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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 15 Dec 2017 10:07:54 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/15/t15133289505x63cqm07seodlr.htm/, Retrieved Wed, 15 May 2024 14:09:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309619, Retrieved Wed, 15 May 2024 14:09:01 +0000

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Estimated Impact

121

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-       [Multiple Regression] [] [2017-12-15 09:07:54] [ca643b0c409f93e6a7ce1fd0961340ec] [Current]

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3,95508	0,02526	0,80199
4,15732	0,02286	0,79322
4,25277	0,01994	0,78584
4,16356	0,02295	0,79224
4,34640	0,02161	0,79051
4,27528	0,01876	0,79448
3,83514	0,02521	0,80246
4,11904	0,01885	0,80396
4,29456	0,01629	0,79619
4,31749	0,01751	0,79385
4,30946	0,01820	0,78705
4,31348	0,01730	0,79145
4,27249	0,01936	0,78298
4,19870	0,01950	0,78226
4,31882	0,01824	0,77622
4,21213	0,02219	0,78786
4,31214	0,02185	0,78426
4,33598	0,01948	0,77902
3,98713	0,02478	0,79494
4,24992	0,02056	0,79205
4,33205	0,02061	0,78240
4,37450	0,02059	0,78381
4,31482	0,02125	0,78160
4,17899	0,02344	0,78965
4,15104	0,02576	0,78965
4,16511	0,02499	0,78700
4,25277	0,02193	0,78141
4,31080	0,02505	0,78212
4,23989	0,02484	0,78179
4,31080	0,02282	0,78004
3,96651	0,02710	0,79782
4,11904	0,02616	0,79619
4,30271	0,02197	0,78332
4,37450	0,02039	0,77772
4,24563	0,02161	0,78456
4,34899	0,02195	0,79236
4,23989	0,02497	0,79116
4,31749	0,02435	0,78537
4,33598	0,02308	0,78386
4,32942	0,02410	0,78673
4,25277	0,02450	0,78678
4,49424	0,02346	0,78657
4,13517	0,02726	0,79831
4,07754	0,02729	0,80821
4,49424	0,02094	0,78764
4,42485	0,02183	0,78897
4,33073	0,02342	0,78657
4,45202	0,02133	0,78753
4,20320	0,02529	0,79057
4,32281	0,02438	0,78819
4,43793	0,02167	0,77815
4,40428	0,02335	0,78298
4,31749	0,02435	0,78759
4,52829	0,02124	0,77601
4,19570	0,02386	0,79931
4,32678	0,02673	0,78982
4,51415	0,02158	0,77513
4,48413	0,02120	0,77493
4,45202	0,02252	0,77968
4,46245	0,01994	0,78193
4,26268	0,02534	0,77221
4,42125	0,02453	0,78026
4,44265	0,02124	0,77368
4,37324	0,02310	0,77589
4,35028	0,02340	0,78684
4,56954	0,01980	0,76959
4,03424	0,02694	0,79372
4,32015	0,02590	0,79074
4,45783	0,02056	0,77593
4,44030	0,02207	0,78352
4,51743	0,02131	0,78008
4,70682	0,02004	0,76966
4,39445	0,02628	0,78288
4,40060	0,02523	0,77663
4,51086	0,02181	0,77047
4,39815	0,02428	0,78411
4,53796	0,02293	0,77341
4,61215	0,02142	0,77213
4,22683	0,02682	0,79139
4,35157	0,02710	0,78942
4,63181	0,02175	0,77191
4,72827	0,02120	0,77232
4,59006	0,02207	0,77169
4,68398	0,02237	0,77684
4,49536	0,02185	0,77705
4,53582	0,02215	0,77785
4,65014	0,01823	0,77221
4,54648	0,02103	0,77995
4,61215	0,02105	0,77684
4,71671	0,01810	0,77015
4,33205	0,02124	0,78313
4,62595	0,02105	0,78573
5,00529	0,01999	0,77284
5,14924	0,01721	0,76614
4,83310	0,01805	0,77364
4,88432	0,01851	0,77919
4,66814	0,02064	0,77688
4,75875	0,01846	0,77638
4,70773	0,01818	0,77622
4,80320	0,01885	0,77296
4,76388	0,02007	0,77755
4,81947	0,01707	0,77326
4,58497	0,02205	0,78481
4,53796	0,02215	0,79098
4,91486	0,01751	0,77288
4,87520	0,01858	0,77318
4,72916	0,02205	0,78594
4,61512	0,02059	0,78965
4,48526	0,02410	0,79673
4,57368	0,02460	0,79163
4,66155	0,02165	0,78678
4,55598	0,02306	0,79230
4,47734	0,02433	0,75977
4,67935	0,02082	0,78226
4,26409	0,02710	0,80169
4,28082	0,02471	0,78532
4,62006	0,01973	0,78293
4,63667	0,02093	0,78507
4,63473	0,02165	0,77964
4,48074	0,01917	0,79022
4,35671	0,02389	0,78732
4,51961	0,02290	0,78441
4,71402	0,01886	0,77505
4,60717	0,02191	0,78004
4,54648	0,02337	0,78004
4,77238	0,01885	0,77225
4,38826	0,02358	0,79507
4,52829	0,02262	0,78781
4,72827	0,01892	0,76783
4,71671	0,01778	0,77065
4,62203	0,01915	0,77093
4,66814	0,01895	0,77065
4,48751	0,02042	0,77418
4,61710	0,02082	0,77065
4,77912	0,01797	0,76323
4,65014	0,02271	0,77262
4,78916	0,01975	0,76601
4,72384	0,02066	0,76877
4,47847	0,02505	0,78652
4,59714	0,02293	0,78441
4,84024	0,01819	0,76397
4,72916	0,01896	0,76840
4,73795	0,02077	0,76799
4,85281	0,01975	0,76816
4,69318	0,02258	0,77326
4,67283	0,02052	0,76984
4,95794	0,01740	0,76311
4,66344	0,02111	0,77247
4,74667	0,02102	0,77213
4,86522	0,01719	0,76620
4,50424	0,02161	0,78841
4,57985	0,02363	0,77557
4,77322	0,02032	0,76813
4,79744	0,01892	0,76873
4,76644	0,02030	0,77446
4,65871	0,02140	0,78406
4,57780	0,02324	0,77862
4,58497	0,02250	0,77836
4,74319	0,02102	0,77068
4,69866	0,02217	0,77155
4,80320	0,02248	0,77195
4,81218	0,02084	0,76860
4,64535	0,02458	0,77692
4,60417	0,02497	0,78689
4,85593	0,02181	0,76671
4,84968	0,02039	0,76443
4,75961	0,02286	0,76750
4,71939	0,02163	0,77353
4,63279	0,02386	0,76507
4,70773	0,02333	0,76786
4,76899	0,02203	0,76646
4,80729	0,02386	0,76740
4,79082	0,02346	0,76510
4,78080	0,02256	0,76697
4,61809	0,02515	0,77721
4,61710	0,02613	0,78411
4,91339	0,02118	0,76376
4,89485	0,02129	0,76766
4,69684	0,02363	0,77100
4,75186	0,02059	0,77642
4,64727	0,02581	0,77414
4,74493	0,02518	0,77177
4,82511	0,02167	0,76560
4,81300	0,02420	0,76620
4,78749	0,02643	0,77012
4,88280	0,02199	0,76158
4,67470	0,02798	0,77726
4,61512	0,02826	0,78279
5,03109	0,02109	0,76244
4,97328	0,02197	0,76409
4,83469	0,02187	0,76358
4,83151	0,02213	0,76942
4,71582	0,02476	0,77266
4,77407	0,02344	0,76452
4,90971	0,02183	0,76138
4,87290	0,02250	0,76723
4,85593	0,02458	0,77086
4,92071	0,02093	0,76115
4,52287	0,02682	0,77700
4,64150	0,02570	0,78198
4,93447	0,02227	0,76104
4,82831	0,02277	0,76541
4,86907	0,02173	0,76652
4,75703	0,02161	0,77228
4,66721	0,02473	0,77529
4,79744	0,02363	0,76470
4,99451	0,01936	0,75790
4,75359	0,02450	0,76736
4,92362	0,02396	0,76299
4,91559	0,02086	0,76085
4,56226	0,02613	0,78008
4,84419	0,02473	0,77977

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)a[t] = + 0.0010037 -17.0196`(1-Bs)(1-B)b`[t] -2.67155`(1-Bs)(1-B)c`[t] -0.328055`(1-Bs)(1-B)a(t-1s)`[t] -0.308145`(1-Bs)(1-B)a(t-2s)`[t] -0.216155`(1-Bs)(1-B)a(t-3s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)a[t] =  +  0.0010037 -17.0196`(1-Bs)(1-B)b`[t] -2.67155`(1-Bs)(1-B)c`[t] -0.328055`(1-Bs)(1-B)a(t-1s)`[t] -0.308145`(1-Bs)(1-B)a(t-2s)`[t] -0.216155`(1-Bs)(1-B)a(t-3s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)a[t] =  +  0.0010037 -17.0196`(1-Bs)(1-B)b`[t] -2.67155`(1-Bs)(1-B)c`[t] -0.328055`(1-Bs)(1-B)a(t-1s)`[t] -0.308145`(1-Bs)(1-B)a(t-2s)`[t] -0.216155`(1-Bs)(1-B)a(t-3s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&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
(1-Bs)(1-B)a[t] = + 0.0010037 -17.0196`(1-Bs)(1-B)b`[t] -2.67155`(1-Bs)(1-B)c`[t] -0.328055`(1-Bs)(1-B)a(t-1s)`[t] -0.308145`(1-Bs)(1-B)a(t-2s)`[t] -0.216155`(1-Bs)(1-B)a(t-3s)`[t] + 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)	+0.001004	0.007111	+1.4110e-01	0.8879	0.444
`(1-Bs)(1-B)b`	-17.02	3.851	-4.4200e+00	1.837e-05	9.183e-06
`(1-Bs)(1-B)c`	-2.671	0.8263	-3.2330e+00	0.001492	0.0007462
`(1-Bs)(1-B)a(t-1s)`	-0.3281	0.07019	-4.6730e+00	6.331e-06	3.165e-06
`(1-Bs)(1-B)a(t-2s)`	-0.3081	0.06965	-4.4240e+00	1.804e-05	9.021e-06
`(1-Bs)(1-B)a(t-3s)`	-0.2162	0.06652	-3.2500e+00	0.001414	0.000707

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +0.001004 &  0.007111 & +1.4110e-01 &  0.8879 &  0.444 \tabularnewline
`(1-Bs)(1-B)b` & -17.02 &  3.851 & -4.4200e+00 &  1.837e-05 &  9.183e-06 \tabularnewline
`(1-Bs)(1-B)c` & -2.671 &  0.8263 & -3.2330e+00 &  0.001492 &  0.0007462 \tabularnewline
`(1-Bs)(1-B)a(t-1s)` & -0.3281 &  0.07019 & -4.6730e+00 &  6.331e-06 &  3.165e-06 \tabularnewline
`(1-Bs)(1-B)a(t-2s)` & -0.3081 &  0.06965 & -4.4240e+00 &  1.804e-05 &  9.021e-06 \tabularnewline
`(1-Bs)(1-B)a(t-3s)` & -0.2162 &  0.06652 & -3.2500e+00 &  0.001414 &  0.000707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+0.001004[/C][C] 0.007111[/C][C]+1.4110e-01[/C][C] 0.8879[/C][C] 0.444[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)b`[/C][C]-17.02[/C][C] 3.851[/C][C]-4.4200e+00[/C][C] 1.837e-05[/C][C] 9.183e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)c`[/C][C]-2.671[/C][C] 0.8263[/C][C]-3.2330e+00[/C][C] 0.001492[/C][C] 0.0007462[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-1s)`[/C][C]-0.3281[/C][C] 0.07019[/C][C]-4.6730e+00[/C][C] 6.331e-06[/C][C] 3.165e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-2s)`[/C][C]-0.3081[/C][C] 0.06965[/C][C]-4.4240e+00[/C][C] 1.804e-05[/C][C] 9.021e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-3s)`[/C][C]-0.2162[/C][C] 0.06652[/C][C]-3.2500e+00[/C][C] 0.001414[/C][C] 0.000707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&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)	+0.001004	0.007111	+1.4110e-01	0.8879	0.444
`(1-Bs)(1-B)b`	-17.02	3.851	-4.4200e+00	1.837e-05	9.183e-06
`(1-Bs)(1-B)c`	-2.671	0.8263	-3.2330e+00	0.001492	0.0007462
`(1-Bs)(1-B)a(t-1s)`	-0.3281	0.07019	-4.6730e+00	6.331e-06	3.165e-06
`(1-Bs)(1-B)a(t-2s)`	-0.3081	0.06965	-4.4240e+00	1.804e-05	9.021e-06
`(1-Bs)(1-B)a(t-3s)`	-0.2162	0.06652	-3.2500e+00	0.001414	0.000707

