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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 computationFri, 12 Dec 2014 14:16:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/12/t1418394221ffscq0uy06lyhkj.htm/, Retrieved Thu, 16 May 2024 15:35:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266736, Retrieved Thu, 16 May 2024 15:35:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-12 14:16:19] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.1 0 1 22 23 48 23 12
2.7 0 1 22 22 50 16 45
2.1 0 1 22 21 150 33 37
2.1 0 1 20 25 154 32 37
2.1 1 0 19 30 109 37 108
2.1 1 1 20 17 68 14 10
2.1 0 1 22 27 194 52 68
2.1 0 0 21 23 158 75 72
2.1 0 1 21 23 159 72 143
2.1 0 0 21 18 67 15 9
2.4 0 0 21 18 147 29 55
1.95 0 1 21 23 39 13 17
2.1 0 1 21 19 100 40 37
2.1 0 1 21 15 111 19 27
1.95 0 1 22 20 138 24 37
2.1 0 1 24 16 101 121 58
2.4 1 1 21 24 131 93 66
2.1 0 1 22 25 101 36 21
2.25 0 1 20 25 114 23 19
2.4 0 0 21 19 165 85 78
2.25 0 1 24 19 114 41 35
2.55 0 1 25 16 111 46 48
1.95 0 1 22 19 75 18 27
2.4 0 1 21 19 82 35 43
2.1 0 1 21 23 121 17 30
2.1 0 1 22 21 32 4 25
2.4 0 0 23 22 150 28 69
2.1 0 1 24 19 117 44 72
2.1 1 1 20 20 71 10 23
2.25 0 1 22 20 165 38 13
2.25 0 1 25 3 154 57 61
2.4 0 1 22 23 126 23 43
2.1 0 0 22 14 138 26 22
2.1 0 0 21 23 149 36 51
2.4 0 0 21 20 145 22 67
2.1 0 1 21 15 120 40 36
1.95 0 0 22 13 138 18 21
2.1 0 0 22 16 109 31 44
2.25 0 0 22 7 132 11 45
2.25 0 1 21 24 172 38 34
2.4 0 0 22 17 169 24 36
2.25 0 1 23 24 114 37 72
2.25 0 1 21 24 156 37 39
2.1 0 0 21 19 172 22 43
2.1 1 1 21 25 68 15 25
2.1 1 1 19 20 89 2 56
2.7 0 1 21 28 167 43 80
2.1 0 0 21 23 113 31 40
2.1 1 0 19 27 115 29 73
2.25 1 0 18 18 78 45 34
2.7 1 0 19 28 118 25 72
2.4 1 1 21 21 87 4 42
2.1 0 0 22 19 173 31 61
2.1 0 1 22 23 2 -4 23
2.4 1 0 19 27 162 66 74
1.95 1 1 20 22 49 61 16
2.7 1 0 19 28 122 32 66
2.1 1 1 21 25 96 31 9
2.25 1 0 19 21 100 39 41
2.1 1 0 20 22 82 19 57
2.7 1 1 21 28 100 31 48
2.1 1 0 19 20 115 36 51
2.1 1 1 21 29 141 42 53
1.65 0 1 21 25 165 21 29
1.65 0 1 21 25 165 21 29
2.1 1 1 19 20 110 25 55
2.1 0 1 25 20 118 32 54
2.1 0 0 21 16 158 26 43
2.1 1 1 20 20 146 28 51
2.1 0 0 25 20 49 32 20
2.4 1 0 19 23 90 41 79
2.4 1 0 20 18 121 29 39
2.1 0 1 22 25 155 33 61
2.25 1 0 19 18 104 17 55
2.4 1 1 20 19 147 13 30
2.1 1 0 19 25 110 32 55
2.1 1 0 19 25 108 30 22
2.4 1 0 18 25 113 34 37
2.4 1 0 19 24 115 59 2
2.1 1 1 21 19 61 13 38
2.1 1 1 19 26 60 23 27
2.4 1 1 20 10 109 10 56
2.1 1 1 20 17 68 5 25
2.7 1 0 19 13 111 31 39
2.1 1 0 19 17 77 19 33
2.1 1 1 22 30 73 32 43
2.25 0 0 21 25 151 30 57
2.1 1 0 19 4 89 25 43
2.4 1 0 19 16 78 48 23
2.25 1 0 19 21 110 35 44
2.25 0 1 23 23 220 67 54
2.1 1 1 19 22 65 15 28
2.1 0 0 20 17 141 22 36
2.4 1 0 19 20 117 18 39
2.25 0 1 22 20 122 33 16
2.1 1 0 19 22 63 46 23
2.1 0 1 25 16 44 24 40
1.65 1 1 19 23 52 14 24
1.65 1 1 20 16 62 23 29
2.7 1 0 19 0 131 12 78
2.1 1 1 19 18 101 38 57
1.95 1 1 20 25 42 12 37
2.25 0 1 20 23 152 28 27
2.4 0 0 21 12 107 41 61
1.95 1 0 19 18 77 12 27
2.1 0 0 21 24 154 31 69
2.4 0 1 23 11 103 33 34
2.1 1 1 19 18 96 34 44
2.1 0 0 21 14 154 41 21
2.4 0 1 22 23 175 21 34
2.4 1 1 20 24 57 20 39
2.4 1 0 18 29 112 44 51
2.25 0 0 21 18 143 52 34
2.4 1 0 20 15 49 7 31
2.1 0 1 21 29 110 29 13
2.1 0 1 21 16 131 11 12
1.8 0 0 21 19 167 26 51
2.7 1 0 19 22 56 24 24
2.1 0 0 21 16 137 7 19
2.1 1 1 19 23 86 60 30
2.4 0 1 21 23 121 13 81
2.55 0 0 21 19 149 20 42
2.55 0 0 22 4 168 52 22
2.1 0 0 21 20 140 28 85
2.1 1 1 22 24 88 25 27
2.1 0 1 22 20 168 39 25
2.25 0 1 22 4 94 9 22
2.25 0 1 22 24 51 19 19
2.1 1 0 21 22 48 13 14
2.1 0 1 22 16 145 60 45
1.95 0 1 23 3 66 19 45
2.4 1 1 19 15 85 34 28
2.1 0 0 22 24 109 14 51
2.4 1 0 21 17 63 17 41
2.4 1 1 19 20 102 45 31
2.4 1 0 19 27 162 66 74
2.25 0 1 20 23 128 24 24
1.95 1 1 20 26 86 48 19
2.1 1 1 18 23 114 29 51
2.1 0 0 21 17 164 -2 73
2.55 0 1 21 20 119 51 24
2.1 0 0 20 22 126 2 61
2.1 0 1 20 19 132 24 23
2.1 0 1 21 24 142 40 14
1.95 0 0 21 19 83 20 54
2.25 1 1 19 23 94 19 51
2.4 1 0 19 15 81 16 62
1.95 0 1 21 27 166 20 36
2.1 1 0 19 26 110 40 59
2.1 1 1 19 22 64 27 24
1.95 0 0 24 22 93 25 26
2.1 1 0 19 18 104 49 54
2.1 1 1 19 15 105 39 39
1.95 1 1 20 22 49 61 16
2.1 1 0 19 27 88 19 36
1.95 1 1 19 10 95 67 31
2.4 1 1 19 20 102 45 31
2.4 1 0 19 17 99 30 42
2.4 1 1 19 23 63 8 39
1.95 1 0 19 19 76 19 25
2.7 1 0 20 13 109 52 31
2.1 1 1 20 27 117 22 38
1.95 1 1 19 23 57 17 31
2.1 1 0 21 16 120 33 17
1.95 1 1 19 25 73 34 22
2.1 1 0 19 2 91 22 55
2.25 1 0 19 26 108 30 62
2.7 1 1 21 20 105 25 51
2.1 0 0 22 23 117 38 30
2.4 1 0 19 22 119 26 49
1.35 1 1 19 24 31 13 16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.73443 + 0.10744programma[t] -0.0522673gender[t] + 0.0158344age[t] -0.00398325NUMERACYTOT[t] + 0.000888826LFM[t] + 0.000528732PRH[t] + 0.00181426CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.73443 +  0.10744programma[t] -0.0522673gender[t] +  0.0158344age[t] -0.00398325NUMERACYTOT[t] +  0.000888826LFM[t] +  0.000528732PRH[t] +  0.00181426CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.73443 +  0.10744programma[t] -0.0522673gender[t] +  0.0158344age[t] -0.00398325NUMERACYTOT[t] +  0.000888826LFM[t] +  0.000528732PRH[t] +  0.00181426CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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
PA[t] = + 1.73443 + 0.10744programma[t] -0.0522673gender[t] + 0.0158344age[t] -0.00398325NUMERACYTOT[t] + 0.000888826LFM[t] + 0.000528732PRH[t] + 0.00181426CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.734430.3862314.4911.33831e-056.69153e-06
programma0.107440.05526311.9440.05359770.0267989
gender-0.05226730.0358667-1.4570.1469670.0734836
age0.01583440.01671950.94710.3450080.172504
NUMERACYTOT-0.003983250.00306179-1.3010.1951110.0975553
LFM0.0008888260.0005619921.5820.1156880.0578439
PRH0.0005287320.0009980830.52970.5970080.298504
CH0.001814260.0008995862.0170.04536040.0226802

\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) & 1.73443 & 0.386231 & 4.491 & 1.33831e-05 & 6.69153e-06 \tabularnewline
programma & 0.10744 & 0.0552631 & 1.944 & 0.0535977 & 0.0267989 \tabularnewline
gender & -0.0522673 & 0.0358667 & -1.457 & 0.146967 & 0.0734836 \tabularnewline
age & 0.0158344 & 0.0167195 & 0.9471 & 0.345008 & 0.172504 \tabularnewline
NUMERACYTOT & -0.00398325 & 0.00306179 & -1.301 & 0.195111 & 0.0975553 \tabularnewline
LFM & 0.000888826 & 0.000561992 & 1.582 & 0.115688 & 0.0578439 \tabularnewline
PRH & 0.000528732 & 0.000998083 & 0.5297 & 0.597008 & 0.298504 \tabularnewline
CH & 0.00181426 & 0.000899586 & 2.017 & 0.0453604 & 0.0226802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&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]1.73443[/C][C]0.386231[/C][C]4.491[/C][C]1.33831e-05[/C][C]6.69153e-06[/C][/ROW]
[ROW][C]programma[/C][C]0.10744[/C][C]0.0552631[/C][C]1.944[/C][C]0.0535977[/C][C]0.0267989[/C][/ROW]
[ROW][C]gender[/C][C]-0.0522673[/C][C]0.0358667[/C][C]-1.457[/C][C]0.146967[/C][C]0.0734836[/C][/ROW]
[ROW][C]age[/C][C]0.0158344[/C][C]0.0167195[/C][C]0.9471[/C][C]0.345008[/C][C]0.172504[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00398325[/C][C]0.00306179[/C][C]-1.301[/C][C]0.195111[/C][C]0.0975553[/C][/ROW]
[ROW][C]LFM[/C][C]0.000888826[/C][C]0.000561992[/C][C]1.582[/C][C]0.115688[/C][C]0.0578439[/C][/ROW]
[ROW][C]PRH[/C][C]0.000528732[/C][C]0.000998083[/C][C]0.5297[/C][C]0.597008[/C][C]0.298504[/C][/ROW]
[ROW][C]CH[/C][C]0.00181426[/C][C]0.000899586[/C][C]2.017[/C][C]0.0453604[/C][C]0.0226802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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)1.734430.3862314.4911.33831e-056.69153e-06
programma0.107440.05526311.9440.05359770.0267989
gender-0.05226730.0358667-1.4570.1469670.0734836
age0.01583440.01671950.94710.3450080.172504
NUMERACYTOT-0.003983250.00306179-1.3010.1951110.0975553
LFM0.0008888260.0005619921.5820.1156880.0578439
PRH0.0005287320.0009980830.52970.5970080.298504
CH0.001814260.0008995862.0170.04536040.0226802






