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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 15:31:03 +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/t1292340549ba7r64phytrtzzk.htm/, Retrieved Thu, 02 May 2024 20:17:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109737, Retrieved Thu, 02 May 2024 20:17:29 +0000
QR Codes:

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

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Linear R...] [2010-12-14 13:02:24] [1429a1a14191a86916b95357f6de790b]
-    D    [Multiple Regression] [Multiple Linear R...] [2010-12-14 15:31:03] [e192c8164fa91adb027f71579ac0a49a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6	2	1	73	62	66
4	1	1	58	54	54
5	1	1	68	41	82
4	1	1	62	49	61
4	1	1	65	49	65
6	1	1	81	72	77
6	1	1	73	78	66
4	2	1	64	58	66
4	1	1	68	58	66
6	1	1	51	23	48
4	1	1	68	39	57
6	1	1	61	63	80
5	1	1	69	46	60
4	1	1	73	58	70
6	2	1	61	39	85
3	2	1	62	44	59
5	1	1	63	49	72
6	1	1	69	57	70
4	2	1	47	76	74
6	2	1	66	63	70
2	1	1	58	18	51
7	2	1	63	40	70
5	1	1	69	59	71
2	2	1	59	62	72
4	1	1	59	70	50
4	2	1	63	65	69
6	2	1	65	56	73
6	1	1	65	45	66
5	2	1	71	57	73
6	1	1	60	50	58
6	2	1	81	40	78
4	1	1	67	58	83
6	2	1	66	49	76
6	1	1	62	49	77
6	1	1	63	27	79
2	2	1	73	51	71
4	2	1	55	75	79
5	1	1	59	65	60
3	1	1	64	47	73
7	2	1	63	49	70
5	1	1	64	65	42
3	1	1	73	61	74
8	1	1	54	46	68
8	1	1	76	69	83
5	2	1	74	55	62
6	2	1	63	78	79
3	2	1	73	58	61
5	2	1	67	34	86
4	2	1	68	67	64
5	1	1	66	45	75
5	2	1	62	68	59
6	2	1	71	49	82
5	1	1	63	19	61
6	1	1	75	72	69
6	1	1	77	59	60
4	2	1	62	46	59
8	1	1	74	56	81
6	2	1	67	45	65
4	2	1	56	53	60
6	2	1	60	67	60
5	2	1	58	73	45
5	1	1	65	46	75
6	2	1	49	70	84
6	1	1	61	38	77
6	2	1	66	54	64
6	2	1	64	46	54
6	2	1	65	46	72
6	1	1	46	45	56
7	2	1	65	47	67
4	2	1	81	25	81
4	1	1	72	63	73
3	2	1	65	46	67
6	2	1	74	69	72
5	1	1	59	43	69
5	1	1	69	49	71
3	2	1	58	39	77
5	1	1	71	65	63
4	2	1	79	54	49
3	2	1	68	50	74
7	1	1	66	42	76
4	2	1	62	45	65
4	1	1	69	50	65
5	2	1	63	55	69
6	1	1	62	38	71
2	1	1	61	40	68
2	2	1	65	51	49
6	1	1	64	49	86
4	2	1	56	39	63
5	2	1	56	57	77
6	1	1	48	30	52
7	1	1	74	51	73
8	1	1	69	48	63
6	1	1	62	56	54
6	1	1	73	66	56
3	1	1	64	72	54
7	1	1	57	28	61
3	1	1	57	52	70
6	2	1	60	53	68
4	2	1	61	70	63
4	1	1	72	63	76
6	1	1	57	46	69
6	1	1	51	45	71
6	1	2	63	68	39
4	1	1	54	54	54
7	1	2	72	60	64
5	1	1	62	50	70
7	1	1	68	66	76
4	1	1	62	56	71
6	2	1	63	54	73
6	1	1	77	72	81
6	1	1	57	34	50
5	1	1	57	39	42
5	1	1	61	66	66
6	1	1	65	27	77
7	1	1	63	63	62
4	2	1	66	65	66
4	1	1	68	63	69
8	1	1	72	49	72
6	1	1	68	42	67
3	1	1	59	51	59
4	1	1	56	50	66
5	1	1	62	64	68
5	2	1	72	68	72
6	2	1	68	66	73
8	1	1	67	59	69
2	1	1	54	32	57
4	2	1	69	62	55
7	1	1	61	52	72
5	1	1	55	34	68
6	2	1	75	63	83
6	1	1	55	48	74
4	1	1	49	53	72
5	2	1	54	39	66
6	1	1	66	51	61
6	1	1	73	60	86
6	2	1	63	70	81
6	2	1	61	40	79
5	1	1	74	61	73
5	2	1	81	35	59
6	1	1	62	39	64
4	1	1	64	31	75
6	1	1	62	36	68
3	1	1	85	51	84
6	1	1	74	55	68
8	1	1	51	67	68
4	1	1	66	40	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Celebrity[t] = + 1.64525178041472 -0.44459465358534Gender[t] + 1.67653317331531Age[t] -0.000771955540575894NV[t] + 0.00519221184566125ANX[t] + 0.032351106163234GR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Celebrity[t] =  +  1.64525178041472 -0.44459465358534Gender[t] +  1.67653317331531Age[t] -0.000771955540575894NV[t] +  0.00519221184566125ANX[t] +  0.032351106163234GR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Celebrity[t] =  +  1.64525178041472 -0.44459465358534Gender[t] +  1.67653317331531Age[t] -0.000771955540575894NV[t] +  0.00519221184566125ANX[t] +  0.032351106163234GR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&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
Celebrity[t] = + 1.64525178041472 -0.44459465358534Gender[t] + 1.67653317331531Age[t] -0.000771955540575894NV[t] + 0.00519221184566125ANX[t] + 0.032351106163234GR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.645251780414721.602231.02690.3062610.153131
Gender-0.444594653585340.235481-1.8880.0610910.030546
Age1.676533173315311.0024411.67250.0966690.048334
NV-0.0007719555405758940.01603-0.04820.961660.48083
ANX0.005192211845661250.0091820.56550.5726480.286324
GR0.0323511061632340.0122842.63350.00940.0047

\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) & 1.64525178041472 & 1.60223 & 1.0269 & 0.306261 & 0.153131 \tabularnewline
Gender & -0.44459465358534 & 0.235481 & -1.888 & 0.061091 & 0.030546 \tabularnewline
Age & 1.67653317331531 & 1.002441 & 1.6725 & 0.096669 & 0.048334 \tabularnewline
NV & -0.000771955540575894 & 0.01603 & -0.0482 & 0.96166 & 0.48083 \tabularnewline
ANX & 0.00519221184566125 & 0.009182 & 0.5655 & 0.572648 & 0.286324 \tabularnewline
GR & 0.032351106163234 & 0.012284 & 2.6335 & 0.0094 & 0.0047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&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]1.64525178041472[/C][C]1.60223[/C][C]1.0269[/C][C]0.306261[/C][C]0.153131[/C][/ROW]
[ROW][C]Gender[/C][C]-0.44459465358534[/C][C]0.235481[/C][C]-1.888[/C][C]0.061091[/C][C]0.030546[/C][/ROW]
[ROW][C]Age[/C][C]1.67653317331531[/C][C]1.002441[/C][C]1.6725[/C][C]0.096669[/C][C]0.048334[/C][/ROW]
[ROW][C]NV[/C][C]-0.000771955540575894[/C][C]0.01603[/C][C]-0.0482[/C][C]0.96166[/C][C]0.48083[/C][/ROW]
[ROW][C]ANX[/C][C]0.00519221184566125[/C][C]0.009182[/C][C]0.5655[/C][C]0.572648[/C][C]0.286324[/C][/ROW]
[ROW][C]GR[/C][C]0.032351106163234[/C][C]0.012284[/C][C]2.6335[/C][C]0.0094[/C][C]0.0047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&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)1.645251780414721.602231.02690.3062610.153131
Gender-0.444594653585340.235481-1.8880.0610910.030546
Age1.676533173315311.0024411.67250.0966690.048334
NV-0.0007719555405758940.01603-0.04820.961660.48083
ANX0.005192211845661250.0091820.56550.5726480.286324
GR0.0323511061632340.0122842.63350.00940.0047






Multiple Linear Regression - Regression Statistics
Multiple R0.287648778695546
R-squared0.082741819885039
Adjusted R-squared0.0499825991666475
F-TEST (value)2.52575665936359
F-TEST (DF numerator)5
F-TEST (DF denominator)140
p-value0.0319025204519411
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.35922235913879
Sum Squared Residuals258.647959021595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.287648778695546 \tabularnewline
R-squared & 0.082741819885039 \tabularnewline
Adjusted R-squared & 0.0499825991666475 \tabularnewline
F-TEST (value) & 2.52575665936359 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 140 \tabularnewline
p-value & 0.0319025204519411 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.35922235913879 \tabularnewline
Sum Squared Residuals & 258.647959021595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.287648778695546[/C][/ROW]
[ROW][C]R-squared[/C][C]0.082741819885039[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0499825991666475[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.52575665936359[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]140[/C][/ROW]
[ROW][C]p-value[/C][C]0.0319025204519411[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.35922235913879[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]258.647959021595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&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 R0.287648778695546
R-squared0.082741819885039
Adjusted R-squared0.0499825991666475
F-TEST (value)2.52575665936359
F-TEST (DF numerator)5
F-TEST (DF denominator)140
p-value0.0319025204519411
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.35922235913879
Sum Squared Residuals258.647959021595






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
164.833333033301751.16666696669825
244.85975605127163-0.859756051271635
355.69036871444282-0.690368714442823
445.05716491302367-1.05716491302367
545.18425347105487-1.18425347105487
665.679536328814680.320463671185325
765.361003076417680.638996923582325
844.81951178578429-0.819511785784295
945.26101861720733-1.26101861720733
1064.510094535860771.48990546413923
1144.87120663667066-0.87120663667066
1265.745298851504940.254701148495056
1355.00383348253941-0.00383348253941515
1445.38656326415739-1.38656326415739
1565.337846644439900.662153355560095
1634.52190698788355-1.52190698788355
1755.41225512527866-0.412255125278662
1865.384458874474030.615541125525971
1945.18490369250186-1.18490369250186
2064.973333358584391.02666664141561
2124.57578310633813-2.57578310633813
2274.856228352755902.14377164724410
2355.42719440432859-0.427194404328585
2425.03824704784922-3.03824704784922
2544.8126550606087-0.812655060608705
2644.9536825427342-0.95368254273420
2765.034813149695030.965186850304966
2865.195835729835460.804164270164538
2955.03537362829724-0.0353736282972399
3064.966847717460781.03315228253922
3165.101142002331410.89885799766859
3245.81175937752288-1.81175937752288
3365.094749029724530.905250970275469
3465.574782611635410.425217388364591
3565.524484207816750.475515792183247
3624.93797423381565-2.93797423381565
3745.33529136714776-1.33529136714776
3855.11020506301274-0.110205063012738
3935.43344985221-2.43344985221000
4074.902958259366862.09704174063314
4154.524025374371650.475974625628353
4235.5315443243473-2.53154432434731
4385.274221664953932.72577833504607
4485.861926107959972.13807389204003
4554.666811170188620.333188829811384
4665.344692358360140.655307641639862
4734.65080865510294-1.65080865510294
4855.33960495813138-0.339604958131377
4944.79845165790647-0.798451657906474
5055.48622372976399-0.486223729763992
5154.646520072179420.353479927820579
5265.284995889001060.715004110998944
5354.900626602113250.0993733978867491
5465.425359212752260.574640787247742
5565.06515659220840.934843407791596
5644.53229141157487-0.532291411574873
5785.731269052721062.26873094727894
5864.717346059005741.28265394099426
