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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 15:49:03 -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/t135214862139ebde4tqmm9o7x.htm/, Retrieved Wed, 01 Feb 2023 16:39:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186291, Retrieved Wed, 01 Feb 2023 16:39:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Vermindering van ...] [2012-11-05 19:01:54] [86dcce9422b96d4554cb918e531c1d5d]
- R PD      [Multiple Regression] [Verwijdering vari...] [2012-11-05 20:49:03] [5f6cd87c5735ffe37dbfae854ce1e663] [Current]
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Dataseries X:
13	12	14	12	53	41	38	1
16	11	18	11	86	39	32	2
19	15	11	14	66	30	35	3
15	6	12	12	67	31	33	4
14	13	16	21	76	34	37	5
13	10	18	12	78	35	29	6
19	12	14	22	53	39	31	7
15	14	14	11	80	34	36	8
14	12	15	10	74	36	35	9
15	6	15	13	76	37	38	10
16	10	17	10	79	38	31	11
16	12	19	8	54	36	34	12
16	12	10	15	67	38	35	13
16	11	16	14	54	39	38	14
17	15	18	10	87	33	37	15
15	12	14	14	58	32	33	16
15	10	14	14	75	36	32	17
20	12	17	11	88	38	38	18
18	11	14	10	64	39	38	19
16	12	16	13	57	32	32	20
16	11	18	7	66	32	33	21
16	12	11	14	68	31	31	22
19	13	14	12	54	39	38	23
16	11	12	14	56	37	39	24
17	9	17	11	86	39	32	25
17	13	9	9	80	41	32	26
16	10	16	11	76	36	35	27
15	14	14	15	69	33	37	28
16	12	15	14	78	33	33	29
14	10	11	13	67	34	33	30
15	12	16	9	80	31	28	31
12	8	13	15	54	27	32	32
14	10	17	10	71	37	31	33
16	12	15	11	84	34	37	34
14	12	14	13	74	34	30	35
7	7	16	8	71	32	33	36
10	6	9	20	63	29	31	37
14	12	15	12	71	36	33	38
16	10	17	10	76	29	31	39
16	10	13	10	69	35	33	40
16	10	15	9	74	37	32	41
14	12	16	14	75	34	33	42
20	15	16	8	54	38	32	43
14	10	12	14	52	35	33	44
14	10	12	11	69	38	28	45
11	12	11	13	68	37	35	46
14	13	15	9	65	38	39	47
15	11	15	11	75	33	34	48
16	11	17	15	74	36	38	49
14	12	13	11	75	38	32	50
16	14	16	10	72	32	38	51
14	10	14	14	67	32	30	52
12	12	11	18	63	32	33	53
16	13	12	14	62	34	38	54
9	5	12	11	63	32	32	55
14	6	15	12	76	37	32	56
16	12	16	13	74	39	34	57
16	12	15	9	67	29	34	58
15	11	12	10	73	37	36	59
16	10	12	15	70	35	34	60
12	7	8	20	53	30	28	61
16	12	13	12	77	38	34	62
16	14	11	12	77	34	35	63
14	11	14	14	52	31	35	64
16	12	15	13	54	34	31	65
17	13	10	11	80	35	37	66
18	14	11	17	66	36	35	67
18	11	12	12	73	30	27	68
12	12	15	13	63	39	40	69
16	12	15	14	69	35	37	70
10	8	14	13	67	38	36	71
14	11	16	15	54	31	38	72
18	14	15	13	81	34	39	73
18	14	15	10	69	38	41	74
16	12	13	11	84	34	27	75
17	9	12	19	80	39	30	76
16	13	17	13	70	37	37	77
16	11	13	17	69	34	31	78
13	12	15	13	77	28	31	79
16	12	13	9	54	37	27	80
16	12	15	11	79	33	36	81
20	12	16	10	30	37	38	82
16	12	15	9	71	35	37	83
15	12	16	12	73	37	33	84
15	11	15	12	72	32	34	85
16	10	14	13	77	33	31	86
14	9	15	13	75	38	39	87
16	12	14	12	69	33	34	88
16	12	13	15	54	29	32	89
15	12	7	22	70	33	33	90
12	9	17	13	73	31	36	91
17	15	13	15	54	36	32	92
16	12	15	13	77	35	41	93
15	12	14	15	82	32	28	94
13	12	13	10	80	29	30	95
16	10	16	11	80	39	36	96
16	13	12	16	69	37	35	97
16	9	14	11	78	35	31	98
16	12	17	11	81	37	34	99
14	10	15	10	76	32	36	100
16	14	17	10	76	38	36	101
16	11	12	16	73	37	35	102
20	15	16	12	85	36	37	103
15	11	11	11	66	32	28	104
16	11	15	16	79	33	39	105
13	12	9	19	68	40	32	106
17	12	16	11	76	38	35	107
16	12	15	16	71	41	39	108
16	11	10	15	54	36	35	109
12	7	10	24	46	43	42	110
16	12	15	14	82	30	34	111
16	14	11	15	74	31	33	112
17	11	13	11	88	32	41	113
13	11	14	15	38	32	33	114
12	10	18	12	76	37	34	115
18	13	16	10	86	37	32	116
14	13	14	14	54	33	40	117
14	8	14	13	70	34	40	118
13	11	14	9	69	33	35	119
16	12	14	15	90	38	36	120
13	11	12	15	54	33	37	121
16	13	14	14	76	31	27	122
13	12	15	11	89	38	39	123
16	14	15	8	76	37	38	124
15	13	15	11	73	33	31	125
16	15	13	11	79	31	33	126
15	10	17	8	90	39	32	127
17	11	17	10	74	44	39	128
15	9	19	11	81	33	36	129
12	11	15	13	72	35	33	130
16	10	13	11	71	32	33	131
10	11	9	20	66	28	32	132
16	8	15	10	77	40	37	133
12	11	15	15	65	27	30	134
14	12	15	12	74	37	38	135
15	12	16	14	82	32	29	136
13	9	11	23	54	28	22	137
15	11	14	14	63	34	35	138
11	10	11	16	54	30	35	139
12	8	15	11	64	35	34	140
8	9	13	12	69	31	35	141
16	8	15	10	54	32	34	142
15	9	16	14	84	30	34	143
17	15	14	12	86	30	35	144
16	11	15	12	77	31	23	145
10	8	16	11	89	40	31	146
18	13	16	12	76	32	27	147
13	12	11	13	60	36	36	148
16	12	12	11	75	32	31	149
13	9	9	19	73	35	32	150
10	7	16	12	85	38	39	151
15	13	13	17	79	42	37	152
16	9	16	9	71	34	38	153
16	6	12	12	72	35	39	154
14	8	9	19	69	35	34	155
10	8	13	18	78	33	31	156
17	15	13	15	54	36	32	157
13	6	14	14	69	32	37	158
15	9	19	11	81	33	36	159
16	11	13	9	84	34	32	160
12	8	12	18	84	32	35	161
13	8	13	16	69	34	36	162




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.03740337916085 + 0.533733913871116software[t] + 0.0559016695036461happiness[t] -0.0698967166353528depression[t] + 0.0051268370936719belonging[t] + 0.107298238771915connected[t] -0.0179858507377762separate[t] -0.00409234584985734t_[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.03740337916085 +  0.533733913871116software[t] +  0.0559016695036461happiness[t] -0.0698967166353528depression[t] +  0.0051268370936719belonging[t] +  0.107298238771915connected[t] -0.0179858507377762separate[t] -0.00409234584985734t_[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.03740337916085 +  0.533733913871116software[t] +  0.0559016695036461happiness[t] -0.0698967166353528depression[t] +  0.0051268370936719belonging[t] +  0.107298238771915connected[t] -0.0179858507377762separate[t] -0.00409234584985734t_[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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
Learning[t] = + 6.03740337916085 + 0.533733913871116software[t] + 0.0559016695036461happiness[t] -0.0698967166353528depression[t] + 0.0051268370936719belonging[t] + 0.107298238771915connected[t] -0.0179858507377762separate[t] -0.00409234584985734t_[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.037403379160852.5823132.3380.0206740.010337
software0.5337339138711160.0692847.703500
happiness0.05590166950364610.0762580.73310.4646360.232318
depression-0.06989671663535280.056017-1.24780.2140040.107002
belonging0.00512683709367190.0146780.34930.727360.36368
connected0.1072982387719150.0471552.27550.0242580.012129
separate-0.01798585073777620.044755-0.40190.6883330.344167
t_-0.004092345849857340.003249-1.25970.20970.10485

\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) & 6.03740337916085 & 2.582313 & 2.338 & 0.020674 & 0.010337 \tabularnewline
software & 0.533733913871116 & 0.069284 & 7.7035 & 0 & 0 \tabularnewline
happiness & 0.0559016695036461 & 0.076258 & 0.7331 & 0.464636 & 0.232318 \tabularnewline
depression & -0.0698967166353528 & 0.056017 & -1.2478 & 0.214004 & 0.107002 \tabularnewline
belonging & 0.0051268370936719 & 0.014678 & 0.3493 & 0.72736 & 0.36368 \tabularnewline
connected & 0.107298238771915 & 0.047155 & 2.2755 & 0.024258 & 0.012129 \tabularnewline
separate & -0.0179858507377762 & 0.044755 & -0.4019 & 0.688333 & 0.344167 \tabularnewline
t_ & -0.00409234584985734 & 0.003249 & -1.2597 & 0.2097 & 0.10485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&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]6.03740337916085[/C][C]2.582313[/C][C]2.338[/C][C]0.020674[/C][C]0.010337[/C][/ROW]
[ROW][C]software[/C][C]0.533733913871116[/C][C]0.069284[/C][C]7.7035[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]0.0559016695036461[/C][C]0.076258[/C][C]0.7331[/C][C]0.464636[/C][C]0.232318[/C][/ROW]
[ROW][C]depression[/C][C]-0.0698967166353528[/C][C]0.056017[/C][C]-1.2478[/C][C]0.214004[/C][C]0.107002[/C][/ROW]
[ROW][C]belonging[/C][C]0.0051268370936719[/C][C]0.014678[/C][C]0.3493[/C][C]0.72736[/C][C]0.36368[/C][/ROW]
[ROW][C]connected[/C][C]0.107298238771915[/C][C]0.047155[/C][C]2.2755[/C][C]0.024258[/C][C]0.012129[/C][/ROW]
[ROW][C]separate[/C][C]-0.0179858507377762[/C][C]0.044755[/C][C]-0.4019[/C][C]0.688333[/C][C]0.344167[/C][/ROW]
[ROW][C]t_[/C][C]-0.00409234584985734[/C][C]0.003249[/C][C]-1.2597[/C][C]0.2097[/C][C]0.10485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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)6.037403379160852.5823132.3380.0206740.010337
software0.5337339138711160.0692847.703500
happiness0.05590166950364610.0762580.73310.4646360.232318
depression-0.06989671663535280.056017-1.24780.2140040.107002
belonging0.00512683709367190.0146780.34930.727360.36368
connected0.1072982387719150.0471552.27550.0242580.012129
separate-0.01798585073777620.044755-0.40190.6883330.344167
t_-0.004092345849857340.003249-1.25970.20970.10485







Multiple Linear Regression - Regression Statistics
Multiple R0.600427258109706
R-squared0.360512892281139
Adjusted R-squared0.331445296475736
F-TEST (value)12.4025700197101
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.53166368477287e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84483623221262
Sum Squared Residuals524.126791447404

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.600427258109706 \tabularnewline
R-squared & 0.360512892281139 \tabularnewline
Adjusted R-squared & 0.331445296475736 \tabularnewline
F-TEST (value) & 12.4025700197101 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.53166368477287e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84483623221262 \tabularnewline
Sum Squared Residuals & 524.126791447404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.600427258109706[/C][/ROW]
[ROW][C]R-squared[/C][C]0.360512892281139[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.331445296475736[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.4025700197101[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]1.53166368477287e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84483623221262[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]524.126791447404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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 R0.600427258109706
R-squared0.360512892281139
Adjusted R-squared0.331445296475736
F-TEST (value)12.4025700197101
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.53166368477287e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84483623221262
Sum Squared Residuals524.126791447404







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3694686007688-3.36946860076883
21616.1876499866718-0.187649986671768
31916.59531301684082.4046869831592
41512.13170732626642.86829267373361
51415.7543814530184-1.75438145301844
61315.1513998731422-2.15139987314217
71915.55725183693673.44274816306326
81516.9014953557987-1.90149535579868
91416.1575548740652-2.15755487406516
101512.80496325582852.19503674417151
111615.50587975959380.494120240406196
121616.4241270566652-0.424127056665221
131615.69090217785890.309097822141133
141615.5450744561360.454925543864025
151717.6106900135167-0.610690013516744
161515.3181692699603-0.31816926996025
171514.7809441327860.219055867213982
182016.39504502843033.6049549715697
191815.74366462535752.25633537464247
201615.49635895584760.50364104415243
211615.51787201805130.482127981948723
221615.10585201999050.894147980009475
231916.60370126549292.3962987345071
