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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 18 Dec 2017 10:52:14 +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/t1513591028twb2mbe64qkd8dt.htm/, Retrieved Tue, 14 May 2024 16:15:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310097, Retrieved Tue, 14 May 2024 16:15:07 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

106

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2017-12-18 09:52:14] [867b6df3e80c046baffd373216517d1f] [Current]

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Dataseries X:

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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	12 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 time12 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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]

12 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310097&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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	12 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)Tobacco[t] = + 2.8053e-05 -0.000875364`(1-Bs)(1-B)FoodProducts`[t] -0.00011978`(1-Bs)(1-B)Beverages`[t] + 0.000103004`(1-Bs)(1-B)DurableConsumerGoods`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)Tobacco[t] =  +  2.8053e-05 -0.000875364`(1-Bs)(1-B)FoodProducts`[t] -0.00011978`(1-Bs)(1-B)Beverages`[t] +  0.000103004`(1-Bs)(1-B)DurableConsumerGoods`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)Tobacco[t] =  +  2.8053e-05 -0.000875364`(1-Bs)(1-B)FoodProducts`[t] -0.00011978`(1-Bs)(1-B)Beverages`[t] +  0.000103004`(1-Bs)(1-B)DurableConsumerGoods`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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] = + 2.8053e-05 -0.000875364`(1-Bs)(1-B)FoodProducts`[t] -0.00011978`(1-Bs)(1-B)Beverages`[t] + 0.000103004`(1-Bs)(1-B)DurableConsumerGoods`[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)	+2.805e-05	0.0006374	+4.4010e-02	0.9649	0.4825
`(1-Bs)(1-B)FoodProducts`	-0.0008754	0.0001616	-5.4170e+00	1.773e-07	8.865e-08
`(1-Bs)(1-B)Beverages`	-0.0001198	6.868e-05	-1.7440e+00	0.08273	0.04136
`(1-Bs)(1-B)DurableConsumerGoods`	+0.000103	7.572e-05	+1.3600e+00	0.1753	0.08764

\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) & +2.805e-05 &  0.0006374 & +4.4010e-02 &  0.9649 &  0.4825 \tabularnewline
`(1-Bs)(1-B)FoodProducts` & -0.0008754 &  0.0001616 & -5.4170e+00 &  1.773e-07 &  8.865e-08 \tabularnewline
`(1-Bs)(1-B)Beverages` & -0.0001198 &  6.868e-05 & -1.7440e+00 &  0.08273 &  0.04136 \tabularnewline
`(1-Bs)(1-B)DurableConsumerGoods` & +0.000103 &  7.572e-05 & +1.3600e+00 &  0.1753 &  0.08764 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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]+2.805e-05[/C][C] 0.0006374[/C][C]+4.4010e-02[/C][C] 0.9649[/C][C] 0.4825[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FoodProducts`[/C][C]-0.0008754[/C][C] 0.0001616[/C][C]-5.4170e+00[/C][C] 1.773e-07[/C][C] 8.865e-08[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Beverages`[/C][C]-0.0001198[/C][C] 6.868e-05[/C][C]-1.7440e+00[/C][C] 0.08273[/C][C] 0.04136[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)DurableConsumerGoods`[/C][C]+0.000103[/C][C] 7.572e-05[/C][C]+1.3600e+00[/C][C] 0.1753[/C][C] 0.08764[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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)	+2.805e-05	0.0006374	+4.4010e-02	0.9649	0.4825
`(1-Bs)(1-B)FoodProducts`	-0.0008754	0.0001616	-5.4170e+00	1.773e-07	8.865e-08
`(1-Bs)(1-B)Beverages`	-0.0001198	6.868e-05	-1.7440e+00	0.08273	0.04136
`(1-Bs)(1-B)DurableConsumerGoods`	+0.000103	7.572e-05	+1.3600e+00	0.1753	0.08764

Multiple Linear Regression - Regression Statistics
Multiple R	0.4781
R-squared	0.2286
Adjusted R-squared	0.2167
F-TEST (value)	19.26
F-TEST (DF numerator)	3
F-TEST (DF denominator)	195
p-value	5.566e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.008992
Sum Squared Residuals	0.01577

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4781 \tabularnewline
R-squared &  0.2286 \tabularnewline
Adjusted R-squared &  0.2167 \tabularnewline
F-TEST (value) &  19.26 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 195 \tabularnewline
p-value &  5.566e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.008992 \tabularnewline
Sum Squared Residuals &  0.01577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4781[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2286[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2167[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 19.26[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]195[/C][/ROW]
[ROW][C]p-value[/C][C] 5.566e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.008992[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.01577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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	0.4781
R-squared	0.2286
Adjusted R-squared	0.2167
F-TEST (value)	19.26
F-TEST (DF numerator)	3
F-TEST (DF denominator)	195
p-value	5.566e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.008992
Sum Squared Residuals	0.01577

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=310097&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=310097&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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	0.009016	0.004816	0.004199
2	0.001358	-0.00323	0.004588
3	-0.003914	0.003332	-0.007246
4	0.009657	0.002138	0.007519
5	-0.007071	-0.002992	-0.004079
6	-0.001227	-0.004672	0.003446
7	0.001281	0.002687	-0.001407
8	0.001358	0.002312	-0.0009541
9	0.004436	0.0006444	0.003792
10	0.003716	0.001544	0.002172
11	-0.02084	0.001109	-0.02195
12	0.009637	-0.002044	0.01168
13	0.003498	-0.0005894	0.004087
14	-0.006772	0.002001	-0.008773
15	0.004007	-0.002841	0.006847
16	0.0002731	0.003043	-0.00277
17	-0.009647	0.00172	-0.01137
18	0.01534	-0.002065	0.01741
19	0.008081	0.003289	0.004792
20	-0.02447	-0.003817	-0.02065
21	0.003766	0.0004589	0.003307
22	-0.01062	0.002905	-0.01352
23	0.01106	-0.006509	0.01757
24	0.008513	0.002801	0.005712
25	-0.01033	-0.0004053	-0.009927
26	0.00913	0.002264	0.006867
27	-0.009494	-0.001877	-0.007617
28	0.01224	0.003977	0.008268
29	-0.004565	-0.004031	-0.000534
30	-0.003687	0.001019	-0.004705
31	0.004877	0.003099	0.001778
32	-0.003558	-0.002499	-0.00106
33	-0.002163	0.0001432	-0.002306
34	0.007967	0.001379	0.006588
35	-0.0005427	-0.0004835	-5.913e-05
36	-0.01257	0.002165	-0.01473
37	0.00151	-0.001154	0.002664
38	-0.01122	-0.00731	-0.003912
39	0.003633	0.004478	-0.0008448
40	0.007736	-0.0007867	0.008523
41	-0.0002584	-0.003814	0.003555
42	0.005085	0.00773	-0.002646
43	-0.01738	-0.004988	-0.01239
44	0.00671	-0.0007042	0.007414
45	0.01286	0.005651	0.007211
46	-0.01143	-0.006978	-0.004454
47	0.006996	0.0007201	0.006276
48	0.00418	0.002658	0.001522
49	0.01384	0.002668	0.01117
50	-0.005876	0.001559	-0.007436
51	0.009741	-0.0009468	0.01069
52	-0.01865	-0.002963	-0.01569
53	0.007227	0.002972	0.004255
54	-0.007335	0.001194	-0.008529
55	0.009209	-0.005848	0.01506
56	0.002303	0.002845	-0.0005421
57	0.005825	0.001088	0.004737
58	-0.003725	-0.002603	-0.001121
59	0.006883	0.00231	0.004573
60	-0.011	-0.0004557	-0.01055
61	-0.00198	0.0002256	-0.002206
62	0.01228	-0.001205	0.01349
63	-0.003715	0.004004	-0.007719
64	-0.006059	-0.006361	0.0003015
65	-0.003705	0.002467	-0.006172
66	0.00487	-0.003271	0.008141
67	0.01106	0.00258	0.008484
68	-0.006109	0.002226	-0.008334
69	-0.01296	-0.005953	-0.007004
70	0.005539	0.003128	0.002411
71	-0.0003794	0.00466	-0.005039
72	-0.008982	-0.008877	-0.0001045
73	0.005669	0.002273	0.003395
74	-0.0003289	-0.000314	-1.485e-05
75	-7.11e-05	-0.001512	0.00144
76	0.002393	0.004266	-0.001873
77	0.002602	-0.0001222	0.002724
78	-0.01408	-0.0026	-0.01148
79	0.007178	0.002323	0.004855
80	0.008724	0.0005596	0.008165
81	-0.008084	-0.005262	-0.002823
82	0.004546	0.00126	0.003286
83	0.00633	0.002187	0.004142
84	-0.01705	-0.005562	-0.01148
85	-0.01151	-0.0009043	-0.0106
86	0.004858	0.01025	-0.005394
87	-0.006472	-0.01174	0.005266
88	0.01243	0.006843	0.005585
89	-0.002558	0.001948	-0.004506
90	0.01373	-0.005509	0.01924
91	-0.01454	0.008769	-0.0233
92	-0.01258	-0.009021	-0.003559
93	0.01434	0.006761	0.007576
94	0.001523	0.005465	-0.003942
95	-0.02371	-0.01099	-0.01272
96	0.0364	0.01768	0.01872
97	0.007184	-0.004596	0.01178
98	-0.005412	-0.00766	0.002249
99	-0.0004686	0.007135	-0.007604
100	-0.005174	0.001521	-0.006694
101	-0.003399	-0.007451	0.004051
102	-0.008861	0.005123	-0.01398
103	0.006579	-0.001509	0.008088
104	0.001422	0.002704	-0.001282
105	0.004647	0.0007019	0.003945
106	-0.008398	-0.0026	-0.005798
107	0.01475	0.001532	0.01322
108	-0.01317	0.0006539	-0.01382
109	0.001408	-0.002612	0.004019
110	-0.003396	-0.004436	0.001039
111	0.005115	0.00472	0.000395
112	0.00824	-0.0007625	0.009002
113	-0.01572	-0.001388	-0.01434
114	0.006495	0.005596	0.0008989
115	-0.004471	-0.007257	0.002786
116	0.002105	0.002415	-0.0003109
117	-0.001734	0.001334	-0.003068
118	-0.002436	-0.006435	0.004
119	0.001026	0.003982	-0.002955
120	-0.005198	-0.004461	-0.0007374
121	-0.001153	0.004434	-0.005587
122	0.001393	0.0001643	0.001229
123	-0.005651	0.0008281	-0.006479
124	-0.005368	-0.009131	0.003764
125	0.02921	0.0119	0.01731
126	-0.005422	0.0009981	-0.00642
127	-0.00745	-0.006588	-0.0008622
128	0.007806	-0.000804	0.00861
129	0.007107	0.001171	0.005936
130	-0.005909	0.001324	-0.007233
131	-0.006987	0.0005439	-0.007531
132	0.01545	-0.001954	0.0174
133	-0.01274	-0.0008547	-0.01189
134	0.01103	0.005231	0.005799
135	0.002473	0.00139	0.001082
136	0.000932	-0.00181	0.002742
137	-0.0185	-0.003371	-0.01513
138	0.01075	-0.003848	0.0146
139	0.004132	0.003873	0.0002591
140	0.00341	0.007684	-0.004274
141	-0.02302	-0.01229	-0.01073
142	0.007308	0.006672	0.0006356
143	0.009866	0.005026	0.00484
144	0.009589	-0.006681	0.01627
145	0.003556	0.006767	-0.003211
146	-0.001214	-0.00034	-0.0008742
147	-0.007949	-0.006222	-0.001727
148	-0.003463	0.003023	-0.006486
149	0.01573	0.004025	0.01171
150	-0.01582	-0.01069	-0.005137
151	0.01064	0.01232	-0.001678
152	-0.009902	-0.005964	-0.003939
153	0.004644	0.004894	-0.0002496
154	0.01985	0.001732	0.01811
155	-0.0285	-0.005844	-0.02265
156	-0.002284	0.005152	-0.007435
157	-0.0109	-0.003383	-0.007521
158	0.005703	0.0004831	0.005219
159	0.001412	-0.005011	0.006423
160	0.00867	0.005856	0.002814
161	-0.005277	-0.006263	0.0009864
162	-0.004044	0.008018	-0.01206
163	-0.001045	-0.003127	0.002082
164	0.000697	-0.006218	0.006915
165	-0.001116	0.004365	-0.005481
166	-0.006961	-0.0002995	-0.006662
167	0.02069	-0.003586	0.02428
168	-0.01297	0.00409	-0.01706
169	0.008297	-0.002294	0.01059
170	-0.006427	-0.003586	-0.002841
171	0.01951	0.005438	0.01407
172	-0.006928	0.002701	-0.009629
173	-0.009851	-0.007835	-0.002016
174	0.01199	0.001532	0.01046
175	-0.006574	0.001338	-0.007912
176	0.00122	0.001775	-0.0005552
177	0.005762	-0.001304	0.007066
178	-0.0102	-0.003749	-0.006447
179	-0.00106	0.002344	-0.003405
180	-0.008141	0.003324	-0.01147
181	-0.0005802	-0.006082	0.005502
182	0.01221	0.0009651	0.01125
183	-0.01438	0.002021	-0.0164
184	0.001928	-0.005792	0.00772
185	0.02923	0.007068	0.02216
186	0.005423	0.007711	-0.002287
187	-0.01622	-0.01214	-0.004076
188	-0.0002036	0.004799	-0.005003
189	0.006453	0.006218	0.000235
190	-0.006762	-0.006739	-2.323e-05
191	0.006298	0.0008469	0.005451
192	-0.0007827	-0.001182	0.0003996
193	-0.009345	0.005138	-0.01448
194	-0.0006665	-0.003866	0.003199
195	0.01208	0.007422	0.00466
196	-0.01878	-0.009557	-0.009224
197	-0.01189	0.003278	-0.01516
198	-0.004741	0.0001581	-0.004899
199	0.0219	-0.0001293	0.02203

