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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 10:11:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291198402q3oewaxujnsd3ma.htm/, Retrieved Sat, 04 May 2024 20:32:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103907, Retrieved Sat, 04 May 2024 20:32:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2010-12-01 10:11:37] [33c49f0356cf94839cc9a2df1ea19a67] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	27	1	5	26	49	35
4	36	1	4	25	45	34
5	25	1	4	17	54	13
2	27	1	3	37	36	35
3	25	2	3	35	36	28
5	44	2	3	15	53	32
4	50	1	4	27	46	35
4	41	1	4	36	42	36
4	48	1	5	25	41	27
4	43	2	4	30	45	29
5	47	2	2	27	47	27
4	41	2	3	33	42	28
3	44	1	2	29	45	29
4	47	2	5	30	40	28
3	40	2	3	25	45	30
3	46	2	3	23	40	25
4	28	1	3	26	42	15
3	56	1	3	24	45	33
4	49	2	4	35	47	31
2	25	2	4	39	31	37
4	41	2	4	23	46	37
3	26	2	3	32	34	34
4	50	1	5	29	43	32
4	47	1	4	26	45	21
3	52	1	2	21	42	25
3	37	2	5	35	51	32
2	41	2	3	23	44	28
4	45	1	4	21	47	22
5	26	2	4	28	47	25
4		1	3	30	41	26
2	52	1	4	21	44	34
5	46	1	2	29	51	34
4	58	1	3	28	46	36
3	54	1	5	19	47	36
4	29	1	3	26	46	26
2	50	2	3	33	38	26
3	43	1	2	34	50	34
3	30	2	3	33	48	33
3	47	2	2	40	36	31
5	45	1	3	24	51	33
	48	2	1	35	35	22
4	48	2	3	35	49	29
4	26	2	4	32	38	24
4	46	1	5	20	47	37
2		2	3	35	36	32
4	50	2	3	35	47	23
3	25	1	4	21	46	29
4	47	1	2	33	43	35
1	47	2	2	40	53	20
2	41	1	3	22	55	28
2	45	2	2	35	39	26
4	41	2	4	20	55	36
3	45	2	5	28	41	26
4	40	2	3	46	33	33
3	29	1	4	18	52	25
3	34	2	5	22	42	29
5	45	1	5	20	56	32
3	52	2	3	25	46	35
2	41	2	4	31	33	24
1	48	2	3	21	51	31
2	45	2	3	23	46	29
5	54	1	2	26	46	27
4	25	2	3	34	50	29
4	26	2	4	31	46	29
3	28	1	4	23	51	27
4	50	2	4	31	48	34
4	48	2	4	26	44	32
2	51	2	3	36	38	31
3	53	2	3	28	42	31
4	37	1	3	34	39	31
3	56	1	2	25	45	16
2	43	1	3	33	31	25
4	34	1	3	46	29	27
4	42	1	3	24	48	32
3	32	2	3	32	38	28
5	31	2	5	33	55	25
1	46	1	3	42	32	25
3	30	2	5	17	51	36
3	47	2	4	36	53	36
5	33	2	4	40	47	36
2	25	1	4	30	45	27
3	25	1	5	19	33	29
3	21	2	4	33	49	32
4	36	2	5	35	46	29
2	50	2	3	23	42	31
4	48	2	3	15	56	34
3	48	2	2	38	35	27
3	25	1	3	37	40	28
3	48	1	4	23	44	32
2	49	2	5	41	46	33
3	27	1	5	34	46	29
2	28	1	3	38	39	32
4	43	2	2	45	35	35
4	48	2	3	27	48	33
2	48	2	4	46	42	27
1	25	1	1	26	39	16
5	49	2	4	44	39	32
4	26	1	3	36	41	26
4	51	1	3	20	52	32
4	25	2	4	44	45	38
3	29	1	3	27	42	24
3	29	1	4	27	44	26
1	43	1	2	41	33	19
5	46	2	3	30	42	37
3	44	1	3	33	46	25
3	25	1	3	37	45	24
2	51	1	2	30	40	23
4	42	1	5	20	48	28
4	53	2	5	44	32	38
3	25	1	4	20	53	28
4	49	2	2	33	39	28
4	51	1	3	31	45	26
2	20	2	3	23	36	21
3	44	2	3	33	38	35
3	38	2	4	33	49	31
3	46	1	5	32	46	34
4	42	2	4	25	43	30
5	29	1		22	37	30
3	46	2	4	16	48	24
3	49	2	2	36	45	27
2	51	2	3	35	32	26
3	38	1	3	25	46	30
1	41	1	1	27	20	15
4	47	2	3	32	42	28
4	44	2	3	36	45	34
4	47	2	3	51	29	29
3	46	2	3	30	51	26
5	44	1	4	20	55	31
2	28	2	3	29	50	28
2	47	2	4	26	44	33
3	28	2	4	20	41	32
3	41	1	5	40	40	33
2	45	2	4	29	47	31
1	46	2	4	32	42	37
3	46	1	4	33	40	27
5	22	2	3	32	51	19
4	33	2	3	34	43	27
4	41	1	4	24	45	31
4	47	2	5	25	41	38
3	25	1	3	41	41	22
5	42	2	3	39	37	35
3	47	2	3	21	46	35
3	50	2	3	38	38	30
3	55	1	5	28	39	41
3	21	1	3	37	45	25
4		1	3	26	46	28
2	52	1	3	30	39	45
2	49	2	4	25	21	21
4	46	2	4	38	31	33
3		1	4	31	35	25
3	45	2	3	31	49	29
2	52	2	3	27	40	31
3		1	3	21	45	29
3	40	2	4	26	46	31
4	49	2	4	37	45	31
1	38	1	5	28	34	25
1	32	1	5	29	41	27
5	46	2	4	33	43	26
4	32	2	3	41	45	26
3	41	2	3	19	48	23
3	43	2	3	37	43	27
4	44	1	4	36	45	24
3	47	1	5	27	45	35
2	28	2	3	33	34	24
1	52	1	1	29	40	32
1	27	1	2	42	40	24
5	45	2	5	27	55	24
4	27	1	4	47	44	38
3	25	1	4	17	44	36
4	28	1	4	34	48	24
5	25	1	3	32	51	18
4	52	1	4	25	49	34
4	44	1	3	27	33	23
2	43	2	3	37	43	35
3	47	2	4	34	44	22
4	52	2	4	27	44	34
3	40	2	2	37	41	28
4	42	1	3	32	45	34
3	45	1	5	26	44	32
4	45	1	2	29	44	24
1	50	1	5	28	40	34
2	49	1	3	19	48	33
3	52	1	2	46	49	33
3	48	2	3	31	46	29
5	51	2	3	42	49	38
4	49	2	4	33	55	24
3	31	2	4	39	51	25
3	43	2	3	27	46	37
3	31	2	3	35	37	33
3	28	2	4	23	43	30
4	43	2	4	32	41	22
3	31	2	3	22	45	28
2	51	2	3	17	39	24
4	58	2	4	35	38	33
2	25	2	5	34	41	37

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time79 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 79 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]79 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time79 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Teamwork[t] = + 45.5524104419848 -0.337389511564904leeftijd[t] -0.230173178231959geslacht[t] -0.101544702076264opleiding[t] + 0.128159964550332Neuroticisme[t] -0.445772756009979Extraversie[t] -0.327842515186265`Openheid `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Teamwork[t] =  +  45.5524104419848 -0.337389511564904leeftijd[t] -0.230173178231959geslacht[t] -0.101544702076264opleiding[t] +  0.128159964550332Neuroticisme[t] -0.445772756009979Extraversie[t] -0.327842515186265`Openheid
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Teamwork[t] =  +  45.5524104419848 -0.337389511564904leeftijd[t] -0.230173178231959geslacht[t] -0.101544702076264opleiding[t] +  0.128159964550332Neuroticisme[t] -0.445772756009979Extraversie[t] -0.327842515186265`Openheid
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Teamwork[t] = + 45.5524104419848 -0.337389511564904leeftijd[t] -0.230173178231959geslacht[t] -0.101544702076264opleiding[t] + 0.128159964550332Neuroticisme[t] -0.445772756009979Extraversie[t] -0.327842515186265`Openheid `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)45.55241044198486.2433977.296100
leeftijd-0.3373895115649040.060011-5.622100
geslacht-0.2301731782319590.067431-3.41340.0007860.000393
opleiding-0.1015447020762640.080315-1.26430.2076770.103838
Neuroticisme0.1281599645503320.0753321.70130.0905460.045273
Extraversie-0.4457727560099790.049427-9.018800
`Openheid `-0.3278425151862650.073437-4.46421.4e-057e-06

\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) & 45.5524104419848 & 6.243397 & 7.2961 & 0 & 0 \tabularnewline
leeftijd & -0.337389511564904 & 0.060011 & -5.6221 & 0 & 0 \tabularnewline
geslacht & -0.230173178231959 & 0.067431 & -3.4134 & 0.000786 & 0.000393 \tabularnewline
opleiding & -0.101544702076264 & 0.080315 & -1.2643 & 0.207677 & 0.103838 \tabularnewline
Neuroticisme & 0.128159964550332 & 0.075332 & 1.7013 & 0.090546 & 0.045273 \tabularnewline
Extraversie & -0.445772756009979 & 0.049427 & -9.0188 & 0 & 0 \tabularnewline
`Openheid
` & -0.327842515186265 & 0.073437 & -4.4642 & 1.4e-05 & 7e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&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]45.5524104419848[/C][C]6.243397[/C][C]7.2961[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.337389511564904[/C][C]0.060011[/C][C]-5.6221[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.230173178231959[/C][C]0.067431[/C][C]-3.4134[/C][C]0.000786[/C][C]0.000393[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.101544702076264[/C][C]0.080315[/C][C]-1.2643[/C][C]0.207677[/C][C]0.103838[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.128159964550332[/C][C]0.075332[/C][C]1.7013[/C][C]0.090546[/C][C]0.045273[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.445772756009979[/C][C]0.049427[/C][C]-9.0188[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Openheid
`[/C][C]-0.327842515186265[/C][C]0.073437[/C][C]-4.4642[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)45.55241044198486.2433977.296100
leeftijd-0.3373895115649040.060011-5.622100
geslacht-0.2301731782319590.067431-3.41340.0007860.000393
opleiding-0.1015447020762640.080315-1.26430.2076770.103838
Neuroticisme0.1281599645503320.0753321.70130.0905460.045273
Extraversie-0.4457727560099790.049427-9.018800
`Openheid `-0.3278425151862650.073437-4.46421.4e-057e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.761884570669444
R-squared0.580468099024164
Adjusted R-squared0.567078783035573
F-TEST (value)43.3530808832056
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.13201861246577
Sum Squared Residuals15678.0276204232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.761884570669444 \tabularnewline
R-squared & 0.580468099024164 \tabularnewline
Adjusted R-squared & 0.567078783035573 \tabularnewline
F-TEST (value) & 43.3530808832056 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 188 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.13201861246577 \tabularnewline
Sum Squared Residuals & 15678.0276204232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.761884570669444[/C][/ROW]
[ROW][C]R-squared[/C][C]0.580468099024164[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.567078783035573[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]43.3530808832056[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]188[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.13201861246577[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15678.0276204232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.761884570669444
R-squared0.580468099024164
Adjusted R-squared0.567078783035573
F-TEST (value)43.3530808832056
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.13201861246577
Sum Squared Residuals15678.0276204232






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
145.71980294341926-1.71980294341926
244.76761561608748-0.767615616087484
3510.3263585417205-5.32635854172049
4213.1276977857554-11.1276977857554
5315.6108813078564-12.6108813078564
65-2.252225615798137.25222561579813
74-0.473132887916834.47313288791683
845.17206090597398-1.17206090597398
944.6953854055761-0.695385405576099
1044.45572825558414-0.455728255584136
1152.688919238178632.31108076182137
1247.28169265765741-3.28169265765741
1334.42344136185339-1.42344136185339
1445.56133180248442-1.56133180248442
1534.60079915441719-1.60079915441719
1636.18821851190832-3.18821851190832
17415.2627624318022-11.2627624318022
183-1.678947362498454.67894736249845
1941.524960466553882.47503953344612
20215.3002576073550-13.3002576073550
2141.164874649361522.83512535063848
22313.8135023235429-10.8135023235429
2342.002488152696331.99751184730367
2445.44644365084527-1.44644365084527
2533.3477338817065-0.347733881706499
2633.36115636403028-0.361156364030282
2725.10854750013413-3.10854750013413
2844.2610348240172-0.261034824017198
29510.3548545718119-5.35485457181194
30434.4869421933677-30.4869421933677
315231.005441633834520.9945583661655
324631.878392640726914.1216073592731
335830.545261344985827.4547386550142
345430.799134756572423.2008652434276
352935.5339208244244-6.53392082442441
365033.132596166736716.8674038332633
374331.570351680981511.4296483190185
383031.2937865201702-1.29378652017020
394729.511087690911717.4889123090883
404518.176645060690526.8233549393095
41218.9048825867587-16.9048825867587
42224.9181328205113-22.9181328205113
43219.1906044400042-17.1906044400042
44137.6840864679064-36.6840864679064
453-0.2235703182480283.22357031824803
4639.5024284907722-6.50242849077221
4744.16794595078603-0.167945950786026
482-0.5117992745917942.51179927459179
492-0.5214480909252762.52144809092528
5032.167925604942780.832074395057219
5123.70713316360349-1.70713316360349
5242.158506976012801.84149302398720
5356.05428614410388-1.05428614410388
5436.21503031400314-3.21503031400314
5549.54429757278717-5.54429757278717
5655.77095462871925-0.770954628719251
575-0.7858966859839555.78589668598396
5834.29959378384177-1.29959378384177
5943.135930497686540.864069502313457
6033.12137372320673-0.121373723206727
6130.5009176001366952.49908239986331
62212.1632459141473-10.1632459141473
6337.89457494853773-4.89457494853773
6449.13557323477626-5.13557323477626
6540.8802296574327483.11977034256725
664-0.94763430290454.9476343029045
6740.2694571748697843.73054282513022
683-2.385239716780865.38523971678086
6936.9815502387068-3.9815502387068
703-2.950109624726685.95010962472668
7125.89541330468906-3.89541330468906
7239.77309412192136-6.77309412192136
7332.078105375809170.921894624190834
7438.62138581381169-5.62138581381169
7539.33227299702776-6.33227299702776
765-1.484815121416426.48481512141642
7737.83350388390858-4.83350388390858
7853.199822711615391.80017728838461
7942.826214148658671.17378585134133
8046.36723984140592-2.36723984140592
81411.2435435967556-7.24354359675559
82518.9690654674542-13.9690654674542
8343.699775972006010.300224027993989
845-2.47698792343957.4769879234395
8533.43715496523528-0.437154965235284
8632.481052491727950.518947508272047
87210.8461592865560-8.84615928655595
883-0.3216351833447283.32163518334473
8942.173171221584461.82682877841554
9055.80127206683214-0.80127206683214
9157.99524473553121-2.99524473553121
9231.194150904431231.80584909556877
932-2.780380849873944.78038084987394
9430.2471485163307182.75285148366928
9546.27951101731234-2.27951101731234
9614.32106370492077-3.32106370492077
9747.07579320239772-3.07579320239772
983-1.220487749338864.22048774933886
99312.2988204037637-9.29882040376366
10043.620007687968110.379992312031893
101311.4677747483351-8.46777474833506
10244.30720047447823-0.307200474478235
10321.673944262640110.326055737359886
10432.448933746499220.551066253500782
105310.2042912879814-7.20429128798145
1063-2.531800498779745.53180049877974
10725.35061140860113-3.35061140860113
10851.144054756717133.85594524328287
