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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 11:32:12 -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/t13516975502wa6h3g6tsszd30.htm/, Retrieved Mon, 29 Apr 2024 03:25:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185448, Retrieved Mon, 29 Apr 2024 03:25:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

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] [x4] [2012-10-31 15:32:12] [e357aba3893873b930815b56a53f1005] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	12	53	32
39	32	16	11	11	86	51
30	35	19	15	14	66	42
31	33	15	6	12	67	41
34	37	14	13	21	76	46
35	29	13	10	12	78	47
39	31	19	12	22	53	37
34	36	15	14	11	80	49
36	35	14	12	10	74	45
37	38	15	6	13	76	47
38	31	16	10	10	79	49
36	34	16	12	8	54	33
38	35	16	12	15	67	42
39	38	16	11	14	54	33
33	37	17	15	10	87	53
32	33	15	12	14	58	36
36	32	15	10	14	75	45
38	38	20	12	11	88	54
39	38	18	11	10	64	41
32	32	16	12	13	57	36
32	33	16	11	7	66	41
31	31	16	12	14	68	44
39	38	19	13	12	54	33
37	39	16	11	14	56	37
39	32	17	9	11	86	52
41	32	17	13	9	80	47
36	35	16	10	11	76	43
33	37	15	14	15	69	44
33	33	16	12	14	78	45
34	33	14	10	13	67	44
31	28	15	12	9	80	49
27	32	12	8	15	54	33
37	31	14	10	10	71	43
34	37	16	12	11	84	54
34	30	14	12	13	74	42
32	33	7	7	8	71	44
29	31	10	6	20	63	37
36	33	14	12	12	71	43
29	31	16	10	10	76	46
35	33	16	10	10	69	42
37	32	16	10	9	74	45
34	33	14	12	14	75	44
38	32	20	15	8	54	33
35	33	14	10	14	52	31
38	28	14	10	11	69	42
37	35	11	12	13	68	40
38	39	14	13	9	65	43
33	34	15	11	11	75	46
36	38	16	11	15	74	42
38	32	14	12	11	75	45
32	38	16	14	10	72	44
32	30	14	10	14	67	40
32	33	12	12	18	63	37
34	38	16	13	14	62	46
32	32	9	5	11	63	36
37	32	14	6	12	76	47
39	34	16	12	13	74	45
29	34	16	12	9	67	42
37	36	15	11	10	73	43
35	34	16	10	15	70	43
30	28	12	7	20	53	32
38	34	16	12	12	77	45
34	35	16	14	12	77	45
31	35	14	11	14	52	31
34	31	16	12	13	54	33
35	37	17	13	11	80	49
36	35	18	14	17	66	42
30	27	18	11	12	73	41
39	40	12	12	13	63	38
35	37	16	12	14	69	42
38	36	10	8	13	67	44
31	38	14	11	15	54	33
34	39	18	14	13	81	48
38	41	18	14	10	69	40
34	27	16	12	11	84	50
39	30	17	9	19	80	49
37	37	16	13	13	70	43
34	31	16	11	17	69	44
28	31	13	12	13	77	47
37	27	16	12	9	54	33
33	36	16	12	11	79	46
37	38	20	12	10	30	0
35	37	16	12	9	71	45
37	33	15	12	12	73	43
32	34	15	11	12	72	44
33	31	16	10	13	77	47
38	39	14	9	13	75	45
33	34	16	12	12	69	42
29	32	16	12	15	54	33
33	33	15	12	22	70	43
31	36	12	9	13	73	46
36	32	17	15	15	54	33
35	41	16	12	13	77	46
32	28	15	12	15	82	48
29	30	13	12	10	80	47
39	36	16	10	11	80	47
37	35	16	13	16	69	43
35	31	16	9	11	78	46
37	34	16	12	11	81	48
32	36	14	10	10	76	46
38	36	16	14	10	76	45
37	35	16	11	16	73	45
36	37	20	15	12	85	52
32	28	15	11	11	66	42
33	39	16	11	16	79	47
40	32	13	12	19	68	41
38	35	17	12	11	76	47
41	39	16	12	16	71	43
36	35	16	11	15	54	33
43	42	12	7	24	46	30
30	34	16	12	14	82	49
31	33	16	14	15	74	44
32	41	17	11	11	88	55
32	33	13	11	15	38	11
37	34	12	10	12	76	47
37	32	18	13	10	86	53
33	40	14	13	14	54	33
34	40	14	8	13	70	44
33	35	13	11	9	69	42
38	36	16	12	15	90	55
33	37	13	11	15	54	33
31	27	16	13	14	76	46
38	39	13	12	11	89	54
37	38	16	14	8	76	47
33	31	15	13	11	73	45
31	33	16	15	11	79	47
39	32	15	10	8	90	55
44	39	17	11	10	74	44
33	36	15	9	11	81	53
35	33	12	11	13	72	44
32	33	16	10	11	71	42
28	32	10	11	20	66	40
40	37	16	8	10	77	46
27	30	12	11	15	65	40
37	38	14	12	12	74	46
32	29	15	12	14	82	53
28	22	13	9	23	54	33
34	35	15	11	14	63	42
30	35	11	10	16	54	35
35	34	12	8	11	64	40
31	35	8	9	12	69	41
32	34	16	8	10	54	33
30	34	15	9	14	84	51
30	35	17	15	12	86	53
31	23	16	11	12	77	46
40	31	10	8	11	89	55
32	27	18	13	12	76	47
36	36	13	12	13	60	38
32	31	16	12	11	75	46
35	32	13	9	19	73	46
38	39	10	7	12	85	53
42	37	15	13	17	79	47
34	38	16	9	9	71	41
35	39	16	6	12	72	44
35	34	14	8	19	69	43
33	31	10	8	18	78	51
36	32	17	15	15	54	33
32	37	13	6	14	69	43
33	36	15	9	11	81	53
34	32	16	11	9	84	51
32	35	12	8	18	84	50
34	36	13	8	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 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 & 14 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&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]14 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=185448&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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 time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 4.004066843185 -0.0482997595122031Connected[t] + 0.0306718810019555Separate[t] + 0.526836579812634Learning[t] -0.00818079228040855Depression[t] + 0.0015368859032072Belonging[t] -0.00467637986962386Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  4.004066843185 -0.0482997595122031Connected[t] +  0.0306718810019555Separate[t] +  0.526836579812634Learning[t] -0.00818079228040855Depression[t] +  0.0015368859032072Belonging[t] -0.00467637986962386Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  4.004066843185 -0.0482997595122031Connected[t] +  0.0306718810019555Separate[t] +  0.526836579812634Learning[t] -0.00818079228040855Depression[t] +  0.0015368859032072Belonging[t] -0.00467637986962386Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185448&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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] = + 4.004066843185 -0.0482997595122031Connected[t] + 0.0306718810019555Separate[t] + 0.526836579812634Learning[t] -0.00818079228040855Depression[t] + 0.0015368859032072Belonging[t] -0.00467637986962386Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.0040668431852.3911141.67460.0960370.048018
Connected-0.04829975951220310.04688-1.03030.3044850.152243
Separate0.03067188100195550.0437350.70130.4841590.242079
Learning0.5268365798126340.0669417.870200
Depression-0.008180792280408550.049081-0.16670.867840.43392
Belonging0.00153688590320720.043720.03520.9720030.486002
Belonging_Final-0.004676379869623860.063046-0.07420.9409680.470484

\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) & 4.004066843185 & 2.391114 & 1.6746 & 0.096037 & 0.048018 \tabularnewline
Connected & -0.0482997595122031 & 0.04688 & -1.0303 & 0.304485 & 0.152243 \tabularnewline
Separate & 0.0306718810019555 & 0.043735 & 0.7013 & 0.484159 & 0.242079 \tabularnewline
Learning & 0.526836579812634 & 0.066941 & 7.8702 & 0 & 0 \tabularnewline
Depression & -0.00818079228040855 & 0.049081 & -0.1667 & 0.86784 & 0.43392 \tabularnewline
Belonging & 0.0015368859032072 & 0.04372 & 0.0352 & 0.972003 & 0.486002 \tabularnewline
Belonging_Final & -0.00467637986962386 & 0.063046 & -0.0742 & 0.940968 & 0.470484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&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]4.004066843185[/C][C]2.391114[/C][C]1.6746[/C][C]0.096037[/C][C]0.048018[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0482997595122031[/C][C]0.04688[/C][C]-1.0303[/C][C]0.304485[/C][C]0.152243[/C][/ROW]
[ROW][C]Separate[/C][C]0.0306718810019555[/C][C]0.043735[/C][C]0.7013[/C][C]0.484159[/C][C]0.242079[/C][/ROW]
[ROW][C]Learning[/C][C]0.526836579812634[/C][C]0.066941[/C][C]7.8702[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Depression[/C][C]-0.00818079228040855[/C][C]0.049081[/C][C]-0.1667[/C][C]0.86784[/C][C]0.43392[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0015368859032072[/C][C]0.04372[/C][C]0.0352[/C][C]0.972003[/C][C]0.486002[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.00467637986962386[/C][C]0.063046[/C][C]-0.0742[/C][C]0.940968[/C][C]0.470484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185448&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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)4.0040668431852.3911141.67460.0960370.048018
Connected-0.04829975951220310.04688-1.03030.3044850.152243
Separate0.03067188100195550.0437350.70130.4841590.242079
Learning0.5268365798126340.0669417.870200
Depression-0.008180792280408550.049081-0.16670.867840.43392
Belonging0.00153688590320720.043720.03520.9720030.486002
Belonging_Final-0.004676379869623860.063046-0.07420.9409680.470484






Multiple Linear Regression - Regression Statistics
Multiple R0.550647838886829
R-squared0.303213042470735
Adjusted R-squared0.276240644114763
F-TEST (value)11.2416047868285
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value2.10551243107204e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8220462940462
Sum Squared Residuals514.577168135362

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.550647838886829 \tabularnewline
R-squared & 0.303213042470735 \tabularnewline
Adjusted R-squared & 0.276240644114763 \tabularnewline
F-TEST (value) & 11.2416047868285 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 2.10551243107204e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8220462940462 \tabularnewline
Sum Squared Residuals & 514.577168135362 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.550647838886829[/C][/ROW]
[ROW][C]R-squared[/C][C]0.303213042470735[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.276240644114763[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.2416047868285[/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]2.10551243107204e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8220462940462[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]514.577168135362[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185448&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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 R0.550647838886829
R-squared0.303213042470735
Adjusted R-squared0.276240644114763
F-TEST (value)11.2416047868285
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value2.10551243107204e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8220462940462
Sum Squared Residuals514.577168135362






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.871825008500342.12817499149966
21111.3349497905143-0.334949790514305
31513.42898033248911.57101966751085
4611.2345653420561-5.23456534205614
51310.60233995097182.39766004902821
6109.853853086091780.146146913908221
71212.8095510172347-0.809551017234653
81411.17243097659052.82756902340949
91210.53598799309121.46401200690882
10611.0757190916234-5.07571909162342
111011.3593530197218-1.35935301972176
121211.60072969664670.39927030335335
131211.45542861057640.544571389423582
141111.5294331884354-0.529433188435412
151512.30530925085432.6946907491457
161211.17945392420240.82054607579762
171010.9396226466795-0.939622646679519
181213.6636717874863-1.66367178748632
191112.5937873372574-1.59378733725739
201211.68226252939030.317737470609741
211111.7524692378554-0.752469237855412
221211.67120432159840.328795678401614
231313.1263045124341-0.12630451243413
241111.6410728407897-0.641072840789693
25911.8571099904573-2.85710999045731
261311.79103263992261.2089673600774
271011.5939068919817-1.5939068919817
281411.22515560239592.77484439760412
291211.64664104374030.353358956259661
301010.5406195518176-0.540619551817622
311211.08831679167230.911683208327704
3289.80947190503917-1.80947190503917
331010.3697428116008-0.36974281160078
341211.71270508166980.287294918330188
351210.46871486987351.53128513012655
3676.996414517168460.00358548283153554
3768.58274983763587-2.58274983763587