Multiple Linear Regression - Regression Statistics
Multiple R	0.6033
R-squared	0.3639
Adjusted R-squared	0.3437
F-TEST (value)	17.96
F-TEST (DF numerator)	5
F-TEST (DF denominator)	157
p-value	4.574e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09077
Sum Squared Residuals	1.294

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6033 \tabularnewline
R-squared &  0.3639 \tabularnewline
Adjusted R-squared &  0.3437 \tabularnewline
F-TEST (value) &  17.96 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  4.574e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.09077 \tabularnewline
Sum Squared Residuals &  1.294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6033[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3639[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3437[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 17.96[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C] 4.574e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.09077[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&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	0.6033
R-squared	0.3639
Adjusted R-squared	0.3437
F-TEST (value)	17.96
F-TEST (DF numerator)	5
F-TEST (DF denominator)	157
p-value	4.574e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09077
Sum Squared Residuals	1.294

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.04201	0.00858	0.03343
2	0.09663	0.07566	0.02097
3	-0.02709	-0.04126	0.01417
4	-0.01014	0.05106	-0.06121
5	-0.03067	-0.02437	-0.006296
6	0.02648	-0.02609	0.05257
7	0.1887	0.112	0.07676
8	-0.2293	-0.1227	-0.1067
9	0.03937	0.05976	-0.02039
10	0.06201	0.007582	0.05443
11	-0.1109	-0.04346	-0.0674
12	0.04905	0.07861	-0.02956
13	0.03896	-0.08091	0.1199
14	-0.09372	-0.001738	-0.09198
15	-0.03576	-0.001875	-0.03388
16	0.06383	0.03802	0.02581
17	0.00846	-0.02818	0.03664
18	-0.2027	-0.08326	-0.1195
19	0.1548	0.07684	0.07799
20	-0.04969	-0.01396	-0.03573
21	0.01249	-0.02773	0.04022
22	0.1092	0.04219	0.06705
23	0.179	-0.008302	0.1873
24	-0.1126	-0.03007	-0.08253
25	-0.1524	0.003833	-0.1563
26	0.08886	0.01801	0.07085
27	-0.0433	-0.005874	-0.03743
28	0.1628	0.07035	0.09242
29	-0.1451	-0.1074	-0.03765
30	0.15	0.1052	0.04482
31	-0.1612	-0.08768	-0.07349
32	0.1426	0.04498	0.09758
33	0.114	0.06953	0.04446
34	-0.2153	-0.0967	-0.1186
35	-0.09547	-0.09574	0.0002664
36	0.1237	0.2028	-0.07909
37	0.03431	-0.01189	0.0462
38	0.00406	-0.01303	0.01709
39	0.00905	0.04223	-0.03318
40	-0.07414	-0.1135	0.03932
41	0.03037	0.09158	-0.06121
42	0.00066	0.06378	-0.06312
43	0.1692	-0.03883	0.208
44	0.0991	-0.06624	0.1653
45	0.04749	0.008198	0.03929
46	-0.1779	0.003372	-0.1813
47	-0.0427	-0.002651	-0.04005
48	-0.02756	-0.05387	0.02631
49	0.05015	0.07398	-0.02383
50	-0.1653	-0.08404	-0.0813
51	0.1991	0.08475	0.1144
52	-0.105	-0.07962	-0.02537
53	-0.04897	0.02835	-0.07732
54	0.1502	-0.02911	0.1793
55	-0.3409	-0.05277	-0.2881
56	-0.00244	0.01015	-0.01259
57	-0.1836	-0.1366	-0.04698
58	0.1701	0.0433	0.1268
59	-0.1653	0.04334	-0.2086
60	0.08632	-0.05232	0.1386
61	-0.00219	-0.0264	0.02421
62	0.1389	0.09276	0.04613
63	-0.201	-0.0938	-0.1072
64	-0.03932	0.1214	-0.1607
65	0.1464	-0.0238	0.1702
66	-0.1808	-0.1241	-0.0567
67	0.06374	0.1981	-0.1344
68	-0.03766	-0.09573	0.05807
69	0.05627	0.01484	0.04143
70	0.1441	0.142	0.002124
71	-0.03995	0.08802	-0.128
72	0.00583	-0.0395	0.04533
73	0.07448	-0.001639	0.07612
74	0.1065	0.03611	0.07043
75	-0.00128	-0.02286	0.02158
76	0.01795	-0.02786	0.04581
77	0.02389	0.05958	-0.03569
78	0.03114	0.03121	-7.29e-05
79	0.1233	-9.726e-05	0.1234
80	-0.1393	0.0179	-0.1572
81	-0.02817	0.06687	-0.09504
82	-0.09274	-0.08654	-0.006199
83	0.2001	0.06447	0.1356
84	-0.0566	0.01658	-0.07318
85	-0.03331	-0.0556	0.02229
86	-0.03239	-0.06644	0.03405
87	-0.02213	-0.02019	-0.001942
88	0.1997	0.1228	0.0769
89	-0.2912	-0.162	-0.1293
90	0.1388	0.03336	0.1054
91	-0.02136	0.02059	-0.04195
92	0.04312	0.07775	-0.03463
93	-0.09952	-0.004215	-0.09531
94	0.1035	-0.05539	0.1589
95	0.06875	-0.003854	0.0726
96	0.021	-0.02822	0.04922
97	-0.1499	0.03103	-0.181
98	0.1231	-0.04847	0.1716
99	-0.1655	0.06972	-0.2352
100	-0.05579	-0.1271	0.07135
101	0.1839	0.1614	0.02245
102	-0.1156	-0.02746	-0.08815
103	-0.04306	-0.08556	0.0425
104	-0.04973	-0.02116	-0.02857
105	0.1353	0.07733	0.05797
106	-0.03979	-0.0446	0.004806
107	-0.2226	-0.1358	-0.08674
108	0.07872	0.0553	0.02342
109	0.02752	0.01345	0.01407
110	-0.1269	-0.0778	-0.04909
111	0.25	0.1287	0.1213
112	0.02131	-0.0549	0.07621
113	-0.1096	-0.01891	-0.09066
114	0.1941	0.03812	0.156
115	-0.1168	-0.03814	-0.07865
116	0.05839	0.06562	-0.007225
117	-0.03047	0.001408	-0.03188
118	-0.05907	-0.009226	-0.04984
119	0.06751	0.05878	0.008729
120	-0.00569	-0.01763	0.01194
121	0.06777	0.03366	0.03411
122	-0.09696	-0.008139	-0.08882
123	0.08283	-0.03697	0.1198
124	-0.121	-0.01267	-0.1083
125	-0.019	0.0167	-0.0357
126	0.00412	-0.04261	0.04673
127	0.04019	0.05536	-0.01517
128	0.04453	0.01877	0.02576
129	-0.01229	-0.05173	0.03944
130	-0.1079	0.01177	-0.1197
131	0.09524	0.06502	0.03022
132	-0.01799	-0.09332	0.07533
133	0.02272	0.01819	0.004531
134	0.01892	0.09566	-0.07674
135	-0.05041	-0.07842	0.02801
136	-0.00904	-0.01518	0.006144
137	0.1053	0.08932	0.01601
138	-0.04539	-0.1076	0.06219
139	-0.05859	0.04869	-0.1073
140	0.1197	0.01694	0.1027
141	-0.03927	-0.02192	-0.01735
142	0.05942	0.115	-0.05561
143	-0.0582	-0.06022	0.002016
144	-0.0111	0.02098	-0.03208
145	-0.03941	-0.006123	-0.03329
146	0.05546	0.01167	0.04379
147	-0.0247	-0.04438	0.01968
148	0.00854	0.03998	-0.03144
149	-0.03053	-0.01433	-0.0162
150	-0.1897	-0.02609	-0.1636
151	0.1782	0.05838	0.1198
152	-0.123	-0.1267	0.003678
153	-0.04835	0.02346	-0.07181
154	0.1794	0.03921	0.1401
155	-0.1089	-0.01716	-0.0917
156	0.02587	0.003693	0.02218
157	0.07198	-0.004917	0.0769
158	0.06143	0.05299	0.008442
159	-0.2041	-0.07899	-0.1251
160	0.187	0.09311	0.09389
161	-0.07281	-0.04692	-0.02589
162	0.04451	0.07787	-0.03336
163	0.1633	-0.02919	0.1925

