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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 10:03:19 -0500
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/Nov/05/t1352127813fg9e2r464y2drhh.htm/, Retrieved Fri, 03 Feb 2023 10:37:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186095, Retrieved Fri, 03 Feb 2023 10:37:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 15:03:19] [a587951ff5ee1fbafa3c9416eb6a1c1d] [Current]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.2629853555223 + 0.336031993936646Separate[t] + 0.324387357179274Learning[t] -0.139120947054739Software[t] + 0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] + 0.0349149616469267Belonging[t] -0.0249943108224535Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.2629853555223 +  0.336031993936646Separate[t] +  0.324387357179274Learning[t] -0.139120947054739Software[t] +  0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] +  0.0349149616469267Belonging[t] -0.0249943108224535Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.2629853555223 +  0.336031993936646Separate[t] +  0.324387357179274Learning[t] -0.139120947054739Software[t] +  0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] +  0.0349149616469267Belonging[t] -0.0249943108224535Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.2629853555223 + 0.336031993936646Separate[t] + 0.324387357179274Learning[t] -0.139120947054739Software[t] + 0.0353030642697486Happiness[t] -0.0228636599266878Depression[t] + 0.0349149616469267Belonging[t] -0.0249943108224535Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.26298535552234.1922484.35642.4e-051.2e-05
Separate0.3360319939366460.070614.7594e-062e-06
Learning0.3243873571792740.1333612.43240.0161450.008073
Software-0.1391209470547390.137233-1.01380.3122890.156145
Happiness0.03530306426974860.1290380.27360.7847680.392384
Depression-0.02286365992668780.095489-0.23940.8110840.405542
Belonging0.03491496164692670.0752030.46430.6431070.321554
Belonging_Final-0.02499431082245350.108077-0.23130.8174170.408708

\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) & 18.2629853555223 & 4.192248 & 4.3564 & 2.4e-05 & 1.2e-05 \tabularnewline
Separate & 0.336031993936646 & 0.07061 & 4.759 & 4e-06 & 2e-06 \tabularnewline
Learning & 0.324387357179274 & 0.133361 & 2.4324 & 0.016145 & 0.008073 \tabularnewline
Software & -0.139120947054739 & 0.137233 & -1.0138 & 0.312289 & 0.156145 \tabularnewline
Happiness & 0.0353030642697486 & 0.129038 & 0.2736 & 0.784768 & 0.392384 \tabularnewline
Depression & -0.0228636599266878 & 0.095489 & -0.2394 & 0.811084 & 0.405542 \tabularnewline
Belonging & 0.0349149616469267 & 0.075203 & 0.4643 & 0.643107 & 0.321554 \tabularnewline
Belonging_Final & -0.0249943108224535 & 0.108077 & -0.2313 & 0.817417 & 0.408708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&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]18.2629853555223[/C][C]4.192248[/C][C]4.3564[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]Separate[/C][C]0.336031993936646[/C][C]0.07061[/C][C]4.759[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.324387357179274[/C][C]0.133361[/C][C]2.4324[/C][C]0.016145[/C][C]0.008073[/C][/ROW]
[ROW][C]Software[/C][C]-0.139120947054739[/C][C]0.137233[/C][C]-1.0138[/C][C]0.312289[/C][C]0.156145[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0353030642697486[/C][C]0.129038[/C][C]0.2736[/C][C]0.784768[/C][C]0.392384[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0228636599266878[/C][C]0.095489[/C][C]-0.2394[/C][C]0.811084[/C][C]0.405542[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0349149616469267[/C][C]0.075203[/C][C]0.4643[/C][C]0.643107[/C][C]0.321554[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0249943108224535[/C][C]0.108077[/C][C]-0.2313[/C][C]0.817417[/C][C]0.408708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=2

As an alternative you can also use a 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)18.26298535552234.1922484.35642.4e-051.2e-05
Separate0.3360319939366460.070614.7594e-062e-06
Learning0.3243873571792740.1333612.43240.0161450.008073
Software-0.1391209470547390.137233-1.01380.3122890.156145
Happiness0.03530306426974860.1290380.27360.7847680.392384
Depression-0.02286365992668780.095489-0.23940.8110840.405542
Belonging0.03491496164692670.0752030.46430.6431070.321554
Belonging_Final-0.02499431082245350.108077-0.23130.8174170.408708







Multiple Linear Regression - Regression Statistics
Multiple R0.427051855095818
R-squared0.182373286940779
Adjusted R-squared0.145208436347178
F-TEST (value)4.90714435892768
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.02586788058146e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1204734771678
Sum Squared Residuals1499.55262714299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.427051855095818 \tabularnewline
R-squared & 0.182373286940779 \tabularnewline
Adjusted R-squared & 0.145208436347178 \tabularnewline
F-TEST (value) & 4.90714435892768 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 5.02586788058146e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.1204734771678 \tabularnewline
Sum Squared Residuals & 1499.55262714299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.427051855095818[/C][/ROW]
[ROW][C]R-squared[/C][C]0.182373286940779[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.145208436347178[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.90714435892768[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]5.02586788058146e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.1204734771678[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1499.55262714299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.427051855095818
R-squared0.182373286940779
Adjusted R-squared0.145208436347178
F-TEST (value)4.90714435892768
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.02586788058146e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.1204734771678
Sum Squared Residuals1499.55262714299







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14134.85033940541346.14966059458662
23934.78780820611364.21219179388636
33035.4235196060377-5.42351960603768
43134.8469343695324-3.84693436953245
53435.0175307771655-1.01753077716547
63532.74346499000832.25653500999167
73934.09083143755334.90916856244672
83435.0894725782011-1.08947257820108
93634.65594931879931.34405068120067
103736.77440866198590.225591338014142
113834.38404164500513.61595835499491
123634.75726411308451.24273588691549
133834.84446861311463.15553138688543
143935.99742188351653.00257811648354
153336.2436617446859-3.24366174468589
163233.8478243951801-1.84782439518007
173634.15863984594861.84136015405143
183837.92197257795260.0780274220473866
193936.81624023893192.1837597610681
203233.894734585242-1.89473458524196
213234.766938715043-2.76693871504304
223133.5434337015664-2.54343370156644
233936.66746325225872.33253674774132
243736.16209430037820.837905699621849
253935.33014008231023.66985991768981
264134.45244088401736.54755911598267
273635.71522387654920.284776123450821
283335.0749569084271-2.07495690842705
293334.6808652521655-1.68086525216554
303433.83291356747040.167086432529573
313132.7957919692103-1.79579196921027
322732.9722704796078-5.97227047960777
333733.60591310240593.39408689759415
343436.0781251801717-2.07812518017167
353432.94687823753371.05312176246635
363232.4100583759496-0.410058375949615
372932.2244325202422-3.22443252024215
383633.88340174777682.11659825222321
392934.3542796925317-5.35427969253167
403534.74070393508730.259296064912704
413734.59773360538412.40226639461589
423433.98764302795840.0123569720415818
433834.85947751995493.14052248004515
443533.64655458780151.35344541219852
453832.35360252684915.64639747315092
463733.38846579463323.61153420536684
473835.61957397424042.38042602575957
483334.7704826199945-1.77048261999448
493636.483211723396-0.483211723395974
503833.58929851017014.41070148982986
513236.0250455726567-4.02504557265669
523233.0078403618329-1.00784036183286
533232.8268789885384-0.826878988538431
543435.5323613848115-1.53236138481151
553232.7118745470261-0.711874547026151
563734.45669300111342.5433069988866
573934.93540412371094.06459587628908
582934.8221339000868-5.8221339000868
593735.36465408415871.63534591584128
603534.93703521594520.0629647840547729
613031.4665091791093-1.46650917910928
623834.95710347576913.04289652423086
633434.9442874470568-0.944287447056812
643134.2501037571595-3.25010375715953
653433.49363757456210.506362425437858
663536.072187976472-1.07218797647202
673635.16966221613310.830337783866912
683033.3177895120583-3.31778951205827
693935.40963919198493.59036080801507
703535.7857415055571-0.785741505557147
713833.9276112074824.07238879251801
723135.3257835092312-4.32578350923122
733437.1202156484346-3.1202156484346
743837.64184556290450.358154437095542
753432.74717635555551.25282364444447
763934.16414465626054.83585534373955
773735.75001099779311.24998900220693
783433.71948475902750.280515240972456
792832.9735992693893-4.97359926938928
803732.17035810998284.82964189001718
813335.76747286458-2.76747286458002
823737.2341581825003-0.234158182500295
833535.8949067960171-0.894906796017078
843734.31292209251972.68707790748033
853234.6928626967719-2.69286269677192
863334.1897001707668-1.18970017076684
873836.3837641175771.61623588242301
883334.7880697793308-1.78806977933084
892933.7133361201059-4.71333612010591
903333.6618130298843-0.661813029884273
913134.7026753158317-3.7026753158317
923633.6203606361212.37963936387903
933537.332075591116-2.33207559111602
943232.682828115227-0.682828115226952
952932.740297011634-3.74029701163399
963936.09093847378372.90906152621628
973734.7979257471442.20207425285596
983534.43445771014430.56554228985567
993735.18585630689521.81414369310483
1003235.3150588193169-3.31505881931686
1013835.50295018481842.4970498151816
1023735.16583886619631.83416113380368
1033637.0556547553594-1.05565475535944
1043232.3988209877631-0.39882098776309
1053336.7753771829888-3.7753771829888
1064032.79636212825977.20363787174031
1073835.66139209632922.33860790367084
1084136.45691378604854.54308621395154
1093634.75464385616131.24535614383866
1104335.95569247317157.04430752682852
1113035.0565798494001-5.05657984940008
1123134.1238819052851-3.12388190528513
1133237.929820867378-5.92982086737802
1143233.2418655055724-1.24186550557238
1153734.02940767921842.97059232078157
1163735.0606099361051.93939006389501
1173335.6718631343822-2.67186313438223