381210.46302474855611.53697525144392
391011.8034693372308-1.80346933723084
401011.5829618603176-1.58296186031757
411011.4575265424788-1.45752654247879
421210.54473384676291.45526615323713
431513.55013273486891.44986726513113
441010.521878649782-0.521878649782013
451010.2228492248655-0.222849224865511
46128.896796701228733.10320329877128
471310.56577757696542.43422242303461
481111.1657316841916-0.165731684191641
491111.6548019739291-0.654801973929142
501210.34072892468371.6592710753163
511411.87647844183432.12352155816567
521010.5557281550342-0.555728155034249
53129.569229065289262.43077093471074
541311.7224341349171.27756586508303
5558.01998937068188-3.01998937068188
56610.3730320180795-4.37303201807946
571211.38954761633670.610452383663344
581211.90853931886670.0914606811332574
591111.0530125682296-0.0530125682296055
601011.5695902859511-1.56959028595107
6179.50412063505911-2.50412063505911
621211.45063882583890.549361174161111
631411.67450974488972.32549025511034
641110.77642144983470.223578550165264
651211.56440961126310.435590388736853
661312.20847625770550.791523742294466
671412.58780281876211.41219718123793
681112.6885648704138-1.68856487041376
69129.482061497249942.51793850275006
701211.67292621520380.327073784796238
7188.33208983752414-0.332089837524141
721110.85397731262740.146022687372639
731412.83480804024631.1651919597537
741412.74646354916111.25353645083888
751211.42469179112880.575308208871247
76911.7351277143998-2.73512771439976
771311.58136799449331.41863200550665
781111.5032995521238-0.503299552123759
791210.24357748649751.7564225135025
801211.32954597784040.67045402215965
811211.7600595696210.239940430378985
821213.8835374698526-1.8835374698526
831211.70287480880330.297125191196653
841210.94463534066291.05536465933708
851111.2105927534531-0.210592753453062
861011.5825884283744-1.58258842837438
87910.5390705071366-1.53907050713658
881211.69387167578310.306128324216881
891211.82021870526530.179781294734699
901211.0514157981980.948584201801973
9199.72372986941483-0.723729869414826
921512.00895696849252.99104303150749
931211.79738409923920.202615900760845
941210.99868243015371.00131756984632
951210.19375888053421.80624111946582
961011.4671215185814-1.46712151858138
971311.4939449697451.506055030255
98911.5085637596836-2.50856375968362
991211.49923778163550.500762218364542
1001010.7582563040787-0.758256304078738
1011411.52680728650042.47319271349959
1021111.4907397536186-0.490739753618585
1031513.7261607352581.27383926474201
1041111.0348737040417-0.0348737040417422
1051111.8064948713552-0.806494871355215
106129.659793805759442.34020619424056
1071212.0054384332914-0.00543843329143206
1081211.42650722751040.573492772489572
1091111.5741360316857-0.574136031685746
11079.27150148472301-2.27150148472302
1111211.80965422741320.190345772586762
1121411.73358860674112.26641139325887
1131112.4602998682579-1.46029986825788
1141110.20377175097320.796228249026844
115109.38070262045810.619297379541898
1161312.48405050170510.51594949829486
1171310.82690234788732.17309765211273
118810.7599333765409-2.75993337654093
1191110.16857619418840.831423805811601
1201211.4606559290470.53934407095297
1211110.19986933278840.800130667211635
1221311.5514591250771.44854087492303
1231210.0080244957031.99197550429695
1241411.64345963283812.35654036716191
1251311.0753186492491.92468135075101
1261511.75996706576993.24003293423007
1271010.8200976116772-0.820097611677231
1281111.8574635603089-0.857463560308901
129911.2035621025274-2.20356210252743
130119.446331062196191.55366893780381
1311011.7227541183802-1.72275411838019
132118.652302996250782.34769700374922
133811.4577401545115-3.45774015451154
134119.732299228883171.26770077111683
1351210.55866591215441.4413340878456
1361211.01415320408790.985846795912075
13799.91584357707678-0.91584357707678
1381111.1238243174802-0.123824317480177
139109.212218137676140.78778186232386
14089.4997749600118-1.4997749600118
14197.611126817178031.38887318282197
142811.7775671501346-3.77756715013465
143911.2765386596678-2.27653865966777
1441512.3709662969232.62903370307702
1451111.4466700715332-0.446670071533177
14688.08086380935547-0.0808638093554696
1471312.56851772988120.431482270118768
1481210.02649917388171.97350082611826
1491211.64885238051060.351147619489388
15099.88559513348837-0.885595133488373
15178.41786280024153-1.41786280024153
1521310.77543590164842.22456409835157
153911.800551968796-2.800551968796
154611.7458894597389-5.74588945973886
155810.481657071301-2.48165707130096
15688.36349635452125-0.363496354521249
1571512.00895696849252.99104303150749
158610.2326393744328-4.23263937443285
159911.2035621025274-2.20356210252743
1601111.5897364008297-0.589736400829724
16189.60205449295541-1.60205449295541
162810.0749772502368-2.0749772502368

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.87182500850034 & 2.12817499149966 \tabularnewline
2 & 11 & 11.3349497905143 & -0.334949790514305 \tabularnewline
3 & 15 & 13.4289803324891 & 1.57101966751085 \tabularnewline
4 & 6 & 11.2345653420561 & -5.23456534205614 \tabularnewline
5 & 13 & 10.6023399509718 & 2.39766004902821 \tabularnewline
6 & 10 & 9.85385308609178 & 0.146146913908221 \tabularnewline
7 & 12 & 12.8095510172347 & -0.809551017234653 \tabularnewline
8 & 14 & 11.1724309765905 & 2.82756902340949 \tabularnewline
9 & 12 & 10.5359879930912 & 1.46401200690882 \tabularnewline
10 & 6 & 11.0757190916234 & -5.07571909162342 \tabularnewline
11 & 10 & 11.3593530197218 & -1.35935301972176 \tabularnewline
12 & 12 & 11.6007296966467 & 0.39927030335335 \tabularnewline
13 & 12 & 11.4554286105764 & 0.544571389423582 \tabularnewline
14 & 11 & 11.5294331884354 & -0.529433188435412 \tabularnewline
15 & 15 & 12.3053092508543 & 2.6946907491457 \tabularnewline
16 & 12 & 11.1794539242024 & 0.82054607579762 \tabularnewline
17 & 10 & 10.9396226466795 & -0.939622646679519 \tabularnewline
18 & 12 & 13.6636717874863 & -1.66367178748632 \tabularnewline
19 & 11 & 12.5937873372574 & -1.59378733725739 \tabularnewline
20 & 12 & 11.6822625293903 & 0.317737470609741 \tabularnewline
21 & 11 & 11.7524692378554 & -0.752469237855412 \tabularnewline
22 & 12 & 11.6712043215984 & 0.328795678401614 \tabularnewline
23 & 13 & 13.1263045124341 & -0.12630451243413 \tabularnewline
24 & 11 & 11.6410728407897 & -0.641072840789693 \tabularnewline
25 & 9 & 11.8571099904573 & -2.85710999045731 \tabularnewline
26 & 13 & 11.7910326399226 & 1.2089673600774 \tabularnewline
27 & 10 & 11.5939068919817 & -1.5939068919817 \tabularnewline
28 & 14 & 11.2251556023959 & 2.77484439760412 \tabularnewline
29 & 12 & 11.6466410437403 & 0.353358956259661 \tabularnewline
30 & 10 & 10.5406195518176 & -0.540619551817622 \tabularnewline
31 & 12 & 11.0883167916723 & 0.911683208327704 \tabularnewline
32 & 8 & 9.80947190503917 & -1.80947190503917 \tabularnewline
33 & 10 & 10.3697428116008 & -0.36974281160078 \tabularnewline
34 & 12 & 11.7127050816698 & 0.287294918330188 \tabularnewline
35 & 12 & 10.4687148698735 & 1.53128513012655 \tabularnewline
36 & 7 & 6.99641451716846 & 0.00358548283153554 \tabularnewline
37 & 6 & 8.58274983763587 & -2.58274983763587 \tabularnewline
38 & 12 & 10.4630247485561 & 1.53697525144392 \tabularnewline
39 & 10 & 11.8034693372308 & -1.80346933723084 \tabularnewline
40 & 10 & 11.5829618603176 & -1.58296186031757 \tabularnewline
41 & 10 & 11.4575265424788 & -1.45752654247879 \tabularnewline
42 & 12 & 10.5447338467629 & 1.45526615323713 \tabularnewline
43 & 15 & 13.5501327348689 & 1.44986726513113 \tabularnewline
44 & 10 & 10.521878649782 & -0.521878649782013 \tabularnewline
45 & 10 & 10.2228492248655 & -0.222849224865511 \tabularnewline
46 & 12 & 8.89679670122873 & 3.10320329877128 \tabularnewline
47 & 13 & 10.5657775769654 & 2.43422242303461 \tabularnewline
48 & 11 & 11.1657316841916 & -0.165731684191641 \tabularnewline
49 & 11 & 11.6548019739291 & -0.654801973929142 \tabularnewline
50 & 12 & 10.3407289246837 & 1.6592710753163 \tabularnewline
51 & 14 & 11.8764784418343 & 2.12352155816567 \tabularnewline
52 & 10 & 10.5557281550342 & -0.555728155034249 \tabularnewline
53 & 12 & 9.56922906528926 & 2.43077093471074 \tabularnewline
54 & 13 & 11.722434134917 & 1.27756586508303 \tabularnewline
55 & 5 & 8.01998937068188 & -3.01998937068188 \tabularnewline
56 & 6 & 10.3730320180795 & -4.37303201807946 \tabularnewline
57 & 12 & 11.3895476163367 & 0.610452383663344 \tabularnewline
58 & 12 & 11.9085393188667 & 0.0914606811332574 \tabularnewline
59 & 11 & 11.0530125682296 & -0.0530125682296055 \tabularnewline
60 & 10 & 11.5695902859511 & -1.56959028595107 \tabularnewline
61 & 7 & 9.50412063505911 & -2.50412063505911 \tabularnewline
62 & 12 & 11.4506388258389 & 0.549361174161111 \tabularnewline
63 & 14 & 11.6745097448897 & 2.32549025511034 \tabularnewline
64 & 11 & 10.7764214498347 & 0.223578550165264 \tabularnewline
65 & 12 & 11.5644096112631 & 0.435590388736853 \tabularnewline
66 & 13 & 12.2084762577055 & 0.791523742294466 \tabularnewline
67 & 14 & 12.5878028187621 & 1.41219718123793 \tabularnewline
68 & 11 & 12.6885648704138 & -1.68856487041376 \tabularnewline
69 & 12 & 9.48206149724994 & 2.51793850275006 \tabularnewline
70 & 12 & 11.6729262152038 & 0.327073784796238 \tabularnewline
71 & 8 & 8.33208983752414 & -0.332089837524141 \tabularnewline
72 & 11 & 10.8539773126274 & 0.146022687372639 \tabularnewline
73 & 14 & 12.8348080402463 & 1.1651919597537 \tabularnewline
74 & 14 & 12.7464635491611 & 1.25353645083888 \tabularnewline
75 & 12 & 11.4246917911288 & 0.575308208871247 \tabularnewline
76 & 9 & 11.7351277143998 & -2.73512771439976 \tabularnewline
77 & 13 & 11.5813679944933 & 1.41863200550665 \tabularnewline
78 & 11 & 11.5032995521238 & -0.503299552123759 \tabularnewline
79 & 12 & 10.2435774864975 & 1.7564225135025 \tabularnewline
80 & 12 & 11.3295459778404 & 0.67045402215965 \tabularnewline
81 & 12 & 11.760059569621 & 0.239940430378985 \tabularnewline
82 & 12 & 13.8835374698526 & -1.8835374698526 \tabularnewline
83 & 12 & 11.7028748088033 & 0.297125191196653 \tabularnewline
84 & 12 & 10.9446353406629 & 1.05536465933708 \tabularnewline
85 & 11 & 11.2105927534531 & -0.210592753453062 \tabularnewline
86 & 10 & 11.5825884283744 & -1.58258842837438 \tabularnewline
87 & 9 & 10.5390705071366 & -1.53907050713658 \tabularnewline
88 & 12 & 11.6938716757831 & 0.306128324216881 \tabularnewline
89 & 12 & 11.8202187052653 & 0.179781294734699 \tabularnewline
90 & 12 & 11.051415798198 & 0.948584201801973 \tabularnewline
91 & 9 & 9.72372986941483 & -0.723729869414826 \tabularnewline
92 & 15 & 12.0089569684925 & 2.99104303150749 \tabularnewline
93 & 12 & 11.7973840992392 & 0.202615900760845 \tabularnewline
94 & 12 & 10.9986824301537 & 1.00131756984632 \tabularnewline
95 & 12 & 10.1937588805342 & 1.80624111946582 \tabularnewline
96 & 10 & 11.4671215185814 & -1.46712151858138 \tabularnewline
97 & 13 & 11.493944969745 & 1.506055030255 \tabularnewline
98 & 9 & 11.5085637596836 & -2.50856375968362 \tabularnewline
99 & 12 & 11.4992377816355 & 0.500762218364542 \tabularnewline
100 & 10 & 10.7582563040787 & -0.758256304078738 \tabularnewline
101 & 14 & 11.5268072865004 & 2.47319271349959 \tabularnewline
