\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) & 21386.0345240392 & 10753.816064 & 1.9887 & 0.052452 & 0.026226 \tabularnewline
x & 4570.5834593585 & 2098.741718 & 2.1778 & 0.034369 & 0.017184 \tabularnewline
y1 & 0.937414189322683 & 0.142928 & 6.5586 & 0 & 0 \tabularnewline
y2 & 0.134008529853577 & 0.194882 & 0.6876 & 0.494989 & 0.247494 \tabularnewline
y3 & 0.126857011464927 & 0.198556 & 0.6389 & 0.525927 & 0.262963 \tabularnewline
y4 & -0.259803480419487 & 0.198715 & -1.3074 & 0.197298 & 0.098649 \tabularnewline
y5 & 0.00246372856114373 & 0.149128 & 0.0165 & 0.986887 & 0.493444 \tabularnewline
M1 & -3682.13734938526 & 2465.403429 & -1.4935 & 0.141845 & 0.070923 \tabularnewline
M2 & -8617.31854672714 & 2763.500072 & -3.1183 & 0.003072 & 0.001536 \tabularnewline
M3 & -7066.4047264724 & 2705.339604 & -2.612 & 0.011979 & 0.00599 \tabularnewline
M4 & -9287.79908724293 & 2398.918476 & -3.8717 & 0.000326 & 0.000163 \tabularnewline
M5 & -5147.02671990541 & 2443.087351 & -2.1068 & 0.040389 & 0.020195 \tabularnewline
M6 & 17983.3445346842 & 2229.790985 & 8.065 & 0 & 0 \tabularnewline
M7 & 782.48771753359 & 3408.61334 & 0.2296 & 0.819408 & 0.409704 \tabularnewline
M8 & -6645.70370737072 & 4500.77405 & -1.4766 & 0.146322 & 0.073161 \tabularnewline
M9 & -15236.7700945788 & 4515.96289 & -3.374 & 0.001474 & 0.000737 \tabularnewline
M10 & -10308.5541016025 & 4463.807728 & -2.3094 & 0.025271 & 0.012636 \tabularnewline
M11 & -1107.26843820283 & 2654.333191 & -0.4172 & 0.678425 & 0.339212 \tabularnewline
t & -68.7912791567189 & 36.037059 & -1.9089 & 0.062263 & 0.031132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&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]21386.0345240392[/C][C]10753.816064[/C][C]1.9887[/C][C]0.052452[/C][C]0.026226[/C][/ROW]
[ROW][C]x[/C][C]4570.5834593585[/C][C]2098.741718[/C][C]2.1778[/C][C]0.034369[/C][C]0.017184[/C][/ROW]
[ROW][C]y1[/C][C]0.937414189322683[/C][C]0.142928[/C][C]6.5586[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y2[/C][C]0.134008529853577[/C][C]0.194882[/C][C]0.6876[/C][C]0.494989[/C][C]0.247494[/C][/ROW]
[ROW][C]y3[/C][C]0.126857011464927[/C][C]0.198556[/C][C]0.6389[/C][C]0.525927[/C][C]0.262963[/C][/ROW]
[ROW][C]y4[/C][C]-0.259803480419487[/C][C]0.198715[/C][C]-1.3074[/C][C]0.197298[/C][C]0.098649[/C][/ROW]
[ROW][C]y5[/C][C]0.00246372856114373[/C][C]0.149128[/C][C]0.0165[/C][C]0.986887[/C][C]0.493444[/C][/ROW]
[ROW][C]M1[/C][C]-3682.13734938526[/C][C]2465.403429[/C][C]-1.4935[/C][C]0.141845[/C][C]0.070923[/C][/ROW]
[ROW][C]M2[/C][C]-8617.31854672714[/C][C]2763.500072[/C][C]-3.1183[/C][C]0.003072[/C][C]0.001536[/C][/ROW]
[ROW][C]M3[/C][C]-7066.4047264724[/C][C]2705.339604[/C][C]-2.612[/C][C]0.011979[/C][C]0.00599[/C][/ROW]
[ROW][C]M4[/C][C]-9287.79908724293[/C][C]2398.918476[/C][C]-3.8717[/C][C]0.000326[/C][C]0.000163[/C][/ROW]
[ROW][C]M5[/C][C]-5147.02671990541[/C][C]2443.087351[/C][C]-2.1068[/C][C]0.040389[/C][C]0.020195[/C][/ROW]
[ROW][C]M6[/C][C]17983.3445346842[/C][C]2229.790985[/C][C]8.065[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]782.48771753359[/C][C]3408.61334[/C][C]0.2296[/C][C]0.819408[/C][C]0.409704[/C][/ROW]
[ROW][C]M8[/C][C]-6645.70370737072[/C][C]4500.77405[/C][C]-1.4766[/C][C]0.146322[/C][C]0.073161[/C][/ROW]
[ROW][C]M9[/C][C]-15236.7700945788[/C][C]4515.96289[/C][C]-3.374[/C][C]0.001474[/C][C]0.000737[/C][/ROW]
[ROW][C]M10[/C][C]-10308.5541016025[/C][C]4463.807728[/C][C]-2.3094[/C][C]0.025271[/C][C]0.012636[/C][/ROW]
[ROW][C]M11[/C][C]-1107.26843820283[/C][C]2654.333191[/C][C]-0.4172[/C][C]0.678425[/C][C]0.339212[/C][/ROW]
[ROW][C]t[/C][C]-68.7912791567189[/C][C]36.037059[/C][C]-1.9089[/C][C]0.062263[/C][C]0.031132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)21386.034524039210753.8160641.98870.0524520.026226