1183436.6740334968865-2.67403349688645
1193334.3586516285645-1.35865162856446
1203835.79983094131762.20016905868237
1213334.5241519010363-1.52415190103631
1223132.3954250431049-1.39542504310488
1233835.95160190474182.04839809525823
1243736.10014674236070.89985325763934
1253333.4393091316037-0.43930913160367
1263134.2464136022439-3.24641360224391
1273934.67551231479734.32448768520269
1284437.20796075250046.79203924749957
1293335.8965303531806-2.89653035318063
1303533.36080497137081.63919502862923
1313234.7876701984545-2.78767019845446
1322831.8946217313785-3.89462173137852
1334036.61302238336853.38697761663155
1342732.1625541814688-5.16255418146878
1353735.59332366993341.40667633006656
1363232.9873583435175-0.987358343517515
1372829.5437015424129-1.54370154241285
1383434.683618273408-0.683618273408011
1393033.2342788000179-3.23427880001787
1403533.98058467643941.01941532356059
1413132.9360570035502-1.93605700355021
1423235.1268083243711-3.1268083243711
1433035.2046996993037-5.20469969930372
1443035.3497432182333-5.3497432182333
1453131.4454843072378-0.445484307237821
1464032.8569764233777.14302357662297
1473233.1355388950338-1.13553889503384
1483634.14394143139781.85605856860215
1493233.8417438554998-1.84174385549984
1503533.26332822354631.73667177645372
1513835.57181843705542.42818156294455
1524235.40721415536066.59278584463943
1533436.7835819386775-2.7835819386775
1543537.2871055660989-2.28710556609887
1553534.33422360153320.665776398466767
1563332.30693427625460.693065723745405
1573633.6203606361212.37963936387903
1583235.5870077412556-3.58700774125556
1593335.8965303531806-2.89653035318063
1603434.5871902813244-0.587190281324418
1613234.499017982794-2.49901798279399
1623434.8167205366189-0.816720536618947

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.8503394054134 & 6.14966059458662 \tabularnewline
2 & 39 & 34.7878082061136 & 4.21219179388636 \tabularnewline
3 & 30 & 35.4235196060377 & -5.42351960603768 \tabularnewline
4 & 31 & 34.8469343695324 & -3.84693436953245 \tabularnewline
5 & 34 & 35.0175307771655 & -1.01753077716547 \tabularnewline
6 & 35 & 32.7434649900083 & 2.25653500999167 \tabularnewline
7 & 39 & 34.0908314375533 & 4.90916856244672 \tabularnewline
8 & 34 & 35.0894725782011 & -1.08947257820108 \tabularnewline
9 & 36 & 34.6559493187993 & 1.34405068120067 \tabularnewline
10 & 37 & 36.7744086619859 & 0.225591338014142 \tabularnewline
11 & 38 & 34.3840416450051 & 3.61595835499491 \tabularnewline
12 & 36 & 34.7572641130845 & 1.24273588691549 \tabularnewline
13 & 38 & 34.8444686131146 & 3.15553138688543 \tabularnewline
14 & 39 & 35.9974218835165 & 3.00257811648354 \tabularnewline
15 & 33 & 36.2436617446859 & -3.24366174468589 \tabularnewline
16 & 32 & 33.8478243951801 & -1.84782439518007 \tabularnewline
17 & 36 & 34.1586398459486 & 1.84136015405143 \tabularnewline
18 & 38 & 37.9219725779526 & 0.0780274220473866 \tabularnewline
19 & 39 & 36.8162402389319 & 2.1837597610681 \tabularnewline
20 & 32 & 33.894734585242 & -1.89473458524196 \tabularnewline
21 & 32 & 34.766938715043 & -2.76693871504304 \tabularnewline
22 & 31 & 33.5434337015664 & -2.54343370156644 \tabularnewline
23 & 39 & 36.6674632522587 & 2.33253674774132 \tabularnewline
24 & 37 & 36.1620943003782 & 0.837905699621849 \tabularnewline
25 & 39 & 35.3301400823102 & 3.66985991768981 \tabularnewline
26 & 41 & 34.4524408840173 & 6.54755911598267 \tabularnewline
27 & 36 & 35.7152238765492 & 0.284776123450821 \tabularnewline
28 & 33 & 35.0749569084271 & -2.07495690842705 \tabularnewline
29 & 33 & 34.6808652521655 & -1.68086525216554 \tabularnewline
30 & 34 & 33.8329135674704 & 0.167086432529573 \tabularnewline
31 & 31 & 32.7957919692103 & -1.79579196921027 \tabularnewline
32 & 27 & 32.9722704796078 & -5.97227047960777 \tabularnewline
33 & 37 & 33.6059131024059 & 3.39408689759415 \tabularnewline
34 & 34 & 36.0781251801717 & -2.07812518017167 \tabularnewline
35 & 34 & 32.9468782375337 & 1.05312176246635 \tabularnewline
36 & 32 & 32.4100583759496 & -0.410058375949615 \tabularnewline
37 & 29 & 32.2244325202422 & -3.22443252024215 \tabularnewline
38 & 36 & 33.8834017477768 & 2.11659825222321 \tabularnewline
39 & 29 & 34.3542796925317 & -5.35427969253167 \tabularnewline
40 & 35 & 34.7407039350873 & 0.259296064912704 \tabularnewline
41 & 37 & 34.5977336053841 & 2.40226639461589 \tabularnewline
42 & 34 & 33.9876430279584 & 0.0123569720415818 \tabularnewline
43 & 38 & 34.8594775199549 & 3.14052248004515 \tabularnewline
44 & 35 & 33.6465545878015 & 1.35344541219852 \tabularnewline
45 & 38 & 32.3536025268491 & 5.64639747315092 \tabularnewline
46 & 37 & 33.3884657946332 & 3.61153420536684 \tabularnewline
47 & 38 & 35.6195739742404 & 2.38042602575957 \tabularnewline
48 & 33 & 34.7704826199945 & -1.77048261999448 \tabularnewline
49 & 36 & 36.483211723396 & -0.483211723395974 \tabularnewline
50 & 38 & 33.5892985101701 & 4.41070148982986 \tabularnewline
51 & 32 & 36.0250455726567 & -4.02504557265669 \tabularnewline
52 & 32 & 33.0078403618329 & -1.00784036183286 \tabularnewline
53 & 32 & 32.8268789885384 & -0.826878988538431 \tabularnewline
54 & 34 & 35.5323613848115 & -1.53236138481151 \tabularnewline
55 & 32 & 32.7118745470261 & -0.711874547026151 \tabularnewline
56 & 37 & 34.4566930011134 & 2.5433069988866 \tabularnewline
57 & 39 & 34.9354041237109 & 4.06459587628908 \tabularnewline
58 & 29 & 34.8221339000868 & -5.8221339000868 \tabularnewline
59 & 37 & 35.3646540841587 & 1.63534591584128 \tabularnewline
60 & 35 & 34.9370352159452 & 0.0629647840547729 \tabularnewline
61 & 30 & 31.4665091791093 & -1.46650917910928 \tabularnewline
62 & 38 & 34.9571034757691 & 3.04289652423086 \tabularnewline
63 & 34 & 34.9442874470568 & -0.944287447056812 \tabularnewline
64 & 31 & 34.2501037571595 & -3.25010375715953 \tabularnewline
65 & 34 & 33.4936375745621 & 0.506362425437858 \tabularnewline
66 & 35 & 36.072187976472 & -1.07218797647202 \tabularnewline
67 & 36 & 35.1696622161331 & 0.830337783866912 \tabularnewline
68 & 30 & 33.3177895120583 & -3.31778951205827 \tabularnewline
69 & 39 & 35.4096391919849 & 3.59036080801507 \tabularnewline
70 & 35 & 35.7857415055571 & -0.785741505557147 \tabularnewline
71 & 38 & 33.927611207482 & 4.07238879251801 \tabularnewline
72 & 31 & 35.3257835092312 & -4.32578350923122 \tabularnewline
73 & 34 & 37.1202156484346 & -3.1202156484346 \tabularnewline
74 & 38 & 37.6418455629045 & 0.358154437095542 \tabularnewline
75 & 34 & 32.7471763555555 & 1.25282364444447 \tabularnewline
76 & 39 & 34.1641446562605 & 4.83585534373955 \tabularnewline
77 & 37 & 35.7500109977931 & 1.24998900220693 \tabularnewline
78 & 34 & 33.7194847590275 & 0.280515240972456 \tabularnewline
79 & 28 & 32.9735992693893 & -4.97359926938928 \tabularnewline
80 & 37 & 32.1703581099828 & 4.82964189001718 \tabularnewline
81 & 33 & 35.76747286458 & -2.76747286458002 \tabularnewline
82 & 37 & 37.2341581825003 & -0.234158182500295 \tabularnewline
83 & 35 & 35.8949067960171 & -0.894906796017078 \tabularnewline
84 & 37 & 34.3129220925197 & 2.68707790748033 \tabularnewline
85 & 32 & 34.6928626967719 & -2.69286269677192 \tabularnewline
86 & 33 & 34.1897001707668 & -1.18970017076684 \tabularnewline
87 & 38 & 36.383764117577 & 1.61623588242301 \tabularnewline
88 & 33 & 34.7880697793308 & -1.78806977933084 \tabularnewline
89 & 29 & 33.7133361201059 & -4.71333612010591 \tabularnewline
90 & 33 & 33.6618130298843 & -0.661813029884273 \tabularnewline
91 & 31 & 34.7026753158317 & -3.7026753158317 \tabularnewline
92 & 36 & 33.620360636121 & 2.37963936387903 \tabularnewline
93 & 35 & 37.332075591116 & -2.33207559111602 \tabularnewline
94 & 32 & 32.682828115227 & -0.682828115226952 \tabularnewline
95 & 29 & 32.740297011634 & -3.74029701163399 \tabularnewline
96 & 39 & 36.0909384737837 & 2.90906152621628 \tabularnewline
97 & 37 & 34.797925747144 & 2.20207425285596 \tabularnewline
98 & 35 & 34.4344577101443 & 0.56554228985567 \tabularnewline
99 & 37 & 35.1858563068952 & 1.81414369310483 \tabularnewline
100 & 32 & 35.3150588193169 & -3.31505881931686 \tabularnewline
101 & 38 & 35.5029501848184 & 2.4970498151816 \tabularnewline
102 & 37 & 35.1658388661963 & 1.83416113380368 \tabularnewline
103 & 36 & 37.0556547553594 & -1.05565475535944 \tabularnewline
104 & 32 & 32.3988209877631 & -0.39882098776309 \tabularnewline
105 & 33 & 36.7753771829888 & -3.7753771829888 \tabularnewline
106 & 40 & 32.7963621282597 & 7.20363787174031 \tabularnewline
107 & 38 & 35.6613920963292 & 2.33860790367084 \tabularnewline
108 & 41 & 36.4569137860485 & 4.54308621395154 \tabularnewline
109 & 36 & 34.7546438561613 & 1.24535614383866 \tabularnewline
110 & 43 & 35.9556924731715 & 7.04430752682852 \tabularnewline
111 & 30 & 35.0565798494001 & -5.05657984940008 \tabularnewline
112 & 31 & 34.1238819052851 & -3.12388190528513 \tabularnewline
113 & 32 & 37.929820867378 & -5.92982086737802 \tabularnewline
114 & 32 & 33.2418655055724 & -1.24186550557238 \tabularnewline
115 & 37 & 34.0294076792184 & 2.97059232078157 \tabularnewline
116 & 37 & 35.060609936105 & 1.93939006389501 \tabularnewline
117 & 33 & 35.6718631343822 & -2.67186313438223 \tabularnewline
118 & 34 & 36.6740334968865 & -2.67403349688645 \tabularnewline
119 & 33 & 34.3586516285645 & -1.35865162856446 \tabularnewline
120 & 38 & 35.7998309413176 & 2.20016905868237 \tabularnewline
121 & 33 & 34.5241519010363 & -1.52415190103631 \tabularnewline
122 & 31 & 32.3954250431049 & -1.39542504310488 \tabularnewline
123 & 38 & 35.9516019047418 & 2.04839809525823 \tabularnewline
124 & 37 & 36.1001467423607 & 0.89985325763934 \tabularnewline
125 & 33 & 33.4393091316037 & -0.43930913160367 \tabularnewline
126 & 31 & 34.2464136022439 & -3.24641360224391 \tabularnewline
127 & 39 & 34.6755123147973 & 4.32448768520269 \tabularnewline
128 & 44 & 37.2079607525004 & 6.79203924749957 \tabularnewline
129 & 33 & 35.8965303531806 & -2.89653035318063 \tabularnewline
130 & 35 & 33.3608049713708 & 1.63919502862923 \tabularnewline
131 & 32 & 34.7876701984545 & -2.78767019845446 \tabularnewline
132 & 28 & 31.8946217313785 & -3.89462173137852 \tabularnewline
133 & 40 & 36.6130223833685 & 3.38697761663155 \tabularnewline
134 & 27 & 32.1625541814688 & -5.16255418146878 \tabularnewline
135 & 37 & 35.5933236699334 & 1.40667633006656 \tabularnewline
136 & 32 & 32.9873583435175 & -0.987358343517515 \tabularnewline
137 & 28 & 29.5437015424129 & -1.54370154241285 \tabularnewline
138 & 34 & 34.683618273408 & -0.683618273408011 \tabularnewline
139 & 30 & 33.2342788000179 & -3.23427880001787 \tabularnewline