Multiple Linear Regression - Regression Statistics
Multiple R0.345482
R-squared0.119358
Adjusted R-squared0.0815391
F-TEST (value)3.15604
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0.00372462
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.212163
Sum Squared Residuals7.33714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.345482 \tabularnewline
R-squared & 0.119358 \tabularnewline
Adjusted R-squared & 0.0815391 \tabularnewline
F-TEST (value) & 3.15604 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value & 0.00372462 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.212163 \tabularnewline
Sum Squared Residuals & 7.33714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.345482[/C][/ROW]
[ROW][C]R-squared[/C][C]0.119358[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0815391[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.15604[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C]0.00372462[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.212163[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.33714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.345482
R-squared0.119358
Adjusted R-squared0.0815391
F-TEST (value)3.15604
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0.00372462
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.212163
Sum Squared Residuals7.33714






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.12.01550.0844978
22.72.077430.622567
32.12.16477-0.0647729
42.12.1202-0.0201976
52.12.33561-0.235614
62.12.12456-0.0245627
72.12.24627-0.14627
82.12.28606-0.186056
92.12.3619-0.261904
102.12.079070.0209337
112.42.241030.158969
121.951.99545-0.0454523
132.12.11616-0.0161647
142.12.11263-0.0126288
151.952.15333-0.203332
162.12.25743-0.157433
172.42.311880.088121
182.12.077850.0221546
192.252.047230.202771
202.42.324380.0756165
212.252.173010.0769882
222.552.224360.325641
231.952.08-0.130004
242.42.108410.291592
252.12.094040.00596357
262.12.022790.077213
272.42.28430.115696
282.12.24439-0.144392
292.12.13675-0.03675
302.252.141190.10881
312.252.34376-0.0937618
322.42.141070.258927
332.12.20334-0.103342
342.12.21934-0.119336
352.42.249360.150643
362.12.14806-0.04806
371.952.20128-0.251281
382.12.21216-0.112157
392.252.25969-0.00968876
402.252.153740.0962563
412.42.243290.156712
422.252.202270.0477261
432.252.148070.101935
442.12.2338-0.133796
452.12.13627-0.0362738
462.12.19256-0.0925552
472.72.219470.480534
482.12.16474-0.064738
492.12.28517-0.185168
502.252.210.0400008
512.72.279920.420078
522.42.194120.205879
532.12.28793-0.187934
542.11.98030.119703
552.42.348320.0516803
561.952.12349-0.173495
572.72.276290.423708
582.12.14059-0.0405925
592.252.242970.00703451
602.12.25727-0.157271
612.72.202950.497046
622.12.27684-0.176838
632.12.2503-0.1503
641.652.12548-0.475479
651.652.12548-0.475479
662.12.22157-0.121567
672.12.21813-0.118131
682.12.23542-0.135417
692.12.26373-0.163728
702.12.14738-0.047384
712.42.296110.10389
722.42.28050.119501
732.12.19683-0.0968263
742.252.27224-0.0222381
752.42.222570.17743
762.12.25762-0.157619
772.12.19491-0.0949135
782.42.212850.187148
792.42.184170.215833
802.12.17648-0.0764795
812.12.10137-0.00136952
822.42.270230.129771
832.12.14702-0.0470181
842.72.276750.42325
852.12.21337-0.113367
862.12.17828-0.0782813
872.252.220860.0291394
882.12.29713-0.19713
892.42.215430.184571
902.252.25518-0.00518162
912.252.28368-0.033678
922.12.11933-0.0193311
932.12.18567-0.0856745
942.42.247330.152673
952.252.105770.144231
962.12.17714-0.0771401
972.12.13866-0.038661
981.652.09601-0.446007
991.652.16244-0.512443
1002.72.407020.292981
1012.12.23204-0.132036
1021.952.11751-0.167515
1032.252.106130.143871
1042.42.246610.153392
1051.952.1948-0.244797
1062.12.24981-0.14981
1072.42.173220.226778
1082.12.20189-0.101892
1092.12.20785-0.107845
1102.42.167240.232761
1112.42.142690.257311
1122.42.226720.173283
1132.252.211540.0384632
1142.42.202310.197693
1152.12.035860.0641378
1162.12.094980.00502169
1171.82.24598-0.445981
1182.72.16110.5389
1192.12.16316-0.0631635
1202.12.16143-0.0614346
1212.42.184450.215551
1222.552.210480.339519
1232.552.283590.266414
1242.12.28074-0.180742
1252.12.18278-0.0827839
1262.12.16616-0.0661562
1272.252.142810.10719
1282.252.024770.22523
1292.12.1617-0.0617
1302.12.20903-0.109035
1311.952.18476-0.234756
1322.42.175040.224964
1332.12.184-0.0840023
1342.42.246050.153951
1352.42.181490.218511
1362.42.348320.0516803
1372.252.077240.172761
1381.952.13902-0.189018
1392.12.1922-0.0921962
1402.12.27639-0.17639
1412.552.11130.4387
1422.12.18721-0.0872079
1432.12.094910.00508663
1442.12.091850.00814885
1451.952.17359-0.22359
1462.252.184970.0650332
1472.42.275920.124084
1481.952.13057-0.180572
1492.12.26512-0.165123
1502.12.11753-0.01753
1511.952.16988-0.219876
1522.12.28734-0.187343
1532.12.21541-0.115413
1541.952.12349-0.173495
1552.12.18875-0.0887541
1561.952.22673-0.276732
1572.42.181490.218511
1582.42.255070.144935
1592.42.129830.270174
1601.952.19-0.239997
1612.72.28740.412604
1622.12.18331-0.083312
1631.952.11474-0.164737
1642.12.26561-0.165612
1651.952.11365-0.163652
1662.12.32706-0.227059
1672.252.2635-0.0135008
1682.72.241530.458465
1692.12.16969-0.0696862
1702.42.263510.136489
1711.352.05832-0.708316

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.1 & 2.0155 & 0.0844978 \tabularnewline
2 & 2.7 & 2.07743 & 0.622567 \tabularnewline
3 & 2.1 & 2.16477 & -0.0647729 \tabularnewline
4 & 2.1 & 2.1202 & -0.0201976 \tabularnewline
5 & 2.1 & 2.33561 & -0.235614 \tabularnewline
6 & 2.1 & 2.12456 & -0.0245627 \tabularnewline
7 & 2.1 & 2.24627 & -0.14627 \tabularnewline
8 & 2.1 & 2.28606 & -0.186056 \tabularnewline
9 & 2.1 & 2.3619 & -0.261904 \tabularnewline
10 & 2.1 & 2.07907 & 0.0209337 \tabularnewline
11 & 2.4 & 2.24103 & 0.158969 \tabularnewline
12 & 1.95 & 1.99545 & -0.0454523 \tabularnewline
13 & 2.1 & 2.11616 & -0.0161647 \tabularnewline
14 & 2.1 & 2.11263 & -0.0126288 \tabularnewline
15 & 1.95 & 2.15333 & -0.203332 \tabularnewline
16 & 2.1 & 2.25743 & -0.157433 \tabularnewline
17 & 2.4 & 2.31188 & 0.088121 \tabularnewline
18 & 2.1 & 2.07785 & 0.0221546 \tabularnewline
19 & 2.25 & 2.04723 & 0.202771 \tabularnewline
20 & 2.4 & 2.32438 & 0.0756165 \tabularnewline
21 & 2.25 & 2.17301 & 0.0769882 \tabularnewline
22 & 2.55 & 2.22436 & 0.325641 \tabularnewline
23 & 1.95 & 2.08 & -0.130004 \tabularnewline
24 & 2.4 & 2.10841 & 0.291592 \tabularnewline
25 & 2.1 & 2.09404 & 0.00596357 \tabularnewline
26 & 2.1 & 2.02279 & 0.077213 \tabularnewline
27 & 2.4 & 2.2843 & 0.115696 \tabularnewline
28 & 2.1 & 2.24439 & -0.144392 \tabularnewline
29 & 2.1 & 2.13675 & -0.03675 \tabularnewline
30 & 2.25 & 2.14119 & 0.10881 \tabularnewline
31 & 2.25 & 2.34376 & -0.0937618 \tabularnewline
32 & 2.4 & 2.14107 & 0.258927 \tabularnewline
33 & 2.1 & 2.20334 & -0.103342 \tabularnewline
34 & 2.1 & 2.21934 & -0.119336 \tabularnewline
35 & 2.4 & 2.24936 & 0.150643 \tabularnewline
36 & 2.1 & 2.14806 & -0.04806 \tabularnewline
37 & 1.95 & 2.20128 & -0.251281 \tabularnewline
38 & 2.1 & 2.21216 & -0.112157 \tabularnewline
39 & 2.25 & 2.25969 & -0.00968876 \tabularnewline
40 & 2.25 & 2.15374 & 0.0962563 \tabularnewline
41 & 2.4 & 2.24329 & 0.156712 \tabularnewline
42 & 2.25 & 2.20227 & 0.0477261 \tabularnewline
43 & 2.25 & 2.14807 & 0.101935 \tabularnewline
44 & 2.1 & 2.2338 & -0.133796 \tabularnewline
45 & 2.1 & 2.13627 & -0.0362738 \tabularnewline
46 & 2.1 & 2.19256 & -0.0925552 \tabularnewline
47 & 2.7 & 2.21947 & 0.480534 \tabularnewline
48 & 2.1 & 2.16474 & -0.064738 \tabularnewline
49 & 2.1 & 2.28517 & -0.185168 \tabularnewline
50 & 2.25 & 2.21 & 0.0400008 \tabularnewline
51 & 2.7 & 2.27992 & 0.420078 \tabularnewline
52 & 2.4 & 2.19412 & 0.205879 \tabularnewline
53 & 2.1 & 2.28793 & -0.187934 \tabularnewline
54 & 2.1 & 1.9803 & 0.119703 \tabularnewline
55 & 2.4 & 2.34832 & 0.0516803 \tabularnewline
56 & 1.95 & 2.12349 & -0.173495 \tabularnewline
57 & 2.7 & 2.27629 & 0.423708 \tabularnewline
