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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Oct 2012 17:07:05 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/30/t1351631397f1myz390di3imkp.htm/, Retrieved Sat, 04 May 2024 04:06:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185351, Retrieved Sat, 04 May 2024 04:06:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [sfdsfds] [2012-10-30 21:07:05] [5821104a6123eb5bf529ba8614395dc8] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
41	38	13	12	14	12	53	32	9	1
39	32	16	11	18	11	86	51	9	2
30	35	19	15	11	14	66	42	9	3
31	33	15	6	12	12	67	41	9	4
34	37	14	13	16	21	76	46	9	5
35	29	13	10	18	12	78	47	9	6
39	31	19	12	14	22	53	37	9	7
34	36	15	14	14	11	80	49	9	8
36	35	14	12	15	10	74	45	9	9
37	38	15	6	15	13	76	47	9	10
38	31	16	10	17	10	79	49	9	11
36	34	16	12	19	8	54	33	9	12
38	35	16	12	10	15	67	42	9	13
39	38	16	11	16	14	54	33	9	14
33	37	17	15	18	10	87	53	9	15
32	33	15	12	14	14	58	36	9	16
36	32	15	10	14	14	75	45	9	17
38	38	20	12	17	11	88	54	9	18
39	38	18	11	14	10	64	41	9	19
32	32	16	12	16	13	57	36	9	20
32	33	16	11	18	7	66	41	9	21
31	31	16	12	11	14	68	44	9	22
39	38	19	13	14	12	54	33	9	23
37	39	16	11	12	14	56	37	9	24
39	32	17	9	17	11	86	52	9	25
41	32	17	13	9	9	80	47	9	26
36	35	16	10	16	11	76	43	9	27
33	37	15	14	14	15	69	44	9	28
33	33	16	12	15	14	78	45	9	29
34	33	14	10	11	13	67	44	9	30
31	28	15	12	16	9	80	49	9	31
27	32	12	8	13	15	54	33	9	32
37	31	14	10	17	10	71	43	9	33
34	37	16	12	15	11	84	54	9	34
34	30	14	12	14	13	74	42	9	35
32	33	7	7	16	8	71	44	9	36
29	31	10	6	9	20	63	37	9	37
36	33	14	12	15	12	71	43	9	38
29	31	16	10	17	10	76	46	9	39
35	33	16	10	13	10	69	42	9	40
37	32	16	10	15	9	74	45	9	41
34	33	14	12	16	14	75	44	9	42
38	32	20	15	16	8	54	33	9	43
35	33	14	10	12	14	52	31	9	44
38	28	14	10	12	11	69	42	9	45
37	35	11	12	11	13	68	40	9	46
38	39	14	13	15	9	65	43	9	47
33	34	15	11	15	11	75	46	9	48
36	38	16	11	17	15	74	42	9	49
38	32	14	12	13	11	75	45	9	50
32	38	16	14	16	10	72	44	9	51
32	30	14	10	14	14	67	40	9	52
32	33	12	12	11	18	63	37	9	53
34	38	16	13	12	14	62	46	9	54
32	32	9	5	12	11	63	36	10	55
37	32	14	6	15	12	76	47	10	56
39	34	16	12	16	13	74	45	10	57
29	34	16	12	15	9	67	42	10	58
37	36	15	11	12	10	73	43	10	59
35	34	16	10	12	15	70	43	10	60
30	28	12	7	8	20	53	32	10	61
38	34	16	12	13	12	77	45	10	62
34	35	16	14	11	12	77	45	10	63
31	35	14	11	14	14	52	31	10	64
34	31	16	12	15	13	54	33	10	65
35	37	17	13	10	11	80	49	10	66
36	35	18	14	11	17	66	42	10	67
30	27	18	11	12	12	73	41	10	68
39	40	12	12	15	13	63	38	10	69
35	37	16	12	15	14	69	42	10	70
38	36	10	8	14	13	67	44	10	71
31	38	14	11	16	15	54	33	10	72
34	39	18	14	15	13	81	48	10	73
38	41	18	14	15	10	69	40	10	74
34	27	16	12	13	11	84	50	10	75
39	30	17	9	12	19	80	49	10	76
37	37	16	13	17	13	70	43	10	77
34	31	16	11	13	17	69	44	10	78
28	31	13	12	15	13	77	47	10	79
37	27	16	12	13	9	54	33	10	80
33	36	16	12	15	11	79	46	10	81
37	38	20	12	16	10	30	0	10	82
35	37	16	12	15	9	71	45	10	83
37	33	15	12	16	12	73	43	10	84
32	34	15	11	15	12	72	44	10	85
33	31	16	10	14	13	77	47	10	86
38	39	14	9	15	13	75	45	10	87
33	34	16	12	14	12	69	42	10	88
29	32	16	12	13	15	54	33	10	89
33	33	15	12	7	22	70	43	10	90
31	36	12	9	17	13	73	46	10	91
36	32	17	15	13	15	54	33	10	92
35	41	16	12	15	13	77	46	10	93
32	28	15	12	14	15	82	48	10	94
29	30	13	12	13	10	80	47	10	95
39	36	16	10	16	11	80	47	10	96
37	35	16	13	12	16	69	43	10	97
35	31	16	9	14	11	78	46	10	98
37	34	16	12	17	11	81	48	10	99
32	36	14	10	15	10	76	46	10	100
38	36	16	14	17	10	76	45	10	101
37	35	16	11	12	16	73	45	10	102
36	37	20	15	16	12	85	52	10	103
32	28	15	11	11	11	66	42	10	104
33	39	16	11	15	16	79	47	10	105
40	32	13	12	9	19	68	41	10	106
38	35	17	12	16	11	76	47	10	107
41	39	16	12	15	16	71	43	10	108
36	35	16	11	10	15	54	33	11	109
43	42	12	7	10	24	46	30	11	110
30	34	16	12	15	14	82	49	11	111
31	33	16	14	11	15	74	44	11	112
32	41	17	11	13	11	88	55	11	113
32	33	13	11	14	15	38	11	11	114
37	34	12	10	18	12	76	47	11	115
37	32	18	13	16	10	86	53	11	116
33	40	14	13	14	14	54	33	11	117
34	40	14	8	14	13	70	44	11	118
33	35	13	11	14	9	69	42	11	119
38	36	16	12	14	15	90	55	11	120
33	37	13	11	12	15	54	33	11	121
31	27	16	13	14	14	76	46	11	122
38	39	13	12	15	11	89	54	11	123
37	38	16	14	15	8	76	47	11	124
33	31	15	13	15	11	73	45	11	125
31	33	16	15	13	11	79	47	11	126
39	32	15	10	17	8	90	55	11	127
44	39	17	11	17	10	74	44	11	128
33	36	15	9	19	11	81	53	11	129
35	33	12	11	15	13	72	44	11	130
32	33	16	10	13	11	71	42	11	131
28	32	10	11	9	20	66	40	11	132
40	37	16	8	15	10	77	46	11	133
27	30	12	11	15	15	65	40	11	134
37	38	14	12	15	12	74	46	11	135
32	29	15	12	16	14	82	53	11	136
28	22	13	9	11	23	54	33	11	137
34	35	15	11	14	14	63	42	11	138
30	35	11	10	11	16	54	35	11	139
35	34	12	8	15	11	64	40	11	140
31	35	8	9	13	12	69	41	11	141
32	34	16	8	15	10	54	33	11	142
30	34	15	9	16	14	84	51	11	143
30	35	17	15	14	12	86	53	11	144
31	23	16	11	15	12	77	46	11	145
40	31	10	8	16	11	89	55	11	146
32	27	18	13	16	12	76	47	11	147
36	36	13	12	11	13	60	38	11	148
32	31	16	12	12	11	75	46	11	149
35	32	13	9	9	19	73	46	11	150
38	39	10	7	16	12	85	53	11	151
42	37	15	13	13	17	79	47	11	152
34	38	16	9	16	9	71	41	11	153
35	39	16	6	12	12	72	44	11	154
35	34	14	8	9	19	69	43	11	155
33	31	10	8	13	18	78	51	11	156
36	32	17	15	13	15	54	33	11	157
32	37	13	6	14	14	69	43	11	158
33	36	15	9	19	11	81	53	11	159
34	32	16	11	13	9	84	51	11	160
32	35	12	8	12	18	84	50	11	161
34	36	13	8	13	16	69	46	11	162

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&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]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.74007460639926 + 0.10629705754098Connected[t] -0.0161704039113756Separate[t] + 0.530388916240316Software[t] + 0.0519271641151573Happiness[t] -0.0642629312219422Depression[t] + 0.0414421853284572Belonging[t] -0.0543502456696904Belonging_Final[t] + 0.239499489964398Month[t] -0.00816613564800637`T\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.74007460639926 +  0.10629705754098Connected[t] -0.0161704039113756Separate[t] +  0.530388916240316Software[t] +  0.0519271641151573Happiness[t] -0.0642629312219422Depression[t] +  0.0414421853284572Belonging[t] -0.0543502456696904Belonging_Final[t] +  0.239499489964398Month[t] -0.00816613564800637`T\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.74007460639926 +  0.10629705754098Connected[t] -0.0161704039113756Separate[t] +  0.530388916240316Software[t] +  0.0519271641151573Happiness[t] -0.0642629312219422Depression[t] +  0.0414421853284572Belonging[t] -0.0543502456696904Belonging_Final[t] +  0.239499489964398Month[t] -0.00816613564800637`T\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.74007460639926 + 0.10629705754098Connected[t] -0.0161704039113756Separate[t] + 0.530388916240316Software[t] + 0.0519271641151573Happiness[t] -0.0642629312219422Depression[t] + 0.0414421853284572Belonging[t] -0.0543502456696904Belonging_Final[t] + 0.239499489964398Month[t] -0.00816613564800637`T\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.740074606399265.2899540.7070.480640.24032
Connected0.106297057540980.0473652.24420.0262640.013132
Separate-0.01617040391137560.04523-0.35750.7212010.3606
Software0.5303889162403160.0696327.61700
Happiness0.05192716411515730.0766330.67760.4990510.249526
Depression-0.06426293122194220.056687-1.13370.2587240.129362
Belonging0.04144218532845720.0448550.92390.3569910.178496
Belonging_Final-0.05435024566969040.064168-0.8470.3983260.199163
Month0.2394994899643980.5384960.44480.6571290.328564
`T\r`-0.008166135648006370.009445-0.86460.3886170.194308

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.74007460639926 & 5.289954 & 0.707 & 0.48064 & 0.24032 \tabularnewline
Connected & 0.10629705754098 & 0.047365 & 2.2442 & 0.026264 & 0.013132 \tabularnewline
Separate & -0.0161704039113756 & 0.04523 & -0.3575 & 0.721201 & 0.3606 \tabularnewline
Software & 0.530388916240316 & 0.069632 & 7.617 & 0 & 0 \tabularnewline
Happiness & 0.0519271641151573 & 0.076633 & 0.6776 & 0.499051 & 0.249526 \tabularnewline
Depression & -0.0642629312219422 & 0.056687 & -1.1337 & 0.258724 & 0.129362 \tabularnewline
Belonging & 0.0414421853284572 & 0.044855 & 0.9239 & 0.356991 & 0.178496 \tabularnewline
Belonging_Final & -0.0543502456696904 & 0.064168 & -0.847 & 0.398326 & 0.199163 \tabularnewline
Month & 0.239499489964398 & 0.538496 & 0.4448 & 0.657129 & 0.328564 \tabularnewline
`T\r` & -0.00816613564800637 & 0.009445 & -0.8646 & 0.388617 & 0.194308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.74007460639926[/C][C]5.289954[/C][C]0.707[/C][C]0.48064[/C][C]0.24032[/C][/ROW]
[ROW][C]Connected[/C][C]0.10629705754098[/C][C]0.047365[/C][C]2.2442[/C][C]0.026264[/C][C]0.013132[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0161704039113756[/C][C]0.04523[/C][C]-0.3575[/C][C]0.721201[/C][C]0.3606[/C][/ROW]
[ROW][C]Software[/C][C]0.530388916240316[/C][C]0.069632[/C][C]7.617[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0519271641151573[/C][C]0.076633[/C][C]0.6776[/C][C]0.499051[/C][C]0.249526[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0642629312219422[/C][C]0.056687[/C][C]-1.1337[/C][C]0.258724[/C][C]0.129362[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0414421853284572[/C][C]0.044855[/C][C]0.9239[/C][C]0.356991[/C][C]0.178496[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0543502456696904[/C][C]0.064168[/C][C]-0.847[/C][C]0.398326[/C][C]0.199163[/C][/ROW]
[ROW][C]Month[/C][C]0.239499489964398[/C][C]0.538496[/C][C]0.4448[/C][C]0.657129[/C][C]0.328564[/C][/ROW]
[ROW][C]`T\r`[/C][C]-0.00816613564800637[/C][C]0.009445[/C][C]-0.8646[/C][C]0.388617[/C][C]0.194308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.740074606399265.2899540.7070.480640.24032
Connected0.106297057540980.0473652.24420.0262640.013132
Separate-0.01617040391137560.04523-0.35750.7212010.3606
Software0.5303889162403160.0696327.61700
Happiness0.05192716411515730.0766330.67760.4990510.249526
Depression-0.06426293122194220.056687-1.13370.2587240.129362
Belonging0.04144218532845720.0448550.92390.3569910.178496
Belonging_Final-0.05435024566969040.064168-0.8470.3983260.199163
Month0.2394994899643980.5384960.44480.6571290.328564
`T\r`-0.008166135648006370.009445-0.86460.3886170.194308






Multiple Linear Regression - Regression Statistics
Multiple R0.603773378399474
R-squared0.364542292463915
Adjusted R-squared0.326916507149278
F-TEST (value)9.68862947086731
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.25052190824704e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85107414612956
Sum Squared Residuals520.824275159329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.603773378399474 \tabularnewline
R-squared & 0.364542292463915 \tabularnewline
Adjusted R-squared & 0.326916507149278 \tabularnewline
F-TEST (value) & 9.68862947086731 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.25052190824704e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85107414612956 \tabularnewline
Sum Squared Residuals & 520.824275159329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.603773378399474[/C][/ROW]
