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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 14 Dec 2010 20:36:58 +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/14/t1292358910278970gys2bq8x4.htm/, Retrieved Thu, 02 May 2024 17:58:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110162, Retrieved Thu, 02 May 2024 17:58:44 +0000
QR Codes:

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

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple Regressi...] [2010-12-14 20:36:58] [278a0539dc236556c5f30b5bc56ff9eb] [Current]
-    D      [Multiple Regression] [WS10 MR] [2011-12-12 15:45:50] [b8fde34a99ee6a7d49500940cae4da2a]
- R         [Multiple Regression] [ws 10] [2011-12-12 16:09:36] [27e29806e0b1d1351a97bc4ee4116294]
- RM        [Multiple Regression] [] [2011-12-13 08:41:42] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	1	22	15	16	17	10
1	2	22	23	24	42	9
1	2	22	26	22	39	30
1	2	23	19	21	22	18
1	2	21	19	23	20	16
1	2	21	16	23	31	20
1	1	24	23	21	42	20
2	1	22	22	20	30	18
1	2	21	19	22	33	21
1	2	23	24	20	29	20
1	1	20	19	12	31	20
2	1	23	25	23	39	20
1	1	20	23	23	44	29
1	2	21	31	30	40	14
2	1	22	29	22	42	25
2	2	22	18	21	28	19
2	2	21	17	21	29	19
1	1	20	22	15	35	25
1	1	21	21	22	26	25
1	2	21	24	24	42	19
1	1	20	22	23	26	19
1	1	21	16	15	30	18
1	2	23	22	24	28	24
1	1	23	21	24	24	18
2	1	21	25	21	26	26
1	1	22	22	21	39	26
2	1	20	24	18	33	24
2	1	23	21	20	50	29
2	1	21	25	19	40	26
1	1	21	29	29	49	28
2	1	23	19	20	31	18
2	1	23	29	23	37	19
2	2	22	25	24	29	21
1	1	21	19	27	37	13
1	1	0	27	28	16	19
1	2	21	25	24	28	26
1	2	21	23	29	29	17
1	1	22	24	24	31	19
2	2	22	23	22	34	28
1	2	22	25	25	30	15
1	1	22	26	24	31	16
1	1	23	23	14	44	18
2	1	0	22	22	35	25
1	2	22	32	24	47	15
1	2	21	22	24	39	24
1	1	23	18	24	34	24
2	1	21	19	24	15	14
2	2	32	23	22	26	19
2	2	32	24	22	25	20
2	1	21	19	21	30	27
1	1	20	16	21	25	20
1	1	21	23	21	33	25
1	1	22	17	15	39	16
1	1	21	17	26	24	19
1	2	21	28	22	44	15
1	2	21	24	24	31	17
1	1	22	21	13	30	22
1	1	21	14	19	21	19
2	1	25	21	10	38	44
1	2	22	20	28	30	19
1	1	21	25	25	31	19
2	2	21	20	24	32	23
1	1	20	17	22	34	19
1	2	21	26	30	43	28
1	2	22	17	22	29	17
4	2	21	17	24	40	22
1	2	23	24	23	31	25
1	2	24	30	20	36	44
2	1	20	25	22	39	19
2	1	21	15	22	31	21
1	1	23	25	19	36	25
1	1	24	18	24	36	28
1	1	22	20	22	36	19
1	1	22	32	26	41	33
2	2	21	14	12	23	19
2	1	20	20	25	23	12
1	2	21	25	29	34	15
1	0	21	25	23	31	23
1	1	22	25	23	26	21
1	1	21	35	17	32	29
1	2	21	29	26	48	15
1	1	22	25	27	37	23
1	2	23	21	23	31	25
1	1	23	21	20	29	18
1	1	21	24	24	44	24
1	1	22	26	22	27	20
1	1	24	24	26	31	19
1	2	21	20	29	37	21
1	1	25	24	20	31	33
2	1	24	18	17	29	17
1	1	21	17	16	25	19
1	2	21	22	24	39	19
1	1	22	22	24	33	23
1	1	22	22	19	26	21
2	1	21	24	29	39	20
1	1	24	32	25	33	19
1	2	21	19	25	22	16
1	2	21	21	24	27	20
1	2	22	23	29	31	18
2	1	22	26	22	40	11
1	2	21	18	23	45	18
2	1	21	19	15	33	23
1	1	22	22	29	26	19
1	2	21	27	21	28	21
2	1	21	21	23	28	16
2	1	22	20	20	30	24
2	2	21	21	25	47	23
1	1	21	20	28	30	20
1	1	21	29	18	37	23
1	1	23	30	25	23	25
1	1	22	23	24	24	24
2	1	21	29	23	15	19
1	2	22	19	25	39	15
1	1	22	26	27	32	20
1	1	22	22	24	23	14
2	1	23	26	24	41	34
2	1	24	27	26	39	24
2	1	22	19	18	42	26
2	2	21	24	26	38	23
1	2	21	26	23	30	18
2	2	22	22	28	19	11
1	1	20	23	20	45	28
1	1	23	25	23	36	15
1	2	21	19	24	36	26
1	1	22	20	21	33	20
2	1	23	25	25	35	21
2	1	21	14	16	29	21
1	2	21	20	19	28	15
1	1	23	27	22	40	30
1	2	21	21	27	28	21
1	2	21	21	24	36	12
1	2	21	14	17	28	23
2	1	23	21	21	42	30
1	1	21	23	21	27	22
2	1	22	18	19	32	21
1	2	22	20	25	27	19
1	2	21	19	24	30	22
1	2	21	15	21	31	18
1	2	24	23	26	26	16
1	1	21	26	25	47	19
1	1	21	21	25	28	20
1	2	20	13	13	31	12
2	2	21	24	25	26	21
1	2	0	17	23	27	14
1	1	24	21	26	28	23
1	1	22	28	22	44	30
1	1	22	22	20	32	21
1	1	22	25	14	25	25
1	1	22	18	23	24	13
1	1	23	27	24	45	17
1	1	22	25	21	37	24
2	1	22	21	24	25	21

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 6.5867952337856 -0.878020762828454Roken[t] -1.3024665063434Geslacht[t] + 0.0879722811529304Leeftijd[t] + 0.401296015151827O[t] + 0.130024469727997CMD[t] + 0.169296265598382PEC[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  6.5867952337856 -0.878020762828454Roken[t] -1.3024665063434Geslacht[t] +  0.0879722811529304Leeftijd[t] +  0.401296015151827O[t] +  0.130024469727997CMD[t] +  0.169296265598382PEC[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  6.5867952337856 -0.878020762828454Roken[t] -1.3024665063434Geslacht[t] +  0.0879722811529304Leeftijd[t] +  0.401296015151827O[t] +  0.130024469727997CMD[t] +  0.169296265598382PEC[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&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
PS[t] = + 6.5867952337856 -0.878020762828454Roken[t] -1.3024665063434Geslacht[t] + 0.0879722811529304Leeftijd[t] + 0.401296015151827O[t] + 0.130024469727997CMD[t] + 0.169296265598382PEC[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.58679523378563.1407852.09720.0377130.018857
Roken-0.8780207628284540.58383-1.50390.1347830.067391
Geslacht-1.30246650634340.609028-2.13860.0341440.017072
Leeftijd0.08797228115293040.0857011.02650.3063620.153181
O0.4012960151518270.0799765.01772e-061e-06
CMD0.1300244697279970.0422593.07690.0025020.001251
PEC0.1692962655983820.0581052.91360.0041390.00207

\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) & 6.5867952337856 & 3.140785 & 2.0972 & 0.037713 & 0.018857 \tabularnewline
Roken & -0.878020762828454 & 0.58383 & -1.5039 & 0.134783 & 0.067391 \tabularnewline
Geslacht & -1.3024665063434 & 0.609028 & -2.1386 & 0.034144 & 0.017072 \tabularnewline
Leeftijd & 0.0879722811529304 & 0.085701 & 1.0265 & 0.306362 & 0.153181 \tabularnewline
O & 0.401296015151827 & 0.079976 & 5.0177 & 2e-06 & 1e-06 \tabularnewline
CMD & 0.130024469727997 & 0.042259 & 3.0769 & 0.002502 & 0.001251 \tabularnewline
PEC & 0.169296265598382 & 0.058105 & 2.9136 & 0.004139 & 0.00207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&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]6.5867952337856[/C][C]3.140785[/C][C]2.0972[/C][C]0.037713[/C][C]0.018857[/C][/ROW]
[ROW][C]Roken[/C][C]-0.878020762828454[/C][C]0.58383[/C][C]-1.5039[/C][C]0.134783[/C][C]0.067391[/C][/ROW]
[ROW][C]Geslacht[/C][C]-1.3024665063434[/C][C]0.609028[/C][C]-2.1386[/C][C]0.034144[/C][C]0.017072[/C][/ROW]
[ROW][C]Leeftijd[/C][C]0.0879722811529304[/C][C]0.085701[/C][C]1.0265[/C][C]0.306362[/C][C]0.153181[/C][/ROW]
[ROW][C]O[/C][C]0.401296015151827[/C][C]0.079976[/C][C]5.0177[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]CMD[/C][C]0.130024469727997[/C][C]0.042259[/C][C]3.0769[/C][C]0.002502[/C][C]0.001251[/C][/ROW]
[ROW][C]PEC[/C][C]0.169296265598382[/C][C]0.058105[/C][C]2.9136[/C][C]0.004139[/C][C]0.00207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&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)6.58679523378563.1407852.09720.0377130.018857
Roken-0.8780207628284540.58383-1.50390.1347830.067391
Geslacht-1.30246650634340.609028-2.13860.0341440.017072
Leeftijd0.08797228115293040.0857011.02650.3063620.153181
O0.4012960151518270.0799765.01772e-061e-06
CMD0.1300244697279970.0422593.07690.0025020.001251
PEC0.1692962655983820.0581052.91360.0041390.00207






Multiple Linear Regression - Regression Statistics
Multiple R0.534702007831309
R-squared0.285906237178833
Adjusted R-squared0.256357529751751
F-TEST (value)9.6757612116998
F-TEST (DF numerator)6
F-TEST (DF denominator)145
p-value5.97857463535689e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5800265148466
Sum Squared Residuals1858.40552781568

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.534702007831309 \tabularnewline
R-squared & 0.285906237178833 \tabularnewline
Adjusted R-squared & 0.256357529751751 \tabularnewline
F-TEST (value) & 9.6757612116998 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 5.97857463535689e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.5800265148466 \tabularnewline
Sum Squared Residuals & 1858.40552781568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.534702007831309[/C][/ROW]
[ROW][C]R-squared[/C][C]0.285906237178833[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.256357529751751[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.6757612116998[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/C][/ROW]
[ROW][C]p-value[/C][C]5.97857463535689e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.5800265148466[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1858.40552781568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&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 R0.534702007831309
R-squared0.285906237178833
Adjusted R-squared0.256357529751751
F-TEST (value)9.6757612116998
F-TEST (DF numerator)6
F-TEST (DF denominator)145
p-value5.97857463535689e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5800265148466
Sum Squared Residuals1858.40552781568






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11515.7877922709388-0.787792270938764
22321.655030126241.34496987376001
32624.01758626431841.98241373568164
41919.4622913577629-0.462291357762933
51919.490297355108-0.490297355107969
61621.5977515845095-5.59775158450947
72323.791812071016-0.791812071015971
82220.43766456279711.5623354372029
91921.625800774412-2.62580077441201
102420.30975916190393.69024083809615
111918.39798964302980.602010356970163
122523.23833764815421.76166235184575
132326.0262303065493-3.02623030654934
143124.56126632453396.43873367546606
152923.98562408902545.01437591097461
161819.4457413977479-1.44574139774791
171719.487793586323-2.48779358632298
182220.96845689538921.03154310461078
192122.695281055053-1.69528105505296
202423.26002050107090.73997949892912
212221.99282719546160.00717280453843304
221619.2212329687135-3.22123296871349
232222.4621038151767-0.462103815176689
242122.2286948490178-1.22869484901781
252521.58526054267113.41473945732894
262224.2415716931164-2.24157169311641
272420.86497897296193.13502102703813
282124.9883851600922-3.98838516009217
292522.60301108855942.39698891144063
302929.0028047616548-0.00280476165483319
311920.655661313678-1.65566131367803
322922.80899244309996.19100755690013
