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Author's title

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 14:49:17 -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/t1352145010g3hkml9bbnh61fx.htm/, Retrieved Fri, 03 Feb 2023 10:10:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186268, Retrieved Fri, 03 Feb 2023 10:10:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2012-11-05 19:49:17] [4e0a07d67ff6ab1ee99ce2372e43edac] [Current]
- R  D    [Multiple Regression] [WS7 goeie versie] [2012-11-05 20:02:23] [d78b9afa8f7e4cb23f8a65a6f0918ac0]
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Dataseries X:
41	38	12	14	12	53	32
39	32	11	18	11	86	51
30	35	15	11	14	66	42
31	33	6	12	12	67	41
34	37	13	16	21	76	46
35	29	10	18	12	78	47
39	31	12	14	22	53	37
34	36	14	14	11	80	49
36	35	12	15	10	74	45
37	38	6	15	13	76	47
38	31	10	17	10	79	49
36	34	12	19	8	54	33
38	35	12	10	15	67	42
39	38	11	16	14	54	33
33	37	15	18	10	87	53
32	33	12	14	14	58	36
36	32	10	14	14	75	45
38	38	12	17	11	88	54
39	38	11	14	10	64	41
32	32	12	16	13	57	36
32	33	11	18	7	66	41
31	31	12	11	14	68	44
39	38	13	14	12	54	33
37	39	11	12	14	56	37
39	32	9	17	11	86	52
41	32	13	9	9	80	47
36	35	10	16	11	76	43
33	37	14	14	15	69	44
33	33	12	15	14	78	45
34	33	10	11	13	67	44
31	28	12	16	9	80	49
27	32	8	13	15	54	33
37	31	10	17	10	71	43
34	37	12	15	11	84	54
34	30	12	14	13	74	42
32	33	7	16	8	71	44
29	31	6	9	20	63	37
36	33	12	15	12	71	43
29	31	10	17	10	76	46
35	33	10	13	10	69	42
37	32	10	15	9	74	45
34	33	12	16	14	75	44
38	32	15	16	8	54	33
35	33	10	12	14	52	31
38	28	10	12	11	69	42
37	35	12	11	13	68	40
38	39	13	15	9	65	43
33	34	11	15	11	75	46
36	38	11	17	15	74	42
38	32	12	13	11	75	45
32	38	14	16	10	72	44
32	30	10	14	14	67	40
32	33	12	11	18	63	37
34	38	13	12	14	62	46
32	32	5	12	11	63	36
37	32	6	15	12	76	47
39	34	12	16	13	74	45
29	34	12	15	9	67	42
37	36	11	12	10	73	43
35	34	10	12	15	70	43
30	28	7	8	20	53	32
38	34	12	13	12	77	45
34	35	14	11	12	77	45
31	35	11	14	14	52	31
34	31	12	15	13	54	33
35	37	13	10	11	80	49
36	35	14	11	17	66	42
30	27	11	12	12	73	41
39	40	12	15	13	63	38
35	37	12	15	14	69	42
38	36	8	14	13	67	44
31	38	11	16	15	54	33
34	39	14	15	13	81	48
38	41	14	15	10	69	40
34	27	12	13	11	84	50
39	30	9	12	19	80	49
37	37	13	17	13	70	43
34	31	11	13	17	69	44
28	31	12	15	13	77	47
37	27	12	13	9	54	33
33	36	12	15	11	79	46
37	38	12	16	10	30	0
35	37	12	15	9	71	45
37	33	12	16	12	73	43
32	34	11	15	12	72	44
33	31	10	14	13	77	47
38	39	9	15	13	75	45
33	34	12	14	12	69	42
29	32	12	13	15	54	33
33	33	12	7	22	70	43
31	36	9	17	13	73	46
36	32	15	13	15	54	33
35	41	12	15	13	77	46
32	28	12	14	15	82	48
29	30	12	13	10	80	47
39	36	10	16	11	80	47
37	35	13	12	16	69	43
35	31	9	14	11	78	46
37	34	12	17	11	81	48
32	36	10	15	10	76	46
38	36	14	17	10	76	45
37	35	11	12	16	73	45
36	37	15	16	12	85	52
32	28	11	11	11	66	42
33	39	11	15	16	79	47
40	32	12	9	19	68	41
38	35	12	16	11	76	47
41	39	12	15	16	71	43
36	35	11	10	15	54	33
43	42	7	10	24	46	30
30	34	12	15	14	82	49
31	33	14	11	15	74	44
32	41	11	13	11	88	55
32	33	11	14	15	38	11
37	34	10	18	12	76	47
37	32	13	16	10	86	53
33	40	13	14	14	54	33
34	40	8	14	13	70	44
33	35	11	14	9	69	42
38	36	12	14	15	90	55
33	37	11	12	15	54	33
31	27	13	14	14	76	46
38	39	12	15	11	89	54
37	38	14	15	8	76	47
33	31	13	15	11	73	45
31	33	15	13	11	79	47
39	32	10	17	8	90	55
44	39	11	17	10	74	44
33	36	9	19	11	81	53
35	33	11	15	13	72	44
32	33	10	13	11	71	42
28	32	11	9	20	66	40
40	37	8	15	10	77	46
27	30	11	15	15	65	40
37	38	12	15	12	74	46
32	29	12	16	14	82	53
28	22	9	11	23	54	33
34	35	11	14	14	63	42
30	35	10	11	16	54	35
35	34	8	15	11	64	40
31	35	9	13	12	69	41
32	34	8	15	10	54	33
30	34	9	16	14	84	51
30	35	15	14	12	86	53
31	23	11	15	12	77	46
40	31	8	16	11	89	55
32	27	13	16	12	76	47
36	36	12	11	13	60	38
32	31	12	12	11	75	46
35	32	9	9	19	73	46
38	39	7	16	12	85	53
42	37	13	13	17	79	47
34	38	9	16	9	71	41
35	39	6	12	12	72	44
35	34	8	9	19	69	43
33	31	8	13	18	78	51
36	32	15	13	15	54	33
32	37	6	14	14	69	43
33	36	9	19	11	81	53
34	32	11	13	9	84	51
32	35	8	12	18	84	50
34	36	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186268&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186268&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 20.8206973025298 + 0.341980886543589Separate[t] + 0.038296948694897Software[t] + 0.0567724161221237Happiness[t] -0.047713000604139Depression[t] + 0.0483024999381042Belonging[t] -0.0435495965731502`Belonging_Final\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  20.8206973025298 +  0.341980886543589Separate[t] +  0.038296948694897Software[t] +  0.0567724161221237Happiness[t] -0.047713000604139Depression[t] +  0.0483024999381042Belonging[t] -0.0435495965731502`Belonging_Final\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  20.8206973025298 +  0.341980886543589Separate[t] +  0.038296948694897Software[t] +  0.0567724161221237Happiness[t] -0.047713000604139Depression[t] +  0.0483024999381042Belonging[t] -0.0435495965731502`Belonging_Final\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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] = + 20.8206973025298 + 0.341980886543589Separate[t] + 0.038296948694897Software[t] + 0.0567724161221237Happiness[t] -0.047713000604139Depression[t] + 0.0483024999381042Belonging[t] -0.0435495965731502`Belonging_Final\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)20.82069730252984.1220945.0511e-061e-06
Separate0.3419808865435890.0716784.77114e-062e-06
Software0.0382969486948970.1180740.32430.7461120.373056
Happiness0.05677241612212370.1307610.43420.6647690.332385
Depression-0.0477130006041390.096434-0.49480.6214620.310731
Belonging0.04830249993810420.0761820.6340.5269880.263494
`Belonging_Final\r`-0.04354959657315020.109504-0.39770.6913990.345699

\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) & 20.8206973025298 & 4.122094 & 5.051 & 1e-06 & 1e-06 \tabularnewline
Separate & 0.341980886543589 & 0.071678 & 4.7711 & 4e-06 & 2e-06 \tabularnewline
Software & 0.038296948694897 & 0.118074 & 0.3243 & 0.746112 & 0.373056 \tabularnewline
Happiness & 0.0567724161221237 & 0.130761 & 0.4342 & 0.664769 & 0.332385 \tabularnewline
Depression & -0.047713000604139 & 0.096434 & -0.4948 & 0.621462 & 0.310731 \tabularnewline
Belonging & 0.0483024999381042 & 0.076182 & 0.634 & 0.526988 & 0.263494 \tabularnewline
`Belonging_Final\r` & -0.0435495965731502 & 0.109504 & -0.3977 & 0.691399 & 0.345699 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&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]20.8206973025298[/C][C]4.122094[/C][C]5.051[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Separate[/C][C]0.341980886543589[/C][C]0.071678[/C][C]4.7711[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Software[/C][C]0.038296948694897[/C][C]0.118074[/C][C]0.3243[/C][C]0.746112[/C][C]0.373056[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0567724161221237[/C][C]0.130761[/C][C]0.4342[/C][C]0.664769[/C][C]0.332385[/C][/ROW]
[ROW][C]Depression[/C][C]-0.047713000604139[/C][C]0.096434[/C][C]-0.4948[/C][C]0.621462[/C][C]0.310731[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0483024999381042[/C][C]0.076182[/C][C]0.634[/C][C]0.526988[/C][C]0.263494[/C][/ROW]
[ROW][C]`Belonging_Final\r`[/C][C]-0.0435495965731502[/C][C]0.109504[/C][C]-0.3977[/C][C]0.691399[/C][C]0.345699[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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)20.82069730252984.1220945.0511e-061e-06
Separate0.3419808865435890.0716784.77114e-062e-06
Software0.0382969486948970.1180740.32430.7461120.373056
Happiness0.05677241612212370.1307610.43420.6647690.332385
Depression-0.0477130006041390.096434-0.49480.6214620.310731
Belonging0.04830249993810420.0761820.6340.5269880.263494
`Belonging_Final\r`-0.04354959657315020.109504-0.39770.6913990.345699







Multiple Linear Regression - Regression Statistics
Multiple R0.38853646818059
R-squared0.150960587106247
Adjusted R-squared0.118094545316811
F-TEST (value)4.59320863989076
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000254585517122985
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16957765393201
Sum Squared Residuals1557.16448816729

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.38853646818059 \tabularnewline
R-squared & 0.150960587106247 \tabularnewline
Adjusted R-squared & 0.118094545316811 \tabularnewline
F-TEST (value) & 4.59320863989076 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.000254585517122985 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.16957765393201 \tabularnewline
Sum Squared Residuals & 1557.16448816729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.38853646818059[/C][/ROW]
[ROW][C]R-squared[/C][C]0.150960587106247[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.118094545316811[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.59320863989076[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.000254585517122985[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.16957765393201[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1557.16448816729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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 R0.38853646818059
R-squared0.150960587106247
Adjusted R-squared0.118094545316811
F-TEST (value)4.59320863989076
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.000254585517122985
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.16957765393201
Sum Squared Residuals1557.16448816729







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.66423760036395.33576239963614
23934.61539816056764.38460183943243
33034.6798790707069-4.67987907070691
43133.8952952732073-2.89529527320731
53435.5459446358744-1.54594463587438
63533.29122393842551.70877606157447
73932.57549340565156.42450659434849
83435.6684070818558-1.6684070818558
93635.23870110131260.761298898687349
103735.90122887369151.09877112630847
113833.97504260339074.02495739660933
123634.77578104058151.22421895941845
133834.50880530783413.49119469216594
143935.64881238606983.35118761393019
153336.487406695461-3.48740669546104
163233.9262212798355-1.92622127983548
173633.93684262569152.06315737430849
183836.61476422255861.38523577744137
193935.8607477830383.13925221696204
203233.6971957262022-1.69719572620217
213234.6176770164971-2.61767701649713
223133.2065704851778-2.20657048517777
233935.70728745242363.29271254757637
243735.68611022170851.31388977829149
253934.43848225048254.5615177495175
264134.16084970073056.8391502992695
273635.35487081246340.64512918753665
283335.5059564495294-2.50595644952943
293334.5570973255613-1.55709732556133
303433.81334886154120.186651138458813
313133.0649369255698-2.06493692556981
322733.2640059717531-6.26400597175307
333733.84992018332473.15007981667527
343435.9660285040184-1.96602850401838
353433.45953404037960.540465959620386
363234.4140950988402-2.41409509884023
372932.6403006334608-3.64030063346084
383634.40150502034921.59849497965082
392933.9607838932958-4.96078389329581
403534.25373688862040.74626311137964
413734.18387754489622.81612245510377
423434.5125118384423-0.512511838442295