241615.05821566552860.941784334471444
251714.97015653487922.02984346512083
261716.97741537673710.0225846232628855
271615.01268344808110.987316551918861
281516.35838227472-1.35838227472004
291615.53070542406110.469294575938897
301414.3563383198313-0.356338319831307
311515.8134924353742-0.813492435374194
321212.4529450032426-0.452945003242588
331415.2675352153757-1.26753521537565
341615.78604970310070.213950296899284
351415.6608948387042-1.66089483870422
36713.1654853046447-6.16548530464467
371011.0706490471844-1.07064904718437
381415.9196746013431-1.91967460134313
391614.41022941556961.58977058443045
401614.75446026320531.24553973679466
411615.19028448674810.809715513251905
421415.6253243250075-1.6253243250075
432017.98132924744142.01867075255857
441414.3154461131684-0.315446113168434
451415.0200241178217-1.02002411782168
461115.6493784659097-4.64937846590968
471416.7021879030267-2.70218790302673
481515.09554072693-0.0955407269299648
491615.1684893298170.831510670183044
501416.0817495054292-2.0817495054292
511616.6156416641287-0.615641664128703
521414.2034760776795-0.20347607767953
531214.7450947839315-2.74509478393154
541615.72976527475910.270234725240869
55911.5639372318229-2.56393723182291
561412.79452716779711.20547283220294
571616.1472143599231-0.14721435992312
581615.25793696373620.742063036263821
591515.3356842101307-0.335684210130711
601614.25436907988371.74563092011632
611211.56025241120380.439747588796208
621615.93702661130730.0629733886926509
631616.441419948367-0.441419948366997
641414.4139717924865-0.413971792486484
651615.47350354010090.526496459899067
661715.99611109265541.00388890734461
671816.23376825130421.76623174869576
681814.56984464944793.43015535055213
691215.8778942277641-3.87789422776414
701615.45943078496660.54056921503337
711013.6640247236302-3.66402472363019
721414.3794357700336-0.37943577003358
731816.50277039667121.49722960332876
741817.04006740921550.959932590784518
751615.6863186916270.313681308372965
761713.92797549484893.07202450515108
771616.3659416481689-0.36594164816889
781614.5720814810381.42791851896195
791314.8903385187259-1.8903385187259
801615.9737399991540.0262600008459599
811615.47876287465490.521237125345077
822015.74247515096624.25752484903375
831615.76596754628260.234032453717408
841515.9048802747126-0.9048802747126
851514.7515484637970.24845153620304
861614.27481379439061.72518620560941
871414.1752399179433-0.175239917943284
881615.30902139810580.690978601894244
891614.5692134228291.43078657717099
901514.23367054135840.76632945864156
911213.5632900801737-1.56329008017373
921716.90922579829620.0907742017038131
931615.40427484085350.595725159146466
941515.142042920973-0.142042920973031
951315.0634123968177-2.06341239681765
961615.05472779839360.945272201606362
971615.82574109812930.174258901870665
981614.05148847822941.94851152177061
991615.99232231911530.00767768088466221
1001414.2807584423478-0.280758442347825
1011617.1671945236212-1.16719452362121
1021614.75831888951251.2416811104875
1032017.31060784857972.6893921514203
1041514.59723801313510.402761986864935
1051614.44337152499711.55662847500286
1061315.2485063446279-2.24850634462791
1071715.96736008537861.03263991462139
1081615.78219961474460.217800385255376
1091614.48305770263991.51694229736012
1101212.299131271077-0.299131271076982
1111614.8757598456941.12424015430603
1121615.72990132569670.270098674303278
1131714.55118459596332.44881540403667
1141314.2109520042943-1.21095200429432
1151214.8197477251753-2.81974772517532
1161816.53208728761451.4679127123855
1171415.3994661882286-1.39946618822857
1181412.98592862192921.01407137807085
1191314.8401290620573-1.84012906205735
1201615.57655925235540.423440747644606
1211314.1878864736576-1.18788647365759
1221615.51101445708730.488985542912675
1231315.8406863615439-2.84068636154388
1241616.9577907230904-0.957790723090437
1251515.8916018022592-0.891601802259165
1261616.6233667886869-0.623366788686897
1271515.3166686703456-0.316668670345588
1281716.03507765029250.964922349707471
1291513.91498888445041.08501111554965
1301214.8373767509715-2.83737675097155
1311614.00051903210461.99948096789543
1321013.2406421825748-3.24064218257483
1331613.92377009809152.07622990190849
1341213.8408977166837-1.84089771668372
1351415.555466550271-1.55546655027102
1361515.1338786001839-0.133878600183892
1371312.17316227693820.826837723061756
1381514.48942812394320.510571876056824
1391113.1687689335099-2.16876893350985
1401213.2760444366432-1.27604443664318
141813.2024413286647-5.20244132866472
1421612.96459337432643.03540662567364
1431513.20975838057621.79024161942382
1441716.4283274356660.571672564334005
1451614.62218801761751.3778119823825
1461014.0260117044624-4.02601170446238
1471815.76760082189082.2323991781092
1481315.0656604029652-2.06566040296517
1491614.9949020148961.00509798510404
1501312.95638437722960.0436156227703756
1511013.0229287128859-3.0229287128859
1521516.1384548925762-1.13845489257621
1531613.80891917517322.19108082482681
1541611.86476748491724.13523251508285
1551412.3457096842591.65429031574102
1561012.5206233415716-2.52062334157161
1571716.64322331805550.35677668194454
1581311.51910448113311.4808955188669
1591513.79221850895461.20778149104537
1601614.85459956009991.14540043990013
1611212.2957793236577-0.295779323657688
1621312.60709015098320.392909849016843

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3694686007688 & -3.36946860076883 \tabularnewline
2 & 16 & 16.1876499866718 & -0.187649986671768 \tabularnewline
3 & 19 & 16.5953130168408 & 2.4046869831592 \tabularnewline
4 & 15 & 12.1317073262664 & 2.86829267373361 \tabularnewline
5 & 14 & 15.7543814530184 & -1.75438145301844 \tabularnewline
6 & 13 & 15.1513998731422 & -2.15139987314217 \tabularnewline
7 & 19 & 15.5572518369367 & 3.44274816306326 \tabularnewline
8 & 15 & 16.9014953557987 & -1.90149535579868 \tabularnewline
9 & 14 & 16.1575548740652 & -2.15755487406516 \tabularnewline
10 & 15 & 12.8049632558285 & 2.19503674417151 \tabularnewline
11 & 16 & 15.5058797595938 & 0.494120240406196 \tabularnewline
12 & 16 & 16.4241270566652 & -0.424127056665221 \tabularnewline
13 & 16 & 15.6909021778589 & 0.309097822141133 \tabularnewline
14 & 16 & 15.545074456136 & 0.454925543864025 \tabularnewline
15 & 17 & 17.6106900135167 & -0.610690013516744 \tabularnewline
16 & 15 & 15.3181692699603 & -0.31816926996025 \tabularnewline
17 & 15 & 14.780944132786 & 0.219055867213982 \tabularnewline
18 & 20 & 16.3950450284303 & 3.6049549715697 \tabularnewline
19 & 18 & 15.7436646253575 & 2.25633537464247 \tabularnewline
20 & 16 & 15.4963589558476 & 0.50364104415243 \tabularnewline
21 & 16 & 15.5178720180513 & 0.482127981948723 \tabularnewline
22 & 16 & 15.1058520199905 & 0.894147980009475 \tabularnewline
23 & 19 & 16.6037012654929 & 2.3962987345071 \tabularnewline
24 & 16 & 15.0582156655286 & 0.941784334471444 \tabularnewline
25 & 17 & 14.9701565348792 & 2.02984346512083 \tabularnewline
26 & 17 & 16.9774153767371 & 0.0225846232628855 \tabularnewline
27 & 16 & 15.0126834480811 & 0.987316551918861 \tabularnewline
28 & 15 & 16.35838227472 & -1.35838227472004 \tabularnewline
29 & 16 & 15.5307054240611 & 0.469294575938897 \tabularnewline
30 & 14 & 14.3563383198313 & -0.356338319831307 \tabularnewline
31 & 15 & 15.8134924353742 & -0.813492435374194 \tabularnewline
32 & 12 & 12.4529450032426 & -0.452945003242588 \tabularnewline
33 & 14 & 15.2675352153757 & -1.26753521537565 \tabularnewline
34 & 16 & 15.7860497031007 & 0.213950296899284 \tabularnewline
35 & 14 & 15.6608948387042 & -1.66089483870422 \tabularnewline
36 & 7 & 13.1654853046447 & -6.16548530464467 \tabularnewline
37 & 10 & 11.0706490471844 & -1.07064904718437 \tabularnewline
38 & 14 & 15.9196746013431 & -1.91967460134313 \tabularnewline
39 & 16 & 14.4102294155696 & 1.58977058443045 \tabularnewline
40 & 16 & 14.7544602632053 & 1.24553973679466 \tabularnewline
41 & 16 & 15.1902844867481 & 0.809715513251905 \tabularnewline
42 & 14 & 15.6253243250075 & -1.6253243250075 \tabularnewline
43 & 20 & 17.9813292474414 & 2.01867075255857 \tabularnewline
44 & 14 & 14.3154461131684 & -0.315446113168434 \tabularnewline
45 & 14 & 15.0200241178217 & -1.02002411782168 \tabularnewline
46 & 11 & 15.6493784659097 & -4.64937846590968 \tabularnewline
47 & 14 & 16.7021879030267 & -2.70218790302673 \tabularnewline
48 & 15 & 15.09554072693 & -0.0955407269299648 \tabularnewline
49 & 16 & 15.168489329817 & 0.831510670183044 \tabularnewline
50 & 14 & 16.0817495054292 & -2.0817495054292 \tabularnewline
51 & 16 & 16.6156416641287 & -0.615641664128703 \tabularnewline
52 & 14 & 14.2034760776795 & -0.20347607767953 \tabularnewline
53 & 12 & 14.7450947839315 & -2.74509478393154 \tabularnewline
54 & 16 & 15.7297652747591 & 0.270234725240869 \tabularnewline
55 & 9 & 11.5639372318229 & -2.56393723182291 \tabularnewline
56 & 14 & 12.7945271677971 & 1.20547283220294 \tabularnewline
57 & 16 & 16.1472143599231 & -0.14721435992312 \tabularnewline
58 & 16 & 15.2579369637362 & 0.742063036263821 \tabularnewline
59 & 15 & 15.3356842101307 & -0.335684210130711 \tabularnewline
60 & 16 & 14.2543690798837 & 1.74563092011632 \tabularnewline
61 & 12 & 11.5602524112038 & 0.439747588796208 \tabularnewline
62 & 16 & 15.9370266113073 & 0.0629733886926509 \tabularnewline
63 & 16 & 16.441419948367 & -0.441419948366997 \tabularnewline
64 & 14 & 14.4139717924865 & -0.413971792486484 \tabularnewline
65 & 16 & 15.4735035401009 & 0.526496459899067 \tabularnewline
66 & 17 & 15.9961110926554 & 1.00388890734461 \tabularnewline
67 & 18 & 16.2337682513042 & 1.76623174869576 \tabularnewline
68 & 18 & 14.5698446494479 & 3.43015535055213 \tabularnewline
69 & 12 & 15.8778942277641 & -3.87789422776414 \tabularnewline
70 & 16 & 15.4594307849666 & 0.54056921503337 \tabularnewline
71 & 10 & 13.6640247236302 & -3.66402472363019 \tabularnewline
72 & 14 & 14.3794357700336 & -0.37943577003358 \tabularnewline
73 & 18 & 16.5027703966712 & 1.49722960332876 \tabularnewline
74 & 18 & 17.0400674092155 & 0.959932590784518 \tabularnewline
75 & 16 & 15.686318691627 & 0.313681308372965 \tabularnewline
76 & 17 & 13.9279754948489 & 3.07202450515108 \tabularnewline
77 & 16 & 16.3659416481689 & -0.36594164816889 \tabularnewline
78 & 16 & 14.572081481038 & 1.42791851896195 \tabularnewline
79 & 13 & 14.8903385187259 & -1.8903385187259 \tabularnewline
80 & 16 & 15.973739999154 & 0.0262600008459599 \tabularnewline
81 & 16 & 15.4787628746549 & 0.521237125345077 \tabularnewline
82 & 20 & 15.7424751509662 & 4.25752484903375 \tabularnewline
83 & 16 & 15.7659675462826 & 0.234032453717408 \tabularnewline
84 & 15 & 15.9048802747126 & -0.9048802747126 \tabularnewline
85 & 15 & 14.751548463797 & 0.24845153620304 \tabularnewline
86 & 16 & 14.2748137943906 & 1.72518620560941 \tabularnewline
87 & 14 & 14.1752399179433 & -0.175239917943284 \tabularnewline
88 & 16 & 15.3090213981058 & 0.690978601894244 \tabularnewline
89 & 16 & 14.569213422829 & 1.43078657717099 \tabularnewline
90 & 15 & 14.2336705413584 & 0.76632945864156 \tabularnewline