x4570.58345935852098.7417182.17780.0343690.017184
y10.9374141893226830.1429286.558600
y20.1340085298535770.1948820.68760.4949890.247494
y30.1268570114649270.1985560.63890.5259270.262963
y4-0.2598034804194870.198715-1.30740.1972980.098649
y50.002463728561143730.1491280.01650.9868870.493444
M1-3682.137349385262465.403429-1.49350.1418450.070923
M2-8617.318546727142763.500072-3.11830.0030720.001536
M3-7066.40472647242705.339604-2.6120.0119790.00599
M4-9287.799087242932398.918476-3.87170.0003260.000163
M5-5147.026719905412443.087351-2.10680.0403890.020195
M617983.34453468422229.7909858.06500
M7782.487717533593408.613340.22960.8194080.409704
M8-6645.703707370724500.77405-1.47660.1463220.073161
M9-15236.77009457884515.96289-3.3740.0014740.000737
M10-10308.55410160254463.807728-2.30940.0252710.012636
M11-1107.268438202832654.333191-0.41720.6784250.339212
t-68.791279156718936.037059-1.90890.0622630.031132







Multiple Linear Regression - Regression Statistics
Multiple R0.986093368796538
R-squared0.972380131984505
Adjusted R-squared0.962022681478694
F-TEST (value)93.8821895831396
F-TEST (DF numerator)18
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3381.62366621292
Sum Squared Residuals548898173.754784

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.986093368796538 \tabularnewline
R-squared & 0.972380131984505 \tabularnewline
Adjusted R-squared & 0.962022681478694 \tabularnewline
F-TEST (value) & 93.8821895831396 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3381.62366621292 \tabularnewline
Sum Squared Residuals & 548898173.754784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.986093368796538[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972380131984505[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.962022681478694[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]93.8821895831396[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3381.62366621292[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]548898173.754784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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 R0.986093368796538
R-squared0.972380131984505
Adjusted R-squared0.962022681478694
F-TEST (value)93.8821895831396
F-TEST (DF numerator)18
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3381.62366621292
Sum Squared Residuals548898173.754784







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1267366267561.66414859-195.664148589884
2264777266038.361251580-1261.36125158039
3258863264427.699636706-5564.69963670626
4254844255057.219632103-213.219632102641
5254868254264.31776012603.682239880169
6277267276732.098444375534.901555624953
7285351281483.0678403013867.93215969854
8286602285598.4227682861003.57723171426
9283042282019.9283992851022.07160071451
10276687278916.036322215-2229.03632221504
11277915279727.825008706-1812.82500870615
12277128280309.114251889-3181.11425188940
13277103276106.809337874996.19066212559
14275037272771.9974481152265.00255188492
15270150271879.539923842-1729.53992384200
16267140264935.6688904942204.33110950609
17264993265273.603130365-280.603130365346
18287259285835.9313487671423.06865123332
19291186290033.7612194161152.23878058413
20292300289699.4441935142600.55580648610
21288186285985.0978975592200.90210244081
22281477281845.379702544-368.37970254389
23282656283313.379022110-657.379022110357
24280190283756.368523286-3566.36852328586
25280408278072.2649827162335.73501728379
26276836274824.6339522222011.36604777802
27275216272351.8600199642864.13998003634
28274352268735.6198718845616.38012811592
29271311271264.73532381146.2646761886604
30289802292082.902146032-2280.90214603200
31290726292041.936627159-1315.93662715915
32292300287723.7831545034576.21684549669
