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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 computationSun, 26 Dec 2010 20:18:12 +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/26/t1293394602qeiw48dzdjmirdu.htm/, Retrieved Mon, 06 May 2024 11:46:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115795, Retrieved Mon, 06 May 2024 11:46:07 +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] [15be6a7406d91c9912c2afdb984c54ee] [Current]
-   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 09:02:26] [d6e648f00513dd750579ba7880c5fbf5]
-             [Multiple Regression] [] [2010-12-28 11:58:43] [e45804683e9a4263debf179d74e04a01]
-           [Multiple Regression] [] [2010-12-27 21:41:58] [d6e648f00513dd750579ba7880c5fbf5]
-           [Multiple Regression] [] [2010-12-28 11:52:19] [e45804683e9a4263debf179d74e04a01]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27	5	26	49	35	18
36	4	25	45	34	10
25	4	17	54	13	23
27	3	37	36	35	14
25	3	35	36	28	20
44	3	15	53	32	15
50	4	27	46	35	18
41	4	36	42	36	19
48	5	25	41	27	19
43	4	30	45	29	14
47	2	27	47	27	15
41	3	33	42	28	14
44	2	29	45	29	16
47	5	30	40	28	13
40	3	25	45	30	13
46	3	23	40	25	14
28	3	26	42	15	23
56	3	24	45	33	17
49	4	35	47	31	14
25	4	39	31	37	21
41	4	23	46	37	15
26	3	32	34	34	19
50	5	29	43	32	20
47	4	26	45	21	18
52	2	21	42	25	13
37	5	35	51	32	20
41	3	23	44	28	12
45	4	21	47	22	17
26	4	28	47	25	13
	3	30	41	26	17
52	4	21	44	34	16
46	2	29	51	34	20
58	3	28	46	36	18
54	5	19	47	36	9
29	3	26	46	26	14
50	3	33	38	26	12
43	2	34	50	34	21
30	3	33	48	33	16
47	2	40	36	31	12
45	3	24	51	33	20
48	1	35	35	22	18
48	3	35	49	29	22
26	4	32	38	24	17
46	5	20	47	37	16
	3	35	36	32	14
50	3	35	47	23	19
25	4	21	46	29	21
47	2	33	43	35	18
47	2	40	53	20	23
41	3	22	55	28	20
45	2	35	39	26	10
41	4	20	55	36	16
45	5	28	41	26	18
40	3	46	33	33	12
29	4	18	52	25	15
34	5	22	42	29	19
45	5	20	56	32	11
52	3	25	46	35	16
41	4	31	33	24	12
48	3	21	51	31	18
45	3	23	46	29	14
54	2	26	46	27	20
25	3	34	50	29	15
26	4	31	46	29	17
28	4	23	51	27	20
50	4	31	48	34	14
48	4	26	44	32	16
51	3	36	38	31	15
53	3	28	42	31	17
37	3	34	39	31	20
56	2	25	45	16	14
43	3	33	31	25	20
34	3	46	29	27	20
42	3	24	48	32	15
32	3	32	38	28	21
31	5	33	55	25	22
46	3	42	32	25	11
30	5	17	51	36	20
47	4	36	53	36	17
33	4	40	47	36	19
25	4	30	45	27	17
25	5	19	33	29	15
21	4	33	49	32	20
36	5	35	46	29	12
50	3	23	42	31	13
48	3	15	56	34	18
48	2	38	35	27	19
25	3	37	40	28	13
48	4	23	44	32	12
49	5	41	46	33	16
27	5	34	46	29	21
28	3	38	39	32	19
43	2	45	35	35	19
48	3	27	48	33	12
48	4	46	42	27	22
25	1	26	39	16	9
49	4	44	39	32	9
26	3	36	41	26	18
51	3	20	52	32	14
25	4	44	45	38	14
29	3	27	42	24	23
29	4	27	44	26	19
43	2	41	33	19	24
46	3	30	42	37	12
44	3	33	46	25	20
25	3	37	45	24	21
51	2	30	40	23	18
42	5	20	48	28	20
53	5	44	32	38	18
25	4	20	53	28	18
49	2	33	39	28	17
51	3	31	45	26	18
20	3	23	36	21	14
44	3	33	38	35	23
38	4	33	49	31	19
46	5	32	46	34	14
42	4	25	43	30	17
29		22	37	30	22
46	4	16	48	24	10
49	2	36	45	27	16
51	3	35	32	26	14
38	3	25	46	30	19
41	1	27	20	15	14
47	3	32	42	28	18
44	3	36	45	34	19
47	3	51	29	29	21
46	3	30	51	26	13
44	4	20	55	31	17
28	3	29	50	28	11
47	4	26	44	33	16
28	4	20	41	32	22
41	5	40	40	33	19
45	4	29	47	31	17
46	4	32	42	37	25
46	4	33	40	27	17
22	3	32	51	19	23
33	3	34	43	27	21
41	4	24	45	31	12
47	5	25	41	38	18
25	3	41	41	22	15
42	3	39	37	35	17
47	3	21	46	35	11
50	3	38	38	30	17
55	5	28	39	41	13
21	3	37	45	25	17
	3	26	46	28	16
52	3	30	39	45	14
49	4	25	21	21	15
46	4	38	31	33	20
	4	31	35	25	14
45	3	31	49	29	16
52	3	27	40	31	14
	3	21	45	29	13
40	4	26	46	31	15
49	4	37	45	31	13
38	5	28	34	25	13
32	5	29	41	27	23
46	4	33	43	26	18
32	3	41	45	26	21
41	3	19	48	23	14
43	3	37	43	27	12
44	4	36	45	24	17
47	5	27	45	35	11
28	3	33	34	24	15
52	1	29	40	32	14
27	2	42	40	24	19
45	5	27	55	24	12
27	4	47	44	38	14
25	4	17	44	36	18
28	4	34	48	24	25
25	3	32	51	18	22
52	4	25	49	34	15
44	3	27	33	23	18
43	3	37	43	35	18
47	4	34	44	22	12
52	4	27	44	34	12
40	2	37	41	28	16
42	3	32	45	34	22
45	5	26	44	32	15
45	2	29	44	24	16
50	5	28	40	34	11
49	3	19	48	33	20
52	2	46	49	33	14
48	3	31	46	29	20
51	3	42	49	38	15
49	4	33	55	24	12
31	4	39	51	25	18
43	3	27	46	37	18
31	3	35	37	33	11
28	4	23	43	30	13
43	4	32	41	22	15
31	3	22	45	28	19
51	3	17	39	24	13
58	4	35	38	33	19
25	5	34	41	37	18

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \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=115795&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/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=115795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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
leeftijd[t] = + 77.3754429800317 -0.381240306445034opleiding[t] -0.524053055241366Neuroticisme[t] -0.0822231173384081Extraversie[t] -0.293690031586825Openheid[t] -0.513130921270904Extrinsieke_waarden[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
leeftijd[t] =  +  77.3754429800317 -0.381240306445034opleiding[t] -0.524053055241366Neuroticisme[t] -0.0822231173384081Extraversie[t] -0.293690031586825Openheid[t] -0.513130921270904Extrinsieke_waarden[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]leeftijd[t] =  +  77.3754429800317 -0.381240306445034opleiding[t] -0.524053055241366Neuroticisme[t] -0.0822231173384081Extraversie[t] -0.293690031586825Openheid[t] -0.513130921270904Extrinsieke_waarden[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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
leeftijd[t] = + 77.3754429800317 -0.381240306445034opleiding[t] -0.524053055241366Neuroticisme[t] -0.0822231173384081Extraversie[t] -0.293690031586825Openheid[t] -0.513130921270904Extrinsieke_waarden[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)77.37544298003176.52304511.861900
opleiding-0.3812403064450340.06365-5.989700
Neuroticisme-0.5240530552413660.084094-6.231800
Extraversie-0.08222311733840810.09282-0.88580.3768350.188418
Openheid-0.2936900315868250.074976-3.91710.0001256.3e-05
Extrinsieke_waarden-0.5131309212709040.073941-6.939700

\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) & 77.3754429800317 & 6.523045 & 11.8619 & 0 & 0 \tabularnewline
opleiding & -0.381240306445034 & 0.06365 & -5.9897 & 0 & 0 \tabularnewline
Neuroticisme & -0.524053055241366 & 0.084094 & -6.2318 & 0 & 0 \tabularnewline
Extraversie & -0.0822231173384081 & 0.09282 & -0.8858 & 0.376835 & 0.188418 \tabularnewline
Openheid & -0.293690031586825 & 0.074976 & -3.9171 & 0.000125 & 6.3e-05 \tabularnewline
Extrinsieke_waarden & -0.513130921270904 & 0.073941 & -6.9397 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&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]77.3754429800317[/C][C]6.523045[/C][C]11.8619[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.381240306445034[/C][C]0.06365[/C][C]-5.9897[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.524053055241366[/C][C]0.084094[/C][C]-6.2318[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.0822231173384081[/C][C]0.09282[/C][C]-0.8858[/C][C]0.376835[/C][C]0.188418[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.293690031586825[/C][C]0.074976[/C][C]-3.9171[/C][C]0.000125[/C][C]6.3e-05[/C][/ROW]
[ROW][C]Extrinsieke_waarden[/C][C]-0.513130921270904[/C][C]0.073941[/C][C]-6.9397[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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)77.37544298003176.52304511.861900
opleiding-0.3812403064450340.06365-5.989700
Neuroticisme-0.5240530552413660.084094-6.231800
Extraversie-0.08222311733840810.09282-0.88580.3768350.188418
Openheid-0.2936900315868250.074976-3.91710.0001256.3e-05
Extrinsieke_waarden-0.5131309212709040.073941-6.939700






Multiple Linear Regression - Regression Statistics
Multiple R0.676772943083343
R-squared0.458021616489690
Adjusted R-squared0.443683564015872
F-TEST (value)31.9444790236379
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.81271872174484
Sum Squared Residuals18198.7058065834

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.676772943083343 \tabularnewline
R-squared & 0.458021616489690 \tabularnewline
Adjusted R-squared & 0.443683564015872 \tabularnewline
F-TEST (value) & 31.9444790236379 \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 & 9.81271872174484 \tabularnewline
Sum Squared Residuals & 18198.7058065834 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.676772943083343[/C][/ROW]
[ROW][C]R-squared[/C][C]0.458021616489690[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.443683564015872[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.9444790236379[/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]9.81271872174484[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18198.7058065834[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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 R0.676772943083343
R-squared0.458021616489690
Adjusted R-squared0.443683564015872
F-TEST (value)31.9444790236379
F-TEST (DF numerator)5
F-TEST (DF denominator)189
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.81271872174484
Sum Squared Residuals18198.7058065834






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12738.2994215735336-11.2994215735336
23643.9323448063280-7.93234480632796
32546.8815498790148-21.8815498790148
42736.4187427892518-9.4187427892518
52536.4438935932169-11.4438935932169
64446.9180561832985-2.91805618329848
75038.403278176752711.5967218232473
84133.20887219607637.79112780392365
94841.31764889890626.68235110109383
104340.72800600297162.27199399702839
114742.97244868881174.02755131118829
124140.07744652729460.922553472705404
134440.98827782856123.01172217143876
144741.56470223607635.43529776392365
154043.9489524753076-3.94895247530756
164646.3634934091455-0.363493409145547
172842.9456100331747-14.9456100331747
185641.539411750704814.4605882492952
194937.355914428914311.6440855710857
202531.2212154469461-6.2212154469461
214141.4515030983573-0.451503098357265
222636.9314897253678-10.9314897253678
235037.075409364058912.9245906359411
244743.12121479154813.87878520845194
255248.14152451266743.85847548733260
263733.27330609390343.7266939060966
274146.1797926875732-5.17979268757325
284545.7964747227621-0.796474722762152
292643.2995569263957-17.2995569263957
30310.638719027923-7.638719027923
31415.2124132407092-11.2124132407092
3221.161788220861200.838211779138798
3336.63875131709352-3.63875131709352
34525.0173443359115-20.0173443359115
35311.4507469147986-8.45074691479858
36317.1537857236842-14.1537857236842
37213.8536155078759-11.8536155078759
3838.11040953847082-5.11040953847081
39214.0958322598179-12.0958322598179
4038.28152208313382-5.28152208313382
41113.9645619499964-12.9645619499964
42316.1663774968611-13.1663774968611
43414.6916293430593-10.6916293430593
44535.8394546440638-30.8394546440638
453529.50607619439515.49392380560494
463536.4469146023797-1.44691460237965
472128.0844714903313-7.08447149033134
483326.33054343023356.66945656976653
494031.21682987596168.78317012403837
502225.8469649680795-3.84696496807946
513533.96564543887231.03435456112769
522020.4440402316894-0.444040231689448
532833.3522008401327-5.3522008401327
544635.944550035218010.0554499647820
551830.6651582234731-12.6651582234731
562228.8218662501492-6.82186625014923
572021.5405593543360-1.54055935433596
582526.0871470925543-1.08714709255433
593135.5940478535116-4.59404785351156
602125.4510823415414-4.45108234154141
612326.6042030905924-3.60420309059243
622635.1628504917917-9.16285049179172
633432.19407778936321.80592221063683
643132.9672127172928-1.96721271729283
652325.4012672486250-2.40126724862503
663123.95733554847477.04266445152533
672625.99801747657110.00198252342885122
683628.30435542464757.69564457535252
692831.3139884695797-3.31398846957972
703427.14406035802086.85593964197917
712536.5155925413594-11.5155925413594
723339.2863509146685-6.28635091466854
734636.65120516438139.34879483561872
742430.1353899682791-6.13538996827909
753234.8180947387095-2.81809473870947
763326.4478569462696.55214305373099
774239.79361694807472.20638305192533
781721.5658598462132-4.56585984621321
793625.161709027553910.8382909724461
804029.634224884241910.3657751157581
813034.7644983077102-4.76449830771017
821940.1436131568629-21.1436131568629
833327.14201210625295.85798789374709
843527.43027853034567.56972146965436
852328.4122905914783-5.41229059147828
861521.6047824701026-6.60478247010257
873839.4387169800255-1.43871698002551
883730.23379944882156.76620055117846
892325.8880281665566-2.88802816655662
904130.733782723981710.2662172760183
913433.15145116921010.848548830789893
923830.52020083074667.4797991692534
934528.491421811597616.5085781884024
942724.64583483839292.35416516160715
954637.54962732543688.45037267456315
962636.9388788559298-10.9388788559298
974435.82203161983588.1779683801642
983630.12161049267765.87838950732237
992030.2353511596742-10.2353511596742
1004429.098085768264914.9019142317351
1012736.3254104836625-9.32541048366253
1022731.7583176299696-4.75831762996962
1034137.81501578483113.18498421516886
1043026.52495550371583.47504449628421