5944.60561973390119-0.605619733901192
6064.675222877578151.32477712242185
6154.222653467284760.777346532715245
6255.49218789715023-0.492187897150229
6365.475717571979080.524282428020919
6465.518440236873710.48155976312629
6564.732496814994031.26750318500597
6664.368991969677551.63100803032245
6764.950539925075191.04946007492481
6864.886991823474071.11300817652594
6974.793976606104682.20602339389532
7045.12031214313619-1.12031214313619
7145.51034959741597-1.51034959741597
7234.78878439425902-1.78878439425902
7365.063013197660210.936986802339787
7455.2871363578773-0.287136357877297
7555.37527228587197-0.375272285871973
7635.08135366175576-2.08135366175576
7755.19799491501553-0.197994915015529
7844.23719480051803-0.237194800518033
7935.03369511816257-2.03369511816257
8075.502998200390241.49700179960976
8144.72120583670862-0.721205836708616
8245.18635786073823-1.18635786073823
8354.901760424277590.0982395757224112
8465.323561644353730.67643835564627
8525.23766470509593-3.23766470509593
8624.23242554254911-2.23242554254911
8765.864398656023360.135601343976637
8844.62998208655164-0.629982086551636
8955.17635738605881-0.176357386058815
9064.678160310055061.32183968994494
9175.446499144186881.55350085581312
9285.111271224720442.88872877527956
9364.867052652800661.13294734719934
9464.97518547263741.0248145273626
9534.94858413125008-1.94858413125008
9674.951988241967662.04801175803234
9735.36776128173263-2.36776128173263
9864.861340761044761.13865923895524
9944.78708087606426-0.787080876064256
10045.60740291590567-1.60740291590567
10165.304256904495430.695743095504568
10265.36839863821970.631601361780305
10366.11985382027483-0.119853820274826
10444.86284387343394-0.86284387343394
10576.88014617972520.119853820274797
10655.35351708033843-0.353517080338432
10775.626067373604961.37393262639504
10845.41702145757563-1.41702145757563
10965.025972637084860.974027362915136
11065.812028575629910.187971424370086
11164.627279345246051.37272065475395
11254.394431555168490.605568444831515
11355.30796000075665-0.307960000756652
11465.458238084409130.541761915590867
11575.161435029485581.83856497051442
11644.85431335762277-0.854313357622772
11745.38403299492534-1.38403299492534
11885.405307525413482.59469247458652
11965.210294333839980.789705666160016
12035.00516299101025-2.00516299101025
12145.22874438892895-1.22874438892895
12255.36150583385122-0.361505833851221
12355.0593648968957-0.0593648968957037
12465.084419401529920.915580598470081
12585.364036103083272.63596389691673
12624.84566853131909-2.84566853131909
12744.48055868766849-0.480558687668486
12875.42937567189681.57062432810320
12955.21114316726541-0.211143167265415
13065.386950138841240.613049861158757
13165.477940770084080.522059229915923
13245.44383135022937-1.44383135022937
13354.728579316122490.27142068387751
13465.064461514552680.935538485447317
13565.914565386460450.085434613539547
13665.367856875921320.632143124078684
13765.148932219306160.851067780693838
13855.4984212626435-0.498421262643496
13954.460509926001660.539490073998343
14065.102296113056750.897703886943246
14145.41507667500589-1.41507667500589
14265.216123902172710.783876097827294
14335.79386980103612-2.79386980103612
14465.305512460753360.694487539246641
14585.385573980334542.61442601966546
14645.26615603355628-1.26615603355628

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6 & 4.83333303330175 & 1.16666696669825 \tabularnewline
2 & 4 & 4.85975605127163 & -0.859756051271635 \tabularnewline
3 & 5 & 5.69036871444282 & -0.690368714442823 \tabularnewline
4 & 4 & 5.05716491302367 & -1.05716491302367 \tabularnewline
5 & 4 & 5.18425347105487 & -1.18425347105487 \tabularnewline
6 & 6 & 5.67953632881468 & 0.320463671185325 \tabularnewline
7 & 6 & 5.36100307641768 & 0.638996923582325 \tabularnewline
8 & 4 & 4.81951178578429 & -0.819511785784295 \tabularnewline
9 & 4 & 5.26101861720733 & -1.26101861720733 \tabularnewline
10 & 6 & 4.51009453586077 & 1.48990546413923 \tabularnewline
11 & 4 & 4.87120663667066 & -0.87120663667066 \tabularnewline
12 & 6 & 5.74529885150494 & 0.254701148495056 \tabularnewline
13 & 5 & 5.00383348253941 & -0.00383348253941515 \tabularnewline
14 & 4 & 5.38656326415739 & -1.38656326415739 \tabularnewline
15 & 6 & 5.33784664443990 & 0.662153355560095 \tabularnewline
16 & 3 & 4.52190698788355 & -1.52190698788355 \tabularnewline
17 & 5 & 5.41225512527866 & -0.412255125278662 \tabularnewline
18 & 6 & 5.38445887447403 & 0.615541125525971 \tabularnewline
19 & 4 & 5.18490369250186 & -1.18490369250186 \tabularnewline
20 & 6 & 4.97333335858439 & 1.02666664141561 \tabularnewline
21 & 2 & 4.57578310633813 & -2.57578310633813 \tabularnewline
22 & 7 & 4.85622835275590 & 2.14377164724410 \tabularnewline
23 & 5 & 5.42719440432859 & -0.427194404328585 \tabularnewline
24 & 2 & 5.03824704784922 & -3.03824704784922 \tabularnewline
25 & 4 & 4.8126550606087 & -0.812655060608705 \tabularnewline
26 & 4 & 4.9536825427342 & -0.95368254273420 \tabularnewline
27 & 6 & 5.03481314969503 & 0.965186850304966 \tabularnewline
28 & 6 & 5.19583572983546 & 0.804164270164538 \tabularnewline
29 & 5 & 5.03537362829724 & -0.0353736282972399 \tabularnewline
30 & 6 & 4.96684771746078 & 1.03315228253922 \tabularnewline
31 & 6 & 5.10114200233141 & 0.89885799766859 \tabularnewline
32 & 4 & 5.81175937752288 & -1.81175937752288 \tabularnewline
33 & 6 & 5.09474902972453 & 0.905250970275469 \tabularnewline
34 & 6 & 5.57478261163541 & 0.425217388364591 \tabularnewline
35 & 6 & 5.52448420781675 & 0.475515792183247 \tabularnewline
36 & 2 & 4.93797423381565 & -2.93797423381565 \tabularnewline
37 & 4 & 5.33529136714776 & -1.33529136714776 \tabularnewline
38 & 5 & 5.11020506301274 & -0.110205063012738 \tabularnewline
39 & 3 & 5.43344985221 & -2.43344985221000 \tabularnewline
40 & 7 & 4.90295825936686 & 2.09704174063314 \tabularnewline
41 & 5 & 4.52402537437165 & 0.475974625628353 \tabularnewline
42 & 3 & 5.5315443243473 & -2.53154432434731 \tabularnewline
43 & 8 & 5.27422166495393 & 2.72577833504607 \tabularnewline
44 & 8 & 5.86192610795997 & 2.13807389204003 \tabularnewline
45 & 5 & 4.66681117018862 & 0.333188829811384 \tabularnewline
46 & 6 & 5.34469235836014 & 0.655307641639862 \tabularnewline
47 & 3 & 4.65080865510294 & -1.65080865510294 \tabularnewline
48 & 5 & 5.33960495813138 & -0.339604958131377 \tabularnewline
49 & 4 & 4.79845165790647 & -0.798451657906474 \tabularnewline
50 & 5 & 5.48622372976399 & -0.486223729763992 \tabularnewline
51 & 5 & 4.64652007217942 & 0.353479927820579 \tabularnewline
52 & 6 & 5.28499588900106 & 0.715004110998944 \tabularnewline
53 & 5 & 4.90062660211325 & 0.0993733978867491 \tabularnewline
54 & 6 & 5.42535921275226 & 0.574640787247742 \tabularnewline
55 & 6 & 5.0651565922084 & 0.934843407791596 \tabularnewline
56 & 4 & 4.53229141157487 & -0.532291411574873 \tabularnewline
57 & 8 & 5.73126905272106 & 2.26873094727894 \tabularnewline
58 & 6 & 4.71734605900574 & 1.28265394099426 \tabularnewline
59 & 4 & 4.60561973390119 & -0.605619733901192 \tabularnewline
60 & 6 & 4.67522287757815 & 1.32477712242185 \tabularnewline
61 & 5 & 4.22265346728476 & 0.777346532715245 \tabularnewline
62 & 5 & 5.49218789715023 & -0.492187897150229 \tabularnewline
63 & 6 & 5.47571757197908 & 0.524282428020919 \tabularnewline
64 & 6 & 5.51844023687371 & 0.48155976312629 \tabularnewline
65 & 6 & 4.73249681499403 & 1.26750318500597 \tabularnewline
66 & 6 & 4.36899196967755 & 1.63100803032245 \tabularnewline
67 & 6 & 4.95053992507519 & 1.04946007492481 \tabularnewline
68 & 6 & 4.88699182347407 & 1.11300817652594 \tabularnewline
69 & 7 & 4.79397660610468 & 2.20602339389532 \tabularnewline
70 & 4 & 5.12031214313619 & -1.12031214313619 \tabularnewline
71 & 4 & 5.51034959741597 & -1.51034959741597 \tabularnewline
72 & 3 & 4.78878439425902 & -1.78878439425902 \tabularnewline
73 & 6 & 5.06301319766021 & 0.936986802339787 \tabularnewline
74 & 5 & 5.2871363578773 & -0.287136357877297 \tabularnewline
75 & 5 & 5.37527228587197 & -0.375272285871973 \tabularnewline
76 & 3 & 5.08135366175576 & -2.08135366175576 \tabularnewline
77 & 5 & 5.19799491501553 & -0.197994915015529 \tabularnewline
78 & 4 & 4.23719480051803 & -0.237194800518033 \tabularnewline
79 & 3 & 5.03369511816257 & -2.03369511816257 \tabularnewline
80 & 7 & 5.50299820039024 & 1.49700179960976 \tabularnewline
81 & 4 & 4.72120583670862 & -0.721205836708616 \tabularnewline
82 & 4 & 5.18635786073823 & -1.18635786073823 \tabularnewline
83 & 5 & 4.90176042427759 & 0.0982395757224112 \tabularnewline
84 & 6 & 5.32356164435373 & 0.67643835564627 \tabularnewline
85 & 2 & 5.23766470509593 & -3.23766470509593 \tabularnewline
86 & 2 & 4.23242554254911 & -2.23242554254911 \tabularnewline
87 & 6 & 5.86439865602336 & 0.135601343976637 \tabularnewline
88 & 4 & 4.62998208655164 & -0.629982086551636 \tabularnewline
89 & 5 & 5.17635738605881 & -0.176357386058815 \tabularnewline
90 & 6 & 4.67816031005506 & 1.32183968994494 \tabularnewline
91 & 7 & 5.44649914418688 & 1.55350085581312 \tabularnewline
92 & 8 & 5.11127122472044 & 2.88872877527956 \tabularnewline
93 & 6 & 4.86705265280066 & 1.13294734719934 \tabularnewline
94 & 6 & 4.9751854726374 & 1.0248145273626 \tabularnewline
95 & 3 & 4.94858413125008 & -1.94858413125008 \tabularnewline
96 & 7 & 4.95198824196766 & 2.04801175803234 \tabularnewline
97 & 3 & 5.36776128173263 & -2.36776128173263 \tabularnewline
98 & 6 & 4.86134076104476 & 1.13865923895524 \tabularnewline
99 & 4 & 4.78708087606426 & -0.787080876064256 \tabularnewline
100 & 4 & 5.60740291590567 & -1.60740291590567 \tabularnewline
101 & 6 & 5.30425690449543 & 0.695743095504568 \tabularnewline
102 & 6 & 5.3683986382197 & 0.631601361780305 \tabularnewline
103 & 6 & 6.11985382027483 & -0.119853820274826 \tabularnewline