10958.3953500290235-3.3953500290235
11041.776279889597252.22372011040275
11120.04893773766724311.95106226233276
112312.7805600875802-9.78056008758025
11337.48857211487927-4.48857211487927
11434.90734134975848-1.90734134975848
1154-0.4567243353815824.45672433538158
11651.849959078054393.15004092194561
117411.5594323098760-7.55943230987598
1182229.551611857446-7.55161185744601
1191628.2615312236473-12.2615312236473
1203628.61320233963697.38679766036315
1213531.90758768299203.09241231700804
1222527.5066961363315-2.50669613633152
1232739.0942890051891-12.0942890051891
1243228.29498854009473.70501145990525
1253626.28626082965939.71373917034072
1265132.808743643383518.1912563566165
1273025.7352148312224.26478516877802
1282021.3709353264023-1.37093532640233
1292925.85559923019283.14440076980722
1302624.19248637988981.80751362011017
1312027.2188378727945-7.21883787279453
1324027.822308525581312.1776914744186
1332926.15063296771752.84936703228246
1343226.69922480800785.30077519199224
1353326.27887681927296.72112318072706
1363225.92028193004466.0797180699554
1373427.92122255393456.07877744606548
1382426.3210953339826-2.32109533398262
1392524.442924400970.557075599030015
1404129.655552489874511.3444475101255
1413928.856748446022810.1432515539772
1422126.2047227355897-5.20472273558967
1433830.48559226765787.51440773234219
1442823.91454403120264.08545596879741
1453714.476310459137522.5236895408625
1464619.320399494670226.6796005053298
1473915.211011688767323.7889883112327
1482118.89069501866852.10930498133154
149318.845688713943522.1543112860565
150254.48060069591420.519399304086
151297.9404516205714821.0595483794285
1523117.129586917535513.8704130824645
15333.85379914569501-0.85379914569501
15442.672825907674511.32717409232549
155112.2298547373185-11.2298547373185
156110.6062574488159-9.60625744881589
15755.70311267211918-0.703112672119176
158410.6618447404868-6.66184474048679
15934.45202919382421-1.45202919382421
16037.00162325190522-4.00162325190522
16146.75668428548451-2.75668428548451
16230.8832637007116272.11673629928837
163216.5453084168261-14.5453084168261
16413.07120620590152-2.07120620590152
165115.6932189535923-14.6932189535923
16650.4764096525586024.5235903474414
16749.7580431355438-5.75804313554379
16837.24370825253617-4.24370825253617
169410.5612782733924-6.56127827339237
170512.0484084041503-7.04840840415034
1714-2.413707592990946.41370759299094
172411.3819048939138-7.3819048939138
17324.37888313041509-2.37888313041509
17436.35948042983969-3.35948042983969
1754-0.1586970620723444.15869706207234
17638.67903948550988-5.67903948550988
17743.74194300062660.258056999373399
17832.859183060859880.140816939140122
17946.17103718222979-2.17103718222979
18012.55596142580341-1.55596142580341
1812-1.295338872325723.29533887232572
18230.8085835819048232.19141641809518
18332.552712608376230.447287391623767
1845-1.337597220971156.33759722097115
1854-0.002643904322756194.00264390432276
18638.29457560000116-5.29457560000116
18731.104280186509301.89571981349070
188311.5015589065257-8.50155890652568
18939.18315417403906-6.18315417403906
19048.79003681502858-4.79003681502858
19137.90850989522285-4.90850989522285
19224.50592643797805-2.50592643797805
19341.844724636187032.15527536381297
194210.1001855224273-8.10018552242727
19545.71980294341946-1.71980294341946

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 5.71980294341926 & -1.71980294341926 \tabularnewline
2 & 4 & 4.76761561608748 & -0.767615616087484 \tabularnewline
3 & 5 & 10.3263585417205 & -5.32635854172049 \tabularnewline
4 & 2 & 13.1276977857554 & -11.1276977857554 \tabularnewline
5 & 3 & 15.6108813078564 & -12.6108813078564 \tabularnewline
6 & 5 & -2.25222561579813 & 7.25222561579813 \tabularnewline
7 & 4 & -0.47313288791683 & 4.47313288791683 \tabularnewline
8 & 4 & 5.17206090597398 & -1.17206090597398 \tabularnewline
9 & 4 & 4.6953854055761 & -0.695385405576099 \tabularnewline
10 & 4 & 4.45572825558414 & -0.455728255584136 \tabularnewline
11 & 5 & 2.68891923817863 & 2.31108076182137 \tabularnewline
12 & 4 & 7.28169265765741 & -3.28169265765741 \tabularnewline
13 & 3 & 4.42344136185339 & -1.42344136185339 \tabularnewline
14 & 4 & 5.56133180248442 & -1.56133180248442 \tabularnewline
15 & 3 & 4.60079915441719 & -1.60079915441719 \tabularnewline
16 & 3 & 6.18821851190832 & -3.18821851190832 \tabularnewline
17 & 4 & 15.2627624318022 & -11.2627624318022 \tabularnewline
18 & 3 & -1.67894736249845 & 4.67894736249845 \tabularnewline
19 & 4 & 1.52496046655388 & 2.47503953344612 \tabularnewline
20 & 2 & 15.3002576073550 & -13.3002576073550 \tabularnewline
21 & 4 & 1.16487464936152 & 2.83512535063848 \tabularnewline
22 & 3 & 13.8135023235429 & -10.8135023235429 \tabularnewline
23 & 4 & 2.00248815269633 & 1.99751184730367 \tabularnewline
24 & 4 & 5.44644365084527 & -1.44644365084527 \tabularnewline
25 & 3 & 3.3477338817065 & -0.347733881706499 \tabularnewline
26 & 3 & 3.36115636403028 & -0.361156364030282 \tabularnewline
27 & 2 & 5.10854750013413 & -3.10854750013413 \tabularnewline
28 & 4 & 4.2610348240172 & -0.261034824017198 \tabularnewline
29 & 5 & 10.3548545718119 & -5.35485457181194 \tabularnewline
30 & 4 & 34.4869421933677 & -30.4869421933677 \tabularnewline
31 & 52 & 31.0054416338345 & 20.9945583661655 \tabularnewline
32 & 46 & 31.8783926407269 & 14.1216073592731 \tabularnewline
33 & 58 & 30.5452613449858 & 27.4547386550142 \tabularnewline
34 & 54 & 30.7991347565724 & 23.2008652434276 \tabularnewline
35 & 29 & 35.5339208244244 & -6.53392082442441 \tabularnewline
36 & 50 & 33.1325961667367 & 16.8674038332633 \tabularnewline
37 & 43 & 31.5703516809815 & 11.4296483190185 \tabularnewline
38 & 30 & 31.2937865201702 & -1.29378652017020 \tabularnewline
39 & 47 & 29.5110876909117 & 17.4889123090883 \tabularnewline
40 & 45 & 18.1766450606905 & 26.8233549393095 \tabularnewline
41 & 2 & 18.9048825867587 & -16.9048825867587 \tabularnewline
42 & 2 & 24.9181328205113 & -22.9181328205113 \tabularnewline
43 & 2 & 19.1906044400042 & -17.1906044400042 \tabularnewline
44 & 1 & 37.6840864679064 & -36.6840864679064 \tabularnewline
45 & 3 & -0.223570318248028 & 3.22357031824803 \tabularnewline
46 & 3 & 9.5024284907722 & -6.50242849077221 \tabularnewline
47 & 4 & 4.16794595078603 & -0.167945950786026 \tabularnewline
48 & 2 & -0.511799274591794 & 2.51179927459179 \tabularnewline
49 & 2 & -0.521448090925276 & 2.52144809092528 \tabularnewline
50 & 3 & 2.16792560494278 & 0.832074395057219 \tabularnewline
51 & 2 & 3.70713316360349 & -1.70713316360349 \tabularnewline
52 & 4 & 2.15850697601280 & 1.84149302398720 \tabularnewline
53 & 5 & 6.05428614410388 & -1.05428614410388 \tabularnewline
54 & 3 & 6.21503031400314 & -3.21503031400314 \tabularnewline
55 & 4 & 9.54429757278717 & -5.54429757278717 \tabularnewline
56 & 5 & 5.77095462871925 & -0.770954628719251 \tabularnewline
57 & 5 & -0.785896685983955 & 5.78589668598396 \tabularnewline
58 & 3 & 4.29959378384177 & -1.29959378384177 \tabularnewline
59 & 4 & 3.13593049768654 & 0.864069502313457 \tabularnewline
60 & 3 & 3.12137372320673 & -0.121373723206727 \tabularnewline
61 & 3 & 0.500917600136695 & 2.49908239986331 \tabularnewline
62 & 2 & 12.1632459141473 & -10.1632459141473 \tabularnewline
63 & 3 & 7.89457494853773 & -4.89457494853773 \tabularnewline
64 & 4 & 9.13557323477626 & -5.13557323477626 \tabularnewline
65 & 4 & 0.880229657432748 & 3.11977034256725 \tabularnewline
66 & 4 & -0.9476343029045 & 4.9476343029045 \tabularnewline
67 & 4 & 0.269457174869784 & 3.73054282513022 \tabularnewline
68 & 3 & -2.38523971678086 & 5.38523971678086 \tabularnewline
69 & 3 & 6.9815502387068 & -3.9815502387068 \tabularnewline
70 & 3 & -2.95010962472668 & 5.95010962472668 \tabularnewline
71 & 2 & 5.89541330468906 & -3.89541330468906 \tabularnewline
72 & 3 & 9.77309412192136 & -6.77309412192136 \tabularnewline
73 & 3 & 2.07810537580917 & 0.921894624190834 \tabularnewline
74 & 3 & 8.62138581381169 & -5.62138581381169 \tabularnewline
75 & 3 & 9.33227299702776 & -6.33227299702776 \tabularnewline
76 & 5 & -1.48481512141642 & 6.48481512141642 \tabularnewline
77 & 3 & 7.83350388390858 & -4.83350388390858 \tabularnewline
78 & 5 & 3.19982271161539 & 1.80017728838461 \tabularnewline
79 & 4 & 2.82621414865867 & 1.17378585134133 \tabularnewline
80 & 4 & 6.36723984140592 & -2.36723984140592 \tabularnewline
81 & 4 & 11.2435435967556 & -7.24354359675559 \tabularnewline
82 & 5 & 18.9690654674542 & -13.9690654674542 \tabularnewline
83 & 4 & 3.69977597200601 & 0.300224027993989 \tabularnewline
84 & 5 & -2.4769879234395 & 7.4769879234395 \tabularnewline
85 & 3 & 3.43715496523528 & -0.437154965235284 \tabularnewline
86 & 3 & 2.48105249172795 & 0.518947508272047 \tabularnewline
87 & 2 & 10.8461592865560 & -8.84615928655595 \tabularnewline
88 & 3 & -0.321635183344728 & 3.32163518334473 \tabularnewline
89 & 4 & 2.17317122158446 & 1.82682877841554 \tabularnewline
90 & 5 & 5.80127206683214 & -0.80127206683214 \tabularnewline
91 & 5 & 7.99524473553121 & -2.99524473553121 \tabularnewline
92 & 3 & 1.19415090443123 & 1.80584909556877 \tabularnewline
93 & 2 & -2.78038084987394 & 4.78038084987394 \tabularnewline
94 & 3 & 0.247148516330718 & 2.75285148366928 \tabularnewline
95 & 4 & 6.27951101731234 & -2.27951101731234 \tabularnewline
96 & 1 & 4.32106370492077 & -3.32106370492077 \tabularnewline
97 & 4 & 7.07579320239772 & -3.07579320239772 \tabularnewline
98 & 3 & -1.22048774933886 & 4.22048774933886 \tabularnewline
99 & 3 & 12.2988204037637 & -9.29882040376366 \tabularnewline
100 & 4 & 3.62000768796811 & 0.379992312031893 \tabularnewline
101 & 3 & 11.4677747483351 & -8.46777474833506 \tabularnewline
102 & 4 & 4.30720047447823 & -0.307200474478235 \tabularnewline
103 & 2 & 1.67394426264011 & 0.326055737359886 \tabularnewline
104 & 3 & 2.44893374649922 & 0.551066253500782 \tabularnewline
105 & 3 & 10.2042912879814 & -7.20429128798145 \tabularnewline
106 & 3 & -2.53180049877974 & 5.53180049877974 \tabularnewline
107 & 2 & 5.35061140860113 & -3.35061140860113 \tabularnewline
108 & 5 & 1.14405475671713 & 3.85594524328287 \tabularnewline
109 & 5 & 8.3953500290235 & -3.3953500290235 \tabularnewline
110 & 4 & 1.77627988959725 & 2.22372011040275 \tabularnewline
111 & 2 & 0.0489377376672431 & 1.95106226233276 \tabularnewline
112 & 3 & 12.7805600875802 & -9.78056008758025 \tabularnewline
113 & 3 & 7.48857211487927 & -4.48857211487927 \tabularnewline
114 & 3 & 4.90734134975848 & -1.90734134975848 \tabularnewline
115 & 4 & -0.456724335381582 & 4.45672433538158 \tabularnewline
116 & 5 & 1.84995907805439 & 3.15004092194561 \tabularnewline
117 & 4 & 11.5594323098760 & -7.55943230987598 \tabularnewline
118 & 22 & 29.551611857446 & -7.55161185744601 \tabularnewline
119 & 16 & 28.2615312236473 & -12.2615312236473 \tabularnewline
120 & 36 & 28.6132023396369 & 7.38679766036315 \tabularnewline
121 & 35 & 31.9075876829920 & 3.09241231700804 \tabularnewline
122 & 25 & 27.5066961363315 & -2.50669613633152 \tabularnewline
123 & 27 & 39.0942890051891 & -12.0942890051891 \tabularnewline
124 & 32 & 28.2949885400947 & 3.70501145990525 \tabularnewline
125 & 36 & 26.2862608296593 & 9.71373917034072 \tabularnewline
126 & 51 & 32.8087436433835 & 18.1912563566165 \tabularnewline
127 & 30 & 25.735214831222 & 4.26478516877802 \tabularnewline
128 & 20 & 21.3709353264023 & -1.37093532640233 \tabularnewline
129 & 29 & 25.8555992301928 & 3.14440076980722 \tabularnewline
130 & 26 & 24.1924863798898 & 1.80751362011017 \tabularnewline
131 & 20 & 27.2188378727945 & -7.21883787279453 \tabularnewline
132 & 40 & 27.8223085255813 & 12.1776914744186 \tabularnewline
133 & 29 & 26.1506329677175 & 2.84936703228246 \tabularnewline
134 & 32 & 26.6992248080078 & 5.30077519199224 \tabularnewline
135 & 33 & 26.2788768192729 & 6.72112318072706 \tabularnewline
136 & 32 & 25.9202819300446 & 6.0797180699554 \tabularnewline
137 & 34 & 27.9212225539345 & 6.07877744606548 \tabularnewline
138 & 24 & 26.3210953339826 & -2.32109533398262 \tabularnewline
139 & 25 & 24.44292440097 & 0.557075599030015 \tabularnewline
140 & 41 & 29.6555524898745 & 11.3444475101255 \tabularnewline
141 & 39 & 28.8567484460228 & 10.1432515539772 \tabularnewline
142 & 21 & 26.2047227355897 & -5.20472273558967 \tabularnewline
143 & 38 & 30.4855922676578 & 7.51440773234219 \tabularnewline
144 & 28 & 23.9145440312026 & 4.08545596879741 \tabularnewline
145 & 37 & 14.4763104591375 & 22.5236895408625 \tabularnewline
146 & 46 & 19.3203994946702 & 26.6796005053298 \tabularnewline
147 & 39 & 15.2110116887673 & 23.7889883112327 \tabularnewline
148 & 21 & 18.8906950186685 & 2.10930498133154 \tabularnewline
149 & 31 & 8.8456887139435 & 22.1543112860565 \tabularnewline
150 & 25 & 4.480600695914 & 20.519399304086 \tabularnewline
151 & 29 & 7.94045162057148 & 21.0595483794285 \tabularnewline
152 & 31 & 17.1295869175355 & 13.8704130824645 \tabularnewline
153 & 3 & 3.85379914569501 & -0.85379914569501 \tabularnewline
154 & 4 & 2.67282590767451 & 1.32717409232549 \tabularnewline
155 & 1 & 12.2298547373185 & -11.2298547373185 \tabularnewline
156 & 1 & 10.6062574488159 & -9.60625744881589 \tabularnewline