102 & 11 & 11.4907397536186 & -0.490739753618585 \tabularnewline
103 & 15 & 13.726160735258 & 1.27383926474201 \tabularnewline
104 & 11 & 11.0348737040417 & -0.0348737040417422 \tabularnewline
105 & 11 & 11.8064948713552 & -0.806494871355215 \tabularnewline
106 & 12 & 9.65979380575944 & 2.34020619424056 \tabularnewline
107 & 12 & 12.0054384332914 & -0.00543843329143206 \tabularnewline
108 & 12 & 11.4265072275104 & 0.573492772489572 \tabularnewline
109 & 11 & 11.5741360316857 & -0.574136031685746 \tabularnewline
110 & 7 & 9.27150148472301 & -2.27150148472302 \tabularnewline
111 & 12 & 11.8096542274132 & 0.190345772586762 \tabularnewline
112 & 14 & 11.7335886067411 & 2.26641139325887 \tabularnewline
113 & 11 & 12.4602998682579 & -1.46029986825788 \tabularnewline
114 & 11 & 10.2037717509732 & 0.796228249026844 \tabularnewline
115 & 10 & 9.3807026204581 & 0.619297379541898 \tabularnewline
116 & 13 & 12.4840505017051 & 0.51594949829486 \tabularnewline
117 & 13 & 10.8269023478873 & 2.17309765211273 \tabularnewline
118 & 8 & 10.7599333765409 & -2.75993337654093 \tabularnewline
119 & 11 & 10.1685761941884 & 0.831423805811601 \tabularnewline
120 & 12 & 11.460655929047 & 0.53934407095297 \tabularnewline
121 & 11 & 10.1998693327884 & 0.800130667211635 \tabularnewline
122 & 13 & 11.551459125077 & 1.44854087492303 \tabularnewline
123 & 12 & 10.008024495703 & 1.99197550429695 \tabularnewline
124 & 14 & 11.6434596328381 & 2.35654036716191 \tabularnewline
125 & 13 & 11.075318649249 & 1.92468135075101 \tabularnewline
126 & 15 & 11.7599670657699 & 3.24003293423007 \tabularnewline
127 & 10 & 10.8200976116772 & -0.820097611677231 \tabularnewline
128 & 11 & 11.8574635603089 & -0.857463560308901 \tabularnewline
129 & 9 & 11.2035621025274 & -2.20356210252743 \tabularnewline
130 & 11 & 9.44633106219619 & 1.55366893780381 \tabularnewline
131 & 10 & 11.7227541183802 & -1.72275411838019 \tabularnewline
132 & 11 & 8.65230299625078 & 2.34769700374922 \tabularnewline
133 & 8 & 11.4577401545115 & -3.45774015451154 \tabularnewline
134 & 11 & 9.73229922888317 & 1.26770077111683 \tabularnewline
135 & 12 & 10.5586659121544 & 1.4413340878456 \tabularnewline
136 & 12 & 11.0141532040879 & 0.985846795912075 \tabularnewline
137 & 9 & 9.91584357707678 & -0.91584357707678 \tabularnewline
138 & 11 & 11.1238243174802 & -0.123824317480177 \tabularnewline
139 & 10 & 9.21221813767614 & 0.78778186232386 \tabularnewline
140 & 8 & 9.4997749600118 & -1.4997749600118 \tabularnewline
141 & 9 & 7.61112681717803 & 1.38887318282197 \tabularnewline
142 & 8 & 11.7775671501346 & -3.77756715013465 \tabularnewline
143 & 9 & 11.2765386596678 & -2.27653865966777 \tabularnewline
144 & 15 & 12.370966296923 & 2.62903370307702 \tabularnewline
145 & 11 & 11.4466700715332 & -0.446670071533177 \tabularnewline
146 & 8 & 8.08086380935547 & -0.0808638093554696 \tabularnewline
147 & 13 & 12.5685177298812 & 0.431482270118768 \tabularnewline
148 & 12 & 10.0264991738817 & 1.97350082611826 \tabularnewline
149 & 12 & 11.6488523805106 & 0.351147619489388 \tabularnewline
150 & 9 & 9.88559513348837 & -0.885595133488373 \tabularnewline
151 & 7 & 8.41786280024153 & -1.41786280024153 \tabularnewline
152 & 13 & 10.7754359016484 & 2.22456409835157 \tabularnewline
153 & 9 & 11.800551968796 & -2.800551968796 \tabularnewline
154 & 6 & 11.7458894597389 & -5.74588945973886 \tabularnewline
155 & 8 & 10.481657071301 & -2.48165707130096 \tabularnewline
156 & 8 & 8.36349635452125 & -0.363496354521249 \tabularnewline
157 & 15 & 12.0089569684925 & 2.99104303150749 \tabularnewline
158 & 6 & 10.2326393744328 & -4.23263937443285 \tabularnewline
159 & 9 & 11.2035621025274 & -2.20356210252743 \tabularnewline
160 & 11 & 11.5897364008297 & -0.589736400829724 \tabularnewline
161 & 8 & 9.60205449295541 & -1.60205449295541 \tabularnewline
162 & 8 & 10.0749772502368 & -2.0749772502368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&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]9.87182500850034[/C][C]2.12817499149966[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.3349497905143[/C][C]-0.334949790514305[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.4289803324891[/C][C]1.57101966751085[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.2345653420561[/C][C]-5.23456534205614[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.6023399509718[/C][C]2.39766004902821[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.85385308609178[/C][C]0.146146913908221[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.8095510172347[/C][C]-0.809551017234653[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.1724309765905[/C][C]2.82756902340949[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.5359879930912[/C][C]1.46401200690882[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.0757190916234[/C][C]-5.07571909162342[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.3593530197218[/C][C]-1.35935301972176[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.6007296966467[/C][C]0.39927030335335[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.4554286105764[/C][C]0.544571389423582[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.5294331884354[/C][C]-0.529433188435412[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.3053092508543[/C][C]2.6946907491457[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.1794539242024[/C][C]0.82054607579762[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.9396226466795[/C][C]-0.939622646679519[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6636717874863[/C][C]-1.66367178748632[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.5937873372574[/C][C]-1.59378733725739[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.6822625293903[/C][C]0.317737470609741[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.7524692378554[/C][C]-0.752469237855412[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.6712043215984[/C][C]0.328795678401614[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.1263045124341[/C][C]-0.12630451243413[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.6410728407897[/C][C]-0.641072840789693[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.8571099904573[/C][C]-2.85710999045731[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]11.7910326399226[/C][C]1.2089673600774[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.5939068919817[/C][C]-1.5939068919817[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.2251556023959[/C][C]2.77484439760412[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.6466410437403[/C][C]0.353358956259661[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.5406195518176[/C][C]-0.540619551817622[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.0883167916723[/C][C]0.911683208327704[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.80947190503917[/C][C]-1.80947190503917[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.3697428116008[/C][C]-0.36974281160078[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.7127050816698[/C][C]0.287294918330188[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.4687148698735[/C][C]1.53128513012655[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.99641451716846[/C][C]0.00358548283153554[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.58274983763587[/C][C]-2.58274983763587[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.4630247485561[/C][C]1.53697525144392[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.8034693372308[/C][C]-1.80346933723084[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.5829618603176[/C][C]-1.58296186031757[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4575265424788[/C][C]-1.45752654247879[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.5447338467629[/C][C]1.45526615323713[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.5501327348689[/C][C]1.44986726513113[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.521878649782[/C][C]-0.521878649782013[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.2228492248655[/C][C]-0.222849224865511[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]8.89679670122873[/C][C]3.10320329877128[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.5657775769654[/C][C]2.43422242303461[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1657316841916[/C][C]-0.165731684191641[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.6548019739291[/C][C]-0.654801973929142[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.3407289246837[/C][C]1.6592710753163[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8764784418343[/C][C]2.12352155816567[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.5557281550342[/C][C]-0.555728155034249[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.56922906528926[/C][C]2.43077093471074[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.722434134917[/C][C]1.27756586508303[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.01998937068188[/C][C]-3.01998937068188[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.3730320180795[/C][C]-4.37303201807946[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.3895476163367[/C][C]0.610452383663344[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9085393188667[/C][C]0.0914606811332574[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.0530125682296[/C][C]-0.0530125682296055[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.5695902859511[/C][C]-1.56959028595107[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.50412063505911[/C][C]-2.50412063505911[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.4506388258389[/C][C]0.549361174161111[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.6745097448897[/C][C]2.32549025511034[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.7764214498347[/C][C]0.223578550165264[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.5644096112631[/C][C]0.435590388736853[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.2084762577055[/C][C]0.791523742294466[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.5878028187621[/C][C]1.41219718123793[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.6885648704138[/C][C]-1.68856487041376[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.48206149724994[/C][C]2.51793850275006[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6729262152038[/C][C]0.327073784796238[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.33208983752414[/C][C]-0.332089837524141[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.8539773126274[/C][C]0.146022687372639[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8348080402463[/C][C]1.1651919597537[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.7464635491611[/C][C]1.25353645083888[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.4246917911288[/C][C]0.575308208871247[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7351277143998[/C][C]-2.73512771439976[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5813679944933[/C][C]1.41863200550665[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.5032995521238[/C][C]-0.503299552123759[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.2