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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 computationTue, 28 Dec 2010 11:58:43 +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/28/t1293537380ual4nrmee42ous4.htm/, Retrieved Sun, 05 May 2024 03:30:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116303, Retrieved Sun, 05 May 2024 03:30:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-26 20:18:12] [dcd1a35a8985187cb1e9de87792355b2]
-   P   [Multiple Regression] [] [2010-12-26 20:25:18] [dcd1a35a8985187cb1e9de87792355b2]
-   PD    [Multiple Regression] [] [2010-12-26 20:50:02] [dcd1a35a8985187cb1e9de87792355b2]
-             [Multiple Regression] [] [2010-12-28 11:58:43] [82d760768aff8bf374d9817688c406af] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22	27	5	26	49	35
23	36	4	25	45	34
27	25	4	17	54	13
19	27	3	37	36	35
15	25	3	35	36	28
29	44	3	15	53	32
25	50	4	27	46	35
25	41	4	36	42	36
21	48	5	25	41	27
22	43	4	30	45	29
22	47	2	27	47	27
24	41	3	33	42	28
22	44	2	29	45	29
23	47	5	30	40	28
19	40	3	25	45	30
19	46	3	23	40	25
21	28	3	26	42	15
20	56	3	24	45	33
23	49	4	35	47	31
11	25	4	39	31	37
21	41	4	23	46	37
19	26	3	32	34	34
21	50	5	29	43	32
23	47	4	26	45	21
19	52	2	21	42	25
22	37	5	35	51	32
19	41	3	23	44	28
23	45	4	21	47	22
29	26	4	28	47	25
27		3	30	41	26
18	52	4	21	44	34
30	46	2	29	51	34
26	58	3	28	46	36
20	54	5	19	47	36
22	29	3	26	46	26
20	50	3	33	38	26
21	43	2	34	50	34
18	30	3	33	48	33
21	47	2	40	36	31
27	45	3	24	51	33
	48	1	35	35	22
18	48	3	35	49	29
24	26	4	32	38	24
24	46	5	20	47	37
17		3	35	36	32
22	50	3	35	47	23
21	25	4	21	46	29
23	47	2	33	43	35
19	47	2	40	53	20
22	41	3	22	55	28
19	45	2	35	39	26
24	41	4	20	55	36
22	45	5	28	41	26
26	40	3	46	33	33
22	29	4	18	52	25
23	34	5	22	42	29
27	45	5	20	56	32
21	52	3	25	46	35
16	41	4	31	33	24
21	48	3	21	51	31
18	45	3	23	46	29
25	54	2	26	46	27
20	25	3	34	50	29
24	26	4	31	46	29
20	28	4	23	51	27
24	50	4	31	48	34
23	48	4	26	44	32
23	51	3	36	38	31
22	53	3	28	42	31
22	37	3	34	39	31
20	56	2	25	45	16
14	43	3	33	31	25
21	34	3	46	29	27
23	42	3	24	48	32
17	32	3	32	38	28
25	31	5	33	55	25
10	46	3	42	32	25
25	30	5	17	51	36
23	47	4	36	53	36
27	33	4	40	47	36
16	25	4	30	45	27
19	25	5	19	33	29
23	21	4	33	49	32
19	36	5	35	46	29
19	50	3	23	42	31
26	48	3	15	56	34
19	48	2	38	35	27
22	25	3	37	40	28
21	48	4	23	44	32
22	49	5	41	46	33
20	27	5	34	46	29
20	28	3	38	39	32
20	43	2	45	35	35
21	48	3	27	48	33
21	48	4	46	42	27
14	25	1	26	39	16
28	49	4	44	39	32
24	26	3	36	41	26
24	51	3	20	52	32
24	25	4	44	45	38
19	29	3	27	42	24
19	29	4	27	44	26
14	43	2	41	33	19
29	46	3	30	42	37
22	44	3	33	46	25
21	25	3	37	45	24
15	51	2	30	40	23
23	42	5	20	48	28
24	53	5	44	32	38
20	25	4	20	53	28
25	49	2	33	39	28
25	51	3	31	45	26
19	20	3	23	36	21
23	44	3	33	38	35
22	38	4	33	49	31
19	46	5	32	46	34
24	42	4	25	43	30
21	29		22	37	30
19	46	4	16	48	24
21	49	2	36	45	27
18	51	3	35	32	26
24	38	3	25	46	30
7	41	1	27	20	15
24	47	3	32	42	28
24	44	3	36	45	34
23	47	3	51	29	29
24	46	3	30	51	26
27	44	4	20	55	31
20	28	3	29	50	28
20	47	4	26	44	33
22	28	4	20	41	32
19	41	5	40	40	33
18	45	4	29	47	31
14	46	4	32	42	37
24	46	4	33	40	27
29	22	3	32	51	19
25	33	3	34	43	27
24	41	4	24	45	31
20	47	5	25	41	38
18	25	3	41	41	22
25	42	3	39	37	35
21	47	3	21	46	35
21	50	3	38	38	30
21	55	5	28	39	41
23	21	3	37	45	25
18		3	26	46	28
23	52	3	30	39	45
13	49	4	25	21	21
23	46	4	38	31	33
17		4	31	35	25
24	45	3	31	49	29
16	52	3	27	40	31
23		3	21	45	29
20	40	4	26	46	31
24	49	4	37	45	31
15	38	5	28	34	25
20	32	5	29	41	27
27	46	4	33	43	26
27	32	3	41	45	26
19	41	3	19	48	23
22	43	3	37	43	27
16	44	4	36	45	24
21	47	5	27	45	35
18	28	3	33	34	24
22	52	1	29	40	32
18	27	2	42	40	24
24	45	5	27	55	24
24	27	4	47	44	38
19	25	4	17	44	36
26	28	4	34	48	24
28	25	3	32	51	18
23	52	4	25	49	34
22	44	3	27	33	23
20	43	3	37	43	35
20	47	4	34	44	22
27	52	4	27	44	34
19	40	2	37	41	28
23	42	3	32	45	34
19	45	5	26	44	32
21	45	2	29	44	24
13	50	5	28	40	34
18	49	3	19	48	33
19	52	2	46	49	33
23	48	3	31	46	29
30	51	3	42	49	38
22	49	4	33	55	24
23	31	4	39	51	25
22	43	3	27	46	37
22	31	3	35	37	33
23	28	4	23	43	30
27	43	4	32	41	22
23	31	3	22	45	28
18	51	3	17	39	24
24	58	4	35	38	33
19	25	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 time12 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 & 12 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=116303&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]12 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=116303&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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 time12 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
Behoefte_affiliatie[t] = + 59.8331365247397 -0.321873599043689leeftijd[t] -0.0894242784873798opleiding[t] + 0.0106074503726078Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Behoefte_affiliatie[t] =  +  59.8331365247397 -0.321873599043689leeftijd[t] -0.0894242784873798opleiding[t] +  0.0106074503726078Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Behoefte_affiliatie[t] =  +  59.8331365247397 -0.321873599043689leeftijd[t] -0.0894242784873798opleiding[t] +  0.0106074503726078Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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
Behoefte_affiliatie[t] = + 59.8331365247397 -0.321873599043689leeftijd[t] -0.0894242784873798opleiding[t] + 0.0106074503726078Neuroticisme[t] -0.3095406594854Extraversie[t] -0.305320732396517`Openheid `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)59.83313652473977.0645928.469400
leeftijd-0.3218735990436890.062584-5.14311e-060
opleiding-0.08942427848737980.078225-1.14320.2544120.127206
Neuroticisme0.01060745037260780.0911740.11630.9075040.453752
Extraversie-0.30954065948540.058358-5.304200
`Openheid `-0.3053207323965170.074876-4.07776.7e-053.3e-05

\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) & 59.8331365247397 & 7.064592 & 8.4694 & 0 & 0 \tabularnewline
leeftijd & -0.321873599043689 & 0.062584 & -5.1431 & 1e-06 & 0 \tabularnewline
opleiding & -0.0894242784873798 & 0.078225 & -1.1432 & 0.254412 & 0.127206 \tabularnewline
Neuroticisme & 0.0106074503726078 & 0.091174 & 0.1163 & 0.907504 & 0.453752 \tabularnewline
Extraversie & -0.3095406594854 & 0.058358 & -5.3042 & 0 & 0 \tabularnewline
`Openheid
` & -0.305320732396517 & 0.074876 & -4.0777 & 6.7e-05 & 3.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&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]59.8331365247397[/C][C]7.064592[/C][C]8.4694[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.321873599043689[/C][C]0.062584[/C][C]-5.1431[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.0894242784873798[/C][C]0.078225[/C][C]-1.1432[/C][C]0.254412[/C][C]0.127206[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.0106074503726078[/C][C]0.091174[/C][C]0.1163[/C][C]0.907504[/C][C]0.453752[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.3095406594854[/C][C]0.058358[/C][C]-5.3042[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Openheid
`[/C][C]-0.305320732396517[/C][C]0.074876[/C][C]-4.0777[/C][C]6.7e-05[/C][C]3.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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)59.83313652473977.0645928.469400
leeftijd-0.3218735990436890.062584-5.14311e-060
opleiding-0.08942427848737980.078225-1.14320.2544120.127206
Neuroticisme0.01060745037260780.0911740.11630.9075040.453752
Extraversie-0.30954065948540.058358-5.304200
`Openheid `-0.3053207323965170.074876-4.07776.7e-053.3e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.601830538297624
R-squared0.362199996827607
Adjusted R-squared0.345326980870666
F-TEST (value)21.466227362785
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.32437127245557
Sum Squared Residuals16432.3970294264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.601830538297624 \tabularnewline
R-squared & 0.362199996827607 \tabularnewline
Adjusted R-squared & 0.345326980870666 \tabularnewline
F-TEST (value) & 21.466227362785 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.32437127245557 \tabularnewline
Sum Squared Residuals & 16432.3970294264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.601830538297624[/C][/ROW]
[ROW][C]R-squared[/C][C]0.362199996827607[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.345326980870666[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.466227362785[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/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]9.32437127245557[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16432.3970294264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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 R0.601830538297624
R-squared0.362199996827607
Adjusted R-squared0.345326980870666
F-TEST (value)21.466227362785
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.32437127245557
Sum Squared Residuals16432.3970294264






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12225.1175037191476-3.11750371914759
22323.8429415262079-0.84294152620789
32730.9245609576659-3.92456095766589
41929.4370628035319-10.4370628035319
51532.1968402276497-17.1968402276497
62919.38561869752969.6143813024704
72518.74306464845966.25693535154041
82522.66823599875132.33176400124866
92123.2664418239135-2.26644182391350
102223.1694672467477-1.16946724674773
112222.0205592022521-0.0205592022521470
122425.1684037852930-1.16840378529303
132223.0158347543062-1.01583475430620
142323.6455726019091-0.645572601909113
151923.8661543381066-4.86615433810662
161924.9880048025089-5.98800480250886
172133.2476779414074-12.2476779414074
182017.78960710584542.21039289415456
192320.06154012058482.93845987941519
201130.9496624565111-19.9496624565111
212120.98685577356930.0131442264306762
221930.6303012020799-11.6303012020799
232120.51943944636320.480560553636825
242324.2821089082547-1.28210890825468
251922.5058912670181-3.50589126701809
262222.2911156602836-0.291115660283576
271924.4432479625962-5.44324796259615
282323.9484168031117-0.948416803111705
292929.2223051403605-0.222305140360495
302743.0758625085064-16.0758625085064
315237.450455702325514.5495442976745
324638.67434375470827.3256562452918
335839.601700257697118.3982997423029
345439.162737551575814.8372624484242
352942.8759554095259-13.8759554095259
365041.85980512473698.14019487526313
374340.65918077107082.34081922892918
383039.7990950120652-9.79909501206515
394738.15486618181768.84513381818241
404532.392076094863412.6079239051366
41125.4439476935796-24.4439476935796
42329.1260721031755-26.1260721031755
43424.9159080638744-20.9159080638744
44543.4070457103041-38.4070457103041
453529.22447878430855.7755212156915
463533.91127600558021.08872399441979
472127.5185657905591-6.51856579055908
483327.90521111527545.09478888472457
494027.611584877782912.3884151222171
502225.2877791951336-3.28777919513362
513531.2971637618193.70283623818099
522023.6882451211629-3.68824512116286
532831.2894928563588-3.28949285635876
544636.29570841974919.70429158025093
551829.0530876863671-11.0530876863671
562228.5516091100055-6.55160911000551
572022.1573180340913-2.15731803409132
582528.1543704591459-3.15437045914591
593131.5139676779368-0.513967677936798
602125.9910725730771-4.99107257307712
612325.3729960749065-2.37299607490649
622634.1701657726983-8.17016577269829
633432.13139122915731.86860877084271
643132.7573745048708-1.75737450487081
652324.5593903594388-1.55939035943877
663125.50751507575645.49248492424363
672626.3505567828462-0.350556782846199
683627.74153388625238.2584661137477
692831.4066900418439-3.40669004184394
703426.77514414040377.22485585959628
712529.8403298621303-4.84032986213031
723336.4018598303324-3.40185983033239
734634.411648096307011.5883519036930
742430.8806902146584-6.88069021465839
753234.240882116718-2.24088211671803
763324.84572358536048.15427641463962
774236.74993720592775.2500627940723
781724.6725863811359-7.67258638113594
793628.40483821733467.59516178266541
804032.69572313338127.30427686661877
813033.8707904565763-3.87079045657634
821939.1403382599544-20.1403382599544
833328.73122741362534.26877258637471
843526.24219327821598.75780672178407
852328.0441725889950-5.04417258899498
861523.5007379467095-8.50073794670946
873837.73199026292410.268009737075918
883728.59783463828498.40216536171506
892326.3483891866514-3.34838918665136
904132.40389731801028.59610268198982
913433.06269523726730.93730476273267
923830.70974843522657.29025156477347
934529.886553416488215.1134465835118
942725.57572445349851.42427554650147
954636.00465693143049.99534306856957
962629.7575110723000-3.75751107230004
974435.70904871557228.29095128442776
983627.86333070127428.13666929872585
992031.5289118550932-11.5289118550932
1004432.259602220066611.7403977799334
1012734.1718421836245-7.17184218362446
1022729.5732814086967-2.57328140869672
1034132.66502999232548.33497000767465
1043028.70335975452191.29664024547813
1053334.3596217800457-1.35962178004566
1063726.964538541117210.0354614588828
1073030.6180941559829-0.618094155982934
1082024.2016441672297-4.20164416722974
1094437.38740836355226.61259163644776
1102024.7570084575150-4.75700845751505
1113328.33883679275944.66116320724062
