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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 18 Dec 2017 11:19:29 +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/18/t1513592681lyfyg0tcv0nr3i1.htm/, Retrieved Tue, 14 May 2024 18:07:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310117, Retrieved Tue, 14 May 2024 18:07:03 +0000

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No (this computation is public)

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Estimated Impact

104

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-       [Multiple Regression] [] [2017-12-18 10:19:29] [867b6df3e80c046baffd373216517d1f] [Current]

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0.315446886	58.5	55.5	87.1
0.304235454	59.8	63	105
0.29532447	64.6	77.2	120.3
0.301356837	62.2	71.1	97
0.286238941	68	90.1	109.9
0.29532447	64.3	91.5	111.7
0.296551173	58.9	76.1	74
0.297800267	64.8	87.8	82.8
0.298912166	67.5	81	116.1
0.284277576	76.2	77.2	117.6
0.295476608	73.7	73.8	112.2
0.302359327	70.4	68.9	100
0.303718778	67.7	68.4	95.4
0.30152292	63.7	65.2	102.3
0.293970322	72.4	78.7	118.3
0.296088574	66	77	98.2
0.290628008	70.1	97.6	119
0.292642675	70.4	88.1	112.8
0.292642675	66.6	98.7	73.3
0.295172671	72.6	93.4	89
0.297642881	74	68	111.8
0.287444363	79	87.9	115.4
0.302359327	76.1	75.8	111.2
0.288397301	72.3	66.3	99.9
0.29939424	71.6	68.4	95.2
0.300696447	67.2	71.3	99.8
0.286371797	73.8	77.4	108.5
0.292496755	70.8	87.1	102.7
0.287309339	71.4	88.5	100.7
0.279677061	70.4	85.9	107.9
0.29502121	70.7	92.7	78.5
0.305632122	70.6	88.5	75.3
0.283636586	75.5	80.2	110.4
0.277204484	82.1	81.8	110.5
0.281503092	74.3	70.4	93.4
0.278605716	76.3	82.2	92.7
0.298116124	74.5	72.8	92.2
0.289086426	71.1	69	93.3
0.28389223	73.3	83	95.5
0.280522804	73.8	92.4	100.6
0.287579663	69	92.3	89.3
0.275382701	71.1	100.5	96
0.287040112	71.9	106.9	80
0.30252782	69	99.5	79.1
0.276973896	77.3	85.9	112.4
0.26837848	82.8	92.6	110.2
0.280644533	74	77.4	93.3
0.277204484	77.6	84.1	95.3
0.284148875	72.3	75.3	86.5
0.276629568	70.7	73.8	94.1
0.260213289	81	100.1	108.2
0.260477301	76.4	90.7	91.3
0.275270552	72.3	96.5	84.9
0.262815191	79.5	111.8	105.9
0.279557139	73.3	97.4	81
0.277668167	74.5	100.8	78.8
0.258824525	82.7	93.7	111.7
0.263090856	83.8	82	105.3
0.263925434	81.6	86	98.8
0.267481496	85.5	84.3	100.3
0.278605716	76.7	73.1	84.5
0.28492489	71.8	75.4	94.1
0.262632108	80.2	97.9	102.5
0.27263688	76.8	97.5	96.8
0.268781351	76.1	106	93.4
0.263553096	80.7	112.8	111
0.272960144	71.3	99.5	71.5
0.280280032	80.9	100.8	81.2
0.26373898	85	102.9	117.3
0.273830375	84.5	88.8	104.8
0.270940354	87.7	91.3	116.9
0.281379743	87.7	88.3	105.9
0.281503092	80.2	77.4	96.8
0.285841968	74.4	80.5	101.6
0.275833288	85.8	96.7	116.2
0.282123373	77	93.8	100.3
0.272208429	84.5	105	107.7
0.26327533	83.6	117.1	108.4
0.277551931	77.7	111.1	75.1
0.295935067	85.7	105.8	88.3
0.273285075	87.9	95.7	115.4
0.27041948	93.7	97.1	116.4
0.273068268	92.3	91	109.5
0.283128276	87	90.9	101.8
0.274270029	89.1	83.5	91.9
0.284277576	81.3	82.3	96.5
0.273940002	92.7	101.7	111.5
0.280158987	83.9	108.3	91.7
0.27263688	87.3	114	99
0.266305447	89.1	118.2	112
0.266499902	86.9	103.4	74.4
0.292060879	91.7	106.8	92.8
0.27813523	93	95.4	115.9
0.267185373	105.3	101.8	126.6
0.274380421	101.6	95.6	112
0.290769928	94.2	94.8	106.6
0.264866355	100.5	94	85.8
0.263367777	95.8	82.4	95.6
0.257888133	95.8	95.8	106
0.257635287	102.1	106.7	105.3
0.262540774	96	114.1	100
0.253651475	96.8	103.9	106.4
0.267580521	98.9	117.4	84.5
0.278605716	93.4	105.9	82.9
0.252100041	105.5	101.7	118.3
0.255487971	110.9	98.7	124.8
0.264206189	98.6	91.3	88.5
0.256883144	102.6	102.3	86.7
0.26738263	93.5	80.5	82
0.273068268	90.8	86.7	84.6
0.262176807	99.7	102.6	98.9
0.261455381	97.8	107.3	90.3
0.261187053	91.1	108	86.6
0.248898569	98.1	124.3	103.9
0.253966767	96	117.1	71.7
0.271571185	93.5	103.9	78.7
0.246487624	101.2	104.7	108.5
0.25452264	105.2	95.9	102.9
0.25484266	98.9	94.2	98.7
0.262267594	101.3	102.7	95
0.259601739	92.1	70.3	83.2
0.266694973	90.6	90.2	86.3
0.252407044	105.4	107.3	108.8
0.256800158	98.4	104.6	93.8
0.264771607	92.7	102.7	87.9
0.236758627	101.2	124.5	110.6
0.248322079	93.4	117.8	84.6
0.261455381	98.3	104.2	83.3
0.23847642	104.3	99.9	115.9
0.244777813	107	91.5	112.4
0.242662207	107.7	95.7	111.8
0.251113336	108.9	91.4	121.4
0.243249217	99.6	86.2	96.8
0.249189	96.1	91.5	108.7
0.23629409	109	115.5	124.4
0.235036669	99.5	113.9	97.2
0.23764042	104.6	131.9	117.3
0.238838633	99.9	121.2	105.3
0.244980286	94.1	105.2	94.9
0.250663534	105.3	107.5	101.4
0.235490556	110.4	113.8	130.6
0.248898569	110.5	100.5	110.4
0.240874401	110	104.8	112.3
0.242338729	108.5	103.8	107.8
0.249921555	104.3	93.1	100.9
0.243118238	101.2	106.2	116.7
0.241252706	109.2	117.5	126.5
0.242467896	99.6	109.9	104.7
0.246003658	105.6	123.6	109.6
0.228701728	106.2	131.7	131.5
0.245592088	102.2	111	93.3
0.25540692	107.5	122	97.1
0.243643996	105.8	110.9	122.6
0.234029114	120.5	108	119
0.233312946	113.2	103.6	117.5
0.244643234	104.3	107.3	104.1
0.261815017	107.7	94.4	94.1
0.258567684	99.2	85.2	103.5
0.255487971	105.1	113.2	111.3
0.248753903	104.3	111.7	110.7
0.248826191	106.1	124.3	107.7
0.247256557	100.8	124	108.5
0.248322079	106.7	133.4	85.4
0.268781351	101.6	112.6	83.2
0.247115977	104.4	115.8	105.4
0.242145536	114.8	112.3	111.8
0.261276363	105.4	103.6	104
0.244108102	104	111.4	102.1
0.258996369	102	95.1	92
0.244845223	96.5	93.4	102.5
0.247468078	102.3	117.3	109.1
0.242145536	105.3	121.5	98.5
0.250887998	101.9	123.1	95.1
0.244041567	102.2	139.3	101.6
0.241063232	102.8	125.8	84.4
0.260477301	100.4	108.6	78.7
0.239508933	110.7	121	114.7
0.233422508	116.4	111.6	116.4
0.245592088	106	99.7	93.2
0.249116255	109.2	116.7	106
0.251038126	103	90.3	87.9
0.245183486	99.8	90.4	97.5
0.241379381	109.8	117.3	108.2
0.255569134	107.3	121.6	103.5
0.257383513	101.2	114.6	93.1
0.24068621	111.8	133.3	113.7
0.249700808	106.9	127.4	73.2
0.262540774	103.5	115	77.3
0.242792123	113.1	112.6	107.1
0.242467896	119.4	108.3	106.9
0.244441964	113.3	107.6	96.6
0.246905755	115	109	101
0.24068621	104.7	89	87.5
0.234251399	107.2	102.5	101.8
0.242662207	116.6	124.5	110.8
0.242467896	111.3	124.2	96.3
0.246210567	111.4	130.8	97.9
0.258738789	115	138.7	114.8
0.273176579	102.4	127.6	77.4
0.269800118	111.4	130.9	87
0.249847879	113.2	136.9	106.6
0.255976633	112.9	125.2	101.8
0.251188643	114.2	131.3	96.6
0.259950432	115.6	124.1	96.4
0.252948222	107.1	103.2	85.4
0.237168364	102.3	118.1	88.9
0.244912714	117.9	136.5	108.6
0.256800158	105.8	117.8	86.7
0.241761143	114.3	145.1	90.7
0.242403275	113.1	158.8	105.1
0.252100041	102.9	136.9	76.8
0.270627309	112.2	132.7	78.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310117&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)Tobacco[t] = + 0.000168091 -0.000699381`(1-Bs)(1-B)FoodProducts`[t] -7.44274e-05`(1-Bs)(1-B)Beverages`[t] + 5.68784e-05`(1-Bs)(1-B)DurableConsumerGoods`[t] -0.427893`(1-Bs)(1-B)Tobacco(t-1)`[t] -0.318787`(1-Bs)(1-B)Tobacco(t-2)`[t] -0.272921`(1-Bs)(1-B)Tobacco(t-3)`[t] -0.153014`(1-Bs)(1-B)Tobacco(t-4)`[t] -0.152757`(1-Bs)(1-B)Tobacco(t-5)`[t] -0.134434`(1-Bs)(1-B)Tobacco(t-6)`[t] -0.0430944`(1-Bs)(1-B)Tobacco(t-7)`[t] -0.0600414`(1-Bs)(1-B)Tobacco(t-8)`[t] -0.0254391`(1-Bs)(1-B)Tobacco(t-9)`[t] + 0.065049`(1-Bs)(1-B)Tobacco(t-10)`[t] -0.0796225`(1-Bs)(1-B)Tobacco(t-11)`[t] -0.498197`(1-Bs)(1-B)Tobacco(t-12)`[t] -0.254722`(1-Bs)(1-B)Tobacco(t-13)`[t] -0.170283`(1-Bs)(1-B)Tobacco(t-14)`[t] -0.0602664`(1-Bs)(1-B)Tobacco(t-15)`[t] -0.0154076`(1-Bs)(1-B)Tobacco(t-16)`[t] -0.0403963`(1-Bs)(1-B)Tobacco(t-17)`[t] + 0.0279956`(1-Bs)(1-B)Tobacco(t-18)`[t] + 0.00224365`(1-Bs)(1-B)Tobacco(t-19)`[t] + 0.0467372`(1-Bs)(1-B)Tobacco(t-20)`[t] + 0.030444`(1-Bs)(1-B)Tobacco(t-21)`[t] + 0.0714623`(1-Bs)(1-B)Tobacco(t-22)`[t] -0.0111519`(1-Bs)(1-B)Tobacco(t-23)`[t] -0.272198`(1-Bs)(1-B)Tobacco(t-24)`[t] -0.184645`(1-Bs)(1-B)Tobacco(t-25)`[t] -0.221473`(1-Bs)(1-B)Tobacco(t-26)`[t] -0.152424`(1-Bs)(1-B)Tobacco(t-27)`[t] -0.0828273`(1-Bs)(1-B)Tobacco(t-28)`[t] -0.116316`(1-Bs)(1-B)Tobacco(t-29)`[t] -0.157904`(1-Bs)(1-B)Tobacco(t-30)`[t] -0.0296304`(1-Bs)(1-B)Tobacco(t-31)`[t] + 0.103369`(1-Bs)(1-B)Tobacco(t-32)`[t] + 0.100296`(1-Bs)(1-B)Tobacco(t-33)`[t] + 0.0284747`(1-Bs)(1-B)Tobacco(t-34)`[t] + 0.138526`(1-Bs)(1-B)Tobacco(t-35)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Tobacco[t] =  +  0.000168091 -0.000699381`(1-Bs)(1-B)FoodProducts`[t] -7.44274e-05`(1-Bs)(1-B)Beverages`[t] +  5.68784e-05`(1-Bs)(1-B)DurableConsumerGoods`[t] -0.427893`(1-Bs)(1-B)Tobacco(t-1)`[t] -0.318787`(1-Bs)(1-B)Tobacco(t-2)`[t] -0.272921`(1-Bs)(1-B)Tobacco(t-3)`[t] -0.153014`(1-Bs)(1-B)Tobacco(t-4)`[t] -0.152757`(1-Bs)(1-B)Tobacco(t-5)`[t] -0.134434`(1-Bs)(1-B)Tobacco(t-6)`[t] -0.0430944`(1-Bs)(1-B)Tobacco(t-7)`[t] -0.0600414`(1-Bs)(1-B)Tobacco(t-8)`[t] -0.0254391`(1-Bs)(1-B)Tobacco(t-9)`[t] +  0.065049`(1-Bs)(1-B)Tobacco(t-10)`[t] -0.0796225`(1-Bs)(1-B)Tobacco(t-11)`[t] -0.498197`(1-Bs)(1-B)Tobacco(t-12)`[t] -0.254722`(1-Bs)(1-B)Tobacco(t-13)`[t] -0.170283`(1-Bs)(1-B)Tobacco(t-14)`[t] -0.0602664`(1-Bs)(1-B)Tobacco(t-15)`[t] -0.0154076`(1-Bs)(1-B)Tobacco(t-16)`[t] -0.0403963`(1-Bs)(1-B)Tobacco(t-17)`[t] +  0.0279956`(1-Bs)(1-B)Tobacco(t-18)`[t] +  0.00224365`(1-Bs)(1-B)Tobacco(t-19)`[t] +  0.0467372`(1-Bs)(1-B)Tobacco(t-20)`[t] +  0.030444`(1-Bs)(1-B)Tobacco(t-21)`[t] +  0.0714623`(1-Bs)(1-B)Tobacco(t-22)`[t] -0.0111519`(1-Bs)(1-B)Tobacco(t-23)`[t] -0.272198`(1-Bs)(1-B)Tobacco(t-24)`[t] -0.184645`(1-Bs)(1-B)Tobacco(t-25)`[t] -0.221473`(1-Bs)(1-B)Tobacco(t-26)`[t] -0.152424`(1-Bs)(1-B)Tobacco(t-27)`[t] -0.0828273`(1-Bs)(1-B)Tobacco(t-28)`[t] -0.116316`(1-Bs)(1-B)Tobacco(t-29)`[t] -0.157904`(1-Bs)(1-B)Tobacco(t-30)`[t] -0.0296304`(1-Bs)(1-B)Tobacco(t-31)`[t] +  0.103369`(1-Bs)(1-B)Tobacco(t-32)`[t] +  0.100296`(1-Bs)(1-B)Tobacco(t-33)`[t] +  0.0284747`(1-Bs)(1-B)Tobacco(t-34)`[t] +  0.138526`(1-Bs)(1-B)Tobacco(t-35)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Tobacco[t] =  +  0.000168091 -0.000699381`(1-Bs)(1-B)FoodProducts`[t] -7.44274e-05`(1-Bs)(1-B)Beverages`[t] +  5.68784e-05`(1-Bs)(1-B)DurableConsumerGoods`[t] -0.427893`(1-Bs)(1-B)Tobacco(t-1)`[t] -0.318787`(1-Bs)(1-B)Tobacco(t-2)`[t] -0.272921`(1-Bs)(1-B)Tobacco(t-3)`[t] -0.153014`(1-Bs)(1-B)Tobacco(t-4)`[t] -0.152757`(1-Bs)(1-B)Tobacco(t-5)`[t] -0.134434`(1-Bs)(1-B)Tobacco(t-6)`[t] -0.0430944`(1-Bs)(1-B)Tobacco(t-7)`[t] -0.0600414`(1-Bs)(1-B)Tobacco(t-8)`[t] -0.0254391`(1-Bs)(1-B)Tobacco(t-9)`[t] +  0.065049`(1-Bs)(1-B)Tobacco(t-10)`[t] -0.0796225`(1-Bs)(1-B)Tobacco(t-11)`[t] -0.498197`(1-Bs)(1-B)Tobacco(t-12)`[t] -0.254722`(1-Bs)(1-B)Tobacco(t-13)`[t] -0.170283`(1-Bs)(1-B)Tobacco(t-14)`[t] -0.0602664`(1-Bs)(1-B)Tobacco(t-15)`[t] -0.0154076`(1-Bs)(1-B)Tobacco(t-16)`[t] -0.0403963`(1-Bs)(1-B)Tobacco(t-17)`[t] +  0.0279956`(1-Bs)(1-B)Tobacco(t-18)`[t] +  0.00224365`(1-Bs)(1-B)Tobacco(t-19)`[t] +  0.0467372`(1-Bs)(1-B)Tobacco(t-20)`[t] +  0.030444`(1-Bs)(1-B)Tobacco(t-21)`[t] +  0.0714623`(1-Bs)(1-B)Tobacco(t-22)`[t] -0.0111519`(1-Bs)(1-B)Tobacco(t-23)`[t] -0.272198`(1-Bs)(1-B)Tobacco(t-24)`[t] -0.184645`(1-Bs)(1-B)Tobacco(t-25)`[t] -0.221473`(1-Bs)(1-B)Tobacco(t-26)`[t] -0.152424`(1-Bs)(1-B)Tobacco(t-27)`[t] -0.0828273`(1-Bs)(1-B)Tobacco(t-28)`[t] -0.116316`(1-Bs)(1-B)Tobacco(t-29)`[t] -0.157904`(1-Bs)(1-B)Tobacco(t-30)`[t] -0.0296304`(1-Bs)(1-B)Tobacco(t-31)`[t] +  0.103369`(1-Bs)(1-B)Tobacco(t-32)`[t] +  0.100296`(1-Bs)(1-B)Tobacco(t-33)`[t] +  0.0284747`(1-Bs)(1-B)Tobacco(t-34)`[t] +  0.138526`(1-Bs)(1-B)Tobacco(t-35)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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)Tobacco[t] = + 0.000168091 -0.000699381`(1-Bs)(1-B)FoodProducts`[t] -7.44274e-05`(1-Bs)(1-B)Beverages`[t] + 5.68784e-05`(1-Bs)(1-B)DurableConsumerGoods`[t] -0.427893`(1-Bs)(1-B)Tobacco(t-1)`[t] -0.318787`(1-Bs)(1-B)Tobacco(t-2)`[t] -0.272921`(1-Bs)(1-B)Tobacco(t-3)`[t] -0.153014`(1-Bs)(1-B)Tobacco(t-4)`[t] -0.152757`(1-Bs)(1-B)Tobacco(t-5)`[t] -0.134434`(1-Bs)(1-B)Tobacco(t-6)`[t] -0.0430944`(1-Bs)(1-B)Tobacco(t-7)`[t] -0.0600414`(1-Bs)(1-B)Tobacco(t-8)`[t] -0.0254391`(1-Bs)(1-B)Tobacco(t-9)`[t] + 0.065049`(1-Bs)(1-B)Tobacco(t-10)`[t] -0.0796225`(1-Bs)(1-B)Tobacco(t-11)`[t] -0.498197`(1-Bs)(1-B)Tobacco(t-12)`[t] -0.254722`(1-Bs)(1-B)Tobacco(t-13)`[t] -0.170283`(1-Bs)(1-B)Tobacco(t-14)`[t] -0.0602664`(1-Bs)(1-B)Tobacco(t-15)`[t] -0.0154076`(1-Bs)(1-B)Tobacco(t-16)`[t] -0.0403963`(1-Bs)(1-B)Tobacco(t-17)`[t] + 0.0279956`(1-Bs)(1-B)Tobacco(t-18)`[t] + 0.00224365`(1-Bs)(1-B)Tobacco(t-19)`[t] + 0.0467372`(1-Bs)(1-B)Tobacco(t-20)`[t] + 0.030444`(1-Bs)(1-B)Tobacco(t-21)`[t] + 0.0714623`(1-Bs)(1-B)Tobacco(t-22)`[t] -0.0111519`(1-Bs)(1-B)Tobacco(t-23)`[t] -0.272198`(1-Bs)(1-B)Tobacco(t-24)`[t] -0.184645`(1-Bs)(1-B)Tobacco(t-25)`[t] -0.221473`(1-Bs)(1-B)Tobacco(t-26)`[t] -0.152424`(1-Bs)(1-B)Tobacco(t-27)`[t] -0.0828273`(1-Bs)(1-B)Tobacco(t-28)`[t] -0.116316`(1-Bs)(1-B)Tobacco(t-29)`[t] -0.157904`(1-Bs)(1-B)Tobacco(t-30)`[t] -0.0296304`(1-Bs)(1-B)Tobacco(t-31)`[t] + 0.103369`(1-Bs)(1-B)Tobacco(t-32)`[t] + 0.100296`(1-Bs)(1-B)Tobacco(t-33)`[t] + 0.0284747`(1-Bs)(1-B)Tobacco(t-34)`[t] + 0.138526`(1-Bs)(1-B)Tobacco(t-35)`[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)	+0.0001681	0.0005815	+2.8900e-01	0.773	0.3865
`(1-Bs)(1-B)FoodProducts`	-0.0006994	0.0001805	-3.8740e+00	0.0001719	8.593e-05
`(1-Bs)(1-B)Beverages`	-7.443e-05	7.318e-05	-1.0170e+00	0.3111	0.1555
`(1-Bs)(1-B)DurableConsumerGoods`	+5.688e-05	7.272e-05	+7.8220e-01	0.4356	0.2178
`(1-Bs)(1-B)Tobacco(t-1)`	-0.4279	0.08345	-5.1280e+00	1.086e-06	5.428e-07
`(1-Bs)(1-B)Tobacco(t-2)`	-0.3188	0.09265	-3.4410e+00	0.0007888	0.0003944
`(1-Bs)(1-B)Tobacco(t-3)`	-0.2729	0.09699	-2.8140e+00	0.005687	0.002844
`(1-Bs)(1-B)Tobacco(t-4)`	-0.153	0.09936	-1.5400e+00	0.1261	0.06304
`(1-Bs)(1-B)Tobacco(t-5)`	-0.1528	0.1002	-1.5240e+00	0.1299	0.06497
`(1-Bs)(1-B)Tobacco(t-6)`	-0.1344	0.1001	-1.3420e+00	0.1819	0.09093
`(1-Bs)(1-B)Tobacco(t-7)`	-0.04309	0.1008	-4.2730e-01	0.6699	0.3349
`(1-Bs)(1-B)Tobacco(t-8)`	-0.06004	0.1003	-5.9870e-01	0.5505	0.2752
`(1-Bs)(1-B)Tobacco(t-9)`	-0.02544	0.1001	-2.5410e-01	0.7998	0.3999
`(1-Bs)(1-B)Tobacco(t-10)`	+0.06505	0.1008	+6.4520e-01	0.5199	0.26
`(1-Bs)(1-B)Tobacco(t-11)`	-0.07962	0.09708	-8.2020e-01	0.4137	0.2068
`(1-Bs)(1-B)Tobacco(t-12)`	-0.4982	0.09558	-5.2120e+00	7.483e-07	3.742e-07
`(1-Bs)(1-B)Tobacco(t-13)`	-0.2547	0.108	-2.3580e+00	0.01993	0.009963
`(1-Bs)(1-B)Tobacco(t-14)`	-0.1703	0.1113	-1.5300e+00	0.1285	0.06425
`(1-Bs)(1-B)Tobacco(t-15)`	-0.06027	0.1133	-5.3190e-01	0.5957	0.2979
`(1-Bs)(1-B)Tobacco(t-16)`	-0.01541	0.1114	-1.3830e-01	0.8903	0.4451
`(1-Bs)(1-B)Tobacco(t-17)`	-0.0404	0.1112	-3.6310e-01	0.7171	0.3586
`(1-Bs)(1-B)Tobacco(t-18)`	+0.028	0.1118	+2.5050e-01	0.8026	0.4013
`(1-Bs)(1-B)Tobacco(t-19)`	+0.002244	0.1124	+1.9970e-02	0.9841	0.4921
`(1-Bs)(1-B)Tobacco(t-20)`	+0.04674	0.1116	+4.1890e-01	0.676	0.338
`(1-Bs)(1-B)Tobacco(t-21)`	+0.03044	0.1108	+2.7470e-01	0.784	0.392
`(1-Bs)(1-B)Tobacco(t-22)`	+0.07146	0.1109	+6.4440e-01	0.5205	0.2603
`(1-Bs)(1-B)Tobacco(t-23)`	-0.01115	0.1067	-1.0450e-01	0.9169	0.4585
`(1-Bs)(1-B)Tobacco(t-24)`	-0.2722	0.1005	-2.7100e+00	0.007682	0.003841
`(1-Bs)(1-B)Tobacco(t-25)`	-0.1847	0.1043	-1.7710e+00	0.07899	0.03949
`(1-Bs)(1-B)Tobacco(t-26)`	-0.2215	0.1061	-2.0870e+00	0.03889	0.01944
`(1-Bs)(1-B)Tobacco(t-27)`	-0.1524	0.1064	-1.4320e+00	0.1545	0.07726
`(1-Bs)(1-B)Tobacco(t-28)`	-0.08283	0.1072	-7.7270e-01	0.4411	0.2206
`(1-Bs)(1-B)Tobacco(t-29)`	-0.1163	0.1094	-1.0630e+00	0.2898	0.1449
`(1-Bs)(1-B)Tobacco(t-30)`	-0.1579	0.1096	-1.4400e+00	0.1524	0.07618
`(1-Bs)(1-B)Tobacco(t-31)`	-0.02963	0.1095	-2.7060e-01	0.7872	0.3936
`(1-Bs)(1-B)Tobacco(t-32)`	+0.1034	0.1063	+9.7210e-01	0.3329	0.1664
`(1-Bs)(1-B)Tobacco(t-33)`	+0.1003	0.1041	+9.6360e-01	0.3371	0.1685
`(1-Bs)(1-B)Tobacco(t-34)`	+0.02848	0.09848	+2.8920e-01	0.7729	0.3865
`(1-Bs)(1-B)Tobacco(t-35)`	+0.1385	0.08581	+1.6140e+00	0.109	0.05448