[ROW][C]R-squared[/C][C]0.364542292463915[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.326916507149278[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.68862947086731[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.25052190824704e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85107414612956[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]520.824275159329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.603773378399474
R-squared0.364542292463915
Adjusted R-squared0.326916507149278
F-TEST (value)9.68862947086731
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.25052190824704e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85107414612956
Sum Squared Residuals520.824275159329






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4088279697896-3.40882796978957
21616.3616102620851-0.361610262085067
31916.57384462378152.42615537621848
41512.20706156489162.79293843510841
51415.896398114974-1.89639811497405
61315.2434803536521-2.2434803536521
71915.34604932763133.65395067236869
81516.9599520164756-1.95995201647558
91416.2047105333854-2.2047105333854
101512.85339183175412.14660816824594
111615.49854043253010.501459567469945
121616.3559762907244-0.355976290724411
131615.67664506889970.323354931100293
141615.52210558048840.477894419511606
151717.6553764240305-0.655376424030509
161515.2717985182772-0.27179851827716
171514.85957812378040.140421876219626
182016.42592799648073.57407200351927
191815.61409218683292.38590781316714
201615.38197945372030.618020546279691
211615.41791435309020.582085646909759
221614.97268385025251.02731614974753
231916.53405972635052.46594027364946
241614.86945443653251.13054556346748
251715.00673389991541.99326610008456
261717.068924210211-0.0689242102109929
271615.17619135413020.823808645869838
281516.2329973069108-1.23299730691076
291615.66355447205110.336445527948857
301414.1559480432815-0.15594804328146
311515.7542053134331-0.75420531343307
321212.3853616995124-0.385361699512414
331415.2071523821234-1.20715238212342
341615.61662893031590.383371069684056
351415.7789836902402-1.77898369024024
36713.0499095835898-6.04990958358984
371011.1390730804921-1.1390730804921
381415.8560814803261-1.85608148032609
391614.35193929754081.64806070245924
401614.66881172823481.33118827176519
411615.10168756066560.898312439334388
421415.6456426199675-1.64564261996752
432017.78314626491642.2168537350836
441414.2205038484227-0.220503848422684
451414.9115341468375-0.911534146837498
461115.6314612382014-4.6314612382014
471416.3726825490431-2.37268254904314
481514.97595056659810.0240494334019046
491615.24475538862040.755244611379624
501416.0043292245094-2.00432922450942
511616.4722042658797-0.47220426587965
521414.1211296994798-0.121129699479778
531214.7096789630403-2.70967896304028
541615.14202833180490.857971668195075
55911.7924121002763-2.79241210027627
561412.87853443660041.12146556339964
571616.2464354592291-0.246435459229137
581615.25337874865380.746621251346185
591515.5071177920042-0.507117792004219
601614.34266822076161.65733177923839
611211.67318471136850.32681528863151
621616.13211571831-0.132115718309967
631616.639514452837-0.639514452836983
641414.4733948319109-0.473394831910915
651615.46956437542630.530435624573701
661716.07784471978430.922155280215729
671816.20531606761321.79468393238675
681814.81525143248323.18474856751682
691215.9240799249445-3.92407992494445
701615.50622596893670.493774031063306
711013.5323166499722-3.53231664997223
721414.3733298113186-0.373329811318643
731816.63933521025491.36066478974514
741817.15430103202990.845698967970113
751615.79656755227430.203432447725716
761714.00275963434132.99724036565873
771616.3472552498339-0.347255249833948
781614.49588972020411.50411027979592
791314.9097229542872-1.90972295428718
801615.88384252563210.116157474367901
811615.60978442990970.390215570090324
822016.58010003165133.4198999683487
831615.64121449527020.358785504729835
841515.9610473227952-0.961047322795247
851514.72711698417730.272883015822655
861614.27134029586021.72865970413976
871414.2126505851834-0.21265058518344
881615.27175132225350.728248677746526
891614.49354123758371.50645876241633
901514.25256193350210.747438066497919
911213.4510375634424-1.45103756344239
921716.80428898214750.19571101785254
931615.43212266455790.567877335442117
941515.2333380158787-0.233338015878683
951315.1147932667923-2.11479326679232
961615.10331601172880.896683988271248
971615.72240654512650.277593454873498
981614.07987016036781.92012983963216
991615.99836123378010.00163876621991991
1001414.2274893378125-0.227489337812515
1011617.1368657862717-1.13686578627166
1021614.67786628438031.32213371561971
1032017.23423283393192.76576716606814
1041514.38558448446340.614415515536571
1051614.45923214460371.54076785539632
1061315.2046128124108-2.20461281241082
1071715.81837095713791.18162904286212
1081615.70136261427890.298637385721081
1091614.57911579705491.42088420294511
1101212.3334274452368-0.33342744523678
1111615.08623651094420.913763489055831
1121615.92955782726740.0704421727326258
1131714.65040271449832.34959728550172
1141314.8857767924122-1.88577679241219
1151214.8812282728164-2.88122827281636
1161816.60956160719211.39043839280794
1171415.4467929398542-1.44679293985422
1181413.02246447465630.977535525343722
1191314.9043300806438-1.90433008064381
1201615.72002285588960.279977144110386
1211314.2337445170634-1.23374451706343
1221615.60875828090020.391241719099804
1231315.9689001865556-2.96890018655563
1241616.9658773338424-0.965877333842365
1251515.9069120208579-0.906912020857936
1261616.7506870871869-0.750687087186898
1271515.3786827579585-0.378682757958487
1281716.12544987354350.874550126456538
1291513.77628396747851.22371603252147
1301214.8699390152938-2.86993901529383
1311614.10442263100711.89557736899294
1321013.3330421125858-3.33304211258581
1331614.0123767606571.98762323934301
1341213.8341890470438-1.83418904704377
1351415.5296861593587-1.52968615935867
1361515.0100554358192-0.0100554358192433
1371312.18734867128970.812651328710258
1381514.28550279279220.714497207207785
1391113.0449242076823-2.04492420768234
1401213.1953298686764-1.19532986867643
141813.2609374367138-5.26093743671379
1421612.89039922238273.10960077761732
1431513.25986446491971.74013553508029
1441716.41671683633330.583283163666676
1451614.64673615604841.35326384395158
1461013.9990576665086-3.99905766650865
1471815.68893189224552.31106810775448
1481314.9322099292929-1.93220992929291
1491614.94699142416621.05300857583376
1501312.89760999573090.102390004269074
1511012.9645495444589-2.96454954445895
1521516.1965981578317-1.19659815783172
1531613.86477842649472.13522157350527
1541611.83346619394834.16653380605169
1551412.29133158912311.70866841087693
1561012.3292318404084-2.32923184040839
1571716.51298965499140.487010345008556
1581311.41960344202681.58039655797315
1591513.53129989803831.46870010196166
1601614.80488019313511.19511980686486
1611212.3684986825072-0.368498682507156
1621312.33297748734070.667022512659322

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.4088279697896 & -3.40882796978957 \tabularnewline
2 & 16 & 16.3616102620851 & -0.361610262085067 \tabularnewline
3 & 19 & 16.5738446237815 & 2.42615537621848 \tabularnewline
4 & 15 & 12.2070615648916 & 2.79293843510841 \tabularnewline
5 & 14 & 15.896398114974 & -1.89639811497405 \tabularnewline
6 & 13 & 15.2434803536521 & -2.2434803536521 \tabularnewline
7 & 19 & 15.3460493276313 & 3.65395067236869 \tabularnewline
8 & 15 & 16.9599520164756 & -1.95995201647558 \tabularnewline
9 & 14 & 16.2047105333854 & -2.2047105333854 \tabularnewline
10 & 15 & 12.8533918317541 & 2.14660816824594 \tabularnewline
11 & 16 & 15.4985404325301 & 0.501459567469945 \tabularnewline
12 & 16 & 16.3559762907244 & -0.355976290724411 \tabularnewline
13 & 16 & 15.6766450688997 & 0.323354931100293 \tabularnewline
14 & 16 & 15.5221055804884 & 0.477894419511606 \tabularnewline
15 & 17 & 17.6553764240305 & -0.655376424030509 \tabularnewline
16 & 15 & 15.2717985182772 & -0.27179851827716 \tabularnewline
17 & 15 & 14.8595781237804 & 0.140421876219626 \tabularnewline
18 & 20 & 16.4259279964807 & 3.57407200351927 \tabularnewline
19 & 18 & 15.6140921868329 & 2.38590781316714 \tabularnewline
20 & 16 & 15.3819794537203 & 0.618020546279691 \tabularnewline
21 & 16 & 15.4179143530902 & 0.582085646909759 \tabularnewline
22 & 16 & 14.9726838502525 & 1.02731614974753 \tabularnewline
23 & 19 & 16.5340597263505 & 2.46594027364946 \tabularnewline
24 & 16 & 14.8694544365325 & 1.13054556346748 \tabularnewline
25 & 17 & 15.0067338999154 & 1.99326610008456 \tabularnewline
26 & 17 & 17.068924210211 & -0.0689242102109929 \tabularnewline
27 & 16 & 15.1761913541302 & 0.823808645869838 \tabularnewline
28 & 15 & 16.2329973069108 & -1.23299730691076 \tabularnewline
29 & 16 & 15.6635544720511 & 0.336445527948857 \tabularnewline
30 & 14 & 14.1559480432815 & -0.15594804328146 \tabularnewline
31 & 15 & 15.7542053134331 & -0.75420531343307 \tabularnewline
32 & 12 & 12.3853616995124 & -0.385361699512414 \tabularnewline
33 & 14 & 15.2071523821234 & -1.20715238212342 \tabularnewline
34 & 16 & 15.6166289303159 & 0.383371069684056 \tabularnewline
35 & 14 & 15.7789836902402 & -1.77898369024024 \tabularnewline
36 & 7 & 13.0499095835898 & -6.04990958358984 \tabularnewline
37 & 10 & 11.1390730804921 & -1.1390730804921 \tabularnewline
38 & 14 & 15.8560814803261 & -1.85608148032609 \tabularnewline
39 & 16 & 14.3519392975408 & 1.64806070245924 \tabularnewline
40 & 16 & 14.6688117282348 & 1.33118827176519 \tabularnewline
41 & 16 & 15.1016875606656 & 0.898312439334388 \tabularnewline
42 & 14 & 15.6456426199675 & -1.64564261996752 \tabularnewline
43 & 20 & 17.7831462649164 & 2.2168537350836 \tabularnewline
44 & 14 & 14.2205038484227 & -0.220503848422684 \tabularnewline
45 & 14 & 14.9115341468375 & -0.911534146837498 \tabularnewline
46 & 11 & 15.6314612382014 & -4.6314612382014 \tabularnewline
47 & 14 & 16.3726825490431 & -2.37268254904314 \tabularnewline
48 & 15 & 14.9759505665981 & 0.0240494334019046 \tabularnewline
49 & 16 & 15.2447553886204 & 0.755244611379624 \tabularnewline
50 & 14 & 16.0043292245094 & -2.00432922450942 \tabularnewline
51 & 16 & 16.4722042658797 & -0.47220426587965 \tabularnewline
52 & 14 & 14.1211296994798 & -0.121129699479778 \tabularnewline
53 & 12 & 14.7096789630403 & -2.70967896304028 \tabularnewline
54 & 16 & 15.1420283318049 & 0.857971668195075 \tabularnewline
55 & 9 & 11.7924121002763 & -2.79241210027627 \tabularnewline
56 & 14 & 12.8785344366004 & 1.12146556339964 \tabularnewline
57 & 16 & 16.2464354592291 & -0.246435459229137 \tabularnewline
58 & 16 & 15.2533787486538 & 0.746621251346185 \tabularnewline
59 & 15 & 15.5071177920042 & -0.507117792004219 \tabularnewline
60 & 16 & 14.3426682207616 & 1.65733177923839 \tabularnewline
61 & 12 & 11.6731847113685 & 0.32681528863151 \tabularnewline
62 & 16 & 16.13211571831 & -0.132115718309967 \tabularnewline
63 & 16 & 16.639514452837 & -0.639514452836983 \tabularnewline
64 & 14 & 14.4733948319109 & -0.473394831910915 \tabularnewline
65 & 16 & 15.4695643754263 & 0.530435624573701 \tabularnewline
66 & 17 & 16.0778447197843 & 0.922155280215729 \tabularnewline
67 & 18 & 16.2053160676132 & 1.79468393238675 \tabularnewline
68 & 18 & 14.8152514324832 & 3.18474856751682 \tabularnewline