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.04201 &  0.00858 &  0.03343 \tabularnewline
2 &  0.09663 &  0.07566 &  0.02097 \tabularnewline
3 & -0.02709 & -0.04126 &  0.01417 \tabularnewline
4 & -0.01014 &  0.05106 & -0.06121 \tabularnewline
5 & -0.03067 & -0.02437 & -0.006296 \tabularnewline
6 &  0.02648 & -0.02609 &  0.05257 \tabularnewline
7 &  0.1887 &  0.112 &  0.07676 \tabularnewline
8 & -0.2293 & -0.1227 & -0.1067 \tabularnewline
9 &  0.03937 &  0.05976 & -0.02039 \tabularnewline
10 &  0.06201 &  0.007582 &  0.05443 \tabularnewline
11 & -0.1109 & -0.04346 & -0.0674 \tabularnewline
12 &  0.04905 &  0.07861 & -0.02956 \tabularnewline
13 &  0.03896 & -0.08091 &  0.1199 \tabularnewline
14 & -0.09372 & -0.001738 & -0.09198 \tabularnewline
15 & -0.03576 & -0.001875 & -0.03388 \tabularnewline
16 &  0.06383 &  0.03802 &  0.02581 \tabularnewline
17 &  0.00846 & -0.02818 &  0.03664 \tabularnewline
18 & -0.2027 & -0.08326 & -0.1195 \tabularnewline
19 &  0.1548 &  0.07684 &  0.07799 \tabularnewline
20 & -0.04969 & -0.01396 & -0.03573 \tabularnewline
21 &  0.01249 & -0.02773 &  0.04022 \tabularnewline
22 &  0.1092 &  0.04219 &  0.06705 \tabularnewline
23 &  0.179 & -0.008302 &  0.1873 \tabularnewline
24 & -0.1126 & -0.03007 & -0.08253 \tabularnewline
25 & -0.1524 &  0.003833 & -0.1563 \tabularnewline
26 &  0.08886 &  0.01801 &  0.07085 \tabularnewline
27 & -0.0433 & -0.005874 & -0.03743 \tabularnewline
28 &  0.1628 &  0.07035 &  0.09242 \tabularnewline
29 & -0.1451 & -0.1074 & -0.03765 \tabularnewline
30 &  0.15 &  0.1052 &  0.04482 \tabularnewline
31 & -0.1612 & -0.08768 & -0.07349 \tabularnewline
32 &  0.1426 &  0.04498 &  0.09758 \tabularnewline
33 &  0.114 &  0.06953 &  0.04446 \tabularnewline
34 & -0.2153 & -0.0967 & -0.1186 \tabularnewline
35 & -0.09547 & -0.09574 &  0.0002664 \tabularnewline
36 &  0.1237 &  0.2028 & -0.07909 \tabularnewline
37 &  0.03431 & -0.01189 &  0.0462 \tabularnewline
38 &  0.00406 & -0.01303 &  0.01709 \tabularnewline
39 &  0.00905 &  0.04223 & -0.03318 \tabularnewline
40 & -0.07414 & -0.1135 &  0.03932 \tabularnewline
41 &  0.03037 &  0.09158 & -0.06121 \tabularnewline
42 &  0.00066 &  0.06378 & -0.06312 \tabularnewline
43 &  0.1692 & -0.03883 &  0.208 \tabularnewline
44 &  0.0991 & -0.06624 &  0.1653 \tabularnewline
45 &  0.04749 &  0.008198 &  0.03929 \tabularnewline
46 & -0.1779 &  0.003372 & -0.1813 \tabularnewline
47 & -0.0427 & -0.002651 & -0.04005 \tabularnewline
48 & -0.02756 & -0.05387 &  0.02631 \tabularnewline
49 &  0.05015 &  0.07398 & -0.02383 \tabularnewline
50 & -0.1653 & -0.08404 & -0.0813 \tabularnewline
51 &  0.1991 &  0.08475 &  0.1144 \tabularnewline
52 & -0.105 & -0.07962 & -0.02537 \tabularnewline
53 & -0.04897 &  0.02835 & -0.07732 \tabularnewline
54 &  0.1502 & -0.02911 &  0.1793 \tabularnewline
55 & -0.3409 & -0.05277 & -0.2881 \tabularnewline
56 & -0.00244 &  0.01015 & -0.01259 \tabularnewline
57 & -0.1836 & -0.1366 & -0.04698 \tabularnewline
58 &  0.1701 &  0.0433 &  0.1268 \tabularnewline
59 & -0.1653 &  0.04334 & -0.2086 \tabularnewline
60 &  0.08632 & -0.05232 &  0.1386 \tabularnewline
61 & -0.00219 & -0.0264 &  0.02421 \tabularnewline
62 &  0.1389 &  0.09276 &  0.04613 \tabularnewline
63 & -0.201 & -0.0938 & -0.1072 \tabularnewline
64 & -0.03932 &  0.1214 & -0.1607 \tabularnewline
65 &  0.1464 & -0.0238 &  0.1702 \tabularnewline
66 & -0.1808 & -0.1241 & -0.0567 \tabularnewline
67 &  0.06374 &  0.1981 & -0.1344 \tabularnewline
68 & -0.03766 & -0.09573 &  0.05807 \tabularnewline
69 &  0.05627 &  0.01484 &  0.04143 \tabularnewline
70 &  0.1441 &  0.142 &  0.002124 \tabularnewline
71 & -0.03995 &  0.08802 & -0.128 \tabularnewline
72 &  0.00583 & -0.0395 &  0.04533 \tabularnewline
73 &  0.07448 & -0.001639 &  0.07612 \tabularnewline
74 &  0.1065 &  0.03611 &  0.07043 \tabularnewline
75 & -0.00128 & -0.02286 &  0.02158 \tabularnewline
76 &  0.01795 & -0.02786 &  0.04581 \tabularnewline
77 &  0.02389 &  0.05958 & -0.03569 \tabularnewline
78 &  0.03114 &  0.03121 & -7.29e-05 \tabularnewline
79 &  0.1233 & -9.726e-05 &  0.1234 \tabularnewline
80 & -0.1393 &  0.0179 & -0.1572 \tabularnewline
81 & -0.02817 &  0.06687 & -0.09504 \tabularnewline
82 & -0.09274 & -0.08654 & -0.006199 \tabularnewline
83 &  0.2001 &  0.06447 &  0.1356 \tabularnewline
84 & -0.0566 &  0.01658 & -0.07318 \tabularnewline
85 & -0.03331 & -0.0556 &  0.02229 \tabularnewline
86 & -0.03239 & -0.06644 &  0.03405 \tabularnewline
87 & -0.02213 & -0.02019 & -0.001942 \tabularnewline
88 &  0.1997 &  0.1228 &  0.0769 \tabularnewline
89 & -0.2912 & -0.162 & -0.1293 \tabularnewline
90 &  0.1388 &  0.03336 &  0.1054 \tabularnewline
91 & -0.02136 &  0.02059 & -0.04195 \tabularnewline
92 &  0.04312 &  0.07775 & -0.03463 \tabularnewline
93 & -0.09952 & -0.004215 & -0.09531 \tabularnewline
94 &  0.1035 & -0.05539 &  0.1589 \tabularnewline
95 &  0.06875 & -0.003854 &  0.0726 \tabularnewline
96 &  0.021 & -0.02822 &  0.04922 \tabularnewline
97 & -0.1499 &  0.03103 & -0.181 \tabularnewline
98 &  0.1231 & -0.04847 &  0.1716 \tabularnewline
99 & -0.1655 &  0.06972 & -0.2352 \tabularnewline
100 & -0.05579 & -0.1271 &  0.07135 \tabularnewline
101 &  0.1839 &  0.1614 &  0.02245 \tabularnewline
102 & -0.1156 & -0.02746 & -0.08815 \tabularnewline
103 & -0.04306 & -0.08556 &  0.0425 \tabularnewline
104 & -0.04973 & -0.02116 & -0.02857 \tabularnewline
105 &  0.1353 &  0.07733 &  0.05797 \tabularnewline
106 & -0.03979 & -0.0446 &  0.004806 \tabularnewline
107 & -0.2226 & -0.1358 & -0.08674 \tabularnewline
108 &  0.07872 &  0.0553 &  0.02342 \tabularnewline
109 &  0.02752 &  0.01345 &  0.01407 \tabularnewline
110 & -0.1269 & -0.0778 & -0.04909 \tabularnewline
111 &  0.25 &  0.1287 &  0.1213 \tabularnewline
112 &  0.02131 & -0.0549 &  0.07621 \tabularnewline
113 & -0.1096 & -0.01891 & -0.09066 \tabularnewline
114 &  0.1941 &  0.03812 &  0.156 \tabularnewline
115 & -0.1168 & -0.03814 & -0.07865 \tabularnewline
116 &  0.05839 &  0.06562 & -0.007225 \tabularnewline
117 & -0.03047 &  0.001408 & -0.03188 \tabularnewline
118 & -0.05907 & -0.009226 & -0.04984 \tabularnewline
119 &  0.06751 &  0.05878 &  0.008729 \tabularnewline
120 & -0.00569 & -0.01763 &  0.01194 \tabularnewline
121 &  0.06777 &  0.03366 &  0.03411 \tabularnewline
122 & -0.09696 & -0.008139 & -0.08882 \tabularnewline
123 &  0.08283 & -0.03697 &  0.1198 \tabularnewline
124 & -0.121 & -0.01267 & -0.1083 \tabularnewline
125 & -0.019 &  0.0167 & -0.0357 \tabularnewline
126 &  0.00412 & -0.04261 &  0.04673 \tabularnewline
127 &  0.04019 &  0.05536 & -0.01517 \tabularnewline
128 &  0.04453 &  0.01877 &  0.02576 \tabularnewline
129 & -0.01229 & -0.05173 &  0.03944 \tabularnewline
130 & -0.1079 &  0.01177 & -0.1197 \tabularnewline
131 &  0.09524 &  0.06502 &  0.03022 \tabularnewline
132 & -0.01799 & -0.09332 &  0.07533 \tabularnewline
133 &  0.02272 &  0.01819 &  0.004531 \tabularnewline
134 &  0.01892 &  0.09566 & -0.07674 \tabularnewline
135 & -0.05041 & -0.07842 &  0.02801 \tabularnewline
136 & -0.00904 & -0.01518 &  0.006144 \tabularnewline
137 &  0.1053 &  0.08932 &  0.01601 \tabularnewline
138 & -0.04539 & -0.1076 &  0.06219 \tabularnewline
139 & -0.05859 &  0.04869 & -0.1073 \tabularnewline
140 &  0.1197 &  0.01694 &  0.1027 \tabularnewline
141 & -0.03927 & -0.02192 & -0.01735 \tabularnewline
142 &  0.05942 &  0.115 & -0.05561 \tabularnewline
143 & -0.0582 & -0.06022 &  0.002016 \tabularnewline
144 & -0.0111 &  0.02098 & -0.03208 \tabularnewline
145 & -0.03941 & -0.006123 & -0.03329 \tabularnewline
146 &  0.05546 &  0.01167 &  0.04379 \tabularnewline
147 & -0.0247 & -0.04438 &  0.01968 \tabularnewline
148 &  0.00854 &  0.03998 & -0.03144 \tabularnewline
149 & -0.03053 & -0.01433 & -0.0162 \tabularnewline
150 & -0.1897 & -0.02609 & -0.1636 \tabularnewline
151 &  0.1782 &  0.05838 &  0.1198 \tabularnewline
152 & -0.123 & -0.1267 &  0.003678 \tabularnewline
153 & -0.04835 &  0.02346 & -0.07181 \tabularnewline
154 &  0.1794 &  0.03921 &  0.1401 \tabularnewline
155 & -0.1089 & -0.01716 & -0.0917 \tabularnewline
156 &  0.02587 &  0.003693 &  0.02218 \tabularnewline
157 &  0.07198 & -0.004917 &  0.0769 \tabularnewline
158 &  0.06143 &  0.05299 &  0.008442 \tabularnewline
159 & -0.2041 & -0.07899 & -0.1251 \tabularnewline
160 &  0.187 &  0.09311 &  0.09389 \tabularnewline
161 & -0.07281 & -0.04692 & -0.02589 \tabularnewline
162 &  0.04451 &  0.07787 & -0.03336 \tabularnewline
163 &  0.1633 & -0.02919 &  0.1925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=5