1053336.2109566022745-3.21095660227445
1063729.91121694663597.08878305336409
1073033.6919584065864-3.69195840658638
1082024.6267340966874-4.62673409668739
1094434.38694648777319.61305351222692
1102025.5991316892990-5.59913168929905
1113329.91820811242343.08179188757662
1123137.7010402460891-6.70104024608906
1132337.0327999915710-14.0327999915710
1143329.44257781750623.55742218249378
1153324.81138796296448.18861203703556
1163226.48195635088575.51804364911432
1172524.05683396692350.943166033076527
1183741.2419483071221-4.24194830712205
1194839.8961190944298.103880905571
1204535.66307449865309.33692550134704
1213243.2926306536891-11.2926306536891
1224638.46185302031877.53814697968128
1232043.1543495196424-23.1543495196424
1244234.29615898182317.70384101817685
1254523.541030916832821.4589690831672
1262935.2570988026026-6.25709880260262
1275145.59530957966775.40469042033231
1285538.583977444040316.4160225559597
1295042.55548019761977.44451980238029
1304442.67003814624291.32996185375712
1314128.281751138835512.7182488611645
1324035.08190769432774.91809230567229
1334735.270878536549411.7291214634506
1344228.277881334677313.7221186653227
1354039.06288461003810.937115389961859
1365137.037772596885913.9622274031141
1374339.21579049822333.78420950177667
1384541.10714711272733.89285288727269
1394129.480340540448011.5196594595520
1404136.78081345708154.21918654291845
1413739.6018243589976-2.60182435899761
1424633.776247676825212.2237523231748
1433836.67094444044551.32905555955446
1443933.3383010517575.66169894824299
1454527.448900828068317.5510991719317
1462814.955362975174913.0446370248251
1474527.91258637726117.087413622739
1482120.15422561320230.845774386797683
1493341.9980838553634-8.9980838553634
1501426.8269451221798-12.8269451221798
1511626.1041038881578-10.1041038881578
1521446.3388547278599-32.3388547278599
1534041.6414841221541-1.64148412215411
1544936.985385474379312.0146145256207
1553843.98721714535-5.98721714535
1563237.1689129928571-5.16891299285709
1574638.14883948160127.85116051839881
1583232.6338163476258-0.633816347625763
1594148.3893007545774-7.3893007545774
1604339.21896306311943.78103693688065
1614437.51274506564486.48725493435517
1624741.69617743654245.30382256345756
1632841.3968606710783-13.3968606710783
1645241.925825469479610.0741745305204
1652734.5157610912369-7.51576109123693
1664543.59140568934251.40859431065747
1672729.2581168969254-2.25811689692537
1682543.5145649322564-18.5145649322564
1692834.2091344539451-6.2091344539451
1702538.6933444721913-13.6933444721913
1715241.037797730619810.9622022693802
1724443.3776993876390.622300612361028
1734333.79065728279939.20934271720067
1744741.79610896299415.20389103700586
1755241.940199970641810.0598000293582
1764037.41843588757082.58156411242923
1774234.48764267083267.51235732916744
1784541.13100001879913.86899998120093
1794542.53895110383382.46104889616623
1805041.87692999957998.12307000042006
1814942.37361491108376.62638508891627
1825231.601985136298920.3980148637011
1834837.424184609211410.5758153907886
1845131.335375971614319.6646240283857
1854940.82832766433948.17167233566064
1863134.6404262430326-3.64042624303255
1874338.19713842002414.80286157997588
1883139.5113986093825-8.51139860938249
1892844.7802645140221-16.7802645140221
1904341.55149166167941.44850833832062
1913143.0297061765799-12.0297061765799
1925150.39685581078990.603144189210117
1935834.942887815431823.0571121845682
1942534.1774020071365-9.17740200713653
1952738.2994215735338-11.2994215735338

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27 & 38.2994215735336 & -11.2994215735336 \tabularnewline
2 & 36 & 43.9323448063280 & -7.93234480632796 \tabularnewline
3 & 25 & 46.8815498790148 & -21.8815498790148 \tabularnewline
4 & 27 & 36.4187427892518 & -9.4187427892518 \tabularnewline
5 & 25 & 36.4438935932169 & -11.4438935932169 \tabularnewline
6 & 44 & 46.9180561832985 & -2.91805618329848 \tabularnewline
7 & 50 & 38.4032781767527 & 11.5967218232473 \tabularnewline
8 & 41 & 33.2088721960763 & 7.79112780392365 \tabularnewline
9 & 48 & 41.3176488989062 & 6.68235110109383 \tabularnewline
10 & 43 & 40.7280060029716 & 2.27199399702839 \tabularnewline
11 & 47 & 42.9724486888117 & 4.02755131118829 \tabularnewline
12 & 41 & 40.0774465272946 & 0.922553472705404 \tabularnewline
13 & 44 & 40.9882778285612 & 3.01172217143876 \tabularnewline
14 & 47 & 41.5647022360763 & 5.43529776392365 \tabularnewline
15 & 40 & 43.9489524753076 & -3.94895247530756 \tabularnewline
16 & 46 & 46.3634934091455 & -0.363493409145547 \tabularnewline
17 & 28 & 42.9456100331747 & -14.9456100331747 \tabularnewline
18 & 56 & 41.5394117507048 & 14.4605882492952 \tabularnewline
19 & 49 & 37.3559144289143 & 11.6440855710857 \tabularnewline
20 & 25 & 31.2212154469461 & -6.2212154469461 \tabularnewline
21 & 41 & 41.4515030983573 & -0.451503098357265 \tabularnewline
22 & 26 & 36.9314897253678 & -10.9314897253678 \tabularnewline
23 & 50 & 37.0754093640589 & 12.9245906359411 \tabularnewline
24 & 47 & 43.1212147915481 & 3.87878520845194 \tabularnewline
25 & 52 & 48.1415245126674 & 3.85847548733260 \tabularnewline
26 & 37 & 33.2733060939034 & 3.7266939060966 \tabularnewline
27 & 41 & 46.1797926875732 & -5.17979268757325 \tabularnewline
28 & 45 & 45.7964747227621 & -0.796474722762152 \tabularnewline
29 & 26 & 43.2995569263957 & -17.2995569263957 \tabularnewline
30 & 3 & 10.638719027923 & -7.638719027923 \tabularnewline
31 & 4 & 15.2124132407092 & -11.2124132407092 \tabularnewline
32 & 2 & 1.16178822086120 & 0.838211779138798 \tabularnewline
33 & 3 & 6.63875131709352 & -3.63875131709352 \tabularnewline
34 & 5 & 25.0173443359115 & -20.0173443359115 \tabularnewline
35 & 3 & 11.4507469147986 & -8.45074691479858 \tabularnewline
36 & 3 & 17.1537857236842 & -14.1537857236842 \tabularnewline
37 & 2 & 13.8536155078759 & -11.8536155078759 \tabularnewline
38 & 3 & 8.11040953847082 & -5.11040953847081 \tabularnewline
39 & 2 & 14.0958322598179 & -12.0958322598179 \tabularnewline
40 & 3 & 8.28152208313382 & -5.28152208313382 \tabularnewline
41 & 1 & 13.9645619499964 & -12.9645619499964 \tabularnewline
42 & 3 & 16.1663774968611 & -13.1663774968611 \tabularnewline
43 & 4 & 14.6916293430593 & -10.6916293430593 \tabularnewline
44 & 5 & 35.8394546440638 & -30.8394546440638 \tabularnewline
45 & 35 & 29.5060761943951 & 5.49392380560494 \tabularnewline
46 & 35 & 36.4469146023797 & -1.44691460237965 \tabularnewline
47 & 21 & 28.0844714903313 & -7.08447149033134 \tabularnewline
48 & 33 & 26.3305434302335 & 6.66945656976653 \tabularnewline
49 & 40 & 31.2168298759616 & 8.78317012403837 \tabularnewline
50 & 22 & 25.8469649680795 & -3.84696496807946 \tabularnewline
51 & 35 & 33.9656454388723 & 1.03435456112769 \tabularnewline
52 & 20 & 20.4440402316894 & -0.444040231689448 \tabularnewline
53 & 28 & 33.3522008401327 & -5.3522008401327 \tabularnewline
54 & 46 & 35.9445500352180 & 10.0554499647820 \tabularnewline
55 & 18 & 30.6651582234731 & -12.6651582234731 \tabularnewline
56 & 22 & 28.8218662501492 & -6.82186625014923 \tabularnewline
57 & 20 & 21.5405593543360 & -1.54055935433596 \tabularnewline
58 & 25 & 26.0871470925543 & -1.08714709255433 \tabularnewline
59 & 31 & 35.5940478535116 & -4.59404785351156 \tabularnewline
60 & 21 & 25.4510823415414 & -4.45108234154141 \tabularnewline
61 & 23 & 26.6042030905924 & -3.60420309059243 \tabularnewline
62 & 26 & 35.1628504917917 & -9.16285049179172 \tabularnewline
63 & 34 & 32.1940777893632 & 1.80592221063683 \tabularnewline
64 & 31 & 32.9672127172928 & -1.96721271729283 \tabularnewline
65 & 23 & 25.4012672486250 & -2.40126724862503 \tabularnewline
66 & 31 & 23.9573355484747 & 7.04266445152533 \tabularnewline
67 & 26 & 25.9980174765711 & 0.00198252342885122 \tabularnewline
68 & 36 & 28.3043554246475 & 7.69564457535252 \tabularnewline
69 & 28 & 31.3139884695797 & -3.31398846957972 \tabularnewline
70 & 34 & 27.1440603580208 & 6.85593964197917 \tabularnewline
71 & 25 & 36.5155925413594 & -11.5155925413594 \tabularnewline
72 & 33 & 39.2863509146685 & -6.28635091466854 \tabularnewline
73 & 46 & 36.6512051643813 & 9.34879483561872 \tabularnewline
74 & 24 & 30.1353899682791 & -6.13538996827909 \tabularnewline
75 & 32 & 34.8180947387095 & -2.81809473870947 \tabularnewline
76 & 33 & 26.447856946269 & 6.55214305373099 \tabularnewline
77 & 42 & 39.7936169480747 & 2.20638305192533 \tabularnewline
78 & 17 & 21.5658598462132 & -4.56585984621321 \tabularnewline
79 & 36 & 25.1617090275539 & 10.8382909724461 \tabularnewline
80 & 40 & 29.6342248842419 & 10.3657751157581 \tabularnewline
81 & 30 & 34.7644983077102 & -4.76449830771017 \tabularnewline
82 & 19 & 40.1436131568629 & -21.1436131568629 \tabularnewline
83 & 33 & 27.1420121062529 & 5.85798789374709 \tabularnewline
84 & 35 & 27.4302785303456 & 7.56972146965436 \tabularnewline
85 & 23 & 28.4122905914783 & -5.41229059147828 \tabularnewline
86 & 15 & 21.6047824701026 & -6.60478247010257 \tabularnewline
87 & 38 & 39.4387169800255 & -1.43871698002551 \tabularnewline
88 & 37 & 30.2337994488215 & 6.76620055117846 \tabularnewline
89 & 23 & 25.8880281665566 & -2.88802816655662 \tabularnewline
90 & 41 & 30.7337827239817 & 10.2662172760183 \tabularnewline
91 & 34 & 33.1514511692101 & 0.848548830789893 \tabularnewline
92 & 38 & 30.5202008307466 & 7.4797991692534 \tabularnewline
93 & 45 & 28.4914218115976 & 16.5085781884024 \tabularnewline
94 & 27 & 24.6458348383929 & 2.35416516160715 \tabularnewline
95 & 46 & 37.5496273254368 & 8.45037267456315 \tabularnewline
96 & 26 & 36.9388788559298 & -10.9388788559298 \tabularnewline
97 & 44 & 35.8220316198358 & 8.1779683801642 \tabularnewline
98 & 36 & 30.1216104926776 & 5.87838950732237 \tabularnewline
99 & 20 & 30.2353511596742 & -10.2353511596742 \tabularnewline
100 & 44 & 29.0980857682649 & 14.9019142317351 \tabularnewline
101 & 27 & 36.3254104836625 & -9.32541048366253 \tabularnewline
102 & 27 & 31.7583176299696 & -4.75831762996962 \tabularnewline
103 & 41 & 37.8150157848311 & 3.18498421516886 \tabularnewline
104 & 30 & 26.5249555037158 & 3.47504449628421 \tabularnewline
105 & 33 & 36.2109566022745 & -3.21095660227445 \tabularnewline
106 & 37 & 29.9112169466359 & 7.08878305336409 \tabularnewline
107 & 30 & 33.6919584065864 & -3.69195840658638 \tabularnewline
108 & 20 & 24.6267340966874 & -4.62673409668739 \tabularnewline
109 & 44 & 34.3869464877731 & 9.61305351222692 \tabularnewline
110 & 20 & 25.5991316892990 & -5.59913168929905 \tabularnewline
111 & 33 & 29.9182081124234 & 3.08179188757662 \tabularnewline
112 & 31 & 37.7010402460891 & -6.70104024608906 \tabularnewline
113 & 23 & 37.0327999915710 & -14.0327999915710 \tabularnewline
114 & 33 & 29.4425778175062 & 3.55742218249378 \tabularnewline
115 & 33 & 24.8113879629644 & 8.18861203703556 \tabularnewline
116 & 32 & 26.4819563508857 & 5.51804364911432 \tabularnewline
117 & 25 & 24.0568339669235 & 0.943166033076527 \tabularnewline
118 & 37 & 41.2419483071221 & -4.24194830712205 \tabularnewline
119 & 48 & 39.896119094429 & 8.103880905571 \tabularnewline
120 & 45 & 35.6630744986530 & 9.33692550134704 \tabularnewline
121 & 32 & 43.2926306536891 & -11.2926306536891 \tabularnewline
122 & 46 & 38.4618530203187 & 7.53814697968128 \tabularnewline
123 & 20 & 43.1543495196424 & -23.1543495196424 \tabularnewline
124 & 42 & 34.2961589818231 & 7.70384101817685 \tabularnewline
125 & 45 & 23.5410309168328 & 21.4589690831672 \tabularnewline
126 & 29 & 35.2570988026026 & -6.25709880260262 \tabularnewline
127 & 51 & 45.5953095796677 & 5.40469042033231 \tabularnewline
128 & 55 & 38.5839774440403 & 16.4160225559597 \tabularnewline
129 & 50 & 42.5554801976197 & 7.44451980238029 \tabularnewline
130 & 44 & 42.6700381462429 & 1.32996185375712 \tabularnewline
131 & 41 & 28.2817511388355 & 12.7182488611645 \tabularnewline
132 & 40 & 35.0819076943277 & 4.91809230567229 \tabularnewline
133 & 47 & 35.2708785365494 & 11.7291214634506 \tabularnewline
134 & 42 & 28.2778813346773 & 13.7221186653227 \tabularnewline
135 & 40 & 39.0628846100381 & 0.937115389961859 \tabularnewline
136 & 51 & 37.0377725968859 & 13.9622274031141 \tabularnewline
137 & 43 & 39.2157904982233 & 3.78420950177667 \tabularnewline
138 & 45 & 41.1071471127273 & 3.89285288727269 \tabularnewline
139 & 41 & 29.4803405404480 & 11.5196594595520 \tabularnewline
140 & 41 & 36.7808134570815 & 4.21918654291845 \tabularnewline
141 & 37 & 39.6018243589976 & -2.60182435899761 \tabularnewline
142 & 46 & 33.7762476768252 & 12.2237523231748 \tabularnewline
143 & 38 & 36.6709444404455 & 1.32905555955446 \tabularnewline
144 & 39 & 33.338301051757 & 5.66169894824299 \tabularnewline
145 & 45 & 27.4489008280683 & 17.5510991719317 \tabularnewline
146 & 28 & 14.9553629751749 & 13.0446370248251 \tabularnewline
147 & 45 & 27.912586377261 & 17.087413622739 \tabularnewline
148 & 21 & 20.1542256132023 & 0.845774386797683 \tabularnewline
149 & 33 & 41.9980838553634 & -8.9980838553634 \tabularnewline