\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) & +0.0001681 &  0.0005815 & +2.8900e-01 &  0.773 &  0.3865 \tabularnewline
`(1-Bs)(1-B)FoodProducts` & -0.0006994 &  0.0001805 & -3.8740e+00 &  0.0001719 &  8.593e-05 \tabularnewline
`(1-Bs)(1-B)Beverages` & -7.443e-05 &  7.318e-05 & -1.0170e+00 &  0.3111 &  0.1555 \tabularnewline
`(1-Bs)(1-B)DurableConsumerGoods` & +5.688e-05 &  7.272e-05 & +7.8220e-01 &  0.4356 &  0.2178 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-1)` & -0.4279 &  0.08345 & -5.1280e+00 &  1.086e-06 &  5.428e-07 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-2)` & -0.3188 &  0.09265 & -3.4410e+00 &  0.0007888 &  0.0003944 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-3)` & -0.2729 &  0.09699 & -2.8140e+00 &  0.005687 &  0.002844 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-4)` & -0.153 &  0.09936 & -1.5400e+00 &  0.1261 &  0.06304 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-5)` & -0.1528 &  0.1002 & -1.5240e+00 &  0.1299 &  0.06497 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-6)` & -0.1344 &  0.1001 & -1.3420e+00 &  0.1819 &  0.09093 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-7)` & -0.04309 &  0.1008 & -4.2730e-01 &  0.6699 &  0.3349 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-8)` & -0.06004 &  0.1003 & -5.9870e-01 &  0.5505 &  0.2752 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-9)` & -0.02544 &  0.1001 & -2.5410e-01 &  0.7998 &  0.3999 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-10)` & +0.06505 &  0.1008 & +6.4520e-01 &  0.5199 &  0.26 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-11)` & -0.07962 &  0.09708 & -8.2020e-01 &  0.4137 &  0.2068 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-12)` & -0.4982 &  0.09558 & -5.2120e+00 &  7.483e-07 &  3.742e-07 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-13)` & -0.2547 &  0.108 & -2.3580e+00 &  0.01993 &  0.009963 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-14)` & -0.1703 &  0.1113 & -1.5300e+00 &  0.1285 &  0.06425 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-15)` & -0.06027 &  0.1133 & -5.3190e-01 &  0.5957 &  0.2979 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-16)` & -0.01541 &  0.1114 & -1.3830e-01 &  0.8903 &  0.4451 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-17)` & -0.0404 &  0.1112 & -3.6310e-01 &  0.7171 &  0.3586 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-18)` & +0.028 &  0.1118 & +2.5050e-01 &  0.8026 &  0.4013 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-19)` & +0.002244 &  0.1124 & +1.9970e-02 &  0.9841 &  0.4921 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-20)` & +0.04674 &  0.1116 & +4.1890e-01 &  0.676 &  0.338 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-21)` & +0.03044 &  0.1108 & +2.7470e-01 &  0.784 &  0.392 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-22)` & +0.07146 &  0.1109 & +6.4440e-01 &  0.5205 &  0.2603 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-23)` & -0.01115 &  0.1067 & -1.0450e-01 &  0.9169 &  0.4585 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-24)` & -0.2722 &  0.1005 & -2.7100e+00 &  0.007682 &  0.003841 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-25)` & -0.1847 &  0.1043 & -1.7710e+00 &  0.07899 &  0.03949 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-26)` & -0.2215 &  0.1061 & -2.0870e+00 &  0.03889 &  0.01944 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-27)` & -0.1524 &  0.1064 & -1.4320e+00 &  0.1545 &  0.07726 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-28)` & -0.08283 &  0.1072 & -7.7270e-01 &  0.4411 &  0.2206 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-29)` & -0.1163 &  0.1094 & -1.0630e+00 &  0.2898 &  0.1449 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-30)` & -0.1579 &  0.1096 & -1.4400e+00 &  0.1524 &  0.07618 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-31)` & -0.02963 &  0.1095 & -2.7060e-01 &  0.7872 &  0.3936 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-32)` & +0.1034 &  0.1063 & +9.7210e-01 &  0.3329 &  0.1664 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-33)` & +0.1003 &  0.1041 & +9.6360e-01 &  0.3371 &  0.1685 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-34)` & +0.02848 &  0.09848 & +2.8920e-01 &  0.7729 &  0.3865 \tabularnewline
`(1-Bs)(1-B)Tobacco(t-35)` & +0.1385 &  0.08581 & +1.6140e+00 &  0.109 &  0.05448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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]+0.0001681[/C][C] 0.0005815[/C][C]+2.8900e-01[/C][C] 0.773[/C][C] 0.3865[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FoodProducts`[/C][C]-0.0006994[/C][C] 0.0001805[/C][C]-3.8740e+00[/C][C] 0.0001719[/C][C] 8.593e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Beverages`[/C][C]-7.443e-05[/C][C] 7.318e-05[/C][C]-1.0170e+00[/C][C] 0.3111[/C][C] 0.1555[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)DurableConsumerGoods`[/C][C]+5.688e-05[/C][C] 7.272e-05[/C][C]+7.8220e-01[/C][C] 0.4356[/C][C] 0.2178[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-1)`[/C][C]-0.4279[/C][C] 0.08345[/C][C]-5.1280e+00[/C][C] 1.086e-06[/C][C] 5.428e-07[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-2)`[/C][C]-0.3188[/C][C] 0.09265[/C][C]-3.4410e+00[/C][C] 0.0007888[/C][C] 0.0003944[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-3)`[/C][C]-0.2729[/C][C] 0.09699[/C][C]-2.8140e+00[/C][C] 0.005687[/C][C] 0.002844[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-4)`[/C][C]-0.153[/C][C] 0.09936[/C][C]-1.5400e+00[/C][C] 0.1261[/C][C] 0.06304[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-5)`[/C][C]-0.1528[/C][C] 0.1002[/C][C]-1.5240e+00[/C][C] 0.1299[/C][C] 0.06497[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-6)`[/C][C]-0.1344[/C][C] 0.1001[/C][C]-1.3420e+00[/C][C] 0.1819[/C][C] 0.09093[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-7)`[/C][C]-0.04309[/C][C] 0.1008[/C][C]-4.2730e-01[/C][C] 0.6699[/C][C] 0.3349[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-8)`[/C][C]-0.06004[/C][C] 0.1003[/C][C]-5.9870e-01[/C][C] 0.5505[/C][C] 0.2752[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-9)`[/C][C]-0.02544[/C][C] 0.1001[/C][C]-2.5410e-01[/C][C] 0.7998[/C][C] 0.3999[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-10)`[/C][C]+0.06505[/C][C] 0.1008[/C][C]+6.4520e-01[/C][C] 0.5199[/C][C] 0.26[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-11)`[/C][C]-0.07962[/C][C] 0.09708[/C][C]-8.2020e-01[/C][C] 0.4137[/C][C] 0.2068[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-12)`[/C][C]-0.4982[/C][C] 0.09558[/C][C]-5.2120e+00[/C][C] 7.483e-07[/C][C] 3.742e-07[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-13)`[/C][C]-0.2547[/C][C] 0.108[/C][C]-2.3580e+00[/C][C] 0.01993[/C][C] 0.009963[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-14)`[/C][C]-0.1703[/C][C] 0.1113[/C][C]-1.5300e+00[/C][C] 0.1285[/C][C] 0.06425[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-15)`[/C][C]-0.06027[/C][C] 0.1133[/C][C]-5.3190e-01[/C][C] 0.5957[/C][C] 0.2979[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-16)`[/C][C]-0.01541[/C][C] 0.1114[/C][C]-1.3830e-01[/C][C] 0.8903[/C][C] 0.4451[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-17)`[/C][C]-0.0404[/C][C] 0.1112[/C][C]-3.6310e-01[/C][C] 0.7171[/C][C] 0.3586[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-18)`[/C][C]+0.028[/C][C] 0.1118[/C][C]+2.5050e-01[/C][C] 0.8026[/C][C] 0.4013[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-19)`[/C][C]+0.002244[/C][C] 0.1124[/C][C]+1.9970e-02[/C][C] 0.9841[/C][C] 0.4921[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-20)`[/C][C]+0.04674[/C][C] 0.1116[/C][C]+4.1890e-01[/C][C] 0.676[/C][C] 0.338[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-21)`[/C][C]+0.03044[/C][C] 0.1108[/C][C]+2.7470e-01[/C][C] 0.784[/C][C] 0.392[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-22)`[/C][C]+0.07146[/C][C] 0.1109[/C][C]+6.4440e-01[/C][C] 0.5205[/C][C] 0.2603[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-23)`[/C][C]-0.01115[/C][C] 0.1067[/C][C]-1.0450e-01[/C][C] 0.9169[/C][C] 0.4585[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-24)`[/C][C]-0.2722[/C][C] 0.1005[/C][C]-2.7100e+00[/C][C] 0.007682[/C][C] 0.003841[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-25)`[/C][C]-0.1847[/C][C] 0.1043[/C][C]-1.7710e+00[/C][C] 0.07899[/C][C] 0.03949[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-26)`[/C][C]-0.2215[/C][C] 0.1061[/C][C]-2.0870e+00[/C][C] 0.03889[/C][C] 0.01944[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-27)`[/C][C]-0.1524[/C][C] 0.1064[/C][C]-1.4320e+00[/C][C] 0.1545[/C][C] 0.07726[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-28)`[/C][C]-0.08283[/C][C] 0.1072[/C][C]-7.7270e-01[/C][C] 0.4411[/C][C] 0.2206[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-29)`[/C][C]-0.1163[/C][C] 0.1094[/C][C]-1.0630e+00[/C][C] 0.2898[/C][C] 0.1449[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-30)`[/C][C]-0.1579[/C][C] 0.1096[/C][C]-1.4400e+00[/C][C] 0.1524[/C][C] 0.07618[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-31)`[/C][C]-0.02963[/C][C] 0.1095[/C][C]-2.7060e-01[/C][C] 0.7872[/C][C] 0.3936[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-32)`[/C][C]+0.1034[/C][C] 0.1063[/C][C]+9.7210e-01[/C][C] 0.3329[/C][C] 0.1664[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-33)`[/C][C]+0.1003[/C][C] 0.1041[/C][C]+9.6360e-01[/C][C] 0.3371[/C][C] 0.1685[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-34)`[/C][C]+0.02848[/C][C] 0.09848[/C][C]+2.8920e-01[/C][C] 0.7729[/C][C] 0.3865[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tobacco(t-35)`[/C][C]+0.1385[/C][C] 0.08581[/C][C]+1.6140e+00[/C][C] 0.109[/C][C] 0.05448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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)	+0.0001681	0.0005815	+2.8900e-01	0.773	0.3865
`(1-Bs)(1-B)FoodProducts`	-0.0006994	0.0001805	-3.8740e+00	0.0001719	8.593e-05
`(1-Bs)(1-B)Beverages`	-7.443e-05	7.318e-05	-1.0170e+00	0.3111	0.1555
`(1-Bs)(1-B)DurableConsumerGoods`	+5.688e-05	7.272e-05	+7.8220e-01	0.4356	0.2178
`(1-Bs)(1-B)Tobacco(t-1)`	-0.4279	0.08345	-5.1280e+00	1.086e-06	5.428e-07
`(1-Bs)(1-B)Tobacco(t-2)`	-0.3188	0.09265	-3.4410e+00	0.0007888	0.0003944
`(1-Bs)(1-B)Tobacco(t-3)`	-0.2729	0.09699	-2.8140e+00	0.005687	0.002844
`(1-Bs)(1-B)Tobacco(t-4)`	-0.153	0.09936	-1.5400e+00	0.1261	0.06304
`(1-Bs)(1-B)Tobacco(t-5)`	-0.1528	0.1002	-1.5240e+00	0.1299	0.06497
`(1-Bs)(1-B)Tobacco(t-6)`	-0.1344	0.1001	-1.3420e+00	0.1819	0.09093
`(1-Bs)(1-B)Tobacco(t-7)`	-0.04309	0.1008	-4.2730e-01	0.6699	0.3349
`(1-Bs)(1-B)Tobacco(t-8)`	-0.06004	0.1003	-5.9870e-01	0.5505	0.2752
`(1-Bs)(1-B)Tobacco(t-9)`	-0.02544	0.1001	-2.5410e-01	0.7998	0.3999
`(1-Bs)(1-B)Tobacco(t-10)`	+0.06505	0.1008	+6.4520e-01	0.5199	0.26
`(1-Bs)(1-B)Tobacco(t-11)`	-0.07962	0.09708	-8.2020e-01	0.4137	0.2068
`(1-Bs)(1-B)Tobacco(t-12)`	-0.4982	0.09558	-5.2120e+00	7.483e-07	3.742e-07
`(1-Bs)(1-B)Tobacco(t-13)`	-0.2547	0.108	-2.3580e+00	0.01993	0.009963
`(1-Bs)(1-B)Tobacco(t-14)`	-0.1703	0.1113	-1.5300e+00	0.1285	0.06425
`(1-Bs)(1-B)Tobacco(t-15)`	-0.06027	0.1133	-5.3190e-01	0.5957	0.2979
`(1-Bs)(1-B)Tobacco(t-16)`	-0.01541	0.1114	-1.3830e-01	0.8903	0.4451
`(1-Bs)(1-B)Tobacco(t-17)`	-0.0404	0.1112	-3.6310e-01	0.7171	0.3586
`(1-Bs)(1-B)Tobacco(t-18)`	+0.028	0.1118	+2.5050e-01	0.8026	0.4013
`(1-Bs)(1-B)Tobacco(t-19)`	+0.002244	0.1124	+1.9970e-02	0.9841	0.4921
`(1-Bs)(1-B)Tobacco(t-20)`	+0.04674	0.1116	+4.1890e-01	0.676	0.338
`(1-Bs)(1-B)Tobacco(t-21)`	+0.03044	0.1108	+2.7470e-01	0.784	0.392
`(1-Bs)(1-B)Tobacco(t-22)`	+0.07146	0.1109	+6.4440e-01	0.5205	0.2603
`(1-Bs)(1-B)Tobacco(t-23)`	-0.01115	0.1067	-1.0450e-01	0.9169	0.4585
`(1-Bs)(1-B)Tobacco(t-24)`	-0.2722	0.1005	-2.7100e+00	0.007682	0.003841
`(1-Bs)(1-B)Tobacco(t-25)`	-0.1847	0.1043	-1.7710e+00	0.07899	0.03949
`(1-Bs)(1-B)Tobacco(t-26)`	-0.2215	0.1061	-2.0870e+00	0.03889	0.01944
`(1-Bs)(1-B)Tobacco(t-27)`	-0.1524	0.1064	-1.4320e+00	0.1545	0.07726
`(1-Bs)(1-B)Tobacco(t-28)`	-0.08283	0.1072	-7.7270e-01	0.4411	0.2206
`(1-Bs)(1-B)Tobacco(t-29)`	-0.1163	0.1094	-1.0630e+00	0.2898	0.1449
`(1-Bs)(1-B)Tobacco(t-30)`	-0.1579	0.1096	-1.4400e+00	0.1524	0.07618
`(1-Bs)(1-B)Tobacco(t-31)`	-0.02963	0.1095	-2.7060e-01	0.7872	0.3936
`(1-Bs)(1-B)Tobacco(t-32)`	+0.1034	0.1063	+9.7210e-01	0.3329	0.1664
`(1-Bs)(1-B)Tobacco(t-33)`	+0.1003	0.1041	+9.6360e-01	0.3371	0.1685
`(1-Bs)(1-B)Tobacco(t-34)`	+0.02848	0.09848	+2.8920e-01	0.7729	0.3865
`(1-Bs)(1-B)Tobacco(t-35)`	+0.1385	0.08581	+1.6140e+00	0.109	0.05448