433834.03639286521273.96360713478731
443533.66401553343861.33598446656144
453832.43934703917625.56065296082385
463734.79640541824892.20359458175114
473836.34501129048941.65498870951059
483334.815463168835-1.81546316883498
493636.2319754311915-0.231975431191525
503834.09980310877163.9001968912284
513236.3449546711523-4.34495467115228
523233.0842088359652-1.08420883596525
533233.7630149321699-1.76301493216992
543435.318591863025-1.31859186302499
553233.5872684216863-1.5872684216863
563733.89705655503413.10294344496587
573934.81035362907884.18964637092123
582934.7369645055259-5.73696450552592
593735.41086448400321.58913551599683
603534.30513325938610.694866740613915
613031.3306054898875-1.33060548988755
623834.83265688113093.16734311886915
633435.13768683282-1.13768683281998
643134.4998190874649-3.49981908746488
653433.28418371344160.715816286558406
663535.745001355216-0.745001355215993
673634.49844312019961.50155687980041
683032.3247096970489-2.32470969704889
693936.57898620891112.42101379108892
703535.6209471620122-0.620947162012202
713834.93301487214853.06698512785147
723135.6010993854657-4.60109938546567
733436.7475482529117-2.74754825291166
743837.34341580113920.65658419886079
753432.60687319263081.39312680736916
763932.92978818204246.07021181795758
773735.82525484692041.17474515307956
783433.18698186685280.813018133147195
792833.7854468599939-5.78544685999389
803731.99356733743955.00643266256045
813335.7309318903695-2.73093189036947
823736.15583802558070.844161974419284
833535.8254683751897-0.825468375189655
843734.55488243634752.44511756365249
853234.7099418615628-2.70994186156283
863333.652080546482-0.652080546481971
873836.3968972995281.603102700472
883334.6336580874676-1.63365808746759
892933.4171937665327-4.41719376653266
903333.4218931853927-0.421893185392698
913135.3443448756921-4.34434487569212
923633.53208461261732.46791538738265
933537.2488053220029-2.24880532200293
943232.8052686861501-0.80526868615009
952933.6179676428328-4.61796764283278
963935.71586331246683.28413668753324
973734.66598949147212.33401050852788
983533.80106169550661.19893830449339
993735.17002075625641.82997924374355
1003235.5571434937695-3.5571434937695
1013835.86742571736652.13257428263351
1023734.69550640068842.30449359931156
1033636.2253804587055-0.225380458705461
1043232.2759640719346-0.275964071934611
1053336.4364630017115-3.4364630017115
1064033.32709032617596.67290967382414
1073835.25726632356052.74273367643946
1084136.26253833719424.73746166280584
1093634.23452222910221.76547777089784
1104335.79001242490517.20998757509494
1113034.9180898255647-4.91808982556474
1123134.2092281546792-3.20922815467923
1133237.3317706724329-5.33177067243286
1143233.9629012461031-1.96290124610311
1153734.90452337126732.09547662873273
1163734.5390610331712.46093896682905
1173336.2958232243025-3.29582322430253
1183436.4458459181372-2.4458459181372
1193335.0804810271287-2.0804810271287
1203835.62268860199162.37731139800841
1213335.0320288344336-2.03202883443358
1223132.3465819424232-1.34658194242321
1233836.89150277679611.10849722320392
1243736.44616946627140.553830533728602
1253333.8130590032909-0.813059003290947
1263134.662785648006-3.662785648006
1273934.68247941102284.31752058897722
1284436.72542212760957.27457787239049
1293335.6348885326374-2.63488853263743
1303534.32024797441510.6797520255849
1313234.3026288878924-2.30262888789243
1322833.1880249735738-5.18802497357377
1334035.87083298286144.1291670171386
1342733.0349602003019-6.03496020030193
1353736.1256681631620.87433183683801
1363233.0907594226763-1.09075942267632
1372829.3872452179347-1.38724521793471
1383434.5521010244794-0.552101024479378
1393034.1181855027789-4.11818550277894
1403534.430542402870.569457597129957
1413134.8475253083775-3.84752530837751
1423234.3000775801052-2.30007758010519
1433034.8694772023321-4.86947720233208
1443035.432626756739-5.43262675673899
1453131.1025654161276-0.102565416127566
1464034.01569070921675.98430929078325
1473232.5120031793026-0.512003179302587
1483634.83906549843391.16093450156608
1493233.6575002095327-1.65750020953274
1503533.23596399691591.76403600308406
1513836.55941704566031.44058295433969
1524235.66783729316576.33216270683427
1533436.2835292180633-2.28352921806328
1543536.0580443024399-1.05804430243992
1553533.81906761127531.18093238872474
1563333.1542533435949-0.154253343594862
1573633.53208461261732.46791538738265
1583235.2908434571475-3.29084345714755
1593335.6348885326374-2.63488853263743
1603434.330357081289-0.330357081289018
1613234.7987690698489-2.79876906984887
1623434.7426092609439-0.742609260943899

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.6642376003639 & 5.33576239963614 \tabularnewline
2 & 39 & 34.6153981605676 & 4.38460183943243 \tabularnewline
3 & 30 & 34.6798790707069 & -4.67987907070691 \tabularnewline
4 & 31 & 33.8952952732073 & -2.89529527320731 \tabularnewline
5 & 34 & 35.5459446358744 & -1.54594463587438 \tabularnewline
6 & 35 & 33.2912239384255 & 1.70877606157447 \tabularnewline
7 & 39 & 32.5754934056515 & 6.42450659434849 \tabularnewline
8 & 34 & 35.6684070818558 & -1.6684070818558 \tabularnewline
9 & 36 & 35.2387011013126 & 0.761298898687349 \tabularnewline
10 & 37 & 35.9012288736915 & 1.09877112630847 \tabularnewline
11 & 38 & 33.9750426033907 & 4.02495739660933 \tabularnewline
12 & 36 & 34.7757810405815 & 1.22421895941845 \tabularnewline
13 & 38 & 34.5088053078341 & 3.49119469216594 \tabularnewline
14 & 39 & 35.6488123860698 & 3.35118761393019 \tabularnewline
15 & 33 & 36.487406695461 & -3.48740669546104 \tabularnewline
16 & 32 & 33.9262212798355 & -1.92622127983548 \tabularnewline
17 & 36 & 33.9368426256915 & 2.06315737430849 \tabularnewline
18 & 38 & 36.6147642225586 & 1.38523577744137 \tabularnewline
19 & 39 & 35.860747783038 & 3.13925221696204 \tabularnewline
20 & 32 & 33.6971957262022 & -1.69719572620217 \tabularnewline
21 & 32 & 34.6176770164971 & -2.61767701649713 \tabularnewline
22 & 31 & 33.2065704851778 & -2.20657048517777 \tabularnewline
23 & 39 & 35.7072874524236 & 3.29271254757637 \tabularnewline
24 & 37 & 35.6861102217085 & 1.31388977829149 \tabularnewline
25 & 39 & 34.4384822504825 & 4.5615177495175 \tabularnewline
26 & 41 & 34.1608497007305 & 6.8391502992695 \tabularnewline
27 & 36 & 35.3548708124634 & 0.64512918753665 \tabularnewline
28 & 33 & 35.5059564495294 & -2.50595644952943 \tabularnewline
29 & 33 & 34.5570973255613 & -1.55709732556133 \tabularnewline
30 & 34 & 33.8133488615412 & 0.186651138458813 \tabularnewline
31 & 31 & 33.0649369255698 & -2.06493692556981 \tabularnewline
32 & 27 & 33.2640059717531 & -6.26400597175307 \tabularnewline
33 & 37 & 33.8499201833247 & 3.15007981667527 \tabularnewline
34 & 34 & 35.9660285040184 & -1.96602850401838 \tabularnewline
35 & 34 & 33.4595340403796 & 0.540465959620386 \tabularnewline
36 & 32 & 34.4140950988402 & -2.41409509884023 \tabularnewline
37 & 29 & 32.6403006334608 & -3.64030063346084 \tabularnewline
38 & 36 & 34.4015050203492 & 1.59849497965082 \tabularnewline
39 & 29 & 33.9607838932958 & -4.96078389329581 \tabularnewline
40 & 35 & 34.2537368886204 & 0.74626311137964 \tabularnewline
41 & 37 & 34.1838775448962 & 2.81612245510377 \tabularnewline
42 & 34 & 34.5125118384423 & -0.512511838442295 \tabularnewline
43 & 38 & 34.0363928652127 & 3.96360713478731 \tabularnewline
44 & 35 & 33.6640155334386 & 1.33598446656144 \tabularnewline
45 & 38 & 32.4393470391762 & 5.56065296082385 \tabularnewline
46 & 37 & 34.7964054182489 & 2.20359458175114 \tabularnewline
47 & 38 & 36.3450112904894 & 1.65498870951059 \tabularnewline
48 & 33 & 34.815463168835 & -1.81546316883498 \tabularnewline
49 & 36 & 36.2319754311915 & -0.231975431191525 \tabularnewline
50 & 38 & 34.0998031087716 & 3.9001968912284 \tabularnewline
51 & 32 & 36.3449546711523 & -4.34495467115228 \tabularnewline
52 & 32 & 33.0842088359652 & -1.08420883596525 \tabularnewline
53 & 32 & 33.7630149321699 & -1.76301493216992 \tabularnewline
54 & 34 & 35.318591863025 & -1.31859186302499 \tabularnewline
55 & 32 & 33.5872684216863 & -1.5872684216863 \tabularnewline
56 & 37 & 33.8970565550341 & 3.10294344496587 \tabularnewline
57 & 39 & 34.8103536290788 & 4.18964637092123 \tabularnewline
58 & 29 & 34.7369645055259 & -5.73696450552592 \tabularnewline
59 & 37 & 35.4108644840032 & 1.58913551599683 \tabularnewline
60 & 35 & 34.3051332593861 & 0.694866740613915 \tabularnewline
61 & 30 & 31.3306054898875 & -1.33060548988755 \tabularnewline
62 & 38 & 34.8326568811309 & 3.16734311886915 \tabularnewline
63 & 34 & 35.13768683282 & -1.13768683281998 \tabularnewline
64 & 31 & 34.4998190874649 & -3.49981908746488 \tabularnewline
65 & 34 & 33.2841837134416 & 0.715816286558406 \tabularnewline
66 & 35 & 35.745001355216 & -0.745001355215993 \tabularnewline
67 & 36 & 34.4984431201996 & 1.50155687980041 \tabularnewline
68 & 30 & 32.3247096970489 & -2.32470969704889 \tabularnewline
69 & 39 & 36.5789862089111 & 2.42101379108892 \tabularnewline
70 & 35 & 35.6209471620122 & -0.620947162012202 \tabularnewline
71 & 38 & 34.9330148721485 & 3.06698512785147 \tabularnewline
72 & 31 & 35.6010993854657 & -4.60109938546567 \tabularnewline
73 & 34 & 36.7475482529117 & -2.74754825291166 \tabularnewline
74 & 38 & 37.3434158011392 & 0.65658419886079 \tabularnewline
75 & 34 & 32.6068731926308 & 1.39312680736916 \tabularnewline
76 & 39 & 32.9297881820424 & 6.07021181795758 \tabularnewline
77 & 37 & 35.8252548469204 & 1.17474515307956 \tabularnewline
78 & 34 & 33.1869818668528 & 0.813018133147195 \tabularnewline
79 & 28 & 33.7854468599939 & -5.78544685999389 \tabularnewline
80 & 37 & 31.9935673374395 & 5.00643266256045 \tabularnewline
81 & 33 & 35.7309318903695 & -2.73093189036947 \tabularnewline
82 & 37 & 36.1558380255807 & 0.844161974419284 \tabularnewline
83 & 35 & 35.8254683751897 & -0.825468375189655 \tabularnewline
84 & 37 & 34.5548824363475 & 2.44511756365249 \tabularnewline
85 & 32 & 34.7099418615628 & -2.70994186156283 \tabularnewline
86 & 33 & 33.652080546482 & -0.652080546481971 \tabularnewline
87 & 38 & 36.396897299528 & 1.603102700472 \tabularnewline
88 & 33 & 34.6336580874676 & -1.63365808746759 \tabularnewline
89 & 29 & 33.4171937665327 & -4.41719376653266 \tabularnewline
90 & 33 & 33.4218931853927 & -0.421893185392698 \tabularnewline
91 & 31 & 35.3443448756921 & -4.34434487569212 \tabularnewline
92 & 36 & 33.5320846126173 & 2.46791538738265 \tabularnewline
93 & 35 & 37.2488053220029 & -2.24880532200293 \tabularnewline
94 & 32 & 32.8052686861501 & -0.80526868615009 \tabularnewline
95 & 29 & 33.6179676428328 & -4.61796764283278 \tabularnewline
96 & 39 & 35.7158633124668 & 3.28413668753324 \tabularnewline
97 & 37 & 34.6659894914721 & 2.33401050852788 \tabularnewline
98 & 35 & 33.8010616955066 & 1.19893830449339 \tabularnewline
99 & 37 & 35.1700207562564 & 1.82997924374355 \tabularnewline
100 & 32 & 35.5571434937695 & -3.5571434937695 \tabularnewline
101 & 38 & 35.8674257173665 & 2.13257428263351 \tabularnewline
102 & 37 & 34.6955064006884 & 2.30449359931156 \tabularnewline