[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] 0.04201[/C][C] 0.00858[/C][C] 0.03343[/C][/ROW]
[ROW][C]2[/C][C] 0.09663[/C][C] 0.07566[/C][C] 0.02097[/C][/ROW]
[ROW][C]3[/C][C]-0.02709[/C][C]-0.04126[/C][C] 0.01417[/C][/ROW]
[ROW][C]4[/C][C]-0.01014[/C][C] 0.05106[/C][C]-0.06121[/C][/ROW]
[ROW][C]5[/C][C]-0.03067[/C][C]-0.02437[/C][C]-0.006296[/C][/ROW]
[ROW][C]6[/C][C] 0.02648[/C][C]-0.02609[/C][C] 0.05257[/C][/ROW]
[ROW][C]7[/C][C] 0.1887[/C][C] 0.112[/C][C] 0.07676[/C][/ROW]
[ROW][C]8[/C][C]-0.2293[/C][C]-0.1227[/C][C]-0.1067[/C][/ROW]
[ROW][C]9[/C][C] 0.03937[/C][C] 0.05976[/C][C]-0.02039[/C][/ROW]
[ROW][C]10[/C][C] 0.06201[/C][C] 0.007582[/C][C] 0.05443[/C][/ROW]
[ROW][C]11[/C][C]-0.1109[/C][C]-0.04346[/C][C]-0.0674[/C][/ROW]
[ROW][C]12[/C][C] 0.04905[/C][C] 0.07861[/C][C]-0.02956[/C][/ROW]
[ROW][C]13[/C][C] 0.03896[/C][C]-0.08091[/C][C] 0.1199[/C][/ROW]
[ROW][C]14[/C][C]-0.09372[/C][C]-0.001738[/C][C]-0.09198[/C][/ROW]
[ROW][C]15[/C][C]-0.03576[/C][C]-0.001875[/C][C]-0.03388[/C][/ROW]
[ROW][C]16[/C][C] 0.06383[/C][C] 0.03802[/C][C] 0.02581[/C][/ROW]
[ROW][C]17[/C][C] 0.00846[/C][C]-0.02818[/C][C] 0.03664[/C][/ROW]
[ROW][C]18[/C][C]-0.2027[/C][C]-0.08326[/C][C]-0.1195[/C][/ROW]
[ROW][C]19[/C][C] 0.1548[/C][C] 0.07684[/C][C] 0.07799[/C][/ROW]
[ROW][C]20[/C][C]-0.04969[/C][C]-0.01396[/C][C]-0.03573[/C][/ROW]
[ROW][C]21[/C][C] 0.01249[/C][C]-0.02773[/C][C] 0.04022[/C][/ROW]
[ROW][C]22[/C][C] 0.1092[/C][C] 0.04219[/C][C] 0.06705[/C][/ROW]
[ROW][C]23[/C][C] 0.179[/C][C]-0.008302[/C][C] 0.1873[/C][/ROW]
[ROW][C]24[/C][C]-0.1126[/C][C]-0.03007[/C][C]-0.08253[/C][/ROW]
[ROW][C]25[/C][C]-0.1524[/C][C] 0.003833[/C][C]-0.1563[/C][/ROW]
[ROW][C]26[/C][C] 0.08886[/C][C] 0.01801[/C][C] 0.07085[/C][/ROW]
[ROW][C]27[/C][C]-0.0433[/C][C]-0.005874[/C][C]-0.03743[/C][/ROW]
[ROW][C]28[/C][C] 0.1628[/C][C] 0.07035[/C][C] 0.09242[/C][/ROW]
[ROW][C]29[/C][C]-0.1451[/C][C]-0.1074[/C][C]-0.03765[/C][/ROW]
[ROW][C]30[/C][C] 0.15[/C][C] 0.1052[/C][C] 0.04482[/C][/ROW]
[ROW][C]31[/C][C]-0.1612[/C][C]-0.08768[/C][C]-0.07349[/C][/ROW]
[ROW][C]32[/C][C] 0.1426[/C][C] 0.04498[/C][C] 0.09758[/C][/ROW]
[ROW][C]33[/C][C] 0.114[/C][C] 0.06953[/C][C] 0.04446[/C][/ROW]
[ROW][C]34[/C][C]-0.2153[/C][C]-0.0967[/C][C]-0.1186[/C][/ROW]
[ROW][C]35[/C][C]-0.09547[/C][C]-0.09574[/C][C] 0.0002664[/C][/ROW]
[ROW][C]36[/C][C] 0.1237[/C][C] 0.2028[/C][C]-0.07909[/C][/ROW]
[ROW][C]37[/C][C] 0.03431[/C][C]-0.01189[/C][C] 0.0462[/C][/ROW]
[ROW][C]38[/C][C] 0.00406[/C][C]-0.01303[/C][C] 0.01709[/C][/ROW]
[ROW][C]39[/C][C] 0.00905[/C][C] 0.04223[/C][C]-0.03318[/C][/ROW]
[ROW][C]40[/C][C]-0.07414[/C][C]-0.1135[/C][C] 0.03932[/C][/ROW]
[ROW][C]41[/C][C] 0.03037[/C][C] 0.09158[/C][C]-0.06121[/C][/ROW]
[ROW][C]42[/C][C] 0.00066[/C][C] 0.06378[/C][C]-0.06312[/C][/ROW]
[ROW][C]43[/C][C] 0.1692[/C][C]-0.03883[/C][C] 0.208[/C][/ROW]
[ROW][C]44[/C][C] 0.0991[/C][C]-0.06624[/C][C] 0.1653[/C][/ROW]
[ROW][C]45[/C][C] 0.04749[/C][C] 0.008198[/C][C] 0.03929[/C][/ROW]
[ROW][C]46[/C][C]-0.1779[/C][C] 0.003372[/C][C]-0.1813[/C][/ROW]
[ROW][C]47[/C][C]-0.0427[/C][C]-0.002651[/C][C]-0.04005[/C][/ROW]
[ROW][C]48[/C][C]-0.02756[/C][C]-0.05387[/C][C] 0.02631[/C][/ROW]
[ROW][C]49[/C][C] 0.05015[/C][C] 0.07398[/C][C]-0.02383[/C][/ROW]
[ROW][C]50[/C][C]-0.1653[/C][C]-0.08404[/C][C]-0.0813[/C][/ROW]
[ROW][C]51[/C][C] 0.1991[/C][C] 0.08475[/C][C] 0.1144[/C][/ROW]
[ROW][C]52[/C][C]-0.105[/C][C]-0.07962[/C][C]-0.02537[/C][/ROW]
[ROW][C]53[/C][C]-0.04897[/C][C] 0.02835[/C][C]-0.07732[/C][/ROW]
[ROW][C]54[/C][C] 0.1502[/C][C]-0.02911[/C][C] 0.1793[/C][/ROW]
[ROW][C]55[/C][C]-0.3409[/C][C]-0.05277[/C][C]-0.2881[/C][/ROW]
[ROW][C]56[/C][C]-0.00244[/C][C] 0.01015[/C][C]-0.01259[/C][/ROW]
[ROW][C]57[/C][C]-0.1836[/C][C]-0.1366[/C][C]-0.04698[/C][/ROW]
[ROW][C]58[/C][C] 0.1701[/C][C] 0.0433[/C][C] 0.1268[/C][/ROW]
[ROW][C]59[/C][C]-0.1653[/C][C] 0.04334[/C][C]-0.2086[/C][/ROW]
[ROW][C]60[/C][C] 0.08632[/C][C]-0.05232[/C][C] 0.1386[/C][/ROW]
[ROW][C]61[/C][C]-0.00219[/C][C]-0.0264[/C][C] 0.02421[/C][/ROW]
[ROW][C]62[/C][C] 0.1389[/C][C] 0.09276[/C][C] 0.04613[/C][/ROW]
[ROW][C]63[/C][C]-0.201[/C][C]-0.0938[/C][C]-0.1072[/C][/ROW]
[ROW][C]64[/C][C]-0.03932[/C][C] 0.1214[/C][C]-0.1607[/C][/ROW]
[ROW][C]65[/C][C] 0.1464[/C][C]-0.0238[/C][C] 0.1702[/C][/ROW]
[ROW][C]66[/C][C]-0.1808[/C][C]-0.1241[/C][C]-0.0567[/C][/ROW]
[ROW][C]67[/C][C] 0.06374[/C][C] 0.1981[/C][C]-0.1344[/C][/ROW]
[ROW][C]68[/C][C]-0.03766[/C][C]-0.09573[/C][C] 0.05807[/C][/ROW]
[ROW][C]69[/C][C] 0.05627[/C][C] 0.01484[/C][C] 0.04143[/C][/ROW]
[ROW][C]70[/C][C] 0.1441[/C][C] 0.142[/C][C] 0.002124[/C][/ROW]
[ROW][C]71[/C][C]-0.03995[/C][C] 0.08802[/C][C]-0.128[/C][/ROW]
[ROW][C]72[/C][C] 0.00583[/C][C]-0.0395[/C][C] 0.04533[/C][/ROW]
[ROW][C]73[/C][C] 0.07448[/C][C]-0.001639[/C][C] 0.07612[/C][/ROW]
[ROW][C]74[/C][C] 0.1065[/C][C] 0.03611[/C][C] 0.07043[/C][/ROW]
[ROW][C]75[/C][C]-0.00128[/C][C]-0.02286[/C][C] 0.02158[/C][/ROW]
[ROW][C]76[/C][C] 0.01795[/C][C]-0.02786[/C][C] 0.04581[/C][/ROW]
[ROW][C]77[/C][C] 0.02389[/C][C] 0.05958[/C][C]-0.03569[/C][/ROW]
[ROW][C]78[/C][C] 0.03114[/C][C] 0.03121[/C][C]-7.29e-05[/C][/ROW]
[ROW][C]79[/C][C] 0.1233[/C][C]-9.726e-05[/C][C] 0.1234[/C][/ROW]
[ROW][C]80[/C][C]-0.1393[/C][C] 0.0179[/C][C]-0.1572[/C][/ROW]
[ROW][C]81[/C][C]-0.02817[/C][C] 0.06687[/C][C]-0.09504[/C][/ROW]
[ROW][C]82[/C][C]-0.09274[/C][C]-0.08654[/C][C]-0.006199[/C][/ROW]
[ROW][C]83[/C][C] 0.2001[/C][C] 0.06447[/C][C] 0.1356[/C][/ROW]
[ROW][C]84[/C][C]-0.0566[/C][C] 0.01658[/C][C]-0.07318[/C][/ROW]
[ROW][C]85[/C][C]-0.03331[/C][C]-0.0556[/C][C] 0.02229[/C][/ROW]
[ROW][C]86[/C][C]-0.03239[/C][C]-0.06644[/C][C] 0.03405[/C][/ROW]
[ROW][C]87[/C][C]-0.02213[/C][C]-0.02019[/C][C]-0.001942[/C][/ROW]
[ROW][C]88[/C][C] 0.1997[/C][C] 0.1228[/C][C] 0.0769[/C][/ROW]
[ROW][C]89[/C][C]-0.2912[/C][C]-0.162[/C][C]-0.1293[/C][/ROW]
[ROW][C]90[/C][C] 0.1388[/C][C] 0.03336[/C][C] 0.1054[/C][/ROW]
[ROW][C]91[/C][C]-0.02136[/C][C] 0.02059[/C][C]-0.04195[/C][/ROW]
[ROW][C]92[/C][C] 0.04312[/C][C] 0.07775[/C][C]-0.03463[/C][/ROW]
[ROW][C]93[/C][C]-0.09952[/C][C]-0.004215[/C][C]-0.09531[/C][/ROW]
[ROW][C]94[/C][C] 0.1035[/C][C]-0.05539[/C][C] 0.1589[/C][/ROW]
[ROW][C]95[/C][C] 0.06875[/C][C]-0.003854[/C][C] 0.0726[/C][/ROW]
[ROW][C]96[/C][C] 0.021[/C][C]-0.02822[/C][C] 0.04922[/C][/ROW]
[ROW][C]97[/C][C]-0.1499[/C][C] 0.03103[/C][C]-0.181[/C][/ROW]
[ROW][C]98[/C][C] 0.1231[/C][C]-0.04847[/C][C] 0.1716[/C][/ROW]
[ROW][C]99[/C][C]-0.1655[/C][C] 0.06972[/C][C]-0.2352[/C][/ROW]
[ROW][C]100[/C][C]-0.05579[/C][C]-0.1271[/C][C] 0.07135[/C][/ROW]
[ROW][C]101[/C][C] 0.1839[/C][C] 0.1614[/C][C] 0.02245[/C][/ROW]
[ROW][C]102[/C][C]-0.1156[/C][C]-0.02746[/C][C]-0.08815[/C][/ROW]
[ROW][C]103[/C][C]-0.04306[/C][C]-0.08556[/C][C] 0.0425[/C][/ROW]
[ROW][C]104[/C][C]-0.04973[/C][C]-0.02116[/C][C]-0.02857[/C][/ROW]
[ROW][C]105[/C][C] 0.1353[/C][C] 0.07733[/C][C] 0.05797[/C][/ROW]
[ROW][C]106[/C][C]-0.03979[/C][C]-0.0446[/C][C] 0.004806[/C][/ROW]
[ROW][C]107[/C][C]-0.2226[/C][C]-0.1358[/C][C]-0.08674[/C][/ROW]
[ROW][C]108[/C][C] 0.07872[/C][C] 0.0553[/C][C] 0.02342[/C][/ROW]
[ROW][C]109[/C][C] 0.02752[/C][C] 0.01345[/C][C] 0.01407[/C][/ROW]
[ROW][C]110[/C][C]-0.1269[/C][C]-0.0778[/C][C]-0.04909[/C][/ROW]
[ROW][C]111[/C][C] 0.25[/C][C] 0.1287[/C][C] 0.1213[/C][/ROW]
[ROW][C]112[/C][C] 0.02131[/C][C]-0.0549[/C][C] 0.07621[/C][/ROW]
[ROW][C]113[/C][C]-0.1096[/C][C]-0.01891[/C][C]-0.09066[/C][/ROW]
[ROW][C]114[/C][C] 0.1941[/C][C] 0.03812[/C][C] 0.156[/C][/ROW]
[ROW][C]115[/C][C]-0.1168[/C][C]-0.03814[/C][C]-0.07865[/C][/ROW]
[ROW][C]116[/C][C] 0.05839[/C][C] 0.06562[/C][C]-0.007225[/C][/ROW]
[ROW][C]117[/C][C]-0.03047[/C][C] 0.001408[/C][C]-0.03188[/C][/ROW]
[ROW][C]118[/C][C]-0.05907[/C][C]-0.009226[/C][C]-0.04984[/C][/ROW]
[ROW][C]119[/C][C] 0.06751[/C][C] 0.05878[/C][C] 0.008729[/C][/ROW]
[ROW][C]120[/C][C]-0.00569[/C][C]-0.01763[/C][C] 0.01194[/C][/ROW]
[ROW][C]121[/C][C] 0.06777[/C][C] 0.03366[/C][C] 0.03411[/C][/ROW]
[ROW][C]122[/C][C]-0.09696[/C][C]-0.008139[/C][C]-0.08882[/C][/ROW]
[ROW][C]123[/C][C] 0.08283[/C][C]-0.03697[/C][C] 0.1198[/C][/ROW]
[ROW][C]124[/C][C]-0.121[/C][C]-0.01267[/C][C]-0.1083[/C][/ROW]
[ROW][C]125[/C][C]-0.019[/C][C] 0.0167[/C][C]-0.0357[/C][/ROW]
[ROW][C]126[/C][C] 0.00412[/C][C]-0.04261[/C][C] 0.04673[/C][/ROW]
[ROW][C]127[/C][C] 0.04019[/C][C] 0.05536[/C][C]-0.01517[/C][/ROW]
[ROW][C]128[/C][C] 0.04453[/C][C] 0.01877[/C][C] 0.02576[/C][/ROW]
[ROW][C]129[/C][C]-0.01229[/C][C]-0.05173[/C][C] 0.03944[/C][/ROW]
[ROW][C]130[/C][C]-0.1079[/C][C] 0.01177[/C][C]-0.1197[/C][/ROW]
[ROW][C]131[/C][C] 0.09524[/C][C] 0.06502[/C][C] 0.03022[/C][/ROW]
[ROW][C]132[/C][C]-0.01799[/C][C]-0.09332[/C][C] 0.07533[/C][/ROW]
[ROW][C]133[/C][C] 0.02272[/C][C] 0.01819[/C][C] 0.004531[/C][/ROW]
[ROW][C]134[/C][C] 0.01892[/C][C] 0.09566[/C][C]-0.07674[/C][/ROW]
[ROW][C]135[/C][C]-0.05041[/C][C]-0.07842[/C][C] 0.02801[/C][/ROW]
[ROW][C]136[/C][C]-0.00904[/C][C]-0.01518[/C][C] 0.006144[/C][/ROW]
[ROW][C]137[/C][C] 0.1053[/C][C] 0.08932[/C][C] 0.01601[/C][/ROW]
[ROW][C]138[/C][C]-0.04539[/C][C]-0.1076[/C][C] 0.06219[/C][/ROW]
[ROW][C]139[/C][C]-0.05859[/C][C] 0.04869[/C][C]-0.1073[/C][/ROW]
[ROW][C]140[/C][C] 0.1197[/C][C] 0.01694[/C][C] 0.1027[/C][/ROW]
[ROW][C]141[/C][C]-0.03927[/C][C]-0.02192[/C][C]-0.01735[/C][/ROW]
[ROW][C]142[/C][C] 0.05942[/C][C] 0.115[/C][C]-0.05561[/C][/ROW]
[ROW][C]143[/C][C]-0.0582[/C][C]-0.06022[/C][C] 0.002016[/C][/ROW]
[ROW][C]144[/C][C]-0.0111[/C][C] 0.02098[/C][C]-0.03208[/C][/ROW]
[ROW][C]145[/C][C]-0.03941[/C][C]-0.006123[/C][C]-0.03329[/C][/ROW]
[ROW][C]146[/C][C] 0.05546[/C][C] 0.01167[/C][C] 0.04379[/C][/ROW]
[ROW][C]147[/C][C]-0.0247[/C][C]-0.04438[/C][C] 0.01968[/C][/ROW]
[ROW][C]148[/C][C] 0.00854[/C][C] 0.03998[/C][C]-0.03144[/C][/ROW]
[ROW][C]149[/C][C]-0.03053[/C][C]-0.01433[/C][C]-0.0162[/C][/ROW]
[ROW][C]150[/C][C]-0.1897[/C][C]-0.02609[/C][C]-0.1636[/C][/ROW]
[ROW][C]151[/C][C] 0.1782[/C][C] 0.05838[/C][C] 0.1198[/C][/ROW]
[ROW][C]152[/C][C]-0.123[/C][C]-0.1267[/C][C] 0.003678[/C][/ROW]
[ROW][C]153[/C][C]-0.04835[/C][C] 0.02346[/C][C]-0.07181[/C][/ROW]
[ROW][C]154[/C][C] 0.1794[/C][C] 0.03921[/C][C] 0.1401[/C][/ROW]
[ROW][C]155[/C][C]-0.1089[/C][C]-0.01716[/C][C]-0.0917[/C][/ROW]
[ROW][C]156[/C][C] 0.02587[/C][C] 0.003693[/C][C] 0.02218[/C][/ROW]
[ROW][C]157[/C][C] 0.07198[/C][C]-0.004917[/C][C] 0.0769[/C][/ROW]
[ROW][C]158[/C][C] 0.06143[/C][C] 0.05299[/C][C] 0.008442[/C][/ROW]
[ROW][C]159[/C][C]-0.2041[/C][C]-0.07899[/C][C]-0.1251[/C][/ROW]
[ROW][C]160[/C][C] 0.187[/C][C] 0.09311[/C][C] 0.09389[/C][/ROW]
[ROW][C]161[/C][C]-0.07281[/C][C]-0.04692[/C][C]-0.02589[/C][/ROW]
[ROW][C]162[/C][C] 0.04451[/C][C] 0.07787[/C][C]-0.03336[/C][/ROW]
[ROW][C]163[/C][C] 0.1633[/C][C]-0.02919[/C][C] 0.1925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=5