69 & 12 & 15.9240799249445 & -3.92407992494445 \tabularnewline
70 & 16 & 15.5062259689367 & 0.493774031063306 \tabularnewline
71 & 10 & 13.5323166499722 & -3.53231664997223 \tabularnewline
72 & 14 & 14.3733298113186 & -0.373329811318643 \tabularnewline
73 & 18 & 16.6393352102549 & 1.36066478974514 \tabularnewline
74 & 18 & 17.1543010320299 & 0.845698967970113 \tabularnewline
75 & 16 & 15.7965675522743 & 0.203432447725716 \tabularnewline
76 & 17 & 14.0027596343413 & 2.99724036565873 \tabularnewline
77 & 16 & 16.3472552498339 & -0.347255249833948 \tabularnewline
78 & 16 & 14.4958897202041 & 1.50411027979592 \tabularnewline
79 & 13 & 14.9097229542872 & -1.90972295428718 \tabularnewline
80 & 16 & 15.8838425256321 & 0.116157474367901 \tabularnewline
81 & 16 & 15.6097844299097 & 0.390215570090324 \tabularnewline
82 & 20 & 16.5801000316513 & 3.4198999683487 \tabularnewline
83 & 16 & 15.6412144952702 & 0.358785504729835 \tabularnewline
84 & 15 & 15.9610473227952 & -0.961047322795247 \tabularnewline
85 & 15 & 14.7271169841773 & 0.272883015822655 \tabularnewline
86 & 16 & 14.2713402958602 & 1.72865970413976 \tabularnewline
87 & 14 & 14.2126505851834 & -0.21265058518344 \tabularnewline
88 & 16 & 15.2717513222535 & 0.728248677746526 \tabularnewline
89 & 16 & 14.4935412375837 & 1.50645876241633 \tabularnewline
90 & 15 & 14.2525619335021 & 0.747438066497919 \tabularnewline
91 & 12 & 13.4510375634424 & -1.45103756344239 \tabularnewline
92 & 17 & 16.8042889821475 & 0.19571101785254 \tabularnewline
93 & 16 & 15.4321226645579 & 0.567877335442117 \tabularnewline
94 & 15 & 15.2333380158787 & -0.233338015878683 \tabularnewline
95 & 13 & 15.1147932667923 & -2.11479326679232 \tabularnewline
96 & 16 & 15.1033160117288 & 0.896683988271248 \tabularnewline
97 & 16 & 15.7224065451265 & 0.277593454873498 \tabularnewline
98 & 16 & 14.0798701603678 & 1.92012983963216 \tabularnewline
99 & 16 & 15.9983612337801 & 0.00163876621991991 \tabularnewline
100 & 14 & 14.2274893378125 & -0.227489337812515 \tabularnewline
101 & 16 & 17.1368657862717 & -1.13686578627166 \tabularnewline
102 & 16 & 14.6778662843803 & 1.32213371561971 \tabularnewline
103 & 20 & 17.2342328339319 & 2.76576716606814 \tabularnewline
104 & 15 & 14.3855844844634 & 0.614415515536571 \tabularnewline
105 & 16 & 14.4592321446037 & 1.54076785539632 \tabularnewline
106 & 13 & 15.2046128124108 & -2.20461281241082 \tabularnewline
107 & 17 & 15.8183709571379 & 1.18162904286212 \tabularnewline
108 & 16 & 15.7013626142789 & 0.298637385721081 \tabularnewline
109 & 16 & 14.5791157970549 & 1.42088420294511 \tabularnewline
110 & 12 & 12.3334274452368 & -0.33342744523678 \tabularnewline
111 & 16 & 15.0862365109442 & 0.913763489055831 \tabularnewline
112 & 16 & 15.9295578272674 & 0.0704421727326258 \tabularnewline
113 & 17 & 14.6504027144983 & 2.34959728550172 \tabularnewline
114 & 13 & 14.8857767924122 & -1.88577679241219 \tabularnewline
115 & 12 & 14.8812282728164 & -2.88122827281636 \tabularnewline
116 & 18 & 16.6095616071921 & 1.39043839280794 \tabularnewline
117 & 14 & 15.4467929398542 & -1.44679293985422 \tabularnewline
118 & 14 & 13.0224644746563 & 0.977535525343722 \tabularnewline
119 & 13 & 14.9043300806438 & -1.90433008064381 \tabularnewline
120 & 16 & 15.7200228558896 & 0.279977144110386 \tabularnewline
121 & 13 & 14.2337445170634 & -1.23374451706343 \tabularnewline
122 & 16 & 15.6087582809002 & 0.391241719099804 \tabularnewline
123 & 13 & 15.9689001865556 & -2.96890018655563 \tabularnewline
124 & 16 & 16.9658773338424 & -0.965877333842365 \tabularnewline
125 & 15 & 15.9069120208579 & -0.906912020857936 \tabularnewline
126 & 16 & 16.7506870871869 & -0.750687087186898 \tabularnewline
127 & 15 & 15.3786827579585 & -0.378682757958487 \tabularnewline
128 & 17 & 16.1254498735435 & 0.874550126456538 \tabularnewline
129 & 15 & 13.7762839674785 & 1.22371603252147 \tabularnewline
130 & 12 & 14.8699390152938 & -2.86993901529383 \tabularnewline
131 & 16 & 14.1044226310071 & 1.89557736899294 \tabularnewline
132 & 10 & 13.3330421125858 & -3.33304211258581 \tabularnewline
133 & 16 & 14.012376760657 & 1.98762323934301 \tabularnewline
134 & 12 & 13.8341890470438 & -1.83418904704377 \tabularnewline
135 & 14 & 15.5296861593587 & -1.52968615935867 \tabularnewline
136 & 15 & 15.0100554358192 & -0.0100554358192433 \tabularnewline
137 & 13 & 12.1873486712897 & 0.812651328710258 \tabularnewline
138 & 15 & 14.2855027927922 & 0.714497207207785 \tabularnewline
139 & 11 & 13.0449242076823 & -2.04492420768234 \tabularnewline
140 & 12 & 13.1953298686764 & -1.19532986867643 \tabularnewline
141 & 8 & 13.2609374367138 & -5.26093743671379 \tabularnewline
142 & 16 & 12.8903992223827 & 3.10960077761732 \tabularnewline
143 & 15 & 13.2598644649197 & 1.74013553508029 \tabularnewline
144 & 17 & 16.4167168363333 & 0.583283163666676 \tabularnewline
145 & 16 & 14.6467361560484 & 1.35326384395158 \tabularnewline
146 & 10 & 13.9990576665086 & -3.99905766650865 \tabularnewline
147 & 18 & 15.6889318922455 & 2.31106810775448 \tabularnewline
148 & 13 & 14.9322099292929 & -1.93220992929291 \tabularnewline
149 & 16 & 14.9469914241662 & 1.05300857583376 \tabularnewline
150 & 13 & 12.8976099957309 & 0.102390004269074 \tabularnewline
151 & 10 & 12.9645495444589 & -2.96454954445895 \tabularnewline
152 & 15 & 16.1965981578317 & -1.19659815783172 \tabularnewline
153 & 16 & 13.8647784264947 & 2.13522157350527 \tabularnewline
154 & 16 & 11.8334661939483 & 4.16653380605169 \tabularnewline
155 & 14 & 12.2913315891231 & 1.70866841087693 \tabularnewline
156 & 10 & 12.3292318404084 & -2.32923184040839 \tabularnewline
157 & 17 & 16.5129896549914 & 0.487010345008556 \tabularnewline
158 & 13 & 11.4196034420268 & 1.58039655797315 \tabularnewline
159 & 15 & 13.5312998980383 & 1.46870010196166 \tabularnewline
160 & 16 & 14.8048801931351 & 1.19511980686486 \tabularnewline
161 & 12 & 12.3684986825072 & -0.368498682507156 \tabularnewline
162 & 13 & 12.3329774873407 & 0.667022512659322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&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]16.4088279697896[/C][C]-3.40882796978957[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.3616102620851[/C][C]-0.361610262085067[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5738446237815[/C][C]2.42615537621848[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2070615648916[/C][C]2.79293843510841[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.896398114974[/C][C]-1.89639811497405[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2434803536521[/C][C]-2.2434803536521[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3460493276313[/C][C]3.65395067236869[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9599520164756[/C][C]-1.95995201647558[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.2047105333854[/C][C]-2.2047105333854[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8533918317541[/C][C]2.14660816824594[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4985404325301[/C][C]0.501459567469945[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3559762907244[/C][C]-0.355976290724411[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6766450688997[/C][C]0.323354931100293[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5221055804884[/C][C]0.477894419511606[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6553764240305[/C][C]-0.655376424030509[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2717985182772[/C][C]-0.27179851827716[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8595781237804[/C][C]0.140421876219626[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4259279964807[/C][C]3.57407200351927[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6140921868329[/C][C]2.38590781316714[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3819794537203[/C][C]0.618020546279691[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4179143530902[/C][C]0.582085646909759[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9726838502525[/C][C]1.02731614974753[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5340597263505[/C][C]2.46594027364946[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8694544365325[/C][C]1.13054556346748[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0067338999154[/C][C]1.99326610008456[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.068924210211[/C][C]-0.0689242102109929[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1761913541302[/C][C]0.823808645869838[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.2329973069108[/C][C]-1.23299730691076[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6635544720511[/C][C]0.336445527948857[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1559480432815[/C][C]-0.15594804328146[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7542053134331[/C][C]-0.75420531343307[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.3853616995124[/C][C]-0.385361699512414[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2071523821234[/C][C]-1.20715238212342[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6166289303159[/C][C]0.383371069684056[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.7789836902402[/C][C]-1.77898369024024[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.0499095835898[/C][C]-6.04990958358984[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.1390730804921[/C][C]-1.1390730804921[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8560814803261[/C][C]-1.85608148032609[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3519392975408[/C][C]1.64806070245924[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6688117282348[/C][C]1.33118827176519[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1016875606656[/C][C]0.898312439334388[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6456426199675[/C][C]-1.64564261996752[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.7831462649164[/C][C]2.2168537350836[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2205038484227[/C][C]-0.220503848422684[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9115341468375[/C][C]-0.911534146837498[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6314612382014[/C][C]-4.6314612382014[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3726825490431[/C][C]-2.37268254904314[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9759505665981[/C][C]0.0240494334019046[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2447553886204[/C][C]0.755244611379624[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0043292245094[/C][C]-2.00432922450942[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.4722042658797[/C][C]-0.47220426587965[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1211296994798[/C][C]-0.121129699479778[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7096789630403[/C][C]-2.70967896304028[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1420283318049[/C][C]0.857971668195075[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.7924121002763[/C][C]-2.79241210027627[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.8785344366004[/C][C]1.12146