103 & 36 & 36.2253804587055 & -0.225380458705461 \tabularnewline
104 & 32 & 32.2759640719346 & -0.275964071934611 \tabularnewline
105 & 33 & 36.4364630017115 & -3.4364630017115 \tabularnewline
106 & 40 & 33.3270903261759 & 6.67290967382414 \tabularnewline
107 & 38 & 35.2572663235605 & 2.74273367643946 \tabularnewline
108 & 41 & 36.2625383371942 & 4.73746166280584 \tabularnewline
109 & 36 & 34.2345222291022 & 1.76547777089784 \tabularnewline
110 & 43 & 35.7900124249051 & 7.20998757509494 \tabularnewline
111 & 30 & 34.9180898255647 & -4.91808982556474 \tabularnewline
112 & 31 & 34.2092281546792 & -3.20922815467923 \tabularnewline
113 & 32 & 37.3317706724329 & -5.33177067243286 \tabularnewline
114 & 32 & 33.9629012461031 & -1.96290124610311 \tabularnewline
115 & 37 & 34.9045233712673 & 2.09547662873273 \tabularnewline
116 & 37 & 34.539061033171 & 2.46093896682905 \tabularnewline
117 & 33 & 36.2958232243025 & -3.29582322430253 \tabularnewline
118 & 34 & 36.4458459181372 & -2.4458459181372 \tabularnewline
119 & 33 & 35.0804810271287 & -2.0804810271287 \tabularnewline
120 & 38 & 35.6226886019916 & 2.37731139800841 \tabularnewline
121 & 33 & 35.0320288344336 & -2.03202883443358 \tabularnewline
122 & 31 & 32.3465819424232 & -1.34658194242321 \tabularnewline
123 & 38 & 36.8915027767961 & 1.10849722320392 \tabularnewline
124 & 37 & 36.4461694662714 & 0.553830533728602 \tabularnewline
125 & 33 & 33.8130590032909 & -0.813059003290947 \tabularnewline
126 & 31 & 34.662785648006 & -3.662785648006 \tabularnewline
127 & 39 & 34.6824794110228 & 4.31752058897722 \tabularnewline
128 & 44 & 36.7254221276095 & 7.27457787239049 \tabularnewline
129 & 33 & 35.6348885326374 & -2.63488853263743 \tabularnewline
130 & 35 & 34.3202479744151 & 0.6797520255849 \tabularnewline
131 & 32 & 34.3026288878924 & -2.30262888789243 \tabularnewline
132 & 28 & 33.1880249735738 & -5.18802497357377 \tabularnewline
133 & 40 & 35.8708329828614 & 4.1291670171386 \tabularnewline
134 & 27 & 33.0349602003019 & -6.03496020030193 \tabularnewline
135 & 37 & 36.125668163162 & 0.87433183683801 \tabularnewline
136 & 32 & 33.0907594226763 & -1.09075942267632 \tabularnewline
137 & 28 & 29.3872452179347 & -1.38724521793471 \tabularnewline
138 & 34 & 34.5521010244794 & -0.552101024479378 \tabularnewline
139 & 30 & 34.1181855027789 & -4.11818550277894 \tabularnewline
140 & 35 & 34.43054240287 & 0.569457597129957 \tabularnewline
141 & 31 & 34.8475253083775 & -3.84752530837751 \tabularnewline
142 & 32 & 34.3000775801052 & -2.30007758010519 \tabularnewline
143 & 30 & 34.8694772023321 & -4.86947720233208 \tabularnewline
144 & 30 & 35.432626756739 & -5.43262675673899 \tabularnewline
145 & 31 & 31.1025654161276 & -0.102565416127566 \tabularnewline
146 & 40 & 34.0156907092167 & 5.98430929078325 \tabularnewline
147 & 32 & 32.5120031793026 & -0.512003179302587 \tabularnewline
148 & 36 & 34.8390654984339 & 1.16093450156608 \tabularnewline
149 & 32 & 33.6575002095327 & -1.65750020953274 \tabularnewline
150 & 35 & 33.2359639969159 & 1.76403600308406 \tabularnewline
151 & 38 & 36.5594170456603 & 1.44058295433969 \tabularnewline
152 & 42 & 35.6678372931657 & 6.33216270683427 \tabularnewline
153 & 34 & 36.2835292180633 & -2.28352921806328 \tabularnewline
154 & 35 & 36.0580443024399 & -1.05804430243992 \tabularnewline
155 & 35 & 33.8190676112753 & 1.18093238872474 \tabularnewline
156 & 33 & 33.1542533435949 & -0.154253343594862 \tabularnewline
157 & 36 & 33.5320846126173 & 2.46791538738265 \tabularnewline
158 & 32 & 35.2908434571475 & -3.29084345714755 \tabularnewline
159 & 33 & 35.6348885326374 & -2.63488853263743 \tabularnewline
160 & 34 & 34.330357081289 & -0.330357081289018 \tabularnewline
161 & 32 & 34.7987690698489 & -2.79876906984887 \tabularnewline
162 & 34 & 34.7426092609439 & -0.742609260943899 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&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]35.6642376003639[/C][C]5.33576239963614[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.6153981605676[/C][C]4.38460183943243[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.6798790707069[/C][C]-4.67987907070691[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]33.8952952732073[/C][C]-2.89529527320731[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.5459446358744[/C][C]-1.54594463587438[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.2912239384255[/C][C]1.70877606157447[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]32.5754934056515[/C][C]6.42450659434849[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.6684070818558[/C][C]-1.6684070818558[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.2387011013126[/C][C]0.761298898687349[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]35.9012288736915[/C][C]1.09877112630847[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]33.9750426033907[/C][C]4.02495739660933[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7757810405815[/C][C]1.22421895941845[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.5088053078341[/C][C]3.49119469216594[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.6488123860698[/C][C]3.35118761393019[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.487406695461[/C][C]-3.48740669546104[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.9262212798355[/C][C]-1.92622127983548[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.9368426256915[/C][C]2.06315737430849[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.6147642225586[/C][C]1.38523577744137[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]35.860747783038[/C][C]3.13925221696204[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.6971957262022[/C][C]-1.69719572620217[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.6176770164971[/C][C]-2.61767701649713[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.2065704851778[/C][C]-2.20657048517777[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]35.7072874524236[/C][C]3.29271254757637[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]35.6861102217085[/C][C]1.31388977829149[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]34.4384822504825[/C][C]4.5615177495175[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.1608497007305[/C][C]6.8391502992695[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.3548708124634[/C][C]0.64512918753665[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.5059564495294[/C][C]-2.50595644952943[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.5570973255613[/C][C]-1.55709732556133[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8133488615412[/C][C]0.186651138458813[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.0649369255698[/C][C]-2.06493692556981[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.2640059717531[/C][C]-6.26400597175307[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.8499201833247[/C][C]3.15007981667527[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.9660285040184[/C][C]-1.96602850401838[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.4595340403796[/C][C]0.540465959620386[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]34.4140950988402[/C][C]-2.41409509884023[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.6403006334608[/C][C]-3.64030063346084[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.4015050203492[/C][C]1.59849497965082[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.9607838932958[/C][C]-4.96078389329581[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.2537368886204[/C][C]0.74626311137964[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.1838775448962[/C][C]2.81612245510377[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.5125118384423[/C][C]-0.512511838442295[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.0363928652127[/C][C]3.96360713478731[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.6640155334386[/C][C]1.33598446656144[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.4393470391762[/C][C]5.56065296082385[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.7964054182489[/C][C]2.20359458175114[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.3450112904894[/C][C]1.65498870951059[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.815463168835[/C][C]-1.81546316883498[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.2319754311915[/C][C]-0.231975431191525[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.0998031087716[/C][C]3.9001968912284[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.3449546711523[/C][C]-4.34495467115228[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0842088359652[/C][C]-1.08420883596525[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.7630149321699[/C][C]-1.76301493216992[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.318591863025[/C][C]-1.31859186302499[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.5872684216863[/C][C]-1.5872684216863[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.8970565550341[/C][C]3.10294344496587[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.8103536290788[/C][C]4.18964637092123[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.7369645055259[/C][C]-5.73696450552592[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.4108644840032[/C][C]1.58913551599683[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.3051332593861[/C][C]0.694866740613915[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.3306054898875[/C][C]-1.33060548988755[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.8326568811309[/C][C]3.16734311886915[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]35.13768683282[/C][C]-1.13768683281998[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.4998190874649[/C][C]-3.49981908746488[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.2841837134416[/C][C]0.715816286558406[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.745001355216[/C][C]-0.745001355215993[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.4984431201996[/C][C]1.50155687980041[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]32.3247096970489[/C][C]-2.32470969704889[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.5789862089111[/C][C]2.42101379108892[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.6209471620122[/C][C]-0.620947162012202[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.9330148721485[/C][C]3.06698512785147[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.6010993854657[/C][C]-4.60109938546567[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.7475482529117[/C][C]-2.74754825291166[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.3434158011392[/C][C]0.65658419886079[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.6068731926308[/C][C]1.39312680736916[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]32.9297881820424[/C][C]6.07021181795758[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8252548469204[/C][C]1.17474515307956[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.1