1123136.1185594972837-5.11855949728375
1132332.0759972549547-9.07599725495469
1143331.72162593218731.27837406781273
1153325.72524509725877.27475490274133
1163228.01911368112883.98088631887125
1172527.8388646312644-2.83886463126441
1183742.8425156230228-5.84251562302282
1194839.13939768246858.86060231753151
1204538.45904469445596.54095530554411
1213241.1596830916966-9.15968309169657
1224641.43266363510294.56733636489714
1232042.658514607755-22.658514607755
1244237.22105253948324.77894746051685
1254530.831246588878514.1687534111215
1262938.7523182355638-9.75231823556378
1275142.17211796096738.82788203903274
1285538.580654777115516.4193452228845
1295040.35423866903039.64576133096974
1304440.19640495413663.80359504586339
1314134.50849293607066.4915070639294
1324037.98654213285182.01345786714819
1334738.08263169807188.91736830192824
1344234.95182658653937.04817341346068
1354038.08372376747861.91627623252136
1365140.522450163083310.4775498369167
1374340.86541191668182.13458808331818
1384539.38439794481035.61560205518969
1394132.81071700090848.18928299909159
1404138.12569275732312.87430724267686
1413739.847843518705-2.84784351870504
1424634.689213419082111.3107865809179
1433838.7857446711579-0.785744671157934
1443932.57682793943616.42317206056392
1454528.115671050132216.8843289498678
1462826.61807526848271.38192473151729
1474537.15916802531867.84083197468136
1482127.1314689730685-6.13146897306853
1493335.8654777966963-2.86547779669632
1502421.38758913957872.61241086042126
1511621.2672686153581-5.26726861535806
1522342.383847373134-19.383847373134
1534039.78509558346920.214904416530849
1544941.5387076613047.46129233869598
1553842.2356109094779-4.23561090947788
1563239.4641123378523-7.46411233785233
1574639.75904438317716.24095561682288
1583241.8293045142391-9.82930451423912
1594143.8411207733459-2.84112077334594
1604342.77220826514760.227791734852427
1614441.96299216181012.03700783818990
1624739.9569520120037.04304798799702
1632842.1311359098207-14.1311359098207
1645241.941182577796110.0588174222039
1652741.1011942399206-14.1011942399206
1664541.63604937568943.36395062431065
1672737.2457898800738-10.2457898800738
1682538.4103544268903-13.4103544268903
1692840.0364179431271-12.0364179431271
1702543.9528100691583-18.9528100691583
1715239.588361699411212.4116383005888
1724443.57725625465080.422743745349176
1734339.07460005967833.92539994032169
1744740.91839019300396.08160980699605
1755240.272438087762911.7275619122371
1764040.626081177185-0.62608117718503
1774240.15779774474231.84220225525766
1784540.04842862138444.9515713786156
1794545.6646677181087-0.664667718108656
1805040.12403114113809.87596885886204
1814941.66167637568157.3383236243185
1825238.358418976352513.6415810236475
1834838.44700471466769.55299528533236
1845137.151859926227813.8481400737722
1854941.72669803620537.27330196379472
1863141.1435026367017-10.1435026367017
1874338.77094241190614.22905758809391
1883138.8929230361987-7.89292303619867
1892839.4151245301093-11.4151245301093
1904342.28669932844700.713300671553051
1913143.2146052189251-12.2146052189250
1925143.00432015268887.9956798473112
1935839.802939817113718.1970601828863
1942537.448188012544-12.4481880125440
1952738.5622024299982-11.5622024299982

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22 & 25.1175037191476 & -3.11750371914759 \tabularnewline
2 & 23 & 23.8429415262079 & -0.84294152620789 \tabularnewline
3 & 27 & 30.9245609576659 & -3.92456095766589 \tabularnewline
4 & 19 & 29.4370628035319 & -10.4370628035319 \tabularnewline
5 & 15 & 32.1968402276497 & -17.1968402276497 \tabularnewline
6 & 29 & 19.3856186975296 & 9.6143813024704 \tabularnewline
7 & 25 & 18.7430646484596 & 6.25693535154041 \tabularnewline
8 & 25 & 22.6682359987513 & 2.33176400124866 \tabularnewline
9 & 21 & 23.2664418239135 & -2.26644182391350 \tabularnewline
10 & 22 & 23.1694672467477 & -1.16946724674773 \tabularnewline
11 & 22 & 22.0205592022521 & -0.0205592022521470 \tabularnewline
12 & 24 & 25.1684037852930 & -1.16840378529303 \tabularnewline
13 & 22 & 23.0158347543062 & -1.01583475430620 \tabularnewline
14 & 23 & 23.6455726019091 & -0.645572601909113 \tabularnewline
15 & 19 & 23.8661543381066 & -4.86615433810662 \tabularnewline
16 & 19 & 24.9880048025089 & -5.98800480250886 \tabularnewline
17 & 21 & 33.2476779414074 & -12.2476779414074 \tabularnewline
18 & 20 & 17.7896071058454 & 2.21039289415456 \tabularnewline
19 & 23 & 20.0615401205848 & 2.93845987941519 \tabularnewline
20 & 11 & 30.9496624565111 & -19.9496624565111 \tabularnewline
21 & 21 & 20.9868557735693 & 0.0131442264306762 \tabularnewline
22 & 19 & 30.6303012020799 & -11.6303012020799 \tabularnewline
23 & 21 & 20.5194394463632 & 0.480560553636825 \tabularnewline
24 & 23 & 24.2821089082547 & -1.28210890825468 \tabularnewline
25 & 19 & 22.5058912670181 & -3.50589126701809 \tabularnewline
26 & 22 & 22.2911156602836 & -0.291115660283576 \tabularnewline
27 & 19 & 24.4432479625962 & -5.44324796259615 \tabularnewline
28 & 23 & 23.9484168031117 & -0.948416803111705 \tabularnewline
29 & 29 & 29.2223051403605 & -0.222305140360495 \tabularnewline
30 & 27 & 43.0758625085064 & -16.0758625085064 \tabularnewline
31 & 52 & 37.4504557023255 & 14.5495442976745 \tabularnewline
32 & 46 & 38.6743437547082 & 7.3256562452918 \tabularnewline
33 & 58 & 39.6017002576971 & 18.3982997423029 \tabularnewline
34 & 54 & 39.1627375515758 & 14.8372624484242 \tabularnewline
35 & 29 & 42.8759554095259 & -13.8759554095259 \tabularnewline
36 & 50 & 41.8598051247369 & 8.14019487526313 \tabularnewline
37 & 43 & 40.6591807710708 & 2.34081922892918 \tabularnewline
38 & 30 & 39.7990950120652 & -9.79909501206515 \tabularnewline
39 & 47 & 38.1548661818176 & 8.84513381818241 \tabularnewline
40 & 45 & 32.3920760948634 & 12.6079239051366 \tabularnewline
41 & 1 & 25.4439476935796 & -24.4439476935796 \tabularnewline
42 & 3 & 29.1260721031755 & -26.1260721031755 \tabularnewline
43 & 4 & 24.9159080638744 & -20.9159080638744 \tabularnewline
44 & 5 & 43.4070457103041 & -38.4070457103041 \tabularnewline
45 & 35 & 29.2244787843085 & 5.7755212156915 \tabularnewline
46 & 35 & 33.9112760055802 & 1.08872399441979 \tabularnewline
47 & 21 & 27.5185657905591 & -6.51856579055908 \tabularnewline
48 & 33 & 27.9052111152754 & 5.09478888472457 \tabularnewline
49 & 40 & 27.6115848777829 & 12.3884151222171 \tabularnewline
50 & 22 & 25.2877791951336 & -3.28777919513362 \tabularnewline
51 & 35 & 31.297163761819 & 3.70283623818099 \tabularnewline
52 & 20 & 23.6882451211629 & -3.68824512116286 \tabularnewline
53 & 28 & 31.2894928563588 & -3.28949285635876 \tabularnewline
54 & 46 & 36.2957084197491 & 9.70429158025093 \tabularnewline
55 & 18 & 29.0530876863671 & -11.0530876863671 \tabularnewline
56 & 22 & 28.5516091100055 & -6.55160911000551 \tabularnewline
57 & 20 & 22.1573180340913 & -2.15731803409132 \tabularnewline
58 & 25 & 28.1543704591459 & -3.15437045914591 \tabularnewline
59 & 31 & 31.5139676779368 & -0.513967677936798 \tabularnewline
60 & 21 & 25.9910725730771 & -4.99107257307712 \tabularnewline
61 & 23 & 25.3729960749065 & -2.37299607490649 \tabularnewline
62 & 26 & 34.1701657726983 & -8.17016577269829 \tabularnewline
63 & 34 & 32.1313912291573 & 1.86860877084271 \tabularnewline
64 & 31 & 32.7573745048708 & -1.75737450487081 \tabularnewline
65 & 23 & 24.5593903594388 & -1.55939035943877 \tabularnewline
66 & 31 & 25.5075150757564 & 5.49248492424363 \tabularnewline
67 & 26 & 26.3505567828462 & -0.350556782846199 \tabularnewline
68 & 36 & 27.7415338862523 & 8.2584661137477 \tabularnewline
69 & 28 & 31.4066900418439 & -3.40669004184394 \tabularnewline
70 & 34 & 26.7751441404037 & 7.22485585959628 \tabularnewline
71 & 25 & 29.8403298621303 & -4.84032986213031 \tabularnewline
72 & 33 & 36.4018598303324 & -3.40185983033239 \tabularnewline
73 & 46 & 34.4116480963070 & 11.5883519036930 \tabularnewline
74 & 24 & 30.8806902146584 & -6.88069021465839 \tabularnewline
75 & 32 & 34.240882116718 & -2.24088211671803 \tabularnewline
76 & 33 & 24.8457235853604 & 8.15427641463962 \tabularnewline
77 & 42 & 36.7499372059277 & 5.2500627940723 \tabularnewline
78 & 17 & 24.6725863811359 & -7.67258638113594 \tabularnewline
79 & 36 & 28.4048382173346 & 7.59516178266541 \tabularnewline
80 & 40 & 32.6957231333812 & 7.30427686661877 \tabularnewline
81 & 30 & 33.8707904565763 & -3.87079045657634 \tabularnewline
82 & 19 & 39.1403382599544 & -20.1403382599544 \tabularnewline
83 & 33 & 28.7312274136253 & 4.26877258637471 \tabularnewline
84 & 35 & 26.2421932782159 & 8.75780672178407 \tabularnewline
85 & 23 & 28.0441725889950 & -5.04417258899498 \tabularnewline
86 & 15 & 23.5007379467095 & -8.50073794670946 \tabularnewline
87 & 38 & 37.7319902629241 & 0.268009737075918 \tabularnewline
88 & 37 & 28.5978346382849 & 8.40216536171506 \tabularnewline
89 & 23 & 26.3483891866514 & -3.34838918665136 \tabularnewline
90 & 41 & 32.4038973180102 & 8.59610268198982 \tabularnewline
91 & 34 & 33.0626952372673 & 0.93730476273267 \tabularnewline
92 & 38 & 30.7097484352265 & 7.29025156477347 \tabularnewline
93 & 45 & 29.8865534164882 & 15.1134465835118 \tabularnewline
94 & 27 & 25.5757244534985 & 1.42427554650147 \tabularnewline
95 & 46 & 36.0046569314304 & 9.99534306856957 \tabularnewline
96 & 26 & 29.7575110723000 & -3.75751107230004 \tabularnewline
97 & 44 & 35.7090487155722 & 8.29095128442776 \tabularnewline
98 & 36 & 27.8633307012742 & 8.13666929872585 \tabularnewline
99 & 20 & 31.5289118550932 & -11.5289118550932 \tabularnewline
100 & 44 & 32.2596022200666 & 11.7403977799334 \tabularnewline
101 & 27 & 34.1718421836245 & -7.17184218362446 \tabularnewline
102 & 27 & 29.5732814086967 & -2.57328140869672 \tabularnewline
103 & 41 & 32.6650299923254 & 8.33497000767465 \tabularnewline
104 & 30 & 28.7033597545219 & 1.29664024547813 \tabularnewline
105 & 33 & 34.3596217800457 & -1.35962178004566 \tabularnewline
106 & 37 & 26.9645385411172 & 10.0354614588828 \tabularnewline
107 & 30 & 30.6180941559829 & -0.618094155982934 \tabularnewline
108 & 20 & 24.2016441672297 & -4.20164416722974 \tabularnewline
109 & 44 & 37.3874083635522 & 6.61259163644776 \tabularnewline
110 & 20 & 24.7570084575150 & -4.75700845751505 \tabularnewline
111 & 33 & 28.3388367927594 & 4.66116320724062 \tabularnewline
112 & 31 & 36.1185594972837 & -5.11855949728375 \tabularnewline
113 & 23 & 32.0759972549547 & -9.07599725495469 \tabularnewline
114 & 33 & 31.7216259321873 & 1.27837406781273 \tabularnewline
115 & 33 & 25.7252450972587 & 7.27475490274133 \tabularnewline
116 & 32 & 28.0191136811288 & 3.98088631887125 \tabularnewline
117 & 25 & 27.8388646312644 & -2.83886463126441 \tabularnewline
118 & 37 & 42.8425156230228 & -5.84251562302282 \tabularnewline
119 & 48 & 39.1393976824685 & 8.86060231753151 \tabularnewline
120 & 45 & 38.4590446944559 & 6.54095530554411 \tabularnewline
121 & 32 & 41.1596830916966 & -9.15968309169657 \tabularnewline
122 & 46 & 41.4326636351029 & 4.56733636489714 \tabularnewline
123 & 20 & 42.658514607755 & -22.658514607755 \tabularnewline
124 & 42 & 37.2210525394832 & 4.77894746051685 \tabularnewline
125 & 45 & 30.8312465888785 & 14.1687534111215 \tabularnewline
126 & 29 & 38.7523182355638 & -9.75231823556378 \tabularnewline
127 & 51 & 42.1721179609673 & 8.82788203903274 \tabularnewline
128 & 55 & 38.5806547771155 & 16.4193452228845 \tabularnewline
129 & 50 & 40.3542386690303 & 9.64576133096974 \tabularnewline
130 & 44 & 40.1964049541366 & 3.80359504586339 \tabularnewline
131 & 41 & 34.5084929360706 & 6.4915070639294 \tabularnewline
132 & 40 & 37.9865421328518 & 2.01345786714819 \tabularnewline
133 & 47 & 38.0826316980718 & 8.91736830192824 \tabularnewline
134 & 42 & 34.9518265865393 & 7.04817341346068 \tabularnewline
135 & 40 & 38.0837237674786 & 1.91627623252136 \tabularnewline
136 & 51 & 40.5224501630833 & 10.4775498369167 \tabularnewline
137 & 43 & 40.8654119166818 & 2.13458808331818 \tabularnewline
138 & 45 & 39.3843979448103 & 5.61560205518969 \tabularnewline
139 & 41 & 32.8107170009084 & 8.18928299909159 \tabularnewline
140 & 41 & 38.1256927573231 & 2.87430724267686 \tabularnewline
141 & 37 & 39.847843518705 & -2.84784351870504 \tabularnewline
142 & 46 & 34.6892134190821 & 11.3107865809179 \tabularnewline
143 & 38 & 38.7857446711579 & -0.785744671157934 \tabularnewline
144 & 39 & 32.5768279394361 & 6.42317206056392 \tabularnewline
145 & 45 & 28.1156710501322 & 16.8843289498678 \tabularnewline
146 & 28 & 26.6180752684827 & 1.38192473151729 \tabularnewline
147 & 45 & 37.1591680253186 & 7.84083197468136 \tabularnewline
148 & 21 & 27.1314689730685 & -6.13146897306853 \tabularnewline
149 & 33 & 35.8654777966963 & -2.86547779669632 \tabularnewline
150 & 24 & 21.3875891395787 & 2.61241086042126 \tabularnewline
151 & 16 & 21.2672686153581 & -5.26726861535806 \tabularnewline
152 & 23 & 42.383847373134 & -19.383847373134 \tabularnewline
153 & 40 & 39.7850955834692 & 0.214904416530849 \tabularnewline
154 & 49 & 41.538707661304 & 7.46129233869598 \tabularnewline
155 & 38 & 42.2356109094779 & -4.23561090947788 \tabularnewline
156 & 32 & 39.4641123378523 & -7.46411233785233 \tabularnewline