332521.11824644412823.88175355587184
341924.1004751106395-5.10047511063948
352720.93961695088216.06038304911788
362522.62475178406762.37524821593241
372323.2375899391693-0.237589939169288
382423.22019012155920.779809878440759
392322.15085062165320.849149378346839
402521.51181009824613.48818990175386
412622.71230132476413.2876986752359
422320.81622409205952.18377590794052
432221.14006261556490.859937384435056
443223.32093006847038.67906993152973
452223.7164284198788-1.7164284198788
461824.5447171398881-6.54471713988807
471919.327324233938-0.327324233937992
482320.46671128497312.53328871502695
492420.50598308084343.49401691915657
501922.2746546871814-3.27465468718143
511621.2295069610283-5.2295069610283
522323.2041563279971-0.204156327997115
531720.1408329462216-3.14083294622163
541723.024638582614-6.02463858261398
552822.04029234782975.95970765217031
562421.49115880286612.50884119713385
572119.18379828195631.81620171804371
581419.8254930673672-5.8254930673672
592122.1305199181195-1.13051991811953
602023.3928832060952-3.39288320609515
612523.53351385555811.46648614444186
622021.758940103356-1.75894010335598
631722.6317269381337-5.63172693813372
642627.3214874520953-1.32148745209528
651720.5164901142594-3.51649011425943
661720.8737980699247-3.87379806992467
672422.62017747480721.37982252519276
683025.37101310551394.62898689448607
692522.40382852394532.59617147605475
701521.790197578471-6.79019757847097
712522.96758226918332.03241773081669
721825.5699234228905-7.56992342289053
732023.0677204398956-3.06772043989557
743227.69317456752024.30682543247979
751415.0959826315886-1.09598263158855
762020.3422511945641-0.342251194564103
772523.54911975661251.45088024338749
782524.71057339399140.289426606008589
792522.50736428896422.49263571103581
803522.146132860055312.8538671399447
812924.1655742873494.83442571265101
822525.8814100477762-0.881410047776233
832122.6201774748072-1.62017747480724
842121.2736331370505-0.273633137050487
852425.6690172748622-1.66901727486218
862622.0667964779423.93320352205802
872424.1987267141688-0.198726714168755
882024.9549707593868-4.95497075938679
892424.2490706227881-0.249070622788071
901819.1104003443211-1.1104003443211
911719.1417029008237-2.14170290082371
922222.8699470918869-0.869947091886888
932224.1574241234088-2.15742412340876
942220.90218022835691.09781977164312
952425.4701691767594-1.47016917675935
963224.05747963847297.94252036152708
971920.5529383248676-1.55293832486762
982121.4789497207493-0.478949720749304
992323.7549074253766-0.754907425376594
1002621.35542743119214.64457256880795
1011823.0795016295047-5.07950162950466
1021919.5797669430609-0.579766943060935
1032224.5765478486784-2.57654784867839
1042720.57438241062026.4256175893798
1052120.95493885644690.0450611435531078
1062021.4534421563874-1.45344215638739
1072124.1106031644278-3.11060316442777
1082024.776673696884-4.776673696884
1092922.18177363025696.81822636974314
1103023.68504025363036.31495974636969
1112323.1565001614552-0.15650016145517
1122919.77250954677819.22749045322193
1131922.6820303257981-3.68203032579812
1142624.72339890234111.2766010976589
1152221.33351303574340.666486964256646
1162626.2698303211394-0.269830321139417
1172725.20738303715621.79261696284381
1181922.5497362940165-3.54973629401647
1192423.34167895202760.658321047972382
1202621.12913458358474.8708654164153
1212219.73022315147172.26977684852833
1222324.7830704652235-1.78307046522347
1232522.87980367380682.1201963261932
1241923.6649475418916-4.66494754189157
1252022.4456472811581-2.44564728115814
1262523.69012806514431.3098719348557
1271419.122372548104-5.12237254810401
1282018.75601278672631.24398721327374
1292725.53804952154271.46195047845731
1302122.9821585015312-1.98215850153116
1312121.2947998235142-0.294799823514223
1321419.3077908812097-5.30779088120966
1332124.5187816830184-3.51878168301841
1342321.9161207128341.08387928716601
1351820.8043062838964-2.80430628389641
1362021.7989217514557-1.79892175145568
1371922.2076156611301-3.20761566113006
1381520.456567023009-5.45656702300905
1392321.73824906239021.26175093760978
1402625.61390537120610.386094628793907
1412123.3127367119725-2.31273671197253
1421316.1424490270512-3.14244902705121
1432421.04149676894312.95850323105694
1441718.2144582077956-1.21445820779565
1452124.4858383673783-3.48583836737829
1462825.97017511930182.02982488069825
1472222.0836230618767-0.0836230618766936
1482519.44286074526335.55713925473672
1491820.8929452247211-2.89294522472114
1502724.78991244770742.21008755229263
1512523.64293022246371.35706977753635
1522121.9006150715596-0.900615071559567

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 15.7877922709388 & -0.787792270938764 \tabularnewline
2 & 23 & 21.65503012624 & 1.34496987376001 \tabularnewline
3 & 26 & 24.0175862643184 & 1.98241373568164 \tabularnewline
4 & 19 & 19.4622913577629 & -0.462291357762933 \tabularnewline
5 & 19 & 19.490297355108 & -0.490297355107969 \tabularnewline
6 & 16 & 21.5977515845095 & -5.59775158450947 \tabularnewline
7 & 23 & 23.791812071016 & -0.791812071015971 \tabularnewline
8 & 22 & 20.4376645627971 & 1.5623354372029 \tabularnewline
9 & 19 & 21.625800774412 & -2.62580077441201 \tabularnewline
10 & 24 & 20.3097591619039 & 3.69024083809615 \tabularnewline
11 & 19 & 18.3979896430298 & 0.602010356970163 \tabularnewline
12 & 25 & 23.2383376481542 & 1.76166235184575 \tabularnewline
13 & 23 & 26.0262303065493 & -3.02623030654934 \tabularnewline
14 & 31 & 24.5612663245339 & 6.43873367546606 \tabularnewline
15 & 29 & 23.9856240890254 & 5.01437591097461 \tabularnewline
16 & 18 & 19.4457413977479 & -1.44574139774791 \tabularnewline
17 & 17 & 19.487793586323 & -2.48779358632298 \tabularnewline
18 & 22 & 20.9684568953892 & 1.03154310461078 \tabularnewline
19 & 21 & 22.695281055053 & -1.69528105505296 \tabularnewline
20 & 24 & 23.2600205010709 & 0.73997949892912 \tabularnewline
21 & 22 & 21.9928271954616 & 0.00717280453843304 \tabularnewline
22 & 16 & 19.2212329687135 & -3.22123296871349 \tabularnewline
23 & 22 & 22.4621038151767 & -0.462103815176689 \tabularnewline
24 & 21 & 22.2286948490178 & -1.22869484901781 \tabularnewline
25 & 25 & 21.5852605426711 & 3.41473945732894 \tabularnewline
26 & 22 & 24.2415716931164 & -2.24157169311641 \tabularnewline
27 & 24 & 20.8649789729619 & 3.13502102703813 \tabularnewline
28 & 21 & 24.9883851600922 & -3.98838516009217 \tabularnewline
29 & 25 & 22.6030110885594 & 2.39698891144063 \tabularnewline
30 & 29 & 29.0028047616548 & -0.00280476165483319 \tabularnewline
31 & 19 & 20.655661313678 & -1.65566131367803 \tabularnewline
32 & 29 & 22.8089924430999 & 6.19100755690013 \tabularnewline
33 & 25 & 21.1182464441282 & 3.88175355587184 \tabularnewline
34 & 19 & 24.1004751106395 & -5.10047511063948 \tabularnewline
35 & 27 & 20.9396169508821 & 6.06038304911788 \tabularnewline
36 & 25 & 22.6247517840676 & 2.37524821593241 \tabularnewline
37 & 23 & 23.2375899391693 & -0.237589939169288 \tabularnewline
38 & 24 & 23.2201901215592 & 0.779809878440759 \tabularnewline
39 & 23 & 22.1508506216532 & 0.849149378346839 \tabularnewline
40 & 25 & 21.5118100982461 & 3.48818990175386 \tabularnewline
41 & 26 & 22.7123013247641 & 3.2876986752359 \tabularnewline
42 & 23 & 20.8162240920595 & 2.18377590794052 \tabularnewline
43 & 22 & 21.1400626155649 & 0.859937384435056 \tabularnewline
44 & 32 & 23.3209300684703 & 8.67906993152973 \tabularnewline
45 & 22 & 23.7164284198788 & -1.7164284198788 \tabularnewline
46 & 18 & 24.5447171398881 & -6.54471713988807 \tabularnewline
47 & 19 & 19.327324233938 & -0.327324233937992 \tabularnewline
48 & 23 & 20.4667112849731 & 2.53328871502695 \tabularnewline
49 & 24 & 20.5059830808434 & 3.49401691915657 \tabularnewline
50 & 19 & 22.2746546871814 & -3.27465468718143 \tabularnewline
51 & 16 & 21.2295069610283 & -5.2295069610283 \tabularnewline
52 & 23 & 23.2041563279971 & -0.204156327997115 \tabularnewline
53 & 17 & 20.1408329462216 & -3.14083294622163 \tabularnewline
54 & 17 & 23.024638582614 & -6.02463858261398 \tabularnewline
55 & 28 & 22.0402923478297 & 5.95970765217031 \tabularnewline
56 & 24 & 21.4911588028661 & 2.50884119713385 \tabularnewline
57 & 21 & 19.1837982819563 & 1.81620171804371 \tabularnewline
58 & 14 & 19.8254930673672 & -5.8254930673672 \tabularnewline
59 & 21 & 22.1305199181195 & -1.13051991811953 \tabularnewline
60 & 20 & 23.3928832060952 & -3.39288320609515 \tabularnewline
61 & 25 & 23.5335138555581 & 1.46648614444186 \tabularnewline
62 & 20 & 21.758940103356 & -1.75894010335598 \tabularnewline
63 & 17 & 22.6317269381337 & -5.63172693813372 \tabularnewline
64 & 26 & 27.3214874520953 & -1.32148745209528 \tabularnewline
65 & 17 & 20.5164901142594 & -3.51649011425943 \tabularnewline
66 & 17 & 20.8737980699247 & -3.87379806992467 \tabularnewline
67 & 24 & 22.6201774748072 & 1.37982252519276 \tabularnewline
68 & 30 & 25.3710131055139 & 4.62898689448607 \tabularnewline
69 & 25 & 22.4038285239453 & 2.59617147605475 \tabularnewline
70 & 15 & 21.790197578471 & -6.79019757847097 \tabularnewline
71 & 25 & 22.9675822691833 & 2.03241773081669 \tabularnewline
72 & 18 & 25.5699234228905 & -7.56992342289053 \tabularnewline
73 & 20 & 23.0677204398956 & -3.06772043989557 \tabularnewline
74 & 32 & 27.6931745675202 & 4.30682543247979 \tabularnewline
75 & 14 & 15.0959826315886 & -1.09598263158855 \tabularnewline
76 & 20 & 20.3422511945641 & -0.342251194564103 \tabularnewline
77 & 25 & 23.5491197566125 & 1.45088024338749 \tabularnewline
78 & 25 & 24.7105733939914 & 0.289426606008589 \tabularnewline
79 & 25 & 22.5073642889642 & 2.49263571103581 \tabularnewline
80 & 35 & 22.1461328600553 & 12.8538671399447 \tabularnewline
81 & 29 & 24.165574287349 & 4.83442571265101 \tabularnewline
82 & 25 & 25.8814100477762 & -0.881410047776233 \tabularnewline
83 & 21 & 22.6201774748072 & -1.62017747480724 \tabularnewline
84 & 21 & 21.2736331370505 & -0.273633137050487 \tabularnewline
85 & 24 & 25.6690172748622 & -1.66901727486218 \tabularnewline
86 & 26 & 22.066796477942 & 3.93320352205802 \tabularnewline