869818668528[/C][C]0.813018133147195[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.7854468599939[/C][C]-5.78544685999389[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]31.9935673374395[/C][C]5.00643266256045[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.7309318903695[/C][C]-2.73093189036947[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.1558380255807[/C][C]0.844161974419284[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8254683751897[/C][C]-0.825468375189655[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.5548824363475[/C][C]2.44511756365249[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.7099418615628[/C][C]-2.70994186156283[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.652080546482[/C][C]-0.652080546481971[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.396897299528[/C][C]1.603102700472[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.6336580874676[/C][C]-1.63365808746759[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.4171937665327[/C][C]-4.41719376653266[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.4218931853927[/C][C]-0.421893185392698[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]35.3443448756921[/C][C]-4.34434487569212[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.5320846126173[/C][C]2.46791538738265[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.2488053220029[/C][C]-2.24880532200293[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.8052686861501[/C][C]-0.80526868615009[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.6179676428328[/C][C]-4.61796764283278[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.7158633124668[/C][C]3.28413668753324[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.6659894914721[/C][C]2.33401050852788[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]33.8010616955066[/C][C]1.19893830449339[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.1700207562564[/C][C]1.82997924374355[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.5571434937695[/C][C]-3.5571434937695[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.8674257173665[/C][C]2.13257428263351[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.6955064006884[/C][C]2.30449359931156[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.2253804587055[/C][C]-0.225380458705461[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.2759640719346[/C][C]-0.275964071934611[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.4364630017115[/C][C]-3.4364630017115[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.3270903261759[/C][C]6.67290967382414[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.2572663235605[/C][C]2.74273367643946[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.2625383371942[/C][C]4.73746166280584[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.2345222291022[/C][C]1.76547777089784[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.7900124249051[/C][C]7.20998757509494[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.9180898255647[/C][C]-4.91808982556474[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.2092281546792[/C][C]-3.20922815467923[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.3317706724329[/C][C]-5.33177067243286[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.9629012461031[/C][C]-1.96290124610311[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.9045233712673[/C][C]2.09547662873273[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.539061033171[/C][C]2.46093896682905[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]36.2958232243025[/C][C]-3.29582322430253[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.4458459181372[/C][C]-2.4458459181372[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]35.0804810271287[/C][C]-2.0804810271287[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.6226886019916[/C][C]2.37731139800841[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]35.0320288344336[/C][C]-2.03202883443358[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.3465819424232[/C][C]-1.34658194242321[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]36.8915027767961[/C][C]1.10849722320392[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.4461694662714[/C][C]0.553830533728602[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.8130590032909[/C][C]-0.813059003290947[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.662785648006[/C][C]-3.662785648006[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6824794110228[/C][C]4.31752058897722[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.7254221276095[/C][C]7.27457787239049[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.6348885326374[/C][C]-2.63488853263743[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]34.3202479744151[/C][C]0.6797520255849[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.3026288878924[/C][C]-2.30262888789243[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.1880249735738[/C][C]-5.18802497357377[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]35.8708329828614[/C][C]4.1291670171386[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]33.0349602003019[/C][C]-6.03496020030193[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]36.125668163162[/C][C]0.87433183683801[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.0907594226763[/C][C]-1.09075942267632[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.3872452179347[/C][C]-1.38724521793471[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.5521010244794[/C][C]-0.552101024479378[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]34.1181855027789[/C][C]-4.11818550277894[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.43054240287[/C][C]0.569457597129957[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]34.8475253083775[/C][C]-3.84752530837751[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.3000775801052[/C][C]-2.30007758010519[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.8694772023321[/C][C]-4.86947720233208[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.432626756739[/C][C]-5.43262675673899[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.1025654161276[/C][C]-0.102565416127566[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]34.0156907092167[/C][C]5.98430929078325[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]32.5120031793026[/C][C]-0.512003179302587[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.8390654984339[/C][C]1.16093450156608[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.6575002095327[/C][C]-1.65750020953274[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2359639969159[/C][C]1.76403600308406[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]36.5594170456603[/C][C]1.44058295433969[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.6678372931657[/C][C]6.33216270683427[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.2835292180633[/C][C]-2.28352921806328[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.0580443024399[/C][C]-1.05804430243992[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.8190676112753[/C][C]1.18093238872474[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.1542533435949[/C][C]-0.154253343594862[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.5320846126173[/C][C]2.46791538738265[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.2908434571475[/C][C]-3.29084345714755[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.6348885326374[/C][C]-2.63488853263743[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.330357081289[/C][C]-0.330357081289018[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.7987690698489[/C][C]-2.79876906984887[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.7426092609439[/C][C]-0.742609260943899[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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
14135.66423760036395.33576239963614
23934.61539816056764.38460183943243
33034.6798790707069-4.67987907070691
43133.8952952732073-2.89529527320731
53435.5459446358744-1.54594463587438
63533.29122393842551.70877606157447
73932.57549340565156.42450659434849
83435.6684070818558-1.6684070818558
93635.23870110131260.761298898687349
103735.90122887369151.09877112630847
113833.97504260339074.02495739660933
123634.77578104058151.22421895941845
133834.50880530783413.49119469216594
143935.64881238606983.35118761393019
153336.487406695461-3.48740669546104
163233.9262212798355-1.92622127983548
173633.93684262569152.06315737430849
183836.61476422255861.38523577744137
193935.8607477830383.13925221696204
203233.6971957262022-1.69719572620217
213234.6176770164971-2.61767701649713
223133.2065704851778-2.20657048517777
233935.70728745242363.29271254757637
243735.68611022170851.31388977829149
253934.43848225048254.5615177495175
264134.16084970073056.8391502992695
273635.35487081246340.64512918753665
283335.5059564495294-2.50595644952943
293334.5570973255613-1.55709732556133
303433.81334886154120.186651138458813
313133.0649369255698-2.06493692556981
322733.2640059717531-6.26400597175307
333733.84992018332473.15007981667527
343435.9660285040184-1.96602850401838
353433.45953404037960.540465959620386
363234.4140950988402-2.41409509884023
372932.6403006334608-3.64030063346084
383634.40150502034921.59849497965082
392933.9607838932958-4.96078389329581
403534.25373688862040.74626311137964
413734.18387754489622.81612245510377
423434.5125118384423-0.512511838442295
433834.03639286521273.96360713478731
443533.66401553343861.33598446656144
453832.43934703917625.56065296082385
463734.79640541824892.20359458175114
473836.34501129048941.65498870951059
483334.815463168835-1.81546316883498
493636.2319754311915-0.231975431191525
503834.09980310877163.9001968912284
513236.3449546711523-4.34495467115228
523233.0842088359652-1.08420883596525
533233.7630149321699-1.76301493216992
543435.318591863025-1.31859186302499
553233.5872684216863-1.5872684216863
563733.89705655503413.10294344496587
573934.81035362907884.18964637092123
582934.7369645055259-5.73696450552592
593735.41086448400321.58913551599683
603534.30513325938610.694866740613915
613031.3306054898875-1.33060548988755
623834.83265688113093.16734311886915
633435.13768683282-1.13768683281998
643134.4998190874649-3.49981908746488
653433.28418371344160.715816286558406
663535.745001355216-0.745001355215993
673634.49844312019961.50155687980041
683032.3247096970489-2.32470969704889
693936.57898620891112.42101379108892
703535.6209471620122-0.620947162012202
713834.93301487214853.06698512785147
723135.6010993854657-4.60109938546567
733436.7475482529117-2.74754825291166
743837.34341580113920.65658419886079
753432.60687319263081.39312680736916
763932.92978818204246.07021181795758
773735.82525484692041.17474515307956
783433.18698186685280.813018133147195
792833.7854468599939-5.78544685999389
803731.99356733743955.00643266256045
813335.7309318903695-2.73093189036947
823736.15583802558070.844161974419284
833535.8254683751897-0.825468375189655