140 & 35 & 33.9805846764394 & 1.01941532356059 \tabularnewline
141 & 31 & 32.9360570035502 & -1.93605700355021 \tabularnewline
142 & 32 & 35.1268083243711 & -3.1268083243711 \tabularnewline
143 & 30 & 35.2046996993037 & -5.20469969930372 \tabularnewline
144 & 30 & 35.3497432182333 & -5.3497432182333 \tabularnewline
145 & 31 & 31.4454843072378 & -0.445484307237821 \tabularnewline
146 & 40 & 32.856976423377 & 7.14302357662297 \tabularnewline
147 & 32 & 33.1355388950338 & -1.13553889503384 \tabularnewline
148 & 36 & 34.1439414313978 & 1.85605856860215 \tabularnewline
149 & 32 & 33.8417438554998 & -1.84174385549984 \tabularnewline
150 & 35 & 33.2633282235463 & 1.73667177645372 \tabularnewline
151 & 38 & 35.5718184370554 & 2.42818156294455 \tabularnewline
152 & 42 & 35.4072141553606 & 6.59278584463943 \tabularnewline
153 & 34 & 36.7835819386775 & -2.7835819386775 \tabularnewline
154 & 35 & 37.2871055660989 & -2.28710556609887 \tabularnewline
155 & 35 & 34.3342236015332 & 0.665776398466767 \tabularnewline
156 & 33 & 32.3069342762546 & 0.693065723745405 \tabularnewline
157 & 36 & 33.620360636121 & 2.37963936387903 \tabularnewline
158 & 32 & 35.5870077412556 & -3.58700774125556 \tabularnewline
159 & 33 & 35.8965303531806 & -2.89653035318063 \tabularnewline
160 & 34 & 34.5871902813244 & -0.587190281324418 \tabularnewline
161 & 32 & 34.499017982794 & -2.49901798279399 \tabularnewline
162 & 34 & 34.8167205366189 & -0.816720536618947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&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]41[/C][C]34.8503394054134[/C][C]6.14966059458662[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.7878082061136[/C][C]4.21219179388636[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.4235196060377[/C][C]-5.42351960603768[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.8469343695324[/C][C]-3.84693436953245[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.0175307771655[/C][C]-1.01753077716547[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.7434649900083[/C][C]2.25653500999167[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.0908314375533[/C][C]4.90916856244672[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.0894725782011[/C][C]-1.08947257820108[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.6559493187993[/C][C]1.34405068120067[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.7744086619859[/C][C]0.225591338014142[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.3840416450051[/C][C]3.61595835499491[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7572641130845[/C][C]1.24273588691549[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.8444686131146[/C][C]3.15553138688543[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.9974218835165[/C][C]3.00257811648354[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.2436617446859[/C][C]-3.24366174468589[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8478243951801[/C][C]-1.84782439518007[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.1586398459486[/C][C]1.84136015405143[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.9219725779526[/C][C]0.0780274220473866[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.8162402389319[/C][C]2.1837597610681[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.894734585242[/C][C]-1.89473458524196[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.766938715043[/C][C]-2.76693871504304[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.5434337015664[/C][C]-2.54343370156644[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.6674632522587[/C][C]2.33253674774132[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.1620943003782[/C][C]0.837905699621849[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.3301400823102[/C][C]3.66985991768981[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.4524408840173[/C][C]6.54755911598267[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.7152238765492[/C][C]0.284776123450821[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.0749569084271[/C][C]-2.07495690842705[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.6808652521655[/C][C]-1.68086525216554[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8329135674704[/C][C]0.167086432529573[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.7957919692103[/C][C]-1.79579196921027[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]32.9722704796078[/C][C]-5.97227047960777[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6059131024059[/C][C]3.39408689759415[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.0781251801717[/C][C]-2.07812518017167[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.9468782375337[/C][C]1.05312176246635[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.4100583759496[/C][C]-0.410058375949615[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.2244325202422[/C][C]-3.22443252024215[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8834017477768[/C][C]2.11659825222321[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3542796925317[/C][C]-5.35427969253167[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.7407039350873[/C][C]0.259296064912704[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.5977336053841[/C][C]2.40226639461589[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.9876430279584[/C][C]0.0123569720415818[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.8594775199549[/C][C]3.14052248004515[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.6465545878015[/C][C]1.35344541219852[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.3536025268491[/C][C]5.64639747315092[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.3884657946332[/C][C]3.61153420536684[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.6195739742404[/C][C]2.38042602575957[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7704826199945[/C][C]-1.77048261999448[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.483211723396[/C][C]-0.483211723395974[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.5892985101701[/C][C]4.41070148982986[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0250455726567[/C][C]-4.02504557265669[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0078403618329[/C][C]-1.00784036183286[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.8268789885384[/C][C]-0.826878988538431[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.5323613848115[/C][C]-1.53236138481151[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.7118745470261[/C][C]-0.711874547026151[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4566930011134[/C][C]2.5433069988866[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9354041237109[/C][C]4.06459587628908[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8221339000868[/C][C]-5.8221339000868[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.3646540841587[/C][C]1.63534591584128[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.9370352159452[/C][C]0.0629647840547729[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.4665091791093[/C][C]-1.46650917910928[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.9571034757691[/C][C]3.04289652423086[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.9442874470568[/C][C]-0.944287447056812[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.2501037571595[/C][C]-3.25010375715953[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.4936375745621[/C][C]0.506362425437858[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.072187976472[/C][C]-1.07218797647202[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.1696622161331[/C][C]0.830337783866912[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.3177895120583[/C][C]-3.31778951205827[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.4096391919849[/C][C]3.59036080801507[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.7857415055571[/C][C]-0.785741505557147[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]33.927611207482[/C][C]4.07238879251801[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3257835092312[/C][C]-4.32578350923122[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]37.1202156484346[/C][C]-3.1202156484346[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.6418455629045[/C][C]0.358154437095542[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7471763555555[/C][C]1.25282364444447[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.1641446562605[/C][C]4.83585534373955[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.7500109977931[/C][C]1.24998900220693[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7194847590275[/C][C]0.280515240972456[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.9735992693893[/C][C]-4.97359926938928[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.1703581099828[/C][C]4.82964189001718[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.76747286458[/C][C]-2.76747286458002[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.2341581825003[/C][C]-0.234158182500295[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8949067960171[/C][C]-0.894906796017078[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.3129220925197[/C][C]2.68707790748033[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6928626967719[/C][C]-2.69286269677192[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.1897001707668[/C][C]-1.18970017076684[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.383764117577[/C][C]1.61623588242301[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.7880697793308[/C][C]-1.78806977933084[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.7133361201059[/C][C]-4.71333612010591[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.6618130298843[/C][C]-0.661813029884273[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7026753158317[/C][C]-3.7026753158317[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.620360636121[/C][C]2.37963936387903[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.332075591116[/C][C]-2.33207559111602[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.682828115227[/C][C]-0.682828115226952[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.740297011634[/C][C]-3.74029701163399[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0909384737837[/C][C]2.90906152621628[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.797925747144[/C][C]2.20207425285596[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