87 & 24 & 24.1987267141688 & -0.198726714168755 \tabularnewline
88 & 20 & 24.9549707593868 & -4.95497075938679 \tabularnewline
89 & 24 & 24.2490706227881 & -0.249070622788071 \tabularnewline
90 & 18 & 19.1104003443211 & -1.1104003443211 \tabularnewline
91 & 17 & 19.1417029008237 & -2.14170290082371 \tabularnewline
92 & 22 & 22.8699470918869 & -0.869947091886888 \tabularnewline
93 & 22 & 24.1574241234088 & -2.15742412340876 \tabularnewline
94 & 22 & 20.9021802283569 & 1.09781977164312 \tabularnewline
95 & 24 & 25.4701691767594 & -1.47016917675935 \tabularnewline
96 & 32 & 24.0574796384729 & 7.94252036152708 \tabularnewline
97 & 19 & 20.5529383248676 & -1.55293832486762 \tabularnewline
98 & 21 & 21.4789497207493 & -0.478949720749304 \tabularnewline
99 & 23 & 23.7549074253766 & -0.754907425376594 \tabularnewline
100 & 26 & 21.3554274311921 & 4.64457256880795 \tabularnewline
101 & 18 & 23.0795016295047 & -5.07950162950466 \tabularnewline
102 & 19 & 19.5797669430609 & -0.579766943060935 \tabularnewline
103 & 22 & 24.5765478486784 & -2.57654784867839 \tabularnewline
104 & 27 & 20.5743824106202 & 6.4256175893798 \tabularnewline
105 & 21 & 20.9549388564469 & 0.0450611435531078 \tabularnewline
106 & 20 & 21.4534421563874 & -1.45344215638739 \tabularnewline
107 & 21 & 24.1106031644278 & -3.11060316442777 \tabularnewline
108 & 20 & 24.776673696884 & -4.776673696884 \tabularnewline
109 & 29 & 22.1817736302569 & 6.81822636974314 \tabularnewline
110 & 30 & 23.6850402536303 & 6.31495974636969 \tabularnewline
111 & 23 & 23.1565001614552 & -0.15650016145517 \tabularnewline
112 & 29 & 19.7725095467781 & 9.22749045322193 \tabularnewline
113 & 19 & 22.6820303257981 & -3.68203032579812 \tabularnewline
114 & 26 & 24.7233989023411 & 1.2766010976589 \tabularnewline
115 & 22 & 21.3335130357434 & 0.666486964256646 \tabularnewline
116 & 26 & 26.2698303211394 & -0.269830321139417 \tabularnewline
117 & 27 & 25.2073830371562 & 1.79261696284381 \tabularnewline
118 & 19 & 22.5497362940165 & -3.54973629401647 \tabularnewline
119 & 24 & 23.3416789520276 & 0.658321047972382 \tabularnewline
120 & 26 & 21.1291345835847 & 4.8708654164153 \tabularnewline
121 & 22 & 19.7302231514717 & 2.26977684852833 \tabularnewline
122 & 23 & 24.7830704652235 & -1.78307046522347 \tabularnewline
123 & 25 & 22.8798036738068 & 2.1201963261932 \tabularnewline
124 & 19 & 23.6649475418916 & -4.66494754189157 \tabularnewline
125 & 20 & 22.4456472811581 & -2.44564728115814 \tabularnewline
126 & 25 & 23.6901280651443 & 1.3098719348557 \tabularnewline
127 & 14 & 19.122372548104 & -5.12237254810401 \tabularnewline
128 & 20 & 18.7560127867263 & 1.24398721327374 \tabularnewline
129 & 27 & 25.5380495215427 & 1.46195047845731 \tabularnewline
130 & 21 & 22.9821585015312 & -1.98215850153116 \tabularnewline
131 & 21 & 21.2947998235142 & -0.294799823514223 \tabularnewline
132 & 14 & 19.3077908812097 & -5.30779088120966 \tabularnewline
133 & 21 & 24.5187816830184 & -3.51878168301841 \tabularnewline
134 & 23 & 21.916120712834 & 1.08387928716601 \tabularnewline
135 & 18 & 20.8043062838964 & -2.80430628389641 \tabularnewline
136 & 20 & 21.7989217514557 & -1.79892175145568 \tabularnewline
137 & 19 & 22.2076156611301 & -3.20761566113006 \tabularnewline
138 & 15 & 20.456567023009 & -5.45656702300905 \tabularnewline
139 & 23 & 21.7382490623902 & 1.26175093760978 \tabularnewline
140 & 26 & 25.6139053712061 & 0.386094628793907 \tabularnewline
141 & 21 & 23.3127367119725 & -2.31273671197253 \tabularnewline
142 & 13 & 16.1424490270512 & -3.14244902705121 \tabularnewline
143 & 24 & 21.0414967689431 & 2.95850323105694 \tabularnewline
144 & 17 & 18.2144582077956 & -1.21445820779565 \tabularnewline
145 & 21 & 24.4858383673783 & -3.48583836737829 \tabularnewline
146 & 28 & 25.9701751193018 & 2.02982488069825 \tabularnewline
147 & 22 & 22.0836230618767 & -0.0836230618766936 \tabularnewline
148 & 25 & 19.4428607452633 & 5.55713925473672 \tabularnewline
149 & 18 & 20.8929452247211 & -2.89294522472114 \tabularnewline
150 & 27 & 24.7899124477074 & 2.21008755229263 \tabularnewline
151 & 25 & 23.6429302224637 & 1.35706977753635 \tabularnewline
152 & 21 & 21.9006150715596 & -0.900615071559567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&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]15[/C][C]15.7877922709388[/C][C]-0.787792270938764[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]21.65503012624[/C][C]1.34496987376001[/C][/ROW]
[ROW][C]3[/C][C]26[/C][C]24.0175862643184[/C][C]1.98241373568164[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]19.4622913577629[/C][C]-0.462291357762933[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]19.490297355108[/C][C]-0.490297355107969[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]21.5977515845095[/C][C]-5.59775158450947[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]23.791812071016[/C][C]-0.791812071015971[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.4376645627971[/C][C]1.5623354372029[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.625800774412[/C][C]-2.62580077441201[/C][/ROW]
[ROW][C]10[/C][C]24[/C][C]20.3097591619039[/C][C]3.69024083809615[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]18.3979896430298[/C][C]0.602010356970163[/C][/ROW]
[ROW][C]12[/C][C]25[/C][C]23.2383376481542[/C][C]1.76166235184575[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]26.0262303065493[/C][C]-3.02623030654934[/C][/ROW]
[ROW][C]14[/C][C]31[/C][C]24.5612663245339[/C][C]6.43873367546606[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]23.9856240890254[/C][C]5.01437591097461[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]19.4457413977479[/C][C]-1.44574139774791[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]19.487793586323[/C][C]-2.48779358632298[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]20.9684568953892[/C][C]1.03154310461078[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]22.695281055053[/C][C]-1.69528105505296[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]23.2600205010709[/C][C]0.73997949892912[/C][/ROW]
[ROW][C]21[/C][C]22[/C][C]21.9928271954616[/C][C]0.00717280453843304[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]19.2212329687135[/C][C]-3.22123296871349[/C][/ROW]
[ROW][C]23[/C][C]22[/C][C]22.4621038151767[/C][C]-0.462103815176689[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]22.2286948490178[/C][C]-1.22869484901781[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]21.5852605426711[/C][C]3.41473945732894[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]24.2415716931164[/C][C]-2.24157169311641[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]20.8649789729619[/C][C]3.13502102703813[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]24.9883851600922[/C][C]-3.98838516009217[/C][/ROW]
[ROW][C]29[/C][C]25[/C][C]22.6030110885594[/C][C]2.39698891144063[/C][/ROW]
[ROW][C]30[/C][C]29[/C][C]29.0028047616548[/C][C]-0.00280476165483319[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]20.655661313678[/C][C]-1.65566131367803[/C][/ROW]
[ROW][C]32[/C][C]29[/C][C]22.8089924430999[/C][C]6.19100755690013[/C][/ROW]
[ROW][C]33[/C][C]25[/C][C]21.1182464441282[/C][C]3.88175355587184[/C][/ROW]
[ROW][C]34[/C][C]19[/C][C]24.1004751106395[/C][C]-5.10047511063948[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]20.9396169508821[/C][C]6.06038304911788[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]22.6247517840676[/C][C]2.37524821593241[/C][/ROW]
[ROW][C]37[/C][C]23[/C][C]23.2375899391693[/C][C]-0.237589939169288[/C][/ROW]
[ROW][C]38[/C][C]24[/C][C]23.2201901215592[/C][C]0.779809878440759[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]22.1508506216532[/C][C]0.849149378346839[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]21.5118100982461[/C][C]3.48818990175386[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]22.7123013247641[/C][C]3.2876986752359[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.8162240920595[/C][C]2.18377590794052[/C][/ROW]
[ROW][C]43[/C][C]22[/C][C]21.1400626155649[/C][C]0.859937384435056[/C][/ROW]
[ROW][C]44[/C][C]32[/C][C]23.3209300684703[/C][C]8.67906993152973[/C][/ROW]
[ROW][C]45[/C][C]22[/C][C]23.7164284198788[/C][C]-1.7164284198788[/C][/ROW]
[ROW][C]46[/C][C]18[/C][C]24.5447171398881[/C][C]-6.54471713988807[/C][/ROW]
[ROW][C]47[/C][C]19[/C][C]19.327324233938[/C][C]-0.327324233937992[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]20.4667112849731[/C][C]2.53328871502695[/C][/ROW]
[ROW][C]49[/C][C]24[/C][C]20.5059830808434[/C][C]3.49401691915657[/C][/ROW]
[ROW][C]50[/C][C]19[/C][C]22.2746546871814[/C][C]-3.27465468718143[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]21.2295069610283[/C][C]-5.2295069610283[/C][/ROW]
[ROW][C]52[/C][C]23[/C][C]23.2041563279971[/C][C]-0.204156327997115[/C][/ROW]
[ROW][C]53[/C][C]17[/C][C]20.1408329462216[/C][C]-3.14083294622163[/C][/ROW]
[ROW][C]54[/C][C]17[/C][C]23.024638582614[/C][C]-6.02463858261398[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]22.0402923478297[/C][C]5.95970765217031[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]21.4911588028661[/C][C]2.50884119713385[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]19.1837982819563[/C][C]1.81620171804371[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]19.8254930673672[/C][C]-5.8254930673672[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]22.1305199181195[/C][C]-1.13051991811953[/C][/ROW]
[ROW][C]60[/C][C]20[/C][C]23.3928832060952[/C][C]-3.39288320609515[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]23.5335138555581[/C][C]1.46648614444186[/C][/ROW]
[ROW][C]62[/C][C]20[/C][C]21.758940103356[/C][C]-1.75894010335598[/C][/ROW]