58 & 2.1 & 2.14059 & -0.0405925 \tabularnewline
59 & 2.25 & 2.24297 & 0.00703451 \tabularnewline
60 & 2.1 & 2.25727 & -0.157271 \tabularnewline
61 & 2.7 & 2.20295 & 0.497046 \tabularnewline
62 & 2.1 & 2.27684 & -0.176838 \tabularnewline
63 & 2.1 & 2.2503 & -0.1503 \tabularnewline
64 & 1.65 & 2.12548 & -0.475479 \tabularnewline
65 & 1.65 & 2.12548 & -0.475479 \tabularnewline
66 & 2.1 & 2.22157 & -0.121567 \tabularnewline
67 & 2.1 & 2.21813 & -0.118131 \tabularnewline
68 & 2.1 & 2.23542 & -0.135417 \tabularnewline
69 & 2.1 & 2.26373 & -0.163728 \tabularnewline
70 & 2.1 & 2.14738 & -0.047384 \tabularnewline
71 & 2.4 & 2.29611 & 0.10389 \tabularnewline
72 & 2.4 & 2.2805 & 0.119501 \tabularnewline
73 & 2.1 & 2.19683 & -0.0968263 \tabularnewline
74 & 2.25 & 2.27224 & -0.0222381 \tabularnewline
75 & 2.4 & 2.22257 & 0.17743 \tabularnewline
76 & 2.1 & 2.25762 & -0.157619 \tabularnewline
77 & 2.1 & 2.19491 & -0.0949135 \tabularnewline
78 & 2.4 & 2.21285 & 0.187148 \tabularnewline
79 & 2.4 & 2.18417 & 0.215833 \tabularnewline
80 & 2.1 & 2.17648 & -0.0764795 \tabularnewline
81 & 2.1 & 2.10137 & -0.00136952 \tabularnewline
82 & 2.4 & 2.27023 & 0.129771 \tabularnewline
83 & 2.1 & 2.14702 & -0.0470181 \tabularnewline
84 & 2.7 & 2.27675 & 0.42325 \tabularnewline
85 & 2.1 & 2.21337 & -0.113367 \tabularnewline
86 & 2.1 & 2.17828 & -0.0782813 \tabularnewline
87 & 2.25 & 2.22086 & 0.0291394 \tabularnewline
88 & 2.1 & 2.29713 & -0.19713 \tabularnewline
89 & 2.4 & 2.21543 & 0.184571 \tabularnewline
90 & 2.25 & 2.25518 & -0.00518162 \tabularnewline
91 & 2.25 & 2.28368 & -0.033678 \tabularnewline
92 & 2.1 & 2.11933 & -0.0193311 \tabularnewline
93 & 2.1 & 2.18567 & -0.0856745 \tabularnewline
94 & 2.4 & 2.24733 & 0.152673 \tabularnewline
95 & 2.25 & 2.10577 & 0.144231 \tabularnewline
96 & 2.1 & 2.17714 & -0.0771401 \tabularnewline
97 & 2.1 & 2.13866 & -0.038661 \tabularnewline
98 & 1.65 & 2.09601 & -0.446007 \tabularnewline
99 & 1.65 & 2.16244 & -0.512443 \tabularnewline
100 & 2.7 & 2.40702 & 0.292981 \tabularnewline
101 & 2.1 & 2.23204 & -0.132036 \tabularnewline
102 & 1.95 & 2.11751 & -0.167515 \tabularnewline
103 & 2.25 & 2.10613 & 0.143871 \tabularnewline
104 & 2.4 & 2.24661 & 0.153392 \tabularnewline
105 & 1.95 & 2.1948 & -0.244797 \tabularnewline
106 & 2.1 & 2.24981 & -0.14981 \tabularnewline
107 & 2.4 & 2.17322 & 0.226778 \tabularnewline
108 & 2.1 & 2.20189 & -0.101892 \tabularnewline
109 & 2.1 & 2.20785 & -0.107845 \tabularnewline
110 & 2.4 & 2.16724 & 0.232761 \tabularnewline
111 & 2.4 & 2.14269 & 0.257311 \tabularnewline
112 & 2.4 & 2.22672 & 0.173283 \tabularnewline
113 & 2.25 & 2.21154 & 0.0384632 \tabularnewline
114 & 2.4 & 2.20231 & 0.197693 \tabularnewline
115 & 2.1 & 2.03586 & 0.0641378 \tabularnewline
116 & 2.1 & 2.09498 & 0.00502169 \tabularnewline
117 & 1.8 & 2.24598 & -0.445981 \tabularnewline
118 & 2.7 & 2.1611 & 0.5389 \tabularnewline
119 & 2.1 & 2.16316 & -0.0631635 \tabularnewline
120 & 2.1 & 2.16143 & -0.0614346 \tabularnewline
121 & 2.4 & 2.18445 & 0.215551 \tabularnewline
122 & 2.55 & 2.21048 & 0.339519 \tabularnewline
123 & 2.55 & 2.28359 & 0.266414 \tabularnewline
124 & 2.1 & 2.28074 & -0.180742 \tabularnewline
125 & 2.1 & 2.18278 & -0.0827839 \tabularnewline
126 & 2.1 & 2.16616 & -0.0661562 \tabularnewline
127 & 2.25 & 2.14281 & 0.10719 \tabularnewline
128 & 2.25 & 2.02477 & 0.22523 \tabularnewline
129 & 2.1 & 2.1617 & -0.0617 \tabularnewline
130 & 2.1 & 2.20903 & -0.109035 \tabularnewline
131 & 1.95 & 2.18476 & -0.234756 \tabularnewline
132 & 2.4 & 2.17504 & 0.224964 \tabularnewline
133 & 2.1 & 2.184 & -0.0840023 \tabularnewline
134 & 2.4 & 2.24605 & 0.153951 \tabularnewline
135 & 2.4 & 2.18149 & 0.218511 \tabularnewline
136 & 2.4 & 2.34832 & 0.0516803 \tabularnewline
137 & 2.25 & 2.07724 & 0.172761 \tabularnewline
138 & 1.95 & 2.13902 & -0.189018 \tabularnewline
139 & 2.1 & 2.1922 & -0.0921962 \tabularnewline
140 & 2.1 & 2.27639 & -0.17639 \tabularnewline
141 & 2.55 & 2.1113 & 0.4387 \tabularnewline
142 & 2.1 & 2.18721 & -0.0872079 \tabularnewline
143 & 2.1 & 2.09491 & 0.00508663 \tabularnewline
144 & 2.1 & 2.09185 & 0.00814885 \tabularnewline
145 & 1.95 & 2.17359 & -0.22359 \tabularnewline
146 & 2.25 & 2.18497 & 0.0650332 \tabularnewline
147 & 2.4 & 2.27592 & 0.124084 \tabularnewline
148 & 1.95 & 2.13057 & -0.180572 \tabularnewline
149 & 2.1 & 2.26512 & -0.165123 \tabularnewline
150 & 2.1 & 2.11753 & -0.01753 \tabularnewline
151 & 1.95 & 2.16988 & -0.219876 \tabularnewline
152 & 2.1 & 2.28734 & -0.187343 \tabularnewline
153 & 2.1 & 2.21541 & -0.115413 \tabularnewline
154 & 1.95 & 2.12349 & -0.173495 \tabularnewline
155 & 2.1 & 2.18875 & -0.0887541 \tabularnewline
156 & 1.95 & 2.22673 & -0.276732 \tabularnewline
157 & 2.4 & 2.18149 & 0.218511 \tabularnewline
158 & 2.4 & 2.25507 & 0.144935 \tabularnewline
159 & 2.4 & 2.12983 & 0.270174 \tabularnewline
160 & 1.95 & 2.19 & -0.239997 \tabularnewline
161 & 2.7 & 2.2874 & 0.412604 \tabularnewline
162 & 2.1 & 2.18331 & -0.083312 \tabularnewline
163 & 1.95 & 2.11474 & -0.164737 \tabularnewline
164 & 2.1 & 2.26561 & -0.165612 \tabularnewline
165 & 1.95 & 2.11365 & -0.163652 \tabularnewline
166 & 2.1 & 2.32706 & -0.227059 \tabularnewline
167 & 2.25 & 2.2635 & -0.0135008 \tabularnewline
168 & 2.7 & 2.24153 & 0.458465 \tabularnewline
169 & 2.1 & 2.16969 & -0.0696862 \tabularnewline
170 & 2.4 & 2.26351 & 0.136489 \tabularnewline
171 & 1.35 & 2.05832 & -0.708316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&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]2.1[/C][C]2.0155[/C][C]0.0844978[/C][/ROW]
[ROW][C]2[/C][C]2.7[/C][C]2.07743[/C][C]0.622567[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.16477[/C][C]-0.0647729[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]2.1202[/C][C]-0.0201976[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]2.33561[/C][C]-0.235614[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.12456[/C][C]-0.0245627[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.24627[/C][C]-0.14627[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.28606[/C][C]-0.186056[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.3619[/C][C]-0.261904[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.07907[/C][C]0.0209337[/C][/ROW]
[ROW][C]11[/C][C]2.4[/C][C]2.24103[/C][C]0.158969[/C][/ROW]
[ROW][C]12[/C][C]1.95[/C][C]1.99545[/C][C]-0.0454523[/C][/ROW]
[ROW][C]13[/C][C]2.1[/C][C]2.11616[/C][C]-0.0161647[/C][/ROW]
[ROW][C]14[/C][C]2.1[/C][C]2.11263[/C][C]-0.0126288[/C][/ROW]
[ROW][C]15[/C][C]1.95[/C][C]2.15333[/C][C]-0.203332[/C][/ROW]
[ROW][C]16[/C][C]2.1[/C][C]2.25743[/C][C]-0.157433[/C][/ROW]
[ROW][C]17[/C][C]2.4[/C][C]2.31188[/C][C]0.088121[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.07785[/C][C]0.0221546[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]2.04723[/C][C]0.202771[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.32438[/C][C]0.0756165[/C][/ROW]
[ROW][C]21[/C][C]2.25[/C][C]2.17301[/C][C]0.0769882[/C][/ROW]
[ROW][C]22[/C][C]2.55[/C][C]2.22436[/C][C]0.325641[/C][/ROW]
[ROW][C]23[/C][C]1.95[/C][C]2.08[/C][C]-0.130004[/C][/ROW]
[ROW][C]24[/C][C]2.4[/C][C]2.10841[/C][C]0.291592[/C][/ROW]
[ROW][C]25[/C][C]2.1[/C][C]2.09404[/C][C]0.00596357[/C][/ROW]
[ROW][C]26[/C][C]2.1[/C][C]2.02279[/C][C]0.077213[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.2843[/C][C]0.115696[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.24439[/C][C]-0.144392[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]2.13675[/C][C]-0.03675[/C][/ROW]
[ROW][C]30[/C][C]2.25[/C][C]2.14119[/C][C]0.10881[/C][/ROW]
[ROW][C]31[/C][C]2.25[/C][C]2.34376[/C][C]-0.0937618[/C][/ROW]
[ROW][C]32[/C][C]2.4[/C][C]2.14107[/C][C]0.258927[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.20334[/C][C]-0.103342[/C][/ROW]
[ROW][C]34[/C][C]2.1[/C][C]2.21934[/C][C]-0.119336[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]2.24936[/C][C]0.150643[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.14806[/C][C]-0.04806[/C][/ROW]