.4344577101443[/C][C]0.56554228985567[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.1858563068952[/C][C]1.81414369310483[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.3150588193169[/C][C]-3.31505881931686[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.5029501848184[/C][C]2.4970498151816[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.1658388661963[/C][C]1.83416113380368[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]37.0556547553594[/C][C]-1.05565475535944[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.3988209877631[/C][C]-0.39882098776309[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.7753771829888[/C][C]-3.7753771829888[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7963621282597[/C][C]7.20363787174031[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.6613920963292[/C][C]2.33860790367084[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.4569137860485[/C][C]4.54308621395154[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.7546438561613[/C][C]1.24535614383866[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.9556924731715[/C][C]7.04430752682852[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]35.0565798494001[/C][C]-5.05657984940008[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.1238819052851[/C][C]-3.12388190528513[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.929820867378[/C][C]-5.92982086737802[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.2418655055724[/C][C]-1.24186550557238[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.0294076792184[/C][C]2.97059232078157[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.060609936105[/C][C]1.93939006389501[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.6718631343822[/C][C]-2.67186313438223[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.6740334968865[/C][C]-2.67403349688645[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.3586516285645[/C][C]-1.35865162856446[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.7998309413176[/C][C]2.20016905868237[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.5241519010363[/C][C]-1.52415190103631[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3954250431049[/C][C]-1.39542504310488[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.9516019047418[/C][C]2.04839809525823[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.1001467423607[/C][C]0.89985325763934[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.4393091316037[/C][C]-0.43930913160367[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2464136022439[/C][C]-3.24641360224391[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6755123147973[/C][C]4.32448768520269[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.2079607525004[/C][C]6.79203924749957[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.8965303531806[/C][C]-2.89653035318063[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.3608049713708[/C][C]1.63919502862923[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.7876701984545[/C][C]-2.78767019845446[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.8946217313785[/C][C]-3.89462173137852[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.6130223833685[/C][C]3.38697761663155[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.1625541814688[/C][C]-5.16255418146878[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.5933236699334[/C][C]1.40667633006656[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9873583435175[/C][C]-0.987358343517515[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.5437015424129[/C][C]-1.54370154241285[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.683618273408[/C][C]-0.683618273408011[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.2342788000179[/C][C]-3.23427880001787[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.9805846764394[/C][C]1.01941532356059[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.9360570035502[/C][C]-1.93605700355021[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.1268083243711[/C][C]-3.1268083243711[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.2046996993037[/C][C]-5.20469969930372[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.3497432182333[/C][C]-5.3497432182333[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.4454843072378[/C][C]-0.445484307237821[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.856976423377[/C][C]7.14302357662297[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.1355388950338[/C][C]-1.13553889503384[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.1439414313978[/C][C]1.85605856860215[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.8417438554998[/C][C]-1.84174385549984[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2633282235463[/C][C]1.73667177645372[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.5718184370554[/C][C]2.42818156294455[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.4072141553606[/C][C]6.59278584463943[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.7835819386775[/C][C]-2.7835819386775[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]37.2871055660989[/C][C]-2.28710556609887[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.3342236015332[/C][C]0.665776398466767[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.3069342762546[/C][C]0.693065723745405[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.620360636121[/C][C]2.37963936387903[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.5870077412556[/C][C]-3.58700774125556[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.8965303531806[/C][C]-2.89653035318063[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.5871902813244[/C][C]-0.587190281324418[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.499017982794[/C][C]-2.49901798279399[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.8167205366189[/C][C]-0.816720536618947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14134.85033940541346.14966059458662
23934.78780820611364.21219179388636
33035.4235196060377-5.42351960603768
43134.8469343695324-3.84693436953245
53435.0175307771655-1.01753077716547
63532.74346499000832.25653500999167
73934.09083143755334.90916856244672
83435.0894725782011-1.08947257820108
93634.65594931879931.34405068120067
103736.77440866198590.225591338014142
113834.38404164500513.61595835499491
123634.75726411308451.24273588691549
133834.84446861311463.15553138688543
143935.99742188351653.00257811648354
153336.2436617446859-3.24366174468589
163233.8478243951801-1.84782439518007
173634.15863984594861.84136015405143
183837.92197257795260.0780274220473866
193936.81624023893192.1837597610681
203233.894734585242-1.89473458524196
213234.766938715043-2.76693871504304
223133.5434337015664-2.54343370156644
233936.66746325225872.33253674774132
243736.16209430037820.837905699621849
253935.33014008231023.66985991768981
264134.45244088401736.54755911598267
273635.71522387654920.284776123450821
283335.0749569084271-2.07495690842705
293334.6808652521655-1.68086525216554
303433.83291356747040.167086432529573
313132.7957919692103-1.79579196921027
322732.9722704796078-5.97227047960777
333733.60591310240593.39408689759415
343436.0781251801717-2.07812518017167
353432.94687823753371.05312176246635
363232.4100583759496-0.410058375949615
372932.2244325202422-3.22443252024215
383633.88340174777682.11659825222321
392934.3542796925317-5.35427969253167
403534.74070393508730.259296064912704
413734.59773360538412.40226639461589
423433.98764302795840.0123569720415818
433834.85947751995493.14052248004515
443533.64655458780151.35344541219852
453832.35360252684915.64639747315092
463733.38846579463323.61153420536684
473835.61957397424042.38042602575957
483334.7704826199945-1.77048261999448
493636.483211723396-0.483211723395974
503833.58929851017014.41070148982986
513236.0250455726567-4.02504557265669
523233.0078403618329-1.00784036183286
533232.8268789885384-0.826878988538431
543435.5323613848115-1.53236138481151
553232.7118745470261-0.711874547026151
563734.45669300111342.5433069988866
573934.93540412371094.06459587628908
582934.8221339000868-5.8221339000868
593735.36465408415871.63534591584128
603534.93703521594520.0629647840547729
613031.4665091791093-1.46650917910928
623834.95710347576913.04289652423086
633434.9442874470568-0.944287447056812
643134.2501037571595-3.25010375715953
653433.49363757456210.506362425437858
663536.072187976472-1.07218797647202
673635.16966221613310.830337783866912
683033.3177895120583-3.31778951205827
693935.40963919198493.59036080801507
703535.7857415055571-0.785741505557147
713833.9276112074824.07238879251801
723135.3257835092312-4.32578350923122
733437.1202156484346-3.1202156484346
743837.64184556290450.358154437095542
753432.74717635555551.25282364444447
763934.16414465626054.83585534373955
773735.75001099779311.24998900220693
783433.71948475902750.280515240972456
792832.9735992693893-4.97359926938928
803732.17035810998284.82964189001718
813335.76747286458-2.76747286458002
823737.2341581825003-0.234158182500295
833535.8949067960171-0.894906796017078
843734.31292209251972.68707790748033
853234.6928626967719-2.69286269677192
863334.1897001707668-1.18970017076684
873836.3837641175771.61623588242301
883334.7880697793308-1.78806977933084
892933.7133361201059-4.71333612010591
903333.6618130298843-0.661813029884273
913134.7026753158317-3.7026753158317
923633.6203606361212.37963936387903
933537.332075591116-2.33207559111602
943232.682828115227-0.682828115226952
952932.740297011634-3.74029701163399
963936.09093847378372.90906152621628
973734.7979257471442.20207425285596
983534.43445771014430.56554228985567
993735.18585630689521.81414369310483
1003235.3150588193169-3.31505881931686
1013835.50295018481842.4970498151816
1023735.16583886619631.83416113380368
1033637.0556547553594-1.05565475535944
1043232.3988209877631-0.39882098776309
1053336.7753771829888-3.7753771829888
1064032.79636212825977.20363787174031
1073835.66139209632922.33860790367084
1084136.45691378604854.54308621395154
1093634.75464385616131.24535614383866
1104335.95569247317157.04430752682852
1113035.0565798494001-5.05657984940008
1123134.1238819052851-3.12388190528513
1133237.929820867378-5.92982086737802
1143233.2418655055724-1.24186550557238
1153734.02940767921842.97059232078157
1163735.0606099361051.93939006389501
1173335.6718631343822-2.67186313438223
1183436.6740334968865-2.67403349688645
1193334.3586516285645-1.35865162856446