435774864975[/C][C]1.7564225135025[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.3295459778404[/C][C]0.67045402215965[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.760059569621[/C][C]0.239940430378985[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.8835374698526[/C][C]-1.8835374698526[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7028748088033[/C][C]0.297125191196653[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.9446353406629[/C][C]1.05536465933708[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.2105927534531[/C][C]-0.210592753453062[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.5825884283744[/C][C]-1.58258842837438[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5390705071366[/C][C]-1.53907050713658[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.6938716757831[/C][C]0.306128324216881[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8202187052653[/C][C]0.179781294734699[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.051415798198[/C][C]0.948584201801973[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.72372986941483[/C][C]-0.723729869414826[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.0089569684925[/C][C]2.99104303150749[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.7973840992392[/C][C]0.202615900760845[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.9986824301537[/C][C]1.00131756984632[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.1937588805342[/C][C]1.80624111946582[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4671215185814[/C][C]-1.46712151858138[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.493944969745[/C][C]1.506055030255[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.5085637596836[/C][C]-2.50856375968362[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4992377816355[/C][C]0.500762218364542[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.7582563040787[/C][C]-0.758256304078738[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.5268072865004[/C][C]2.47319271349959[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.4907397536186[/C][C]-0.490739753618585[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.726160735258[/C][C]1.27383926474201[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.0348737040417[/C][C]-0.0348737040417422[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.8064948713552[/C][C]-0.806494871355215[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.65979380575944[/C][C]2.34020619424056[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.0054384332914[/C][C]-0.00543843329143206[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.4265072275104[/C][C]0.573492772489572[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.5741360316857[/C][C]-0.574136031685746[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.27150148472301[/C][C]-2.27150148472302[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.8096542274132[/C][C]0.190345772586762[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.7335886067411[/C][C]2.26641139325887[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.4602998682579[/C][C]-1.46029986825788[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.2037717509732[/C][C]0.796228249026844[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.3807026204581[/C][C]0.619297379541898[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.4840505017051[/C][C]0.51594949829486[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.8269023478873[/C][C]2.17309765211273[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.7599333765409[/C][C]-2.75993337654093[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.1685761941884[/C][C]0.831423805811601[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.460655929047[/C][C]0.53934407095297[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.1998693327884[/C][C]0.800130667211635[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.551459125077[/C][C]1.44854087492303[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.008024495703[/C][C]1.99197550429695[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.6434596328381[/C][C]2.35654036716191[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.075318649249[/C][C]1.92468135075101[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.7599670657699[/C][C]3.24003293423007[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.8200976116772[/C][C]-0.820097611677231[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8574635603089[/C][C]-0.857463560308901[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.2035621025274[/C][C]-2.20356210252743[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.44633106219619[/C][C]1.55366893780381[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.7227541183802[/C][C]-1.72275411838019[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.65230299625078[/C][C]2.34769700374922[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.4577401545115[/C][C]-3.45774015451154[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.73229922888317[/C][C]1.26770077111683[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.5586659121544[/C][C]1.4413340878456[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.0141532040879[/C][C]0.985846795912075[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.91584357707678[/C][C]-0.91584357707678[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1238243174802[/C][C]-0.123824317480177[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.21221813767614[/C][C]0.78778186232386[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.4997749600118[/C][C]-1.4997749600118[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.61112681717803[/C][C]1.38887318282197[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.7775671501346[/C][C]-3.77756715013465[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.2765386596678[/C][C]-2.27653865966777[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.370966296923[/C][C]2.62903370307702[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.4466700715332[/C][C]-0.446670071533177[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.08086380935547[/C][C]-0.0808638093554696[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.5685177298812[/C][C]0.431482270118768[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.0264991738817[/C][C]1.97350082611826[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.6488523805106[/C][C]0.351147619489388[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.88559513348837[/C][C]-0.885595133488373[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.41786280024153[/C][C]-1.41786280024153[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.7754359016484[/C][C]2.22456409835157[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.800551968796[/C][C]-2.800551968796[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.7458894597389[/C][C]-5.74588945973886[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.481657071301[/C][C]-2.48165707130096[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.36349635452125[/C][C]-0.363496354521249[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.0089569684925[/C][C]2.99104303150749[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2326393744328[/C][C]-4.23263937443285[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.2035621025274[/C][C]-2.20356210252743[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.5897364008297[/C][C]-0.589736400829724[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.60205449295541[/C][C]-1.60205449295541[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.0749772502368[/C][C]-2.0749772502368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185448&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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
1129.871825008500342.12817499149966
21111.3349497905143-0.334949790514305
31513.42898033248911.57101966751085
4611.2345653420561-5.23456534205614
51310.60233995097182.39766004902821
6109.853853086091780.146146913908221
71212.8095510172347-0.809551017234653
81411.17243097659052.82756902340949
91210.53598799309121.46401200690882
10611.0757190916234-5.07571909162342
111011.3593530197218-1.35935301972176
121211.60072969664670.39927030335335
131211.45542861057640.544571389423582
141111.5294331884354-0.529433188435412
151512.30530925085432.6946907491457
161211.17945392420240.82054607579762
171010.9396226466795-0.939622646679519
181213.6636717874863-1.66367178748632
191112.5937873372574-1.59378733725739
201211.68226252939030.317737470609741
211111.7524692378554-0.752469237855412
221211.67120432159840.328795678401614
231313.1263045124341-0.12630451243413
241111.6410728407897-0.641072840789693
25911.8571099904573-2.85710999045731
261311.79103263992261.2089673600774
271011.5939068919817-1.5939068919817
281411.22515560239592.77484439760412
291211.64664104374030.353358956259661
301010.5406195518176-0.540619551817622
311211.08831679167230.911683208327704
3289.80947190503917-1.80947190503917
331010.3697428116008-0.36974281160078
341211.71270508166980.287294918330188
351210.46871486987351.53128513012655
3676.996414517168460.00358548283153554
3768.58274983763587-2.58274983763587
381210.46302474855611.53697525144392
391011.8034693372308-1.80346933723084
401011.5829618603176-1.58296186031757
411011.4575265424788-1.45752654247879
421210.54473384676291.45526615323713
431513.55013273486891.44986726513113
441010.521878649782-0.521878649782013
451010.2228492248655-0.222849224865511
46128.896796701228733.10320329877128
471310.56577757696542.43422242303461
481111.1657316841916-0.165731684191641
491111.6548019739291-0.654801973929142
501210.34072892468371.6592710753163
511411.87647844183432.12352155816567
521010.5557281550342-0.555728155034249
53129.569229065289262.43077093471074
541311.7224341349171.27756586508303
5558.01998937068188-3.01998937068188
56610.3730320180795-4.37303201807946
571211.38954761633670.610452383663344
581211.90853931886670.0914606811332574
591111.0530125682296-0.0530125682296055
601011.5695902859511-1.56959028595107
6179.50412063505911-2.50412063505911
621211.45063882583890.549361174161111
631411.67450974488972.32549025511034
641110.77642144983470.223578550165264
651211.56440961126310.435590388736853
661312.20847625770550.791523742294466
671412.58780281876211.41219718123793
681112.6885648704138-1.68856487041376
69129.482061497249942.51793850275006
701211.67292621520380.327073784796238
7188.33208983752414-0.332089837524141
721110.85397731262740.146022687372639
731412.83480804024631.1651919597537
741412.74646354916111.25353645083888
751211.42469179112880.575308208871247
76911.7351277143998-2.73512771439976
771311.58136799449331.41863200550665
781111.5032995521238-0.503299552123759
791210.24357748649751.7564225135025
801211.32954597784040.67045402215965
811211.7600595696210.239940430378985
821213.8835374698526-1.8835374698526
831211.70287480880330.297125191196653
841210.94463534066291.05536465933708
851111.2105927534531-0.210592753453062
861011.5825884283744-1.58258842837438
87910.5390705071366-1.53907050713658
881211.69387167578310.306128324216881