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.009016 &  0.004816 &  0.004199 \tabularnewline
2 &  0.001358 & -0.00323 &  0.004588 \tabularnewline
3 & -0.003914 &  0.003332 & -0.007246 \tabularnewline
4 &  0.009657 &  0.002138 &  0.007519 \tabularnewline
5 & -0.007071 & -0.002992 & -0.004079 \tabularnewline
6 & -0.001227 & -0.004672 &  0.003446 \tabularnewline
7 &  0.001281 &  0.002687 & -0.001407 \tabularnewline
8 &  0.001358 &  0.002312 & -0.0009541 \tabularnewline
9 &  0.004436 &  0.0006444 &  0.003792 \tabularnewline
10 &  0.003716 &  0.001544 &  0.002172 \tabularnewline
11 & -0.02084 &  0.001109 & -0.02195 \tabularnewline
12 &  0.009637 & -0.002044 &  0.01168 \tabularnewline
13 &  0.003498 & -0.0005894 &  0.004087 \tabularnewline
14 & -0.006772 &  0.002001 & -0.008773 \tabularnewline
15 &  0.004007 & -0.002841 &  0.006847 \tabularnewline
16 &  0.0002731 &  0.003043 & -0.00277 \tabularnewline
17 & -0.009647 &  0.00172 & -0.01137 \tabularnewline
18 &  0.01534 & -0.002065 &  0.01741 \tabularnewline
19 &  0.008081 &  0.003289 &  0.004792 \tabularnewline
20 & -0.02447 & -0.003817 & -0.02065 \tabularnewline
21 &  0.003766 &  0.0004589 &  0.003307 \tabularnewline
22 & -0.01062 &  0.002905 & -0.01352 \tabularnewline
23 &  0.01106 & -0.006509 &  0.01757 \tabularnewline
24 &  0.008513 &  0.002801 &  0.005712 \tabularnewline
25 & -0.01033 & -0.0004053 & -0.009927 \tabularnewline
26 &  0.00913 &  0.002264 &  0.006867 \tabularnewline
27 & -0.009494 & -0.001877 & -0.007617 \tabularnewline
28 &  0.01224 &  0.003977 &  0.008268 \tabularnewline
29 & -0.004565 & -0.004031 & -0.000534 \tabularnewline
30 & -0.003687 &  0.001019 & -0.004705 \tabularnewline
31 &  0.004877 &  0.003099 &  0.001778 \tabularnewline
32 & -0.003558 & -0.002499 & -0.00106 \tabularnewline
33 & -0.002163 &  0.0001432 & -0.002306 \tabularnewline
34 &  0.007967 &  0.001379 &  0.006588 \tabularnewline
35 & -0.0005427 & -0.0004835 & -5.913e-05 \tabularnewline
36 & -0.01257 &  0.002165 & -0.01473 \tabularnewline
37 &  0.00151 & -0.001154 &  0.002664 \tabularnewline
38 & -0.01122 & -0.00731 & -0.003912 \tabularnewline
39 &  0.003633 &  0.004478 & -0.0008448 \tabularnewline
40 &  0.007736 & -0.0007867 &  0.008523 \tabularnewline
41 & -0.0002584 & -0.003814 &  0.003555 \tabularnewline
42 &  0.005085 &  0.00773 & -0.002646 \tabularnewline
43 & -0.01738 & -0.004988 & -0.01239 \tabularnewline
44 &  0.00671 & -0.0007042 &  0.007414 \tabularnewline
45 &  0.01286 &  0.005651 &  0.007211 \tabularnewline
46 & -0.01143 & -0.006978 & -0.004454 \tabularnewline
47 &  0.006996 &  0.0007201 &  0.006276 \tabularnewline
48 &  0.00418 &  0.002658 &  0.001522 \tabularnewline
49 &  0.01384 &  0.002668 &  0.01117 \tabularnewline
50 & -0.005876 &  0.001559 & -0.007436 \tabularnewline
51 &  0.009741 & -0.0009468 &  0.01069 \tabularnewline
52 & -0.01865 & -0.002963 & -0.01569 \tabularnewline
53 &  0.007227 &  0.002972 &  0.004255 \tabularnewline
54 & -0.007335 &  0.001194 & -0.008529 \tabularnewline
55 &  0.009209 & -0.005848 &  0.01506 \tabularnewline
56 &  0.002303 &  0.002845 & -0.0005421 \tabularnewline
57 &  0.005825 &  0.001088 &  0.004737 \tabularnewline
58 & -0.003725 & -0.002603 & -0.001121 \tabularnewline
59 &  0.006883 &  0.00231 &  0.004573 \tabularnewline
60 & -0.011 & -0.0004557 & -0.01055 \tabularnewline
61 & -0.00198 &  0.0002256 & -0.002206 \tabularnewline
62 &  0.01228 & -0.001205 &  0.01349 \tabularnewline
63 & -0.003715 &  0.004004 & -0.007719 \tabularnewline
64 & -0.006059 & -0.006361 &  0.0003015 \tabularnewline
65 & -0.003705 &  0.002467 & -0.006172 \tabularnewline
66 &  0.00487 & -0.003271 &  0.008141 \tabularnewline
67 &  0.01106 &  0.00258 &  0.008484 \tabularnewline
68 & -0.006109 &  0.002226 & -0.008334 \tabularnewline
69 & -0.01296 & -0.005953 & -0.007004 \tabularnewline
70 &  0.005539 &  0.003128 &  0.002411 \tabularnewline
71 & -0.0003794 &  0.00466 & -0.005039 \tabularnewline
72 & -0.008982 & -0.008877 & -0.0001045 \tabularnewline
73 &  0.005669 &  0.002273 &  0.003395 \tabularnewline
74 & -0.0003289 & -0.000314 & -1.485e-05 \tabularnewline
75 & -7.11e-05 & -0.001512 &  0.00144 \tabularnewline
76 &  0.002393 &  0.004266 & -0.001873 \tabularnewline
77 &  0.002602 & -0.0001222 &  0.002724 \tabularnewline
78 & -0.01408 & -0.0026 & -0.01148 \tabularnewline
79 &  0.007178 &  0.002323 &  0.004855 \tabularnewline
80 &  0.008724 &  0.0005596 &  0.008165 \tabularnewline
81 & -0.008084 & -0.005262 & -0.002823 \tabularnewline
82 &  0.004546 &  0.00126 &  0.003286 \tabularnewline
83 &  0.00633 &  0.002187 &  0.004142 \tabularnewline
84 & -0.01705 & -0.005562 & -0.01148 \tabularnewline
85 & -0.01151 & -0.0009043 & -0.0106 \tabularnewline
86 &  0.004858 &  0.01025 & -0.005394 \tabularnewline
87 & -0.006472 & -0.01174 &  0.005266 \tabularnewline
88 &  0.01243 &  0.006843 &  0.005585 \tabularnewline
89 & -0.002558 &  0.001948 & -0.004506 \tabularnewline
90 &  0.01373 & -0.005509 &  0.01924 \tabularnewline
91 & -0.01454 &  0.008769 & -0.0233 \tabularnewline
92 & -0.01258 & -0.009021 & -0.003559 \tabularnewline
93 &  0.01434 &  0.006761 &  0.007576 \tabularnewline
94 &  0.001523 &  0.005465 & -0.003942 \tabularnewline
95 & -0.02371 & -0.01099 & -0.01272 \tabularnewline
96 &  0.0364 &  0.01768 &  0.01872 \tabularnewline
97 &  0.007184 & -0.004596 &  0.01178 \tabularnewline
98 & -0.005412 & -0.00766 &  0.002249 \tabularnewline
99 & -0.0004686 &  0.007135 & -0.007604 \tabularnewline
100 & -0.005174 &  0.001521 & -0.006694 \tabularnewline
101 & -0.003399 & -0.007451 &  0.004051 \tabularnewline
102 & -0.008861 &  0.005123 & -0.01398 \tabularnewline
103 &  0.006579 & -0.001509 &  0.008088 \tabularnewline
104 &  0.001422 &  0.002704 & -0.001282 \tabularnewline
105 &  0.004647 &  0.0007019 &  0.003945 \tabularnewline
106 & -0.008398 & -0.0026 & -0.005798 \tabularnewline
107 &  0.01475 &  0.001532 &  0.01322 \tabularnewline
108 & -0.01317 &  0.0006539 & -0.01382 \tabularnewline
109 &  0.001408 & -0.002612 &  0.004019 \tabularnewline
110 & -0.003396 & -0.004436 &  0.001039 \tabularnewline
111 &  0.005115 &  0.00472 &  0.000395 \tabularnewline
112 &  0.00824 & -0.0007625 &  0.009002 \tabularnewline
113 & -0.01572 & -0.001388 & -0.01434 \tabularnewline
114 &  0.006495 &  0.005596 &  0.0008989 \tabularnewline
115 & -0.004471 & -0.007257 &  0.002786 \tabularnewline
116 &  0.002105 &  0.002415 & -0.0003109 \tabularnewline
117 & -0.001734 &  0.001334 & -0.003068 \tabularnewline
118 & -0.002436 & -0.006435 &  0.004 \tabularnewline
119 &  0.001026 &  0.003982 & -0.002955 \tabularnewline
120 & -0.005198 & -0.004461 & -0.0007374 \tabularnewline
121 & -0.001153 &  0.004434 & -0.005587 \tabularnewline
122 &  0.001393 &  0.0001643 &  0.001229 \tabularnewline
123 & -0.005651 &  0.0008281 & -0.006479 \tabularnewline
124 & -0.005368 & -0.009131 &  0.003764 \tabularnewline
125 &  0.02921 &  0.0119 &  0.01731 \tabularnewline
126 & -0.005422 &  0.0009981 & -0.00642 \tabularnewline
127 & -0.00745 & -0.006588 & -0.0008622 \tabularnewline
128 &  0.007806 & -0.000804 &  0.00861 \tabularnewline
129 &  0.007107 &  0.001171 &  0.005936 \tabularnewline
130 & -0.005909 &  0.001324 & -0.007233 \tabularnewline
131 & -0.006987 &  0.0005439 & -0.007531 \tabularnewline
132 &  0.01545 & -0.001954 &  0.0174 \tabularnewline
133 & -0.01274 & -0.0008547 & -0.01189 \tabularnewline
134 &  0.01103 &  0.005231 &  0.005799 \tabularnewline
135 &  0.002473 &  0.00139 &  0.001082 \tabularnewline
136 &  0.000932 & -0.00181 &  0.002742 \tabularnewline
137 & -0.0185 & -0.003371 & -0.01513 \tabularnewline
138 &  0.01075 & -0.003848 &  0.0146 \tabularnewline
139 &  0.004132 &  0.003873 &  0.0002591 \tabularnewline
140 &  0.00341 &  0.007684 & -0.004274 \tabularnewline
141 & -0.02302 & -0.01229 & -0.01073 \tabularnewline
142 &  0.007308 &  0.006672 &  0.0006356 \tabularnewline
143 &  0.009866 &  0.005026 &  0.00484 \tabularnewline
144 &  0.009589 & -0.006681 &  0.01627 \tabularnewline
145 &  0.003556 &  0.006767 & -0.003211 \tabularnewline
146 & -0.001214 & -0.00034 & -0.0008742 \tabularnewline
147 & -0.007949 & -0.006222 & -0.001727 \tabularnewline
148 & -0.003463 &  0.003023 & -0.006486 \tabularnewline
149 &  0.01573 &  0.004025 &  0.01171 \tabularnewline
150 & -0.01582 & -0.01069 & -0.005137 \tabularnewline
151 &  0.01064 &  0.01232 & -0.001678 \tabularnewline
152 & -0.009902 & -0.005964 & -0.003939 \tabularnewline
153 &  0.004644 &  0.004894 & -0.0002496 \tabularnewline
154 &  0.01985 &  0.001732 &  0.01811 \tabularnewline
155 & -0.0285 & -0.005844 & -0.02265 \tabularnewline
156 & -0.002284 &  0.005152 & -0.007435 \tabularnewline
157 & -0.0109 & -0.003383 & -0.007521 \tabularnewline
158 &  0.005703 &  0.0004831 &  0.005219 \tabularnewline
159 &  0.001412 & -0.005011 &  0.006423 \tabularnewline
160 &  0.00867 &  0.005856 &  0.002814 \tabularnewline
161 & -0.005277 & -0.006263 &  0.0009864 \tabularnewline
162 & -0.004044 &  0.008018 & -0.01206 \tabularnewline
163 & -0.001045 & -0.003127 &  0.002082 \tabularnewline
164 &  0.000697 & -0.006218 &  0.006915 \tabularnewline
165 & -0.001116 &  0.004365 & -0.005481 \tabularnewline
166 & -0.006961 & -0.0002995 & -0.006662 \tabularnewline
167 &  0.02069 & -0.003586 &  0.02428 \tabularnewline
168 & -0.01297 &  0.00409 & -0.01706 \tabularnewline
169 &  0.008297 & -0.002294 &  0.01059 \tabularnewline
170 & -0.006427 & -0.003586 & -0.002841 \tabularnewline
171 &  0.01951 &  0.005438 &  0.01407 \tabularnewline
172 & -0.006928 &  0.002701 & -0.009629 \tabularnewline
173 & -0.009851 & -0.007835 & -0.002016 \tabularnewline
174 &  0.01199 &  0.001532 &  0.01046 \tabularnewline
175 & -0.006574 &  0.001338 & -0.007912 \tabularnewline
176 &  0.00122 &  0.001775 & -0.0005552 \tabularnewline
177 &  0.005762 & -0.001304 &  0.007066 \tabularnewline
178 & -0.0102 & -0.003749 & -0.006447 \tabularnewline
179 & -0.00106 &  0.002344 & -0.003405 \tabularnewline
180 & -0.008141 &  0.003324 & -0.01147 \tabularnewline
181 & -0.0005802 & -0.006082 &  0.005502 \tabularnewline
182 &  0.01221 &  0.0009651 &  0.01125 \tabularnewline
183 & -0.01438 &  0.002021 & -0.0164 \tabularnewline
184 &  0.001928 & -0.005792 &  0.00772 \tabularnewline
185 &  0.02923 &  0.007068 &  0.02216 \tabularnewline
186 &  0.005423 &  0.007711 & -0.002287 \tabularnewline
187 & -0.01622 & -0.01214 & -0.004076 \tabularnewline
188 & -0.0002036 &  0.004799 & -0.005003 \tabularnewline
