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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 31 Oct 2012 11:27:23 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/31/t13516972566s5yjbvhbs2o5wt.htm/, Retrieved Sun, 28 Apr 2024 22:34:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185447, Retrieved Sun, 28 Apr 2024 22:34:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-       [Multiple Regression] [mu lin reg] [2012-10-31 10:14:49] [2f324ead08cc3849e52bae5d3f3d905a]
-   PD      [Multiple Regression] [x5] [2012-10-31 15:27:23] [e357aba3893873b930815b56a53f1005] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	53	32
39	32	16	11	18	86	51
30	35	19	15	11	66	42
31	33	15	6	12	67	41
34	37	14	13	16	76	46
35	29	13	10	18	78	47
39	31	19	12	14	53	37
34	36	15	14	14	80	49
36	35	14	12	15	74	45
37	38	15	6	15	76	47
38	31	16	10	17	79	49
36	34	16	12	19	54	33
38	35	16	12	10	67	42
39	38	16	11	16	54	33
33	37	17	15	18	87	53
32	33	15	12	14	58	36
36	32	15	10	14	75	45
38	38	20	12	17	88	54
39	38	18	11	14	64	41
32	32	16	12	16	57	36
32	33	16	11	18	66	41
31	31	16	12	11	68	44
39	38	19	13	14	54	33
37	39	16	11	12	56	37
39	32	17	9	17	86	52
41	32	17	13	9	80	47
36	35	16	10	16	76	43
33	37	15	14	14	69	44
33	33	16	12	15	78	45
34	33	14	10	11	67	44
31	28	15	12	16	80	49
27	32	12	8	13	54	33
37	31	14	10	17	71	43
34	37	16	12	15	84	54
34	30	14	12	14	74	42
32	33	7	7	16	71	44
29	31	10	6	9	63	37
36	33	14	12	15	71	43
29	31	16	10	17	76	46
35	33	16	10	13	69	42
37	32	16	10	15	74	45
34	33	14	12	16	75	44
38	32	20	15	16	54	33
35	33	14	10	12	52	31
38	28	14	10	12	69	42
37	35	11	12	11	68	40
38	39	14	13	15	65	43
33	34	15	11	15	75	46
36	38	16	11	17	74	42
38	32	14	12	13	75	45
32	38	16	14	16	72	44
32	30	14	10	14	67	40
32	33	12	12	11	63	37
34	38	16	13	12	62	46
32	32	9	5	12	63	36
37	32	14	6	15	76	47
39	34	16	12	16	74	45
29	34	16	12	15	67	42
37	36	15	11	12	73	43
35	34	16	10	12	70	43
30	28	12	7	8	53	32
38	34	16	12	13	77	45
34	35	16	14	11	77	45
31	35	14	11	14	52	31
34	31	16	12	15	54	33
35	37	17	13	10	80	49
36	35	18	14	11	66	42
30	27	18	11	12	73	41
39	40	12	12	15	63	38
35	37	16	12	15	69	42
38	36	10	8	14	67	44
31	38	14	11	16	54	33
34	39	18	14	15	81	48
38	41	18	14	15	69	40
34	27	16	12	13	84	50
39	30	17	9	12	80	49
37	37	16	13	17	70	43
34	31	16	11	13	69	44
28	31	13	12	15	77	47
37	27	16	12	13	54	33
33	36	16	12	15	79	46
37	38	20	12	16	30	0
35	37	16	12	15	71	45
37	33	15	12	16	73	43
32	34	15	11	15	72	44
33	31	16	10	14	77	47
38	39	14	9	15	75	45
33	34	16	12	14	69	42
29	32	16	12	13	54	33
33	33	15	12	7	70	43
31	36	12	9	17	73	46
36	32	17	15	13	54	33
35	41	16	12	15	77	46
32	28	15	12	14	82	48
29	30	13	12	13	80	47
39	36	16	10	16	80	47
37	35	16	13	12	69	43
35	31	16	9	14	78	46
37	34	16	12	17	81	48
32	36	14	10	15	76	46
38	36	16	14	17	76	45
37	35	16	11	12	73	45
36	37	20	15	16	85	52
32	28	15	11	11	66	42
33	39	16	11	15	79	47
40	32	13	12	9	68	41
38	35	17	12	16	76	47
41	39	16	12	15	71	43
36	35	16	11	10	54	33
43	42	12	7	10	46	30
30	34	16	12	15	82	49
31	33	16	14	11	74	44
32	41	17	11	13	88	55
32	33	13	11	14	38	11
37	34	12	10	18	76	47
37	32	18	13	16	86	53
33	40	14	13	14	54	33
34	40	14	8	14	70	44
33	35	13	11	14	69	42
38	36	16	12	14	90	55
33	37	13	11	12	54	33
31	27	16	13	14	76	46
38	39	13	12	15	89	54
37	38	16	14	15	76	47
33	31	15	13	15	73	45
31	33	16	15	13	79	47
39	32	15	10	17	90	55
44	39	17	11	17	74	44
33	36	15	9	19	81	53
35	33	12	11	15	72	44
32	33	16	10	13	71	42
28	32	10	11	9	66	40
40	37	16	8	15	77	46
27	30	12	11	15	65	40
37	38	14	12	15	74	46
32	29	15	12	16	82	53
28	22	13	9	11	54	33
34	35	15	11	14	63	42
30	35	11	10	11	54	35
35	34	12	8	15	64	40
31	35	8	9	13	69	41
32	34	16	8	15	54	33
30	34	15	9	16	84	51
30	35	17	15	14	86	53
31	23	16	11	15	77	46
40	31	10	8	16	89	55
32	27	18	13	16	76	47
36	36	13	12	11	60	38
32	31	16	12	12	75	46
35	32	13	9	9	73	46
38	39	10	7	16	85	53
42	37	15	13	13	79	47
34	38	16	9	16	71	41
35	39	16	6	12	72	44
35	34	14	8	9	69	43
33	31	10	8	13	78	51
36	32	17	15	13	54	33
32	37	13	6	14	69	43
33	36	15	9	19	81	53
34	32	16	11	13	84	51
32	35	12	8	12	84	50
34	36	13	8	13	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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 time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 3.88584939634547 -0.0473347328558422Connected[t] + 0.033582457223669Separate[t] + 0.53232381448206Learning[t] -0.0262529956639021Happiness[t] + 0.00665043508903866Belonging[t] -0.00920921561511311Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  3.88584939634547 -0.0473347328558422Connected[t] +  0.033582457223669Separate[t] +  0.53232381448206Learning[t] -0.0262529956639021Happiness[t] +  0.00665043508903866Belonging[t] -0.00920921561511311Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  3.88584939634547 -0.0473347328558422Connected[t] +  0.033582457223669Separate[t] +  0.53232381448206Learning[t] -0.0262529956639021Happiness[t] +  0.00665043508903866Belonging[t] -0.00920921561511311Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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
Software[t] = + 3.88584939634547 -0.0473347328558422Connected[t] + 0.033582457223669Separate[t] + 0.53232381448206Learning[t] -0.0262529956639021Happiness[t] + 0.00665043508903866Belonging[t] -0.00920921561511311Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.885849396345472.0459061.89930.0593790.029689
Connected-0.04733473285584220.04687-1.00990.3141070.157054
Separate0.0335824572236690.0440560.76230.4470560.223528
Learning0.532323814482060.0663238.026300
Happiness-0.02625299566390210.066274-0.39610.6925550.346278
Belonging0.006650435089038660.0433920.15330.8783890.439194
Belonging_Final-0.009209215615113110.062663-0.1470.8833510.441676

\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) & 3.88584939634547 & 2.045906 & 1.8993 & 0.059379 & 0.029689 \tabularnewline
Connected & -0.0473347328558422 & 0.04687 & -1.0099 & 0.314107 & 0.157054 \tabularnewline
Separate & 0.033582457223669 & 0.044056 & 0.7623 & 0.447056 & 0.223528 \tabularnewline
Learning & 0.53232381448206 & 0.066323 & 8.0263 & 0 & 0 \tabularnewline
Happiness & -0.0262529956639021 & 0.066274 & -0.3961 & 0.692555 & 0.346278 \tabularnewline
Belonging & 0.00665043508903866 & 0.043392 & 0.1533 & 0.878389 & 0.439194 \tabularnewline
Belonging_Final & -0.00920921561511311 & 0.062663 & -0.147 & 0.883351 & 0.441676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&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]3.88584939634547[/C][C]2.045906[/C][C]1.8993[/C][C]0.059379[/C][C]0.029689[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0473347328558422[/C][C]0.04687[/C][C]-1.0099[/C][C]0.314107[/C][C]0.157054[/C][/ROW]
[ROW][C]Separate[/C][C]0.033582457223669[/C][C]0.044056[/C][C]0.7623[/C][C]0.447056[/C][C]0.223528[/C][/ROW]
[ROW][C]Learning[/C][C]0.53232381448206[/C][C]0.066323[/C][C]8.0263[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0262529956639021[/C][C]0.066274[/C][C]-0.3961[/C][C]0.692555[/C][C]0.346278[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00665043508903866[/C][C]0.043392[/C][C]0.1533[/C][C]0.878389[/C][C]0.439194[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.00920921561511311[/C][C]0.062663[/C][C]-0.147[/C][C]0.883351[/C][C]0.441676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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)3.885849396345472.0459061.89930.0593790.029689
Connected-0.04733473285584220.04687-1.00990.3141070.157054
Separate0.0335824572236690.0440560.76230.4470560.223528
Learning0.532323814482060.0663238.026300
Happiness-0.02625299566390210.066274-0.39610.6925550.346278
Belonging0.006650435089038660.0433920.15330.8783890.439194
Belonging_Final-0.009209215615113110.062663-0.1470.8833510.441676






Multiple Linear Regression - Regression Statistics
Multiple R0.5511741764828
R-squared0.303792972821493
Adjusted R-squared0.276843023382325
F-TEST (value)11.2724876722764
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.98117744432125e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82128789875121
Sum Squared Residuals514.148889571327

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.5511741764828 \tabularnewline
R-squared & 0.303792972821493 \tabularnewline
Adjusted R-squared & 0.276843023382325 \tabularnewline
F-TEST (value) & 11.2724876722764 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.98117744432125e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.82128789875121 \tabularnewline
Sum Squared Residuals & 514.148889571327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.5511741764828[/C][/ROW]
[ROW][C]R-squared[/C][C]0.303792972821493[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.276843023382325[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.2724876722764[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.98117744432125e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.82128789875121[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]514.148889571327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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 R0.5511741764828
R-squared0.303792972821493
Adjusted R-squared0.276843023382325
F-TEST (value)11.2724876722764
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.98117744432125e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.82128789875121
Sum Squared Residuals514.148889571327






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.831704532762942.16829546723706
21111.2613279771743-0.261327977174305
31513.51870459639661.48129540360337
4611.2645163462055-5.26451634620546
51310.63331401712072.36668598287927
6109.736581475228630.263418524771369
71212.8391936067257-0.839193606725732
81411.18353545921772.81646454078226
91210.49364097806261.50635902193736
10611.0742598703077-5.07425987030772
111011.2731986340773-1.27319863407734
121211.39719605274810.602803947251933
131211.57595872085670.424041279143345
141111.4682806700669-0.468280670066923
151512.23380447876862.76619552123142
161211.1508677842960.849132215703967
171010.9581208516266-0.958120851626634
181213.651378930297-1.65137893029704
191112.5782649163283-1.57826491632833
201211.59045271513760.409547284862419
211111.5853370187592-0.585337018759228
221211.73495103014780.265048969852215
231313.1177581048409-0.117758104840907
241111.6780085833755-0.67800858337551
25911.8106955717052-2.81069557170515
261311.9321935388461.06780646115398
271011.5637549127712-1.56375491277116
281411.23734394139342.76265605860661
291211.65972963150310.340270368496896
301010.5888136819744-0.588813681974438
311211.00437400866810.995625991331894
3289.78426645005848-1.78426645005848
331010.2579375509474-0.257937550947428
341211.70374439754020.296255602459848
351210.47427880016521.52572119983478
3676.852553208348350.147446791651652
3768.71939593415551-2.71939593415551
381210.42494318957841.57505681042159
391011.7068875713581-1.70688757135814
401011.5853398881632-1.58533988816322
411011.4102065024999-1.41020650249991
421210.51075218436721.48924781563276
431513.4434159175091.55658408249101
441010.5351892301156-0.535189230115584
451010.2370287701771-0.237028770177126
46128.960490251957563.03950974804244
471310.49186585667452.50813414332549