843734.55488243634752.44511756365249
853234.7099418615628-2.70994186156283
863333.652080546482-0.652080546481971
873836.3968972995281.603102700472
883334.6336580874676-1.63365808746759
892933.4171937665327-4.41719376653266
903333.4218931853927-0.421893185392698
913135.3443448756921-4.34434487569212
923633.53208461261732.46791538738265
933537.2488053220029-2.24880532200293
943232.8052686861501-0.80526868615009
952933.6179676428328-4.61796764283278
963935.71586331246683.28413668753324
973734.66598949147212.33401050852788
983533.80106169550661.19893830449339
993735.17002075625641.82997924374355
1003235.5571434937695-3.5571434937695
1013835.86742571736652.13257428263351
1023734.69550640068842.30449359931156
1033636.2253804587055-0.225380458705461
1043232.2759640719346-0.275964071934611
1053336.4364630017115-3.4364630017115
1064033.32709032617596.67290967382414
1073835.25726632356052.74273367643946
1084136.26253833719424.73746166280584
1093634.23452222910221.76547777089784
1104335.79001242490517.20998757509494
1113034.9180898255647-4.91808982556474
1123134.2092281546792-3.20922815467923
1133237.3317706724329-5.33177067243286
1143233.9629012461031-1.96290124610311
1153734.90452337126732.09547662873273
1163734.5390610331712.46093896682905
1173336.2958232243025-3.29582322430253
1183436.4458459181372-2.4458459181372
1193335.0804810271287-2.0804810271287
1203835.62268860199162.37731139800841
1213335.0320288344336-2.03202883443358
1223132.3465819424232-1.34658194242321
1233836.89150277679611.10849722320392
1243736.44616946627140.553830533728602
1253333.8130590032909-0.813059003290947
1263134.662785648006-3.662785648006
1273934.68247941102284.31752058897722
1284436.72542212760957.27457787239049
1293335.6348885326374-2.63488853263743
1303534.32024797441510.6797520255849
1313234.3026288878924-2.30262888789243
1322833.1880249735738-5.18802497357377
1334035.87083298286144.1291670171386
1342733.0349602003019-6.03496020030193
1353736.1256681631620.87433183683801
1363233.0907594226763-1.09075942267632
1372829.3872452179347-1.38724521793471
1383434.5521010244794-0.552101024479378
1393034.1181855027789-4.11818550277894
1403534.430542402870.569457597129957
1413134.8475253083775-3.84752530837751
1423234.3000775801052-2.30007758010519
1433034.8694772023321-4.86947720233208
1443035.432626756739-5.43262675673899
1453131.1025654161276-0.102565416127566
1464034.01569070921675.98430929078325
1473232.5120031793026-0.512003179302587
1483634.83906549843391.16093450156608
1493233.6575002095327-1.65750020953274
1503533.23596399691591.76403600308406
1513836.55941704566031.44058295433969
1524235.66783729316576.33216270683427
1533436.2835292180633-2.28352921806328
1543536.0580443024399-1.05804430243992
1553533.81906761127531.18093238872474
1563333.1542533435949-0.154253343594862
1573633.53208461261732.46791538738265
1583235.2908434571475-3.29084345714755
1593335.6348885326374-2.63488853263743
1603434.330357081289-0.330357081289018
1613234.7987690698489-2.79876906984887
1623434.7426092609439-0.742609260943899







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3291000607319590.6582001214639180.670899939268041
110.1880822161397480.3761644322794960.811917783860252
120.8040231977086140.3919536045827710.195976802291386
130.8771638996596650.2456722006806690.122836100340335
140.8209763647761680.3580472704476640.179023635223832
150.7884943065471740.4230113869056510.211505693452826
160.8104090914869860.3791818170260280.189590908513014
170.7532913921707060.4934172156585880.246708607829294
180.7025134512012820.5949730975974360.297486548798718
190.6542120545625710.6915758908748580.345787945437429
200.6888020655447320.6223958689105370.311197934455268
210.7132104758152550.573579048369490.286789524184745
220.6677727266555510.6644545466888970.332227273344449
230.6431618244256560.7136763511486880.356838175574344
240.574733943531150.85053211293770.42526605646885
250.5988693599024850.8022612801950290.401130640097515
260.8073972782819160.3852054434361680.192602721718084
270.7752202243114580.4495595513770840.224779775688542
280.751973208839530.4960535823209390.24802679116047
290.7419179784216840.5161640431566320.258082021578316
300.6918968015322660.6162063969354670.308103198467734
310.6646188182278750.670762363544250.335381181772125
320.8627693489971720.2744613020056550.137230651002828
330.8498635112210230.3002729775579540.150136488778977
340.8289466312215380.3421067375569240.171053368778462
350.7897839337279880.4204321325440240.210216066272012
360.7823354906109040.4353290187781920.217664509389096
370.7963708365638580.4072583268722830.203629163436142
380.7602606181214650.479478763757070.239739381878535
390.8172784830854410.3654430338291190.182721516914559
400.7795687268019040.4408625463961920.220431273198096
410.7643514923700750.471297015259850.235648507629925
420.7232175006698260.5535649986603480.276782499330174
430.7215174268287310.5569651463425380.278482573171269
440.6794082509143320.6411834981713350.320591749085668
450.7515311379755590.4969377240488820.248468862024441
460.7199133386028670.5601733227942660.280086661397133
470.6845875012973260.6308249974053470.315412498702674
480.6574516907770820.6850966184458360.342548309222918
490.6095310597087340.7809378805825310.390468940291266
500.6160012932786680.7679974134426630.383998706721332
510.6723343583373240.6553312833253520.327665641662676
520.6359475844073520.7281048311852970.364052415592648
530.6075052957118950.784989408576210.392494704288105
540.5629515672056970.8740968655886050.437048432794303
550.5304487271651030.9391025456697950.469551272834897
560.5335019810570880.9329960378858250.466498018942912
570.5623567941098860.8752864117802280.437643205890114
580.677694694892750.6446106102144990.322305305107249
590.6419553590260820.7160892819478350.358044640973918
600.5975503411362820.8048993177274370.402449658863718
610.5589177599931640.8821644800136730.441082240006836
620.5493342592745310.9013314814509380.450665740725469
630.5139528545807040.9720942908385920.486047145419296
640.5247794226127690.9504411547744630.475220577387231
650.4794744045385460.9589488090770920.520525595461454
660.4362236661313810.8724473322627610.563776333868619
670.3997830519289730.7995661038579460.600216948071027
680.3866134873895770.7732269747791540.613386512610423
690.3667295102257480.7334590204514960.633270489774252
700.3252074677487060.6504149354974120.674792532251294
710.3230915980928250.6461831961856510.676908401907175
720.3653778332181910.7307556664363820.634622166781809
730.3546765513393830.7093531026787670.645323448660617
740.3143028874241530.6286057748483050.685697112575847
750.281663536048450.56332707209690.71833646395155
760.3819809346710310.7639618693420630.618019065328969
770.3429373237307610.6858746474615230.657062676269239
780.3031667457250350.606333491450070.696833254274965
790.4084678088031890.8169356176063780.591532191196811
800.4933474966760240.9866949933520480.506652503323976
810.4796778441316120.9593556882632230.520322155868388
820.4407094034180750.881418806836150.559290596581925
830.397964017692590.7959280353851790.60203598230741
840.3799154399609010.7598308799218030.620084560039099
850.3656147255857220.7312294511714440.634385274414278
860.3250993910694170.6501987821388340.674900608930583
870.2954419885541050.5908839771082110.704558011445895
880.2653474797684710.5306949595369420.734652520231529
890.2957591954737680.5915183909475370.704240804526232
900.2576649756602840.5153299513205690.742335024339716
910.2902139515962530.5804279031925050.709786048403747
920.2747633526485740.5495267052971470.725236647351426
930.2572299823497480.5144599646994960.742770017650252
940.2236227715759820.4472455431519650.776377228424018
950.2546397761238110.5092795522476210.745360223876189
960.2569722483831880.5139444967663770.743027751616812
970.2388156477731840.4776312955463670.761184352226816
980.2121780098980170.4243560197960330.787821990101983
990.1899183880508680.3798367761017370.810081611949132
1000.1911711513900830.3823423027801660.808828848609917
1010.1743501618249170.3487003236498330.825649838175083
1020.1597463370904540.3194926741809090.840253662909546
1030.1323187573096920.2646375146193830.867681242690308
1040.1114480398583790.2228960797167590.888551960141621
1050.1222224959777370.2444449919554730.877777504022263
1060.2337483903126740.4674967806253490.766251609687326
1070.2253723837247960.4507447674495920.774627616275204
1080.2585314074306220.5170628148612440.741468592569378
1090.248212139413360.496424278826720.75178786058664
1100.4764107038132930.9528214076265850.523589296186707
1110.5496770906967190.9006458186065630.450322909303281
1120.5344946932620040.9310106134759930.465505306737996
1130.6375099189030840.7249801621938330.362490081096916
1140.598576429218890.8028471415622210.40142357078111
1150.569614305673610.860771388652780.43038569432639
1160.54286036088090.91427927823820.4571396391191
1170.5213750259447960.9572499481104080.478624974055204
1180.4921546523406620.9843093046813240.507845347659338
1190.4524331958610290.9048663917220570.547566804138971
1200.4178340055347090.8356680110694180.582165994465291
1210.373692680864110.747385361728220.62630731913589
1220.3278666850550590.6557333701101180.672133314944941
1230.2815894811234040.5631789622468070.718410518876596
1240.2373730787025310.4747461574050630.762626921297469
1250.1967494505314930.3934989010629870.803250549468507
1260.2133858018278120.4267716036556240.786614198172188
1270.23253732116180.4650746423236010.7674626788382
1280.4509456527433970.9018913054867940.549054347256603
1290.4162268683634790.8324537367269580.583773131636521
1300.3695963379808060.7391926759616120.630403662019194
1310.3290475455853610.6580950911707220.670952454414639
1320.4011139265317340.8022278530634670.598886073468266
1330.4811140216450550.962228043290110.518885978354945
1340.5826829508431470.8346340983137050.417317049156853
1350.5292627006623530.9414745986752930.470737299337647
1360.4660344411897820.9320688823795650.533965558810218
1370.419051790852180.838103581704360.58094820914782
1380.3511829679531960.7023659359063910.648817032046804
1390.3791039568371910.7582079136743810.620896043162809
1400.3292866402599120.6585732805198240.670713359740088
1410.3208334748562290.6416669497124590.679166525143771
1420.2635448727385360.5270897454770720.736455127261464
1430.3345273814691390.6690547629382780.665472618530861
1440.6615820158699950.6768359682600110.338417984130005
1450.5744740214707230.8510519570585530.425525978529277
1460.9747338197992610.0505323604014770.0252661802007385
1470.9644480279953180.07110394400936470.0355519720046824
1480.9548201390244040.09035972195119290.0451798609755964
1490.9423992204355070.1152015591289870.0576007795644933
1500.8878933995849780.2242132008300430.112106600415022
1510.8879202679751870.2241594640496270.112079732024813
1520.965043276265650.06991344746869990.03495672373435

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.329100060731959 & 0.658200121463918 & 0.670899939268041 \tabularnewline