33278506283796.886025194-5290.88602519388
34269826271242.246361088-1416.24636108839
35265861270394.643246172-4533.64324617248
36269034264396.5592962884637.44070371187
37264176265575.190327744-1399.19032774368
38255198258160.590272551-2962.59027255119
39253353251986.8477172121366.15228278776
40246057245313.607828147743.392171853363
41235372242429.989723120-7057.98972311957
42258556256584.0685195311971.93148046879
43260993259147.2191576991845.78084230060
44254663257577.082035586-2914.0820355856
45250643249009.0491165641633.95088343566
46243422243511.336502552-89.3365025524604
47247105243957.0218542913147.9781457088
48248541248620.91483001-79.9148300099232
49245039246822.446502492-1783.44650249211
50237080241061.436900374-3981.43690037385
51237085233820.9019032443264.09809675594
52225554229660.572305417-4106.57230541694
53226839222827.6151675614011.38483243935
54247934247608.302226826325.69777317352
55248333253372.494842279-5039.49484227919
56246969252235.267848111-5266.26784811145
57245098244664.038561397433.961438602899
58246263242160.00111164102.99888839978
59255765251909.130868723855.86913128019
60264319262129.0430985272189.95690147333
61268347268300.62470058446.3752994162896
62273046269116.9801751573929.01982484249
63273963274163.150799032-200.150799031771
64267430271674.311471956-4244.31147195579
65271993269315.7388950232677.26110497673
66292710294684.697314469-1974.69731446858
67295881296391.520313145-510.520313144927

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 267366 & 267561.66414859 & -195.664148589884 \tabularnewline
2 & 264777 & 266038.361251580 & -1261.36125158039 \tabularnewline
3 & 258863 & 264427.699636706 & -5564.69963670626 \tabularnewline
4 & 254844 & 255057.219632103 & -213.219632102641 \tabularnewline
5 & 254868 & 254264.31776012 & 603.682239880169 \tabularnewline
6 & 277267 & 276732.098444375 & 534.901555624953 \tabularnewline
7 & 285351 & 281483.067840301 & 3867.93215969854 \tabularnewline
8 & 286602 & 285598.422768286 & 1003.57723171426 \tabularnewline
9 & 283042 & 282019.928399285 & 1022.07160071451 \tabularnewline
10 & 276687 & 278916.036322215 & -2229.03632221504 \tabularnewline
11 & 277915 & 279727.825008706 & -1812.82500870615 \tabularnewline
12 & 277128 & 280309.114251889 & -3181.11425188940 \tabularnewline
13 & 277103 & 276106.809337874 & 996.19066212559 \tabularnewline
14 & 275037 & 272771.997448115 & 2265.00255188492 \tabularnewline
15 & 270150 & 271879.539923842 & -1729.53992384200 \tabularnewline
16 & 267140 & 264935.668890494 & 2204.33110950609 \tabularnewline
17 & 264993 & 265273.603130365 & -280.603130365346 \tabularnewline
18 & 287259 & 285835.931348767 & 1423.06865123332 \tabularnewline
19 & 291186 & 290033.761219416 & 1152.23878058413 \tabularnewline
20 & 292300 & 289699.444193514 & 2600.55580648610 \tabularnewline
21 & 288186 & 285985.097897559 & 2200.90210244081 \tabularnewline
22 & 281477 & 281845.379702544 & -368.37970254389 \tabularnewline
23 & 282656 & 283313.379022110 & -657.379022110357 \tabularnewline
24 & 280190 & 283756.368523286 & -3566.36852328586 \tabularnewline
25 & 280408 & 278072.264982716 & 2335.73501728379 \tabularnewline
26 & 276836 & 274824.633952222 & 2011.36604777802 \tabularnewline
27 & 275216 & 272351.860019964 & 2864.13998003634 \tabularnewline
28 & 274352 & 268735.619871884 & 5616.38012811592 \tabularnewline
29 & 271311 & 271264.735323811 & 46.2646761886604 \tabularnewline
30 & 289802 & 292082.902146032 & -2280.90214603200 \tabularnewline
31 & 290726 & 292041.936627159 & -1315.93662715915 \tabularnewline
32 & 292300 & 287723.783154503 & 4576.21684549669 \tabularnewline
33 & 278506 & 283796.886025194 & -5290.88602519388 \tabularnewline
34 & 269826 & 271242.246361088 & -1416.24636108839 \tabularnewline