91 & 12 & 13.5632900801737 & -1.56329008017373 \tabularnewline
92 & 17 & 16.9092257982962 & 0.0907742017038131 \tabularnewline
93 & 16 & 15.4042748408535 & 0.595725159146466 \tabularnewline
94 & 15 & 15.142042920973 & -0.142042920973031 \tabularnewline
95 & 13 & 15.0634123968177 & -2.06341239681765 \tabularnewline
96 & 16 & 15.0547277983936 & 0.945272201606362 \tabularnewline
97 & 16 & 15.8257410981293 & 0.174258901870665 \tabularnewline
98 & 16 & 14.0514884782294 & 1.94851152177061 \tabularnewline
99 & 16 & 15.9923223191153 & 0.00767768088466221 \tabularnewline
100 & 14 & 14.2807584423478 & -0.280758442347825 \tabularnewline
101 & 16 & 17.1671945236212 & -1.16719452362121 \tabularnewline
102 & 16 & 14.7583188895125 & 1.2416811104875 \tabularnewline
103 & 20 & 17.3106078485797 & 2.6893921514203 \tabularnewline
104 & 15 & 14.5972380131351 & 0.402761986864935 \tabularnewline
105 & 16 & 14.4433715249971 & 1.55662847500286 \tabularnewline
106 & 13 & 15.2485063446279 & -2.24850634462791 \tabularnewline
107 & 17 & 15.9673600853786 & 1.03263991462139 \tabularnewline
108 & 16 & 15.7821996147446 & 0.217800385255376 \tabularnewline
109 & 16 & 14.4830577026399 & 1.51694229736012 \tabularnewline
110 & 12 & 12.299131271077 & -0.299131271076982 \tabularnewline
111 & 16 & 14.875759845694 & 1.12424015430603 \tabularnewline
112 & 16 & 15.7299013256967 & 0.270098674303278 \tabularnewline
113 & 17 & 14.5511845959633 & 2.44881540403667 \tabularnewline
114 & 13 & 14.2109520042943 & -1.21095200429432 \tabularnewline
115 & 12 & 14.8197477251753 & -2.81974772517532 \tabularnewline
116 & 18 & 16.5320872876145 & 1.4679127123855 \tabularnewline
117 & 14 & 15.3994661882286 & -1.39946618822857 \tabularnewline
118 & 14 & 12.9859286219292 & 1.01407137807085 \tabularnewline
119 & 13 & 14.8401290620573 & -1.84012906205735 \tabularnewline
120 & 16 & 15.5765592523554 & 0.423440747644606 \tabularnewline
121 & 13 & 14.1878864736576 & -1.18788647365759 \tabularnewline
122 & 16 & 15.5110144570873 & 0.488985542912675 \tabularnewline
123 & 13 & 15.8406863615439 & -2.84068636154388 \tabularnewline
124 & 16 & 16.9577907230904 & -0.957790723090437 \tabularnewline
125 & 15 & 15.8916018022592 & -0.891601802259165 \tabularnewline
126 & 16 & 16.6233667886869 & -0.623366788686897 \tabularnewline
127 & 15 & 15.3166686703456 & -0.316668670345588 \tabularnewline
128 & 17 & 16.0350776502925 & 0.964922349707471 \tabularnewline
129 & 15 & 13.9149888844504 & 1.08501111554965 \tabularnewline
130 & 12 & 14.8373767509715 & -2.83737675097155 \tabularnewline
131 & 16 & 14.0005190321046 & 1.99948096789543 \tabularnewline
132 & 10 & 13.2406421825748 & -3.24064218257483 \tabularnewline
133 & 16 & 13.9237700980915 & 2.07622990190849 \tabularnewline
134 & 12 & 13.8408977166837 & -1.84089771668372 \tabularnewline
135 & 14 & 15.555466550271 & -1.55546655027102 \tabularnewline
136 & 15 & 15.1338786001839 & -0.133878600183892 \tabularnewline
137 & 13 & 12.1731622769382 & 0.826837723061756 \tabularnewline
138 & 15 & 14.4894281239432 & 0.510571876056824 \tabularnewline
139 & 11 & 13.1687689335099 & -2.16876893350985 \tabularnewline
140 & 12 & 13.2760444366432 & -1.27604443664318 \tabularnewline
141 & 8 & 13.2024413286647 & -5.20244132866472 \tabularnewline
142 & 16 & 12.9645933743264 & 3.03540662567364 \tabularnewline
143 & 15 & 13.2097583805762 & 1.79024161942382 \tabularnewline
144 & 17 & 16.428327435666 & 0.571672564334005 \tabularnewline
145 & 16 & 14.6221880176175 & 1.3778119823825 \tabularnewline
146 & 10 & 14.0260117044624 & -4.02601170446238 \tabularnewline
147 & 18 & 15.7676008218908 & 2.2323991781092 \tabularnewline
148 & 13 & 15.0656604029652 & -2.06566040296517 \tabularnewline
149 & 16 & 14.994902014896 & 1.00509798510404 \tabularnewline
150 & 13 & 12.9563843772296 & 0.0436156227703756 \tabularnewline
151 & 10 & 13.0229287128859 & -3.0229287128859 \tabularnewline
152 & 15 & 16.1384548925762 & -1.13845489257621 \tabularnewline
153 & 16 & 13.8089191751732 & 2.19108082482681 \tabularnewline
154 & 16 & 11.8647674849172 & 4.13523251508285 \tabularnewline
155 & 14 & 12.345709684259 & 1.65429031574102 \tabularnewline
156 & 10 & 12.5206233415716 & -2.52062334157161 \tabularnewline
157 & 17 & 16.6432233180555 & 0.35677668194454 \tabularnewline
158 & 13 & 11.5191044811331 & 1.4808955188669 \tabularnewline
159 & 15 & 13.7922185089546 & 1.20778149104537 \tabularnewline
160 & 16 & 14.8545995600999 & 1.14540043990013 \tabularnewline
161 & 12 & 12.2957793236577 & -0.295779323657688 \tabularnewline
162 & 13 & 12.6070901509832 & 0.392909849016843 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&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]13[/C][C]16.3694686007688[/C][C]-3.36946860076883[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1876499866718[/C][C]-0.187649986671768[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5953130168408[/C][C]2.4046869831592[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1317073262664[/C][C]2.86829267373361[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7543814530184[/C][C]-1.75438145301844[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1513998731422[/C][C]-2.15139987314217[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5572518369367[/C][C]3.44274816306326[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9014953557987[/C][C]-1.90149535579868[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1575548740652[/C][C]-2.15755487406516[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8049632558285[/C][C]2.19503674417151[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5058797595938[/C][C]0.494120240406196[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.4241270566652[/C][C]-0.424127056665221[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6909021778589[/C][C]0.309097822141133[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.545074456136[/C][C]0.454925543864025[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6106900135167[/C][C]-0.610690013516744[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3181692699603[/C][C]-0.31816926996025[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.780944132786[/C][C]0.219055867213982[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3950450284303[/C][C]3.6049549715697[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7436646253575[/C][C]2.25633537464247[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4963589558476[/C][C]0.50364104415243[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5178720180513[/C][C]0.482127981948723[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1058520199905[/C][C]0.894147980009475[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6037012654929[/C][C]2.3962987345071[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0582156655286[/C][C]0.941784334471444[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9701565348792[/C][C]2.02984346512083[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9774153767371[/C][C]0.0225846232628855[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0126834480811[/C][C]0.987316551918861[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.35838227472[/C][C]-1.35838227472004[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5307054240611[/C][C]0.469294575938897[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3563383198313[/C][C]-0.356338319831307[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8134924353742[/C][C]-0.813492435374194[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4529450032426[/C][C]-0.452945003242588[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2675352153757[/C][C]-1.26753521537565[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.7860497031007[/C][C]0.213950296899284[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6608948387042[/C][C]-1.66089483870422[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1654853046447[/C][C]-6.16548530464467[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0706490471844[/C][C]-1.07064904718437[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9196746013431[/C][C]-1.91967460134313[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4102294155696[/C][C]1.58977058443045[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7544602632053[/C][C]1.24553973679466[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1902844867481[/C][C]0.809715513251905[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6253243250075[/C][C]-1.6253243250075[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9813292474414[/C][C]2.01867075255857[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3154461131684[/C][C]-0.315446113168434[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0200241178217[/C][C]-1.02002411782168[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6493784659097[/C][C]-4.64937846590968[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7021879030267[/C][C]-2.70218790302673[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.09554072693[/C][C]-0.0955407269299648[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.168489329817[/C][C]0.831510670183044[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0817495054292[/C][C]-2.0817495054292[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6156416641287[/C][C]-0.615641664128703[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2034760776795[/C][C]-0.20347607767953[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7450947839315[/C][C]-2.74509478393154[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7297652747591[/C][C]0.270234725240869[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5639372318229[/C][C]-2.56393723182291[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7945271677971[/C][C]1.20547283220294[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1472143599231[/C][C]-0.14721435992312[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2579369637362[/C][C]0.742063036263821[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3356842101307[/C][C]-0.335684210130711[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2543690798837[/C][C]1.74563092011632[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5602524112038[/C][C]0.439747588796208[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9370266113073[/C][C]0.0629733886926509[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.441419948367[/C][C]-0.441419948366997[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4139717924865[/C][C]-0.413971792486484[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4735035401009[/C][C]0.526496459899067[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9961110926554[/C][C]1.00388890734461[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2337682513042[/C][C]1.76623174869576[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5698446494479[/C][C]3.43015535055213[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8778942277641[/C][C]-3.87789422776414[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4594307849666[/C][C]0.54056921