11 & 0.188082216139748 & 0.376164432279496 & 0.811917783860252 \tabularnewline
12 & 0.804023197708614 & 0.391953604582771 & 0.195976802291386 \tabularnewline
13 & 0.877163899659665 & 0.245672200680669 & 0.122836100340335 \tabularnewline
14 & 0.820976364776168 & 0.358047270447664 & 0.179023635223832 \tabularnewline
15 & 0.788494306547174 & 0.423011386905651 & 0.211505693452826 \tabularnewline
16 & 0.810409091486986 & 0.379181817026028 & 0.189590908513014 \tabularnewline
17 & 0.753291392170706 & 0.493417215658588 & 0.246708607829294 \tabularnewline
18 & 0.702513451201282 & 0.594973097597436 & 0.297486548798718 \tabularnewline
19 & 0.654212054562571 & 0.691575890874858 & 0.345787945437429 \tabularnewline
20 & 0.688802065544732 & 0.622395868910537 & 0.311197934455268 \tabularnewline
21 & 0.713210475815255 & 0.57357904836949 & 0.286789524184745 \tabularnewline
22 & 0.667772726655551 & 0.664454546688897 & 0.332227273344449 \tabularnewline
23 & 0.643161824425656 & 0.713676351148688 & 0.356838175574344 \tabularnewline
24 & 0.57473394353115 & 0.8505321129377 & 0.42526605646885 \tabularnewline
25 & 0.598869359902485 & 0.802261280195029 & 0.401130640097515 \tabularnewline
26 & 0.807397278281916 & 0.385205443436168 & 0.192602721718084 \tabularnewline
27 & 0.775220224311458 & 0.449559551377084 & 0.224779775688542 \tabularnewline
28 & 0.75197320883953 & 0.496053582320939 & 0.24802679116047 \tabularnewline
29 & 0.741917978421684 & 0.516164043156632 & 0.258082021578316 \tabularnewline
30 & 0.691896801532266 & 0.616206396935467 & 0.308103198467734 \tabularnewline
31 & 0.664618818227875 & 0.67076236354425 & 0.335381181772125 \tabularnewline
32 & 0.862769348997172 & 0.274461302005655 & 0.137230651002828 \tabularnewline
33 & 0.849863511221023 & 0.300272977557954 & 0.150136488778977 \tabularnewline
34 & 0.828946631221538 & 0.342106737556924 & 0.171053368778462 \tabularnewline
35 & 0.789783933727988 & 0.420432132544024 & 0.210216066272012 \tabularnewline
36 & 0.782335490610904 & 0.435329018778192 & 0.217664509389096 \tabularnewline
37 & 0.796370836563858 & 0.407258326872283 & 0.203629163436142 \tabularnewline
38 & 0.760260618121465 & 0.47947876375707 & 0.239739381878535 \tabularnewline
39 & 0.817278483085441 & 0.365443033829119 & 0.182721516914559 \tabularnewline
40 & 0.779568726801904 & 0.440862546396192 & 0.220431273198096 \tabularnewline
41 & 0.764351492370075 & 0.47129701525985 & 0.235648507629925 \tabularnewline
42 & 0.723217500669826 & 0.553564998660348 & 0.276782499330174 \tabularnewline
43 & 0.721517426828731 & 0.556965146342538 & 0.278482573171269 \tabularnewline
44 & 0.679408250914332 & 0.641183498171335 & 0.320591749085668 \tabularnewline
45 & 0.751531137975559 & 0.496937724048882 & 0.248468862024441 \tabularnewline
46 & 0.719913338602867 & 0.560173322794266 & 0.280086661397133 \tabularnewline
47 & 0.684587501297326 & 0.630824997405347 & 0.315412498702674 \tabularnewline
48 & 0.657451690777082 & 0.685096618445836 & 0.342548309222918 \tabularnewline
49 & 0.609531059708734 & 0.780937880582531 & 0.390468940291266 \tabularnewline
50 & 0.616001293278668 & 0.767997413442663 & 0.383998706721332 \tabularnewline
51 & 0.672334358337324 & 0.655331283325352 & 0.327665641662676 \tabularnewline
52 & 0.635947584407352 & 0.728104831185297 & 0.364052415592648 \tabularnewline
53 & 0.607505295711895 & 0.78498940857621 & 0.392494704288105 \tabularnewline
54 & 0.562951567205697 & 0.874096865588605 & 0.437048432794303 \tabularnewline
55 & 0.530448727165103 & 0.939102545669795 & 0.469551272834897 \tabularnewline
56 & 0.533501981057088 & 0.932996037885825 & 0.466498018942912 \tabularnewline
57 & 0.562356794109886 & 0.875286411780228 & 0.437643205890114 \tabularnewline
58 & 0.67769469489275 & 0.644610610214499 & 0.322305305107249 \tabularnewline
59 & 0.641955359026082 & 0.716089281947835 & 0.358044640973918 \tabularnewline
60 & 0.597550341136282 & 0.804899317727437 & 0.402449658863718 \tabularnewline
61 & 0.558917759993164 & 0.882164480013673 & 0.441082240006836 \tabularnewline
62 & 0.549334259274531 & 0.901331481450938 & 0.450665740725469 \tabularnewline
63 & 0.513952854580704 & 0.972094290838592 & 0.486047145419296 \tabularnewline
64 & 0.524779422612769 & 0.950441154774463 & 0.475220577387231 \tabularnewline
65 & 0.479474404538546 & 0.958948809077092 & 0.520525595461454 \tabularnewline
66 & 0.436223666131381 & 0.872447332262761 & 0.563776333868619 \tabularnewline
67 & 0.399783051928973 & 0.799566103857946 & 0.600216948071027 \tabularnewline
68 & 0.386613487389577 & 0.773226974779154 & 0.613386512610423 \tabularnewline
69 & 0.366729510225748 & 0.733459020451496 & 0.633270489774252 \tabularnewline
70 & 0.325207467748706 & 0.650414935497412 & 0.674792532251294 \tabularnewline
71 & 0.323091598092825 & 0.646183196185651 & 0.676908401907175 \tabularnewline
72 & 0.365377833218191 & 0.730755666436382 & 0.634622166781809 \tabularnewline
73 & 0.354676551339383 & 0.709353102678767 & 0.645323448660617 \tabularnewline
74 & 0.314302887424153 & 0.628605774848305 & 0.685697112575847 \tabularnewline
75 & 0.28166353604845 & 0.5633270720969 & 0.71833646395155 \tabularnewline
76 & 0.381980934671031 & 0.763961869342063 & 0.618019065328969 \tabularnewline
77 & 0.342937323730761 & 0.685874647461523 & 0.657062676269239 \tabularnewline
78 & 0.303166745725035 & 0.60633349145007 & 0.696833254274965 \tabularnewline
79 & 0.408467808803189 & 0.816935617606378 & 0.591532191196811 \tabularnewline
80 & 0.493347496676024 & 0.986694993352048 & 0.506652503323976 \tabularnewline
81 & 0.479677844131612 & 0.959355688263223 & 0.520322155868388 \tabularnewline
82 & 0.440709403418075 & 0.88141880683615 & 0.559290596581925 \tabularnewline
83 & 0.39796401769259 & 0.795928035385179 & 0.60203598230741 \tabularnewline
84 & 0.379915439960901 & 0.759830879921803 & 0.620084560039099 \tabularnewline
85 & 0.365614725585722 & 0.731229451171444 & 0.634385274414278 \tabularnewline
86 & 0.325099391069417 & 0.650198782138834 & 0.674900608930583 \tabularnewline
87 & 0.295441988554105 & 0.590883977108211 & 0.704558011445895 \tabularnewline
88 & 0.265347479768471 & 0.530694959536942 & 0.734652520231529 \tabularnewline
89 & 0.295759195473768 & 0.591518390947537 & 0.704240804526232 \tabularnewline
90 & 0.257664975660284 & 0.515329951320569 & 0.742335024339716 \tabularnewline
91 & 0.290213951596253 & 0.580427903192505 & 0.709786048403747 \tabularnewline
92 & 0.274763352648574 & 0.549526705297147 & 0.725236647351426 \tabularnewline
93 & 0.257229982349748 & 0.514459964699496 & 0.742770017650252 \tabularnewline
94 & 0.223622771575982 & 0.447245543151965 & 0.776377228424018 \tabularnewline
95 & 0.254639776123811 & 0.509279552247621 & 0.745360223876189 \tabularnewline
96 & 0.256972248383188 & 0.513944496766377 & 0.743027751616812 \tabularnewline
97 & 0.238815647773184 & 0.477631295546367 & 0.761184352226816 \tabularnewline
98 & 0.212178009898017 & 0.424356019796033 & 0.787821990101983 \tabularnewline
99 & 0.189918388050868 & 0.379836776101737 & 0.810081611949132 \tabularnewline
100 & 0.191171151390083 & 0.382342302780166 & 0.808828848609917 \tabularnewline
101 & 0.174350161824917 & 0.348700323649833 & 0.825649838175083 \tabularnewline
102 & 0.159746337090454 & 0.319492674180909 & 0.840253662909546 \tabularnewline
103 & 0.132318757309692 & 0.264637514619383 & 0.867681242690308 \tabularnewline
104 & 0.111448039858379 & 0.222896079716759 & 0.888551960141621 \tabularnewline
105 & 0.122222495977737 & 0.244444991955473 & 0.877777504022263 \tabularnewline
106 & 0.233748390312674 & 0.467496780625349 & 0.766251609687326 \tabularnewline
107 & 0.225372383724796 & 0.450744767449592 & 0.774627616275204 \tabularnewline
108 & 0.258531407430622 & 0.517062814861244 & 0.741468592569378 \tabularnewline
109 & 0.24821213941336 & 0.49642427882672 & 0.75178786058664 \tabularnewline
110 & 0.476410703813293 & 0.952821407626585 & 0.523589296186707 \tabularnewline
111 & 0.549677090696719 & 0.900645818606563 & 0.450322909303281 \tabularnewline
112 & 0.534494693262004 & 0.931010613475993 & 0.465505306737996 \tabularnewline
113 & 0.637509918903084 & 0.724980162193833 & 0.362490081096916 \tabularnewline
114 & 0.59857642921889 & 0.802847141562221 & 0.40142357078111 \tabularnewline
115 & 0.56961430567361 & 0.86077138865278 & 0.43038569432639 \tabularnewline
116 & 0.5428603608809 & 0.9142792782382 & 0.4571396391191 \tabularnewline
117 & 0.521375025944796 & 0.957249948110408 & 0.478624974055204 \tabularnewline
118 & 0.492154652340662 & 0.984309304681324 & 0.507845347659338 \tabularnewline
119 & 0.452433195861029 & 0.904866391722057 & 0.547566804138971 \tabularnewline
120 & 0.417834005534709 & 0.835668011069418 & 0.582165994465291 \tabularnewline
121 & 0.37369268086411 & 0.74738536172822 & 0.62630731913589 \tabularnewline
122 & 0.327866685055059 & 0.655733370110118 & 0.672133314944941 \tabularnewline
123 & 0.281589481123404 & 0.563178962246807 & 0.718410518876596 \tabularnewline
124 & 0.237373078702531 & 0.474746157405063 & 0.762626921297469 \tabularnewline
125 & 0.196749450531493 & 0.393498901062987 & 0.803250549468507 \tabularnewline
126 & 0.213385801827812 & 0.426771603655624 & 0.786614198172188 \tabularnewline
127 & 0.2325373211618 & 0.465074642323601 & 0.7674626788382 \tabularnewline
128 & 0.450945652743397 & 0.901891305486794 & 0.549054347256603 \tabularnewline
129 & 0.416226868363479 & 0.832453736726958 & 0.583773131636521 \tabularnewline
130 & 0.369596337980806 & 0.739192675961612 & 0.630403662019194 \tabularnewline
131 & 0.329047545585361 & 0.658095091170722 & 0.670952454414639 \tabularnewline
132 & 0.401113926531734 & 0.802227853063467 & 0.598886073468266 \tabularnewline
133 & 0.481114021645055 & 0.96222804329011 & 0.518885978354945 \tabularnewline
134 & 0.582682950843147 & 0.834634098313705 & 0.417317049156853 \tabularnewline
135 & 0.529262700662353 & 0.941474598675293 & 0.470737299337647 \tabularnewline
136 & 0.466034441189782 & 0.932068882379565 & 0.533965558810218 \tabularnewline
137 & 0.41905179085218 & 0.83810358170436 & 0.58094820914782 \tabularnewline
138 & 0.351182967953196 & 0.702365935906391 & 0.648817032046804 \tabularnewline
139 & 0.379103956837191 & 0.758207913674381 & 0.620896043162809 \tabularnewline
140 & 0.329286640259912 & 0.658573280519824 & 0.670713359740088 \tabularnewline
141 & 0.320833474856229 & 0.641666949712459 & 0.679166525143771 \tabularnewline
142 & 0.263544872738536 & 0.527089745477072 & 0.736455127261464 \tabularnewline
143 & 0.334527381469139 & 0.669054762938278 & 0.665472618530861 \tabularnewline
144 & 0.661582015869995 & 0.676835968260011 & 0.338417984130005 \tabularnewline
145 & 0.574474021470723 & 0.851051957058553 & 0.425525978529277 \tabularnewline
146 & 0.974733819799261 & 0.050532360401477 & 0.0252661802007385 \tabularnewline
147 & 0.964448027995318 & 0.0711039440093647 & 0.0355519720046824 \tabularnewline
148 & 0.954820139024404 & 0.0903597219511929 & 0.0451798609755964 \tabularnewline
149 & 0.942399220435507 & 0.115201559128987 & 0.0576007795644933 \tabularnewline