157 & 46 & 39.7590443831771 & 6.24095561682288 \tabularnewline
158 & 32 & 41.8293045142391 & -9.82930451423912 \tabularnewline
159 & 41 & 43.8411207733459 & -2.84112077334594 \tabularnewline
160 & 43 & 42.7722082651476 & 0.227791734852427 \tabularnewline
161 & 44 & 41.9629921618101 & 2.03700783818990 \tabularnewline
162 & 47 & 39.956952012003 & 7.04304798799702 \tabularnewline
163 & 28 & 42.1311359098207 & -14.1311359098207 \tabularnewline
164 & 52 & 41.9411825777961 & 10.0588174222039 \tabularnewline
165 & 27 & 41.1011942399206 & -14.1011942399206 \tabularnewline
166 & 45 & 41.6360493756894 & 3.36395062431065 \tabularnewline
167 & 27 & 37.2457898800738 & -10.2457898800738 \tabularnewline
168 & 25 & 38.4103544268903 & -13.4103544268903 \tabularnewline
169 & 28 & 40.0364179431271 & -12.0364179431271 \tabularnewline
170 & 25 & 43.9528100691583 & -18.9528100691583 \tabularnewline
171 & 52 & 39.5883616994112 & 12.4116383005888 \tabularnewline
172 & 44 & 43.5772562546508 & 0.422743745349176 \tabularnewline
173 & 43 & 39.0746000596783 & 3.92539994032169 \tabularnewline
174 & 47 & 40.9183901930039 & 6.08160980699605 \tabularnewline
175 & 52 & 40.2724380877629 & 11.7275619122371 \tabularnewline
176 & 40 & 40.626081177185 & -0.62608117718503 \tabularnewline
177 & 42 & 40.1577977447423 & 1.84220225525766 \tabularnewline
178 & 45 & 40.0484286213844 & 4.9515713786156 \tabularnewline
179 & 45 & 45.6646677181087 & -0.664667718108656 \tabularnewline
180 & 50 & 40.1240311411380 & 9.87596885886204 \tabularnewline
181 & 49 & 41.6616763756815 & 7.3383236243185 \tabularnewline
182 & 52 & 38.3584189763525 & 13.6415810236475 \tabularnewline
183 & 48 & 38.4470047146676 & 9.55299528533236 \tabularnewline
184 & 51 & 37.1518599262278 & 13.8481400737722 \tabularnewline
185 & 49 & 41.7266980362053 & 7.27330196379472 \tabularnewline
186 & 31 & 41.1435026367017 & -10.1435026367017 \tabularnewline
187 & 43 & 38.7709424119061 & 4.22905758809391 \tabularnewline
188 & 31 & 38.8929230361987 & -7.89292303619867 \tabularnewline
189 & 28 & 39.4151245301093 & -11.4151245301093 \tabularnewline
190 & 43 & 42.2866993284470 & 0.713300671553051 \tabularnewline
191 & 31 & 43.2146052189251 & -12.2146052189250 \tabularnewline
192 & 51 & 43.0043201526888 & 7.9956798473112 \tabularnewline
193 & 58 & 39.8029398171137 & 18.1970601828863 \tabularnewline
194 & 25 & 37.448188012544 & -12.4481880125440 \tabularnewline
195 & 27 & 38.5622024299982 & -11.5622024299982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&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]22[/C][C]25.1175037191476[/C][C]-3.11750371914759[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]23.8429415262079[/C][C]-0.84294152620789[/C][/ROW]
[ROW][C]3[/C][C]27[/C][C]30.9245609576659[/C][C]-3.92456095766589[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]29.4370628035319[/C][C]-10.4370628035319[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]32.1968402276497[/C][C]-17.1968402276497[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]19.3856186975296[/C][C]9.6143813024704[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]18.7430646484596[/C][C]6.25693535154041[/C][/ROW]
[ROW][C]8[/C][C]25[/C][C]22.6682359987513[/C][C]2.33176400124866[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]23.2664418239135[/C][C]-2.26644182391350[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]23.1694672467477[/C][C]-1.16946724674773[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]22.0205592022521[/C][C]-0.0205592022521470[/C][/ROW]
[ROW][C]12[/C][C]24[/C][C]25.1684037852930[/C][C]-1.16840378529303[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]23.0158347543062[/C][C]-1.01583475430620[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]23.6455726019091[/C][C]-0.645572601909113[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]23.8661543381066[/C][C]-4.86615433810662[/C][/ROW]
[ROW][C]16[/C][C]19[/C][C]24.9880048025089[/C][C]-5.98800480250886[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]33.2476779414074[/C][C]-12.2476779414074[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]17.7896071058454[/C][C]2.21039289415456[/C][/ROW]
[ROW][C]19[/C][C]23[/C][C]20.0615401205848[/C][C]2.93845987941519[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]30.9496624565111[/C][C]-19.9496624565111[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]20.9868557735693[/C][C]0.0131442264306762[/C][/ROW]
[ROW][C]22[/C][C]19[/C][C]30.6303012020799[/C][C]-11.6303012020799[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.5194394463632[/C][C]0.480560553636825[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]24.2821089082547[/C][C]-1.28210890825468[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]22.5058912670181[/C][C]-3.50589126701809[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]22.2911156602836[/C][C]-0.291115660283576[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]24.4432479625962[/C][C]-5.44324796259615[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]23.9484168031117[/C][C]-0.948416803111705[/C][/ROW]
[ROW][C]29[/C][C]29[/C][C]29.2223051403605[/C][C]-0.222305140360495[/C][/ROW]
[ROW][C]30[/C][C]27[/C][C]43.0758625085064[/C][C]-16.0758625085064[/C][/ROW]
[ROW][C]31[/C][C]52[/C][C]37.4504557023255[/C][C]14.5495442976745[/C][/ROW]
[ROW][C]32[/C][C]46[/C][C]38.6743437547082[/C][C]7.3256562452918[/C][/ROW]
[ROW][C]33[/C][C]58[/C][C]39.6017002576971[/C][C]18.3982997423029[/C][/ROW]
[ROW][C]34[/C][C]54[/C][C]39.1627375515758[/C][C]14.8372624484242[/C][/ROW]
[ROW][C]35[/C][C]29[/C][C]42.8759554095259[/C][C]-13.8759554095259[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]41.8598051247369[/C][C]8.14019487526313[/C][/ROW]
[ROW][C]37[/C][C]43[/C][C]40.6591807710708[/C][C]2.34081922892918[/C][/ROW]
[ROW][C]38[/C][C]30[/C][C]39.7990950120652[/C][C]-9.79909501206515[/C][/ROW]
[ROW][C]39[/C][C]47[/C][C]38.1548661818176[/C][C]8.84513381818241[/C][/ROW]
[ROW][C]40[/C][C]45[/C][C]32.3920760948634[/C][C]12.6079239051366[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]25.4439476935796[/C][C]-24.4439476935796[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]29.1260721031755[/C][C]-26.1260721031755[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]24.9159080638744[/C][C]-20.9159080638744[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]43.4070457103041[/C][C]-38.4070457103041[/C][/ROW]
[ROW][C]45[/C][C]35[/C][C]29.2244787843085[/C][C]5.7755212156915[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]33.9112760055802[/C][C]1.08872399441979[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]27.5185657905591[/C][C]-6.51856579055908[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]27.9052111152754[/C][C]5.09478888472457[/C][/ROW]
[ROW][C]49[/C][C]40[/C][C]27.6115848777829[/C][C]12.3884151222171[/C][/ROW]
[ROW][C]50[/C][C]22[/C][C]25.2877791951336[/C][C]-3.28777919513362[/C][/ROW]
[ROW][C]51[/C][C]35[/C][C]31.297163761819[/C][C]3.70283623818099[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]23.6882451211629[/C][C]-3.68824512116286[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]31.2894928563588[/C][C]-3.28949285635876[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]36.2957084197491[/C][C]9.70429158025093[/C][/ROW]
[ROW][C]55[/C][C]18[/C][C]29.0530876863671[/C][C]-11.0530876863671[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]28.5516091100055[/C][C]-6.55160911000551[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]22.1573180340913[/C][C]-2.15731803409132[/C][/ROW]
[ROW][C]58[/C][C]25[/C][C]28.1543704591459[/C][C]-3.15437045914591[/C][/ROW]
[ROW][C]59[/C][C]31[/C][C]31.5139676779368[/C][C]-0.513967677936798[/C][/ROW]
[ROW][C]60[/C][C]21[/C][C]25.9910725730771[/C][C]-4.99107257307712[/C][/ROW]
[ROW][C]61[/C][C]23[/C][C]25.3729960749065[/C][C]-2.37299607490649[/C][/ROW]
[ROW][C]62[/C][C]26[/C][C]34.1701657726983[/C][C]-8.17016577269829[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]32.1313912291573[/C][C]1.86860877084271[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]32.7573745048708[/C][C]-1.75737450487081[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]24.5593903594388[/C][C]-1.55939035943877[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]25.5075150757564[/C][C]5.49248492424363[/C][/ROW]
[ROW][C]67[/C][C]26[/C][C]26.3505567828462[/C][C]-0.350556782846199[/C][/ROW]
[ROW][C]68[/C][C]36[/C][C]27.7415338862523[/C][C]8.2584661137477[/C][/ROW]
[ROW][C]69[/C][C]28[/C][C]31.4066900418439[/C][C]-3.40669004184394[/C][/ROW]
[ROW][C]70[/C][C]34[/C][C]26.7751441404037[/C][C]7.22485585959628[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]29.8403298621303[/C][C]-4.84032986213031[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]36.4018598303324[/C][C]-3.40185983033239[/C][/ROW]
[ROW][C]73[/C][C]46[/C][C]34.4116480963070[/C][C]11.5883519036930[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]30.8806902146584[/C][C]-6.88069021465839[/C][/ROW]
[ROW][C]75[/C][C]32[/C][C]34.240882116718[/C][C]-2.24088211671803[/C][/ROW]
[ROW][C]76[/C][C]33[/C][C]24.8457235853604[/C][C]8.15427641463962[/C][/ROW]
[ROW][C]77[/C][C]42[/C][C]36.7499372059277[/C][C]5.2500627940723[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]24.6725863811359[/C][C]-7.67258638113594[/C][/ROW]
[ROW][C]79[/C][C]36[/C][C]28.4048382173346[/C][C]7.59516178266541[/C][/ROW]
[ROW][C]80[/C][C]40[/C][C]32.6957231333812[/C][C]7.30427686661877[/C][/ROW]
[ROW][C]81[/C][C]30[/C][C]33.8707904565763[/C][C]-3.87079045657634[/C][/ROW]
[ROW][C]82[/C][C]19[/C][C]39.1403382599544[/C][C]-20.1403382599544[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]28.7312274136253[/C][C]4.26877258637471[/C][/ROW]
[ROW][C]84[/C][C]35[/C][C]26.2421932782159[/C][C]8.75780672178407[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]28.0441725889950[/C][C]-5.04417258899498[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]23.5007379467095[/C][C]-8.50073794670946[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]37.7319902629241[/C][C]0.268009737075918[/C][/ROW]
[ROW][C]88[/C][C]37[/C][C]28.5978346382849[/C][C]8.40216536171506[/C][/ROW]
[ROW][C]89[/C][C]23[/C][C]26.3483891866514[/C][C]-3.34838918665136[/C][/ROW]
[ROW][C]90[/C][C]41[/C][C]32.4038973180102[/C][C]8.59610268198982[/C][/ROW]
[ROW][C]91[/C][C]34[/C][C]33.0626952372673[/C][C]0.93730476273267[/C][/ROW]
[ROW][C]92[/C][C]38[/C][C]30.7097484352265[/C][C]7.29025156477347[/C][/ROW]
[ROW][C]93[/C][C]45[/C][C]29.8865534164882[/C][C]15.1134465835118[/C][/ROW]
[ROW][C]94[/C][C]27[/C][C]25.5757244534985[/C][C]1.42427554650147[/C][/ROW]
[ROW][C]95[/C][C]46[/C][C]36.0046569314304[/C][C]9.99534306856957[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]29.7575110723000[/C][C]-3.75751107230004[/C][/ROW]
[ROW][C]97[/C][C]44[/C][C]35.7090487155722[/C][C]8.29095128442776[/C][/ROW]
[ROW][C]98[/C][C]36[/C][C]27.8633307012742[/C][C]8.13666929872585[/C][/ROW]
[ROW][C]99[/C][C]20[/C][C]31.5289118550932[/C][C]-11.5289118550932[/C][/ROW]
[ROW][C]100[/C][C]44[/C][C]32.2596022200666[/C][C]11.7403977799334[/C][/ROW]
[ROW][C]101[/C][C]27[/C][C]34.1718421836245[/C][C]-7.17184218362446[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]29.5732814086967[/C][C]-2.57328140869672[/C][/ROW]
[ROW][C]103[/C][C]41[/C][C]32.6650299923254[/C][C]8.33497000767465[/C][/ROW]
[ROW][C]104[/C][C]30[/C][C]28.7033597545219[/C][C]1.29664024547813[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]34.3596217800457[/C][C]-1.35962178004566[/C][/ROW]
[ROW][C]106[/C][C]37[/C][C]26.9645385411172[/C][C]10.0354614588828[/C][/ROW]
[ROW][C]107[/C][C]30[/C][C]30.6180941559829[/C][C]-0.618094155982934[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]24.2016441672297[/C][C]-4.20164416722974[/C][/ROW]
[ROW][C]109[/C][C]44[/C][C]37.3874083635522[/C][C]6.61259163644776[/C][/ROW]
[ROW][C]110[/C][C]20[/C][C]24.7570084575150[/C][C]-4.75700845751505[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]28.3388367927594[/C][C]4.66116320724062[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]36.1185594972837[/C][C]-5.11855949728375[/C][/ROW]
[ROW][C]113[/C][C]23[/C][C]32.0759972549547[/C][C]-9.07599725495469[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]31.7216259321873[/C][C]1.27837406781273[/C][/ROW]
[ROW][C]115[/C][C]33[/C][C]25.7252450972587[/C][C]7.27475490274133[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]28.0191136811288[/C][C]3.98088631887125[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]27.8388646312644[/C][C]-2.83886463126441[/C][/ROW]
[ROW][C]118[/C][C]37[/C][C]42.8425156230228[/C][C]-5.84251562302282[/C][/ROW]
[ROW][C]119[/C][C]48[/C][C]39.1393976824685[/C][C]8.86060231753151[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]38.4590446944559[/C][C]6.54095530554411[/C][/ROW]
[ROW][C]121[/C][C]32[/C][C]41.1596830916966[/C][C]-9.15968309169657[/C][/ROW]
[ROW][C]122[/C][C]46[/C][C]41.4326636351029[/C][C]4.56733636489714[/C][/ROW]
[ROW][C]123[/C][C]20[/C][C]42.658514607755[/C][C]-22.658514607755[/C][/ROW]