503337[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6640247236302[/C][C]-3.66402472363019[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3794357700336[/C][C]-0.37943577003358[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5027703966712[/C][C]1.49722960332876[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0400674092155[/C][C]0.959932590784518[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.686318691627[/C][C]0.313681308372965[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9279754948489[/C][C]3.07202450515108[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3659416481689[/C][C]-0.36594164816889[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.572081481038[/C][C]1.42791851896195[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8903385187259[/C][C]-1.8903385187259[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.973739999154[/C][C]0.0262600008459599[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4787628746549[/C][C]0.521237125345077[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7424751509662[/C][C]4.25752484903375[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7659675462826[/C][C]0.234032453717408[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9048802747126[/C][C]-0.9048802747126[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.751548463797[/C][C]0.24845153620304[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2748137943906[/C][C]1.72518620560941[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1752399179433[/C][C]-0.175239917943284[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3090213981058[/C][C]0.690978601894244[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.569213422829[/C][C]1.43078657717099[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2336705413584[/C][C]0.76632945864156[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5632900801737[/C][C]-1.56329008017373[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9092257982962[/C][C]0.0907742017038131[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4042748408535[/C][C]0.595725159146466[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.142042920973[/C][C]-0.142042920973031[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0634123968177[/C][C]-2.06341239681765[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0547277983936[/C][C]0.945272201606362[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8257410981293[/C][C]0.174258901870665[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0514884782294[/C][C]1.94851152177061[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9923223191153[/C][C]0.00767768088466221[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2807584423478[/C][C]-0.280758442347825[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1671945236212[/C][C]-1.16719452362121[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7583188895125[/C][C]1.2416811104875[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3106078485797[/C][C]2.6893921514203[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5972380131351[/C][C]0.402761986864935[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4433715249971[/C][C]1.55662847500286[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2485063446279[/C][C]-2.24850634462791[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9673600853786[/C][C]1.03263991462139[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7821996147446[/C][C]0.217800385255376[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4830577026399[/C][C]1.51694229736012[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.299131271077[/C][C]-0.299131271076982[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.875759845694[/C][C]1.12424015430603[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7299013256967[/C][C]0.270098674303278[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5511845959633[/C][C]2.44881540403667[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.2109520042943[/C][C]-1.21095200429432[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8197477251753[/C][C]-2.81974772517532[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5320872876145[/C][C]1.4679127123855[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3994661882286[/C][C]-1.39946618822857[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9859286219292[/C][C]1.01407137807085[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8401290620573[/C][C]-1.84012906205735[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5765592523554[/C][C]0.423440747644606[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1878864736576[/C][C]-1.18788647365759[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5110144570873[/C][C]0.488985542912675[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8406863615439[/C][C]-2.84068636154388[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9577907230904[/C][C]-0.957790723090437[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8916018022592[/C][C]-0.891601802259165[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6233667886869[/C][C]-0.623366788686897[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3166686703456[/C][C]-0.316668670345588[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0350776502925[/C][C]0.964922349707471[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.9149888844504[/C][C]1.08501111554965[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8373767509715[/C][C]-2.83737675097155[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0005190321046[/C][C]1.99948096789543[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2406421825748[/C][C]-3.24064218257483[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9237700980915[/C][C]2.07622990190849[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8408977166837[/C][C]-1.84089771668372[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.555466550271[/C][C]-1.55546655027102[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1338786001839[/C][C]-0.133878600183892[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1731622769382[/C][C]0.826837723061756[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4894281239432[/C][C]0.510571876056824[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1687689335099[/C][C]-2.16876893350985[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2760444366432[/C][C]-1.27604443664318[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2024413286647[/C][C]-5.20244132866472[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9645933743264[/C][C]3.03540662567364[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2097583805762[/C][C]1.79024161942382[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.428327435666[/C][C]0.571672564334005[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6221880176175[/C][C]1.3778119823825[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0260117044624[/C][C]-4.02601170446238[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7676008218908[/C][C]2.2323991781092[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0656604029652[/C][C]-2.06566040296517[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.994902014896[/C][C]1.00509798510404[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9563843772296[/C][C]0.0436156227703756[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0229287128859[/C][C]-3.0229287128859[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1384548925762[/C][C]-1.13845489257621[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8089191751732[/C][C]2.19108082482681[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8647674849172[/C][C]4.13523251508285[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.345709684259[/C][C]1.65429031574102[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5206233415716[/C][C]-2.52062334157161[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.6432233180555[/C][C]0.35677668194454[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5191044811331[/C][C]1.4808955188669[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.7922185089546[/C][C]1.20778149104537[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8545995600999[/C][C]1.14540043990013[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2957793236577[/C][C]-0.295779323657688[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6070901509832[/C][C]0.392909849016843[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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
11316.3694686007688-3.36946860076883
21616.1876499866718-0.187649986671768
31916.59531301684082.4046869831592
41512.13170732626642.86829267373361
51415.7543814530184-1.75438145301844
61315.1513998731422-2.15139987314217
71915.55725183693673.44274816306326
81516.9014953557987-1.90149535579868
91416.1575548740652-2.15755487406516
101512.80496325582852.19503674417151
111615.50587975959380.494120240406196
121616.4241270566652-0.424127056665221
131615.69090217785890.309097822141133
141615.5450744561360.454925543864025
151717.6106900135167-0.610690013516744
161515.3181692699603-0.31816926996025
171514.7809441327860.219055867213982
182016.39504502843033.6049549715697
191815.74366462535752.25633537464247
201615.49635895584760.50364104415243
211615.51787201805130.482127981948723
221615.10585201999050.894147980009475
231916.60370126549292.3962987345071
241615.05821566552860.941784334471444
251714.97015653487922.02984346512083
261716.97741537673710.0225846232628855
271615.01268344808110.987316551918861
281516.35838227472-1.35838227472004
291615.53070542406110.469294575938897
301414.3563383198313-0.356338319831307
311515.8134924353742-0.813492435374194
321212.4529450032426-0.452945003242588
331415.2675352153757-1.26753521537565
341615.78604970310070.213950296899284
351415.6608948387042-1.66089483870422
36713.1654853046447-6.16548530464467
371011.0706490471844-1.07064904718437
381415.9196746013431-1.91967460134313
391614.41022941556961.58977058443045
401614.75446026320531.24553973679466
411615.19028448674810.809715513251905
421415.6253243250075-1.6253243250075
432017.98132924744142.01867075255857
441414.3154461131684-0.315446113168434
451415.0200241178217-1.02002411782168
461115.6493784659097-4.64937846590968
471416.7021879030267-2.70218790302673
481515.09554072693-0.0955407269299648
491615.1684893298170.831510670183044
501416.0817495054292-2.0817495054292
511616.6156416641287-0.615641664128703
521414.2034760776795-0.20347607767953
531214.7450947839315-2.74509478393154
541615.72976527475910.270234725240869
55911.5639372318229-2.56393723182291
561412.79452716779711.20547283220294
571616.1472143599231-0.14721435992312
581615.25793696373620.742063036263821
591515.3356842101307-0.335684210130711
601614.25436907988371.74563092011632
611211.56025241120380.439747588796208
621615.93702661130730.0629733886926509
631616.441419948367-0.441419948366997
641414.4139717924865-0.413971792486484
651615.47350354010090.526496459899067
661715.99611109265541.00388890734461
671816.23376825130421.76623174869576