481111.1318277533625-0.131827753362484
491111.6341576342153-0.634157634215303
501210.35738056709681.64261943290321
511411.81803025989432.18196974010573
521010.5408136514839-0.540813651483862
53129.656698287671642.34330171232836
541311.74344999471761.25655000528242
5558.009100606953-3.009100606953
56610.3404413124836-4.34044131248364
571211.35644895557170.643551044428337
581211.83712388101610.162876118983944
591111.1027395000454-0.102739500045421
601011.6426165605247-1.64261656052471
6179.64175618144186-2.64175618144186
621211.50249398068630.497506019313673
631411.77792136066122.22207863933883
641110.73918708465850.260812915341513
651211.49613012944440.503869870555647
661312.33944279520530.66055720479471
671412.70237238477951.29762761522048
681112.7472303896997-1.7472303896997
69129.446211159975712.55378884002429
701211.66716372573010.332836274269913
7188.29216787730213-0.29216787730213
721110.78231090394950.217689096050459
731412.87086092937521.12913907062483
741412.74255541625161.25744458374842
751211.45726267909170.542737320908282
76911.8625206718884-2.86252067188844
771311.51742948816451.48257051183548
781111.5470912753415-0.547091275341493
791210.20719807156951.79280192843047
801211.272302093310.727697906690046
811211.7579182226480.242081777351964
821213.8365390668687-1.83653906686865
831211.65283694906280.347163050937175
841210.89698014571881.10301985428119
851111.1776296121814-0.177629612181436
861011.5937488464004-1.5937488464004
87910.5399517763347-1.53995177633467
881211.68733881543470.312661184565334
891211.8188922422750.181107757724963
901211.30264473285020.697355267149823
9199.63088382856944-0.630883828569445
921512.01987292676622.9801270732338
931211.81786017287660.18213982712338
941211.03205535293330.967944647066742
951210.19873817808491.80126182191506
961011.445098049323-1.44509804932301
971311.57487911694771.42512088305234
98911.5149390313929-2.51493903139287
991211.44379082439740.556209175602624
1001010.7206540212726-0.720654021272643
1011411.4579964773892.54200352261098
1021111.5830624260736-0.583062426073586
1031513.73718606041211.26281393958793
1041111.059662672191-0.0596626721910247
1051111.8494563787039-0.849456378703931
106129.825683086395842.17431691360416
1071211.96457239908110.0354276009189152
1081211.45441189760530.545588102394664
1091111.6670554709469-0.667055470946855
11079.41591844972643-2.41591844972643
1111211.825081165190.174918834809998
1121411.84201855512942.15798144487065
1131112.5349660226974-1.53496602269742
1141110.1834418439290.816558156071032
115109.264199610975150.735800389024846
1161312.45473263194770.545267368052312
1171310.8073123440132.192687655987
118810.7650832008155-2.76508320081553
1191110.12394982921220.876050170787844
1201211.53776939947610.462230600523865
1211110.22674714918770.773252850812264
1221311.55664726374351.44335273625652
1231210.01785111256311.98214888743687
1241411.60658368478982.39341631521022
1251311.02698872712851.97301127287149
1261511.79513709240143.2048629075986
1271010.7450220362519-0.74502203625185
1281111.8029676118441-0.802967611844067
129911.0694187863827-2.06941878638265
130119.405071512944061.59492848705594
1311011.7406449569088-1.74064495690882
132118.79263678265682.2073632173432
133811.4468566797027-3.44685667970273
134119.673285820956971.32671417904303
1351210.53784440126271.46215559873731
1361210.96708574075361.03291425924644
13799.98593695077581-0.985936950775813
1381111.1013601147862-0.101360114786201
139109.244773368777480.755226631222519
14089.42228735191587-1.42228735191587
14197.592462433792561.40753756620744
142811.691546966827-3.69154696682704
143911.2613867939919-2.26138679399189
1441512.40700531045532.59299468954466
1451111.4027148742739-0.402714874273946
14688.0220883343369-0.0220883343369024
1471312.51224495290880.487755047091154
1481210.07127002151911.92872997848089
1491211.68949791602110.310502083978915
150910.0495628480447-1.04956284804468
15178.37723414871201-1.37723414871201
1521310.87646104539982.12353895460024
153911.7443380059389-2.74433800593889
154611.814620501206-5.81462050120602
155810.6500774834633-2.65007748346326
15688.39587252780053-0.395872527800532
1571512.01987292676622.9801270732338
158610.2292402609002-4.22924026090022
159911.0694187863827-2.06941878638265
1601111.615965749595-0.61596574959495
16189.71754954032842-1.71754954032842
162810.0996136867834-2.09961368678343

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.83170453276294 & 2.16829546723706 \tabularnewline
2 & 11 & 11.2613279771743 & -0.261327977174305 \tabularnewline
3 & 15 & 13.5187045963966 & 1.48129540360337 \tabularnewline
4 & 6 & 11.2645163462055 & -5.26451634620546 \tabularnewline
5 & 13 & 10.6333140171207 & 2.36668598287927 \tabularnewline
6 & 10 & 9.73658147522863 & 0.263418524771369 \tabularnewline
7 & 12 & 12.8391936067257 & -0.839193606725732 \tabularnewline
8 & 14 & 11.1835354592177 & 2.81646454078226 \tabularnewline
9 & 12 & 10.4936409780626 & 1.50635902193736 \tabularnewline
10 & 6 & 11.0742598703077 & -5.07425987030772 \tabularnewline
11 & 10 & 11.2731986340773 & -1.27319863407734 \tabularnewline
12 & 12 & 11.3971960527481 & 0.602803947251933 \tabularnewline
13 & 12 & 11.5759587208567 & 0.424041279143345 \tabularnewline
14 & 11 & 11.4682806700669 & -0.468280670066923 \tabularnewline
15 & 15 & 12.2338044787686 & 2.76619552123142 \tabularnewline
16 & 12 & 11.150867784296 & 0.849132215703967 \tabularnewline
17 & 10 & 10.9581208516266 & -0.958120851626634 \tabularnewline
18 & 12 & 13.651378930297 & -1.65137893029704 \tabularnewline
19 & 11 & 12.5782649163283 & -1.57826491632833 \tabularnewline
20 & 12 & 11.5904527151376 & 0.409547284862419 \tabularnewline
21 & 11 & 11.5853370187592 & -0.585337018759228 \tabularnewline
22 & 12 & 11.7349510301478 & 0.265048969852215 \tabularnewline
23 & 13 & 13.1177581048409 & -0.117758104840907 \tabularnewline
24 & 11 & 11.6780085833755 & -0.67800858337551 \tabularnewline
25 & 9 & 11.8106955717052 & -2.81069557170515 \tabularnewline
26 & 13 & 11.932193538846 & 1.06780646115398 \tabularnewline
27 & 10 & 11.5637549127712 & -1.56375491277116 \tabularnewline
28 & 14 & 11.2373439413934 & 2.76265605860661 \tabularnewline
29 & 12 & 11.6597296315031 & 0.340270368496896 \tabularnewline
30 & 10 & 10.5888136819744 & -0.588813681974438 \tabularnewline
31 & 12 & 11.0043740086681 & 0.995625991331894 \tabularnewline
32 & 8 & 9.78426645005848 & -1.78426645005848 \tabularnewline
33 & 10 & 10.2579375509474 & -0.257937550947428 \tabularnewline
34 & 12 & 11.7037443975402 & 0.296255602459848 \tabularnewline
35 & 12 & 10.4742788001652 & 1.52572119983478 \tabularnewline
36 & 7 & 6.85255320834835 & 0.147446791651652 \tabularnewline
37 & 6 & 8.71939593415551 & -2.71939593415551 \tabularnewline
38 & 12 & 10.4249431895784 & 1.57505681042159 \tabularnewline
39 & 10 & 11.7068875713581 & -1.70688757135814 \tabularnewline
40 & 10 & 11.5853398881632 & -1.58533988816322 \tabularnewline
41 & 10 & 11.4102065024999 & -1.41020650249991 \tabularnewline
42 & 12 & 10.5107521843672 & 1.48924781563276 \tabularnewline
43 & 15 & 13.443415917509 & 1.55658408249101 \tabularnewline
44 & 10 & 10.5351892301156 & -0.535189230115584 \tabularnewline
45 & 10 & 10.2370287701771 & -0.237028770177126 \tabularnewline
46 & 12 & 8.96049025195756 & 3.03950974804244 \tabularnewline
47 & 13 & 10.4918658566745 & 2.50813414332549 \tabularnewline
48 & 11 & 11.1318277533625 & -0.131827753362484 \tabularnewline
49 & 11 & 11.6341576342153 & -0.634157634215303 \tabularnewline
50 & 12 & 10.3573805670968 & 1.64261943290321 \tabularnewline
51 & 14 & 11.8180302598943 & 2.18196974010573 \tabularnewline
52 & 10 & 10.5408136514839 & -0.540813651483862 \tabularnewline
53 & 12 & 9.65669828767164 & 2.34330171232836 \tabularnewline
54 & 13 & 11.7434499947176 & 1.25655000528242 \tabularnewline
55 & 5 & 8.009100606953 & -3.009100606953 \tabularnewline
56 & 6 & 10.3404413124836 & -4.34044131248364 \tabularnewline
57 & 12 & 11.3564489555717 & 0.643551044428337 \tabularnewline
58 & 12 & 11.8371238810161 & 0.162876118983944 \tabularnewline
59 & 11 & 11.1027395000454 & -0.102739500045421 \tabularnewline
60 & 10 & 11.6426165605247 & -1.64261656052471 \tabularnewline
61 & 7 & 9.64175618144186 & -2.64175618144186 \tabularnewline
62 & 12 & 11.5024939806863 & 0.497506019313673 \tabularnewline
63 & 14 & 11.7779213606612 & 2.22207863933883 \tabularnewline
64 & 11 & 10.7391870846585 & 0.260812915341513 \tabularnewline
65 & 12 & 11.4961301294444 & 0.503869870555647 \tabularnewline
66 & 13 & 12.3394427952053 & 0.66055720479471 \tabularnewline
67 & 14 & 12.7023723847795 & 1.29762761522048 \tabularnewline
68 & 11 & 12.7472303896997 & -1.7472303896997 \tabularnewline
69 & 12 & 9.44621115997571 & 2.55378884002429 \tabularnewline
70 & 12 & 11.6671637257301 & 0.332836274269913 \tabularnewline
71 & 8 & 8.29216787730213 & -0.29216787730213 \tabularnewline
72 & 11 & 10.7823109039495 & 0.217689096050459 \tabularnewline
73 & 14 & 12.8708609293752 & 1.12913907062483 \tabularnewline
74 & 14 & 12.7425554162516 & 1.25744458374842 \tabularnewline
75 & 12 & 11.4572626790917 & 0.542737320908282 \tabularnewline
76 & 9 & 11.8625206718884 & -2.86252067188844 \tabularnewline
77 & 13 & 11.5174294881645 & 1.48257051183548 \tabularnewline
78 & 11 & 11.5470912753415 & -0.547091275341493 \tabularnewline
79 & 12 & 10.2071980715695 & 1.79280192843047 \tabularnewline
80 & 12 & 11.27230209331 & 0.727697906690046 \tabularnewline
81 & 12 & 11.757918222648 & 0.242081777351964 \tabularnewline
82 & 12 & 13.8365390668687 & -1.83653906686865 \tabularnewline
83 & 12 & 11.6528369490628 & 0.347163050937175 \tabularnewline
84 & 12 & 10.8969801457188 & 1.10301985428119 \tabularnewline
85 & 11 & 11.1776296121814 & -0.177629612181436 \tabularnewline
86 & 10 & 11.5937488464004 & -1.5937488464004 \tabularnewline
87 & 9 & 10.5399517763347 & -1.53995177633467 \tabularnewline
88 & 12 & 11.6873388154347 & 0.312661184565334 \tabularnewline
89 & 12 & 11.818892242275 & 0.181107757724963 \tabularnewline
90 & 12 & 11.3026447328502 & 0.697355267149823 \tabularnewline
91 & 9 & 9.63088382856944 & -0.630883828569445 \tabularnewline
92 & 15 & 12.0198729267662 & 2.9801270732338 \tabularnewline
93 & 12 & 11.8178601728766 & 0.18213982712338 \tabularnewline
94 & 12 & 11.0320553529333 & 0.967944647066742 \tabularnewline
95 & 12 & 10.1987381780849 & 1.80126182191506 \tabularnewline
96 & 10 & 11.445098049323 & -1.44509804932301 \tabularnewline
97 & 13 & 11.5748791169477 & 1.42512088305234 \tabularnewline
98 & 9 & 11.5149390313929 & -2.51493903139287 \tabularnewline
99 & 12 & 11.4437908243974 & 0.556209175602624 \tabularnewline
100 & 10 & 10.7206540212726 & -0.720654021272643 \tabularnewline
101 & 14 & 11.457996477389 & 2.54200352261098 \tabularnewline
102 & 11 & 11.5830624260736 & -0.583062426073586 \tabularnewline
103 & 15 & 13.7371860604121 & 1.26281393958793 \tabularnewline
104 & 11 & 11.059662672191 & -0.0596626721910247 \tabularnewline
105 & 11 & 11.8494563787039 & -0.849456378703931 \tabularnewline
106 & 12 & 9.82568308639584 & 2.17431691360416 \tabularnewline