[ROW][C]124[/C][C]42[/C][C]37.2210525394832[/C][C]4.77894746051685[/C][/ROW]
[ROW][C]125[/C][C]45[/C][C]30.8312465888785[/C][C]14.1687534111215[/C][/ROW]
[ROW][C]126[/C][C]29[/C][C]38.7523182355638[/C][C]-9.75231823556378[/C][/ROW]
[ROW][C]127[/C][C]51[/C][C]42.1721179609673[/C][C]8.82788203903274[/C][/ROW]
[ROW][C]128[/C][C]55[/C][C]38.5806547771155[/C][C]16.4193452228845[/C][/ROW]
[ROW][C]129[/C][C]50[/C][C]40.3542386690303[/C][C]9.64576133096974[/C][/ROW]
[ROW][C]130[/C][C]44[/C][C]40.1964049541366[/C][C]3.80359504586339[/C][/ROW]
[ROW][C]131[/C][C]41[/C][C]34.5084929360706[/C][C]6.4915070639294[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]37.9865421328518[/C][C]2.01345786714819[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]38.0826316980718[/C][C]8.91736830192824[/C][/ROW]
[ROW][C]134[/C][C]42[/C][C]34.9518265865393[/C][C]7.04817341346068[/C][/ROW]
[ROW][C]135[/C][C]40[/C][C]38.0837237674786[/C][C]1.91627623252136[/C][/ROW]
[ROW][C]136[/C][C]51[/C][C]40.5224501630833[/C][C]10.4775498369167[/C][/ROW]
[ROW][C]137[/C][C]43[/C][C]40.8654119166818[/C][C]2.13458808331818[/C][/ROW]
[ROW][C]138[/C][C]45[/C][C]39.3843979448103[/C][C]5.61560205518969[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]32.8107170009084[/C][C]8.18928299909159[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]38.1256927573231[/C][C]2.87430724267686[/C][/ROW]
[ROW][C]141[/C][C]37[/C][C]39.847843518705[/C][C]-2.84784351870504[/C][/ROW]
[ROW][C]142[/C][C]46[/C][C]34.6892134190821[/C][C]11.3107865809179[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]38.7857446711579[/C][C]-0.785744671157934[/C][/ROW]
[ROW][C]144[/C][C]39[/C][C]32.5768279394361[/C][C]6.42317206056392[/C][/ROW]
[ROW][C]145[/C][C]45[/C][C]28.1156710501322[/C][C]16.8843289498678[/C][/ROW]
[ROW][C]146[/C][C]28[/C][C]26.6180752684827[/C][C]1.38192473151729[/C][/ROW]
[ROW][C]147[/C][C]45[/C][C]37.1591680253186[/C][C]7.84083197468136[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]27.1314689730685[/C][C]-6.13146897306853[/C][/ROW]
[ROW][C]149[/C][C]33[/C][C]35.8654777966963[/C][C]-2.86547779669632[/C][/ROW]
[ROW][C]150[/C][C]24[/C][C]21.3875891395787[/C][C]2.61241086042126[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]21.2672686153581[/C][C]-5.26726861535806[/C][/ROW]
[ROW][C]152[/C][C]23[/C][C]42.383847373134[/C][C]-19.383847373134[/C][/ROW]
[ROW][C]153[/C][C]40[/C][C]39.7850955834692[/C][C]0.214904416530849[/C][/ROW]
[ROW][C]154[/C][C]49[/C][C]41.538707661304[/C][C]7.46129233869598[/C][/ROW]
[ROW][C]155[/C][C]38[/C][C]42.2356109094779[/C][C]-4.23561090947788[/C][/ROW]
[ROW][C]156[/C][C]32[/C][C]39.4641123378523[/C][C]-7.46411233785233[/C][/ROW]
[ROW][C]157[/C][C]46[/C][C]39.7590443831771[/C][C]6.24095561682288[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]41.8293045142391[/C][C]-9.82930451423912[/C][/ROW]
[ROW][C]159[/C][C]41[/C][C]43.8411207733459[/C][C]-2.84112077334594[/C][/ROW]
[ROW][C]160[/C][C]43[/C][C]42.7722082651476[/C][C]0.227791734852427[/C][/ROW]
[ROW][C]161[/C][C]44[/C][C]41.9629921618101[/C][C]2.03700783818990[/C][/ROW]
[ROW][C]162[/C][C]47[/C][C]39.956952012003[/C][C]7.04304798799702[/C][/ROW]
[ROW][C]163[/C][C]28[/C][C]42.1311359098207[/C][C]-14.1311359098207[/C][/ROW]
[ROW][C]164[/C][C]52[/C][C]41.9411825777961[/C][C]10.0588174222039[/C][/ROW]
[ROW][C]165[/C][C]27[/C][C]41.1011942399206[/C][C]-14.1011942399206[/C][/ROW]
[ROW][C]166[/C][C]45[/C][C]41.6360493756894[/C][C]3.36395062431065[/C][/ROW]
[ROW][C]167[/C][C]27[/C][C]37.2457898800738[/C][C]-10.2457898800738[/C][/ROW]
[ROW][C]168[/C][C]25[/C][C]38.4103544268903[/C][C]-13.4103544268903[/C][/ROW]
[ROW][C]169[/C][C]28[/C][C]40.0364179431271[/C][C]-12.0364179431271[/C][/ROW]
[ROW][C]170[/C][C]25[/C][C]43.9528100691583[/C][C]-18.9528100691583[/C][/ROW]
[ROW][C]171[/C][C]52[/C][C]39.5883616994112[/C][C]12.4116383005888[/C][/ROW]
[ROW][C]172[/C][C]44[/C][C]43.5772562546508[/C][C]0.422743745349176[/C][/ROW]
[ROW][C]173[/C][C]43[/C][C]39.0746000596783[/C][C]3.92539994032169[/C][/ROW]
[ROW][C]174[/C][C]47[/C][C]40.9183901930039[/C][C]6.08160980699605[/C][/ROW]
[ROW][C]175[/C][C]52[/C][C]40.2724380877629[/C][C]11.7275619122371[/C][/ROW]
[ROW][C]176[/C][C]40[/C][C]40.626081177185[/C][C]-0.62608117718503[/C][/ROW]
[ROW][C]177[/C][C]42[/C][C]40.1577977447423[/C][C]1.84220225525766[/C][/ROW]
[ROW][C]178[/C][C]45[/C][C]40.0484286213844[/C][C]4.9515713786156[/C][/ROW]
[ROW][C]179[/C][C]45[/C][C]45.6646677181087[/C][C]-0.664667718108656[/C][/ROW]
[ROW][C]180[/C][C]50[/C][C]40.1240311411380[/C][C]9.87596885886204[/C][/ROW]
[ROW][C]181[/C][C]49[/C][C]41.6616763756815[/C][C]7.3383236243185[/C][/ROW]
[ROW][C]182[/C][C]52[/C][C]38.3584189763525[/C][C]13.6415810236475[/C][/ROW]
[ROW][C]183[/C][C]48[/C][C]38.4470047146676[/C][C]9.55299528533236[/C][/ROW]
[ROW][C]184[/C][C]51[/C][C]37.1518599262278[/C][C]13.8481400737722[/C][/ROW]
[ROW][C]185[/C][C]49[/C][C]41.7266980362053[/C][C]7.27330196379472[/C][/ROW]
[ROW][C]186[/C][C]31[/C][C]41.1435026367017[/C][C]-10.1435026367017[/C][/ROW]
[ROW][C]187[/C][C]43[/C][C]38.7709424119061[/C][C]4.22905758809391[/C][/ROW]
[ROW][C]188[/C][C]31[/C][C]38.8929230361987[/C][C]-7.89292303619867[/C][/ROW]
[ROW][C]189[/C][C]28[/C][C]39.4151245301093[/C][C]-11.4151245301093[/C][/ROW]
[ROW][C]190[/C][C]43[/C][C]42.2866993284470[/C][C]0.713300671553051[/C][/ROW]
[ROW][C]191[/C][C]31[/C][C]43.2146052189251[/C][C]-12.2146052189250[/C][/ROW]
[ROW][C]192[/C][C]51[/C][C]43.0043201526888[/C][C]7.9956798473112[/C][/ROW]
[ROW][C]193[/C][C]58[/C][C]39.8029398171137[/C][C]18.1970601828863[/C][/ROW]
[ROW][C]194[/C][C]25[/C][C]37.448188012544[/C][C]-12.4481880125440[/C][/ROW]
[ROW][C]195[/C][C]27[/C][C]38.5622024299982[/C][C]-11.5622024299982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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
12225.1175037191476-3.11750371914759
22323.8429415262079-0.84294152620789
32730.9245609576659-3.92456095766589
41929.4370628035319-10.4370628035319
51532.1968402276497-17.1968402276497
62919.38561869752969.6143813024704
72518.74306464845966.25693535154041
82522.66823599875132.33176400124866
92123.2664418239135-2.26644182391350
102223.1694672467477-1.16946724674773
112222.0205592022521-0.0205592022521470
122425.1684037852930-1.16840378529303
132223.0158347543062-1.01583475430620
142323.6455726019091-0.645572601909113
151923.8661543381066-4.86615433810662
161924.9880048025089-5.98800480250886
172133.2476779414074-12.2476779414074
182017.78960710584542.21039289415456
192320.06154012058482.93845987941519
201130.9496624565111-19.9496624565111
212120.98685577356930.0131442264306762
221930.6303012020799-11.6303012020799
232120.51943944636320.480560553636825
242324.2821089082547-1.28210890825468
251922.5058912670181-3.50589126701809
262222.2911156602836-0.291115660283576
271924.4432479625962-5.44324796259615
282323.9484168031117-0.948416803111705
292929.2223051403605-0.222305140360495
302743.0758625085064-16.0758625085064
315237.450455702325514.5495442976745
324638.67434375470827.3256562452918
335839.601700257697118.3982997423029
345439.162737551575814.8372624484242
352942.8759554095259-13.8759554095259
365041.85980512473698.14019487526313
374340.65918077107082.34081922892918
383039.7990950120652-9.79909501206515
394738.15486618181768.84513381818241
404532.392076094863412.6079239051366
41125.4439476935796-24.4439476935796
42329.1260721031755-26.1260721031755
43424.9159080638744-20.9159080638744
44543.4070457103041-38.4070457103041
453529.22447878430855.7755212156915
463533.91127600558021.08872399441979
472127.5185657905591-6.51856579055908
483327.90521111527545.09478888472457
494027.611584877782912.3884151222171
502225.2877791951336-3.28777919513362
513531.2971637618193.70283623818099
522023.6882451211629-3.68824512116286
532831.2894928563588-3.28949285635876
544636.29570841974919.70429158025093
551829.0530876863671-11.0530876863671
562228.5516091100055-6.55160911000551
572022.1573180340913-2.15731803409132
582528.1543704591459-3.15437045914591
593131.5139676779368-0.513967677936798
602125.9910725730771-4.99107257307712
612325.3729960749065-2.37299607490649
622634.1701657726983-8.17016577269829
633432.13139122915731.86860877084271
643132.7573745048708-1.75737450487081
652324.5593903594388-1.55939035943877
663125.50751507575645.49248492424363
672626.3505567828462-0.350556782846199
683627.74153388625238.2584661137477
692831.4066900418439-3.40669004184394
703426.77514414040377.22485585959628
712529.8403298621303-4.84032986213031
723336.4018598303324-3.40185983033239
734634.411648096307011.5883519036930
742430.8806902146584-6.88069021465839
753234.240882116718-2.24088211671803
763324.84572358536048.15427641463962
774236.74993720592775.2500627940723
781724.6725863811359-7.67258638113594
793628.40483821733467.59516178266541
804032.69572313338127.30427686661877
813033.8707904565763-3.87079045657634
821939.1403382599544-20.1403382599544
833328.73122741362534.26877258637471
843526.24219327821598.75780672178407
852328.0441725889950-5.04417258899498
861523.5007379467095-8.50073794670946
873837.73199026292410.268009737075918
883728.59783463828498.40216536171506
892326.3483891866514-3.34838918665136
904132.40389731801028.59610268198982
913433.06269523726730.93730476273267
923830.70974843522657.29025156477347
934529.886553416488215.1134465835118
942725.57572445349851.42427554650147
954636.00465693143049.99534306856957
962629.7575110723000-3.75751107230004
974435.70904871557228.29095128442776
983627.86333070127428.13666929872585
992031.5289118550932-11.5289118550932
1004432.259602220066611.7403977799334
1012734.1718421836245-7.17184218362446
1022729.5732814086967-2.57328140869672
1034132.66502999232548.33497000767465
1043028.70335975452191.29664024547813
1053334.3596217800457-1.35962178004566
1063726.964538541117210.0354614588828
1073030.6180941559829-0.618094155982934
1082024.2016441672297-4.20164416722974
1094437.38740836355226.61259163644776
1102024.7570084575150-4.75700845751505
1113328.33883679275944.66116320724062
1123136.1185594972837-5.11855949728375
1132332.0759972549547-9.07599725495469
1143331.72162593218731.27837406781273
1153325.72524509725877.27475490274133
1163228.01911368112883.98088631887125
1172527.8388646312644-2.83886463126441
1183742.8425156230228-5.84251562302282
1194839.13939768246858.86060231753151
1204538.45904469445596.54095530554411
1213241.1596830916966-9.15968309169657
1224641.43266363510294.56733636489714
1232042.658514607755-22.658514607755
1244237.22105253948324.77894746051685
1254530.831246588878514.1687534111215
1262938.7523182355638-9.75231823556378
1275142.17211796096738.82788203903274
1285538.580654777115516.4193452228845
1295040.35423866903039.64576133096974
1304440.19640495413663.80359504586339
1314134.50849293607066.4915070639294
1324037.98654213285182.01345786714819
1334738.08263169807188.91736830192824
1344234.95182658653937.04817341346068
1354038.08372376747861.91627623252136
1365140.522450163083310.4775498369167
1374340.86541191668182.13458808331818
1384539.38439794481035.61560205518969
1394132.81071700090848.18928299909159
1404138.12569275732312.87430724267686
1413739.847843518705-2.84784351870504
1424634.689213419082111.3107865809179
1433838.7857446711579-0.785744671157934
1443932.57682793943616.42317206056392
1454528.115671050132216.8843289498678
1462826.61807526848271.38192473151729
1474537.15916802531867.84083197468136
1482127.1314689730685-6.13146897306853
1493335.8654777966963-2.86547779669632
1502421.38758913957872.61241086042126
1511621.2672686153581-5.26726861535806
1522342.383847373134-19.383847373134
1534039.78509558346920.214904416530849
1544941.5387076613047.46129233869598
1553842.2356109094779-4.23561090947788
1563239.4641123378523-7.46411233785233
1574639.75904438317716.24095561682288
1583241.8293045142391-9.82930451423912
1594143.8411207733459-2.84112077334594
1604342.77220826514760.227791734852427
1614441.96299216181012.03700783818990
1624739.9569520120037.04304798799702
1632842.1311359098207-14.1311359098207
1645241.941182577796110.0588174222039
1652741.1011942399206-14.1011942399206
1664541.63604937568943.36395062431065
1672737.2457898800738-10.2457898800738
1682538.4103544268903-13.4103544268903
1692840.0364179431271-12.0364179431271
1702543.9528100691583-18.9528100691583
1715239.588361699411212.4116383005888
1724443.57725625465080.422743745349176
1734339.07460005967833.92539994032169
1744740.91839019300396.08160980699605
1755240.272438087762911.7275619122371
1764040.626081177185-0.62608117718503
1774240.15779774474231.84220225525766
1784540.04842862138444.9515713786156
1794545.6646677181087-0.664667718108656
1805040.12403114113809.87596885886204
1814941.66167637568157.3383236243185
1825238.358418976352513.6415810236475
1834838.44700471466769.55299528533236
1845137.151859926227813.8481400737722
1854941.72669803620537.27330196379472
1863141.1435026367017-10.1435026367017
1874338.77094241190614.22905758809391
1883138.8929230361987-7.89292303619867
1892839.4151245301093-11.4151245301093
1904342.28669932844700.713300671553051
1913143.2146052189251-12.2146052189250