150 & 14 & 26.8269451221798 & -12.8269451221798 \tabularnewline
151 & 16 & 26.1041038881578 & -10.1041038881578 \tabularnewline
152 & 14 & 46.3388547278599 & -32.3388547278599 \tabularnewline
153 & 40 & 41.6414841221541 & -1.64148412215411 \tabularnewline
154 & 49 & 36.9853854743793 & 12.0146145256207 \tabularnewline
155 & 38 & 43.98721714535 & -5.98721714535 \tabularnewline
156 & 32 & 37.1689129928571 & -5.16891299285709 \tabularnewline
157 & 46 & 38.1488394816012 & 7.85116051839881 \tabularnewline
158 & 32 & 32.6338163476258 & -0.633816347625763 \tabularnewline
159 & 41 & 48.3893007545774 & -7.3893007545774 \tabularnewline
160 & 43 & 39.2189630631194 & 3.78103693688065 \tabularnewline
161 & 44 & 37.5127450656448 & 6.48725493435517 \tabularnewline
162 & 47 & 41.6961774365424 & 5.30382256345756 \tabularnewline
163 & 28 & 41.3968606710783 & -13.3968606710783 \tabularnewline
164 & 52 & 41.9258254694796 & 10.0741745305204 \tabularnewline
165 & 27 & 34.5157610912369 & -7.51576109123693 \tabularnewline
166 & 45 & 43.5914056893425 & 1.40859431065747 \tabularnewline
167 & 27 & 29.2581168969254 & -2.25811689692537 \tabularnewline
168 & 25 & 43.5145649322564 & -18.5145649322564 \tabularnewline
169 & 28 & 34.2091344539451 & -6.2091344539451 \tabularnewline
170 & 25 & 38.6933444721913 & -13.6933444721913 \tabularnewline
171 & 52 & 41.0377977306198 & 10.9622022693802 \tabularnewline
172 & 44 & 43.377699387639 & 0.622300612361028 \tabularnewline
173 & 43 & 33.7906572827993 & 9.20934271720067 \tabularnewline
174 & 47 & 41.7961089629941 & 5.20389103700586 \tabularnewline
175 & 52 & 41.9401999706418 & 10.0598000293582 \tabularnewline
176 & 40 & 37.4184358875708 & 2.58156411242923 \tabularnewline
177 & 42 & 34.4876426708326 & 7.51235732916744 \tabularnewline
178 & 45 & 41.1310000187991 & 3.86899998120093 \tabularnewline
179 & 45 & 42.5389511038338 & 2.46104889616623 \tabularnewline
180 & 50 & 41.8769299995799 & 8.12307000042006 \tabularnewline
181 & 49 & 42.3736149110837 & 6.62638508891627 \tabularnewline
182 & 52 & 31.6019851362989 & 20.3980148637011 \tabularnewline
183 & 48 & 37.4241846092114 & 10.5758153907886 \tabularnewline
184 & 51 & 31.3353759716143 & 19.6646240283857 \tabularnewline
185 & 49 & 40.8283276643394 & 8.17167233566064 \tabularnewline
186 & 31 & 34.6404262430326 & -3.64042624303255 \tabularnewline
187 & 43 & 38.1971384200241 & 4.80286157997588 \tabularnewline
188 & 31 & 39.5113986093825 & -8.51139860938249 \tabularnewline
189 & 28 & 44.7802645140221 & -16.7802645140221 \tabularnewline
190 & 43 & 41.5514916616794 & 1.44850833832062 \tabularnewline
191 & 31 & 43.0297061765799 & -12.0297061765799 \tabularnewline
192 & 51 & 50.3968558107899 & 0.603144189210117 \tabularnewline
193 & 58 & 34.9428878154318 & 23.0571121845682 \tabularnewline
194 & 25 & 34.1774020071365 & -9.17740200713653 \tabularnewline
195 & 27 & 38.2994215735338 & -11.2994215735338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&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]27[/C][C]38.2994215735336[/C][C]-11.2994215735336[/C][/ROW]
[ROW][C]2[/C][C]36[/C][C]43.9323448063280[/C][C]-7.93234480632796[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]46.8815498790148[/C][C]-21.8815498790148[/C][/ROW]
[ROW][C]4[/C][C]27[/C][C]36.4187427892518[/C][C]-9.4187427892518[/C][/ROW]
[ROW][C]5[/C][C]25[/C][C]36.4438935932169[/C][C]-11.4438935932169[/C][/ROW]
[ROW][C]6[/C][C]44[/C][C]46.9180561832985[/C][C]-2.91805618329848[/C][/ROW]
[ROW][C]7[/C][C]50[/C][C]38.4032781767527[/C][C]11.5967218232473[/C][/ROW]
[ROW][C]8[/C][C]41[/C][C]33.2088721960763[/C][C]7.79112780392365[/C][/ROW]
[ROW][C]9[/C][C]48[/C][C]41.3176488989062[/C][C]6.68235110109383[/C][/ROW]
[ROW][C]10[/C][C]43[/C][C]40.7280060029716[/C][C]2.27199399702839[/C][/ROW]
[ROW][C]11[/C][C]47[/C][C]42.9724486888117[/C][C]4.02755131118829[/C][/ROW]
[ROW][C]12[/C][C]41[/C][C]40.0774465272946[/C][C]0.922553472705404[/C][/ROW]
[ROW][C]13[/C][C]44[/C][C]40.9882778285612[/C][C]3.01172217143876[/C][/ROW]
[ROW][C]14[/C][C]47[/C][C]41.5647022360763[/C][C]5.43529776392365[/C][/ROW]
[ROW][C]15[/C][C]40[/C][C]43.9489524753076[/C][C]-3.94895247530756[/C][/ROW]
[ROW][C]16[/C][C]46[/C][C]46.3634934091455[/C][C]-0.363493409145547[/C][/ROW]
[ROW][C]17[/C][C]28[/C][C]42.9456100331747[/C][C]-14.9456100331747[/C][/ROW]
[ROW][C]18[/C][C]56[/C][C]41.5394117507048[/C][C]14.4605882492952[/C][/ROW]
[ROW][C]19[/C][C]49[/C][C]37.3559144289143[/C][C]11.6440855710857[/C][/ROW]
[ROW][C]20[/C][C]25[/C][C]31.2212154469461[/C][C]-6.2212154469461[/C][/ROW]
[ROW][C]21[/C][C]41[/C][C]41.4515030983573[/C][C]-0.451503098357265[/C][/ROW]
[ROW][C]22[/C][C]26[/C][C]36.9314897253678[/C][C]-10.9314897253678[/C][/ROW]
[ROW][C]23[/C][C]50[/C][C]37.0754093640589[/C][C]12.9245906359411[/C][/ROW]
[ROW][C]24[/C][C]47[/C][C]43.1212147915481[/C][C]3.87878520845194[/C][/ROW]
[ROW][C]25[/C][C]52[/C][C]48.1415245126674[/C][C]3.85847548733260[/C][/ROW]
[ROW][C]26[/C][C]37[/C][C]33.2733060939034[/C][C]3.7266939060966[/C][/ROW]
[ROW][C]27[/C][C]41[/C][C]46.1797926875732[/C][C]-5.17979268757325[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]45.7964747227621[/C][C]-0.796474722762152[/C][/ROW]
[ROW][C]29[/C][C]26[/C][C]43.2995569263957[/C][C]-17.2995569263957[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]10.638719027923[/C][C]-7.638719027923[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]15.2124132407092[/C][C]-11.2124132407092[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]1.16178822086120[/C][C]0.838211779138798[/C][/ROW]
[ROW][C]33[/C][C]3[/C][C]6.63875131709352[/C][C]-3.63875131709352[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]25.0173443359115[/C][C]-20.0173443359115[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]11.4507469147986[/C][C]-8.45074691479858[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]17.1537857236842[/C][C]-14.1537857236842[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]13.8536155078759[/C][C]-11.8536155078759[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]8.11040953847082[/C][C]-5.11040953847081[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]14.0958322598179[/C][C]-12.0958322598179[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]8.28152208313382[/C][C]-5.28152208313382[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]13.9645619499964[/C][C]-12.9645619499964[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]16.1663774968611[/C][C]-13.1663774968611[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]14.6916293430593[/C][C]-10.6916293430593[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]35.8394546440638[/C][C]-30.8394546440638[/C][/ROW]
[ROW][C]45[/C][C]35[/C][C]29.5060761943951[/C][C]5.49392380560494[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]36.4469146023797[/C][C]-1.44691460237965[/C][/ROW]
[ROW][C]47[/C][C]21[/C][C]28.0844714903313[/C][C]-7.08447149033134[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]26.3305434302335[/C][C]6.66945656976653[/C][/ROW]
[ROW][C]49[/C][C]40[/C][C]31.2168298759616[/C][C]8.78317012403837[/C][/ROW]
[ROW][C]50[/C][C]22[/C][C]25.8469649680795[/C][C]-3.84696496807946[/C][/ROW]
[ROW][C]51[/C][C]35[/C][C]33.9656454388723[/C][C]1.03435456112769[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]20.4440402316894[/C][C]-0.444040231689448[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]33.3522008401327[/C][C]-5.3522008401327[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]35.9445500352180[/C][C]10.0554499647820[/C][/ROW]
[ROW][C]55[/C][C]18[/C][C]30.6651582234731[/C][C]-12.6651582234731[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]28.8218662501492[/C][C]-6.82186625014923[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.5405593543360[/C][C]-1.54055935433596[/C][/ROW]
[ROW][C]58[/C][C]25[/C][C]26.0871470925543[/C][C]-1.08714709255433[/C][/ROW]
[ROW][C]59[/C][C]31[/C][C]35.5940478535116[/C][C]-4.59404785351156[/C][/ROW]
[ROW][C]60[/C][C]21[/C][C]25.4510823415414[/C][C]-4.45108234154141[/C][/ROW]
[ROW][C]61[/C][C]23[/C][C]26.6042030905924[/C][C]-3.60420309059243[/C][/ROW]
[ROW][C]62[/C][C]26[/C][C]35.1628504917917[/C][C]-9.16285049179172[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]32.1940777893632[/C][C]1.80592221063683[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]32.9672127172928[/C][C]-1.96721271729283[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]25.4012672486250[/C][C]-2.40126724862503[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]23.9573355484747[/C][C]7.04266445152533[/C][/ROW]
[ROW][C]67[/C][C]26[/C][C]25.9980174765711[/C][C]0.00198252342885122[/C][/ROW]
[ROW][C]68[/C][C]36[/C][C]28.3043554246475[/C][C]7.69564457535252[/C][/ROW]
[ROW][C]69[/C][C]28[/C][C]31.3139884695797[/C][C]-3.31398846957972[/C][/ROW]
[ROW][C]70[/C][C]34[/C][C]27.1440603580208[/C][C]6.85593964197917[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]36.5155925413594[/C][C]-11.5155925413594[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]39.2863509146685[/C][C]-6.28635091466854[/C][/ROW]
[ROW][C]73[/C][C]46[/C][C]36.6512051643813[/C][C]9.34879483561872[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]30.1353899682791[/C][C]-6.13538996827909[/C][/ROW]
[ROW][C]75[/C][C]32[/C][C]34.8180947387095[/C][C]-2.81809473870947[/C][/ROW]
[ROW][C]76[/C][C]33[/C][C]26.447856946269[/C][C]6.55214305373099[/C][/ROW]
[ROW][C]77[/C][C]42[/C][C]39.7936169480747[/C][C]2.20638305192533[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]21.5658598462132[/C][C]-4.56585984621321[/C][/ROW]
[ROW][C]79[/C][C]36[/C][C]25.1617090275539[/C][C]10.8382909724461[/C][/ROW]
[ROW][C]80[/C][C]40[/C][C]29.6342248842419[/C][C]10.3657751157581[/C][/ROW]
[ROW][C]81[/C][C]30[/C][C]34.7644983077102[/C][C]-4.76449830771017[/C][/ROW]
[ROW][C]82[/C][C]19[/C][C]40.1436131568629[/C][C]-21.1436131568629[/C][/ROW]
[ROW][C]83[/C][C]33[/C][C]27.1420121062529[/C][C]5.85798789374709[/C][/ROW]
[ROW][C]84[/C][C]35[/C][C]27.4302785303456[/C][C]7.56972146965436[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]28.4122905914783[/C][C]-5.41229059147828[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]21.6047824701026[/C][C]-6.60478247010257[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]39.4387169800255[/C][C]-1.43871698002551[/C][/ROW]
[ROW][C]88[/C][C]37[/C][C]30.2337994488215[/C][C]6.76620055117846[/C][/ROW]
[ROW][C]89[/C][C]23[/C][C]25.8880281665566[/C][C]-2.88802816655662[/C][/ROW]
[ROW][C]90[/C][C]41[/C][C]30.7337827239817[/C][C]10.2662172760183[/C][/ROW]
[ROW][C]91[/C][C]34[/C][C]33.1514511692101[/C][C]0.848548830789893[/C][/ROW]
[ROW][C]92[/C][C]38[/C][C]30.5202008307466[/C][C]7.4797991692534[/C][/ROW]
[ROW][C]93[/C][C]45[/C][C]28.4914218115976[/C][C]16.5085781884024[/C][/ROW]
[ROW][C]94[/C][C]27[/C][C]24.6458348383929[/C][C]2.35416516160715[/C][/ROW]
[ROW][C]95[/C][C]46[/C][C]37.5496273254368[/C][C]8.45037267456315[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]36.9388788559298[/C][C]-10.9388788559298[/C][/ROW]
[ROW][C]97[/C][C]44[/C][C]35.8220316198358[/C][C]8.1779683801642[/C][/ROW]
[ROW][C]98[/C][C]36[/C][C]30.1216104926776[/C][C]5.87838950732237[/C][/ROW]
[ROW][C]99[/C][C]20[/C][C]30.2353511596742[/C][C]-10.2353511596742[/C][/ROW]
[ROW][C]100[/C][C]44[/C][C]29.0980857682649[/C][C]14.9019142317351[/C][/ROW]
[ROW][C]101[/C][C]27[/C][C]36.3254104836625[/C][C]-9.32541048366253[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]31.7583176299696[/C][C]-4.75831762996962[/C][/ROW]
[ROW][C]103[/C][C]41[/C][C]37.8150157848311[/C][C]3.18498421516886[/C][/ROW]
[ROW][C]104[/C][C]30[/C][C]26.5249555037158[/C][C]3.47504449628421[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.2109566022745[/C][C]-3.21095660227445[/C][/ROW]
[ROW][C]106[/C][C]37[/C][C]29.9112169466359[/C][C]7.08878305336409[/C][/ROW]
[ROW][C]107[/C][C]30[/C][C]33.6919584065864[/C][C]-3.69195840658638[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]24.6267340966874[/C][C]-4.62673409668739[/C][/ROW]
[ROW][C]109[/C][C]44[/C][C]34.3869464877731[/C][C]9.61305351222692[/C][/ROW]
[ROW][C]110[/C][C]20[/C][C]25.5991316892990[/C][C]-5.59913168929905[/C][/ROW]
[ROW][C]111[/C][C]33[/C][C]29.9182081124234[/C][C]3.08179188757662[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]37.7010402460891[/C][C]-6.70104024608906[/C][/ROW]
[ROW][C]113[/C][C]23[/C][C]37.0327999915710[/C][C]-14.0327999915710[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]29.4425778175062[/C][C]3.55742218249378[/C][/ROW]
[ROW][C]115[/C][C]33[/C][C]24.8113879629644[/C][C]8.18861203703556[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]26.4819563508857[/C][C]5.51804364911432[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]24.0568339669235[/C][C]0.943166033076527[/C][/ROW]
[ROW][C]118[/C][C]37[/C][C]41.2419483071221[/C][C]-4.24194830712205[/C][/ROW]
[ROW][C]119[/C][C]48[/C][C]39.896119094429[/C][C]8.103880905571[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]35.6630744986530[/C][C]9.33692550134704[/C][/ROW]