107 & 12 & 11.9645723990811 & 0.0354276009189152 \tabularnewline
108 & 12 & 11.4544118976053 & 0.545588102394664 \tabularnewline
109 & 11 & 11.6670554709469 & -0.667055470946855 \tabularnewline
110 & 7 & 9.41591844972643 & -2.41591844972643 \tabularnewline
111 & 12 & 11.82508116519 & 0.174918834809998 \tabularnewline
112 & 14 & 11.8420185551294 & 2.15798144487065 \tabularnewline
113 & 11 & 12.5349660226974 & -1.53496602269742 \tabularnewline
114 & 11 & 10.183441843929 & 0.816558156071032 \tabularnewline
115 & 10 & 9.26419961097515 & 0.735800389024846 \tabularnewline
116 & 13 & 12.4547326319477 & 0.545267368052312 \tabularnewline
117 & 13 & 10.807312344013 & 2.192687655987 \tabularnewline
118 & 8 & 10.7650832008155 & -2.76508320081553 \tabularnewline
119 & 11 & 10.1239498292122 & 0.876050170787844 \tabularnewline
120 & 12 & 11.5377693994761 & 0.462230600523865 \tabularnewline
121 & 11 & 10.2267471491877 & 0.773252850812264 \tabularnewline
122 & 13 & 11.5566472637435 & 1.44335273625652 \tabularnewline
123 & 12 & 10.0178511125631 & 1.98214888743687 \tabularnewline
124 & 14 & 11.6065836847898 & 2.39341631521022 \tabularnewline
125 & 13 & 11.0269887271285 & 1.97301127287149 \tabularnewline
126 & 15 & 11.7951370924014 & 3.2048629075986 \tabularnewline
127 & 10 & 10.7450220362519 & -0.74502203625185 \tabularnewline
128 & 11 & 11.8029676118441 & -0.802967611844067 \tabularnewline
129 & 9 & 11.0694187863827 & -2.06941878638265 \tabularnewline
130 & 11 & 9.40507151294406 & 1.59492848705594 \tabularnewline
131 & 10 & 11.7406449569088 & -1.74064495690882 \tabularnewline
132 & 11 & 8.7926367826568 & 2.2073632173432 \tabularnewline
133 & 8 & 11.4468566797027 & -3.44685667970273 \tabularnewline
134 & 11 & 9.67328582095697 & 1.32671417904303 \tabularnewline
135 & 12 & 10.5378444012627 & 1.46215559873731 \tabularnewline
136 & 12 & 10.9670857407536 & 1.03291425924644 \tabularnewline
137 & 9 & 9.98593695077581 & -0.985936950775813 \tabularnewline
138 & 11 & 11.1013601147862 & -0.101360114786201 \tabularnewline
139 & 10 & 9.24477336877748 & 0.755226631222519 \tabularnewline
140 & 8 & 9.42228735191587 & -1.42228735191587 \tabularnewline
141 & 9 & 7.59246243379256 & 1.40753756620744 \tabularnewline
142 & 8 & 11.691546966827 & -3.69154696682704 \tabularnewline
143 & 9 & 11.2613867939919 & -2.26138679399189 \tabularnewline
144 & 15 & 12.4070053104553 & 2.59299468954466 \tabularnewline
145 & 11 & 11.4027148742739 & -0.402714874273946 \tabularnewline
146 & 8 & 8.0220883343369 & -0.0220883343369024 \tabularnewline
147 & 13 & 12.5122449529088 & 0.487755047091154 \tabularnewline
148 & 12 & 10.0712700215191 & 1.92872997848089 \tabularnewline
149 & 12 & 11.6894979160211 & 0.310502083978915 \tabularnewline
150 & 9 & 10.0495628480447 & -1.04956284804468 \tabularnewline
151 & 7 & 8.37723414871201 & -1.37723414871201 \tabularnewline
152 & 13 & 10.8764610453998 & 2.12353895460024 \tabularnewline
153 & 9 & 11.7443380059389 & -2.74433800593889 \tabularnewline
154 & 6 & 11.814620501206 & -5.81462050120602 \tabularnewline
155 & 8 & 10.6500774834633 & -2.65007748346326 \tabularnewline
156 & 8 & 8.39587252780053 & -0.395872527800532 \tabularnewline
157 & 15 & 12.0198729267662 & 2.9801270732338 \tabularnewline
158 & 6 & 10.2292402609002 & -4.22924026090022 \tabularnewline
159 & 9 & 11.0694187863827 & -2.06941878638265 \tabularnewline
160 & 11 & 11.615965749595 & -0.61596574959495 \tabularnewline
161 & 8 & 9.71754954032842 & -1.71754954032842 \tabularnewline
162 & 8 & 10.0996136867834 & -2.09961368678343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&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]12[/C][C]9.83170453276294[/C][C]2.16829546723706[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.2613279771743[/C][C]-0.261327977174305[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5187045963966[/C][C]1.48129540360337[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.2645163462055[/C][C]-5.26451634620546[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.6333140171207[/C][C]2.36668598287927[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.73658147522863[/C][C]0.263418524771369[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.8391936067257[/C][C]-0.839193606725732[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.1835354592177[/C][C]2.81646454078226[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.4936409780626[/C][C]1.50635902193736[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0742598703077[/C][C]-5.07425987030772[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.2731986340773[/C][C]-1.27319863407734[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.3971960527481[/C][C]0.602803947251933[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.5759587208567[/C][C]0.424041279143345[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.4682806700669[/C][C]-0.468280670066923[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.2338044787686[/C][C]2.76619552123142[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.150867784296[/C][C]0.849132215703967[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.9581208516266[/C][C]-0.958120851626634[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.651378930297[/C][C]-1.65137893029704[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.5782649163283[/C][C]-1.57826491632833[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5904527151376[/C][C]0.409547284862419[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.5853370187592[/C][C]-0.585337018759228[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.7349510301478[/C][C]0.265048969852215[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1177581048409[/C][C]-0.117758104840907[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.6780085833755[/C][C]-0.67800858337551[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.8106955717052[/C][C]-2.81069557170515[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]11.932193538846[/C][C]1.06780646115398[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.5637549127712[/C][C]-1.56375491277116[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.2373439413934[/C][C]2.76265605860661[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.6597296315031[/C][C]0.340270368496896[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.5888136819744[/C][C]-0.588813681974438[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0043740086681[/C][C]0.995625991331894[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.78426645005848[/C][C]-1.78426645005848[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.2579375509474[/C][C]-0.257937550947428[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7037443975402[/C][C]0.296255602459848[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.4742788001652[/C][C]1.52572119983478[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.85255320834835[/C][C]0.147446791651652[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.71939593415551[/C][C]-2.71939593415551[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.4249431895784[/C][C]1.57505681042159[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.7068875713581[/C][C]-1.70688757135814[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.5853398881632[/C][C]-1.58533988816322[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4102065024999[/C][C]-1.41020650249991[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.5107521843672[/C][C]1.48924781563276[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.443415917509[/C][C]1.55658408249101[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.5351892301156[/C][C]-0.535189230115584[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.2370287701771[/C][C]-0.237028770177126[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]8.96049025195756[/C][C]3.03950974804244[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.4918658566745[/C][C]2.50813414332549[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1318277533625[/C][C]-0.131827753362484[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.6341576342153[/C][C]-0.634157634215303[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.3573805670968[/C][C]1.64261943290321[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8180302598943[/C][C]2.18196974010573[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5408136514839[/C][C]-0.540813651483862[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.65669828767164[/C][C]2.34330171232836[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7434499947176[/C][C]1.25655000528242[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.009100606953[/C][C]-3.009100606953[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.3404413124836[/C][C]-4.34044131248364[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3564489555717[/C][C]0.643551044428337[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.8371238810161[/C][C]0.162876118983944[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.1027395000454[/C][C]-0.102739500045421[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.6426165605247[/C][C]-1.64261656052471[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.64175618144186[/C][C]-2.64175618144186[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.5024939806863[/C][C]0.497506019313673[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.7779213606612[/C][C]2.22207863933883[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7391870846585[/C][C]0.260812915341513[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.4961301294444[/C][C]0.503869870555647[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.3394427952053[/C][C]0.66055720479471[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.7023723847795[/C][C]1.29762761522048[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.7472303896997[/C][C]-1.7472303896997[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.44621115997571[/C][C]2.55378884002429[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6671637257301[/C][C]0.332836274269913[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.29216787730213[/C][C]-0.29216787730213[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7823109039495[/C][C]0.217689096050459[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8708609293752[/C][C]1.12913907062483[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.7425554162516[/C][C]1.25744458374842[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4572626790917[/C][C]0.542737320908282[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.8625206718884[/C][C]-2.86252067188844[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5174294881645[/C][C]1.48257051183548[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.5470912753415[/C][C]-0.547091275341493[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.2071980715695[/C][C]1.79280192843047[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.27230209331[/C][C]0.727697906690046[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.757918222648[/C][C]0.242081777351964[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.8365390668687[/C][