556339964[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.2464354592291[/C][C]-0.246435459229137[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2533787486538[/C][C]0.746621251346185[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.5071177920042[/C][C]-0.507117792004219[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3426682207616[/C][C]1.65733177923839[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.6731847113685[/C][C]0.32681528863151[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.13211571831[/C][C]-0.132115718309967[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.639514452837[/C][C]-0.639514452836983[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4733948319109[/C][C]-0.473394831910915[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4695643754263[/C][C]0.530435624573701[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0778447197843[/C][C]0.922155280215729[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2053160676132[/C][C]1.79468393238675[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.8152514324832[/C][C]3.18474856751682[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9240799249445[/C][C]-3.92407992494445[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.5062259689367[/C][C]0.493774031063306[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5323166499722[/C][C]-3.53231664997223[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3733298113186[/C][C]-0.373329811318643[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.6393352102549[/C][C]1.36066478974514[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1543010320299[/C][C]0.845698967970113[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7965675522743[/C][C]0.203432447725716[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.0027596343413[/C][C]2.99724036565873[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3472552498339[/C][C]-0.347255249833948[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4958897202041[/C][C]1.50411027979592[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.9097229542872[/C][C]-1.90972295428718[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8838425256321[/C][C]0.116157474367901[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.6097844299097[/C][C]0.390215570090324[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.5801000316513[/C][C]3.4198999683487[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6412144952702[/C][C]0.358785504729835[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9610473227952[/C][C]-0.961047322795247[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7271169841773[/C][C]0.272883015822655[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2713402958602[/C][C]1.72865970413976[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2126505851834[/C][C]-0.21265058518344[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2717513222535[/C][C]0.728248677746526[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.4935412375837[/C][C]1.50645876241633[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2525619335021[/C][C]0.747438066497919[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4510375634424[/C][C]-1.45103756344239[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8042889821475[/C][C]0.19571101785254[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4321226645579[/C][C]0.567877335442117[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2333380158787[/C][C]-0.233338015878683[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1147932667923[/C][C]-2.11479326679232[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1033160117288[/C][C]0.896683988271248[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7224065451265[/C][C]0.277593454873498[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0798701603678[/C][C]1.92012983963216[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9983612337801[/C][C]0.00163876621991991[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2274893378125[/C][C]-0.227489337812515[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1368657862717[/C][C]-1.13686578627166[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.6778662843803[/C][C]1.32213371561971[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.2342328339319[/C][C]2.76576716606814[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.3855844844634[/C][C]0.614415515536571[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4592321446037[/C][C]1.54076785539632[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2046128124108[/C][C]-2.20461281241082[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.8183709571379[/C][C]1.18162904286212[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7013626142789[/C][C]0.298637385721081[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5791157970549[/C][C]1.42088420294511[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3334274452368[/C][C]-0.33342744523678[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0862365109442[/C][C]0.913763489055831[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.9295578272674[/C][C]0.0704421727326258[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6504027144983[/C][C]2.34959728550172[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.8857767924122[/C][C]-1.88577679241219[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8812282728164[/C][C]-2.88122827281636[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6095616071921[/C][C]1.39043839280794[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4467929398542[/C][C]-1.44679293985422[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0224644746563[/C][C]0.977535525343722[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9043300806438[/C][C]-1.90433008064381[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7200228558896[/C][C]0.279977144110386[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2337445170634[/C][C]-1.23374451706343[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6087582809002[/C][C]0.391241719099804[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9689001865556[/C][C]-2.96890018655563[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9658773338424[/C][C]-0.965877333842365[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.9069120208579[/C][C]-0.906912020857936[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.7506870871869[/C][C]-0.750687087186898[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3786827579585[/C][C]-0.378682757958487[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.1254498735435[/C][C]0.874550126456538[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7762839674785[/C][C]1.22371603252147[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8699390152938[/C][C]-2.86993901529383[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1044226310071[/C][C]1.89557736899294[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3330421125858[/C][C]-3.33304211258581[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.012376760657[/C][C]1.98762323934301[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8341890470438[/C][C]-1.83418904704377[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5296861593587[/C][C]-1.52968615935867[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.0100554358192[/C][C]-0.0100554358192433[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1873486712897[/C][C]0.812651328710258[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2855027927922[/C][C]0.714497207207785[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.0449242076823[/C][C]-2.04492420768234[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1953298686764[/C][C]-1.19532986867643[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2609374367138[/C][C]-5.26093743671379[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8903992223827[/C][C]3.10960077761732[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2598644649197[/C][C]1.74013553508029[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4167168363333[/C][C]0.583283163666676[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6467361560484[/C][C]1.35326384395158[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9990576665086[/C][C]-3.99905766650865[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.6889318922455[/C][C]2.31106810775448[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9322099292929[/C][C]-1.93220992929291[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9469914241662[/C][C]1.05300857583376[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.8976099957309[/C][C]0.102390004269074[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9645495444589[/C][C]-2.96454954445895[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1965981578317[/C][C]-1.19659815783172[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8647784264947[/C][C]2.13522157350527[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8334661939483[/C][C]4.16653380605169[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.2913315891231[/C][C]1.70866841087693[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3292318404084[/C][C]-2.32923184040839[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.5129896549914[/C][C]0.487010345008556[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4196034420268[/C][C]1.58039655797315[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.5312998980383[/C][C]1.46870010196166[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8048801931351[/C][C]1.19511980686486[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3684986825072[/C][C]-0.368498682507156[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.3329774873407[/C][C]0.667022512659322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.4088279697896-3.40882796978957
21616.3616102620851-0.361610262085067
31916.57384462378152.42615537621848
41512.20706156489162.79293843510841
51415.896398114974-1.89639811497405
61315.2434803536521-2.2434803536521
71915.34604932763133.65395067236869
81516.9599520164756-1.95995201647558
91416.2047105333854-2.2047105333854
101512.85339183175412.14660816824594
111615.49854043253010.501459567469945
121616.3559762907244-0.355976290724411
131615.67664506889970.323354931100293
141615.52210558048840.477894419511606
151717.6553764240305-0.655376424030509
161515.2717985182772-0.27179851827716
171514.85957812378040.140421876219626
182016.42592799648073.57407200351927
191815.61409218683292.38590781316714
201615.38197945372030.618020546279691
211615.41791435309020.582085646909759
221614.97268385025251.02731614974753
231916.53405972635052.46594027364946
241614.86945443653251.13054556346748
251715.00673389991541.99326610008456
261717.068924210211-0.0689242102109929
271615.17619135413020.823808645869838
281516.2329973069108-1.23299730691076
291615.66355447205110.336445527948857
301414.1559480432815-0.15594804328146
311515.7542053134331-0.75420531343307
321212.3853616995124-0.385361699512414
331415.2071523821234-1.20715238212342
341615.61662893031590.383371069684056
351415.7789836902402-1.77898369024024
36713.0499095835898-6.04990958358984
371011.1390730804921-1.1390730804921