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	0.04201	0.00858	0.03343
2	0.09663	0.07566	0.02097
3	-0.02709	-0.04126	0.01417
4	-0.01014	0.05106	-0.06121
5	-0.03067	-0.02437	-0.006296
6	0.02648	-0.02609	0.05257
7	0.1887	0.112	0.07676
8	-0.2293	-0.1227	-0.1067
9	0.03937	0.05976	-0.02039
10	0.06201	0.007582	0.05443
11	-0.1109	-0.04346	-0.0674
12	0.04905	0.07861	-0.02956
13	0.03896	-0.08091	0.1199
14	-0.09372	-0.001738	-0.09198
15	-0.03576	-0.001875	-0.03388
16	0.06383	0.03802	0.02581
17	0.00846	-0.02818	0.03664
18	-0.2027	-0.08326	-0.1195
19	0.1548	0.07684	0.07799
20	-0.04969	-0.01396	-0.03573
21	0.01249	-0.02773	0.04022
22	0.1092	0.04219	0.06705
23	0.179	-0.008302	0.1873
24	-0.1126	-0.03007	-0.08253
25	-0.1524	0.003833	-0.1563
26	0.08886	0.01801	0.07085
27	-0.0433	-0.005874	-0.03743
28	0.1628	0.07035	0.09242
29	-0.1451	-0.1074	-0.03765
30	0.15	0.1052	0.04482
31	-0.1612	-0.08768	-0.07349
32	0.1426	0.04498	0.09758
33	0.114	0.06953	0.04446
34	-0.2153	-0.0967	-0.1186
35	-0.09547	-0.09574	0.0002664
36	0.1237	0.2028	-0.07909
37	0.03431	-0.01189	0.0462
38	0.00406	-0.01303	0.01709
39	0.00905	0.04223	-0.03318
40	-0.07414	-0.1135	0.03932
41	0.03037	0.09158	-0.06121
42	0.00066	0.06378	-0.06312
43	0.1692	-0.03883	0.208
44	0.0991	-0.06624	0.1653
45	0.04749	0.008198	0.03929
46	-0.1779	0.003372	-0.1813
47	-0.0427	-0.002651	-0.04005
48	-0.02756	-0.05387	0.02631
49	0.05015	0.07398	-0.02383
50	-0.1653	-0.08404	-0.0813
51	0.1991	0.08475	0.1144
52	-0.105	-0.07962	-0.02537
53	-0.04897	0.02835	-0.07732
54	0.1502	-0.02911	0.1793
55	-0.3409	-0.05277	-0.2881
56	-0.00244	0.01015	-0.01259
57	-0.1836	-0.1366	-0.04698
58	0.1701	0.0433	0.1268
59	-0.1653	0.04334	-0.2086
60	0.08632	-0.05232	0.1386
61	-0.00219	-0.0264	0.02421
62	0.1389	0.09276	0.04613
63	-0.201	-0.0938	-0.1072
64	-0.03932	0.1214	-0.1607
65	0.1464	-0.0238	0.1702
66	-0.1808	-0.1241	-0.0567
67	0.06374	0.1981	-0.1344
68	-0.03766	-0.09573	0.05807
69	0.05627	0.01484	0.04143
70	0.1441	0.142	0.002124
71	-0.03995	0.08802	-0.128
72	0.00583	-0.0395	0.04533
73	0.07448	-0.001639	0.07612
74	0.1065	0.03611	0.07043
75	-0.00128	-0.02286	0.02158
76	0.01795	-0.02786	0.04581
77	0.02389	0.05958	-0.03569
78	0.03114	0.03121	-7.29e-05
79	0.1233	-9.726e-05	0.1234
80	-0.1393	0.0179	-0.1572
81	-0.02817	0.06687	-0.09504
82	-0.09274	-0.08654	-0.006199
83	0.2001	0.06447	0.1356
84	-0.0566	0.01658	-0.07318
85	-0.03331	-0.0556	0.02229
86	-0.03239	-0.06644	0.03405
87	-0.02213	-0.02019	-0.001942
88	0.1997	0.1228	0.0769
89	-0.2912	-0.162	-0.1293
90	0.1388	0.03336	0.1054
91	-0.02136	0.02059	-0.04195
92	0.04312	0.07775	-0.03463
93	-0.09952	-0.004215	-0.09531
94	0.1035	-0.05539	0.1589
95	0.06875	-0.003854	0.0726
96	0.021	-0.02822	0.04922
97	-0.1499	0.03103	-0.181
98	0.1231	-0.04847	0.1716
99	-0.1655	0.06972	-0.2352
100	-0.05579	-0.1271	0.07135
101	0.1839	0.1614	0.02245
102	-0.1156	-0.02746	-0.08815
103	-0.04306	-0.08556	0.0425
104	-0.04973	-0.02116	-0.02857
105	0.1353	0.07733	0.05797
106	-0.03979	-0.0446	0.004806
107	-0.2226	-0.1358	-0.08674
108	0.07872	0.0553	0.02342
109	0.02752	0.01345	0.01407
110	-0.1269	-0.0778	-0.04909
111	0.25	0.1287	0.1213
112	0.02131	-0.0549	0.07621
113	-0.1096	-0.01891	-0.09066
114	0.1941	0.03812	0.156
115	-0.1168	-0.03814	-0.07865
116	0.05839	0.06562	-0.007225
117	-0.03047	0.001408	-0.03188
118	-0.05907	-0.009226	-0.04984
119	0.06751	0.05878	0.008729
120	-0.00569	-0.01763	0.01194
121	0.06777	0.03366	0.03411
122	-0.09696	-0.008139	-0.08882
123	0.08283	-0.03697	0.1198
124	-0.121	-0.01267	-0.1083
125	-0.019	0.0167	-0.0357
126	0.00412	-0.04261	0.04673
127	0.04019	0.05536	-0.01517
128	0.04453	0.01877	0.02576
129	-0.01229	-0.05173	0.03944
130	-0.1079	0.01177	-0.1197
131	0.09524	0.06502	0.03022
132	-0.01799	-0.09332	0.07533
133	0.02272	0.01819	0.004531
134	0.01892	0.09566	-0.07674
135	-0.05041	-0.07842	0.02801
136	-0.00904	-0.01518	0.006144
137	0.1053	0.08932	0.01601
138	-0.04539	-0.1076	0.06219
139	-0.05859	0.04869	-0.1073
140	0.1197	0.01694	0.1027
141	-0.03927	-0.02192	-0.01735
142	0.05942	0.115	-0.05561
143	-0.0582	-0.06022	0.002016
144	-0.0111	0.02098	-0.03208
145	-0.03941	-0.006123	-0.03329
146	0.05546	0.01167	0.04379
147	-0.0247	-0.04438	0.01968
148	0.00854	0.03998	-0.03144
149	-0.03053	-0.01433	-0.0162
150	-0.1897	-0.02609	-0.1636
151	0.1782	0.05838	0.1198
152	-0.123	-0.1267	0.003678
153	-0.04835	0.02346	-0.07181
154	0.1794	0.03921	0.1401
155	-0.1089	-0.01716	-0.0917
156	0.02587	0.003693	0.02218
157	0.07198	-0.004917	0.0769
158	0.06143	0.05299	0.008442
159	-0.2041	-0.07899	-0.1251
160	0.187	0.09311	0.09389
161	-0.07281	-0.04692	-0.02589
162	0.04451	0.07787	-0.03336
163	0.1633	-0.02919	0.1925

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.3387	0.6774	0.6613
10	0.2971	0.5943	0.7029
11	0.2317	0.4635	0.7683
12	0.1787	0.3573	0.8213
13	0.252	0.5041	0.748
14	0.2405	0.4811	0.7595
15	0.2	0.4	0.8
16	0.1415	0.283	0.8585
17	0.09594	0.1919	0.9041
18	0.08807	0.1761	0.9119
19	0.07483	0.1497	0.9252
20	0.06862	0.1372	0.9314
21	0.0553	0.1106	0.9447
22	0.04453	0.08906	0.9555
23	0.1502	0.3004	0.8498
24	0.128	0.256	0.872
25	0.2745	0.5491	0.7255
26	0.2471	0.4943	0.7529
27	0.2033	0.4066	0.7967
28	0.1875	0.3749	0.8125
29	0.1467	0.2935	0.8533
30	0.1153	0.2307	0.8847
31	0.08918	0.1784	0.9108
32	0.07383	0.1477	0.9262
33	0.05526	0.1105	0.9447
34	0.05331	0.1066	0.9467
35	0.04345	0.0869	0.9565
36	0.09268	0.1854	0.9073
37	0.07473	0.1495	0.9253
38	0.05588	0.1118	0.9441
39	0.04394	0.08788	0.9561
40	0.04058	0.08117	0.9594
41	0.03914	0.07828	0.9609
42	0.03792	0.07584	0.9621
43	0.08465	0.1693	0.9153
44	0.2426	0.4852	0.7574
45	0.2095	0.4191	0.7905
46	0.3721	0.7443	0.6279
47	0.3286	0.6571	0.6714
48	0.2843	0.5686	0.7157
49	0.2481	0.4961	0.7519
50	0.233	0.4659	0.767
51	0.2482	0.4964	0.7518
52	0.2113	0.4227	0.7887
53	0.207	0.414	0.793
54	0.3459	0.6918	0.6541
55	0.7432	0.5136	0.2568
56	0.7029	0.5942	0.2971
57	0.6688	0.6624	0.3312
58	0.7006	0.5987	0.2994
59	0.8423	0.3154	0.1577
60	0.8746	0.2508	0.1254
61	0.8504	0.2992	0.1496
62	0.828	0.3441	0.172
63	0.8356	0.3289	0.1644
64	0.8901	0.2198	0.1099
65	0.9306	0.1387	0.06937
66	0.9216	0.1569	0.07845
67	0.9459	0.1083	0.05415
68	0.9376	0.1247	0.06237
69	0.9256	0.1488	0.07441
70	0.9096	0.1808	0.09038
71	0.9283	0.1435	0.07173
72	0.917	0.1659	0.08296
73	0.9115	0.1771	0.08855
74	0.9041	0.1917	0.09586
75	0.8849	0.2301	0.1151
76	0.8776	0.2447	0.1224
77	0.879	0.2419	0.121
78	0.8557	0.2885	0.1443
79	0.8903	0.2193	0.1097
80	0.9534	0.09314	0.04657
81	0.9538	0.09231	0.04616
82	0.9415	0.117	0.05849
83	0.9526	0.09488	0.04744
84	0.9471	0.1057	0.05285
85	0.9341	0.1319	0.06593
86	0.9194	0.1612	0.0806
87	0.9021	0.1958	0.09789
88	0.8928	0.2144	0.1072
89	0.9068	0.1864	0.0932
90	0.9121	0.1759	0.08793
91	0.8959	0.2082	0.1041
92	0.8757	0.2486	0.1243
93	0.875	0.2499	0.125
94	0.9156	0.1688	0.08439
95	0.906	0.188	0.09399
96	0.8942	0.2117	0.1058
97	0.9536	0.09281	0.04641
98	0.977	0.04602	0.02301
99	0.998	0.004023	0.002012
100	0.9978	0.004348	0.002174
101	0.9968	0.006323	0.003161
102	0.9977	0.004577	0.002288
103	0.997	0.006071	0.003036
104	0.9957	0.008524	0.004262
105	0.9947	0.01055	0.005277
106	0.9927	0.01459	0.007297
107	0.993	0.01398	0.006988
108	0.9902	0.01955	0.009773
109	0.9894	0.02121	0.0106
110	0.9867	0.02656	0.01328
111	0.9913	0.01741	0.008703
112	0.9902	0.01966	0.009831
113	0.99	0.02008	0.01004
114	0.9968	0.00644	0.00322
115	0.9964	0.007184	0.003592
116	0.9946	0.01075	0.005376
117	0.9941	0.0117	0.005851
118	0.9923	0.01544	0.007722
119	0.9942	0.01155	0.005776
120	0.9917	0.01662	0.008308
121	0.9881	0.02377	0.01189
122	0.986	0.02799	0.014
123	0.9829	0.03413	0.01707
124	0.9839	0.03228	0.01614
125	0.9804	0.03926	0.01963
126	0.9725	0.05501	0.02751
127	0.9641	0.07176	0.03588
128	0.9505	0.09909	0.04955
129	0.9347	0.1307	0.06535
130	0.9363	0.1273	0.06367
131	0.9141	0.1718	0.08592
132	0.9215	0.157	0.07851
133	0.9036	0.1929	0.09643
134	0.8798	0.2403	0.1202
135	0.8569	0.2862	0.1431
136	0.8463	0.3074	0.1537
137	0.8049	0.3903	0.1951
138	0.787	0.4261	0.213
139	0.8864	0.2271	0.1136
140	0.8601	0.2798	0.1399
141	0.8141	0.3718	0.1859
142	0.7902	0.4196	0.2098
143	0.7291	0.5418	0.2709
144	0.693	0.614	0.307
145	0.6361	0.7278	0.3639
146	0.5604	0.8793	0.4396
147	0.5487	0.9026	0.4513
148	0.4896	0.9792	0.5104
149	0.569	0.862	0.431
150	0.5886	0.8227	0.4114
151	0.7356	0.5289	0.2644
152	0.6289	0.7421	0.3711
153	0.6002	0.7997	0.3998
154	0.4402	0.8804	0.5598