[ROW][C]121[/C][C]32[/C][C]43.2926306536891[/C][C]-11.2926306536891[/C][/ROW]
[ROW][C]122[/C][C]46[/C][C]38.4618530203187[/C][C]7.53814697968128[/C][/ROW]
[ROW][C]123[/C][C]20[/C][C]43.1543495196424[/C][C]-23.1543495196424[/C][/ROW]
[ROW][C]124[/C][C]42[/C][C]34.2961589818231[/C][C]7.70384101817685[/C][/ROW]
[ROW][C]125[/C][C]45[/C][C]23.5410309168328[/C][C]21.4589690831672[/C][/ROW]
[ROW][C]126[/C][C]29[/C][C]35.2570988026026[/C][C]-6.25709880260262[/C][/ROW]
[ROW][C]127[/C][C]51[/C][C]45.5953095796677[/C][C]5.40469042033231[/C][/ROW]
[ROW][C]128[/C][C]55[/C][C]38.5839774440403[/C][C]16.4160225559597[/C][/ROW]
[ROW][C]129[/C][C]50[/C][C]42.5554801976197[/C][C]7.44451980238029[/C][/ROW]
[ROW][C]130[/C][C]44[/C][C]42.6700381462429[/C][C]1.32996185375712[/C][/ROW]
[ROW][C]131[/C][C]41[/C][C]28.2817511388355[/C][C]12.7182488611645[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]35.0819076943277[/C][C]4.91809230567229[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]35.2708785365494[/C][C]11.7291214634506[/C][/ROW]
[ROW][C]134[/C][C]42[/C][C]28.2778813346773[/C][C]13.7221186653227[/C][/ROW]
[ROW][C]135[/C][C]40[/C][C]39.0628846100381[/C][C]0.937115389961859[/C][/ROW]
[ROW][C]136[/C][C]51[/C][C]37.0377725968859[/C][C]13.9622274031141[/C][/ROW]
[ROW][C]137[/C][C]43[/C][C]39.2157904982233[/C][C]3.78420950177667[/C][/ROW]
[ROW][C]138[/C][C]45[/C][C]41.1071471127273[/C][C]3.89285288727269[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]29.4803405404480[/C][C]11.5196594595520[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]36.7808134570815[/C][C]4.21918654291845[/C][/ROW]
[ROW][C]141[/C][C]37[/C][C]39.6018243589976[/C][C]-2.60182435899761[/C][/ROW]
[ROW][C]142[/C][C]46[/C][C]33.7762476768252[/C][C]12.2237523231748[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]36.6709444404455[/C][C]1.32905555955446[/C][/ROW]
[ROW][C]144[/C][C]39[/C][C]33.338301051757[/C][C]5.66169894824299[/C][/ROW]
[ROW][C]145[/C][C]45[/C][C]27.4489008280683[/C][C]17.5510991719317[/C][/ROW]
[ROW][C]146[/C][C]28[/C][C]14.9553629751749[/C][C]13.0446370248251[/C][/ROW]
[ROW][C]147[/C][C]45[/C][C]27.912586377261[/C][C]17.087413622739[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]20.1542256132023[/C][C]0.845774386797683[/C][/ROW]
[ROW][C]149[/C][C]33[/C][C]41.9980838553634[/C][C]-8.9980838553634[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]26.8269451221798[/C][C]-12.8269451221798[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]26.1041038881578[/C][C]-10.1041038881578[/C][/ROW]
[ROW][C]152[/C][C]14[/C][C]46.3388547278599[/C][C]-32.3388547278599[/C][/ROW]
[ROW][C]153[/C][C]40[/C][C]41.6414841221541[/C][C]-1.64148412215411[/C][/ROW]
[ROW][C]154[/C][C]49[/C][C]36.9853854743793[/C][C]12.0146145256207[/C][/ROW]
[ROW][C]155[/C][C]38[/C][C]43.98721714535[/C][C]-5.98721714535[/C][/ROW]
[ROW][C]156[/C][C]32[/C][C]37.1689129928571[/C][C]-5.16891299285709[/C][/ROW]
[ROW][C]157[/C][C]46[/C][C]38.1488394816012[/C][C]7.85116051839881[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]32.6338163476258[/C][C]-0.633816347625763[/C][/ROW]
[ROW][C]159[/C][C]41[/C][C]48.3893007545774[/C][C]-7.3893007545774[/C][/ROW]
[ROW][C]160[/C][C]43[/C][C]39.2189630631194[/C][C]3.78103693688065[/C][/ROW]
[ROW][C]161[/C][C]44[/C][C]37.5127450656448[/C][C]6.48725493435517[/C][/ROW]
[ROW][C]162[/C][C]47[/C][C]41.6961774365424[/C][C]5.30382256345756[/C][/ROW]
[ROW][C]163[/C][C]28[/C][C]41.3968606710783[/C][C]-13.3968606710783[/C][/ROW]
[ROW][C]164[/C][C]52[/C][C]41.9258254694796[/C][C]10.0741745305204[/C][/ROW]
[ROW][C]165[/C][C]27[/C][C]34.5157610912369[/C][C]-7.51576109123693[/C][/ROW]
[ROW][C]166[/C][C]45[/C][C]43.5914056893425[/C][C]1.40859431065747[/C][/ROW]
[ROW][C]167[/C][C]27[/C][C]29.2581168969254[/C][C]-2.25811689692537[/C][/ROW]
[ROW][C]168[/C][C]25[/C][C]43.5145649322564[/C][C]-18.5145649322564[/C][/ROW]
[ROW][C]169[/C][C]28[/C][C]34.2091344539451[/C][C]-6.2091344539451[/C][/ROW]
[ROW][C]170[/C][C]25[/C][C]38.6933444721913[/C][C]-13.6933444721913[/C][/ROW]
[ROW][C]171[/C][C]52[/C][C]41.0377977306198[/C][C]10.9622022693802[/C][/ROW]
[ROW][C]172[/C][C]44[/C][C]43.377699387639[/C][C]0.622300612361028[/C][/ROW]
[ROW][C]173[/C][C]43[/C][C]33.7906572827993[/C][C]9.20934271720067[/C][/ROW]
[ROW][C]174[/C][C]47[/C][C]41.7961089629941[/C][C]5.20389103700586[/C][/ROW]
[ROW][C]175[/C][C]52[/C][C]41.9401999706418[/C][C]10.0598000293582[/C][/ROW]
[ROW][C]176[/C][C]40[/C][C]37.4184358875708[/C][C]2.58156411242923[/C][/ROW]
[ROW][C]177[/C][C]42[/C][C]34.4876426708326[/C][C]7.51235732916744[/C][/ROW]
[ROW][C]178[/C][C]45[/C][C]41.1310000187991[/C][C]3.86899998120093[/C][/ROW]
[ROW][C]179[/C][C]45[/C][C]42.5389511038338[/C][C]2.46104889616623[/C][/ROW]
[ROW][C]180[/C][C]50[/C][C]41.8769299995799[/C][C]8.12307000042006[/C][/ROW]
[ROW][C]181[/C][C]49[/C][C]42.3736149110837[/C][C]6.62638508891627[/C][/ROW]
[ROW][C]182[/C][C]52[/C][C]31.6019851362989[/C][C]20.3980148637011[/C][/ROW]
[ROW][C]183[/C][C]48[/C][C]37.4241846092114[/C][C]10.5758153907886[/C][/ROW]
[ROW][C]184[/C][C]51[/C][C]31.3353759716143[/C][C]19.6646240283857[/C][/ROW]
[ROW][C]185[/C][C]49[/C][C]40.8283276643394[/C][C]8.17167233566064[/C][/ROW]
[ROW][C]186[/C][C]31[/C][C]34.6404262430326[/C][C]-3.64042624303255[/C][/ROW]
[ROW][C]187[/C][C]43[/C][C]38.1971384200241[/C][C]4.80286157997588[/C][/ROW]
[ROW][C]188[/C][C]31[/C][C]39.5113986093825[/C][C]-8.51139860938249[/C][/ROW]
[ROW][C]189[/C][C]28[/C][C]44.7802645140221[/C][C]-16.7802645140221[/C][/ROW]
[ROW][C]190[/C][C]43[/C][C]41.5514916616794[/C][C]1.44850833832062[/C][/ROW]
[ROW][C]191[/C][C]31[/C][C]43.0297061765799[/C][C]-12.0297061765799[/C][/ROW]
[ROW][C]192[/C][C]51[/C][C]50.3968558107899[/C][C]0.603144189210117[/C][/ROW]
[ROW][C]193[/C][C]58[/C][C]34.9428878154318[/C][C]23.0571121845682[/C][/ROW]
[ROW][C]194[/C][C]25[/C][C]34.1774020071365[/C][C]-9.17740200713653[/C][/ROW]
[ROW][C]195[/C][C]27[/C][C]38.2994215735338[/C][C]-11.2994215735338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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
12738.2994215735336-11.2994215735336
23643.9323448063280-7.93234480632796
32546.8815498790148-21.8815498790148
42736.4187427892518-9.4187427892518
52536.4438935932169-11.4438935932169
64446.9180561832985-2.91805618329848
75038.403278176752711.5967218232473
84133.20887219607637.79112780392365
94841.31764889890626.68235110109383
104340.72800600297162.27199399702839
114742.97244868881174.02755131118829
124140.07744652729460.922553472705404
134440.98827782856123.01172217143876
144741.56470223607635.43529776392365
154043.9489524753076-3.94895247530756
164646.3634934091455-0.363493409145547
172842.9456100331747-14.9456100331747
185641.539411750704814.4605882492952
194937.355914428914311.6440855710857
202531.2212154469461-6.2212154469461
214141.4515030983573-0.451503098357265
222636.9314897253678-10.9314897253678
235037.075409364058912.9245906359411
244743.12121479154813.87878520845194
255248.14152451266743.85847548733260
263733.27330609390343.7266939060966
274146.1797926875732-5.17979268757325
284545.7964747227621-0.796474722762152
292643.2995569263957-17.2995569263957
30310.638719027923-7.638719027923
31415.2124132407092-11.2124132407092
3221.161788220861200.838211779138798
3336.63875131709352-3.63875131709352
34525.0173443359115-20.0173443359115
35311.4507469147986-8.45074691479858
36317.1537857236842-14.1537857236842
37213.8536155078759-11.8536155078759
3838.11040953847082-5.11040953847081
39214.0958322598179-12.0958322598179
4038.28152208313382-5.28152208313382
41113.9645619499964-12.9645619499964
42316.1663774968611-13.1663774968611
43414.6916293430593-10.6916293430593
44535.8394546440638-30.8394546440638
453529.50607619439515.49392380560494
463536.4469146023797-1.44691460237965
472128.0844714903313-7.08447149033134
483326.33054343023356.66945656976653
494031.21682987596168.78317012403837
502225.8469649680795-3.84696496807946
513533.96564543887231.03435456112769
522020.4440402316894-0.444040231689448
532833.3522008401327-5.3522008401327
544635.944550035218010.0554499647820
551830.6651582234731-12.6651582234731
562228.8218662501492-6.82186625014923
572021.5405593543360-1.54055935433596
582526.0871470925543-1.08714709255433
593135.5940478535116-4.59404785351156
602125.4510823415414-4.45108234154141
612326.6042030905924-3.60420309059243
622635.1628504917917-9.16285049179172
633432.19407778936321.80592221063683
643132.9672127172928-1.96721271729283
652325.4012672486250-2.40126724862503
663123.95733554847477.04266445152533
672625.99801747657110.00198252342885122
683628.30435542464757.69564457535252
692831.3139884695797-3.31398846957972
703427.14406035802086.85593964197917
712536.5155925413594-11.5155925413594
723339.2863509146685-6.28635091466854
734636.65120516438139.34879483561872
742430.1353899682791-6.13538996827909
753234.8180947387095-2.81809473870947
763326.4478569462696.55214305373099
774239.79361694807472.20638305192533
781721.5658598462132-4.56585984621321
793625.161709027553910.8382909724461
804029.634224884241910.3657751157581
813034.7644983077102-4.76449830771017
821940.1436131568629-21.1436131568629
833327.14201210625295.85798789374709
843527.43027853034567.56972146965436
852328.4122905914783-5.41229059147828
861521.6047824701026-6.60478247010257
873839.4387169800255-1.43871698002551
883730.23379944882156.76620055117846
892325.8880281665566-2.88802816655662
904130.733782723981710.2662172760183
913433.15145116921010.848548830789893
923830.52020083074667.4797991692534
934528.491421811597616.5085781884024
942724.64583483839292.35416516160715
954637.54962732543688.45037267456315
962636.9388788559298-10.9388788559298
974435.82203161983588.1779683801642
983630.12161049267765.87838950732237
992030.2353511596742-10.2353511596742
1004429.098085768264914.9019142317351
1012736.3254104836625-9.32541048366253
1022731.7583176299696-4.75831762996962
1034137.81501578483113.18498421516886
1043026.52495550371583.47504449628421
1053336.2109566022745-3.21095660227445
1063729.91121694663597.08878305336409
1073033.6919584065864-3.69195840658638
1082024.6267340966874-4.62673409668739
1094434.38694648777319.61305351222692
1102025.5991316892990-5.59913168929905
1113329.91820811242343.08179188757662
1123137.7010402460891-6.70104024608906
1132337.0327999915710-14.0327999915710
1143329.44257781750623.55742218249378
1153324.81138796296448.18861203703556
1163226.48195635088575.51804364911432
1172524.05683396692350.943166033076527
1183741.2419483071221-4.24194830712205
1194839.8961190944298.103880905571
1204535.66307449865309.33692550134704
1213243.2926306536891-11.2926306536891
1224638.46185302031877.53814697968128
1232043.1543495196424-23.1543495196424
1244234.29615898182317.70384101817685
1254523.541030916832821.4589690831672
1262935.2570988026026-6.25709880260262
1275145.59530957966775.40469042033231
1285538.583977444040316.4160225559597
1295042.55548019761977.44451980238029
1304442.67003814624291.32996185375712
1314128.281751138835512.7182488611645
1324035.08190769432774.91809230567229
1334735.270878536549411.7291214634506
1344228.277881334677313.7221186653227
1354039.06288461003810.937115389961859
1365137.037772596885913.9622274031141
1374339.21579049822333.78420950177667
1384541.10714711272733.89285288727269
1394129.480340540448011.5196594595520
1404136.78081345708154.21918654291845
1413739.6018243589976-2.60182435899761
1424633.776247676825212.2237523231748
1433836.67094444044551.32905555955446
1443933.3383010517575.66169894824299
1454527.448900828068317.5510991719317
1462814.955362975174913.0446370248251
1474527.91258637726117.087413622739
1482120.15422561320230.845774386797683
1493341.9980838553634-8.9980838553634
1501426.8269451221798-12.8269451221798
1511626.1041038881578-10.1041038881578
1521446.3388547278599-32.3388547278599
1534041.6414841221541-1.64148412215411
1544936.985385474379312.0146145256207
1553843.98721714535-5.98721714535
1563237.1689129928571-5.16891299285709
1574638.14883948160127.85116051839881
1583232.6338163476258-0.633816347625763
1594148.3893007545774-7.3893007545774
1604339.21896306311943.78103693688065
1614437.51274506564486.48725493435517
1624741.69617743654245.30382256345756
1632841.3968606710783-13.3968606710783
1645241.925825469479610.0741745305204
1652734.5157610912369-7.51576109123693
1664543.59140568934251.40859431065747
1672729.2581168969254-2.25811689692537
1682543.5145649322564-18.5145649322564
1692834.2091344539451-6.2091344539451
1702538.6933444721913-13.6933444721913
1715241.037797730619810.9622022693802
1724443.3776993876390.622300612361028
1734333.79065728279939.20934271720067
1744741.79610896299415.20389103700586
1755241.940199970641810.0598000293582
1764037.41843588757082.58156411242923
1774234.48764267083267.51235732916744
1784541.13100001879913.86899998120093
1794542.53895110383382.46104889616623
1805041.87692999957998.12307000042006