C]-1.83653906686865[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.6528369490628[/C][C]0.347163050937175[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.8969801457188[/C][C]1.10301985428119[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1776296121814[/C][C]-0.177629612181436[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5937488464004[/C][C]-1.5937488464004[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5399517763347[/C][C]-1.53995177633467[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.6873388154347[/C][C]0.312661184565334[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.818892242275[/C][C]0.181107757724963[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.3026447328502[/C][C]0.697355267149823[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.63088382856944[/C][C]-0.630883828569445[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.0198729267662[/C][C]2.9801270732338[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.8178601728766[/C][C]0.18213982712338[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.0320553529333[/C][C]0.967944647066742[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.1987381780849[/C][C]1.80126182191506[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.445098049323[/C][C]-1.44509804932301[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5748791169477[/C][C]1.42512088305234[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.5149390313929[/C][C]-2.51493903139287[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4437908243974[/C][C]0.556209175602624[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7206540212726[/C][C]-0.720654021272643[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.457996477389[/C][C]2.54200352261098[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.5830624260736[/C][C]-0.583062426073586[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.7371860604121[/C][C]1.26281393958793[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.059662672191[/C][C]-0.0596626721910247[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.8494563787039[/C][C]-0.849456378703931[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.82568308639584[/C][C]2.17431691360416[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.9645723990811[/C][C]0.0354276009189152[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.4544118976053[/C][C]0.545588102394664[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.6670554709469[/C][C]-0.667055470946855[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.41591844972643[/C][C]-2.41591844972643[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.82508116519[/C][C]0.174918834809998[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.8420185551294[/C][C]2.15798144487065[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5349660226974[/C][C]-1.53496602269742[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.183441843929[/C][C]0.816558156071032[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.26419961097515[/C][C]0.735800389024846[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4547326319477[/C][C]0.545267368052312[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.807312344013[/C][C]2.192687655987[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7650832008155[/C][C]-2.76508320081553[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.1239498292122[/C][C]0.876050170787844[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.5377693994761[/C][C]0.462230600523865[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.2267471491877[/C][C]0.773252850812264[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.5566472637435[/C][C]1.44335273625652[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.0178511125631[/C][C]1.98214888743687[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.6065836847898[/C][C]2.39341631521022[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.0269887271285[/C][C]1.97301127287149[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.7951370924014[/C][C]3.2048629075986[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.7450220362519[/C][C]-0.74502203625185[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8029676118441[/C][C]-0.802967611844067[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.0694187863827[/C][C]-2.06941878638265[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.40507151294406[/C][C]1.59492848705594[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.7406449569088[/C][C]-1.74064495690882[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.7926367826568[/C][C]2.2073632173432[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.4468566797027[/C][C]-3.44685667970273[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.67328582095697[/C][C]1.32671417904303[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.5378444012627[/C][C]1.46215559873731[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.9670857407536[/C][C]1.03291425924644[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.98593695077581[/C][C]-0.985936950775813[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1013601147862[/C][C]-0.101360114786201[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.24477336877748[/C][C]0.755226631222519[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.42228735191587[/C][C]-1.42228735191587[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.59246243379256[/C][C]1.40753756620744[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.691546966827[/C][C]-3.69154696682704[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.2613867939919[/C][C]-2.26138679399189[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.4070053104553[/C][C]2.59299468954466[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.4027148742739[/C][C]-0.402714874273946[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.0220883343369[/C][C]-0.0220883343369024[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.5122449529088[/C][C]0.487755047091154[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.0712700215191[/C][C]1.92872997848089[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.6894979160211[/C][C]0.310502083978915[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]10.0495628480447[/C][C]-1.04956284804468[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.37723414871201[/C][C]-1.37723414871201[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.8764610453998[/C][C]2.12353895460024[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.7443380059389[/C][C]-2.74433800593889[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.814620501206[/C][C]-5.81462050120602[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.6500774834633[/C][C]-2.65007748346326[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.39587252780053[/C][C]-0.395872527800532[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.0198729267662[/C][C]2.9801270732338[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2292402609002[/C][C]-4.22924026090022[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.0694187863827[/C][C]-2.06941878638265[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.615965749595[/C][C]-0.61596574959495[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.71754954032842[/C][C]-1.71754954032842[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.0996136867834[/C][C]-2.09961368678343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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
1129.831704532762942.16829546723706
21111.2613279771743-0.261327977174305
31513.51870459639661.48129540360337
4611.2645163462055-5.26451634620546
51310.63331401712072.36668598287927
6109.736581475228630.263418524771369
71212.8391936067257-0.839193606725732
81411.18353545921772.81646454078226
91210.49364097806261.50635902193736
10611.0742598703077-5.07425987030772
111011.2731986340773-1.27319863407734
121211.39719605274810.602803947251933
131211.57595872085670.424041279143345
141111.4682806700669-0.468280670066923
151512.23380447876862.76619552123142
161211.1508677842960.849132215703967
171010.9581208516266-0.958120851626634
181213.651378930297-1.65137893029704
191112.5782649163283-1.57826491632833
201211.59045271513760.409547284862419
211111.5853370187592-0.585337018759228
221211.73495103014780.265048969852215
231313.1177581048409-0.117758104840907
241111.6780085833755-0.67800858337551
25911.8106955717052-2.81069557170515
261311.9321935388461.06780646115398
271011.5637549127712-1.56375491277116
281411.23734394139342.76265605860661
291211.65972963150310.340270368496896
301010.5888136819744-0.588813681974438
311211.00437400866810.995625991331894
3289.78426645005848-1.78426645005848
331010.2579375509474-0.257937550947428
341211.70374439754020.296255602459848
351210.47427880016521.52572119983478
3676.852553208348350.147446791651652
3768.71939593415551-2.71939593415551
381210.42494318957841.57505681042159
391011.7068875713581-1.70688757135814
401011.5853398881632-1.58533988816322
411011.4102065024999-1.41020650249991
421210.51075218436721.48924781563276
431513.4434159175091.55658408249101
441010.5351892301156-0.535189230115584
451010.2370287701771-0.237028770177126
46128.960490251957563.03950974804244
471310.49186585667452.50813414332549
481111.1318277533625-0.131827753362484
491111.6341576342153-0.634157634215303
501210.35738056709681.64261943290321
511411.81803025989432.18196974010573
521010.5408136514839-0.540813651483862
53129.656698287671642.34330171232836
541311.74344999471761.25655000528242
5558.009100606953-3.009100606953
56610.3404413124836-4.34044131248364
571211.35644895557170.643551044428337
581211.83712388101610.162876118983944
591111.1027395000454-0.102739500045421
601011.6426165605247-1.64261656052471
6179.64175618144186-2.64175618144186
621211.50249398068630.497506019313673
631411.77792136066122.22207863933883
641110.73918708465850.260812915341513
651211.49613012944440.503869870555647
661312.33944279520530.66055720479471
671412.70237238477951.29762761522048
681112.7472303896997-1.7472303896997
69129.446211159975712.55378884002429
701211.66716372573010.332836274269913
7188.29216787730213-0.29216787730213
721110.78231090394950.217689096050459
731412.87086092937521.12913907062483
741412.74255541625161.25744458374842
751211.45726267909170.542737320908282
76911.8625206718884-2.86252067188844
771311.51742948816451.48257051183548
781111.5470912753415-0.547091275341493
791210.20719807156951.79280192843047
801211.272302093310.727697906690046
811211.7579182226480.242081777351964
821213.8365390668687-1.83653906686865
831211.65283694906280.347163050937175
841210.89698014571881.10301985428119
851111.1776296121814-0.177629612181436
861011.5937488464004-1.5937488464004
87910.5399517763347-1.53995177633467
881211.68733881543470.312661184565334
891211.8188922422750.181107757724963
901211.30264473285020.697355267149823
9199.63088382856944-0.630883828569445
921512.01987292676622.9801270732338
931211.81786017287660.18213982712338
941211.03205535293330.967944647066742
951210.19873817808491.80126182191506
961011.445098049323-1.44509804932301