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.3387 &  0.6774 &  0.6613 \tabularnewline
10 &  0.2971 &  0.5943 &  0.7029 \tabularnewline
11 &  0.2317 &  0.4635 &  0.7683 \tabularnewline
12 &  0.1787 &  0.3573 &  0.8213 \tabularnewline
13 &  0.252 &  0.5041 &  0.748 \tabularnewline
14 &  0.2405 &  0.4811 &  0.7595 \tabularnewline
15 &  0.2 &  0.4 &  0.8 \tabularnewline
16 &  0.1415 &  0.283 &  0.8585 \tabularnewline
17 &  0.09594 &  0.1919 &  0.9041 \tabularnewline
18 &  0.08807 &  0.1761 &  0.9119 \tabularnewline
19 &  0.07483 &  0.1497 &  0.9252 \tabularnewline
20 &  0.06862 &  0.1372 &  0.9314 \tabularnewline
21 &  0.0553 &  0.1106 &  0.9447 \tabularnewline
22 &  0.04453 &  0.08906 &  0.9555 \tabularnewline
23 &  0.1502 &  0.3004 &  0.8498 \tabularnewline
24 &  0.128 &  0.256 &  0.872 \tabularnewline
25 &  0.2745 &  0.5491 &  0.7255 \tabularnewline
26 &  0.2471 &  0.4943 &  0.7529 \tabularnewline
27 &  0.2033 &  0.4066 &  0.7967 \tabularnewline
28 &  0.1875 &  0.3749 &  0.8125 \tabularnewline
29 &  0.1467 &  0.2935 &  0.8533 \tabularnewline
30 &  0.1153 &  0.2307 &  0.8847 \tabularnewline
31 &  0.08918 &  0.1784 &  0.9108 \tabularnewline
32 &  0.07383 &  0.1477 &  0.9262 \tabularnewline
33 &  0.05526 &  0.1105 &  0.9447 \tabularnewline
34 &  0.05331 &  0.1066 &  0.9467 \tabularnewline
35 &  0.04345 &  0.0869 &  0.9565 \tabularnewline
36 &  0.09268 &  0.1854 &  0.9073 \tabularnewline
37 &  0.07473 &  0.1495 &  0.9253 \tabularnewline
38 &  0.05588 &  0.1118 &  0.9441 \tabularnewline
39 &  0.04394 &  0.08788 &  0.9561 \tabularnewline
40 &  0.04058 &  0.08117 &  0.9594 \tabularnewline
41 &  0.03914 &  0.07828 &  0.9609 \tabularnewline
42 &  0.03792 &  0.07584 &  0.9621 \tabularnewline
43 &  0.08465 &  0.1693 &  0.9153 \tabularnewline
44 &  0.2426 &  0.4852 &  0.7574 \tabularnewline
45 &  0.2095 &  0.4191 &  0.7905 \tabularnewline
46 &  0.3721 &  0.7443 &  0.6279 \tabularnewline
47 &  0.3286 &  0.6571 &  0.6714 \tabularnewline
48 &  0.2843 &  0.5686 &  0.7157 \tabularnewline
49 &  0.2481 &  0.4961 &  0.7519 \tabularnewline
50 &  0.233 &  0.4659 &  0.767 \tabularnewline
51 &  0.2482 &  0.4964 &  0.7518 \tabularnewline
52 &  0.2113 &  0.4227 &  0.7887 \tabularnewline
53 &  0.207 &  0.414 &  0.793 \tabularnewline
54 &  0.3459 &  0.6918 &  0.6541 \tabularnewline
55 &  0.7432 &  0.5136 &  0.2568 \tabularnewline
56 &  0.7029 &  0.5942 &  0.2971 \tabularnewline
57 &  0.6688 &  0.6624 &  0.3312 \tabularnewline
58 &  0.7006 &  0.5987 &  0.2994 \tabularnewline
59 &  0.8423 &  0.3154 &  0.1577 \tabularnewline
60 &  0.8746 &  0.2508 &  0.1254 \tabularnewline
61 &  0.8504 &  0.2992 &  0.1496 \tabularnewline
62 &  0.828 &  0.3441 &  0.172 \tabularnewline
63 &  0.8356 &  0.3289 &  0.1644 \tabularnewline
64 &  0.8901 &  0.2198 &  0.1099 \tabularnewline
65 &  0.9306 &  0.1387 &  0.06937 \tabularnewline
66 &  0.9216 &  0.1569 &  0.07845 \tabularnewline
67 &  0.9459 &  0.1083 &  0.05415 \tabularnewline
68 &  0.9376 &  0.1247 &  0.06237 \tabularnewline
69 &  0.9256 &  0.1488 &  0.07441 \tabularnewline
70 &  0.9096 &  0.1808 &  0.09038 \tabularnewline
71 &  0.9283 &  0.1435 &  0.07173 \tabularnewline
72 &  0.917 &  0.1659 &  0.08296 \tabularnewline
73 &  0.9115 &  0.1771 &  0.08855 \tabularnewline
74 &  0.9041 &  0.1917 &  0.09586 \tabularnewline
75 &  0.8849 &  0.2301 &  0.1151 \tabularnewline
76 &  0.8776 &  0.2447 &  0.1224 \tabularnewline
77 &  0.879 &  0.2419 &  0.121 \tabularnewline
78 &  0.8557 &  0.2885 &  0.1443 \tabularnewline
79 &  0.8903 &  0.2193 &  0.1097 \tabularnewline
80 &  0.9534 &  0.09314 &  0.04657 \tabularnewline
81 &  0.9538 &  0.09231 &  0.04616 \tabularnewline
82 &  0.9415 &  0.117 &  0.05849 \tabularnewline
83 &  0.9526 &  0.09488 &  0.04744 \tabularnewline
84 &  0.9471 &  0.1057 &  0.05285 \tabularnewline
85 &  0.9341 &  0.1319 &  0.06593 \tabularnewline
86 &  0.9194 &  0.1612 &  0.0806 \tabularnewline
87 &  0.9021 &  0.1958 &  0.09789 \tabularnewline
88 &  0.8928 &  0.2144 &  0.1072 \tabularnewline
89 &  0.9068 &  0.1864 &  0.0932 \tabularnewline
90 &  0.9121 &  0.1759 &  0.08793 \tabularnewline
91 &  0.8959 &  0.2082 &  0.1041 \tabularnewline
92 &  0.8757 &  0.2486 &  0.1243 \tabularnewline
93 &  0.875 &  0.2499 &  0.125 \tabularnewline
94 &  0.9156 &  0.1688 &  0.08439 \tabularnewline
95 &  0.906 &  0.188 &  0.09399 \tabularnewline
96 &  0.8942 &  0.2117 &  0.1058 \tabularnewline
97 &  0.9536 &  0.09281 &  0.04641 \tabularnewline
98 &  0.977 &  0.04602 &  0.02301 \tabularnewline
99 &  0.998 &  0.004023 &  0.002012 \tabularnewline
100 &  0.9978 &  0.004348 &  0.002174 \tabularnewline
101 &  0.9968 &  0.006323 &  0.003161 \tabularnewline
102 &  0.9977 &  0.004577 &  0.002288 \tabularnewline
103 &  0.997 &  0.006071 &  0.003036 \tabularnewline
104 &  0.9957 &  0.008524 &  0.004262 \tabularnewline
105 &  0.9947 &  0.01055 &  0.005277 \tabularnewline
106 &  0.9927 &  0.01459 &  0.007297 \tabularnewline
107 &  0.993 &  0.01398 &  0.006988 \tabularnewline
108 &  0.9902 &  0.01955 &  0.009773 \tabularnewline
109 &  0.9894 &  0.02121 &  0.0106 \tabularnewline
110 &  0.9867 &  0.02656 &  0.01328 \tabularnewline
111 &  0.9913 &  0.01741 &  0.008703 \tabularnewline
112 &  0.9902 &  0.01966 &  0.009831 \tabularnewline
113 &  0.99 &  0.02008 &  0.01004 \tabularnewline
114 &  0.9968 &  0.00644 &  0.00322 \tabularnewline
115 &  0.9964 &  0.007184 &  0.003592 \tabularnewline
116 &  0.9946 &  0.01075 &  0.005376 \tabularnewline
117 &  0.9941 &  0.0117 &  0.005851 \tabularnewline
118 &  0.9923 &  0.01544 &  0.007722 \tabularnewline
119 &  0.9942 &  0.01155 &  0.005776 \tabularnewline
120 &  0.9917 &  0.01662 &  0.008308 \tabularnewline
121 &  0.9881 &  0.02377 &  0.01189 \tabularnewline
122 &  0.986 &  0.02799 &  0.014 \tabularnewline
123 &  0.9829 &  0.03413 &  0.01707 \tabularnewline
124 &  0.9839 &  0.03228 &  0.01614 \tabularnewline
125 &  0.9804 &  0.03926 &  0.01963 \tabularnewline
126 &  0.9725 &  0.05501 &  0.02751 \tabularnewline
127 &  0.9641 &  0.07176 &  0.03588 \tabularnewline
128 &  0.9505 &  0.09909 &  0.04955 \tabularnewline
129 &  0.9347 &  0.1307 &  0.06535 \tabularnewline
130 &  0.9363 &  0.1273 &  0.06367 \tabularnewline
131 &  0.9141 &  0.1718 &  0.08592 \tabularnewline
132 &  0.9215 &  0.157 &  0.07851 \tabularnewline
133 &  0.9036 &  0.1929 &  0.09643 \tabularnewline
134 &  0.8798 &  0.2403 &  0.1202 \tabularnewline
135 &  0.8569 &  0.2862 &  0.1431 \tabularnewline
136 &  0.8463 &  0.3074 &  0.1537 \tabularnewline
137 &  0.8049 &  0.3903 &  0.1951 \tabularnewline
138 &  0.787 &  0.4261 &  0.213 \tabularnewline
139 &  0.8864 &  0.2271 &  0.1136 \tabularnewline
140 &  0.8601 &  0.2798 &  0.1399 \tabularnewline
141 &  0.8141 &  0.3718 &  0.1859 \tabularnewline
142 &  0.7902 &  0.4196 &  0.2098 \tabularnewline
143 &  0.7291 &  0.5418 &  0.2709 \tabularnewline
144 &  0.693 &  0.614 &  0.307 \tabularnewline
145 &  0.6361 &  0.7278 &  0.3639 \tabularnewline
146 &  0.5604 &  0.8793 &  0.4396 \tabularnewline
147 &  0.5487 &  0.9026 &  0.4513 \tabularnewline
148 &  0.4896 &  0.9792 &  0.5104 \tabularnewline
149 &  0.569 &  0.862 &  0.431 \tabularnewline
150 &  0.5886 &  0.8227 &  0.4114 \tabularnewline
151 &  0.7356 &  0.5289 &  0.2644 \tabularnewline
152 &  0.6289 &  0.7421 &  0.3711 \tabularnewline
153 &  0.6002 &  0.7997 &  0.3998 \tabularnewline
154 &  0.4402 &  0.8804 &  0.5598 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=6