1814942.37361491108376.62638508891627
1825231.601985136298920.3980148637011
1834837.424184609211410.5758153907886
1845131.335375971614319.6646240283857
1854940.82832766433948.17167233566064
1863134.6404262430326-3.64042624303255
1874338.19713842002414.80286157997588
1883139.5113986093825-8.51139860938249
1892844.7802645140221-16.7802645140221
1904341.55149166167941.44850833832062
1913143.0297061765799-12.0297061765799
1925150.39685581078990.603144189210117
1935834.942887815431823.0571121845682
1942534.1774020071365-9.17740200713653
1952738.2994215735338-11.2994215735338






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7535967480585980.4928065038828050.246403251941402
100.8292755453476680.3414489093046640.170724454652332
110.861900933606440.2761981327871210.138099066393560
120.8049392945124780.3901214109750430.195060705487522
130.7336977193494660.5326045613010690.266302280650534
140.7042542702840720.5914914594318550.295745729715928
150.6179483745266350.764103250946730.382051625473365
160.5337301080695840.9325397838608320.466269891930416
170.4644084872161110.9288169744322210.535591512783889
180.533763989659970.9324720206800610.466236010340030
190.5612227585951290.8775544828097420.438777241404871
200.5234380996171840.9531238007656310.476561900382816
210.4530304317101130.9060608634202260.546969568289887
220.4235298762672640.8470597525345280.576470123732736
230.4961256125904080.9922512251808150.503874387409592
240.4737891076537850.947578215307570.526210892346215
250.4576244678418780.9152489356837560.542375532158122
260.3909490198441520.7818980396883050.609050980155848
270.3453679313692760.6907358627385530.654632068630724
280.2898470472136530.5796940944273070.710152952786347
290.4290526379193540.8581052758387080.570947362080646
300.3717524705537920.7435049411075840.628247529446208
310.3272467208601110.6544934417202210.67275327913989
320.2856641600557010.5713283201114030.714335839944299
330.2370854765843280.4741709531686560.762914523415672
340.2490425395583330.4980850791166670.750957460441667
350.2146439186462130.4292878372924270.785356081353787
360.1934957310637460.3869914621274920.806504268936254
370.1750756114060210.3501512228120430.824924388593979
380.1521902763226230.3043805526452470.847809723677377
390.1389804113177830.2779608226355650.861019588682217
400.1232605040885130.2465210081770270.876739495911486
410.1276398958572060.2552797917144130.872360104142794
420.1352646000640480.2705292001280950.864735399935952
430.1538709194413870.3077418388827730.846129080558613
440.2831159729787870.5662319459575740.716884027021213
450.2641373113690970.5282746227381940.735862688630903
460.2937737696469290.5875475392938580.706226230353071
470.3192142665342850.6384285330685690.680785733465715
480.2952354769328630.5904709538657260.704764523067137
490.2916311640677510.5832623281355010.70836883593225
500.2738630414548990.5477260829097980.726136958545101
510.2380047274572160.4760094549144310.761995272542784
520.2055592266513330.4111184533026660.794440773348667
530.1798871154133630.3597742308267270.820112884586637
540.3191972663690790.6383945327381590.68080273363092
550.3309387428249550.661877485649910.669061257175045
560.3351908362890380.6703816725780760.664809163710962
570.3073706624807570.6147413249615140.692629337519243
580.2720652231361130.5441304462722260.727934776863887
590.2689332304317820.5378664608635630.731066769568218
600.2431105417264170.4862210834528350.756889458273583
610.2338993583160570.4677987166321140.766100641683943
620.2128262550235570.4256525100471150.787173744976443
630.236446899014780.472893798029560.76355310098522
640.2180019818758740.4360039637517480.781998018124126
650.1943191936558670.3886383873117350.805680806344133
660.1795518893974940.3591037787949880.820448110602506
670.1534680571869620.3069361143739240.846531942813038
680.1367990080732570.2735980161465140.863200991926743
690.1155484277999540.2310968555999080.884451572200046
700.09893848844243240.1978769768848650.901061511557568
710.1134314305554100.2268628611108200.88656856944459
720.09530832993142960.1906166598628590.90469167006857
730.1051282125919250.2102564251838500.894871787408075
740.0928822942540280.1857645885080560.907117705745972
750.07788597018707210.1557719403741440.922114029812928
760.0712960543851190.1425921087702380.928703945614881
770.06613385937807220.1322677187561440.933866140621928
780.06199357744892440.1239871548978490.938006422551076
790.09496513501842530.1899302700368510.905034864981575
800.1434300626057320.2868601252114630.856569937394268
810.1238722899925470.2477445799850940.876127710007453
820.1915845350979640.3831690701959270.808415464902036
830.1824647465232930.3649294930465860.817535253476707
840.1677417442354890.3354834884709780.832258255764511
850.1590829699038200.3181659398076400.84091703009618
860.1670373187092740.3340746374185490.832962681290726
870.147111703099550.29422340619910.85288829690045
880.1341500721673950.2683001443347900.865849927832605
890.1204289692359710.2408579384719420.879571030764029
900.1528498710576630.3056997421153250.847150128942337
910.1355744096757140.2711488193514290.864425590324285
920.1265407186610770.2530814373221540.873459281338923
930.1683644414486980.3367288828973960.831635558551302
940.1432613853486840.2865227706973690.856738614651316
950.1655234325200300.3310468650400590.83447656747997
960.1758076297434910.3516152594869820.82419237025651
970.1915990331688490.3831980663376990.80840096683115
980.1770866765181390.3541733530362770.822913323481861
990.1873000379491850.3746000758983690.812699962050815
1000.2422218630062520.4844437260125040.757778136993748
1010.2295730571932800.4591461143865590.77042694280672
1020.2066602978001050.413320595600210.793339702199895
1030.1970244192809330.3940488385618660.802975580719067
1040.1697268565170680.3394537130341360.830273143482932
1050.1457334986741950.291466997348390.854266501325805
1060.1424334310842860.2848668621685720.857566568915714
1070.1222468491043530.2444936982087070.877753150895647
1080.1118869828602210.2237739657204420.888113017139779
1090.1160060092203640.2320120184407270.883993990779636
1100.1059679947038260.2119359894076520.894032005296174
1110.09170493832597730.1834098766519550.908295061674023
1120.07898230612408140.1579646122481630.921017693875919
1130.09107665645375050.1821533129075010.90892334354625
1140.07585014950596050.1517002990119210.92414985049404
1150.0686783957223840.1373567914447680.931321604277616
1160.05849659180117650.1169931836023530.941503408198823
1170.05198740593534210.1039748118706840.948012594064658
1180.04735030380610320.09470060761220650.952649696193897
1190.06445884043427430.1289176808685490.935541159565726
1200.07219381637612810.1443876327522560.927806183623872
1210.07361455147045370.1472291029409070.926385448529546
1220.07420066100393170.1484013220078630.925799338996068
1230.1812279453546750.362455890709350.818772054645325
1240.1810057464171440.3620114928342880.818994253582856
1250.2620490019617190.5240980039234380.737950998038281
1260.2797529222848750.559505844569750.720247077715125
1270.2586560151053300.5173120302106590.74134398489467
1280.3309990610734120.6619981221468240.669000938926588
1290.3123848595708690.6247697191417380.687615140429131
1300.2765507208935770.5531014417871530.723449279106423
1310.2690589577586980.5381179155173950.730941042241302
1320.2362839143585320.4725678287170640.763716085641468
1330.2263527713534390.4527055427068790.77364722864656
1340.2161334454789830.4322668909579670.783866554521017
1350.1878597098597240.3757194197194490.812140290140276
1360.2035589077553360.4071178155106720.796441092244664
1370.1740196317332000.3480392634663990.8259803682668
1380.1497414715623280.2994829431246550.850258528437672
1390.1398648506778770.2797297013557540.860135149322123
1400.1164634454805600.2329268909611190.88353655451944
1410.09901646114885120.1980329222977020.900983538851149
1420.1024502765466400.2049005530932800.89754972345336
1430.08521785893271630.1704357178654330.914782141067284
1440.08534512121820010.1706902424364000.9146548787818
1450.1992090013188090.3984180026376180.800790998681191
1460.2127979243978020.4255958487956050.787202075602198
1470.2766588112118270.5533176224236550.723341188788173
1480.2369117835537550.473823567107510.763088216446245
1490.237728716557510.475457433115020.76227128344249
1500.2408068172215420.4816136344430830.759193182778458
1510.2344039440813520.4688078881627040.765596055918648
1520.6913839963107330.6172320073785340.308616003689267
1530.6457975817980410.7084048364039170.354202418201959
1540.6365195987823520.7269608024352970.363480401217648
1550.5871514570817160.8256970858365670.412848542918284
1560.5385910548573260.9228178902853480.461408945142674
1570.5443676305163640.9112647389672710.455632369483636
1580.4910299014181980.9820598028363950.508970098581802
1590.4527827562831250.905565512566250.547217243716875
1600.4020676655054320.8041353310108650.597932334494568
1610.3838016222785240.7676032445570470.616198377721476
1620.3366352693571010.6732705387142010.6633647306429
1630.3544983410117550.708996682023510.645501658988245
1640.3114632060132540.6229264120265070.688536793986746
1650.3305945294544000.6611890589088010.6694054705456
1660.283309241630840.566618483261680.71669075836916
1670.3562841654838090.7125683309676180.643715834516191
1680.4511752497811400.9023504995622810.54882475021886
1690.3889072152024110.7778144304048230.611092784797589
1700.4175725000646540.8351450001293090.582427499935346
1710.4155094259547270.8310188519094550.584490574045273
1720.3506197855094240.7012395710188480.649380214490576
1730.2939723321249050.587944664249810.706027667875095
1740.2448757238132080.4897514476264160.755124276186792
1750.2346674928785100.4693349857570200.76533250712149
1760.2035678585056210.4071357170112410.79643214149438
1770.1543215704013040.3086431408026080.845678429598696
1780.1366647675724600.2733295351449200.86333523242754
1790.09980845110990690.1996169022198140.900191548890093
1800.1843492857855550.368698571571110.815650714214445
1810.1511972410438790.3023944820877590.84880275895612
1820.1131115851795230.2262231703590470.886888414820477
1830.07266999515208930.1453399903041790.92733000484791
1840.06546125410550950.1309225082110190.93453874589449
1850.2301986595729790.4603973191459580.769801340427021
1860.1358092910666510.2716185821333020.864190708933349

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.753596748058598 & 0.492806503882805 & 0.246403251941402 \tabularnewline
10 & 0.829275545347668 & 0.341448909304664 & 0.170724454652332 \tabularnewline
11 & 0.86190093360644 & 0.276198132787121 & 0.138099066393560 \tabularnewline
12 & 0.804939294512478 & 0.390121410975043 & 0.195060705487522 \tabularnewline
13 & 0.733697719349466 & 0.532604561301069 & 0.266302280650534 \tabularnewline
14 & 0.704254270284072 & 0.591491459431855 & 0.295745729715928 \tabularnewline
15 & 0.617948374526635 & 0.76410325094673 & 0.382051625473365 \tabularnewline
16 & 0.533730108069584 & 0.932539783860832 & 0.466269891930416 \tabularnewline
17 & 0.464408487216111 & 0.928816974432221 & 0.535591512783889 \tabularnewline
18 & 0.53376398965997 & 0.932472020680061 & 0.466236010340030 \tabularnewline
19 & 0.561222758595129 & 0.877554482809742 & 0.438777241404871 \tabularnewline
20 & 0.523438099617184 & 0.953123800765631 & 0.476561900382816 \tabularnewline
21 & 0.453030431710113 & 0.906060863420226 & 0.546969568289887 \tabularnewline
22 & 0.423529876267264 & 0.847059752534528 & 0.576470123732736 \tabularnewline
23 & 0.496125612590408 & 0.992251225180815 & 0.503874387409592 \tabularnewline
24 & 0.473789107653785 & 0.94757821530757 & 0.526210892346215 \tabularnewline
25 & 0.457624467841878 & 0.915248935683756 & 0.542375532158122 \tabularnewline
26 & 0.390949019844152 & 0.781898039688305 & 0.609050980155848 \tabularnewline
27 & 0.345367931369276 & 0.690735862738553 & 0.654632068630724 \tabularnewline
28 & 0.289847047213653 & 0.579694094427307 & 0.710152952786347 \tabularnewline
29 & 0.429052637919354 & 0.858105275838708 & 0.570947362080646 \tabularnewline
30 & 0.371752470553792 & 0.743504941107584 & 0.628247529446208 \tabularnewline
31 & 0.327246720860111 & 0.654493441720221 & 0.67275327913989 \tabularnewline
32 & 0.285664160055701 & 0.571328320111403 & 0.714335839944299 \tabularnewline
33 & 0.237085476584328 & 0.474170953168656 & 0.762914523415672 \tabularnewline
34 & 0.249042539558333 & 0.498085079116667 & 0.750957460441667 \tabularnewline
35 & 0.214643918646213 & 0.429287837292427 & 0.785356081353787 \tabularnewline
36 & 0.193495731063746 & 0.386991462127492 & 0.806504268936254 \tabularnewline
37 & 0.175075611406021 & 0.350151222812043 & 0.824924388593979 \tabularnewline
38 & 0.152190276322623 & 0.304380552645247 & 0.847809723677377 \tabularnewline
39 & 0.138980411317783 & 0.277960822635565 & 0.861019588682217 \tabularnewline
40 & 0.123260504088513 & 0.246521008177027 & 0.876739495911486 \tabularnewline