157 & 5 & 5.70311267211918 & -0.703112672119176 \tabularnewline
158 & 4 & 10.6618447404868 & -6.66184474048679 \tabularnewline
159 & 3 & 4.45202919382421 & -1.45202919382421 \tabularnewline
160 & 3 & 7.00162325190522 & -4.00162325190522 \tabularnewline
161 & 4 & 6.75668428548451 & -2.75668428548451 \tabularnewline
162 & 3 & 0.883263700711627 & 2.11673629928837 \tabularnewline
163 & 2 & 16.5453084168261 & -14.5453084168261 \tabularnewline
164 & 1 & 3.07120620590152 & -2.07120620590152 \tabularnewline
165 & 1 & 15.6932189535923 & -14.6932189535923 \tabularnewline
166 & 5 & 0.476409652558602 & 4.5235903474414 \tabularnewline
167 & 4 & 9.7580431355438 & -5.75804313554379 \tabularnewline
168 & 3 & 7.24370825253617 & -4.24370825253617 \tabularnewline
169 & 4 & 10.5612782733924 & -6.56127827339237 \tabularnewline
170 & 5 & 12.0484084041503 & -7.04840840415034 \tabularnewline
171 & 4 & -2.41370759299094 & 6.41370759299094 \tabularnewline
172 & 4 & 11.3819048939138 & -7.3819048939138 \tabularnewline
173 & 2 & 4.37888313041509 & -2.37888313041509 \tabularnewline
174 & 3 & 6.35948042983969 & -3.35948042983969 \tabularnewline
175 & 4 & -0.158697062072344 & 4.15869706207234 \tabularnewline
176 & 3 & 8.67903948550988 & -5.67903948550988 \tabularnewline
177 & 4 & 3.7419430006266 & 0.258056999373399 \tabularnewline
178 & 3 & 2.85918306085988 & 0.140816939140122 \tabularnewline
179 & 4 & 6.17103718222979 & -2.17103718222979 \tabularnewline
180 & 1 & 2.55596142580341 & -1.55596142580341 \tabularnewline
181 & 2 & -1.29533887232572 & 3.29533887232572 \tabularnewline
182 & 3 & 0.808583581904823 & 2.19141641809518 \tabularnewline
183 & 3 & 2.55271260837623 & 0.447287391623767 \tabularnewline
184 & 5 & -1.33759722097115 & 6.33759722097115 \tabularnewline
185 & 4 & -0.00264390432275619 & 4.00264390432276 \tabularnewline
186 & 3 & 8.29457560000116 & -5.29457560000116 \tabularnewline
187 & 3 & 1.10428018650930 & 1.89571981349070 \tabularnewline
188 & 3 & 11.5015589065257 & -8.50155890652568 \tabularnewline
189 & 3 & 9.18315417403906 & -6.18315417403906 \tabularnewline
190 & 4 & 8.79003681502858 & -4.79003681502858 \tabularnewline
191 & 3 & 7.90850989522285 & -4.90850989522285 \tabularnewline
192 & 2 & 4.50592643797805 & -2.50592643797805 \tabularnewline
193 & 4 & 1.84472463618703 & 2.15527536381297 \tabularnewline
194 & 2 & 10.1001855224273 & -8.10018552242727 \tabularnewline
195 & 4 & 5.71980294341946 & -1.71980294341946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4[/C][C]5.71980294341926[/C][C]-1.71980294341926[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]4.76761561608748[/C][C]-0.767615616087484[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]10.3263585417205[/C][C]-5.32635854172049[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]13.1276977857554[/C][C]-11.1276977857554[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]15.6108813078564[/C][C]-12.6108813078564[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]-2.25222561579813[/C][C]7.25222561579813[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]-0.47313288791683[/C][C]4.47313288791683[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]5.17206090597398[/C][C]-1.17206090597398[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.6953854055761[/C][C]-0.695385405576099[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.45572825558414[/C][C]-0.455728255584136[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]2.68891923817863[/C][C]2.31108076182137[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]7.28169265765741[/C][C]-3.28169265765741[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]4.42344136185339[/C][C]-1.42344136185339[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]5.56133180248442[/C][C]-1.56133180248442[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]4.60079915441719[/C][C]-1.60079915441719[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]6.18821851190832[/C][C]-3.18821851190832[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]15.2627624318022[/C][C]-11.2627624318022[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]-1.67894736249845[/C][C]4.67894736249845[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]1.52496046655388[/C][C]2.47503953344612[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]15.3002576073550[/C][C]-13.3002576073550[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]1.16487464936152[/C][C]2.83512535063848[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]13.8135023235429[/C][C]-10.8135023235429[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]2.00248815269633[/C][C]1.99751184730367[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]5.44644365084527[/C][C]-1.44644365084527[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.3477338817065[/C][C]-0.347733881706499[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]3.36115636403028[/C][C]-0.361156364030282[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]5.10854750013413[/C][C]-3.10854750013413[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]4.2610348240172[/C][C]-0.261034824017198[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]10.3548545718119[/C][C]-5.35485457181194[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]34.4869421933677[/C][C]-30.4869421933677[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]31.0054416338345[/C][C]20.9945583661655[/C][/ROW]
[ROW][C]32[/C][C]46[/C][C]31.8783926407269[/C][C]14.1216073592731[/C][/ROW]
[ROW][C]33[/C][C]58[/C][C]30.5452613449858[/C][C]27.4547386550142[/C][/ROW]
[ROW][C]34[/C][C]54[/C][C]30.7991347565724[/C][C]23.2008652434276[/C][/ROW]
[ROW][C]35[/C][C]29[/C][C]35.5339208244244[/C][C]-6.53392082442441[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]33.1325961667367[/C][C]16.8674038332633[/C][/ROW]
[ROW][C]37[/C][C]43[/C][C]31.5703516809815[/C][C]11.4296483190185[/C][/ROW]
[ROW][C]38[/C][C]30[/C][C]31.2937865201702[/C][C]-1.29378652017020[/C][/ROW]
[ROW][C]39[/C][C]47[/C][C]29.5110876909117[/C][C]17.4889123090883[/C][/ROW]
[ROW][C]40[/C][C]45[/C][C]18.1766450606905[/C][C]26.8233549393095[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]18.9048825867587[/C][C]-16.9048825867587[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]24.9181328205113[/C][C]-22.9181328205113[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]19.1906044400042[/C][C]-17.1906044400042[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]37.6840864679064[/C][C]-36.6840864679064[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]-0.223570318248028[/C][C]3.22357031824803[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]9.5024284907722[/C][C]-6.50242849077221[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.16794595078603[/C][C]-0.167945950786026[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]-0.511799274591794[/C][C]2.51179927459179[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]-0.521448090925276[/C][C]2.52144809092528[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.16792560494278[/C][C]0.832074395057219[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]3.70713316360349[/C][C]-1.70713316360349[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]2.15850697601280[/C][C]1.84149302398720[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]6.05428614410388[/C][C]-1.05428614410388[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]6.21503031400314[/C][C]-3.21503031400314[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]9.54429757278717[/C][C]-5.54429757278717[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.77095462871925[/C][C]-0.770954628719251[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]-0.785896685983955[/C][C]5.78589668598396[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]4.29959378384177[/C][C]-1.29959378384177[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]3.13593049768654[/C][C]0.864069502313457[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]3.12137372320673[/C][C]-0.121373723206727[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]0.500917600136695[/C][C]2.49908239986331[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]12.1632459141473[/C][C]-10.1632459141473[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]7.89457494853773[/C][C]-4.89457494853773[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]9.13557323477626[/C][C]-5.13557323477626[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]0.880229657432748[/C][C]3.11977034256725[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]-0.9476343029045[/C][C]4.9476343029045[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]0.269457174869784[/C][C]3.73054282513022[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-2.38523971678086[/C][C]5.38523971678086[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]6.9815502387068[/C][C]-3.9815502387068[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]-2.95010962472668[/C][C]5.95010962472668[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]5.89541330468906[/C][C]-3.89541330468906[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]9.77309412192136[/C][C]-6.77309412192136[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.07810537580917[/C][C]0.921894624190834[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]8.62138581381169[/C][C]-5.62138581381169[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]9.33227299702776[/C][C]-6.33227299702776[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]-1.48481512141642[/C][C]6.48481512141642[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]7.83350388390858[/C][C]-4.83350388390858[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]3.19982271161539[/C][C]1.80017728838461[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]2.82621414865867[/C][C]1.17378585134133[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]6.36723984140592[/C][C]-2.36723984140592[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]11.2435435967556[/C][C]-7.24354359675559[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]18.9690654674542[/C][C]-13.9690654674542[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.69977597200601[/C][C]0.300224027993989[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]-2.4769879234395[/C][C]7.4769879234395[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]3.43715496523528[/C][C]-0.437154965235284[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]2.48105249172795[/C][C]0.518947508272047[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]10.8461592865560[/C][C]-8.84615928655595[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]-0.321635183344728[/C][C]3.32163518334473[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]2.17317122158446[/C][C]1.82682877841554[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]5.80127206683214[/C][C]-0.80127206683214[/C][/ROW]
[ROW][C]91[/C][C]5[/C][C]7.99524473553121[/C][C]-2.99524473553121[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]1.19415090443123[/C][C]1.80584909556877[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]-2.78038084987394[/C][C]4.78038084987394[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]0.247148516330718[/C][C]2.75285148366928[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]6.27951101731234[/C][C]-2.27951101731234[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]4.32106370492077[/C][C]-3.32106370492077[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]7.07579320239772[/C][C]-3.07579320239772[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]-1.22048774933886[/C][C]4.22048774933886[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]12.2988204037637[/C][C]-9.29882040376366[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.62000768796811[/C][C]0.379992312031893[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]11.4677747483351[/C][C]-8.46777474833506[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]4.30720047447823[/C][C]-0.307200474478235[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.67394426264011[/C][C]0.326055737359886[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.44893374649922[/C][C]0.551066253500782[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]10.2042912879814[/C][C]-7.20429128798145[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]-2.53180049877974[/C][C]5.53180049877974[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]5.35061140860113[/C][C]-3.35061140860113[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]1.14405475671713[/C][C]3.85594524328287[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]8.3953500290235[/C][C]-3.3953500290235[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]1.77627988959725[/C][C]2.22372011040275[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]0.0489377376672431[/C][C]1.95106226233276[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]12.7805600875802[/C][C]-9.78056008758025[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]7.48857211487927[/C][C]-4.48857211487927[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]4.90734134975848[/C][C]-1.90734134975848[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]-0.456724335381582[/C][C]4.45672433538158[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]1.84995907805439[/C][C]3.15004092194561[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]11.5594323098760[/C][C]-7.55943230987598[/C][/ROW]