35 & 265861 & 270394.643246172 & -4533.64324617248 \tabularnewline
36 & 269034 & 264396.559296288 & 4637.44070371187 \tabularnewline
37 & 264176 & 265575.190327744 & -1399.19032774368 \tabularnewline
38 & 255198 & 258160.590272551 & -2962.59027255119 \tabularnewline
39 & 253353 & 251986.847717212 & 1366.15228278776 \tabularnewline
40 & 246057 & 245313.607828147 & 743.392171853363 \tabularnewline
41 & 235372 & 242429.989723120 & -7057.98972311957 \tabularnewline
42 & 258556 & 256584.068519531 & 1971.93148046879 \tabularnewline
43 & 260993 & 259147.219157699 & 1845.78084230060 \tabularnewline
44 & 254663 & 257577.082035586 & -2914.0820355856 \tabularnewline
45 & 250643 & 249009.049116564 & 1633.95088343566 \tabularnewline
46 & 243422 & 243511.336502552 & -89.3365025524604 \tabularnewline
47 & 247105 & 243957.021854291 & 3147.9781457088 \tabularnewline
48 & 248541 & 248620.91483001 & -79.9148300099232 \tabularnewline
49 & 245039 & 246822.446502492 & -1783.44650249211 \tabularnewline
50 & 237080 & 241061.436900374 & -3981.43690037385 \tabularnewline
51 & 237085 & 233820.901903244 & 3264.09809675594 \tabularnewline
52 & 225554 & 229660.572305417 & -4106.57230541694 \tabularnewline
53 & 226839 & 222827.615167561 & 4011.38483243935 \tabularnewline
54 & 247934 & 247608.302226826 & 325.69777317352 \tabularnewline
55 & 248333 & 253372.494842279 & -5039.49484227919 \tabularnewline
56 & 246969 & 252235.267848111 & -5266.26784811145 \tabularnewline
57 & 245098 & 244664.038561397 & 433.961438602899 \tabularnewline
58 & 246263 & 242160.0011116 & 4102.99888839978 \tabularnewline
59 & 255765 & 251909.13086872 & 3855.86913128019 \tabularnewline
60 & 264319 & 262129.043098527 & 2189.95690147333 \tabularnewline
61 & 268347 & 268300.624700584 & 46.3752994162896 \tabularnewline
62 & 273046 & 269116.980175157 & 3929.01982484249 \tabularnewline
63 & 273963 & 274163.150799032 & -200.150799031771 \tabularnewline
64 & 267430 & 271674.311471956 & -4244.31147195579 \tabularnewline
65 & 271993 & 269315.738895023 & 2677.26110497673 \tabularnewline
66 & 292710 & 294684.697314469 & -1974.69731446858 \tabularnewline
67 & 295881 & 296391.520313145 & -510.520313144927 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&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]267366[/C][C]267561.66414859[/C][C]-195.664148589884[/C][/ROW]
[ROW][C]2[/C][C]264777[/C][C]266038.361251580[/C][C]-1261.36125158039[/C][/ROW]
[ROW][C]3[/C][C]258863[/C][C]264427.699636706[/C][C]-5564.69963670626[/C][/ROW]
[ROW][C]4[/C][C]254844[/C][C]255057.219632103[/C][C]-213.219632102641[/C][/ROW]
[ROW][C]5[/C][C]254868[/C][C]254264.31776012[/C][C]603.682239880169[/C][/ROW]
[ROW][C]6[/C][C]277267[/C][C]276732.098444375[/C][C]534.901555624953[/C][/ROW]
[ROW][C]7[/C][C]285351[/C][C]281483.067840301[/C][C]3867.93215969854[/C][/ROW]
[ROW][C]8[/C][C]286602[/C][C]285598.422768286[/C][C]1003.57723171426[/C][/ROW]
[ROW][C]9[/C][C]283042[/C][C]282019.928399285[/C][C]1022.07160071451[/C][/ROW]
[ROW][C]10[/C][C]276687[/C][C]278916.036322215[/C][C]-2229.03632221504[/C][/ROW]
[ROW][C]11[/C][C]277915[/C][C]279727.825008706[/C][C]-1812.82500870615[/C][/ROW]
[ROW][C]12[/C][C]277128[/C][C]280309.114251889[/C][C]-3181.11425188940[/C][/ROW]
[ROW][C]13[/C][C]277103[/C][C]276106.809337874[/C][C]996.19066212559[/C][/ROW]
[ROW][C]14[/C][C]275037[/C][C]272771.997448115[/C][C]2265.00255188492[/C][/ROW]
[ROW][C]15[/C][C]270150[/C][C]271879.539923842[/C][C]-1729.53992384200[/C][/ROW]
[ROW][C]16[/C][C]267140[/C][C]264935.668890494[/C][C]2204.33110950609[/C][/ROW]
[ROW][C]17[/C][C]264993[/C][C]265273.603130365[/C][C]-280.603130365346[/C][/ROW]
[ROW][C]18[/C][C]287259[/C][C]285835.931348767[/C][C]1423.06865123332[/C][/ROW]