381415.8560814803261-1.85608148032609
391614.35193929754081.64806070245924
401614.66881172823481.33118827176519
411615.10168756066560.898312439334388
421415.6456426199675-1.64564261996752
432017.78314626491642.2168537350836
441414.2205038484227-0.220503848422684
451414.9115341468375-0.911534146837498
461115.6314612382014-4.6314612382014
471416.3726825490431-2.37268254904314
481514.97595056659810.0240494334019046
491615.24475538862040.755244611379624
501416.0043292245094-2.00432922450942
511616.4722042658797-0.47220426587965
521414.1211296994798-0.121129699479778
531214.7096789630403-2.70967896304028
541615.14202833180490.857971668195075
55911.7924121002763-2.79241210027627
561412.87853443660041.12146556339964
571616.2464354592291-0.246435459229137
581615.25337874865380.746621251346185
591515.5071177920042-0.507117792004219
601614.34266822076161.65733177923839
611211.67318471136850.32681528863151
621616.13211571831-0.132115718309967
631616.639514452837-0.639514452836983
641414.4733948319109-0.473394831910915
651615.46956437542630.530435624573701
661716.07784471978430.922155280215729
671816.20531606761321.79468393238675
681814.81525143248323.18474856751682
691215.9240799249445-3.92407992494445
701615.50622596893670.493774031063306
711013.5323166499722-3.53231664997223
721414.3733298113186-0.373329811318643
731816.63933521025491.36066478974514
741817.15430103202990.845698967970113
751615.79656755227430.203432447725716
761714.00275963434132.99724036565873
771616.3472552498339-0.347255249833948
781614.49588972020411.50411027979592
791314.9097229542872-1.90972295428718
801615.88384252563210.116157474367901
811615.60978442990970.390215570090324
822016.58010003165133.4198999683487
831615.64121449527020.358785504729835
841515.9610473227952-0.961047322795247
851514.72711698417730.272883015822655
861614.27134029586021.72865970413976
871414.2126505851834-0.21265058518344
881615.27175132225350.728248677746526
891614.49354123758371.50645876241633
901514.25256193350210.747438066497919
911213.4510375634424-1.45103756344239
921716.80428898214750.19571101785254
931615.43212266455790.567877335442117
941515.2333380158787-0.233338015878683
951315.1147932667923-2.11479326679232
961615.10331601172880.896683988271248
971615.72240654512650.277593454873498
981614.07987016036781.92012983963216
991615.99836123378010.00163876621991991
1001414.2274893378125-0.227489337812515
1011617.1368657862717-1.13686578627166
1021614.67786628438031.32213371561971
1032017.23423283393192.76576716606814
1041514.38558448446340.614415515536571
1051614.45923214460371.54076785539632
1061315.2046128124108-2.20461281241082
1071715.81837095713791.18162904286212
1081615.70136261427890.298637385721081
1091614.57911579705491.42088420294511
1101212.3334274452368-0.33342744523678
1111615.08623651094420.913763489055831
1121615.92955782726740.0704421727326258
1131714.65040271449832.34959728550172
1141314.8857767924122-1.88577679241219
1151214.8812282728164-2.88122827281636
1161816.60956160719211.39043839280794
1171415.4467929398542-1.44679293985422
1181413.02246447465630.977535525343722
1191314.9043300806438-1.90433008064381
1201615.72002285588960.279977144110386
1211314.2337445170634-1.23374451706343
1221615.60875828090020.391241719099804
1231315.9689001865556-2.96890018655563
1241616.9658773338424-0.965877333842365
1251515.9069120208579-0.906912020857936
1261616.7506870871869-0.750687087186898
1271515.3786827579585-0.378682757958487
1281716.12544987354350.874550126456538
1291513.77628396747851.22371603252147
1301214.8699390152938-2.86993901529383
1311614.10442263100711.89557736899294
1321013.3330421125858-3.33304211258581
1331614.0123767606571.98762323934301
1341213.8341890470438-1.83418904704377
1351415.5296861593587-1.52968615935867
1361515.0100554358192-0.0100554358192433
1371312.18734867128970.812651328710258
1381514.28550279279220.714497207207785
1391113.0449242076823-2.04492420768234
1401213.1953298686764-1.19532986867643
141813.2609374367138-5.26093743671379
1421612.89039922238273.10960077761732
1431513.25986446491971.74013553508029
1441716.41671683633330.583283163666676
1451614.64673615604841.35326384395158
1461013.9990576665086-3.99905766650865
1471815.68893189224552.31106810775448
1481314.9322099292929-1.93220992929291
1491614.94699142416621.05300857583376
1501312.89760999573090.102390004269074
1511012.9645495444589-2.96454954445895
1521516.1965981578317-1.19659815783172
1531613.86477842649472.13522157350527
1541611.83346619394834.16653380605169
1551412.29133158912311.70866841087693
1561012.3292318404084-2.32923184040839
1571716.51298965499140.487010345008556
1581311.41960344202681.58039655797315
1591513.53129989803831.46870010196166
1601614.80488019313511.19511980686486
1611212.3684986825072-0.368498682507156
1621312.33297748734070.667022512659322






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7350477299511560.5299045400976890.264952270048844
140.7093652564506990.5812694870986020.290634743549301
150.5875381672437980.8249236655124050.412461832756202
160.4743750045100920.9487500090201850.525624995489908
170.3958536917275870.7917073834551730.604146308272413
180.5500489667629840.8999020664740320.449951033237016
190.4639860939310250.927972187862050.536013906068975
200.3752441588448290.7504883176896570.624755841155171
210.3047520518411090.6095041036822170.695247948158891
220.2967272798294740.5934545596589490.703272720170526
230.4221430541071940.8442861082143870.577856945892806
240.4942549245932760.9885098491865530.505745075406724
250.4395413319927770.8790826639855530.560458668007223
260.3713319066188480.7426638132376970.628668093381152
270.3422309310228680.6844618620457370.657769068977132
280.4453952860818760.8907905721637520.554604713918124
290.3875729063908280.7751458127816560.612427093609172
300.4967960785336750.9935921570673510.503203921466325
310.445878684330320.8917573686606390.55412131566968
320.4084331702523120.8168663405046240.591566829747688
330.3964150900283340.7928301800566670.603584909971666
340.3644179900975780.7288359801951560.635582009902422
350.3167311290116480.6334622580232950.683268870988352
360.8609319019914530.2781361960170930.139068098008547
370.8337842691010710.3324314617978580.166215730898929
380.8159082231757310.3681835536485380.184091776824269
390.8459954608416680.3080090783166640.154004539158332
400.8332948754019140.3334102491961710.166705124598086
410.8054683452346950.389063309530610.194531654765305
420.778306906741260.443386186517480.22169309325874
430.8064189036607240.3871621926785520.193581096339276
440.7664332107323530.4671335785352940.233566789267647
450.7371131531845510.5257736936308990.26288684681545
460.8763630578542660.2472738842914680.123636942145734
470.9008005713472840.1983988573054310.0991994286527155
480.8794022185246940.2411955629506130.120597781475306
490.875679412115220.2486411757695610.12432058788478
500.8717648253798140.2564703492403720.128235174620186
510.8469002511924180.3061994976151640.153099748807582
520.8194320317869810.3611359364260390.180567968213019
530.8421775570371260.3156448859257470.157822442962874
540.8111281899106980.3777436201786050.188871810089302
550.8174970060313590.3650059879372820.182502993968641
560.8073393380543850.3853213238912310.192660661945615
570.7715027068203860.4569945863592280.228497293179614
580.7485740288015780.5028519423968440.251425971198422
590.7118121296730430.5763757406539140.288187870326957
600.7023824233903070.5952351532193860.297617576609693
610.6597253045806240.6805493908387510.340274695419376
620.6146541812840590.7706916374318810.385345818715941
630.5730399517943090.8539200964113830.426960048205691
640.5259317779360960.9481364441278070.474068222063904
650.4816436630432920.9632873260865840.518356336956708
660.4439493732417460.8878987464834930.556050626758254
670.4322666662222460.8645333324444930.567733333777754
680.5542171421277380.8915657157445240.445782857872262
690.693353400959110.613293198081780.30664659904089
700.6542973028504080.6914053942991840.345702697149592
710.7802306267543120.4395387464913760.219769373245688
720.7454355137859730.5091289724280540.254564486214027
730.7356423230376130.5287153539247740.264357676962387
740.7155279973012230.5689440053975540.284472002698777
750.6738173830324720.6523652339350560.326182616967528
760.7279389689021940.5441220621956110.272061031097806
770.6882712136652540.6234575726694930.311728786334746
780.6696520638062590.6606958723874830.330347936193741
790.6826943722661920.6346112554676170.317305627733808
800.6391581308616030.7216837382767940.360841869138397
810.6009273128701160.7981453742597680.399072687129884
820.7353871004699210.5292257990601580.264612899530079
830.6984828958389560.6030342083220890.301517104161045
840.6676414560329520.6647170879340960.332358543967048
850.6254701885417830.7490596229164340.374529811458217
860.6146262123932370.7707475752135270.385373787606763
870.5699029630956590.8601940738086810.430097036904341
880.5271981529293190.9456036941413620.472801847070681
890.5012531134020990.9974937731958020.498746886597901
900.4617031726262180.9234063452524350.538296827373782
910.453192375152390.906384750304780.54680762484761
920.4083066481294640.8166132962589270.591693351870536
930.367048019932950.7340960398658990.63295198006705
940.3238857438289830.6477714876579650.676114256171017
950.3617960429312610.7235920858625230.638203957068739
960.3261469525736630.6522939051473260.673853047426337
970.2836541136066020.5673082272132050.716345886393398
980.2751700375702810.5503400751405620.724829962429719
990.2368564892167120.4737129784334240.763143510783288
1000.2149215660634770.4298431321269540.785078433936523
1010.2043618768492220.4087237536984450.795638123150778
1020.1793874692785420.3587749385570830.820612530721458
1030.201511471408940.4030229428178790.79848852859106
1040.1716027467569430.3432054935138860.828397253243057
1050.155795662593060.311591325186120.84420433740694
1060.1745421125471450.349084225094290.825457887452855
1070.148070445119380.2961408902387610.85192955488062
1080.1206864165818920.2413728331637850.879313583418108
1090.1139260314348220.2278520628696450.886073968565178
1100.1139666537640920.2279333075281840.886033346235908
1110.1008369630144730.2016739260289460.899163036985527
1120.08631700092600690.1726340018520140.913682999073993
1130.1128167966636660.2256335933273330.887183203336334
1140.1081970597281230.2163941194562460.891802940271877
1150.1309768702360880.2619537404721760.869023129763912
1160.1351528304048510.2703056608097030.864847169595149
1170.1161042372146040.2322084744292070.883895762785396
1180.1161709150495860.2323418300991730.883829084950414
1190.1074155656270240.2148311312540490.892584434372976
1200.1208961522177390.2417923044354780.879103847782261
1210.0984966414227820.1969932828455640.901503358577218
1220.08575339193961290.1715067838792260.914246608060387
1230.08416505390938750.1683301078187750.915834946090613
1240.06517130135573860.1303426027114770.934828698644261
1250.04968220465614570.09936440931229140.950317795343854
1260.03712283553875170.07424567107750340.962877164461248
1270.0266818487647430.0533636975294860.973318151235257