[ROW][C]37[/C][C]1.95[/C][C]2.20128[/C][C]-0.251281[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.21216[/C][C]-0.112157[/C][/ROW]
[ROW][C]39[/C][C]2.25[/C][C]2.25969[/C][C]-0.00968876[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.15374[/C][C]0.0962563[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.24329[/C][C]0.156712[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.20227[/C][C]0.0477261[/C][/ROW]
[ROW][C]43[/C][C]2.25[/C][C]2.14807[/C][C]0.101935[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.2338[/C][C]-0.133796[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]2.13627[/C][C]-0.0362738[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.19256[/C][C]-0.0925552[/C][/ROW]
[ROW][C]47[/C][C]2.7[/C][C]2.21947[/C][C]0.480534[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]2.16474[/C][C]-0.064738[/C][/ROW]
[ROW][C]49[/C][C]2.1[/C][C]2.28517[/C][C]-0.185168[/C][/ROW]
[ROW][C]50[/C][C]2.25[/C][C]2.21[/C][C]0.0400008[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.27992[/C][C]0.420078[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.19412[/C][C]0.205879[/C][/ROW]
[ROW][C]53[/C][C]2.1[/C][C]2.28793[/C][C]-0.187934[/C][/ROW]
[ROW][C]54[/C][C]2.1[/C][C]1.9803[/C][C]0.119703[/C][/ROW]
[ROW][C]55[/C][C]2.4[/C][C]2.34832[/C][C]0.0516803[/C][/ROW]
[ROW][C]56[/C][C]1.95[/C][C]2.12349[/C][C]-0.173495[/C][/ROW]
[ROW][C]57[/C][C]2.7[/C][C]2.27629[/C][C]0.423708[/C][/ROW]
[ROW][C]58[/C][C]2.1[/C][C]2.14059[/C][C]-0.0405925[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.24297[/C][C]0.00703451[/C][/ROW]
[ROW][C]60[/C][C]2.1[/C][C]2.25727[/C][C]-0.157271[/C][/ROW]
[ROW][C]61[/C][C]2.7[/C][C]2.20295[/C][C]0.497046[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]2.27684[/C][C]-0.176838[/C][/ROW]
[ROW][C]63[/C][C]2.1[/C][C]2.2503[/C][C]-0.1503[/C][/ROW]
[ROW][C]64[/C][C]1.65[/C][C]2.12548[/C][C]-0.475479[/C][/ROW]
[ROW][C]65[/C][C]1.65[/C][C]2.12548[/C][C]-0.475479[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.22157[/C][C]-0.121567[/C][/ROW]
[ROW][C]67[/C][C]2.1[/C][C]2.21813[/C][C]-0.118131[/C][/ROW]
[ROW][C]68[/C][C]2.1[/C][C]2.23542[/C][C]-0.135417[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.26373[/C][C]-0.163728[/C][/ROW]
[ROW][C]70[/C][C]2.1[/C][C]2.14738[/C][C]-0.047384[/C][/ROW]
[ROW][C]71[/C][C]2.4[/C][C]2.29611[/C][C]0.10389[/C][/ROW]
[ROW][C]72[/C][C]2.4[/C][C]2.2805[/C][C]0.119501[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.19683[/C][C]-0.0968263[/C][/ROW]
[ROW][C]74[/C][C]2.25[/C][C]2.27224[/C][C]-0.0222381[/C][/ROW]
[ROW][C]75[/C][C]2.4[/C][C]2.22257[/C][C]0.17743[/C][/ROW]
[ROW][C]76[/C][C]2.1[/C][C]2.25762[/C][C]-0.157619[/C][/ROW]
[ROW][C]77[/C][C]2.1[/C][C]2.19491[/C][C]-0.0949135[/C][/ROW]
[ROW][C]78[/C][C]2.4[/C][C]2.21285[/C][C]0.187148[/C][/ROW]
[ROW][C]79[/C][C]2.4[/C][C]2.18417[/C][C]0.215833[/C][/ROW]
[ROW][C]80[/C][C]2.1[/C][C]2.17648[/C][C]-0.0764795[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.10137[/C][C]-0.00136952[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.27023[/C][C]0.129771[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]2.14702[/C][C]-0.0470181[/C][/ROW]
[ROW][C]84[/C][C]2.7[/C][C]2.27675[/C][C]0.42325[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.21337[/C][C]-0.113367[/C][/ROW]
[ROW][C]86[/C][C]2.1[/C][C]2.17828[/C][C]-0.0782813[/C][/ROW]
[ROW][C]87[/C][C]2.25[/C][C]2.22086[/C][C]0.0291394[/C][/ROW]
[ROW][C]88[/C][C]2.1[/C][C]2.29713[/C][C]-0.19713[/C][/ROW]
[ROW][C]89[/C][C]2.4[/C][C]2.21543[/C][C]0.184571[/C][/ROW]
[ROW][C]90[/C][C]2.25[/C][C]2.25518[/C][C]-0.00518162[/C][/ROW]
[ROW][C]91[/C][C]2.25[/C][C]2.28368[/C][C]-0.033678[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]2.11933[/C][C]-0.0193311[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]2.18567[/C][C]-0.0856745[/C][/ROW]
[ROW][C]94[/C][C]2.4[/C][C]2.24733[/C][C]0.152673[/C][/ROW]
[ROW][C]95[/C][C]2.25[/C][C]2.10577[/C][C]0.144231[/C][/ROW]
[ROW][C]96[/C][C]2.1[/C][C]2.17714[/C][C]-0.0771401[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]2.13866[/C][C]-0.038661[/C][/ROW]
[ROW][C]98[/C][C]1.65[/C][C]2.09601[/C][C]-0.446007[/C][/ROW]
[ROW][C]99[/C][C]1.65[/C][C]2.16244[/C][C]-0.512443[/C][/ROW]
[ROW][C]100[/C][C]2.7[/C][C]2.40702[/C][C]0.292981[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]2.23204[/C][C]-0.132036[/C][/ROW]
[ROW][C]102[/C][C]1.95[/C][C]2.11751[/C][C]-0.167515[/C][/ROW]
[ROW][C]103[/C][C]2.25[/C][C]2.10613[/C][C]0.143871[/C][/ROW]
[ROW][C]104[/C][C]2.4[/C][C]2.24661[/C][C]0.153392[/C][/ROW]
[ROW][C]105[/C][C]1.95[/C][C]2.1948[/C][C]-0.244797[/C][/ROW]
[ROW][C]106[/C][C]2.1[/C][C]2.24981[/C][C]-0.14981[/C][/ROW]
[ROW][C]107[/C][C]2.4[/C][C]2.17322[/C][C]0.226778[/C][/ROW]
[ROW][C]108[/C][C]2.1[/C][C]2.20189[/C][C]-0.101892[/C][/ROW]
[ROW][C]109[/C][C]2.1[/C][C]2.20785[/C][C]-0.107845[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.16724[/C][C]0.232761[/C][/ROW]
[ROW][C]111[/C][C]2.4[/C][C]2.14269[/C][C]0.257311[/C][/ROW]
[ROW][C]112[/C][C]2.4[/C][C]2.22672[/C][C]0.173283[/C][/ROW]
[ROW][C]113[/C][C]2.25[/C][C]2.21154[/C][C]0.0384632[/C][/ROW]
[ROW][C]114[/C][C]2.4[/C][C]2.20231[/C][C]0.197693[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]2.03586[/C][C]0.0641378[/C][/ROW]
[ROW][C]116[/C][C]2.1[/C][C]2.09498[/C][C]0.00502169[/C][/ROW]
[ROW][C]117[/C][C]1.8[/C][C]2.24598[/C][C]-0.445981[/C][/ROW]
[ROW][C]118[/C][C]2.7[/C][C]2.1611[/C][C]0.5389[/C][/ROW]
[ROW][C]119[/C][C]2.1[/C][C]2.16316[/C][C]-0.0631635[/C][/ROW]
[ROW][C]120[/C][C]2.1[/C][C]2.16143[/C][C]-0.0614346[/C][/ROW]
[ROW][C]121[/C][C]2.4[/C][C]2.18445[/C][C]0.215551[/C][/ROW]
[ROW][C]122[/C][C]2.55[/C][C]2.21048[/C][C]0.339519[/C][/ROW]
[ROW][C]123[/C][C]2.55[/C][C]2.28359[/C][C]0.266414[/C][/ROW]
[ROW][C]124[/C][C]2.1[/C][C]2.28074[/C][C]-0.180742[/C][/ROW]
[ROW][C]125[/C][C]2.1[/C][C]2.18278[/C][C]-0.0827839[/C][/ROW]
[ROW][C]126[/C][C]2.1[/C][C]2.16616[/C][C]-0.0661562[/C][/ROW]
[ROW][C]127[/C][C]2.25[/C][C]2.14281[/C][C]0.10719[/C][/ROW]
[ROW][C]128[/C][C]2.25[/C][C]2.02477[/C][C]0.22523[/C][/ROW]
[ROW][C]129[/C][C]2.1[/C][C]2.1617[/C][C]-0.0617[/C][/ROW]
[ROW][C]130[/C][C]2.1[/C][C]2.20903[/C][C]-0.109035[/C][/ROW]
[ROW][C]131[/C][C]1.95[/C][C]2.18476[/C][C]-0.234756[/C][/ROW]
[ROW][C]132[/C][C]2.4[/C][C]2.17504[/C][C]0.224964[/C][/ROW]
[ROW][C]133[/C][C]2.1[/C][C]2.184[/C][C]-0.0840023[/C][/ROW]
[ROW][C]134[/C][C]2.4[/C][C]2.24605[/C][C]0.153951[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]2.18149[/C][C]0.218511[/C][/ROW]
[ROW][C]136[/C][C]2.4[/C][C]2.34832[/C][C]0.0516803[/C][/ROW]
[ROW][C]137[/C][C]2.25[/C][C]2.07724[/C][C]0.172761[/C][/ROW]
[ROW][C]138[/C][C]1.95[/C][C]2.13902[/C][C]-0.189018[/C][/ROW]
[ROW][C]139[/C][C]2.1[/C][C]2.1922[/C][C]-0.0921962[/C][/ROW]
[ROW][C]140[/C][C]2.1[/C][C]2.27639[/C][C]-0.17639[/C][/ROW]
[ROW][C]141[/C][C]2.55[/C][C]2.1113[/C][C]0.4387[/C][/ROW]
[ROW][C]142[/C][C]2.1[/C][C]2.18721[/C][C]-0.0872079[/C][/ROW]
[ROW][C]143[/C][C]2.1[/C][C]2.09491[/C][C]0.00508663[/C][/ROW]
[ROW][C]144[/C][C]2.1[/C][C]2.09185[/C][C]0.00814885[/C][/ROW]
[ROW][C]145[/C][C]1.95[/C][C]2.17359[/C][C]-0.22359[/C][/ROW]
[ROW][C]146[/C][C]2.25[/C][C]2.18497[/C][C]0.0650332[/C][/ROW]
[ROW][C]147[/C][C]2.4[/C][C]2.27592[/C][C]0.124084[/C][/ROW]
[ROW][C]148[/C][C]1.95[/C][C]2.13057[/C][C]-0.180572[/C][/ROW]
[ROW][C]149[/C][C]2.1[/C][C]2.26512[/C][C]-0.165123[/C][/ROW]
[ROW][C]150[/C][C]2.1[/C][C]2.11753[/C][C]-0.01753[/C][/ROW]
[ROW][C]151[/C][C]1.95[/C][C]2.16988[/C][C]-0.219876[/C][/ROW]
[ROW][C]152[/C][C]2.1[/C][C]2.28734[/C][C]-0.187343[/C][/ROW]
[ROW][C]153[/C][C]2.1[/C][C]2.21541[/C][C]-0.115413[/C][/ROW]
[ROW][C]154[/C][C]1.95[/C][C]2.12349[/C][C]-0.173495[/C][/ROW]
[ROW][C]155[/C][C]2.1[/C][C]2.18875[/C][C]-0.0887541[/C][/ROW]
[ROW][C]156[/C][C]1.95[/C][C]2.22673[/C][C]-0.276732[/C][/ROW]
[ROW][C]157[/C][C]2.4[/C][C]2.18149[/C][C]0.218511[/C][/ROW]
[ROW][C]158[/C][C]2.4[/C][C]2.25507[/C][C]0.144935[/C][/ROW]