[ROW][C]63[/C][C]17[/C][C]22.6317269381337[/C][C]-5.63172693813372[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]27.3214874520953[/C][C]-1.32148745209528[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]20.5164901142594[/C][C]-3.51649011425943[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]20.8737980699247[/C][C]-3.87379806992467[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]22.6201774748072[/C][C]1.37982252519276[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]25.3710131055139[/C][C]4.62898689448607[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]22.4038285239453[/C][C]2.59617147605475[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]21.790197578471[/C][C]-6.79019757847097[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]22.9675822691833[/C][C]2.03241773081669[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]25.5699234228905[/C][C]-7.56992342289053[/C][/ROW]
[ROW][C]73[/C][C]20[/C][C]23.0677204398956[/C][C]-3.06772043989557[/C][/ROW]
[ROW][C]74[/C][C]32[/C][C]27.6931745675202[/C][C]4.30682543247979[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]15.0959826315886[/C][C]-1.09598263158855[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]20.3422511945641[/C][C]-0.342251194564103[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.5491197566125[/C][C]1.45088024338749[/C][/ROW]
[ROW][C]78[/C][C]25[/C][C]24.7105733939914[/C][C]0.289426606008589[/C][/ROW]
[ROW][C]79[/C][C]25[/C][C]22.5073642889642[/C][C]2.49263571103581[/C][/ROW]
[ROW][C]80[/C][C]35[/C][C]22.1461328600553[/C][C]12.8538671399447[/C][/ROW]
[ROW][C]81[/C][C]29[/C][C]24.165574287349[/C][C]4.83442571265101[/C][/ROW]
[ROW][C]82[/C][C]25[/C][C]25.8814100477762[/C][C]-0.881410047776233[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]22.6201774748072[/C][C]-1.62017747480724[/C][/ROW]
[ROW][C]84[/C][C]21[/C][C]21.2736331370505[/C][C]-0.273633137050487[/C][/ROW]
[ROW][C]85[/C][C]24[/C][C]25.6690172748622[/C][C]-1.66901727486218[/C][/ROW]
[ROW][C]86[/C][C]26[/C][C]22.066796477942[/C][C]3.93320352205802[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.1987267141688[/C][C]-0.198726714168755[/C][/ROW]
[ROW][C]88[/C][C]20[/C][C]24.9549707593868[/C][C]-4.95497075938679[/C][/ROW]
[ROW][C]89[/C][C]24[/C][C]24.2490706227881[/C][C]-0.249070622788071[/C][/ROW]
[ROW][C]90[/C][C]18[/C][C]19.1104003443211[/C][C]-1.1104003443211[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]19.1417029008237[/C][C]-2.14170290082371[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]22.8699470918869[/C][C]-0.869947091886888[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]24.1574241234088[/C][C]-2.15742412340876[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]20.9021802283569[/C][C]1.09781977164312[/C][/ROW]
[ROW][C]95[/C][C]24[/C][C]25.4701691767594[/C][C]-1.47016917675935[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]24.0574796384729[/C][C]7.94252036152708[/C][/ROW]
[ROW][C]97[/C][C]19[/C][C]20.5529383248676[/C][C]-1.55293832486762[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]21.4789497207493[/C][C]-0.478949720749304[/C][/ROW]
[ROW][C]99[/C][C]23[/C][C]23.7549074253766[/C][C]-0.754907425376594[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]21.3554274311921[/C][C]4.64457256880795[/C][/ROW]
[ROW][C]101[/C][C]18[/C][C]23.0795016295047[/C][C]-5.07950162950466[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]19.5797669430609[/C][C]-0.579766943060935[/C][/ROW]
[ROW][C]103[/C][C]22[/C][C]24.5765478486784[/C][C]-2.57654784867839[/C][/ROW]
[ROW][C]104[/C][C]27[/C][C]20.5743824106202[/C][C]6.4256175893798[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]20.9549388564469[/C][C]0.0450611435531078[/C][/ROW]
[ROW][C]106[/C][C]20[/C][C]21.4534421563874[/C][C]-1.45344215638739[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]24.1106031644278[/C][C]-3.11060316442777[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]24.776673696884[/C][C]-4.776673696884[/C][/ROW]
[ROW][C]109[/C][C]29[/C][C]22.1817736302569[/C][C]6.81822636974314[/C][/ROW]
[ROW][C]110[/C][C]30[/C][C]23.6850402536303[/C][C]6.31495974636969[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]23.1565001614552[/C][C]-0.15650016145517[/C][/ROW]
[ROW][C]112[/C][C]29[/C][C]19.7725095467781[/C][C]9.22749045322193[/C][/ROW]
[ROW][C]113[/C][C]19[/C][C]22.6820303257981[/C][C]-3.68203032579812[/C][/ROW]
[ROW][C]114[/C][C]26[/C][C]24.7233989023411[/C][C]1.2766010976589[/C][/ROW]
[ROW][C]115[/C][C]22[/C][C]21.3335130357434[/C][C]0.666486964256646[/C][/ROW]
[ROW][C]116[/C][C]26[/C][C]26.2698303211394[/C][C]-0.269830321139417[/C][/ROW]
[ROW][C]117[/C][C]27[/C][C]25.2073830371562[/C][C]1.79261696284381[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.5497362940165[/C][C]-3.54973629401647[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]23.3416789520276[/C][C]0.658321047972382[/C][/ROW]
[ROW][C]120[/C][C]26[/C][C]21.1291345835847[/C][C]4.8708654164153[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]19.7302231514717[/C][C]2.26977684852833[/C][/ROW]
[ROW][C]122[/C][C]23[/C][C]24.7830704652235[/C][C]-1.78307046522347[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]22.8798036738068[/C][C]2.1201963261932[/C][/ROW]
[ROW][C]124[/C][C]19[/C][C]23.6649475418916[/C][C]-4.66494754189157[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]22.4456472811581[/C][C]-2.44564728115814[/C][/ROW]
[ROW][C]126[/C][C]25[/C][C]23.6901280651443[/C][C]1.3098719348557[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]19.122372548104[/C][C]-5.12237254810401[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]18.7560127867263[/C][C]1.24398721327374[/C][/ROW]
[ROW][C]129[/C][C]27[/C][C]25.5380495215427[/C][C]1.46195047845731[/C][/ROW]
[ROW][C]130[/C][C]21[/C][C]22.9821585015312[/C][C]-1.98215850153116[/C][/ROW]
[ROW][C]131[/C][C]21[/C][C]21.2947998235142[/C][C]-0.294799823514223[/C][/ROW]
[ROW][C]132[/C][C]14[/C][C]19.3077908812097[/C][C]-5.30779088120966[/C][/ROW]
[ROW][C]133[/C][C]21[/C][C]24.5187816830184[/C][C]-3.51878168301841[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.916120712834[/C][C]1.08387928716601[/C][/ROW]
[ROW][C]135[/C][C]18[/C][C]20.8043062838964[/C][C]-2.80430628389641[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]21.7989217514557[/C][C]-1.79892175145568[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]22.2076156611301[/C][C]-3.20761566113006[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]20.456567023009[/C][C]-5.45656702300905[/C][/ROW]
[ROW][C]139[/C][C]23[/C][C]21.7382490623902[/C][C]1.26175093760978[/C][/ROW]
[ROW][C]140[/C][C]26[/C][C]25.6139053712061[/C][C]0.386094628793907[/C][/ROW]
[ROW][C]141[/C][C]21[/C][C]23.3127367119725[/C][C]-2.31273671197253[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]16.1424490270512[/C][C]-3.14244902705121[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]21.0414967689431[/C][C]2.95850323105694[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]18.2144582077956[/C][C]-1.21445820779565[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]24.4858383673783[/C][C]-3.48583836737829[/C][/ROW]
[ROW][C]146[/C][C]28[/C][C]25.9701751193018[/C][C]2.02982488069825[/C][/ROW]
[ROW][C]147[/C][C]22[/C][C]22.0836230618767[/C][C]-0.0836230618766936[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]19.4428607452633[/C][C]5.55713925473672[/C][/ROW]
[ROW][C]149[/C][C]18[/C][C]20.8929452247211[/C][C]-2.89294522472114[/C][/ROW]
[ROW][C]150[/C][C]27[/C][C]24.7899124477074[/C][C]2.21008755229263[/C][/ROW]
[ROW][C]151[/C][C]25[/C][C]23.6429302224637[/C][C]1.35706977753635[/C][/ROW]
[ROW][C]152[/C][C]21[/C][C]21.9006150715596[/C][C]-0.900615071559567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&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
11515.7877922709388-0.787792270938764
22321.655030126241.34496987376001
32624.01758626431841.98241373568164
41919.4622913577629-0.462291357762933
51919.490297355108-0.490297355107969
61621.5977515845095-5.59775158450947
72323.791812071016-0.791812071015971
82220.43766456279711.5623354372029
91921.625800774412-2.62580077441201
102420.30975916190393.69024083809615
111918.39798964302980.602010356970163
122523.23833764815421.76166235184575
132326.0262303065493-3.02623030654934
143124.56126632453396.43873367546606
152923.98562408902545.01437591097461
161819.4457413977479-1.44574139774791
171719.487793586323-2.48779358632298
182220.96845689538921.03154310461078
192122.695281055053-1.69528105505296
202423.26002050107090.73997949892912
212221.99282719546160.00717280453843304
221619.2212329687135-3.22123296871349
232222.4621038151767-0.462103815176689
242122.2286948490178-1.22869484901781
252521.58526054267113.41473945732894
262224.2415716931164-2.24157169311641
272420.86497897296193.13502102703813
282124.9883851600922-3.98838516009217
292522.60301108855942.39698891144063
302929.0028047616548-0.00280476165483319
311920.655661313678-1.65566131367803
322922.80899244309996.19100755690013
332521.11824644412823.88175355587184
341924.1004751106395-5.10047511063948
352720.93961695088216.06038304911788
362522.62475178406762.37524821593241
372323.2375899391693-0.237589939169288
382423.22019012155920.779809878440759
392322.15085062165320.849149378346839
402521.51181009824613.48818990175386
412622.71230132476413.2876986752359
422320.81622409205952.18377590794052
432221.14006261556490.859937384435056
443223.32093006847038.67906993152973
452223.7164284198788-1.7164284198788
461824.5447171398881-6.54471713988807
471919.327324233938-0.327324233937992
482320.46671128497312.53328871502695
492420.50598308084343.49401691915657
501922.2746546871814-3.27465468718143
511621.2295069610283-5.2295069610283
522323.2041563279971-0.204156327997115
531720.1408329462216-3.14083294622163
541723.024638582614-6.02463858261398
552822.04029234782975.95970765217031
562421.49115880286612.50884119713385
572119.18379828195631.81620171804371
581419.8254930673672-5.8254930673672
592122.1305199181195-1.13051991811953
602023.3928832060952-3.39288320609515
612523.53351385555811.46648614444186
622021.758940103356-1.75894010335598
631722.6317269381337-5.63172693813372
642627.3214874520953-1.32148745209528
651720.5164901142594-3.51649011425943
661720.8737980699247-3.87379806992467
672422.62017747480721.37982252519276