1280.02506710815624680.05013421631249360.974932891843753
1290.02730235965031030.05460471930062060.97269764034969
1300.02709972883853050.05419945767706090.972900271161469
1310.02926073024356750.05852146048713510.970739269756432
1320.03105690294464840.06211380588929680.968943097055352
1330.06465272839456680.1293054567891340.935347271605433
1340.06686186369578290.1337237273915660.933138136304217
1350.05047234699843260.1009446939968650.949527653001567
1360.04044115521877750.08088231043755490.959558844781223
1370.02853364750960070.05706729501920140.971466352490399
1380.03393284323431940.06786568646863870.966067156765681
1390.02781545167447010.05563090334894010.97218454832553
1400.01790554516046340.03581109032092690.982094454839537
1410.4593509949905920.9187019899811840.540649005009408
1420.3980550575217690.7961101150435380.601944942478231
1430.3260032705631260.6520065411262520.673996729436874
1440.2425303494916320.4850606989832640.757469650508368
1450.1714685210743680.3429370421487350.828531478925632
1460.2016392543541520.4032785087083040.798360745645848
1470.2193763140507510.4387526281015020.780623685949249
1480.4603528319308540.9207056638617090.539647168069145
1490.4439263838866240.8878527677732490.556073616113376

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.735047729951156 & 0.529904540097689 & 0.264952270048844 \tabularnewline
14 & 0.709365256450699 & 0.581269487098602 & 0.290634743549301 \tabularnewline
15 & 0.587538167243798 & 0.824923665512405 & 0.412461832756202 \tabularnewline
16 & 0.474375004510092 & 0.948750009020185 & 0.525624995489908 \tabularnewline
17 & 0.395853691727587 & 0.791707383455173 & 0.604146308272413 \tabularnewline
18 & 0.550048966762984 & 0.899902066474032 & 0.449951033237016 \tabularnewline
19 & 0.463986093931025 & 0.92797218786205 & 0.536013906068975 \tabularnewline
20 & 0.375244158844829 & 0.750488317689657 & 0.624755841155171 \tabularnewline
21 & 0.304752051841109 & 0.609504103682217 & 0.695247948158891 \tabularnewline
22 & 0.296727279829474 & 0.593454559658949 & 0.703272720170526 \tabularnewline
23 & 0.422143054107194 & 0.844286108214387 & 0.577856945892806 \tabularnewline
24 & 0.494254924593276 & 0.988509849186553 & 0.505745075406724 \tabularnewline
25 & 0.439541331992777 & 0.879082663985553 & 0.560458668007223 \tabularnewline
26 & 0.371331906618848 & 0.742663813237697 & 0.628668093381152 \tabularnewline
27 & 0.342230931022868 & 0.684461862045737 & 0.657769068977132 \tabularnewline
28 & 0.445395286081876 & 0.890790572163752 & 0.554604713918124 \tabularnewline
29 & 0.387572906390828 & 0.775145812781656 & 0.612427093609172 \tabularnewline
30 & 0.496796078533675 & 0.993592157067351 & 0.503203921466325 \tabularnewline
31 & 0.44587868433032 & 0.891757368660639 & 0.55412131566968 \tabularnewline
32 & 0.408433170252312 & 0.816866340504624 & 0.591566829747688 \tabularnewline
33 & 0.396415090028334 & 0.792830180056667 & 0.603584909971666 \tabularnewline
34 & 0.364417990097578 & 0.728835980195156 & 0.635582009902422 \tabularnewline
35 & 0.316731129011648 & 0.633462258023295 & 0.683268870988352 \tabularnewline
36 & 0.860931901991453 & 0.278136196017093 & 0.139068098008547 \tabularnewline
37 & 0.833784269101071 & 0.332431461797858 & 0.166215730898929 \tabularnewline
38 & 0.815908223175731 & 0.368183553648538 & 0.184091776824269 \tabularnewline
39 & 0.845995460841668 & 0.308009078316664 & 0.154004539158332 \tabularnewline
40 & 0.833294875401914 & 0.333410249196171 & 0.166705124598086 \tabularnewline
41 & 0.805468345234695 & 0.38906330953061 & 0.194531654765305 \tabularnewline
42 & 0.77830690674126 & 0.44338618651748 & 0.22169309325874 \tabularnewline
43 & 0.806418903660724 & 0.387162192678552 & 0.193581096339276 \tabularnewline
44 & 0.766433210732353 & 0.467133578535294 & 0.233566789267647 \tabularnewline
45 & 0.737113153184551 & 0.525773693630899 & 0.26288684681545 \tabularnewline
46 & 0.876363057854266 & 0.247273884291468 & 0.123636942145734 \tabularnewline
47 & 0.900800571347284 & 0.198398857305431 & 0.0991994286527155 \tabularnewline
48 & 0.879402218524694 & 0.241195562950613 & 0.120597781475306 \tabularnewline
49 & 0.87567941211522 & 0.248641175769561 & 0.12432058788478 \tabularnewline
50 & 0.871764825379814 & 0.256470349240372 & 0.128235174620186 \tabularnewline
51 & 0.846900251192418 & 0.306199497615164 & 0.153099748807582 \tabularnewline
52 & 0.819432031786981 & 0.361135936426039 & 0.180567968213019 \tabularnewline
53 & 0.842177557037126 & 0.315644885925747 & 0.157822442962874 \tabularnewline
54 & 0.811128189910698 & 0.377743620178605 & 0.188871810089302 \tabularnewline
55 & 0.817497006031359 & 0.365005987937282 & 0.182502993968641 \tabularnewline
56 & 0.807339338054385 & 0.385321323891231 & 0.192660661945615 \tabularnewline
57 & 0.771502706820386 & 0.456994586359228 & 0.228497293179614 \tabularnewline
58 & 0.748574028801578 & 0.502851942396844 & 0.251425971198422 \tabularnewline
59 & 0.711812129673043 & 0.576375740653914 & 0.288187870326957 \tabularnewline
60 & 0.702382423390307 & 0.595235153219386 & 0.297617576609693 \tabularnewline
61 & 0.659725304580624 & 0.680549390838751 & 0.340274695419376 \tabularnewline
62 & 0.614654181284059 & 0.770691637431881 & 0.385345818715941 \tabularnewline
63 & 0.573039951794309 & 0.853920096411383 & 0.426960048205691 \tabularnewline
64 & 0.525931777936096 & 0.948136444127807 & 0.474068222063904 \tabularnewline
65 & 0.481643663043292 & 0.963287326086584 & 0.518356336956708 \tabularnewline
66 & 0.443949373241746 & 0.887898746483493 & 0.556050626758254 \tabularnewline
67 & 0.432266666222246 & 0.864533332444493 & 0.567733333777754 \tabularnewline
68 & 0.554217142127738 & 0.891565715744524 & 0.445782857872262 \tabularnewline
69 & 0.69335340095911 & 0.61329319808178 & 0.30664659904089 \tabularnewline
70 & 0.654297302850408 & 0.691405394299184 & 0.345702697149592 \tabularnewline
71 & 0.780230626754312 & 0.439538746491376 & 0.219769373245688 \tabularnewline
72 & 0.745435513785973 & 0.509128972428054 & 0.254564486214027 \tabularnewline
73 & 0.735642323037613 & 0.528715353924774 & 0.264357676962387 \tabularnewline
74 & 0.715527997301223 & 0.568944005397554 & 0.284472002698777 \tabularnewline
75 & 0.673817383032472 & 0.652365233935056 & 0.326182616967528 \tabularnewline
76 & 0.727938968902194 & 0.544122062195611 & 0.272061031097806 \tabularnewline
77 & 0.688271213665254 & 0.623457572669493 & 0.311728786334746 \tabularnewline
78 & 0.669652063806259 & 0.660695872387483 & 0.330347936193741 \tabularnewline
79 & 0.682694372266192 & 0.634611255467617 & 0.317305627733808 \tabularnewline
80 & 0.639158130861603 & 0.721683738276794 & 0.360841869138397 \tabularnewline
81 & 0.600927312870116 & 0.798145374259768 & 0.399072687129884 \tabularnewline
82 & 0.735387100469921 & 0.529225799060158 & 0.264612899530079 \tabularnewline
83 & 0.698482895838956 & 0.603034208322089 & 0.301517104161045 \tabularnewline
84 & 0.667641456032952 & 0.664717087934096 & 0.332358543967048 \tabularnewline
85 & 0.625470188541783 & 0.749059622916434 & 0.374529811458217 \tabularnewline
86 & 0.614626212393237 & 0.770747575213527 & 0.385373787606763 \tabularnewline
87 & 0.569902963095659 & 0.860194073808681 & 0.430097036904341 \tabularnewline
88 & 0.527198152929319 & 0.945603694141362 & 0.472801847070681 \tabularnewline
89 & 0.501253113402099 & 0.997493773195802 & 0.498746886597901 \tabularnewline
90 & 0.461703172626218 & 0.923406345252435 & 0.538296827373782 \tabularnewline
91 & 0.45319237515239 & 0.90638475030478 & 0.54680762484761 \tabularnewline
92 & 0.408306648129464 & 0.816613296258927 & 0.591693351870536 \tabularnewline
93 & 0.36704801993295 & 0.734096039865899 & 0.63295198006705 \tabularnewline
94 & 0.323885743828983 & 0.647771487657965 & 0.676114256171017 \tabularnewline
95 & 0.361796042931261 & 0.723592085862523 & 0.638203957068739 \tabularnewline
96 & 0.326146952573663 & 0.652293905147326 & 0.673853047426337 \tabularnewline
97 & 0.283654113606602 & 0.567308227213205 & 0.716345886393398 \tabularnewline
98 & 0.275170037570281 & 0.550340075140562 & 0.724829962429719 \tabularnewline
99 & 0.236856489216712 & 0.473712978433424 & 0.763143510783288 \tabularnewline
100 & 0.214921566063477 & 0.429843132126954 & 0.785078433936523 \tabularnewline
101 & 0.204361876849222 & 0.408723753698445 & 0.795638123150778 \tabularnewline
102 & 0.179387469278542 & 0.358774938557083 & 0.820612530721458 \tabularnewline
103 & 0.20151147140894 & 0.403022942817879 & 0.79848852859106 \tabularnewline
104 & 0.171602746756943 & 0.343205493513886 & 0.828397253243057 \tabularnewline
105 & 0.15579566259306 & 0.31159132518612 & 0.84420433740694 \tabularnewline
106 & 0.174542112547145 & 0.34908422509429 & 0.825457887452855 \tabularnewline
107 & 0.14807044511938 & 0.296140890238761 & 0.85192955488062 \tabularnewline
108 & 0.120686416581892 & 0.241372833163785 & 0.879313583418108 \tabularnewline
109 & 0.113926031434822 & 0.227852062869645 & 0.886073968565178 \tabularnewline
110 & 0.113966653764092 & 0.227933307528184 & 0.886033346235908 \tabularnewline
111 & 0.100836963014473 & 0.201673926028946 & 0.899163036985527 \tabularnewline
112 & 0.0863170009260069 & 0.172634001852014 & 0.913682999073993 \tabularnewline
113 & 0.112816796663666 & 0.225633593327333 & 0.887183203336334 \tabularnewline
114 & 0.108197059728123 & 0.216394119456246 & 0.891802940271877 \tabularnewline
115 & 0.130976870236088 & 0.261953740472176 & 0.869023129763912 \tabularnewline
116 & 0.135152830404851 & 0.270305660809703 & 0.864847169595149 \tabularnewline
117 & 0.116104237214604 & 0.232208474429207 & 0.883895762785396 \tabularnewline
118 & 0.116170915049586 & 0.232341830099173 & 0.883829084950414 \tabularnewline
119 & 0.107415565627024 & 0.214831131254049 & 0.892584434372976 \tabularnewline
120 & 0.120896152217739 & 0.241792304435478 & 0.879103847782261 \tabularnewline
121 & 0.098496641422782 & 0.196993282845564 & 0.901503358577218 \tabularnewline
122 & 0.0857533919396129 & 0.171506783879226 & 0.914246608060387 \tabularnewline
123 & 0.0841650539093875 & 0.168330107818775 & 0.915834946090613 \tabularnewline
124 & 0.0651713013557386 & 0.130342602711477 & 0.934828698644261 \tabularnewline
125 & 0.0496822046561457 & 0.0993644093122914 & 0.950317795343854 \tabularnewline
126 & 0.0371228355387517 & 0.0742456710775034 & 0.962877164461248 \tabularnewline
127 & 0.026681848764743 & 0.053363697529486 & 0.973318151235257 \tabularnewline
128 & 0.0250671081562468 & 0.0501342163124936 & 0.974932891843753 \tabularnewline
129 & 0.0273023596503103 & 0.0546047193006206 & 0.97269764034969 \tabularnewline
130 & 0.0270997288385305 & 0.0541994576770609 & 0.972900271161469 \tabularnewline
131 & 0.0292607302435675 & 0.0585214604871351 & 0.970739269756432 \tabularnewline
132 & 0.0310569029446484 & 0.0621138058892968 & 0.968943097055352 \tabularnewline
133 & 0.0646527283945668 & 0.129305456789134 & 0.935347271605433 \tabularnewline
134 & 0.0668618636957829 & 0.133723727391566 & 0.933138136304217 \tabularnewline
135 & 0.0504723469984326 & 0.100944693996865 & 0.949527653001567 \tabularnewline
136 & 0.0404411552187775 & 0.0808823104375549 & 0.959558844781223 \tabularnewline