[ROW][C]19[/C][C]291186[/C][C]290033.761219416[/C][C]1152.23878058413[/C][/ROW]
[ROW][C]20[/C][C]292300[/C][C]289699.444193514[/C][C]2600.55580648610[/C][/ROW]
[ROW][C]21[/C][C]288186[/C][C]285985.097897559[/C][C]2200.90210244081[/C][/ROW]
[ROW][C]22[/C][C]281477[/C][C]281845.379702544[/C][C]-368.37970254389[/C][/ROW]
[ROW][C]23[/C][C]282656[/C][C]283313.379022110[/C][C]-657.379022110357[/C][/ROW]
[ROW][C]24[/C][C]280190[/C][C]283756.368523286[/C][C]-3566.36852328586[/C][/ROW]
[ROW][C]25[/C][C]280408[/C][C]278072.264982716[/C][C]2335.73501728379[/C][/ROW]
[ROW][C]26[/C][C]276836[/C][C]274824.633952222[/C][C]2011.36604777802[/C][/ROW]
[ROW][C]27[/C][C]275216[/C][C]272351.860019964[/C][C]2864.13998003634[/C][/ROW]
[ROW][C]28[/C][C]274352[/C][C]268735.619871884[/C][C]5616.38012811592[/C][/ROW]
[ROW][C]29[/C][C]271311[/C][C]271264.735323811[/C][C]46.2646761886604[/C][/ROW]
[ROW][C]30[/C][C]289802[/C][C]292082.902146032[/C][C]-2280.90214603200[/C][/ROW]
[ROW][C]31[/C][C]290726[/C][C]292041.936627159[/C][C]-1315.93662715915[/C][/ROW]
[ROW][C]32[/C][C]292300[/C][C]287723.783154503[/C][C]4576.21684549669[/C][/ROW]
[ROW][C]33[/C][C]278506[/C][C]283796.886025194[/C][C]-5290.88602519388[/C][/ROW]
[ROW][C]34[/C][C]269826[/C][C]271242.246361088[/C][C]-1416.24636108839[/C][/ROW]
[ROW][C]35[/C][C]265861[/C][C]270394.643246172[/C][C]-4533.64324617248[/C][/ROW]
[ROW][C]36[/C][C]269034[/C][C]264396.559296288[/C][C]4637.44070371187[/C][/ROW]
[ROW][C]37[/C][C]264176[/C][C]265575.190327744[/C][C]-1399.19032774368[/C][/ROW]
[ROW][C]38[/C][C]255198[/C][C]258160.590272551[/C][C]-2962.59027255119[/C][/ROW]
[ROW][C]39[/C][C]253353[/C][C]251986.847717212[/C][C]1366.15228278776[/C][/ROW]
[ROW][C]40[/C][C]246057[/C][C]245313.607828147[/C][C]743.392171853363[/C][/ROW]
[ROW][C]41[/C][C]235372[/C][C]242429.989723120[/C][C]-7057.98972311957[/C][/ROW]
[ROW][C]42[/C][C]258556[/C][C]256584.068519531[/C][C]1971.93148046879[/C][/ROW]
[ROW][C]43[/C][C]260993[/C][C]259147.219157699[/C][C]1845.78084230060[/C][/ROW]
[ROW][C]44[/C][C]254663[/C][C]257577.082035586[/C][C]-2914.0820355856[/C][/ROW]
[ROW][C]45[/C][C]250643[/C][C]249009.049116564[/C][C]1633.95088343566[/C][/ROW]
[ROW][C]46[/C][C]243422[/C][C]243511.336502552[/C][C]-89.3365025524604[/C][/ROW]
[ROW][C]47[/C][C]247105[/C][C]243957.021854291[/C][C]3147.9781457088[/C][/ROW]
[ROW][C]48[/C][C]248541[/C][C]248620.91483001[/C][C]-79.9148300099232[/C][/ROW]
[ROW][C]49[/C][C]245039[/C][C]246822.446502492[/C][C]-1783.44650249211[/C][/ROW]
[ROW][C]50[/C][C]237080[/C][C]241061.436900374[/C][C]-3981.43690037385[/C][/ROW]
[ROW][C]51[/C][C]237085[/C][C]233820.901903244[/C][C]3264.09809675594[/C][/ROW]
[ROW][C]52[/C][C]225554[/C][C]229660.572305417[/C][C]-4106.57230541694[/C][/ROW]
[ROW][C]53[/C][C]226839[/C][C]222827.615167561[/C][C]4011.38483243935[/C][/ROW]
[ROW][C]54[/C][C]247934[/C][C]247608.302226826[/C][C]325.69777317352[/C][/ROW]
[ROW][C]55[/C][C]248333[/C][C]253372.494842279[/C][C]-5039.49484227919[/C][/ROW]
[ROW][C]56[/C][C]246969[/C][C]252235.267848111[/C][C]-5266.26784811145[/C][/ROW]
[ROW][C]57[/C][C]245098[/C][C]244664.038561397[/C][C]433.961438602899[/C][/ROW]
[ROW][C]58[/C][C]246263[/C][C]242160.0011116[/C][C]4102.99888839978[/C][/ROW]
[ROW][C]59[/C][C]255765[/C][C]251909.13086872[/C][C]3855.86913128019[/C][/ROW]
[ROW][C]60[/C][C]264319[/C][C]262129.043098527[/C][C]2189.95690147333[/C][/ROW]
[ROW][C]61[/C][C]268347[/C][C]268300.624700584[/C][C]46.3752994162896[/C][/ROW]
[ROW][C]62[/C][C]273046[/C][C]269116.980175157[/C][C]3929.01982484249[/C][/ROW]
[ROW][C]63[/C][C]273963[/C][C]274163.150799032[/C][C]-200.150799031771[/C][/ROW]
[ROW][C]64[/C][C]267430[/C][C]271674.311471956[/C][C]-4244.31147195579[/C][/ROW]
[ROW][C]65[/C][C]271993[/C][C]269315.738895023[/C][C]2677.26110497673[/C][/ROW]