683025.37101310551394.62898689448607
692522.40382852394532.59617147605475
701521.790197578471-6.79019757847097
712522.96758226918332.03241773081669
721825.5699234228905-7.56992342289053
732023.0677204398956-3.06772043989557
743227.69317456752024.30682543247979
751415.0959826315886-1.09598263158855
762020.3422511945641-0.342251194564103
772523.54911975661251.45088024338749
782524.71057339399140.289426606008589
792522.50736428896422.49263571103581
803522.146132860055312.8538671399447
812924.1655742873494.83442571265101
822525.8814100477762-0.881410047776233
832122.6201774748072-1.62017747480724
842121.2736331370505-0.273633137050487
852425.6690172748622-1.66901727486218
862622.0667964779423.93320352205802
872424.1987267141688-0.198726714168755
882024.9549707593868-4.95497075938679
892424.2490706227881-0.249070622788071
901819.1104003443211-1.1104003443211
911719.1417029008237-2.14170290082371
922222.8699470918869-0.869947091886888
932224.1574241234088-2.15742412340876
942220.90218022835691.09781977164312
952425.4701691767594-1.47016917675935
963224.05747963847297.94252036152708
971920.5529383248676-1.55293832486762
982121.4789497207493-0.478949720749304
992323.7549074253766-0.754907425376594
1002621.35542743119214.64457256880795
1011823.0795016295047-5.07950162950466
1021919.5797669430609-0.579766943060935
1032224.5765478486784-2.57654784867839
1042720.57438241062026.4256175893798
1052120.95493885644690.0450611435531078
1062021.4534421563874-1.45344215638739
1072124.1106031644278-3.11060316442777
1082024.776673696884-4.776673696884
1092922.18177363025696.81822636974314
1103023.68504025363036.31495974636969
1112323.1565001614552-0.15650016145517
1122919.77250954677819.22749045322193
1131922.6820303257981-3.68203032579812
1142624.72339890234111.2766010976589
1152221.33351303574340.666486964256646
1162626.2698303211394-0.269830321139417
1172725.20738303715621.79261696284381
1181922.5497362940165-3.54973629401647
1192423.34167895202760.658321047972382
1202621.12913458358474.8708654164153
1212219.73022315147172.26977684852833
1222324.7830704652235-1.78307046522347
1232522.87980367380682.1201963261932
1241923.6649475418916-4.66494754189157
1252022.4456472811581-2.44564728115814
1262523.69012806514431.3098719348557
1271419.122372548104-5.12237254810401
1282018.75601278672631.24398721327374
1292725.53804952154271.46195047845731
1302122.9821585015312-1.98215850153116
1312121.2947998235142-0.294799823514223
1321419.3077908812097-5.30779088120966
1332124.5187816830184-3.51878168301841
1342321.9161207128341.08387928716601
1351820.8043062838964-2.80430628389641
1362021.7989217514557-1.79892175145568
1371922.2076156611301-3.20761566113006
1381520.456567023009-5.45656702300905
1392321.73824906239021.26175093760978
1402625.61390537120610.386094628793907
1412123.3127367119725-2.31273671197253
1421316.1424490270512-3.14244902705121
1432421.04149676894312.95850323105694
1441718.2144582077956-1.21445820779565
1452124.4858383673783-3.48583836737829
1462825.97017511930182.02982488069825
1472222.0836230618767-0.0836230618766936
1482519.44286074526335.55713925473672
1491820.8929452247211-2.89294522472114
1502724.78991244770742.21008755229263
1512523.64293022246371.35706977753635
1522121.9006150715596-0.900615071559567






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4323565507207640.8647131014415270.567643449279236
110.400250264633140.800500529266280.59974973536686
120.272652823880510.545305647761020.72734717611949
130.1860516696718250.372103339343650.813948330328175
140.5421684247527010.9156631504945980.457831575247299
150.505331261216780.989337477566440.49466873878322
160.4953708150007220.9907416300014440.504629184999278
170.4454401513969050.890880302793810.554559848603095
180.3887336635859130.7774673271718250.611266336414087
190.3068823659235370.6137647318470750.693117634076463
200.233868234600550.4677364692010990.76613176539945
210.1760810902534130.3521621805068260.823918909746587
220.1566928661617070.3133857323234140.843307133838293
230.1124475462748930.2248950925497850.887552453725107
240.08285616447364780.1657123289472960.917143835526352
250.08928396247219710.1785679249443940.910716037527803
260.07731657059747680.1546331411949540.922683429402523
270.0660563187031210.1321126374062420.933943681296879
280.1119145193760950.2238290387521910.888085480623905
290.08968284369514550.1793656873902910.910317156304854
300.06684737187662570.1336947437532510.933152628123374
310.05509486085402670.1101897217080530.944905139145973
320.08390518373895580.1678103674779120.916094816261044
330.07498048271549790.1499609654309960.925019517284502
340.1241222047307370.2482444094614750.875877795269263
350.1177833989011760.2355667978023520.882216601098824
360.1013346701878860.2026693403757710.898665329812114
370.07840698293381580.1568139658676320.921593017066184
380.06173462987179050.1234692597435810.93826537012821
390.04706020242372690.09412040484745380.952939797576273
400.04742624894546950.0948524978909390.95257375105453
410.04911300730793320.09822601461586650.950886992692067
420.04812785021748370.09625570043496750.951872149782516
430.05414567924604460.1082913584920890.945854320753955
440.1480827548625380.2961655097250770.851917245137461
450.131225732466770.262451464933540.86877426753323
460.201948238120140.4038964762402790.79805176187986
470.1682627673683260.3365255347366520.831737232631674
480.1491781130321680.2983562260643370.850821886967831
490.141313002411360.282626004822720.85868699758864
500.1373479833319850.274695966663970.862652016668015
510.1578296258823560.3156592517647120.842170374117644
520.1308173531703460.2616347063406920.869182646829654
530.1231722176730590.2463444353461180.876827782326941
540.1627581528744640.3255163057489280.837241847125536
550.192887083778650.3857741675572990.80711291622135
560.1708634812018040.3417269624036080.829136518798196
570.1657682556040880.3315365112081750.834231744395912
580.2007836975016690.4015673950033390.799216302498331
590.1743151530089650.348630306017930.825684846991035
600.1788118011856420.3576236023712840.821188198814358
610.1608688352170060.3217376704340120.839131164782994
620.1578267469268030.3156534938536050.842173253073197
630.2024232794046880.4048465588093770.797576720595312
640.1748649833979510.3497299667959020.825135016602049
650.180452819334590.360905638669180.81954718066541
660.2761426972744430.5522853945488860.723857302725557
670.2439221121311260.4878442242622510.756077887868874
680.2878023154194130.5756046308388260.712197684580587
690.2702878427012270.5405756854024530.729712157298773
700.3709894490344870.7419788980689740.629010550965513
710.3450293999208850.690058799841770.654970600079115
720.4833918815142330.9667837630284660.516608118485767
730.467807965876080.935615931752160.53219203412392
740.5129185014915880.9741629970168240.487081498508412
750.47368575216190.94737150432380.5263142478381
760.4292641211075020.8585282422150030.570735878892498
770.3935146713426520.7870293426853030.606485328657348
780.3671814061710890.7343628123421780.632818593828911
790.3498679225909380.6997358451818760.650132077409062
800.8627714859834760.2744570280330490.137228514016524
810.9000511603137470.1998976793725070.0999488396862534
820.8778910577076690.2442178845846620.122108942292331
830.8560359543443680.2879280913112640.143964045655632
840.828329695345460.3433406093090790.171670304654539
850.8009905493612060.3980189012775880.199009450638794
860.805865019484170.3882699610316620.194134980515831
870.7723659940154750.455268011969050.227634005984525
880.794084430544320.4118311389113610.20591556945568
890.7575092467994240.4849815064011520.242490753200576
900.7250034133025630.5499931733948740.274996586697437
910.7070116687404760.5859766625190490.292988331259524
920.6692851296410170.6614297407179660.330714870358983
930.6419511386310420.7160977227379160.358048861368958
940.597663867805290.804672264389420.40233613219471
950.5552333309040360.8895333381919280.444766669095964
960.730350851259370.539298297481260.26964914874063
970.6950452143927680.6099095712144650.304954785607232
980.6494063295885020.7011873408229950.350593670411498
990.6015571199861640.7968857600276720.398442880013836
1000.6487829223141350.702434155371730.351217077685865
1010.6649124288392760.6701751423214480.335087571160724
1020.6170878915629880.7658242168740230.382912108437012
1030.6100945816441880.7798108367116230.389905418355812
1040.7384213647905220.5231572704189560.261578635209478
1050.6943051386286390.6113897227427220.305694861371361
1060.6573931100257260.6852137799485470.342606889974274
1070.6238059697383120.7523880605233760.376194030261688
1080.7037021356550180.5925957286899630.296297864344982
1090.8488919042986430.3022161914027130.151108095701357
1100.889299595453510.2214008090929790.11070040454649
1110.8631703077448060.2736593845103890.136829692255194
1120.9660440691899130.06791186162017310.0339559308100866
1130.966638648618350.06672270276329840.0333613513816492
1140.9543765757176850.09124684856463050.0456234242823153
1150.9377822393025360.1244355213949270.0622177606974636
1160.9170608964926140.1658782070147710.0829391035073855
1170.8987490472894240.2025019054211510.101250952710576
1180.8843564477227520.2312871045544950.115643552277248
1190.857526863494670.2849462730106610.142473136505331
1200.9181293421640770.1637413156718460.0818706578359229
1210.9173706281173560.1652587437652880.082629371882644
1220.8951516732066790.2096966535866420.104848326793321
1230.8737237568957880.2525524862084230.126276243104212
1240.8824294605463230.2351410789073540.117570539453677
1250.8607572395955260.2784855208089490.139242760404475
1260.8392485743050460.3215028513899080.160751425694954
1270.852849104183870.2943017916322590.147150895816129
1280.8413275843671740.3173448312656510.158672415632826
1290.7949393280510830.4101213438978340.205060671948917
1300.7360956605111030.5278086789777930.263904339488897
1310.6830115164132360.6339769671735280.316988483586764
1320.7256306148505580.5487387702988840.274369385149442