104 & 4 & 4.86284387343394 & -0.86284387343394 \tabularnewline
105 & 7 & 6.8801461797252 & 0.119853820274797 \tabularnewline
106 & 5 & 5.35351708033843 & -0.353517080338432 \tabularnewline
107 & 7 & 5.62606737360496 & 1.37393262639504 \tabularnewline
108 & 4 & 5.41702145757563 & -1.41702145757563 \tabularnewline
109 & 6 & 5.02597263708486 & 0.974027362915136 \tabularnewline
110 & 6 & 5.81202857562991 & 0.187971424370086 \tabularnewline
111 & 6 & 4.62727934524605 & 1.37272065475395 \tabularnewline
112 & 5 & 4.39443155516849 & 0.605568444831515 \tabularnewline
113 & 5 & 5.30796000075665 & -0.307960000756652 \tabularnewline
114 & 6 & 5.45823808440913 & 0.541761915590867 \tabularnewline
115 & 7 & 5.16143502948558 & 1.83856497051442 \tabularnewline
116 & 4 & 4.85431335762277 & -0.854313357622772 \tabularnewline
117 & 4 & 5.38403299492534 & -1.38403299492534 \tabularnewline
118 & 8 & 5.40530752541348 & 2.59469247458652 \tabularnewline
119 & 6 & 5.21029433383998 & 0.789705666160016 \tabularnewline
120 & 3 & 5.00516299101025 & -2.00516299101025 \tabularnewline
121 & 4 & 5.22874438892895 & -1.22874438892895 \tabularnewline
122 & 5 & 5.36150583385122 & -0.361505833851221 \tabularnewline
123 & 5 & 5.0593648968957 & -0.0593648968957037 \tabularnewline
124 & 6 & 5.08441940152992 & 0.915580598470081 \tabularnewline
125 & 8 & 5.36403610308327 & 2.63596389691673 \tabularnewline
126 & 2 & 4.84566853131909 & -2.84566853131909 \tabularnewline
127 & 4 & 4.48055868766849 & -0.480558687668486 \tabularnewline
128 & 7 & 5.4293756718968 & 1.57062432810320 \tabularnewline
129 & 5 & 5.21114316726541 & -0.211143167265415 \tabularnewline
130 & 6 & 5.38695013884124 & 0.613049861158757 \tabularnewline
131 & 6 & 5.47794077008408 & 0.522059229915923 \tabularnewline
132 & 4 & 5.44383135022937 & -1.44383135022937 \tabularnewline
133 & 5 & 4.72857931612249 & 0.27142068387751 \tabularnewline
134 & 6 & 5.06446151455268 & 0.935538485447317 \tabularnewline
135 & 6 & 5.91456538646045 & 0.085434613539547 \tabularnewline
136 & 6 & 5.36785687592132 & 0.632143124078684 \tabularnewline
137 & 6 & 5.14893221930616 & 0.851067780693838 \tabularnewline
138 & 5 & 5.4984212626435 & -0.498421262643496 \tabularnewline
139 & 5 & 4.46050992600166 & 0.539490073998343 \tabularnewline
140 & 6 & 5.10229611305675 & 0.897703886943246 \tabularnewline
141 & 4 & 5.41507667500589 & -1.41507667500589 \tabularnewline
142 & 6 & 5.21612390217271 & 0.783876097827294 \tabularnewline
143 & 3 & 5.79386980103612 & -2.79386980103612 \tabularnewline
144 & 6 & 5.30551246075336 & 0.694487539246641 \tabularnewline
145 & 8 & 5.38557398033454 & 2.61442601966546 \tabularnewline
146 & 4 & 5.26615603355628 & -1.26615603355628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&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]6[/C][C]4.83333303330175[/C][C]1.16666696669825[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]4.85975605127163[/C][C]-0.859756051271635[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]5.69036871444282[/C][C]-0.690368714442823[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]5.05716491302367[/C][C]-1.05716491302367[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]5.18425347105487[/C][C]-1.18425347105487[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.67953632881468[/C][C]0.320463671185325[/C][/ROW]
[ROW][C]7[/C][C]6[/C][C]5.36100307641768[/C][C]0.638996923582325[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.81951178578429[/C][C]-0.819511785784295[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.26101861720733[/C][C]-1.26101861720733[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]4.51009453586077[/C][C]1.48990546413923[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]4.87120663667066[/C][C]-0.87120663667066[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]5.74529885150494[/C][C]0.254701148495056[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.00383348253941[/C][C]-0.00383348253941515[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]5.38656326415739[/C][C]-1.38656326415739[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.33784664443990[/C][C]0.662153355560095[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]4.52190698788355[/C][C]-1.52190698788355[/C][/ROW]
[ROW][C]17[/C][C]5[/C][C]5.41225512527866[/C][C]-0.412255125278662[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]5.38445887447403[/C][C]0.615541125525971[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]5.18490369250186[/C][C]-1.18490369250186[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]4.97333335858439[/C][C]1.02666664141561[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]4.57578310633813[/C][C]-2.57578310633813[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]4.85622835275590[/C][C]2.14377164724410[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]5.42719440432859[/C][C]-0.427194404328585[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]5.03824704784922[/C][C]-3.03824704784922[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.8126550606087[/C][C]-0.812655060608705[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]4.9536825427342[/C][C]-0.95368254273420[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]5.03481314969503[/C][C]0.965186850304966[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]5.19583572983546[/C][C]0.804164270164538[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]5.03537362829724[/C][C]-0.0353736282972399[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.96684771746078[/C][C]1.03315228253922[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]5.10114200233141[/C][C]0.89885799766859[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]5.81175937752288[/C][C]-1.81175937752288[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]5.09474902972453[/C][C]0.905250970275469[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]5.57478261163541[/C][C]0.425217388364591[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]5.52448420781675[/C][C]0.475515792183247[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]4.93797423381565[/C][C]-2.93797423381565[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]5.33529136714776[/C][C]-1.33529136714776[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]5.11020506301274[/C][C]-0.110205063012738[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]5.43344985221[/C][C]-2.43344985221000[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]4.90295825936686[/C][C]2.09704174063314[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.52402537437165[/C][C]0.475974625628353[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]5.5315443243473[/C][C]-2.53154432434731[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]5.27422166495393[/C][C]2.72577833504607[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]5.86192610795997[/C][C]2.13807389204003[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]4.66681117018862[/C][C]0.333188829811384[/C][/ROW]
[ROW][C]46[/C][C]6[/C][C]5.34469235836014[/C][C]0.655307641639862[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]4.65080865510294[/C][C]-1.65080865510294[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]5.33960495813138[/C][C]-0.339604958131377[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.79845165790647[/C][C]-0.798451657906474[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]5.48622372976399[/C][C]-0.486223729763992[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]4.64652007217942[/C][C]0.353479927820579[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.28499588900106[/C][C]0.715004110998944[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.90062660211325[/C][C]0.0993733978867491[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]5.42535921275226[/C][C]0.574640787247742[/C][/ROW]
[ROW][C]55[/C][C]6[/C][C]5.0651565922084[/C][C]0.934843407791596[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]4.53229141157487[/C][C]-0.532291411574873[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]5.73126905272106[/C][C]2.26873094727894[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]4.71734605900574[/C][C]1.28265394099426[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.60561973390119[/C][C]-0.605619733901192[/C][/ROW]
[ROW][C]60[/C][C]6[/C][C]4.67522287757815[/C][C]1.32477712242185[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]4.22265346728476[/C][C]0.777346532715245[/C][/ROW]
[ROW][C]62[/C][C]5[/C][C]5.49218789715023[/C][C]-0.492187897150229[/C][/ROW]
[ROW][C]63[/C][C]6[/C][C]5.47571757197908[/C][C]0.524282428020919[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]5.51844023687371[/C][C]0.48155976312629[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]4.73249681499403[/C][C]1.26750318500597[/C][/ROW]
[ROW][C]66[/C][C]6[/C][C]4.36899196967755[/C][C]1.63100803032245[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]4.95053992507519[/C][C]1.04946007492481[/C][/ROW]
[ROW][C]68[/C][C]6[/C][C]4.88699182347407[/C][C]1.11300817652594[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]4.79397660610468[/C][C]2.20602339389532[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]5.12031214313619[/C][C]-1.12031214313619[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]5.51034959741597[/C][C]-1.51034959741597[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]4.78878439425902[/C][C]-1.78878439425902[/C][/ROW]
[ROW][C]73[/C][C]6[/C][C]5.06301319766021[/C][C]0.936986802339787[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]5.2871363578773[/C][C]-0.287136357877297[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]5.37527228587197[/C][C]-0.375272285871973[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]5.08135366175576[/C][C]-2.08135366175576[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]5.19799491501553[/C][C]-0.197994915015529[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]4.23719480051803[/C][C]-0.237194800518033[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]5.03369511816257[/C][C]-2.03369511816257[/C][/ROW]