891211.82021870526530.179781294734699
901211.0514157981980.948584201801973
9199.72372986941483-0.723729869414826
921512.00895696849252.99104303150749
931211.79738409923920.202615900760845
941210.99868243015371.00131756984632
951210.19375888053421.80624111946582
961011.4671215185814-1.46712151858138
971311.4939449697451.506055030255
98911.5085637596836-2.50856375968362
991211.49923778163550.500762218364542
1001010.7582563040787-0.758256304078738
1011411.52680728650042.47319271349959
1021111.4907397536186-0.490739753618585
1031513.7261607352581.27383926474201
1041111.0348737040417-0.0348737040417422
1051111.8064948713552-0.806494871355215
106129.659793805759442.34020619424056
1071212.0054384332914-0.00543843329143206
1081211.42650722751040.573492772489572
1091111.5741360316857-0.574136031685746
11079.27150148472301-2.27150148472302
1111211.80965422741320.190345772586762
1121411.73358860674112.26641139325887
1131112.4602998682579-1.46029986825788
1141110.20377175097320.796228249026844
115109.38070262045810.619297379541898
1161312.48405050170510.51594949829486
1171310.82690234788732.17309765211273
118810.7599333765409-2.75993337654093
1191110.16857619418840.831423805811601
1201211.4606559290470.53934407095297
1211110.19986933278840.800130667211635
1221311.5514591250771.44854087492303
1231210.0080244957031.99197550429695
1241411.64345963283812.35654036716191
1251311.0753186492491.92468135075101
1261511.75996706576993.24003293423007
1271010.8200976116772-0.820097611677231
1281111.8574635603089-0.857463560308901
129911.2035621025274-2.20356210252743
130119.446331062196191.55366893780381
1311011.7227541183802-1.72275411838019
132118.652302996250782.34769700374922
133811.4577401545115-3.45774015451154
134119.732299228883171.26770077111683
1351210.55866591215441.4413340878456
1361211.01415320408790.985846795912075
13799.91584357707678-0.91584357707678
1381111.1238243174802-0.123824317480177
139109.212218137676140.78778186232386
14089.4997749600118-1.4997749600118
14197.611126817178031.38887318282197
142811.7775671501346-3.77756715013465
143911.2765386596678-2.27653865966777
1441512.3709662969232.62903370307702
1451111.4466700715332-0.446670071533177
14688.08086380935547-0.0808638093554696
1471312.56851772988120.431482270118768
1481210.02649917388171.97350082611826
1491211.64885238051060.351147619489388
15099.88559513348837-0.885595133488373
15178.41786280024153-1.41786280024153
1521310.77543590164842.22456409835157
153911.800551968796-2.800551968796
154611.7458894597389-5.74588945973886
155810.481657071301-2.48165707130096
15688.36349635452125-0.363496354521249
1571512.00895696849252.99104303150749
158610.2326393744328-4.23263937443285
159911.2035621025274-2.20356210252743
1601111.5897364008297-0.589736400829724
16189.60205449295541-1.60205449295541
162810.0749772502368-2.0749772502368






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9999285177550.0001429644899994377.14822449997183e-05
110.999783016972830.0004339660543404310.000216983027170216
120.9994272692028160.001145461594367580.000572730797183788
130.9986757838650750.002648432269849770.00132421613492488
140.9979763718001120.004047256399776660.00202362819988833
150.9980607370091560.003878525981687330.00193926299084366
160.9967213817542490.006557236491502170.00327861824575108
170.9947188973450430.01056220530991320.00528110265495659
180.9936504287722050.01269914245558920.0063495712277946
190.99042038173560.01915923652880020.00957961826440011
200.984529433517310.03094113296537910.0154705664826895
210.9760560176574040.04788796468519240.0239439823425962
220.9651501302674090.06969973946518250.0348498697325913
230.9490822346471570.1018355307056850.0509177653528427
240.9293377145803760.1413245708392470.0706622854196237
250.9301824737810650.1396350524378690.0698175262189345
260.9293441796795260.1413116406409470.0706558203204737
270.9309024678422720.1381950643154560.0690975321577278
280.9393001566752660.1213996866494680.0606998433247338
290.9180061057442840.1639877885114320.081993894255716
300.8942227583604570.2115544832790870.105777241639543
310.8755237370491660.2489525259016680.124476262950834
320.8953208316217320.2093583367565360.104679168378268
330.8660652200323770.2678695599352460.133934779967623
340.8314612932077820.3370774135844360.168538706792218
350.8174459281916150.365108143616770.182554071808385
360.7780129385609290.4439741228781430.221987061439071
370.8234263081723230.3531473836553540.176573691827677
380.8142199482635950.3715601034728090.185780051736405
390.8038805660349480.3922388679301040.196119433965052
400.7851663861987680.4296672276024630.214833613801232
410.7605874569648070.4788250860703850.239412543035193
420.7441322262802690.5117355474394620.255867773719731
430.7432500233750470.5134999532499060.256749976624953
440.7008648235137830.5982703529724330.299135176486217
450.6548014534782310.6903970930435390.345198546521769
460.7193927406510550.561214518697890.280607259348945
470.7304691559864350.539061688027130.269530844013565
480.6861598494230830.6276803011538340.313840150576917
490.6489101416465570.7021797167068860.351089858353443
500.6363198952785290.7273602094429420.363680104721471
510.6410758964974810.7178482070050390.358924103502519
520.5945898479171350.8108203041657310.405410152082865
530.6233106275629920.7533787448740170.376689372437008
540.5904418656067620.8191162687864770.409558134393238
550.6785717331420710.6428565337158580.321428266857929
560.8425848753827590.3148302492344830.157415124617242
570.8146869449989950.370626110002010.185313055001005
580.7805439219819580.4389121560360840.219456078018042
590.7433801718837360.5132396562325280.256619828116264
600.7304555255572840.5390889488854320.269544474442716
610.7444710884081010.5110578231837980.255528911591899
620.7080021820959420.5839956358081160.291997817904058
630.7276601002823190.5446797994353620.272339899717681
640.6869007062012080.6261985875975850.313099293798792
650.6514475380383380.6971049239233240.348552461961662
660.6127544437084830.7744911125830340.387245556291517
670.5955350691592260.8089298616815490.404464930840774
680.57747887951430.8450422409713990.4225211204857
690.5938107221293460.8123785557413080.406189277870654
700.5496570126097460.9006859747805090.450342987390254
710.5083953095018030.9832093809963950.491604690498197
720.4647819562273480.9295639124546960.535218043772652
730.4346619322631280.8693238645262560.565338067736872
740.4117246621026750.8234493242053490.588275337897325
750.3865524895335070.7731049790670140.613447510466493
760.436809647832230.8736192956644610.56319035216777
770.4175598157201170.8351196314402340.582440184279883
780.3771025988409960.7542051976819910.622897401159004
790.3790055137419750.758011027483950.620994486258025
800.3562303952356530.7124607904713070.643769604764347
810.3157383441672610.6314766883345220.684261655832739
820.3289547125599020.6579094251198050.671045287440098
830.294029408551250.58805881710250.70597059144875
840.2663991078587340.5327982157174680.733600892141266
850.2301890471910280.4603780943820560.769810952808972
860.2222895269656410.4445790539312820.777710473034359
870.2229035002852780.4458070005705560.777096499714722
880.1905152435277080.3810304870554160.809484756472292
890.1616481570013950.3232963140027910.838351842998605
900.1428594900000180.2857189800000350.857140509999982
910.1238357760030810.2476715520061620.876164223996919
920.1738893000553360.3477786001106720.826110699944664
930.1494769890167040.2989539780334080.850523010983296
940.1339582149010260.2679164298020520.866041785098974
950.1308625120002080.2617250240004160.869137487999792
960.1249341144687060.2498682289374120.875065885531294
970.1181071477357380.2362142954714770.881892852264262
980.1481470884608160.2962941769216330.851852911539184
990.1232857462112910.2465714924225810.876714253788709
1000.1048384532230110.2096769064460220.895161546776989
1010.1201366829358440.2402733658716870.879863317064156
1020.09925722495104060.1985144499020810.900742775048959
1030.09244308311200560.1848861662240110.907556916887994
1040.07441744188661380.1488348837732280.925582558113386
1050.06186915307869410.1237383061573880.938130846921306
1060.06745434595544750.1349086919108950.932545654044552
1070.05331342208205570.1066268441641110.946686577917944
1080.04454171902831330.08908343805662660.955458280971687
1090.03469482286766960.06938964573533920.96530517713233
1100.0359088779906970.07181775598139390.964091122009303
1110.02731414187977680.05462828375955360.972685858120223
1120.0319971629726260.06399432594525190.968002837027374
1130.02817265723918150.0563453144783630.971827342760819
1140.02313076377933010.04626152755866010.97686923622067
1150.01738284537584740.03476569075169480.982617154624153
1160.01322909605801720.02645819211603440.986770903941983
1170.01788209520836510.03576419041673020.982117904791635
1180.02065570223052480.04131140446104970.979344297769475
1190.01612443140974690.03224886281949380.983875568590253
1200.01233038446989690.02466076893979370.987669615530103
1210.0100110723446480.0200221446892960.989988927655352
1220.008222648006278820.01644529601255760.991777351993721
1230.009737295227609480.0194745904552190.990262704772391
1240.01725458798490310.03450917596980610.982745412015097
1250.01828930185592520.03657860371185040.981710698144075
1260.0479642037512110.09592840750242210.952035796248789
1270.03660512110155890.07321024220311790.963394878898441
1280.02766723606364530.05533447212729050.972332763936355
1290.02433898905686070.04867797811372130.975661010943139
1300.02226109649561940.04452219299123880.977738903504381
1310.01749429309515630.03498858619031260.982505706904844
1320.02175510801744560.04351021603489120.978244891982554
1330.03346481459659470.06692962919318930.966535185403405
1340.03486295542485890.06972591084971780.965137044575141
1350.037126685936110.074253371872220.96287331406389
1360.02966686763922080.05933373527844160.970333132360779
1370.0283522936798420.05670458735968410.971647706320158
1380.02092073769079050.04184147538158090.979079262309209
1390.02180874303037670.04361748606075350.978191256969623
1400.01564036687836040.03128073375672090.98435963312164
1410.04949099878499120.09898199756998230.950509001215009
1420.05366984127647260.1073396825529450.946330158723527
1430.0396957004032150.079391400806430.960304299596785
1440.3564431294762280.7128862589524560.643556870523772
1450.3620515888373810.7241031776747630.637948411162619
1460.6361510565013020.7276978869973960.363848943498698
1470.5605049735623060.8789900528753880.439495026437694
1480.583618719456310.832762561087380.41638128054369
1490.477550511798230.955101023596460.52244948820177
1500.4296327519929880.8592655039859750.570367248007012
1510.3019698148579690.6039396297159380.698030185142031
1520.4262035687906170.8524071375812340.573796431209383

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.999928517755 & 0.000142964489999437 & 7.14822449997183e-05 \tabularnewline
11 & 0.99978301697283 & 0.000433966054340431 & 0.000216983027170216 \tabularnewline
12 & 0.999427269202816 & 0.00114546159436758 & 0.000572730797183788 \tabularnewline
13 & 0.998675783865075 & 0.00264843226984977 & 0.00132421613492488 \tabularnewline