1203835.79983094131762.20016905868237
1213334.5241519010363-1.52415190103631
1223132.3954250431049-1.39542504310488
1233835.95160190474182.04839809525823
1243736.10014674236070.89985325763934
1253333.4393091316037-0.43930913160367
1263134.2464136022439-3.24641360224391
1273934.67551231479734.32448768520269
1284437.20796075250046.79203924749957
1293335.8965303531806-2.89653035318063
1303533.36080497137081.63919502862923
1313234.7876701984545-2.78767019845446
1322831.8946217313785-3.89462173137852
1334036.61302238336853.38697761663155
1342732.1625541814688-5.16255418146878
1353735.59332366993341.40667633006656
1363232.9873583435175-0.987358343517515
1372829.5437015424129-1.54370154241285
1383434.683618273408-0.683618273408011
1393033.2342788000179-3.23427880001787
1403533.98058467643941.01941532356059
1413132.9360570035502-1.93605700355021
1423235.1268083243711-3.1268083243711
1433035.2046996993037-5.20469969930372
1443035.3497432182333-5.3497432182333
1453131.4454843072378-0.445484307237821
1464032.8569764233777.14302357662297
1473233.1355388950338-1.13553889503384
1483634.14394143139781.85605856860215
1493233.8417438554998-1.84174385549984
1503533.26332822354631.73667177645372
1513835.57181843705542.42818156294455
1524235.40721415536066.59278584463943
1533436.7835819386775-2.7835819386775
1543537.2871055660989-2.28710556609887
1553534.33422360153320.665776398466767
1563332.30693427625460.693065723745405
1573633.6203606361212.37963936387903
1583235.5870077412556-3.58700774125556
1593335.8965303531806-2.89653035318063
1603434.5871902813244-0.587190281324418
1613234.499017982794-2.49901798279399
1623434.8167205366189-0.816720536618947







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1858689979561010.3717379959122020.814131002043899
120.8711265198208840.2577469603582330.128873480179116
130.9177856073633610.1644287852732790.0822143926366395
140.8782549710655490.2434900578689030.121745028934451
150.8409748745624570.3180502508750850.159025125437543
160.8650212906900450.269957418619910.134978709309955
170.8216938897658040.3566122204683920.178306110234196
180.7890062562805150.4219874874389710.210993743719485
190.7427074467205680.5145851065588640.257292553279432
200.7705474341228570.4589051317542860.229452565877143
210.7902548525762610.4194902948474780.209745147423739
220.7492177578348370.5015644843303270.250782242165163
230.7059472602460190.5881054795079620.294052739753981
240.6388008599893830.7223982800212340.361199140010617
250.6332683712805110.7334632574389790.366731628719489
260.7795034963227430.4409930073545140.220496503677257
270.7731025924187280.4537948151625450.226897407581272
280.7293224658350680.5413550683298630.270677534164932
290.7383900771481540.5232198457036930.261609922851846
300.6826102923862960.6347794152274080.317389707613704
310.6451341949604360.7097316100791280.354865805039564
320.8036532056193840.3926935887612330.196346794380616
330.8012536826432240.3974926347135520.198746317356776
340.7650746236652690.4698507526694620.234925376334731
350.717559050892450.56488189821510.28244094910755
360.6711103240942140.6577793518115720.328889675905786
370.6590730000160950.6818539999678110.340926999983905
380.6262948773022420.7474102453955170.373705122697758
390.7442330541417220.5115338917165560.255766945858278
400.6971112879335630.6057774241328730.302888712066437
410.6672128522335550.6655742955328910.332787147766445
420.6154486431136750.769102713772650.384551356886325
430.5861251239017570.8277497521964870.413874876098243
440.5375554111245320.9248891777509360.462444588875468
450.6347300884154850.7305398231690310.365269911584515
460.64104054700080.7179189059984010.3589594529992
470.6237605004674870.7524789990650270.376239499532513
480.5930500522892990.8138998954214020.406949947710701
490.5453454367630860.9093091264738280.454654563236914
500.5723870471296280.8552259057407450.427612952870372
510.6188628680546390.7622742638907220.381137131945361
520.5798674180973140.8402651638053710.420132581902686
530.5349780662111850.930043867577630.465021933788815
540.4862341915947640.9724683831895280.513765808405236
550.4413071656985930.8826143313971860.558692834301407
560.4304866465178270.8609732930356540.569513353482173
570.4579753075115840.9159506150231680.542024692488416
580.5930836817318480.8138326365363050.406916318268152
590.5539529181863690.8920941636272620.446047081813631
600.505559403401580.9888811931968410.49444059659842
610.4690263593896760.9380527187793520.530973640610324
620.4535232639334230.9070465278668460.546476736066577
630.4198101516587640.8396203033175290.580189848341236
640.4262472069042050.852494413808410.573752793095795
650.3807441904738630.7614883809477270.619255809526137
660.3447150556384750.6894301112769510.655284944361525
670.3042239283740670.6084478567481350.695776071625933
680.3348925450790330.6697850901580660.665107454920967
690.3454863652890560.6909727305781130.654513634710944
700.3060211139377050.6120422278754110.693978886062295
710.339072452269970.678144904539940.66092754773003
720.3779145721855590.7558291443711180.622085427814441
730.3789251460676610.7578502921353220.621074853932339
740.3354068212201780.6708136424403560.664593178779822
750.30077185882630.6015437176525990.6992281411737
760.3563087178066860.7126174356133730.643691282193314
770.3187674452748230.6375348905496450.681232554725177
780.2782536788275070.5565073576550140.721746321172493
790.3421658677495780.6843317354991570.657834132250422
800.4178073496552030.8356146993104050.582192650344797
810.4075681614624820.8151363229249630.592431838537518
820.3629451928759740.7258903857519490.637054807124026
830.3234050647725990.6468101295451980.676594935227401
840.31179469515420.6235893903084010.6882053048458
850.2988765327212880.5977530654425760.701123467278712
860.2650828659056180.5301657318112360.734917134094382
870.2382934810190260.4765869620380530.761706518980974
880.2130079935830820.4260159871661640.786992006416918
890.2503840176144410.5007680352288830.749615982385559
900.2164345260985070.4328690521970130.783565473901493
910.2306292760226840.4612585520453680.769370723977316
920.2160680742553650.432136148510730.783931925744635
930.2024424479209670.4048848958419350.797557552079033
940.1722363295019220.3444726590038440.827763670498078
950.1795821426805190.3591642853610380.820417857319481
960.175492461448020.350984922896040.82450753855198
970.1602890452649620.3205780905299230.839710954735038
980.1370486479003810.2740972958007620.862951352099619
990.1202701079778680.2405402159557350.879729892022132
1000.1191637999744470.2383275999488940.880836200025553
1010.109987033288770.219974066577540.89001296671123
1020.09652946431977560.1930589286395510.903470535680224
1030.07908564216874750.1581712843374950.920914357831253
1040.06490920954573540.1298184190914710.935090790454265
1050.07627202492009060.1525440498401810.923727975079909
1060.1827067294171980.3654134588343960.817293270582802
1070.1698591350137210.3397182700274430.830140864986279
1080.1937948316745130.3875896633490260.806205168325487
1090.1800406058931180.3600812117862360.819959394106882
1100.3807243372514540.7614486745029090.619275662748546
1110.4567150875552450.9134301751104910.543284912444755
1120.4402094206972810.8804188413945620.559790579302719
1130.5690955350178820.8618089299642360.430904464982118
1140.5222926214597440.9554147570805120.477707378540256
1150.5077196772307020.9845606455385960.492280322769298
1160.4720816983318360.9441633966636730.527918301668164
1170.4423322543281330.8846645086562660.557667745671867
1180.4183084114809510.8366168229619010.581691588519049
1190.3725985470299160.7451970940598320.627401452970084
1200.3364030956608270.6728061913216540.663596904339173
1210.2919728559341240.5839457118682480.708027144065876
1220.2509440689365070.5018881378730140.749055931063493
1230.2156456226379230.4312912452758450.784354377362077
1240.1784445227088760.3568890454177520.821555477291124
1250.143758678157620.287517356315240.85624132184238
1260.1534414046925950.306882809385190.846558595307405
1270.1707955489856040.3415910979712070.829204451014396
1280.3496285532832850.699257106566570.650371446716715
1290.3207509728858020.6415019457716040.679249027114198
1300.2840011166064660.5680022332129320.715998883393534
1310.2523594790835020.5047189581670040.747640520916498
1320.2947143677287810.5894287354575620.705285632271219
1330.3658416073725560.7316832147451120.634158392627444
1340.4761863918852570.9523727837705140.523813608114743
1350.4202245722774820.8404491445549630.579775427722518
1360.358131421700580.7162628434011590.64186857829942
1370.3143919327905470.6287838655810940.685608067209453
1380.2543868428273890.5087736856547780.745613157172611
1390.3035720261943950.607144052388790.696427973805605
1400.248588645879610.497177291759220.75141135412039
1410.4765229413846080.9530458827692170.523477058615392
1420.4149872337799880.8299744675599770.585012766220012
1430.4412122143355560.8824244286711130.558787785664444
1440.7027614498989560.5944771002020880.297238550101044
1450.6132553504205320.7734892991589350.386744649579468
1460.9442888645712040.1114222708575910.0557111354287957
1470.924296562059530.151406875880940.0757034379404701
1480.9464807511732260.1070384976535480.0535192488267738
1490.9447529667845710.1104940664308580.0552470332154288
1500.8824242881943190.2351514236113630.117575711805681
1510.7693679172047750.461264165590450.230632082795225

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.185868997956101 & 0.371737995912202 & 0.814131002043899 \tabularnewline
12 & 0.871126519820884 & 0.257746960358233 & 0.128873480179116 \tabularnewline
13 & 0.917785607363361 & 0.164428785273279 & 0.0822143926366395 \tabularnewline
14 & 0.878254971065549 & 0.243490057868903 & 0.121745028934451 \tabularnewline
15 & 0.840974874562457 & 0.318050250875085 & 0.159025125437543 \tabularnewline
16 & 0.865021290690045 & 0.26995741861991 & 0.134978709309955 \tabularnewline
17 & 0.821693889765804 & 0.356612220468392 & 0.178306110234196 \tabularnewline
18 & 0.789006256280515 & 0.421987487438971 & 0.210993743719485 \tabularnewline
19 & 0.742707446720568 & 0.514585106558864 & 0.257292553279432 \tabularnewline
20 & 0.770547434122857 & 0.458905131754286 & 0.229452565877143 \tabularnewline
21 & 0.790254852576261 & 0.419490294847478 & 0.209745147423739 \tabularnewline
22 & 0.749217757834837 & 0.501564484330327 & 0.250782242165163 \tabularnewline
23 & 0.705947260246019 & 0.588105479507962 & 0.294052739753981 \tabularnewline
24 & 0.638800859989383 & 0.722398280021234 & 0.361199140010617 \tabularnewline