Multiple Linear Regression - Regression Statistics
Multiple R	0.7843
R-squared	0.6152
Adjusted R-squared	0.4982
F-TEST (value)	5.259
F-TEST (DF numerator)	38
F-TEST (DF denominator)	125
p-value	9.319e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.007376
Sum Squared Residuals	0.006801

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7843 \tabularnewline
R-squared &  0.6152 \tabularnewline
Adjusted R-squared &  0.4982 \tabularnewline
F-TEST (value) &  5.259 \tabularnewline
F-TEST (DF numerator) & 38 \tabularnewline
F-TEST (DF denominator) & 125 \tabularnewline
p-value &  9.319e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.007376 \tabularnewline
Sum Squared Residuals &  0.006801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7843[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6152[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4982[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.259[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]38[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]125[/C][/ROW]
[ROW][C]p-value[/C][C] 9.319e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.007376[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.006801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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	0.7843
R-squared	0.6152
Adjusted R-squared	0.4982
F-TEST (value)	5.259
F-TEST (DF numerator)	38
F-TEST (DF denominator)	125
p-value	9.319e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.007376
Sum Squared Residuals	0.006801

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=310117&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=310117&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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	-0.01257	-0.001851	-0.01072
2	0.00151	0.005265	-0.003755
3	-0.01122	-0.005188	-0.006034
4	0.003633	0.0132	-0.009566
5	0.007736	-0.002489	0.01023
6	-0.0002584	0.002842	-0.003101
7	0.005085	0.001949	0.003135
8	-0.01738	-0.01231	-0.005069
9	0.00671	0.01192	-0.005207
10	0.01286	0.006409	0.006453
11	-0.01143	-0.009098	-0.002334
12	0.006996	0.002137	0.004859
13	0.00418	0.00214	0.002039
14	0.01384	0.002643	0.0112
15	-0.005876	0.0008596	-0.006736
16	0.009741	0.0004913	0.009249
17	-0.01865	-0.01648	-0.002173
18	0.007227	0.0005217	0.006705
19	-0.007335	-0.0034	-0.003935
20	0.009209	0.002442	0.006766
21	0.002303	0.0007109	0.001592
22	0.005825	-0.006281	0.01211
23	-0.003725	-0.006105	0.002381
24	0.006883	-0.001054	0.007937
25	-0.011	-0.006053	-0.004948
26	-0.00198	-0.003328	0.001348
27	0.01228	0.001271	0.01101
28	-0.003715	8.253e-05	-0.003797
29	-0.006059	-0.002222	-0.003838
30	-0.003705	0.003998	-0.007703
31	0.00487	0.006086	-0.001216
32	0.01106	0.004515	0.006549
33	-0.006109	-0.007612	0.001503
34	-0.01296	-0.009374	-0.003583
35	0.005539	0.004112	0.001427
36	-0.0003794	-0.001276	0.0008968
37	-0.008982	-0.002624	-0.006358
38	0.005669	0.003835	0.001834
39	-0.0003289	-0.006959	0.00663
40	-7.11e-05	-0.005409	0.005338
41	0.002393	0.00734	-0.004947
42	0.002602	0.005289	-0.002688
43	-0.01408	-0.006093	-0.00799
44	0.007178	-0.001337	0.008515
45	0.008724	0.004042	0.004682
46	-0.008084	-0.003789	-0.004295
47	0.004546	0.003604	0.0009425
48	0.00633	0.0008821	0.005447
49	-0.01705	-0.0002674	-0.01678
50	-0.01151	-0.0003765	-0.01113
51	0.004858	0.01543	-0.01057
52	-0.006472	-0.00729	0.0008181
53	0.01243	0.01154	0.0008916
54	-0.002558	0.002125	-0.004683
55	0.01373	0.00614	0.007595
56	-0.01454	-0.003253	-0.01128
57	-0.01258	-0.009392	-0.003188
58	0.01434	0.01431	2.361e-05
59	0.001523	0.003098	-0.001574
60	-0.02371	-0.01046	-0.01325
61	0.0364	0.02893	0.007476
62	0.007184	-0.002569	0.009753
63	-0.005412	-0.008408	0.002997
64	-0.0004686	0.00443	-0.004898
65	-0.005174	-0.005404	0.0002303
66	-0.003399	-0.008845	0.005446
67	-0.008861	-0.0001843	-0.008677
68	0.006579	0.00781	-0.001231
69	0.001422	0.006037	-0.004615
70	0.004647	-0.003275	0.007922
71	-0.008398	-0.004511	-0.003887
72	0.01475	0.007768	0.00698
73	-0.01317	-0.01066	-0.002502
74	0.001408	-0.004801	0.006209
75	-0.003396	-0.005509	0.002112
76	0.005115	0.01088	-0.005767
77	0.00824	0.002084	0.006155
78	-0.01572	-0.004759	-0.01097
79	0.006495	0.0146	-0.008103
80	-0.004471	-0.0008775	-0.003594
81	0.002105	0.00474	-0.002635
82	-0.001734	-0.00379	0.002057
83	-0.002436	-0.001063	-0.001372
84	0.001026	0.001779	-0.0007528
85	-0.005198	-0.008044	0.002846
86	-0.001153	0.007688	-0.008842
87	0.001393	0.001896	-0.0005034
88	-0.005651	-0.004512	-0.001138
89	-0.005368	-0.0103	0.004934
90	0.02921	0.02393	0.005277
91	-0.005422	-0.01105	0.005632
92	-0.00745	-0.009973	0.002523
93	0.007806	0.002677	0.005129
94	0.007107	0.004396	0.002711
95	-0.005909	-0.00439	-0.001518
96	-0.006987	0.0002239	-0.007211
97	0.01545	0.007326	0.008121
98	-0.01274	-0.007658	-0.005085
99	0.01103	0.005948	0.005081
100	0.002473	0.0003806	0.002092
101	0.000932	-0.003356	0.004288
102	-0.0185	-0.0185	4.676e-06
103	0.01075	0.001651	0.009098
104	0.004132	0.007582	-0.003451
105	0.00341	0.0008129	0.002597
106	-0.02302	-0.01995	-0.003078
107	0.007308	0.01632	-0.009015
108	0.009866	0.01014	-0.0002783
109	0.009589	-0.007891	0.01748
110	0.003556	0.003154	0.0004023
111	-0.001214	-0.008607	0.007393
112	-0.007949	-0.006927	-0.001022
113	-0.003463	0.002464	-0.005927
114	0.01573	0.007974	0.007758
115	-0.01582	-0.01528	-0.0005464
116	0.01064	0.005662	0.004983
117	-0.009902	-0.01192	0.002018
118	0.004644	0.01404	-0.009393
119	0.01985	0.001618	0.01823
120	-0.0285	-0.02208	-0.006418
121	-0.002284	0.0004002	-0.002684
122	-0.0109	-0.0003464	-0.01056
123	0.005703	0.007182	-0.001479
124	0.001412	-0.001995	0.003406
125	0.00867	0.01264	-0.003967
126	-0.005277	-0.006199	0.000922
127	-0.004044	0.007035	-0.01108
128	-0.001045	-0.001935	0.0008897
129	0.000697	0.00272	-0.002023
130	-0.001116	0.0042	-0.005316
131	-0.006961	-0.005629	-0.001332
132	0.02069	0.01518	0.005511
133	-0.01297	-0.00346	-0.009507
134	0.008297	0.003957	0.004339
135	-0.006427	-0.007848	0.001421
136	0.01951	0.009012	0.0105
137	-0.006928	-0.01277	0.005841
138	-0.009851	-0.01451	0.004654
139	0.01199	0.007007	0.004986
140	-0.006574	-0.003116	-0.003458
141	0.00122	0.004955	-0.003736
142	0.005762	0.0008919	0.00487
143	-0.0102	-0.005803	-0.004392
144	-0.00106	0.0002403	-0.001301
145	-0.008141	0.008446	-0.01659
146	-0.0005802	0.00709	-0.00767
147	0.01221	0.007905	0.00431
148	-0.01438	-0.009025	-0.005359
149	0.001928	-9.357e-05	0.002022
150	0.02923	0.01221	0.01702
151	0.005423	-0.001311	0.006735
152	-0.01622	-0.01732	0.001105
153	-0.0002036	-0.001997	0.001794
154	0.006453	0.006845	-0.0003918
155	-0.006762	-0.009807	0.003045
156	0.006298	0.0003205	0.005977
157	-0.0007827	0.006392	-0.007174
158	-0.009345	0.003728	-0.01307
159	-0.0006665	-0.004724	0.004057
160	0.01208	0.01161	0.0004671
161	-0.01878	-0.01254	-0.006243
162	-0.01189	-0.009163	-0.002723
163	-0.004741	-0.002513	-0.002228
164	0.0219	0.01466	0.007248