[ROW][C]66[/C][C]292710[/C][C]294684.697314469[/C][C]-1974.69731446858[/C][/ROW]
[ROW][C]67[/C][C]295881[/C][C]296391.520313145[/C][C]-510.520313144927[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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 IndexActualsInterpolationForecastResidualsPrediction Error
1267366267561.66414859-195.664148589884
2264777266038.361251580-1261.36125158039
3258863264427.699636706-5564.69963670626
4254844255057.219632103-213.219632102641
5254868254264.31776012603.682239880169
6277267276732.098444375534.901555624953
7285351281483.0678403013867.93215969854
8286602285598.4227682861003.57723171426
9283042282019.9283992851022.07160071451
10276687278916.036322215-2229.03632221504
11277915279727.825008706-1812.82500870615
12277128280309.114251889-3181.11425188940
13277103276106.809337874996.19066212559
14275037272771.9974481152265.00255188492
15270150271879.539923842-1729.53992384200
16267140264935.6688904942204.33110950609
17264993265273.603130365-280.603130365346
18287259285835.9313487671423.06865123332
19291186290033.7612194161152.23878058413
20292300289699.4441935142600.55580648610
21288186285985.0978975592200.90210244081
22281477281845.379702544-368.37970254389
23282656283313.379022110-657.379022110357
24280190283756.368523286-3566.36852328586
25280408278072.2649827162335.73501728379
26276836274824.6339522222011.36604777802
27275216272351.8600199642864.13998003634
28274352268735.6198718845616.38012811592
29271311271264.73532381146.2646761886604
30289802292082.902146032-2280.90214603200
31290726292041.936627159-1315.93662715915
32292300287723.7831545034576.21684549669
33278506283796.886025194-5290.88602519388
34269826271242.246361088-1416.24636108839
35265861270394.643246172-4533.64324617248
36269034264396.5592962884637.44070371187
37264176265575.190327744-1399.19032774368
38255198258160.590272551-2962.59027255119
39253353251986.8477172121366.15228278776
40246057245313.607828147743.392171853363
41235372242429.989723120-7057.98972311957
42258556256584.0685195311971.93148046879
43260993259147.2191576991845.78084230060
44254663257577.082035586-2914.0820355856
45250643249009.0491165641633.95088343566
46243422243511.336502552-89.3365025524604
47247105243957.0218542913147.9781457088
48248541248620.91483001-79.9148300099232
49245039246822.446502492-1783.44650249211
50237080241061.436900374-3981.43690037385
51237085233820.9019032443264.09809675594
52225554229660.572305417-4106.57230541694
53226839222827.6151675614011.38483243935
54247934247608.302226826325.69777317352
55248333253372.494842279-5039.49484227919
56246969252235.267848111-5266.26784811145
57245098244664.038561397433.961438602899
58246263242160.00111164102.99888839978
59255765251909.130868723855.86913128019
60264319262129.0430985272189.95690147333
61268347268300.62470058446.3752994162896
62273046269116.9801751573929.01982484249
63273963274163.150799032-200.150799031771
64267430271674.311471956-4244.31147195579
65271993269315.7388950232677.26110497673
66292710294684.697314469-1974.69731446858
67295881296391.520313145-510.520313144927







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.03550838950274520.07101677900549030.964491610497255
230.008718877521055660.01743775504211130.991281122478944
240.002398350739516090.004796701479032180.997601649260484
250.0007746473172055660.001549294634411130.999225352682794
260.0001953909257153570.0003907818514307140.999804609074285
270.000425127276705250.00085025455341050.999574872723295
280.001622836190662680.003245672381325360.998377163809337
290.0005665615453073810.001133123090614760.999433438454693
300.0002812158386770750.000562431677354150.999718784161323
310.0001016996016543050.0002033992033086100.999898300398346