971311.57487911694771.42512088305234
98911.5149390313929-2.51493903139287
991211.44379082439740.556209175602624
1001010.7206540212726-0.720654021272643
1011411.4579964773892.54200352261098
1021111.5830624260736-0.583062426073586
1031513.73718606041211.26281393958793
1041111.059662672191-0.0596626721910247
1051111.8494563787039-0.849456378703931
106129.825683086395842.17431691360416
1071211.96457239908110.0354276009189152
1081211.45441189760530.545588102394664
1091111.6670554709469-0.667055470946855
11079.41591844972643-2.41591844972643
1111211.825081165190.174918834809998
1121411.84201855512942.15798144487065
1131112.5349660226974-1.53496602269742
1141110.1834418439290.816558156071032
115109.264199610975150.735800389024846
1161312.45473263194770.545267368052312
1171310.8073123440132.192687655987
118810.7650832008155-2.76508320081553
1191110.12394982921220.876050170787844
1201211.53776939947610.462230600523865
1211110.22674714918770.773252850812264
1221311.55664726374351.44335273625652
1231210.01785111256311.98214888743687
1241411.60658368478982.39341631521022
1251311.02698872712851.97301127287149
1261511.79513709240143.2048629075986
1271010.7450220362519-0.74502203625185
1281111.8029676118441-0.802967611844067
129911.0694187863827-2.06941878638265
130119.405071512944061.59492848705594
1311011.7406449569088-1.74064495690882
132118.79263678265682.2073632173432
133811.4468566797027-3.44685667970273
134119.673285820956971.32671417904303
1351210.53784440126271.46215559873731
1361210.96708574075361.03291425924644
13799.98593695077581-0.985936950775813
1381111.1013601147862-0.101360114786201
139109.244773368777480.755226631222519
14089.42228735191587-1.42228735191587
14197.592462433792561.40753756620744
142811.691546966827-3.69154696682704
143911.2613867939919-2.26138679399189
1441512.40700531045532.59299468954466
1451111.4027148742739-0.402714874273946
14688.0220883343369-0.0220883343369024
1471312.51224495290880.487755047091154
1481210.07127002151911.92872997848089
1491211.68949791602110.310502083978915
150910.0495628480447-1.04956284804468
15178.37723414871201-1.37723414871201
1521310.87646104539982.12353895460024
153911.7443380059389-2.74433800593889
154611.814620501206-5.81462050120602
155810.6500774834633-2.65007748346326
15688.39587252780053-0.395872527800532
1571512.01987292676622.9801270732338
158610.2292402609002-4.22924026090022
159911.0694187863827-2.06941878638265
1601111.615965749595-0.61596574959495
16189.71754954032842-1.71754954032842
162810.0996136867834-2.09961368678343






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9998721618558670.0002556762882653980.000127838144132699
110.9996213752530780.0007572494938437760.000378624746921888
120.9995415085091710.0009169829816572650.000458491490828633
130.9990473231196340.001905353760732240.000952676880366122
140.9983690852251990.003261829549602710.00163091477480135
150.9980125532468540.003974893506292710.00198744675314636
160.9966732541801820.006653491639635280.00332674581981764
170.9940669975342640.01186600493147210.00593300246573606
180.992966907950870.01406618409825980.00703309204912992
190.990044969206840.01991006158632050.00995503079316027
200.9837373430306760.03252531393864880.0162626569693244
210.9774404093057070.04511918138858610.022559590694293
220.9672371883214830.06552562335703480.0327628116785174
230.9526207760476050.0947584479047910.0473792239523955
240.9350451035259230.1299097929481550.0649548964740774
250.9348145371428410.1303709257143180.0651854628571588
260.9343856194551360.1312287610897280.0656143805448642
270.931913112751530.136173774496940.0680868872484701
280.9405255372772230.1189489254455540.0594744627227771
290.9200911537948990.1598176924102010.0799088462051005
300.899015498219530.2019690035609410.10098450178047
310.8788621558990140.2422756882019730.121137844100986
320.8965436041970.2069127916060.103456395803
330.8672287731827380.2655424536345240.132771226817262
340.8328422393253610.3343155213492780.167157760674639
350.8201302705104540.3597394589790930.179869729489546
360.7829397859557470.4341204280885060.217060214044253
370.8234662326362220.3530675347275560.176533767363778
380.8147127665299120.3705744669401760.185287233470088
390.8041814134725610.3916371730548780.195818586527439
400.7846057104009720.4307885791980560.215394289599028
410.759294193564580.4814116128708390.24070580643542
420.7437397943227860.5125204113544290.256260205677214
430.7405414053102570.5189171893794850.259458594689743
440.6973931387440420.6052137225119160.302606861255958
450.6519129842892080.6961740314215840.348087015710792
460.719652412615940.5606951747681190.28034758738406
470.7335010198758130.5329979602483750.266498980124187
480.6893222248078250.621355550384350.310677775192175
490.6536544345467070.6926911309065860.346345565453293
500.6413146021318670.7173707957362650.358685397868133
510.6501063553041240.6997872893917530.349893644695876
520.6039472638790760.7921054722418480.396052736120924
530.6257221233545810.7485557532908380.374277876645419
540.5922436110793540.8155127778412920.407756388920646
550.679838551572620.6403228968547610.32016144842738
560.8413208079007640.3173583841984730.158679192099236
570.8135283438812990.3729433122374030.186471656118701
580.7794817459747030.4410365080505930.220518254025297
590.7423150349939030.5153699300121940.257684965006097
600.7307687669337540.5384624661324920.269231233066246
610.7507068686687190.4985862626625610.249293131331281
620.7147779178646220.5704441642707550.285222082135378
630.7316907146974250.5366185706051490.268309285302575
640.6913800714878260.6172398570243480.308619928512174
650.6567922956470530.6864154087058940.343207704352947
660.6166491107433430.7667017785133140.383350889256657
670.5952834730277570.8094330539444870.404716526972243
680.5786789711327810.8426420577344380.421321028867219
690.5971961284851220.8056077430297560.402803871514878
700.5533012687935550.8933974624128910.446698731206445
710.5119330763701440.9761338472597120.488066923629856
720.4689864137023390.9379728274046780.531013586297661
730.437402453868050.8748049077360990.56259754613195
740.4147406438414840.8294812876829690.585259356158516
750.3890770689649230.7781541379298460.610922931035077
760.4527821017997020.9055642035994040.547217898200298
770.4381996178587110.8763992357174230.561800382141289
780.3966979859421680.7933959718843360.603302014057832
790.4004261817426040.8008523634852080.599573818257396
800.3824299689185210.7648599378370420.617570031081479
810.3406095970104730.6812191940209470.659390402989527
820.348875423081910.697750846163820.65112457691809
830.3123746259109370.6247492518218730.687625374089063
840.2847047776223690.5694095552447390.715295222377631
850.2470580822773290.4941161645546580.752941917722671
860.2395236949876430.4790473899752860.760476305012357
870.2410690476749260.4821380953498520.758930952325074
880.2071999104177020.4143998208354050.792800089582298
890.1765711225201390.3531422450402790.823428877479861
900.151926802627320.3038536052546410.84807319737268
910.1315606836129740.2631213672259470.868439316387026
920.1831969500114580.3663939000229160.816803049988542
930.1583282117980090.3166564235960180.841671788201991
940.1413552959255510.2827105918511020.858644704074449
950.1375329175930320.2750658351860640.862467082406968
960.1306361018909860.2612722037819730.869363898109014
970.1214377018110770.2428754036221540.878562298188923
980.1533747325319390.3067494650638770.846625267468061
990.1283230624438050.256646124887610.871676937556195
1000.108839243126020.2176784862520390.89116075687398
1010.1292829721305110.2585659442610210.870717027869489
1020.1082164797852050.216432959570410.891783520214795
1030.1013736402031480.2027472804062960.898626359796852
1040.08257200596776540.1651440119355310.917427994032235
1050.06949697286981220.1389939457396240.930503027130188
1060.06953990015534070.1390798003106810.930460099844659
1070.05520259655981870.1104051931196370.944797403440181
1080.046010001454350.09202000290870.95398999854565
1090.03622808463686730.07245616927373460.963771915363133
1100.04097916702743970.08195833405487950.95902083297256
1110.03145642587720690.06291285175441380.968543574122793
1120.03396953275045750.0679390655009150.966030467249543
1130.03008351092904350.0601670218580870.969916489070957
1140.02419014090010460.04838028180020930.975809859099895
1150.0186202342320740.0372404684641480.981379765767926
1160.01422954664734330.02845909329468660.985770453352657
1170.02013263887812590.04026527775625180.979867361121874
1180.02323802598353470.04647605196706940.976761974016465
1190.01865919531301490.03731839062602990.981340804686985
1200.01389805809602730.02779611619205460.986101941903973
1210.01136490559925130.02272981119850250.988635094400749
1220.009291106272633770.01858221254526750.990708893727366
1230.01084846575875870.02169693151751750.989151534241241
1240.01957614378280550.03915228756561110.980423856217194
1250.0212527540753150.042505508150630.978747245924685
1260.05128436153924190.1025687230784840.948715638460758
1270.0390993681506310.0781987363012620.960900631849369
1280.02993981678155860.05987963356311720.970060183218441
1290.02541825376970350.0508365075394070.974581746230296
1300.02401460460421220.04802920920842440.975985395395788
1310.01912414643934360.03824829287868730.980875853560656
1320.02178890351117950.0435778070223590.97821109648882
1330.03289144496316090.06578288992632180.967108555036839
1340.03542033416369710.07084066832739410.964579665836303
1350.03897838271173610.07795676542347230.961021617288264
1360.03196585229501710.06393170459003420.968034147704983
1370.02978443825067620.05956887650135230.970215561749324
1380.02219320248745070.04438640497490140.977806797512549
1390.02278362936986620.04556725873973240.977216370630134
1400.0161198585646640.0322397171293280.983880141435336
1410.04693237412069220.09386474824138440.953067625879308
1420.05149625290127110.1029925058025420.948503747098729
1430.03766357489739140.07532714979478280.962336425102609
1440.3638674727226290.7277349454452590.636132527277371
1450.3936346854330860.7872693708661730.606365314566914
1460.6796595616221150.6406808767557690.320340438377885
1470.7084642651159290.5830714697681420.291535734884071
1480.9308867289194830.1382265421610340.0691132710805172
1490.9078711920476280.1842576159047450.0921288079523723
1500.837577232192030.324845535615940.16242276780797
1510.7405444961979420.5189110076041170.259455503802058
1520.6157248242905440.7685503514189110.384275175709456

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.999872161855867 & 0.000255676288265398 & 0.000127838144132699 \tabularnewline
11 & 0.999621375253078 & 0.000757249493843776 & 0.000378624746921888 \tabularnewline
12 & 0.999541508509171 & 0.000916982981657265 & 0.000458491490828633 \tabularnewline
13 & 0.999047323119634 & 0.00190535376073224 & 0.000952676880366122 \tabularnewline
14 & 0.998369085225199 & 0.00326182954960271 & 0.00163091477480135 \tabularnewline
15 & 0.998012553246854 & 0.00397489350629271 & 0.00198744675314636 \tabularnewline
16 & 0.996673254180182 & 0.00665349163963528 & 0.00332674581981764 \tabularnewline