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -0.01257 & -0.001851 & -0.01072 \tabularnewline
2 &  0.00151 &  0.005265 & -0.003755 \tabularnewline
3 & -0.01122 & -0.005188 & -0.006034 \tabularnewline
4 &  0.003633 &  0.0132 & -0.009566 \tabularnewline
5 &  0.007736 & -0.002489 &  0.01023 \tabularnewline
6 & -0.0002584 &  0.002842 & -0.003101 \tabularnewline
7 &  0.005085 &  0.001949 &  0.003135 \tabularnewline
8 & -0.01738 & -0.01231 & -0.005069 \tabularnewline
9 &  0.00671 &  0.01192 & -0.005207 \tabularnewline
10 &  0.01286 &  0.006409 &  0.006453 \tabularnewline
11 & -0.01143 & -0.009098 & -0.002334 \tabularnewline
12 &  0.006996 &  0.002137 &  0.004859 \tabularnewline
13 &  0.00418 &  0.00214 &  0.002039 \tabularnewline
14 &  0.01384 &  0.002643 &  0.0112 \tabularnewline
15 & -0.005876 &  0.0008596 & -0.006736 \tabularnewline
16 &  0.009741 &  0.0004913 &  0.009249 \tabularnewline
17 & -0.01865 & -0.01648 & -0.002173 \tabularnewline
18 &  0.007227 &  0.0005217 &  0.006705 \tabularnewline
19 & -0.007335 & -0.0034 & -0.003935 \tabularnewline
20 &  0.009209 &  0.002442 &  0.006766 \tabularnewline
21 &  0.002303 &  0.0007109 &  0.001592 \tabularnewline
22 &  0.005825 & -0.006281 &  0.01211 \tabularnewline
23 & -0.003725 & -0.006105 &  0.002381 \tabularnewline
24 &  0.006883 & -0.001054 &  0.007937 \tabularnewline
25 & -0.011 & -0.006053 & -0.004948 \tabularnewline
26 & -0.00198 & -0.003328 &  0.001348 \tabularnewline
27 &  0.01228 &  0.001271 &  0.01101 \tabularnewline
28 & -0.003715 &  8.253e-05 & -0.003797 \tabularnewline
29 & -0.006059 & -0.002222 & -0.003838 \tabularnewline
30 & -0.003705 &  0.003998 & -0.007703 \tabularnewline
31 &  0.00487 &  0.006086 & -0.001216 \tabularnewline
32 &  0.01106 &  0.004515 &  0.006549 \tabularnewline
33 & -0.006109 & -0.007612 &  0.001503 \tabularnewline
34 & -0.01296 & -0.009374 & -0.003583 \tabularnewline
35 &  0.005539 &  0.004112 &  0.001427 \tabularnewline
36 & -0.0003794 & -0.001276 &  0.0008968 \tabularnewline
37 & -0.008982 & -0.002624 & -0.006358 \tabularnewline
38 &  0.005669 &  0.003835 &  0.001834 \tabularnewline
39 & -0.0003289 & -0.006959 &  0.00663 \tabularnewline
40 & -7.11e-05 & -0.005409 &  0.005338 \tabularnewline
41 &  0.002393 &  0.00734 & -0.004947 \tabularnewline
42 &  0.002602 &  0.005289 & -0.002688 \tabularnewline
43 & -0.01408 & -0.006093 & -0.00799 \tabularnewline
44 &  0.007178 & -0.001337 &  0.008515 \tabularnewline
45 &  0.008724 &  0.004042 &  0.004682 \tabularnewline
46 & -0.008084 & -0.003789 & -0.004295 \tabularnewline
47 &  0.004546 &  0.003604 &  0.0009425 \tabularnewline
48 &  0.00633 &  0.0008821 &  0.005447 \tabularnewline
49 & -0.01705 & -0.0002674 & -0.01678 \tabularnewline
50 & -0.01151 & -0.0003765 & -0.01113 \tabularnewline
51 &  0.004858 &  0.01543 & -0.01057 \tabularnewline
52 & -0.006472 & -0.00729 &  0.0008181 \tabularnewline
53 &  0.01243 &  0.01154 &  0.0008916 \tabularnewline
54 & -0.002558 &  0.002125 & -0.004683 \tabularnewline
55 &  0.01373 &  0.00614 &  0.007595 \tabularnewline
56 & -0.01454 & -0.003253 & -0.01128 \tabularnewline
57 & -0.01258 & -0.009392 & -0.003188 \tabularnewline
58 &  0.01434 &  0.01431 &  2.361e-05 \tabularnewline
59 &  0.001523 &  0.003098 & -0.001574 \tabularnewline
60 & -0.02371 & -0.01046 & -0.01325 \tabularnewline
61 &  0.0364 &  0.02893 &  0.007476 \tabularnewline
62 &  0.007184 & -0.002569 &  0.009753 \tabularnewline
63 & -0.005412 & -0.008408 &  0.002997 \tabularnewline
64 & -0.0004686 &  0.00443 & -0.004898 \tabularnewline
65 & -0.005174 & -0.005404 &  0.0002303 \tabularnewline
66 & -0.003399 & -0.008845 &  0.005446 \tabularnewline
67 & -0.008861 & -0.0001843 & -0.008677 \tabularnewline
68 &  0.006579 &  0.00781 & -0.001231 \tabularnewline
69 &  0.001422 &  0.006037 & -0.004615 \tabularnewline
70 &  0.004647 & -0.003275 &  0.007922 \tabularnewline
71 & -0.008398 & -0.004511 & -0.003887 \tabularnewline
72 &  0.01475 &  0.007768 &  0.00698 \tabularnewline
73 & -0.01317 & -0.01066 & -0.002502 \tabularnewline
74 &  0.001408 & -0.004801 &  0.006209 \tabularnewline
75 & -0.003396 & -0.005509 &  0.002112 \tabularnewline
76 &  0.005115 &  0.01088 & -0.005767 \tabularnewline
77 &  0.00824 &  0.002084 &  0.006155 \tabularnewline
78 & -0.01572 & -0.004759 & -0.01097 \tabularnewline
79 &  0.006495 &  0.0146 & -0.008103 \tabularnewline
80 & -0.004471 & -0.0008775 & -0.003594 \tabularnewline
81 &  0.002105 &  0.00474 & -0.002635 \tabularnewline
82 & -0.001734 & -0.00379 &  0.002057 \tabularnewline
83 & -0.002436 & -0.001063 & -0.001372 \tabularnewline
84 &  0.001026 &  0.001779 & -0.0007528 \tabularnewline
85 & -0.005198 & -0.008044 &  0.002846 \tabularnewline
86 & -0.001153 &  0.007688 & -0.008842 \tabularnewline
87 &  0.001393 &  0.001896 & -0.0005034 \tabularnewline
88 & -0.005651 & -0.004512 & -0.001138 \tabularnewline
89 & -0.005368 & -0.0103 &  0.004934 \tabularnewline
90 &  0.02921 &  0.02393 &  0.005277 \tabularnewline
91 & -0.005422 & -0.01105 &  0.005632 \tabularnewline
92 & -0.00745 & -0.009973 &  0.002523 \tabularnewline
93 &  0.007806 &  0.002677 &  0.005129 \tabularnewline
94 &  0.007107 &  0.004396 &  0.002711 \tabularnewline
95 & -0.005909 & -0.00439 & -0.001518 \tabularnewline
96 & -0.006987 &  0.0002239 & -0.007211 \tabularnewline
97 &  0.01545 &  0.007326 &  0.008121 \tabularnewline
98 & -0.01274 & -0.007658 & -0.005085 \tabularnewline
99 &  0.01103 &  0.005948 &  0.005081 \tabularnewline
100 &  0.002473 &  0.0003806 &  0.002092 \tabularnewline
101 &  0.000932 & -0.003356 &  0.004288 \tabularnewline
102 & -0.0185 & -0.0185 &  4.676e-06 \tabularnewline
103 &  0.01075 &  0.001651 &  0.009098 \tabularnewline
104 &  0.004132 &  0.007582 & -0.003451 \tabularnewline
105 &  0.00341 &  0.0008129 &  0.002597 \tabularnewline
106 & -0.02302 & -0.01995 & -0.003078 \tabularnewline
107 &  0.007308 &  0.01632 & -0.009015 \tabularnewline
108 &  0.009866 &  0.01014 & -0.0002783 \tabularnewline
109 &  0.009589 & -0.007891 &  0.01748 \tabularnewline
110 &  0.003556 &  0.003154 &  0.0004023 \tabularnewline
111 & -0.001214 & -0.008607 &  0.007393 \tabularnewline
112 & -0.007949 & -0.006927 & -0.001022 \tabularnewline
113 & -0.003463 &  0.002464 & -0.005927 \tabularnewline
114 &  0.01573 &  0.007974 &  0.007758 \tabularnewline
115 & -0.01582 & -0.01528 & -0.0005464 \tabularnewline
116 &  0.01064 &  0.005662 &  0.004983 \tabularnewline
117 & -0.009902 & -0.01192 &  0.002018 \tabularnewline
118 &  0.004644 &  0.01404 & -0.009393 \tabularnewline
119 &  0.01985 &  0.001618 &  0.01823 \tabularnewline
120 & -0.0285 & -0.02208 & -0.006418 \tabularnewline
121 & -0.002284 &  0.0004002 & -0.002684 \tabularnewline
122 & -0.0109 & -0.0003464 & -0.01056 \tabularnewline
123 &  0.005703 &  0.007182 & -0.001479 \tabularnewline
124 &  0.001412 & -0.001995 &  0.003406 \tabularnewline
125 &  0.00867 &  0.01264 & -0.003967 \tabularnewline
126 & -0.005277 & -0.006199 &  0.000922 \tabularnewline
127 & -0.004044 &  0.007035 & -0.01108 \tabularnewline
128 & -0.001045 & -0.001935 &  0.0008897 \tabularnewline
129 &  0.000697 &  0.00272 & -0.002023 \tabularnewline
130 & -0.001116 &  0.0042 & -0.005316 \tabularnewline
131 & -0.006961 & -0.005629 & -0.001332 \tabularnewline
132 &  0.02069 &  0.01518 &  0.005511 \tabularnewline
133 & -0.01297 & -0.00346 & -0.009507 \tabularnewline
134 &  0.008297 &  0.003957 &  0.004339 \tabularnewline
135 & -0.006427 & -0.007848 &  0.001421 \tabularnewline
136 &  0.01951 &  0.009012 &  0.0105 \tabularnewline
137 & -0.006928 & -0.01277 &  0.005841 \tabularnewline
138 & -0.009851 & -0.01451 &  0.004654 \tabularnewline
139 &  0.01199 &  0.007007 &  0.004986 \tabularnewline
140 & -0.006574 & -0.003116 & -0.003458 \tabularnewline
141 &  0.00122 &  0.004955 & -0.003736 \tabularnewline
142 &  0.005762 &  0.0008919 &  0.00487 \tabularnewline
143 & -0.0102 & -0.005803 & -0.004392 \tabularnewline
144 & -0.00106 &  0.0002403 & -0.001301 \tabularnewline
145 & -0.008141 &  0.008446 & -0.01659 \tabularnewline
146 & -0.0005802 &  0.00709 & -0.00767 \tabularnewline
147 &  0.01221 &  0.007905 &  0.00431 \tabularnewline
148 & -0.01438 & -0.009025 & -0.005359 \tabularnewline
149 &  0.001928 & -9.357e-05 &  0.002022 \tabularnewline
150 &  0.02923 &  0.01221 &  0.01702 \tabularnewline
151 &  0.005423 & -0.001311 &  0.006735 \tabularnewline
152 & -0.01622 & -0.01732 &  0.001105 \tabularnewline
153 & -0.0002036 & -0.001997 &  0.001794 \tabularnewline
154 &  0.006453 &  0.006845 & -0.0003918 \tabularnewline
155 & -0.006762 & -0.009807 &  0.003045 \tabularnewline
156 &  0.006298 &  0.0003205 &  0.005977 \tabularnewline
157 & -0.0007827 &  0.006392 & -0.007174 \tabularnewline
158 & -0.009345 &  0.003728 & -0.01307 \tabularnewline
159 & -0.0006665 & -0.004724 &  0.004057 \tabularnewline
160 &  0.01208 &  0.01161 &  0.0004671 \tabularnewline
161 & -0.01878 & -0.01254 & -0.006243 \tabularnewline
162 & -0.01189 & -0.009163 & -0.002723 \tabularnewline
163 & -0.004741 & -0.002513 & -0.002228 \tabularnewline
164 &  0.0219 &  0.01466 &  0.007248 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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]-0.01257[/C][C]-0.001851[/C][C]-0.01072[/C][/ROW]
[ROW][C]2[/C][C] 0.00151[/C][C] 0.005265[/C][C]-0.003755[/C][/ROW]
[ROW][C]3[/C][C]-0.01122[/C][C]-0.005188[/C][C]-0.006034[/C][/ROW]
[ROW][C]4[/C][C] 0.003633[/C][C] 0.0132[/C][C]-0.009566[/C][/ROW]
[ROW][C]5[/C][C] 0.007736[/C][C]-0.002489[/C][C] 0.01023[/C][/ROW]
[ROW][C]6[/C][C]-0.0002584[/C][C] 0.002842[/C][C]-0.003101[/C][/ROW]
[ROW][C]7[/C][C] 0.005085[/C][C] 0.001949[/C][C] 0.003135[/C][/ROW]
[ROW][C]8[/C][C]-0.01738[/C][C]-0.01231[/C][C]-0.005069[/C][/ROW]
[ROW][C]9[/C][C] 0.00671[/C][C] 0.01192[/C][C]-0.005207[/C][/ROW]
[ROW][C]10[/C][C] 0.01286[/C][C] 0.006409[/C][C] 0.006453[/C][/ROW]
[ROW][C]11[/C][C]-0.01143[/C][C]-0.009098[/C][C]-0.002334[/C][/ROW]
[ROW][C]12[/C][C] 0.006996[/C][C] 0.002137[/C][C] 0.004859[/C][/ROW]
[ROW][C]13[/C][C] 0.00418[/C][C] 0.00214[/C][C] 0.002039[/C][/ROW]
[ROW][C]14[/C][C] 0.01384[/C][C] 0.002643[/C][C] 0.0112[/C][/ROW]
[ROW][C]15[/C][C]-0.005876[/C][C] 0.0008596[/C][C]-0.006736[/C][/ROW]
[ROW][C]16[/C][C] 0.009741[/C][C] 0.0004913[/C][C] 0.009249[/C][/ROW]
[ROW][C]17[/C][C]-0.01865[/C][C]-0.01648[/C][C]-0.002173[/C][/ROW]
[ROW][C]18[/C][C] 0.007227[/C][C] 0.0005217[/C][C] 0.006705[/C][/ROW]
[ROW][C]19[/C][C]-0.007335[/C][C]-0.0034[/C][C]-0.003935[/C][/ROW]
[ROW][C]20[/C][C] 0.009209[/C][C] 0.002442[/C][C] 0.006766[/C][/ROW]
[ROW][C]21[/C][C] 0.002303[/C][C] 0.0007109[/C][C] 0.001592[/C][/ROW]
[ROW][C]22[/C][C] 0.005825[/C][C]-0.006281[/C][C] 0.01211[/C][/ROW]
[ROW][C]23[/C][C]-0.003725[/C][C]-0.006105[/C][C] 0.002381[/C][/ROW]
[ROW][C]24[/C][C] 0.006883[/C][C]-0.001054[/C][C] 0.007937[/C][/ROW]
[ROW][C]25[/C][C]-0.011[/C][C]-0.006053[/C][C]-0.004948[/C][/ROW]
[ROW][C]26[/C][C]-0.00198[/C][C]-0.003328[/C][C] 0.001348[/C][/ROW]
[ROW][C]27[/C][C] 0.01228[/C][C] 0.001271[/C][C] 0.01101[/C][/ROW]
[ROW][C]28[/C][C]-0.003715[/C][C] 8.253e-05[/C][C]-0.003797[/C][/ROW]
[ROW][C]29[/C][C]-0.006059[/C][C]-0.002222[/C][C]-0.003838[/C][/ROW]
[ROW][C]30[/C][C]-0.003705[/C][C] 0.003998[/C][C]-0.007703[/C][/ROW]
[ROW][C]31[/C][C] 0.00487[/C][C] 0.006086[/C][C]-0.001216[/C][/ROW]
[ROW][C]32[/C][C] 0.01106[/C][C] 0.004515[/C][C] 0.006549[/C][/ROW]
[ROW][C]33[/C][C]-0.006109[/C][C]-0.007612[/C][C] 0.001503[/C][/ROW]
[ROW][C]34[/C][C]-0.01296[/C][C]-0.009374[/C][C]-0.003583[/C][/ROW]
[ROW][C]35[/C][C] 0.005539[/C][C] 0.004112[/C][C] 0.001427[/C][/ROW]
[ROW][C]36[/C][C]-0.0003794[/C][C]-0.001276[/C][C] 0.0008968[/C][/ROW]
[ROW][C]37[/C][C]-0.008982[/C][C]-0.002624[/C][C]-0.006358[/C][/ROW]
[ROW][C]38[/C][C] 0.005669[/C][C] 0.003835[/C][C] 0.001834[/C][/ROW]
[ROW][C]39[/C][C]-0.0003289[/C][C]-0.006959[/C][C] 0.00663[/C][/ROW]
[ROW][C]40[/C][C]-7.11e-05[/C][C]-0.005409[/C][C] 0.005338[/C][/ROW]
[ROW][C]41[/C][C] 0.002393[/C][C] 0.00734[/C][C]-0.004947[/C][/ROW]
[ROW][C]42[/C][C] 0.002602[/C][C] 0.005289[/C][C]-0.002688[/C][/ROW]
[ROW][C]43[/C][C]-0.01408[/C][C]-0.006093[/C][C]-0.00799[/C][/ROW]
[ROW][C]44[/C][C] 0.007178[/C][C]-0.001337[/C][C] 0.008515[/C][/ROW]
[ROW][C]45[/C][C] 0.008724[/C][C] 0.004042[/C][C] 0.004682[/C][/ROW]
[ROW][C]46[/C][C]-0.008084[/C][C]-0.003789[/C][C]-0.004295[/C][/ROW]
[ROW][C]47[/C][C] 0.004546[/C][C] 0.003604[/C][C] 0.0009425[/C][/ROW]
[ROW][C]48[/C][C] 0.00633[/C][C] 0.0008821[/C][C] 0.005447[/C][/ROW]
[ROW][C]49[/C][C]-0.01705[/C][C]-0.0002674[/C][C]-0.01678[/C][/ROW]
[ROW][C]50[/C][C]-0.01151[/C][C]-0.0003765[/C][C]-0.01113[/C][/ROW]
[ROW][C]51[/C][C] 0.004858[/C][C] 0.01543[/C][C]-0.01057[/C][/ROW]
[ROW][C]52[/C][C]-0.006472[/C][C]-0.00729[/C][C] 0.0008181[/C][/ROW]
[ROW][C]53[/C][C] 0.01243[/C][C] 0.01154[/C][C] 0.0008916[/C][/ROW]
[ROW][C]54[/C][C]-0.002558[/C][C] 0.002125[/C][C]-0.004683[/C][/ROW]