25 & 0.633268371280511 & 0.733463257438979 & 0.366731628719489 \tabularnewline
26 & 0.779503496322743 & 0.440993007354514 & 0.220496503677257 \tabularnewline
27 & 0.773102592418728 & 0.453794815162545 & 0.226897407581272 \tabularnewline
28 & 0.729322465835068 & 0.541355068329863 & 0.270677534164932 \tabularnewline
29 & 0.738390077148154 & 0.523219845703693 & 0.261609922851846 \tabularnewline
30 & 0.682610292386296 & 0.634779415227408 & 0.317389707613704 \tabularnewline
31 & 0.645134194960436 & 0.709731610079128 & 0.354865805039564 \tabularnewline
32 & 0.803653205619384 & 0.392693588761233 & 0.196346794380616 \tabularnewline
33 & 0.801253682643224 & 0.397492634713552 & 0.198746317356776 \tabularnewline
34 & 0.765074623665269 & 0.469850752669462 & 0.234925376334731 \tabularnewline
35 & 0.71755905089245 & 0.5648818982151 & 0.28244094910755 \tabularnewline
36 & 0.671110324094214 & 0.657779351811572 & 0.328889675905786 \tabularnewline
37 & 0.659073000016095 & 0.681853999967811 & 0.340926999983905 \tabularnewline
38 & 0.626294877302242 & 0.747410245395517 & 0.373705122697758 \tabularnewline
39 & 0.744233054141722 & 0.511533891716556 & 0.255766945858278 \tabularnewline
40 & 0.697111287933563 & 0.605777424132873 & 0.302888712066437 \tabularnewline
41 & 0.667212852233555 & 0.665574295532891 & 0.332787147766445 \tabularnewline
42 & 0.615448643113675 & 0.76910271377265 & 0.384551356886325 \tabularnewline
43 & 0.586125123901757 & 0.827749752196487 & 0.413874876098243 \tabularnewline
44 & 0.537555411124532 & 0.924889177750936 & 0.462444588875468 \tabularnewline
45 & 0.634730088415485 & 0.730539823169031 & 0.365269911584515 \tabularnewline
46 & 0.6410405470008 & 0.717918905998401 & 0.3589594529992 \tabularnewline
47 & 0.623760500467487 & 0.752478999065027 & 0.376239499532513 \tabularnewline
48 & 0.593050052289299 & 0.813899895421402 & 0.406949947710701 \tabularnewline
49 & 0.545345436763086 & 0.909309126473828 & 0.454654563236914 \tabularnewline
50 & 0.572387047129628 & 0.855225905740745 & 0.427612952870372 \tabularnewline
51 & 0.618862868054639 & 0.762274263890722 & 0.381137131945361 \tabularnewline
52 & 0.579867418097314 & 0.840265163805371 & 0.420132581902686 \tabularnewline
53 & 0.534978066211185 & 0.93004386757763 & 0.465021933788815 \tabularnewline
54 & 0.486234191594764 & 0.972468383189528 & 0.513765808405236 \tabularnewline
55 & 0.441307165698593 & 0.882614331397186 & 0.558692834301407 \tabularnewline
56 & 0.430486646517827 & 0.860973293035654 & 0.569513353482173 \tabularnewline
57 & 0.457975307511584 & 0.915950615023168 & 0.542024692488416 \tabularnewline
58 & 0.593083681731848 & 0.813832636536305 & 0.406916318268152 \tabularnewline
59 & 0.553952918186369 & 0.892094163627262 & 0.446047081813631 \tabularnewline
60 & 0.50555940340158 & 0.988881193196841 & 0.49444059659842 \tabularnewline
61 & 0.469026359389676 & 0.938052718779352 & 0.530973640610324 \tabularnewline
62 & 0.453523263933423 & 0.907046527866846 & 0.546476736066577 \tabularnewline
63 & 0.419810151658764 & 0.839620303317529 & 0.580189848341236 \tabularnewline
64 & 0.426247206904205 & 0.85249441380841 & 0.573752793095795 \tabularnewline
65 & 0.380744190473863 & 0.761488380947727 & 0.619255809526137 \tabularnewline
66 & 0.344715055638475 & 0.689430111276951 & 0.655284944361525 \tabularnewline
67 & 0.304223928374067 & 0.608447856748135 & 0.695776071625933 \tabularnewline
68 & 0.334892545079033 & 0.669785090158066 & 0.665107454920967 \tabularnewline
69 & 0.345486365289056 & 0.690972730578113 & 0.654513634710944 \tabularnewline
70 & 0.306021113937705 & 0.612042227875411 & 0.693978886062295 \tabularnewline
71 & 0.33907245226997 & 0.67814490453994 & 0.66092754773003 \tabularnewline
72 & 0.377914572185559 & 0.755829144371118 & 0.622085427814441 \tabularnewline
73 & 0.378925146067661 & 0.757850292135322 & 0.621074853932339 \tabularnewline
74 & 0.335406821220178 & 0.670813642440356 & 0.664593178779822 \tabularnewline
75 & 0.3007718588263 & 0.601543717652599 & 0.6992281411737 \tabularnewline
76 & 0.356308717806686 & 0.712617435613373 & 0.643691282193314 \tabularnewline
77 & 0.318767445274823 & 0.637534890549645 & 0.681232554725177 \tabularnewline
78 & 0.278253678827507 & 0.556507357655014 & 0.721746321172493 \tabularnewline
79 & 0.342165867749578 & 0.684331735499157 & 0.657834132250422 \tabularnewline
80 & 0.417807349655203 & 0.835614699310405 & 0.582192650344797 \tabularnewline
81 & 0.407568161462482 & 0.815136322924963 & 0.592431838537518 \tabularnewline
82 & 0.362945192875974 & 0.725890385751949 & 0.637054807124026 \tabularnewline
83 & 0.323405064772599 & 0.646810129545198 & 0.676594935227401 \tabularnewline
84 & 0.3117946951542 & 0.623589390308401 & 0.6882053048458 \tabularnewline
85 & 0.298876532721288 & 0.597753065442576 & 0.701123467278712 \tabularnewline
86 & 0.265082865905618 & 0.530165731811236 & 0.734917134094382 \tabularnewline
87 & 0.238293481019026 & 0.476586962038053 & 0.761706518980974 \tabularnewline
88 & 0.213007993583082 & 0.426015987166164 & 0.786992006416918 \tabularnewline
89 & 0.250384017614441 & 0.500768035228883 & 0.749615982385559 \tabularnewline
90 & 0.216434526098507 & 0.432869052197013 & 0.783565473901493 \tabularnewline
91 & 0.230629276022684 & 0.461258552045368 & 0.769370723977316 \tabularnewline
92 & 0.216068074255365 & 0.43213614851073 & 0.783931925744635 \tabularnewline
93 & 0.202442447920967 & 0.404884895841935 & 0.797557552079033 \tabularnewline
94 & 0.172236329501922 & 0.344472659003844 & 0.827763670498078 \tabularnewline
95 & 0.179582142680519 & 0.359164285361038 & 0.820417857319481 \tabularnewline
96 & 0.17549246144802 & 0.35098492289604 & 0.82450753855198 \tabularnewline
97 & 0.160289045264962 & 0.320578090529923 & 0.839710954735038 \tabularnewline
98 & 0.137048647900381 & 0.274097295800762 & 0.862951352099619 \tabularnewline
99 & 0.120270107977868 & 0.240540215955735 & 0.879729892022132 \tabularnewline
100 & 0.119163799974447 & 0.238327599948894 & 0.880836200025553 \tabularnewline
101 & 0.10998703328877 & 0.21997406657754 & 0.89001296671123 \tabularnewline
102 & 0.0965294643197756 & 0.193058928639551 & 0.903470535680224 \tabularnewline
103 & 0.0790856421687475 & 0.158171284337495 & 0.920914357831253 \tabularnewline
104 & 0.0649092095457354 & 0.129818419091471 & 0.935090790454265 \tabularnewline
105 & 0.0762720249200906 & 0.152544049840181 & 0.923727975079909 \tabularnewline
106 & 0.182706729417198 & 0.365413458834396 & 0.817293270582802 \tabularnewline
107 & 0.169859135013721 & 0.339718270027443 & 0.830140864986279 \tabularnewline
108 & 0.193794831674513 & 0.387589663349026 & 0.806205168325487 \tabularnewline
109 & 0.180040605893118 & 0.360081211786236 & 0.819959394106882 \tabularnewline
110 & 0.380724337251454 & 0.761448674502909 & 0.619275662748546 \tabularnewline
111 & 0.456715087555245 & 0.913430175110491 & 0.543284912444755 \tabularnewline
112 & 0.440209420697281 & 0.880418841394562 & 0.559790579302719 \tabularnewline
113 & 0.569095535017882 & 0.861808929964236 & 0.430904464982118 \tabularnewline
114 & 0.522292621459744 & 0.955414757080512 & 0.477707378540256 \tabularnewline
115 & 0.507719677230702 & 0.984560645538596 & 0.492280322769298 \tabularnewline
116 & 0.472081698331836 & 0.944163396663673 & 0.527918301668164 \tabularnewline
117 & 0.442332254328133 & 0.884664508656266 & 0.557667745671867 \tabularnewline
118 & 0.418308411480951 & 0.836616822961901 & 0.581691588519049 \tabularnewline
119 & 0.372598547029916 & 0.745197094059832 & 0.627401452970084 \tabularnewline
120 & 0.336403095660827 & 0.672806191321654 & 0.663596904339173 \tabularnewline
121 & 0.291972855934124 & 0.583945711868248 & 0.708027144065876 \tabularnewline
122 & 0.250944068936507 & 0.501888137873014 & 0.749055931063493 \tabularnewline
123 & 0.215645622637923 & 0.431291245275845 & 0.784354377362077 \tabularnewline
124 & 0.178444522708876 & 0.356889045417752 & 0.821555477291124 \tabularnewline
125 & 0.14375867815762 & 0.28751735631524 & 0.85624132184238 \tabularnewline
126 & 0.153441404692595 & 0.30688280938519 & 0.846558595307405 \tabularnewline
127 & 0.170795548985604 & 0.341591097971207 & 0.829204451014396 \tabularnewline
128 & 0.349628553283285 & 0.69925710656657 & 0.650371446716715 \tabularnewline
129 & 0.320750972885802 & 0.641501945771604 & 0.679249027114198 \tabularnewline
130 & 0.284001116606466 & 0.568002233212932 & 0.715998883393534 \tabularnewline
131 & 0.252359479083502 & 0.504718958167004 & 0.747640520916498 \tabularnewline
132 & 0.294714367728781 & 0.589428735457562 & 0.705285632271219 \tabularnewline
133 & 0.365841607372556 & 0.731683214745112 & 0.634158392627444 \tabularnewline
134 & 0.476186391885257 & 0.952372783770514 & 0.523813608114743 \tabularnewline
135 & 0.420224572277482 & 0.840449144554963 & 0.579775427722518 \tabularnewline
136 & 0.35813142170058 & 0.716262843401159 & 0.64186857829942 \tabularnewline
137 & 0.314391932790547 & 0.628783865581094 & 0.685608067209453 \tabularnewline
138 & 0.254386842827389 & 0.508773685654778 & 0.745613157172611 \tabularnewline
139 & 0.303572026194395 & 0.60714405238879 & 0.696427973805605 \tabularnewline
140 & 0.24858864587961 & 0.49717729175922 & 0.75141135412039 \tabularnewline
141 & 0.476522941384608 & 0.953045882769217 & 0.523477058615392 \tabularnewline
142 & 0.414987233779988 & 0.829974467559977 & 0.585012766220012 \tabularnewline
143 & 0.441212214335556 & 0.882424428671113 & 0.558787785664444 \tabularnewline
144 & 0.702761449898956 & 0.594477100202088 & 0.297238550101044 \tabularnewline
145 & 0.613255350420532 & 0.773489299158935 & 0.386744649579468 \tabularnewline
146 & 0.944288864571204 & 0.111422270857591 & 0.0557111354287957 \tabularnewline
147 & 0.92429656205953 & 0.15140687588094 & 0.0757034379404701 \tabularnewline
148 & 0.946480751173226 & 0.107038497653548 & 0.0535192488267738 \tabularnewline
149 & 0.944752966784571 & 0.110494066430858 & 0.0552470332154288 \tabularnewline
150 & 0.882424288194319 & 0.235151423611363 & 0.117575711805681 \tabularnewline
151 & 0.769367917204775 & 0.46126416559045 & 0.230632082795225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&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]11[/C][C]0.185868997956101[/C][C]0.371737995912202[/C][C]0.814131002043899[/C][/ROW]
[ROW][C]12[/C][C]0.871126519820884[/C][C]0.257746960358233[/C][C]0.128873480179116[/C][/ROW]
[ROW][C]13[/C][C]0.917785607363361[/C][C]0.164428785273279[/C][C]0.0822143926366395[/C][/ROW]