189 &  0.006453 &  0.006218 &  0.000235 \tabularnewline
190 & -0.006762 & -0.006739 & -2.323e-05 \tabularnewline
191 &  0.006298 &  0.0008469 &  0.005451 \tabularnewline
192 & -0.0007827 & -0.001182 &  0.0003996 \tabularnewline
193 & -0.009345 &  0.005138 & -0.01448 \tabularnewline
194 & -0.0006665 & -0.003866 &  0.003199 \tabularnewline
195 &  0.01208 &  0.007422 &  0.00466 \tabularnewline
196 & -0.01878 & -0.009557 & -0.009224 \tabularnewline
197 & -0.01189 &  0.003278 & -0.01516 \tabularnewline
198 & -0.004741 &  0.0001581 & -0.004899 \tabularnewline
199 &  0.0219 & -0.0001293 &  0.02203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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] 0.009016[/C][C] 0.004816[/C][C] 0.004199[/C][/ROW]
[ROW][C]2[/C][C] 0.001358[/C][C]-0.00323[/C][C] 0.004588[/C][/ROW]
[ROW][C]3[/C][C]-0.003914[/C][C] 0.003332[/C][C]-0.007246[/C][/ROW]
[ROW][C]4[/C][C] 0.009657[/C][C] 0.002138[/C][C] 0.007519[/C][/ROW]
[ROW][C]5[/C][C]-0.007071[/C][C]-0.002992[/C][C]-0.004079[/C][/ROW]
[ROW][C]6[/C][C]-0.001227[/C][C]-0.004672[/C][C] 0.003446[/C][/ROW]
[ROW][C]7[/C][C] 0.001281[/C][C] 0.002687[/C][C]-0.001407[/C][/ROW]
[ROW][C]8[/C][C] 0.001358[/C][C] 0.002312[/C][C]-0.0009541[/C][/ROW]
[ROW][C]9[/C][C] 0.004436[/C][C] 0.0006444[/C][C] 0.003792[/C][/ROW]
[ROW][C]10[/C][C] 0.003716[/C][C] 0.001544[/C][C] 0.002172[/C][/ROW]
[ROW][C]11[/C][C]-0.02084[/C][C] 0.001109[/C][C]-0.02195[/C][/ROW]
[ROW][C]12[/C][C] 0.009637[/C][C]-0.002044[/C][C] 0.01168[/C][/ROW]
[ROW][C]13[/C][C] 0.003498[/C][C]-0.0005894[/C][C] 0.004087[/C][/ROW]
[ROW][C]14[/C][C]-0.006772[/C][C] 0.002001[/C][C]-0.008773[/C][/ROW]
[ROW][C]15[/C][C] 0.004007[/C][C]-0.002841[/C][C] 0.006847[/C][/ROW]
[ROW][C]16[/C][C] 0.0002731[/C][C] 0.003043[/C][C]-0.00277[/C][/ROW]
[ROW][C]17[/C][C]-0.009647[/C][C] 0.00172[/C][C]-0.01137[/C][/ROW]
[ROW][C]18[/C][C] 0.01534[/C][C]-0.002065[/C][C] 0.01741[/C][/ROW]
[ROW][C]19[/C][C] 0.008081[/C][C] 0.003289[/C][C] 0.004792[/C][/ROW]
[ROW][C]20[/C][C]-0.02447[/C][C]-0.003817[/C][C]-0.02065[/C][/ROW]
[ROW][C]21[/C][C] 0.003766[/C][C] 0.0004589[/C][C] 0.003307[/C][/ROW]
[ROW][C]22[/C][C]-0.01062[/C][C] 0.002905[/C][C]-0.01352[/C][/ROW]
[ROW][C]23[/C][C] 0.01106[/C][C]-0.006509[/C][C] 0.01757[/C][/ROW]
[ROW][C]24[/C][C] 0.008513[/C][C] 0.002801[/C][C] 0.005712[/C][/ROW]
[ROW][C]25[/C][C]-0.01033[/C][C]-0.0004053[/C][C]-0.009927[/C][/ROW]
[ROW][C]26[/C][C] 0.00913[/C][C] 0.002264[/C][C] 0.006867[/C][/ROW]
[ROW][C]27[/C][C]-0.009494[/C][C]-0.001877[/C][C]-0.007617[/C][/ROW]
[ROW][C]28[/C][C] 0.01224[/C][C] 0.003977[/C][C] 0.008268[/C][/ROW]
[ROW][C]29[/C][C]-0.004565[/C][C]-0.004031[/C][C]-0.000534[/C][/ROW]
[ROW][C]30[/C][C]-0.003687[/C][C] 0.001019[/C][C]-0.004705[/C][/ROW]
[ROW][C]31[/C][C] 0.004877[/C][C] 0.003099[/C][C] 0.001778[/C][/ROW]
[ROW][C]32[/C][C]-0.003558[/C][C]-0.002499[/C][C]-0.00106[/C][/ROW]
[ROW][C]33[/C][C]-0.002163[/C][C] 0.0001432[/C][C]-0.002306[/C][/ROW]
[ROW][C]34[/C][C] 0.007967[/C][C] 0.001379[/C][C] 0.006588[/C][/ROW]
[ROW][C]35[/C][C]-0.0005427[/C][C]-0.0004835[/C][C]-5.913e-05[/C][/ROW]
[ROW][C]36[/C][C]-0.01257[/C][C] 0.002165[/C][C]-0.01473[/C][/ROW]
[ROW][C]37[/C][C] 0.00151[/C][C]-0.001154[/C][C] 0.002664[/C][/ROW]
[ROW][C]38[/C][C]-0.01122[/C][C]-0.00731[/C][C]-0.003912[/C][/ROW]
[ROW][C]39[/C][C] 0.003633[/C][C] 0.004478[/C][C]-0.0008448[/C][/ROW]
[ROW][C]40[/C][C] 0.007736[/C][C]-0.0007867[/C][C] 0.008523[/C][/ROW]
[ROW][C]41[/C][C]-0.0002584[/C][C]-0.003814[/C][C] 0.003555[/C][/ROW]
[ROW][C]42[/C][C] 0.005085[/C][C] 0.00773[/C][C]-0.002646[/C][/ROW]
[ROW][C]43[/C][C]-0.01738[/C][C]-0.004988[/C][C]-0.01239[/C][/ROW]
[ROW][C]44[/C][C] 0.00671[/C][C]-0.0007042[/C][C] 0.007414[/C][/ROW]
[ROW][C]45[/C][C] 0.01286[/C][C] 0.005651[/C][C] 0.007211[/C][/ROW]
[ROW][C]46[/C][C]-0.01143[/C][C]-0.006978[/C][C]-0.004454[/C][/ROW]
[ROW][C]47[/C][C] 0.006996[/C][C] 0.0007201[/C][C] 0.006276[/C][/ROW]
[ROW][C]48[/C][C] 0.00418[/C][C] 0.002658[/C][C] 0.001522[/C][/ROW]
[ROW][C]49[/C][C] 0.01384[/C][C] 0.002668[/C][C] 0.01117[/C][/ROW]
[ROW][C]50[/C][C]-0.005876[/C][C] 0.001559[/C][C]-0.007436[/C][/ROW]
[ROW][C]51[/C][C] 0.009741[/C][C]-0.0009468[/C][C] 0.01069[/C][/ROW]
[ROW][C]52[/C][C]-0.01865[/C][C]-0.002963[/C][C]-0.01569[/C][/ROW]
[ROW][C]53[/C][C] 0.007227[/C][C] 0.002972[/C][C] 0.004255[/C][/ROW]
[ROW][C]54[/C][C]-0.007335[/C][C] 0.001194[/C][C]-0.008529[/C][/ROW]
[ROW][C]55[/C][C] 0.009209[/C][C]-0.005848[/C][C] 0.01506[/C][/ROW]
[ROW][C]56[/C][C] 0.002303[/C][C] 0.002845[/C][C]-0.0005421[/C][/ROW]
[ROW][C]57[/C][C] 0.005825[/C][C] 0.001088[/C][C] 0.004737[/C][/ROW]
[ROW][C]58[/C][C]-0.003725[/C][C]-0.002603[/C][C]-0.001121[/C][/ROW]
[ROW][C]59[/C][C] 0.006883[/C][C] 0.00231[/C][C] 0.004573[/C][/ROW]
[ROW][C]60[/C][C]-0.011[/C][C]-0.0004557[/C][C]-0.01055[/C][/ROW]
[ROW][C]61[/C][C]-0.00198[/C][C] 0.0002256[/C][C]-0.002206[/C][/ROW]
[ROW][C]62[/C][C] 0.01228[/C][C]-0.001205[/C][C] 0.01349[/C][/ROW]
[ROW][C]63[/C][C]-0.003715[/C][C] 0.004004[/C][C]-0.007719[/C][/ROW]
[ROW][C]64[/C][C]-0.006059[/C][C]-0.006361[/C][C] 0.0003015[/C][/ROW]
[ROW][C]65[/C][C]-0.003705[/C][C] 0.002467[/C][C]-0.006172[/C][/ROW]
[ROW][C]66[/C][C] 0.00487[/C][C]-0.003271[/C][C] 0.008141[/C][/ROW]
[ROW][C]67[/C][C] 0.01106[/C][C] 0.00258[/C][C] 0.008484[/C][/ROW]
[ROW][C]68[/C][C]-0.006109[/C][C] 0.002226[/C][C]-0.008334[/C][/ROW]
[ROW][C]69[/C][C]-0.01296[/C][C]-0.005953[/C][C]-0.007004[/C][/ROW]
[ROW][C]70[/C][C] 0.005539[/C][C] 0.003128[/C][C] 0.002411[/C][/ROW]
[ROW][C]71[/C][C]-0.0003794[/C][C] 0.00466[/C][C]-0.005039[/C][/ROW]
[ROW][C]72[/C][C]-0.008982[/C][C]-0.008877[/C][C]-0.0001045[/C][/ROW]
[ROW][C]73[/C][C] 0.005669[/C][C] 0.002273[/C][C] 0.003395[/C][/ROW]
[ROW][C]74[/C][C]-0.0003289[/C][C]-0.000314[/C][C]-1.485e-05[/C][/ROW]
[ROW][C]75[/C][C]-7.11e-05[/C][C]-0.001512[/C][C] 0.00144[/C][/ROW]
[ROW][C]76[/C][C] 0.002393[/C][C] 0.004266[/C][C]-0.001873[/C][/ROW]
[ROW][C]77[/C][C] 0.002602[/C][C]-0.0001222[/C][C] 0.002724[/C][/ROW]
[ROW][C]78[/C][C]-0.01408[/C][C]-0.0026[/C][C]-0.01148[/C][/ROW]
[ROW][C]79[/C][C] 0.007178[/C][C] 0.002323[/C][C] 0.004855[/C][/ROW]
[ROW][C]80[/C][C] 0.008724[/C][C] 0.0005596[/C][C] 0.008165[/C][/ROW]
[ROW][C]81[/C][C]-0.008084[/C][C]-0.005262[/C][C]-0.002823[/C][/ROW]
[ROW][C]82[/C][C] 0.004546[/C][C] 0.00126[/C][C] 0.003286[/C][/ROW]
[ROW][C]83[/C][C] 0.00633[/C][C] 0.002187[/C][C] 0.004142[/C][/ROW]
[ROW][C]84[/C][C]-0.01705[/C][C]-0.005562[/C][C]-0.01148[/C][/ROW]
[ROW][C]85[/C][C]-0.01151[/C][C]-0.0009043[/C][C]-0.0106[/C][/ROW]
[ROW][C]86[/C][C] 0.004858[/C][C] 0.01025[/C][C]-0.005394[/C][/ROW]
[ROW][C]87[/C][C]-0.006472[/C][C]-0.01174[/C][C] 0.005266[/C][/ROW]
[ROW][C]88[/C][C] 0.01243[/C][C] 0.006843[/C][C] 0.005585[/C][/ROW]
[ROW][C]89[/C][C]-0.002558[/C][C] 0.001948[/C][C]-0.004506[/C][/ROW]
[ROW][C]90[/C][C] 0.01373[/C][C]-0.005509[/C][C] 0.01924[/C][/ROW]
[ROW][C]91[/C][C]-0.01454[/C][C] 0.008769[/C][C]-0.0233[/C][/ROW]
[ROW][C]92[/C][C]-0.01258[/C][C]-0.009021[/C][C]-0.003559[/C][/ROW]
[ROW][C]93[/C][C] 0.01434[/C][C] 0.006761[/C][C] 0.007576[/C][/ROW]
[ROW][C]94[/C][C] 0.001523[/C][C] 0.005465[/C][C]-0.003942[/C][/ROW]
[ROW][C]95[/C][C]-0.02371[/C][C]-0.01099[/C][C]-0.01272[/C][/ROW]
[ROW][C]96[/C][C] 0.0364[/C][C] 0.01768[/C][C] 0.01872[/C][/ROW]
[ROW][C]97[/C][C] 0.007184[/C][C]-0.004596[/C][C] 0.01178[/C][/ROW]
[ROW][C]98[/C][C]-0.005412[/C][C]-0.00766[/C][C] 0.002249[/C][/ROW]
[ROW][C]99[/C][C]-0.0004686[/C][C] 0.007135[/C][C]-0.007604[/C][/ROW]
[ROW][C]100[/C][C]-0.005174[/C][C] 0.001521[/C][C]-0.006694[/C][/ROW]
[ROW][C]101[/C][C]-0.003399[/C][C]-0.007451[/C][C] 0.004051[/C][/ROW]
[ROW][C]102[/C][C]-0.008861[/C][C] 0.005123[/C][C]-0.01398[/C][/ROW]
[ROW][C]103[/C][C] 0.006579[/C][C]-0.001509[/C][C] 0.008088[/C][/ROW]
[ROW][C]104[/C][C] 0.001422[/C][C] 0.002704[/C][C]-0.001282[/C][/ROW]
[ROW][C]105[/C][C] 0.004647[/C][C] 0.0007019[/C][C] 0.003945[/C][/ROW]
[ROW][C]106[/C][C]-0.008398[/C][C]-0.0026[/C][C]-0.005798[/C][/ROW]
[ROW][C]107[/C][C] 0.01475[/C][C] 0.001532[/C][C] 0.01322[/C][/ROW]
[ROW][C]108[/C][C]-0.01317[/C][C] 0.0006539[/C][C]-0.01382[/C][/ROW]
[ROW][C]109[/C][C] 0.001408[/C][C]-0.002612[/C][C] 0.004019[/C][/ROW]
[ROW][C]110[/C][C]-0.003396[/C][C]-0.004436[/C][C] 0.001039[/C][/ROW]
[ROW][C]111[/C][C] 0.005115[/C][C] 0.00472[/C][C] 0.000395[/C][/ROW]
[ROW][C]112[/C][C] 0.00824[/C][C]-0.0007625[/C][C] 0.009002[/C][/ROW]
[ROW][C]113[/C][C]-0.01572[/C][C]-0.001388[/C][C]-0.01434[/C][/ROW]
[ROW][C]114[/C][C] 0.006495[/C][C] 0.005596[/C][C] 0.0008989[/C][/ROW]
[ROW][C]115[/C][C]-0.004471[/C][C]-0.007257[/C][C] 0.002786[/C][/ROW]
[ROW][C]116[/C][C] 0.002105[/C][C] 0.002415[/C][C]-0.0003109[/C][/ROW]
[ROW][C]117[/C][C]-0.001734[/C][C] 0.001334[/C][C]-0.003068[/C][/ROW]
[ROW][C]118[/C][C]-0.002436[/C][C]-0.006435[/C][C] 0.004[/C][/ROW]
[ROW][C]119[/C][C] 0.001026[/C][C] 0.003982[/C][C]-0.002955[/C][/ROW]
[ROW][C]120[/C][C]-0.005198[/C][C]-0.004461[/C][C]-0.0007374[/C][/ROW]
[ROW][C]121[/C][C]-0.001153[/C][C] 0.004434[/C][C]-0.005587[/C][/ROW]
[ROW][C]122[/C][C] 0.001393[/C][C] 0.0001643[/C][C] 0.001229[/C][/ROW]
[ROW][C]123[/C][C]-0.005651[/C][C] 0.0008281[/C][C]-0.006479[/C][/ROW]
[ROW][C]124[/C][C]-0.005368[/C][C]-0.009131[/C][C] 0.003764[/C][/ROW]
[ROW][C]125[/C][C] 0.02921[/C][C] 0.0119[/C][C] 0.01731[/C][/ROW]
[ROW][C]126[/C][C]-0.005422[/C][C] 0.0009981[/C][C]-0.00642[/C][/ROW]
[ROW][C]127[/C][C]-0.00745[/C][C]-0.006588[/C][C]-0.0008622[/C][/ROW]
[ROW][C]128[/C][C] 0.007806[/C][C]-0.000804[/C][C] 0.00861[/C][/ROW]
[ROW][C]129[/C][C] 0.007107[/C][C] 0.001171[/C][C] 0.005936[/C][/ROW]
[ROW][C]130[/C][C]-0.005909[/C][C] 0.001324[/C][C]-0.007233[/C][/ROW]
[ROW][C]131[/C][C]-0.006987[/C][C] 0.0005439[/C][C]-0.007531[/C][/ROW]
[ROW][C]132[/C][C] 0.01545[/C][C]-0.001954[/C][C] 0.0174[/C][/ROW]
[ROW][C]133[/C][C]-0.01274[/C][C]-0.0008547[/C][C]-0.01189[/C][/ROW]
[ROW][C]134[/C][C] 0.01103[/C][C] 0.005231[/C][C] 0.005799[/C][/ROW]
[ROW][C]135[/C][C] 0.002473[/C][C] 0.00139[/C][C] 0.001082[/C][/ROW]
[ROW][C]136[/C][C] 0.000932[/C][C]-0.00181[/C][C] 0.002742[/C][/ROW]
[ROW][C]137[/C][C]-0.0185[/C][C]-0.003371[/C][C]-0.01513[/C][/ROW]
[ROW][C]138[/C][C] 0.01075[/C][C]-0.003848[/C][C] 0.0146[/C][/ROW]
[ROW][C]139[/C][C] 0.004132[/C][C] 0.003873[/C][C] 0.0002591[/C][/ROW]
[ROW][C]140[/C][C] 0.00341[/C][C] 0.007684[/C][C]-0.004274[/C][/ROW]
[ROW][C]141[/C][C]-0.02302[/C][C]-0.01229[/C][C]-0.01073[/C][/ROW]