[ROW][C]118[/C][C]22[/C][C]29.551611857446[/C][C]-7.55161185744601[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]28.2615312236473[/C][C]-12.2615312236473[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]28.6132023396369[/C][C]7.38679766036315[/C][/ROW]
[ROW][C]121[/C][C]35[/C][C]31.9075876829920[/C][C]3.09241231700804[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]27.5066961363315[/C][C]-2.50669613633152[/C][/ROW]
[ROW][C]123[/C][C]27[/C][C]39.0942890051891[/C][C]-12.0942890051891[/C][/ROW]
[ROW][C]124[/C][C]32[/C][C]28.2949885400947[/C][C]3.70501145990525[/C][/ROW]
[ROW][C]125[/C][C]36[/C][C]26.2862608296593[/C][C]9.71373917034072[/C][/ROW]
[ROW][C]126[/C][C]51[/C][C]32.8087436433835[/C][C]18.1912563566165[/C][/ROW]
[ROW][C]127[/C][C]30[/C][C]25.735214831222[/C][C]4.26478516877802[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.3709353264023[/C][C]-1.37093532640233[/C][/ROW]
[ROW][C]129[/C][C]29[/C][C]25.8555992301928[/C][C]3.14440076980722[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]24.1924863798898[/C][C]1.80751362011017[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]27.2188378727945[/C][C]-7.21883787279453[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]27.8223085255813[/C][C]12.1776914744186[/C][/ROW]
[ROW][C]133[/C][C]29[/C][C]26.1506329677175[/C][C]2.84936703228246[/C][/ROW]
[ROW][C]134[/C][C]32[/C][C]26.6992248080078[/C][C]5.30077519199224[/C][/ROW]
[ROW][C]135[/C][C]33[/C][C]26.2788768192729[/C][C]6.72112318072706[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]25.9202819300446[/C][C]6.0797180699554[/C][/ROW]
[ROW][C]137[/C][C]34[/C][C]27.9212225539345[/C][C]6.07877744606548[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]26.3210953339826[/C][C]-2.32109533398262[/C][/ROW]
[ROW][C]139[/C][C]25[/C][C]24.44292440097[/C][C]0.557075599030015[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]29.6555524898745[/C][C]11.3444475101255[/C][/ROW]
[ROW][C]141[/C][C]39[/C][C]28.8567484460228[/C][C]10.1432515539772[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]26.2047227355897[/C][C]-5.20472273558967[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]30.4855922676578[/C][C]7.51440773234219[/C][/ROW]
[ROW][C]144[/C][C]28[/C][C]23.9145440312026[/C][C]4.08545596879741[/C][/ROW]
[ROW][C]145[/C][C]37[/C][C]14.4763104591375[/C][C]22.5236895408625[/C][/ROW]
[ROW][C]146[/C][C]46[/C][C]19.3203994946702[/C][C]26.6796005053298[/C][/ROW]
[ROW][C]147[/C][C]39[/C][C]15.2110116887673[/C][C]23.7889883112327[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]18.8906950186685[/C][C]2.10930498133154[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]8.8456887139435[/C][C]22.1543112860565[/C][/ROW]
[ROW][C]150[/C][C]25[/C][C]4.480600695914[/C][C]20.519399304086[/C][/ROW]
[ROW][C]151[/C][C]29[/C][C]7.94045162057148[/C][C]21.0595483794285[/C][/ROW]
[ROW][C]152[/C][C]31[/C][C]17.1295869175355[/C][C]13.8704130824645[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]3.85379914569501[/C][C]-0.85379914569501[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]2.67282590767451[/C][C]1.32717409232549[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]12.2298547373185[/C][C]-11.2298547373185[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]10.6062574488159[/C][C]-9.60625744881589[/C][/ROW]
[ROW][C]157[/C][C]5[/C][C]5.70311267211918[/C][C]-0.703112672119176[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]10.6618447404868[/C][C]-6.66184474048679[/C][/ROW]
[ROW][C]159[/C][C]3[/C][C]4.45202919382421[/C][C]-1.45202919382421[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]7.00162325190522[/C][C]-4.00162325190522[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]6.75668428548451[/C][C]-2.75668428548451[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]0.883263700711627[/C][C]2.11673629928837[/C][/ROW]
[ROW][C]163[/C][C]2[/C][C]16.5453084168261[/C][C]-14.5453084168261[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]3.07120620590152[/C][C]-2.07120620590152[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]15.6932189535923[/C][C]-14.6932189535923[/C][/ROW]
[ROW][C]166[/C][C]5[/C][C]0.476409652558602[/C][C]4.5235903474414[/C][/ROW]
[ROW][C]167[/C][C]4[/C][C]9.7580431355438[/C][C]-5.75804313554379[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]7.24370825253617[/C][C]-4.24370825253617[/C][/ROW]
[ROW][C]169[/C][C]4[/C][C]10.5612782733924[/C][C]-6.56127827339237[/C][/ROW]
[ROW][C]170[/C][C]5[/C][C]12.0484084041503[/C][C]-7.04840840415034[/C][/ROW]
[ROW][C]171[/C][C]4[/C][C]-2.41370759299094[/C][C]6.41370759299094[/C][/ROW]
[ROW][C]172[/C][C]4[/C][C]11.3819048939138[/C][C]-7.3819048939138[/C][/ROW]
[ROW][C]173[/C][C]2[/C][C]4.37888313041509[/C][C]-2.37888313041509[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]6.35948042983969[/C][C]-3.35948042983969[/C][/ROW]
[ROW][C]175[/C][C]4[/C][C]-0.158697062072344[/C][C]4.15869706207234[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]8.67903948550988[/C][C]-5.67903948550988[/C][/ROW]
[ROW][C]177[/C][C]4[/C][C]3.7419430006266[/C][C]0.258056999373399[/C][/ROW]
[ROW][C]178[/C][C]3[/C][C]2.85918306085988[/C][C]0.140816939140122[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]6.17103718222979[/C][C]-2.17103718222979[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]2.55596142580341[/C][C]-1.55596142580341[/C][/ROW]
[ROW][C]181[/C][C]2[/C][C]-1.29533887232572[/C][C]3.29533887232572[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]0.808583581904823[/C][C]2.19141641809518[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]2.55271260837623[/C][C]0.447287391623767[/C][/ROW]
[ROW][C]184[/C][C]5[/C][C]-1.33759722097115[/C][C]6.33759722097115[/C][/ROW]
[ROW][C]185[/C][C]4[/C][C]-0.00264390432275619[/C][C]4.00264390432276[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]8.29457560000116[/C][C]-5.29457560000116[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]1.10428018650930[/C][C]1.89571981349070[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]11.5015589065257[/C][C]-8.50155890652568[/C][/ROW]
[ROW][C]189[/C][C]3[/C][C]9.18315417403906[/C][C]-6.18315417403906[/C][/ROW]
[ROW][C]190[/C][C]4[/C][C]8.79003681502858[/C][C]-4.79003681502858[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]7.90850989522285[/C][C]-4.90850989522285[/C][/ROW]
[ROW][C]192[/C][C]2[/C][C]4.50592643797805[/C][C]-2.50592643797805[/C][/ROW]
[ROW][C]193[/C][C]4[/C][C]1.84472463618703[/C][C]2.15527536381297[/C][/ROW]
[ROW][C]194[/C][C]2[/C][C]10.1001855224273[/C][C]-8.10018552242727[/C][/ROW]
[ROW][C]195[/C][C]4[/C][C]5.71980294341946[/C][C]-1.71980294341946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
145.71980294341926-1.71980294341926
244.76761561608748-0.767615616087484
3510.3263585417205-5.32635854172049
4213.1276977857554-11.1276977857554
5315.6108813078564-12.6108813078564
65-2.252225615798137.25222561579813
74-0.473132887916834.47313288791683
845.17206090597398-1.17206090597398
944.6953854055761-0.695385405576099
1044.45572825558414-0.455728255584136
1152.688919238178632.31108076182137
1247.28169265765741-3.28169265765741
1334.42344136185339-1.42344136185339
1445.56133180248442-1.56133180248442
1534.60079915441719-1.60079915441719
1636.18821851190832-3.18821851190832
17415.2627624318022-11.2627624318022
183-1.678947362498454.67894736249845
1941.524960466553882.47503953344612
20215.3002576073550-13.3002576073550
2141.164874649361522.83512535063848
22313.8135023235429-10.8135023235429
2342.002488152696331.99751184730367
2445.44644365084527-1.44644365084527
2533.3477338817065-0.347733881706499
2633.36115636403028-0.361156364030282
2725.10854750013413-3.10854750013413
2844.2610348240172-0.261034824017198
29510.3548545718119-5.35485457181194
30434.4869421933677-30.4869421933677
315231.005441633834520.9945583661655
324631.878392640726914.1216073592731
335830.545261344985827.4547386550142
345430.799134756572423.2008652434276
352935.5339208244244-6.53392082442441
365033.132596166736716.8674038332633
374331.570351680981511.4296483190185
383031.2937865201702-1.29378652017020
394729.511087690911717.4889123090883
404518.176645060690526.8233549393095
41218.9048825867587-16.9048825867587
42224.9181328205113-22.9181328205113
43219.1906044400042-17.1906044400042
44137.6840864679064-36.6840864679064
453-0.2235703182480283.22357031824803
4639.5024284907722-6.50242849077221
4744.16794595078603-0.167945950786026
482-0.5117992745917942.51179927459179
492-0.5214480909252762.52144809092528
5032.167925604942780.832074395057219
5123.70713316360349-1.70713316360349
5242.158506976012801.84149302398720
5356.05428614410388-1.05428614410388
5436.21503031400314-3.21503031400314
5549.54429757278717-5.54429757278717
5655.77095462871925-0.770954628719251
575-0.7858966859839555.78589668598396
5834.29959378384177-1.29959378384177
5943.135930497686540.864069502313457
6033.12137372320673-0.121373723206727
6130.5009176001366952.49908239986331
62212.1632459141473-10.1632459141473
6337.89457494853773-4.89457494853773
6449.13557323477626-5.13557323477626
6540.8802296574327483.11977034256725
664-0.94763430290454.9476343029045
6740.2694571748697843.73054282513022
683-2.385239716780865.38523971678086
6936.9815502387068-3.9815502387068
703-2.950109624726685.95010962472668
7125.89541330468906-3.89541330468906
7239.77309412192136-6.77309412192136
7332.078105375809170.921894624190834
7438.62138581381169-5.62138581381169
7539.33227299702776-6.33227299702776
765-1.484815121416426.48481512141642
7737.83350388390858-4.83350388390858
7853.199822711615391.80017728838461
7942.826214148658671.17378585134133
8046.36723984140592-2.36723984140592
81411.2435435967556-7.24354359675559
82518.9690654674542-13.9690654674542
8343.699775972006010.300224027993989
845-2.47698792343957.4769879234395
8533.43715496523528-0.437154965235284
8632.481052491727950.518947508272047
87210.8461592865560-8.84615928655595
883-0.3216351833447283.32163518334473
8942.173171221584461.82682877841554
9055.80127206683214-0.80127206683214
9157.99524473553121-2.99524473553121
9231.194150904431231.80584909556877
932-2.780380849873944.78038084987394
9430.2471485163307182.75285148366928
9546.27951101731234-2.27951101731234
9614.32106370492077-3.32106370492077
9747.07579320239772-3.07579320239772
983-1.220487749338864.22048774933886
99312.2988204037637-9.29882040376366
10043.620007687968110.379992312031893
101311.4677747483351-8.46777474833506
10244.30720047447823-0.307200474478235
10321.673944262640110.326055737359886
10432.448933746499220.551066253500782
105310.2042912879814-7.20429128798145
1063-2.531800498779745.53180049877974
10725.35061140860113-3.35061140860113
10851.144054756717133.85594524328287
10958.3953500290235-3.3953500290235
11041.776279889597252.22372011040275
11120.04893773766724311.95106226233276
112312.7805600875802-9.78056008758025
11337.48857211487927-4.48857211487927
11434.90734134975848-1.90734134975848
1154-0.4567243353815824.45672433538158
11651.849959078054393.15004092194561
117411.5594323098760-7.55943230987598
1182229.551611857446-7.55161185744601
1191628.2615312236473-12.2615312236473
1203628.61320233963697.38679766036315
1213531.90758768299203.09241231700804
1222527.5066961363315-2.50669613633152
1232739.0942890051891-12.0942890051891
1243228.29498854009473.70501145990525
1253626.28626082965939.71373917034072
1265132.808743643383518.1912563566165
1273025.7352148312224.26478516877802
1282021.3709353264023-1.37093532640233
1292925.85559923019283.14440076980722
1302624.19248637988981.80751362011017
1312027.2188378727945-7.21883787279453
1324027.822308525581312.1776914744186
1332926.15063296771752.84936703228246
1343226.69922480800785.30077519199224
1353326.27887681927296.72112318072706
1363225.92028193004466.0797180699554
1373427.92122255393456.07877744606548
1382426.3210953339826-2.32109533398262
1392524.442924400970.557075599030015
1404129.655552489874511.3444475101255
1413928.856748446022810.1432515539772
1422126.2047227355897-5.20472273558967
1433830.48559226765787.51440773234219
1442823.91454403120264.08545596879741
1453714.476310459137522.5236895408625
1464619.320399494670226.6796005053298
1473915.211011688767323.7889883112327
1482118.89069501866852.10930498133154
149318.845688713943522.1543112860565
150254.48060069591420.519399304086
151297.9404516205714821.0595483794285
1523117.129586917535513.8704130824645
15333.85379914569501-0.85379914569501
15442.672825907674511.32717409232549
155112.2298547373185-11.2298547373185
156110.6062574488159-9.60625744881589
15755.70311267211918-0.703112672119176
158410.6618447404868-6.66184474048679
15934.45202919382421-1.45202919382421
16037.00162325190522-4.00162325190522
16146.75668428548451-2.75668428548451
16230.8832637007116272.11673629928837
163216.5453084168261-14.5453084168261
16413.07120620590152-2.07120620590152
165115.6932189535923-14.6932189535923