681814.56984464944793.43015535055213
691215.8778942277641-3.87789422776414
701615.45943078496660.54056921503337
711013.6640247236302-3.66402472363019
721414.3794357700336-0.37943577003358
731816.50277039667121.49722960332876
741817.04006740921550.959932590784518
751615.6863186916270.313681308372965
761713.92797549484893.07202450515108
771616.3659416481689-0.36594164816889
781614.5720814810381.42791851896195
791314.8903385187259-1.8903385187259
801615.9737399991540.0262600008459599
811615.47876287465490.521237125345077
822015.74247515096624.25752484903375
831615.76596754628260.234032453717408
841515.9048802747126-0.9048802747126
851514.7515484637970.24845153620304
861614.27481379439061.72518620560941
871414.1752399179433-0.175239917943284
881615.30902139810580.690978601894244
891614.5692134228291.43078657717099
901514.23367054135840.76632945864156
911213.5632900801737-1.56329008017373
921716.90922579829620.0907742017038131
931615.40427484085350.595725159146466
941515.142042920973-0.142042920973031
951315.0634123968177-2.06341239681765
961615.05472779839360.945272201606362
971615.82574109812930.174258901870665
981614.05148847822941.94851152177061
991615.99232231911530.00767768088466221
1001414.2807584423478-0.280758442347825
1011617.1671945236212-1.16719452362121
1021614.75831888951251.2416811104875
1032017.31060784857972.6893921514203
1041514.59723801313510.402761986864935
1051614.44337152499711.55662847500286
1061315.2485063446279-2.24850634462791
1071715.96736008537861.03263991462139
1081615.78219961474460.217800385255376
1091614.48305770263991.51694229736012
1101212.299131271077-0.299131271076982
1111614.8757598456941.12424015430603
1121615.72990132569670.270098674303278
1131714.55118459596332.44881540403667
1141314.2109520042943-1.21095200429432
1151214.8197477251753-2.81974772517532
1161816.53208728761451.4679127123855
1171415.3994661882286-1.39946618822857
1181412.98592862192921.01407137807085
1191314.8401290620573-1.84012906205735
1201615.57655925235540.423440747644606
1211314.1878864736576-1.18788647365759
1221615.51101445708730.488985542912675
1231315.8406863615439-2.84068636154388
1241616.9577907230904-0.957790723090437
1251515.8916018022592-0.891601802259165
1261616.6233667886869-0.623366788686897
1271515.3166686703456-0.316668670345588
1281716.03507765029250.964922349707471
1291513.91498888445041.08501111554965
1301214.8373767509715-2.83737675097155
1311614.00051903210461.99948096789543
1321013.2406421825748-3.24064218257483
1331613.92377009809152.07622990190849
1341213.8408977166837-1.84089771668372
1351415.555466550271-1.55546655027102
1361515.1338786001839-0.133878600183892
1371312.17316227693820.826837723061756
1381514.48942812394320.510571876056824
1391113.1687689335099-2.16876893350985
1401213.2760444366432-1.27604443664318
141813.2024413286647-5.20244132866472
1421612.96459337432643.03540662567364
1431513.20975838057621.79024161942382
1441716.4283274356660.571672564334005
1451614.62218801761751.3778119823825
1461014.0260117044624-4.02601170446238
1471815.76760082189082.2323991781092
1481315.0656604029652-2.06566040296517
1491614.9949020148961.00509798510404
1501312.95638437722960.0436156227703756
1511013.0229287128859-3.0229287128859
1521516.1384548925762-1.13845489257621
1531613.80891917517322.19108082482681
1541611.86476748491724.13523251508285
1551412.3457096842591.65429031574102
1561012.5206233415716-2.52062334157161
1571716.64322331805550.35677668194454
1581311.51910448113311.4808955188669
1591513.79221850895461.20778149104537
1601614.85459956009991.14540043990013
1611212.2957793236577-0.295779323657688
1621312.60709015098320.392909849016843







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4486066592534970.8972133185069940.551393340746503
120.8555500736453980.2888998527092040.144449926354602
130.827069958513810.3458600829723790.17293004148619
140.7471813236026220.5056373527947570.252818676397378
150.6978558710044610.6042882579910780.302144128995539
160.7338414342450510.5323171315098980.266158565754949
170.6725046578282680.6549906843434640.327495342171732
180.8701977711199710.2596044577600580.129802228880029
190.8437131190126010.3125737619747980.156286880987399
200.79302343345760.41395313308480.2069765665424
210.7297402726027710.5405194547944580.270259727397229
220.6901368694046510.6197262611906970.309863130595348
230.6569771559720320.6860456880559360.343022844027968
240.6332110499261120.7335779001477750.366788950073888
250.5760205405531330.8479589188937350.423979459446867
260.5272615918190170.9454768163619660.472738408180983
270.4767341302161760.9534682604323520.523265869783824
280.5268661386101410.9462677227797170.473133861389859
290.4683533367394320.9367066734788640.531646663260568
300.4818419834153410.9636839668306820.518158016584659
310.4280943154306240.8561886308612490.571905684569376
320.4260226992294430.8520453984588860.573977300770557
330.4124433253022350.8248866506044690.587556674697765
340.3544906089640270.7089812179280540.645509391035973
350.3397720838730650.679544167746130.660227916126935
360.8389319914532660.3221360170934670.161068008546734
370.8220022388755680.3559955222488640.177997761124432
380.8059035228947110.3881929542105780.194096477105289
390.8379217691317520.3241564617364960.162078230868248
400.8246556961699750.3506886076600510.175344303830025
410.7984735603927160.4030528792145690.201526439607284
420.7770922073522570.4458155852954870.222907792647743
430.7963143524141480.4073712951717030.203685647585852
440.7583841966177870.4832316067644250.241615803382213
450.7259241716047650.5481516567904710.274075828395235
460.8832785166531370.2334429666937270.116721483346864
470.8917518624743250.2164962750513490.108248137525675
480.8706670298317650.2586659403364710.129332970168235
490.8561719849744670.2876560300510660.143828015025533
500.8493351628481140.3013296743037730.150664837151886
510.8211236274255260.3577527451489480.178876372574474
520.7894814014381240.4210371971237520.210518598561876
530.8047844972955320.3904310054089350.195215502704467
540.7782745027933120.4434509944133750.221725497206687
550.7987812393877730.4024375212244550.201218760612227
560.7891519744402040.4216960511195930.210848025559796
570.7536831696206980.4926336607586050.246316830379302
580.7420654117012630.5158691765974730.257934588298737
590.7072486852396670.5855026295206670.292751314760333
600.714477516415470.5710449671690590.28552248358453
610.679893940941720.6402121181165610.32010605905828
620.639003551022110.721992897955780.36099644897789
630.5993113155254140.8013773689491710.400688684474586
640.555854602225840.888290795548320.44414539777416
650.5179424467561850.964115106487630.482057553243815
660.4926711801393510.9853423602787020.507328819860649
670.487299933640960.974599867281920.51270006635904
680.606620332478140.786759335043720.39337966752186
690.7330287515111640.5339424969776730.266971248488836
700.699284882520650.6014302349587010.30071511747935
710.8031724483397670.3936551033204650.196827551660233
720.7736727077918420.4526545844163160.226327292208158
730.7652459119390390.4695081761219210.234754088060961
740.7428999812591150.514200037481770.257100018740885
750.704224111546390.5915517769072210.29577588845361
760.7539064386598080.4921871226803840.246093561340192
770.7170617669141260.5658764661717480.282938233085874
780.6957060572470930.6085878855058140.304293942752907
790.7019989070817520.5960021858364970.298001092918248
800.6631052581429860.6737894837140290.336894741857014
810.6239138376929350.752172324614130.376086162307065
820.7867498019851760.4265003960296480.213250198014824
830.7520315398264610.4959369203470770.247968460173539
840.7247301256288370.5505397487423260.275269874371163
850.685012932585660.629974134828680.31498706741434
860.6711116958472550.6577766083054910.328888304152745
870.6286382627588090.7427234744823830.371361737241191
880.5871078995707360.8257842008585280.412892100429264
890.5640636477167310.8718727045665390.435936352283269
900.5262331513577960.9475336972844080.473766848642204
910.5166095901300650.966780819739870.483390409869935
920.476084039890370.952168079780740.52391596010963
930.433218028680370.866436057360740.56678197131963
940.3880789875408210.7761579750816420.611921012459179
950.414131208570830.828262417141660.58586879142917
960.3761539031965760.7523078063931530.623846096803424
970.333975605398640.667951210797280.66602439460136
980.3264350056208240.6528700112416490.673564994379176
990.284273358000570.5685467160011390.71572664199943
1000.2498076882795310.4996153765590620.750192311720469
1010.2270075836441040.4540151672882080.772992416355896
1020.2069408422105770.4138816844211530.793059157789423
1030.249539172157470.4990783443149390.75046082784253
1040.2127262737610720.4254525475221440.787273726238928
1050.205812233853220.411624467706440.79418776614678
1060.2170214841487260.4340429682974520.782978515851274
1070.1952969269810370.3905938539620740.804703073018963
1080.1739292529005550.347858505801110.826070747099445
1090.1693812607914820.3387625215829640.830618739208518
1100.1659158416124810.3318316832249620.834084158387519
1110.1523708989595420.3047417979190850.847629101040458
1120.1314083922648470.2628167845296940.868591607735153
1130.1728677899257180.3457355798514350.827132210074282
1140.1485926353093310.2971852706186620.851407364690669
1150.168267404985250.3365348099705010.83173259501475
1160.1721740893719710.3443481787439430.827825910628029
1170.1476768276109660.2953536552219320.852323172389034
1180.1453075418861410.2906150837722820.854692458113859
1190.1351655437422990.2703310874845990.864834456257701
1200.151349185376260.302698370752520.84865081462374
1210.1251440047353240.2502880094706490.874855995264676
1220.1116541925160630.2233083850321270.888345807483937
1230.1075628649705280.2151257299410550.892437135029473
1240.08514807150659420.1702961430131880.914851928493406
1250.06662187894200040.1332437578840010.933378121058
1260.0509034631611370.1018069263222740.949096536838863
1270.03751935549989360.07503871099978720.962480644500106
1280.03830487495778920.07660974991557850.961695125042211
1290.03726502248909390.07453004497818770.962734977510906
1300.0369644058933030.0739288117866060.963035594106697
1310.04241924033199040.08483848066398080.95758075966801
1320.04382966417951740.08765932835903470.956170335820483
1330.09727011491954090.1945402298390820.902729885080459
1340.09826125097755290.1965225019551060.901738749022447
1350.07749328776236560.1549865755247310.922506712237634
1360.05966367308612580.1193273461722520.940336326913874
1370.04620191181357090.09240382362714170.953798088186429
1380.04233600402430720.08467200804861450.957663995975693
1390.04138384639884830.08276769279769670.958616153601152
1400.02885385836979620.05770771673959240.971146141630204
1410.4440387556731170.8880775113462330.555961244326884
1420.3908925952100580.7817851904201170.609107404789942
1430.3464844509721330.6929689019442660.653515549027867
1440.2752041388124330.5504082776248660.724795861187567
1450.2269140001625130.4538280003250250.773085999837487