[ROW][C]80[/C][C]7[/C][C]5.50299820039024[/C][C]1.49700179960976[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.72120583670862[/C][C]-0.721205836708616[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]5.18635786073823[/C][C]-1.18635786073823[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]4.90176042427759[/C][C]0.0982395757224112[/C][/ROW]
[ROW][C]84[/C][C]6[/C][C]5.32356164435373[/C][C]0.67643835564627[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]5.23766470509593[/C][C]-3.23766470509593[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]4.23242554254911[/C][C]-2.23242554254911[/C][/ROW]
[ROW][C]87[/C][C]6[/C][C]5.86439865602336[/C][C]0.135601343976637[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.62998208655164[/C][C]-0.629982086551636[/C][/ROW]
[ROW][C]89[/C][C]5[/C][C]5.17635738605881[/C][C]-0.176357386058815[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]4.67816031005506[/C][C]1.32183968994494[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]5.44649914418688[/C][C]1.55350085581312[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]5.11127122472044[/C][C]2.88872877527956[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]4.86705265280066[/C][C]1.13294734719934[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]4.9751854726374[/C][C]1.0248145273626[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]4.94858413125008[/C][C]-1.94858413125008[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]4.95198824196766[/C][C]2.04801175803234[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]5.36776128173263[/C][C]-2.36776128173263[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]4.86134076104476[/C][C]1.13865923895524[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]4.78708087606426[/C][C]-0.787080876064256[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]5.60740291590567[/C][C]-1.60740291590567[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]5.30425690449543[/C][C]0.695743095504568[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]5.3683986382197[/C][C]0.631601361780305[/C][/ROW]
[ROW][C]103[/C][C]6[/C][C]6.11985382027483[/C][C]-0.119853820274826[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]4.86284387343394[/C][C]-0.86284387343394[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]6.8801461797252[/C][C]0.119853820274797[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]5.35351708033843[/C][C]-0.353517080338432[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]5.62606737360496[/C][C]1.37393262639504[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]5.41702145757563[/C][C]-1.41702145757563[/C][/ROW]
[ROW][C]109[/C][C]6[/C][C]5.02597263708486[/C][C]0.974027362915136[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]5.81202857562991[/C][C]0.187971424370086[/C][/ROW]
[ROW][C]111[/C][C]6[/C][C]4.62727934524605[/C][C]1.37272065475395[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]4.39443155516849[/C][C]0.605568444831515[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]5.30796000075665[/C][C]-0.307960000756652[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]5.45823808440913[/C][C]0.541761915590867[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]5.16143502948558[/C][C]1.83856497051442[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.85431335762277[/C][C]-0.854313357622772[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]5.38403299492534[/C][C]-1.38403299492534[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]5.40530752541348[/C][C]2.59469247458652[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.21029433383998[/C][C]0.789705666160016[/C][/ROW]
[ROW][C]120[/C][C]3[/C][C]5.00516299101025[/C][C]-2.00516299101025[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]5.22874438892895[/C][C]-1.22874438892895[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]5.36150583385122[/C][C]-0.361505833851221[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.0593648968957[/C][C]-0.0593648968957037[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]5.08441940152992[/C][C]0.915580598470081[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]5.36403610308327[/C][C]2.63596389691673[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]4.84566853131909[/C][C]-2.84566853131909[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]4.48055868766849[/C][C]-0.480558687668486[/C][/ROW]
[ROW][C]128[/C][C]7[/C][C]5.4293756718968[/C][C]1.57062432810320[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.21114316726541[/C][C]-0.211143167265415[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]5.38695013884124[/C][C]0.613049861158757[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]5.47794077008408[/C][C]0.522059229915923[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]5.44383135022937[/C][C]-1.44383135022937[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]4.72857931612249[/C][C]0.27142068387751[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]5.06446151455268[/C][C]0.935538485447317[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]5.91456538646045[/C][C]0.085434613539547[/C][/ROW]
[ROW][C]136[/C][C]6[/C][C]5.36785687592132[/C][C]0.632143124078684[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]5.14893221930616[/C][C]0.851067780693838[/C][/ROW]
[ROW][C]138[/C][C]5[/C][C]5.4984212626435[/C][C]-0.498421262643496[/C][/ROW]
[ROW][C]139[/C][C]5[/C][C]4.46050992600166[/C][C]0.539490073998343[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]5.10229611305675[/C][C]0.897703886943246[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]5.41507667500589[/C][C]-1.41507667500589[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.21612390217271[/C][C]0.783876097827294[/C][/ROW]
[ROW][C]143[/C][C]3[/C][C]5.79386980103612[/C][C]-2.79386980103612[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]5.30551246075336[/C][C]0.694487539246641[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]5.38557398033454[/C][C]2.61442601966546[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]5.26615603355628[/C][C]-1.26615603355628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&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
164.833333033301751.16666696669825
244.85975605127163-0.859756051271635
355.69036871444282-0.690368714442823
445.05716491302367-1.05716491302367
545.18425347105487-1.18425347105487
665.679536328814680.320463671185325
765.361003076417680.638996923582325
844.81951178578429-0.819511785784295
945.26101861720733-1.26101861720733
1064.510094535860771.48990546413923
1144.87120663667066-0.87120663667066
1265.745298851504940.254701148495056
1355.00383348253941-0.00383348253941515
1445.38656326415739-1.38656326415739
1565.337846644439900.662153355560095
1634.52190698788355-1.52190698788355
1755.41225512527866-0.412255125278662
1865.384458874474030.615541125525971
1945.18490369250186-1.18490369250186
2064.973333358584391.02666664141561
2124.57578310633813-2.57578310633813
2274.856228352755902.14377164724410
2355.42719440432859-0.427194404328585
2425.03824704784922-3.03824704784922
2544.8126550606087-0.812655060608705
2644.9536825427342-0.95368254273420
2765.034813149695030.965186850304966
2865.195835729835460.804164270164538
2955.03537362829724-0.0353736282972399
3064.966847717460781.03315228253922
3165.101142002331410.89885799766859
3245.81175937752288-1.81175937752288
3365.094749029724530.905250970275469
3465.574782611635410.425217388364591
3565.524484207816750.475515792183247
3624.93797423381565-2.93797423381565
3745.33529136714776-1.33529136714776
3855.11020506301274-0.110205063012738
3935.43344985221-2.43344985221000
4074.902958259366862.09704174063314
4154.524025374371650.475974625628353
4235.5315443243473-2.53154432434731
4385.274221664953932.72577833504607
4485.861926107959972.13807389204003
4554.666811170188620.333188829811384
4665.344692358360140.655307641639862
4734.65080865510294-1.65080865510294
4855.33960495813138-0.339604958131377
4944.79845165790647-0.798451657906474
5055.48622372976399-0.486223729763992
5154.646520072179420.353479927820579
5265.284995889001060.715004110998944
5354.900626602113250.0993733978867491
5465.425359212752260.574640787247742
5565.06515659220840.934843407791596
5644.53229141157487-0.532291411574873
5785.731269052721062.26873094727894
5864.717346059005741.28265394099426
5944.60561973390119-0.605619733901192
6064.675222877578151.32477712242185
6154.222653467284760.777346532715245
6255.49218789715023-0.492187897150229
6365.475717571979080.524282428020919
6465.518440236873710.48155976312629
6564.732496814994031.26750318500597
6664.368991969677551.63100803032245
6764.950539925075191.04946007492481
6864.886991823474071.11300817652594
6974.793976606104682.20602339389532
7045.12031214313619-1.12031214313619
7145.51034959741597-1.51034959741597
7234.78878439425902-1.78878439425902
7365.063013197660210.936986802339787
7455.2871363578773-0.287136357877297
7555.37527228587197-0.375272285871973
7635.08135366175576-2.08135366175576
7755.19799491501553-0.197994915015529
7844.23719480051803-0.237194800518033
7935.03369511816257-2.03369511816257
8075.502998200390241.49700179960976
8144.72120583670862-0.721205836708616
8245.18635786073823-1.18635786073823
8354.901760424277590.0982395757224112
8465.323561644353730.67643835564627
8525.23766470509593-3.23766470509593
8624.23242554254911-2.23242554254911
8765.864398656023360.135601343976637
8844.62998208655164-0.629982086551636