41 & 0.127639895857206 & 0.255279791714413 & 0.872360104142794 \tabularnewline
42 & 0.135264600064048 & 0.270529200128095 & 0.864735399935952 \tabularnewline
43 & 0.153870919441387 & 0.307741838882773 & 0.846129080558613 \tabularnewline
44 & 0.283115972978787 & 0.566231945957574 & 0.716884027021213 \tabularnewline
45 & 0.264137311369097 & 0.528274622738194 & 0.735862688630903 \tabularnewline
46 & 0.293773769646929 & 0.587547539293858 & 0.706226230353071 \tabularnewline
47 & 0.319214266534285 & 0.638428533068569 & 0.680785733465715 \tabularnewline
48 & 0.295235476932863 & 0.590470953865726 & 0.704764523067137 \tabularnewline
49 & 0.291631164067751 & 0.583262328135501 & 0.70836883593225 \tabularnewline
50 & 0.273863041454899 & 0.547726082909798 & 0.726136958545101 \tabularnewline
51 & 0.238004727457216 & 0.476009454914431 & 0.761995272542784 \tabularnewline
52 & 0.205559226651333 & 0.411118453302666 & 0.794440773348667 \tabularnewline
53 & 0.179887115413363 & 0.359774230826727 & 0.820112884586637 \tabularnewline
54 & 0.319197266369079 & 0.638394532738159 & 0.68080273363092 \tabularnewline
55 & 0.330938742824955 & 0.66187748564991 & 0.669061257175045 \tabularnewline
56 & 0.335190836289038 & 0.670381672578076 & 0.664809163710962 \tabularnewline
57 & 0.307370662480757 & 0.614741324961514 & 0.692629337519243 \tabularnewline
58 & 0.272065223136113 & 0.544130446272226 & 0.727934776863887 \tabularnewline
59 & 0.268933230431782 & 0.537866460863563 & 0.731066769568218 \tabularnewline
60 & 0.243110541726417 & 0.486221083452835 & 0.756889458273583 \tabularnewline
61 & 0.233899358316057 & 0.467798716632114 & 0.766100641683943 \tabularnewline
62 & 0.212826255023557 & 0.425652510047115 & 0.787173744976443 \tabularnewline
63 & 0.23644689901478 & 0.47289379802956 & 0.76355310098522 \tabularnewline
64 & 0.218001981875874 & 0.436003963751748 & 0.781998018124126 \tabularnewline
65 & 0.194319193655867 & 0.388638387311735 & 0.805680806344133 \tabularnewline
66 & 0.179551889397494 & 0.359103778794988 & 0.820448110602506 \tabularnewline
67 & 0.153468057186962 & 0.306936114373924 & 0.846531942813038 \tabularnewline
68 & 0.136799008073257 & 0.273598016146514 & 0.863200991926743 \tabularnewline
69 & 0.115548427799954 & 0.231096855599908 & 0.884451572200046 \tabularnewline
70 & 0.0989384884424324 & 0.197876976884865 & 0.901061511557568 \tabularnewline
71 & 0.113431430555410 & 0.226862861110820 & 0.88656856944459 \tabularnewline
72 & 0.0953083299314296 & 0.190616659862859 & 0.90469167006857 \tabularnewline
73 & 0.105128212591925 & 0.210256425183850 & 0.894871787408075 \tabularnewline
74 & 0.092882294254028 & 0.185764588508056 & 0.907117705745972 \tabularnewline
75 & 0.0778859701870721 & 0.155771940374144 & 0.922114029812928 \tabularnewline
76 & 0.071296054385119 & 0.142592108770238 & 0.928703945614881 \tabularnewline
77 & 0.0661338593780722 & 0.132267718756144 & 0.933866140621928 \tabularnewline
78 & 0.0619935774489244 & 0.123987154897849 & 0.938006422551076 \tabularnewline
79 & 0.0949651350184253 & 0.189930270036851 & 0.905034864981575 \tabularnewline
80 & 0.143430062605732 & 0.286860125211463 & 0.856569937394268 \tabularnewline
81 & 0.123872289992547 & 0.247744579985094 & 0.876127710007453 \tabularnewline
82 & 0.191584535097964 & 0.383169070195927 & 0.808415464902036 \tabularnewline
83 & 0.182464746523293 & 0.364929493046586 & 0.817535253476707 \tabularnewline
84 & 0.167741744235489 & 0.335483488470978 & 0.832258255764511 \tabularnewline
85 & 0.159082969903820 & 0.318165939807640 & 0.84091703009618 \tabularnewline
86 & 0.167037318709274 & 0.334074637418549 & 0.832962681290726 \tabularnewline
87 & 0.14711170309955 & 0.2942234061991 & 0.85288829690045 \tabularnewline
88 & 0.134150072167395 & 0.268300144334790 & 0.865849927832605 \tabularnewline
89 & 0.120428969235971 & 0.240857938471942 & 0.879571030764029 \tabularnewline
90 & 0.152849871057663 & 0.305699742115325 & 0.847150128942337 \tabularnewline
91 & 0.135574409675714 & 0.271148819351429 & 0.864425590324285 \tabularnewline
92 & 0.126540718661077 & 0.253081437322154 & 0.873459281338923 \tabularnewline
93 & 0.168364441448698 & 0.336728882897396 & 0.831635558551302 \tabularnewline
94 & 0.143261385348684 & 0.286522770697369 & 0.856738614651316 \tabularnewline
95 & 0.165523432520030 & 0.331046865040059 & 0.83447656747997 \tabularnewline
96 & 0.175807629743491 & 0.351615259486982 & 0.82419237025651 \tabularnewline
97 & 0.191599033168849 & 0.383198066337699 & 0.80840096683115 \tabularnewline
98 & 0.177086676518139 & 0.354173353036277 & 0.822913323481861 \tabularnewline
99 & 0.187300037949185 & 0.374600075898369 & 0.812699962050815 \tabularnewline
100 & 0.242221863006252 & 0.484443726012504 & 0.757778136993748 \tabularnewline
101 & 0.229573057193280 & 0.459146114386559 & 0.77042694280672 \tabularnewline
102 & 0.206660297800105 & 0.41332059560021 & 0.793339702199895 \tabularnewline
103 & 0.197024419280933 & 0.394048838561866 & 0.802975580719067 \tabularnewline
104 & 0.169726856517068 & 0.339453713034136 & 0.830273143482932 \tabularnewline
105 & 0.145733498674195 & 0.29146699734839 & 0.854266501325805 \tabularnewline
106 & 0.142433431084286 & 0.284866862168572 & 0.857566568915714 \tabularnewline
107 & 0.122246849104353 & 0.244493698208707 & 0.877753150895647 \tabularnewline
108 & 0.111886982860221 & 0.223773965720442 & 0.888113017139779 \tabularnewline
109 & 0.116006009220364 & 0.232012018440727 & 0.883993990779636 \tabularnewline
110 & 0.105967994703826 & 0.211935989407652 & 0.894032005296174 \tabularnewline
111 & 0.0917049383259773 & 0.183409876651955 & 0.908295061674023 \tabularnewline
112 & 0.0789823061240814 & 0.157964612248163 & 0.921017693875919 \tabularnewline
113 & 0.0910766564537505 & 0.182153312907501 & 0.90892334354625 \tabularnewline
114 & 0.0758501495059605 & 0.151700299011921 & 0.92414985049404 \tabularnewline
115 & 0.068678395722384 & 0.137356791444768 & 0.931321604277616 \tabularnewline
116 & 0.0584965918011765 & 0.116993183602353 & 0.941503408198823 \tabularnewline
117 & 0.0519874059353421 & 0.103974811870684 & 0.948012594064658 \tabularnewline
118 & 0.0473503038061032 & 0.0947006076122065 & 0.952649696193897 \tabularnewline
119 & 0.0644588404342743 & 0.128917680868549 & 0.935541159565726 \tabularnewline
120 & 0.0721938163761281 & 0.144387632752256 & 0.927806183623872 \tabularnewline
121 & 0.0736145514704537 & 0.147229102940907 & 0.926385448529546 \tabularnewline
122 & 0.0742006610039317 & 0.148401322007863 & 0.925799338996068 \tabularnewline
123 & 0.181227945354675 & 0.36245589070935 & 0.818772054645325 \tabularnewline
124 & 0.181005746417144 & 0.362011492834288 & 0.818994253582856 \tabularnewline
125 & 0.262049001961719 & 0.524098003923438 & 0.737950998038281 \tabularnewline
126 & 0.279752922284875 & 0.55950584456975 & 0.720247077715125 \tabularnewline
127 & 0.258656015105330 & 0.517312030210659 & 0.74134398489467 \tabularnewline
128 & 0.330999061073412 & 0.661998122146824 & 0.669000938926588 \tabularnewline
129 & 0.312384859570869 & 0.624769719141738 & 0.687615140429131 \tabularnewline
130 & 0.276550720893577 & 0.553101441787153 & 0.723449279106423 \tabularnewline
131 & 0.269058957758698 & 0.538117915517395 & 0.730941042241302 \tabularnewline
132 & 0.236283914358532 & 0.472567828717064 & 0.763716085641468 \tabularnewline
133 & 0.226352771353439 & 0.452705542706879 & 0.77364722864656 \tabularnewline
134 & 0.216133445478983 & 0.432266890957967 & 0.783866554521017 \tabularnewline
135 & 0.187859709859724 & 0.375719419719449 & 0.812140290140276 \tabularnewline
136 & 0.203558907755336 & 0.407117815510672 & 0.796441092244664 \tabularnewline
137 & 0.174019631733200 & 0.348039263466399 & 0.8259803682668 \tabularnewline
138 & 0.149741471562328 & 0.299482943124655 & 0.850258528437672 \tabularnewline
139 & 0.139864850677877 & 0.279729701355754 & 0.860135149322123 \tabularnewline
140 & 0.116463445480560 & 0.232926890961119 & 0.88353655451944 \tabularnewline
141 & 0.0990164611488512 & 0.198032922297702 & 0.900983538851149 \tabularnewline
142 & 0.102450276546640 & 0.204900553093280 & 0.89754972345336 \tabularnewline
143 & 0.0852178589327163 & 0.170435717865433 & 0.914782141067284 \tabularnewline
144 & 0.0853451212182001 & 0.170690242436400 & 0.9146548787818 \tabularnewline
145 & 0.199209001318809 & 0.398418002637618 & 0.800790998681191 \tabularnewline
146 & 0.212797924397802 & 0.425595848795605 & 0.787202075602198 \tabularnewline
147 & 0.276658811211827 & 0.553317622423655 & 0.723341188788173 \tabularnewline
148 & 0.236911783553755 & 0.47382356710751 & 0.763088216446245 \tabularnewline
149 & 0.23772871655751 & 0.47545743311502 & 0.76227128344249 \tabularnewline
150 & 0.240806817221542 & 0.481613634443083 & 0.759193182778458 \tabularnewline
151 & 0.234403944081352 & 0.468807888162704 & 0.765596055918648 \tabularnewline
152 & 0.691383996310733 & 0.617232007378534 & 0.308616003689267 \tabularnewline
153 & 0.645797581798041 & 0.708404836403917 & 0.354202418201959 \tabularnewline
154 & 0.636519598782352 & 0.726960802435297 & 0.363480401217648 \tabularnewline
155 & 0.587151457081716 & 0.825697085836567 & 0.412848542918284 \tabularnewline
156 & 0.538591054857326 & 0.922817890285348 & 0.461408945142674 \tabularnewline
157 & 0.544367630516364 & 0.911264738967271 & 0.455632369483636 \tabularnewline
158 & 0.491029901418198 & 0.982059802836395 & 0.508970098581802 \tabularnewline
159 & 0.452782756283125 & 0.90556551256625 & 0.547217243716875 \tabularnewline
160 & 0.402067665505432 & 0.804135331010865 & 0.597932334494568 \tabularnewline
161 & 0.383801622278524 & 0.767603244557047 & 0.616198377721476 \tabularnewline
162 & 0.336635269357101 & 0.673270538714201 & 0.6633647306429 \tabularnewline
163 & 0.354498341011755 & 0.70899668202351 & 0.645501658988245 \tabularnewline
164 & 0.311463206013254 & 0.622926412026507 & 0.688536793986746 \tabularnewline
165 & 0.330594529454400 & 0.661189058908801 & 0.6694054705456 \tabularnewline
166 & 0.28330924163084 & 0.56661848326168 & 0.71669075836916 \tabularnewline
167 & 0.356284165483809 & 0.712568330967618 & 0.643715834516191 \tabularnewline
168 & 0.451175249781140 & 0.902350499562281 & 0.54882475021886 \tabularnewline
169 & 0.388907215202411 & 0.777814430404823 & 0.611092784797589 \tabularnewline
170 & 0.417572500064654 & 0.835145000129309 & 0.582427499935346 \tabularnewline
171 & 0.415509425954727 & 0.831018851909455 & 0.584490574045273 \tabularnewline
172 & 0.350619785509424 & 0.701239571018848 & 0.649380214490576 \tabularnewline
173 & 0.293972332124905 & 0.58794466424981 & 0.706027667875095 \tabularnewline
174 & 0.244875723813208 & 0.489751447626416 & 0.755124276186792 \tabularnewline
175 & 0.234667492878510 & 0.469334985757020 & 0.76533250712149 \tabularnewline
176 & 0.203567858505621 & 0.407135717011241 & 0.79643214149438 \tabularnewline
177 & 0.154321570401304 & 0.308643140802608 & 0.845678429598696 \tabularnewline
178 & 0.136664767572460 & 0.273329535144920 & 0.86333523242754 \tabularnewline
179 & 0.0998084511099069 & 0.199616902219814 & 0.900191548890093 \tabularnewline
180 & 0.184349285785555 & 0.36869857157111 & 0.815650714214445 \tabularnewline
181 & 0.151197241043879 & 0.302394482087759 & 0.84880275895612 \tabularnewline
182 & 0.113111585179523 & 0.226223170359047 & 0.886888414820477 \tabularnewline
183 & 0.0726699951520893 & 0.145339990304179 & 0.92733000484791 \tabularnewline
184 & 0.0654612541055095 & 0.130922508211019 & 0.93453874589449 \tabularnewline
185 & 0.230198659572979 & 0.460397319145958 & 0.769801340427021 \tabularnewline
186 & 0.135809291066651 & 0.271618582133302 & 0.864190708933349 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&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]0.753596748058598[/C][C]0.492806503882805[/C][C]0.246403251941402[/C][/ROW]
[ROW][C]10[/C][C]0.829275545347668[/C][C]0.341448909304664[/C][C]0.170724454652332[/C][/ROW]
[ROW][C]11[/C][C]0.86190093360644[/C][C]0.276198132787121[/C][C]0.138099066393560[/C][/ROW]
[ROW][C]12[/C][C]0.804939294512478[/C][C]0.390121410975043[/C][C]0.195060705487522[/C][/ROW]
[ROW][C]13[/C][C]0.733697719349466[/C][C]0.532604561301069[/C][C]0.266302280650534[/C][/ROW]
[ROW][C]14[/C][C]0.704254270284072[/C][C]0.591491459431855[/C][C]0.295745729715928[/C][/ROW]
[ROW][C]15[/C][C]0.617948374526635[/C][C]0.76410325094673[/C][C]0.382051625473365[/C][/ROW]
[ROW][C]16[/C][C]0.533730108069584[/C][C]0.932539783860832[/C][C]0.466269891930416[/C][/ROW]
[ROW][C]17[/C][C]0.464408487216111[/C][C]0.928816974432221[/C][C]0.535591512783889[/C][/ROW]
[ROW][C]18[/C][C]0.53376398965997[/C][C]0.932472020680061[/C][C]0.466236010340030[/C][/ROW]
[ROW][C]19[/C][C]0.561222758595129[/C][C]0.877554482809742[/C][C]0.438777241404871[/C][/ROW]
[ROW][C]20[/C][C]0.523438099617184[/C][C]0.953123800765631[/C][C]0.476561900382816[/C][/ROW]
[ROW][C]21[/C][C]0.453030431710113[/C][C]0.906060863420226[/C][C]0.546969568289887[/C][/ROW]
[ROW][C]22[/C][C]0.423529876267264[/C][C]0.847059752534528[/C][C]0.576470123732736[/C][/ROW]