14 & 0.997976371800112 & 0.00404725639977666 & 0.00202362819988833 \tabularnewline
15 & 0.998060737009156 & 0.00387852598168733 & 0.00193926299084366 \tabularnewline
16 & 0.996721381754249 & 0.00655723649150217 & 0.00327861824575108 \tabularnewline
17 & 0.994718897345043 & 0.0105622053099132 & 0.00528110265495659 \tabularnewline
18 & 0.993650428772205 & 0.0126991424555892 & 0.0063495712277946 \tabularnewline
19 & 0.9904203817356 & 0.0191592365288002 & 0.00957961826440011 \tabularnewline
20 & 0.98452943351731 & 0.0309411329653791 & 0.0154705664826895 \tabularnewline
21 & 0.976056017657404 & 0.0478879646851924 & 0.0239439823425962 \tabularnewline
22 & 0.965150130267409 & 0.0696997394651825 & 0.0348498697325913 \tabularnewline
23 & 0.949082234647157 & 0.101835530705685 & 0.0509177653528427 \tabularnewline
24 & 0.929337714580376 & 0.141324570839247 & 0.0706622854196237 \tabularnewline
25 & 0.930182473781065 & 0.139635052437869 & 0.0698175262189345 \tabularnewline
26 & 0.929344179679526 & 0.141311640640947 & 0.0706558203204737 \tabularnewline
27 & 0.930902467842272 & 0.138195064315456 & 0.0690975321577278 \tabularnewline
28 & 0.939300156675266 & 0.121399686649468 & 0.0606998433247338 \tabularnewline
29 & 0.918006105744284 & 0.163987788511432 & 0.081993894255716 \tabularnewline
30 & 0.894222758360457 & 0.211554483279087 & 0.105777241639543 \tabularnewline
31 & 0.875523737049166 & 0.248952525901668 & 0.124476262950834 \tabularnewline
32 & 0.895320831621732 & 0.209358336756536 & 0.104679168378268 \tabularnewline
33 & 0.866065220032377 & 0.267869559935246 & 0.133934779967623 \tabularnewline
34 & 0.831461293207782 & 0.337077413584436 & 0.168538706792218 \tabularnewline
35 & 0.817445928191615 & 0.36510814361677 & 0.182554071808385 \tabularnewline
36 & 0.778012938560929 & 0.443974122878143 & 0.221987061439071 \tabularnewline
37 & 0.823426308172323 & 0.353147383655354 & 0.176573691827677 \tabularnewline
38 & 0.814219948263595 & 0.371560103472809 & 0.185780051736405 \tabularnewline
39 & 0.803880566034948 & 0.392238867930104 & 0.196119433965052 \tabularnewline
40 & 0.785166386198768 & 0.429667227602463 & 0.214833613801232 \tabularnewline
41 & 0.760587456964807 & 0.478825086070385 & 0.239412543035193 \tabularnewline
42 & 0.744132226280269 & 0.511735547439462 & 0.255867773719731 \tabularnewline
43 & 0.743250023375047 & 0.513499953249906 & 0.256749976624953 \tabularnewline
44 & 0.700864823513783 & 0.598270352972433 & 0.299135176486217 \tabularnewline
45 & 0.654801453478231 & 0.690397093043539 & 0.345198546521769 \tabularnewline
46 & 0.719392740651055 & 0.56121451869789 & 0.280607259348945 \tabularnewline
47 & 0.730469155986435 & 0.53906168802713 & 0.269530844013565 \tabularnewline
48 & 0.686159849423083 & 0.627680301153834 & 0.313840150576917 \tabularnewline
49 & 0.648910141646557 & 0.702179716706886 & 0.351089858353443 \tabularnewline
50 & 0.636319895278529 & 0.727360209442942 & 0.363680104721471 \tabularnewline
51 & 0.641075896497481 & 0.717848207005039 & 0.358924103502519 \tabularnewline
52 & 0.594589847917135 & 0.810820304165731 & 0.405410152082865 \tabularnewline
53 & 0.623310627562992 & 0.753378744874017 & 0.376689372437008 \tabularnewline
54 & 0.590441865606762 & 0.819116268786477 & 0.409558134393238 \tabularnewline
55 & 0.678571733142071 & 0.642856533715858 & 0.321428266857929 \tabularnewline
56 & 0.842584875382759 & 0.314830249234483 & 0.157415124617242 \tabularnewline
57 & 0.814686944998995 & 0.37062611000201 & 0.185313055001005 \tabularnewline
58 & 0.780543921981958 & 0.438912156036084 & 0.219456078018042 \tabularnewline
59 & 0.743380171883736 & 0.513239656232528 & 0.256619828116264 \tabularnewline
60 & 0.730455525557284 & 0.539088948885432 & 0.269544474442716 \tabularnewline
61 & 0.744471088408101 & 0.511057823183798 & 0.255528911591899 \tabularnewline
62 & 0.708002182095942 & 0.583995635808116 & 0.291997817904058 \tabularnewline
63 & 0.727660100282319 & 0.544679799435362 & 0.272339899717681 \tabularnewline
64 & 0.686900706201208 & 0.626198587597585 & 0.313099293798792 \tabularnewline
65 & 0.651447538038338 & 0.697104923923324 & 0.348552461961662 \tabularnewline
66 & 0.612754443708483 & 0.774491112583034 & 0.387245556291517 \tabularnewline
67 & 0.595535069159226 & 0.808929861681549 & 0.404464930840774 \tabularnewline
68 & 0.5774788795143 & 0.845042240971399 & 0.4225211204857 \tabularnewline
69 & 0.593810722129346 & 0.812378555741308 & 0.406189277870654 \tabularnewline
70 & 0.549657012609746 & 0.900685974780509 & 0.450342987390254 \tabularnewline
71 & 0.508395309501803 & 0.983209380996395 & 0.491604690498197 \tabularnewline
72 & 0.464781956227348 & 0.929563912454696 & 0.535218043772652 \tabularnewline
73 & 0.434661932263128 & 0.869323864526256 & 0.565338067736872 \tabularnewline
74 & 0.411724662102675 & 0.823449324205349 & 0.588275337897325 \tabularnewline
75 & 0.386552489533507 & 0.773104979067014 & 0.613447510466493 \tabularnewline
76 & 0.43680964783223 & 0.873619295664461 & 0.56319035216777 \tabularnewline
77 & 0.417559815720117 & 0.835119631440234 & 0.582440184279883 \tabularnewline
78 & 0.377102598840996 & 0.754205197681991 & 0.622897401159004 \tabularnewline
79 & 0.379005513741975 & 0.75801102748395 & 0.620994486258025 \tabularnewline
80 & 0.356230395235653 & 0.712460790471307 & 0.643769604764347 \tabularnewline
81 & 0.315738344167261 & 0.631476688334522 & 0.684261655832739 \tabularnewline
82 & 0.328954712559902 & 0.657909425119805 & 0.671045287440098 \tabularnewline
83 & 0.29402940855125 & 0.5880588171025 & 0.70597059144875 \tabularnewline
84 & 0.266399107858734 & 0.532798215717468 & 0.733600892141266 \tabularnewline
85 & 0.230189047191028 & 0.460378094382056 & 0.769810952808972 \tabularnewline
86 & 0.222289526965641 & 0.444579053931282 & 0.777710473034359 \tabularnewline
87 & 0.222903500285278 & 0.445807000570556 & 0.777096499714722 \tabularnewline
88 & 0.190515243527708 & 0.381030487055416 & 0.809484756472292 \tabularnewline
89 & 0.161648157001395 & 0.323296314002791 & 0.838351842998605 \tabularnewline
90 & 0.142859490000018 & 0.285718980000035 & 0.857140509999982 \tabularnewline
91 & 0.123835776003081 & 0.247671552006162 & 0.876164223996919 \tabularnewline
92 & 0.173889300055336 & 0.347778600110672 & 0.826110699944664 \tabularnewline
93 & 0.149476989016704 & 0.298953978033408 & 0.850523010983296 \tabularnewline
94 & 0.133958214901026 & 0.267916429802052 & 0.866041785098974 \tabularnewline
95 & 0.130862512000208 & 0.261725024000416 & 0.869137487999792 \tabularnewline
96 & 0.124934114468706 & 0.249868228937412 & 0.875065885531294 \tabularnewline
97 & 0.118107147735738 & 0.236214295471477 & 0.881892852264262 \tabularnewline
98 & 0.148147088460816 & 0.296294176921633 & 0.851852911539184 \tabularnewline
99 & 0.123285746211291 & 0.246571492422581 & 0.876714253788709 \tabularnewline
100 & 0.104838453223011 & 0.209676906446022 & 0.895161546776989 \tabularnewline
101 & 0.120136682935844 & 0.240273365871687 & 0.879863317064156 \tabularnewline
102 & 0.0992572249510406 & 0.198514449902081 & 0.900742775048959 \tabularnewline
103 & 0.0924430831120056 & 0.184886166224011 & 0.907556916887994 \tabularnewline
104 & 0.0744174418866138 & 0.148834883773228 & 0.925582558113386 \tabularnewline
105 & 0.0618691530786941 & 0.123738306157388 & 0.938130846921306 \tabularnewline
106 & 0.0674543459554475 & 0.134908691910895 & 0.932545654044552 \tabularnewline
107 & 0.0533134220820557 & 0.106626844164111 & 0.946686577917944 \tabularnewline
108 & 0.0445417190283133 & 0.0890834380566266 & 0.955458280971687 \tabularnewline
109 & 0.0346948228676696 & 0.0693896457353392 & 0.96530517713233 \tabularnewline
110 & 0.035908877990697 & 0.0718177559813939 & 0.964091122009303 \tabularnewline
111 & 0.0273141418797768 & 0.0546282837595536 & 0.972685858120223 \tabularnewline
112 & 0.031997162972626 & 0.0639943259452519 & 0.968002837027374 \tabularnewline
113 & 0.0281726572391815 & 0.056345314478363 & 0.971827342760819 \tabularnewline
114 & 0.0231307637793301 & 0.0462615275586601 & 0.97686923622067 \tabularnewline
115 & 0.0173828453758474 & 0.0347656907516948 & 0.982617154624153 \tabularnewline
116 & 0.0132290960580172 & 0.0264581921160344 & 0.986770903941983 \tabularnewline
117 & 0.0178820952083651 & 0.0357641904167302 & 0.982117904791635 \tabularnewline
118 & 0.0206557022305248 & 0.0413114044610497 & 0.979344297769475 \tabularnewline
119 & 0.0161244314097469 & 0.0322488628194938 & 0.983875568590253 \tabularnewline
120 & 0.0123303844698969 & 0.0246607689397937 & 0.987669615530103 \tabularnewline
121 & 0.010011072344648 & 0.020022144689296 & 0.989988927655352 \tabularnewline
122 & 0.00822264800627882 & 0.0164452960125576 & 0.991777351993721 \tabularnewline
123 & 0.00973729522760948 & 0.019474590455219 & 0.990262704772391 \tabularnewline
124 & 0.0172545879849031 & 0.0345091759698061 & 0.982745412015097 \tabularnewline
125 & 0.0182893018559252 & 0.0365786037118504 & 0.981710698144075 \tabularnewline
126 & 0.047964203751211 & 0.0959284075024221 & 0.952035796248789 \tabularnewline
127 & 0.0366051211015589 & 0.0732102422031179 & 0.963394878898441 \tabularnewline
128 & 0.0276672360636453 & 0.0553344721272905 & 0.972332763936355 \tabularnewline
129 & 0.0243389890568607 & 0.0486779781137213 & 0.975661010943139 \tabularnewline
130 & 0.0222610964956194 & 0.0445221929912388 & 0.977738903504381 \tabularnewline
131 & 0.0174942930951563 & 0.0349885861903126 & 0.982505706904844 \tabularnewline
132 & 0.0217551080174456 & 0.0435102160348912 & 0.978244891982554 \tabularnewline
133 & 0.0334648145965947 & 0.0669296291931893 & 0.966535185403405 \tabularnewline
134 & 0.0348629554248589 & 0.0697259108497178 & 0.965137044575141 \tabularnewline
135 & 0.03712668593611 & 0.07425337187222 & 0.96287331406389 \tabularnewline
136 & 0.0296668676392208 & 0.0593337352784416 & 0.970333132360779 \tabularnewline
137 & 0.028352293679842 & 0.0567045873596841 & 0.971647706320158 \tabularnewline
138 & 0.0209207376907905 & 0.0418414753815809 & 0.979079262309209 \tabularnewline
139 & 0.0218087430303767 & 0.0436174860607535 & 0.978191256969623 \tabularnewline
140 & 0.0156403668783604 & 0.0312807337567209 & 0.98435963312164 \tabularnewline
141 & 0.0494909987849912 & 0.0989819975699823 & 0.950509001215009 \tabularnewline
142 & 0.0536698412764726 & 0.107339682552945 & 0.946330158723527 \tabularnewline
143 & 0.039695700403215 & 0.07939140080643 & 0.960304299596785 \tabularnewline
144 & 0.356443129476228 & 0.712886258952456 & 0.643556870523772 \tabularnewline
145 & 0.362051588837381 & 0.724103177674763 & 0.637948411162619 \tabularnewline
146 & 0.636151056501302 & 0.727697886997396 & 0.363848943498698 \tabularnewline
147 & 0.560504973562306 & 0.878990052875388 & 0.439495026437694 \tabularnewline
148 & 0.58361871945631 & 0.83276256108738 & 0.41638128054369 \tabularnewline
149 & 0.47755051179823 & 0.95510102359646 & 0.52244948820177 \tabularnewline
150 & 0.429632751992988 & 0.859265503985975 & 0.570367248007012 \tabularnewline
151 & 0.301969814857969 & 0.603939629715938 & 0.698030185142031 \tabularnewline