[ROW][C]142[/C][C] 0.007308[/C][C] 0.006672[/C][C] 0.0006356[/C][/ROW]
[ROW][C]143[/C][C] 0.009866[/C][C] 0.005026[/C][C] 0.00484[/C][/ROW]
[ROW][C]144[/C][C] 0.009589[/C][C]-0.006681[/C][C] 0.01627[/C][/ROW]
[ROW][C]145[/C][C] 0.003556[/C][C] 0.006767[/C][C]-0.003211[/C][/ROW]
[ROW][C]146[/C][C]-0.001214[/C][C]-0.00034[/C][C]-0.0008742[/C][/ROW]
[ROW][C]147[/C][C]-0.007949[/C][C]-0.006222[/C][C]-0.001727[/C][/ROW]
[ROW][C]148[/C][C]-0.003463[/C][C] 0.003023[/C][C]-0.006486[/C][/ROW]
[ROW][C]149[/C][C] 0.01573[/C][C] 0.004025[/C][C] 0.01171[/C][/ROW]
[ROW][C]150[/C][C]-0.01582[/C][C]-0.01069[/C][C]-0.005137[/C][/ROW]
[ROW][C]151[/C][C] 0.01064[/C][C] 0.01232[/C][C]-0.001678[/C][/ROW]
[ROW][C]152[/C][C]-0.009902[/C][C]-0.005964[/C][C]-0.003939[/C][/ROW]
[ROW][C]153[/C][C] 0.004644[/C][C] 0.004894[/C][C]-0.0002496[/C][/ROW]
[ROW][C]154[/C][C] 0.01985[/C][C] 0.001732[/C][C] 0.01811[/C][/ROW]
[ROW][C]155[/C][C]-0.0285[/C][C]-0.005844[/C][C]-0.02265[/C][/ROW]
[ROW][C]156[/C][C]-0.002284[/C][C] 0.005152[/C][C]-0.007435[/C][/ROW]
[ROW][C]157[/C][C]-0.0109[/C][C]-0.003383[/C][C]-0.007521[/C][/ROW]
[ROW][C]158[/C][C] 0.005703[/C][C] 0.0004831[/C][C] 0.005219[/C][/ROW]
[ROW][C]159[/C][C] 0.001412[/C][C]-0.005011[/C][C] 0.006423[/C][/ROW]
[ROW][C]160[/C][C] 0.00867[/C][C] 0.005856[/C][C] 0.002814[/C][/ROW]
[ROW][C]161[/C][C]-0.005277[/C][C]-0.006263[/C][C] 0.0009864[/C][/ROW]
[ROW][C]162[/C][C]-0.004044[/C][C] 0.008018[/C][C]-0.01206[/C][/ROW]
[ROW][C]163[/C][C]-0.001045[/C][C]-0.003127[/C][C] 0.002082[/C][/ROW]
[ROW][C]164[/C][C] 0.000697[/C][C]-0.006218[/C][C] 0.006915[/C][/ROW]
[ROW][C]165[/C][C]-0.001116[/C][C] 0.004365[/C][C]-0.005481[/C][/ROW]
[ROW][C]166[/C][C]-0.006961[/C][C]-0.0002995[/C][C]-0.006662[/C][/ROW]
[ROW][C]167[/C][C] 0.02069[/C][C]-0.003586[/C][C] 0.02428[/C][/ROW]
[ROW][C]168[/C][C]-0.01297[/C][C] 0.00409[/C][C]-0.01706[/C][/ROW]
[ROW][C]169[/C][C] 0.008297[/C][C]-0.002294[/C][C] 0.01059[/C][/ROW]
[ROW][C]170[/C][C]-0.006427[/C][C]-0.003586[/C][C]-0.002841[/C][/ROW]
[ROW][C]171[/C][C] 0.01951[/C][C] 0.005438[/C][C] 0.01407[/C][/ROW]
[ROW][C]172[/C][C]-0.006928[/C][C] 0.002701[/C][C]-0.009629[/C][/ROW]
[ROW][C]173[/C][C]-0.009851[/C][C]-0.007835[/C][C]-0.002016[/C][/ROW]
[ROW][C]174[/C][C] 0.01199[/C][C] 0.001532[/C][C] 0.01046[/C][/ROW]
[ROW][C]175[/C][C]-0.006574[/C][C] 0.001338[/C][C]-0.007912[/C][/ROW]
[ROW][C]176[/C][C] 0.00122[/C][C] 0.001775[/C][C]-0.0005552[/C][/ROW]
[ROW][C]177[/C][C] 0.005762[/C][C]-0.001304[/C][C] 0.007066[/C][/ROW]
[ROW][C]178[/C][C]-0.0102[/C][C]-0.003749[/C][C]-0.006447[/C][/ROW]
[ROW][C]179[/C][C]-0.00106[/C][C] 0.002344[/C][C]-0.003405[/C][/ROW]
[ROW][C]180[/C][C]-0.008141[/C][C] 0.003324[/C][C]-0.01147[/C][/ROW]
[ROW][C]181[/C][C]-0.0005802[/C][C]-0.006082[/C][C] 0.005502[/C][/ROW]
[ROW][C]182[/C][C] 0.01221[/C][C] 0.0009651[/C][C] 0.01125[/C][/ROW]
[ROW][C]183[/C][C]-0.01438[/C][C] 0.002021[/C][C]-0.0164[/C][/ROW]
[ROW][C]184[/C][C] 0.001928[/C][C]-0.005792[/C][C] 0.00772[/C][/ROW]
[ROW][C]185[/C][C] 0.02923[/C][C] 0.007068[/C][C] 0.02216[/C][/ROW]
[ROW][C]186[/C][C] 0.005423[/C][C] 0.007711[/C][C]-0.002287[/C][/ROW]
[ROW][C]187[/C][C]-0.01622[/C][C]-0.01214[/C][C]-0.004076[/C][/ROW]
[ROW][C]188[/C][C]-0.0002036[/C][C] 0.004799[/C][C]-0.005003[/C][/ROW]
[ROW][C]189[/C][C] 0.006453[/C][C] 0.006218[/C][C] 0.000235[/C][/ROW]
[ROW][C]190[/C][C]-0.006762[/C][C]-0.006739[/C][C]-2.323e-05[/C][/ROW]
[ROW][C]191[/C][C] 0.006298[/C][C] 0.0008469[/C][C] 0.005451[/C][/ROW]
[ROW][C]192[/C][C]-0.0007827[/C][C]-0.001182[/C][C] 0.0003996[/C][/ROW]
[ROW][C]193[/C][C]-0.009345[/C][C] 0.005138[/C][C]-0.01448[/C][/ROW]
[ROW][C]194[/C][C]-0.0006665[/C][C]-0.003866[/C][C] 0.003199[/C][/ROW]
[ROW][C]195[/C][C] 0.01208[/C][C] 0.007422[/C][C] 0.00466[/C][/ROW]
[ROW][C]196[/C][C]-0.01878[/C][C]-0.009557[/C][C]-0.009224[/C][/ROW]
[ROW][C]197[/C][C]-0.01189[/C][C] 0.003278[/C][C]-0.01516[/C][/ROW]
[ROW][C]198[/C][C]-0.004741[/C][C] 0.0001581[/C][C]-0.004899[/C][/ROW]
[ROW][C]199[/C][C] 0.0219[/C][C]-0.0001293[/C][C] 0.02203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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	0.009016	0.004816	0.004199
2	0.001358	-0.00323	0.004588
3	-0.003914	0.003332	-0.007246
4	0.009657	0.002138	0.007519
5	-0.007071	-0.002992	-0.004079
6	-0.001227	-0.004672	0.003446
7	0.001281	0.002687	-0.001407
8	0.001358	0.002312	-0.0009541
9	0.004436	0.0006444	0.003792
10	0.003716	0.001544	0.002172
11	-0.02084	0.001109	-0.02195
12	0.009637	-0.002044	0.01168
13	0.003498	-0.0005894	0.004087
14	-0.006772	0.002001	-0.008773
15	0.004007	-0.002841	0.006847
16	0.0002731	0.003043	-0.00277
17	-0.009647	0.00172	-0.01137
18	0.01534	-0.002065	0.01741
19	0.008081	0.003289	0.004792
20	-0.02447	-0.003817	-0.02065
21	0.003766	0.0004589	0.003307
22	-0.01062	0.002905	-0.01352
23	0.01106	-0.006509	0.01757
24	0.008513	0.002801	0.005712
25	-0.01033	-0.0004053	-0.009927
26	0.00913	0.002264	0.006867
27	-0.009494	-0.001877	-0.007617
28	0.01224	0.003977	0.008268
29	-0.004565	-0.004031	-0.000534
30	-0.003687	0.001019	-0.004705
31	0.004877	0.003099	0.001778
32	-0.003558	-0.002499	-0.00106
33	-0.002163	0.0001432	-0.002306
34	0.007967	0.001379	0.006588
35	-0.0005427	-0.0004835	-5.913e-05
36	-0.01257	0.002165	-0.01473
37	0.00151	-0.001154	0.002664
38	-0.01122	-0.00731	-0.003912
39	0.003633	0.004478	-0.0008448
40	0.007736	-0.0007867	0.008523
41	-0.0002584	-0.003814	0.003555
42	0.005085	0.00773	-0.002646
43	-0.01738	-0.004988	-0.01239
44	0.00671	-0.0007042	0.007414
45	0.01286	0.005651	0.007211
46	-0.01143	-0.006978	-0.004454
47	0.006996	0.0007201	0.006276
48	0.00418	0.002658	0.001522
49	0.01384	0.002668	0.01117
50	-0.005876	0.001559	-0.007436
51	0.009741	-0.0009468	0.01069
52	-0.01865	-0.002963	-0.01569
53	0.007227	0.002972	0.004255
54	-0.007335	0.001194	-0.008529
55	0.009209	-0.005848	0.01506
56	0.002303	0.002845	-0.0005421
57	0.005825	0.001088	0.004737
58	-0.003725	-0.002603	-0.001121
59	0.006883	0.00231	0.004573
60	-0.011	-0.0004557	-0.01055
61	-0.00198	0.0002256	-0.002206
62	0.01228	-0.001205	0.01349
63	-0.003715	0.004004	-0.007719
64	-0.006059	-0.006361	0.0003015
65	-0.003705	0.002467	-0.006172
66	0.00487	-0.003271	0.008141
67	0.01106	0.00258	0.008484
68	-0.006109	0.002226	-0.008334
69	-0.01296	-0.005953	-0.007004
70	0.005539	0.003128	0.002411
71	-0.0003794	0.00466	-0.005039
72	-0.008982	-0.008877	-0.0001045
73	0.005669	0.002273	0.003395
74	-0.0003289	-0.000314	-1.485e-05
75	-7.11e-05	-0.001512	0.00144
76	0.002393	0.004266	-0.001873
77	0.002602	-0.0001222	0.002724
78	-0.01408	-0.0026	-0.01148
79	0.007178	0.002323	0.004855
80	0.008724	0.0005596	0.008165
81	-0.008084	-0.005262	-0.002823
82	0.004546	0.00126	0.003286
83	0.00633	0.002187	0.004142
84	-0.01705	-0.005562	-0.01148
85	-0.01151	-0.0009043	-0.0106
86	0.004858	0.01025	-0.005394
87	-0.006472	-0.01174	0.005266
88	0.01243	0.006843	0.005585
89	-0.002558	0.001948	-0.004506
90	0.01373	-0.005509	0.01924
91	-0.01454	0.008769	-0.0233
92	-0.01258	-0.009021	-0.003559
93	0.01434	0.006761	0.007576
94	0.001523	0.005465	-0.003942
95	-0.02371	-0.01099	-0.01272
96	0.0364	0.01768	0.01872
97	0.007184	-0.004596	0.01178
98	-0.005412	-0.00766	0.002249
99	-0.0004686	0.007135	-0.007604
100	-0.005174	0.001521	-0.006694
101	-0.003399	-0.007451	0.004051
102	-0.008861	0.005123	-0.01398
103	0.006579	-0.001509	0.008088
104	0.001422	0.002704	-0.001282
105	0.004647	0.0007019	0.003945
106	-0.008398	-0.0026	-0.005798
107	0.01475	0.001532	0.01322
108	-0.01317	0.0006539	-0.01382
109	0.001408	-0.002612	0.004019
110	-0.003396	-0.004436	0.001039
111	0.005115	0.00472	0.000395
112	0.00824	-0.0007625	0.009002
113	-0.01572	-0.001388	-0.01434
114	0.006495	0.005596	0.0008989
115	-0.004471	-0.007257	0.002786
116	0.002105	0.002415	-0.0003109
117	-0.001734	0.001334	-0.003068
118	-0.002436	-0.006435	0.004
119	0.001026	0.003982	-0.002955
120	-0.005198	-0.004461	-0.0007374
121	-0.001153	0.004434	-0.005587
122	0.001393	0.0001643	0.001229
123	-0.005651	0.0008281	-0.006479
124	-0.005368	-0.009131	0.003764
125	0.02921	0.0119	0.01731
126	-0.005422	0.0009981	-0.00642
127	-0.00745	-0.006588	-0.0008622
128	0.007806	-0.000804	0.00861
129	0.007107	0.001171	0.005936
130	-0.005909	0.001324	-0.007233
131	-0.006987	0.0005439	-0.007531
132	0.01545	-0.001954	0.0174
133	-0.01274	-0.0008547	-0.01189
134	0.01103	0.005231	0.005799
135	0.002473	0.00139	0.001082
136	0.000932	-0.00181	0.002742
137	-0.0185	-0.003371	-0.01513
138	0.01075	-0.003848	0.0146
139	0.004132	0.003873	0.0002591
140	0.00341	0.007684	-0.004274
141	-0.02302	-0.01229	-0.01073
142	0.007308	0.006672	0.0006356
143	0.009866	0.005026	0.00484
144	0.009589	-0.006681	0.01627
145	0.003556	0.006767	-0.003211
146	-0.001214	-0.00034	-0.0008742
147	-0.007949	-0.006222	-0.001727
148	-0.003463	0.003023	-0.006486
149	0.01573	0.004025	0.01171
150	-0.01582	-0.01069	-0.005137
151	0.01064	0.01232	-0.001678
152	-0.009902	-0.005964	-0.003939
153	0.004644	0.004894	-0.0002496
154	0.01985	0.001732	0.01811
155	-0.0285	-0.005844	-0.02265
156	-0.002284	0.005152	-0.007435
157	-0.0109	-0.003383	-0.007521
158	0.005703	0.0004831	0.005219
159	0.001412	-0.005011	0.006423
160	0.00867	0.005856	0.002814
161	-0.005277	-0.006263	0.0009864
162	-0.004044	0.008018	-0.01206
163	-0.001045	-0.003127	0.002082
164	0.000697	-0.006218	0.006915
165	-0.001116	0.004365	-0.005481
166	-0.006961	-0.0002995	-0.006662
167	0.02069	-0.003586	0.02428
168	-0.01297	0.00409	-0.01706
169	0.008297	-0.002294	0.01059
170	-0.006427	-0.003586	-0.002841
171	0.01951	0.005438	0.01407
172	-0.006928	0.002701	-0.009629
173	-0.009851	-0.007835	-0.002016
174	0.01199	0.001532	0.01046
175	-0.006574	0.001338	-0.007912
176	0.00122	0.001775	-0.0005552
177	0.005762	-0.001304	0.007066
178	-0.0102	-0.003749	-0.006447
179	-0.00106	0.002344	-0.003405
180	-0.008141	0.003324	-0.01147
181	-0.0005802	-0.006082	0.005502
182	0.01221	0.0009651	0.01125
183	-0.01438	0.002021	-0.0164
184	0.001928	-0.005792	0.00772
185	0.02923	0.007068	0.02216
186	0.005423	0.007711	-0.002287
187	-0.01622	-0.01214	-0.004076
188	-0.0002036	0.004799	-0.005003
189	0.006453	0.006218	0.000235
190	-0.006762	-0.006739	-2.323e-05
191	0.006298	0.0008469	0.005451
192	-0.0007827	-0.001182	0.0003996
193	-0.009345	0.005138	-0.01448
194	-0.0006665	-0.003866	0.003199
195	0.01208	0.007422	0.00466
196	-0.01878	-0.009557	-0.009224
197	-0.01189	0.003278	-0.01516
198	-0.004741	0.0001581	-0.004899
199	0.0219	-0.0001293	0.02203