1460.2226673520807620.4453347041615240.777332647919238
1470.3326755380714050.665351076142810.667324461928595
1480.7047239024428720.5905521951142550.295276097557128
1490.6094826169244840.7810347661510320.390517383075516
1500.4672857941824210.9345715883648420.532714205817579
1510.7059022443093520.5881955113812950.294097755690648

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.448606659253497 & 0.897213318506994 & 0.551393340746503 \tabularnewline
12 & 0.855550073645398 & 0.288899852709204 & 0.144449926354602 \tabularnewline
13 & 0.82706995851381 & 0.345860082972379 & 0.17293004148619 \tabularnewline
14 & 0.747181323602622 & 0.505637352794757 & 0.252818676397378 \tabularnewline
15 & 0.697855871004461 & 0.604288257991078 & 0.302144128995539 \tabularnewline
16 & 0.733841434245051 & 0.532317131509898 & 0.266158565754949 \tabularnewline
17 & 0.672504657828268 & 0.654990684343464 & 0.327495342171732 \tabularnewline
18 & 0.870197771119971 & 0.259604457760058 & 0.129802228880029 \tabularnewline
19 & 0.843713119012601 & 0.312573761974798 & 0.156286880987399 \tabularnewline
20 & 0.7930234334576 & 0.4139531330848 & 0.2069765665424 \tabularnewline
21 & 0.729740272602771 & 0.540519454794458 & 0.270259727397229 \tabularnewline
22 & 0.690136869404651 & 0.619726261190697 & 0.309863130595348 \tabularnewline
23 & 0.656977155972032 & 0.686045688055936 & 0.343022844027968 \tabularnewline
24 & 0.633211049926112 & 0.733577900147775 & 0.366788950073888 \tabularnewline
25 & 0.576020540553133 & 0.847958918893735 & 0.423979459446867 \tabularnewline
26 & 0.527261591819017 & 0.945476816361966 & 0.472738408180983 \tabularnewline
27 & 0.476734130216176 & 0.953468260432352 & 0.523265869783824 \tabularnewline
28 & 0.526866138610141 & 0.946267722779717 & 0.473133861389859 \tabularnewline
29 & 0.468353336739432 & 0.936706673478864 & 0.531646663260568 \tabularnewline
30 & 0.481841983415341 & 0.963683966830682 & 0.518158016584659 \tabularnewline
31 & 0.428094315430624 & 0.856188630861249 & 0.571905684569376 \tabularnewline
32 & 0.426022699229443 & 0.852045398458886 & 0.573977300770557 \tabularnewline
33 & 0.412443325302235 & 0.824886650604469 & 0.587556674697765 \tabularnewline
34 & 0.354490608964027 & 0.708981217928054 & 0.645509391035973 \tabularnewline
35 & 0.339772083873065 & 0.67954416774613 & 0.660227916126935 \tabularnewline
36 & 0.838931991453266 & 0.322136017093467 & 0.161068008546734 \tabularnewline
37 & 0.822002238875568 & 0.355995522248864 & 0.177997761124432 \tabularnewline
38 & 0.805903522894711 & 0.388192954210578 & 0.194096477105289 \tabularnewline
39 & 0.837921769131752 & 0.324156461736496 & 0.162078230868248 \tabularnewline
40 & 0.824655696169975 & 0.350688607660051 & 0.175344303830025 \tabularnewline
41 & 0.798473560392716 & 0.403052879214569 & 0.201526439607284 \tabularnewline
42 & 0.777092207352257 & 0.445815585295487 & 0.222907792647743 \tabularnewline
43 & 0.796314352414148 & 0.407371295171703 & 0.203685647585852 \tabularnewline
44 & 0.758384196617787 & 0.483231606764425 & 0.241615803382213 \tabularnewline
45 & 0.725924171604765 & 0.548151656790471 & 0.274075828395235 \tabularnewline
46 & 0.883278516653137 & 0.233442966693727 & 0.116721483346864 \tabularnewline
47 & 0.891751862474325 & 0.216496275051349 & 0.108248137525675 \tabularnewline
48 & 0.870667029831765 & 0.258665940336471 & 0.129332970168235 \tabularnewline
49 & 0.856171984974467 & 0.287656030051066 & 0.143828015025533 \tabularnewline
50 & 0.849335162848114 & 0.301329674303773 & 0.150664837151886 \tabularnewline
51 & 0.821123627425526 & 0.357752745148948 & 0.178876372574474 \tabularnewline
52 & 0.789481401438124 & 0.421037197123752 & 0.210518598561876 \tabularnewline
53 & 0.804784497295532 & 0.390431005408935 & 0.195215502704467 \tabularnewline
54 & 0.778274502793312 & 0.443450994413375 & 0.221725497206687 \tabularnewline
55 & 0.798781239387773 & 0.402437521224455 & 0.201218760612227 \tabularnewline
56 & 0.789151974440204 & 0.421696051119593 & 0.210848025559796 \tabularnewline
57 & 0.753683169620698 & 0.492633660758605 & 0.246316830379302 \tabularnewline
58 & 0.742065411701263 & 0.515869176597473 & 0.257934588298737 \tabularnewline
59 & 0.707248685239667 & 0.585502629520667 & 0.292751314760333 \tabularnewline
60 & 0.71447751641547 & 0.571044967169059 & 0.28552248358453 \tabularnewline
61 & 0.67989394094172 & 0.640212118116561 & 0.32010605905828 \tabularnewline
62 & 0.63900355102211 & 0.72199289795578 & 0.36099644897789 \tabularnewline
63 & 0.599311315525414 & 0.801377368949171 & 0.400688684474586 \tabularnewline
64 & 0.55585460222584 & 0.88829079554832 & 0.44414539777416 \tabularnewline
65 & 0.517942446756185 & 0.96411510648763 & 0.482057553243815 \tabularnewline
66 & 0.492671180139351 & 0.985342360278702 & 0.507328819860649 \tabularnewline
67 & 0.48729993364096 & 0.97459986728192 & 0.51270006635904 \tabularnewline
68 & 0.60662033247814 & 0.78675933504372 & 0.39337966752186 \tabularnewline
69 & 0.733028751511164 & 0.533942496977673 & 0.266971248488836 \tabularnewline
70 & 0.69928488252065 & 0.601430234958701 & 0.30071511747935 \tabularnewline
71 & 0.803172448339767 & 0.393655103320465 & 0.196827551660233 \tabularnewline
72 & 0.773672707791842 & 0.452654584416316 & 0.226327292208158 \tabularnewline
73 & 0.765245911939039 & 0.469508176121921 & 0.234754088060961 \tabularnewline
74 & 0.742899981259115 & 0.51420003748177 & 0.257100018740885 \tabularnewline
75 & 0.70422411154639 & 0.591551776907221 & 0.29577588845361 \tabularnewline
76 & 0.753906438659808 & 0.492187122680384 & 0.246093561340192 \tabularnewline
77 & 0.717061766914126 & 0.565876466171748 & 0.282938233085874 \tabularnewline
78 & 0.695706057247093 & 0.608587885505814 & 0.304293942752907 \tabularnewline
79 & 0.701998907081752 & 0.596002185836497 & 0.298001092918248 \tabularnewline
80 & 0.663105258142986 & 0.673789483714029 & 0.336894741857014 \tabularnewline
81 & 0.623913837692935 & 0.75217232461413 & 0.376086162307065 \tabularnewline
82 & 0.786749801985176 & 0.426500396029648 & 0.213250198014824 \tabularnewline
83 & 0.752031539826461 & 0.495936920347077 & 0.247968460173539 \tabularnewline
84 & 0.724730125628837 & 0.550539748742326 & 0.275269874371163 \tabularnewline
85 & 0.68501293258566 & 0.62997413482868 & 0.31498706741434 \tabularnewline
86 & 0.671111695847255 & 0.657776608305491 & 0.328888304152745 \tabularnewline
87 & 0.628638262758809 & 0.742723474482383 & 0.371361737241191 \tabularnewline
88 & 0.587107899570736 & 0.825784200858528 & 0.412892100429264 \tabularnewline
89 & 0.564063647716731 & 0.871872704566539 & 0.435936352283269 \tabularnewline
90 & 0.526233151357796 & 0.947533697284408 & 0.473766848642204 \tabularnewline
91 & 0.516609590130065 & 0.96678081973987 & 0.483390409869935 \tabularnewline
92 & 0.47608403989037 & 0.95216807978074 & 0.52391596010963 \tabularnewline
93 & 0.43321802868037 & 0.86643605736074 & 0.56678197131963 \tabularnewline
94 & 0.388078987540821 & 0.776157975081642 & 0.611921012459179 \tabularnewline
95 & 0.41413120857083 & 0.82826241714166 & 0.58586879142917 \tabularnewline
96 & 0.376153903196576 & 0.752307806393153 & 0.623846096803424 \tabularnewline
97 & 0.33397560539864 & 0.66795121079728 & 0.66602439460136 \tabularnewline
98 & 0.326435005620824 & 0.652870011241649 & 0.673564994379176 \tabularnewline
99 & 0.28427335800057 & 0.568546716001139 & 0.71572664199943 \tabularnewline
100 & 0.249807688279531 & 0.499615376559062 & 0.750192311720469 \tabularnewline
101 & 0.227007583644104 & 0.454015167288208 & 0.772992416355896 \tabularnewline
102 & 0.206940842210577 & 0.413881684421153 & 0.793059157789423 \tabularnewline
103 & 0.24953917215747 & 0.499078344314939 & 0.75046082784253 \tabularnewline
104 & 0.212726273761072 & 0.425452547522144 & 0.787273726238928 \tabularnewline
105 & 0.20581223385322 & 0.41162446770644 & 0.79418776614678 \tabularnewline
106 & 0.217021484148726 & 0.434042968297452 & 0.782978515851274 \tabularnewline
107 & 0.195296926981037 & 0.390593853962074 & 0.804703073018963 \tabularnewline
108 & 0.173929252900555 & 0.34785850580111 & 0.826070747099445 \tabularnewline
109 & 0.169381260791482 & 0.338762521582964 & 0.830618739208518 \tabularnewline
110 & 0.165915841612481 & 0.331831683224962 & 0.834084158387519 \tabularnewline
111 & 0.152370898959542 & 0.304741797919085 & 0.847629101040458 \tabularnewline
112 & 0.131408392264847 & 0.262816784529694 & 0.868591607735153 \tabularnewline
113 & 0.172867789925718 & 0.345735579851435 & 0.827132210074282 \tabularnewline
114 & 0.148592635309331 & 0.297185270618662 & 0.851407364690669 \tabularnewline
115 & 0.16826740498525 & 0.336534809970501 & 0.83173259501475 \tabularnewline
116 & 0.172174089371971 & 0.344348178743943 & 0.827825910628029 \tabularnewline
117 & 0.147676827610966 & 0.295353655221932 & 0.852323172389034 \tabularnewline
118 & 0.145307541886141 & 0.290615083772282 & 0.854692458113859 \tabularnewline
119 & 0.135165543742299 & 0.270331087484599 & 0.864834456257701 \tabularnewline
120 & 0.15134918537626 & 0.30269837075252 & 0.84865081462374 \tabularnewline
121 & 0.125144004735324 & 0.250288009470649 & 0.874855995264676 \tabularnewline
122 & 0.111654192516063 & 0.223308385032127 & 0.888345807483937 \tabularnewline
123 & 0.107562864970528 & 0.215125729941055 & 0.892437135029473 \tabularnewline
124 & 0.0851480715065942 & 0.170296143013188 & 0.914851928493406 \tabularnewline
125 & 0.0666218789420004 & 0.133243757884001 & 0.933378121058 \tabularnewline
126 & 0.050903463161137 & 0.101806926322274 & 0.949096536838863 \tabularnewline
127 & 0.0375193554998936 & 0.0750387109997872 & 0.962480644500106 \tabularnewline
128 & 0.0383048749577892 & 0.0766097499155785 & 0.961695125042211 \tabularnewline
129 & 0.0372650224890939 & 0.0745300449781877 & 0.962734977510906 \tabularnewline
130 & 0.036964405893303 & 0.073928811786606 & 0.963035594106697 \tabularnewline
131 & 0.0424192403319904 & 0.0848384806639808 & 0.95758075966801 \tabularnewline
132 & 0.0438296641795174 & 0.0876593283590347 & 0.956170335820483 \tabularnewline
133 & 0.0972701149195409 & 0.194540229839082 & 0.902729885080459 \tabularnewline
134 & 0.0982612509775529 & 0.196522501955106 & 0.901738749022447 \tabularnewline
135 & 0.0774932877623656 & 0.154986575524731 & 0.922506712237634 \tabularnewline
136 & 0.0596636730861258 & 0.119327346172252 & 0.940336326913874 \tabularnewline
137 & 0.0462019118135709 & 0.0924038236271417 & 0.953798088186429 \tabularnewline
138 & 0.0423360040243072 & 0.0846720080486145 & 0.957663995975693 \tabularnewline
139 & 0.0413838463988483 & 0.0827676927976967 & 0.958616153601152 \tabularnewline
140 & 0.0288538583697962 & 0.0577077167395924 & 0.971146141630204 \tabularnewline
141 & 0.444038755673117 & 0.888077511346233 & 0.555961244326884 \tabularnewline
142 & 0.390892595210058 & 0.781785190420117 & 0.609107404789942 \tabularnewline
143 & 0.346484450972133 & 0.692968901944266 & 0.653515549027867 \tabularnewline
144 & 0.275204138812433 & 0.550408277624866 & 0.724795861187567 \tabularnewline