152 & 0.426203568790617 & 0.852407137581234 & 0.573796431209383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&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]0.999928517755[/C][C]0.000142964489999437[/C][C]7.14822449997183e-05[/C][/ROW]
[ROW][C]11[/C][C]0.99978301697283[/C][C]0.000433966054340431[/C][C]0.000216983027170216[/C][/ROW]
[ROW][C]12[/C][C]0.999427269202816[/C][C]0.00114546159436758[/C][C]0.000572730797183788[/C][/ROW]
[ROW][C]13[/C][C]0.998675783865075[/C][C]0.00264843226984977[/C][C]0.00132421613492488[/C][/ROW]
[ROW][C]14[/C][C]0.997976371800112[/C][C]0.00404725639977666[/C][C]0.00202362819988833[/C][/ROW]
[ROW][C]15[/C][C]0.998060737009156[/C][C]0.00387852598168733[/C][C]0.00193926299084366[/C][/ROW]
[ROW][C]16[/C][C]0.996721381754249[/C][C]0.00655723649150217[/C][C]0.00327861824575108[/C][/ROW]
[ROW][C]17[/C][C]0.994718897345043[/C][C]0.0105622053099132[/C][C]0.00528110265495659[/C][/ROW]
[ROW][C]18[/C][C]0.993650428772205[/C][C]0.0126991424555892[/C][C]0.0063495712277946[/C][/ROW]
[ROW][C]19[/C][C]0.9904203817356[/C][C]0.0191592365288002[/C][C]0.00957961826440011[/C][/ROW]
[ROW][C]20[/C][C]0.98452943351731[/C][C]0.0309411329653791[/C][C]0.0154705664826895[/C][/ROW]
[ROW][C]21[/C][C]0.976056017657404[/C][C]0.0478879646851924[/C][C]0.0239439823425962[/C][/ROW]
[ROW][C]22[/C][C]0.965150130267409[/C][C]0.0696997394651825[/C][C]0.0348498697325913[/C][/ROW]
[ROW][C]23[/C][C]0.949082234647157[/C][C]0.101835530705685[/C][C]0.0509177653528427[/C][/ROW]
[ROW][C]24[/C][C]0.929337714580376[/C][C]0.141324570839247[/C][C]0.0706622854196237[/C][/ROW]
[ROW][C]25[/C][C]0.930182473781065[/C][C]0.139635052437869[/C][C]0.0698175262189345[/C][/ROW]
[ROW][C]26[/C][C]0.929344179679526[/C][C]0.141311640640947[/C][C]0.0706558203204737[/C][/ROW]
[ROW][C]27[/C][C]0.930902467842272[/C][C]0.138195064315456[/C][C]0.0690975321577278[/C][/ROW]
[ROW][C]28[/C][C]0.939300156675266[/C][C]0.121399686649468[/C][C]0.0606998433247338[/C][/ROW]
[ROW][C]29[/C][C]0.918006105744284[/C][C]0.163987788511432[/C][C]0.081993894255716[/C][/ROW]
[ROW][C]30[/C][C]0.894222758360457[/C][C]0.211554483279087[/C][C]0.105777241639543[/C][/ROW]
[ROW][C]31[/C][C]0.875523737049166[/C][C]0.248952525901668[/C][C]0.124476262950834[/C][/ROW]
[ROW][C]32[/C][C]0.895320831621732[/C][C]0.209358336756536[/C][C]0.104679168378268[/C][/ROW]
[ROW][C]33[/C][C]0.866065220032377[/C][C]0.267869559935246[/C][C]0.133934779967623[/C][/ROW]
[ROW][C]34[/C][C]0.831461293207782[/C][C]0.337077413584436[/C][C]0.168538706792218[/C][/ROW]
[ROW][C]35[/C][C]0.817445928191615[/C][C]0.36510814361677[/C][C]0.182554071808385[/C][/ROW]
[ROW][C]36[/C][C]0.778012938560929[/C][C]0.443974122878143[/C][C]0.221987061439071[/C][/ROW]
[ROW][C]37[/C][C]0.823426308172323[/C][C]0.353147383655354[/C][C]0.176573691827677[/C][/ROW]
[ROW][C]38[/C][C]0.814219948263595[/C][C]0.371560103472809[/C][C]0.185780051736405[/C][/ROW]
[ROW][C]39[/C][C]0.803880566034948[/C][C]0.392238867930104[/C][C]0.196119433965052[/C][/ROW]
[ROW][C]40[/C][C]0.785166386198768[/C][C]0.429667227602463[/C][C]0.214833613801232[/C][/ROW]
[ROW][C]41[/C][C]0.760587456964807[/C][C]0.478825086070385[/C][C]0.239412543035193[/C][/ROW]
[ROW][C]42[/C][C]0.744132226280269[/C][C]0.511735547439462[/C][C]0.255867773719731[/C][/ROW]
[ROW][C]43[/C][C]0.743250023375047[/C][C]0.513499953249906[/C][C]0.256749976624953[/C][/ROW]
[ROW][C]44[/C][C]0.700864823513783[/C][C]0.598270352972433[/C][C]0.299135176486217[/C][/ROW]
[ROW][C]45[/C][C]0.654801453478231[/C][C]0.690397093043539[/C][C]0.345198546521769[/C][/ROW]
[ROW][C]46[/C][C]0.719392740651055[/C][C]0.56121451869789[/C][C]0.280607259348945[/C][/ROW]
[ROW][C]47[/C][C]0.730469155986435[/C][C]0.53906168802713[/C][C]0.269530844013565[/C][/ROW]
[ROW][C]48[/C][C]0.686159849423083[/C][C]0.627680301153834[/C][C]0.313840150576917[/C][/ROW]
[ROW][C]49[/C][C]0.648910141646557[/C][C]0.702179716706886[/C][C]0.351089858353443[/C][/ROW]
[ROW][C]50[/C][C]0.636319895278529[/C][C]0.727360209442942[/C][C]0.363680104721471[/C][/ROW]
[ROW][C]51[/C][C]0.641075896497481[/C][C]0.717848207005039[/C][C]0.358924103502519[/C][/ROW]
[ROW][C]52[/C][C]0.594589847917135[/C][C]0.810820304165731[/C][C]0.405410152082865[/C][/ROW]
[ROW][C]53[/C][C]0.623310627562992[/C][C]0.753378744874017[/C][C]0.376689372437008[/C][/ROW]
[ROW][C]54[/C][C]0.590441865606762[/C][C]0.819116268786477[/C][C]0.409558134393238[/C][/ROW]
[ROW][C]55[/C][C]0.678571733142071[/C][C]0.642856533715858[/C][C]0.321428266857929[/C][/ROW]
[ROW][C]56[/C][C]0.842584875382759[/C][C]0.314830249234483[/C][C]0.157415124617242[/C][/ROW]
[ROW][C]57[/C][C]0.814686944998995[/C][C]0.37062611000201[/C][C]0.185313055001005[/C][/ROW]
[ROW][C]58[/C][C]0.780543921981958[/C][C]0.438912156036084[/C][C]0.219456078018042[/C][/ROW]
[ROW][C]59[/C][C]0.743380171883736[/C][C]0.513239656232528[/C][C]0.256619828116264[/C][/ROW]
[ROW][C]60[/C][C]0.730455525557284[/C][C]0.539088948885432[/C][C]0.269544474442716[/C][/ROW]
[ROW][C]61[/C][C]0.744471088408101[/C][C]0.511057823183798[/C][C]0.255528911591899[/C][/ROW]
[ROW][C]62[/C][C]0.708002182095942[/C][C]0.583995635808116[/C][C]0.291997817904058[/C][/ROW]
[ROW][C]63[/C][C]0.727660100282319[/C][C]0.544679799435362[/C][C]0.272339899717681[/C][/ROW]
[ROW][C]64[/C][C]0.686900706201208[/C][C]0.626198587597585[/C][C]0.313099293798792[/C][/ROW]
[ROW][C]65[/C][C]0.651447538038338[/C][C]0.697104923923324[/C][C]0.348552461961662[/C][/ROW]
[ROW][C]66[/C][C]0.612754443708483[/C][C]0.774491112583034[/C][C]0.387245556291517[/C][/ROW]
[ROW][C]67[/C][C]0.595535069159226[/C][C]0.808929861681549[/C][C]0.404464930840774[/C][/ROW]
[ROW][C]68[/C][C]0.5774788795143[/C][C]0.845042240971399[/C][C]0.4225211204857[/C][/ROW]
[ROW][C]69[/C][C]0.593810722129346[/C][C]0.812378555741308[/C][C]0.406189277870654[/C][/ROW]
[ROW][C]70[/C][C]0.549657012609746[/C][C]0.900685974780509[/C][C]0.450342987390254[/C][/ROW]
[ROW][C]71[/C][C]0.508395309501803[/C][C]0.983209380996395[/C][C]0.491604690498197[/C][/ROW]
[ROW][C]72[/C][C]0.464781956227348[/C][C]0.929563912454696[/C][C]0.535218043772652[/C][/ROW]
[ROW][C]73[/C][C]0.434661932263128[/C][C]0.869323864526256[/C][C]0.565338067736872[/C][/ROW]
[ROW][C]74[/C][C]0.411724662102675[/C][C]0.823449324205349[/C][C]0.588275337897325[/C][/ROW]
[ROW][C]75[/C][C]0.386552489533507[/C][C]0.773104979067014[/C][C]0.613447510466493[/C][/ROW]
[ROW][C]76[/C][C]0.43680964783223[/C][C]0.873619295664461[/C][C]0.56319035216777[/C][/ROW]
[ROW][C]77[/C][C]0.417559815720117[/C][C]0.835119631440234[/C][C]0.582440184279883[/C][/ROW]
[ROW][C]78[/C][C]0.377102598840996[/C][C]0.754205197681991[/C][C]0.622897401159004[/C][/ROW]
[ROW][C]79[/C][C]0.379005513741975[/C][C]0.75801102748395[/C][C]0.620994486258025[/C][/ROW]
[ROW][C]80[/C][C]0.356230395235653[/C][C]0.712460790471307[/C][C]0.643769604764347[/C][/ROW]
[ROW][C]81[/C][C]0.315738344167261[/C][C]0.631476688334522[/C][C]0.684261655832739[/C][/ROW]
[ROW][C]82[/C][C]0.328954712559902[/C][C]0.657909425119805[/C][C]0.671045287440098[/C][/ROW]
[ROW][C]83[/C][C]0.29402940855125[/C][C]0.5880588171025[/C][C]0.70597059144875[/C][/ROW]
[ROW][C]84[/C][C]0.266399107858734[/C][C]0.532798215717468[/C][C]0.733600892141266[/C][/ROW]
[ROW][C]85[/C][C]0.230189047191028[/C][C]0.460378094382056[/C][C]0.769810952808972[/C][/ROW]
[ROW][C]86[/C][C]0.222289526965641[/C][C]0.444579053931282[/C][C]0.777710473034359[/C][/ROW]
[ROW][C]87[/C][C]0.222903500285278[/C][C]0.445807000570556[/C][C]0.777096499714722[/C][/ROW]
[ROW][C]88[/C][C]0.190515243527708[/C][C]0.381030487055416[/C][C]0.809484756472292[/C][/ROW]
[ROW][C]89[/C][C]0.161648157001395[/C][C]0.323296314002791[/C][C]0.838351842998605[/C][/ROW]
[ROW][C]90[/C][C]0.142859490000018[/C][C]0.285718980000035[/C][C]0.857140509999982[/C][/ROW]
[ROW][C]91[/C][C]0.123835776003081[/C][C]0.247671552006162[/C][C]0.876164223996919[/C][/ROW]
[ROW][C]92[/C][C]0.173889300055336[/C][C]0.347778600110672[/C][C]0.826110699944664[/C][/ROW]
[ROW][C]93[/C][C]0.149476989016704[/C][C]0.298953978033408[/C][C]0.850523010983296[/C][/ROW]
[ROW][C]94[/C][C]0.133958214901026[/C][C]0.267916429802052[/C][C]0.866041785098974[/C][/ROW]
[ROW][C]95[/C][C]0.130862512000208[/C][C]0.261725024000416[/C][C]0.869137487999792[/C][/ROW]
[ROW][C]96[/C][C]0.124934114468706[/C][C]0.249868228937412[/C][C]0.875065885531294[/C][/ROW]
[ROW][C]97[/C][C]0.118107147735738[/C][C]0.236214295471477[/C][C]0.881892852264262[/C][/ROW]
[ROW][C]98[/C][C]0.148147088460816[/C][C]0.296294176921633[/C][C]0.851852911539184[/C][/ROW]
[ROW][C]99[/C][C]0.123285746211291[/C][C]0.246571492422581[/C][C]0.876714253788709[/C][/ROW]
[ROW][C]100[/C][C]0.104838453223011[/C][C]0.209676906446022[/C][C]0.895161546776989[/C][/ROW]
[ROW][C]101[/C][C]0.120136682935844[/C][C]0.240273365871687[/C][C]0.879863317064156[/C][/ROW]
[ROW][C]102[/C][C]0.0992572249510406[/C][C]0.198514449902081[/C][C]0.900742775048959[/C][/ROW]
[ROW][C]103[/C][C]0.0924430831120056[/C][C]0.184886166224011[/C][C]0.907556916887994[/C][/ROW]
[ROW][C]104[/C][C]0.0744174418866138[/C][C]0.148834883773228[/C][C]0.925582558113386[/C][/ROW]
[ROW][C]105[/C][C]0.0618691530786941[/C][C]0.123738306157388[/C][C]0.938130846921306[/C][/ROW]
[ROW][C]106[/C][C]0.0674543459554475[/C][C]0.134908691910895[/C][C]0.932545654044552[/C][/ROW]
[ROW][C]107[/C][C]0.0533134220820557[/C][C]0.106626844164111[/C][C]0.946686577917944[/C][/ROW]
[ROW][C]108[/C][C]0.0445417190283133[/C][C]0.0890834380566266[/C][C]0.955458280971687[/C][/ROW]
[ROW][C]109[/C][C]0.0346948228676696[/C][C]0.0693896457353392[/C][C]0.96530517713233[/C][/ROW]
[ROW][C]110[/C][C]0.035908877990697[/C][C]0.0718177559813939[/C][C]0.964091122009303[/C][/ROW]
[ROW][C]111[/C][C]0.0273141418797768[/C][C]0.0546282837595536[/C][C]0.972685858120223[/C][/ROW]
[ROW][C]112[/C][C]0.031997162972626[/C][C]0.0639943259452519[/C][C]0.968002837027374[/C][/ROW]
[ROW][C]113[/C][C]0.0281726572391815[/C][C]0.056345314478363[/C][C]0.971827342760819[/C][/ROW]
[ROW][C]114[/C][C]0.0231307637793301[/C][C]0.0462615275586601[/C][C]0.97686923622067[/C][/ROW]
[ROW][C]115[/C][C]0.0173828453758474[/C][C]0.0347656907516948[/C][C]0.982617154624153[/C][/ROW]
[ROW][C]116[/C][C]0.0132290960580172[/C][C]0.0264581921160344[/C][C]0.986770903941983[/C][/ROW]
[ROW][C]117[/C][C]0.0178820952083651[/C][C]0.0357641904167302[/C][C]0.982117904791635[/C][/ROW]
[ROW][C]118[/C][C]0.0206557022305248[/C][C]0.0413114044610497[/C][C]0.979344297769475[/C][/ROW]
[ROW][C]119[/C][C]0.0161244314097469[/C][C]0.0322488628194938[/C][C]0.983875568590253[/C][/ROW]
[ROW][C]120[/C][C]0.0123303844698969[/C][C]0.0246607689397937[/C][C]0.987669615530103[/C][/ROW]
[ROW][C]121[/C][C]0.010011072344648[/C][C]0.020022144689296[/C][C]0.989988927655352[/C][/ROW]
[ROW][C]122[/C][C]0.00822264800627882[/C][C]0.0164452960125576[/C][C]0.991777351993721[/C][/ROW]
[ROW][C]123[/C][C]0.00973729522760948[/C][C]0.019474590455219[/C][C]0.990262704772391[/C][/ROW]
[ROW][C]124[/C][C]0.0172545879849031[/C][C]0.0345091759698061[/C][C]0.982745412015097[/C][/ROW]
[ROW][C]125[/C][C]0.0182893018559252[/C][C]0.0365786037118504[/C][C]0.981710698144075[/C][/ROW]
[ROW][C]126[/C][C]0.047964203751211[/C][C]0.0959284075024221[/C][C]0.952035796248789[/C][/ROW]