137 & 0.0285336475096007 & 0.0570672950192014 & 0.971466352490399 \tabularnewline
138 & 0.0339328432343194 & 0.0678656864686387 & 0.966067156765681 \tabularnewline
139 & 0.0278154516744701 & 0.0556309033489401 & 0.97218454832553 \tabularnewline
140 & 0.0179055451604634 & 0.0358110903209269 & 0.982094454839537 \tabularnewline
141 & 0.459350994990592 & 0.918701989981184 & 0.540649005009408 \tabularnewline
142 & 0.398055057521769 & 0.796110115043538 & 0.601944942478231 \tabularnewline
143 & 0.326003270563126 & 0.652006541126252 & 0.673996729436874 \tabularnewline
144 & 0.242530349491632 & 0.485060698983264 & 0.757469650508368 \tabularnewline
145 & 0.171468521074368 & 0.342937042148735 & 0.828531478925632 \tabularnewline
146 & 0.201639254354152 & 0.403278508708304 & 0.798360745645848 \tabularnewline
147 & 0.219376314050751 & 0.438752628101502 & 0.780623685949249 \tabularnewline
148 & 0.460352831930854 & 0.920705663861709 & 0.539647168069145 \tabularnewline
149 & 0.443926383886624 & 0.887852767773249 & 0.556073616113376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&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]13[/C][C]0.735047729951156[/C][C]0.529904540097689[/C][C]0.264952270048844[/C][/ROW]
[ROW][C]14[/C][C]0.709365256450699[/C][C]0.581269487098602[/C][C]0.290634743549301[/C][/ROW]
[ROW][C]15[/C][C]0.587538167243798[/C][C]0.824923665512405[/C][C]0.412461832756202[/C][/ROW]
[ROW][C]16[/C][C]0.474375004510092[/C][C]0.948750009020185[/C][C]0.525624995489908[/C][/ROW]
[ROW][C]17[/C][C]0.395853691727587[/C][C]0.791707383455173[/C][C]0.604146308272413[/C][/ROW]
[ROW][C]18[/C][C]0.550048966762984[/C][C]0.899902066474032[/C][C]0.449951033237016[/C][/ROW]
[ROW][C]19[/C][C]0.463986093931025[/C][C]0.92797218786205[/C][C]0.536013906068975[/C][/ROW]
[ROW][C]20[/C][C]0.375244158844829[/C][C]0.750488317689657[/C][C]0.624755841155171[/C][/ROW]
[ROW][C]21[/C][C]0.304752051841109[/C][C]0.609504103682217[/C][C]0.695247948158891[/C][/ROW]
[ROW][C]22[/C][C]0.296727279829474[/C][C]0.593454559658949[/C][C]0.703272720170526[/C][/ROW]
[ROW][C]23[/C][C]0.422143054107194[/C][C]0.844286108214387[/C][C]0.577856945892806[/C][/ROW]
[ROW][C]24[/C][C]0.494254924593276[/C][C]0.988509849186553[/C][C]0.505745075406724[/C][/ROW]
[ROW][C]25[/C][C]0.439541331992777[/C][C]0.879082663985553[/C][C]0.560458668007223[/C][/ROW]
[ROW][C]26[/C][C]0.371331906618848[/C][C]0.742663813237697[/C][C]0.628668093381152[/C][/ROW]
[ROW][C]27[/C][C]0.342230931022868[/C][C]0.684461862045737[/C][C]0.657769068977132[/C][/ROW]
[ROW][C]28[/C][C]0.445395286081876[/C][C]0.890790572163752[/C][C]0.554604713918124[/C][/ROW]
[ROW][C]29[/C][C]0.387572906390828[/C][C]0.775145812781656[/C][C]0.612427093609172[/C][/ROW]
[ROW][C]30[/C][C]0.496796078533675[/C][C]0.993592157067351[/C][C]0.503203921466325[/C][/ROW]
[ROW][C]31[/C][C]0.44587868433032[/C][C]0.891757368660639[/C][C]0.55412131566968[/C][/ROW]
[ROW][C]32[/C][C]0.408433170252312[/C][C]0.816866340504624[/C][C]0.591566829747688[/C][/ROW]
[ROW][C]33[/C][C]0.396415090028334[/C][C]0.792830180056667[/C][C]0.603584909971666[/C][/ROW]
[ROW][C]34[/C][C]0.364417990097578[/C][C]0.728835980195156[/C][C]0.635582009902422[/C][/ROW]
[ROW][C]35[/C][C]0.316731129011648[/C][C]0.633462258023295[/C][C]0.683268870988352[/C][/ROW]
[ROW][C]36[/C][C]0.860931901991453[/C][C]0.278136196017093[/C][C]0.139068098008547[/C][/ROW]
[ROW][C]37[/C][C]0.833784269101071[/C][C]0.332431461797858[/C][C]0.166215730898929[/C][/ROW]
[ROW][C]38[/C][C]0.815908223175731[/C][C]0.368183553648538[/C][C]0.184091776824269[/C][/ROW]
[ROW][C]39[/C][C]0.845995460841668[/C][C]0.308009078316664[/C][C]0.154004539158332[/C][/ROW]
[ROW][C]40[/C][C]0.833294875401914[/C][C]0.333410249196171[/C][C]0.166705124598086[/C][/ROW]
[ROW][C]41[/C][C]0.805468345234695[/C][C]0.38906330953061[/C][C]0.194531654765305[/C][/ROW]
[ROW][C]42[/C][C]0.77830690674126[/C][C]0.44338618651748[/C][C]0.22169309325874[/C][/ROW]
[ROW][C]43[/C][C]0.806418903660724[/C][C]0.387162192678552[/C][C]0.193581096339276[/C][/ROW]
[ROW][C]44[/C][C]0.766433210732353[/C][C]0.467133578535294[/C][C]0.233566789267647[/C][/ROW]
[ROW][C]45[/C][C]0.737113153184551[/C][C]0.525773693630899[/C][C]0.26288684681545[/C][/ROW]
[ROW][C]46[/C][C]0.876363057854266[/C][C]0.247273884291468[/C][C]0.123636942145734[/C][/ROW]
[ROW][C]47[/C][C]0.900800571347284[/C][C]0.198398857305431[/C][C]0.0991994286527155[/C][/ROW]
[ROW][C]48[/C][C]0.879402218524694[/C][C]0.241195562950613[/C][C]0.120597781475306[/C][/ROW]
[ROW][C]49[/C][C]0.87567941211522[/C][C]0.248641175769561[/C][C]0.12432058788478[/C][/ROW]
[ROW][C]50[/C][C]0.871764825379814[/C][C]0.256470349240372[/C][C]0.128235174620186[/C][/ROW]
[ROW][C]51[/C][C]0.846900251192418[/C][C]0.306199497615164[/C][C]0.153099748807582[/C][/ROW]
[ROW][C]52[/C][C]0.819432031786981[/C][C]0.361135936426039[/C][C]0.180567968213019[/C][/ROW]
[ROW][C]53[/C][C]0.842177557037126[/C][C]0.315644885925747[/C][C]0.157822442962874[/C][/ROW]
[ROW][C]54[/C][C]0.811128189910698[/C][C]0.377743620178605[/C][C]0.188871810089302[/C][/ROW]
[ROW][C]55[/C][C]0.817497006031359[/C][C]0.365005987937282[/C][C]0.182502993968641[/C][/ROW]
[ROW][C]56[/C][C]0.807339338054385[/C][C]0.385321323891231[/C][C]0.192660661945615[/C][/ROW]
[ROW][C]57[/C][C]0.771502706820386[/C][C]0.456994586359228[/C][C]0.228497293179614[/C][/ROW]
[ROW][C]58[/C][C]0.748574028801578[/C][C]0.502851942396844[/C][C]0.251425971198422[/C][/ROW]
[ROW][C]59[/C][C]0.711812129673043[/C][C]0.576375740653914[/C][C]0.288187870326957[/C][/ROW]
[ROW][C]60[/C][C]0.702382423390307[/C][C]0.595235153219386[/C][C]0.297617576609693[/C][/ROW]
[ROW][C]61[/C][C]0.659725304580624[/C][C]0.680549390838751[/C][C]0.340274695419376[/C][/ROW]
[ROW][C]62[/C][C]0.614654181284059[/C][C]0.770691637431881[/C][C]0.385345818715941[/C][/ROW]
[ROW][C]63[/C][C]0.573039951794309[/C][C]0.853920096411383[/C][C]0.426960048205691[/C][/ROW]
[ROW][C]64[/C][C]0.525931777936096[/C][C]0.948136444127807[/C][C]0.474068222063904[/C][/ROW]
[ROW][C]65[/C][C]0.481643663043292[/C][C]0.963287326086584[/C][C]0.518356336956708[/C][/ROW]
[ROW][C]66[/C][C]0.443949373241746[/C][C]0.887898746483493[/C][C]0.556050626758254[/C][/ROW]
[ROW][C]67[/C][C]0.432266666222246[/C][C]0.864533332444493[/C][C]0.567733333777754[/C][/ROW]
[ROW][C]68[/C][C]0.554217142127738[/C][C]0.891565715744524[/C][C]0.445782857872262[/C][/ROW]
[ROW][C]69[/C][C]0.69335340095911[/C][C]0.61329319808178[/C][C]0.30664659904089[/C][/ROW]
[ROW][C]70[/C][C]0.654297302850408[/C][C]0.691405394299184[/C][C]0.345702697149592[/C][/ROW]
[ROW][C]71[/C][C]0.780230626754312[/C][C]0.439538746491376[/C][C]0.219769373245688[/C][/ROW]
[ROW][C]72[/C][C]0.745435513785973[/C][C]0.509128972428054[/C][C]0.254564486214027[/C][/ROW]
[ROW][C]73[/C][C]0.735642323037613[/C][C]0.528715353924774[/C][C]0.264357676962387[/C][/ROW]
[ROW][C]74[/C][C]0.715527997301223[/C][C]0.568944005397554[/C][C]0.284472002698777[/C][/ROW]
[ROW][C]75[/C][C]0.673817383032472[/C][C]0.652365233935056[/C][C]0.326182616967528[/C][/ROW]
[ROW][C]76[/C][C]0.727938968902194[/C][C]0.544122062195611[/C][C]0.272061031097806[/C][/ROW]
[ROW][C]77[/C][C]0.688271213665254[/C][C]0.623457572669493[/C][C]0.311728786334746[/C][/ROW]
[ROW][C]78[/C][C]0.669652063806259[/C][C]0.660695872387483[/C][C]0.330347936193741[/C][/ROW]
[ROW][C]79[/C][C]0.682694372266192[/C][C]0.634611255467617[/C][C]0.317305627733808[/C][/ROW]
[ROW][C]80[/C][C]0.639158130861603[/C][C]0.721683738276794[/C][C]0.360841869138397[/C][/ROW]
[ROW][C]81[/C][C]0.600927312870116[/C][C]0.798145374259768[/C][C]0.399072687129884[/C][/ROW]
[ROW][C]82[/C][C]0.735387100469921[/C][C]0.529225799060158[/C][C]0.264612899530079[/C][/ROW]
[ROW][C]83[/C][C]0.698482895838956[/C][C]0.603034208322089[/C][C]0.301517104161045[/C][/ROW]
[ROW][C]84[/C][C]0.667641456032952[/C][C]0.664717087934096[/C][C]0.332358543967048[/C][/ROW]
[ROW][C]85[/C][C]0.625470188541783[/C][C]0.749059622916434[/C][C]0.374529811458217[/C][/ROW]
[ROW][C]86[/C][C]0.614626212393237[/C][C]0.770747575213527[/C][C]0.385373787606763[/C][/ROW]
[ROW][C]87[/C][C]0.569902963095659[/C][C]0.860194073808681[/C][C]0.430097036904341[/C][/ROW]
[ROW][C]88[/C][C]0.527198152929319[/C][C]0.945603694141362[/C][C]0.472801847070681[/C][/ROW]
[ROW][C]89[/C][C]0.501253113402099[/C][C]0.997493773195802[/C][C]0.498746886597901[/C][/ROW]
[ROW][C]90[/C][C]0.461703172626218[/C][C]0.923406345252435[/C][C]0.538296827373782[/C][/ROW]
[ROW][C]91[/C][C]0.45319237515239[/C][C]0.90638475030478[/C][C]0.54680762484761[/C][/ROW]
[ROW][C]92[/C][C]0.408306648129464[/C][C]0.816613296258927[/C][C]0.591693351870536[/C][/ROW]
[ROW][C]93[/C][C]0.36704801993295[/C][C]0.734096039865899[/C][C]0.63295198006705[/C][/ROW]
[ROW][C]94[/C][C]0.323885743828983[/C][C]0.647771487657965[/C][C]0.676114256171017[/C][/ROW]
[ROW][C]95[/C][C]0.361796042931261[/C][C]0.723592085862523[/C][C]0.638203957068739[/C][/ROW]
[ROW][C]96[/C][C]0.326146952573663[/C][C]0.652293905147326[/C][C]0.673853047426337[/C][/ROW]
[ROW][C]97[/C][C]0.283654113606602[/C][C]0.567308227213205[/C][C]0.716345886393398[/C][/ROW]
[ROW][C]98[/C][C]0.275170037570281[/C][C]0.550340075140562[/C][C]0.724829962429719[/C][/ROW]
[ROW][C]99[/C][C]0.236856489216712[/C][C]0.473712978433424[/C][C]0.763143510783288[/C][/ROW]
[ROW][C]100[/C][C]0.214921566063477[/C][C]0.429843132126954[/C][C]0.785078433936523[/C][/ROW]
[ROW][C]101[/C][C]0.204361876849222[/C][C]0.408723753698445[/C][C]0.795638123150778[/C][/ROW]
[ROW][C]102[/C][C]0.179387469278542[/C][C]0.358774938557083[/C][C]0.820612530721458[/C][/ROW]
[ROW][C]103[/C][C]0.20151147140894[/C][C]0.403022942817879[/C][C]0.79848852859106[/C][/ROW]
[ROW][C]104[/C][C]0.171602746756943[/C][C]0.343205493513886[/C][C]0.828397253243057[/C][/ROW]
[ROW][C]105[/C][C]0.15579566259306[/C][C]0.31159132518612[/C][C]0.84420433740694[/C][/ROW]
[ROW][C]106[/C][C]0.174542112547145[/C][C]0.34908422509429[/C][C]0.825457887452855[/C][/ROW]
[ROW][C]107[/C][C]0.14807044511938[/C][C]0.296140890238761[/C][C]0.85192955488062[/C][/ROW]
[ROW][C]108[/C][C]0.120686416581892[/C][C]0.241372833163785[/C][C]0.879313583418108[/C][/ROW]
[ROW][C]109[/C][C]0.113926031434822[/C][C]0.227852062869645[/C][C]0.886073968565178[/C][/ROW]
[ROW][C]110[/C][C]0.113966653764092[/C][C]0.227933307528184[/C][C]0.886033346235908[/C][/ROW]
[ROW][C]111[/C][C]0.100836963014473[/C][C]0.201673926028946[/C][C]0.899163036985527[/C][/ROW]
[ROW][C]112[/C][C]0.0863170009260069[/C][C]0.172634001852014[/C][C]0.913682999073993[/C][/ROW]
[ROW][C]113[/C][C]0.112816796663666[/C][C]0.225633593327333[/C][C]0.887183203336334[/C][/ROW]
[ROW][C]114[/C][C]0.108197059728123[/C][C]0.216394119456246[/C][C]0.891802940271877[/C][/ROW]
[ROW][C]115[/C][C]0.130976870236088[/C][C]0.261953740472176[/C][C]0.869023129763912[/C][/ROW]
[ROW][C]116[/C][C]0.135152830404851[/C][C]0.270305660809703[/C][C]0.864847169595149[/C][/ROW]
[ROW][C]117[/C][C]0.116104237214604[/C][C]0.232208474429207[/C][C]0.883895762785396[/C][/ROW]
[ROW][C]118[/C][C]0.116170915049586[/C][C]0.232341830099173[/C][C]0.883829084950414[/C][/ROW]
[ROW][C]119[/C][C]0.107415565627024[/C][C]0.214831131254049[/C][C]0.892584434372976[/C][/ROW]