145 & 0.226914000162513 & 0.453828000325025 & 0.773085999837487 \tabularnewline
146 & 0.222667352080762 & 0.445334704161524 & 0.777332647919238 \tabularnewline
147 & 0.332675538071405 & 0.66535107614281 & 0.667324461928595 \tabularnewline
148 & 0.704723902442872 & 0.590552195114255 & 0.295276097557128 \tabularnewline
149 & 0.609482616924484 & 0.781034766151032 & 0.390517383075516 \tabularnewline
150 & 0.467285794182421 & 0.934571588364842 & 0.532714205817579 \tabularnewline
151 & 0.705902244309352 & 0.588195511381295 & 0.294097755690648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.448606659253497[/C][C]0.897213318506994[/C][C]0.551393340746503[/C][/ROW]
[ROW][C]12[/C][C]0.855550073645398[/C][C]0.288899852709204[/C][C]0.144449926354602[/C][/ROW]
[ROW][C]13[/C][C]0.82706995851381[/C][C]0.345860082972379[/C][C]0.17293004148619[/C][/ROW]
[ROW][C]14[/C][C]0.747181323602622[/C][C]0.505637352794757[/C][C]0.252818676397378[/C][/ROW]
[ROW][C]15[/C][C]0.697855871004461[/C][C]0.604288257991078[/C][C]0.302144128995539[/C][/ROW]
[ROW][C]16[/C][C]0.733841434245051[/C][C]0.532317131509898[/C][C]0.266158565754949[/C][/ROW]
[ROW][C]17[/C][C]0.672504657828268[/C][C]0.654990684343464[/C][C]0.327495342171732[/C][/ROW]
[ROW][C]18[/C][C]0.870197771119971[/C][C]0.259604457760058[/C][C]0.129802228880029[/C][/ROW]
[ROW][C]19[/C][C]0.843713119012601[/C][C]0.312573761974798[/C][C]0.156286880987399[/C][/ROW]
[ROW][C]20[/C][C]0.7930234334576[/C][C]0.4139531330848[/C][C]0.2069765665424[/C][/ROW]
[ROW][C]21[/C][C]0.729740272602771[/C][C]0.540519454794458[/C][C]0.270259727397229[/C][/ROW]
[ROW][C]22[/C][C]0.690136869404651[/C][C]0.619726261190697[/C][C]0.309863130595348[/C][/ROW]
[ROW][C]23[/C][C]0.656977155972032[/C][C]0.686045688055936[/C][C]0.343022844027968[/C][/ROW]
[ROW][C]24[/C][C]0.633211049926112[/C][C]0.733577900147775[/C][C]0.366788950073888[/C][/ROW]
[ROW][C]25[/C][C]0.576020540553133[/C][C]0.847958918893735[/C][C]0.423979459446867[/C][/ROW]
[ROW][C]26[/C][C]0.527261591819017[/C][C]0.945476816361966[/C][C]0.472738408180983[/C][/ROW]
[ROW][C]27[/C][C]0.476734130216176[/C][C]0.953468260432352[/C][C]0.523265869783824[/C][/ROW]
[ROW][C]28[/C][C]0.526866138610141[/C][C]0.946267722779717[/C][C]0.473133861389859[/C][/ROW]
[ROW][C]29[/C][C]0.468353336739432[/C][C]0.936706673478864[/C][C]0.531646663260568[/C][/ROW]
[ROW][C]30[/C][C]0.481841983415341[/C][C]0.963683966830682[/C][C]0.518158016584659[/C][/ROW]
[ROW][C]31[/C][C]0.428094315430624[/C][C]0.856188630861249[/C][C]0.571905684569376[/C][/ROW]
[ROW][C]32[/C][C]0.426022699229443[/C][C]0.852045398458886[/C][C]0.573977300770557[/C][/ROW]
[ROW][C]33[/C][C]0.412443325302235[/C][C]0.824886650604469[/C][C]0.587556674697765[/C][/ROW]
[ROW][C]34[/C][C]0.354490608964027[/C][C]0.708981217928054[/C][C]0.645509391035973[/C][/ROW]
[ROW][C]35[/C][C]0.339772083873065[/C][C]0.67954416774613[/C][C]0.660227916126935[/C][/ROW]
[ROW][C]36[/C][C]0.838931991453266[/C][C]0.322136017093467[/C][C]0.161068008546734[/C][/ROW]
[ROW][C]37[/C][C]0.822002238875568[/C][C]0.355995522248864[/C][C]0.177997761124432[/C][/ROW]
[ROW][C]38[/C][C]0.805903522894711[/C][C]0.388192954210578[/C][C]0.194096477105289[/C][/ROW]
[ROW][C]39[/C][C]0.837921769131752[/C][C]0.324156461736496[/C][C]0.162078230868248[/C][/ROW]
[ROW][C]40[/C][C]0.824655696169975[/C][C]0.350688607660051[/C][C]0.175344303830025[/C][/ROW]
[ROW][C]41[/C][C]0.798473560392716[/C][C]0.403052879214569[/C][C]0.201526439607284[/C][/ROW]
[ROW][C]42[/C][C]0.777092207352257[/C][C]0.445815585295487[/C][C]0.222907792647743[/C][/ROW]
[ROW][C]43[/C][C]0.796314352414148[/C][C]0.407371295171703[/C][C]0.203685647585852[/C][/ROW]
[ROW][C]44[/C][C]0.758384196617787[/C][C]0.483231606764425[/C][C]0.241615803382213[/C][/ROW]
[ROW][C]45[/C][C]0.725924171604765[/C][C]0.548151656790471[/C][C]0.274075828395235[/C][/ROW]
[ROW][C]46[/C][C]0.883278516653137[/C][C]0.233442966693727[/C][C]0.116721483346864[/C][/ROW]
[ROW][C]47[/C][C]0.891751862474325[/C][C]0.216496275051349[/C][C]0.108248137525675[/C][/ROW]
[ROW][C]48[/C][C]0.870667029831765[/C][C]0.258665940336471[/C][C]0.129332970168235[/C][/ROW]
[ROW][C]49[/C][C]0.856171984974467[/C][C]0.287656030051066[/C][C]0.143828015025533[/C][/ROW]
[ROW][C]50[/C][C]0.849335162848114[/C][C]0.301329674303773[/C][C]0.150664837151886[/C][/ROW]
[ROW][C]51[/C][C]0.821123627425526[/C][C]0.357752745148948[/C][C]0.178876372574474[/C][/ROW]
[ROW][C]52[/C][C]0.789481401438124[/C][C]0.421037197123752[/C][C]0.210518598561876[/C][/ROW]
[ROW][C]53[/C][C]0.804784497295532[/C][C]0.390431005408935[/C][C]0.195215502704467[/C][/ROW]
[ROW][C]54[/C][C]0.778274502793312[/C][C]0.443450994413375[/C][C]0.221725497206687[/C][/ROW]
[ROW][C]55[/C][C]0.798781239387773[/C][C]0.402437521224455[/C][C]0.201218760612227[/C][/ROW]
[ROW][C]56[/C][C]0.789151974440204[/C][C]0.421696051119593[/C][C]0.210848025559796[/C][/ROW]
[ROW][C]57[/C][C]0.753683169620698[/C][C]0.492633660758605[/C][C]0.246316830379302[/C][/ROW]
[ROW][C]58[/C][C]0.742065411701263[/C][C]0.515869176597473[/C][C]0.257934588298737[/C][/ROW]
[ROW][C]59[/C][C]0.707248685239667[/C][C]0.585502629520667[/C][C]0.292751314760333[/C][/ROW]
[ROW][C]60[/C][C]0.71447751641547[/C][C]0.571044967169059[/C][C]0.28552248358453[/C][/ROW]
[ROW][C]61[/C][C]0.67989394094172[/C][C]0.640212118116561[/C][C]0.32010605905828[/C][/ROW]
[ROW][C]62[/C][C]0.63900355102211[/C][C]0.72199289795578[/C][C]0.36099644897789[/C][/ROW]
[ROW][C]63[/C][C]0.599311315525414[/C][C]0.801377368949171[/C][C]0.400688684474586[/C][/ROW]
[ROW][C]64[/C][C]0.55585460222584[/C][C]0.88829079554832[/C][C]0.44414539777416[/C][/ROW]
[ROW][C]65[/C][C]0.517942446756185[/C][C]0.96411510648763[/C][C]0.482057553243815[/C][/ROW]
[ROW][C]66[/C][C]0.492671180139351[/C][C]0.985342360278702[/C][C]0.507328819860649[/C][/ROW]
[ROW][C]67[/C][C]0.48729993364096[/C][C]0.97459986728192[/C][C]0.51270006635904[/C][/ROW]
[ROW][C]68[/C][C]0.60662033247814[/C][C]0.78675933504372[/C][C]0.39337966752186[/C][/ROW]
[ROW][C]69[/C][C]0.733028751511164[/C][C]0.533942496977673[/C][C]0.266971248488836[/C][/ROW]
[ROW][C]70[/C][C]0.69928488252065[/C][C]0.601430234958701[/C][C]0.30071511747935[/C][/ROW]
[ROW][C]71[/C][C]0.803172448339767[/C][C]0.393655103320465[/C][C]0.196827551660233[/C][/ROW]
[ROW][C]72[/C][C]0.773672707791842[/C][C]0.452654584416316[/C][C]0.226327292208158[/C][/ROW]
[ROW][C]73[/C][C]0.765245911939039[/C][C]0.469508176121921[/C][C]0.234754088060961[/C][/ROW]
[ROW][C]74[/C][C]0.742899981259115[/C][C]0.51420003748177[/C][C]0.257100018740885[/C][/ROW]
[ROW][C]75[/C][C]0.70422411154639[/C][C]0.591551776907221[/C][C]0.29577588845361[/C][/ROW]
[ROW][C]76[/C][C]0.753906438659808[/C][C]0.492187122680384[/C][C]0.246093561340192[/C][/ROW]
[ROW][C]77[/C][C]0.717061766914126[/C][C]0.565876466171748[/C][C]0.282938233085874[/C][/ROW]
[ROW][C]78[/C][C]0.695706057247093[/C][C]0.608587885505814[/C][C]0.304293942752907[/C][/ROW]
[ROW][C]79[/C][C]0.701998907081752[/C][C]0.596002185836497[/C][C]0.298001092918248[/C][/ROW]
[ROW][C]80[/C][C]0.663105258142986[/C][C]0.673789483714029[/C][C]0.336894741857014[/C][/ROW]
[ROW][C]81[/C][C]0.623913837692935[/C][C]0.75217232461413[/C][C]0.376086162307065[/C][/ROW]
[ROW][C]82[/C][C]0.786749801985176[/C][C]0.426500396029648[/C][C]0.213250198014824[/C][/ROW]
[ROW][C]83[/C][C]0.752031539826461[/C][C]0.495936920347077[/C][C]0.247968460173539[/C][/ROW]
[ROW][C]84[/C][C]0.724730125628837[/C][C]0.550539748742326[/C][C]0.275269874371163[/C][/ROW]
[ROW][C]85[/C][C]0.68501293258566[/C][C]0.62997413482868[/C][C]0.31498706741434[/C][/ROW]
[ROW][C]86[/C][C]0.671111695847255[/C][C]0.657776608305491[/C][C]0.328888304152745[/C][/ROW]
[ROW][C]87[/C][C]0.628638262758809[/C][C]0.742723474482383[/C][C]0.371361737241191[/C][/ROW]
[ROW][C]88[/C][C]0.587107899570736[/C][C]0.825784200858528[/C][C]0.412892100429264[/C][/ROW]
[ROW][C]89[/C][C]0.564063647716731[/C][C]0.871872704566539[/C][C]0.435936352283269[/C][/ROW]
[ROW][C]90[/C][C]0.526233151357796[/C][C]0.947533697284408[/C][C]0.473766848642204[/C][/ROW]
[ROW][C]91[/C][C]0.516609590130065[/C][C]0.96678081973987[/C][C]0.483390409869935[/C][/ROW]
[ROW][C]92[/C][C]0.47608403989037[/C][C]0.95216807978074[/C][C]0.52391596010963[/C][/ROW]
[ROW][C]93[/C][C]0.43321802868037[/C][C]0.86643605736074[/C][C]0.56678197131963[/C][/ROW]
[ROW][C]94[/C][C]0.388078987540821[/C][C]0.776157975081642[/C][C]0.611921012459179[/C][/ROW]
[ROW][C]95[/C][C]0.41413120857083[/C][C]0.82826241714166[/C][C]0.58586879142917[/C][/ROW]
[ROW][C]96[/C][C]0.376153903196576[/C][C]0.752307806393153[/C][C]0.623846096803424[/C][/ROW]
[ROW][C]97[/C][C]0.33397560539864[/C][C]0.66795121079728[/C][C]0.66602439460136[/C][/ROW]
[ROW][C]98[/C][C]0.326435005620824[/C][C]0.652870011241649[/C][C]0.673564994379176[/C][/ROW]
[ROW][C]99[/C][C]0.28427335800057[/C][C]0.568546716001139[/C][C]0.71572664199943[/C][/ROW]
[ROW][C]100[/C][C]0.249807688279531[/C][C]0.499615376559062[/C][C]0.750192311720469[/C][/ROW]
[ROW][C]101[/C][C]0.227007583644104[/C][C]0.454015167288208[/C][C]0.772992416355896[/C][/ROW]
[ROW][C]102[/C][C]0.206940842210577[/C][C]0.413881684421153[/C][C]0.793059157789423[/C][/ROW]
[ROW][C]103[/C][C]0.24953917215747[/C][C]0.499078344314939[/C][C]0.75046082784253[/C][/ROW]
[ROW][C]104[/C][C]0.212726273761072[/C][C]0.425452547522144[/C][C]0.787273726238928[/C][/ROW]
[ROW][C]105[/C][C]0.20581223385322[/C][C]0.41162446770644[/C][C]0.79418776614678[/C][/ROW]
[ROW][C]106[/C][C]0.217021484148726[/C][C]0.434042968297452[/C][C]0.782978515851274[/C][/ROW]
[ROW][C]107[/C][C]0.195296926981037[/C][C]0.390593853962074[/C][C]0.804703073018963[/C][/ROW]
[ROW][C]108[/C][C]0.173929252900555[/C][C]0.34785850580111[/C][C]0.826070747099445[/C][/ROW]
[ROW][C]109[/C][C]0.169381260791482[/C][C]0.338762521582964[/C][C]0.830618739208518[/C][/ROW]
[ROW][C]110[/C][C]0.165915841612481[/C][C]0.331831683224962[/C][C]0.834084158387519[/C][/ROW]
[ROW][C]111[/C][C]0.152370898959542[/C][C]0.304741797919085[/C][C]0.847629101040458[/C][/ROW]
[ROW][C]112[/C][C]0.131408392264847[/C][C]0.262816784529694[/C][C]0.868591607735153[/C][/ROW]
[ROW][C]113[/C][C]0.172867789925718[/C][C]0.345735579851435[/C][C]0.827132210074282[/C][/ROW]
[ROW][C]114[/C][C]0.148592635309331[/C][C]0.297185270618662[/C][C]0.851407364690669[/C][/ROW]
[ROW][C]115[/C][C]0.16826740498525[/C][C]0.336534809970501[/C][C]0.83173259501475[/C][/ROW]
[ROW][C]116[/C][C]0.172174089371971[/C][C]0.344348178743943[/C][C]0.827825910628029[/C][/ROW]
[ROW][C]117[/C][C]0.147676827610966[/C][C]0.295353655221932[/C][C]0.852323172389034[/C][/ROW]
[ROW][C]118[/C][C]0.145307541886141[/C][C]0.290615083772282[/C][C]0.854692458113859[/C][/ROW]
[ROW][C]119[/C][C]0.135165543742299[/C][C]0.270331087484599[/C][C]0.864834456257701[/C][/ROW]
[ROW][C]120[/C][C]0.15134918537626[/C][C]0.30269837075252[/C][C]0.84865081462374[/C][/ROW]
[ROW][C]121[/C][C]0.125144004735324[/C][C]0.250288009470649[/C][C]0.874855995264676[/C][/ROW]
[ROW][C]122[/C][C]0.111654192516063[/C][C]0.223308385032127[/C][C]0.888345807483937[/C][/ROW]