[ROW][C]159[/C][C]2.4[/C][C]2.12983[/C][C]0.270174[/C][/ROW]
[ROW][C]160[/C][C]1.95[/C][C]2.19[/C][C]-0.239997[/C][/ROW]
[ROW][C]161[/C][C]2.7[/C][C]2.2874[/C][C]0.412604[/C][/ROW]
[ROW][C]162[/C][C]2.1[/C][C]2.18331[/C][C]-0.083312[/C][/ROW]
[ROW][C]163[/C][C]1.95[/C][C]2.11474[/C][C]-0.164737[/C][/ROW]
[ROW][C]164[/C][C]2.1[/C][C]2.26561[/C][C]-0.165612[/C][/ROW]
[ROW][C]165[/C][C]1.95[/C][C]2.11365[/C][C]-0.163652[/C][/ROW]
[ROW][C]166[/C][C]2.1[/C][C]2.32706[/C][C]-0.227059[/C][/ROW]
[ROW][C]167[/C][C]2.25[/C][C]2.2635[/C][C]-0.0135008[/C][/ROW]
[ROW][C]168[/C][C]2.7[/C][C]2.24153[/C][C]0.458465[/C][/ROW]
[ROW][C]169[/C][C]2.1[/C][C]2.16969[/C][C]-0.0696862[/C][/ROW]
[ROW][C]170[/C][C]2.4[/C][C]2.26351[/C][C]0.136489[/C][/ROW]
[ROW][C]171[/C][C]1.35[/C][C]2.05832[/C][C]-0.708316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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
12.12.01550.0844978
22.72.077430.622567
32.12.16477-0.0647729
42.12.1202-0.0201976
52.12.33561-0.235614
62.12.12456-0.0245627
72.12.24627-0.14627
82.12.28606-0.186056
92.12.3619-0.261904
102.12.079070.0209337
112.42.241030.158969
121.951.99545-0.0454523
132.12.11616-0.0161647
142.12.11263-0.0126288
151.952.15333-0.203332
162.12.25743-0.157433
172.42.311880.088121
182.12.077850.0221546
192.252.047230.202771
202.42.324380.0756165
212.252.173010.0769882
222.552.224360.325641
231.952.08-0.130004
242.42.108410.291592
252.12.094040.00596357
262.12.022790.077213
272.42.28430.115696
282.12.24439-0.144392
292.12.13675-0.03675
302.252.141190.10881
312.252.34376-0.0937618
322.42.141070.258927
332.12.20334-0.103342
342.12.21934-0.119336
352.42.249360.150643
362.12.14806-0.04806
371.952.20128-0.251281
382.12.21216-0.112157
392.252.25969-0.00968876
402.252.153740.0962563
412.42.243290.156712
422.252.202270.0477261
432.252.148070.101935
442.12.2338-0.133796
452.12.13627-0.0362738
462.12.19256-0.0925552
472.72.219470.480534
482.12.16474-0.064738
492.12.28517-0.185168
502.252.210.0400008
512.72.279920.420078
522.42.194120.205879
532.12.28793-0.187934
542.11.98030.119703
552.42.348320.0516803
561.952.12349-0.173495
572.72.276290.423708
582.12.14059-0.0405925
592.252.242970.00703451
602.12.25727-0.157271
612.72.202950.497046
622.12.27684-0.176838
632.12.2503-0.1503
641.652.12548-0.475479
651.652.12548-0.475479
662.12.22157-0.121567
672.12.21813-0.118131
682.12.23542-0.135417
692.12.26373-0.163728
702.12.14738-0.047384
712.42.296110.10389
722.42.28050.119501
732.12.19683-0.0968263
742.252.27224-0.0222381
752.42.222570.17743
762.12.25762-0.157619
772.12.19491-0.0949135
782.42.212850.187148
792.42.184170.215833
802.12.17648-0.0764795
812.12.10137-0.00136952
822.42.270230.129771
832.12.14702-0.0470181
842.72.276750.42325
852.12.21337-0.113367
862.12.17828-0.0782813
872.252.220860.0291394
882.12.29713-0.19713
892.42.215430.184571
902.252.25518-0.00518162
912.252.28368-0.033678
922.12.11933-0.0193311
932.12.18567-0.0856745
942.42.247330.152673
952.252.105770.144231
962.12.17714-0.0771401
972.12.13866-0.038661
981.652.09601-0.446007
991.652.16244-0.512443
1002.72.407020.292981
1012.12.23204-0.132036
1021.952.11751-0.167515
1032.252.106130.143871
1042.42.246610.153392
1051.952.1948-0.244797
1062.12.24981-0.14981
1072.42.173220.226778
1082.12.20189-0.101892
1092.12.20785-0.107845
1102.42.167240.232761
1112.42.142690.257311
1122.42.226720.173283
1132.252.211540.0384632
1142.42.202310.197693
1152.12.035860.0641378
1162.12.094980.00502169
1171.82.24598-0.445981
1182.72.16110.5389
1192.12.16316-0.0631635
1202.12.16143-0.0614346
1212.42.184450.215551
1222.552.210480.339519
1232.552.283590.266414
1242.12.28074-0.180742
1252.12.18278-0.0827839
1262.12.16616-0.0661562
1272.252.142810.10719
1282.252.024770.22523
1292.12.1617-0.0617
1302.12.20903-0.109035
1311.952.18476-0.234756
1322.42.175040.224964
1332.12.184-0.0840023
1342.42.246050.153951
1352.42.181490.218511
1362.42.348320.0516803
1372.252.077240.172761
1381.952.13902-0.189018
1392.12.1922-0.0921962
1402.12.27639-0.17639
1412.552.11130.4387
1422.12.18721-0.0872079
1432.12.094910.00508663
1442.12.091850.00814885
1451.952.17359-0.22359
1462.252.184970.0650332
1472.42.275920.124084
1481.952.13057-0.180572
1492.12.26512-0.165123
1502.12.11753-0.01753
1511.952.16988-0.219876
1522.12.28734-0.187343
1532.12.21541-0.115413
1541.952.12349-0.173495
1552.12.18875-0.0887541
1561.952.22673-0.276732
1572.42.181490.218511
1582.42.255070.144935
1592.42.129830.270174
1601.952.19-0.239997
1612.72.28740.412604
1622.12.18331-0.083312
1631.952.11474-0.164737
1642.12.26561-0.165612
1651.952.11365-0.163652
1662.12.32706-0.227059
1672.252.2635-0.0135008
1682.72.241530.458465
1692.12.16969-0.0696862
1702.42.263510.136489
1711.352.05832-0.708316






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7580240.4839530.241976
120.7558850.488230.244115
130.6388840.7222320.361116
140.5488010.9023990.451199
150.6271830.7456330.372817
160.5306860.9386280.469314
170.670240.6595190.32976
180.5804350.8391290.419565
190.581520.836960.41848
200.5799410.8401190.420059
210.4996010.9992030.500399
220.5046150.9907690.495385
230.5174550.9650890.482545
240.5544120.8911760.445588
250.4800660.9601330.519934
260.4253330.8506650.574667
270.3630540.7261080.636946
280.3585070.7170130.641493
290.299310.598620.70069
300.2571060.5142120.742894
310.2159570.4319140.784043
320.2287950.4575890.771205
330.2015630.4031260.798437
340.1709670.3419340.829033
350.1598840.3197680.840116
360.1254350.250870.874565
370.141430.2828610.85857
380.1183640.2367270.881636
390.09360810.1872160.906392
400.07720440.1544090.922796
410.07347720.1469540.926523
420.05544810.1108960.944552
430.04370880.08741760.956291
440.035070.07013990.96493
450.02662930.05325860.973371
460.01927950.03855890.980721
470.06837890.1367580.931621
480.05430910.1086180.945691
490.04487090.08974170.955129
500.04385760.08771520.956142
510.1144220.2288430.885578
520.1074950.2149890.892505
530.1013680.2027360.898632
540.08813650.1762730.911864
550.07266390.1453280.927336
560.06794140.1358830.932059
570.1265640.2531280.873436
580.1079840.2159670.892016
590.08703410.1740680.912966
600.08159350.1631870.918407
610.1640870.3281740.835913
620.1506140.3012280.849386
630.157320.3146410.84268
640.3335240.6670490.666476
650.5178460.9643070.482154
660.4799230.9598460.520077
670.4623650.9247310.537635
680.4301160.8602310.569884
690.4064550.8129090.593545
700.3792740.7585480.620726
710.3464210.6928420.653579
720.3232920.6465840.676708
730.2921350.584270.707865
740.2538210.5076420.746179
750.2482540.4965080.751746
760.2349930.4699870.765007
770.2079960.4159920.792004
780.2015820.4031640.798418
790.2030390.4060780.796961
800.178320.356640.82168
810.1526610.3053220.847339
820.1400080.2800160.859992
830.1177480.2354950.882252
840.1993780.3987560.800622
850.179120.3582410.82088
860.1579140.3158280.842086
870.1323870.2647740.867613
880.1282490.2564970.871751
890.121820.243640.87818
900.1003520.2007050.899648
910.08624490.172490.913755
920.07058460.1411690.929415
930.05813590.1162720.941864
940.05145660.1029130.948543
950.04506170.09012350.954938
960.03689520.07379040.963105
970.02898440.05796870.971016
980.06273580.1254720.937264
990.1522710.3045420.847729
1000.1762770.3525540.823723
1010.15810.3162010.8419
1020.1462950.292590.853705
1030.1320440.2640880.867956
1040.1215080.2430160.878492
1050.1259790.2519580.874021
1060.1131750.2263510.886825
1070.1143010.2286020.885699
1080.09790310.1958060.902097
1090.08354960.1670990.91645
1100.08306070.1661210.916939
1110.09178330.1835670.908217
1120.08471330.1694270.915287
1130.06815380.1363080.931846
1140.06632070.1326410.933679
1150.05366360.1073270.946336
1160.04173210.08346420.958268
1170.09135770.1827150.908642
1180.2831650.5663310.716835
1190.2467540.4935090.753246
1200.2102230.4204470.789777
1210.215460.4309210.78454
1220.2665590.5331170.733441
1230.2688470.5376940.731153
1240.2488530.4977060.751147
1250.2192450.4384890.780755
1260.1959320.3918630.804068
1270.1711720.3423450.828828