[ROW][C]55[/C][C] 0.01373[/C][C] 0.00614[/C][C] 0.007595[/C][/ROW]
[ROW][C]56[/C][C]-0.01454[/C][C]-0.003253[/C][C]-0.01128[/C][/ROW]
[ROW][C]57[/C][C]-0.01258[/C][C]-0.009392[/C][C]-0.003188[/C][/ROW]
[ROW][C]58[/C][C] 0.01434[/C][C] 0.01431[/C][C] 2.361e-05[/C][/ROW]
[ROW][C]59[/C][C] 0.001523[/C][C] 0.003098[/C][C]-0.001574[/C][/ROW]
[ROW][C]60[/C][C]-0.02371[/C][C]-0.01046[/C][C]-0.01325[/C][/ROW]
[ROW][C]61[/C][C] 0.0364[/C][C] 0.02893[/C][C] 0.007476[/C][/ROW]
[ROW][C]62[/C][C] 0.007184[/C][C]-0.002569[/C][C] 0.009753[/C][/ROW]
[ROW][C]63[/C][C]-0.005412[/C][C]-0.008408[/C][C] 0.002997[/C][/ROW]
[ROW][C]64[/C][C]-0.0004686[/C][C] 0.00443[/C][C]-0.004898[/C][/ROW]
[ROW][C]65[/C][C]-0.005174[/C][C]-0.005404[/C][C] 0.0002303[/C][/ROW]
[ROW][C]66[/C][C]-0.003399[/C][C]-0.008845[/C][C] 0.005446[/C][/ROW]
[ROW][C]67[/C][C]-0.008861[/C][C]-0.0001843[/C][C]-0.008677[/C][/ROW]
[ROW][C]68[/C][C] 0.006579[/C][C] 0.00781[/C][C]-0.001231[/C][/ROW]
[ROW][C]69[/C][C] 0.001422[/C][C] 0.006037[/C][C]-0.004615[/C][/ROW]
[ROW][C]70[/C][C] 0.004647[/C][C]-0.003275[/C][C] 0.007922[/C][/ROW]
[ROW][C]71[/C][C]-0.008398[/C][C]-0.004511[/C][C]-0.003887[/C][/ROW]
[ROW][C]72[/C][C] 0.01475[/C][C] 0.007768[/C][C] 0.00698[/C][/ROW]
[ROW][C]73[/C][C]-0.01317[/C][C]-0.01066[/C][C]-0.002502[/C][/ROW]
[ROW][C]74[/C][C] 0.001408[/C][C]-0.004801[/C][C] 0.006209[/C][/ROW]
[ROW][C]75[/C][C]-0.003396[/C][C]-0.005509[/C][C] 0.002112[/C][/ROW]
[ROW][C]76[/C][C] 0.005115[/C][C] 0.01088[/C][C]-0.005767[/C][/ROW]
[ROW][C]77[/C][C] 0.00824[/C][C] 0.002084[/C][C] 0.006155[/C][/ROW]
[ROW][C]78[/C][C]-0.01572[/C][C]-0.004759[/C][C]-0.01097[/C][/ROW]
[ROW][C]79[/C][C] 0.006495[/C][C] 0.0146[/C][C]-0.008103[/C][/ROW]
[ROW][C]80[/C][C]-0.004471[/C][C]-0.0008775[/C][C]-0.003594[/C][/ROW]
[ROW][C]81[/C][C] 0.002105[/C][C] 0.00474[/C][C]-0.002635[/C][/ROW]
[ROW][C]82[/C][C]-0.001734[/C][C]-0.00379[/C][C] 0.002057[/C][/ROW]
[ROW][C]83[/C][C]-0.002436[/C][C]-0.001063[/C][C]-0.001372[/C][/ROW]
[ROW][C]84[/C][C] 0.001026[/C][C] 0.001779[/C][C]-0.0007528[/C][/ROW]
[ROW][C]85[/C][C]-0.005198[/C][C]-0.008044[/C][C] 0.002846[/C][/ROW]
[ROW][C]86[/C][C]-0.001153[/C][C] 0.007688[/C][C]-0.008842[/C][/ROW]
[ROW][C]87[/C][C] 0.001393[/C][C] 0.001896[/C][C]-0.0005034[/C][/ROW]
[ROW][C]88[/C][C]-0.005651[/C][C]-0.004512[/C][C]-0.001138[/C][/ROW]
[ROW][C]89[/C][C]-0.005368[/C][C]-0.0103[/C][C] 0.004934[/C][/ROW]
[ROW][C]90[/C][C] 0.02921[/C][C] 0.02393[/C][C] 0.005277[/C][/ROW]
[ROW][C]91[/C][C]-0.005422[/C][C]-0.01105[/C][C] 0.005632[/C][/ROW]
[ROW][C]92[/C][C]-0.00745[/C][C]-0.009973[/C][C] 0.002523[/C][/ROW]
[ROW][C]93[/C][C] 0.007806[/C][C] 0.002677[/C][C] 0.005129[/C][/ROW]
[ROW][C]94[/C][C] 0.007107[/C][C] 0.004396[/C][C] 0.002711[/C][/ROW]
[ROW][C]95[/C][C]-0.005909[/C][C]-0.00439[/C][C]-0.001518[/C][/ROW]
[ROW][C]96[/C][C]-0.006987[/C][C] 0.0002239[/C][C]-0.007211[/C][/ROW]
[ROW][C]97[/C][C] 0.01545[/C][C] 0.007326[/C][C] 0.008121[/C][/ROW]
[ROW][C]98[/C][C]-0.01274[/C][C]-0.007658[/C][C]-0.005085[/C][/ROW]
[ROW][C]99[/C][C] 0.01103[/C][C] 0.005948[/C][C] 0.005081[/C][/ROW]
[ROW][C]100[/C][C] 0.002473[/C][C] 0.0003806[/C][C] 0.002092[/C][/ROW]
[ROW][C]101[/C][C] 0.000932[/C][C]-0.003356[/C][C] 0.004288[/C][/ROW]
[ROW][C]102[/C][C]-0.0185[/C][C]-0.0185[/C][C] 4.676e-06[/C][/ROW]
[ROW][C]103[/C][C] 0.01075[/C][C] 0.001651[/C][C] 0.009098[/C][/ROW]
[ROW][C]104[/C][C] 0.004132[/C][C] 0.007582[/C][C]-0.003451[/C][/ROW]
[ROW][C]105[/C][C] 0.00341[/C][C] 0.0008129[/C][C] 0.002597[/C][/ROW]
[ROW][C]106[/C][C]-0.02302[/C][C]-0.01995[/C][C]-0.003078[/C][/ROW]
[ROW][C]107[/C][C] 0.007308[/C][C] 0.01632[/C][C]-0.009015[/C][/ROW]
[ROW][C]108[/C][C] 0.009866[/C][C] 0.01014[/C][C]-0.0002783[/C][/ROW]
[ROW][C]109[/C][C] 0.009589[/C][C]-0.007891[/C][C] 0.01748[/C][/ROW]
[ROW][C]110[/C][C] 0.003556[/C][C] 0.003154[/C][C] 0.0004023[/C][/ROW]
[ROW][C]111[/C][C]-0.001214[/C][C]-0.008607[/C][C] 0.007393[/C][/ROW]
[ROW][C]112[/C][C]-0.007949[/C][C]-0.006927[/C][C]-0.001022[/C][/ROW]
[ROW][C]113[/C][C]-0.003463[/C][C] 0.002464[/C][C]-0.005927[/C][/ROW]
[ROW][C]114[/C][C] 0.01573[/C][C] 0.007974[/C][C] 0.007758[/C][/ROW]
[ROW][C]115[/C][C]-0.01582[/C][C]-0.01528[/C][C]-0.0005464[/C][/ROW]
[ROW][C]116[/C][C] 0.01064[/C][C] 0.005662[/C][C] 0.004983[/C][/ROW]
[ROW][C]117[/C][C]-0.009902[/C][C]-0.01192[/C][C] 0.002018[/C][/ROW]
[ROW][C]118[/C][C] 0.004644[/C][C] 0.01404[/C][C]-0.009393[/C][/ROW]
[ROW][C]119[/C][C] 0.01985[/C][C] 0.001618[/C][C] 0.01823[/C][/ROW]
[ROW][C]120[/C][C]-0.0285[/C][C]-0.02208[/C][C]-0.006418[/C][/ROW]
[ROW][C]121[/C][C]-0.002284[/C][C] 0.0004002[/C][C]-0.002684[/C][/ROW]
[ROW][C]122[/C][C]-0.0109[/C][C]-0.0003464[/C][C]-0.01056[/C][/ROW]
[ROW][C]123[/C][C] 0.005703[/C][C] 0.007182[/C][C]-0.001479[/C][/ROW]
[ROW][C]124[/C][C] 0.001412[/C][C]-0.001995[/C][C] 0.003406[/C][/ROW]
[ROW][C]125[/C][C] 0.00867[/C][C] 0.01264[/C][C]-0.003967[/C][/ROW]
[ROW][C]126[/C][C]-0.005277[/C][C]-0.006199[/C][C] 0.000922[/C][/ROW]
[ROW][C]127[/C][C]-0.004044[/C][C] 0.007035[/C][C]-0.01108[/C][/ROW]
[ROW][C]128[/C][C]-0.001045[/C][C]-0.001935[/C][C] 0.0008897[/C][/ROW]
[ROW][C]129[/C][C] 0.000697[/C][C] 0.00272[/C][C]-0.002023[/C][/ROW]
[ROW][C]130[/C][C]-0.001116[/C][C] 0.0042[/C][C]-0.005316[/C][/ROW]
[ROW][C]131[/C][C]-0.006961[/C][C]-0.005629[/C][C]-0.001332[/C][/ROW]
[ROW][C]132[/C][C] 0.02069[/C][C] 0.01518[/C][C] 0.005511[/C][/ROW]
[ROW][C]133[/C][C]-0.01297[/C][C]-0.00346[/C][C]-0.009507[/C][/ROW]
[ROW][C]134[/C][C] 0.008297[/C][C] 0.003957[/C][C] 0.004339[/C][/ROW]
[ROW][C]135[/C][C]-0.006427[/C][C]-0.007848[/C][C] 0.001421[/C][/ROW]
[ROW][C]136[/C][C] 0.01951[/C][C] 0.009012[/C][C] 0.0105[/C][/ROW]
[ROW][C]137[/C][C]-0.006928[/C][C]-0.01277[/C][C] 0.005841[/C][/ROW]
[ROW][C]138[/C][C]-0.009851[/C][C]-0.01451[/C][C] 0.004654[/C][/ROW]
[ROW][C]139[/C][C] 0.01199[/C][C] 0.007007[/C][C] 0.004986[/C][/ROW]
[ROW][C]140[/C][C]-0.006574[/C][C]-0.003116[/C][C]-0.003458[/C][/ROW]
[ROW][C]141[/C][C] 0.00122[/C][C] 0.004955[/C][C]-0.003736[/C][/ROW]
[ROW][C]142[/C][C] 0.005762[/C][C] 0.0008919[/C][C] 0.00487[/C][/ROW]
[ROW][C]143[/C][C]-0.0102[/C][C]-0.005803[/C][C]-0.004392[/C][/ROW]
[ROW][C]144[/C][C]-0.00106[/C][C] 0.0002403[/C][C]-0.001301[/C][/ROW]
[ROW][C]145[/C][C]-0.008141[/C][C] 0.008446[/C][C]-0.01659[/C][/ROW]
[ROW][C]146[/C][C]-0.0005802[/C][C] 0.00709[/C][C]-0.00767[/C][/ROW]
[ROW][C]147[/C][C] 0.01221[/C][C] 0.007905[/C][C] 0.00431[/C][/ROW]
[ROW][C]148[/C][C]-0.01438[/C][C]-0.009025[/C][C]-0.005359[/C][/ROW]
[ROW][C]149[/C][C] 0.001928[/C][C]-9.357e-05[/C][C] 0.002022[/C][/ROW]
[ROW][C]150[/C][C] 0.02923[/C][C] 0.01221[/C][C] 0.01702[/C][/ROW]
[ROW][C]151[/C][C] 0.005423[/C][C]-0.001311[/C][C] 0.006735[/C][/ROW]
[ROW][C]152[/C][C]-0.01622[/C][C]-0.01732[/C][C] 0.001105[/C][/ROW]
[ROW][C]153[/C][C]-0.0002036[/C][C]-0.001997[/C][C] 0.001794[/C][/ROW]
[ROW][C]154[/C][C] 0.006453[/C][C] 0.006845[/C][C]-0.0003918[/C][/ROW]
[ROW][C]155[/C][C]-0.006762[/C][C]-0.009807[/C][C] 0.003045[/C][/ROW]
[ROW][C]156[/C][C] 0.006298[/C][C] 0.0003205[/C][C] 0.005977[/C][/ROW]
[ROW][C]157[/C][C]-0.0007827[/C][C] 0.006392[/C][C]-0.007174[/C][/ROW]
[ROW][C]158[/C][C]-0.009345[/C][C] 0.003728[/C][C]-0.01307[/C][/ROW]
[ROW][C]159[/C][C]-0.0006665[/C][C]-0.004724[/C][C] 0.004057[/C][/ROW]
[ROW][C]160[/C][C] 0.01208[/C][C] 0.01161[/C][C] 0.0004671[/C][/ROW]
[ROW][C]161[/C][C]-0.01878[/C][C]-0.01254[/C][C]-0.006243[/C][/ROW]
[ROW][C]162[/C][C]-0.01189[/C][C]-0.009163[/C][C]-0.002723[/C][/ROW]
[ROW][C]163[/C][C]-0.004741[/C][C]-0.002513[/C][C]-0.002228[/C][/ROW]
[ROW][C]164[/C][C] 0.0219[/C][C] 0.01466[/C][C] 0.007248[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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	-0.01257	-0.001851	-0.01072
2	0.00151	0.005265	-0.003755
3	-0.01122	-0.005188	-0.006034
4	0.003633	0.0132	-0.009566
5	0.007736	-0.002489	0.01023
6	-0.0002584	0.002842	-0.003101
7	0.005085	0.001949	0.003135
8	-0.01738	-0.01231	-0.005069
9	0.00671	0.01192	-0.005207
10	0.01286	0.006409	0.006453
11	-0.01143	-0.009098	-0.002334
12	0.006996	0.002137	0.004859
13	0.00418	0.00214	0.002039
14	0.01384	0.002643	0.0112
15	-0.005876	0.0008596	-0.006736
16	0.009741	0.0004913	0.009249
17	-0.01865	-0.01648	-0.002173
18	0.007227	0.0005217	0.006705
19	-0.007335	-0.0034	-0.003935
20	0.009209	0.002442	0.006766
21	0.002303	0.0007109	0.001592
22	0.005825	-0.006281	0.01211
23	-0.003725	-0.006105	0.002381
24	0.006883	-0.001054	0.007937
25	-0.011	-0.006053	-0.004948
26	-0.00198	-0.003328	0.001348
27	0.01228	0.001271	0.01101
28	-0.003715	8.253e-05	-0.003797
29	-0.006059	-0.002222	-0.003838
30	-0.003705	0.003998	-0.007703
31	0.00487	0.006086	-0.001216
32	0.01106	0.004515	0.006549
33	-0.006109	-0.007612	0.001503
34	-0.01296	-0.009374	-0.003583
35	0.005539	0.004112	0.001427
36	-0.0003794	-0.001276	0.0008968
37	-0.008982	-0.002624	-0.006358
38	0.005669	0.003835	0.001834
39	-0.0003289	-0.006959	0.00663
40	-7.11e-05	-0.005409	0.005338
41	0.002393	0.00734	-0.004947
42	0.002602	0.005289	-0.002688
43	-0.01408	-0.006093	-0.00799
44	0.007178	-0.001337	0.008515
45	0.008724	0.004042	0.004682
46	-0.008084	-0.003789	-0.004295
47	0.004546	0.003604	0.0009425
48	0.00633	0.0008821	0.005447
49	-0.01705	-0.0002674	-0.01678
50	-0.01151	-0.0003765	-0.01113
51	0.004858	0.01543	-0.01057
52	-0.006472	-0.00729	0.0008181
53	0.01243	0.01154	0.0008916
54	-0.002558	0.002125	-0.004683
55	0.01373	0.00614	0.007595
56	-0.01454	-0.003253	-0.01128
57	-0.01258	-0.009392	-0.003188
58	0.01434	0.01431	2.361e-05
59	0.001523	0.003098	-0.001574
60	-0.02371	-0.01046	-0.01325
61	0.0364	0.02893	0.007476
62	0.007184	-0.002569	0.009753
63	-0.005412	-0.008408	0.002997
64	-0.0004686	0.00443	-0.004898
65	-0.005174	-0.005404	0.0002303
66	-0.003399	-0.008845	0.005446
67	-0.008861	-0.0001843	-0.008677
68	0.006579	0.00781	-0.001231
69	0.001422	0.006037	-0.004615
70	0.004647	-0.003275	0.007922
71	-0.008398	-0.004511	-0.003887
72	0.01475	0.007768	0.00698
73	-0.01317	-0.01066	-0.002502
74	0.001408	-0.004801	0.006209
75	-0.003396	-0.005509	0.002112
76	0.005115	0.01088	-0.005767
77	0.00824	0.002084	0.006155
78	-0.01572	-0.004759	-0.01097
79	0.006495	0.0146	-0.008103
80	-0.004471	-0.0008775	-0.003594
81	0.002105	0.00474	-0.002635
82	-0.001734	-0.00379	0.002057
83	-0.002436	-0.001063	-0.001372
84	0.001026	0.001779	-0.0007528
85	-0.005198	-0.008044	0.002846
86	-0.001153	0.007688	-0.008842
87	0.001393	0.001896	-0.0005034
88	-0.005651	-0.004512	-0.001138
89	-0.005368	-0.0103	0.004934
90	0.02921	0.02393	0.005277
91	-0.005422	-0.01105	0.005632
92	-0.00745	-0.009973	0.002523
93	0.007806	0.002677	0.005129
94	0.007107	0.004396	0.002711
95	-0.005909	-0.00439	-0.001518
96	-0.006987	0.0002239	-0.007211
97	0.01545	0.007326	0.008121
98	-0.01274	-0.007658	-0.005085
99	0.01103	0.005948	0.005081
100	0.002473	0.0003806	0.002092
101	0.000932	-0.003356	0.004288
102	-0.0185	-0.0185	4.676e-06
103	0.01075	0.001651	0.009098
104	0.004132	0.007582	-0.003451
105	0.00341	0.0008129	0.002597
106	-0.02302	-0.01995	-0.003078
107	0.007308	0.01632	-0.009015
108	0.009866	0.01014	-0.0002783
109	0.009589	-0.007891	0.01748
110	0.003556	0.003154	0.0004023
111	-0.001214	-0.008607	0.007393
112	-0.007949	-0.006927	-0.001022
113	-0.003463	0.002464	-0.005927
114	0.01573	0.007974	0.007758
115	-0.01582	-0.01528	-0.0005464
116	0.01064	0.005662	0.004983
117	-0.009902	-0.01192	0.002018
118	0.004644	0.01404	-0.009393
119	0.01985	0.001618	0.01823
120	-0.0285	-0.02208	-0.006418
121	-0.002284	0.0004002	-0.002684
122	-0.0109	-0.0003464	-0.01056
123	0.005703	0.007182	-0.001479
124	0.001412	-0.001995	0.003406
125	0.00867	0.01264	-0.003967
126	-0.005277	-0.006199	0.000922
127	-0.004044	0.007035	-0.01108
128	-0.001045	-0.001935	0.0008897
129	0.000697	0.00272	-0.002023
130	-0.001116	0.0042	-0.005316
131	-0.006961	-0.005629	-0.001332
132	0.02069	0.01518	0.005511
133	-0.01297	-0.00346	-0.009507
134	0.008297	0.003957	0.004339
135	-0.006427	-0.007848	0.001421
136	0.01951	0.009012	0.0105
137	-0.006928	-0.01277	0.005841
138	-0.009851	-0.01451	0.004654
139	0.01199	0.007007	0.004986
140	-0.006574	-0.003116	-0.003458
141	0.00122	0.004955	-0.003736
142	0.005762	0.0008919	0.00487
143	-0.0102	-0.005803	-0.004392
144	-0.00106	0.0002403	-0.001301
145	-0.008141	0.008446	-0.01659
146	-0.0005802	0.00709	-0.00767
147	0.01221	0.007905	0.00431
148	-0.01438	-0.009025	-0.005359
149	0.001928	-9.357e-05	0.002022
150	0.02923	0.01221	0.01702
151	0.005423	-0.001311	0.006735
152	-0.01622	-0.01732	0.001105
153	-0.0002036	-0.001997	0.001794
154	0.006453	0.006845	-0.0003918
155	-0.006762	-0.009807	0.003045
156	0.006298	0.0003205	0.005977
157	-0.0007827	0.006392	-0.007174
158	-0.009345	0.003728	-0.01307
159	-0.0006665	-0.004724	0.004057
160	0.01208	0.01161	0.0004671
161	-0.01878	-0.01254	-0.006243
162	-0.01189	-0.009163	-0.002723
163	-0.004741	-0.002513	-0.002228
164	0.0219	0.01466	0.007248