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.42	0.84	0.58
8	0.2568	0.5135	0.7432
9	0.1453	0.2906	0.8547
10	0.07909	0.1582	0.9209
11	0.6411	0.7177	0.3589
12	0.6792	0.6416	0.3208
13	0.5919	0.8161	0.4081
14	0.5606	0.8788	0.4394
15	0.478	0.956	0.522
16	0.3963	0.7927	0.6037
17	0.4406	0.8811	0.5594
18	0.5843	0.8313	0.4157
19	0.5618	0.8764	0.4382
20	0.8594	0.2812	0.1406
21	0.8204	0.3592	0.1796
22	0.8434	0.3131	0.1566
23	0.8785	0.243	0.1215
24	0.8656	0.2687	0.1344
25	0.8745	0.2509	0.1255
26	0.8696	0.2607	0.1304
27	0.8642	0.2716	0.1358
28	0.8674	0.2651	0.1326
29	0.8366	0.3268	0.1634
30	0.8034	0.3933	0.1966
31	0.7663	0.4673	0.2336
32	0.7222	0.5555	0.2778
33	0.6763	0.6474	0.3237
34	0.6557	0.6886	0.3443
35	0.603	0.7941	0.397
36	0.6719	0.6563	0.3281
37	0.6247	0.7506	0.3753
38	0.5984	0.8032	0.4016
39	0.5467	0.9065	0.4533
40	0.5391	0.9218	0.4609
41	0.491	0.9821	0.509
42	0.4407	0.8813	0.5593
43	0.4994	0.9989	0.5006
44	0.4832	0.9664	0.5168
45	0.4724	0.9447	0.5276
46	0.4375	0.8751	0.5625
47	0.4126	0.8252	0.5874
48	0.3671	0.7342	0.6329
49	0.3879	0.7757	0.6121
50	0.3721	0.7441	0.6279
51	0.3803	0.7605	0.6197
52	0.4714	0.9427	0.5286
53	0.434	0.8681	0.566
54	0.4191	0.8383	0.5809
55	0.4924	0.9849	0.5076
56	0.4469	0.8938	0.5531
57	0.4166	0.8331	0.5834
58	0.379	0.758	0.621
59	0.3518	0.7037	0.6482
60	0.3748	0.7496	0.6252
61	0.3345	0.669	0.6655
62	0.3779	0.7558	0.6221
63	0.3618	0.7236	0.6382
64	0.3214	0.6429	0.6786
65	0.2935	0.587	0.7065
66	0.2837	0.5675	0.7163
67	0.2748	0.5495	0.7252
68	0.2671	0.5342	0.7329
69	0.2589	0.5178	0.7411
70	0.2317	0.4634	0.7683
71	0.2112	0.4224	0.7888
72	0.1812	0.3623	0.8188
73	0.1574	0.3148	0.8426
74	0.1328	0.2655	0.8672
75	0.1122	0.2245	0.8878
76	0.09407	0.1881	0.9059
77	0.07817	0.1563	0.9218
78	0.08859	0.1772	0.9114
79	0.07647	0.1529	0.9235
80	0.07504	0.1501	0.925
81	0.06291	0.1258	0.9371
82	0.05293	0.1059	0.9471
83	0.04419	0.08837	0.9558
84	0.04699	0.09398	0.953
85	0.05318	0.1064	0.9468
86	0.04777	0.09554	0.9522
87	0.04087	0.08175	0.9591
88	0.03631	0.07262	0.9637
89	0.03035	0.0607	0.9697
90	0.06334	0.1267	0.9367
91	0.1642	0.3283	0.8358
92	0.1455	0.291	0.8545
93	0.1393	0.2785	0.8607
94	0.1216	0.2432	0.8784
95	0.1406	0.2812	0.8594
96	0.2174	0.4349	0.7826
97	0.254	0.5081	0.746
98	0.2259	0.4519	0.7741
99	0.2174	0.4348	0.7826
100	0.2059	0.4119	0.7941
101	0.1851	0.3702	0.8149
102	0.2325	0.465	0.7675
103	0.2255	0.4511	0.7745
104	0.1962	0.3925	0.8038
105	0.1792	0.3585	0.8208
106	0.1822	0.3645	0.8178
107	0.2185	0.437	0.7815
108	0.2719	0.5439	0.7281
109	0.2474	0.4949	0.7526
110	0.2163	0.4325	0.7837
111	0.1874	0.3749	0.8126
112	0.1883	0.3765	0.8117
113	0.2362	0.4725	0.7638
114	0.2109	0.4218	0.7891
115	0.1912	0.3824	0.8088
116	0.1644	0.3287	0.8356
117	0.1426	0.2851	0.8574
118	0.1241	0.2481	0.8759
119	0.1069	0.2139	0.8931
120	0.08895	0.1779	0.9111
121	0.07926	0.1585	0.9207
122	0.06529	0.1306	0.9347
123	0.06021	0.1204	0.9398
124	0.05452	0.109	0.9455
125	0.08876	0.1775	0.9112
126	0.08016	0.1603	0.9198
127	0.06569	0.1314	0.9343
128	0.06598	0.132	0.934
129	0.05775	0.1155	0.9422
130	0.05268	0.1054	0.9473
131	0.05037	0.1007	0.9496
132	0.09389	0.1878	0.9061
133	0.1038	0.2077	0.8962
134	0.09177	0.1835	0.9082
135	0.07576	0.1515	0.9242
136	0.0635	0.127	0.9365
137	0.07604	0.1521	0.924
138	0.08706	0.1741	0.9129
139	0.07119	0.1424	0.9288
140	0.06002	0.12	0.94
141	0.06518	0.1304	0.9348
142	0.05233	0.1047	0.9477
143	0.04529	0.09057	0.9547
144	0.06363	0.1273	0.9364
145	0.05257	0.1051	0.9474
146	0.04154	0.08309	0.9585
147	0.03247	0.06494	0.9675
148	0.02821	0.05642	0.9718
149	0.0309	0.0618	0.9691
150	0.02509	0.05018	0.9749
151	0.01915	0.03829	0.9809
152	0.01503	0.03006	0.985
153	0.01113	0.02225	0.9889
154	0.02411	0.04823	0.9759
155	0.08841	0.1768	0.9116
156	0.07868	0.1574	0.9213
157	0.07346	0.1469	0.9265
158	0.06149	0.123	0.9385
159	0.05321	0.1064	0.9468
160	0.04209	0.08418	0.9579
161	0.03184	0.06367	0.9682
162	0.04163	0.08325	0.9584
163	0.03177	0.06354	0.9682
164	0.02575	0.05149	0.9743
165	0.02063	0.04126	0.9794
166	0.01658	0.03317	0.9834
167	0.07921	0.1584	0.9208
168	0.1541	0.3082	0.8459
169	0.1597	0.3195	0.8403
170	0.1293	0.2586	0.8707
171	0.2108	0.4216	0.7892
172	0.2398	0.4796	0.7602
173	0.1987	0.3973	0.8013
174	0.2359	0.4718	0.7641
175	0.2052	0.4104	0.7948
176	0.1802	0.3603	0.8198
177	0.1769	0.3538	0.8231
178	0.1437	0.2874	0.8563
179	0.1489	0.2978	0.8511
180	0.1283	0.2567	0.8717
181	0.1231	0.2463	0.8769
182	0.1175	0.2351	0.8825
183	0.2528	0.5057	0.7472
184	0.4376	0.8752	0.5624
185	0.8364	0.3271	0.1636
186	0.8915	0.2169	0.1085
187	0.8624	0.2752	0.1376
188	0.9046	0.1908	0.09541
189	0.9164	0.1672	0.08361
190	0.8466	0.3069	0.1534
191	0.7997	0.4007	0.2003
192	0.667	0.666	0.333