150 & 0.887893399584978 & 0.224213200830043 & 0.112106600415022 \tabularnewline
151 & 0.887920267975187 & 0.224159464049627 & 0.112079732024813 \tabularnewline
152 & 0.96504327626565 & 0.0699134474686999 & 0.03495672373435 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.329100060731959[/C][C]0.658200121463918[/C][C]0.670899939268041[/C][/ROW]
[ROW][C]11[/C][C]0.188082216139748[/C][C]0.376164432279496[/C][C]0.811917783860252[/C][/ROW]
[ROW][C]12[/C][C]0.804023197708614[/C][C]0.391953604582771[/C][C]0.195976802291386[/C][/ROW]
[ROW][C]13[/C][C]0.877163899659665[/C][C]0.245672200680669[/C][C]0.122836100340335[/C][/ROW]
[ROW][C]14[/C][C]0.820976364776168[/C][C]0.358047270447664[/C][C]0.179023635223832[/C][/ROW]
[ROW][C]15[/C][C]0.788494306547174[/C][C]0.423011386905651[/C][C]0.211505693452826[/C][/ROW]
[ROW][C]16[/C][C]0.810409091486986[/C][C]0.379181817026028[/C][C]0.189590908513014[/C][/ROW]
[ROW][C]17[/C][C]0.753291392170706[/C][C]0.493417215658588[/C][C]0.246708607829294[/C][/ROW]
[ROW][C]18[/C][C]0.702513451201282[/C][C]0.594973097597436[/C][C]0.297486548798718[/C][/ROW]
[ROW][C]19[/C][C]0.654212054562571[/C][C]0.691575890874858[/C][C]0.345787945437429[/C][/ROW]
[ROW][C]20[/C][C]0.688802065544732[/C][C]0.622395868910537[/C][C]0.311197934455268[/C][/ROW]
[ROW][C]21[/C][C]0.713210475815255[/C][C]0.57357904836949[/C][C]0.286789524184745[/C][/ROW]
[ROW][C]22[/C][C]0.667772726655551[/C][C]0.664454546688897[/C][C]0.332227273344449[/C][/ROW]
[ROW][C]23[/C][C]0.643161824425656[/C][C]0.713676351148688[/C][C]0.356838175574344[/C][/ROW]
[ROW][C]24[/C][C]0.57473394353115[/C][C]0.8505321129377[/C][C]0.42526605646885[/C][/ROW]
[ROW][C]25[/C][C]0.598869359902485[/C][C]0.802261280195029[/C][C]0.401130640097515[/C][/ROW]
[ROW][C]26[/C][C]0.807397278281916[/C][C]0.385205443436168[/C][C]0.192602721718084[/C][/ROW]
[ROW][C]27[/C][C]0.775220224311458[/C][C]0.449559551377084[/C][C]0.224779775688542[/C][/ROW]
[ROW][C]28[/C][C]0.75197320883953[/C][C]0.496053582320939[/C][C]0.24802679116047[/C][/ROW]
[ROW][C]29[/C][C]0.741917978421684[/C][C]0.516164043156632[/C][C]0.258082021578316[/C][/ROW]
[ROW][C]30[/C][C]0.691896801532266[/C][C]0.616206396935467[/C][C]0.308103198467734[/C][/ROW]
[ROW][C]31[/C][C]0.664618818227875[/C][C]0.67076236354425[/C][C]0.335381181772125[/C][/ROW]
[ROW][C]32[/C][C]0.862769348997172[/C][C]0.274461302005655[/C][C]0.137230651002828[/C][/ROW]
[ROW][C]33[/C][C]0.849863511221023[/C][C]0.300272977557954[/C][C]0.150136488778977[/C][/ROW]
[ROW][C]34[/C][C]0.828946631221538[/C][C]0.342106737556924[/C][C]0.171053368778462[/C][/ROW]
[ROW][C]35[/C][C]0.789783933727988[/C][C]0.420432132544024[/C][C]0.210216066272012[/C][/ROW]
[ROW][C]36[/C][C]0.782335490610904[/C][C]0.435329018778192[/C][C]0.217664509389096[/C][/ROW]
[ROW][C]37[/C][C]0.796370836563858[/C][C]0.407258326872283[/C][C]0.203629163436142[/C][/ROW]
[ROW][C]38[/C][C]0.760260618121465[/C][C]0.47947876375707[/C][C]0.239739381878535[/C][/ROW]
[ROW][C]39[/C][C]0.817278483085441[/C][C]0.365443033829119[/C][C]0.182721516914559[/C][/ROW]
[ROW][C]40[/C][C]0.779568726801904[/C][C]0.440862546396192[/C][C]0.220431273198096[/C][/ROW]
[ROW][C]41[/C][C]0.764351492370075[/C][C]0.47129701525985[/C][C]0.235648507629925[/C][/ROW]
[ROW][C]42[/C][C]0.723217500669826[/C][C]0.553564998660348[/C][C]0.276782499330174[/C][/ROW]
[ROW][C]43[/C][C]0.721517426828731[/C][C]0.556965146342538[/C][C]0.278482573171269[/C][/ROW]
[ROW][C]44[/C][C]0.679408250914332[/C][C]0.641183498171335[/C][C]0.320591749085668[/C][/ROW]
[ROW][C]45[/C][C]0.751531137975559[/C][C]0.496937724048882[/C][C]0.248468862024441[/C][/ROW]
[ROW][C]46[/C][C]0.719913338602867[/C][C]0.560173322794266[/C][C]0.280086661397133[/C][/ROW]
[ROW][C]47[/C][C]0.684587501297326[/C][C]0.630824997405347[/C][C]0.315412498702674[/C][/ROW]
[ROW][C]48[/C][C]0.657451690777082[/C][C]0.685096618445836[/C][C]0.342548309222918[/C][/ROW]
[ROW][C]49[/C][C]0.609531059708734[/C][C]0.780937880582531[/C][C]0.390468940291266[/C][/ROW]
[ROW][C]50[/C][C]0.616001293278668[/C][C]0.767997413442663[/C][C]0.383998706721332[/C][/ROW]
[ROW][C]51[/C][C]0.672334358337324[/C][C]0.655331283325352[/C][C]0.327665641662676[/C][/ROW]
[ROW][C]52[/C][C]0.635947584407352[/C][C]0.728104831185297[/C][C]0.364052415592648[/C][/ROW]
[ROW][C]53[/C][C]0.607505295711895[/C][C]0.78498940857621[/C][C]0.392494704288105[/C][/ROW]
[ROW][C]54[/C][C]0.562951567205697[/C][C]0.874096865588605[/C][C]0.437048432794303[/C][/ROW]
[ROW][C]55[/C][C]0.530448727165103[/C][C]0.939102545669795[/C][C]0.469551272834897[/C][/ROW]
[ROW][C]56[/C][C]0.533501981057088[/C][C]0.932996037885825[/C][C]0.466498018942912[/C][/ROW]
[ROW][C]57[/C][C]0.562356794109886[/C][C]0.875286411780228[/C][C]0.437643205890114[/C][/ROW]
[ROW][C]58[/C][C]0.67769469489275[/C][C]0.644610610214499[/C][C]0.322305305107249[/C][/ROW]
[ROW][C]59[/C][C]0.641955359026082[/C][C]0.716089281947835[/C][C]0.358044640973918[/C][/ROW]
[ROW][C]60[/C][C]0.597550341136282[/C][C]0.804899317727437[/C][C]0.402449658863718[/C][/ROW]
[ROW][C]61[/C][C]0.558917759993164[/C][C]0.882164480013673[/C][C]0.441082240006836[/C][/ROW]
[ROW][C]62[/C][C]0.549334259274531[/C][C]0.901331481450938[/C][C]0.450665740725469[/C][/ROW]
[ROW][C]63[/C][C]0.513952854580704[/C][C]0.972094290838592[/C][C]0.486047145419296[/C][/ROW]
[ROW][C]64[/C][C]0.524779422612769[/C][C]0.950441154774463[/C][C]0.475220577387231[/C][/ROW]
[ROW][C]65[/C][C]0.479474404538546[/C][C]0.958948809077092[/C][C]0.520525595461454[/C][/ROW]
[ROW][C]66[/C][C]0.436223666131381[/C][C]0.872447332262761[/C][C]0.563776333868619[/C][/ROW]
[ROW][C]67[/C][C]0.399783051928973[/C][C]0.799566103857946[/C][C]0.600216948071027[/C][/ROW]
[ROW][C]68[/C][C]0.386613487389577[/C][C]0.773226974779154[/C][C]0.613386512610423[/C][/ROW]
[ROW][C]69[/C][C]0.366729510225748[/C][C]0.733459020451496[/C][C]0.633270489774252[/C][/ROW]
[ROW][C]70[/C][C]0.325207467748706[/C][C]0.650414935497412[/C][C]0.674792532251294[/C][/ROW]
[ROW][C]71[/C][C]0.323091598092825[/C][C]0.646183196185651[/C][C]0.676908401907175[/C][/ROW]
[ROW][C]72[/C][C]0.365377833218191[/C][C]0.730755666436382[/C][C]0.634622166781809[/C][/ROW]
[ROW][C]73[/C][C]0.354676551339383[/C][C]0.709353102678767[/C][C]0.645323448660617[/C][/ROW]
[ROW][C]74[/C][C]0.314302887424153[/C][C]0.628605774848305[/C][C]0.685697112575847[/C][/ROW]
[ROW][C]75[/C][C]0.28166353604845[/C][C]0.5633270720969[/C][C]0.71833646395155[/C][/ROW]
[ROW][C]76[/C][C]0.381980934671031[/C][C]0.763961869342063[/C][C]0.618019065328969[/C][/ROW]
[ROW][C]77[/C][C]0.342937323730761[/C][C]0.685874647461523[/C][C]0.657062676269239[/C][/ROW]
[ROW][C]78[/C][C]0.303166745725035[/C][C]0.60633349145007[/C][C]0.696833254274965[/C][/ROW]
[ROW][C]79[/C][C]0.408467808803189[/C][C]0.816935617606378[/C][C]0.591532191196811[/C][/ROW]
[ROW][C]80[/C][C]0.493347496676024[/C][C]0.986694993352048[/C][C]0.506652503323976[/C][/ROW]
[ROW][C]81[/C][C]0.479677844131612[/C][C]0.959355688263223[/C][C]0.520322155868388[/C][/ROW]
[ROW][C]82[/C][C]0.440709403418075[/C][C]0.88141880683615[/C][C]0.559290596581925[/C][/ROW]
[ROW][C]83[/C][C]0.39796401769259[/C][C]0.795928035385179[/C][C]0.60203598230741[/C][/ROW]
[ROW][C]84[/C][C]0.379915439960901[/C][C]0.759830879921803[/C][C]0.620084560039099[/C][/ROW]
[ROW][C]85[/C][C]0.365614725585722[/C][C]0.731229451171444[/C][C]0.634385274414278[/C][/ROW]
[ROW][C]86[/C][C]0.325099391069417[/C][C]0.650198782138834[/C][C]0.674900608930583[/C][/ROW]
[ROW][C]87[/C][C]0.295441988554105[/C][C]0.590883977108211[/C][C]0.704558011445895[/C][/ROW]
[ROW][C]88[/C][C]0.265347479768471[/C][C]0.530694959536942[/C][C]0.734652520231529[/C][/ROW]
[ROW][C]89[/C][C]0.295759195473768[/C][C]0.591518390947537[/C][C]0.704240804526232[/C][/ROW]
[ROW][C]90[/C][C]0.257664975660284[/C][C]0.515329951320569[/C][C]0.742335024339716[/C][/ROW]
[ROW][C]91[/C][C]0.290213951596253[/C][C]0.580427903192505[/C][C]0.709786048403747[/C][/ROW]
[ROW][C]92[/C][C]0.274763352648574[/C][C]0.549526705297147[/C][C]0.725236647351426[/C][/ROW]
[ROW][C]93[/C][C]0.257229982349748[/C][C]0.514459964699496[/C][C]0.742770017650252[/C][/ROW]
[ROW][C]94[/C][C]0.223622771575982[/C][C]0.447245543151965[/C][C]0.776377228424018[/C][/ROW]
[ROW][C]95[/C][C]0.254639776123811[/C][C]0.509279552247621[/C][C]0.745360223876189[/C][/ROW]
[ROW][C]96[/C][C]0.256972248383188[/C][C]0.513944496766377[/C][C]0.743027751616812[/C][/ROW]
[ROW][C]97[/C][C]0.238815647773184[/C][C]0.477631295546367[/C][C]0.761184352226816[/C][/ROW]
[ROW][C]98[/C][C]0.212178009898017[/C][C]0.424356019796033[/C][C]0.787821990101983[/C][/ROW]
[ROW][C]99[/C][C]0.189918388050868[/C][C]0.379836776101737[/C][C]0.810081611949132[/C][/ROW]
[ROW][C]100[/C][C]0.191171151390083[/C][C]0.382342302780166[/C][C]0.808828848609917[/C][/ROW]
[ROW][C]101[/C][C]0.174350161824917[/C][C]0.348700323649833[/C][C]0.825649838175083[/C][/ROW]
[ROW][C]102[/C][C]0.159746337090454[/C][C]0.319492674180909[/C][C]0.840253662909546[/C][/ROW]
[ROW][C]103[/C][C]0.132318757309692[/C][C]0.264637514619383[/C][C]0.867681242690308[/C][/ROW]
[ROW][C]104[/C][C]0.111448039858379[/C][C]0.222896079716759[/C][C]0.888551960141621[/C][/ROW]
[ROW][C]105[/C][C]0.122222495977737[/C][C]0.244444991955473[/C][C]0.877777504022263[/C][/ROW]
[ROW][C]106[/C][C]0.233748390312674[/C][C]0.467496780625349[/C][C]0.766251609687326[/C][/ROW]
[ROW][C]107[/C][C]0.225372383724796[/C][C]0.450744767449592[/C][C]0.774627616275204[/C][/ROW]
[ROW][C]108[/C][C]0.258531407430622[/C][C]0.517062814861244[/C][C]0.741468592569378[/C][/ROW]
[ROW][C]109[/C][C]0.24821213941336[/C][C]0.49642427882672[/C][C]0.75178786058664[/C][/ROW]
[ROW][C]110[/C][C]0.476410703813293[/C][C]0.952821407626585[/C][C]0.523589296186707[/C][/ROW]
[ROW][C]111[/C][C]0.549677090696719[/C][C]0.900645818606563[/C][C]0.450322909303281[/C][/ROW]
[ROW][C]112[/C][C]0.534494693262004[/C][C]0.931010613475993[/C][C]0.465505306737996[/C][/ROW]
[ROW][C]113[/C][C]0.637509918903084[/C][C]0.724980162193833[/C][C]0.362490081096916[/C][/ROW]
[ROW][C]114[/C][C]0.59857642921889[/C][C]0.802847141562221[/C][C]0.40142357078111[/C][/ROW]
[ROW][C]115[/C][C]0.56961430567361[/C][C]0.86077138865278[/C][C]0.43038569432639[/C][/ROW]
[ROW][C]116[/C][C]0.5428603608809[/C][C]0.9142792782382[/C][C]0.4571396391191[/C][/ROW]
[ROW][C]117[/C][C]0.521375025944796[/C][C]0.957249948110408[/C][C]0.478624974055204[/C][/ROW]
[ROW][C]118[/C][C]0.492154652340662[/C][C]0.984309304681324[/C][C]0.507845347659338[/C][/ROW]
[ROW][C]119[/C][C]0.452433195861029[/C][C]0.904866391722057[/C][C]0.547566804138971[/C][/ROW]
[ROW][C]120[/C][C]0.417834005534709[/C][C]0.835668011069418[/C][C]0.582165994465291[/C][/ROW]
[ROW][C]121[/C][C]0.37369268086411[/C][C]0.74738536172822[/C][C]0.62630731913589[/C][/ROW]
[ROW][C]122[/C][C]0.327866685055059[/C][C]0.655733370110118[/C][C]0.672133314944941[/C][/ROW]
[ROW][C]123[/C][C]0.281589481123404[/C][C]0.563178962246807[/C][C]0.718410518876596[/C][/ROW]
[ROW][C]124[/C][C]0.237373078702531[/C][C]0.474746157405063[/C][C]0.762626921297469[/C][/ROW]
[ROW][C]125[/C][C]0.196749450531493[/C][C]0.393498901062987[/C][C]0.803250549468507[/C][/ROW]