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
42	0.4433	0.8866	0.5567
43	0.3851	0.7701	0.6149
44	0.2806	0.5612	0.7194
45	0.1757	0.3514	0.8243
46	0.3725	0.745	0.6275
47	0.2696	0.5393	0.7304
48	0.3101	0.6203	0.6899
49	0.2735	0.547	0.7265
50	0.232	0.464	0.768
51	0.1877	0.3754	0.8123
52	0.2541	0.5081	0.7459
53	0.217	0.4341	0.783
54	0.1626	0.3252	0.8374
55	0.3083	0.6165	0.6917
56	0.2741	0.5482	0.7259
57	0.2406	0.4812	0.7594
58	0.1926	0.3853	0.8074
59	0.1477	0.2954	0.8523
60	0.1497	0.2993	0.8503
61	0.2348	0.4696	0.7652
62	0.3301	0.6602	0.6699
63	0.289	0.578	0.711
64	0.2545	0.509	0.7455
65	0.2558	0.5116	0.7442
66	0.2356	0.4712	0.7644
67	0.3367	0.6733	0.6633
68	0.3085	0.617	0.6915
69	0.3041	0.6081	0.6959
70	0.2852	0.5705	0.7148
71	0.2784	0.5569	0.7216
72	0.25	0.5	0.75
73	0.2345	0.4689	0.7655
74	0.2456	0.4913	0.7544
75	0.2123	0.4245	0.7877
76	0.2146	0.4292	0.7854
77	0.2437	0.4873	0.7563
78	0.3554	0.7108	0.6446
79	0.418	0.836	0.582
80	0.3856	0.7712	0.6144
81	0.3283	0.6567	0.6717
82	0.3518	0.7036	0.6482
83	0.31	0.62	0.69
84	0.3083	0.6165	0.6917
85	0.2671	0.5343	0.7329
86	0.2984	0.5969	0.7016
87	0.263	0.526	0.737
88	0.2221	0.4442	0.7779
89	0.1905	0.381	0.8095
90	0.2363	0.4726	0.7637
91	0.2353	0.4705	0.7647
92	0.2209	0.4417	0.7791
93	0.1834	0.3669	0.8166
94	0.1536	0.3072	0.8464
95	0.1224	0.2448	0.8776
96	0.1489	0.2978	0.8511
97	0.1373	0.2747	0.8627
98	0.1549	0.3098	0.8451
99	0.1391	0.2782	0.8609
100	0.1113	0.2226	0.8887
101	0.08302	0.166	0.917
102	0.09178	0.1836	0.9082
103	0.07435	0.1487	0.9256
104	0.05306	0.1061	0.9469
105	0.05009	0.1002	0.9499
106	0.03516	0.07032	0.9648
107	0.04074	0.08149	0.9593
108	0.02768	0.05535	0.9723
109	0.1004	0.2008	0.8996
110	0.07382	0.1476	0.9262
111	0.1079	0.2158	0.8921
112	0.08138	0.1628	0.9186
113	0.06179	0.1236	0.9382
114	0.05171	0.1034	0.9483
115	0.03265	0.0653	0.9673
116	0.02153	0.04307	0.9785
117	0.02514	0.05029	0.9749
118	0.01921	0.03843	0.9808
119	0.07594	0.1519	0.9241
120	0.07836	0.1567	0.9216
121	0.06694	0.1339	0.9331
122	0.03653	0.07306	0.9635