1925143.00432015268887.9956798473112
1935839.802939817113718.1970601828863
1942537.448188012544-12.4481880125440
1952738.5622024299982-11.5622024299982






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.01145892327362450.0229178465472490.988541076726375
100.005580255084646560.01116051016929310.994419744915353
110.003830394951385410.007660789902770820.996169605048615
120.001446881691108900.002893763382217790.99855311830889
130.0004068296906227460.0008136593812454930.999593170309377
140.0001108453200228620.0002216906400457230.999889154679977
157.23420617892117e-050.0001446841235784230.99992765793821
161.75447938266853e-053.50895876533705e-050.999982455206173
175.37635136478092e-061.07527027295618e-050.999994623648635
182.89050727484676e-065.78101454969352e-060.999997109492725
191.20016921465064e-062.40033842930127e-060.999998799830785
209.13359193218533e-071.82671838643707e-060.999999086640807
212.61232875895234e-075.22465751790467e-070.999999738767124
222.52252598181453e-075.04505196362905e-070.999999747747402
237.28640359549451e-081.45728071909890e-070.999999927135964
241.84663738701327e-083.69327477402655e-080.999999981533626
255.44503040070167e-091.08900608014033e-080.99999999455497
264.41030311973868e-098.82060623947737e-090.999999995589697
271.95896596257071e-093.91793192514142e-090.999999998041034
285.15437507545988e-101.03087501509198e-090.999999999484563
291.68819821438689e-093.37639642877377e-090.999999998311802
305.97728836986143e-101.19545767397229e-090.999999999402271
310.0001506816645327530.0003013633290655070.999849318335467
328.04157568769302e-050.0001608315137538600.999919584243123
330.0003659071099302150.0007318142198604310.99963409289007
340.001647329585041560.003294659170083110.998352670414958
350.002663321124423540.005326642248847070.997336678875576
360.002758548276660560.005517096553321130.99724145172334
370.004570073596382140.009140147192764290.995429926403618
380.0244512042985660.0489024085971320.975548795701434
390.01834191306157650.03668382612315290.981658086938423
400.01465260367781120.02930520735562250.985347396322189
410.04461983549027190.08923967098054380.955380164509728
420.1543526594979820.3087053189959650.845647340502018
430.1944954064896870.3889908129793740.805504593510313
440.3794246752763580.7588493505527170.620575324723642
450.3365929552365330.6731859104730660.663407044763467
460.7802383812630820.4395232374738370.219761618736918
470.7705109611154850.458978077769030.229489038884515
480.7431219071644850.513756185671030.256878092835515
490.842645505336920.3147089893261600.157354494663080
500.815705503819520.3685889923609610.184294496180480
510.7956943690302840.4086112619394320.204305630969716
520.7744545300198640.4510909399602730.225545469980136
530.7386582097333680.5226835805332650.261341790266632
540.8652085778887780.2695828442224440.134791422111222
550.855211145068370.2895777098632580.144788854931629
560.8560807565885060.2878384868229870.143919243411494
570.8443511105909670.3112977788180660.155648889409033
580.818575428099170.3628491438016610.181424571900831
590.7913918252118410.4172163495763180.208608174788159
600.7676274514122940.4647450971754120.232372548587706
610.763725584282320.472548831435360.23627441571768
620.7607276491621360.4785447016757280.239272350837864
630.7926213191611820.4147573616776360.207378680838818
640.7854981822013880.4290036355972240.214501817798612
650.7588638528346710.4822722943306570.241136147165329
660.7296985227489090.5406029545021830.270301477251091
670.6976887222051810.6046225555896380.302311277794819
680.6705091697279110.6589816605441790.329490830272089
690.6340033364609590.7319933270780820.365996663539041
700.5992892304148620.8014215391702760.400710769585138
710.5652256227820440.8695487544359130.434774377217956
720.5259812483444870.9480375033110250.474018751655513
730.5571203989054750.8857592021890490.442879601094525
740.5310714331946770.9378571336106460.468928566805323
750.4967245435716770.9934490871433540.503275456428323
760.4975102186618580.9950204373237150.502489781338142
770.5026291752231360.9947416495537280.497370824776864
780.506425799985830.987148400028340.49357420001417
790.5345128147011360.9309743705977280.465487185298864
800.5973395850832530.8053208298334950.402660414916747
810.5679743958868760.8640512082262470.432025604113124
820.6602304954978440.6795390090043120.339769504502156
830.6423772413587080.7152455172825840.357622758641292
840.6221470619979980.7557058760040050.377852938002002
850.6119803703959520.7760392592080960.388019629604048
860.6288448101885920.7423103796228170.371155189811408
870.603137017927940.7937259641441210.396862982072060
880.5820966521661640.8358066956676720.417903347833836
890.558679888516550.88264022296690.44132011148345
900.5981570962200240.8036858075599510.401842903779976
910.5703241980178440.8593516039643130.429675801982156
920.5482742532700930.9034514934598140.451725746729907
930.5937088658824070.8125822682351860.406291134117593
940.5526818640052970.8946362719894060.447318135994703
950.5989642361095760.8020715277808480.401035763890424
960.5788046377310230.8423907245379540.421195362268977
970.5971376621099160.8057246757801680.402862337890084
980.5757269424536080.8485461150927830.424273057546392
990.585142003008530.829715993982940.41485799699147
1000.6366055336629420.7267889326741150.363394466337058
1010.6150445118132010.7699109763735980.384955488186799
1020.5818110708045920.8363778583908150.418188929195408
1030.5682678482017760.8634643035964480.431732151798224
1040.5267289341565010.9465421316869980.473271065843499
1050.4890101526964460.9780203053928920.510989847303554
1060.4840850905159250.968170181031850.515914909484075
1070.4432511969017210.8865023938034420.556748803098279
1080.4231130511259580.8462261022519150.576886948874042
1090.4207056633130160.8414113266260330.579294336686984
1100.3978120416231980.7956240832463960.602187958376802
1110.3644817518685330.7289635037370660.635518248131467
1120.3313349396436060.6626698792872120.668665060356394
1130.3452325100207790.6904650200415580.654767489979221
1140.307222544628450.61444508925690.69277745537155
1150.2850532358517730.5701064717035470.714946764148227
1160.2567796639805740.5135593279611480.743220336019426
1170.2335237746186240.4670475492372480.766476225381376
1180.2177409541022750.435481908204550.782259045897725
1190.2691910251967240.5383820503934490.730808974803276
1200.2775299358784840.5550598717569680.722470064121516
1210.277320878782890.554641757565780.72267912121711
1220.2639758976801430.5279517953602850.736024102319857
1230.4644935868123720.9289871736247450.535506413187628
1240.4556162528207060.9112325056414120.544383747179294
1250.5266866220320220.9466267559359560.473313377967978
1260.555556359736650.8888872805266990.444443640263349
1270.5483862502290480.9032274995419050.451613749770952
1280.6390285959796840.7219428080406330.360971404020317
1290.6331501455055310.7336997089889380.366849854494469
1300.5993304172637780.8013391654724440.400669582736222
1310.571528528041580.856942943916840.42847147195842
1320.5288282133904890.9423435732190220.471171786609511
1330.5100020592937680.9799958814124640.489997940706232
1340.4781713570186970.9563427140373940.521828642981303
1350.4409046013920020.8818092027840050.559095398607998
1360.4433797600969560.8867595201939120.556620239903044
1370.3990746657787180.7981493315574360.600925334221282
1380.3654779510367080.7309559020734160.634522048963292
1390.3474507874875120.6949015749750240.652549212512488
1400.3073774481161690.6147548962323380.692622551883831
1410.2716633034827370.5433266069654730.728336696517264
1420.285226503807550.57045300761510.71477349619245
1430.2497082878113290.4994165756226580.750291712188671
1440.2652288173313150.530457634662630.734771182668685
1450.41189522304880.82379044609760.5881047769512
1460.3811166337940840.7622332675881690.618883366205916
1470.3997748748402530.7995497496805070.600225125159747
1480.3590553327199950.718110665439990.640944667280005
1490.3150504962313190.6301009924626380.684949503768681
1500.2734384535569040.5468769071138080.726561546443096
1510.2372028669615420.4744057339230850.762797133038458
1520.3962798011294870.7925596022589750.603720198870513
1530.346859501475780.693719002951560.65314049852422
1540.3160260606152070.6320521212304150.683973939384793
1550.2726923804286900.5453847608573790.72730761957131
1560.2379211878790650.4758423757581290.762078812120935
1570.2408579872699450.4817159745398890.759142012730055
1580.2372864158285480.4745728316570950.762713584171453
1590.1988108996124740.3976217992249480.801189100387526
1600.1627581812124260.3255163624248510.837241818787574
1610.1370372142810660.2740744285621310.862962785718934
1620.1154269284110260.2308538568220520.884573071588974
1630.1217476227680370.2434952455360740.878252377231963
1640.1047490751731660.2094981503463310.895250924826834
1650.1257839601781310.2515679203562610.874216039821869
1660.1140611495062330.2281222990124660.885938850493767
1670.1649481814534920.3298963629069830.835051818546508
1680.2030215628754230.4060431257508450.796978437124577
1690.1955942832168850.391188566433770.804405716783115
1700.3019372081604860.6038744163209730.698062791839514
1710.3189872460807770.6379744921615540.681012753919223
1720.2590627623262120.5181255246524240.740937237673788
1730.2072318528499320.4144637056998630.792768147150068
1740.1814383989223160.3628767978446330.818561601077684
1750.1857026871275360.3714053742550730.814297312872464
1760.1563470849791660.3126941699583320.843652915020834
1770.1160174150441170.2320348300882340.883982584955883
1780.1020605940961490.2041211881922980.897939405903851
1790.0901741680289290.1803483360578580.90982583197107
1800.1351662217223720.2703324434447440.864833778277628
1810.1115344902226880.2230689804453760.888465509777312
1820.07726311056732140.1545262211346430.922736889432679
1830.05402914401000270.1080582880200050.945970855989997
1840.0512555479809750.102511095961950.948744452019025
1850.08654840045558130.1730968009111630.913451599544419
1860.04484173696538020.08968347393076030.95515826303462

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0114589232736245 & 0.022917846547249 & 0.988541076726375 \tabularnewline
10 & 0.00558025508464656 & 0.0111605101692931 & 0.994419744915353 \tabularnewline
11 & 0.00383039495138541 & 0.00766078990277082 & 0.996169605048615 \tabularnewline
12 & 0.00144688169110890 & 0.00289376338221779 & 0.99855311830889 \tabularnewline
13 & 0.000406829690622746 & 0.000813659381245493 & 0.999593170309377 \tabularnewline
14 & 0.000110845320022862 & 0.000221690640045723 & 0.999889154679977 \tabularnewline
15 & 7.23420617892117e-05 & 0.000144684123578423 & 0.99992765793821 \tabularnewline
16 & 1.75447938266853e-05 & 3.50895876533705e-05 & 0.999982455206173 \tabularnewline
17 & 5.37635136478092e-06 & 1.07527027295618e-05 & 0.999994623648635 \tabularnewline
18 & 2.89050727484676e-06 & 5.78101454969352e-06 & 0.999997109492725 \tabularnewline
19 & 1.20016921465064e-06 & 2.40033842930127e-06 & 0.999998799830785 \tabularnewline
20 & 9.13359193218533e-07 & 1.82671838643707e-06 & 0.999999086640807 \tabularnewline
21 & 2.61232875895234e-07 & 5.22465751790467e-07 & 0.999999738767124 \tabularnewline
22 & 2.52252598181453e-07 & 5.04505196362905e-07 & 0.999999747747402 \tabularnewline
23 & 7.28640359549451e-08 & 1.45728071909890e-07 & 0.999999927135964 \tabularnewline
24 & 1.84663738701327e-08 & 3.69327477402655e-08 & 0.999999981533626 \tabularnewline
25 & 5.44503040070167e-09 & 1.08900608014033e-08 & 0.99999999455497 \tabularnewline
26 & 4.41030311973868e-09 & 8.82060623947737e-09 & 0.999999995589697 \tabularnewline
27 & 1.95896596257071e-09 & 3.91793192514142e-09 & 0.999999998041034 \tabularnewline
28 & 5.15437507545988e-10 & 1.03087501509198e-09 & 0.999999999484563 \tabularnewline
29 & 1.68819821438689e-09 & 3.37639642877377e-09 & 0.999999998311802 \tabularnewline
30 & 5.97728836986143e-10 & 1.19545767397229e-09 & 0.999999999402271 \tabularnewline
31 & 0.000150681664532753 & 0.000301363329065507 & 0.999849318335467 \tabularnewline
32 & 8.04157568769302e-05 & 0.000160831513753860 & 0.999919584243123 \tabularnewline
33 & 0.000365907109930215 & 0.000731814219860431 & 0.99963409289007 \tabularnewline
34 & 0.00164732958504156 & 0.00329465917008311 & 0.998352670414958 \tabularnewline
35 & 0.00266332112442354 & 0.00532664224884707 & 0.997336678875576 \tabularnewline
36 & 0.00275854827666056 & 0.00551709655332113 & 0.99724145172334 \tabularnewline
37 & 0.00457007359638214 & 0.00914014719276429 & 0.995429926403618 \tabularnewline
38 & 0.024451204298566 & 0.048902408597132 & 0.975548795701434 \tabularnewline
39 & 0.0183419130615765 & 0.0366838261231529 & 0.981658086938423 \tabularnewline
40 & 0.0146526036778112 & 0.0293052073556225 & 0.985347396322189 \tabularnewline
41 & 0.0446198354902719 & 0.0892396709805438 & 0.955380164509728 \tabularnewline
42 & 0.154352659497982 & 0.308705318995965 & 0.845647340502018 \tabularnewline
43 & 0.194495406489687 & 0.388990812979374 & 0.805504593510313 \tabularnewline
44 & 0.379424675276358 & 0.758849350552717 & 0.620575324723642 \tabularnewline