16650.4764096525586024.5235903474414
16749.7580431355438-5.75804313554379
16837.24370825253617-4.24370825253617
169410.5612782733924-6.56127827339237
170512.0484084041503-7.04840840415034
1714-2.413707592990946.41370759299094
172411.3819048939138-7.3819048939138
17324.37888313041509-2.37888313041509
17436.35948042983969-3.35948042983969
1754-0.1586970620723444.15869706207234
17638.67903948550988-5.67903948550988
17743.74194300062660.258056999373399
17832.859183060859880.140816939140122
17946.17103718222979-2.17103718222979
18012.55596142580341-1.55596142580341
1812-1.295338872325723.29533887232572
18230.8085835819048232.19141641809518
18332.552712608376230.447287391623767
1845-1.337597220971156.33759722097115
1854-0.002643904322756194.00264390432276
18638.29457560000116-5.29457560000116
18731.104280186509301.89571981349070
188311.5015589065257-8.50155890652568
18939.18315417403906-6.18315417403906
19048.79003681502858-4.79003681502858
19137.90850989522285-4.90850989522285
19224.50592643797805-2.50592643797805
19341.844724636187032.15527536381297
194210.1001855224273-8.10018552242727
19545.71980294341946-1.71980294341946






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0001733003160171490.0003466006320342970.999826699683983
111.09596655118670e-052.19193310237339e-050.999989040334488
124.4972658468272e-078.9945316936544e-070.999999550273415
139.23387117627485e-081.84677423525497e-070.999999907661288
145.26853697282594e-091.05370739456519e-080.999999994731463
151.94165482223767e-093.88330964447534e-090.999999998058345
161.32325023848456e-102.64650047696912e-100.999999999867675
171.23875477757535e-112.47750955515070e-110.999999999987612
181.29386150494948e-122.58772300989896e-120.999999999998706
191.78041354711013e-133.56082709422026e-130.999999999999822
201.13842776791048e-142.27685553582096e-140.999999999999989
216.53066267983013e-161.30613253596603e-151
227.41432159546549e-171.4828643190931e-161
234.27471698570774e-188.54943397141549e-181
242.35921643593315e-194.7184328718663e-191
251.26725445490410e-202.53450890980819e-201
265.16731020361028e-201.03346204072206e-191
271.11516895865872e-192.23033791731743e-191
288.35863912376145e-211.67172782475229e-201
291.47617221958398e-212.95234443916797e-211
303.95025166909836e-227.90050333819672e-221
310.05342893665272910.1068578733054580.946571063347271
320.1336434435780230.2672868871560460.866356556421977
330.3529090595166130.7058181190332250.647090940483387
340.4119613547820420.8239227095640830.588038645217958
350.3729195716529330.7458391433058660.627080428347067
360.5247968927346760.9504062145306490.475203107265324
370.5249421219978520.9501157560042960.475057878002148
380.63538828306280.7292234338744010.364611716937201
390.759364926223680.481270147552640.24063507377632
400.9164987250011470.1670025499977060.0835012749988528
410.9763017051855630.04739658962887480.0236982948144374
420.987835665724880.02432866855024150.0121643342751208
430.9932069070473580.01358618590528390.00679309295264195
440.9991843079886880.001631384022624990.000815692011312493
450.9989020883883130.002195823223374280.00109791161168714
460.9997491689185180.0005016621629643960.000250831081482198
470.999722545127140.0005549097457199820.000277454872859991
480.9996092697101120.0007814605797765550.000390730289888278
490.999537363232490.0009252735350205740.000462636767510287
500.9993451650226780.001309669954644410.000654834977322207
510.9990304040534920.001939191893016540.000969595946508271
520.9987724505593880.002455098881223020.00122754944061151
530.9982673837152140.003465232569573020.00173261628478651
540.9983018021261050.003396395747790060.00169819787389503
550.997872516700590.004254966598821890.00212748329941094
560.997196978123550.005606043752900820.00280302187645041
570.9967566540666450.006486691866709730.00324334593335487
580.9955407246795690.008918550640862130.00445927532043107
590.9941308869380230.01173822612395350.00586911306197673
600.9921933726277150.01561325474456970.00780662737228484
610.9919139363867190.01617212722656190.00808606361328093
620.9928353977874470.01432920442510670.00716460221255335
630.9934122206519310.01317555869613800.00658777934806898
640.9930253785868230.01394924282635310.00697462141317653
650.991087010974340.01782597805131850.00891298902565924
660.9887700515183830.02245989696323450.0112299484816173
670.986532723323790.02693455335241880.0134672766762094
680.9849200265389010.03015994692219810.0150799734610990
690.980568150805560.03886369838888050.0194318491944402
700.980369205144920.03926158971016220.0196307948550811
710.9748394742471870.05032105150562540.0251605257528127
720.9691103551466270.06177928970674580.0308896448533729
730.962642330920550.0747153381588980.037357669079449
740.9570526289090650.08589474218187010.0429473710909351
750.9505030074706580.0989939850586840.049496992529342
760.9442191800266940.1115616399466120.0557808199733058
770.939380821417690.1212383571646180.0606191785823088
780.9286800932934220.1426398134131560.071319906706578
790.9160743937504570.1678512124990860.083925606249543
800.913862064873620.1722758702527580.086137935126379
810.91492223406320.1701555318735990.0850777659367993
820.925451415260380.149097169479240.07454858473962
830.91094312827210.17811374345580.0890568717279
840.9037393575064670.1925212849870670.0962606424935336
850.88983191116350.2203361776730000.110168088836500
860.8738741675565350.2522516648869290.126125832443465
870.8762633241833630.2474733516332740.123736675816637
880.8594602936211320.2810794127577370.140539706378868
890.8405110976137780.3189778047724440.159488902386222
900.8303790295007560.3392419409984880.169620970499244
910.8219016419756540.3561967160486910.178098358024346
920.79470440073340.4105911985332010.205295599266601
930.7778525798716050.4442948402567900.222147420128395
940.7514508733404610.4970982533190790.248549126659539
950.7531011069616620.4937977860766760.246898893038338
960.7223265871617210.5553468256765570.277673412838279
970.7056850816124020.5886298367751970.294314918387598
980.6882257069489760.6235485861020480.311774293051024
990.7155955486970110.5688089026059780.284404451302989
1000.6899220872689520.6201558254620970.310077912731049
1010.694971321700390.6100573565992190.305028678299610
1020.6576009830788230.6847980338423540.342399016921177
1030.6205880261759630.7588239476480740.379411973824037
1040.5837792148582910.8324415702834170.416220785141709
1050.6117662543937330.7764674912125350.388233745606267
1060.5872991134997160.8254017730005690.412700886500284
1070.549046915444560.901906169110880.45095308455544
1080.5274538932827440.9450922134345120.472546106717256
1090.5116224613434560.9767550773130880.488377538656544
1100.474227870785630.948455741571260.52577212921437
1110.4515196763959380.9030393527918750.548480323604062
1120.5438421757101270.9123156485797450.456157824289873
1130.5048428474615440.9903143050769130.495157152538456
1140.4734126414480820.9468252828961640.526587358551918
1150.4320691577962740.8641383155925480.567930842203726
1160.3903455266881670.7806910533763340.609654473311833
1170.5162896020896510.9674207958206970.483710397910349
1180.5206423395318700.9587153209362590.479357660468129
1190.5687432760856640.8625134478286720.431256723914336
1200.6153280718444220.7693438563111560.384671928155578
1210.6526504896453830.6946990207092340.347349510354617
1220.6316431574299540.7367136851400920.368356842570046
1230.6137638123022180.7724723753955630.386236187697782
1240.5905694226986190.8188611546027620.409430577301381
1250.5897568969761080.8204862060477850.410243103023892
1260.840159196234860.3196816075302800.159840803765140
1270.8159309931620760.3681380136758490.184069006837924
1280.8299922097161440.3400155805677110.170007790283856
1290.8009757075262720.3980485849474560.199024292473728
1300.8058111941818790.3883776116362420.194188805818121
1310.8426335095610120.3147329808779760.157366490438988
1320.867919443593470.2641611128130590.132080556406529
1330.8415485191345570.3169029617308860.158451480865443
1340.8123154395236830.3753691209526340.187684560476317
1350.842765569341460.3144688613170800.157234430658540
1360.8381165221465660.3237669557068680.161883477853434
1370.8232419260288930.3535161479422140.176758073971107
1380.8092587004591950.3814825990816090.190741299540805
1390.8375614875623190.3248770248753620.162438512437681
1400.8881289010297460.2237421979405080.111871098970254
1410.9065084217027250.1869831565945510.0934915782972754
1420.9129673519242740.1740652961514520.0870326480757262
1430.9624207682763330.07515846344733330.0375792317236666
1440.9578358005052340.0843283989895320.042164199494766
1450.9960818120211980.0078363759576030.0039181879788015
1460.9996802689594670.0006394620810652480.000319731040532624
1470.999989384855692.12302886198245e-051.06151443099122e-05
1480.9999970499389245.9001221519506e-062.9500610759753e-06
1490.9999999957163928.56721525843379e-094.28360762921689e-09
1500.9999999963058277.38834608377859e-093.69417304188929e-09
1510.999999995013659.97269972848212e-094.98634986424106e-09
15211.97211809558101e-289.86059047790503e-29
15311.79342634232052e-278.96713171160262e-28
15411.63849121687569e-268.19245608437843e-27
15516.31488591141798e-263.15744295570899e-26
15615.65211273469902e-262.82605636734951e-26
15711.75488531999251e-258.77442659996257e-26
15811.42145609548231e-247.10728047741154e-25
15911.34856560813031e-236.74282804065154e-24
16011.28973693201584e-226.44868466007922e-23
16111.19822851170118e-215.99114255850592e-22
16211.08336948582014e-205.41684742910069e-21
16317.72228051228693e-203.86114025614347e-20
16413.00243264016310e-191.50121632008155e-19
16511.48322475497325e-197.41612377486624e-20
16617.83125610163132e-193.91562805081566e-19
16717.49157140913349e-183.74578570456674e-18
16817.35901551977213e-173.67950775988606e-17
16917.21733212541535e-163.60866606270767e-16
1700.9999999999999984.34233208701896e-152.17116604350948e-15
1710.9999999999999853.06461143034374e-141.53230571517187e-14
1720.9999999999999421.16353383088316e-135.81766915441578e-14
1730.9999999999997824.36928453228393e-132.18464226614196e-13
1740.9999999999978894.2218706624973e-122.11093533124865e-12
1750.9999999999857922.84151886905057e-111.42075943452529e-11
1760.99999999987562.48799165963366e-101.24399582981683e-10
1770.9999999992215161.55696890534207e-097.78484452671035e-10
1780.999999993528421.29431613466353e-086.47158067331767e-09
1790.9999999749810185.00379630783757e-082.50189815391879e-08
1800.9999999359847141.28030571956975e-076.40152859784877e-08
1810.9999995431921269.13615748349212e-074.56807874174606e-07
1820.9999988792569052.24148618897964e-061.12074309448982e-06
1830.999990278107081.94437858419535e-059.72189292097676e-06
1840.9999035752424230.0001928495151545949.6424757577297e-05
1850.9988024978326250.002395004334749250.00119750216737462

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.000173300316017149 & 0.000346600632034297 & 0.999826699683983 \tabularnewline
11 & 1.09596655118670e-05 & 2.19193310237339e-05 & 0.999989040334488 \tabularnewline
12 & 4.4972658468272e-07 & 8.9945316936544e-07 & 0.999999550273415 \tabularnewline
13 & 9.23387117627485e-08 & 1.84677423525497e-07 & 0.999999907661288 \tabularnewline
14 & 5.26853697282594e-09 & 1.05370739456519e-08 & 0.999999994731463 \tabularnewline
15 & 1.94165482223767e-09 & 3.88330964447534e-09 & 0.999999998058345 \tabularnewline
16 & 1.32325023848456e-10 & 2.64650047696912e-10 & 0.999999999867675 \tabularnewline
17 & 1.23875477757535e-11 & 2.47750955515070e-11 & 0.999999999987612 \tabularnewline
18 & 1.29386150494948e-12 & 2.58772300989896e-12 & 0.999999999998706 \tabularnewline
19 & 1.78041354711013e-13 & 3.56082709422026e-13 & 0.999999999999822 \tabularnewline
20 & 1.13842776791048e-14 & 2.27685553582096e-14 & 0.999999999999989 \tabularnewline
21 & 6.53066267983013e-16 & 1.30613253596603e-15 & 1 \tabularnewline
22 & 7.41432159546549e-17 & 1.4828643190931e-16 & 1 \tabularnewline
23 & 4.27471698570774e-18 & 8.54943397141549e-18 & 1 \tabularnewline
24 & 2.35921643593315e-19 & 4.7184328718663e-19 & 1 \tabularnewline
25 & 1.26725445490410e-20 & 2.53450890980819e-20 & 1 \tabularnewline
26 & 5.16731020361028e-20 & 1.03346204072206e-19 & 1 \tabularnewline
27 & 1.11516895865872e-19 & 2.23033791731743e-19 & 1 \tabularnewline
28 & 8.35863912376145e-21 & 1.67172782475229e-20 & 1 \tabularnewline
29 & 1.47617221958398e-21 & 2.95234443916797e-21 & 1 \tabularnewline
30 & 3.95025166909836e-22 & 7.90050333819672e-22 & 1 \tabularnewline
31 & 0.0534289366527291 & 0.106857873305458 & 0.946571063347271 \tabularnewline
32 & 0.133643443578023 & 0.267286887156046 & 0.866356556421977 \tabularnewline
33 & 0.352909059516613 & 0.705818119033225 & 0.647090940483387 \tabularnewline
34 & 0.411961354782042 & 0.823922709564083 & 0.588038645217958 \tabularnewline
35 & 0.372919571652933 & 0.745839143305866 & 0.627080428347067 \tabularnewline
36 & 0.524796892734676 & 0.950406214530649 & 0.475203107265324 \tabularnewline
37 & 0.524942121997852 & 0.950115756004296 & 0.475057878002148 \tabularnewline
38 & 0.6353882830628 & 0.729223433874401 & 0.364611716937201 \tabularnewline