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
42 &  0.4433 &  0.8866 &  0.5567 \tabularnewline
43 &  0.3851 &  0.7701 &  0.6149 \tabularnewline
44 &  0.2806 &  0.5612 &  0.7194 \tabularnewline
45 &  0.1757 &  0.3514 &  0.8243 \tabularnewline
46 &  0.3725 &  0.745 &  0.6275 \tabularnewline
47 &  0.2696 &  0.5393 &  0.7304 \tabularnewline
48 &  0.3101 &  0.6203 &  0.6899 \tabularnewline
49 &  0.2735 &  0.547 &  0.7265 \tabularnewline
50 &  0.232 &  0.464 &  0.768 \tabularnewline
51 &  0.1877 &  0.3754 &  0.8123 \tabularnewline
52 &  0.2541 &  0.5081 &  0.7459 \tabularnewline
53 &  0.217 &  0.4341 &  0.783 \tabularnewline
54 &  0.1626 &  0.3252 &  0.8374 \tabularnewline
55 &  0.3083 &  0.6165 &  0.6917 \tabularnewline
56 &  0.2741 &  0.5482 &  0.7259 \tabularnewline
57 &  0.2406 &  0.4812 &  0.7594 \tabularnewline
58 &  0.1926 &  0.3853 &  0.8074 \tabularnewline
59 &  0.1477 &  0.2954 &  0.8523 \tabularnewline
60 &  0.1497 &  0.2993 &  0.8503 \tabularnewline
61 &  0.2348 &  0.4696 &  0.7652 \tabularnewline
62 &  0.3301 &  0.6602 &  0.6699 \tabularnewline
63 &  0.289 &  0.578 &  0.711 \tabularnewline
64 &  0.2545 &  0.509 &  0.7455 \tabularnewline
65 &  0.2558 &  0.5116 &  0.7442 \tabularnewline
66 &  0.2356 &  0.4712 &  0.7644 \tabularnewline
67 &  0.3367 &  0.6733 &  0.6633 \tabularnewline
68 &  0.3085 &  0.617 &  0.6915 \tabularnewline
69 &  0.3041 &  0.6081 &  0.6959 \tabularnewline
70 &  0.2852 &  0.5705 &  0.7148 \tabularnewline
71 &  0.2784 &  0.5569 &  0.7216 \tabularnewline
72 &  0.25 &  0.5 &  0.75 \tabularnewline
73 &  0.2345 &  0.4689 &  0.7655 \tabularnewline
74 &  0.2456 &  0.4913 &  0.7544 \tabularnewline
75 &  0.2123 &  0.4245 &  0.7877 \tabularnewline
76 &  0.2146 &  0.4292 &  0.7854 \tabularnewline
77 &  0.2437 &  0.4873 &  0.7563 \tabularnewline
78 &  0.3554 &  0.7108 &  0.6446 \tabularnewline
79 &  0.418 &  0.836 &  0.582 \tabularnewline
80 &  0.3856 &  0.7712 &  0.6144 \tabularnewline
81 &  0.3283 &  0.6567 &  0.6717 \tabularnewline
82 &  0.3518 &  0.7036 &  0.6482 \tabularnewline
83 &  0.31 &  0.62 &  0.69 \tabularnewline
84 &  0.3083 &  0.6165 &  0.6917 \tabularnewline
85 &  0.2671 &  0.5343 &  0.7329 \tabularnewline
86 &  0.2984 &  0.5969 &  0.7016 \tabularnewline
87 &  0.263 &  0.526 &  0.737 \tabularnewline
88 &  0.2221 &  0.4442 &  0.7779 \tabularnewline
89 &  0.1905 &  0.381 &  0.8095 \tabularnewline
90 &  0.2363 &  0.4726 &  0.7637 \tabularnewline
91 &  0.2353 &  0.4705 &  0.7647 \tabularnewline
92 &  0.2209 &  0.4417 &  0.7791 \tabularnewline
93 &  0.1834 &  0.3669 &  0.8166 \tabularnewline
94 &  0.1536 &  0.3072 &  0.8464 \tabularnewline
95 &  0.1224 &  0.2448 &  0.8776 \tabularnewline
96 &  0.1489 &  0.2978 &  0.8511 \tabularnewline
97 &  0.1373 &  0.2747 &  0.8627 \tabularnewline
98 &  0.1549 &  0.3098 &  0.8451 \tabularnewline
99 &  0.1391 &  0.2782 &  0.8609 \tabularnewline
100 &  0.1113 &  0.2226 &  0.8887 \tabularnewline
101 &  0.08302 &  0.166 &  0.917 \tabularnewline
102 &  0.09178 &  0.1836 &  0.9082 \tabularnewline
103 &  0.07435 &  0.1487 &  0.9256 \tabularnewline
104 &  0.05306 &  0.1061 &  0.9469 \tabularnewline
105 &  0.05009 &  0.1002 &  0.9499 \tabularnewline
106 &  0.03516 &  0.07032 &  0.9648 \tabularnewline
107 &  0.04074 &  0.08149 &  0.9593 \tabularnewline
108 &  0.02768 &  0.05535 &  0.9723 \tabularnewline
109 &  0.1004 &  0.2008 &  0.8996 \tabularnewline
110 &  0.07382 &  0.1476 &  0.9262 \tabularnewline
111 &  0.1079 &  0.2158 &  0.8921 \tabularnewline
112 &  0.08138 &  0.1628 &  0.9186 \tabularnewline
113 &  0.06179 &  0.1236 &  0.9382 \tabularnewline
114 &  0.05171 &  0.1034 &  0.9483 \tabularnewline
115 &  0.03265 &  0.0653 &  0.9673 \tabularnewline
116 &  0.02153 &  0.04307 &  0.9785 \tabularnewline
117 &  0.02514 &  0.05029 &  0.9749 \tabularnewline
118 &  0.01921 &  0.03843 &  0.9808 \tabularnewline
119 &  0.07594 &  0.1519 &  0.9241 \tabularnewline
120 &  0.07836 &  0.1567 &  0.9216 \tabularnewline
121 &  0.06694 &  0.1339 &  0.9331 \tabularnewline
122 &  0.03653 &  0.07306 &  0.9635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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]42[/C][C] 0.4433[/C][C] 0.8866[/C][C] 0.5567[/C][/ROW]
[ROW][C]43[/C][C] 0.3851[/C][C] 0.7701[/C][C] 0.6149[/C][/ROW]
[ROW][C]44[/C][C] 0.2806[/C][C] 0.5612[/C][C] 0.7194[/C][/ROW]
[ROW][C]45[/C][C] 0.1757[/C][C] 0.3514[/C][C] 0.8243[/C][/ROW]
[ROW][C]46[/C][C] 0.3725[/C][C] 0.745[/C][C] 0.6275[/C][/ROW]
[ROW][C]47[/C][C] 0.2696[/C][C] 0.5393[/C][C] 0.7304[/C][/ROW]
[ROW][C]48[/C][C] 0.3101[/C][C] 0.6203[/C][C] 0.6899[/C][/ROW]
[ROW][C]49[/C][C] 0.2735[/C][C] 0.547[/C][C] 0.7265[/C][/ROW]
[ROW][C]50[/C][C] 0.232[/C][C] 0.464[/C][C] 0.768[/C][/ROW]
[ROW][C]51[/C][C] 0.1877[/C][C] 0.3754[/C][C] 0.8123[/C][/ROW]
[ROW][C]52[/C][C] 0.2541[/C][C] 0.5081[/C][C] 0.7459[/C][/ROW]
[ROW][C]53[/C][C] 0.217[/C][C] 0.4341[/C][C] 0.783[/C][/ROW]
[ROW][C]54[/C][C] 0.1626[/C][C] 0.3252[/C][C] 0.8374[/C][/ROW]
[ROW][C]55[/C][C] 0.3083[/C][C] 0.6165[/C][C] 0.6917[/C][/ROW]
[ROW][C]56[/C][C] 0.2741[/C][C] 0.5482[/C][C] 0.7259[/C][/ROW]
[ROW][C]57[/C][C] 0.2406[/C][C] 0.4812[/C][C] 0.7594[/C][/ROW]
[ROW][C]58[/C][C] 0.1926[/C][C] 0.3853[/C][C] 0.8074[/C][/ROW]
[ROW][C]59[/C][C] 0.1477[/C][C] 0.2954[/C][C] 0.8523[/C][/ROW]
[ROW][C]60[/C][C] 0.1497[/C][C] 0.2993[/C][C] 0.8503[/C][/ROW]
[ROW][C]61[/C][C] 0.2348[/C][C] 0.4696[/C][C] 0.7652[/C][/ROW]
[ROW][C]62[/C][C] 0.3301[/C][C] 0.6602[/C][C] 0.6699[/C][/ROW]
[ROW][C]63[/C][C] 0.289[/C][C] 0.578[/C][C] 0.711[/C][/ROW]
[ROW][C]64[/C][C] 0.2545[/C][C] 0.509[/C][C] 0.7455[/C][/ROW]
[ROW][C]65[/C][C] 0.2558[/C][C] 0.5116[/C][C] 0.7442[/C][/ROW]
[ROW][C]66[/C][C] 0.2356[/C][C] 0.4712[/C][C] 0.7644[/C][/ROW]
[ROW][C]67[/C][C] 0.3367[/C][C] 0.6733[/C][C] 0.6633[/C][/ROW]
[ROW][C]68[/C][C] 0.3085[/C][C] 0.617[/C][C] 0.6915[/C][/ROW]
[ROW][C]69[/C][C] 0.3041[/C][C] 0.6081[/C][C] 0.6959[/C][/ROW]
[ROW][C]70[/C][C] 0.2852[/C][C] 0.5705[/C][C] 0.7148[/C][/ROW]
[ROW][C]71[/C][C] 0.2784[/C][C] 0.5569[/C][C] 0.7216[/C][/ROW]
[ROW][C]72[/C][C] 0.25[/C][C] 0.5[/C][C] 0.75[/C][/ROW]
[ROW][C]73[/C][C] 0.2345[/C][C] 0.4689[/C][C] 0.7655[/C][/ROW]
[ROW][C]74[/C][C] 0.2456[/C][C] 0.4913[/C][C] 0.7544[/C][/ROW]
[ROW][C]75[/C][C] 0.2123[/C][C] 0.4245[/C][C] 0.7877[/C][/ROW]
[ROW][C]76[/C][C] 0.2146[/C][C] 0.4292[/C][C] 0.7854[/C][/ROW]
[ROW][C]77[/C][C] 0.2437[/C][C] 0.4873[/C][C] 0.7563[/C][/ROW]
[ROW][C]78[/C][C] 0.3554[/C][C] 0.7108[/C][C] 0.6446[/C][/ROW]
[ROW][C]79[/C][C] 0.418[/C][C] 0.836[/C][C] 0.582[/C][/ROW]
[ROW][C]80[/C][C] 0.3856[/C][C] 0.7712[/C][C] 0.6144[/C][/ROW]
[ROW][C]81[/C][C] 0.3283[/C][C] 0.6567[/C][C] 0.6717[/C][/ROW]
[ROW][C]82[/C][C] 0.3518[/C][C] 0.7036[/C][C] 0.6482[/C][/ROW]
[ROW][C]83[/C][C] 0.31[/C][C] 0.62[/C][C] 0.69[/C][/ROW]
[ROW][C]84[/C][C] 0.3083[/C][C] 0.6165[/C][C] 0.6917[/C][/ROW]
[ROW][C]85[/C][C] 0.2671[/C][C] 0.5343[/C][C] 0.7329[/C][/ROW]
[ROW][C]86[/C][C] 0.2984[/C][C] 0.5969[/C][C] 0.7016[/C][/ROW]
[ROW][C]87[/C][C] 0.263[/C][C] 0.526[/C][C] 0.737[/C][/ROW]
[ROW][C]88[/C][C] 0.2221[/C][C] 0.4442[/C][C] 0.7779[/C][/ROW]
[ROW][C]89[/C][C] 0.1905[/C][C] 0.381[/C][C] 0.8095[/C][/ROW]
[ROW][C]90[/C][C] 0.2363[/C][C] 0.4726[/C][C] 0.7637[/C][/ROW]
[ROW][C]91[/C][C] 0.2353[/C][C] 0.4705[/C][C] 0.7647[/C][/ROW]
[ROW][C]92[/C][C] 0.2209[/C][C] 0.4417[/C][C] 0.7791[/C][/ROW]
[ROW][C]93[/C][C] 0.1834[/C][C] 0.3669[/C][C] 0.8166[/C][/ROW]
[ROW][C]94[/C][C] 0.1536[/C][C] 0.3072[/C][C] 0.8464[/C][/ROW]
[ROW][C]95[/C][C] 0.1224[/C][C] 0.2448[/C][C] 0.8776[/C][/ROW]
[ROW][C]96[/C][C] 0.1489[/C][C] 0.2978[/C][C] 0.8511[/C][/ROW]
[ROW][C]97[/C][C] 0.1373[/C][C] 0.2747[/C][C] 0.8627[/C][/ROW]
[ROW][C]98[/C][C] 0.1549[/C][C] 0.3098[/C][C] 0.8451[/C][/ROW]
[ROW][C]99[/C][C] 0.1391[/C][C] 0.2782[/C][C] 0.8609[/C][/ROW]
[ROW][C]100[/C][C] 0.1113[/C][C] 0.2226[/C][C] 0.8887[/C][/ROW]
[ROW][C]101[/C][C] 0.08302[/C][C] 0.166[/C][C] 0.917[/C][/ROW]
[ROW][C]102[/C][C] 0.09178[/C][C] 0.1836[/C][C] 0.9082[/C][/ROW]
[ROW][C]103[/C][C] 0.07435[/C][C] 0.1487[/C][C] 0.9256[/C][/ROW]
[ROW][C]104[/C][C] 0.05306[/C][C] 0.1061[/C][C] 0.9469[/C][/ROW]
[ROW][C]105[/C][C] 0.05009[/C][C] 0.1002[/C][C] 0.9499[/C][/ROW]
[ROW][C]106[/C][C] 0.03516[/C][C] 0.07032[/C][C] 0.9648[/C][/ROW]
[ROW][C]107[/C][C] 0.04074[/C][C] 0.08149[/C][C] 0.9593[/C][/ROW]
[ROW][C]108[/C][C] 0.02768[/C][C] 0.05535[/C][C] 0.9723[/C][/ROW]
[ROW][C]109[/C][C] 0.1004[/C][C] 0.2008[/C][C] 0.8996[/C][/ROW]
[ROW][C]110[/C][C] 0.07382[/C][C] 0.1476[/C][C] 0.9262[/C][/ROW]
[ROW][C]111[/C][C] 0.1079[/C][C] 0.2158[/C][C] 0.8921[/C][/ROW]
[ROW][C]112[/C][C] 0.08138[/C][C] 0.1628[/C][C] 0.9186[/C][/ROW]
[ROW][C]113[/C][C] 0.06179[/C][C] 0.1236[/C][C] 0.9382[/C][/ROW]
[ROW][C]114[/C][C] 0.05171[/C][C] 0.1034[/C][C] 0.9483[/C][/ROW]
[ROW][C]115[/C][C] 0.03265[/C][C] 0.0653[/C][C] 0.9673[/C][/ROW]
[ROW][C]116[/C][C] 0.02153[/C][C] 0.04307[/C][C] 0.9785[/C][/ROW]
[ROW][C]117[/C][C] 0.02514[/C][C] 0.05029[/C][C] 0.9749[/C][/ROW]
[ROW][C]118[/C][C] 0.01921[/C][C] 0.03843[/C][C] 0.9808[/C][/ROW]
[ROW][C]119[/C][C] 0.07594[/C][C] 0.1519[/C][C] 0.9241[/C][/ROW]
[ROW][C]120[/C][C] 0.07836[/C][C] 0.1567[/C][C] 0.9216[/C][/ROW]
[ROW][C]121[/C][C] 0.06694[/C][C] 0.1339[/C][C] 0.9331[/C][/ROW]
[ROW][C]122[/C][C] 0.03653[/C][C] 0.07306[/C][C] 0.9635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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
42	0.4433	0.8866	0.5567
43	0.3851	0.7701	0.6149
44	0.2806	0.5612	0.7194
45	0.1757	0.3514	0.8243
46	0.3725	0.745	0.6275
47	0.2696	0.5393	0.7304
48	0.3101	0.6203	0.6899
49	0.2735	0.547	0.7265
50	0.232	0.464	0.768
51	0.1877	0.3754	0.8123
52	0.2541	0.5081	0.7459
53	0.217	0.4341	0.783
54	0.1626	0.3252	0.8374
55	0.3083	0.6165	0.6917
56	0.2741	0.5482	0.7259
57	0.2406	0.4812	0.7594
58	0.1926	0.3853	0.8074
59	0.1477	0.2954	0.8523
60	0.1497	0.2993	0.8503
61	0.2348	0.4696	0.7652
62	0.3301	0.6602	0.6699
63	0.289	0.578	0.711
64	0.2545	0.509	0.7455
65	0.2558	0.5116	0.7442
66	0.2356	0.4712	0.7644
67	0.3367	0.6733	0.6633
68	0.3085	0.617	0.6915
69	0.3041	0.6081	0.6959
70	0.2852	0.5705	0.7148
71	0.2784	0.5569	0.7216
72	0.25	0.5	0.75
73	0.2345	0.4689	0.7655
74	0.2456	0.4913	0.7544
75	0.2123	0.4245	0.7877
76	0.2146	0.4292	0.7854
77	0.2437	0.4873	0.7563
78	0.3554	0.7108	0.6446
79	0.418	0.836	0.582
80	0.3856	0.7712	0.6144
81	0.3283	0.6567	0.6717
82	0.3518	0.7036	0.6482
83	0.31	0.62	0.69
84	0.3083	0.6165	0.6917
85	0.2671	0.5343	0.7329
86	0.2984	0.5969	0.7016
87	0.263	0.526	0.737
88	0.2221	0.4442	0.7779
89	0.1905	0.381	0.8095
90	0.2363	0.4726	0.7637
91	0.2353	0.4705	0.7647
92	0.2209	0.4417	0.7791
93	0.1834	0.3669	0.8166
94	0.1536	0.3072	0.8464
95	0.1224	0.2448	0.8776
96	0.1489	0.2978	0.8511
97	0.1373	0.2747	0.8627
98	0.1549	0.3098	0.8451
99	0.1391	0.2782	0.8609
100	0.1113	0.2226	0.8887
101	0.08302	0.166	0.917
102	0.09178	0.1836	0.9082
103	0.07435	0.1487	0.9256
104	0.05306	0.1061	0.9469
105	0.05009	0.1002	0.9499
106	0.03516	0.07032	0.9648
107	0.04074	0.08149	0.9593
108	0.02768	0.05535	0.9723
109	0.1004	0.2008	0.8996
110	0.07382	0.1476	0.9262
111	0.1079	0.2158	0.8921
112	0.08138	0.1628	0.9186
113	0.06179	0.1236	0.9382
114	0.05171	0.1034	0.9483
115	0.03265	0.0653	0.9673
116	0.02153	0.04307	0.9785
117	0.02514	0.05029	0.9749
118	0.01921	0.03843	0.9808
119	0.07594	0.1519	0.9241
120	0.07836	0.1567	0.9216
121	0.06694	0.1339	0.9331
122	0.03653	0.07306	0.9635

