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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 computationWed, 03 Dec 2014 19:19:30 +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/03/t1417634504uan0wozic85tt4n.htm/, Retrieved Thu, 16 May 2024 14:36:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263030, Retrieved Thu, 16 May 2024 14:36:22 +0000
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Estimated Impact100

Tree of Dependent Computations

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Dataset

Dataseries X:
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Histogram
Boxplots 
'4.35' 0 1 22 23 48 23 12
'12.7' 0 1 22 22 50 16 45
'18.1' 0 1 22 21 150 33 37
'17.85' 0 1 20 25 154 32 37
'16.6' 1 0 19 30 109 37 108
'12.6' 1 1 20 17 68 14 10
'17.1' 0 1 22 27 194 52 68
'19.1' 0 0 21 23 158 75 72
'16.1' 0 1 21 23 159 72 143
'13.35' 0 0 21 18 67 15 9
'18.4' 0 0 21 18 147 29 55
'14.7' 0 1 21 23 39 13 17
'10.6' 0 1 21 19 100 40 37
'12.6' 0 1 21 15 111 19 27
'16.2' 0 1 22 20 138 24 37
'13.6' 0 1 24 16 101 121 58
'18.9' 1 1 21 24 131 93 66
'14.1' 0 1 22 25 101 36 21
'14.5' 0 1 20 25 114 23 19
'16.15' 0 0 21 19 165 85 78
'14.75' 0 1 24 19 114 41 35
'14.8' 0 1 25 16 111 46 48
'12.45' 0 1 22 19 75 18 27
'12.65' 0 1 21 19 82 35 43
'17.35' 0 1 21 23 121 17 30
'8.6' 0 1 22 21 32 4 25
'18.4' 0 0 23 22 150 28 69
'16.1' 0 1 24 19 117 44 72
'11.6' 1 1 20 20 71 10 23
'17.75' 0 1 22 20 165 38 13
'15.25' 0 1 25 3 154 57 61
'17.65' 0 1 22 23 126 23 43
'15.6' 0 0 22 14 138 26 22
'16.35' 0 0 21 23 149 36 51
'17.65' 0 0 21 20 145 22 67
'13.6' 0 1 21 15 120 40 36
'11.7' 0 0 22 13 138 18 21
'14.35' 0 0 22 16 109 31 44
'14.75' 0 0 22 7 132 11 45
'18.25' 0 1 21 24 172 38 34
'9.9' 0 0 22 17 169 24 36
16 0 1 23 24 114 37 72
'18.25' 0 1 21 24 156 37 39
'16.85' 0 0 21 19 172 22 43
'14.6' 1 1 21 25 68 15 25
'13.85' 1 1 19 20 89 2 56
'18.95' 0 1 21 28 167 43 80
'15.6' 0 0 21 23 113 31 40
'14.85' 1 0 19 27 115 29 73
'11.75' 1 0 18 18 78 45 34
'18.45' 1 0 19 28 118 25 72
'15.9' 1 1 21 21 87 4 42
'17.1' 0 0 22 19 173 31 61
'16.1' 0 1 22 23 2 -4 23
'19.9' 1 0 19 27 162 66 74
'10.95' 1 1 20 22 49 61 16
'18.45' 1 0 19 28 122 32 66
'15.1' 1 1 21 25 96 31 9
15 1 0 19 21 100 39 41
'11.35' 1 0 20 22 82 19 57
'15.95' 1 1 21 28 100 31 48
'18.1' 1 0 19 20 115 36 51
'14.6' 1 1 21 29 141 42 53
'15.4' 0 1 21 25 165 21 29
'15.4' 0 1 21 25 165 21 29
'17.6' 1 1 19 20 110 25 55
'13.35' 0 1 25 20 118 32 54
'19.1' 0 0 21 16 158 26 43
'15.35' 1 1 20 20 146 28 51
'7.6' 0 0 25 20 49 32 20
'13.4' 1 0 19 23 90 41 79
'13.9' 1 0 20 18 121 29 39
'19.1' 0 1 22 25 155 33 61
'15.25' 1 0 19 18 104 17 55
'12.9' 1 1 20 19 147 13 30
'16.1' 1 0 19 25 110 32 55
'17.35' 1 0 19 25 108 30 22
'13.15' 1 0 18 25 113 34 37
'12.15' 1 0 19 24 115 59 2
'12.6' 1 1 21 19 61 13 38
'10.35' 1 1 19 26 60 23 27
'15.4' 1 1 20 10 109 10 56
'9.6' 1 1 20 17 68 5 25
'18.2' 1 0 19 13 111 31 39
'13.6' 1 0 19 17 77 19 33
'14.85' 1 1 22 30 73 32 43
'14.75' 0 0 21 25 151 30 57
'14.1' 1 0 19 4 89 25 43
'14.9' 1 0 19 16 78 48 23
'16.25' 1 0 19 21 110 35 44
'19.25' 0 1 23 23 220 67 54
'13.6' 1 1 19 22 65 15 28
'13.6' 0 0 20 17 141 22 36
'15.65' 1 0 19 20 117 18 39
'12.75' 0 1 22 20 122 33 16
'14.6' 1 0 19 22 63 46 23
'9.85' 0 1 25 16 44 24 40
'12.65' 1 1 19 23 52 14 24
'11.9' 1 1 20 16 62 23 29
'19.2' 1 0 19 0 131 12 78
'16.6' 1 1 19 18 101 38 57
'11.2' 1 1 20 25 42 12 37
'15.25' 0 1 20 23 152 28 27
'11.9' 0 0 21 12 107 41 61
'13.2' 1 0 19 18 77 12 27
'16.35' 0 0 21 24 154 31 69