39 & 0.75936492622368 & 0.48127014755264 & 0.24063507377632 \tabularnewline
40 & 0.916498725001147 & 0.167002549997706 & 0.0835012749988528 \tabularnewline
41 & 0.976301705185563 & 0.0473965896288748 & 0.0236982948144374 \tabularnewline
42 & 0.98783566572488 & 0.0243286685502415 & 0.0121643342751208 \tabularnewline
43 & 0.993206907047358 & 0.0135861859052839 & 0.00679309295264195 \tabularnewline
44 & 0.999184307988688 & 0.00163138402262499 & 0.000815692011312493 \tabularnewline
45 & 0.998902088388313 & 0.00219582322337428 & 0.00109791161168714 \tabularnewline
46 & 0.999749168918518 & 0.000501662162964396 & 0.000250831081482198 \tabularnewline
47 & 0.99972254512714 & 0.000554909745719982 & 0.000277454872859991 \tabularnewline
48 & 0.999609269710112 & 0.000781460579776555 & 0.000390730289888278 \tabularnewline
49 & 0.99953736323249 & 0.000925273535020574 & 0.000462636767510287 \tabularnewline
50 & 0.999345165022678 & 0.00130966995464441 & 0.000654834977322207 \tabularnewline
51 & 0.999030404053492 & 0.00193919189301654 & 0.000969595946508271 \tabularnewline
52 & 0.998772450559388 & 0.00245509888122302 & 0.00122754944061151 \tabularnewline
53 & 0.998267383715214 & 0.00346523256957302 & 0.00173261628478651 \tabularnewline
54 & 0.998301802126105 & 0.00339639574779006 & 0.00169819787389503 \tabularnewline
55 & 0.99787251670059 & 0.00425496659882189 & 0.00212748329941094 \tabularnewline
56 & 0.99719697812355 & 0.00560604375290082 & 0.00280302187645041 \tabularnewline
57 & 0.996756654066645 & 0.00648669186670973 & 0.00324334593335487 \tabularnewline
58 & 0.995540724679569 & 0.00891855064086213 & 0.00445927532043107 \tabularnewline
59 & 0.994130886938023 & 0.0117382261239535 & 0.00586911306197673 \tabularnewline
60 & 0.992193372627715 & 0.0156132547445697 & 0.00780662737228484 \tabularnewline
61 & 0.991913936386719 & 0.0161721272265619 & 0.00808606361328093 \tabularnewline
62 & 0.992835397787447 & 0.0143292044251067 & 0.00716460221255335 \tabularnewline
63 & 0.993412220651931 & 0.0131755586961380 & 0.00658777934806898 \tabularnewline
64 & 0.993025378586823 & 0.0139492428263531 & 0.00697462141317653 \tabularnewline
65 & 0.99108701097434 & 0.0178259780513185 & 0.00891298902565924 \tabularnewline
66 & 0.988770051518383 & 0.0224598969632345 & 0.0112299484816173 \tabularnewline
67 & 0.98653272332379 & 0.0269345533524188 & 0.0134672766762094 \tabularnewline
68 & 0.984920026538901 & 0.0301599469221981 & 0.0150799734610990 \tabularnewline
69 & 0.98056815080556 & 0.0388636983888805 & 0.0194318491944402 \tabularnewline
70 & 0.98036920514492 & 0.0392615897101622 & 0.0196307948550811 \tabularnewline
71 & 0.974839474247187 & 0.0503210515056254 & 0.0251605257528127 \tabularnewline
72 & 0.969110355146627 & 0.0617792897067458 & 0.0308896448533729 \tabularnewline
73 & 0.96264233092055 & 0.074715338158898 & 0.037357669079449 \tabularnewline
74 & 0.957052628909065 & 0.0858947421818701 & 0.0429473710909351 \tabularnewline
75 & 0.950503007470658 & 0.098993985058684 & 0.049496992529342 \tabularnewline
76 & 0.944219180026694 & 0.111561639946612 & 0.0557808199733058 \tabularnewline
77 & 0.93938082141769 & 0.121238357164618 & 0.0606191785823088 \tabularnewline
78 & 0.928680093293422 & 0.142639813413156 & 0.071319906706578 \tabularnewline
79 & 0.916074393750457 & 0.167851212499086 & 0.083925606249543 \tabularnewline
80 & 0.91386206487362 & 0.172275870252758 & 0.086137935126379 \tabularnewline
81 & 0.9149222340632 & 0.170155531873599 & 0.0850777659367993 \tabularnewline
82 & 0.92545141526038 & 0.14909716947924 & 0.07454858473962 \tabularnewline
83 & 0.9109431282721 & 0.1781137434558 & 0.0890568717279 \tabularnewline
84 & 0.903739357506467 & 0.192521284987067 & 0.0962606424935336 \tabularnewline
85 & 0.8898319111635 & 0.220336177673000 & 0.110168088836500 \tabularnewline
86 & 0.873874167556535 & 0.252251664886929 & 0.126125832443465 \tabularnewline
87 & 0.876263324183363 & 0.247473351633274 & 0.123736675816637 \tabularnewline
88 & 0.859460293621132 & 0.281079412757737 & 0.140539706378868 \tabularnewline
89 & 0.840511097613778 & 0.318977804772444 & 0.159488902386222 \tabularnewline
90 & 0.830379029500756 & 0.339241940998488 & 0.169620970499244 \tabularnewline
91 & 0.821901641975654 & 0.356196716048691 & 0.178098358024346 \tabularnewline
92 & 0.7947044007334 & 0.410591198533201 & 0.205295599266601 \tabularnewline
93 & 0.777852579871605 & 0.444294840256790 & 0.222147420128395 \tabularnewline
94 & 0.751450873340461 & 0.497098253319079 & 0.248549126659539 \tabularnewline
95 & 0.753101106961662 & 0.493797786076676 & 0.246898893038338 \tabularnewline
96 & 0.722326587161721 & 0.555346825676557 & 0.277673412838279 \tabularnewline
97 & 0.705685081612402 & 0.588629836775197 & 0.294314918387598 \tabularnewline
98 & 0.688225706948976 & 0.623548586102048 & 0.311774293051024 \tabularnewline
99 & 0.715595548697011 & 0.568808902605978 & 0.284404451302989 \tabularnewline
100 & 0.689922087268952 & 0.620155825462097 & 0.310077912731049 \tabularnewline
101 & 0.69497132170039 & 0.610057356599219 & 0.305028678299610 \tabularnewline
102 & 0.657600983078823 & 0.684798033842354 & 0.342399016921177 \tabularnewline
103 & 0.620588026175963 & 0.758823947648074 & 0.379411973824037 \tabularnewline
104 & 0.583779214858291 & 0.832441570283417 & 0.416220785141709 \tabularnewline
105 & 0.611766254393733 & 0.776467491212535 & 0.388233745606267 \tabularnewline
106 & 0.587299113499716 & 0.825401773000569 & 0.412700886500284 \tabularnewline
107 & 0.54904691544456 & 0.90190616911088 & 0.45095308455544 \tabularnewline
108 & 0.527453893282744 & 0.945092213434512 & 0.472546106717256 \tabularnewline
109 & 0.511622461343456 & 0.976755077313088 & 0.488377538656544 \tabularnewline
110 & 0.47422787078563 & 0.94845574157126 & 0.52577212921437 \tabularnewline
111 & 0.451519676395938 & 0.903039352791875 & 0.548480323604062 \tabularnewline
112 & 0.543842175710127 & 0.912315648579745 & 0.456157824289873 \tabularnewline
113 & 0.504842847461544 & 0.990314305076913 & 0.495157152538456 \tabularnewline
114 & 0.473412641448082 & 0.946825282896164 & 0.526587358551918 \tabularnewline
115 & 0.432069157796274 & 0.864138315592548 & 0.567930842203726 \tabularnewline
116 & 0.390345526688167 & 0.780691053376334 & 0.609654473311833 \tabularnewline
117 & 0.516289602089651 & 0.967420795820697 & 0.483710397910349 \tabularnewline
118 & 0.520642339531870 & 0.958715320936259 & 0.479357660468129 \tabularnewline
119 & 0.568743276085664 & 0.862513447828672 & 0.431256723914336 \tabularnewline
120 & 0.615328071844422 & 0.769343856311156 & 0.384671928155578 \tabularnewline
121 & 0.652650489645383 & 0.694699020709234 & 0.347349510354617 \tabularnewline
122 & 0.631643157429954 & 0.736713685140092 & 0.368356842570046 \tabularnewline
123 & 0.613763812302218 & 0.772472375395563 & 0.386236187697782 \tabularnewline
124 & 0.590569422698619 & 0.818861154602762 & 0.409430577301381 \tabularnewline
125 & 0.589756896976108 & 0.820486206047785 & 0.410243103023892 \tabularnewline
126 & 0.84015919623486 & 0.319681607530280 & 0.159840803765140 \tabularnewline
127 & 0.815930993162076 & 0.368138013675849 & 0.184069006837924 \tabularnewline
128 & 0.829992209716144 & 0.340015580567711 & 0.170007790283856 \tabularnewline
129 & 0.800975707526272 & 0.398048584947456 & 0.199024292473728 \tabularnewline
130 & 0.805811194181879 & 0.388377611636242 & 0.194188805818121 \tabularnewline
131 & 0.842633509561012 & 0.314732980877976 & 0.157366490438988 \tabularnewline
132 & 0.86791944359347 & 0.264161112813059 & 0.132080556406529 \tabularnewline
133 & 0.841548519134557 & 0.316902961730886 & 0.158451480865443 \tabularnewline
134 & 0.812315439523683 & 0.375369120952634 & 0.187684560476317 \tabularnewline
135 & 0.84276556934146 & 0.314468861317080 & 0.157234430658540 \tabularnewline
136 & 0.838116522146566 & 0.323766955706868 & 0.161883477853434 \tabularnewline
137 & 0.823241926028893 & 0.353516147942214 & 0.176758073971107 \tabularnewline
138 & 0.809258700459195 & 0.381482599081609 & 0.190741299540805 \tabularnewline
139 & 0.837561487562319 & 0.324877024875362 & 0.162438512437681 \tabularnewline
140 & 0.888128901029746 & 0.223742197940508 & 0.111871098970254 \tabularnewline
141 & 0.906508421702725 & 0.186983156594551 & 0.0934915782972754 \tabularnewline
142 & 0.912967351924274 & 0.174065296151452 & 0.0870326480757262 \tabularnewline
143 & 0.962420768276333 & 0.0751584634473333 & 0.0375792317236666 \tabularnewline
144 & 0.957835800505234 & 0.084328398989532 & 0.042164199494766 \tabularnewline
145 & 0.996081812021198 & 0.007836375957603 & 0.0039181879788015 \tabularnewline
146 & 0.999680268959467 & 0.000639462081065248 & 0.000319731040532624 \tabularnewline
147 & 0.99998938485569 & 2.12302886198245e-05 & 1.06151443099122e-05 \tabularnewline
148 & 0.999997049938924 & 5.9001221519506e-06 & 2.9500610759753e-06 \tabularnewline
149 & 0.999999995716392 & 8.56721525843379e-09 & 4.28360762921689e-09 \tabularnewline
150 & 0.999999996305827 & 7.38834608377859e-09 & 3.69417304188929e-09 \tabularnewline
151 & 0.99999999501365 & 9.97269972848212e-09 & 4.98634986424106e-09 \tabularnewline
152 & 1 & 1.97211809558101e-28 & 9.86059047790503e-29 \tabularnewline
153 & 1 & 1.79342634232052e-27 & 8.96713171160262e-28 \tabularnewline
154 & 1 & 1.63849121687569e-26 & 8.19245608437843e-27 \tabularnewline
155 & 1 & 6.31488591141798e-26 & 3.15744295570899e-26 \tabularnewline
156 & 1 & 5.65211273469902e-26 & 2.82605636734951e-26 \tabularnewline
157 & 1 & 1.75488531999251e-25 & 8.77442659996257e-26 \tabularnewline
158 & 1 & 1.42145609548231e-24 & 7.10728047741154e-25 \tabularnewline
159 & 1 & 1.34856560813031e-23 & 6.74282804065154e-24 \tabularnewline
160 & 1 & 1.28973693201584e-22 & 6.44868466007922e-23 \tabularnewline
161 & 1 & 1.19822851170118e-21 & 5.99114255850592e-22 \tabularnewline
162 & 1 & 1.08336948582014e-20 & 5.41684742910069e-21 \tabularnewline
163 & 1 & 7.72228051228693e-20 & 3.86114025614347e-20 \tabularnewline
164 & 1 & 3.00243264016310e-19 & 1.50121632008155e-19 \tabularnewline
165 & 1 & 1.48322475497325e-19 & 7.41612377486624e-20 \tabularnewline
166 & 1 & 7.83125610163132e-19 & 3.91562805081566e-19 \tabularnewline
167 & 1 & 7.49157140913349e-18 & 3.74578570456674e-18 \tabularnewline
168 & 1 & 7.35901551977213e-17 & 3.67950775988606e-17 \tabularnewline
169 & 1 & 7.21733212541535e-16 & 3.60866606270767e-16 \tabularnewline
170 & 0.999999999999998 & 4.34233208701896e-15 & 2.17116604350948e-15 \tabularnewline
171 & 0.999999999999985 & 3.06461143034374e-14 & 1.53230571517187e-14 \tabularnewline
172 & 0.999999999999942 & 1.16353383088316e-13 & 5.81766915441578e-14 \tabularnewline
173 & 0.999999999999782 & 4.36928453228393e-13 & 2.18464226614196e-13 \tabularnewline
174 & 0.999999999997889 & 4.2218706624973e-12 & 2.11093533124865e-12 \tabularnewline
175 & 0.999999999985792 & 2.84151886905057e-11 & 1.42075943452529e-11 \tabularnewline
176 & 0.9999999998756 & 2.48799165963366e-10 & 1.24399582981683e-10 \tabularnewline
177 & 0.999999999221516 & 1.55696890534207e-09 & 7.78484452671035e-10 \tabularnewline
178 & 0.99999999352842 & 1.29431613466353e-08 & 6.47158067331767e-09 \tabularnewline
179 & 0.999999974981018 & 5.00379630783757e-08 & 2.50189815391879e-08 \tabularnewline
180 & 0.999999935984714 & 1.28030571956975e-07 & 6.40152859784877e-08 \tabularnewline
181 & 0.999999543192126 & 9.13615748349212e-07 & 4.56807874174606e-07 \tabularnewline
182 & 0.999998879256905 & 2.24148618897964e-06 & 1.12074309448982e-06 \tabularnewline
183 & 0.99999027810708 & 1.94437858419535e-05 & 9.72189292097676e-06 \tabularnewline
184 & 0.999903575242423 & 0.000192849515154594 & 9.6424757577297e-05 \tabularnewline
185 & 0.998802497832625 & 0.00239500433474925 & 0.00119750216737462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.000173300316017149[/C][C]0.000346600632034297[/C][C]0.999826699683983[/C][/ROW]
[ROW][C]11[/C][C]1.09596655118670e-05[/C][C]2.19193310237339e-05[/C][C]0.999989040334488[/C][/ROW]
[ROW][C]12[/C][C]4.4972658468272e-07[/C][C]8.9945316936544e-07[/C][C]0.999999550273415[/C][/ROW]
[ROW][C]13[/C][C]9.23387117627485e-08[/C][C]1.84677423525497e-07[/C][C]0.999999907661288[/C][/ROW]
[ROW][C]14[/C][C]5.26853697282594e-09[/C][C]1.05370739456519e-08[/C][C]0.999999994731463[/C][/ROW]
[ROW][C]15[/C][C]1.94165482223767e-09[/C][C]3.88330964447534e-09[/C][C]0.999999998058345[/C][/ROW]
[ROW][C]16[/C][C]1.32325023848456e-10[/C][C]2.64650047696912e-10[/C][C]0.999999999867675[/C][/ROW]
[ROW][C]17[/C][C]1.23875477757535e-11[/C][C]2.47750955515070e-11[/C][C]0.999999999987612[/C][/ROW]
[ROW][C]18[/C][C]1.29386150494948e-12[/C][C]2.58772300989896e-12[/C][C]0.999999999998706[/C][/ROW]
[ROW][C]19[/C][C]1.78041354711013e-13[/C][C]3.56082709422026e-13[/C][C]0.999999999999822[/C][/ROW]
[ROW][C]20[/C][C]1.13842776791048e-14[/C][C]2.27685553582096e-14[/C][C]0.999999999999989[/C][/ROW]