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0246914	OK
10% type I error level	8	0.0987654	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 2 & 0.0246914 & OK \tabularnewline
10% type I error level & 8 & 0.0987654 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0246914[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0987654[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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	0	0	OK
5% type I error level	2	0.0246914	OK
10% type I error level	8	0.0987654	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.5136, df1 = 2, df2 = 123, p-value = 0.2242
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.81749, df1 = 76, df2 = 49, p-value = 0.7876
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.088687, df1 = 2, df2 = 123, p-value = 0.9152

\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 = 1.5136, df1 = 2, df2 = 123, p-value = 0.2242
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.81749, df1 = 76, df2 = 49, p-value = 0.7876
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.088687, df1 = 2, df2 = 123, p-value = 0.9152
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&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 = 1.5136, df1 = 2, df2 = 123, p-value = 0.2242
[/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 = 0.81749, df1 = 76, df2 = 49, p-value = 0.7876
[/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 = 0.088687, df1 = 2, df2 = 123, p-value = 0.9152
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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 = 1.5136, df1 = 2, df2 = 123, p-value = 0.2242
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.81749, df1 = 76, df2 = 49, p-value = 0.7876
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.088687, df1 = 2, df2 = 123, p-value = 0.9152

Variance Inflation Factors (Multicollinearity)

> vif
        `(1-Bs)(1-B)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         3.113183                          1.891100 
`(1-Bs)(1-B)DurableConsumerGoods`         `(1-Bs)(1-B)Tobacco(t-1)` 
                         1.858948                          2.200203 
        `(1-Bs)(1-B)Tobacco(t-2)`         `(1-Bs)(1-B)Tobacco(t-3)` 
                         2.718848                          2.956259 
        `(1-Bs)(1-B)Tobacco(t-4)`         `(1-Bs)(1-B)Tobacco(t-5)` 
                         3.040393                          3.070269 
        `(1-Bs)(1-B)Tobacco(t-6)`         `(1-Bs)(1-B)Tobacco(t-7)` 
                         3.068375                          3.099189 
        `(1-Bs)(1-B)Tobacco(t-8)`         `(1-Bs)(1-B)Tobacco(t-9)` 
                         3.092479                          3.091747 
       `(1-Bs)(1-B)Tobacco(t-10)`        `(1-Bs)(1-B)Tobacco(t-11)` 
                         3.141148                          2.924568 
       `(1-Bs)(1-B)Tobacco(t-12)`        `(1-Bs)(1-B)Tobacco(t-13)` 
                         2.847170                          3.604314 
       `(1-Bs)(1-B)Tobacco(t-14)`        `(1-Bs)(1-B)Tobacco(t-15)` 
                         3.845738                          3.788892 
       `(1-Bs)(1-B)Tobacco(t-16)`        `(1-Bs)(1-B)Tobacco(t-17)` 
                         3.800573                          3.755864 
       `(1-Bs)(1-B)Tobacco(t-18)`        `(1-Bs)(1-B)Tobacco(t-19)` 
                         3.811536                          3.873553 
       `(1-Bs)(1-B)Tobacco(t-20)`        `(1-Bs)(1-B)Tobacco(t-21)` 
                         3.803205                          3.757499 
       `(1-Bs)(1-B)Tobacco(t-22)`        `(1-Bs)(1-B)Tobacco(t-23)` 
                         3.748712                          3.466888 
       `(1-Bs)(1-B)Tobacco(t-24)`        `(1-Bs)(1-B)Tobacco(t-25)` 
                         3.088572                          3.405151 
       `(1-Bs)(1-B)Tobacco(t-26)`        `(1-Bs)(1-B)Tobacco(t-27)` 
                         3.499885                          3.504201 
       `(1-Bs)(1-B)Tobacco(t-28)`        `(1-Bs)(1-B)Tobacco(t-29)` 
                         3.545334                          3.611965 
       `(1-Bs)(1-B)Tobacco(t-30)`        `(1-Bs)(1-B)Tobacco(t-31)` 
                         3.618146                          3.604591 
       `(1-Bs)(1-B)Tobacco(t-32)`        `(1-Bs)(1-B)Tobacco(t-33)` 
                         3.382614                          3.158633 
       `(1-Bs)(1-B)Tobacco(t-34)`        `(1-Bs)(1-B)Tobacco(t-35)` 
                         2.819506                          2.151623

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        `(1-Bs)(1-B)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         3.113183                          1.891100 
`(1-Bs)(1-B)DurableConsumerGoods`         `(1-Bs)(1-B)Tobacco(t-1)` 
                         1.858948                          2.200203 
        `(1-Bs)(1-B)Tobacco(t-2)`         `(1-Bs)(1-B)Tobacco(t-3)` 
                         2.718848                          2.956259 
        `(1-Bs)(1-B)Tobacco(t-4)`         `(1-Bs)(1-B)Tobacco(t-5)` 
                         3.040393                          3.070269 
        `(1-Bs)(1-B)Tobacco(t-6)`         `(1-Bs)(1-B)Tobacco(t-7)` 
                         3.068375                          3.099189 
        `(1-Bs)(1-B)Tobacco(t-8)`         `(1-Bs)(1-B)Tobacco(t-9)` 
                         3.092479                          3.091747 
       `(1-Bs)(1-B)Tobacco(t-10)`        `(1-Bs)(1-B)Tobacco(t-11)` 
                         3.141148                          2.924568 
       `(1-Bs)(1-B)Tobacco(t-12)`        `(1-Bs)(1-B)Tobacco(t-13)` 
                         2.847170                          3.604314 
       `(1-Bs)(1-B)Tobacco(t-14)`        `(1-Bs)(1-B)Tobacco(t-15)` 
                         3.845738                          3.788892 
       `(1-Bs)(1-B)Tobacco(t-16)`        `(1-Bs)(1-B)Tobacco(t-17)` 
                         3.800573                          3.755864 
       `(1-Bs)(1-B)Tobacco(t-18)`        `(1-Bs)(1-B)Tobacco(t-19)` 
                         3.811536                          3.873553 
       `(1-Bs)(1-B)Tobacco(t-20)`        `(1-Bs)(1-B)Tobacco(t-21)` 
                         3.803205                          3.757499 
       `(1-Bs)(1-B)Tobacco(t-22)`        `(1-Bs)(1-B)Tobacco(t-23)` 
                         3.748712                          3.466888 
       `(1-Bs)(1-B)Tobacco(t-24)`        `(1-Bs)(1-B)Tobacco(t-25)` 
                         3.088572                          3.405151 
       `(1-Bs)(1-B)Tobacco(t-26)`        `(1-Bs)(1-B)Tobacco(t-27)` 
                         3.499885                          3.504201 
       `(1-Bs)(1-B)Tobacco(t-28)`        `(1-Bs)(1-B)Tobacco(t-29)` 
                         3.545334                          3.611965 
       `(1-Bs)(1-B)Tobacco(t-30)`        `(1-Bs)(1-B)Tobacco(t-31)` 
                         3.618146                          3.604591 
       `(1-Bs)(1-B)Tobacco(t-32)`        `(1-Bs)(1-B)Tobacco(t-33)` 
                         3.382614                          3.158633 
       `(1-Bs)(1-B)Tobacco(t-34)`        `(1-Bs)(1-B)Tobacco(t-35)` 
                         2.819506                          2.151623 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310117&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
        `(1-Bs)(1-B)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         3.113183                          1.891100 
`(1-Bs)(1-B)DurableConsumerGoods`         `(1-Bs)(1-B)Tobacco(t-1)` 
                         1.858948                          2.200203 
        `(1-Bs)(1-B)Tobacco(t-2)`         `(1-Bs)(1-B)Tobacco(t-3)` 
                         2.718848                          2.956259 
        `(1-Bs)(1-B)Tobacco(t-4)`         `(1-Bs)(1-B)Tobacco(t-5)` 
                         3.040393                          3.070269 
        `(1-Bs)(1-B)Tobacco(t-6)`         `(1-Bs)(1-B)Tobacco(t-7)` 
                         3.068375                          3.099189 
        `(1-Bs)(1-B)Tobacco(t-8)`         `(1-Bs)(1-B)Tobacco(t-9)` 
                         3.092479                          3.091747 
       `(1-Bs)(1-B)Tobacco(t-10)`        `(1-Bs)(1-B)Tobacco(t-11)` 
                         3.141148                          2.924568 
       `(1-Bs)(1-B)Tobacco(t-12)`        `(1-Bs)(1-B)Tobacco(t-13)` 
                         2.847170                          3.604314 
       `(1-Bs)(1-B)Tobacco(t-14)`        `(1-Bs)(1-B)Tobacco(t-15)` 
                         3.845738                          3.788892 
       `(1-Bs)(1-B)Tobacco(t-16)`        `(1-Bs)(1-B)Tobacco(t-17)` 
                         3.800573                          3.755864 
       `(1-Bs)(1-B)Tobacco(t-18)`        `(1-Bs)(1-B)Tobacco(t-19)` 
                         3.811536                          3.873553 
       `(1-Bs)(1-B)Tobacco(t-20)`        `(1-Bs)(1-B)Tobacco(t-21)` 
                         3.803205                          3.757499 
       `(1-Bs)(1-B)Tobacco(t-22)`        `(1-Bs)(1-B)Tobacco(t-23)` 
                         3.748712                          3.466888 
       `(1-Bs)(1-B)Tobacco(t-24)`        `(1-Bs)(1-B)Tobacco(t-25)` 
                         3.088572                          3.405151 
       `(1-Bs)(1-B)Tobacco(t-26)`        `(1-Bs)(1-B)Tobacco(t-27)` 
                         3.499885                          3.504201 
       `(1-Bs)(1-B)Tobacco(t-28)`        `(1-Bs)(1-B)Tobacco(t-29)` 
                         3.545334                          3.611965 
       `(1-Bs)(1-B)Tobacco(t-30)`        `(1-Bs)(1-B)Tobacco(t-31)` 
                         3.618146                          3.604591 
       `(1-Bs)(1-B)Tobacco(t-32)`        `(1-Bs)(1-B)Tobacco(t-33)` 
                         3.382614                          3.158633 
       `(1-Bs)(1-B)Tobacco(t-34)`        `(1-Bs)(1-B)Tobacco(t-35)` 
                         2.819506                          2.151623 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310117&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310117&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)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         3.113183                          1.891100 
`(1-Bs)(1-B)DurableConsumerGoods`         `(1-Bs)(1-B)Tobacco(t-1)` 
                         1.858948                          2.200203 
        `(1-Bs)(1-B)Tobacco(t-2)`         `(1-Bs)(1-B)Tobacco(t-3)` 
                         2.718848                          2.956259 
        `(1-Bs)(1-B)Tobacco(t-4)`         `(1-Bs)(1-B)Tobacco(t-5)` 
                         3.040393                          3.070269 
        `(1-Bs)(1-B)Tobacco(t-6)`         `(1-Bs)(1-B)Tobacco(t-7)` 
                         3.068375                          3.099189 
        `(1-Bs)(1-B)Tobacco(t-8)`         `(1-Bs)(1-B)Tobacco(t-9)` 
                         3.092479                          3.091747 
       `(1-Bs)(1-B)Tobacco(t-10)`        `(1-Bs)(1-B)Tobacco(t-11)` 
                         3.141148                          2.924568 
       `(1-Bs)(1-B)Tobacco(t-12)`        `(1-Bs)(1-B)Tobacco(t-13)` 
                         2.847170                          3.604314 
       `(1-Bs)(1-B)Tobacco(t-14)`        `(1-Bs)(1-B)Tobacco(t-15)` 
                         3.845738                          3.788892 
       `(1-Bs)(1-B)Tobacco(t-16)`        `(1-Bs)(1-B)Tobacco(t-17)` 
                         3.800573                          3.755864 
       `(1-Bs)(1-B)Tobacco(t-18)`        `(1-Bs)(1-B)Tobacco(t-19)` 
                         3.811536                          3.873553 
       `(1-Bs)(1-B)Tobacco(t-20)`        `(1-Bs)(1-B)Tobacco(t-21)` 
                         3.803205                          3.757499 
       `(1-Bs)(1-B)Tobacco(t-22)`        `(1-Bs)(1-B)Tobacco(t-23)` 
                         3.748712                          3.466888 
       `(1-Bs)(1-B)Tobacco(t-24)`        `(1-Bs)(1-B)Tobacco(t-25)` 
                         3.088572                          3.405151 
       `(1-Bs)(1-B)Tobacco(t-26)`        `(1-Bs)(1-B)Tobacco(t-27)` 
                         3.499885                          3.504201 
       `(1-Bs)(1-B)Tobacco(t-28)`        `(1-Bs)(1-B)Tobacco(t-29)` 
                         3.545334                          3.611965 
       `(1-Bs)(1-B)Tobacco(t-30)`        `(1-Bs)(1-B)Tobacco(t-31)` 
                         3.618146                          3.604591 
       `(1-Bs)(1-B)Tobacco(t-32)`        `(1-Bs)(1-B)Tobacco(t-33)` 
                         3.382614                          3.158633 
       `(1-Bs)(1-B)Tobacco(t-34)`        `(1-Bs)(1-B)Tobacco(t-35)` 
                         2.819506                          2.151623

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 35 ; par5 = 0 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '0'
par4 <- '36'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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