320.0001283460681494850.000256692136298970.99987165393185
330.05411254283962830.1082250856792570.945887457160372
340.03498890207464270.06997780414928540.965011097925357
350.05187883211082020.1037576642216400.94812116788918
360.07586821115667940.1517364223133590.92413178884332
370.04669201851220890.09338403702441780.953307981487791
380.04140704049185260.08281408098370510.958592959508147
390.07224404525163290.1444880905032660.927755954748367
400.1218666858177950.2437333716355910.878133314182205
410.2776732463870210.5553464927740410.72232675361298
420.2990255013179780.5980510026359560.700974498682022
430.5193715983755630.9612568032488730.480628401624437
440.4617664958123190.9235329916246390.538233504187681
450.701052087868460.597895824263080.29894791213154

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.0355083895027452 & 0.0710167790054903 & 0.964491610497255 \tabularnewline
23 & 0.00871887752105566 & 0.0174377550421113 & 0.991281122478944 \tabularnewline
24 & 0.00239835073951609 & 0.00479670147903218 & 0.997601649260484 \tabularnewline
25 & 0.000774647317205566 & 0.00154929463441113 & 0.999225352682794 \tabularnewline
26 & 0.000195390925715357 & 0.000390781851430714 & 0.999804609074285 \tabularnewline
27 & 0.00042512727670525 & 0.0008502545534105 & 0.999574872723295 \tabularnewline
28 & 0.00162283619066268 & 0.00324567238132536 & 0.998377163809337 \tabularnewline
29 & 0.000566561545307381 & 0.00113312309061476 & 0.999433438454693 \tabularnewline
30 & 0.000281215838677075 & 0.00056243167735415 & 0.999718784161323 \tabularnewline
31 & 0.000101699601654305 & 0.000203399203308610 & 0.999898300398346 \tabularnewline
32 & 0.000128346068149485 & 0.00025669213629897 & 0.99987165393185 \tabularnewline
33 & 0.0541125428396283 & 0.108225085679257 & 0.945887457160372 \tabularnewline
34 & 0.0349889020746427 & 0.0699778041492854 & 0.965011097925357 \tabularnewline
35 & 0.0518788321108202 & 0.103757664221640 & 0.94812116788918 \tabularnewline
36 & 0.0758682111566794 & 0.151736422313359 & 0.92413178884332 \tabularnewline
37 & 0.0466920185122089 & 0.0933840370244178 & 0.953307981487791 \tabularnewline
38 & 0.0414070404918526 & 0.0828140809837051 & 0.958592959508147 \tabularnewline
39 & 0.0722440452516329 & 0.144488090503266 & 0.927755954748367 \tabularnewline
40 & 0.121866685817795 & 0.243733371635591 & 0.878133314182205 \tabularnewline
41 & 0.277673246387021 & 0.555346492774041 & 0.72232675361298 \tabularnewline
42 & 0.299025501317978 & 0.598051002635956 & 0.700974498682022 \tabularnewline
43 & 0.519371598375563 & 0.961256803248873 & 0.480628401624437 \tabularnewline
44 & 0.461766495812319 & 0.923532991624639 & 0.538233504187681 \tabularnewline
45 & 0.70105208786846 & 0.59789582426308 & 0.29894791213154 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&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]22[/C][C]0.0355083895027452[/C][C]0.0710167790054903[/C][C]0.964491610497255[/C][/ROW]
[ROW][C]23[/C][C]0.00871887752105566[/C][C]0.0174377550421113[/C][C]0.991281122478944[/C][/ROW]
[ROW][C]24[/C][C]0.00239835073951609[/C][C]0.00479670147903218[/C][C]0.997601649260484[/C][/ROW]
[ROW][C]25[/C][C]0.000774647317205566[/C][C]0.00154929463441113[/C][C]0.999225352682794[/C][/ROW]
[ROW][C]26[/C][C]0.000195390925715357[/C][C]0.000390781851430714[/C][C]0.999804609074285[/C][/ROW]
[ROW][C]27[/C][C]0.00042512727670525[/C][C]0.0008502545534105[/C][C]0.999574872723295[/C][/ROW]
[ROW][C]28[/C][C]0.00162283619066268[/C][C]0.00324567238132536[/C][C]0.998377163809337[/C][/ROW]
[ROW][C]29[/C][C]0.000566561545307381[/C][C]0.00113312309061476[/C][C]0.999433438454693[/C][/ROW]
[ROW][C]30[/C][C]0.000281215838677075[/C][C]0.00056243167735415[/C][C]0.999718784161323[/C][/ROW]