[ROW][C]21[/C][C]6.53066267983013e-16[/C][C]1.30613253596603e-15[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]7.41432159546549e-17[/C][C]1.4828643190931e-16[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]4.27471698570774e-18[/C][C]8.54943397141549e-18[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]2.35921643593315e-19[/C][C]4.7184328718663e-19[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.26725445490410e-20[/C][C]2.53450890980819e-20[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]5.16731020361028e-20[/C][C]1.03346204072206e-19[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.11516895865872e-19[/C][C]2.23033791731743e-19[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]8.35863912376145e-21[/C][C]1.67172782475229e-20[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.47617221958398e-21[/C][C]2.95234443916797e-21[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]3.95025166909836e-22[/C][C]7.90050333819672e-22[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0.0534289366527291[/C][C]0.106857873305458[/C][C]0.946571063347271[/C][/ROW]
[ROW][C]32[/C][C]0.133643443578023[/C][C]0.267286887156046[/C][C]0.866356556421977[/C][/ROW]
[ROW][C]33[/C][C]0.352909059516613[/C][C]0.705818119033225[/C][C]0.647090940483387[/C][/ROW]
[ROW][C]34[/C][C]0.411961354782042[/C][C]0.823922709564083[/C][C]0.588038645217958[/C][/ROW]
[ROW][C]35[/C][C]0.372919571652933[/C][C]0.745839143305866[/C][C]0.627080428347067[/C][/ROW]
[ROW][C]36[/C][C]0.524796892734676[/C][C]0.950406214530649[/C][C]0.475203107265324[/C][/ROW]
[ROW][C]37[/C][C]0.524942121997852[/C][C]0.950115756004296[/C][C]0.475057878002148[/C][/ROW]
[ROW][C]38[/C][C]0.6353882830628[/C][C]0.729223433874401[/C][C]0.364611716937201[/C][/ROW]
[ROW][C]39[/C][C]0.75936492622368[/C][C]0.48127014755264[/C][C]0.24063507377632[/C][/ROW]
[ROW][C]40[/C][C]0.916498725001147[/C][C]0.167002549997706[/C][C]0.0835012749988528[/C][/ROW]
[ROW][C]41[/C][C]0.976301705185563[/C][C]0.0473965896288748[/C][C]0.0236982948144374[/C][/ROW]
[ROW][C]42[/C][C]0.98783566572488[/C][C]0.0243286685502415[/C][C]0.0121643342751208[/C][/ROW]
[ROW][C]43[/C][C]0.993206907047358[/C][C]0.0135861859052839[/C][C]0.00679309295264195[/C][/ROW]
[ROW][C]44[/C][C]0.999184307988688[/C][C]0.00163138402262499[/C][C]0.000815692011312493[/C][/ROW]
[ROW][C]45[/C][C]0.998902088388313[/C][C]0.00219582322337428[/C][C]0.00109791161168714[/C][/ROW]
[ROW][C]46[/C][C]0.999749168918518[/C][C]0.000501662162964396[/C][C]0.000250831081482198[/C][/ROW]
[ROW][C]47[/C][C]0.99972254512714[/C][C]0.000554909745719982[/C][C]0.000277454872859991[/C][/ROW]
[ROW][C]48[/C][C]0.999609269710112[/C][C]0.000781460579776555[/C][C]0.000390730289888278[/C][/ROW]
[ROW][C]49[/C][C]0.99953736323249[/C][C]0.000925273535020574[/C][C]0.000462636767510287[/C][/ROW]
[ROW][C]50[/C][C]0.999345165022678[/C][C]0.00130966995464441[/C][C]0.000654834977322207[/C][/ROW]
[ROW][C]51[/C][C]0.999030404053492[/C][C]0.00193919189301654[/C][C]0.000969595946508271[/C][/ROW]
[ROW][C]52[/C][C]0.998772450559388[/C][C]0.00245509888122302[/C][C]0.00122754944061151[/C][/ROW]
[ROW][C]53[/C][C]0.998267383715214[/C][C]0.00346523256957302[/C][C]0.00173261628478651[/C][/ROW]
[ROW][C]54[/C][C]0.998301802126105[/C][C]0.00339639574779006[/C][C]0.00169819787389503[/C][/ROW]
[ROW][C]55[/C][C]0.99787251670059[/C][C]0.00425496659882189[/C][C]0.00212748329941094[/C][/ROW]
[ROW][C]56[/C][C]0.99719697812355[/C][C]0.00560604375290082[/C][C]0.00280302187645041[/C][/ROW]
[ROW][C]57[/C][C]0.996756654066645[/C][C]0.00648669186670973[/C][C]0.00324334593335487[/C][/ROW]
[ROW][C]58[/C][C]0.995540724679569[/C][C]0.00891855064086213[/C][C]0.00445927532043107[/C][/ROW]
[ROW][C]59[/C][C]0.994130886938023[/C][C]0.0117382261239535[/C][C]0.00586911306197673[/C][/ROW]
[ROW][C]60[/C][C]0.992193372627715[/C][C]0.0156132547445697[/C][C]0.00780662737228484[/C][/ROW]
[ROW][C]61[/C][C]0.991913936386719[/C][C]0.0161721272265619[/C][C]0.00808606361328093[/C][/ROW]
[ROW][C]62[/C][C]0.992835397787447[/C][C]0.0143292044251067[/C][C]0.00716460221255335[/C][/ROW]
[ROW][C]63[/C][C]0.993412220651931[/C][C]0.0131755586961380[/C][C]0.00658777934806898[/C][/ROW]
[ROW][C]64[/C][C]0.993025378586823[/C][C]0.0139492428263531[/C][C]0.00697462141317653[/C][/ROW]
[ROW][C]65[/C][C]0.99108701097434[/C][C]0.0178259780513185[/C][C]0.00891298902565924[/C][/ROW]
[ROW][C]66[/C][C]0.988770051518383[/C][C]0.0224598969632345[/C][C]0.0112299484816173[/C][/ROW]
[ROW][C]67[/C][C]0.98653272332379[/C][C]0.0269345533524188[/C][C]0.0134672766762094[/C][/ROW]
[ROW][C]68[/C][C]0.984920026538901[/C][C]0.0301599469221981[/C][C]0.0150799734610990[/C][/ROW]
[ROW][C]69[/C][C]0.98056815080556[/C][C]0.0388636983888805[/C][C]0.0194318491944402[/C][/ROW]
[ROW][C]70[/C][C]0.98036920514492[/C][C]0.0392615897101622[/C][C]0.0196307948550811[/C][/ROW]
[ROW][C]71[/C][C]0.974839474247187[/C][C]0.0503210515056254[/C][C]0.0251605257528127[/C][/ROW]
[ROW][C]72[/C][C]0.969110355146627[/C][C]0.0617792897067458[/C][C]0.0308896448533729[/C][/ROW]
[ROW][C]73[/C][C]0.96264233092055[/C][C]0.074715338158898[/C][C]0.037357669079449[/C][/ROW]
[ROW][C]74[/C][C]0.957052628909065[/C][C]0.0858947421818701[/C][C]0.0429473710909351[/C][/ROW]
[ROW][C]75[/C][C]0.950503007470658[/C][C]0.098993985058684[/C][C]0.049496992529342[/C][/ROW]
[ROW][C]76[/C][C]0.944219180026694[/C][C]0.111561639946612[/C][C]0.0557808199733058[/C][/ROW]
[ROW][C]77[/C][C]0.93938082141769[/C][C]0.121238357164618[/C][C]0.0606191785823088[/C][/ROW]
[ROW][C]78[/C][C]0.928680093293422[/C][C]0.142639813413156[/C][C]0.071319906706578[/C][/ROW]
[ROW][C]79[/C][C]0.916074393750457[/C][C]0.167851212499086[/C][C]0.083925606249543[/C][/ROW]
[ROW][C]80[/C][C]0.91386206487362[/C][C]0.172275870252758[/C][C]0.086137935126379[/C][/ROW]
[ROW][C]81[/C][C]0.9149222340632[/C][C]0.170155531873599[/C][C]0.0850777659367993[/C][/ROW]
[ROW][C]82[/C][C]0.92545141526038[/C][C]0.14909716947924[/C][C]0.07454858473962[/C][/ROW]
[ROW][C]83[/C][C]0.9109431282721[/C][C]0.1781137434558[/C][C]0.0890568717279[/C][/ROW]
[ROW][C]84[/C][C]0.903739357506467[/C][C]0.192521284987067[/C][C]0.0962606424935336[/C][/ROW]
[ROW][C]85[/C][C]0.8898319111635[/C][C]0.220336177673000[/C][C]0.110168088836500[/C][/ROW]
[ROW][C]86[/C][C]0.873874167556535[/C][C]0.252251664886929[/C][C]0.126125832443465[/C][/ROW]
[ROW][C]87[/C][C]0.876263324183363[/C][C]0.247473351633274[/C][C]0.123736675816637[/C][/ROW]
[ROW][C]88[/C][C]0.859460293621132[/C][C]0.281079412757737[/C][C]0.140539706378868[/C][/ROW]
[ROW][C]89[/C][C]0.840511097613778[/C][C]0.318977804772444[/C][C]0.159488902386222[/C][/ROW]
[ROW][C]90[/C][C]0.830379029500756[/C][C]0.339241940998488[/C][C]0.169620970499244[/C][/ROW]
[ROW][C]91[/C][C]0.821901641975654[/C][C]0.356196716048691[/C][C]0.178098358024346[/C][/ROW]
[ROW][C]92[/C][C]0.7947044007334[/C][C]0.410591198533201[/C][C]0.205295599266601[/C][/ROW]
[ROW][C]93[/C][C]0.777852579871605[/C][C]0.444294840256790[/C][C]0.222147420128395[/C][/ROW]
[ROW][C]94[/C][C]0.751450873340461[/C][C]0.497098253319079[/C][C]0.248549126659539[/C][/ROW]
[ROW][C]95[/C][C]0.753101106961662[/C][C]0.493797786076676[/C][C]0.246898893038338[/C][/ROW]
[ROW][C]96[/C][C]0.722326587161721[/C][C]0.555346825676557[/C][C]0.277673412838279[/C][/ROW]
[ROW][C]97[/C][C]0.705685081612402[/C][C]0.588629836775197[/C][C]0.294314918387598[/C][/ROW]
[ROW][C]98[/C][C]0.688225706948976[/C][C]0.623548586102048[/C][C]0.311774293051024[/C][/ROW]
[ROW][C]99[/C][C]0.715595548697011[/C][C]0.568808902605978[/C][C]0.284404451302989[/C][/ROW]
[ROW][C]100[/C][C]0.689922087268952[/C][C]0.620155825462097[/C][C]0.310077912731049[/C][/ROW]
[ROW][C]101[/C][C]0.69497132170039[/C][C]0.610057356599219[/C][C]0.305028678299610[/C][/ROW]
[ROW][C]102[/C][C]0.657600983078823[/C][C]0.684798033842354[/C][C]0.342399016921177[/C][/ROW]
[ROW][C]103[/C][C]0.620588026175963[/C][C]0.758823947648074[/C][C]0.379411973824037[/C][/ROW]
[ROW][C]104[/C][C]0.583779214858291[/C][C]0.832441570283417[/C][C]0.416220785141709[/C][/ROW]
[ROW][C]105[/C][C]0.611766254393733[/C][C]0.776467491212535[/C][C]0.388233745606267[/C][/ROW]
[ROW][C]106[/C][C]0.587299113499716[/C][C]0.825401773000569[/C][C]0.412700886500284[/C][/ROW]
[ROW][C]107[/C][C]0.54904691544456[/C][C]0.90190616911088[/C][C]0.45095308455544[/C][/ROW]
[ROW][C]108[/C][C]0.527453893282744[/C][C]0.945092213434512[/C][C]0.472546106717256[/C][/ROW]
[ROW][C]109[/C][C]0.511622461343456[/C][C]0.976755077313088[/C][C]0.488377538656544[/C][/ROW]
[ROW][C]110[/C][C]0.47422787078563[/C][C]0.94845574157126[/C][C]0.52577212921437[/C][/ROW]
[ROW][C]111[/C][C]0.451519676395938[/C][C]0.903039352791875[/C][C]0.548480323604062[/C][/ROW]
[ROW][C]112[/C][C]0.543842175710127[/C][C]0.912315648579745[/C][C]0.456157824289873[/C][/ROW]
[ROW][C]113[/C][C]0.504842847461544[/C][C]0.990314305076913[/C][C]0.495157152538456[/C][/ROW]
[ROW][C]114[/C][C]0.473412641448082[/C][C]0.946825282896164[/C][C]0.526587358551918[/C][/ROW]
[ROW][C]115[/C][C]0.432069157796274[/C][C]0.864138315592548[/C][C]0.567930842203726[/C][/ROW]
[ROW][C]116[/C][C]0.390345526688167[/C][C]0.780691053376334[/C][C]0.609654473311833[/C][/ROW]
[ROW][C]117[/C][C]0.516289602089651[/C][C]0.967420795820697[/C][C]0.483710397910349[/C][/ROW]
[ROW][C]118[/C][C]0.520642339531870[/C][C]0.958715320936259[/C][C]0.479357660468129[/C][/ROW]
[ROW][C]119[/C][C]0.568743276085664[/C][C]0.862513447828672[/C][C]0.431256723914336[/C][/ROW]
[ROW][C]120[/C][C]0.615328071844422[/C][C]0.769343856311156[/C][C]0.384671928155578[/C][/ROW]
[ROW][C]121[/C][C]0.652650489645383[/C][C]0.694699020709234[/C][C]0.347349510354617[/C][/ROW]
[ROW][C]122[/C][C]0.631643157429954[/C][C]0.736713685140092[/C][C]0.368356842570046[/C][/ROW]
[ROW][C]123[/C][C]0.613763812302218[/C][C]0.772472375395563[/C][C]0.386236187697782[/C][/ROW]
[ROW][C]124[/C][C]0.590569422698619[/C][C]0.818861154602762[/C][C]0.409430577301381[/C][/ROW]
[ROW][C]125[/C][C]0.589756896976108[/C][C]0.820486206047785[/C][C]0.410243103023892[/C][/ROW]
[ROW][C]126[/C][C]0.84015919623486[/C][C]0.319681607530280[/C][C]0.159840803765140[/C][/ROW]
[ROW][C]127[/C][C]0.815930993162076[/C][C]0.368138013675849[/C][C]0.184069006837924[/C][/ROW]
[ROW][C]128[/C][C]0.829992209716144[/C][C]0.340015580567711[/C][C]0.170007790283856[/C][/ROW]
[ROW][C]129[/C][C]0.800975707526272[/C][C]0.398048584947456[/C][C]0.199024292473728[/C][/ROW]
[ROW][C]130[/C][C]0.805811194181879[/C][C]0.388377611636242[/C][C]0.194188805818121[/C][/ROW]
[ROW][C]131[/C][C]0.842633509561012[/C][C]0.314732980877976[/C][C]0.157366490438988[/C][/ROW]
[ROW][C]132[/C][C]0.86791944359347[/C][C]0.264161112813059[/C][C]0.132080556406529[/C][/ROW]
[ROW][C]133[/C][C]0.841548519134557[/C][C]0.316902961730886[/C][C]0.158451480865443[/C][/ROW]
[ROW][C]134[/C][C]0.812315439523683[/C][C]0.375369120952634[/C][C]0.187684560476317[/C][/ROW]
[ROW][C]135[/C][C]0.84276556934146[/C][C]0.314468861317080[/C][C]0.157234430658540[/C][/ROW]
[ROW][C]136[/C][C]0.838116522146566[/C][C]0.323766955706868[/C][C]0.161883477853434[/C][/ROW]
[ROW][C]137[/C][C]0.823241926028893[/C][C]0.353516147942214[/C][C]0.176758073971107[/C][/ROW]
[ROW][C]138[/C][C]0.809258700459195[/C][C]0.381482599081609[/C][C]0.190741299540805[/C][/ROW]
[ROW][C]139[/C][C]0.837561487562319[/C][C]0.324877024875362[/C][C]0.162438512437681[/C][/ROW]
[ROW][C]140[/C][C]0.888128901029746[/C][C]0.223742197940508[/C][C]0.111871098970254[/C][/ROW]
[ROW][C]141[/C][C]0.906508421702725[/C][C]0.186983156594551[/C][C]0.0934915782972754[/C][/ROW]
[ROW][C]142[/C][C]0.912967351924274[/C][C]0.174065296151452[/C][C]0.0870326480757262[/C][/ROW]
[ROW][C]143[/C][C]0.962420768276333[/C][C]0.0751584634473333[/C][C]0.0375792317236666[/C][/ROW]
[ROW][C]144[/C][C]0.957835800505234[/C][C]0.084328398989532[/C][C]0.042164199494766[/C][/ROW]
[ROW][C]145[/C][C]0.996081812021198[/C][C]0.007836375957603[/C][C]0.0039181879788015[/C][/ROW]
[ROW][C]146[/C][C]0.999680268959467[/C][C]0.000639462081065248[/C][C]0.000319731040532624[/C][/ROW]
[ROW][C]147[/C][C]0.99998938485569[/C][C]2.12302886198245e-05[/C][C]1.06151443099122e-05[/C][/ROW]
[ROW][C]148[/C][C]0.999997049938924[/C][C]5.9001221519506e-06[/C][C]2.9500610759753e-06[/C][/ROW]
[ROW][C]149[/C][C]0.999999995716392[/C][C]8.56721525843379e-09[/C][C]4.28360762921689e-09[/C][/ROW]
[ROW][C]150[/C][C]0.999999996305827[/C][C]7.38834608377859e-09[/C][C]3.69417304188929e-09[/C][/ROW]
[ROW][C]151[/C][C]0.99999999501365[/C][C]9.97269972848212e-09[/C][C]4.98634986424106e-09[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.97211809558101e-28[/C][C]9.86059047790503e-29[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.79342634232052e-27[/C][C]8.96713171160262e-28[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.63849121687569e-26[/C][C]8.19245608437843e-27[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]6.31488591141798e-26[/C][C]3.15744295570899e-26[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]5.65211273469902e-26[/C][C]2.82605636734951e-26[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.75488531999251e-25[/C][C]8.77442659996257e-26[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.42145609548231e-24[/C][C]7.10728047741154e-25[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.34856560813031e-23[/C][C]6.74282804065154e-24[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.28973693201584e-22[/C][C]6.44868466007922e-23[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.19822851170118e-21[/C][C]5.99114255850592e-22[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]1.08336948582014e-20[/C][C]5.41684742910069e-21[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]7.72228051228693e-20[/C][C]3.86114025614347e-20[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]3.00243264016310e-19[/C][C]1.50121632008155e-19[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.48322475497325e-19[/C][C]7.41612377486624e-20[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]7.83125610163132e-19[/C][C]3.91562805081566e-19[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]7.49157140913349e-18[/C][C]3.74578570456674e-18[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]7.35901551977213e-17[/C][C]3.67950775988606e-17[/C][/ROW]