[ROW][C]123[/C][C]0.107562864970528[/C][C]0.215125729941055[/C][C]0.892437135029473[/C][/ROW]
[ROW][C]124[/C][C]0.0851480715065942[/C][C]0.170296143013188[/C][C]0.914851928493406[/C][/ROW]
[ROW][C]125[/C][C]0.0666218789420004[/C][C]0.133243757884001[/C][C]0.933378121058[/C][/ROW]
[ROW][C]126[/C][C]0.050903463161137[/C][C]0.101806926322274[/C][C]0.949096536838863[/C][/ROW]
[ROW][C]127[/C][C]0.0375193554998936[/C][C]0.0750387109997872[/C][C]0.962480644500106[/C][/ROW]
[ROW][C]128[/C][C]0.0383048749577892[/C][C]0.0766097499155785[/C][C]0.961695125042211[/C][/ROW]
[ROW][C]129[/C][C]0.0372650224890939[/C][C]0.0745300449781877[/C][C]0.962734977510906[/C][/ROW]
[ROW][C]130[/C][C]0.036964405893303[/C][C]0.073928811786606[/C][C]0.963035594106697[/C][/ROW]
[ROW][C]131[/C][C]0.0424192403319904[/C][C]0.0848384806639808[/C][C]0.95758075966801[/C][/ROW]
[ROW][C]132[/C][C]0.0438296641795174[/C][C]0.0876593283590347[/C][C]0.956170335820483[/C][/ROW]
[ROW][C]133[/C][C]0.0972701149195409[/C][C]0.194540229839082[/C][C]0.902729885080459[/C][/ROW]
[ROW][C]134[/C][C]0.0982612509775529[/C][C]0.196522501955106[/C][C]0.901738749022447[/C][/ROW]
[ROW][C]135[/C][C]0.0774932877623656[/C][C]0.154986575524731[/C][C]0.922506712237634[/C][/ROW]
[ROW][C]136[/C][C]0.0596636730861258[/C][C]0.119327346172252[/C][C]0.940336326913874[/C][/ROW]
[ROW][C]137[/C][C]0.0462019118135709[/C][C]0.0924038236271417[/C][C]0.953798088186429[/C][/ROW]
[ROW][C]138[/C][C]0.0423360040243072[/C][C]0.0846720080486145[/C][C]0.957663995975693[/C][/ROW]
[ROW][C]139[/C][C]0.0413838463988483[/C][C]0.0827676927976967[/C][C]0.958616153601152[/C][/ROW]
[ROW][C]140[/C][C]0.0288538583697962[/C][C]0.0577077167395924[/C][C]0.971146141630204[/C][/ROW]
[ROW][C]141[/C][C]0.444038755673117[/C][C]0.888077511346233[/C][C]0.555961244326884[/C][/ROW]
[ROW][C]142[/C][C]0.390892595210058[/C][C]0.781785190420117[/C][C]0.609107404789942[/C][/ROW]
[ROW][C]143[/C][C]0.346484450972133[/C][C]0.692968901944266[/C][C]0.653515549027867[/C][/ROW]
[ROW][C]144[/C][C]0.275204138812433[/C][C]0.550408277624866[/C][C]0.724795861187567[/C][/ROW]
[ROW][C]145[/C][C]0.226914000162513[/C][C]0.453828000325025[/C][C]0.773085999837487[/C][/ROW]
[ROW][C]146[/C][C]0.222667352080762[/C][C]0.445334704161524[/C][C]0.777332647919238[/C][/ROW]
[ROW][C]147[/C][C]0.332675538071405[/C][C]0.66535107614281[/C][C]0.667324461928595[/C][/ROW]
[ROW][C]148[/C][C]0.704723902442872[/C][C]0.590552195114255[/C][C]0.295276097557128[/C][/ROW]
[ROW][C]149[/C][C]0.609482616924484[/C][C]0.781034766151032[/C][C]0.390517383075516[/C][/ROW]
[ROW][C]150[/C][C]0.467285794182421[/C][C]0.934571588364842[/C][C]0.532714205817579[/C][/ROW]
[ROW][C]151[/C][C]0.705902244309352[/C][C]0.588195511381295[/C][C]0.294097755690648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4486066592534970.8972133185069940.551393340746503
120.8555500736453980.2888998527092040.144449926354602
130.827069958513810.3458600829723790.17293004148619
140.7471813236026220.5056373527947570.252818676397378
150.6978558710044610.6042882579910780.302144128995539
160.7338414342450510.5323171315098980.266158565754949
170.6725046578282680.6549906843434640.327495342171732
180.8701977711199710.2596044577600580.129802228880029
190.8437131190126010.3125737619747980.156286880987399
200.79302343345760.41395313308480.2069765665424
210.7297402726027710.5405194547944580.270259727397229
220.6901368694046510.6197262611906970.309863130595348
230.6569771559720320.6860456880559360.343022844027968
240.6332110499261120.7335779001477750.366788950073888
250.5760205405531330.8479589188937350.423979459446867
260.5272615918190170.9454768163619660.472738408180983
270.4767341302161760.9534682604323520.523265869783824
280.5268661386101410.9462677227797170.473133861389859
290.4683533367394320.9367066734788640.531646663260568
300.4818419834153410.9636839668306820.518158016584659
310.4280943154306240.8561886308612490.571905684569376
320.4260226992294430.8520453984588860.573977300770557
330.4124433253022350.8248866506044690.587556674697765
340.3544906089640270.7089812179280540.645509391035973
350.3397720838730650.679544167746130.660227916126935
360.8389319914532660.3221360170934670.161068008546734
370.8220022388755680.3559955222488640.177997761124432
380.8059035228947110.3881929542105780.194096477105289
390.8379217691317520.3241564617364960.162078230868248
400.8246556961699750.3506886076600510.175344303830025
410.7984735603927160.4030528792145690.201526439607284
420.7770922073522570.4458155852954870.222907792647743
430.7963143524141480.4073712951717030.203685647585852
440.7583841966177870.4832316067644250.241615803382213
450.7259241716047650.5481516567904710.274075828395235
460.8832785166531370.2334429666937270.116721483346864
470.8917518624743250.2164962750513490.108248137525675
480.8706670298317650.2586659403364710.129332970168235
490.8561719849744670.2876560300510660.143828015025533
500.8493351628481140.3013296743037730.150664837151886
510.8211236274255260.3577527451489480.178876372574474
520.7894814014381240.4210371971237520.210518598561876
530.8047844972955320.3904310054089350.195215502704467
540.7782745027933120.4434509944133750.221725497206687
550.7987812393877730.4024375212244550.201218760612227
560.7891519744402040.4216960511195930.210848025559796
570.7536831696206980.4926336607586050.246316830379302
580.7420654117012630.5158691765974730.257934588298737
590.7072486852396670.5855026295206670.292751314760333
600.714477516415470.5710449671690590.28552248358453
610.679893940941720.6402121181165610.32010605905828
620.639003551022110.721992897955780.36099644897789
630.5993113155254140.8013773689491710.400688684474586
640.555854602225840.888290795548320.44414539777416
650.5179424467561850.964115106487630.482057553243815
660.4926711801393510.9853423602787020.507328819860649
670.487299933640960.974599867281920.51270006635904
680.606620332478140.786759335043720.39337966752186
690.7330287515111640.5339424969776730.266971248488836
700.699284882520650.6014302349587010.30071511747935
710.8031724483397670.3936551033204650.196827551660233
720.7736727077918420.4526545844163160.226327292208158
730.7652459119390390.4695081761219210.234754088060961
740.7428999812591150.514200037481770.257100018740885
750.704224111546390.5915517769072210.29577588845361
760.7539064386598080.4921871226803840.246093561340192
770.7170617669141260.5658764661717480.282938233085874
780.6957060572470930.6085878855058140.304293942752907
790.7019989070817520.5960021858364970.298001092918248
800.6631052581429860.6737894837140290.336894741857014
810.6239138376929350.752172324614130.376086162307065
820.7867498019851760.4265003960296480.213250198014824
830.7520315398264610.4959369203470770.247968460173539
840.7247301256288370.5505397487423260.275269874371163
850.685012932585660.629974134828680.31498706741434
860.6711116958472550.6577766083054910.328888304152745
870.6286382627588090.7427234744823830.371361737241191
880.5871078995707360.8257842008585280.412892100429264
890.5640636477167310.8718727045665390.435936352283269
900.5262331513577960.9475336972844080.473766848642204
910.5166095901300650.966780819739870.483390409869935
920.476084039890370.952168079780740.52391596010963
930.433218028680370.866436057360740.56678197131963
940.3880789875408210.7761579750816420.611921012459179
950.414131208570830.828262417141660.58586879142917
960.3761539031965760.7523078063931530.623846096803424
970.333975605398640.667951210797280.66602439460136
980.3264350056208240.6528700112416490.673564994379176
990.284273358000570.5685467160011390.71572664199943
1000.2498076882795310.4996153765590620.750192311720469
1010.2270075836441040.4540151672882080.772992416355896
1020.2069408422105770.4138816844211530.793059157789423
1030.249539172157470.4990783443149390.75046082784253
1040.2127262737610720.4254525475221440.787273726238928
1050.205812233853220.411624467706440.79418776614678
1060.2170214841487260.4340429682974520.782978515851274
1070.1952969269810370.3905938539620740.804703073018963
1080.1739292529005550.347858505801110.826070747099445
1090.1693812607914820.3387625215829640.830618739208518
1100.1659158416124810.3318316832249620.834084158387519
1110.1523708989595420.3047417979190850.847629101040458
1120.1314083922648470.2628167845296940.868591607735153
1130.1728677899257180.3457355798514350.827132210074282
1140.1485926353093310.2971852706186620.851407364690669
1150.168267404985250.3365348099705010.83173259501475
1160.1721740893719710.3443481787439430.827825910628029
1170.1476768276109660.2953536552219320.852323172389034
1180.1453075418861410.2906150837722820.854692458113859
1190.1351655437422990.2703310874845990.864834456257701
1200.151349185376260.302698370752520.84865081462374
1210.1251440047353240.2502880094706490.874855995264676
1220.1116541925160630.2233083850321270.888345807483937
1230.1075628649705280.2151257299410550.892437135029473
1240.08514807150659420.1702961430131880.914851928493406
1250.06662187894200040.1332437578840010.933378121058
1260.0509034631611370.1018069263222740.949096536838863
1270.03751935549989360.07503871099978720.962480644500106
1280.03830487495778920.07660974991557850.961695125042211
1290.03726502248909390.07453004497818770.962734977510906
1300.0369644058933030.0739288117866060.963035594106697
1310.04241924033199040.08483848066398080.95758075966801
1320.04382966417951740.08765932835903470.956170335820483
1330.09727011491954090.1945402298390820.902729885080459
1340.09826125097755290.1965225019551060.901738749022447
1350.07749328776236560.1549865755247310.922506712237634
1360.05966367308612580.1193273461722520.940336326913874
1370.04620191181357090.09240382362714170.953798088186429
1380.04233600402430720.08467200804861450.957663995975693
1390.04138384639884830.08276769279769670.958616153601152
1400.02885385836979620.05770771673959240.971146141630204
1410.4440387556731170.8880775113462330.555961244326884
1420.3908925952100580.7817851904201170.609107404789942
1430.3464844509721330.6929689019442660.653515549027867
1440.2752041388124330.5504082776248660.724795861187567
1450.2269140001625130.4538280003250250.773085999837487
1460.2226673520807620.4453347041615240.777332647919238
1470.3326755380714050.665351076142810.667324461928595
1480.7047239024428720.5905521951142550.295276097557128
1490.6094826169244840.7810347661510320.390517383075516
1500.4672857941824210.9345715883648420.532714205817579
1510.7059022443093520.5881955113812950.294097755690648







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 level100.0709219858156028OK

\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 & 10 & 0.0709219858156028 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186291&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]10[/C][C]0.0709219858156028[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186291&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186291&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 level100.0709219858156028OK



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')
}