[ROW][C]120[/C][C]0.120896152217739[/C][C]0.241792304435478[/C][C]0.879103847782261[/C][/ROW]
[ROW][C]121[/C][C]0.098496641422782[/C][C]0.196993282845564[/C][C]0.901503358577218[/C][/ROW]
[ROW][C]122[/C][C]0.0857533919396129[/C][C]0.171506783879226[/C][C]0.914246608060387[/C][/ROW]
[ROW][C]123[/C][C]0.0841650539093875[/C][C]0.168330107818775[/C][C]0.915834946090613[/C][/ROW]
[ROW][C]124[/C][C]0.0651713013557386[/C][C]0.130342602711477[/C][C]0.934828698644261[/C][/ROW]
[ROW][C]125[/C][C]0.0496822046561457[/C][C]0.0993644093122914[/C][C]0.950317795343854[/C][/ROW]
[ROW][C]126[/C][C]0.0371228355387517[/C][C]0.0742456710775034[/C][C]0.962877164461248[/C][/ROW]
[ROW][C]127[/C][C]0.026681848764743[/C][C]0.053363697529486[/C][C]0.973318151235257[/C][/ROW]
[ROW][C]128[/C][C]0.0250671081562468[/C][C]0.0501342163124936[/C][C]0.974932891843753[/C][/ROW]
[ROW][C]129[/C][C]0.0273023596503103[/C][C]0.0546047193006206[/C][C]0.97269764034969[/C][/ROW]
[ROW][C]130[/C][C]0.0270997288385305[/C][C]0.0541994576770609[/C][C]0.972900271161469[/C][/ROW]
[ROW][C]131[/C][C]0.0292607302435675[/C][C]0.0585214604871351[/C][C]0.970739269756432[/C][/ROW]
[ROW][C]132[/C][C]0.0310569029446484[/C][C]0.0621138058892968[/C][C]0.968943097055352[/C][/ROW]
[ROW][C]133[/C][C]0.0646527283945668[/C][C]0.129305456789134[/C][C]0.935347271605433[/C][/ROW]
[ROW][C]134[/C][C]0.0668618636957829[/C][C]0.133723727391566[/C][C]0.933138136304217[/C][/ROW]
[ROW][C]135[/C][C]0.0504723469984326[/C][C]0.100944693996865[/C][C]0.949527653001567[/C][/ROW]
[ROW][C]136[/C][C]0.0404411552187775[/C][C]0.0808823104375549[/C][C]0.959558844781223[/C][/ROW]
[ROW][C]137[/C][C]0.0285336475096007[/C][C]0.0570672950192014[/C][C]0.971466352490399[/C][/ROW]
[ROW][C]138[/C][C]0.0339328432343194[/C][C]0.0678656864686387[/C][C]0.966067156765681[/C][/ROW]
[ROW][C]139[/C][C]0.0278154516744701[/C][C]0.0556309033489401[/C][C]0.97218454832553[/C][/ROW]
[ROW][C]140[/C][C]0.0179055451604634[/C][C]0.0358110903209269[/C][C]0.982094454839537[/C][/ROW]
[ROW][C]141[/C][C]0.459350994990592[/C][C]0.918701989981184[/C][C]0.540649005009408[/C][/ROW]
[ROW][C]142[/C][C]0.398055057521769[/C][C]0.796110115043538[/C][C]0.601944942478231[/C][/ROW]
[ROW][C]143[/C][C]0.326003270563126[/C][C]0.652006541126252[/C][C]0.673996729436874[/C][/ROW]
[ROW][C]144[/C][C]0.242530349491632[/C][C]0.485060698983264[/C][C]0.757469650508368[/C][/ROW]
[ROW][C]145[/C][C]0.171468521074368[/C][C]0.342937042148735[/C][C]0.828531478925632[/C][/ROW]
[ROW][C]146[/C][C]0.201639254354152[/C][C]0.403278508708304[/C][C]0.798360745645848[/C][/ROW]
[ROW][C]147[/C][C]0.219376314050751[/C][C]0.438752628101502[/C][C]0.780623685949249[/C][/ROW]
[ROW][C]148[/C][C]0.460352831930854[/C][C]0.920705663861709[/C][C]0.539647168069145[/C][/ROW]
[ROW][C]149[/C][C]0.443926383886624[/C][C]0.887852767773249[/C][C]0.556073616113376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7350477299511560.5299045400976890.264952270048844
140.7093652564506990.5812694870986020.290634743549301
150.5875381672437980.8249236655124050.412461832756202
160.4743750045100920.9487500090201850.525624995489908
170.3958536917275870.7917073834551730.604146308272413
180.5500489667629840.8999020664740320.449951033237016
190.4639860939310250.927972187862050.536013906068975
200.3752441588448290.7504883176896570.624755841155171
210.3047520518411090.6095041036822170.695247948158891
220.2967272798294740.5934545596589490.703272720170526
230.4221430541071940.8442861082143870.577856945892806
240.4942549245932760.9885098491865530.505745075406724
250.4395413319927770.8790826639855530.560458668007223
260.3713319066188480.7426638132376970.628668093381152
270.3422309310228680.6844618620457370.657769068977132
280.4453952860818760.8907905721637520.554604713918124
290.3875729063908280.7751458127816560.612427093609172
300.4967960785336750.9935921570673510.503203921466325
310.445878684330320.8917573686606390.55412131566968
320.4084331702523120.8168663405046240.591566829747688
330.3964150900283340.7928301800566670.603584909971666
340.3644179900975780.7288359801951560.635582009902422
350.3167311290116480.6334622580232950.683268870988352
360.8609319019914530.2781361960170930.139068098008547
370.8337842691010710.3324314617978580.166215730898929
380.8159082231757310.3681835536485380.184091776824269
390.8459954608416680.3080090783166640.154004539158332
400.8332948754019140.3334102491961710.166705124598086
410.8054683452346950.389063309530610.194531654765305
420.778306906741260.443386186517480.22169309325874
430.8064189036607240.3871621926785520.193581096339276
440.7664332107323530.4671335785352940.233566789267647
450.7371131531845510.5257736936308990.26288684681545
460.8763630578542660.2472738842914680.123636942145734
470.9008005713472840.1983988573054310.0991994286527155
480.8794022185246940.2411955629506130.120597781475306
490.875679412115220.2486411757695610.12432058788478
500.8717648253798140.2564703492403720.128235174620186
510.8469002511924180.3061994976151640.153099748807582
520.8194320317869810.3611359364260390.180567968213019
530.8421775570371260.3156448859257470.157822442962874
540.8111281899106980.3777436201786050.188871810089302
550.8174970060313590.3650059879372820.182502993968641
560.8073393380543850.3853213238912310.192660661945615
570.7715027068203860.4569945863592280.228497293179614
580.7485740288015780.5028519423968440.251425971198422
590.7118121296730430.5763757406539140.288187870326957
600.7023824233903070.5952351532193860.297617576609693
610.6597253045806240.6805493908387510.340274695419376
620.6146541812840590.7706916374318810.385345818715941
630.5730399517943090.8539200964113830.426960048205691
640.5259317779360960.9481364441278070.474068222063904
650.4816436630432920.9632873260865840.518356336956708
660.4439493732417460.8878987464834930.556050626758254
670.4322666662222460.8645333324444930.567733333777754
680.5542171421277380.8915657157445240.445782857872262
690.693353400959110.613293198081780.30664659904089
700.6542973028504080.6914053942991840.345702697149592
710.7802306267543120.4395387464913760.219769373245688
720.7454355137859730.5091289724280540.254564486214027
730.7356423230376130.5287153539247740.264357676962387
740.7155279973012230.5689440053975540.284472002698777
750.6738173830324720.6523652339350560.326182616967528
760.7279389689021940.5441220621956110.272061031097806
770.6882712136652540.6234575726694930.311728786334746
780.6696520638062590.6606958723874830.330347936193741
790.6826943722661920.6346112554676170.317305627733808
800.6391581308616030.7216837382767940.360841869138397
810.6009273128701160.7981453742597680.399072687129884
820.7353871004699210.5292257990601580.264612899530079
830.6984828958389560.6030342083220890.301517104161045
840.6676414560329520.6647170879340960.332358543967048
850.6254701885417830.7490596229164340.374529811458217
860.6146262123932370.7707475752135270.385373787606763
870.5699029630956590.8601940738086810.430097036904341
880.5271981529293190.9456036941413620.472801847070681
890.5012531134020990.9974937731958020.498746886597901
900.4617031726262180.9234063452524350.538296827373782
910.453192375152390.906384750304780.54680762484761
920.4083066481294640.8166132962589270.591693351870536
930.367048019932950.7340960398658990.63295198006705
940.3238857438289830.6477714876579650.676114256171017
950.3617960429312610.7235920858625230.638203957068739
960.3261469525736630.6522939051473260.673853047426337
970.2836541136066020.5673082272132050.716345886393398
980.2751700375702810.5503400751405620.724829962429719
990.2368564892167120.4737129784334240.763143510783288
1000.2149215660634770.4298431321269540.785078433936523
1010.2043618768492220.4087237536984450.795638123150778
1020.1793874692785420.3587749385570830.820612530721458
1030.201511471408940.4030229428178790.79848852859106
1040.1716027467569430.3432054935138860.828397253243057
1050.155795662593060.311591325186120.84420433740694
1060.1745421125471450.349084225094290.825457887452855
1070.148070445119380.2961408902387610.85192955488062
1080.1206864165818920.2413728331637850.879313583418108
1090.1139260314348220.2278520628696450.886073968565178
1100.1139666537640920.2279333075281840.886033346235908
1110.1008369630144730.2016739260289460.899163036985527
1120.08631700092600690.1726340018520140.913682999073993
1130.1128167966636660.2256335933273330.887183203336334
1140.1081970597281230.2163941194562460.891802940271877
1150.1309768702360880.2619537404721760.869023129763912
1160.1351528304048510.2703056608097030.864847169595149
1170.1161042372146040.2322084744292070.883895762785396
1180.1161709150495860.2323418300991730.883829084950414
1190.1074155656270240.2148311312540490.892584434372976
1200.1208961522177390.2417923044354780.879103847782261
1210.0984966414227820.1969932828455640.901503358577218
1220.08575339193961290.1715067838792260.914246608060387
1230.08416505390938750.1683301078187750.915834946090613
1240.06517130135573860.1303426027114770.934828698644261
1250.04968220465614570.09936440931229140.950317795343854
1260.03712283553875170.07424567107750340.962877164461248
1270.0266818487647430.0533636975294860.973318151235257
1280.02506710815624680.05013421631249360.974932891843753
1290.02730235965031030.05460471930062060.97269764034969
1300.02709972883853050.05419945767706090.972900271161469
1310.02926073024356750.05852146048713510.970739269756432
1320.03105690294464840.06211380588929680.968943097055352
1330.06465272839456680.1293054567891340.935347271605433
1340.06686186369578290.1337237273915660.933138136304217
1350.05047234699843260.1009446939968650.949527653001567
1360.04044115521877750.08088231043755490.959558844781223
1370.02853364750960070.05706729501920140.971466352490399
1380.03393284323431940.06786568646863870.966067156765681
1390.02781545167447010.05563090334894010.97218454832553
1400.01790554516046340.03581109032092690.982094454839537
1410.4593509949905920.9187019899811840.540649005009408
1420.3980550575217690.7961101150435380.601944942478231
1430.3260032705631260.6520065411262520.673996729436874
1440.2425303494916320.4850606989832640.757469650508368
1450.1714685210743680.3429370421487350.828531478925632
1460.2016392543541520.4032785087083040.798360745645848
1470.2193763140507510.4387526281015020.780623685949249
1480.4603528319308540.9207056638617090.539647168069145
1490.4439263838866240.8878527677732490.556073616113376






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level130.0948905109489051OK

\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 & 1 & 0.0072992700729927 & OK \tabularnewline
10% type I error level & 13 & 0.0948905109489051 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185351&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]1[/C][C]0.0072992700729927[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0948905109489051[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185351&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185351&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level130.0948905109489051OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 0 ;
Parameters (R input):
par1 = 3 ; 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')
}