17 & 0.994066997534264 & 0.0118660049314721 & 0.00593300246573606 \tabularnewline
18 & 0.99296690795087 & 0.0140661840982598 & 0.00703309204912992 \tabularnewline
19 & 0.99004496920684 & 0.0199100615863205 & 0.00995503079316027 \tabularnewline
20 & 0.983737343030676 & 0.0325253139386488 & 0.0162626569693244 \tabularnewline
21 & 0.977440409305707 & 0.0451191813885861 & 0.022559590694293 \tabularnewline
22 & 0.967237188321483 & 0.0655256233570348 & 0.0327628116785174 \tabularnewline
23 & 0.952620776047605 & 0.094758447904791 & 0.0473792239523955 \tabularnewline
24 & 0.935045103525923 & 0.129909792948155 & 0.0649548964740774 \tabularnewline
25 & 0.934814537142841 & 0.130370925714318 & 0.0651854628571588 \tabularnewline
26 & 0.934385619455136 & 0.131228761089728 & 0.0656143805448642 \tabularnewline
27 & 0.93191311275153 & 0.13617377449694 & 0.0680868872484701 \tabularnewline
28 & 0.940525537277223 & 0.118948925445554 & 0.0594744627227771 \tabularnewline
29 & 0.920091153794899 & 0.159817692410201 & 0.0799088462051005 \tabularnewline
30 & 0.89901549821953 & 0.201969003560941 & 0.10098450178047 \tabularnewline
31 & 0.878862155899014 & 0.242275688201973 & 0.121137844100986 \tabularnewline
32 & 0.896543604197 & 0.206912791606 & 0.103456395803 \tabularnewline
33 & 0.867228773182738 & 0.265542453634524 & 0.132771226817262 \tabularnewline
34 & 0.832842239325361 & 0.334315521349278 & 0.167157760674639 \tabularnewline
35 & 0.820130270510454 & 0.359739458979093 & 0.179869729489546 \tabularnewline
36 & 0.782939785955747 & 0.434120428088506 & 0.217060214044253 \tabularnewline
37 & 0.823466232636222 & 0.353067534727556 & 0.176533767363778 \tabularnewline
38 & 0.814712766529912 & 0.370574466940176 & 0.185287233470088 \tabularnewline
39 & 0.804181413472561 & 0.391637173054878 & 0.195818586527439 \tabularnewline
40 & 0.784605710400972 & 0.430788579198056 & 0.215394289599028 \tabularnewline
41 & 0.75929419356458 & 0.481411612870839 & 0.24070580643542 \tabularnewline
42 & 0.743739794322786 & 0.512520411354429 & 0.256260205677214 \tabularnewline
43 & 0.740541405310257 & 0.518917189379485 & 0.259458594689743 \tabularnewline
44 & 0.697393138744042 & 0.605213722511916 & 0.302606861255958 \tabularnewline
45 & 0.651912984289208 & 0.696174031421584 & 0.348087015710792 \tabularnewline
46 & 0.71965241261594 & 0.560695174768119 & 0.28034758738406 \tabularnewline
47 & 0.733501019875813 & 0.532997960248375 & 0.266498980124187 \tabularnewline
48 & 0.689322224807825 & 0.62135555038435 & 0.310677775192175 \tabularnewline
49 & 0.653654434546707 & 0.692691130906586 & 0.346345565453293 \tabularnewline
50 & 0.641314602131867 & 0.717370795736265 & 0.358685397868133 \tabularnewline
51 & 0.650106355304124 & 0.699787289391753 & 0.349893644695876 \tabularnewline
52 & 0.603947263879076 & 0.792105472241848 & 0.396052736120924 \tabularnewline
53 & 0.625722123354581 & 0.748555753290838 & 0.374277876645419 \tabularnewline
54 & 0.592243611079354 & 0.815512777841292 & 0.407756388920646 \tabularnewline
55 & 0.67983855157262 & 0.640322896854761 & 0.32016144842738 \tabularnewline
56 & 0.841320807900764 & 0.317358384198473 & 0.158679192099236 \tabularnewline
57 & 0.813528343881299 & 0.372943312237403 & 0.186471656118701 \tabularnewline
58 & 0.779481745974703 & 0.441036508050593 & 0.220518254025297 \tabularnewline
59 & 0.742315034993903 & 0.515369930012194 & 0.257684965006097 \tabularnewline
60 & 0.730768766933754 & 0.538462466132492 & 0.269231233066246 \tabularnewline
61 & 0.750706868668719 & 0.498586262662561 & 0.249293131331281 \tabularnewline
62 & 0.714777917864622 & 0.570444164270755 & 0.285222082135378 \tabularnewline
63 & 0.731690714697425 & 0.536618570605149 & 0.268309285302575 \tabularnewline
64 & 0.691380071487826 & 0.617239857024348 & 0.308619928512174 \tabularnewline
65 & 0.656792295647053 & 0.686415408705894 & 0.343207704352947 \tabularnewline
66 & 0.616649110743343 & 0.766701778513314 & 0.383350889256657 \tabularnewline
67 & 0.595283473027757 & 0.809433053944487 & 0.404716526972243 \tabularnewline
68 & 0.578678971132781 & 0.842642057734438 & 0.421321028867219 \tabularnewline
69 & 0.597196128485122 & 0.805607743029756 & 0.402803871514878 \tabularnewline
70 & 0.553301268793555 & 0.893397462412891 & 0.446698731206445 \tabularnewline
71 & 0.511933076370144 & 0.976133847259712 & 0.488066923629856 \tabularnewline
72 & 0.468986413702339 & 0.937972827404678 & 0.531013586297661 \tabularnewline
73 & 0.43740245386805 & 0.874804907736099 & 0.56259754613195 \tabularnewline
74 & 0.414740643841484 & 0.829481287682969 & 0.585259356158516 \tabularnewline
75 & 0.389077068964923 & 0.778154137929846 & 0.610922931035077 \tabularnewline
76 & 0.452782101799702 & 0.905564203599404 & 0.547217898200298 \tabularnewline
77 & 0.438199617858711 & 0.876399235717423 & 0.561800382141289 \tabularnewline
78 & 0.396697985942168 & 0.793395971884336 & 0.603302014057832 \tabularnewline
79 & 0.400426181742604 & 0.800852363485208 & 0.599573818257396 \tabularnewline
80 & 0.382429968918521 & 0.764859937837042 & 0.617570031081479 \tabularnewline
81 & 0.340609597010473 & 0.681219194020947 & 0.659390402989527 \tabularnewline
82 & 0.34887542308191 & 0.69775084616382 & 0.65112457691809 \tabularnewline
83 & 0.312374625910937 & 0.624749251821873 & 0.687625374089063 \tabularnewline
84 & 0.284704777622369 & 0.569409555244739 & 0.715295222377631 \tabularnewline
85 & 0.247058082277329 & 0.494116164554658 & 0.752941917722671 \tabularnewline
86 & 0.239523694987643 & 0.479047389975286 & 0.760476305012357 \tabularnewline
87 & 0.241069047674926 & 0.482138095349852 & 0.758930952325074 \tabularnewline
88 & 0.207199910417702 & 0.414399820835405 & 0.792800089582298 \tabularnewline
89 & 0.176571122520139 & 0.353142245040279 & 0.823428877479861 \tabularnewline
90 & 0.15192680262732 & 0.303853605254641 & 0.84807319737268 \tabularnewline
91 & 0.131560683612974 & 0.263121367225947 & 0.868439316387026 \tabularnewline
92 & 0.183196950011458 & 0.366393900022916 & 0.816803049988542 \tabularnewline
93 & 0.158328211798009 & 0.316656423596018 & 0.841671788201991 \tabularnewline
94 & 0.141355295925551 & 0.282710591851102 & 0.858644704074449 \tabularnewline
95 & 0.137532917593032 & 0.275065835186064 & 0.862467082406968 \tabularnewline
96 & 0.130636101890986 & 0.261272203781973 & 0.869363898109014 \tabularnewline
97 & 0.121437701811077 & 0.242875403622154 & 0.878562298188923 \tabularnewline
98 & 0.153374732531939 & 0.306749465063877 & 0.846625267468061 \tabularnewline
99 & 0.128323062443805 & 0.25664612488761 & 0.871676937556195 \tabularnewline
100 & 0.10883924312602 & 0.217678486252039 & 0.89116075687398 \tabularnewline
101 & 0.129282972130511 & 0.258565944261021 & 0.870717027869489 \tabularnewline
102 & 0.108216479785205 & 0.21643295957041 & 0.891783520214795 \tabularnewline
103 & 0.101373640203148 & 0.202747280406296 & 0.898626359796852 \tabularnewline
104 & 0.0825720059677654 & 0.165144011935531 & 0.917427994032235 \tabularnewline
105 & 0.0694969728698122 & 0.138993945739624 & 0.930503027130188 \tabularnewline
106 & 0.0695399001553407 & 0.139079800310681 & 0.930460099844659 \tabularnewline
107 & 0.0552025965598187 & 0.110405193119637 & 0.944797403440181 \tabularnewline
108 & 0.04601000145435 & 0.0920200029087 & 0.95398999854565 \tabularnewline
109 & 0.0362280846368673 & 0.0724561692737346 & 0.963771915363133 \tabularnewline
110 & 0.0409791670274397 & 0.0819583340548795 & 0.95902083297256 \tabularnewline
111 & 0.0314564258772069 & 0.0629128517544138 & 0.968543574122793 \tabularnewline
112 & 0.0339695327504575 & 0.067939065500915 & 0.966030467249543 \tabularnewline
113 & 0.0300835109290435 & 0.060167021858087 & 0.969916489070957 \tabularnewline
114 & 0.0241901409001046 & 0.0483802818002093 & 0.975809859099895 \tabularnewline
115 & 0.018620234232074 & 0.037240468464148 & 0.981379765767926 \tabularnewline
116 & 0.0142295466473433 & 0.0284590932946866 & 0.985770453352657 \tabularnewline
117 & 0.0201326388781259 & 0.0402652777562518 & 0.979867361121874 \tabularnewline
118 & 0.0232380259835347 & 0.0464760519670694 & 0.976761974016465 \tabularnewline
119 & 0.0186591953130149 & 0.0373183906260299 & 0.981340804686985 \tabularnewline
120 & 0.0138980580960273 & 0.0277961161920546 & 0.986101941903973 \tabularnewline
121 & 0.0113649055992513 & 0.0227298111985025 & 0.988635094400749 \tabularnewline
122 & 0.00929110627263377 & 0.0185822125452675 & 0.990708893727366 \tabularnewline
123 & 0.0108484657587587 & 0.0216969315175175 & 0.989151534241241 \tabularnewline
124 & 0.0195761437828055 & 0.0391522875656111 & 0.980423856217194 \tabularnewline
125 & 0.021252754075315 & 0.04250550815063 & 0.978747245924685 \tabularnewline
126 & 0.0512843615392419 & 0.102568723078484 & 0.948715638460758 \tabularnewline
127 & 0.039099368150631 & 0.078198736301262 & 0.960900631849369 \tabularnewline
128 & 0.0299398167815586 & 0.0598796335631172 & 0.970060183218441 \tabularnewline
129 & 0.0254182537697035 & 0.050836507539407 & 0.974581746230296 \tabularnewline
130 & 0.0240146046042122 & 0.0480292092084244 & 0.975985395395788 \tabularnewline
131 & 0.0191241464393436 & 0.0382482928786873 & 0.980875853560656 \tabularnewline
132 & 0.0217889035111795 & 0.043577807022359 & 0.97821109648882 \tabularnewline
133 & 0.0328914449631609 & 0.0657828899263218 & 0.967108555036839 \tabularnewline
134 & 0.0354203341636971 & 0.0708406683273941 & 0.964579665836303 \tabularnewline
135 & 0.0389783827117361 & 0.0779567654234723 & 0.961021617288264 \tabularnewline
136 & 0.0319658522950171 & 0.0639317045900342 & 0.968034147704983 \tabularnewline
137 & 0.0297844382506762 & 0.0595688765013523 & 0.970215561749324 \tabularnewline
138 & 0.0221932024874507 & 0.0443864049749014 & 0.977806797512549 \tabularnewline
139 & 0.0227836293698662 & 0.0455672587397324 & 0.977216370630134 \tabularnewline
140 & 0.016119858564664 & 0.032239717129328 & 0.983880141435336 \tabularnewline
141 & 0.0469323741206922 & 0.0938647482413844 & 0.953067625879308 \tabularnewline
142 & 0.0514962529012711 & 0.102992505802542 & 0.948503747098729 \tabularnewline
143 & 0.0376635748973914 & 0.0753271497947828 & 0.962336425102609 \tabularnewline
144 & 0.363867472722629 & 0.727734945445259 & 0.636132527277371 \tabularnewline
145 & 0.393634685433086 & 0.787269370866173 & 0.606365314566914 \tabularnewline
146 & 0.679659561622115 & 0.640680876755769 & 0.320340438377885 \tabularnewline
147 & 0.708464265115929 & 0.583071469768142 & 0.291535734884071 \tabularnewline
148 & 0.930886728919483 & 0.138226542161034 & 0.0691132710805172 \tabularnewline
149 & 0.907871192047628 & 0.184257615904745 & 0.0921288079523723 \tabularnewline
150 & 0.83757723219203 & 0.32484553561594 & 0.16242276780797 \tabularnewline
151 & 0.740544496197942 & 0.518911007604117 & 0.259455503802058 \tabularnewline
152 & 0.615724824290544 & 0.768550351418911 & 0.384275175709456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&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]0.999872161855867[/C][C]0.000255676288265398[/C][C]0.000127838144132699[/C][/ROW]
[ROW][C]11[/C][C]0.999621375253078[/C][C]0.000757249493843776[/C][C]0.000378624746921888[/C][/ROW]