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.42 &  0.84 &  0.58 \tabularnewline
8 &  0.2568 &  0.5135 &  0.7432 \tabularnewline
9 &  0.1453 &  0.2906 &  0.8547 \tabularnewline
10 &  0.07909 &  0.1582 &  0.9209 \tabularnewline
11 &  0.6411 &  0.7177 &  0.3589 \tabularnewline
12 &  0.6792 &  0.6416 &  0.3208 \tabularnewline
13 &  0.5919 &  0.8161 &  0.4081 \tabularnewline
14 &  0.5606 &  0.8788 &  0.4394 \tabularnewline
15 &  0.478 &  0.956 &  0.522 \tabularnewline
16 &  0.3963 &  0.7927 &  0.6037 \tabularnewline
17 &  0.4406 &  0.8811 &  0.5594 \tabularnewline
18 &  0.5843 &  0.8313 &  0.4157 \tabularnewline
19 &  0.5618 &  0.8764 &  0.4382 \tabularnewline
20 &  0.8594 &  0.2812 &  0.1406 \tabularnewline
21 &  0.8204 &  0.3592 &  0.1796 \tabularnewline
22 &  0.8434 &  0.3131 &  0.1566 \tabularnewline
23 &  0.8785 &  0.243 &  0.1215 \tabularnewline
24 &  0.8656 &  0.2687 &  0.1344 \tabularnewline
25 &  0.8745 &  0.2509 &  0.1255 \tabularnewline
26 &  0.8696 &  0.2607 &  0.1304 \tabularnewline
27 &  0.8642 &  0.2716 &  0.1358 \tabularnewline
28 &  0.8674 &  0.2651 &  0.1326 \tabularnewline
29 &  0.8366 &  0.3268 &  0.1634 \tabularnewline
30 &  0.8034 &  0.3933 &  0.1966 \tabularnewline
31 &  0.7663 &  0.4673 &  0.2336 \tabularnewline
32 &  0.7222 &  0.5555 &  0.2778 \tabularnewline
33 &  0.6763 &  0.6474 &  0.3237 \tabularnewline
34 &  0.6557 &  0.6886 &  0.3443 \tabularnewline
35 &  0.603 &  0.7941 &  0.397 \tabularnewline
36 &  0.6719 &  0.6563 &  0.3281 \tabularnewline
37 &  0.6247 &  0.7506 &  0.3753 \tabularnewline
38 &  0.5984 &  0.8032 &  0.4016 \tabularnewline
39 &  0.5467 &  0.9065 &  0.4533 \tabularnewline
40 &  0.5391 &  0.9218 &  0.4609 \tabularnewline
41 &  0.491 &  0.9821 &  0.509 \tabularnewline
42 &  0.4407 &  0.8813 &  0.5593 \tabularnewline
43 &  0.4994 &  0.9989 &  0.5006 \tabularnewline
44 &  0.4832 &  0.9664 &  0.5168 \tabularnewline
45 &  0.4724 &  0.9447 &  0.5276 \tabularnewline
46 &  0.4375 &  0.8751 &  0.5625 \tabularnewline
47 &  0.4126 &  0.8252 &  0.5874 \tabularnewline
48 &  0.3671 &  0.7342 &  0.6329 \tabularnewline
49 &  0.3879 &  0.7757 &  0.6121 \tabularnewline
50 &  0.3721 &  0.7441 &  0.6279 \tabularnewline
51 &  0.3803 &  0.7605 &  0.6197 \tabularnewline
52 &  0.4714 &  0.9427 &  0.5286 \tabularnewline
53 &  0.434 &  0.8681 &  0.566 \tabularnewline
54 &  0.4191 &  0.8383 &  0.5809 \tabularnewline
55 &  0.4924 &  0.9849 &  0.5076 \tabularnewline
56 &  0.4469 &  0.8938 &  0.5531 \tabularnewline
57 &  0.4166 &  0.8331 &  0.5834 \tabularnewline
58 &  0.379 &  0.758 &  0.621 \tabularnewline
59 &  0.3518 &  0.7037 &  0.6482 \tabularnewline
60 &  0.3748 &  0.7496 &  0.6252 \tabularnewline
61 &  0.3345 &  0.669 &  0.6655 \tabularnewline
62 &  0.3779 &  0.7558 &  0.6221 \tabularnewline
63 &  0.3618 &  0.7236 &  0.6382 \tabularnewline
64 &  0.3214 &  0.6429 &  0.6786 \tabularnewline
65 &  0.2935 &  0.587 &  0.7065 \tabularnewline
66 &  0.2837 &  0.5675 &  0.7163 \tabularnewline
67 &  0.2748 &  0.5495 &  0.7252 \tabularnewline
68 &  0.2671 &  0.5342 &  0.7329 \tabularnewline
69 &  0.2589 &  0.5178 &  0.7411 \tabularnewline
70 &  0.2317 &  0.4634 &  0.7683 \tabularnewline
71 &  0.2112 &  0.4224 &  0.7888 \tabularnewline
72 &  0.1812 &  0.3623 &  0.8188 \tabularnewline
73 &  0.1574 &  0.3148 &  0.8426 \tabularnewline
74 &  0.1328 &  0.2655 &  0.8672 \tabularnewline
75 &  0.1122 &  0.2245 &  0.8878 \tabularnewline
76 &  0.09407 &  0.1881 &  0.9059 \tabularnewline
77 &  0.07817 &  0.1563 &  0.9218 \tabularnewline
78 &  0.08859 &  0.1772 &  0.9114 \tabularnewline
79 &  0.07647 &  0.1529 &  0.9235 \tabularnewline
80 &  0.07504 &  0.1501 &  0.925 \tabularnewline
81 &  0.06291 &  0.1258 &  0.9371 \tabularnewline
82 &  0.05293 &  0.1059 &  0.9471 \tabularnewline
83 &  0.04419 &  0.08837 &  0.9558 \tabularnewline
84 &  0.04699 &  0.09398 &  0.953 \tabularnewline
85 &  0.05318 &  0.1064 &  0.9468 \tabularnewline
86 &  0.04777 &  0.09554 &  0.9522 \tabularnewline
87 &  0.04087 &  0.08175 &  0.9591 \tabularnewline
88 &  0.03631 &  0.07262 &  0.9637 \tabularnewline
89 &  0.03035 &  0.0607 &  0.9697 \tabularnewline
90 &  0.06334 &  0.1267 &  0.9367 \tabularnewline
91 &  0.1642 &  0.3283 &  0.8358 \tabularnewline
92 &  0.1455 &  0.291 &  0.8545 \tabularnewline
93 &  0.1393 &  0.2785 &  0.8607 \tabularnewline
94 &  0.1216 &  0.2432 &  0.8784 \tabularnewline
95 &  0.1406 &  0.2812 &  0.8594 \tabularnewline
96 &  0.2174 &  0.4349 &  0.7826 \tabularnewline
97 &  0.254 &  0.5081 &  0.746 \tabularnewline
98 &  0.2259 &  0.4519 &  0.7741 \tabularnewline
99 &  0.2174 &  0.4348 &  0.7826 \tabularnewline
100 &  0.2059 &  0.4119 &  0.7941 \tabularnewline
101 &  0.1851 &  0.3702 &  0.8149 \tabularnewline
102 &  0.2325 &  0.465 &  0.7675 \tabularnewline
103 &  0.2255 &  0.4511 &  0.7745 \tabularnewline
104 &  0.1962 &  0.3925 &  0.8038 \tabularnewline
105 &  0.1792 &  0.3585 &  0.8208 \tabularnewline
106 &  0.1822 &  0.3645 &  0.8178 \tabularnewline
107 &  0.2185 &  0.437 &  0.7815 \tabularnewline
108 &  0.2719 &  0.5439 &  0.7281 \tabularnewline
109 &  0.2474 &  0.4949 &  0.7526 \tabularnewline
110 &  0.2163 &  0.4325 &  0.7837 \tabularnewline
111 &  0.1874 &  0.3749 &  0.8126 \tabularnewline
112 &  0.1883 &  0.3765 &  0.8117 \tabularnewline
113 &  0.2362 &  0.4725 &  0.7638 \tabularnewline
114 &  0.2109 &  0.4218 &  0.7891 \tabularnewline
115 &  0.1912 &  0.3824 &  0.8088 \tabularnewline
116 &  0.1644 &  0.3287 &  0.8356 \tabularnewline
117 &  0.1426 &  0.2851 &  0.8574 \tabularnewline
118 &  0.1241 &  0.2481 &  0.8759 \tabularnewline
119 &  0.1069 &  0.2139 &  0.8931 \tabularnewline
120 &  0.08895 &  0.1779 &  0.9111 \tabularnewline
121 &  0.07926 &  0.1585 &  0.9207 \tabularnewline
122 &  0.06529 &  0.1306 &  0.9347 \tabularnewline
123 &  0.06021 &  0.1204 &  0.9398 \tabularnewline
124 &  0.05452 &  0.109 &  0.9455 \tabularnewline
125 &  0.08876 &  0.1775 &  0.9112 \tabularnewline
126 &  0.08016 &  0.1603 &  0.9198 \tabularnewline
127 &  0.06569 &  0.1314 &  0.9343 \tabularnewline
128 &  0.06598 &  0.132 &  0.934 \tabularnewline
129 &  0.05775 &  0.1155 &  0.9422 \tabularnewline
130 &  0.05268 &  0.1054 &  0.9473 \tabularnewline
131 &  0.05037 &  0.1007 &  0.9496 \tabularnewline
132 &  0.09389 &  0.1878 &  0.9061 \tabularnewline
133 &  0.1038 &  0.2077 &  0.8962 \tabularnewline
134 &  0.09177 &  0.1835 &  0.9082 \tabularnewline
135 &  0.07576 &  0.1515 &  0.9242 \tabularnewline
136 &  0.0635 &  0.127 &  0.9365 \tabularnewline
137 &  0.07604 &  0.1521 &  0.924 \tabularnewline
138 &  0.08706 &  0.1741 &  0.9129 \tabularnewline
139 &  0.07119 &  0.1424 &  0.9288 \tabularnewline
140 &  0.06002 &  0.12 &  0.94 \tabularnewline
141 &  0.06518 &  0.1304 &  0.9348 \tabularnewline
142 &  0.05233 &  0.1047 &  0.9477 \tabularnewline
143 &  0.04529 &  0.09057 &  0.9547 \tabularnewline
144 &  0.06363 &  0.1273 &  0.9364 \tabularnewline
145 &  0.05257 &  0.1051 &  0.9474 \tabularnewline
146 &  0.04154 &  0.08309 &  0.9585 \tabularnewline
147 &  0.03247 &  0.06494 &  0.9675 \tabularnewline
148 &  0.02821 &  0.05642 &  0.9718 \tabularnewline
149 &  0.0309 &  0.0618 &  0.9691 \tabularnewline
150 &  0.02509 &  0.05018 &  0.9749 \tabularnewline
151 &  0.01915 &  0.03829 &  0.9809 \tabularnewline
152 &  0.01503 &  0.03006 &  0.985 \tabularnewline
153 &  0.01113 &  0.02225 &  0.9889 \tabularnewline
154 &  0.02411 &  0.04823 &  0.9759 \tabularnewline
155 &  0.08841 &  0.1768 &  0.9116 \tabularnewline
156 &  0.07868 &  0.1574 &  0.9213 \tabularnewline
157 &  0.07346 &  0.1469 &  0.9265 \tabularnewline
158 &  0.06149 &  0.123 &  0.9385 \tabularnewline
159 &  0.05321 &  0.1064 &  0.9468 \tabularnewline
160 &  0.04209 &  0.08418 &  0.9579 \tabularnewline
161 &  0.03184 &  0.06367 &  0.9682 \tabularnewline
162 &  0.04163 &  0.08325 &  0.9584 \tabularnewline
163 &  0.03177 &  0.06354 &  0.9682 \tabularnewline
164 &  0.02575 &  0.05149 &  0.9743 \tabularnewline
165 &  0.02063 &  0.04126 &  0.9794 \tabularnewline
166 &  0.01658 &  0.03317 &  0.9834 \tabularnewline
167 &  0.07921 &  0.1584 &  0.9208 \tabularnewline
168 &  0.1541 &  0.3082 &  0.8459 \tabularnewline
169 &  0.1597 &  0.3195 &  0.8403 \tabularnewline
170 &  0.1293 &  0.2586 &  0.8707 \tabularnewline
171 &  0.2108 &  0.4216 &  0.7892 \tabularnewline
172 &  0.2398 &  0.4796 &  0.7602 \tabularnewline
173 &  0.1987 &  0.3973 &  0.8013 \tabularnewline
174 &  0.2359 &  0.4718 &  0.7641 \tabularnewline
175 &  0.2052 &  0.4104 &  0.7948 \tabularnewline
176 &  0.1802 &  0.3603 &  0.8198 \tabularnewline
177 &  0.1769 &  0.3538 &  0.8231 \tabularnewline
178 &  0.1437 &  0.2874 &  0.8563 \tabularnewline
179 &  0.1489 &  0.2978 &  0.8511 \tabularnewline
180 &  0.1283 &  0.2567 &  0.8717 \tabularnewline
181 &  0.1231 &  0.2463 &  0.8769 \tabularnewline
182 &  0.1175 &  0.2351 &  0.8825 \tabularnewline
183 &  0.2528 &  0.5057 &  0.7472 \tabularnewline
184 &  0.4376 &  0.8752 &  0.5624 \tabularnewline
185 &  0.8364 &  0.3271 &  0.1636 \tabularnewline
186 &  0.8915 &  0.2169 &  0.1085 \tabularnewline
187 &  0.8624 &  0.2752 &  0.1376 \tabularnewline
188 &  0.9046 &  0.1908 &  0.09541 \tabularnewline
189 &  0.9164 &  0.1672 &  0.08361 \tabularnewline
190 &  0.8466 &  0.3069 &  0.1534 \tabularnewline
191 &  0.7997 &  0.4007 &  0.2003 \tabularnewline
192 &  0.667 &  0.666 &  0.333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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]7[/C][C] 0.42[/C][C] 0.84[/C][C] 0.58[/C][/ROW]
[ROW][C]8[/C][C] 0.2568[/C][C] 0.5135[/C][C] 0.7432[/C][/ROW]
[ROW][C]9[/C][C] 0.1453[/C][C] 0.2906[/C][C] 0.8547[/C][/ROW]
[ROW][C]10[/C][C] 0.07909[/C][C] 0.1582[/C][C] 0.9209[/C][/ROW]
[ROW][C]11[/C][C] 0.6411[/C][C] 0.7177[/C][C] 0.3589[/C][/ROW]
[ROW][C]12[/C][C] 0.6792[/C][C] 0.6416[/C][C] 0.3208[/C][/ROW]
[ROW][C]13[/C][C] 0.5919[/C][C] 0.8161[/C][C] 0.4081[/C][/ROW]
[ROW][C]14[/C][C] 0.5606[/C][C] 0.8788[/C][C] 0.4394[/C][/ROW]
[ROW][C]15[/C][C] 0.478[/C][C] 0.956[/C][C] 0.522[/C][/ROW]
[ROW][C]16[/C][C] 0.3963[/C][C] 0.7927[/C][C] 0.6037[/C][/ROW]
[ROW][C]17[/C][C] 0.4406[/C][C] 0.8811[/C][C] 0.5594[/C][/ROW]
[ROW][C]18[/C][C] 0.5843[/C][C] 0.8313[/C][C] 0.4157[/C][/ROW]
[ROW][C]19[/C][C] 0.5618[/C][C] 0.8764[/C][C] 0.4382[/C][/ROW]
[ROW][C]20[/C][C] 0.8594[/C][C] 0.2812[/C][C] 0.1406[/C][/ROW]
[ROW][C]21[/C][C] 0.8204[/C][C] 0.3592[/C][C] 0.1796[/C][/ROW]
[ROW][C]22[/C][C] 0.8434[/C][C] 0.3131[/C][C] 0.1566[/C][/ROW]
[ROW][C]23[/C][C] 0.8785[/C][C] 0.243[/C][C] 0.1215[/C][/ROW]
[ROW][C]24[/C][C] 0.8656[/C][C] 0.2687[/C][C] 0.1344[/C][/ROW]
[ROW][C]25[/C][C] 0.8745[/C][C] 0.2509[/C][C] 0.1255[/C][/ROW]
[ROW][C]26[/C][C] 0.8696[/C][C] 0.2607[/C][C] 0.1304[/C][/ROW]
[ROW][C]27[/C][C] 0.8642[/C][C] 0.2716[/C][C] 0.1358[/C][/ROW]
[ROW][C]28[/C][C] 0.8674[/C][C] 0.2651[/C][C] 0.1326[/C][/ROW]
[ROW][C]29[/C][C] 0.8366[/C][C] 0.3268[/C][C] 0.1634[/C][/ROW]
[ROW][C]30[/C][C] 0.8034[/C][C] 0.3933[/C][C] 0.1966[/C][/ROW]
[ROW][C]31[/C][C] 0.7663[/C][C] 0.4673[/C][C] 0.2336[/C][/ROW]
[ROW][C]32[/C][C] 0.7222[/C][C] 0.5555[/C][C] 0.2778[/C][/ROW]
[ROW][C]33[/C][C] 0.6763[/C][C] 0.6474[/C][C] 0.3237[/C][/ROW]
[ROW][C]34[/C][C] 0.6557[/C][C] 0.6886[/C][C] 0.3443[/C][/ROW]
[ROW][C]35[/C][C] 0.603[/C][C] 0.7941[/C][C] 0.397[/C][/ROW]
[ROW][C]36[/C][C] 0.6719[/C][C] 0.6563[/C][C] 0.3281[/C][/ROW]
[ROW][C]37[/C][C] 0.6247[/C][C] 0.7506[/C][C] 0.3753[/C][/ROW]
[ROW][C]38[/C][C] 0.5984[/C][C] 0.8032[/C][C] 0.4016[/C][/ROW]
[ROW][C]39[/C][C] 0.5467[/C][C] 0.9065[/C][C] 0.4533[/C][/ROW]
[ROW][C]40[/C][C] 0.5391[/C][C] 0.9218[/C][C] 0.4609[/C][/ROW]
[ROW][C]41[/C][C] 0.491[/C][C] 0.9821[/C][C] 0.509[/C][/ROW]
[ROW][C]42[/C][C] 0.4407[/C][C] 0.8813[/C][C] 0.5593[/C][/ROW]
[ROW][C]43[/C][C] 0.4994[/C][C] 0.9989[/C][C] 0.5006[/C][/ROW]
[ROW][C]44[/C][C] 0.4832[/C][C] 0.9664[/C][C] 0.5168[/C][/ROW]
[ROW][C]45[/C][C] 0.4724[/C][C] 0.9447[/C][C] 0.5276[/C][/ROW]
[ROW][C]46[/C][C] 0.4375[/C][C] 0.8751[/C][C] 0.5625[/C][/ROW]
[ROW][C]47[/C][C] 0.4126[/C][C] 0.8252[/C][C] 0.5874[/C][/ROW]
[ROW][C]48[/C][C] 0.3671[/C][C] 0.7342[/C][C] 0.6329[/C][/ROW]
[ROW][C]49[/C][C] 0.3879[/C][C] 0.7757[/C][C] 0.6121[/C][/ROW]
[ROW][C]50[/C][C] 0.3721[/C][C] 0.7441[/C][C] 0.6279[/C][/ROW]
[ROW][C]51[/C][C] 0.3803[/C][C] 0.7605[/C][C] 0.6197[/C][/ROW]
[ROW][C]52[/C][C] 0.4714[/C][C] 0.9427[/C][C] 0.5286[/C][/ROW]
[ROW][C]53[/C][C] 0.434[/C][C] 0.8681[/C][C] 0.566[/C][/ROW]
[ROW][C]54[/C][C] 0.4191[/C][C] 0.8383[/C][C] 0.5809[/C][/ROW]
[ROW][C]55[/C][C] 0.4924[/C][C] 0.9849[/C][C] 0.5076[/C][/ROW]
[ROW][C]56[/C][C] 0.4469[/C][C] 0.8938[/C][C] 0.5531[/C][/ROW]
[ROW][C]57[/C][C] 0.4166[/C][C] 0.8331[/C][C] 0.5834[/C][/ROW]
[ROW][C]58[/C][C] 0.379[/C][C] 0.758[/C][C] 0.621[/C][/ROW]
[ROW][C]59[/C][C] 0.3518[/C][C] 0.7037[/C][C] 0.6482[/C][/ROW]
[ROW][C]60[/C][C] 0.3748[/C][C] 0.7496[/C][C] 0.6252[/C][/ROW]
[ROW][C]61[/C][C] 0.3345[/C][C] 0.669[/C][C] 0.6655[/C][/ROW]
[ROW][C]62[/C][C] 0.3779[/C][C] 0.7558[/C][C] 0.6221[/C][/ROW]
[ROW][C]63[/C][C] 0.3618[/C][C] 0.7236[/C][C] 0.6382[/C][/ROW]