[ROW][C]23[/C][C]0.496125612590408[/C][C]0.992251225180815[/C][C]0.503874387409592[/C][/ROW]
[ROW][C]24[/C][C]0.473789107653785[/C][C]0.94757821530757[/C][C]0.526210892346215[/C][/ROW]
[ROW][C]25[/C][C]0.457624467841878[/C][C]0.915248935683756[/C][C]0.542375532158122[/C][/ROW]
[ROW][C]26[/C][C]0.390949019844152[/C][C]0.781898039688305[/C][C]0.609050980155848[/C][/ROW]
[ROW][C]27[/C][C]0.345367931369276[/C][C]0.690735862738553[/C][C]0.654632068630724[/C][/ROW]
[ROW][C]28[/C][C]0.289847047213653[/C][C]0.579694094427307[/C][C]0.710152952786347[/C][/ROW]
[ROW][C]29[/C][C]0.429052637919354[/C][C]0.858105275838708[/C][C]0.570947362080646[/C][/ROW]
[ROW][C]30[/C][C]0.371752470553792[/C][C]0.743504941107584[/C][C]0.628247529446208[/C][/ROW]
[ROW][C]31[/C][C]0.327246720860111[/C][C]0.654493441720221[/C][C]0.67275327913989[/C][/ROW]
[ROW][C]32[/C][C]0.285664160055701[/C][C]0.571328320111403[/C][C]0.714335839944299[/C][/ROW]
[ROW][C]33[/C][C]0.237085476584328[/C][C]0.474170953168656[/C][C]0.762914523415672[/C][/ROW]
[ROW][C]34[/C][C]0.249042539558333[/C][C]0.498085079116667[/C][C]0.750957460441667[/C][/ROW]
[ROW][C]35[/C][C]0.214643918646213[/C][C]0.429287837292427[/C][C]0.785356081353787[/C][/ROW]
[ROW][C]36[/C][C]0.193495731063746[/C][C]0.386991462127492[/C][C]0.806504268936254[/C][/ROW]
[ROW][C]37[/C][C]0.175075611406021[/C][C]0.350151222812043[/C][C]0.824924388593979[/C][/ROW]
[ROW][C]38[/C][C]0.152190276322623[/C][C]0.304380552645247[/C][C]0.847809723677377[/C][/ROW]
[ROW][C]39[/C][C]0.138980411317783[/C][C]0.277960822635565[/C][C]0.861019588682217[/C][/ROW]
[ROW][C]40[/C][C]0.123260504088513[/C][C]0.246521008177027[/C][C]0.876739495911486[/C][/ROW]
[ROW][C]41[/C][C]0.127639895857206[/C][C]0.255279791714413[/C][C]0.872360104142794[/C][/ROW]
[ROW][C]42[/C][C]0.135264600064048[/C][C]0.270529200128095[/C][C]0.864735399935952[/C][/ROW]
[ROW][C]43[/C][C]0.153870919441387[/C][C]0.307741838882773[/C][C]0.846129080558613[/C][/ROW]
[ROW][C]44[/C][C]0.283115972978787[/C][C]0.566231945957574[/C][C]0.716884027021213[/C][/ROW]
[ROW][C]45[/C][C]0.264137311369097[/C][C]0.528274622738194[/C][C]0.735862688630903[/C][/ROW]
[ROW][C]46[/C][C]0.293773769646929[/C][C]0.587547539293858[/C][C]0.706226230353071[/C][/ROW]
[ROW][C]47[/C][C]0.319214266534285[/C][C]0.638428533068569[/C][C]0.680785733465715[/C][/ROW]
[ROW][C]48[/C][C]0.295235476932863[/C][C]0.590470953865726[/C][C]0.704764523067137[/C][/ROW]
[ROW][C]49[/C][C]0.291631164067751[/C][C]0.583262328135501[/C][C]0.70836883593225[/C][/ROW]
[ROW][C]50[/C][C]0.273863041454899[/C][C]0.547726082909798[/C][C]0.726136958545101[/C][/ROW]
[ROW][C]51[/C][C]0.238004727457216[/C][C]0.476009454914431[/C][C]0.761995272542784[/C][/ROW]
[ROW][C]52[/C][C]0.205559226651333[/C][C]0.411118453302666[/C][C]0.794440773348667[/C][/ROW]
[ROW][C]53[/C][C]0.179887115413363[/C][C]0.359774230826727[/C][C]0.820112884586637[/C][/ROW]
[ROW][C]54[/C][C]0.319197266369079[/C][C]0.638394532738159[/C][C]0.68080273363092[/C][/ROW]
[ROW][C]55[/C][C]0.330938742824955[/C][C]0.66187748564991[/C][C]0.669061257175045[/C][/ROW]
[ROW][C]56[/C][C]0.335190836289038[/C][C]0.670381672578076[/C][C]0.664809163710962[/C][/ROW]
[ROW][C]57[/C][C]0.307370662480757[/C][C]0.614741324961514[/C][C]0.692629337519243[/C][/ROW]
[ROW][C]58[/C][C]0.272065223136113[/C][C]0.544130446272226[/C][C]0.727934776863887[/C][/ROW]
[ROW][C]59[/C][C]0.268933230431782[/C][C]0.537866460863563[/C][C]0.731066769568218[/C][/ROW]
[ROW][C]60[/C][C]0.243110541726417[/C][C]0.486221083452835[/C][C]0.756889458273583[/C][/ROW]
[ROW][C]61[/C][C]0.233899358316057[/C][C]0.467798716632114[/C][C]0.766100641683943[/C][/ROW]
[ROW][C]62[/C][C]0.212826255023557[/C][C]0.425652510047115[/C][C]0.787173744976443[/C][/ROW]
[ROW][C]63[/C][C]0.23644689901478[/C][C]0.47289379802956[/C][C]0.76355310098522[/C][/ROW]
[ROW][C]64[/C][C]0.218001981875874[/C][C]0.436003963751748[/C][C]0.781998018124126[/C][/ROW]
[ROW][C]65[/C][C]0.194319193655867[/C][C]0.388638387311735[/C][C]0.805680806344133[/C][/ROW]
[ROW][C]66[/C][C]0.179551889397494[/C][C]0.359103778794988[/C][C]0.820448110602506[/C][/ROW]
[ROW][C]67[/C][C]0.153468057186962[/C][C]0.306936114373924[/C][C]0.846531942813038[/C][/ROW]
[ROW][C]68[/C][C]0.136799008073257[/C][C]0.273598016146514[/C][C]0.863200991926743[/C][/ROW]
[ROW][C]69[/C][C]0.115548427799954[/C][C]0.231096855599908[/C][C]0.884451572200046[/C][/ROW]
[ROW][C]70[/C][C]0.0989384884424324[/C][C]0.197876976884865[/C][C]0.901061511557568[/C][/ROW]
[ROW][C]71[/C][C]0.113431430555410[/C][C]0.226862861110820[/C][C]0.88656856944459[/C][/ROW]
[ROW][C]72[/C][C]0.0953083299314296[/C][C]0.190616659862859[/C][C]0.90469167006857[/C][/ROW]
[ROW][C]73[/C][C]0.105128212591925[/C][C]0.210256425183850[/C][C]0.894871787408075[/C][/ROW]
[ROW][C]74[/C][C]0.092882294254028[/C][C]0.185764588508056[/C][C]0.907117705745972[/C][/ROW]
[ROW][C]75[/C][C]0.0778859701870721[/C][C]0.155771940374144[/C][C]0.922114029812928[/C][/ROW]
[ROW][C]76[/C][C]0.071296054385119[/C][C]0.142592108770238[/C][C]0.928703945614881[/C][/ROW]
[ROW][C]77[/C][C]0.0661338593780722[/C][C]0.132267718756144[/C][C]0.933866140621928[/C][/ROW]
[ROW][C]78[/C][C]0.0619935774489244[/C][C]0.123987154897849[/C][C]0.938006422551076[/C][/ROW]
[ROW][C]79[/C][C]0.0949651350184253[/C][C]0.189930270036851[/C][C]0.905034864981575[/C][/ROW]
[ROW][C]80[/C][C]0.143430062605732[/C][C]0.286860125211463[/C][C]0.856569937394268[/C][/ROW]
[ROW][C]81[/C][C]0.123872289992547[/C][C]0.247744579985094[/C][C]0.876127710007453[/C][/ROW]
[ROW][C]82[/C][C]0.191584535097964[/C][C]0.383169070195927[/C][C]0.808415464902036[/C][/ROW]
[ROW][C]83[/C][C]0.182464746523293[/C][C]0.364929493046586[/C][C]0.817535253476707[/C][/ROW]
[ROW][C]84[/C][C]0.167741744235489[/C][C]0.335483488470978[/C][C]0.832258255764511[/C][/ROW]
[ROW][C]85[/C][C]0.159082969903820[/C][C]0.318165939807640[/C][C]0.84091703009618[/C][/ROW]
[ROW][C]86[/C][C]0.167037318709274[/C][C]0.334074637418549[/C][C]0.832962681290726[/C][/ROW]
[ROW][C]87[/C][C]0.14711170309955[/C][C]0.2942234061991[/C][C]0.85288829690045[/C][/ROW]
[ROW][C]88[/C][C]0.134150072167395[/C][C]0.268300144334790[/C][C]0.865849927832605[/C][/ROW]
[ROW][C]89[/C][C]0.120428969235971[/C][C]0.240857938471942[/C][C]0.879571030764029[/C][/ROW]
[ROW][C]90[/C][C]0.152849871057663[/C][C]0.305699742115325[/C][C]0.847150128942337[/C][/ROW]
[ROW][C]91[/C][C]0.135574409675714[/C][C]0.271148819351429[/C][C]0.864425590324285[/C][/ROW]
[ROW][C]92[/C][C]0.126540718661077[/C][C]0.253081437322154[/C][C]0.873459281338923[/C][/ROW]
[ROW][C]93[/C][C]0.168364441448698[/C][C]0.336728882897396[/C][C]0.831635558551302[/C][/ROW]
[ROW][C]94[/C][C]0.143261385348684[/C][C]0.286522770697369[/C][C]0.856738614651316[/C][/ROW]
[ROW][C]95[/C][C]0.165523432520030[/C][C]0.331046865040059[/C][C]0.83447656747997[/C][/ROW]
[ROW][C]96[/C][C]0.175807629743491[/C][C]0.351615259486982[/C][C]0.82419237025651[/C][/ROW]
[ROW][C]97[/C][C]0.191599033168849[/C][C]0.383198066337699[/C][C]0.80840096683115[/C][/ROW]
[ROW][C]98[/C][C]0.177086676518139[/C][C]0.354173353036277[/C][C]0.822913323481861[/C][/ROW]
[ROW][C]99[/C][C]0.187300037949185[/C][C]0.374600075898369[/C][C]0.812699962050815[/C][/ROW]
[ROW][C]100[/C][C]0.242221863006252[/C][C]0.484443726012504[/C][C]0.757778136993748[/C][/ROW]
[ROW][C]101[/C][C]0.229573057193280[/C][C]0.459146114386559[/C][C]0.77042694280672[/C][/ROW]
[ROW][C]102[/C][C]0.206660297800105[/C][C]0.41332059560021[/C][C]0.793339702199895[/C][/ROW]
[ROW][C]103[/C][C]0.197024419280933[/C][C]0.394048838561866[/C][C]0.802975580719067[/C][/ROW]
[ROW][C]104[/C][C]0.169726856517068[/C][C]0.339453713034136[/C][C]0.830273143482932[/C][/ROW]
[ROW][C]105[/C][C]0.145733498674195[/C][C]0.29146699734839[/C][C]0.854266501325805[/C][/ROW]
[ROW][C]106[/C][C]0.142433431084286[/C][C]0.284866862168572[/C][C]0.857566568915714[/C][/ROW]
[ROW][C]107[/C][C]0.122246849104353[/C][C]0.244493698208707[/C][C]0.877753150895647[/C][/ROW]
[ROW][C]108[/C][C]0.111886982860221[/C][C]0.223773965720442[/C][C]0.888113017139779[/C][/ROW]
[ROW][C]109[/C][C]0.116006009220364[/C][C]0.232012018440727[/C][C]0.883993990779636[/C][/ROW]
[ROW][C]110[/C][C]0.105967994703826[/C][C]0.211935989407652[/C][C]0.894032005296174[/C][/ROW]
[ROW][C]111[/C][C]0.0917049383259773[/C][C]0.183409876651955[/C][C]0.908295061674023[/C][/ROW]
[ROW][C]112[/C][C]0.0789823061240814[/C][C]0.157964612248163[/C][C]0.921017693875919[/C][/ROW]
[ROW][C]113[/C][C]0.0910766564537505[/C][C]0.182153312907501[/C][C]0.90892334354625[/C][/ROW]
[ROW][C]114[/C][C]0.0758501495059605[/C][C]0.151700299011921[/C][C]0.92414985049404[/C][/ROW]
[ROW][C]115[/C][C]0.068678395722384[/C][C]0.137356791444768[/C][C]0.931321604277616[/C][/ROW]
[ROW][C]116[/C][C]0.0584965918011765[/C][C]0.116993183602353[/C][C]0.941503408198823[/C][/ROW]
[ROW][C]117[/C][C]0.0519874059353421[/C][C]0.103974811870684[/C][C]0.948012594064658[/C][/ROW]
[ROW][C]118[/C][C]0.0473503038061032[/C][C]0.0947006076122065[/C][C]0.952649696193897[/C][/ROW]
[ROW][C]119[/C][C]0.0644588404342743[/C][C]0.128917680868549[/C][C]0.935541159565726[/C][/ROW]
[ROW][C]120[/C][C]0.0721938163761281[/C][C]0.144387632752256[/C][C]0.927806183623872[/C][/ROW]
[ROW][C]121[/C][C]0.0736145514704537[/C][C]0.147229102940907[/C][C]0.926385448529546[/C][/ROW]
[ROW][C]122[/C][C]0.0742006610039317[/C][C]0.148401322007863[/C][C]0.925799338996068[/C][/ROW]
[ROW][C]123[/C][C]0.181227945354675[/C][C]0.36245589070935[/C][C]0.818772054645325[/C][/ROW]
[ROW][C]124[/C][C]0.181005746417144[/C][C]0.362011492834288[/C][C]0.818994253582856[/C][/ROW]
[ROW][C]125[/C][C]0.262049001961719[/C][C]0.524098003923438[/C][C]0.737950998038281[/C][/ROW]
[ROW][C]126[/C][C]0.279752922284875[/C][C]0.55950584456975[/C][C]0.720247077715125[/C][/ROW]
[ROW][C]127[/C][C]0.258656015105330[/C][C]0.517312030210659[/C][C]0.74134398489467[/C][/ROW]
[ROW][C]128[/C][C]0.330999061073412[/C][C]0.661998122146824[/C][C]0.669000938926588[/C][/ROW]
[ROW][C]129[/C][C]0.312384859570869[/C][C]0.624769719141738[/C][C]0.687615140429131[/C][/ROW]
[ROW][C]130[/C][C]0.276550720893577[/C][C]0.553101441787153[/C][C]0.723449279106423[/C][/ROW]
[ROW][C]131[/C][C]0.269058957758698[/C][C]0.538117915517395[/C][C]0.730941042241302[/C][/ROW]
[ROW][C]132[/C][C]0.236283914358532[/C][C]0.472567828717064[/C][C]0.763716085641468[/C][/ROW]
[ROW][C]133[/C][C]0.226352771353439[/C][C]0.452705542706879[/C][C]0.77364722864656[/C][/ROW]
[ROW][C]134[/C][C]0.216133445478983[/C][C]0.432266890957967[/C][C]0.783866554521017[/C][/ROW]
[ROW][C]135[/C][C]0.187859709859724[/C][C]0.375719419719449[/C][C]0.812140290140276[/C][/ROW]
[ROW][C]136[/C][C]0.203558907755336[/C][C]0.407117815510672[/C][C]0.796441092244664[/C][/ROW]
[ROW][C]137[/C][C]0.174019631733200[/C][C]0.348039263466399[/C][C]0.8259803682668[/C][/ROW]
[ROW][C]138[/C][C]0.149741471562328[/C][C]0.299482943124655[/C][C]0.850258528437672[/C][/ROW]
[ROW][C]139[/C][C]0.139864850677877[/C][C]0.279729701355754[/C][C]0.860135149322123[/C][/ROW]
[ROW][C]140[/C][C]0.116463445480560[/C][C]0.232926890961119[/C][C]0.88353655451944[/C][/ROW]
[ROW][C]141[/C][C]0.0990164611488512[/C][C]0.198032922297702[/C][C]0.900983538851149[/C][/ROW]
[ROW][C]142[/C][C]0.102450276546640[/C][C]0.204900553093280[/C][C]0.89754972345336[/C][/ROW]
[ROW][C]143[/C][C]0.0852178589327163[/C][C]0.170435717865433[/C][C]0.914782141067284[/C][/ROW]
[ROW][C]144[/C][C]0.0853451212182001[/C][C]0.170690242436400[/C][C]0.9146548787818[/C][/ROW]
[ROW][C]145[/C][C]0.199209001318809[/C][C]0.398418002637618[/C][C]0.800790998681191[/C][/ROW]
[ROW][C]146[/C][C]0.212797924397802[/C][C]0.425595848795605[/C][C]0.787202075602198[/C][/ROW]
[ROW][C]147[/C][C]0.276658811211827[/C][C]0.553317622423655[/C][C]0.723341188788173[/C][/ROW]
[ROW][C]148[/C][C]0.236911783553755[/C][C]0.47382356710751[/C][C]0.763088216446245[/C][/ROW]
[ROW][C]149[/C][C]0.23772871655751[/C][C]0.47545743311502[/C][C]0.76227128344249[/C][/ROW]
[ROW][C]150[/C][C]0.240806817221542[/C][C]0.481613634443083[/C][C]0.759193182778458[/C][/ROW]
[ROW][C]151[/C][C]0.234403944081352[/C][C]0.468807888162704[/C][C]0.765596055918648[/C][/ROW]
[ROW][C]152[/C][C]0.691383996310733[/C][C]0.617232007378534[/C][C]0.308616003689267[/C][/ROW]
[ROW][C]153[/C][C]0.645797581798041[/C][C]0.708404836403917[/C][C]0.354202418201959[/C][/ROW]
[ROW][C]154[/C][C]0.636519598782352[/C][C]0.726960802435297[/C][C]0.363480401217648[/C][/ROW]
[ROW][C]155[/C][C]0.587151457081716[/C][C]0.825697085836567[/C][C]0.412848542918284[/C][/ROW]
[ROW][C]156[/C][C]0.538591054857326[/C][C]0.922817890285348[/C][C]0.461408945142674[/C][/ROW]
[ROW][C]157[/C][C]0.544367630516364[/C][C]0.911264738967271[/C][C]0.455632369483636[/C][/ROW]
[ROW][C]158[/C][C]0.491029901418198[/C][C]0.982059802836395[/C][C]0.508970098581802[/C][/ROW]
[ROW][C]159[/C][C]0.452782756283125[/C][C]0.90556551256625[/C][C]0.547217243716875[/C][/ROW]
[ROW][C]160[/C][C]0.402067665505432[/C][C]0.804135331010865[/C][C]0.597932334494568[/C][/ROW]