45 & 0.336592955236533 & 0.673185910473066 & 0.663407044763467 \tabularnewline
46 & 0.780238381263082 & 0.439523237473837 & 0.219761618736918 \tabularnewline
47 & 0.770510961115485 & 0.45897807776903 & 0.229489038884515 \tabularnewline
48 & 0.743121907164485 & 0.51375618567103 & 0.256878092835515 \tabularnewline
49 & 0.84264550533692 & 0.314708989326160 & 0.157354494663080 \tabularnewline
50 & 0.81570550381952 & 0.368588992360961 & 0.184294496180480 \tabularnewline
51 & 0.795694369030284 & 0.408611261939432 & 0.204305630969716 \tabularnewline
52 & 0.774454530019864 & 0.451090939960273 & 0.225545469980136 \tabularnewline
53 & 0.738658209733368 & 0.522683580533265 & 0.261341790266632 \tabularnewline
54 & 0.865208577888778 & 0.269582844222444 & 0.134791422111222 \tabularnewline
55 & 0.85521114506837 & 0.289577709863258 & 0.144788854931629 \tabularnewline
56 & 0.856080756588506 & 0.287838486822987 & 0.143919243411494 \tabularnewline
57 & 0.844351110590967 & 0.311297778818066 & 0.155648889409033 \tabularnewline
58 & 0.81857542809917 & 0.362849143801661 & 0.181424571900831 \tabularnewline
59 & 0.791391825211841 & 0.417216349576318 & 0.208608174788159 \tabularnewline
60 & 0.767627451412294 & 0.464745097175412 & 0.232372548587706 \tabularnewline
61 & 0.76372558428232 & 0.47254883143536 & 0.23627441571768 \tabularnewline
62 & 0.760727649162136 & 0.478544701675728 & 0.239272350837864 \tabularnewline
63 & 0.792621319161182 & 0.414757361677636 & 0.207378680838818 \tabularnewline
64 & 0.785498182201388 & 0.429003635597224 & 0.214501817798612 \tabularnewline
65 & 0.758863852834671 & 0.482272294330657 & 0.241136147165329 \tabularnewline
66 & 0.729698522748909 & 0.540602954502183 & 0.270301477251091 \tabularnewline
67 & 0.697688722205181 & 0.604622555589638 & 0.302311277794819 \tabularnewline
68 & 0.670509169727911 & 0.658981660544179 & 0.329490830272089 \tabularnewline
69 & 0.634003336460959 & 0.731993327078082 & 0.365996663539041 \tabularnewline
70 & 0.599289230414862 & 0.801421539170276 & 0.400710769585138 \tabularnewline
71 & 0.565225622782044 & 0.869548754435913 & 0.434774377217956 \tabularnewline
72 & 0.525981248344487 & 0.948037503311025 & 0.474018751655513 \tabularnewline
73 & 0.557120398905475 & 0.885759202189049 & 0.442879601094525 \tabularnewline
74 & 0.531071433194677 & 0.937857133610646 & 0.468928566805323 \tabularnewline
75 & 0.496724543571677 & 0.993449087143354 & 0.503275456428323 \tabularnewline
76 & 0.497510218661858 & 0.995020437323715 & 0.502489781338142 \tabularnewline
77 & 0.502629175223136 & 0.994741649553728 & 0.497370824776864 \tabularnewline
78 & 0.50642579998583 & 0.98714840002834 & 0.49357420001417 \tabularnewline
79 & 0.534512814701136 & 0.930974370597728 & 0.465487185298864 \tabularnewline
80 & 0.597339585083253 & 0.805320829833495 & 0.402660414916747 \tabularnewline
81 & 0.567974395886876 & 0.864051208226247 & 0.432025604113124 \tabularnewline
82 & 0.660230495497844 & 0.679539009004312 & 0.339769504502156 \tabularnewline
83 & 0.642377241358708 & 0.715245517282584 & 0.357622758641292 \tabularnewline
84 & 0.622147061997998 & 0.755705876004005 & 0.377852938002002 \tabularnewline
85 & 0.611980370395952 & 0.776039259208096 & 0.388019629604048 \tabularnewline
86 & 0.628844810188592 & 0.742310379622817 & 0.371155189811408 \tabularnewline
87 & 0.60313701792794 & 0.793725964144121 & 0.396862982072060 \tabularnewline
88 & 0.582096652166164 & 0.835806695667672 & 0.417903347833836 \tabularnewline
89 & 0.55867988851655 & 0.8826402229669 & 0.44132011148345 \tabularnewline
90 & 0.598157096220024 & 0.803685807559951 & 0.401842903779976 \tabularnewline
91 & 0.570324198017844 & 0.859351603964313 & 0.429675801982156 \tabularnewline
92 & 0.548274253270093 & 0.903451493459814 & 0.451725746729907 \tabularnewline
93 & 0.593708865882407 & 0.812582268235186 & 0.406291134117593 \tabularnewline
94 & 0.552681864005297 & 0.894636271989406 & 0.447318135994703 \tabularnewline
95 & 0.598964236109576 & 0.802071527780848 & 0.401035763890424 \tabularnewline
96 & 0.578804637731023 & 0.842390724537954 & 0.421195362268977 \tabularnewline
97 & 0.597137662109916 & 0.805724675780168 & 0.402862337890084 \tabularnewline
98 & 0.575726942453608 & 0.848546115092783 & 0.424273057546392 \tabularnewline
99 & 0.58514200300853 & 0.82971599398294 & 0.41485799699147 \tabularnewline
100 & 0.636605533662942 & 0.726788932674115 & 0.363394466337058 \tabularnewline
101 & 0.615044511813201 & 0.769910976373598 & 0.384955488186799 \tabularnewline
102 & 0.581811070804592 & 0.836377858390815 & 0.418188929195408 \tabularnewline
103 & 0.568267848201776 & 0.863464303596448 & 0.431732151798224 \tabularnewline
104 & 0.526728934156501 & 0.946542131686998 & 0.473271065843499 \tabularnewline
105 & 0.489010152696446 & 0.978020305392892 & 0.510989847303554 \tabularnewline
106 & 0.484085090515925 & 0.96817018103185 & 0.515914909484075 \tabularnewline
107 & 0.443251196901721 & 0.886502393803442 & 0.556748803098279 \tabularnewline
108 & 0.423113051125958 & 0.846226102251915 & 0.576886948874042 \tabularnewline
109 & 0.420705663313016 & 0.841411326626033 & 0.579294336686984 \tabularnewline
110 & 0.397812041623198 & 0.795624083246396 & 0.602187958376802 \tabularnewline
111 & 0.364481751868533 & 0.728963503737066 & 0.635518248131467 \tabularnewline
112 & 0.331334939643606 & 0.662669879287212 & 0.668665060356394 \tabularnewline
113 & 0.345232510020779 & 0.690465020041558 & 0.654767489979221 \tabularnewline
114 & 0.30722254462845 & 0.6144450892569 & 0.69277745537155 \tabularnewline
115 & 0.285053235851773 & 0.570106471703547 & 0.714946764148227 \tabularnewline
116 & 0.256779663980574 & 0.513559327961148 & 0.743220336019426 \tabularnewline
117 & 0.233523774618624 & 0.467047549237248 & 0.766476225381376 \tabularnewline
118 & 0.217740954102275 & 0.43548190820455 & 0.782259045897725 \tabularnewline
119 & 0.269191025196724 & 0.538382050393449 & 0.730808974803276 \tabularnewline
120 & 0.277529935878484 & 0.555059871756968 & 0.722470064121516 \tabularnewline
121 & 0.27732087878289 & 0.55464175756578 & 0.72267912121711 \tabularnewline
122 & 0.263975897680143 & 0.527951795360285 & 0.736024102319857 \tabularnewline
123 & 0.464493586812372 & 0.928987173624745 & 0.535506413187628 \tabularnewline
124 & 0.455616252820706 & 0.911232505641412 & 0.544383747179294 \tabularnewline
125 & 0.526686622032022 & 0.946626755935956 & 0.473313377967978 \tabularnewline
126 & 0.55555635973665 & 0.888887280526699 & 0.444443640263349 \tabularnewline
127 & 0.548386250229048 & 0.903227499541905 & 0.451613749770952 \tabularnewline
128 & 0.639028595979684 & 0.721942808040633 & 0.360971404020317 \tabularnewline
129 & 0.633150145505531 & 0.733699708988938 & 0.366849854494469 \tabularnewline
130 & 0.599330417263778 & 0.801339165472444 & 0.400669582736222 \tabularnewline
131 & 0.57152852804158 & 0.85694294391684 & 0.42847147195842 \tabularnewline
132 & 0.528828213390489 & 0.942343573219022 & 0.471171786609511 \tabularnewline
133 & 0.510002059293768 & 0.979995881412464 & 0.489997940706232 \tabularnewline
134 & 0.478171357018697 & 0.956342714037394 & 0.521828642981303 \tabularnewline
135 & 0.440904601392002 & 0.881809202784005 & 0.559095398607998 \tabularnewline
136 & 0.443379760096956 & 0.886759520193912 & 0.556620239903044 \tabularnewline
137 & 0.399074665778718 & 0.798149331557436 & 0.600925334221282 \tabularnewline
138 & 0.365477951036708 & 0.730955902073416 & 0.634522048963292 \tabularnewline
139 & 0.347450787487512 & 0.694901574975024 & 0.652549212512488 \tabularnewline
140 & 0.307377448116169 & 0.614754896232338 & 0.692622551883831 \tabularnewline
141 & 0.271663303482737 & 0.543326606965473 & 0.728336696517264 \tabularnewline
142 & 0.28522650380755 & 0.5704530076151 & 0.71477349619245 \tabularnewline
143 & 0.249708287811329 & 0.499416575622658 & 0.750291712188671 \tabularnewline
144 & 0.265228817331315 & 0.53045763466263 & 0.734771182668685 \tabularnewline
145 & 0.4118952230488 & 0.8237904460976 & 0.5881047769512 \tabularnewline
146 & 0.381116633794084 & 0.762233267588169 & 0.618883366205916 \tabularnewline
147 & 0.399774874840253 & 0.799549749680507 & 0.600225125159747 \tabularnewline
148 & 0.359055332719995 & 0.71811066543999 & 0.640944667280005 \tabularnewline
149 & 0.315050496231319 & 0.630100992462638 & 0.684949503768681 \tabularnewline
150 & 0.273438453556904 & 0.546876907113808 & 0.726561546443096 \tabularnewline
151 & 0.237202866961542 & 0.474405733923085 & 0.762797133038458 \tabularnewline
152 & 0.396279801129487 & 0.792559602258975 & 0.603720198870513 \tabularnewline
153 & 0.34685950147578 & 0.69371900295156 & 0.65314049852422 \tabularnewline
154 & 0.316026060615207 & 0.632052121230415 & 0.683973939384793 \tabularnewline
155 & 0.272692380428690 & 0.545384760857379 & 0.72730761957131 \tabularnewline
156 & 0.237921187879065 & 0.475842375758129 & 0.762078812120935 \tabularnewline
157 & 0.240857987269945 & 0.481715974539889 & 0.759142012730055 \tabularnewline
158 & 0.237286415828548 & 0.474572831657095 & 0.762713584171453 \tabularnewline
159 & 0.198810899612474 & 0.397621799224948 & 0.801189100387526 \tabularnewline
160 & 0.162758181212426 & 0.325516362424851 & 0.837241818787574 \tabularnewline
161 & 0.137037214281066 & 0.274074428562131 & 0.862962785718934 \tabularnewline
162 & 0.115426928411026 & 0.230853856822052 & 0.884573071588974 \tabularnewline
163 & 0.121747622768037 & 0.243495245536074 & 0.878252377231963 \tabularnewline
164 & 0.104749075173166 & 0.209498150346331 & 0.895250924826834 \tabularnewline
165 & 0.125783960178131 & 0.251567920356261 & 0.874216039821869 \tabularnewline
166 & 0.114061149506233 & 0.228122299012466 & 0.885938850493767 \tabularnewline
167 & 0.164948181453492 & 0.329896362906983 & 0.835051818546508 \tabularnewline
168 & 0.203021562875423 & 0.406043125750845 & 0.796978437124577 \tabularnewline
169 & 0.195594283216885 & 0.39118856643377 & 0.804405716783115 \tabularnewline
170 & 0.301937208160486 & 0.603874416320973 & 0.698062791839514 \tabularnewline
171 & 0.318987246080777 & 0.637974492161554 & 0.681012753919223 \tabularnewline
172 & 0.259062762326212 & 0.518125524652424 & 0.740937237673788 \tabularnewline
173 & 0.207231852849932 & 0.414463705699863 & 0.792768147150068 \tabularnewline
174 & 0.181438398922316 & 0.362876797844633 & 0.818561601077684 \tabularnewline
175 & 0.185702687127536 & 0.371405374255073 & 0.814297312872464 \tabularnewline
176 & 0.156347084979166 & 0.312694169958332 & 0.843652915020834 \tabularnewline
177 & 0.116017415044117 & 0.232034830088234 & 0.883982584955883 \tabularnewline
178 & 0.102060594096149 & 0.204121188192298 & 0.897939405903851 \tabularnewline
179 & 0.090174168028929 & 0.180348336057858 & 0.90982583197107 \tabularnewline
180 & 0.135166221722372 & 0.270332443444744 & 0.864833778277628 \tabularnewline
181 & 0.111534490222688 & 0.223068980445376 & 0.888465509777312 \tabularnewline
182 & 0.0772631105673214 & 0.154526221134643 & 0.922736889432679 \tabularnewline
183 & 0.0540291440100027 & 0.108058288020005 & 0.945970855989997 \tabularnewline
184 & 0.051255547980975 & 0.10251109596195 & 0.948744452019025 \tabularnewline
185 & 0.0865484004555813 & 0.173096800911163 & 0.913451599544419 \tabularnewline
186 & 0.0448417369653802 & 0.0896834739307603 & 0.95515826303462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&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]9[/C][C]0.0114589232736245[/C][C]0.022917846547249[/C][C]0.988541076726375[/C][/ROW]
[ROW][C]10[/C][C]0.00558025508464656[/C][C]0.0111605101692931[/C][C]0.994419744915353[/C][/ROW]
[ROW][C]11[/C][C]0.00383039495138541[/C][C]0.00766078990277082[/C][C]0.996169605048615[/C][/ROW]
[ROW][C]12[/C][C]0.00144688169110890[/C][C]0.00289376338221779[/C][C]0.99855311830889[/C][/ROW]
[ROW][C]13[/C][C]0.000406829690622746[/C][C]0.000813659381245493[/C][C]0.999593170309377[/C][/ROW]
[ROW][C]14[/C][C]0.000110845320022862[/C][C]0.000221690640045723[/C][C]0.999889154679977[/C][/ROW]
[ROW][C]15[/C][C]7.23420617892117e-05[/C][C]0.000144684123578423[/C][C]0.99992765793821[/C][/ROW]
[ROW][C]16[/C][C]1.75447938266853e-05[/C][C]3.50895876533705e-05[/C][C]0.999982455206173[/C][/ROW]
[ROW][C]17[/C][C]5.37635136478092e-06[/C][C]1.07527027295618e-05[/C][C]0.999994623648635[/C][/ROW]
[ROW][C]18[/C][C]2.89050727484676e-06[/C][C]5.78101454969352e-06[/C][C]0.999997109492725[/C][/ROW]
[ROW][C]19[/C][C]1.20016921465064e-06[/C][C]2.40033842930127e-06[/C][C]0.999998799830785[/C][/ROW]
[ROW][C]20[/C][C]9.13359193218533e-07[/C][C]1.82671838643707e-06[/C][C]0.999999086640807[/C][/ROW]
[ROW][C]21[/C][C]2.61232875895234e-07[/C][C]5.22465751790467e-07[/C][C]0.999999738767124[/C][/ROW]
[ROW][C]22[/C][C]2.52252598181453e-07[/C][C]5.04505196362905e-07[/C][C]0.999999747747402[/C][/ROW]
[ROW][C]23[/C][C]7.28640359549451e-08[/C][C]1.45728071909890e-07[/C][C]0.999999927135964[/C][/ROW]
[ROW][C]24[/C][C]1.84663738701327e-08[/C][C]3.69327477402655e-08[/C][C]0.999999981533626[/C][/ROW]
[ROW][C]25[/C][C]5.44503040070167e-09[/C][C]1.08900608014033e-08[/C][C]0.99999999455497[/C][/ROW]