[ROW][C]169[/C][C]1[/C][C]7.21733212541535e-16[/C][C]3.60866606270767e-16[/C][/ROW]
[ROW][C]170[/C][C]0.999999999999998[/C][C]4.34233208701896e-15[/C][C]2.17116604350948e-15[/C][/ROW]
[ROW][C]171[/C][C]0.999999999999985[/C][C]3.06461143034374e-14[/C][C]1.53230571517187e-14[/C][/ROW]
[ROW][C]172[/C][C]0.999999999999942[/C][C]1.16353383088316e-13[/C][C]5.81766915441578e-14[/C][/ROW]
[ROW][C]173[/C][C]0.999999999999782[/C][C]4.36928453228393e-13[/C][C]2.18464226614196e-13[/C][/ROW]
[ROW][C]174[/C][C]0.999999999997889[/C][C]4.2218706624973e-12[/C][C]2.11093533124865e-12[/C][/ROW]
[ROW][C]175[/C][C]0.999999999985792[/C][C]2.84151886905057e-11[/C][C]1.42075943452529e-11[/C][/ROW]
[ROW][C]176[/C][C]0.9999999998756[/C][C]2.48799165963366e-10[/C][C]1.24399582981683e-10[/C][/ROW]
[ROW][C]177[/C][C]0.999999999221516[/C][C]1.55696890534207e-09[/C][C]7.78484452671035e-10[/C][/ROW]
[ROW][C]178[/C][C]0.99999999352842[/C][C]1.29431613466353e-08[/C][C]6.47158067331767e-09[/C][/ROW]
[ROW][C]179[/C][C]0.999999974981018[/C][C]5.00379630783757e-08[/C][C]2.50189815391879e-08[/C][/ROW]
[ROW][C]180[/C][C]0.999999935984714[/C][C]1.28030571956975e-07[/C][C]6.40152859784877e-08[/C][/ROW]
[ROW][C]181[/C][C]0.999999543192126[/C][C]9.13615748349212e-07[/C][C]4.56807874174606e-07[/C][/ROW]
[ROW][C]182[/C][C]0.999998879256905[/C][C]2.24148618897964e-06[/C][C]1.12074309448982e-06[/C][/ROW]
[ROW][C]183[/C][C]0.99999027810708[/C][C]1.94437858419535e-05[/C][C]9.72189292097676e-06[/C][/ROW]
[ROW][C]184[/C][C]0.999903575242423[/C][C]0.000192849515154594[/C][C]9.6424757577297e-05[/C][/ROW]
[ROW][C]185[/C][C]0.998802497832625[/C][C]0.00239500433474925[/C][C]0.00119750216737462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0001733003160171490.0003466006320342970.999826699683983
111.09596655118670e-052.19193310237339e-050.999989040334488
124.4972658468272e-078.9945316936544e-070.999999550273415
139.23387117627485e-081.84677423525497e-070.999999907661288
145.26853697282594e-091.05370739456519e-080.999999994731463
151.94165482223767e-093.88330964447534e-090.999999998058345
161.32325023848456e-102.64650047696912e-100.999999999867675
171.23875477757535e-112.47750955515070e-110.999999999987612
181.29386150494948e-122.58772300989896e-120.999999999998706
191.78041354711013e-133.56082709422026e-130.999999999999822
201.13842776791048e-142.27685553582096e-140.999999999999989
216.53066267983013e-161.30613253596603e-151
227.41432159546549e-171.4828643190931e-161
234.27471698570774e-188.54943397141549e-181
242.35921643593315e-194.7184328718663e-191
251.26725445490410e-202.53450890980819e-201
265.16731020361028e-201.03346204072206e-191
271.11516895865872e-192.23033791731743e-191
288.35863912376145e-211.67172782475229e-201
291.47617221958398e-212.95234443916797e-211
303.95025166909836e-227.90050333819672e-221
310.05342893665272910.1068578733054580.946571063347271
320.1336434435780230.2672868871560460.866356556421977
330.3529090595166130.7058181190332250.647090940483387
340.4119613547820420.8239227095640830.588038645217958
350.3729195716529330.7458391433058660.627080428347067
360.5247968927346760.9504062145306490.475203107265324
370.5249421219978520.9501157560042960.475057878002148
380.63538828306280.7292234338744010.364611716937201
390.759364926223680.481270147552640.24063507377632
400.9164987250011470.1670025499977060.0835012749988528
410.9763017051855630.04739658962887480.0236982948144374
420.987835665724880.02432866855024150.0121643342751208
430.9932069070473580.01358618590528390.00679309295264195
440.9991843079886880.001631384022624990.000815692011312493
450.9989020883883130.002195823223374280.00109791161168714
460.9997491689185180.0005016621629643960.000250831081482198
470.999722545127140.0005549097457199820.000277454872859991
480.9996092697101120.0007814605797765550.000390730289888278
490.999537363232490.0009252735350205740.000462636767510287
500.9993451650226780.001309669954644410.000654834977322207
510.9990304040534920.001939191893016540.000969595946508271
520.9987724505593880.002455098881223020.00122754944061151
530.9982673837152140.003465232569573020.00173261628478651
540.9983018021261050.003396395747790060.00169819787389503
550.997872516700590.004254966598821890.00212748329941094
560.997196978123550.005606043752900820.00280302187645041
570.9967566540666450.006486691866709730.00324334593335487
580.9955407246795690.008918550640862130.00445927532043107
590.9941308869380230.01173822612395350.00586911306197673
600.9921933726277150.01561325474456970.00780662737228484
610.9919139363867190.01617212722656190.00808606361328093
620.9928353977874470.01432920442510670.00716460221255335
630.9934122206519310.01317555869613800.00658777934806898
640.9930253785868230.01394924282635310.00697462141317653
650.991087010974340.01782597805131850.00891298902565924
660.9887700515183830.02245989696323450.0112299484816173
670.986532723323790.02693455335241880.0134672766762094
680.9849200265389010.03015994692219810.0150799734610990
690.980568150805560.03886369838888050.0194318491944402
700.980369205144920.03926158971016220.0196307948550811
710.9748394742471870.05032105150562540.0251605257528127
720.9691103551466270.06177928970674580.0308896448533729
730.962642330920550.0747153381588980.037357669079449
740.9570526289090650.08589474218187010.0429473710909351
750.9505030074706580.0989939850586840.049496992529342
760.9442191800266940.1115616399466120.0557808199733058
770.939380821417690.1212383571646180.0606191785823088
780.9286800932934220.1426398134131560.071319906706578
790.9160743937504570.1678512124990860.083925606249543
800.913862064873620.1722758702527580.086137935126379
810.91492223406320.1701555318735990.0850777659367993
820.925451415260380.149097169479240.07454858473962
830.91094312827210.17811374345580.0890568717279
840.9037393575064670.1925212849870670.0962606424935336
850.88983191116350.2203361776730000.110168088836500
860.8738741675565350.2522516648869290.126125832443465
870.8762633241833630.2474733516332740.123736675816637
880.8594602936211320.2810794127577370.140539706378868
890.8405110976137780.3189778047724440.159488902386222
900.8303790295007560.3392419409984880.169620970499244
910.8219016419756540.3561967160486910.178098358024346
920.79470440073340.4105911985332010.205295599266601
930.7778525798716050.4442948402567900.222147420128395
940.7514508733404610.4970982533190790.248549126659539
950.7531011069616620.4937977860766760.246898893038338
960.7223265871617210.5553468256765570.277673412838279
970.7056850816124020.5886298367751970.294314918387598
980.6882257069489760.6235485861020480.311774293051024
990.7155955486970110.5688089026059780.284404451302989
1000.6899220872689520.6201558254620970.310077912731049
1010.694971321700390.6100573565992190.305028678299610
1020.6576009830788230.6847980338423540.342399016921177
1030.6205880261759630.7588239476480740.379411973824037
1040.5837792148582910.8324415702834170.416220785141709
1050.6117662543937330.7764674912125350.388233745606267
1060.5872991134997160.8254017730005690.412700886500284
1070.549046915444560.901906169110880.45095308455544
1080.5274538932827440.9450922134345120.472546106717256
1090.5116224613434560.9767550773130880.488377538656544
1100.474227870785630.948455741571260.52577212921437
1110.4515196763959380.9030393527918750.548480323604062
1120.5438421757101270.9123156485797450.456157824289873
1130.5048428474615440.9903143050769130.495157152538456
1140.4734126414480820.9468252828961640.526587358551918
1150.4320691577962740.8641383155925480.567930842203726
1160.3903455266881670.7806910533763340.609654473311833
1170.5162896020896510.9674207958206970.483710397910349
1180.5206423395318700.9587153209362590.479357660468129
1190.5687432760856640.8625134478286720.431256723914336
1200.6153280718444220.7693438563111560.384671928155578
1210.6526504896453830.6946990207092340.347349510354617
1220.6316431574299540.7367136851400920.368356842570046
1230.6137638123022180.7724723753955630.386236187697782
1240.5905694226986190.8188611546027620.409430577301381
1250.5897568969761080.8204862060477850.410243103023892
1260.840159196234860.3196816075302800.159840803765140
1270.8159309931620760.3681380136758490.184069006837924
1280.8299922097161440.3400155805677110.170007790283856
1290.8009757075262720.3980485849474560.199024292473728
1300.8058111941818790.3883776116362420.194188805818121
1310.8426335095610120.3147329808779760.157366490438988
1320.867919443593470.2641611128130590.132080556406529
1330.8415485191345570.3169029617308860.158451480865443
1340.8123154395236830.3753691209526340.187684560476317
1350.842765569341460.3144688613170800.157234430658540
1360.8381165221465660.3237669557068680.161883477853434
1370.8232419260288930.3535161479422140.176758073971107
1380.8092587004591950.3814825990816090.190741299540805
1390.8375614875623190.3248770248753620.162438512437681
1400.8881289010297460.2237421979405080.111871098970254
1410.9065084217027250.1869831565945510.0934915782972754
1420.9129673519242740.1740652961514520.0870326480757262
1430.9624207682763330.07515846344733330.0375792317236666
1440.9578358005052340.0843283989895320.042164199494766
1450.9960818120211980.0078363759576030.0039181879788015
1460.9996802689594670.0006394620810652480.000319731040532624
1470.999989384855692.12302886198245e-051.06151443099122e-05
1480.9999970499389245.9001221519506e-062.9500610759753e-06
1490.9999999957163928.56721525843379e-094.28360762921689e-09
1500.9999999963058277.38834608377859e-093.69417304188929e-09
1510.999999995013659.97269972848212e-094.98634986424106e-09
15211.97211809558101e-289.86059047790503e-29
15311.79342634232052e-278.96713171160262e-28
15411.63849121687569e-268.19245608437843e-27
15516.31488591141798e-263.15744295570899e-26
15615.65211273469902e-262.82605636734951e-26
15711.75488531999251e-258.77442659996257e-26
15811.42145609548231e-247.10728047741154e-25
15911.34856560813031e-236.74282804065154e-24
16011.28973693201584e-226.44868466007922e-23
16111.19822851170118e-215.99114255850592e-22
16211.08336948582014e-205.41684742910069e-21
16317.72228051228693e-203.86114025614347e-20
16413.00243264016310e-191.50121632008155e-19
16511.48322475497325e-197.41612377486624e-20
16617.83125610163132e-193.91562805081566e-19
16717.49157140913349e-183.74578570456674e-18
16817.35901551977213e-173.67950775988606e-17
16917.21733212541535e-163.60866606270767e-16
1700.9999999999999984.34233208701896e-152.17116604350948e-15
1710.9999999999999853.06461143034374e-141.53230571517187e-14
1720.9999999999999421.16353383088316e-135.81766915441578e-14
1730.9999999999997824.36928453228393e-132.18464226614196e-13
1740.9999999999978894.2218706624973e-122.11093533124865e-12
1750.9999999999857922.84151886905057e-111.42075943452529e-11
1760.99999999987562.48799165963366e-101.24399582981683e-10
1770.9999999992215161.55696890534207e-097.78484452671035e-10
1780.999999993528421.29431613466353e-086.47158067331767e-09
1790.9999999749810185.00379630783757e-082.50189815391879e-08
1800.9999999359847141.28030571956975e-076.40152859784877e-08
1810.9999995431921269.13615748349212e-074.56807874174606e-07
1820.9999988792569052.24148618897964e-061.12074309448982e-06
1830.999990278107081.94437858419535e-059.72189292097676e-06
1840.9999035752424230.0001928495151545949.6424757577297e-05
1850.9988024978326250.002395004334749250.00119750216737462






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level770.4375NOK
5% type I error level920.522727272727273NOK
10% type I error level990.5625NOK

\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 & 77 & 0.4375 & NOK \tabularnewline
5% type I error level & 92 & 0.522727272727273 & NOK \tabularnewline
10% type I error level & 99 & 0.5625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103907&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]77[/C][C]0.4375[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]92[/C][C]0.522727272727273[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]99[/C][C]0.5625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103907&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103907&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level770.4375NOK
5% type I error level920.522727272727273NOK
10% type I error level990.5625NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}