8955.17635738605881-0.176357386058815
9064.678160310055061.32183968994494
9175.446499144186881.55350085581312
9285.111271224720442.88872877527956
9364.867052652800661.13294734719934
9464.97518547263741.0248145273626
9534.94858413125008-1.94858413125008
9674.951988241967662.04801175803234
9735.36776128173263-2.36776128173263
9864.861340761044761.13865923895524
9944.78708087606426-0.787080876064256
10045.60740291590567-1.60740291590567
10165.304256904495430.695743095504568
10265.36839863821970.631601361780305
10366.11985382027483-0.119853820274826
10444.86284387343394-0.86284387343394
10576.88014617972520.119853820274797
10655.35351708033843-0.353517080338432
10775.626067373604961.37393262639504
10845.41702145757563-1.41702145757563
10965.025972637084860.974027362915136
11065.812028575629910.187971424370086
11164.627279345246051.37272065475395
11254.394431555168490.605568444831515
11355.30796000075665-0.307960000756652
11465.458238084409130.541761915590867
11575.161435029485581.83856497051442
11644.85431335762277-0.854313357622772
11745.38403299492534-1.38403299492534
11885.405307525413482.59469247458652
11965.210294333839980.789705666160016
12035.00516299101025-2.00516299101025
12145.22874438892895-1.22874438892895
12255.36150583385122-0.361505833851221
12355.0593648968957-0.0593648968957037
12465.084419401529920.915580598470081
12585.364036103083272.63596389691673
12624.84566853131909-2.84566853131909
12744.48055868766849-0.480558687668486
12875.42937567189681.57062432810320
12955.21114316726541-0.211143167265415
13065.386950138841240.613049861158757
13165.477940770084080.522059229915923
13245.44383135022937-1.44383135022937
13354.728579316122490.27142068387751
13465.064461514552680.935538485447317
13565.914565386460450.085434613539547
13665.367856875921320.632143124078684
13765.148932219306160.851067780693838
13855.4984212626435-0.498421262643496
13954.460509926001660.539490073998343
14065.102296113056750.897703886943246
14145.41507667500589-1.41507667500589
14265.216123902172710.783876097827294
14335.79386980103612-2.79386980103612
14465.305512460753360.694487539246641
14585.385573980334542.61442601966546
14645.26615603355628-1.26615603355628






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1865014888941510.3730029777883020.813498511105849
100.3530604485735350.7061208971470710.646939551426465
110.5016156439785390.9967687120429220.498384356021461
120.4561417789866110.9122835579732220.543858221013389
130.3418483290312390.6836966580624780.658151670968761
140.3023751864542220.6047503729084450.697624813545778
150.2271412467766490.4542824935532980.772858753223351
160.2718421953906260.5436843907812510.728157804609374
170.1974083394568880.3948166789137760.802591660543112
180.1685208354658490.3370416709316980.831479164534151
190.1377180072015750.2754360144031490.862281992798425
200.1346371118608670.2692742237217350.865362888139133
210.2072845209286010.4145690418572030.792715479071399
220.3091241041053860.6182482082107720.690875895894614
230.2451636384562620.4903272769125230.754836361543738
240.5046083672119590.9907832655760820.495391632788041
250.441353830985510.882707661971020.55864616901449
260.3919630695537570.7839261391075140.608036930446243
270.3627863730247750.7255727460495510.637213626975225
280.3442861962095130.6885723924190260.655713803790487
290.2864904643822770.5729809287645540.713509535617723
300.3110095393420070.6220190786840140.688990460657993
310.2612235905477960.5224471810955920.738776409452204
320.2835945968732350.567189193746470.716405403126765
330.2517611111919650.503522222383930.748238888808035
340.2216626914032370.4433253828064740.778337308596763
350.1838559573822270.3677119147644540.816144042617773
360.3987136691084550.797427338216910.601286330891545
370.3698764213472160.7397528426944310.630123578652784
380.3252397877914290.6504795755828570.674760212208571
390.4198736083519520.8397472167039050.580126391648048
400.5112210427992630.9775579144014730.488778957200737
410.4783250667576570.9566501335153140.521674933242343
420.5794039400748470.8411921198503050.420596059925153
430.7579047897941270.4841904204117470.242095210205873
440.8273303186751260.3453393626497490.172669681324875
450.7935968447087810.4128063105824370.206403155291219
460.7664242677116380.4671514645767250.233575732288362
470.7766562246377520.4466875507244950.223343775362248
480.7394255278972130.5211489442055740.260574472102787
490.7064868141463120.5870263717073760.293513185853688
500.6647623771198840.6704752457602320.335237622880116
510.6245598326740220.7508803346519560.375440167325978
520.5877946344169280.8244107311661440.412205365583072
530.5378285663424720.9243428673150550.462171433657528
540.5009098339807850.998180332038430.499090166019215
550.4771953142965440.9543906285930870.522804685703456
560.4322502852107920.8645005704215840.567749714789208
570.5117266011567930.9765467976864140.488273398843207
580.5074740854389260.9850518291221470.492525914561074
590.4646968666456240.9293937332912480.535303133354376
600.4695743514497760.9391487028995530.530425648550224
610.441274656361640.882549312723280.55872534363836
620.3974521648875210.7949043297750420.602547835112479
630.3590149692753430.7180299385506860.640985030724657
640.3203235995796380.6406471991592760.679676400420362
650.3126576090863250.625315218172650.687342390913675
660.3290037676577470.6580075353154950.670996232342253
670.309789997636660.619579995273320.69021000236334
680.2995186753529390.5990373507058790.70048132464706
690.3685993990649760.7371987981299510.631400600935024
700.3557000906123700.7114001812247390.64429990938763
710.3633825632129120.7267651264258240.636617436787088
720.3928394375850470.7856788751700930.607160562414953
730.3688584141118890.7377168282237780.631141585888111
740.3253203192231080.6506406384462160.674679680776892
750.2855499745506750.5710999491013490.714450025449325
760.3356108430744520.6712216861489040.664389156925548
770.2928859485006540.5857718970013080.707114051499346
780.2536542219096350.5073084438192700.746345778090365
790.3000512856857750.6001025713715510.699948714314225
800.3062999587974640.6125999175949270.693700041202536
810.2757040631276110.5514081262552220.724295936872389
820.2650486897317320.5300973794634630.734951310268268
830.2262789555418110.4525579110836220.773721044458189
840.1986773684387610.3973547368775220.801322631561239
850.3855584632066890.7711169264133770.614441536793311
860.474670298040070.949340596080140.52532970195993
870.4257486140104250.8514972280208510.574251385989575
880.3942545685628710.7885091371257420.605745431437129
890.3484109049082430.6968218098164860.651589095091757
900.3403515664051150.680703132810230.659648433594885
910.3476538749704360.6953077499408730.652346125029564
920.5047652413496240.9904695173007520.495234758650376
930.487794677059810.975589354119620.51220532294019
940.4716419895623570.9432839791247130.528358010437643
950.5120969199747850.975806160050430.487903080025215
960.5722269251287580.8555461497424840.427773074871242
970.6735955276456240.6528089447087510.326404472354376
980.6486112324426920.7027775351146150.351388767557308
990.6301027163889750.739794567222050.369897283611025
1000.6535674632754490.6928650734491030.346432536724551
1010.6142306982421510.7715386035156980.385769301757849
1020.5713292422568510.8573415154862970.428670757743149
1030.5186739475267870.9626521049464260.481326052473213
1040.4939799915476110.9879599830952220.506020008452389
1050.4387252213997220.8774504427994450.561274778600278
1060.3886676364407740.7773352728815480.611332363559226
1070.3782819362574570.7565638725149140.621718063742543
1080.3874164970507820.7748329941015640.612583502949218
1090.3500619445681230.7001238891362450.649938055431877
1100.2982799555987290.5965599111974570.701720044401271
1110.2961761748020930.5923523496041850.703823825197907
1120.2604180985415720.5208361970831440.739581901458428
1130.2213862194460100.4427724388920190.77861378055399
1140.1949986238893160.3899972477786320.805001376110684
1150.2102011084680870.4204022169361740.789798891531913
1160.1995546554920340.3991093109840690.800445344507966
1170.2116768592419580.4233537184839160.788323140758042
1180.356550183829160.713100367658320.64344981617084
1190.3393543633384150.6787087266768290.660645636661585
1200.4184886959947140.8369773919894280.581511304005286
1210.4240743098167650.848148619633530.575925690183235
1220.3943126392915610.7886252785831220.605687360708439
1230.3558506417794460.7117012835588910.644149358220554
1240.2945134578428830.5890269156857650.705486542157117
1250.4152905241806610.8305810483613220.584709475819339
1260.6992356782619840.6015286434760330.300764321738016
1270.8373316129051110.3253367741897780.162668387094889
1280.8493601443051170.3012797113897660.150639855694883
1290.7867392242449820.4265215515100370.213260775755018
1300.7209999005587480.5580001988825030.279000099441252
1310.6466698517962460.7066602964075070.353330148203754
1320.8594423346868930.2811153306262140.140557665313107
1330.8829879338536740.2340241322926510.117012066146326
1340.8092268848810610.3815462302378780.190773115118939
1350.886474417303870.2270511653922600.113525582696130
1360.8500557791906630.2998884416186730.149944220809337
1370.7513036391789370.4973927216421250.248696360821063

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.186501488894151 & 0.373002977788302 & 0.813498511105849 \tabularnewline
10 & 0.353060448573535 & 0.706120897147071 & 0.646939551426465 \tabularnewline
11 & 0.501615643978539 & 0.996768712042922 & 0.498384356021461 \tabularnewline
12 & 0.456141778986611 & 0.912283557973222 & 0.543858221013389 \tabularnewline
13 & 0.341848329031239 & 0.683696658062478 & 0.658151670968761 \tabularnewline
14 & 0.302375186454222 & 0.604750372908445 & 0.697624813545778 \tabularnewline