[ROW][C]26[/C][C]4.41030311973868e-09[/C][C]8.82060623947737e-09[/C][C]0.999999995589697[/C][/ROW]
[ROW][C]27[/C][C]1.95896596257071e-09[/C][C]3.91793192514142e-09[/C][C]0.999999998041034[/C][/ROW]
[ROW][C]28[/C][C]5.15437507545988e-10[/C][C]1.03087501509198e-09[/C][C]0.999999999484563[/C][/ROW]
[ROW][C]29[/C][C]1.68819821438689e-09[/C][C]3.37639642877377e-09[/C][C]0.999999998311802[/C][/ROW]
[ROW][C]30[/C][C]5.97728836986143e-10[/C][C]1.19545767397229e-09[/C][C]0.999999999402271[/C][/ROW]
[ROW][C]31[/C][C]0.000150681664532753[/C][C]0.000301363329065507[/C][C]0.999849318335467[/C][/ROW]
[ROW][C]32[/C][C]8.04157568769302e-05[/C][C]0.000160831513753860[/C][C]0.999919584243123[/C][/ROW]
[ROW][C]33[/C][C]0.000365907109930215[/C][C]0.000731814219860431[/C][C]0.99963409289007[/C][/ROW]
[ROW][C]34[/C][C]0.00164732958504156[/C][C]0.00329465917008311[/C][C]0.998352670414958[/C][/ROW]
[ROW][C]35[/C][C]0.00266332112442354[/C][C]0.00532664224884707[/C][C]0.997336678875576[/C][/ROW]
[ROW][C]36[/C][C]0.00275854827666056[/C][C]0.00551709655332113[/C][C]0.99724145172334[/C][/ROW]
[ROW][C]37[/C][C]0.00457007359638214[/C][C]0.00914014719276429[/C][C]0.995429926403618[/C][/ROW]
[ROW][C]38[/C][C]0.024451204298566[/C][C]0.048902408597132[/C][C]0.975548795701434[/C][/ROW]
[ROW][C]39[/C][C]0.0183419130615765[/C][C]0.0366838261231529[/C][C]0.981658086938423[/C][/ROW]
[ROW][C]40[/C][C]0.0146526036778112[/C][C]0.0293052073556225[/C][C]0.985347396322189[/C][/ROW]
[ROW][C]41[/C][C]0.0446198354902719[/C][C]0.0892396709805438[/C][C]0.955380164509728[/C][/ROW]
[ROW][C]42[/C][C]0.154352659497982[/C][C]0.308705318995965[/C][C]0.845647340502018[/C][/ROW]
[ROW][C]43[/C][C]0.194495406489687[/C][C]0.388990812979374[/C][C]0.805504593510313[/C][/ROW]
[ROW][C]44[/C][C]0.379424675276358[/C][C]0.758849350552717[/C][C]0.620575324723642[/C][/ROW]
[ROW][C]45[/C][C]0.336592955236533[/C][C]0.673185910473066[/C][C]0.663407044763467[/C][/ROW]
[ROW][C]46[/C][C]0.780238381263082[/C][C]0.439523237473837[/C][C]0.219761618736918[/C][/ROW]
[ROW][C]47[/C][C]0.770510961115485[/C][C]0.45897807776903[/C][C]0.229489038884515[/C][/ROW]
[ROW][C]48[/C][C]0.743121907164485[/C][C]0.51375618567103[/C][C]0.256878092835515[/C][/ROW]
[ROW][C]49[/C][C]0.84264550533692[/C][C]0.314708989326160[/C][C]0.157354494663080[/C][/ROW]
[ROW][C]50[/C][C]0.81570550381952[/C][C]0.368588992360961[/C][C]0.184294496180480[/C][/ROW]
[ROW][C]51[/C][C]0.795694369030284[/C][C]0.408611261939432[/C][C]0.204305630969716[/C][/ROW]
[ROW][C]52[/C][C]0.774454530019864[/C][C]0.451090939960273[/C][C]0.225545469980136[/C][/ROW]
[ROW][C]53[/C][C]0.738658209733368[/C][C]0.522683580533265[/C][C]0.261341790266632[/C][/ROW]
[ROW][C]54[/C][C]0.865208577888778[/C][C]0.269582844222444[/C][C]0.134791422111222[/C][/ROW]
[ROW][C]55[/C][C]0.85521114506837[/C][C]0.289577709863258[/C][C]0.144788854931629[/C][/ROW]
[ROW][C]56[/C][C]0.856080756588506[/C][C]0.287838486822987[/C][C]0.143919243411494[/C][/ROW]
[ROW][C]57[/C][C]0.844351110590967[/C][C]0.311297778818066[/C][C]0.155648889409033[/C][/ROW]
[ROW][C]58[/C][C]0.81857542809917[/C][C]0.362849143801661[/C][C]0.181424571900831[/C][/ROW]
[ROW][C]59[/C][C]0.791391825211841[/C][C]0.417216349576318[/C][C]0.208608174788159[/C][/ROW]
[ROW][C]60[/C][C]0.767627451412294[/C][C]0.464745097175412[/C][C]0.232372548587706[/C][/ROW]
[ROW][C]61[/C][C]0.76372558428232[/C][C]0.47254883143536[/C][C]0.23627441571768[/C][/ROW]
[ROW][C]62[/C][C]0.760727649162136[/C][C]0.478544701675728[/C][C]0.239272350837864[/C][/ROW]
[ROW][C]63[/C][C]0.792621319161182[/C][C]0.414757361677636[/C][C]0.207378680838818[/C][/ROW]
[ROW][C]64[/C][C]0.785498182201388[/C][C]0.429003635597224[/C][C]0.214501817798612[/C][/ROW]
[ROW][C]65[/C][C]0.758863852834671[/C][C]0.482272294330657[/C][C]0.241136147165329[/C][/ROW]
[ROW][C]66[/C][C]0.729698522748909[/C][C]0.540602954502183[/C][C]0.270301477251091[/C][/ROW]
[ROW][C]67[/C][C]0.697688722205181[/C][C]0.604622555589638[/C][C]0.302311277794819[/C][/ROW]
[ROW][C]68[/C][C]0.670509169727911[/C][C]0.658981660544179[/C][C]0.329490830272089[/C][/ROW]
[ROW][C]69[/C][C]0.634003336460959[/C][C]0.731993327078082[/C][C]0.365996663539041[/C][/ROW]
[ROW][C]70[/C][C]0.599289230414862[/C][C]0.801421539170276[/C][C]0.400710769585138[/C][/ROW]
[ROW][C]71[/C][C]0.565225622782044[/C][C]0.869548754435913[/C][C]0.434774377217956[/C][/ROW]
[ROW][C]72[/C][C]0.525981248344487[/C][C]0.948037503311025[/C][C]0.474018751655513[/C][/ROW]
[ROW][C]73[/C][C]0.557120398905475[/C][C]0.885759202189049[/C][C]0.442879601094525[/C][/ROW]
[ROW][C]74[/C][C]0.531071433194677[/C][C]0.937857133610646[/C][C]0.468928566805323[/C][/ROW]
[ROW][C]75[/C][C]0.496724543571677[/C][C]0.993449087143354[/C][C]0.503275456428323[/C][/ROW]
[ROW][C]76[/C][C]0.497510218661858[/C][C]0.995020437323715[/C][C]0.502489781338142[/C][/ROW]
[ROW][C]77[/C][C]0.502629175223136[/C][C]0.994741649553728[/C][C]0.497370824776864[/C][/ROW]
[ROW][C]78[/C][C]0.50642579998583[/C][C]0.98714840002834[/C][C]0.49357420001417[/C][/ROW]
[ROW][C]79[/C][C]0.534512814701136[/C][C]0.930974370597728[/C][C]0.465487185298864[/C][/ROW]
[ROW][C]80[/C][C]0.597339585083253[/C][C]0.805320829833495[/C][C]0.402660414916747[/C][/ROW]
[ROW][C]81[/C][C]0.567974395886876[/C][C]0.864051208226247[/C][C]0.432025604113124[/C][/ROW]
[ROW][C]82[/C][C]0.660230495497844[/C][C]0.679539009004312[/C][C]0.339769504502156[/C][/ROW]
[ROW][C]83[/C][C]0.642377241358708[/C][C]0.715245517282584[/C][C]0.357622758641292[/C][/ROW]
[ROW][C]84[/C][C]0.622147061997998[/C][C]0.755705876004005[/C][C]0.377852938002002[/C][/ROW]
[ROW][C]85[/C][C]0.611980370395952[/C][C]0.776039259208096[/C][C]0.388019629604048[/C][/ROW]
[ROW][C]86[/C][C]0.628844810188592[/C][C]0.742310379622817[/C][C]0.371155189811408[/C][/ROW]
[ROW][C]87[/C][C]0.60313701792794[/C][C]0.793725964144121[/C][C]0.396862982072060[/C][/ROW]
[ROW][C]88[/C][C]0.582096652166164[/C][C]0.835806695667672[/C][C]0.417903347833836[/C][/ROW]
[ROW][C]89[/C][C]0.55867988851655[/C][C]0.8826402229669[/C][C]0.44132011148345[/C][/ROW]
[ROW][C]90[/C][C]0.598157096220024[/C][C]0.803685807559951[/C][C]0.401842903779976[/C][/ROW]
[ROW][C]91[/C][C]0.570324198017844[/C][C]0.859351603964313[/C][C]0.429675801982156[/C][/ROW]
[ROW][C]92[/C][C]0.548274253270093[/C][C]0.903451493459814[/C][C]0.451725746729907[/C][/ROW]
[ROW][C]93[/C][C]0.593708865882407[/C][C]0.812582268235186[/C][C]0.406291134117593[/C][/ROW]
[ROW][C]94[/C][C]0.552681864005297[/C][C]0.894636271989406[/C][C]0.447318135994703[/C][/ROW]
[ROW][C]95[/C][C]0.598964236109576[/C][C]0.802071527780848[/C][C]0.401035763890424[/C][/ROW]
[ROW][C]96[/C][C]0.578804637731023[/C][C]0.842390724537954[/C][C]0.421195362268977[/C][/ROW]
[ROW][C]97[/C][C]0.597137662109916[/C][C]0.805724675780168[/C][C]0.402862337890084[/C][/ROW]
[ROW][C]98[/C][C]0.575726942453608[/C][C]0.848546115092783[/C][C]0.424273057546392[/C][/ROW]
[ROW][C]99[/C][C]0.58514200300853[/C][C]0.82971599398294[/C][C]0.41485799699147[/C][/ROW]
[ROW][C]100[/C][C]0.636605533662942[/C][C]0.726788932674115[/C][C]0.363394466337058[/C][/ROW]
[ROW][C]101[/C][C]0.615044511813201[/C][C]0.769910976373598[/C][C]0.384955488186799[/C][/ROW]
[ROW][C]102[/C][C]0.581811070804592[/C][C]0.836377858390815[/C][C]0.418188929195408[/C][/ROW]
[ROW][C]103[/C][C]0.568267848201776[/C][C]0.863464303596448[/C][C]0.431732151798224[/C][/ROW]
[ROW][C]104[/C][C]0.526728934156501[/C][C]0.946542131686998[/C][C]0.473271065843499[/C][/ROW]
[ROW][C]105[/C][C]0.489010152696446[/C][C]0.978020305392892[/C][C]0.510989847303554[/C][/ROW]
[ROW][C]106[/C][C]0.484085090515925[/C][C]0.96817018103185[/C][C]0.515914909484075[/C][/ROW]
[ROW][C]107[/C][C]0.443251196901721[/C][C]0.886502393803442[/C][C]0.556748803098279[/C][/ROW]
[ROW][C]108[/C][C]0.423113051125958[/C][C]0.846226102251915[/C][C]0.576886948874042[/C][/ROW]
[ROW][C]109[/C][C]0.420705663313016[/C][C]0.841411326626033[/C][C]0.579294336686984[/C][/ROW]
[ROW][C]110[/C][C]0.397812041623198[/C][C]0.795624083246396[/C][C]0.602187958376802[/C][/ROW]
[ROW][C]111[/C][C]0.364481751868533[/C][C]0.728963503737066[/C][C]0.635518248131467[/C][/ROW]
[ROW][C]112[/C][C]0.331334939643606[/C][C]0.662669879287212[/C][C]0.668665060356394[/C][/ROW]
[ROW][C]113[/C][C]0.345232510020779[/C][C]0.690465020041558[/C][C]0.654767489979221[/C][/ROW]
[ROW][C]114[/C][C]0.30722254462845[/C][C]0.6144450892569[/C][C]0.69277745537155[/C][/ROW]
[ROW][C]115[/C][C]0.285053235851773[/C][C]0.570106471703547[/C][C]0.714946764148227[/C][/ROW]
[ROW][C]116[/C][C]0.256779663980574[/C][C]0.513559327961148[/C][C]0.743220336019426[/C][/ROW]
[ROW][C]117[/C][C]0.233523774618624[/C][C]0.467047549237248[/C][C]0.766476225381376[/C][/ROW]
[ROW][C]118[/C][C]0.217740954102275[/C][C]0.43548190820455[/C][C]0.782259045897725[/C][/ROW]
[ROW][C]119[/C][C]0.269191025196724[/C][C]0.538382050393449[/C][C]0.730808974803276[/C][/ROW]
[ROW][C]120[/C][C]0.277529935878484[/C][C]0.555059871756968[/C][C]0.722470064121516[/C][/ROW]
[ROW][C]121[/C][C]0.27732087878289[/C][C]0.55464175756578[/C][C]0.72267912121711[/C][/ROW]
[ROW][C]122[/C][C]0.263975897680143[/C][C]0.527951795360285[/C][C]0.736024102319857[/C][/ROW]
[ROW][C]123[/C][C]0.464493586812372[/C][C]0.928987173624745[/C][C]0.535506413187628[/C][/ROW]
[ROW][C]124[/C][C]0.455616252820706[/C][C]0.911232505641412[/C][C]0.544383747179294[/C][/ROW]
[ROW][C]125[/C][C]0.526686622032022[/C][C]0.946626755935956[/C][C]0.473313377967978[/C][/ROW]
[ROW][C]126[/C][C]0.55555635973665[/C][C]0.888887280526699[/C][C]0.444443640263349[/C][/ROW]
[ROW][C]127[/C][C]0.548386250229048[/C][C]0.903227499541905[/C][C]0.451613749770952[/C][/ROW]
[ROW][C]128[/C][C]0.639028595979684[/C][C]0.721942808040633[/C][C]0.360971404020317[/C][/ROW]
[ROW][C]129[/C][C]0.633150145505531[/C][C]0.733699708988938[/C][C]0.366849854494469[/C][/ROW]
[ROW][C]130[/C][C]0.599330417263778[/C][C]0.801339165472444[/C][C]0.400669582736222[/C][/ROW]
[ROW][C]131[/C][C]0.57152852804158[/C][C]0.85694294391684[/C][C]0.42847147195842[/C][/ROW]
[ROW][C]132[/C][C]0.528828213390489[/C][C]0.942343573219022[/C][C]0.471171786609511[/C][/ROW]
[ROW][C]133[/C][C]0.510002059293768[/C][C]0.979995881412464[/C][C]0.489997940706232[/C][/ROW]
[ROW][C]134[/C][C]0.478171357018697[/C][C]0.956342714037394[/C][C]0.521828642981303[/C][/ROW]
[ROW][C]135[/C][C]0.440904601392002[/C][C]0.881809202784005[/C][C]0.559095398607998[/C][/ROW]
[ROW][C]136[/C][C]0.443379760096956[/C][C]0.886759520193912[/C][C]0.556620239903044[/C][/ROW]
[ROW][C]137[/C][C]0.399074665778718[/C][C]0.798149331557436[/C][C]0.600925334221282[/C][/ROW]
[ROW][C]138[/C][C]0.365477951036708[/C][C]0.730955902073416[/C][C]0.634522048963292[/C][/ROW]
[ROW][C]139[/C][C]0.347450787487512[/C][C]0.694901574975024[/C][C]0.652549212512488[/C][/ROW]
[ROW][C]140[/C][C]0.307377448116169[/C][C]0.614754896232338[/C][C]0.692622551883831[/C][/ROW]
[ROW][C]141[/C][C]0.271663303482737[/C][C]0.543326606965473[/C][C]0.728336696517264[/C][/ROW]
[ROW][C]142[/C][C]0.28522650380755[/C][C]0.5704530076151[/C][C]0.71477349619245[/C][/ROW]
[ROW][C]143[/C][C]0.249708287811329[/C][C]0.499416575622658[/C][C]0.750291712188671[/C][/ROW]
[ROW][C]144[/C][C]0.265228817331315[/C][C]0.53045763466263[/C][C]0.734771182668685[/C][/ROW]
[ROW][C]145[/C][C]0.4118952230488[/C][C]0.8237904460976[/C][C]0.5881047769512[/C][/ROW]
[ROW][C]146[/C][C]0.381116633794084[/C][C]0.762233267588169[/C][C]0.618883366205916[/C][/ROW]
[ROW][C]147[/C][C]0.399774874840253[/C][C]0.799549749680507[/C][C]0.600225125159747[/C][/ROW]
[ROW][C]148[/C][C]0.359055332719995[/C][C]0.71811066543999[/C][C]0.640944667280005[/C][/ROW]
[ROW][C]149[/C][C]0.315050496231319[/C][C]0.630100992462638[/C][C]0.684949503768681[/C][/ROW]
[ROW][C]150[/C][C]0.273438453556904[/C][C]0.546876907113808[/C][C]0.726561546443096[/C][/ROW]
[ROW][C]151[/C][C]0.237202866961542[/C][C]0.474405733923085[/C][C]0.762797133038458[/C][/ROW]
[ROW][C]152[/C][C]0.396279801129487[/C][C]0.792559602258975[/C][C]0.603720198870513[/C][/ROW]
[ROW][C]153[/C][C]0.34685950147578[/C][C]0.69371900295156[/C][C]0.65314049852422[/C][/ROW]
[ROW][C]154[/C][C]0.316026060615207[/C][C]0.632052121230415[/C][C]0.683973939384793[/C][/ROW]
[ROW][C]155[/C][C]0.272692380428690[/C][C]0.545384760857379[/C][C]0.72730761957131[/C][/ROW]
[ROW][C]156[/C][C]0.237921187879065[/C][C]0.475842375758129[/C][C]0.762078812120935[/C][/ROW]
[ROW][C]157[/C][C]0.240857987269945[/C][C]0.481715974539889[/C][C]0.759142012730055[/C][/ROW]
[ROW][C]158[/C][C]0.237286415828548[/C][C]0.474572831657095[/C][C]0.762713584171453[/C][/ROW]
[ROW][C]159[/C][C]0.198810899612474[/C][C]0.397621799224948[/C][C]0.801189100387526[/C][/ROW]
[ROW][C]160[/C][C]0.162758181212426[/C][C]0.325516362424851[/C][C]0.837241818787574[/C][/ROW]
[ROW][C]161[/C][C]0.137037214281066[/C][C]0.274074428562131[/C][C]0.862962785718934[/C][/ROW]
[ROW][C]162[/C][C]0.115426928411026[/C][C]0.230853856822052[/C][C]0.884573071588974[/C][/ROW]
[ROW][C]163[/C][C]0.121747622768037[/C][C]0.243495245536074[/C][C]0.878252377231963[/C][/ROW]
[ROW][C]164[/C][C]0.104749075173166[/C][C]0.209498150346331[/C][C]0.895250924826834[/C][/ROW]