[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]9[/C][C] 0.3387[/C][C] 0.6774[/C][C] 0.6613[/C][/ROW]
[ROW][C]10[/C][C] 0.2971[/C][C] 0.5943[/C][C] 0.7029[/C][/ROW]
[ROW][C]11[/C][C] 0.2317[/C][C] 0.4635[/C][C] 0.7683[/C][/ROW]
[ROW][C]12[/C][C] 0.1787[/C][C] 0.3573[/C][C] 0.8213[/C][/ROW]
[ROW][C]13[/C][C] 0.252[/C][C] 0.5041[/C][C] 0.748[/C][/ROW]
[ROW][C]14[/C][C] 0.2405[/C][C] 0.4811[/C][C] 0.7595[/C][/ROW]
[ROW][C]15[/C][C] 0.2[/C][C] 0.4[/C][C] 0.8[/C][/ROW]
[ROW][C]16[/C][C] 0.1415[/C][C] 0.283[/C][C] 0.8585[/C][/ROW]
[ROW][C]17[/C][C] 0.09594[/C][C] 0.1919[/C][C] 0.9041[/C][/ROW]
[ROW][C]18[/C][C] 0.08807[/C][C] 0.1761[/C][C] 0.9119[/C][/ROW]
[ROW][C]19[/C][C] 0.07483[/C][C] 0.1497[/C][C] 0.9252[/C][/ROW]
[ROW][C]20[/C][C] 0.06862[/C][C] 0.1372[/C][C] 0.9314[/C][/ROW]
[ROW][C]21[/C][C] 0.0553[/C][C] 0.1106[/C][C] 0.9447[/C][/ROW]
[ROW][C]22[/C][C] 0.04453[/C][C] 0.08906[/C][C] 0.9555[/C][/ROW]
[ROW][C]23[/C][C] 0.1502[/C][C] 0.3004[/C][C] 0.8498[/C][/ROW]
[ROW][C]24[/C][C] 0.128[/C][C] 0.256[/C][C] 0.872[/C][/ROW]
[ROW][C]25[/C][C] 0.2745[/C][C] 0.5491[/C][C] 0.7255[/C][/ROW]
[ROW][C]26[/C][C] 0.2471[/C][C] 0.4943[/C][C] 0.7529[/C][/ROW]
[ROW][C]27[/C][C] 0.2033[/C][C] 0.4066[/C][C] 0.7967[/C][/ROW]
[ROW][C]28[/C][C] 0.1875[/C][C] 0.3749[/C][C] 0.8125[/C][/ROW]
[ROW][C]29[/C][C] 0.1467[/C][C] 0.2935[/C][C] 0.8533[/C][/ROW]
[ROW][C]30[/C][C] 0.1153[/C][C] 0.2307[/C][C] 0.8847[/C][/ROW]
[ROW][C]31[/C][C] 0.08918[/C][C] 0.1784[/C][C] 0.9108[/C][/ROW]
[ROW][C]32[/C][C] 0.07383[/C][C] 0.1477[/C][C] 0.9262[/C][/ROW]
[ROW][C]33[/C][C] 0.05526[/C][C] 0.1105[/C][C] 0.9447[/C][/ROW]
[ROW][C]34[/C][C] 0.05331[/C][C] 0.1066[/C][C] 0.9467[/C][/ROW]
[ROW][C]35[/C][C] 0.04345[/C][C] 0.0869[/C][C] 0.9565[/C][/ROW]
[ROW][C]36[/C][C] 0.09268[/C][C] 0.1854[/C][C] 0.9073[/C][/ROW]
[ROW][C]37[/C][C] 0.07473[/C][C] 0.1495[/C][C] 0.9253[/C][/ROW]
[ROW][C]38[/C][C] 0.05588[/C][C] 0.1118[/C][C] 0.9441[/C][/ROW]
[ROW][C]39[/C][C] 0.04394[/C][C] 0.08788[/C][C] 0.9561[/C][/ROW]
[ROW][C]40[/C][C] 0.04058[/C][C] 0.08117[/C][C] 0.9594[/C][/ROW]
[ROW][C]41[/C][C] 0.03914[/C][C] 0.07828[/C][C] 0.9609[/C][/ROW]
[ROW][C]42[/C][C] 0.03792[/C][C] 0.07584[/C][C] 0.9621[/C][/ROW]
[ROW][C]43[/C][C] 0.08465[/C][C] 0.1693[/C][C] 0.9153[/C][/ROW]
[ROW][C]44[/C][C] 0.2426[/C][C] 0.4852[/C][C] 0.7574[/C][/ROW]
[ROW][C]45[/C][C] 0.2095[/C][C] 0.4191[/C][C] 0.7905[/C][/ROW]
[ROW][C]46[/C][C] 0.3721[/C][C] 0.7443[/C][C] 0.6279[/C][/ROW]
[ROW][C]47[/C][C] 0.3286[/C][C] 0.6571[/C][C] 0.6714[/C][/ROW]
[ROW][C]48[/C][C] 0.2843[/C][C] 0.5686[/C][C] 0.7157[/C][/ROW]
[ROW][C]49[/C][C] 0.2481[/C][C] 0.4961[/C][C] 0.7519[/C][/ROW]
[ROW][C]50[/C][C] 0.233[/C][C] 0.4659[/C][C] 0.767[/C][/ROW]
[ROW][C]51[/C][C] 0.2482[/C][C] 0.4964[/C][C] 0.7518[/C][/ROW]
[ROW][C]52[/C][C] 0.2113[/C][C] 0.4227[/C][C] 0.7887[/C][/ROW]
[ROW][C]53[/C][C] 0.207[/C][C] 0.414[/C][C] 0.793[/C][/ROW]
[ROW][C]54[/C][C] 0.3459[/C][C] 0.6918[/C][C] 0.6541[/C][/ROW]
[ROW][C]55[/C][C] 0.7432[/C][C] 0.5136[/C][C] 0.2568[/C][/ROW]
[ROW][C]56[/C][C] 0.7029[/C][C] 0.5942[/C][C] 0.2971[/C][/ROW]
[ROW][C]57[/C][C] 0.6688[/C][C] 0.6624[/C][C] 0.3312[/C][/ROW]
[ROW][C]58[/C][C] 0.7006[/C][C] 0.5987[/C][C] 0.2994[/C][/ROW]
[ROW][C]59[/C][C] 0.8423[/C][C] 0.3154[/C][C] 0.1577[/C][/ROW]
[ROW][C]60[/C][C] 0.8746[/C][C] 0.2508[/C][C] 0.1254[/C][/ROW]
[ROW][C]61[/C][C] 0.8504[/C][C] 0.2992[/C][C] 0.1496[/C][/ROW]
[ROW][C]62[/C][C] 0.828[/C][C] 0.3441[/C][C] 0.172[/C][/ROW]
[ROW][C]63[/C][C] 0.8356[/C][C] 0.3289[/C][C] 0.1644[/C][/ROW]
[ROW][C]64[/C][C] 0.8901[/C][C] 0.2198[/C][C] 0.1099[/C][/ROW]
[ROW][C]65[/C][C] 0.9306[/C][C] 0.1387[/C][C] 0.06937[/C][/ROW]
[ROW][C]66[/C][C] 0.9216[/C][C] 0.1569[/C][C] 0.07845[/C][/ROW]
[ROW][C]67[/C][C] 0.9459[/C][C] 0.1083[/C][C] 0.05415[/C][/ROW]
[ROW][C]68[/C][C] 0.9376[/C][C] 0.1247[/C][C] 0.06237[/C][/ROW]
[ROW][C]69[/C][C] 0.9256[/C][C] 0.1488[/C][C] 0.07441[/C][/ROW]
[ROW][C]70[/C][C] 0.9096[/C][C] 0.1808[/C][C] 0.09038[/C][/ROW]
[ROW][C]71[/C][C] 0.9283[/C][C] 0.1435[/C][C] 0.07173[/C][/ROW]
[ROW][C]72[/C][C] 0.917[/C][C] 0.1659[/C][C] 0.08296[/C][/ROW]
[ROW][C]73[/C][C] 0.9115[/C][C] 0.1771[/C][C] 0.08855[/C][/ROW]
[ROW][C]74[/C][C] 0.9041[/C][C] 0.1917[/C][C] 0.09586[/C][/ROW]
[ROW][C]75[/C][C] 0.8849[/C][C] 0.2301[/C][C] 0.1151[/C][/ROW]
[ROW][C]76[/C][C] 0.8776[/C][C] 0.2447[/C][C] 0.1224[/C][/ROW]
[ROW][C]77[/C][C] 0.879[/C][C] 0.2419[/C][C] 0.121[/C][/ROW]
[ROW][C]78[/C][C] 0.8557[/C][C] 0.2885[/C][C] 0.1443[/C][/ROW]
[ROW][C]79[/C][C] 0.8903[/C][C] 0.2193[/C][C] 0.1097[/C][/ROW]
[ROW][C]80[/C][C] 0.9534[/C][C] 0.09314[/C][C] 0.04657[/C][/ROW]
[ROW][C]81[/C][C] 0.9538[/C][C] 0.09231[/C][C] 0.04616[/C][/ROW]
[ROW][C]82[/C][C] 0.9415[/C][C] 0.117[/C][C] 0.05849[/C][/ROW]
[ROW][C]83[/C][C] 0.9526[/C][C] 0.09488[/C][C] 0.04744[/C][/ROW]
[ROW][C]84[/C][C] 0.9471[/C][C] 0.1057[/C][C] 0.05285[/C][/ROW]
[ROW][C]85[/C][C] 0.9341[/C][C] 0.1319[/C][C] 0.06593[/C][/ROW]
[ROW][C]86[/C][C] 0.9194[/C][C] 0.1612[/C][C] 0.0806[/C][/ROW]
[ROW][C]87[/C][C] 0.9021[/C][C] 0.1958[/C][C] 0.09789[/C][/ROW]
[ROW][C]88[/C][C] 0.8928[/C][C] 0.2144[/C][C] 0.1072[/C][/ROW]
[ROW][C]89[/C][C] 0.9068[/C][C] 0.1864[/C][C] 0.0932[/C][/ROW]
[ROW][C]90[/C][C] 0.9121[/C][C] 0.1759[/C][C] 0.08793[/C][/ROW]
[ROW][C]91[/C][C] 0.8959[/C][C] 0.2082[/C][C] 0.1041[/C][/ROW]
[ROW][C]92[/C][C] 0.8757[/C][C] 0.2486[/C][C] 0.1243[/C][/ROW]
[ROW][C]93[/C][C] 0.875[/C][C] 0.2499[/C][C] 0.125[/C][/ROW]
[ROW][C]94[/C][C] 0.9156[/C][C] 0.1688[/C][C] 0.08439[/C][/ROW]
[ROW][C]95[/C][C] 0.906[/C][C] 0.188[/C][C] 0.09399[/C][/ROW]
[ROW][C]96[/C][C] 0.8942[/C][C] 0.2117[/C][C] 0.1058[/C][/ROW]
[ROW][C]97[/C][C] 0.9536[/C][C] 0.09281[/C][C] 0.04641[/C][/ROW]
[ROW][C]98[/C][C] 0.977[/C][C] 0.04602[/C][C] 0.02301[/C][/ROW]
[ROW][C]99[/C][C] 0.998[/C][C] 0.004023[/C][C] 0.002012[/C][/ROW]
[ROW][C]100[/C][C] 0.9978[/C][C] 0.004348[/C][C] 0.002174[/C][/ROW]
[ROW][C]101[/C][C] 0.9968[/C][C] 0.006323[/C][C] 0.003161[/C][/ROW]
[ROW][C]102[/C][C] 0.9977[/C][C] 0.004577[/C][C] 0.002288[/C][/ROW]
[ROW][C]103[/C][C] 0.997[/C][C] 0.006071[/C][C] 0.003036[/C][/ROW]
[ROW][C]104[/C][C] 0.9957[/C][C] 0.008524[/C][C] 0.004262[/C][/ROW]
[ROW][C]105[/C][C] 0.9947[/C][C] 0.01055[/C][C] 0.005277[/C][/ROW]
[ROW][C]106[/C][C] 0.9927[/C][C] 0.01459[/C][C] 0.007297[/C][/ROW]
[ROW][C]107[/C][C] 0.993[/C][C] 0.01398[/C][C] 0.006988[/C][/ROW]
[ROW][C]108[/C][C] 0.9902[/C][C] 0.01955[/C][C] 0.009773[/C][/ROW]
[ROW][C]109[/C][C] 0.9894[/C][C] 0.02121[/C][C] 0.0106[/C][/ROW]
[ROW][C]110[/C][C] 0.9867[/C][C] 0.02656[/C][C] 0.01328[/C][/ROW]
[ROW][C]111[/C][C] 0.9913[/C][C] 0.01741[/C][C] 0.008703[/C][/ROW]
[ROW][C]112[/C][C] 0.9902[/C][C] 0.01966[/C][C] 0.009831[/C][/ROW]
[ROW][C]113[/C][C] 0.99[/C][C] 0.02008[/C][C] 0.01004[/C][/ROW]
[ROW][C]114[/C][C] 0.9968[/C][C] 0.00644[/C][C] 0.00322[/C][/ROW]
[ROW][C]115[/C][C] 0.9964[/C][C] 0.007184[/C][C] 0.003592[/C][/ROW]
[ROW][C]116[/C][C] 0.9946[/C][C] 0.01075[/C][C] 0.005376[/C][/ROW]
[ROW][C]117[/C][C] 0.9941[/C][C] 0.0117[/C][C] 0.005851[/C][/ROW]
[ROW][C]118[/C][C] 0.9923[/C][C] 0.01544[/C][C] 0.007722[/C][/ROW]
[ROW][C]119[/C][C] 0.9942[/C][C] 0.01155[/C][C] 0.005776[/C][/ROW]
[ROW][C]120[/C][C] 0.9917[/C][C] 0.01662[/C][C] 0.008308[/C][/ROW]
[ROW][C]121[/C][C] 0.9881[/C][C] 0.02377[/C][C] 0.01189[/C][/ROW]
[ROW][C]122[/C][C] 0.986[/C][C] 0.02799[/C][C] 0.014[/C][/ROW]
[ROW][C]123[/C][C] 0.9829[/C][C] 0.03413[/C][C] 0.01707[/C][/ROW]
[ROW][C]124[/C][C] 0.9839[/C][C] 0.03228[/C][C] 0.01614[/C][/ROW]
[ROW][C]125[/C][C] 0.9804[/C][C] 0.03926[/C][C] 0.01963[/C][/ROW]
[ROW][C]126[/C][C] 0.9725[/C][C] 0.05501[/C][C] 0.02751[/C][/ROW]
[ROW][C]127[/C][C] 0.9641[/C][C] 0.07176[/C][C] 0.03588[/C][/ROW]
[ROW][C]128[/C][C] 0.9505[/C][C] 0.09909[/C][C] 0.04955[/C][/ROW]
[ROW][C]129[/C][C] 0.9347[/C][C] 0.1307[/C][C] 0.06535[/C][/ROW]
[ROW][C]130[/C][C] 0.9363[/C][C] 0.1273[/C][C] 0.06367[/C][/ROW]
[ROW][C]131[/C][C] 0.9141[/C][C] 0.1718[/C][C] 0.08592[/C][/ROW]
[ROW][C]132[/C][C] 0.9215[/C][C] 0.157[/C][C] 0.07851[/C][/ROW]
[ROW][C]133[/C][C] 0.9036[/C][C] 0.1929[/C][C] 0.09643[/C][/ROW]
[ROW][C]134[/C][C] 0.8798[/C][C] 0.2403[/C][C] 0.1202[/C][/ROW]
[ROW][C]135[/C][C] 0.8569[/C][C] 0.2862[/C][C] 0.1431[/C][/ROW]
[ROW][C]136[/C][C] 0.8463[/C][C] 0.3074[/C][C] 0.1537[/C][/ROW]
[ROW][C]137[/C][C] 0.8049[/C][C] 0.3903[/C][C] 0.1951[/C][/ROW]
[ROW][C]138[/C][C] 0.787[/C][C] 0.4261[/C][C] 0.213[/C][/ROW]
[ROW][C]139[/C][C] 0.8864[/C][C] 0.2271[/C][C] 0.1136[/C][/ROW]
[ROW][C]140[/C][C] 0.8601[/C][C] 0.2798[/C][C] 0.1399[/C][/ROW]
[ROW][C]141[/C][C] 0.8141[/C][C] 0.3718[/C][C] 0.1859[/C][/ROW]
[ROW][C]142[/C][C] 0.7902[/C][C] 0.4196[/C][C] 0.2098[/C][/ROW]
[ROW][C]143[/C][C] 0.7291[/C][C] 0.5418[/C][C] 0.2709[/C][/ROW]
[ROW][C]144[/C][C] 0.693[/C][C] 0.614[/C][C] 0.307[/C][/ROW]
[ROW][C]145[/C][C] 0.6361[/C][C] 0.7278[/C][C] 0.3639[/C][/ROW]
[ROW][C]146[/C][C] 0.5604[/C][C] 0.8793[/C][C] 0.4396[/C][/ROW]
[ROW][C]147[/C][C] 0.5487[/C][C] 0.9026[/C][C] 0.4513[/C][/ROW]
[ROW][C]148[/C][C] 0.4896[/C][C] 0.9792[/C][C] 0.5104[/C][/ROW]
[ROW][C]149[/C][C] 0.569[/C][C] 0.862[/C][C] 0.431[/C][/ROW]
[ROW][C]150[/C][C] 0.5886[/C][C] 0.8227[/C][C] 0.4114[/C][/ROW]
[ROW][C]151[/C][C] 0.7356[/C][C] 0.5289[/C][C] 0.2644[/C][/ROW]
[ROW][C]152[/C][C] 0.6289[/C][C] 0.7421[/C][C] 0.3711[/C][/ROW]
[ROW][C]153[/C][C] 0.6002[/C][C] 0.7997[/C][C] 0.3998[/C][/ROW]
[ROW][C]154[/C][C] 0.4402[/C][C] 0.8804[/C][C] 0.5598[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=6