1330.7852748238978460.4294503522043080.214725176102154
1340.7198058999890740.5603882000218530.280194100010926
1350.763936047235970.4721279055280610.236063952764031
1360.6816872687793930.6366254624412140.318312731220607
1370.6262777742248250.747444451550350.373722225775175
1380.7787404380346080.4425191239307840.221259561965392
1390.7939583629327020.4120832741345950.206041637067298
1400.6802550160950740.6394899678098530.319744983904926
1410.5390455944298510.9219088111402970.460954405570149
1420.9541252719430620.09174945611387520.0458747280569376

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.432356550720764 & 0.864713101441527 & 0.567643449279236 \tabularnewline
11 & 0.40025026463314 & 0.80050052926628 & 0.59974973536686 \tabularnewline
12 & 0.27265282388051 & 0.54530564776102 & 0.72734717611949 \tabularnewline
13 & 0.186051669671825 & 0.37210333934365 & 0.813948330328175 \tabularnewline
14 & 0.542168424752701 & 0.915663150494598 & 0.457831575247299 \tabularnewline
15 & 0.50533126121678 & 0.98933747756644 & 0.49466873878322 \tabularnewline
16 & 0.495370815000722 & 0.990741630001444 & 0.504629184999278 \tabularnewline
17 & 0.445440151396905 & 0.89088030279381 & 0.554559848603095 \tabularnewline
18 & 0.388733663585913 & 0.777467327171825 & 0.611266336414087 \tabularnewline
19 & 0.306882365923537 & 0.613764731847075 & 0.693117634076463 \tabularnewline
20 & 0.23386823460055 & 0.467736469201099 & 0.76613176539945 \tabularnewline
21 & 0.176081090253413 & 0.352162180506826 & 0.823918909746587 \tabularnewline
22 & 0.156692866161707 & 0.313385732323414 & 0.843307133838293 \tabularnewline
23 & 0.112447546274893 & 0.224895092549785 & 0.887552453725107 \tabularnewline
24 & 0.0828561644736478 & 0.165712328947296 & 0.917143835526352 \tabularnewline
25 & 0.0892839624721971 & 0.178567924944394 & 0.910716037527803 \tabularnewline
26 & 0.0773165705974768 & 0.154633141194954 & 0.922683429402523 \tabularnewline
27 & 0.066056318703121 & 0.132112637406242 & 0.933943681296879 \tabularnewline
28 & 0.111914519376095 & 0.223829038752191 & 0.888085480623905 \tabularnewline
29 & 0.0896828436951455 & 0.179365687390291 & 0.910317156304854 \tabularnewline
30 & 0.0668473718766257 & 0.133694743753251 & 0.933152628123374 \tabularnewline
31 & 0.0550948608540267 & 0.110189721708053 & 0.944905139145973 \tabularnewline
32 & 0.0839051837389558 & 0.167810367477912 & 0.916094816261044 \tabularnewline
33 & 0.0749804827154979 & 0.149960965430996 & 0.925019517284502 \tabularnewline
34 & 0.124122204730737 & 0.248244409461475 & 0.875877795269263 \tabularnewline
35 & 0.117783398901176 & 0.235566797802352 & 0.882216601098824 \tabularnewline
36 & 0.101334670187886 & 0.202669340375771 & 0.898665329812114 \tabularnewline
37 & 0.0784069829338158 & 0.156813965867632 & 0.921593017066184 \tabularnewline
38 & 0.0617346298717905 & 0.123469259743581 & 0.93826537012821 \tabularnewline
39 & 0.0470602024237269 & 0.0941204048474538 & 0.952939797576273 \tabularnewline
40 & 0.0474262489454695 & 0.094852497890939 & 0.95257375105453 \tabularnewline
41 & 0.0491130073079332 & 0.0982260146158665 & 0.950886992692067 \tabularnewline
42 & 0.0481278502174837 & 0.0962557004349675 & 0.951872149782516 \tabularnewline
43 & 0.0541456792460446 & 0.108291358492089 & 0.945854320753955 \tabularnewline
44 & 0.148082754862538 & 0.296165509725077 & 0.851917245137461 \tabularnewline
45 & 0.13122573246677 & 0.26245146493354 & 0.86877426753323 \tabularnewline
46 & 0.20194823812014 & 0.403896476240279 & 0.79805176187986 \tabularnewline
47 & 0.168262767368326 & 0.336525534736652 & 0.831737232631674 \tabularnewline
48 & 0.149178113032168 & 0.298356226064337 & 0.850821886967831 \tabularnewline
49 & 0.14131300241136 & 0.28262600482272 & 0.85868699758864 \tabularnewline
50 & 0.137347983331985 & 0.27469596666397 & 0.862652016668015 \tabularnewline
51 & 0.157829625882356 & 0.315659251764712 & 0.842170374117644 \tabularnewline
52 & 0.130817353170346 & 0.261634706340692 & 0.869182646829654 \tabularnewline
53 & 0.123172217673059 & 0.246344435346118 & 0.876827782326941 \tabularnewline
54 & 0.162758152874464 & 0.325516305748928 & 0.837241847125536 \tabularnewline
55 & 0.19288708377865 & 0.385774167557299 & 0.80711291622135 \tabularnewline
56 & 0.170863481201804 & 0.341726962403608 & 0.829136518798196 \tabularnewline
57 & 0.165768255604088 & 0.331536511208175 & 0.834231744395912 \tabularnewline
58 & 0.200783697501669 & 0.401567395003339 & 0.799216302498331 \tabularnewline
59 & 0.174315153008965 & 0.34863030601793 & 0.825684846991035 \tabularnewline
60 & 0.178811801185642 & 0.357623602371284 & 0.821188198814358 \tabularnewline
61 & 0.160868835217006 & 0.321737670434012 & 0.839131164782994 \tabularnewline
62 & 0.157826746926803 & 0.315653493853605 & 0.842173253073197 \tabularnewline
63 & 0.202423279404688 & 0.404846558809377 & 0.797576720595312 \tabularnewline
64 & 0.174864983397951 & 0.349729966795902 & 0.825135016602049 \tabularnewline
65 & 0.18045281933459 & 0.36090563866918 & 0.81954718066541 \tabularnewline
66 & 0.276142697274443 & 0.552285394548886 & 0.723857302725557 \tabularnewline
67 & 0.243922112131126 & 0.487844224262251 & 0.756077887868874 \tabularnewline
68 & 0.287802315419413 & 0.575604630838826 & 0.712197684580587 \tabularnewline
69 & 0.270287842701227 & 0.540575685402453 & 0.729712157298773 \tabularnewline
70 & 0.370989449034487 & 0.741978898068974 & 0.629010550965513 \tabularnewline
71 & 0.345029399920885 & 0.69005879984177 & 0.654970600079115 \tabularnewline
72 & 0.483391881514233 & 0.966783763028466 & 0.516608118485767 \tabularnewline
73 & 0.46780796587608 & 0.93561593175216 & 0.53219203412392 \tabularnewline
74 & 0.512918501491588 & 0.974162997016824 & 0.487081498508412 \tabularnewline
75 & 0.4736857521619 & 0.9473715043238 & 0.5263142478381 \tabularnewline
76 & 0.429264121107502 & 0.858528242215003 & 0.570735878892498 \tabularnewline
77 & 0.393514671342652 & 0.787029342685303 & 0.606485328657348 \tabularnewline
78 & 0.367181406171089 & 0.734362812342178 & 0.632818593828911 \tabularnewline
79 & 0.349867922590938 & 0.699735845181876 & 0.650132077409062 \tabularnewline
80 & 0.862771485983476 & 0.274457028033049 & 0.137228514016524 \tabularnewline
81 & 0.900051160313747 & 0.199897679372507 & 0.0999488396862534 \tabularnewline
82 & 0.877891057707669 & 0.244217884584662 & 0.122108942292331 \tabularnewline
83 & 0.856035954344368 & 0.287928091311264 & 0.143964045655632 \tabularnewline
84 & 0.82832969534546 & 0.343340609309079 & 0.171670304654539 \tabularnewline
85 & 0.800990549361206 & 0.398018901277588 & 0.199009450638794 \tabularnewline
86 & 0.80586501948417 & 0.388269961031662 & 0.194134980515831 \tabularnewline
87 & 0.772365994015475 & 0.45526801196905 & 0.227634005984525 \tabularnewline
88 & 0.79408443054432 & 0.411831138911361 & 0.20591556945568 \tabularnewline
89 & 0.757509246799424 & 0.484981506401152 & 0.242490753200576 \tabularnewline
90 & 0.725003413302563 & 0.549993173394874 & 0.274996586697437 \tabularnewline
91 & 0.707011668740476 & 0.585976662519049 & 0.292988331259524 \tabularnewline
92 & 0.669285129641017 & 0.661429740717966 & 0.330714870358983 \tabularnewline
93 & 0.641951138631042 & 0.716097722737916 & 0.358048861368958 \tabularnewline
94 & 0.59766386780529 & 0.80467226438942 & 0.40233613219471 \tabularnewline
95 & 0.555233330904036 & 0.889533338191928 & 0.444766669095964 \tabularnewline
96 & 0.73035085125937 & 0.53929829748126 & 0.26964914874063 \tabularnewline
97 & 0.695045214392768 & 0.609909571214465 & 0.304954785607232 \tabularnewline
98 & 0.649406329588502 & 0.701187340822995 & 0.350593670411498 \tabularnewline
99 & 0.601557119986164 & 0.796885760027672 & 0.398442880013836 \tabularnewline
100 & 0.648782922314135 & 0.70243415537173 & 0.351217077685865 \tabularnewline
101 & 0.664912428839276 & 0.670175142321448 & 0.335087571160724 \tabularnewline
102 & 0.617087891562988 & 0.765824216874023 & 0.382912108437012 \tabularnewline
103 & 0.610094581644188 & 0.779810836711623 & 0.389905418355812 \tabularnewline
104 & 0.738421364790522 & 0.523157270418956 & 0.261578635209478 \tabularnewline
105 & 0.694305138628639 & 0.611389722742722 & 0.305694861371361 \tabularnewline
106 & 0.657393110025726 & 0.685213779948547 & 0.342606889974274 \tabularnewline
107 & 0.623805969738312 & 0.752388060523376 & 0.376194030261688 \tabularnewline
108 & 0.703702135655018 & 0.592595728689963 & 0.296297864344982 \tabularnewline
109 & 0.848891904298643 & 0.302216191402713 & 0.151108095701357 \tabularnewline
110 & 0.88929959545351 & 0.221400809092979 & 0.11070040454649 \tabularnewline
111 & 0.863170307744806 & 0.273659384510389 & 0.136829692255194 \tabularnewline
112 & 0.966044069189913 & 0.0679118616201731 & 0.0339559308100866 \tabularnewline
113 & 0.96663864861835 & 0.0667227027632984 & 0.0333613513816492 \tabularnewline
114 & 0.954376575717685 & 0.0912468485646305 & 0.0456234242823153 \tabularnewline
115 & 0.937782239302536 & 0.124435521394927 & 0.0622177606974636 \tabularnewline
116 & 0.917060896492614 & 0.165878207014771 & 0.0829391035073855 \tabularnewline
117 & 0.898749047289424 & 0.202501905421151 & 0.101250952710576 \tabularnewline
118 & 0.884356447722752 & 0.231287104554495 & 0.115643552277248 \tabularnewline
119 & 0.85752686349467 & 0.284946273010661 & 0.142473136505331 \tabularnewline
120 & 0.918129342164077 & 0.163741315671846 & 0.0818706578359229 \tabularnewline
121 & 0.917370628117356 & 0.165258743765288 & 0.082629371882644 \tabularnewline
122 & 0.895151673206679 & 0.209696653586642 & 0.104848326793321 \tabularnewline
123 & 0.873723756895788 & 0.252552486208423 & 0.126276243104212 \tabularnewline
124 & 0.882429460546323 & 0.235141078907354 & 0.117570539453677 \tabularnewline
125 & 0.860757239595526 & 0.278485520808949 & 0.139242760404475 \tabularnewline
126 & 0.839248574305046 & 0.321502851389908 & 0.160751425694954 \tabularnewline
127 & 0.85284910418387 & 0.294301791632259 & 0.147150895816129 \tabularnewline
128 & 0.841327584367174 & 0.317344831265651 & 0.158672415632826 \tabularnewline