[ROW][C]31[/C][C]0.000101699601654305[/C][C]0.000203399203308610[/C][C]0.999898300398346[/C][/ROW]
[ROW][C]32[/C][C]0.000128346068149485[/C][C]0.00025669213629897[/C][C]0.99987165393185[/C][/ROW]
[ROW][C]33[/C][C]0.0541125428396283[/C][C]0.108225085679257[/C][C]0.945887457160372[/C][/ROW]
[ROW][C]34[/C][C]0.0349889020746427[/C][C]0.0699778041492854[/C][C]0.965011097925357[/C][/ROW]
[ROW][C]35[/C][C]0.0518788321108202[/C][C]0.103757664221640[/C][C]0.94812116788918[/C][/ROW]
[ROW][C]36[/C][C]0.0758682111566794[/C][C]0.151736422313359[/C][C]0.92413178884332[/C][/ROW]
[ROW][C]37[/C][C]0.0466920185122089[/C][C]0.0933840370244178[/C][C]0.953307981487791[/C][/ROW]
[ROW][C]38[/C][C]0.0414070404918526[/C][C]0.0828140809837051[/C][C]0.958592959508147[/C][/ROW]
[ROW][C]39[/C][C]0.0722440452516329[/C][C]0.144488090503266[/C][C]0.927755954748367[/C][/ROW]
[ROW][C]40[/C][C]0.121866685817795[/C][C]0.243733371635591[/C][C]0.878133314182205[/C][/ROW]
[ROW][C]41[/C][C]0.277673246387021[/C][C]0.555346492774041[/C][C]0.72232675361298[/C][/ROW]
[ROW][C]42[/C][C]0.299025501317978[/C][C]0.598051002635956[/C][C]0.700974498682022[/C][/ROW]
[ROW][C]43[/C][C]0.519371598375563[/C][C]0.961256803248873[/C][C]0.480628401624437[/C][/ROW]
[ROW][C]44[/C][C]0.461766495812319[/C][C]0.923532991624639[/C][C]0.538233504187681[/C][/ROW]
[ROW][C]45[/C][C]0.70105208786846[/C][C]0.59789582426308[/C][C]0.29894791213154[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.03550838950274520.07101677900549030.964491610497255
230.008718877521055660.01743775504211130.991281122478944
240.002398350739516090.004796701479032180.997601649260484
250.0007746473172055660.001549294634411130.999225352682794
260.0001953909257153570.0003907818514307140.999804609074285
270.000425127276705250.00085025455341050.999574872723295
280.001622836190662680.003245672381325360.998377163809337
290.0005665615453073810.001133123090614760.999433438454693
300.0002812158386770750.000562431677354150.999718784161323
310.0001016996016543050.0002033992033086100.999898300398346
320.0001283460681494850.000256692136298970.99987165393185
330.05411254283962830.1082250856792570.945887457160372
340.03498890207464270.06997780414928540.965011097925357
350.05187883211082020.1037576642216400.94812116788918
360.07586821115667940.1517364223133590.92413178884332
370.04669201851220890.09338403702441780.953307981487791
380.04140704049185260.08281408098370510.958592959508147
390.07224404525163290.1444880905032660.927755954748367
400.1218666858177950.2437333716355910.878133314182205
410.2776732463870210.5553464927740410.72232675361298
420.2990255013179780.5980510026359560.700974498682022
430.5193715983755630.9612568032488730.480628401624437
440.4617664958123190.9235329916246390.538233504187681
450.701052087868460.597895824263080.29894791213154







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.375NOK
5% type I error level100.416666666666667NOK
10% type I error level140.583333333333333NOK

\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 & 9 & 0.375 & NOK \tabularnewline
5% type I error level & 10 & 0.416666666666667 & NOK \tabularnewline
10% type I error level & 14 & 0.583333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58403&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]9[/C][C]0.375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.416666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.583333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58403&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58403&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 testsOK/NOK
1% type I error level90.375NOK
5% type I error level100.416666666666667NOK
10% type I error level140.583333333333333NOK



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')
}