[ROW][C]14[/C][C]0.878254971065549[/C][C]0.243490057868903[/C][C]0.121745028934451[/C][/ROW]
[ROW][C]15[/C][C]0.840974874562457[/C][C]0.318050250875085[/C][C]0.159025125437543[/C][/ROW]
[ROW][C]16[/C][C]0.865021290690045[/C][C]0.26995741861991[/C][C]0.134978709309955[/C][/ROW]
[ROW][C]17[/C][C]0.821693889765804[/C][C]0.356612220468392[/C][C]0.178306110234196[/C][/ROW]
[ROW][C]18[/C][C]0.789006256280515[/C][C]0.421987487438971[/C][C]0.210993743719485[/C][/ROW]
[ROW][C]19[/C][C]0.742707446720568[/C][C]0.514585106558864[/C][C]0.257292553279432[/C][/ROW]
[ROW][C]20[/C][C]0.770547434122857[/C][C]0.458905131754286[/C][C]0.229452565877143[/C][/ROW]
[ROW][C]21[/C][C]0.790254852576261[/C][C]0.419490294847478[/C][C]0.209745147423739[/C][/ROW]
[ROW][C]22[/C][C]0.749217757834837[/C][C]0.501564484330327[/C][C]0.250782242165163[/C][/ROW]
[ROW][C]23[/C][C]0.705947260246019[/C][C]0.588105479507962[/C][C]0.294052739753981[/C][/ROW]
[ROW][C]24[/C][C]0.638800859989383[/C][C]0.722398280021234[/C][C]0.361199140010617[/C][/ROW]
[ROW][C]25[/C][C]0.633268371280511[/C][C]0.733463257438979[/C][C]0.366731628719489[/C][/ROW]
[ROW][C]26[/C][C]0.779503496322743[/C][C]0.440993007354514[/C][C]0.220496503677257[/C][/ROW]
[ROW][C]27[/C][C]0.773102592418728[/C][C]0.453794815162545[/C][C]0.226897407581272[/C][/ROW]
[ROW][C]28[/C][C]0.729322465835068[/C][C]0.541355068329863[/C][C]0.270677534164932[/C][/ROW]
[ROW][C]29[/C][C]0.738390077148154[/C][C]0.523219845703693[/C][C]0.261609922851846[/C][/ROW]
[ROW][C]30[/C][C]0.682610292386296[/C][C]0.634779415227408[/C][C]0.317389707613704[/C][/ROW]
[ROW][C]31[/C][C]0.645134194960436[/C][C]0.709731610079128[/C][C]0.354865805039564[/C][/ROW]
[ROW][C]32[/C][C]0.803653205619384[/C][C]0.392693588761233[/C][C]0.196346794380616[/C][/ROW]
[ROW][C]33[/C][C]0.801253682643224[/C][C]0.397492634713552[/C][C]0.198746317356776[/C][/ROW]
[ROW][C]34[/C][C]0.765074623665269[/C][C]0.469850752669462[/C][C]0.234925376334731[/C][/ROW]
[ROW][C]35[/C][C]0.71755905089245[/C][C]0.5648818982151[/C][C]0.28244094910755[/C][/ROW]
[ROW][C]36[/C][C]0.671110324094214[/C][C]0.657779351811572[/C][C]0.328889675905786[/C][/ROW]
[ROW][C]37[/C][C]0.659073000016095[/C][C]0.681853999967811[/C][C]0.340926999983905[/C][/ROW]
[ROW][C]38[/C][C]0.626294877302242[/C][C]0.747410245395517[/C][C]0.373705122697758[/C][/ROW]
[ROW][C]39[/C][C]0.744233054141722[/C][C]0.511533891716556[/C][C]0.255766945858278[/C][/ROW]
[ROW][C]40[/C][C]0.697111287933563[/C][C]0.605777424132873[/C][C]0.302888712066437[/C][/ROW]
[ROW][C]41[/C][C]0.667212852233555[/C][C]0.665574295532891[/C][C]0.332787147766445[/C][/ROW]
[ROW][C]42[/C][C]0.615448643113675[/C][C]0.76910271377265[/C][C]0.384551356886325[/C][/ROW]
[ROW][C]43[/C][C]0.586125123901757[/C][C]0.827749752196487[/C][C]0.413874876098243[/C][/ROW]
[ROW][C]44[/C][C]0.537555411124532[/C][C]0.924889177750936[/C][C]0.462444588875468[/C][/ROW]
[ROW][C]45[/C][C]0.634730088415485[/C][C]0.730539823169031[/C][C]0.365269911584515[/C][/ROW]
[ROW][C]46[/C][C]0.6410405470008[/C][C]0.717918905998401[/C][C]0.3589594529992[/C][/ROW]
[ROW][C]47[/C][C]0.623760500467487[/C][C]0.752478999065027[/C][C]0.376239499532513[/C][/ROW]
[ROW][C]48[/C][C]0.593050052289299[/C][C]0.813899895421402[/C][C]0.406949947710701[/C][/ROW]
[ROW][C]49[/C][C]0.545345436763086[/C][C]0.909309126473828[/C][C]0.454654563236914[/C][/ROW]
[ROW][C]50[/C][C]0.572387047129628[/C][C]0.855225905740745[/C][C]0.427612952870372[/C][/ROW]
[ROW][C]51[/C][C]0.618862868054639[/C][C]0.762274263890722[/C][C]0.381137131945361[/C][/ROW]
[ROW][C]52[/C][C]0.579867418097314[/C][C]0.840265163805371[/C][C]0.420132581902686[/C][/ROW]
[ROW][C]53[/C][C]0.534978066211185[/C][C]0.93004386757763[/C][C]0.465021933788815[/C][/ROW]
[ROW][C]54[/C][C]0.486234191594764[/C][C]0.972468383189528[/C][C]0.513765808405236[/C][/ROW]
[ROW][C]55[/C][C]0.441307165698593[/C][C]0.882614331397186[/C][C]0.558692834301407[/C][/ROW]
[ROW][C]56[/C][C]0.430486646517827[/C][C]0.860973293035654[/C][C]0.569513353482173[/C][/ROW]
[ROW][C]57[/C][C]0.457975307511584[/C][C]0.915950615023168[/C][C]0.542024692488416[/C][/ROW]
[ROW][C]58[/C][C]0.593083681731848[/C][C]0.813832636536305[/C][C]0.406916318268152[/C][/ROW]
[ROW][C]59[/C][C]0.553952918186369[/C][C]0.892094163627262[/C][C]0.446047081813631[/C][/ROW]
[ROW][C]60[/C][C]0.50555940340158[/C][C]0.988881193196841[/C][C]0.49444059659842[/C][/ROW]
[ROW][C]61[/C][C]0.469026359389676[/C][C]0.938052718779352[/C][C]0.530973640610324[/C][/ROW]
[ROW][C]62[/C][C]0.453523263933423[/C][C]0.907046527866846[/C][C]0.546476736066577[/C][/ROW]
[ROW][C]63[/C][C]0.419810151658764[/C][C]0.839620303317529[/C][C]0.580189848341236[/C][/ROW]
[ROW][C]64[/C][C]0.426247206904205[/C][C]0.85249441380841[/C][C]0.573752793095795[/C][/ROW]
[ROW][C]65[/C][C]0.380744190473863[/C][C]0.761488380947727[/C][C]0.619255809526137[/C][/ROW]
[ROW][C]66[/C][C]0.344715055638475[/C][C]0.689430111276951[/C][C]0.655284944361525[/C][/ROW]
[ROW][C]67[/C][C]0.304223928374067[/C][C]0.608447856748135[/C][C]0.695776071625933[/C][/ROW]
[ROW][C]68[/C][C]0.334892545079033[/C][C]0.669785090158066[/C][C]0.665107454920967[/C][/ROW]
[ROW][C]69[/C][C]0.345486365289056[/C][C]0.690972730578113[/C][C]0.654513634710944[/C][/ROW]
[ROW][C]70[/C][C]0.306021113937705[/C][C]0.612042227875411[/C][C]0.693978886062295[/C][/ROW]
[ROW][C]71[/C][C]0.33907245226997[/C][C]0.67814490453994[/C][C]0.66092754773003[/C][/ROW]
[ROW][C]72[/C][C]0.377914572185559[/C][C]0.755829144371118[/C][C]0.622085427814441[/C][/ROW]
[ROW][C]73[/C][C]0.378925146067661[/C][C]0.757850292135322[/C][C]0.621074853932339[/C][/ROW]
[ROW][C]74[/C][C]0.335406821220178[/C][C]0.670813642440356[/C][C]0.664593178779822[/C][/ROW]
[ROW][C]75[/C][C]0.3007718588263[/C][C]0.601543717652599[/C][C]0.6992281411737[/C][/ROW]
[ROW][C]76[/C][C]0.356308717806686[/C][C]0.712617435613373[/C][C]0.643691282193314[/C][/ROW]
[ROW][C]77[/C][C]0.318767445274823[/C][C]0.637534890549645[/C][C]0.681232554725177[/C][/ROW]
[ROW][C]78[/C][C]0.278253678827507[/C][C]0.556507357655014[/C][C]0.721746321172493[/C][/ROW]
[ROW][C]79[/C][C]0.342165867749578[/C][C]0.684331735499157[/C][C]0.657834132250422[/C][/ROW]
[ROW][C]80[/C][C]0.417807349655203[/C][C]0.835614699310405[/C][C]0.582192650344797[/C][/ROW]
[ROW][C]81[/C][C]0.407568161462482[/C][C]0.815136322924963[/C][C]0.592431838537518[/C][/ROW]
[ROW][C]82[/C][C]0.362945192875974[/C][C]0.725890385751949[/C][C]0.637054807124026[/C][/ROW]
[ROW][C]83[/C][C]0.323405064772599[/C][C]0.646810129545198[/C][C]0.676594935227401[/C][/ROW]
[ROW][C]84[/C][C]0.3117946951542[/C][C]0.623589390308401[/C][C]0.6882053048458[/C][/ROW]
[ROW][C]85[/C][C]0.298876532721288[/C][C]0.597753065442576[/C][C]0.701123467278712[/C][/ROW]
[ROW][C]86[/C][C]0.265082865905618[/C][C]0.530165731811236[/C][C]0.734917134094382[/C][/ROW]
[ROW][C]87[/C][C]0.238293481019026[/C][C]0.476586962038053[/C][C]0.761706518980974[/C][/ROW]
[ROW][C]88[/C][C]0.213007993583082[/C][C]0.426015987166164[/C][C]0.786992006416918[/C][/ROW]
[ROW][C]89[/C][C]0.250384017614441[/C][C]0.500768035228883[/C][C]0.749615982385559[/C][/ROW]
[ROW][C]90[/C][C]0.216434526098507[/C][C]0.432869052197013[/C][C]0.783565473901493[/C][/ROW]
[ROW][C]91[/C][C]0.230629276022684[/C][C]0.461258552045368[/C][C]0.769370723977316[/C][/ROW]
[ROW][C]92[/C][C]0.216068074255365[/C][C]0.43213614851073[/C][C]0.783931925744635[/C][/ROW]
[ROW][C]93[/C][C]0.202442447920967[/C][C]0.404884895841935[/C][C]0.797557552079033[/C][/ROW]
[ROW][C]94[/C][C]0.172236329501922[/C][C]0.344472659003844[/C][C]0.827763670498078[/C][/ROW]
[ROW][C]95[/C][C]0.179582142680519[/C][C]0.359164285361038[/C][C]0.820417857319481[/C][/ROW]
[ROW][C]96[/C][C]0.17549246144802[/C][C]0.35098492289604[/C][C]0.82450753855198[/C][/ROW]
[ROW][C]97[/C][C]0.160289045264962[/C][C]0.320578090529923[/C][C]0.839710954735038[/C][/ROW]
[ROW][C]98[/C][C]0.137048647900381[/C][C]0.274097295800762[/C][C]0.862951352099619[/C][/ROW]
[ROW][C]99[/C][C]0.120270107977868[/C][C]0.240540215955735[/C][C]0.879729892022132[/C][/ROW]
[ROW][C]100[/C][C]0.119163799974447[/C][C]0.238327599948894[/C][C]0.880836200025553[/C][/ROW]
[ROW][C]101[/C][C]0.10998703328877[/C][C]0.21997406657754[/C][C]0.89001296671123[/C][/ROW]
[ROW][C]102[/C][C]0.0965294643197756[/C][C]0.193058928639551[/C][C]0.903470535680224[/C][/ROW]
[ROW][C]103[/C][C]0.0790856421687475[/C][C]0.158171284337495[/C][C]0.920914357831253[/C][/ROW]
[ROW][C]104[/C][C]0.0649092095457354[/C][C]0.129818419091471[/C][C]0.935090790454265[/C][/ROW]
[ROW][C]105[/C][C]0.0762720249200906[/C][C]0.152544049840181[/C][C]0.923727975079909[/C][/ROW]
[ROW][C]106[/C][C]0.182706729417198[/C][C]0.365413458834396[/C][C]0.817293270582802[/C][/ROW]
[ROW][C]107[/C][C]0.169859135013721[/C][C]0.339718270027443[/C][C]0.830140864986279[/C][/ROW]
[ROW][C]108[/C][C]0.193794831674513[/C][C]0.387589663349026[/C][C]0.806205168325487[/C][/ROW]
[ROW][C]109[/C][C]0.180040605893118[/C][C]0.360081211786236[/C][C]0.819959394106882[/C][/ROW]
[ROW][C]110[/C][C]0.380724337251454[/C][C]0.761448674502909[/C][C]0.619275662748546[/C][/ROW]
[ROW][C]111[/C][C]0.456715087555245[/C][C]0.913430175110491[/C][C]0.543284912444755[/C][/ROW]
[ROW][C]112[/C][C]0.440209420697281[/C][C]0.880418841394562[/C][C]0.559790579302719[/C][/ROW]
[ROW][C]113[/C][C]0.569095535017882[/C][C]0.861808929964236[/C][C]0.430904464982118[/C][/ROW]
[ROW][C]114[/C][C]0.522292621459744[/C][C]0.955414757080512[/C][C]0.477707378540256[/C][/ROW]
[ROW][C]115[/C][C]0.507719677230702[/C][C]0.984560645538596[/C][C]0.492280322769298[/C][/ROW]
[ROW][C]116[/C][C]0.472081698331836[/C][C]0.944163396663673[/C][C]0.527918301668164[/C][/ROW]
[ROW][C]117[/C][C]0.442332254328133[/C][C]0.884664508656266[/C][C]0.557667745671867[/C][/ROW]
[ROW][C]118[/C][C]0.418308411480951[/C][C]0.836616822961901[/C][C]0.581691588519049[/C][/ROW]
[ROW][C]119[/C][C]0.372598547029916[/C][C]0.745197094059832[/C][C]0.627401452970084[/C][/ROW]
[ROW][C]120[/C][C]0.336403095660827[/C][C]0.672806191321654[/C][C]0.663596904339173[/C][/ROW]
[ROW][C]121[/C][C]0.291972855934124[/C][C]0.583945711868248[/C][C]0.708027144065876[/C][/ROW]
[ROW][C]122[/C][C]0.250944068936507[/C][C]0.501888137873014[/C][C]0.749055931063493[/C][/ROW]
[ROW][C]123[/C][C]0.215645622637923[/C][C]0.431291245275845[/C][C]0.784354377362077[/C][/ROW]
[ROW][C]124[/C][C]0.178444522708876[/C][C]0.356889045417752[/C][C]0.821555477291124[/C][/ROW]
[ROW][C]125[/C][C]0.14375867815762[/C][C]0.28751735631524[/C][C]0.85624132184238[/C][/ROW]
[ROW][C]126[/C][C]0.153441404692595[/C][C]0.30688280938519[/C][C]0.846558595307405[/C][/ROW]