129 & 0.794939328051083 & 0.410121343897834 & 0.205060671948917 \tabularnewline
130 & 0.736095660511103 & 0.527808678977793 & 0.263904339488897 \tabularnewline
131 & 0.683011516413236 & 0.633976967173528 & 0.316988483586764 \tabularnewline
132 & 0.725630614850558 & 0.548738770298884 & 0.274369385149442 \tabularnewline
133 & 0.785274823897846 & 0.429450352204308 & 0.214725176102154 \tabularnewline
134 & 0.719805899989074 & 0.560388200021853 & 0.280194100010926 \tabularnewline
135 & 0.76393604723597 & 0.472127905528061 & 0.236063952764031 \tabularnewline
136 & 0.681687268779393 & 0.636625462441214 & 0.318312731220607 \tabularnewline
137 & 0.626277774224825 & 0.74744445155035 & 0.373722225775175 \tabularnewline
138 & 0.778740438034608 & 0.442519123930784 & 0.221259561965392 \tabularnewline
139 & 0.793958362932702 & 0.412083274134595 & 0.206041637067298 \tabularnewline
140 & 0.680255016095074 & 0.639489967809853 & 0.319744983904926 \tabularnewline
141 & 0.539045594429851 & 0.921908811140297 & 0.460954405570149 \tabularnewline
142 & 0.954125271943062 & 0.0917494561138752 & 0.0458747280569376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.432356550720764[/C][C]0.864713101441527[/C][C]0.567643449279236[/C][/ROW]
[ROW][C]11[/C][C]0.40025026463314[/C][C]0.80050052926628[/C][C]0.59974973536686[/C][/ROW]
[ROW][C]12[/C][C]0.27265282388051[/C][C]0.54530564776102[/C][C]0.72734717611949[/C][/ROW]
[ROW][C]13[/C][C]0.186051669671825[/C][C]0.37210333934365[/C][C]0.813948330328175[/C][/ROW]
[ROW][C]14[/C][C]0.542168424752701[/C][C]0.915663150494598[/C][C]0.457831575247299[/C][/ROW]
[ROW][C]15[/C][C]0.50533126121678[/C][C]0.98933747756644[/C][C]0.49466873878322[/C][/ROW]
[ROW][C]16[/C][C]0.495370815000722[/C][C]0.990741630001444[/C][C]0.504629184999278[/C][/ROW]
[ROW][C]17[/C][C]0.445440151396905[/C][C]0.89088030279381[/C][C]0.554559848603095[/C][/ROW]
[ROW][C]18[/C][C]0.388733663585913[/C][C]0.777467327171825[/C][C]0.611266336414087[/C][/ROW]
[ROW][C]19[/C][C]0.306882365923537[/C][C]0.613764731847075[/C][C]0.693117634076463[/C][/ROW]
[ROW][C]20[/C][C]0.23386823460055[/C][C]0.467736469201099[/C][C]0.76613176539945[/C][/ROW]
[ROW][C]21[/C][C]0.176081090253413[/C][C]0.352162180506826[/C][C]0.823918909746587[/C][/ROW]
[ROW][C]22[/C][C]0.156692866161707[/C][C]0.313385732323414[/C][C]0.843307133838293[/C][/ROW]
[ROW][C]23[/C][C]0.112447546274893[/C][C]0.224895092549785[/C][C]0.887552453725107[/C][/ROW]
[ROW][C]24[/C][C]0.0828561644736478[/C][C]0.165712328947296[/C][C]0.917143835526352[/C][/ROW]
[ROW][C]25[/C][C]0.0892839624721971[/C][C]0.178567924944394[/C][C]0.910716037527803[/C][/ROW]
[ROW][C]26[/C][C]0.0773165705974768[/C][C]0.154633141194954[/C][C]0.922683429402523[/C][/ROW]
[ROW][C]27[/C][C]0.066056318703121[/C][C]0.132112637406242[/C][C]0.933943681296879[/C][/ROW]
[ROW][C]28[/C][C]0.111914519376095[/C][C]0.223829038752191[/C][C]0.888085480623905[/C][/ROW]
[ROW][C]29[/C][C]0.0896828436951455[/C][C]0.179365687390291[/C][C]0.910317156304854[/C][/ROW]
[ROW][C]30[/C][C]0.0668473718766257[/C][C]0.133694743753251[/C][C]0.933152628123374[/C][/ROW]
[ROW][C]31[/C][C]0.0550948608540267[/C][C]0.110189721708053[/C][C]0.944905139145973[/C][/ROW]
[ROW][C]32[/C][C]0.0839051837389558[/C][C]0.167810367477912[/C][C]0.916094816261044[/C][/ROW]
[ROW][C]33[/C][C]0.0749804827154979[/C][C]0.149960965430996[/C][C]0.925019517284502[/C][/ROW]
[ROW][C]34[/C][C]0.124122204730737[/C][C]0.248244409461475[/C][C]0.875877795269263[/C][/ROW]
[ROW][C]35[/C][C]0.117783398901176[/C][C]0.235566797802352[/C][C]0.882216601098824[/C][/ROW]
[ROW][C]36[/C][C]0.101334670187886[/C][C]0.202669340375771[/C][C]0.898665329812114[/C][/ROW]
[ROW][C]37[/C][C]0.0784069829338158[/C][C]0.156813965867632[/C][C]0.921593017066184[/C][/ROW]
[ROW][C]38[/C][C]0.0617346298717905[/C][C]0.123469259743581[/C][C]0.93826537012821[/C][/ROW]
[ROW][C]39[/C][C]0.0470602024237269[/C][C]0.0941204048474538[/C][C]0.952939797576273[/C][/ROW]
[ROW][C]40[/C][C]0.0474262489454695[/C][C]0.094852497890939[/C][C]0.95257375105453[/C][/ROW]
[ROW][C]41[/C][C]0.0491130073079332[/C][C]0.0982260146158665[/C][C]0.950886992692067[/C][/ROW]
[ROW][C]42[/C][C]0.0481278502174837[/C][C]0.0962557004349675[/C][C]0.951872149782516[/C][/ROW]
[ROW][C]43[/C][C]0.0541456792460446[/C][C]0.108291358492089[/C][C]0.945854320753955[/C][/ROW]
[ROW][C]44[/C][C]0.148082754862538[/C][C]0.296165509725077[/C][C]0.851917245137461[/C][/ROW]
[ROW][C]45[/C][C]0.13122573246677[/C][C]0.26245146493354[/C][C]0.86877426753323[/C][/ROW]
[ROW][C]46[/C][C]0.20194823812014[/C][C]0.403896476240279[/C][C]0.79805176187986[/C][/ROW]
[ROW][C]47[/C][C]0.168262767368326[/C][C]0.336525534736652[/C][C]0.831737232631674[/C][/ROW]
[ROW][C]48[/C][C]0.149178113032168[/C][C]0.298356226064337[/C][C]0.850821886967831[/C][/ROW]
[ROW][C]49[/C][C]0.14131300241136[/C][C]0.28262600482272[/C][C]0.85868699758864[/C][/ROW]
[ROW][C]50[/C][C]0.137347983331985[/C][C]0.27469596666397[/C][C]0.862652016668015[/C][/ROW]
[ROW][C]51[/C][C]0.157829625882356[/C][C]0.315659251764712[/C][C]0.842170374117644[/C][/ROW]
[ROW][C]52[/C][C]0.130817353170346[/C][C]0.261634706340692[/C][C]0.869182646829654[/C][/ROW]
[ROW][C]53[/C][C]0.123172217673059[/C][C]0.246344435346118[/C][C]0.876827782326941[/C][/ROW]
[ROW][C]54[/C][C]0.162758152874464[/C][C]0.325516305748928[/C][C]0.837241847125536[/C][/ROW]
[ROW][C]55[/C][C]0.19288708377865[/C][C]0.385774167557299[/C][C]0.80711291622135[/C][/ROW]
[ROW][C]56[/C][C]0.170863481201804[/C][C]0.341726962403608[/C][C]0.829136518798196[/C][/ROW]
[ROW][C]57[/C][C]0.165768255604088[/C][C]0.331536511208175[/C][C]0.834231744395912[/C][/ROW]
[ROW][C]58[/C][C]0.200783697501669[/C][C]0.401567395003339[/C][C]0.799216302498331[/C][/ROW]
[ROW][C]59[/C][C]0.174315153008965[/C][C]0.34863030601793[/C][C]0.825684846991035[/C][/ROW]
[ROW][C]60[/C][C]0.178811801185642[/C][C]0.357623602371284[/C][C]0.821188198814358[/C][/ROW]
[ROW][C]61[/C][C]0.160868835217006[/C][C]0.321737670434012[/C][C]0.839131164782994[/C][/ROW]
[ROW][C]62[/C][C]0.157826746926803[/C][C]0.315653493853605[/C][C]0.842173253073197[/C][/ROW]
[ROW][C]63[/C][C]0.202423279404688[/C][C]0.404846558809377[/C][C]0.797576720595312[/C][/ROW]
[ROW][C]64[/C][C]0.174864983397951[/C][C]0.349729966795902[/C][C]0.825135016602049[/C][/ROW]
[ROW][C]65[/C][C]0.18045281933459[/C][C]0.36090563866918[/C][C]0.81954718066541[/C][/ROW]
[ROW][C]66[/C][C]0.276142697274443[/C][C]0.552285394548886[/C][C]0.723857302725557[/C][/ROW]
[ROW][C]67[/C][C]0.243922112131126[/C][C]0.487844224262251[/C][C]0.756077887868874[/C][/ROW]
[ROW][C]68[/C][C]0.287802315419413[/C][C]0.575604630838826[/C][C]0.712197684580587[/C][/ROW]
[ROW][C]69[/C][C]0.270287842701227[/C][C]0.540575685402453[/C][C]0.729712157298773[/C][/ROW]
[ROW][C]70[/C][C]0.370989449034487[/C][C]0.741978898068974[/C][C]0.629010550965513[/C][/ROW]
[ROW][C]71[/C][C]0.345029399920885[/C][C]0.69005879984177[/C][C]0.654970600079115[/C][/ROW]
[ROW][C]72[/C][C]0.483391881514233[/C][C]0.966783763028466[/C][C]0.516608118485767[/C][/ROW]
[ROW][C]73[/C][C]0.46780796587608[/C][C]0.93561593175216[/C][C]0.53219203412392[/C][/ROW]
[ROW][C]74[/C][C]0.512918501491588[/C][C]0.974162997016824[/C][C]0.487081498508412[/C][/ROW]
[ROW][C]75[/C][C]0.4736857521619[/C][C]0.9473715043238[/C][C]0.5263142478381[/C][/ROW]
[ROW][C]76[/C][C]0.429264121107502[/C][C]0.858528242215003[/C][C]0.570735878892498[/C][/ROW]
[ROW][C]77[/C][C]0.393514671342652[/C][C]0.787029342685303[/C][C]0.606485328657348[/C][/ROW]
[ROW][C]78[/C][C]0.367181406171089[/C][C]0.734362812342178[/C][C]0.632818593828911[/C][/ROW]
[ROW][C]79[/C][C]0.349867922590938[/C][C]0.699735845181876[/C][C]0.650132077409062[/C][/ROW]
[ROW][C]80[/C][C]0.862771485983476[/C][C]0.274457028033049[/C][C]0.137228514016524[/C][/ROW]
[ROW][C]81[/C][C]0.900051160313747[/C][C]0.199897679372507[/C][C]0.0999488396862534[/C][/ROW]
[ROW][C]82[/C][C]0.877891057707669[/C][C]0.244217884584662[/C][C]0.122108942292331[/C][/ROW]
[ROW][C]83[/C][C]0.856035954344368[/C][C]0.287928091311264[/C][C]0.143964045655632[/C][/ROW]
[ROW][C]84[/C][C]0.82832969534546[/C][C]0.343340609309079[/C][C]0.171670304654539[/C][/ROW]
[ROW][C]85[/C][C]0.800990549361206[/C][C]0.398018901277588[/C][C]0.199009450638794[/C][/ROW]
[ROW][C]86[/C][C]0.80586501948417[/C][C]0.388269961031662[/C][C]0.194134980515831[/C][/ROW]
[ROW][C]87[/C][C]0.772365994015475[/C][C]0.45526801196905[/C][C]0.227634005984525[/C][/ROW]
[ROW][C]88[/C][C]0.79408443054432[/C][C]0.411831138911361[/C][C]0.20591556945568[/C][/ROW]
[ROW][C]89[/C][C]0.757509246799424[/C][C]0.484981506401152[/C][C]0.242490753200576[/C][/ROW]
[ROW][C]90[/C][C]0.725003413302563[/C][C]0.549993173394874[/C][C]0.274996586697437[/C][/ROW]
[ROW][C]91[/C][C]0.707011668740476[/C][C]0.585976662519049[/C][C]0.292988331259524[/C][/ROW]
[ROW][C]92[/C][C]0.669285129641017[/C][C]0.661429740717966[/C][C]0.330714870358983[/C][/ROW]
[ROW][C]93[/C][C]0.641951138631042[/C][C]0.716097722737916[/C][C]0.358048861368958[/C][/ROW]
[ROW][C]94[/C][C]0.59766386780529[/C][C]0.80467226438942[/C][C]0.40233613219471[/C][/ROW]
[ROW][C]95[/C][C]0.555233330904036[/C][C]0.889533338191928[/C][C]0.444766669095964[/C][/ROW]
[ROW][C]96[/C][C]0.73035085125937[/C][C]0.53929829748126[/C][C]0.26964914874063[/C][/ROW]
[ROW][C]97[/C][C]0.695045214392768[/C][C]0.609909571214465[/C][C]0.304954785607232[/C][/ROW]
[ROW][C]98[/C][C]0.649406329588502[/C][C]0.701187340822995[/C][C]0.350593670411498[/C][/ROW]
[ROW][C]99[/C][C]0.601557119986164[/C][C]0.796885760027672[/C][C]0.398442880013836[/C][/ROW]
[ROW][C]100[/C][C]0.648782922314135[/C][C]0.70243415537173[/C][C]0.351217077685865[/C][/ROW]
[ROW][C]101[/C][C]0.664912428839276[/C][C]0.670175142321448[/C][C]0.335087571160724[/C][/ROW]
[ROW][C]102[/C][C]0.617087891562988[/C][C]0.765824216874023[/C][C]0.382912108437012[/C][/ROW]
[ROW][C]103[/C][C]0.610094581644188[/C][C]0.779810836711623[/C][C]0.389905418355812[/C][/ROW]
[ROW][C]104[/C][C]0.738421364790522[/C][C]0.523157270418956[/C][C]0.261578635209478[/C][/ROW]