[ROW][C]12[/C][C]0.999541508509171[/C][C]0.000916982981657265[/C][C]0.000458491490828633[/C][/ROW]
[ROW][C]13[/C][C]0.999047323119634[/C][C]0.00190535376073224[/C][C]0.000952676880366122[/C][/ROW]
[ROW][C]14[/C][C]0.998369085225199[/C][C]0.00326182954960271[/C][C]0.00163091477480135[/C][/ROW]
[ROW][C]15[/C][C]0.998012553246854[/C][C]0.00397489350629271[/C][C]0.00198744675314636[/C][/ROW]
[ROW][C]16[/C][C]0.996673254180182[/C][C]0.00665349163963528[/C][C]0.00332674581981764[/C][/ROW]
[ROW][C]17[/C][C]0.994066997534264[/C][C]0.0118660049314721[/C][C]0.00593300246573606[/C][/ROW]
[ROW][C]18[/C][C]0.99296690795087[/C][C]0.0140661840982598[/C][C]0.00703309204912992[/C][/ROW]
[ROW][C]19[/C][C]0.99004496920684[/C][C]0.0199100615863205[/C][C]0.00995503079316027[/C][/ROW]
[ROW][C]20[/C][C]0.983737343030676[/C][C]0.0325253139386488[/C][C]0.0162626569693244[/C][/ROW]
[ROW][C]21[/C][C]0.977440409305707[/C][C]0.0451191813885861[/C][C]0.022559590694293[/C][/ROW]
[ROW][C]22[/C][C]0.967237188321483[/C][C]0.0655256233570348[/C][C]0.0327628116785174[/C][/ROW]
[ROW][C]23[/C][C]0.952620776047605[/C][C]0.094758447904791[/C][C]0.0473792239523955[/C][/ROW]
[ROW][C]24[/C][C]0.935045103525923[/C][C]0.129909792948155[/C][C]0.0649548964740774[/C][/ROW]
[ROW][C]25[/C][C]0.934814537142841[/C][C]0.130370925714318[/C][C]0.0651854628571588[/C][/ROW]
[ROW][C]26[/C][C]0.934385619455136[/C][C]0.131228761089728[/C][C]0.0656143805448642[/C][/ROW]
[ROW][C]27[/C][C]0.93191311275153[/C][C]0.13617377449694[/C][C]0.0680868872484701[/C][/ROW]
[ROW][C]28[/C][C]0.940525537277223[/C][C]0.118948925445554[/C][C]0.0594744627227771[/C][/ROW]
[ROW][C]29[/C][C]0.920091153794899[/C][C]0.159817692410201[/C][C]0.0799088462051005[/C][/ROW]
[ROW][C]30[/C][C]0.89901549821953[/C][C]0.201969003560941[/C][C]0.10098450178047[/C][/ROW]
[ROW][C]31[/C][C]0.878862155899014[/C][C]0.242275688201973[/C][C]0.121137844100986[/C][/ROW]
[ROW][C]32[/C][C]0.896543604197[/C][C]0.206912791606[/C][C]0.103456395803[/C][/ROW]
[ROW][C]33[/C][C]0.867228773182738[/C][C]0.265542453634524[/C][C]0.132771226817262[/C][/ROW]
[ROW][C]34[/C][C]0.832842239325361[/C][C]0.334315521349278[/C][C]0.167157760674639[/C][/ROW]
[ROW][C]35[/C][C]0.820130270510454[/C][C]0.359739458979093[/C][C]0.179869729489546[/C][/ROW]
[ROW][C]36[/C][C]0.782939785955747[/C][C]0.434120428088506[/C][C]0.217060214044253[/C][/ROW]
[ROW][C]37[/C][C]0.823466232636222[/C][C]0.353067534727556[/C][C]0.176533767363778[/C][/ROW]
[ROW][C]38[/C][C]0.814712766529912[/C][C]0.370574466940176[/C][C]0.185287233470088[/C][/ROW]
[ROW][C]39[/C][C]0.804181413472561[/C][C]0.391637173054878[/C][C]0.195818586527439[/C][/ROW]
[ROW][C]40[/C][C]0.784605710400972[/C][C]0.430788579198056[/C][C]0.215394289599028[/C][/ROW]
[ROW][C]41[/C][C]0.75929419356458[/C][C]0.481411612870839[/C][C]0.24070580643542[/C][/ROW]
[ROW][C]42[/C][C]0.743739794322786[/C][C]0.512520411354429[/C][C]0.256260205677214[/C][/ROW]
[ROW][C]43[/C][C]0.740541405310257[/C][C]0.518917189379485[/C][C]0.259458594689743[/C][/ROW]
[ROW][C]44[/C][C]0.697393138744042[/C][C]0.605213722511916[/C][C]0.302606861255958[/C][/ROW]
[ROW][C]45[/C][C]0.651912984289208[/C][C]0.696174031421584[/C][C]0.348087015710792[/C][/ROW]
[ROW][C]46[/C][C]0.71965241261594[/C][C]0.560695174768119[/C][C]0.28034758738406[/C][/ROW]
[ROW][C]47[/C][C]0.733501019875813[/C][C]0.532997960248375[/C][C]0.266498980124187[/C][/ROW]
[ROW][C]48[/C][C]0.689322224807825[/C][C]0.62135555038435[/C][C]0.310677775192175[/C][/ROW]
[ROW][C]49[/C][C]0.653654434546707[/C][C]0.692691130906586[/C][C]0.346345565453293[/C][/ROW]
[ROW][C]50[/C][C]0.641314602131867[/C][C]0.717370795736265[/C][C]0.358685397868133[/C][/ROW]
[ROW][C]51[/C][C]0.650106355304124[/C][C]0.699787289391753[/C][C]0.349893644695876[/C][/ROW]
[ROW][C]52[/C][C]0.603947263879076[/C][C]0.792105472241848[/C][C]0.396052736120924[/C][/ROW]
[ROW][C]53[/C][C]0.625722123354581[/C][C]0.748555753290838[/C][C]0.374277876645419[/C][/ROW]
[ROW][C]54[/C][C]0.592243611079354[/C][C]0.815512777841292[/C][C]0.407756388920646[/C][/ROW]
[ROW][C]55[/C][C]0.67983855157262[/C][C]0.640322896854761[/C][C]0.32016144842738[/C][/ROW]
[ROW][C]56[/C][C]0.841320807900764[/C][C]0.317358384198473[/C][C]0.158679192099236[/C][/ROW]
[ROW][C]57[/C][C]0.813528343881299[/C][C]0.372943312237403[/C][C]0.186471656118701[/C][/ROW]
[ROW][C]58[/C][C]0.779481745974703[/C][C]0.441036508050593[/C][C]0.220518254025297[/C][/ROW]
[ROW][C]59[/C][C]0.742315034993903[/C][C]0.515369930012194[/C][C]0.257684965006097[/C][/ROW]
[ROW][C]60[/C][C]0.730768766933754[/C][C]0.538462466132492[/C][C]0.269231233066246[/C][/ROW]
[ROW][C]61[/C][C]0.750706868668719[/C][C]0.498586262662561[/C][C]0.249293131331281[/C][/ROW]
[ROW][C]62[/C][C]0.714777917864622[/C][C]0.570444164270755[/C][C]0.285222082135378[/C][/ROW]
[ROW][C]63[/C][C]0.731690714697425[/C][C]0.536618570605149[/C][C]0.268309285302575[/C][/ROW]
[ROW][C]64[/C][C]0.691380071487826[/C][C]0.617239857024348[/C][C]0.308619928512174[/C][/ROW]
[ROW][C]65[/C][C]0.656792295647053[/C][C]0.686415408705894[/C][C]0.343207704352947[/C][/ROW]
[ROW][C]66[/C][C]0.616649110743343[/C][C]0.766701778513314[/C][C]0.383350889256657[/C][/ROW]
[ROW][C]67[/C][C]0.595283473027757[/C][C]0.809433053944487[/C][C]0.404716526972243[/C][/ROW]
[ROW][C]68[/C][C]0.578678971132781[/C][C]0.842642057734438[/C][C]0.421321028867219[/C][/ROW]
[ROW][C]69[/C][C]0.597196128485122[/C][C]0.805607743029756[/C][C]0.402803871514878[/C][/ROW]
[ROW][C]70[/C][C]0.553301268793555[/C][C]0.893397462412891[/C][C]0.446698731206445[/C][/ROW]
[ROW][C]71[/C][C]0.511933076370144[/C][C]0.976133847259712[/C][C]0.488066923629856[/C][/ROW]
[ROW][C]72[/C][C]0.468986413702339[/C][C]0.937972827404678[/C][C]0.531013586297661[/C][/ROW]
[ROW][C]73[/C][C]0.43740245386805[/C][C]0.874804907736099[/C][C]0.56259754613195[/C][/ROW]
[ROW][C]74[/C][C]0.414740643841484[/C][C]0.829481287682969[/C][C]0.585259356158516[/C][/ROW]
[ROW][C]75[/C][C]0.389077068964923[/C][C]0.778154137929846[/C][C]0.610922931035077[/C][/ROW]
[ROW][C]76[/C][C]0.452782101799702[/C][C]0.905564203599404[/C][C]0.547217898200298[/C][/ROW]
[ROW][C]77[/C][C]0.438199617858711[/C][C]0.876399235717423[/C][C]0.561800382141289[/C][/ROW]
[ROW][C]78[/C][C]0.396697985942168[/C][C]0.793395971884336[/C][C]0.603302014057832[/C][/ROW]
[ROW][C]79[/C][C]0.400426181742604[/C][C]0.800852363485208[/C][C]0.599573818257396[/C][/ROW]
[ROW][C]80[/C][C]0.382429968918521[/C][C]0.764859937837042[/C][C]0.617570031081479[/C][/ROW]
[ROW][C]81[/C][C]0.340609597010473[/C][C]0.681219194020947[/C][C]0.659390402989527[/C][/ROW]
[ROW][C]82[/C][C]0.34887542308191[/C][C]0.69775084616382[/C][C]0.65112457691809[/C][/ROW]
[ROW][C]83[/C][C]0.312374625910937[/C][C]0.624749251821873[/C][C]0.687625374089063[/C][/ROW]
[ROW][C]84[/C][C]0.284704777622369[/C][C]0.569409555244739[/C][C]0.715295222377631[/C][/ROW]
[ROW][C]85[/C][C]0.247058082277329[/C][C]0.494116164554658[/C][C]0.752941917722671[/C][/ROW]
[ROW][C]86[/C][C]0.239523694987643[/C][C]0.479047389975286[/C][C]0.760476305012357[/C][/ROW]
[ROW][C]87[/C][C]0.241069047674926[/C][C]0.482138095349852[/C][C]0.758930952325074[/C][/ROW]
[ROW][C]88[/C][C]0.207199910417702[/C][C]0.414399820835405[/C][C]0.792800089582298[/C][/ROW]
[ROW][C]89[/C][C]0.176571122520139[/C][C]0.353142245040279[/C][C]0.823428877479861[/C][/ROW]
[ROW][C]90[/C][C]0.15192680262732[/C][C]0.303853605254641[/C][C]0.84807319737268[/C][/ROW]
[ROW][C]91[/C][C]0.131560683612974[/C][C]0.263121367225947[/C][C]0.868439316387026[/C][/ROW]
[ROW][C]92[/C][C]0.183196950011458[/C][C]0.366393900022916[/C][C]0.816803049988542[/C][/ROW]
[ROW][C]93[/C][C]0.158328211798009[/C][C]0.316656423596018[/C][C]0.841671788201991[/C][/ROW]
[ROW][C]94[/C][C]0.141355295925551[/C][C]0.282710591851102[/C][C]0.858644704074449[/C][/ROW]
[ROW][C]95[/C][C]0.137532917593032[/C][C]0.275065835186064[/C][C]0.862467082406968[/C][/ROW]
[ROW][C]96[/C][C]0.130636101890986[/C][C]0.261272203781973[/C][C]0.869363898109014[/C][/ROW]
[ROW][C]97[/C][C]0.121437701811077[/C][C]0.242875403622154[/C][C]0.878562298188923[/C][/ROW]
[ROW][C]98[/C][C]0.153374732531939[/C][C]0.306749465063877[/C][C]0.846625267468061[/C][/ROW]
[ROW][C]99[/C][C]0.128323062443805[/C][C]0.25664612488761[/C][C]0.871676937556195[/C][/ROW]
[ROW][C]100[/C][C]0.10883924312602[/C][C]0.217678486252039[/C][C]0.89116075687398[/C][/ROW]
[ROW][C]101[/C][C]0.129282972130511[/C][C]0.258565944261021[/C][C]0.870717027869489[/C][/ROW]
[ROW][C]102[/C][C]0.108216479785205[/C][C]0.21643295957041[/C][C]0.891783520214795[/C][/ROW]
[ROW][C]103[/C][C]0.101373640203148[/C][C]0.202747280406296[/C][C]0.898626359796852[/C][/ROW]
[ROW][C]104[/C][C]0.0825720059677654[/C][C]0.165144011935531[/C][C]0.917427994032235[/C][/ROW]
[ROW][C]105[/C][C]0.0694969728698122[/C][C]0.138993945739624[/C][C]0.930503027130188[/C][/ROW]
[ROW][C]106[/C][C]0.0695399001553407[/C][C]0.139079800310681[/C][C]0.930460099844659[/C][/ROW]
[ROW][C]107[/C][C]0.0552025965598187[/C][C]0.110405193119637[/C][C]0.944797403440181[/C][/ROW]
[ROW][C]108[/C][C]0.04601000145435[/C][C]0.0920200029087[/C][C]0.95398999854565[/C][/ROW]
[ROW][C]109[/C][C]0.0362280846368673[/C][C]0.0724561692737346[/C][C]0.963771915363133[/C][/ROW]
[ROW][C]110[/C][C]0.0409791670274397[/C][C]0.0819583340548795[/C][C]0.95902083297256[/C][/ROW]
[ROW][C]111[/C][C]0.0314564258772069[/C][C]0.0629128517544138[/C][C]0.968543574122793[/C][/ROW]
[ROW][C]112[/C][C]0.0339695327504575[/C][C]0.067939065500915[/C][C]0.966030467249543[/C][/ROW]
[ROW][C]113[/C][C]0.0300835109290435[/C][C]0.060167021858087[/C][C]0.969916489070957[/C][/ROW]
[ROW][C]114[/C][C]0.0241901409001046[/C][C]0.0483802818002093[/C][C]0.975809859099895[/C][/ROW]
[ROW][C]115[/C][C]0.018620234232074[/C][C]0.037240468464148[/C][C]0.981379765767926[/C][/ROW]
[ROW][C]116[/C][C]0.0142295466473433[/C][C]0.0284590932946866[/C][C]0.985770453352657[/C][/ROW]
[ROW][C]117[/C][C]0.0201326388781259[/C][C]0.0402652777562518[/C][C]0.979867361121874[/C][/ROW]
[ROW][C]118[/C][C]0.0232380259835347[/C][C]0.0464760519670694[/C][C]0.976761974016465[/C][/ROW]
[ROW][C]119[/C][C]0.0186591953130149[/C][C]0.0373183906260299[/C][C]0.981340804686985[/C][/ROW]
[ROW][C]120[/C][C]0.0138980580960273[/C][C]0.0277961161920546[/C][C]0.986101941903973[/C][/ROW]
[ROW][C]121[/C][C]0.0113649055992513[/C][C]0.0227298111985025[/C][C]0.988635094400749[/C][/ROW]
[ROW][C]122[/C][C]0.00929110627263377[/C][C]0.0185822125452675[/C][C]0.990708893727366[/C][/ROW]
[ROW][C]123[/C][C]0.0108484657587587[/C][C]0.0216969315175175[/C][C]0.989151534241241[/C][/ROW]
[ROW][C]124[/C][C]0.0195761437828055[/C][C]0.0391522875656111[/C][C]0.980423856217194[/C][/ROW]
[ROW][C]125[/C][C]0.021252754075315[/C][C]0.04250550815063[/C][C]0.978747245924685[/C][/ROW]
[ROW][C]126[/C][C]0.0512843615392419[/C][C]0.102568723078484[/C][C]0.948715638460758[/C][/ROW]
[ROW][C]127[/C][C]0.039099368150631[/C][C]0.078198736301262[/C][C]0.960900631849369[/C][/ROW]
[ROW][C]128[/C][C]0.0299398167815586[/C][C]0.0598796335631172[/C][C]0.970060183218441[/C][/ROW]
[ROW][C]129[/C][C]0.0254182537697035[/C][C]0.050836507539407[/C][C]0.974581746230296[/C][/ROW]