15 & 0.227141246776649 & 0.454282493553298 & 0.772858753223351 \tabularnewline
16 & 0.271842195390626 & 0.543684390781251 & 0.728157804609374 \tabularnewline
17 & 0.197408339456888 & 0.394816678913776 & 0.802591660543112 \tabularnewline
18 & 0.168520835465849 & 0.337041670931698 & 0.831479164534151 \tabularnewline
19 & 0.137718007201575 & 0.275436014403149 & 0.862281992798425 \tabularnewline
20 & 0.134637111860867 & 0.269274223721735 & 0.865362888139133 \tabularnewline
21 & 0.207284520928601 & 0.414569041857203 & 0.792715479071399 \tabularnewline
22 & 0.309124104105386 & 0.618248208210772 & 0.690875895894614 \tabularnewline
23 & 0.245163638456262 & 0.490327276912523 & 0.754836361543738 \tabularnewline
24 & 0.504608367211959 & 0.990783265576082 & 0.495391632788041 \tabularnewline
25 & 0.44135383098551 & 0.88270766197102 & 0.55864616901449 \tabularnewline
26 & 0.391963069553757 & 0.783926139107514 & 0.608036930446243 \tabularnewline
27 & 0.362786373024775 & 0.725572746049551 & 0.637213626975225 \tabularnewline
28 & 0.344286196209513 & 0.688572392419026 & 0.655713803790487 \tabularnewline
29 & 0.286490464382277 & 0.572980928764554 & 0.713509535617723 \tabularnewline
30 & 0.311009539342007 & 0.622019078684014 & 0.688990460657993 \tabularnewline
31 & 0.261223590547796 & 0.522447181095592 & 0.738776409452204 \tabularnewline
32 & 0.283594596873235 & 0.56718919374647 & 0.716405403126765 \tabularnewline
33 & 0.251761111191965 & 0.50352222238393 & 0.748238888808035 \tabularnewline
34 & 0.221662691403237 & 0.443325382806474 & 0.778337308596763 \tabularnewline
35 & 0.183855957382227 & 0.367711914764454 & 0.816144042617773 \tabularnewline
36 & 0.398713669108455 & 0.79742733821691 & 0.601286330891545 \tabularnewline
37 & 0.369876421347216 & 0.739752842694431 & 0.630123578652784 \tabularnewline
38 & 0.325239787791429 & 0.650479575582857 & 0.674760212208571 \tabularnewline
39 & 0.419873608351952 & 0.839747216703905 & 0.580126391648048 \tabularnewline
40 & 0.511221042799263 & 0.977557914401473 & 0.488778957200737 \tabularnewline
41 & 0.478325066757657 & 0.956650133515314 & 0.521674933242343 \tabularnewline
42 & 0.579403940074847 & 0.841192119850305 & 0.420596059925153 \tabularnewline
43 & 0.757904789794127 & 0.484190420411747 & 0.242095210205873 \tabularnewline
44 & 0.827330318675126 & 0.345339362649749 & 0.172669681324875 \tabularnewline
45 & 0.793596844708781 & 0.412806310582437 & 0.206403155291219 \tabularnewline
46 & 0.766424267711638 & 0.467151464576725 & 0.233575732288362 \tabularnewline
47 & 0.776656224637752 & 0.446687550724495 & 0.223343775362248 \tabularnewline
48 & 0.739425527897213 & 0.521148944205574 & 0.260574472102787 \tabularnewline
49 & 0.706486814146312 & 0.587026371707376 & 0.293513185853688 \tabularnewline
50 & 0.664762377119884 & 0.670475245760232 & 0.335237622880116 \tabularnewline
51 & 0.624559832674022 & 0.750880334651956 & 0.375440167325978 \tabularnewline
52 & 0.587794634416928 & 0.824410731166144 & 0.412205365583072 \tabularnewline
53 & 0.537828566342472 & 0.924342867315055 & 0.462171433657528 \tabularnewline
54 & 0.500909833980785 & 0.99818033203843 & 0.499090166019215 \tabularnewline
55 & 0.477195314296544 & 0.954390628593087 & 0.522804685703456 \tabularnewline
56 & 0.432250285210792 & 0.864500570421584 & 0.567749714789208 \tabularnewline
57 & 0.511726601156793 & 0.976546797686414 & 0.488273398843207 \tabularnewline
58 & 0.507474085438926 & 0.985051829122147 & 0.492525914561074 \tabularnewline
59 & 0.464696866645624 & 0.929393733291248 & 0.535303133354376 \tabularnewline
60 & 0.469574351449776 & 0.939148702899553 & 0.530425648550224 \tabularnewline
61 & 0.44127465636164 & 0.88254931272328 & 0.55872534363836 \tabularnewline
62 & 0.397452164887521 & 0.794904329775042 & 0.602547835112479 \tabularnewline
63 & 0.359014969275343 & 0.718029938550686 & 0.640985030724657 \tabularnewline
64 & 0.320323599579638 & 0.640647199159276 & 0.679676400420362 \tabularnewline
65 & 0.312657609086325 & 0.62531521817265 & 0.687342390913675 \tabularnewline
66 & 0.329003767657747 & 0.658007535315495 & 0.670996232342253 \tabularnewline
67 & 0.30978999763666 & 0.61957999527332 & 0.69021000236334 \tabularnewline
68 & 0.299518675352939 & 0.599037350705879 & 0.70048132464706 \tabularnewline
69 & 0.368599399064976 & 0.737198798129951 & 0.631400600935024 \tabularnewline
70 & 0.355700090612370 & 0.711400181224739 & 0.64429990938763 \tabularnewline
71 & 0.363382563212912 & 0.726765126425824 & 0.636617436787088 \tabularnewline
72 & 0.392839437585047 & 0.785678875170093 & 0.607160562414953 \tabularnewline
73 & 0.368858414111889 & 0.737716828223778 & 0.631141585888111 \tabularnewline
74 & 0.325320319223108 & 0.650640638446216 & 0.674679680776892 \tabularnewline
75 & 0.285549974550675 & 0.571099949101349 & 0.714450025449325 \tabularnewline
76 & 0.335610843074452 & 0.671221686148904 & 0.664389156925548 \tabularnewline
77 & 0.292885948500654 & 0.585771897001308 & 0.707114051499346 \tabularnewline
78 & 0.253654221909635 & 0.507308443819270 & 0.746345778090365 \tabularnewline
79 & 0.300051285685775 & 0.600102571371551 & 0.699948714314225 \tabularnewline
80 & 0.306299958797464 & 0.612599917594927 & 0.693700041202536 \tabularnewline
81 & 0.275704063127611 & 0.551408126255222 & 0.724295936872389 \tabularnewline
82 & 0.265048689731732 & 0.530097379463463 & 0.734951310268268 \tabularnewline
83 & 0.226278955541811 & 0.452557911083622 & 0.773721044458189 \tabularnewline
84 & 0.198677368438761 & 0.397354736877522 & 0.801322631561239 \tabularnewline
85 & 0.385558463206689 & 0.771116926413377 & 0.614441536793311 \tabularnewline
86 & 0.47467029804007 & 0.94934059608014 & 0.52532970195993 \tabularnewline
87 & 0.425748614010425 & 0.851497228020851 & 0.574251385989575 \tabularnewline
88 & 0.394254568562871 & 0.788509137125742 & 0.605745431437129 \tabularnewline
89 & 0.348410904908243 & 0.696821809816486 & 0.651589095091757 \tabularnewline
90 & 0.340351566405115 & 0.68070313281023 & 0.659648433594885 \tabularnewline
91 & 0.347653874970436 & 0.695307749940873 & 0.652346125029564 \tabularnewline
92 & 0.504765241349624 & 0.990469517300752 & 0.495234758650376 \tabularnewline
93 & 0.48779467705981 & 0.97558935411962 & 0.51220532294019 \tabularnewline
94 & 0.471641989562357 & 0.943283979124713 & 0.528358010437643 \tabularnewline
95 & 0.512096919974785 & 0.97580616005043 & 0.487903080025215 \tabularnewline
96 & 0.572226925128758 & 0.855546149742484 & 0.427773074871242 \tabularnewline
97 & 0.673595527645624 & 0.652808944708751 & 0.326404472354376 \tabularnewline
98 & 0.648611232442692 & 0.702777535114615 & 0.351388767557308 \tabularnewline
99 & 0.630102716388975 & 0.73979456722205 & 0.369897283611025 \tabularnewline
100 & 0.653567463275449 & 0.692865073449103 & 0.346432536724551 \tabularnewline
101 & 0.614230698242151 & 0.771538603515698 & 0.385769301757849 \tabularnewline
102 & 0.571329242256851 & 0.857341515486297 & 0.428670757743149 \tabularnewline
103 & 0.518673947526787 & 0.962652104946426 & 0.481326052473213 \tabularnewline
104 & 0.493979991547611 & 0.987959983095222 & 0.506020008452389 \tabularnewline
105 & 0.438725221399722 & 0.877450442799445 & 0.561274778600278 \tabularnewline
106 & 0.388667636440774 & 0.777335272881548 & 0.611332363559226 \tabularnewline
107 & 0.378281936257457 & 0.756563872514914 & 0.621718063742543 \tabularnewline
108 & 0.387416497050782 & 0.774832994101564 & 0.612583502949218 \tabularnewline
109 & 0.350061944568123 & 0.700123889136245 & 0.649938055431877 \tabularnewline
110 & 0.298279955598729 & 0.596559911197457 & 0.701720044401271 \tabularnewline
111 & 0.296176174802093 & 0.592352349604185 & 0.703823825197907 \tabularnewline
112 & 0.260418098541572 & 0.520836197083144 & 0.739581901458428 \tabularnewline
113 & 0.221386219446010 & 0.442772438892019 & 0.77861378055399 \tabularnewline
114 & 0.194998623889316 & 0.389997247778632 & 0.805001376110684 \tabularnewline
115 & 0.210201108468087 & 0.420402216936174 & 0.789798891531913 \tabularnewline
116 & 0.199554655492034 & 0.399109310984069 & 0.800445344507966 \tabularnewline
117 & 0.211676859241958 & 0.423353718483916 & 0.788323140758042 \tabularnewline
118 & 0.35655018382916 & 0.71310036765832 & 0.64344981617084 \tabularnewline
119 & 0.339354363338415 & 0.678708726676829 & 0.660645636661585 \tabularnewline
120 & 0.418488695994714 & 0.836977391989428 & 0.581511304005286 \tabularnewline
121 & 0.424074309816765 & 0.84814861963353 & 0.575925690183235 \tabularnewline
122 & 0.394312639291561 & 0.788625278583122 & 0.605687360708439 \tabularnewline
123 & 0.355850641779446 & 0.711701283558891 & 0.644149358220554 \tabularnewline
124 & 0.294513457842883 & 0.589026915685765 & 0.705486542157117 \tabularnewline
125 & 0.415290524180661 & 0.830581048361322 & 0.584709475819339 \tabularnewline
126 & 0.699235678261984 & 0.601528643476033 & 0.300764321738016 \tabularnewline
127 & 0.837331612905111 & 0.325336774189778 & 0.162668387094889 \tabularnewline
128 & 0.849360144305117 & 0.301279711389766 & 0.150639855694883 \tabularnewline
129 & 0.786739224244982 & 0.426521551510037 & 0.213260775755018 \tabularnewline
130 & 0.720999900558748 & 0.558000198882503 & 0.279000099441252 \tabularnewline
131 & 0.646669851796246 & 0.706660296407507 & 0.353330148203754 \tabularnewline
132 & 0.859442334686893 & 0.281115330626214 & 0.140557665313107 \tabularnewline
133 & 0.882987933853674 & 0.234024132292651 & 0.117012066146326 \tabularnewline
134 & 0.809226884881061 & 0.381546230237878 & 0.190773115118939 \tabularnewline
135 & 0.88647441730387 & 0.227051165392260 & 0.113525582696130 \tabularnewline
136 & 0.850055779190663 & 0.299888441618673 & 0.149944220809337 \tabularnewline