1280.2272520.4545030.772748
1290.1946310.3892620.805369
1300.1812440.3624880.818756
1310.1872510.3745030.812749
1320.188810.3776190.81119
1330.1552640.3105280.844736
1340.1579050.315810.842095
1350.1515680.3031370.848432
1360.1278230.2556470.872177
1370.1307470.2614950.869253
1380.1205720.2411430.879428
1390.1018010.2036020.898199
1400.1099590.2199190.890041
1410.2770330.5540660.722967
1420.2270730.4541470.772927
1430.2098550.419710.790145
1440.2089690.4179390.791031
1450.1733110.3466230.826689
1460.1325750.265150.867425
1470.1092810.2185630.890719
1480.09519830.1903970.904802
1490.0986860.1973720.901314
1500.08520490.170410.914795
1510.0705630.1411260.929437
1520.08256620.1651320.917434
1530.06130930.1226190.938691
1540.04066970.08133930.95933
1550.02452260.04904520.975477
1560.062240.124480.93776
1570.0375080.0750160.962492
1580.02694590.05389180.973054
1590.2272520.4545050.772748
1600.2782690.5565390.721731

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.758024 & 0.483953 & 0.241976 \tabularnewline
12 & 0.755885 & 0.48823 & 0.244115 \tabularnewline
13 & 0.638884 & 0.722232 & 0.361116 \tabularnewline
14 & 0.548801 & 0.902399 & 0.451199 \tabularnewline
15 & 0.627183 & 0.745633 & 0.372817 \tabularnewline
16 & 0.530686 & 0.938628 & 0.469314 \tabularnewline
17 & 0.67024 & 0.659519 & 0.32976 \tabularnewline
18 & 0.580435 & 0.839129 & 0.419565 \tabularnewline
19 & 0.58152 & 0.83696 & 0.41848 \tabularnewline
20 & 0.579941 & 0.840119 & 0.420059 \tabularnewline
21 & 0.499601 & 0.999203 & 0.500399 \tabularnewline
22 & 0.504615 & 0.990769 & 0.495385 \tabularnewline
23 & 0.517455 & 0.965089 & 0.482545 \tabularnewline
24 & 0.554412 & 0.891176 & 0.445588 \tabularnewline
25 & 0.480066 & 0.960133 & 0.519934 \tabularnewline
26 & 0.425333 & 0.850665 & 0.574667 \tabularnewline
27 & 0.363054 & 0.726108 & 0.636946 \tabularnewline
28 & 0.358507 & 0.717013 & 0.641493 \tabularnewline
29 & 0.29931 & 0.59862 & 0.70069 \tabularnewline
30 & 0.257106 & 0.514212 & 0.742894 \tabularnewline
31 & 0.215957 & 0.431914 & 0.784043 \tabularnewline
32 & 0.228795 & 0.457589 & 0.771205 \tabularnewline
33 & 0.201563 & 0.403126 & 0.798437 \tabularnewline
34 & 0.170967 & 0.341934 & 0.829033 \tabularnewline
35 & 0.159884 & 0.319768 & 0.840116 \tabularnewline
36 & 0.125435 & 0.25087 & 0.874565 \tabularnewline
37 & 0.14143 & 0.282861 & 0.85857 \tabularnewline
38 & 0.118364 & 0.236727 & 0.881636 \tabularnewline
39 & 0.0936081 & 0.187216 & 0.906392 \tabularnewline
40 & 0.0772044 & 0.154409 & 0.922796 \tabularnewline
41 & 0.0734772 & 0.146954 & 0.926523 \tabularnewline
42 & 0.0554481 & 0.110896 & 0.944552 \tabularnewline
43 & 0.0437088 & 0.0874176 & 0.956291 \tabularnewline
44 & 0.03507 & 0.0701399 & 0.96493 \tabularnewline
45 & 0.0266293 & 0.0532586 & 0.973371 \tabularnewline
46 & 0.0192795 & 0.0385589 & 0.980721 \tabularnewline
47 & 0.0683789 & 0.136758 & 0.931621 \tabularnewline
48 & 0.0543091 & 0.108618 & 0.945691 \tabularnewline
49 & 0.0448709 & 0.0897417 & 0.955129 \tabularnewline
50 & 0.0438576 & 0.0877152 & 0.956142 \tabularnewline
51 & 0.114422 & 0.228843 & 0.885578 \tabularnewline
52 & 0.107495 & 0.214989 & 0.892505 \tabularnewline
53 & 0.101368 & 0.202736 & 0.898632 \tabularnewline
54 & 0.0881365 & 0.176273 & 0.911864 \tabularnewline
55 & 0.0726639 & 0.145328 & 0.927336 \tabularnewline
56 & 0.0679414 & 0.135883 & 0.932059 \tabularnewline
57 & 0.126564 & 0.253128 & 0.873436 \tabularnewline
58 & 0.107984 & 0.215967 & 0.892016 \tabularnewline
59 & 0.0870341 & 0.174068 & 0.912966 \tabularnewline
60 & 0.0815935 & 0.163187 & 0.918407 \tabularnewline
61 & 0.164087 & 0.328174 & 0.835913 \tabularnewline
62 & 0.150614 & 0.301228 & 0.849386 \tabularnewline
63 & 0.15732 & 0.314641 & 0.84268 \tabularnewline
64 & 0.333524 & 0.667049 & 0.666476 \tabularnewline
65 & 0.517846 & 0.964307 & 0.482154 \tabularnewline
66 & 0.479923 & 0.959846 & 0.520077 \tabularnewline
67 & 0.462365 & 0.924731 & 0.537635 \tabularnewline
68 & 0.430116 & 0.860231 & 0.569884 \tabularnewline
69 & 0.406455 & 0.812909 & 0.593545 \tabularnewline
70 & 0.379274 & 0.758548 & 0.620726 \tabularnewline
71 & 0.346421 & 0.692842 & 0.653579 \tabularnewline
72 & 0.323292 & 0.646584 & 0.676708 \tabularnewline
73 & 0.292135 & 0.58427 & 0.707865 \tabularnewline
74 & 0.253821 & 0.507642 & 0.746179 \tabularnewline
75 & 0.248254 & 0.496508 & 0.751746 \tabularnewline
76 & 0.234993 & 0.469987 & 0.765007 \tabularnewline
77 & 0.207996 & 0.415992 & 0.792004 \tabularnewline
78 & 0.201582 & 0.403164 & 0.798418 \tabularnewline
79 & 0.203039 & 0.406078 & 0.796961 \tabularnewline
80 & 0.17832 & 0.35664 & 0.82168 \tabularnewline
81 & 0.152661 & 0.305322 & 0.847339 \tabularnewline
82 & 0.140008 & 0.280016 & 0.859992 \tabularnewline
83 & 0.117748 & 0.235495 & 0.882252 \tabularnewline
84 & 0.199378 & 0.398756 & 0.800622 \tabularnewline
85 & 0.17912 & 0.358241 & 0.82088 \tabularnewline
86 & 0.157914 & 0.315828 & 0.842086 \tabularnewline
87 & 0.132387 & 0.264774 & 0.867613 \tabularnewline
88 & 0.128249 & 0.256497 & 0.871751 \tabularnewline
89 & 0.12182 & 0.24364 & 0.87818 \tabularnewline
90 & 0.100352 & 0.200705 & 0.899648 \tabularnewline
91 & 0.0862449 & 0.17249 & 0.913755 \tabularnewline
92 & 0.0705846 & 0.141169 & 0.929415 \tabularnewline
93 & 0.0581359 & 0.116272 & 0.941864 \tabularnewline
94 & 0.0514566 & 0.102913 & 0.948543 \tabularnewline
95 & 0.0450617 & 0.0901235 & 0.954938 \tabularnewline
96 & 0.0368952 & 0.0737904 & 0.963105 \tabularnewline
97 & 0.0289844 & 0.0579687 & 0.971016 \tabularnewline
98 & 0.0627358 & 0.125472 & 0.937264 \tabularnewline
99 & 0.152271 & 0.304542 & 0.847729 \tabularnewline
100 & 0.176277 & 0.352554 & 0.823723 \tabularnewline
101 & 0.1581 & 0.316201 & 0.8419 \tabularnewline
102 & 0.146295 & 0.29259 & 0.853705 \tabularnewline
103 & 0.132044 & 0.264088 & 0.867956 \tabularnewline
104 & 0.121508 & 0.243016 & 0.878492 \tabularnewline
105 & 0.125979 & 0.251958 & 0.874021 \tabularnewline
106 & 0.113175 & 0.226351 & 0.886825 \tabularnewline
107 & 0.114301 & 0.228602 & 0.885699 \tabularnewline
108 & 0.0979031 & 0.195806 & 0.902097 \tabularnewline
109 & 0.0835496 & 0.167099 & 0.91645 \tabularnewline
110 & 0.0830607 & 0.166121 & 0.916939 \tabularnewline
111 & 0.0917833 & 0.183567 & 0.908217 \tabularnewline
112 & 0.0847133 & 0.169427 & 0.915287 \tabularnewline
113 & 0.0681538 & 0.136308 & 0.931846 \tabularnewline
114 & 0.0663207 & 0.132641 & 0.933679 \tabularnewline
115 & 0.0536636 & 0.107327 & 0.946336 \tabularnewline
116 & 0.0417321 & 0.0834642 & 0.958268 \tabularnewline
117 & 0.0913577 & 0.182715 & 0.908642 \tabularnewline
118 & 0.283165 & 0.566331 & 0.716835 \tabularnewline
119 & 0.246754 & 0.493509 & 0.753246 \tabularnewline
120 & 0.210223 & 0.420447 & 0.789777 \tabularnewline
121 & 0.21546 & 0.430921 & 0.78454 \tabularnewline
122 & 0.266559 & 0.533117 & 0.733441 \tabularnewline
123 & 0.268847 & 0.537694 & 0.731153 \tabularnewline
124 & 0.248853 & 0.497706 & 0.751147 \tabularnewline
125 & 0.219245 & 0.438489 & 0.780755 \tabularnewline
126 & 0.195932 & 0.391863 & 0.804068 \tabularnewline
127 & 0.171172 & 0.342345 & 0.828828 \tabularnewline
128 & 0.227252 & 0.454503 & 0.772748 \tabularnewline
129 & 0.194631 & 0.389262 & 0.805369 \tabularnewline
130 & 0.181244 & 0.362488 & 0.818756 \tabularnewline
131 & 0.187251 & 0.374503 & 0.812749 \tabularnewline
132 & 0.18881 & 0.377619 & 0.81119 \tabularnewline
133 & 0.155264 & 0.310528 & 0.844736 \tabularnewline
134 & 0.157905 & 0.31581 & 0.842095 \tabularnewline
135 & 0.151568 & 0.303137 & 0.848432 \tabularnewline
136 & 0.127823 & 0.255647 & 0.872177 \tabularnewline
137 & 0.130747 & 0.261495 & 0.869253 \tabularnewline
138 & 0.120572 & 0.241143 & 0.879428 \tabularnewline
139 & 0.101801 & 0.203602 & 0.898199 \tabularnewline
140 & 0.109959 & 0.219919 & 0.890041 \tabularnewline
141 & 0.277033 & 0.554066 & 0.722967 \tabularnewline
142 & 0.227073 & 0.454147 & 0.772927 \tabularnewline
143 & 0.209855 & 0.41971 & 0.790145 \tabularnewline