[ROW][C]105[/C][C]0.694305138628639[/C][C]0.611389722742722[/C][C]0.305694861371361[/C][/ROW]
[ROW][C]106[/C][C]0.657393110025726[/C][C]0.685213779948547[/C][C]0.342606889974274[/C][/ROW]
[ROW][C]107[/C][C]0.623805969738312[/C][C]0.752388060523376[/C][C]0.376194030261688[/C][/ROW]
[ROW][C]108[/C][C]0.703702135655018[/C][C]0.592595728689963[/C][C]0.296297864344982[/C][/ROW]
[ROW][C]109[/C][C]0.848891904298643[/C][C]0.302216191402713[/C][C]0.151108095701357[/C][/ROW]
[ROW][C]110[/C][C]0.88929959545351[/C][C]0.221400809092979[/C][C]0.11070040454649[/C][/ROW]
[ROW][C]111[/C][C]0.863170307744806[/C][C]0.273659384510389[/C][C]0.136829692255194[/C][/ROW]
[ROW][C]112[/C][C]0.966044069189913[/C][C]0.0679118616201731[/C][C]0.0339559308100866[/C][/ROW]
[ROW][C]113[/C][C]0.96663864861835[/C][C]0.0667227027632984[/C][C]0.0333613513816492[/C][/ROW]
[ROW][C]114[/C][C]0.954376575717685[/C][C]0.0912468485646305[/C][C]0.0456234242823153[/C][/ROW]
[ROW][C]115[/C][C]0.937782239302536[/C][C]0.124435521394927[/C][C]0.0622177606974636[/C][/ROW]
[ROW][C]116[/C][C]0.917060896492614[/C][C]0.165878207014771[/C][C]0.0829391035073855[/C][/ROW]
[ROW][C]117[/C][C]0.898749047289424[/C][C]0.202501905421151[/C][C]0.101250952710576[/C][/ROW]
[ROW][C]118[/C][C]0.884356447722752[/C][C]0.231287104554495[/C][C]0.115643552277248[/C][/ROW]
[ROW][C]119[/C][C]0.85752686349467[/C][C]0.284946273010661[/C][C]0.142473136505331[/C][/ROW]
[ROW][C]120[/C][C]0.918129342164077[/C][C]0.163741315671846[/C][C]0.0818706578359229[/C][/ROW]
[ROW][C]121[/C][C]0.917370628117356[/C][C]0.165258743765288[/C][C]0.082629371882644[/C][/ROW]
[ROW][C]122[/C][C]0.895151673206679[/C][C]0.209696653586642[/C][C]0.104848326793321[/C][/ROW]
[ROW][C]123[/C][C]0.873723756895788[/C][C]0.252552486208423[/C][C]0.126276243104212[/C][/ROW]
[ROW][C]124[/C][C]0.882429460546323[/C][C]0.235141078907354[/C][C]0.117570539453677[/C][/ROW]
[ROW][C]125[/C][C]0.860757239595526[/C][C]0.278485520808949[/C][C]0.139242760404475[/C][/ROW]
[ROW][C]126[/C][C]0.839248574305046[/C][C]0.321502851389908[/C][C]0.160751425694954[/C][/ROW]
[ROW][C]127[/C][C]0.85284910418387[/C][C]0.294301791632259[/C][C]0.147150895816129[/C][/ROW]
[ROW][C]128[/C][C]0.841327584367174[/C][C]0.317344831265651[/C][C]0.158672415632826[/C][/ROW]
[ROW][C]129[/C][C]0.794939328051083[/C][C]0.410121343897834[/C][C]0.205060671948917[/C][/ROW]
[ROW][C]130[/C][C]0.736095660511103[/C][C]0.527808678977793[/C][C]0.263904339488897[/C][/ROW]
[ROW][C]131[/C][C]0.683011516413236[/C][C]0.633976967173528[/C][C]0.316988483586764[/C][/ROW]
[ROW][C]132[/C][C]0.725630614850558[/C][C]0.548738770298884[/C][C]0.274369385149442[/C][/ROW]
[ROW][C]133[/C][C]0.785274823897846[/C][C]0.429450352204308[/C][C]0.214725176102154[/C][/ROW]
[ROW][C]134[/C][C]0.719805899989074[/C][C]0.560388200021853[/C][C]0.280194100010926[/C][/ROW]
[ROW][C]135[/C][C]0.76393604723597[/C][C]0.472127905528061[/C][C]0.236063952764031[/C][/ROW]
[ROW][C]136[/C][C]0.681687268779393[/C][C]0.636625462441214[/C][C]0.318312731220607[/C][/ROW]
[ROW][C]137[/C][C]0.626277774224825[/C][C]0.74744445155035[/C][C]0.373722225775175[/C][/ROW]
[ROW][C]138[/C][C]0.778740438034608[/C][C]0.442519123930784[/C][C]0.221259561965392[/C][/ROW]
[ROW][C]139[/C][C]0.793958362932702[/C][C]0.412083274134595[/C][C]0.206041637067298[/C][/ROW]
[ROW][C]140[/C][C]0.680255016095074[/C][C]0.639489967809853[/C][C]0.319744983904926[/C][/ROW]
[ROW][C]141[/C][C]0.539045594429851[/C][C]0.921908811140297[/C][C]0.460954405570149[/C][/ROW]
[ROW][C]142[/C][C]0.954125271943062[/C][C]0.0917494561138752[/C][C]0.0458747280569376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4323565507207640.8647131014415270.567643449279236
110.400250264633140.800500529266280.59974973536686
120.272652823880510.545305647761020.72734717611949
130.1860516696718250.372103339343650.813948330328175
140.5421684247527010.9156631504945980.457831575247299
150.505331261216780.989337477566440.49466873878322
160.4953708150007220.9907416300014440.504629184999278
170.4454401513969050.890880302793810.554559848603095
180.3887336635859130.7774673271718250.611266336414087
190.3068823659235370.6137647318470750.693117634076463
200.233868234600550.4677364692010990.76613176539945
210.1760810902534130.3521621805068260.823918909746587
220.1566928661617070.3133857323234140.843307133838293
230.1124475462748930.2248950925497850.887552453725107
240.08285616447364780.1657123289472960.917143835526352
250.08928396247219710.1785679249443940.910716037527803
260.07731657059747680.1546331411949540.922683429402523
270.0660563187031210.1321126374062420.933943681296879
280.1119145193760950.2238290387521910.888085480623905
290.08968284369514550.1793656873902910.910317156304854
300.06684737187662570.1336947437532510.933152628123374
310.05509486085402670.1101897217080530.944905139145973
320.08390518373895580.1678103674779120.916094816261044
330.07498048271549790.1499609654309960.925019517284502
340.1241222047307370.2482444094614750.875877795269263
350.1177833989011760.2355667978023520.882216601098824
360.1013346701878860.2026693403757710.898665329812114
370.07840698293381580.1568139658676320.921593017066184
380.06173462987179050.1234692597435810.93826537012821
390.04706020242372690.09412040484745380.952939797576273
400.04742624894546950.0948524978909390.95257375105453
410.04911300730793320.09822601461586650.950886992692067
420.04812785021748370.09625570043496750.951872149782516
430.05414567924604460.1082913584920890.945854320753955
440.1480827548625380.2961655097250770.851917245137461
450.131225732466770.262451464933540.86877426753323
460.201948238120140.4038964762402790.79805176187986
470.1682627673683260.3365255347366520.831737232631674
480.1491781130321680.2983562260643370.850821886967831
490.141313002411360.282626004822720.85868699758864
500.1373479833319850.274695966663970.862652016668015
510.1578296258823560.3156592517647120.842170374117644
520.1308173531703460.2616347063406920.869182646829654
530.1231722176730590.2463444353461180.876827782326941
540.1627581528744640.3255163057489280.837241847125536
550.192887083778650.3857741675572990.80711291622135
560.1708634812018040.3417269624036080.829136518798196
570.1657682556040880.3315365112081750.834231744395912
580.2007836975016690.4015673950033390.799216302498331
590.1743151530089650.348630306017930.825684846991035
600.1788118011856420.3576236023712840.821188198814358
610.1608688352170060.3217376704340120.839131164782994
620.1578267469268030.3156534938536050.842173253073197
630.2024232794046880.4048465588093770.797576720595312
640.1748649833979510.3497299667959020.825135016602049
650.180452819334590.360905638669180.81954718066541
660.2761426972744430.5522853945488860.723857302725557
670.2439221121311260.4878442242622510.756077887868874
680.2878023154194130.5756046308388260.712197684580587
690.2702878427012270.5405756854024530.729712157298773
700.3709894490344870.7419788980689740.629010550965513
710.3450293999208850.690058799841770.654970600079115
720.4833918815142330.9667837630284660.516608118485767
730.467807965876080.935615931752160.53219203412392
740.5129185014915880.9741629970168240.487081498508412
750.47368575216190.94737150432380.5263142478381
760.4292641211075020.8585282422150030.570735878892498
770.3935146713426520.7870293426853030.606485328657348
780.3671814061710890.7343628123421780.632818593828911
790.3498679225909380.6997358451818760.650132077409062
800.8627714859834760.2744570280330490.137228514016524
810.9000511603137470.1998976793725070.0999488396862534
820.8778910577076690.2442178845846620.122108942292331
830.8560359543443680.2879280913112640.143964045655632
840.828329695345460.3433406093090790.171670304654539
850.8009905493612060.3980189012775880.199009450638794
860.805865019484170.3882699610316620.194134980515831
870.7723659940154750.455268011969050.227634005984525
880.794084430544320.4118311389113610.20591556945568
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900.7250034133025630.5499931733948740.274996586697437
910.7070116687404760.5859766625190490.292988331259524
920.6692851296410170.6614297407179660.330714870358983
930.6419511386310420.7160977227379160.358048861368958
940.597663867805290.804672264389420.40233613219471
950.5552333309040360.8895333381919280.444766669095964
960.730350851259370.539298297481260.26964914874063
970.6950452143927680.6099095712144650.304954785607232
980.6494063295885020.7011873408229950.350593670411498
990.6015571199861640.7968857600276720.398442880013836
1000.6487829223141350.702434155371730.351217077685865
1010.6649124288392760.6701751423214480.335087571160724
1020.6170878915629880.7658242168740230.382912108437012
1030.6100945816441880.7798108367116230.389905418355812
1040.7384213647905220.5231572704189560.261578635209478
1050.6943051386286390.6113897227427220.305694861371361
1060.6573931100257260.6852137799485470.342606889974274
1070.6238059697383120.7523880605233760.376194030261688
1080.7037021356550180.5925957286899630.296297864344982
1090.8488919042986430.3022161914027130.151108095701357
1100.889299595453510.2214008090929790.11070040454649
1110.8631703077448060.2736593845103890.136829692255194
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1200.9181293421640770.1637413156718460.0818706578359229
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1420.9541252719430620.09174945611387520.0458747280569376






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 level80.0601503759398496OK

\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 & 8 & 0.0601503759398496 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110162&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]8[/C][C]0.0601503759398496[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110162&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110162&T=6

As an alternative you can also use a QR Code:  
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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 level80.0601503759398496OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}