[ROW][C]64[/C][C] 0.3214[/C][C] 0.6429[/C][C] 0.6786[/C][/ROW]
[ROW][C]65[/C][C] 0.2935[/C][C] 0.587[/C][C] 0.7065[/C][/ROW]
[ROW][C]66[/C][C] 0.2837[/C][C] 0.5675[/C][C] 0.7163[/C][/ROW]
[ROW][C]67[/C][C] 0.2748[/C][C] 0.5495[/C][C] 0.7252[/C][/ROW]
[ROW][C]68[/C][C] 0.2671[/C][C] 0.5342[/C][C] 0.7329[/C][/ROW]
[ROW][C]69[/C][C] 0.2589[/C][C] 0.5178[/C][C] 0.7411[/C][/ROW]
[ROW][C]70[/C][C] 0.2317[/C][C] 0.4634[/C][C] 0.7683[/C][/ROW]
[ROW][C]71[/C][C] 0.2112[/C][C] 0.4224[/C][C] 0.7888[/C][/ROW]
[ROW][C]72[/C][C] 0.1812[/C][C] 0.3623[/C][C] 0.8188[/C][/ROW]
[ROW][C]73[/C][C] 0.1574[/C][C] 0.3148[/C][C] 0.8426[/C][/ROW]
[ROW][C]74[/C][C] 0.1328[/C][C] 0.2655[/C][C] 0.8672[/C][/ROW]
[ROW][C]75[/C][C] 0.1122[/C][C] 0.2245[/C][C] 0.8878[/C][/ROW]
[ROW][C]76[/C][C] 0.09407[/C][C] 0.1881[/C][C] 0.9059[/C][/ROW]
[ROW][C]77[/C][C] 0.07817[/C][C] 0.1563[/C][C] 0.9218[/C][/ROW]
[ROW][C]78[/C][C] 0.08859[/C][C] 0.1772[/C][C] 0.9114[/C][/ROW]
[ROW][C]79[/C][C] 0.07647[/C][C] 0.1529[/C][C] 0.9235[/C][/ROW]
[ROW][C]80[/C][C] 0.07504[/C][C] 0.1501[/C][C] 0.925[/C][/ROW]
[ROW][C]81[/C][C] 0.06291[/C][C] 0.1258[/C][C] 0.9371[/C][/ROW]
[ROW][C]82[/C][C] 0.05293[/C][C] 0.1059[/C][C] 0.9471[/C][/ROW]
[ROW][C]83[/C][C] 0.04419[/C][C] 0.08837[/C][C] 0.9558[/C][/ROW]
[ROW][C]84[/C][C] 0.04699[/C][C] 0.09398[/C][C] 0.953[/C][/ROW]
[ROW][C]85[/C][C] 0.05318[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]86[/C][C] 0.04777[/C][C] 0.09554[/C][C] 0.9522[/C][/ROW]
[ROW][C]87[/C][C] 0.04087[/C][C] 0.08175[/C][C] 0.9591[/C][/ROW]
[ROW][C]88[/C][C] 0.03631[/C][C] 0.07262[/C][C] 0.9637[/C][/ROW]
[ROW][C]89[/C][C] 0.03035[/C][C] 0.0607[/C][C] 0.9697[/C][/ROW]
[ROW][C]90[/C][C] 0.06334[/C][C] 0.1267[/C][C] 0.9367[/C][/ROW]
[ROW][C]91[/C][C] 0.1642[/C][C] 0.3283[/C][C] 0.8358[/C][/ROW]
[ROW][C]92[/C][C] 0.1455[/C][C] 0.291[/C][C] 0.8545[/C][/ROW]
[ROW][C]93[/C][C] 0.1393[/C][C] 0.2785[/C][C] 0.8607[/C][/ROW]
[ROW][C]94[/C][C] 0.1216[/C][C] 0.2432[/C][C] 0.8784[/C][/ROW]
[ROW][C]95[/C][C] 0.1406[/C][C] 0.2812[/C][C] 0.8594[/C][/ROW]
[ROW][C]96[/C][C] 0.2174[/C][C] 0.4349[/C][C] 0.7826[/C][/ROW]
[ROW][C]97[/C][C] 0.254[/C][C] 0.5081[/C][C] 0.746[/C][/ROW]
[ROW][C]98[/C][C] 0.2259[/C][C] 0.4519[/C][C] 0.7741[/C][/ROW]
[ROW][C]99[/C][C] 0.2174[/C][C] 0.4348[/C][C] 0.7826[/C][/ROW]
[ROW][C]100[/C][C] 0.2059[/C][C] 0.4119[/C][C] 0.7941[/C][/ROW]
[ROW][C]101[/C][C] 0.1851[/C][C] 0.3702[/C][C] 0.8149[/C][/ROW]
[ROW][C]102[/C][C] 0.2325[/C][C] 0.465[/C][C] 0.7675[/C][/ROW]
[ROW][C]103[/C][C] 0.2255[/C][C] 0.4511[/C][C] 0.7745[/C][/ROW]
[ROW][C]104[/C][C] 0.1962[/C][C] 0.3925[/C][C] 0.8038[/C][/ROW]
[ROW][C]105[/C][C] 0.1792[/C][C] 0.3585[/C][C] 0.8208[/C][/ROW]
[ROW][C]106[/C][C] 0.1822[/C][C] 0.3645[/C][C] 0.8178[/C][/ROW]
[ROW][C]107[/C][C] 0.2185[/C][C] 0.437[/C][C] 0.7815[/C][/ROW]
[ROW][C]108[/C][C] 0.2719[/C][C] 0.5439[/C][C] 0.7281[/C][/ROW]
[ROW][C]109[/C][C] 0.2474[/C][C] 0.4949[/C][C] 0.7526[/C][/ROW]
[ROW][C]110[/C][C] 0.2163[/C][C] 0.4325[/C][C] 0.7837[/C][/ROW]
[ROW][C]111[/C][C] 0.1874[/C][C] 0.3749[/C][C] 0.8126[/C][/ROW]
[ROW][C]112[/C][C] 0.1883[/C][C] 0.3765[/C][C] 0.8117[/C][/ROW]
[ROW][C]113[/C][C] 0.2362[/C][C] 0.4725[/C][C] 0.7638[/C][/ROW]
[ROW][C]114[/C][C] 0.2109[/C][C] 0.4218[/C][C] 0.7891[/C][/ROW]
[ROW][C]115[/C][C] 0.1912[/C][C] 0.3824[/C][C] 0.8088[/C][/ROW]
[ROW][C]116[/C][C] 0.1644[/C][C] 0.3287[/C][C] 0.8356[/C][/ROW]
[ROW][C]117[/C][C] 0.1426[/C][C] 0.2851[/C][C] 0.8574[/C][/ROW]
[ROW][C]118[/C][C] 0.1241[/C][C] 0.2481[/C][C] 0.8759[/C][/ROW]
[ROW][C]119[/C][C] 0.1069[/C][C] 0.2139[/C][C] 0.8931[/C][/ROW]
[ROW][C]120[/C][C] 0.08895[/C][C] 0.1779[/C][C] 0.9111[/C][/ROW]
[ROW][C]121[/C][C] 0.07926[/C][C] 0.1585[/C][C] 0.9207[/C][/ROW]
[ROW][C]122[/C][C] 0.06529[/C][C] 0.1306[/C][C] 0.9347[/C][/ROW]
[ROW][C]123[/C][C] 0.06021[/C][C] 0.1204[/C][C] 0.9398[/C][/ROW]
[ROW][C]124[/C][C] 0.05452[/C][C] 0.109[/C][C] 0.9455[/C][/ROW]
[ROW][C]125[/C][C] 0.08876[/C][C] 0.1775[/C][C] 0.9112[/C][/ROW]
[ROW][C]126[/C][C] 0.08016[/C][C] 0.1603[/C][C] 0.9198[/C][/ROW]
[ROW][C]127[/C][C] 0.06569[/C][C] 0.1314[/C][C] 0.9343[/C][/ROW]
[ROW][C]128[/C][C] 0.06598[/C][C] 0.132[/C][C] 0.934[/C][/ROW]
[ROW][C]129[/C][C] 0.05775[/C][C] 0.1155[/C][C] 0.9422[/C][/ROW]
[ROW][C]130[/C][C] 0.05268[/C][C] 0.1054[/C][C] 0.9473[/C][/ROW]
[ROW][C]131[/C][C] 0.05037[/C][C] 0.1007[/C][C] 0.9496[/C][/ROW]
[ROW][C]132[/C][C] 0.09389[/C][C] 0.1878[/C][C] 0.9061[/C][/ROW]
[ROW][C]133[/C][C] 0.1038[/C][C] 0.2077[/C][C] 0.8962[/C][/ROW]
[ROW][C]134[/C][C] 0.09177[/C][C] 0.1835[/C][C] 0.9082[/C][/ROW]
[ROW][C]135[/C][C] 0.07576[/C][C] 0.1515[/C][C] 0.9242[/C][/ROW]
[ROW][C]136[/C][C] 0.0635[/C][C] 0.127[/C][C] 0.9365[/C][/ROW]
[ROW][C]137[/C][C] 0.07604[/C][C] 0.1521[/C][C] 0.924[/C][/ROW]
[ROW][C]138[/C][C] 0.08706[/C][C] 0.1741[/C][C] 0.9129[/C][/ROW]
[ROW][C]139[/C][C] 0.07119[/C][C] 0.1424[/C][C] 0.9288[/C][/ROW]
[ROW][C]140[/C][C] 0.06002[/C][C] 0.12[/C][C] 0.94[/C][/ROW]
[ROW][C]141[/C][C] 0.06518[/C][C] 0.1304[/C][C] 0.9348[/C][/ROW]
[ROW][C]142[/C][C] 0.05233[/C][C] 0.1047[/C][C] 0.9477[/C][/ROW]
[ROW][C]143[/C][C] 0.04529[/C][C] 0.09057[/C][C] 0.9547[/C][/ROW]
[ROW][C]144[/C][C] 0.06363[/C][C] 0.1273[/C][C] 0.9364[/C][/ROW]
[ROW][C]145[/C][C] 0.05257[/C][C] 0.1051[/C][C] 0.9474[/C][/ROW]
[ROW][C]146[/C][C] 0.04154[/C][C] 0.08309[/C][C] 0.9585[/C][/ROW]
[ROW][C]147[/C][C] 0.03247[/C][C] 0.06494[/C][C] 0.9675[/C][/ROW]
[ROW][C]148[/C][C] 0.02821[/C][C] 0.05642[/C][C] 0.9718[/C][/ROW]
[ROW][C]149[/C][C] 0.0309[/C][C] 0.0618[/C][C] 0.9691[/C][/ROW]
[ROW][C]150[/C][C] 0.02509[/C][C] 0.05018[/C][C] 0.9749[/C][/ROW]
[ROW][C]151[/C][C] 0.01915[/C][C] 0.03829[/C][C] 0.9809[/C][/ROW]
[ROW][C]152[/C][C] 0.01503[/C][C] 0.03006[/C][C] 0.985[/C][/ROW]
[ROW][C]153[/C][C] 0.01113[/C][C] 0.02225[/C][C] 0.9889[/C][/ROW]
[ROW][C]154[/C][C] 0.02411[/C][C] 0.04823[/C][C] 0.9759[/C][/ROW]
[ROW][C]155[/C][C] 0.08841[/C][C] 0.1768[/C][C] 0.9116[/C][/ROW]
[ROW][C]156[/C][C] 0.07868[/C][C] 0.1574[/C][C] 0.9213[/C][/ROW]
[ROW][C]157[/C][C] 0.07346[/C][C] 0.1469[/C][C] 0.9265[/C][/ROW]
[ROW][C]158[/C][C] 0.06149[/C][C] 0.123[/C][C] 0.9385[/C][/ROW]
[ROW][C]159[/C][C] 0.05321[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]160[/C][C] 0.04209[/C][C] 0.08418[/C][C] 0.9579[/C][/ROW]
[ROW][C]161[/C][C] 0.03184[/C][C] 0.06367[/C][C] 0.9682[/C][/ROW]
[ROW][C]162[/C][C] 0.04163[/C][C] 0.08325[/C][C] 0.9584[/C][/ROW]
[ROW][C]163[/C][C] 0.03177[/C][C] 0.06354[/C][C] 0.9682[/C][/ROW]
[ROW][C]164[/C][C] 0.02575[/C][C] 0.05149[/C][C] 0.9743[/C][/ROW]
[ROW][C]165[/C][C] 0.02063[/C][C] 0.04126[/C][C] 0.9794[/C][/ROW]
[ROW][C]166[/C][C] 0.01658[/C][C] 0.03317[/C][C] 0.9834[/C][/ROW]
[ROW][C]167[/C][C] 0.07921[/C][C] 0.1584[/C][C] 0.9208[/C][/ROW]
[ROW][C]168[/C][C] 0.1541[/C][C] 0.3082[/C][C] 0.8459[/C][/ROW]
[ROW][C]169[/C][C] 0.1597[/C][C] 0.3195[/C][C] 0.8403[/C][/ROW]
[ROW][C]170[/C][C] 0.1293[/C][C] 0.2586[/C][C] 0.8707[/C][/ROW]
[ROW][C]171[/C][C] 0.2108[/C][C] 0.4216[/C][C] 0.7892[/C][/ROW]
[ROW][C]172[/C][C] 0.2398[/C][C] 0.4796[/C][C] 0.7602[/C][/ROW]
[ROW][C]173[/C][C] 0.1987[/C][C] 0.3973[/C][C] 0.8013[/C][/ROW]
[ROW][C]174[/C][C] 0.2359[/C][C] 0.4718[/C][C] 0.7641[/C][/ROW]
[ROW][C]175[/C][C] 0.2052[/C][C] 0.4104[/C][C] 0.7948[/C][/ROW]
[ROW][C]176[/C][C] 0.1802[/C][C] 0.3603[/C][C] 0.8198[/C][/ROW]
[ROW][C]177[/C][C] 0.1769[/C][C] 0.3538[/C][C] 0.8231[/C][/ROW]
[ROW][C]178[/C][C] 0.1437[/C][C] 0.2874[/C][C] 0.8563[/C][/ROW]
[ROW][C]179[/C][C] 0.1489[/C][C] 0.2978[/C][C] 0.8511[/C][/ROW]
[ROW][C]180[/C][C] 0.1283[/C][C] 0.2567[/C][C] 0.8717[/C][/ROW]
[ROW][C]181[/C][C] 0.1231[/C][C] 0.2463[/C][C] 0.8769[/C][/ROW]
[ROW][C]182[/C][C] 0.1175[/C][C] 0.2351[/C][C] 0.8825[/C][/ROW]
[ROW][C]183[/C][C] 0.2528[/C][C] 0.5057[/C][C] 0.7472[/C][/ROW]
[ROW][C]184[/C][C] 0.4376[/C][C] 0.8752[/C][C] 0.5624[/C][/ROW]
[ROW][C]185[/C][C] 0.8364[/C][C] 0.3271[/C][C] 0.1636[/C][/ROW]
[ROW][C]186[/C][C] 0.8915[/C][C] 0.2169[/C][C] 0.1085[/C][/ROW]
[ROW][C]187[/C][C] 0.8624[/C][C] 0.2752[/C][C] 0.1376[/C][/ROW]
[ROW][C]188[/C][C] 0.9046[/C][C] 0.1908[/C][C] 0.09541[/C][/ROW]
[ROW][C]189[/C][C] 0.9164[/C][C] 0.1672[/C][C] 0.08361[/C][/ROW]
[ROW][C]190[/C][C] 0.8466[/C][C] 0.3069[/C][C] 0.1534[/C][/ROW]
[ROW][C]191[/C][C] 0.7997[/C][C] 0.4007[/C][C] 0.2003[/C][/ROW]
[ROW][C]192[/C][C] 0.667[/C][C] 0.666[/C][C] 0.333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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
7	0.42	0.84	0.58
8	0.2568	0.5135	0.7432
9	0.1453	0.2906	0.8547
10	0.07909	0.1582	0.9209
11	0.6411	0.7177	0.3589
12	0.6792	0.6416	0.3208
13	0.5919	0.8161	0.4081
14	0.5606	0.8788	0.4394
15	0.478	0.956	0.522
16	0.3963	0.7927	0.6037
17	0.4406	0.8811	0.5594
18	0.5843	0.8313	0.4157
19	0.5618	0.8764	0.4382
20	0.8594	0.2812	0.1406
21	0.8204	0.3592	0.1796
22	0.8434	0.3131	0.1566
23	0.8785	0.243	0.1215
24	0.8656	0.2687	0.1344
25	0.8745	0.2509	0.1255
26	0.8696	0.2607	0.1304
27	0.8642	0.2716	0.1358
28	0.8674	0.2651	0.1326
29	0.8366	0.3268	0.1634
30	0.8034	0.3933	0.1966
31	0.7663	0.4673	0.2336
32	0.7222	0.5555	0.2778
33	0.6763	0.6474	0.3237
34	0.6557	0.6886	0.3443
35	0.603	0.7941	0.397
36	0.6719	0.6563	0.3281
37	0.6247	0.7506	0.3753
38	0.5984	0.8032	0.4016
39	0.5467	0.9065	0.4533
40	0.5391	0.9218	0.4609
41	0.491	0.9821	0.509
42	0.4407	0.8813	0.5593
43	0.4994	0.9989	0.5006
44	0.4832	0.9664	0.5168
45	0.4724	0.9447	0.5276
46	0.4375	0.8751	0.5625
47	0.4126	0.8252	0.5874
48	0.3671	0.7342	0.6329
49	0.3879	0.7757	0.6121
50	0.3721	0.7441	0.6279
51	0.3803	0.7605	0.6197
52	0.4714	0.9427	0.5286
53	0.434	0.8681	0.566
54	0.4191	0.8383	0.5809
55	0.4924	0.9849	0.5076
56	0.4469	0.8938	0.5531
57	0.4166	0.8331	0.5834
58	0.379	0.758	0.621
59	0.3518	0.7037	0.6482
60	0.3748	0.7496	0.6252
61	0.3345	0.669	0.6655
62	0.3779	0.7558	0.6221
63	0.3618	0.7236	0.6382
64	0.3214	0.6429	0.6786
65	0.2935	0.587	0.7065
66	0.2837	0.5675	0.7163
67	0.2748	0.5495	0.7252
68	0.2671	0.5342	0.7329
69	0.2589	0.5178	0.7411
70	0.2317	0.4634	0.7683
71	0.2112	0.4224	0.7888
72	0.1812	0.3623	0.8188
73	0.1574	0.3148	0.8426
74	0.1328	0.2655	0.8672
75	0.1122	0.2245	0.8878
76	0.09407	0.1881	0.9059
77	0.07817	0.1563	0.9218
78	0.08859	0.1772	0.9114
79	0.07647	0.1529	0.9235
80	0.07504	0.1501	0.925
81	0.06291	0.1258	0.9371
82	0.05293	0.1059	0.9471
83	0.04419	0.08837	0.9558
84	0.04699	0.09398	0.953
85	0.05318	0.1064	0.9468
86	0.04777	0.09554	0.9522
87	0.04087	0.08175	0.9591
88	0.03631	0.07262	0.9637
89	0.03035	0.0607	0.9697
90	0.06334	0.1267	0.9367
91	0.1642	0.3283	0.8358
92	0.1455	0.291	0.8545
93	0.1393	0.2785	0.8607
94	0.1216	0.2432	0.8784
95	0.1406	0.2812	0.8594
96	0.2174	0.4349	0.7826
97	0.254	0.5081	0.746
98	0.2259	0.4519	0.7741
99	0.2174	0.4348	0.7826
100	0.2059	0.4119	0.7941
101	0.1851	0.3702	0.8149
102	0.2325	0.465	0.7675
103	0.2255	0.4511	0.7745
104	0.1962	0.3925	0.8038
105	0.1792	0.3585	0.8208
106	0.1822	0.3645	0.8178
107	0.2185	0.437	0.7815
108	0.2719	0.5439	0.7281
109	0.2474	0.4949	0.7526
110	0.2163	0.4325	0.7837
111	0.1874	0.3749	0.8126
112	0.1883	0.3765	0.8117
113	0.2362	0.4725	0.7638
114	0.2109	0.4218	0.7891
115	0.1912	0.3824	0.8088
116	0.1644	0.3287	0.8356
117	0.1426	0.2851	0.8574
118	0.1241	0.2481	0.8759
119	0.1069	0.2139	0.8931
120	0.08895	0.1779	0.9111
121	0.07926	0.1585	0.9207
122	0.06529	0.1306	0.9347
123	0.06021	0.1204	0.9398
124	0.05452	0.109	0.9455
125	0.08876	0.1775	0.9112
126	0.08016	0.1603	0.9198
127	0.06569	0.1314	0.9343
128	0.06598	0.132	0.934
129	0.05775	0.1155	0.9422
130	0.05268	0.1054	0.9473
131	0.05037	0.1007	0.9496
132	0.09389	0.1878	0.9061
133	0.1038	0.2077	0.8962
134	0.09177	0.1835	0.9082
135	0.07576	0.1515	0.9242
136	0.0635	0.127	0.9365
137	0.07604	0.1521	0.924
138	0.08706	0.1741	0.9129
139	0.07119	0.1424	0.9288
140	0.06002	0.12	0.94
141	0.06518	0.1304	0.9348
142	0.05233	0.1047	0.9477
143	0.04529	0.09057	0.9547
144	0.06363	0.1273	0.9364
145	0.05257	0.1051	0.9474
146	0.04154	0.08309	0.9585
147	0.03247	0.06494	0.9675
148	0.02821	0.05642	0.9718
149	0.0309	0.0618	0.9691
150	0.02509	0.05018	0.9749
151	0.01915	0.03829	0.9809
152	0.01503	0.03006	0.985
153	0.01113	0.02225	0.9889
154	0.02411	0.04823	0.9759
155	0.08841	0.1768	0.9116
156	0.07868	0.1574	0.9213
157	0.07346	0.1469	0.9265
158	0.06149	0.123	0.9385
159	0.05321	0.1064	0.9468
160	0.04209	0.08418	0.9579
161	0.03184	0.06367	0.9682
162	0.04163	0.08325	0.9584
163	0.03177	0.06354	0.9682
164	0.02575	0.05149	0.9743
165	0.02063	0.04126	0.9794
166	0.01658	0.03317	0.9834
167	0.07921	0.1584	0.9208
168	0.1541	0.3082	0.8459
169	0.1597	0.3195	0.8403
170	0.1293	0.2586	0.8707
171	0.2108	0.4216	0.7892
172	0.2398	0.4796	0.7602
173	0.1987	0.3973	0.8013
174	0.2359	0.4718	0.7641
175	0.2052	0.4104	0.7948
176	0.1802	0.3603	0.8198
177	0.1769	0.3538	0.8231
178	0.1437	0.2874	0.8563
179	0.1489	0.2978	0.8511
180	0.1283	0.2567	0.8717
181	0.1231	0.2463	0.8769
182	0.1175	0.2351	0.8825
183	0.2528	0.5057	0.7472
184	0.4376	0.8752	0.5624
185	0.8364	0.3271	0.1636
186	0.8915	0.2169	0.1085
187	0.8624	0.2752	0.1376
188	0.9046	0.1908	0.09541
189	0.9164	0.1672	0.08361
190	0.8466	0.3069	0.1534
191	0.7997	0.4007	0.2003
192	0.667	0.666	0.333