144 & 0.208969 & 0.417939 & 0.791031 \tabularnewline
145 & 0.173311 & 0.346623 & 0.826689 \tabularnewline
146 & 0.132575 & 0.26515 & 0.867425 \tabularnewline
147 & 0.109281 & 0.218563 & 0.890719 \tabularnewline
148 & 0.0951983 & 0.190397 & 0.904802 \tabularnewline
149 & 0.098686 & 0.197372 & 0.901314 \tabularnewline
150 & 0.0852049 & 0.17041 & 0.914795 \tabularnewline
151 & 0.070563 & 0.141126 & 0.929437 \tabularnewline
152 & 0.0825662 & 0.165132 & 0.917434 \tabularnewline
153 & 0.0613093 & 0.122619 & 0.938691 \tabularnewline
154 & 0.0406697 & 0.0813393 & 0.95933 \tabularnewline
155 & 0.0245226 & 0.0490452 & 0.975477 \tabularnewline
156 & 0.06224 & 0.12448 & 0.93776 \tabularnewline
157 & 0.037508 & 0.075016 & 0.962492 \tabularnewline
158 & 0.0269459 & 0.0538918 & 0.973054 \tabularnewline
159 & 0.227252 & 0.454505 & 0.772748 \tabularnewline
160 & 0.278269 & 0.556539 & 0.721731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.758024[/C][C]0.483953[/C][C]0.241976[/C][/ROW]
[ROW][C]12[/C][C]0.755885[/C][C]0.48823[/C][C]0.244115[/C][/ROW]
[ROW][C]13[/C][C]0.638884[/C][C]0.722232[/C][C]0.361116[/C][/ROW]
[ROW][C]14[/C][C]0.548801[/C][C]0.902399[/C][C]0.451199[/C][/ROW]
[ROW][C]15[/C][C]0.627183[/C][C]0.745633[/C][C]0.372817[/C][/ROW]
[ROW][C]16[/C][C]0.530686[/C][C]0.938628[/C][C]0.469314[/C][/ROW]
[ROW][C]17[/C][C]0.67024[/C][C]0.659519[/C][C]0.32976[/C][/ROW]
[ROW][C]18[/C][C]0.580435[/C][C]0.839129[/C][C]0.419565[/C][/ROW]
[ROW][C]19[/C][C]0.58152[/C][C]0.83696[/C][C]0.41848[/C][/ROW]
[ROW][C]20[/C][C]0.579941[/C][C]0.840119[/C][C]0.420059[/C][/ROW]
[ROW][C]21[/C][C]0.499601[/C][C]0.999203[/C][C]0.500399[/C][/ROW]
[ROW][C]22[/C][C]0.504615[/C][C]0.990769[/C][C]0.495385[/C][/ROW]
[ROW][C]23[/C][C]0.517455[/C][C]0.965089[/C][C]0.482545[/C][/ROW]
[ROW][C]24[/C][C]0.554412[/C][C]0.891176[/C][C]0.445588[/C][/ROW]
[ROW][C]25[/C][C]0.480066[/C][C]0.960133[/C][C]0.519934[/C][/ROW]
[ROW][C]26[/C][C]0.425333[/C][C]0.850665[/C][C]0.574667[/C][/ROW]
[ROW][C]27[/C][C]0.363054[/C][C]0.726108[/C][C]0.636946[/C][/ROW]
[ROW][C]28[/C][C]0.358507[/C][C]0.717013[/C][C]0.641493[/C][/ROW]
[ROW][C]29[/C][C]0.29931[/C][C]0.59862[/C][C]0.70069[/C][/ROW]
[ROW][C]30[/C][C]0.257106[/C][C]0.514212[/C][C]0.742894[/C][/ROW]
[ROW][C]31[/C][C]0.215957[/C][C]0.431914[/C][C]0.784043[/C][/ROW]
[ROW][C]32[/C][C]0.228795[/C][C]0.457589[/C][C]0.771205[/C][/ROW]
[ROW][C]33[/C][C]0.201563[/C][C]0.403126[/C][C]0.798437[/C][/ROW]
[ROW][C]34[/C][C]0.170967[/C][C]0.341934[/C][C]0.829033[/C][/ROW]
[ROW][C]35[/C][C]0.159884[/C][C]0.319768[/C][C]0.840116[/C][/ROW]
[ROW][C]36[/C][C]0.125435[/C][C]0.25087[/C][C]0.874565[/C][/ROW]
[ROW][C]37[/C][C]0.14143[/C][C]0.282861[/C][C]0.85857[/C][/ROW]
[ROW][C]38[/C][C]0.118364[/C][C]0.236727[/C][C]0.881636[/C][/ROW]
[ROW][C]39[/C][C]0.0936081[/C][C]0.187216[/C][C]0.906392[/C][/ROW]
[ROW][C]40[/C][C]0.0772044[/C][C]0.154409[/C][C]0.922796[/C][/ROW]
[ROW][C]41[/C][C]0.0734772[/C][C]0.146954[/C][C]0.926523[/C][/ROW]
[ROW][C]42[/C][C]0.0554481[/C][C]0.110896[/C][C]0.944552[/C][/ROW]
[ROW][C]43[/C][C]0.0437088[/C][C]0.0874176[/C][C]0.956291[/C][/ROW]
[ROW][C]44[/C][C]0.03507[/C][C]0.0701399[/C][C]0.96493[/C][/ROW]
[ROW][C]45[/C][C]0.0266293[/C][C]0.0532586[/C][C]0.973371[/C][/ROW]
[ROW][C]46[/C][C]0.0192795[/C][C]0.0385589[/C][C]0.980721[/C][/ROW]
[ROW][C]47[/C][C]0.0683789[/C][C]0.136758[/C][C]0.931621[/C][/ROW]
[ROW][C]48[/C][C]0.0543091[/C][C]0.108618[/C][C]0.945691[/C][/ROW]
[ROW][C]49[/C][C]0.0448709[/C][C]0.0897417[/C][C]0.955129[/C][/ROW]
[ROW][C]50[/C][C]0.0438576[/C][C]0.0877152[/C][C]0.956142[/C][/ROW]
[ROW][C]51[/C][C]0.114422[/C][C]0.228843[/C][C]0.885578[/C][/ROW]
[ROW][C]52[/C][C]0.107495[/C][C]0.214989[/C][C]0.892505[/C][/ROW]
[ROW][C]53[/C][C]0.101368[/C][C]0.202736[/C][C]0.898632[/C][/ROW]
[ROW][C]54[/C][C]0.0881365[/C][C]0.176273[/C][C]0.911864[/C][/ROW]
[ROW][C]55[/C][C]0.0726639[/C][C]0.145328[/C][C]0.927336[/C][/ROW]
[ROW][C]56[/C][C]0.0679414[/C][C]0.135883[/C][C]0.932059[/C][/ROW]
[ROW][C]57[/C][C]0.126564[/C][C]0.253128[/C][C]0.873436[/C][/ROW]
[ROW][C]58[/C][C]0.107984[/C][C]0.215967[/C][C]0.892016[/C][/ROW]
[ROW][C]59[/C][C]0.0870341[/C][C]0.174068[/C][C]0.912966[/C][/ROW]
[ROW][C]60[/C][C]0.0815935[/C][C]0.163187[/C][C]0.918407[/C][/ROW]
[ROW][C]61[/C][C]0.164087[/C][C]0.328174[/C][C]0.835913[/C][/ROW]
[ROW][C]62[/C][C]0.150614[/C][C]0.301228[/C][C]0.849386[/C][/ROW]
[ROW][C]63[/C][C]0.15732[/C][C]0.314641[/C][C]0.84268[/C][/ROW]
[ROW][C]64[/C][C]0.333524[/C][C]0.667049[/C][C]0.666476[/C][/ROW]
[ROW][C]65[/C][C]0.517846[/C][C]0.964307[/C][C]0.482154[/C][/ROW]
[ROW][C]66[/C][C]0.479923[/C][C]0.959846[/C][C]0.520077[/C][/ROW]
[ROW][C]67[/C][C]0.462365[/C][C]0.924731[/C][C]0.537635[/C][/ROW]
[ROW][C]68[/C][C]0.430116[/C][C]0.860231[/C][C]0.569884[/C][/ROW]
[ROW][C]69[/C][C]0.406455[/C][C]0.812909[/C][C]0.593545[/C][/ROW]
[ROW][C]70[/C][C]0.379274[/C][C]0.758548[/C][C]0.620726[/C][/ROW]
[ROW][C]71[/C][C]0.346421[/C][C]0.692842[/C][C]0.653579[/C][/ROW]
[ROW][C]72[/C][C]0.323292[/C][C]0.646584[/C][C]0.676708[/C][/ROW]
[ROW][C]73[/C][C]0.292135[/C][C]0.58427[/C][C]0.707865[/C][/ROW]
[ROW][C]74[/C][C]0.253821[/C][C]0.507642[/C][C]0.746179[/C][/ROW]
[ROW][C]75[/C][C]0.248254[/C][C]0.496508[/C][C]0.751746[/C][/ROW]
[ROW][C]76[/C][C]0.234993[/C][C]0.469987[/C][C]0.765007[/C][/ROW]
[ROW][C]77[/C][C]0.207996[/C][C]0.415992[/C][C]0.792004[/C][/ROW]
[ROW][C]78[/C][C]0.201582[/C][C]0.403164[/C][C]0.798418[/C][/ROW]
[ROW][C]79[/C][C]0.203039[/C][C]0.406078[/C][C]0.796961[/C][/ROW]
[ROW][C]80[/C][C]0.17832[/C][C]0.35664[/C][C]0.82168[/C][/ROW]
[ROW][C]81[/C][C]0.152661[/C][C]0.305322[/C][C]0.847339[/C][/ROW]
[ROW][C]82[/C][C]0.140008[/C][C]0.280016[/C][C]0.859992[/C][/ROW]
[ROW][C]83[/C][C]0.117748[/C][C]0.235495[/C][C]0.882252[/C][/ROW]
[ROW][C]84[/C][C]0.199378[/C][C]0.398756[/C][C]0.800622[/C][/ROW]
[ROW][C]85[/C][C]0.17912[/C][C]0.358241[/C][C]0.82088[/C][/ROW]
[ROW][C]86[/C][C]0.157914[/C][C]0.315828[/C][C]0.842086[/C][/ROW]
[ROW][C]87[/C][C]0.132387[/C][C]0.264774[/C][C]0.867613[/C][/ROW]
[ROW][C]88[/C][C]0.128249[/C][C]0.256497[/C][C]0.871751[/C][/ROW]
[ROW][C]89[/C][C]0.12182[/C][C]0.24364[/C][C]0.87818[/C][/ROW]
[ROW][C]90[/C][C]0.100352[/C][C]0.200705[/C][C]0.899648[/C][/ROW]
[ROW][C]91[/C][C]0.0862449[/C][C]0.17249[/C][C]0.913755[/C][/ROW]
[ROW][C]92[/C][C]0.0705846[/C][C]0.141169[/C][C]0.929415[/C][/ROW]
[ROW][C]93[/C][C]0.0581359[/C][C]0.116272[/C][C]0.941864[/C][/ROW]
[ROW][C]94[/C][C]0.0514566[/C][C]0.102913[/C][C]0.948543[/C][/ROW]
[ROW][C]95[/C][C]0.0450617[/C][C]0.0901235[/C][C]0.954938[/C][/ROW]
[ROW][C]96[/C][C]0.0368952[/C][C]0.0737904[/C][C]0.963105[/C][/ROW]
[ROW][C]97[/C][C]0.0289844[/C][C]0.0579687[/C][C]0.971016[/C][/ROW]
[ROW][C]98[/C][C]0.0627358[/C][C]0.125472[/C][C]0.937264[/C][/ROW]
[ROW][C]99[/C][C]0.152271[/C][C]0.304542[/C][C]0.847729[/C][/ROW]
[ROW][C]100[/C][C]0.176277[/C][C]0.352554[/C][C]0.823723[/C][/ROW]
[ROW][C]101[/C][C]0.1581[/C][C]0.316201[/C][C]0.8419[/C][/ROW]
[ROW][C]102[/C][C]0.146295[/C][C]0.29259[/C][C]0.853705[/C][/ROW]
[ROW][C]103[/C][C]0.132044[/C][C]0.264088[/C][C]0.867956[/C][/ROW]
[ROW][C]104[/C][C]0.121508[/C][C]0.243016[/C][C]0.878492[/C][/ROW]
[ROW][C]105[/C][C]0.125979[/C][C]0.251958[/C][C]0.874021[/C][/ROW]
[ROW][C]106[/C][C]0.113175[/C][C]0.226351[/C][C]0.886825[/C][/ROW]
[ROW][C]107[/C][C]0.114301[/C][C]0.228602[/C][C]0.885699[/C][/ROW]
[ROW][C]108[/C][C]0.0979031[/C][C]0.195806[/C][C]0.902097[/C][/ROW]
[ROW][C]109[/C][C]0.0835496[/C][C]0.167099[/C][C]0.91645[/C][/ROW]
[ROW][C]110[/C][C]0.0830607[/C][C]0.166121[/C][C]0.916939[/C][/ROW]
[ROW][C]111[/C][C]0.0917833[/C][C]0.183567[/C][C]0.908217[/C][/ROW]
[ROW][C]112[/C][C]0.0847133[/C][C]0.169427[/C][C]0.915287[/C][/ROW]
[ROW][C]113[/C][C]0.0681538[/C][C]0.136308[/C][C]0.931846[/C][/ROW]
[ROW][C]114[/C][C]0.0663207[/C][C]0.132641[/C][C]0.933679[/C][/ROW]
[ROW][C]115[/C][C]0.0536636[/C][C]0.107327[/C][C]0.946336[/C][/ROW]
[ROW][C]116[/C][C]0.0417321[/C][C]0.0834642[/C][C]0.958268[/C][/ROW]