[ROW][C]165[/C][C]0.125783960178131[/C][C]0.251567920356261[/C][C]0.874216039821869[/C][/ROW]
[ROW][C]166[/C][C]0.114061149506233[/C][C]0.228122299012466[/C][C]0.885938850493767[/C][/ROW]
[ROW][C]167[/C][C]0.164948181453492[/C][C]0.329896362906983[/C][C]0.835051818546508[/C][/ROW]
[ROW][C]168[/C][C]0.203021562875423[/C][C]0.406043125750845[/C][C]0.796978437124577[/C][/ROW]
[ROW][C]169[/C][C]0.195594283216885[/C][C]0.39118856643377[/C][C]0.804405716783115[/C][/ROW]
[ROW][C]170[/C][C]0.301937208160486[/C][C]0.603874416320973[/C][C]0.698062791839514[/C][/ROW]
[ROW][C]171[/C][C]0.318987246080777[/C][C]0.637974492161554[/C][C]0.681012753919223[/C][/ROW]
[ROW][C]172[/C][C]0.259062762326212[/C][C]0.518125524652424[/C][C]0.740937237673788[/C][/ROW]
[ROW][C]173[/C][C]0.207231852849932[/C][C]0.414463705699863[/C][C]0.792768147150068[/C][/ROW]
[ROW][C]174[/C][C]0.181438398922316[/C][C]0.362876797844633[/C][C]0.818561601077684[/C][/ROW]
[ROW][C]175[/C][C]0.185702687127536[/C][C]0.371405374255073[/C][C]0.814297312872464[/C][/ROW]
[ROW][C]176[/C][C]0.156347084979166[/C][C]0.312694169958332[/C][C]0.843652915020834[/C][/ROW]
[ROW][C]177[/C][C]0.116017415044117[/C][C]0.232034830088234[/C][C]0.883982584955883[/C][/ROW]
[ROW][C]178[/C][C]0.102060594096149[/C][C]0.204121188192298[/C][C]0.897939405903851[/C][/ROW]
[ROW][C]179[/C][C]0.090174168028929[/C][C]0.180348336057858[/C][C]0.90982583197107[/C][/ROW]
[ROW][C]180[/C][C]0.135166221722372[/C][C]0.270332443444744[/C][C]0.864833778277628[/C][/ROW]
[ROW][C]181[/C][C]0.111534490222688[/C][C]0.223068980445376[/C][C]0.888465509777312[/C][/ROW]
[ROW][C]182[/C][C]0.0772631105673214[/C][C]0.154526221134643[/C][C]0.922736889432679[/C][/ROW]
[ROW][C]183[/C][C]0.0540291440100027[/C][C]0.108058288020005[/C][C]0.945970855989997[/C][/ROW]
[ROW][C]184[/C][C]0.051255547980975[/C][C]0.10251109596195[/C][C]0.948744452019025[/C][/ROW]
[ROW][C]185[/C][C]0.0865484004555813[/C][C]0.173096800911163[/C][C]0.913451599544419[/C][/ROW]
[ROW][C]186[/C][C]0.0448417369653802[/C][C]0.0896834739307603[/C][C]0.95515826303462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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
90.01145892327362450.0229178465472490.988541076726375
100.005580255084646560.01116051016929310.994419744915353
110.003830394951385410.007660789902770820.996169605048615
120.001446881691108900.002893763382217790.99855311830889
130.0004068296906227460.0008136593812454930.999593170309377
140.0001108453200228620.0002216906400457230.999889154679977
157.23420617892117e-050.0001446841235784230.99992765793821
161.75447938266853e-053.50895876533705e-050.999982455206173
175.37635136478092e-061.07527027295618e-050.999994623648635
182.89050727484676e-065.78101454969352e-060.999997109492725
191.20016921465064e-062.40033842930127e-060.999998799830785
209.13359193218533e-071.82671838643707e-060.999999086640807
212.61232875895234e-075.22465751790467e-070.999999738767124
222.52252598181453e-075.04505196362905e-070.999999747747402
237.28640359549451e-081.45728071909890e-070.999999927135964
241.84663738701327e-083.69327477402655e-080.999999981533626
255.44503040070167e-091.08900608014033e-080.99999999455497
264.41030311973868e-098.82060623947737e-090.999999995589697
271.95896596257071e-093.91793192514142e-090.999999998041034
285.15437507545988e-101.03087501509198e-090.999999999484563
291.68819821438689e-093.37639642877377e-090.999999998311802
305.97728836986143e-101.19545767397229e-090.999999999402271
310.0001506816645327530.0003013633290655070.999849318335467
328.04157568769302e-050.0001608315137538600.999919584243123
330.0003659071099302150.0007318142198604310.99963409289007
340.001647329585041560.003294659170083110.998352670414958
350.002663321124423540.005326642248847070.997336678875576
360.002758548276660560.005517096553321130.99724145172334
370.004570073596382140.009140147192764290.995429926403618
380.0244512042985660.0489024085971320.975548795701434
390.01834191306157650.03668382612315290.981658086938423
400.01465260367781120.02930520735562250.985347396322189
410.04461983549027190.08923967098054380.955380164509728
420.1543526594979820.3087053189959650.845647340502018
430.1944954064896870.3889908129793740.805504593510313
440.3794246752763580.7588493505527170.620575324723642
450.3365929552365330.6731859104730660.663407044763467
460.7802383812630820.4395232374738370.219761618736918
470.7705109611154850.458978077769030.229489038884515
480.7431219071644850.513756185671030.256878092835515
490.842645505336920.3147089893261600.157354494663080
500.815705503819520.3685889923609610.184294496180480
510.7956943690302840.4086112619394320.204305630969716
520.7744545300198640.4510909399602730.225545469980136
530.7386582097333680.5226835805332650.261341790266632
540.8652085778887780.2695828442224440.134791422111222
550.855211145068370.2895777098632580.144788854931629
560.8560807565885060.2878384868229870.143919243411494
570.8443511105909670.3112977788180660.155648889409033
580.818575428099170.3628491438016610.181424571900831
590.7913918252118410.4172163495763180.208608174788159
600.7676274514122940.4647450971754120.232372548587706
610.763725584282320.472548831435360.23627441571768
620.7607276491621360.4785447016757280.239272350837864
630.7926213191611820.4147573616776360.207378680838818
640.7854981822013880.4290036355972240.214501817798612
650.7588638528346710.4822722943306570.241136147165329
660.7296985227489090.5406029545021830.270301477251091
670.6976887222051810.6046225555896380.302311277794819
680.6705091697279110.6589816605441790.329490830272089
690.6340033364609590.7319933270780820.365996663539041
700.5992892304148620.8014215391702760.400710769585138
710.5652256227820440.8695487544359130.434774377217956
720.5259812483444870.9480375033110250.474018751655513
730.5571203989054750.8857592021890490.442879601094525
740.5310714331946770.9378571336106460.468928566805323
750.4967245435716770.9934490871433540.503275456428323
760.4975102186618580.9950204373237150.502489781338142
770.5026291752231360.9947416495537280.497370824776864
780.506425799985830.987148400028340.49357420001417
790.5345128147011360.9309743705977280.465487185298864
800.5973395850832530.8053208298334950.402660414916747
810.5679743958868760.8640512082262470.432025604113124
820.6602304954978440.6795390090043120.339769504502156
830.6423772413587080.7152455172825840.357622758641292
840.6221470619979980.7557058760040050.377852938002002
850.6119803703959520.7760392592080960.388019629604048
860.6288448101885920.7423103796228170.371155189811408
870.603137017927940.7937259641441210.396862982072060
880.5820966521661640.8358066956676720.417903347833836
890.558679888516550.88264022296690.44132011148345
900.5981570962200240.8036858075599510.401842903779976
910.5703241980178440.8593516039643130.429675801982156
920.5482742532700930.9034514934598140.451725746729907
930.5937088658824070.8125822682351860.406291134117593
940.5526818640052970.8946362719894060.447318135994703
950.5989642361095760.8020715277808480.401035763890424
960.5788046377310230.8423907245379540.421195362268977
970.5971376621099160.8057246757801680.402862337890084
980.5757269424536080.8485461150927830.424273057546392
990.585142003008530.829715993982940.41485799699147
1000.6366055336629420.7267889326741150.363394466337058
1010.6150445118132010.7699109763735980.384955488186799
1020.5818110708045920.8363778583908150.418188929195408
1030.5682678482017760.8634643035964480.431732151798224
1040.5267289341565010.9465421316869980.473271065843499
1050.4890101526964460.9780203053928920.510989847303554
1060.4840850905159250.968170181031850.515914909484075
1070.4432511969017210.8865023938034420.556748803098279
1080.4231130511259580.8462261022519150.576886948874042
1090.4207056633130160.8414113266260330.579294336686984
1100.3978120416231980.7956240832463960.602187958376802
1110.3644817518685330.7289635037370660.635518248131467
1120.3313349396436060.6626698792872120.668665060356394
1130.3452325100207790.6904650200415580.654767489979221
1140.307222544628450.61444508925690.69277745537155
1150.2850532358517730.5701064717035470.714946764148227
1160.2567796639805740.5135593279611480.743220336019426
1170.2335237746186240.4670475492372480.766476225381376
1180.2177409541022750.435481908204550.782259045897725
1190.2691910251967240.5383820503934490.730808974803276
1200.2775299358784840.5550598717569680.722470064121516
1210.277320878782890.554641757565780.72267912121711
1220.2639758976801430.5279517953602850.736024102319857
1230.4644935868123720.9289871736247450.535506413187628
1240.4556162528207060.9112325056414120.544383747179294
1250.5266866220320220.9466267559359560.473313377967978
1260.555556359736650.8888872805266990.444443640263349
1270.5483862502290480.9032274995419050.451613749770952
1280.6390285959796840.7219428080406330.360971404020317
1290.6331501455055310.7336997089889380.366849854494469
1300.5993304172637780.8013391654724440.400669582736222
1310.571528528041580.856942943916840.42847147195842
1320.5288282133904890.9423435732190220.471171786609511
1330.5100020592937680.9799958814124640.489997940706232
1340.4781713570186970.9563427140373940.521828642981303
1350.4409046013920020.8818092027840050.559095398607998
1360.4433797600969560.8867595201939120.556620239903044
1370.3990746657787180.7981493315574360.600925334221282
1380.3654779510367080.7309559020734160.634522048963292
1390.3474507874875120.6949015749750240.652549212512488
1400.3073774481161690.6147548962323380.692622551883831
1410.2716633034827370.5433266069654730.728336696517264
1420.285226503807550.57045300761510.71477349619245
1430.2497082878113290.4994165756226580.750291712188671
1440.2652288173313150.530457634662630.734771182668685
1450.41189522304880.82379044609760.5881047769512
1460.3811166337940840.7622332675881690.618883366205916
1470.3997748748402530.7995497496805070.600225125159747
1480.3590553327199950.718110665439990.640944667280005
1490.3150504962313190.6301009924626380.684949503768681
1500.2734384535569040.5468769071138080.726561546443096
1510.2372028669615420.4744057339230850.762797133038458
1520.3962798011294870.7925596022589750.603720198870513
1530.346859501475780.693719002951560.65314049852422
1540.3160260606152070.6320521212304150.683973939384793
1550.2726923804286900.5453847608573790.72730761957131
1560.2379211878790650.4758423757581290.762078812120935
1570.2408579872699450.4817159745398890.759142012730055
1580.2372864158285480.4745728316570950.762713584171453
1590.1988108996124740.3976217992249480.801189100387526
1600.1627581812124260.3255163624248510.837241818787574
1610.1370372142810660.2740744285621310.862962785718934
1620.1154269284110260.2308538568220520.884573071588974
1630.1217476227680370.2434952455360740.878252377231963
1640.1047490751731660.2094981503463310.895250924826834
1650.1257839601781310.2515679203562610.874216039821869
1660.1140611495062330.2281222990124660.885938850493767
1670.1649481814534920.3298963629069830.835051818546508
1680.2030215628754230.4060431257508450.796978437124577
1690.1955942832168850.391188566433770.804405716783115
1700.3019372081604860.6038744163209730.698062791839514
1710.3189872460807770.6379744921615540.681012753919223
1720.2590627623262120.5181255246524240.740937237673788
1730.2072318528499320.4144637056998630.792768147150068
1740.1814383989223160.3628767978446330.818561601077684
1750.1857026871275360.3714053742550730.814297312872464
1760.1563470849791660.3126941699583320.843652915020834
1770.1160174150441170.2320348300882340.883982584955883
1780.1020605940961490.2041211881922980.897939405903851
1790.0901741680289290.1803483360578580.90982583197107
1800.1351662217223720.2703324434447440.864833778277628
1810.1115344902226880.2230689804453760.888465509777312
1820.07726311056732140.1545262211346430.922736889432679
1830.05402914401000270.1080582880200050.945970855989997
1840.0512555479809750.102511095961950.948744452019025
1850.08654840045558130.1730968009111630.913451599544419
1860.04484173696538020.08968347393076030.95515826303462






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.151685393258427NOK
5% type I error level320.179775280898876NOK
10% type I error level340.191011235955056NOK

\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 & 27 & 0.151685393258427 & NOK \tabularnewline
5% type I error level & 32 & 0.179775280898876 & NOK \tabularnewline
10% type I error level & 34 & 0.191011235955056 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116303&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]27[/C][C]0.151685393258427[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.179775280898876[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.191011235955056[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116303&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116303&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 level270.151685393258427NOK
5% type I error level320.179775280898876NOK
10% type I error level340.191011235955056NOK


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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}