[ROW][C]126[/C][C]0.213385801827812[/C][C]0.426771603655624[/C][C]0.786614198172188[/C][/ROW]
[ROW][C]127[/C][C]0.2325373211618[/C][C]0.465074642323601[/C][C]0.7674626788382[/C][/ROW]
[ROW][C]128[/C][C]0.450945652743397[/C][C]0.901891305486794[/C][C]0.549054347256603[/C][/ROW]
[ROW][C]129[/C][C]0.416226868363479[/C][C]0.832453736726958[/C][C]0.583773131636521[/C][/ROW]
[ROW][C]130[/C][C]0.369596337980806[/C][C]0.739192675961612[/C][C]0.630403662019194[/C][/ROW]
[ROW][C]131[/C][C]0.329047545585361[/C][C]0.658095091170722[/C][C]0.670952454414639[/C][/ROW]
[ROW][C]132[/C][C]0.401113926531734[/C][C]0.802227853063467[/C][C]0.598886073468266[/C][/ROW]
[ROW][C]133[/C][C]0.481114021645055[/C][C]0.96222804329011[/C][C]0.518885978354945[/C][/ROW]
[ROW][C]134[/C][C]0.582682950843147[/C][C]0.834634098313705[/C][C]0.417317049156853[/C][/ROW]
[ROW][C]135[/C][C]0.529262700662353[/C][C]0.941474598675293[/C][C]0.470737299337647[/C][/ROW]
[ROW][C]136[/C][C]0.466034441189782[/C][C]0.932068882379565[/C][C]0.533965558810218[/C][/ROW]
[ROW][C]137[/C][C]0.41905179085218[/C][C]0.83810358170436[/C][C]0.58094820914782[/C][/ROW]
[ROW][C]138[/C][C]0.351182967953196[/C][C]0.702365935906391[/C][C]0.648817032046804[/C][/ROW]
[ROW][C]139[/C][C]0.379103956837191[/C][C]0.758207913674381[/C][C]0.620896043162809[/C][/ROW]
[ROW][C]140[/C][C]0.329286640259912[/C][C]0.658573280519824[/C][C]0.670713359740088[/C][/ROW]
[ROW][C]141[/C][C]0.320833474856229[/C][C]0.641666949712459[/C][C]0.679166525143771[/C][/ROW]
[ROW][C]142[/C][C]0.263544872738536[/C][C]0.527089745477072[/C][C]0.736455127261464[/C][/ROW]
[ROW][C]143[/C][C]0.334527381469139[/C][C]0.669054762938278[/C][C]0.665472618530861[/C][/ROW]
[ROW][C]144[/C][C]0.661582015869995[/C][C]0.676835968260011[/C][C]0.338417984130005[/C][/ROW]
[ROW][C]145[/C][C]0.574474021470723[/C][C]0.851051957058553[/C][C]0.425525978529277[/C][/ROW]
[ROW][C]146[/C][C]0.974733819799261[/C][C]0.050532360401477[/C][C]0.0252661802007385[/C][/ROW]
[ROW][C]147[/C][C]0.964448027995318[/C][C]0.0711039440093647[/C][C]0.0355519720046824[/C][/ROW]
[ROW][C]148[/C][C]0.954820139024404[/C][C]0.0903597219511929[/C][C]0.0451798609755964[/C][/ROW]
[ROW][C]149[/C][C]0.942399220435507[/C][C]0.115201559128987[/C][C]0.0576007795644933[/C][/ROW]
[ROW][C]150[/C][C]0.887893399584978[/C][C]0.224213200830043[/C][C]0.112106600415022[/C][/ROW]
[ROW][C]151[/C][C]0.887920267975187[/C][C]0.224159464049627[/C][C]0.112079732024813[/C][/ROW]
[ROW][C]152[/C][C]0.96504327626565[/C][C]0.0699134474686999[/C][C]0.03495672373435[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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
100.3291000607319590.6582001214639180.670899939268041
110.1880822161397480.3761644322794960.811917783860252
120.8040231977086140.3919536045827710.195976802291386
130.8771638996596650.2456722006806690.122836100340335
140.8209763647761680.3580472704476640.179023635223832
150.7884943065471740.4230113869056510.211505693452826
160.8104090914869860.3791818170260280.189590908513014
170.7532913921707060.4934172156585880.246708607829294
180.7025134512012820.5949730975974360.297486548798718
190.6542120545625710.6915758908748580.345787945437429
200.6888020655447320.6223958689105370.311197934455268
210.7132104758152550.573579048369490.286789524184745
220.6677727266555510.6644545466888970.332227273344449
230.6431618244256560.7136763511486880.356838175574344
240.574733943531150.85053211293770.42526605646885
250.5988693599024850.8022612801950290.401130640097515
260.8073972782819160.3852054434361680.192602721718084
270.7752202243114580.4495595513770840.224779775688542
280.751973208839530.4960535823209390.24802679116047
290.7419179784216840.5161640431566320.258082021578316
300.6918968015322660.6162063969354670.308103198467734
310.6646188182278750.670762363544250.335381181772125
320.8627693489971720.2744613020056550.137230651002828
330.8498635112210230.3002729775579540.150136488778977
340.8289466312215380.3421067375569240.171053368778462
350.7897839337279880.4204321325440240.210216066272012
360.7823354906109040.4353290187781920.217664509389096
370.7963708365638580.4072583268722830.203629163436142
380.7602606181214650.479478763757070.239739381878535
390.8172784830854410.3654430338291190.182721516914559
400.7795687268019040.4408625463961920.220431273198096
410.7643514923700750.471297015259850.235648507629925
420.7232175006698260.5535649986603480.276782499330174
430.7215174268287310.5569651463425380.278482573171269
440.6794082509143320.6411834981713350.320591749085668
450.7515311379755590.4969377240488820.248468862024441
460.7199133386028670.5601733227942660.280086661397133
470.6845875012973260.6308249974053470.315412498702674
480.6574516907770820.6850966184458360.342548309222918
490.6095310597087340.7809378805825310.390468940291266
500.6160012932786680.7679974134426630.383998706721332
510.6723343583373240.6553312833253520.327665641662676
520.6359475844073520.7281048311852970.364052415592648
530.6075052957118950.784989408576210.392494704288105
540.5629515672056970.8740968655886050.437048432794303
550.5304487271651030.9391025456697950.469551272834897
560.5335019810570880.9329960378858250.466498018942912
570.5623567941098860.8752864117802280.437643205890114
580.677694694892750.6446106102144990.322305305107249
590.6419553590260820.7160892819478350.358044640973918
600.5975503411362820.8048993177274370.402449658863718
610.5589177599931640.8821644800136730.441082240006836
620.5493342592745310.9013314814509380.450665740725469
630.5139528545807040.9720942908385920.486047145419296
640.5247794226127690.9504411547744630.475220577387231
650.4794744045385460.9589488090770920.520525595461454
660.4362236661313810.8724473322627610.563776333868619
670.3997830519289730.7995661038579460.600216948071027
680.3866134873895770.7732269747791540.613386512610423
690.3667295102257480.7334590204514960.633270489774252
700.3252074677487060.6504149354974120.674792532251294
710.3230915980928250.6461831961856510.676908401907175
720.3653778332181910.7307556664363820.634622166781809
730.3546765513393830.7093531026787670.645323448660617
740.3143028874241530.6286057748483050.685697112575847
750.281663536048450.56332707209690.71833646395155
760.3819809346710310.7639618693420630.618019065328969
770.3429373237307610.6858746474615230.657062676269239
780.3031667457250350.606333491450070.696833254274965
790.4084678088031890.8169356176063780.591532191196811
800.4933474966760240.9866949933520480.506652503323976
810.4796778441316120.9593556882632230.520322155868388
820.4407094034180750.881418806836150.559290596581925
830.397964017692590.7959280353851790.60203598230741
840.3799154399609010.7598308799218030.620084560039099
850.3656147255857220.7312294511714440.634385274414278
860.3250993910694170.6501987821388340.674900608930583
870.2954419885541050.5908839771082110.704558011445895
880.2653474797684710.5306949595369420.734652520231529
890.2957591954737680.5915183909475370.704240804526232
900.2576649756602840.5153299513205690.742335024339716
910.2902139515962530.5804279031925050.709786048403747
920.2747633526485740.5495267052971470.725236647351426
930.2572299823497480.5144599646994960.742770017650252
940.2236227715759820.4472455431519650.776377228424018
950.2546397761238110.5092795522476210.745360223876189
960.2569722483831880.5139444967663770.743027751616812
970.2388156477731840.4776312955463670.761184352226816
980.2121780098980170.4243560197960330.787821990101983
990.1899183880508680.3798367761017370.810081611949132
1000.1911711513900830.3823423027801660.808828848609917
1010.1743501618249170.3487003236498330.825649838175083
1020.1597463370904540.3194926741809090.840253662909546
1030.1323187573096920.2646375146193830.867681242690308
1040.1114480398583790.2228960797167590.888551960141621
1050.1222224959777370.2444449919554730.877777504022263
1060.2337483903126740.4674967806253490.766251609687326
1070.2253723837247960.4507447674495920.774627616275204
1080.2585314074306220.5170628148612440.741468592569378
1090.248212139413360.496424278826720.75178786058664
1100.4764107038132930.9528214076265850.523589296186707
1110.5496770906967190.9006458186065630.450322909303281
1120.5344946932620040.9310106134759930.465505306737996
1130.6375099189030840.7249801621938330.362490081096916
1140.598576429218890.8028471415622210.40142357078111
1150.569614305673610.860771388652780.43038569432639
1160.54286036088090.91427927823820.4571396391191
1170.5213750259447960.9572499481104080.478624974055204
1180.4921546523406620.9843093046813240.507845347659338
1190.4524331958610290.9048663917220570.547566804138971
1200.4178340055347090.8356680110694180.582165994465291
1210.373692680864110.747385361728220.62630731913589
1220.3278666850550590.6557333701101180.672133314944941
1230.2815894811234040.5631789622468070.718410518876596
1240.2373730787025310.4747461574050630.762626921297469
1250.1967494505314930.3934989010629870.803250549468507
1260.2133858018278120.4267716036556240.786614198172188
1270.23253732116180.4650746423236010.7674626788382
1280.4509456527433970.9018913054867940.549054347256603
1290.4162268683634790.8324537367269580.583773131636521
1300.3695963379808060.7391926759616120.630403662019194
1310.3290475455853610.6580950911707220.670952454414639
1320.4011139265317340.8022278530634670.598886073468266
1330.4811140216450550.962228043290110.518885978354945
1340.5826829508431470.8346340983137050.417317049156853
1350.5292627006623530.9414745986752930.470737299337647
1360.4660344411897820.9320688823795650.533965558810218
1370.419051790852180.838103581704360.58094820914782
1380.3511829679531960.7023659359063910.648817032046804
1390.3791039568371910.7582079136743810.620896043162809
1400.3292866402599120.6585732805198240.670713359740088
1410.3208334748562290.6416669497124590.679166525143771
1420.2635448727385360.5270897454770720.736455127261464
1430.3345273814691390.6690547629382780.665472618530861
1440.6615820158699950.6768359682600110.338417984130005
1450.5744740214707230.8510519570585530.425525978529277
1460.9747338197992610.0505323604014770.0252661802007385
1470.9644480279953180.07110394400936470.0355519720046824
1480.9548201390244040.09035972195119290.0451798609755964
1490.9423992204355070.1152015591289870.0576007795644933
1500.8878933995849780.2242132008300430.112106600415022
1510.8879202679751870.2241594640496270.112079732024813
1520.965043276265650.06991344746869990.03495672373435







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 level40.027972027972028OK

\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 & 4 & 0.027972027972028 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186268&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]4[/C][C]0.027972027972028[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186268&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186268&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 level40.027972027972028OK



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')
}