[ROW][C]127[/C][C]0.170795548985604[/C][C]0.341591097971207[/C][C]0.829204451014396[/C][/ROW]
[ROW][C]128[/C][C]0.349628553283285[/C][C]0.69925710656657[/C][C]0.650371446716715[/C][/ROW]
[ROW][C]129[/C][C]0.320750972885802[/C][C]0.641501945771604[/C][C]0.679249027114198[/C][/ROW]
[ROW][C]130[/C][C]0.284001116606466[/C][C]0.568002233212932[/C][C]0.715998883393534[/C][/ROW]
[ROW][C]131[/C][C]0.252359479083502[/C][C]0.504718958167004[/C][C]0.747640520916498[/C][/ROW]
[ROW][C]132[/C][C]0.294714367728781[/C][C]0.589428735457562[/C][C]0.705285632271219[/C][/ROW]
[ROW][C]133[/C][C]0.365841607372556[/C][C]0.731683214745112[/C][C]0.634158392627444[/C][/ROW]
[ROW][C]134[/C][C]0.476186391885257[/C][C]0.952372783770514[/C][C]0.523813608114743[/C][/ROW]
[ROW][C]135[/C][C]0.420224572277482[/C][C]0.840449144554963[/C][C]0.579775427722518[/C][/ROW]
[ROW][C]136[/C][C]0.35813142170058[/C][C]0.716262843401159[/C][C]0.64186857829942[/C][/ROW]
[ROW][C]137[/C][C]0.314391932790547[/C][C]0.628783865581094[/C][C]0.685608067209453[/C][/ROW]
[ROW][C]138[/C][C]0.254386842827389[/C][C]0.508773685654778[/C][C]0.745613157172611[/C][/ROW]
[ROW][C]139[/C][C]0.303572026194395[/C][C]0.60714405238879[/C][C]0.696427973805605[/C][/ROW]
[ROW][C]140[/C][C]0.24858864587961[/C][C]0.49717729175922[/C][C]0.75141135412039[/C][/ROW]
[ROW][C]141[/C][C]0.476522941384608[/C][C]0.953045882769217[/C][C]0.523477058615392[/C][/ROW]
[ROW][C]142[/C][C]0.414987233779988[/C][C]0.829974467559977[/C][C]0.585012766220012[/C][/ROW]
[ROW][C]143[/C][C]0.441212214335556[/C][C]0.882424428671113[/C][C]0.558787785664444[/C][/ROW]
[ROW][C]144[/C][C]0.702761449898956[/C][C]0.594477100202088[/C][C]0.297238550101044[/C][/ROW]
[ROW][C]145[/C][C]0.613255350420532[/C][C]0.773489299158935[/C][C]0.386744649579468[/C][/ROW]
[ROW][C]146[/C][C]0.944288864571204[/C][C]0.111422270857591[/C][C]0.0557111354287957[/C][/ROW]
[ROW][C]147[/C][C]0.92429656205953[/C][C]0.15140687588094[/C][C]0.0757034379404701[/C][/ROW]
[ROW][C]148[/C][C]0.946480751173226[/C][C]0.107038497653548[/C][C]0.0535192488267738[/C][/ROW]
[ROW][C]149[/C][C]0.944752966784571[/C][C]0.110494066430858[/C][C]0.0552470332154288[/C][/ROW]
[ROW][C]150[/C][C]0.882424288194319[/C][C]0.235151423611363[/C][C]0.117575711805681[/C][/ROW]
[ROW][C]151[/C][C]0.769367917204775[/C][C]0.46126416559045[/C][C]0.230632082795225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1858689979561010.3717379959122020.814131002043899
120.8711265198208840.2577469603582330.128873480179116
130.9177856073633610.1644287852732790.0822143926366395
140.8782549710655490.2434900578689030.121745028934451
150.8409748745624570.3180502508750850.159025125437543
160.8650212906900450.269957418619910.134978709309955
170.8216938897658040.3566122204683920.178306110234196
180.7890062562805150.4219874874389710.210993743719485
190.7427074467205680.5145851065588640.257292553279432
200.7705474341228570.4589051317542860.229452565877143
210.7902548525762610.4194902948474780.209745147423739
220.7492177578348370.5015644843303270.250782242165163
230.7059472602460190.5881054795079620.294052739753981
240.6388008599893830.7223982800212340.361199140010617
250.6332683712805110.7334632574389790.366731628719489
260.7795034963227430.4409930073545140.220496503677257
270.7731025924187280.4537948151625450.226897407581272
280.7293224658350680.5413550683298630.270677534164932
290.7383900771481540.5232198457036930.261609922851846
300.6826102923862960.6347794152274080.317389707613704
310.6451341949604360.7097316100791280.354865805039564
320.8036532056193840.3926935887612330.196346794380616
330.8012536826432240.3974926347135520.198746317356776
340.7650746236652690.4698507526694620.234925376334731
350.717559050892450.56488189821510.28244094910755
360.6711103240942140.6577793518115720.328889675905786
370.6590730000160950.6818539999678110.340926999983905
380.6262948773022420.7474102453955170.373705122697758
390.7442330541417220.5115338917165560.255766945858278
400.6971112879335630.6057774241328730.302888712066437
410.6672128522335550.6655742955328910.332787147766445
420.6154486431136750.769102713772650.384551356886325
430.5861251239017570.8277497521964870.413874876098243
440.5375554111245320.9248891777509360.462444588875468
450.6347300884154850.7305398231690310.365269911584515
460.64104054700080.7179189059984010.3589594529992
470.6237605004674870.7524789990650270.376239499532513
480.5930500522892990.8138998954214020.406949947710701
490.5453454367630860.9093091264738280.454654563236914
500.5723870471296280.8552259057407450.427612952870372
510.6188628680546390.7622742638907220.381137131945361
520.5798674180973140.8402651638053710.420132581902686
530.5349780662111850.930043867577630.465021933788815
540.4862341915947640.9724683831895280.513765808405236
550.4413071656985930.8826143313971860.558692834301407
560.4304866465178270.8609732930356540.569513353482173
570.4579753075115840.9159506150231680.542024692488416
580.5930836817318480.8138326365363050.406916318268152
590.5539529181863690.8920941636272620.446047081813631
600.505559403401580.9888811931968410.49444059659842
610.4690263593896760.9380527187793520.530973640610324
620.4535232639334230.9070465278668460.546476736066577
630.4198101516587640.8396203033175290.580189848341236
640.4262472069042050.852494413808410.573752793095795
650.3807441904738630.7614883809477270.619255809526137
660.3447150556384750.6894301112769510.655284944361525
670.3042239283740670.6084478567481350.695776071625933
680.3348925450790330.6697850901580660.665107454920967
690.3454863652890560.6909727305781130.654513634710944
700.3060211139377050.6120422278754110.693978886062295
710.339072452269970.678144904539940.66092754773003
720.3779145721855590.7558291443711180.622085427814441
730.3789251460676610.7578502921353220.621074853932339
740.3354068212201780.6708136424403560.664593178779822
750.30077185882630.6015437176525990.6992281411737
760.3563087178066860.7126174356133730.643691282193314
770.3187674452748230.6375348905496450.681232554725177
780.2782536788275070.5565073576550140.721746321172493
790.3421658677495780.6843317354991570.657834132250422
800.4178073496552030.8356146993104050.582192650344797
810.4075681614624820.8151363229249630.592431838537518
820.3629451928759740.7258903857519490.637054807124026
830.3234050647725990.6468101295451980.676594935227401
840.31179469515420.6235893903084010.6882053048458
850.2988765327212880.5977530654425760.701123467278712
860.2650828659056180.5301657318112360.734917134094382
870.2382934810190260.4765869620380530.761706518980974
880.2130079935830820.4260159871661640.786992006416918
890.2503840176144410.5007680352288830.749615982385559
900.2164345260985070.4328690521970130.783565473901493
910.2306292760226840.4612585520453680.769370723977316
920.2160680742553650.432136148510730.783931925744635
930.2024424479209670.4048848958419350.797557552079033
940.1722363295019220.3444726590038440.827763670498078
950.1795821426805190.3591642853610380.820417857319481
960.175492461448020.350984922896040.82450753855198
970.1602890452649620.3205780905299230.839710954735038
980.1370486479003810.2740972958007620.862951352099619
990.1202701079778680.2405402159557350.879729892022132
1000.1191637999744470.2383275999488940.880836200025553
1010.109987033288770.219974066577540.89001296671123
1020.09652946431977560.1930589286395510.903470535680224
1030.07908564216874750.1581712843374950.920914357831253
1040.06490920954573540.1298184190914710.935090790454265
1050.07627202492009060.1525440498401810.923727975079909
1060.1827067294171980.3654134588343960.817293270582802
1070.1698591350137210.3397182700274430.830140864986279
1080.1937948316745130.3875896633490260.806205168325487
1090.1800406058931180.3600812117862360.819959394106882
1100.3807243372514540.7614486745029090.619275662748546
1110.4567150875552450.9134301751104910.543284912444755
1120.4402094206972810.8804188413945620.559790579302719
1130.5690955350178820.8618089299642360.430904464982118
1140.5222926214597440.9554147570805120.477707378540256
1150.5077196772307020.9845606455385960.492280322769298
1160.4720816983318360.9441633966636730.527918301668164
1170.4423322543281330.8846645086562660.557667745671867
1180.4183084114809510.8366168229619010.581691588519049
1190.3725985470299160.7451970940598320.627401452970084
1200.3364030956608270.6728061913216540.663596904339173
1210.2919728559341240.5839457118682480.708027144065876
1220.2509440689365070.5018881378730140.749055931063493
1230.2156456226379230.4312912452758450.784354377362077
1240.1784445227088760.3568890454177520.821555477291124
1250.143758678157620.287517356315240.85624132184238
1260.1534414046925950.306882809385190.846558595307405
1270.1707955489856040.3415910979712070.829204451014396
1280.3496285532832850.699257106566570.650371446716715
1290.3207509728858020.6415019457716040.679249027114198
1300.2840011166064660.5680022332129320.715998883393534
1310.2523594790835020.5047189581670040.747640520916498
1320.2947143677287810.5894287354575620.705285632271219
1330.3658416073725560.7316832147451120.634158392627444
1340.4761863918852570.9523727837705140.523813608114743
1350.4202245722774820.8404491445549630.579775427722518
1360.358131421700580.7162628434011590.64186857829942
1370.3143919327905470.6287838655810940.685608067209453
1380.2543868428273890.5087736856547780.745613157172611
1390.3035720261943950.607144052388790.696427973805605
1400.248588645879610.497177291759220.75141135412039
1410.4765229413846080.9530458827692170.523477058615392
1420.4149872337799880.8299744675599770.585012766220012
1430.4412122143355560.8824244286711130.558787785664444
1440.7027614498989560.5944771002020880.297238550101044
1450.6132553504205320.7734892991589350.386744649579468
1460.9442888645712040.1114222708575910.0557111354287957
1470.924296562059530.151406875880940.0757034379404701
1480.9464807511732260.1070384976535480.0535192488267738
1490.9447529667845710.1104940664308580.0552470332154288
1500.8824242881943190.2351514236113630.117575711805681
1510.7693679172047750.461264165590450.230632082795225







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186095&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186095&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186095&T=6

As an alternative you can also use a QR Code:  

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

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



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