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	6	0.0322581	OK
10% type I error level	23	0.123656	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 6 & 0.0322581 & OK \tabularnewline
10% type I error level & 23 & 0.123656 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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]6[/C][C]0.0322581[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.123656[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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	6	0.0322581	OK
10% type I error level	23	0.123656	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.2231, df1 = 2, df2 = 193, p-value = 0.006179
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4451, df1 = 6, df2 = 189, p-value = 0.02669
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6606, df1 = 2, df2 = 193, p-value = 0.1927

\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 = 5.2231, df1 = 2, df2 = 193, p-value = 0.006179
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.4451, df1 = 6, df2 = 189, p-value = 0.02669
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6606, df1 = 2, df2 = 193, p-value = 0.1927
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&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 = 5.2231, df1 = 2, df2 = 193, p-value = 0.006179
[/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 = 2.4451, df1 = 6, df2 = 189, p-value = 0.02669
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6606, df1 = 2, df2 = 193, p-value = 0.1927
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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 = 5.2231, df1 = 2, df2 = 193, p-value = 0.006179
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4451, df1 = 6, df2 = 189, p-value = 0.02669
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6606, df1 = 2, df2 = 193, p-value = 0.1927

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)FoodProducts` `(1-Bs)(1-B)Beverages` 1.792701 1.379357 `(1-Bs)(1-B)DurableConsumerGoods` 1.552478

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        `(1-Bs)(1-B)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         1.792701                          1.379357 
`(1-Bs)(1-B)DurableConsumerGoods` 
                         1.552478 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310097&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
        `(1-Bs)(1-B)FoodProducts`            `(1-Bs)(1-B)Beverages` 
                         1.792701                          1.379357 
`(1-Bs)(1-B)DurableConsumerGoods` 
                         1.552478 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310097&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310097&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` 1.792701 1.379357 `(1-Bs)(1-B)DurableConsumerGoods` 1.552478

Figure 1

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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 = 0 ; 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