[ROW][C]161[/C][C]0.383801622278524[/C][C]0.767603244557047[/C][C]0.616198377721476[/C][/ROW]
[ROW][C]162[/C][C]0.336635269357101[/C][C]0.673270538714201[/C][C]0.6633647306429[/C][/ROW]
[ROW][C]163[/C][C]0.354498341011755[/C][C]0.70899668202351[/C][C]0.645501658988245[/C][/ROW]
[ROW][C]164[/C][C]0.311463206013254[/C][C]0.622926412026507[/C][C]0.688536793986746[/C][/ROW]
[ROW][C]165[/C][C]0.330594529454400[/C][C]0.661189058908801[/C][C]0.6694054705456[/C][/ROW]
[ROW][C]166[/C][C]0.28330924163084[/C][C]0.56661848326168[/C][C]0.71669075836916[/C][/ROW]
[ROW][C]167[/C][C]0.356284165483809[/C][C]0.712568330967618[/C][C]0.643715834516191[/C][/ROW]
[ROW][C]168[/C][C]0.451175249781140[/C][C]0.902350499562281[/C][C]0.54882475021886[/C][/ROW]
[ROW][C]169[/C][C]0.388907215202411[/C][C]0.777814430404823[/C][C]0.611092784797589[/C][/ROW]
[ROW][C]170[/C][C]0.417572500064654[/C][C]0.835145000129309[/C][C]0.582427499935346[/C][/ROW]
[ROW][C]171[/C][C]0.415509425954727[/C][C]0.831018851909455[/C][C]0.584490574045273[/C][/ROW]
[ROW][C]172[/C][C]0.350619785509424[/C][C]0.701239571018848[/C][C]0.649380214490576[/C][/ROW]
[ROW][C]173[/C][C]0.293972332124905[/C][C]0.58794466424981[/C][C]0.706027667875095[/C][/ROW]
[ROW][C]174[/C][C]0.244875723813208[/C][C]0.489751447626416[/C][C]0.755124276186792[/C][/ROW]
[ROW][C]175[/C][C]0.234667492878510[/C][C]0.469334985757020[/C][C]0.76533250712149[/C][/ROW]
[ROW][C]176[/C][C]0.203567858505621[/C][C]0.407135717011241[/C][C]0.79643214149438[/C][/ROW]
[ROW][C]177[/C][C]0.154321570401304[/C][C]0.308643140802608[/C][C]0.845678429598696[/C][/ROW]
[ROW][C]178[/C][C]0.136664767572460[/C][C]0.273329535144920[/C][C]0.86333523242754[/C][/ROW]
[ROW][C]179[/C][C]0.0998084511099069[/C][C]0.199616902219814[/C][C]0.900191548890093[/C][/ROW]
[ROW][C]180[/C][C]0.184349285785555[/C][C]0.36869857157111[/C][C]0.815650714214445[/C][/ROW]
[ROW][C]181[/C][C]0.151197241043879[/C][C]0.302394482087759[/C][C]0.84880275895612[/C][/ROW]
[ROW][C]182[/C][C]0.113111585179523[/C][C]0.226223170359047[/C][C]0.886888414820477[/C][/ROW]
[ROW][C]183[/C][C]0.0726699951520893[/C][C]0.145339990304179[/C][C]0.92733000484791[/C][/ROW]
[ROW][C]184[/C][C]0.0654612541055095[/C][C]0.130922508211019[/C][C]0.93453874589449[/C][/ROW]
[ROW][C]185[/C][C]0.230198659572979[/C][C]0.460397319145958[/C][C]0.769801340427021[/C][/ROW]
[ROW][C]186[/C][C]0.135809291066651[/C][C]0.271618582133302[/C][C]0.864190708933349[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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
90.7535967480585980.4928065038828050.246403251941402
100.8292755453476680.3414489093046640.170724454652332
110.861900933606440.2761981327871210.138099066393560
120.8049392945124780.3901214109750430.195060705487522
130.7336977193494660.5326045613010690.266302280650534
140.7042542702840720.5914914594318550.295745729715928
150.6179483745266350.764103250946730.382051625473365
160.5337301080695840.9325397838608320.466269891930416
170.4644084872161110.9288169744322210.535591512783889
180.533763989659970.9324720206800610.466236010340030
190.5612227585951290.8775544828097420.438777241404871
200.5234380996171840.9531238007656310.476561900382816
210.4530304317101130.9060608634202260.546969568289887
220.4235298762672640.8470597525345280.576470123732736
230.4961256125904080.9922512251808150.503874387409592
240.4737891076537850.947578215307570.526210892346215
250.4576244678418780.9152489356837560.542375532158122
260.3909490198441520.7818980396883050.609050980155848
270.3453679313692760.6907358627385530.654632068630724
280.2898470472136530.5796940944273070.710152952786347
290.4290526379193540.8581052758387080.570947362080646
300.3717524705537920.7435049411075840.628247529446208
310.3272467208601110.6544934417202210.67275327913989
320.2856641600557010.5713283201114030.714335839944299
330.2370854765843280.4741709531686560.762914523415672
340.2490425395583330.4980850791166670.750957460441667
350.2146439186462130.4292878372924270.785356081353787
360.1934957310637460.3869914621274920.806504268936254
370.1750756114060210.3501512228120430.824924388593979
380.1521902763226230.3043805526452470.847809723677377
390.1389804113177830.2779608226355650.861019588682217
400.1232605040885130.2465210081770270.876739495911486
410.1276398958572060.2552797917144130.872360104142794
420.1352646000640480.2705292001280950.864735399935952
430.1538709194413870.3077418388827730.846129080558613
440.2831159729787870.5662319459575740.716884027021213
450.2641373113690970.5282746227381940.735862688630903
460.2937737696469290.5875475392938580.706226230353071
470.3192142665342850.6384285330685690.680785733465715
480.2952354769328630.5904709538657260.704764523067137
490.2916311640677510.5832623281355010.70836883593225
500.2738630414548990.5477260829097980.726136958545101
510.2380047274572160.4760094549144310.761995272542784
520.2055592266513330.4111184533026660.794440773348667
530.1798871154133630.3597742308267270.820112884586637
540.3191972663690790.6383945327381590.68080273363092
550.3309387428249550.661877485649910.669061257175045
560.3351908362890380.6703816725780760.664809163710962
570.3073706624807570.6147413249615140.692629337519243
580.2720652231361130.5441304462722260.727934776863887
590.2689332304317820.5378664608635630.731066769568218
600.2431105417264170.4862210834528350.756889458273583
610.2338993583160570.4677987166321140.766100641683943
620.2128262550235570.4256525100471150.787173744976443
630.236446899014780.472893798029560.76355310098522
640.2180019818758740.4360039637517480.781998018124126
650.1943191936558670.3886383873117350.805680806344133
660.1795518893974940.3591037787949880.820448110602506
670.1534680571869620.3069361143739240.846531942813038
680.1367990080732570.2735980161465140.863200991926743
690.1155484277999540.2310968555999080.884451572200046
700.09893848844243240.1978769768848650.901061511557568
710.1134314305554100.2268628611108200.88656856944459
720.09530832993142960.1906166598628590.90469167006857
730.1051282125919250.2102564251838500.894871787408075
740.0928822942540280.1857645885080560.907117705745972
750.07788597018707210.1557719403741440.922114029812928
760.0712960543851190.1425921087702380.928703945614881
770.06613385937807220.1322677187561440.933866140621928
780.06199357744892440.1239871548978490.938006422551076
790.09496513501842530.1899302700368510.905034864981575
800.1434300626057320.2868601252114630.856569937394268
810.1238722899925470.2477445799850940.876127710007453
820.1915845350979640.3831690701959270.808415464902036
830.1824647465232930.3649294930465860.817535253476707
840.1677417442354890.3354834884709780.832258255764511
850.1590829699038200.3181659398076400.84091703009618
860.1670373187092740.3340746374185490.832962681290726
870.147111703099550.29422340619910.85288829690045
880.1341500721673950.2683001443347900.865849927832605
890.1204289692359710.2408579384719420.879571030764029
900.1528498710576630.3056997421153250.847150128942337
910.1355744096757140.2711488193514290.864425590324285
920.1265407186610770.2530814373221540.873459281338923
930.1683644414486980.3367288828973960.831635558551302
940.1432613853486840.2865227706973690.856738614651316
950.1655234325200300.3310468650400590.83447656747997
960.1758076297434910.3516152594869820.82419237025651
970.1915990331688490.3831980663376990.80840096683115
980.1770866765181390.3541733530362770.822913323481861
990.1873000379491850.3746000758983690.812699962050815
1000.2422218630062520.4844437260125040.757778136993748
1010.2295730571932800.4591461143865590.77042694280672
1020.2066602978001050.413320595600210.793339702199895
1030.1970244192809330.3940488385618660.802975580719067
1040.1697268565170680.3394537130341360.830273143482932
1050.1457334986741950.291466997348390.854266501325805
1060.1424334310842860.2848668621685720.857566568915714
1070.1222468491043530.2444936982087070.877753150895647
1080.1118869828602210.2237739657204420.888113017139779
1090.1160060092203640.2320120184407270.883993990779636
1100.1059679947038260.2119359894076520.894032005296174
1110.09170493832597730.1834098766519550.908295061674023
1120.07898230612408140.1579646122481630.921017693875919
1130.09107665645375050.1821533129075010.90892334354625
1140.07585014950596050.1517002990119210.92414985049404
1150.0686783957223840.1373567914447680.931321604277616
1160.05849659180117650.1169931836023530.941503408198823
1170.05198740593534210.1039748118706840.948012594064658
1180.04735030380610320.09470060761220650.952649696193897
1190.06445884043427430.1289176808685490.935541159565726
1200.07219381637612810.1443876327522560.927806183623872
1210.07361455147045370.1472291029409070.926385448529546
1220.07420066100393170.1484013220078630.925799338996068
1230.1812279453546750.362455890709350.818772054645325
1240.1810057464171440.3620114928342880.818994253582856
1250.2620490019617190.5240980039234380.737950998038281
1260.2797529222848750.559505844569750.720247077715125
1270.2586560151053300.5173120302106590.74134398489467
1280.3309990610734120.6619981221468240.669000938926588
1290.3123848595708690.6247697191417380.687615140429131
1300.2765507208935770.5531014417871530.723449279106423
1310.2690589577586980.5381179155173950.730941042241302
1320.2362839143585320.4725678287170640.763716085641468
1330.2263527713534390.4527055427068790.77364722864656
1340.2161334454789830.4322668909579670.783866554521017
1350.1878597098597240.3757194197194490.812140290140276
1360.2035589077553360.4071178155106720.796441092244664
1370.1740196317332000.3480392634663990.8259803682668
1380.1497414715623280.2994829431246550.850258528437672
1390.1398648506778770.2797297013557540.860135149322123
1400.1164634454805600.2329268909611190.88353655451944
1410.09901646114885120.1980329222977020.900983538851149
1420.1024502765466400.2049005530932800.89754972345336
1430.08521785893271630.1704357178654330.914782141067284
1440.08534512121820010.1706902424364000.9146548787818
1450.1992090013188090.3984180026376180.800790998681191
1460.2127979243978020.4255958487956050.787202075602198
1470.2766588112118270.5533176224236550.723341188788173
1480.2369117835537550.473823567107510.763088216446245
1490.237728716557510.475457433115020.76227128344249
1500.2408068172215420.4816136344430830.759193182778458
1510.2344039440813520.4688078881627040.765596055918648
1520.6913839963107330.6172320073785340.308616003689267
1530.6457975817980410.7084048364039170.354202418201959
1540.6365195987823520.7269608024352970.363480401217648
1550.5871514570817160.8256970858365670.412848542918284
1560.5385910548573260.9228178902853480.461408945142674
1570.5443676305163640.9112647389672710.455632369483636
1580.4910299014181980.9820598028363950.508970098581802
1590.4527827562831250.905565512566250.547217243716875
1600.4020676655054320.8041353310108650.597932334494568
1610.3838016222785240.7676032445570470.616198377721476
1620.3366352693571010.6732705387142010.6633647306429
1630.3544983410117550.708996682023510.645501658988245
1640.3114632060132540.6229264120265070.688536793986746
1650.3305945294544000.6611890589088010.6694054705456
1660.283309241630840.566618483261680.71669075836916
1670.3562841654838090.7125683309676180.643715834516191
1680.4511752497811400.9023504995622810.54882475021886
1690.3889072152024110.7778144304048230.611092784797589
1700.4175725000646540.8351450001293090.582427499935346
1710.4155094259547270.8310188519094550.584490574045273
1720.3506197855094240.7012395710188480.649380214490576
1730.2939723321249050.587944664249810.706027667875095
1740.2448757238132080.4897514476264160.755124276186792
1750.2346674928785100.4693349857570200.76533250712149
1760.2035678585056210.4071357170112410.79643214149438
1770.1543215704013040.3086431408026080.845678429598696
1780.1366647675724600.2733295351449200.86333523242754
1790.09980845110990690.1996169022198140.900191548890093
1800.1843492857855550.368698571571110.815650714214445
1810.1511972410438790.3023944820877590.84880275895612
1820.1131115851795230.2262231703590470.886888414820477
1830.07266999515208930.1453399903041790.92733000484791
1840.06546125410550950.1309225082110190.93453874589449
1850.2301986595729790.4603973191459580.769801340427021
1860.1358092910666510.2716185821333020.864190708933349






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00561797752808989OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00561797752808989 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115795&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00561797752808989[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115795&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115795&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 level00OK
5% type I error level00OK
10% type I error level10.00561797752808989OK


Figures (Output of Computation)

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

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