[ROW][C]130[/C][C]0.0240146046042122[/C][C]0.0480292092084244[/C][C]0.975985395395788[/C][/ROW]
[ROW][C]131[/C][C]0.0191241464393436[/C][C]0.0382482928786873[/C][C]0.980875853560656[/C][/ROW]
[ROW][C]132[/C][C]0.0217889035111795[/C][C]0.043577807022359[/C][C]0.97821109648882[/C][/ROW]
[ROW][C]133[/C][C]0.0328914449631609[/C][C]0.0657828899263218[/C][C]0.967108555036839[/C][/ROW]
[ROW][C]134[/C][C]0.0354203341636971[/C][C]0.0708406683273941[/C][C]0.964579665836303[/C][/ROW]
[ROW][C]135[/C][C]0.0389783827117361[/C][C]0.0779567654234723[/C][C]0.961021617288264[/C][/ROW]
[ROW][C]136[/C][C]0.0319658522950171[/C][C]0.0639317045900342[/C][C]0.968034147704983[/C][/ROW]
[ROW][C]137[/C][C]0.0297844382506762[/C][C]0.0595688765013523[/C][C]0.970215561749324[/C][/ROW]
[ROW][C]138[/C][C]0.0221932024874507[/C][C]0.0443864049749014[/C][C]0.977806797512549[/C][/ROW]
[ROW][C]139[/C][C]0.0227836293698662[/C][C]0.0455672587397324[/C][C]0.977216370630134[/C][/ROW]
[ROW][C]140[/C][C]0.016119858564664[/C][C]0.032239717129328[/C][C]0.983880141435336[/C][/ROW]
[ROW][C]141[/C][C]0.0469323741206922[/C][C]0.0938647482413844[/C][C]0.953067625879308[/C][/ROW]
[ROW][C]142[/C][C]0.0514962529012711[/C][C]0.102992505802542[/C][C]0.948503747098729[/C][/ROW]
[ROW][C]143[/C][C]0.0376635748973914[/C][C]0.0753271497947828[/C][C]0.962336425102609[/C][/ROW]
[ROW][C]144[/C][C]0.363867472722629[/C][C]0.727734945445259[/C][C]0.636132527277371[/C][/ROW]
[ROW][C]145[/C][C]0.393634685433086[/C][C]0.787269370866173[/C][C]0.606365314566914[/C][/ROW]
[ROW][C]146[/C][C]0.679659561622115[/C][C]0.640680876755769[/C][C]0.320340438377885[/C][/ROW]
[ROW][C]147[/C][C]0.708464265115929[/C][C]0.583071469768142[/C][C]0.291535734884071[/C][/ROW]
[ROW][C]148[/C][C]0.930886728919483[/C][C]0.138226542161034[/C][C]0.0691132710805172[/C][/ROW]
[ROW][C]149[/C][C]0.907871192047628[/C][C]0.184257615904745[/C][C]0.0921288079523723[/C][/ROW]
[ROW][C]150[/C][C]0.83757723219203[/C][C]0.32484553561594[/C][C]0.16242276780797[/C][/ROW]
[ROW][C]151[/C][C]0.740544496197942[/C][C]0.518911007604117[/C][C]0.259455503802058[/C][/ROW]
[ROW][C]152[/C][C]0.615724824290544[/C][C]0.768550351418911[/C][C]0.384275175709456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&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
100.9998721618558670.0002556762882653980.000127838144132699
110.9996213752530780.0007572494938437760.000378624746921888
120.9995415085091710.0009169829816572650.000458491490828633
130.9990473231196340.001905353760732240.000952676880366122
140.9983690852251990.003261829549602710.00163091477480135
150.9980125532468540.003974893506292710.00198744675314636
160.9966732541801820.006653491639635280.00332674581981764
170.9940669975342640.01186600493147210.00593300246573606
180.992966907950870.01406618409825980.00703309204912992
190.990044969206840.01991006158632050.00995503079316027
200.9837373430306760.03252531393864880.0162626569693244
210.9774404093057070.04511918138858610.022559590694293
220.9672371883214830.06552562335703480.0327628116785174
230.9526207760476050.0947584479047910.0473792239523955
240.9350451035259230.1299097929481550.0649548964740774
250.9348145371428410.1303709257143180.0651854628571588
260.9343856194551360.1312287610897280.0656143805448642
270.931913112751530.136173774496940.0680868872484701
280.9405255372772230.1189489254455540.0594744627227771
290.9200911537948990.1598176924102010.0799088462051005
300.899015498219530.2019690035609410.10098450178047
310.8788621558990140.2422756882019730.121137844100986
320.8965436041970.2069127916060.103456395803
330.8672287731827380.2655424536345240.132771226817262
340.8328422393253610.3343155213492780.167157760674639
350.8201302705104540.3597394589790930.179869729489546
360.7829397859557470.4341204280885060.217060214044253
370.8234662326362220.3530675347275560.176533767363778
380.8147127665299120.3705744669401760.185287233470088
390.8041814134725610.3916371730548780.195818586527439
400.7846057104009720.4307885791980560.215394289599028
410.759294193564580.4814116128708390.24070580643542
420.7437397943227860.5125204113544290.256260205677214
430.7405414053102570.5189171893794850.259458594689743
440.6973931387440420.6052137225119160.302606861255958
450.6519129842892080.6961740314215840.348087015710792
460.719652412615940.5606951747681190.28034758738406
470.7335010198758130.5329979602483750.266498980124187
480.6893222248078250.621355550384350.310677775192175
490.6536544345467070.6926911309065860.346345565453293
500.6413146021318670.7173707957362650.358685397868133
510.6501063553041240.6997872893917530.349893644695876
520.6039472638790760.7921054722418480.396052736120924
530.6257221233545810.7485557532908380.374277876645419
540.5922436110793540.8155127778412920.407756388920646
550.679838551572620.6403228968547610.32016144842738
560.8413208079007640.3173583841984730.158679192099236
570.8135283438812990.3729433122374030.186471656118701
580.7794817459747030.4410365080505930.220518254025297
590.7423150349939030.5153699300121940.257684965006097
600.7307687669337540.5384624661324920.269231233066246
610.7507068686687190.4985862626625610.249293131331281
620.7147779178646220.5704441642707550.285222082135378
630.7316907146974250.5366185706051490.268309285302575
640.6913800714878260.6172398570243480.308619928512174
650.6567922956470530.6864154087058940.343207704352947
660.6166491107433430.7667017785133140.383350889256657
670.5952834730277570.8094330539444870.404716526972243
680.5786789711327810.8426420577344380.421321028867219
690.5971961284851220.8056077430297560.402803871514878
700.5533012687935550.8933974624128910.446698731206445
710.5119330763701440.9761338472597120.488066923629856
720.4689864137023390.9379728274046780.531013586297661
730.437402453868050.8748049077360990.56259754613195
740.4147406438414840.8294812876829690.585259356158516
750.3890770689649230.7781541379298460.610922931035077
760.4527821017997020.9055642035994040.547217898200298
770.4381996178587110.8763992357174230.561800382141289
780.3966979859421680.7933959718843360.603302014057832
790.4004261817426040.8008523634852080.599573818257396
800.3824299689185210.7648599378370420.617570031081479
810.3406095970104730.6812191940209470.659390402989527
820.348875423081910.697750846163820.65112457691809
830.3123746259109370.6247492518218730.687625374089063
840.2847047776223690.5694095552447390.715295222377631
850.2470580822773290.4941161645546580.752941917722671
860.2395236949876430.4790473899752860.760476305012357
870.2410690476749260.4821380953498520.758930952325074
880.2071999104177020.4143998208354050.792800089582298
890.1765711225201390.3531422450402790.823428877479861
900.151926802627320.3038536052546410.84807319737268
910.1315606836129740.2631213672259470.868439316387026
920.1831969500114580.3663939000229160.816803049988542
930.1583282117980090.3166564235960180.841671788201991
940.1413552959255510.2827105918511020.858644704074449
950.1375329175930320.2750658351860640.862467082406968
960.1306361018909860.2612722037819730.869363898109014
970.1214377018110770.2428754036221540.878562298188923
980.1533747325319390.3067494650638770.846625267468061
990.1283230624438050.256646124887610.871676937556195
1000.108839243126020.2176784862520390.89116075687398
1010.1292829721305110.2585659442610210.870717027869489
1020.1082164797852050.216432959570410.891783520214795
1030.1013736402031480.2027472804062960.898626359796852
1040.08257200596776540.1651440119355310.917427994032235
1050.06949697286981220.1389939457396240.930503027130188
1060.06953990015534070.1390798003106810.930460099844659
1070.05520259655981870.1104051931196370.944797403440181
1080.046010001454350.09202000290870.95398999854565
1090.03622808463686730.07245616927373460.963771915363133
1100.04097916702743970.08195833405487950.95902083297256
1110.03145642587720690.06291285175441380.968543574122793
1120.03396953275045750.0679390655009150.966030467249543
1130.03008351092904350.0601670218580870.969916489070957
1140.02419014090010460.04838028180020930.975809859099895
1150.0186202342320740.0372404684641480.981379765767926
1160.01422954664734330.02845909329468660.985770453352657
1170.02013263887812590.04026527775625180.979867361121874
1180.02323802598353470.04647605196706940.976761974016465
1190.01865919531301490.03731839062602990.981340804686985
1200.01389805809602730.02779611619205460.986101941903973
1210.01136490559925130.02272981119850250.988635094400749
1220.009291106272633770.01858221254526750.990708893727366
1230.01084846575875870.02169693151751750.989151534241241
1240.01957614378280550.03915228756561110.980423856217194
1250.0212527540753150.042505508150630.978747245924685
1260.05128436153924190.1025687230784840.948715638460758
1270.0390993681506310.0781987363012620.960900631849369
1280.02993981678155860.05987963356311720.970060183218441
1290.02541825376970350.0508365075394070.974581746230296
1300.02401460460421220.04802920920842440.975985395395788
1310.01912414643934360.03824829287868730.980875853560656
1320.02178890351117950.0435778070223590.97821109648882
1330.03289144496316090.06578288992632180.967108555036839
1340.03542033416369710.07084066832739410.964579665836303
1350.03897838271173610.07795676542347230.961021617288264
1360.03196585229501710.06393170459003420.968034147704983
1370.02978443825067620.05956887650135230.970215561749324
1380.02219320248745070.04438640497490140.977806797512549
1390.02278362936986620.04556725873973240.977216370630134
1400.0161198585646640.0322397171293280.983880141435336
1410.04693237412069220.09386474824138440.953067625879308
1420.05149625290127110.1029925058025420.948503747098729
1430.03766357489739140.07532714979478280.962336425102609
1440.3638674727226290.7277349454452590.636132527277371
1450.3936346854330860.7872693708661730.606365314566914
1460.6796595616221150.6406808767557690.320340438377885
1470.7084642651159290.5830714697681420.291535734884071
1480.9308867289194830.1382265421610340.0691132710805172
1490.9078711920476280.1842576159047450.0921288079523723
1500.837577232192030.324845535615940.16242276780797
1510.7405444961979420.5189110076041170.259455503802058
1520.6157248242905440.7685503514189110.384275175709456






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level300.20979020979021NOK
10% type I error level480.335664335664336NOK

\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 & 7 & 0.048951048951049 & NOK \tabularnewline
5% type I error level & 30 & 0.20979020979021 & NOK \tabularnewline
10% type I error level & 48 & 0.335664335664336 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185447&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]7[/C][C]0.048951048951049[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.20979020979021[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.335664335664336[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185447&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185447&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level300.20979020979021NOK
10% type I error level480.335664335664336NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}