137 & 0.751303639178937 & 0.497392721642125 & 0.248696360821063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.186501488894151[/C][C]0.373002977788302[/C][C]0.813498511105849[/C][/ROW]
[ROW][C]10[/C][C]0.353060448573535[/C][C]0.706120897147071[/C][C]0.646939551426465[/C][/ROW]
[ROW][C]11[/C][C]0.501615643978539[/C][C]0.996768712042922[/C][C]0.498384356021461[/C][/ROW]
[ROW][C]12[/C][C]0.456141778986611[/C][C]0.912283557973222[/C][C]0.543858221013389[/C][/ROW]
[ROW][C]13[/C][C]0.341848329031239[/C][C]0.683696658062478[/C][C]0.658151670968761[/C][/ROW]
[ROW][C]14[/C][C]0.302375186454222[/C][C]0.604750372908445[/C][C]0.697624813545778[/C][/ROW]
[ROW][C]15[/C][C]0.227141246776649[/C][C]0.454282493553298[/C][C]0.772858753223351[/C][/ROW]
[ROW][C]16[/C][C]0.271842195390626[/C][C]0.543684390781251[/C][C]0.728157804609374[/C][/ROW]
[ROW][C]17[/C][C]0.197408339456888[/C][C]0.394816678913776[/C][C]0.802591660543112[/C][/ROW]
[ROW][C]18[/C][C]0.168520835465849[/C][C]0.337041670931698[/C][C]0.831479164534151[/C][/ROW]
[ROW][C]19[/C][C]0.137718007201575[/C][C]0.275436014403149[/C][C]0.862281992798425[/C][/ROW]
[ROW][C]20[/C][C]0.134637111860867[/C][C]0.269274223721735[/C][C]0.865362888139133[/C][/ROW]
[ROW][C]21[/C][C]0.207284520928601[/C][C]0.414569041857203[/C][C]0.792715479071399[/C][/ROW]
[ROW][C]22[/C][C]0.309124104105386[/C][C]0.618248208210772[/C][C]0.690875895894614[/C][/ROW]
[ROW][C]23[/C][C]0.245163638456262[/C][C]0.490327276912523[/C][C]0.754836361543738[/C][/ROW]
[ROW][C]24[/C][C]0.504608367211959[/C][C]0.990783265576082[/C][C]0.495391632788041[/C][/ROW]
[ROW][C]25[/C][C]0.44135383098551[/C][C]0.88270766197102[/C][C]0.55864616901449[/C][/ROW]
[ROW][C]26[/C][C]0.391963069553757[/C][C]0.783926139107514[/C][C]0.608036930446243[/C][/ROW]
[ROW][C]27[/C][C]0.362786373024775[/C][C]0.725572746049551[/C][C]0.637213626975225[/C][/ROW]
[ROW][C]28[/C][C]0.344286196209513[/C][C]0.688572392419026[/C][C]0.655713803790487[/C][/ROW]
[ROW][C]29[/C][C]0.286490464382277[/C][C]0.572980928764554[/C][C]0.713509535617723[/C][/ROW]
[ROW][C]30[/C][C]0.311009539342007[/C][C]0.622019078684014[/C][C]0.688990460657993[/C][/ROW]
[ROW][C]31[/C][C]0.261223590547796[/C][C]0.522447181095592[/C][C]0.738776409452204[/C][/ROW]
[ROW][C]32[/C][C]0.283594596873235[/C][C]0.56718919374647[/C][C]0.716405403126765[/C][/ROW]
[ROW][C]33[/C][C]0.251761111191965[/C][C]0.50352222238393[/C][C]0.748238888808035[/C][/ROW]
[ROW][C]34[/C][C]0.221662691403237[/C][C]0.443325382806474[/C][C]0.778337308596763[/C][/ROW]
[ROW][C]35[/C][C]0.183855957382227[/C][C]0.367711914764454[/C][C]0.816144042617773[/C][/ROW]
[ROW][C]36[/C][C]0.398713669108455[/C][C]0.79742733821691[/C][C]0.601286330891545[/C][/ROW]
[ROW][C]37[/C][C]0.369876421347216[/C][C]0.739752842694431[/C][C]0.630123578652784[/C][/ROW]
[ROW][C]38[/C][C]0.325239787791429[/C][C]0.650479575582857[/C][C]0.674760212208571[/C][/ROW]
[ROW][C]39[/C][C]0.419873608351952[/C][C]0.839747216703905[/C][C]0.580126391648048[/C][/ROW]
[ROW][C]40[/C][C]0.511221042799263[/C][C]0.977557914401473[/C][C]0.488778957200737[/C][/ROW]
[ROW][C]41[/C][C]0.478325066757657[/C][C]0.956650133515314[/C][C]0.521674933242343[/C][/ROW]
[ROW][C]42[/C][C]0.579403940074847[/C][C]0.841192119850305[/C][C]0.420596059925153[/C][/ROW]
[ROW][C]43[/C][C]0.757904789794127[/C][C]0.484190420411747[/C][C]0.242095210205873[/C][/ROW]
[ROW][C]44[/C][C]0.827330318675126[/C][C]0.345339362649749[/C][C]0.172669681324875[/C][/ROW]
[ROW][C]45[/C][C]0.793596844708781[/C][C]0.412806310582437[/C][C]0.206403155291219[/C][/ROW]
[ROW][C]46[/C][C]0.766424267711638[/C][C]0.467151464576725[/C][C]0.233575732288362[/C][/ROW]
[ROW][C]47[/C][C]0.776656224637752[/C][C]0.446687550724495[/C][C]0.223343775362248[/C][/ROW]
[ROW][C]48[/C][C]0.739425527897213[/C][C]0.521148944205574[/C][C]0.260574472102787[/C][/ROW]
[ROW][C]49[/C][C]0.706486814146312[/C][C]0.587026371707376[/C][C]0.293513185853688[/C][/ROW]
[ROW][C]50[/C][C]0.664762377119884[/C][C]0.670475245760232[/C][C]0.335237622880116[/C][/ROW]
[ROW][C]51[/C][C]0.624559832674022[/C][C]0.750880334651956[/C][C]0.375440167325978[/C][/ROW]
[ROW][C]52[/C][C]0.587794634416928[/C][C]0.824410731166144[/C][C]0.412205365583072[/C][/ROW]
[ROW][C]53[/C][C]0.537828566342472[/C][C]0.924342867315055[/C][C]0.462171433657528[/C][/ROW]
[ROW][C]54[/C][C]0.500909833980785[/C][C]0.99818033203843[/C][C]0.499090166019215[/C][/ROW]
[ROW][C]55[/C][C]0.477195314296544[/C][C]0.954390628593087[/C][C]0.522804685703456[/C][/ROW]
[ROW][C]56[/C][C]0.432250285210792[/C][C]0.864500570421584[/C][C]0.567749714789208[/C][/ROW]
[ROW][C]57[/C][C]0.511726601156793[/C][C]0.976546797686414[/C][C]0.488273398843207[/C][/ROW]
[ROW][C]58[/C][C]0.507474085438926[/C][C]0.985051829122147[/C][C]0.492525914561074[/C][/ROW]
[ROW][C]59[/C][C]0.464696866645624[/C][C]0.929393733291248[/C][C]0.535303133354376[/C][/ROW]
[ROW][C]60[/C][C]0.469574351449776[/C][C]0.939148702899553[/C][C]0.530425648550224[/C][/ROW]
[ROW][C]61[/C][C]0.44127465636164[/C][C]0.88254931272328[/C][C]0.55872534363836[/C][/ROW]
[ROW][C]62[/C][C]0.397452164887521[/C][C]0.794904329775042[/C][C]0.602547835112479[/C][/ROW]
[ROW][C]63[/C][C]0.359014969275343[/C][C]0.718029938550686[/C][C]0.640985030724657[/C][/ROW]
[ROW][C]64[/C][C]0.320323599579638[/C][C]0.640647199159276[/C][C]0.679676400420362[/C][/ROW]
[ROW][C]65[/C][C]0.312657609086325[/C][C]0.62531521817265[/C][C]0.687342390913675[/C][/ROW]
[ROW][C]66[/C][C]0.329003767657747[/C][C]0.658007535315495[/C][C]0.670996232342253[/C][/ROW]
[ROW][C]67[/C][C]0.30978999763666[/C][C]0.61957999527332[/C][C]0.69021000236334[/C][/ROW]
[ROW][C]68[/C][C]0.299518675352939[/C][C]0.599037350705879[/C][C]0.70048132464706[/C][/ROW]
[ROW][C]69[/C][C]0.368599399064976[/C][C]0.737198798129951[/C][C]0.631400600935024[/C][/ROW]
[ROW][C]70[/C][C]0.355700090612370[/C][C]0.711400181224739[/C][C]0.64429990938763[/C][/ROW]
[ROW][C]71[/C][C]0.363382563212912[/C][C]0.726765126425824[/C][C]0.636617436787088[/C][/ROW]
[ROW][C]72[/C][C]0.392839437585047[/C][C]0.785678875170093[/C][C]0.607160562414953[/C][/ROW]
[ROW][C]73[/C][C]0.368858414111889[/C][C]0.737716828223778[/C][C]0.631141585888111[/C][/ROW]
[ROW][C]74[/C][C]0.325320319223108[/C][C]0.650640638446216[/C][C]0.674679680776892[/C][/ROW]
[ROW][C]75[/C][C]0.285549974550675[/C][C]0.571099949101349[/C][C]0.714450025449325[/C][/ROW]
[ROW][C]76[/C][C]0.335610843074452[/C][C]0.671221686148904[/C][C]0.664389156925548[/C][/ROW]
[ROW][C]77[/C][C]0.292885948500654[/C][C]0.585771897001308[/C][C]0.707114051499346[/C][/ROW]
[ROW][C]78[/C][C]0.253654221909635[/C][C]0.507308443819270[/C][C]0.746345778090365[/C][/ROW]
[ROW][C]79[/C][C]0.300051285685775[/C][C]0.600102571371551[/C][C]0.699948714314225[/C][/ROW]
[ROW][C]80[/C][C]0.306299958797464[/C][C]0.612599917594927[/C][C]0.693700041202536[/C][/ROW]
[ROW][C]81[/C][C]0.275704063127611[/C][C]0.551408126255222[/C][C]0.724295936872389[/C][/ROW]
[ROW][C]82[/C][C]0.265048689731732[/C][C]0.530097379463463[/C][C]0.734951310268268[/C][/ROW]
[ROW][C]83[/C][C]0.226278955541811[/C][C]0.452557911083622[/C][C]0.773721044458189[/C][/ROW]
[ROW][C]84[/C][C]0.198677368438761[/C][C]0.397354736877522[/C][C]0.801322631561239[/C][/ROW]
[ROW][C]85[/C][C]0.385558463206689[/C][C]0.771116926413377[/C][C]0.614441536793311[/C][/ROW]
[ROW][C]86[/C][C]0.47467029804007[/C][C]0.94934059608014[/C][C]0.52532970195993[/C][/ROW]
[ROW][C]87[/C][C]0.425748614010425[/C][C]0.851497228020851[/C][C]0.574251385989575[/C][/ROW]
[ROW][C]88[/C][C]0.394254568562871[/C][C]0.788509137125742[/C][C]0.605745431437129[/C][/ROW]
[ROW][C]89[/C][C]0.348410904908243[/C][C]0.696821809816486[/C][C]0.651589095091757[/C][/ROW]
[ROW][C]90[/C][C]0.340351566405115[/C][C]0.68070313281023[/C][C]0.659648433594885[/C][/ROW]
[ROW][C]91[/C][C]0.347653874970436[/C][C]0.695307749940873[/C][C]0.652346125029564[/C][/ROW]
[ROW][C]92[/C][C]0.504765241349624[/C][C]0.990469517300752[/C][C]0.495234758650376[/C][/ROW]
[ROW][C]93[/C][C]0.48779467705981[/C][C]0.97558935411962[/C][C]0.51220532294019[/C][/ROW]
[ROW][C]94[/C][C]0.471641989562357[/C][C]0.943283979124713[/C][C]0.528358010437643[/C][/ROW]
[ROW][C]95[/C][C]0.512096919974785[/C][C]0.97580616005043[/C][C]0.487903080025215[/C][/ROW]
[ROW][C]96[/C][C]0.572226925128758[/C][C]0.855546149742484[/C][C]0.427773074871242[/C][/ROW]
[ROW][C]97[/C][C]0.673595527645624[/C][C]0.652808944708751[/C][C]0.326404472354376[/C][/ROW]
[ROW][C]98[/C][C]0.648611232442692[/C][C]0.702777535114615[/C][C]0.351388767557308[/C][/ROW]
[ROW][C]99[/C][C]0.630102716388975[/C][C]0.73979456722205[/C][C]0.369897283611025[/C][/ROW]
[ROW][C]100[/C][C]0.653567463275449[/C][C]0.692865073449103[/C][C]0.346432536724551[/C][/ROW]
[ROW][C]101[/C][C]0.614230698242151[/C][C]0.771538603515698[/C][C]0.385769301757849[/C][/ROW]
[ROW][C]102[/C][C]0.571329242256851[/C][C]0.857341515486297[/C][C]0.428670757743149[/C][/ROW]
[ROW][C]103[/C][C]0.518673947526787[/C][C]0.962652104946426[/C][C]0.481326052473213[/C][/ROW]
[ROW][C]104[/C][C]0.493979991547611[/C][C]0.987959983095222[/C][C]0.506020008452389[/C][/ROW]
[ROW][C]105[/C][C]0.438725221399722[/C][C]0.877450442799445[/C][C]0.561274778600278[/C][/ROW]
[ROW][C]106[/C][C]0.388667636440774[/C][C]0.777335272881548[/C][C]0.611332363559226[/C][/ROW]
[ROW][C]107[/C][C]0.378281936257457[/C][C]0.756563872514914[/C][C]0.621718063742543[/C][/ROW]
[ROW][C]108[/C][C]0.387416497050782[/C][C]0.774832994101564[/C][C]0.612583502949218[/C][/ROW]
[ROW][C]109[/C][C]0.350061944568123[/C][C]0.700123889136245[/C][C]0.649938055431877[/C][/ROW]
[ROW][C]110[/C][C]0.298279955598729[/C][C]0.596559911197457[/C][C]0.701720044401271[/C][/ROW]
[ROW][C]111[/C][C]0.296176174802093[/C][C]0.592352349604185[/C][C]0.703823825197907[/C][/ROW]
[ROW][C]112[/C][C]0.260418098541572[/C][C]0.520836197083144[/C][C]0.739581901458428[/C][/ROW]