[ROW][C]127[/C][C]0.0366051211015589[/C][C]0.0732102422031179[/C][C]0.963394878898441[/C][/ROW]
[ROW][C]128[/C][C]0.0276672360636453[/C][C]0.0553344721272905[/C][C]0.972332763936355[/C][/ROW]
[ROW][C]129[/C][C]0.0243389890568607[/C][C]0.0486779781137213[/C][C]0.975661010943139[/C][/ROW]
[ROW][C]130[/C][C]0.0222610964956194[/C][C]0.0445221929912388[/C][C]0.977738903504381[/C][/ROW]
[ROW][C]131[/C][C]0.0174942930951563[/C][C]0.0349885861903126[/C][C]0.982505706904844[/C][/ROW]
[ROW][C]132[/C][C]0.0217551080174456[/C][C]0.0435102160348912[/C][C]0.978244891982554[/C][/ROW]
[ROW][C]133[/C][C]0.0334648145965947[/C][C]0.0669296291931893[/C][C]0.966535185403405[/C][/ROW]
[ROW][C]134[/C][C]0.0348629554248589[/C][C]0.0697259108497178[/C][C]0.965137044575141[/C][/ROW]
[ROW][C]135[/C][C]0.03712668593611[/C][C]0.07425337187222[/C][C]0.96287331406389[/C][/ROW]
[ROW][C]136[/C][C]0.0296668676392208[/C][C]0.0593337352784416[/C][C]0.970333132360779[/C][/ROW]
[ROW][C]137[/C][C]0.028352293679842[/C][C]0.0567045873596841[/C][C]0.971647706320158[/C][/ROW]
[ROW][C]138[/C][C]0.0209207376907905[/C][C]0.0418414753815809[/C][C]0.979079262309209[/C][/ROW]
[ROW][C]139[/C][C]0.0218087430303767[/C][C]0.0436174860607535[/C][C]0.978191256969623[/C][/ROW]
[ROW][C]140[/C][C]0.0156403668783604[/C][C]0.0312807337567209[/C][C]0.98435963312164[/C][/ROW]
[ROW][C]141[/C][C]0.0494909987849912[/C][C]0.0989819975699823[/C][C]0.950509001215009[/C][/ROW]
[ROW][C]142[/C][C]0.0536698412764726[/C][C]0.107339682552945[/C][C]0.946330158723527[/C][/ROW]
[ROW][C]143[/C][C]0.039695700403215[/C][C]0.07939140080643[/C][C]0.960304299596785[/C][/ROW]
[ROW][C]144[/C][C]0.356443129476228[/C][C]0.712886258952456[/C][C]0.643556870523772[/C][/ROW]
[ROW][C]145[/C][C]0.362051588837381[/C][C]0.724103177674763[/C][C]0.637948411162619[/C][/ROW]
[ROW][C]146[/C][C]0.636151056501302[/C][C]0.727697886997396[/C][C]0.363848943498698[/C][/ROW]
[ROW][C]147[/C][C]0.560504973562306[/C][C]0.878990052875388[/C][C]0.439495026437694[/C][/ROW]
[ROW][C]148[/C][C]0.58361871945631[/C][C]0.83276256108738[/C][C]0.41638128054369[/C][/ROW]
[ROW][C]149[/C][C]0.47755051179823[/C][C]0.95510102359646[/C][C]0.52244948820177[/C][/ROW]
[ROW][C]150[/C][C]0.429632751992988[/C][C]0.859265503985975[/C][C]0.570367248007012[/C][/ROW]
[ROW][C]151[/C][C]0.301969814857969[/C][C]0.603939629715938[/C][C]0.698030185142031[/C][/ROW]
[ROW][C]152[/C][C]0.426203568790617[/C][C]0.852407137581234[/C][C]0.573796431209383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185448&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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
100.9999285177550.0001429644899994377.14822449997183e-05
110.999783016972830.0004339660543404310.000216983027170216
120.9994272692028160.001145461594367580.000572730797183788
130.9986757838650750.002648432269849770.00132421613492488
140.9979763718001120.004047256399776660.00202362819988833
150.9980607370091560.003878525981687330.00193926299084366
160.9967213817542490.006557236491502170.00327861824575108
170.9947188973450430.01056220530991320.00528110265495659
180.9936504287722050.01269914245558920.0063495712277946
190.99042038173560.01915923652880020.00957961826440011
200.984529433517310.03094113296537910.0154705664826895
210.9760560176574040.04788796468519240.0239439823425962
220.9651501302674090.06969973946518250.0348498697325913
230.9490822346471570.1018355307056850.0509177653528427
240.9293377145803760.1413245708392470.0706622854196237
250.9301824737810650.1396350524378690.0698175262189345
260.9293441796795260.1413116406409470.0706558203204737
270.9309024678422720.1381950643154560.0690975321577278
280.9393001566752660.1213996866494680.0606998433247338
290.9180061057442840.1639877885114320.081993894255716
300.8942227583604570.2115544832790870.105777241639543
310.8755237370491660.2489525259016680.124476262950834
320.8953208316217320.2093583367565360.104679168378268
330.8660652200323770.2678695599352460.133934779967623
340.8314612932077820.3370774135844360.168538706792218
350.8174459281916150.365108143616770.182554071808385
360.7780129385609290.4439741228781430.221987061439071
370.8234263081723230.3531473836553540.176573691827677
380.8142199482635950.3715601034728090.185780051736405
390.8038805660349480.3922388679301040.196119433965052
400.7851663861987680.4296672276024630.214833613801232
410.7605874569648070.4788250860703850.239412543035193
420.7441322262802690.5117355474394620.255867773719731
430.7432500233750470.5134999532499060.256749976624953
440.7008648235137830.5982703529724330.299135176486217
450.6548014534782310.6903970930435390.345198546521769
460.7193927406510550.561214518697890.280607259348945
470.7304691559864350.539061688027130.269530844013565
480.6861598494230830.6276803011538340.313840150576917
490.6489101416465570.7021797167068860.351089858353443
500.6363198952785290.7273602094429420.363680104721471
510.6410758964974810.7178482070050390.358924103502519
520.5945898479171350.8108203041657310.405410152082865
530.6233106275629920.7533787448740170.376689372437008
540.5904418656067620.8191162687864770.409558134393238
550.6785717331420710.6428565337158580.321428266857929
560.8425848753827590.3148302492344830.157415124617242
570.8146869449989950.370626110002010.185313055001005
580.7805439219819580.4389121560360840.219456078018042
590.7433801718837360.5132396562325280.256619828116264
600.7304555255572840.5390889488854320.269544474442716
610.7444710884081010.5110578231837980.255528911591899
620.7080021820959420.5839956358081160.291997817904058
630.7276601002823190.5446797994353620.272339899717681
640.6869007062012080.6261985875975850.313099293798792
650.6514475380383380.6971049239233240.348552461961662
660.6127544437084830.7744911125830340.387245556291517
670.5955350691592260.8089298616815490.404464930840774
680.57747887951430.8450422409713990.4225211204857
690.5938107221293460.8123785557413080.406189277870654
700.5496570126097460.9006859747805090.450342987390254
710.5083953095018030.9832093809963950.491604690498197
720.4647819562273480.9295639124546960.535218043772652
730.4346619322631280.8693238645262560.565338067736872
740.4117246621026750.8234493242053490.588275337897325
750.3865524895335070.7731049790670140.613447510466493
760.436809647832230.8736192956644610.56319035216777
770.4175598157201170.8351196314402340.582440184279883
780.3771025988409960.7542051976819910.622897401159004
790.3790055137419750.758011027483950.620994486258025
800.3562303952356530.7124607904713070.643769604764347
810.3157383441672610.6314766883345220.684261655832739
820.3289547125599020.6579094251198050.671045287440098
830.294029408551250.58805881710250.70597059144875
840.2663991078587340.5327982157174680.733600892141266
850.2301890471910280.4603780943820560.769810952808972
860.2222895269656410.4445790539312820.777710473034359
870.2229035002852780.4458070005705560.777096499714722
880.1905152435277080.3810304870554160.809484756472292
890.1616481570013950.3232963140027910.838351842998605
900.1428594900000180.2857189800000350.857140509999982
910.1238357760030810.2476715520061620.876164223996919
920.1738893000553360.3477786001106720.826110699944664
930.1494769890167040.2989539780334080.850523010983296
940.1339582149010260.2679164298020520.866041785098974
950.1308625120002080.2617250240004160.869137487999792
960.1249341144687060.2498682289374120.875065885531294
970.1181071477357380.2362142954714770.881892852264262
980.1481470884608160.2962941769216330.851852911539184
990.1232857462112910.2465714924225810.876714253788709
1000.1048384532230110.2096769064460220.895161546776989
1010.1201366829358440.2402733658716870.879863317064156
1020.09925722495104060.1985144499020810.900742775048959
1030.09244308311200560.1848861662240110.907556916887994
1040.07441744188661380.1488348837732280.925582558113386
1050.06186915307869410.1237383061573880.938130846921306
1060.06745434595544750.1349086919108950.932545654044552
1070.05331342208205570.1066268441641110.946686577917944
1080.04454171902831330.08908343805662660.955458280971687
1090.03469482286766960.06938964573533920.96530517713233
1100.0359088779906970.07181775598139390.964091122009303
1110.02731414187977680.05462828375955360.972685858120223
1120.0319971629726260.06399432594525190.968002837027374
1130.02817265723918150.0563453144783630.971827342760819
1140.02313076377933010.04626152755866010.97686923622067
1150.01738284537584740.03476569075169480.982617154624153
1160.01322909605801720.02645819211603440.986770903941983
1170.01788209520836510.03576419041673020.982117904791635
1180.02065570223052480.04131140446104970.979344297769475
1190.01612443140974690.03224886281949380.983875568590253
1200.01233038446989690.02466076893979370.987669615530103
1210.0100110723446480.0200221446892960.989988927655352
1220.008222648006278820.01644529601255760.991777351993721
1230.009737295227609480.0194745904552190.990262704772391
1240.01725458798490310.03450917596980610.982745412015097
1250.01828930185592520.03657860371185040.981710698144075
1260.0479642037512110.09592840750242210.952035796248789
1270.03660512110155890.07321024220311790.963394878898441
1280.02766723606364530.05533447212729050.972332763936355
1290.02433898905686070.04867797811372130.975661010943139
1300.02226109649561940.04452219299123880.977738903504381
1310.01749429309515630.03498858619031260.982505706904844
1320.02175510801744560.04351021603489120.978244891982554
1330.03346481459659470.06692962919318930.966535185403405
1340.03486295542485890.06972591084971780.965137044575141
1350.037126685936110.074253371872220.96287331406389
1360.02966686763922080.05933373527844160.970333132360779
1370.0283522936798420.05670458735968410.971647706320158
1380.02092073769079050.04184147538158090.979079262309209
1390.02180874303037670.04361748606075350.978191256969623
1400.01564036687836040.03128073375672090.98435963312164
1410.04949099878499120.09898199756998230.950509001215009
1420.05366984127647260.1073396825529450.946330158723527
1430.0396957004032150.079391400806430.960304299596785
1440.3564431294762280.7128862589524560.643556870523772
1450.3620515888373810.7241031776747630.637948411162619
1460.6361510565013020.7276978869973960.363848943498698
1470.5605049735623060.8789900528753880.439495026437694
1480.583618719456310.832762561087380.41638128054369
1490.477550511798230.955101023596460.52244948820177
1500.4296327519929880.8592655039859750.570367248007012
1510.3019698148579690.6039396297159380.698030185142031
1520.4262035687906170.8524071375812340.573796431209383






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.048951048951049NOK
5% type I error level310.216783216783217NOK
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 & 31 & 0.216783216783217 & NOK \tabularnewline
10% type I error level & 48 & 0.335664335664336 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185448&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]31[/C][C]0.216783216783217[/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=185448&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185448&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 level310.216783216783217NOK
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')
}