[ROW][C]117[/C][C]0.0913577[/C][C]0.182715[/C][C]0.908642[/C][/ROW]
[ROW][C]118[/C][C]0.283165[/C][C]0.566331[/C][C]0.716835[/C][/ROW]
[ROW][C]119[/C][C]0.246754[/C][C]0.493509[/C][C]0.753246[/C][/ROW]
[ROW][C]120[/C][C]0.210223[/C][C]0.420447[/C][C]0.789777[/C][/ROW]
[ROW][C]121[/C][C]0.21546[/C][C]0.430921[/C][C]0.78454[/C][/ROW]
[ROW][C]122[/C][C]0.266559[/C][C]0.533117[/C][C]0.733441[/C][/ROW]
[ROW][C]123[/C][C]0.268847[/C][C]0.537694[/C][C]0.731153[/C][/ROW]
[ROW][C]124[/C][C]0.248853[/C][C]0.497706[/C][C]0.751147[/C][/ROW]
[ROW][C]125[/C][C]0.219245[/C][C]0.438489[/C][C]0.780755[/C][/ROW]
[ROW][C]126[/C][C]0.195932[/C][C]0.391863[/C][C]0.804068[/C][/ROW]
[ROW][C]127[/C][C]0.171172[/C][C]0.342345[/C][C]0.828828[/C][/ROW]
[ROW][C]128[/C][C]0.227252[/C][C]0.454503[/C][C]0.772748[/C][/ROW]
[ROW][C]129[/C][C]0.194631[/C][C]0.389262[/C][C]0.805369[/C][/ROW]
[ROW][C]130[/C][C]0.181244[/C][C]0.362488[/C][C]0.818756[/C][/ROW]
[ROW][C]131[/C][C]0.187251[/C][C]0.374503[/C][C]0.812749[/C][/ROW]
[ROW][C]132[/C][C]0.18881[/C][C]0.377619[/C][C]0.81119[/C][/ROW]
[ROW][C]133[/C][C]0.155264[/C][C]0.310528[/C][C]0.844736[/C][/ROW]
[ROW][C]134[/C][C]0.157905[/C][C]0.31581[/C][C]0.842095[/C][/ROW]
[ROW][C]135[/C][C]0.151568[/C][C]0.303137[/C][C]0.848432[/C][/ROW]
[ROW][C]136[/C][C]0.127823[/C][C]0.255647[/C][C]0.872177[/C][/ROW]
[ROW][C]137[/C][C]0.130747[/C][C]0.261495[/C][C]0.869253[/C][/ROW]
[ROW][C]138[/C][C]0.120572[/C][C]0.241143[/C][C]0.879428[/C][/ROW]
[ROW][C]139[/C][C]0.101801[/C][C]0.203602[/C][C]0.898199[/C][/ROW]
[ROW][C]140[/C][C]0.109959[/C][C]0.219919[/C][C]0.890041[/C][/ROW]
[ROW][C]141[/C][C]0.277033[/C][C]0.554066[/C][C]0.722967[/C][/ROW]
[ROW][C]142[/C][C]0.227073[/C][C]0.454147[/C][C]0.772927[/C][/ROW]
[ROW][C]143[/C][C]0.209855[/C][C]0.41971[/C][C]0.790145[/C][/ROW]
[ROW][C]144[/C][C]0.208969[/C][C]0.417939[/C][C]0.791031[/C][/ROW]
[ROW][C]145[/C][C]0.173311[/C][C]0.346623[/C][C]0.826689[/C][/ROW]
[ROW][C]146[/C][C]0.132575[/C][C]0.26515[/C][C]0.867425[/C][/ROW]
[ROW][C]147[/C][C]0.109281[/C][C]0.218563[/C][C]0.890719[/C][/ROW]
[ROW][C]148[/C][C]0.0951983[/C][C]0.190397[/C][C]0.904802[/C][/ROW]
[ROW][C]149[/C][C]0.098686[/C][C]0.197372[/C][C]0.901314[/C][/ROW]
[ROW][C]150[/C][C]0.0852049[/C][C]0.17041[/C][C]0.914795[/C][/ROW]
[ROW][C]151[/C][C]0.070563[/C][C]0.141126[/C][C]0.929437[/C][/ROW]
[ROW][C]152[/C][C]0.0825662[/C][C]0.165132[/C][C]0.917434[/C][/ROW]
[ROW][C]153[/C][C]0.0613093[/C][C]0.122619[/C][C]0.938691[/C][/ROW]
[ROW][C]154[/C][C]0.0406697[/C][C]0.0813393[/C][C]0.95933[/C][/ROW]
[ROW][C]155[/C][C]0.0245226[/C][C]0.0490452[/C][C]0.975477[/C][/ROW]
[ROW][C]156[/C][C]0.06224[/C][C]0.12448[/C][C]0.93776[/C][/ROW]
[ROW][C]157[/C][C]0.037508[/C][C]0.075016[/C][C]0.962492[/C][/ROW]
[ROW][C]158[/C][C]0.0269459[/C][C]0.0538918[/C][C]0.973054[/C][/ROW]
[ROW][C]159[/C][C]0.227252[/C][C]0.454505[/C][C]0.772748[/C][/ROW]
[ROW][C]160[/C][C]0.278269[/C][C]0.556539[/C][C]0.721731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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
110.7580240.4839530.241976
120.7558850.488230.244115
130.6388840.7222320.361116
140.5488010.9023990.451199
150.6271830.7456330.372817
160.5306860.9386280.469314
170.670240.6595190.32976
180.5804350.8391290.419565
190.581520.836960.41848
200.5799410.8401190.420059
210.4996010.9992030.500399
220.5046150.9907690.495385
230.5174550.9650890.482545
240.5544120.8911760.445588
250.4800660.9601330.519934
260.4253330.8506650.574667
270.3630540.7261080.636946
280.3585070.7170130.641493
290.299310.598620.70069
300.2571060.5142120.742894
310.2159570.4319140.784043
320.2287950.4575890.771205
330.2015630.4031260.798437
340.1709670.3419340.829033
350.1598840.3197680.840116
360.1254350.250870.874565
370.141430.2828610.85857
380.1183640.2367270.881636
390.09360810.1872160.906392
400.07720440.1544090.922796
410.07347720.1469540.926523
420.05544810.1108960.944552
430.04370880.08741760.956291
440.035070.07013990.96493
450.02662930.05325860.973371
460.01927950.03855890.980721
470.06837890.1367580.931621
480.05430910.1086180.945691
490.04487090.08974170.955129
500.04385760.08771520.956142
510.1144220.2288430.885578
520.1074950.2149890.892505
530.1013680.2027360.898632
540.08813650.1762730.911864
550.07266390.1453280.927336
560.06794140.1358830.932059
570.1265640.2531280.873436
580.1079840.2159670.892016
590.08703410.1740680.912966
600.08159350.1631870.918407
610.1640870.3281740.835913
620.1506140.3012280.849386
630.157320.3146410.84268
640.3335240.6670490.666476
650.5178460.9643070.482154
660.4799230.9598460.520077
670.4623650.9247310.537635
680.4301160.8602310.569884
690.4064550.8129090.593545
700.3792740.7585480.620726
710.3464210.6928420.653579
720.3232920.6465840.676708
730.2921350.584270.707865
740.2538210.5076420.746179
750.2482540.4965080.751746
760.2349930.4699870.765007
770.2079960.4159920.792004
780.2015820.4031640.798418
790.2030390.4060780.796961
800.178320.356640.82168
810.1526610.3053220.847339
820.1400080.2800160.859992
830.1177480.2354950.882252
840.1993780.3987560.800622
850.179120.3582410.82088
860.1579140.3158280.842086
870.1323870.2647740.867613
880.1282490.2564970.871751
890.121820.243640.87818
900.1003520.2007050.899648
910.08624490.172490.913755
920.07058460.1411690.929415
930.05813590.1162720.941864
940.05145660.1029130.948543
950.04506170.09012350.954938
960.03689520.07379040.963105
970.02898440.05796870.971016
980.06273580.1254720.937264
990.1522710.3045420.847729
1000.1762770.3525540.823723
1010.15810.3162010.8419
1020.1462950.292590.853705
1030.1320440.2640880.867956
1040.1215080.2430160.878492
1050.1259790.2519580.874021
1060.1131750.2263510.886825
1070.1143010.2286020.885699
1080.09790310.1958060.902097
1090.08354960.1670990.91645
1100.08306070.1661210.916939
1110.09178330.1835670.908217
1120.08471330.1694270.915287
1130.06815380.1363080.931846
1140.06632070.1326410.933679
1150.05366360.1073270.946336
1160.04173210.08346420.958268
1170.09135770.1827150.908642
1180.2831650.5663310.716835
1190.2467540.4935090.753246
1200.2102230.4204470.789777
1210.215460.4309210.78454
1220.2665590.5331170.733441
1230.2688470.5376940.731153
1240.2488530.4977060.751147
1250.2192450.4384890.780755
1260.1959320.3918630.804068
1270.1711720.3423450.828828
1280.2272520.4545030.772748
1290.1946310.3892620.805369
1300.1812440.3624880.818756
1310.1872510.3745030.812749
1320.188810.3776190.81119
1330.1552640.3105280.844736
1340.1579050.315810.842095
1350.1515680.3031370.848432
1360.1278230.2556470.872177
1370.1307470.2614950.869253
1380.1205720.2411430.879428
1390.1018010.2036020.898199
1400.1099590.2199190.890041
1410.2770330.5540660.722967
1420.2270730.4541470.772927
1430.2098550.419710.790145
1440.2089690.4179390.791031
1450.1733110.3466230.826689
1460.1325750.265150.867425
1470.1092810.2185630.890719
1480.09519830.1903970.904802
1490.0986860.1973720.901314
1500.08520490.170410.914795
1510.0705630.1411260.929437
1520.08256620.1651320.917434
1530.06130930.1226190.938691
1540.04066970.08133930.95933
1550.02452260.04904520.975477
1560.062240.124480.93776
1570.0375080.0750160.962492
1580.02694590.05389180.973054
1590.2272520.4545050.772748
1600.2782690.5565390.721731






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0133333OK
10% type I error level140.0933333OK

\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 & 2 & 0.0133333 & OK \tabularnewline
10% type I error level & 14 & 0.0933333 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266736&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]2[/C][C]0.0133333[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.0933333[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266736&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266736&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 level20.0133333OK
10% type I error level140.0933333OK


Figures (Output of Computation)

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