[ROW][C]113[/C][C]0.221386219446010[/C][C]0.442772438892019[/C][C]0.77861378055399[/C][/ROW]
[ROW][C]114[/C][C]0.194998623889316[/C][C]0.389997247778632[/C][C]0.805001376110684[/C][/ROW]
[ROW][C]115[/C][C]0.210201108468087[/C][C]0.420402216936174[/C][C]0.789798891531913[/C][/ROW]
[ROW][C]116[/C][C]0.199554655492034[/C][C]0.399109310984069[/C][C]0.800445344507966[/C][/ROW]
[ROW][C]117[/C][C]0.211676859241958[/C][C]0.423353718483916[/C][C]0.788323140758042[/C][/ROW]
[ROW][C]118[/C][C]0.35655018382916[/C][C]0.71310036765832[/C][C]0.64344981617084[/C][/ROW]
[ROW][C]119[/C][C]0.339354363338415[/C][C]0.678708726676829[/C][C]0.660645636661585[/C][/ROW]
[ROW][C]120[/C][C]0.418488695994714[/C][C]0.836977391989428[/C][C]0.581511304005286[/C][/ROW]
[ROW][C]121[/C][C]0.424074309816765[/C][C]0.84814861963353[/C][C]0.575925690183235[/C][/ROW]
[ROW][C]122[/C][C]0.394312639291561[/C][C]0.788625278583122[/C][C]0.605687360708439[/C][/ROW]
[ROW][C]123[/C][C]0.355850641779446[/C][C]0.711701283558891[/C][C]0.644149358220554[/C][/ROW]
[ROW][C]124[/C][C]0.294513457842883[/C][C]0.589026915685765[/C][C]0.705486542157117[/C][/ROW]
[ROW][C]125[/C][C]0.415290524180661[/C][C]0.830581048361322[/C][C]0.584709475819339[/C][/ROW]
[ROW][C]126[/C][C]0.699235678261984[/C][C]0.601528643476033[/C][C]0.300764321738016[/C][/ROW]
[ROW][C]127[/C][C]0.837331612905111[/C][C]0.325336774189778[/C][C]0.162668387094889[/C][/ROW]
[ROW][C]128[/C][C]0.849360144305117[/C][C]0.301279711389766[/C][C]0.150639855694883[/C][/ROW]
[ROW][C]129[/C][C]0.786739224244982[/C][C]0.426521551510037[/C][C]0.213260775755018[/C][/ROW]
[ROW][C]130[/C][C]0.720999900558748[/C][C]0.558000198882503[/C][C]0.279000099441252[/C][/ROW]
[ROW][C]131[/C][C]0.646669851796246[/C][C]0.706660296407507[/C][C]0.353330148203754[/C][/ROW]
[ROW][C]132[/C][C]0.859442334686893[/C][C]0.281115330626214[/C][C]0.140557665313107[/C][/ROW]
[ROW][C]133[/C][C]0.882987933853674[/C][C]0.234024132292651[/C][C]0.117012066146326[/C][/ROW]
[ROW][C]134[/C][C]0.809226884881061[/C][C]0.381546230237878[/C][C]0.190773115118939[/C][/ROW]
[ROW][C]135[/C][C]0.88647441730387[/C][C]0.227051165392260[/C][C]0.113525582696130[/C][/ROW]
[ROW][C]136[/C][C]0.850055779190663[/C][C]0.299888441618673[/C][C]0.149944220809337[/C][/ROW]
[ROW][C]137[/C][C]0.751303639178937[/C][C]0.497392721642125[/C][C]0.248696360821063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1865014888941510.3730029777883020.813498511105849
100.3530604485735350.7061208971470710.646939551426465
110.5016156439785390.9967687120429220.498384356021461
120.4561417789866110.9122835579732220.543858221013389
130.3418483290312390.6836966580624780.658151670968761
140.3023751864542220.6047503729084450.697624813545778
150.2271412467766490.4542824935532980.772858753223351
160.2718421953906260.5436843907812510.728157804609374
170.1974083394568880.3948166789137760.802591660543112
180.1685208354658490.3370416709316980.831479164534151
190.1377180072015750.2754360144031490.862281992798425
200.1346371118608670.2692742237217350.865362888139133
210.2072845209286010.4145690418572030.792715479071399
220.3091241041053860.6182482082107720.690875895894614
230.2451636384562620.4903272769125230.754836361543738
240.5046083672119590.9907832655760820.495391632788041
250.441353830985510.882707661971020.55864616901449
260.3919630695537570.7839261391075140.608036930446243
270.3627863730247750.7255727460495510.637213626975225
280.3442861962095130.6885723924190260.655713803790487
290.2864904643822770.5729809287645540.713509535617723
300.3110095393420070.6220190786840140.688990460657993
310.2612235905477960.5224471810955920.738776409452204
320.2835945968732350.567189193746470.716405403126765
330.2517611111919650.503522222383930.748238888808035
340.2216626914032370.4433253828064740.778337308596763
350.1838559573822270.3677119147644540.816144042617773
360.3987136691084550.797427338216910.601286330891545
370.3698764213472160.7397528426944310.630123578652784
380.3252397877914290.6504795755828570.674760212208571
390.4198736083519520.8397472167039050.580126391648048
400.5112210427992630.9775579144014730.488778957200737
410.4783250667576570.9566501335153140.521674933242343
420.5794039400748470.8411921198503050.420596059925153
430.7579047897941270.4841904204117470.242095210205873
440.8273303186751260.3453393626497490.172669681324875
450.7935968447087810.4128063105824370.206403155291219
460.7664242677116380.4671514645767250.233575732288362
470.7766562246377520.4466875507244950.223343775362248
480.7394255278972130.5211489442055740.260574472102787
490.7064868141463120.5870263717073760.293513185853688
500.6647623771198840.6704752457602320.335237622880116
510.6245598326740220.7508803346519560.375440167325978
520.5877946344169280.8244107311661440.412205365583072
530.5378285663424720.9243428673150550.462171433657528
540.5009098339807850.998180332038430.499090166019215
550.4771953142965440.9543906285930870.522804685703456
560.4322502852107920.8645005704215840.567749714789208
570.5117266011567930.9765467976864140.488273398843207
580.5074740854389260.9850518291221470.492525914561074
590.4646968666456240.9293937332912480.535303133354376
600.4695743514497760.9391487028995530.530425648550224
610.441274656361640.882549312723280.55872534363836
620.3974521648875210.7949043297750420.602547835112479
630.3590149692753430.7180299385506860.640985030724657
640.3203235995796380.6406471991592760.679676400420362
650.3126576090863250.625315218172650.687342390913675
660.3290037676577470.6580075353154950.670996232342253
670.309789997636660.619579995273320.69021000236334
680.2995186753529390.5990373507058790.70048132464706
690.3685993990649760.7371987981299510.631400600935024
700.3557000906123700.7114001812247390.64429990938763
710.3633825632129120.7267651264258240.636617436787088
720.3928394375850470.7856788751700930.607160562414953
730.3688584141118890.7377168282237780.631141585888111
740.3253203192231080.6506406384462160.674679680776892
750.2855499745506750.5710999491013490.714450025449325
760.3356108430744520.6712216861489040.664389156925548
770.2928859485006540.5857718970013080.707114051499346
780.2536542219096350.5073084438192700.746345778090365
790.3000512856857750.6001025713715510.699948714314225
800.3062999587974640.6125999175949270.693700041202536
810.2757040631276110.5514081262552220.724295936872389
820.2650486897317320.5300973794634630.734951310268268
830.2262789555418110.4525579110836220.773721044458189
840.1986773684387610.3973547368775220.801322631561239
850.3855584632066890.7711169264133770.614441536793311
860.474670298040070.949340596080140.52532970195993
870.4257486140104250.8514972280208510.574251385989575
880.3942545685628710.7885091371257420.605745431437129
890.3484109049082430.6968218098164860.651589095091757
900.3403515664051150.680703132810230.659648433594885
910.3476538749704360.6953077499408730.652346125029564
920.5047652413496240.9904695173007520.495234758650376
930.487794677059810.975589354119620.51220532294019
940.4716419895623570.9432839791247130.528358010437643
950.5120969199747850.975806160050430.487903080025215
960.5722269251287580.8555461497424840.427773074871242
970.6735955276456240.6528089447087510.326404472354376
980.6486112324426920.7027775351146150.351388767557308
990.6301027163889750.739794567222050.369897283611025
1000.6535674632754490.6928650734491030.346432536724551
1010.6142306982421510.7715386035156980.385769301757849
1020.5713292422568510.8573415154862970.428670757743149
1030.5186739475267870.9626521049464260.481326052473213
1040.4939799915476110.9879599830952220.506020008452389
1050.4387252213997220.8774504427994450.561274778600278
1060.3886676364407740.7773352728815480.611332363559226
1070.3782819362574570.7565638725149140.621718063742543
1080.3874164970507820.7748329941015640.612583502949218
1090.3500619445681230.7001238891362450.649938055431877
1100.2982799555987290.5965599111974570.701720044401271
1110.2961761748020930.5923523496041850.703823825197907
1120.2604180985415720.5208361970831440.739581901458428
1130.2213862194460100.4427724388920190.77861378055399
1140.1949986238893160.3899972477786320.805001376110684
1150.2102011084680870.4204022169361740.789798891531913
1160.1995546554920340.3991093109840690.800445344507966
1170.2116768592419580.4233537184839160.788323140758042
1180.356550183829160.713100367658320.64344981617084
1190.3393543633384150.6787087266768290.660645636661585
1200.4184886959947140.8369773919894280.581511304005286
1210.4240743098167650.848148619633530.575925690183235
1220.3943126392915610.7886252785831220.605687360708439
1230.3558506417794460.7117012835588910.644149358220554
1240.2945134578428830.5890269156857650.705486542157117
1250.4152905241806610.8305810483613220.584709475819339
1260.6992356782619840.6015286434760330.300764321738016
1270.8373316129051110.3253367741897780.162668387094889
1280.8493601443051170.3012797113897660.150639855694883
1290.7867392242449820.4265215515100370.213260775755018
1300.7209999005587480.5580001988825030.279000099441252
1310.6466698517962460.7066602964075070.353330148203754
1320.8594423346868930.2811153306262140.140557665313107
1330.8829879338536740.2340241322926510.117012066146326
1340.8092268848810610.3815462302378780.190773115118939
1350.886474417303870.2270511653922600.113525582696130
1360.8500557791906630.2998884416186730.149944220809337
1370.7513036391789370.4973927216421250.248696360821063






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109737&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109737&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109737&T=6

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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 level00OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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