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
9	0.3387	0.6774	0.6613
10	0.2971	0.5943	0.7029
11	0.2317	0.4635	0.7683
12	0.1787	0.3573	0.8213
13	0.252	0.5041	0.748
14	0.2405	0.4811	0.7595
15	0.2	0.4	0.8
16	0.1415	0.283	0.8585
17	0.09594	0.1919	0.9041
18	0.08807	0.1761	0.9119
19	0.07483	0.1497	0.9252
20	0.06862	0.1372	0.9314
21	0.0553	0.1106	0.9447
22	0.04453	0.08906	0.9555
23	0.1502	0.3004	0.8498
24	0.128	0.256	0.872
25	0.2745	0.5491	0.7255
26	0.2471	0.4943	0.7529
27	0.2033	0.4066	0.7967
28	0.1875	0.3749	0.8125
29	0.1467	0.2935	0.8533
30	0.1153	0.2307	0.8847
31	0.08918	0.1784	0.9108
32	0.07383	0.1477	0.9262
33	0.05526	0.1105	0.9447
34	0.05331	0.1066	0.9467
35	0.04345	0.0869	0.9565
36	0.09268	0.1854	0.9073
37	0.07473	0.1495	0.9253
38	0.05588	0.1118	0.9441
39	0.04394	0.08788	0.9561
40	0.04058	0.08117	0.9594
41	0.03914	0.07828	0.9609
42	0.03792	0.07584	0.9621
43	0.08465	0.1693	0.9153
44	0.2426	0.4852	0.7574
45	0.2095	0.4191	0.7905
46	0.3721	0.7443	0.6279
47	0.3286	0.6571	0.6714
48	0.2843	0.5686	0.7157
49	0.2481	0.4961	0.7519
50	0.233	0.4659	0.767
51	0.2482	0.4964	0.7518
52	0.2113	0.4227	0.7887
53	0.207	0.414	0.793
54	0.3459	0.6918	0.6541
55	0.7432	0.5136	0.2568
56	0.7029	0.5942	0.2971
57	0.6688	0.6624	0.3312
58	0.7006	0.5987	0.2994
59	0.8423	0.3154	0.1577
60	0.8746	0.2508	0.1254
61	0.8504	0.2992	0.1496
62	0.828	0.3441	0.172
63	0.8356	0.3289	0.1644
64	0.8901	0.2198	0.1099
65	0.9306	0.1387	0.06937
66	0.9216	0.1569	0.07845
67	0.9459	0.1083	0.05415
68	0.9376	0.1247	0.06237
69	0.9256	0.1488	0.07441
70	0.9096	0.1808	0.09038
71	0.9283	0.1435	0.07173
72	0.917	0.1659	0.08296
73	0.9115	0.1771	0.08855
74	0.9041	0.1917	0.09586
75	0.8849	0.2301	0.1151
76	0.8776	0.2447	0.1224
77	0.879	0.2419	0.121
78	0.8557	0.2885	0.1443
79	0.8903	0.2193	0.1097
80	0.9534	0.09314	0.04657
81	0.9538	0.09231	0.04616
82	0.9415	0.117	0.05849
83	0.9526	0.09488	0.04744
84	0.9471	0.1057	0.05285
85	0.9341	0.1319	0.06593
86	0.9194	0.1612	0.0806
87	0.9021	0.1958	0.09789
88	0.8928	0.2144	0.1072
89	0.9068	0.1864	0.0932
90	0.9121	0.1759	0.08793
91	0.8959	0.2082	0.1041
92	0.8757	0.2486	0.1243
93	0.875	0.2499	0.125
94	0.9156	0.1688	0.08439
95	0.906	0.188	0.09399
96	0.8942	0.2117	0.1058
97	0.9536	0.09281	0.04641
98	0.977	0.04602	0.02301
99	0.998	0.004023	0.002012
100	0.9978	0.004348	0.002174
101	0.9968	0.006323	0.003161
102	0.9977	0.004577	0.002288
103	0.997	0.006071	0.003036
104	0.9957	0.008524	0.004262
105	0.9947	0.01055	0.005277
106	0.9927	0.01459	0.007297
107	0.993	0.01398	0.006988
108	0.9902	0.01955	0.009773
109	0.9894	0.02121	0.0106
110	0.9867	0.02656	0.01328
111	0.9913	0.01741	0.008703
112	0.9902	0.01966	0.009831
113	0.99	0.02008	0.01004
114	0.9968	0.00644	0.00322
115	0.9964	0.007184	0.003592
116	0.9946	0.01075	0.005376
117	0.9941	0.0117	0.005851
118	0.9923	0.01544	0.007722
119	0.9942	0.01155	0.005776
120	0.9917	0.01662	0.008308
121	0.9881	0.02377	0.01189
122	0.986	0.02799	0.014
123	0.9829	0.03413	0.01707
124	0.9839	0.03228	0.01614
125	0.9804	0.03926	0.01963
126	0.9725	0.05501	0.02751
127	0.9641	0.07176	0.03588
128	0.9505	0.09909	0.04955
129	0.9347	0.1307	0.06535
130	0.9363	0.1273	0.06367
131	0.9141	0.1718	0.08592
132	0.9215	0.157	0.07851
133	0.9036	0.1929	0.09643
134	0.8798	0.2403	0.1202
135	0.8569	0.2862	0.1431
136	0.8463	0.3074	0.1537
137	0.8049	0.3903	0.1951
138	0.787	0.4261	0.213
139	0.8864	0.2271	0.1136
140	0.8601	0.2798	0.1399
141	0.8141	0.3718	0.1859
142	0.7902	0.4196	0.2098
143	0.7291	0.5418	0.2709
144	0.693	0.614	0.307
145	0.6361	0.7278	0.3639
146	0.5604	0.8793	0.4396
147	0.5487	0.9026	0.4513
148	0.4896	0.9792	0.5104
149	0.569	0.862	0.431
150	0.5886	0.8227	0.4114
151	0.7356	0.5289	0.2644
152	0.6289	0.7421	0.3711
153	0.6002	0.7997	0.3998
154	0.4402	0.8804	0.5598

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.05479	NOK
5% type I error level	28	0.191781	NOK
10% type I error level	41	0.280822	NOK

\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 & 8 &  0.05479 & NOK \tabularnewline
5% type I error level & 28 & 0.191781 & NOK \tabularnewline
10% type I error level & 41 & 0.280822 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=7

[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]8[/C][C] 0.05479[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.191781[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.280822[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=7

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	8	0.05479	NOK
5% type I error level	28	0.191781	NOK
10% type I error level	41	0.280822	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7094, df1 = 2, df2 = 155, p-value = 0.1844
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4616, df1 = 10, df2 = 147, p-value = 0.1594
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3777, df1 = 2, df2 = 155, p-value = 0.2552

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7094, df1 = 2, df2 = 155, p-value = 0.1844
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4616, df1 = 10, df2 = 147, p-value = 0.1594
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3777, df1 = 2, df2 = 155, p-value = 0.2552
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7094, df1 = 2, df2 = 155, p-value = 0.1844
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4616, df1 = 10, df2 = 147, p-value = 0.1594
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3777, df1 = 2, df2 = 155, p-value = 0.2552
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7094, df1 = 2, df2 = 155, p-value = 0.1844
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4616, df1 = 10, df2 = 147, p-value = 0.1594
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3777, df1 = 2, df2 = 155, p-value = 0.2552

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)b` `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 1.116608 1.091584 1.216434 `(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 1.234483 1.173841

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.116608             1.091584             1.216434 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.234483             1.173841 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309619&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.116608             1.091584             1.216434 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.234483             1.173841 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309619&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309619&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)b` `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 1.116608 1.091584 1.216434 `(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 1.234483 1.173841

Figure 1

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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 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code