'12.4' 0 1 23 11 103 33 34
'15.85' 1 1 19 18 96 34 44
'14.35' 0 0 21 14 154 41 21
'18.15' 0 1 22 23 175 21 34
'11.15' 1 1 20 24 57 20 39
'15.65' 1 0 18 29 112 44 51
'17.75' 0 0 21 18 143 52 34
'7.65' 1 0 20 15 49 7 31
'12.35' 0 1 21 29 110 29 13
'15.6' 0 1 21 16 131 11 12
'19.3' 0 0 21 19 167 26 51
'15.2' 1 0 19 22 56 24 24
'17.1' 0 0 21 16 137 7 19
'15.6' 1 1 19 23 86 60 30
'18.4' 0 1 21 23 121 13 81
'19.05' 0 0 21 19 149 20 42
'18.55' 0 0 22 4 168 52 22
'19.1' 0 0 21 20 140 28 85
'13.1' 1 1 22 24 88 25 27
'12.85' 0 1 22 20 168 39 25
'9.5' 0 1 22 4 94 9 22
'4.5' 0 1 22 24 51 19 19
'11.85' 1 0 21 22 48 13 14
'13.6' 0 1 22 16 145 60 45
'11.7' 0 1 23 3 66 19 45
'12.4' 1 1 19 15 85 34 28
'13.35' 0 0 22 24 109 14 51
'11.4' 1 0 21 17 63 17 41
'14.9' 1 1 19 20 102 45 31
'19.9' 1 0 19 27 162 66 74
'17.75' 0 1 20 23 128 24 24
'11.2' 1 1 20 26 86 48 19
'14.6' 1 1 18 23 114 29 51
'17.6' 0 0 21 17 164 -2 73
'14.05' 0 1 21 20 119 51 24
'16.1' 0 0 20 22 126 2 61
'13.35' 0 1 20 19 132 24 23
'11.85' 0 1 21 24 142 40 14
'11.95' 0 0 21 19 83 20 54
'14.75' 1 1 19 23 94 19 51
'15.15' 1 0 19 15 81 16 62
'13.2' 0 1 21 27 166 20 36
'16.85' 1 0 19 26 110 40 59
'7.85' 1 1 19 22 64 27 24
'7.7' 0 0 24 22 93 25 26
'12.6' 1 0 19 18 104 49 54
'7.85' 1 1 19 15 105 39 39
'10.95' 1 1 20 22 49 61 16
'12.35' 1 0 19 27 88 19 36
'9.95' 1 1 19 10 95 67 31
'14.9' 1 1 19 20 102 45 31
'16.65' 1 0 19 17 99 30 42
'13.4' 1 1 19 23 63 8 39
'13.95' 1 0 19 19 76 19 25
'15.7' 1 0 20 13 109 52 31
'16.85' 1 1 20 27 117 22 38
'10.95' 1 1 19 23 57 17 31
'15.35' 1 0 21 16 120 33 17
'12.2' 1 1 19 25 73 34 22
'15.1' 1 0 19 2 91 22 55
'17.75' 1 0 19 26 108 30 62
'15.2' 1 1 21 20 105 25 51
'14.6' 0 0 22 23 117 38 30
'16.65' 1 0 19 22 119 26 49
'8.1' 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 time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&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]6 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=263030&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.5639 + 0.571557programma[t] -0.343148gender[t] -0.166793age[t] + 0.0390598NUMERACYTOT[t] + 0.0481977LFM[t] -0.00899497PRH[t] + 0.0361013CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.5639 +  0.571557programma[t] -0.343148gender[t] -0.166793age[t] +  0.0390598NUMERACYTOT[t] +  0.0481977LFM[t] -0.00899497PRH[t] +  0.0361013CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.5639 +  0.571557programma[t] -0.343148gender[t] -0.166793age[t] +  0.0390598NUMERACYTOT[t] +  0.0481977LFM[t] -0.00899497PRH[t] +  0.0361013CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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
TOT[t] = + 10.5639 + 0.571557programma[t] -0.343148gender[t] -0.166793age[t] + 0.0390598NUMERACYTOT[t] + 0.0481977LFM[t] -0.00899497PRH[t] + 0.0361013CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.56393.939622.6810.008084540.00404227
programma0.5715570.5636941.0140.3121090.156055
gender-0.3431480.365847-0.9380.3496550.174828
age-0.1667930.170542-0.9780.3295140.164757
NUMERACYTOT0.03905980.03123081.2510.2128430.106422
LFM0.04819770.005732428.4081.98428e-149.92138e-15
PRH-0.008994970.0101806-0.88350.3782470.189123
CH0.03610130.009175933.9340.0001232276.16133e-05

\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) & 10.5639 & 3.93962 & 2.681 & 0.00808454 & 0.00404227 \tabularnewline
programma & 0.571557 & 0.563694 & 1.014 & 0.312109 & 0.156055 \tabularnewline
gender & -0.343148 & 0.365847 & -0.938 & 0.349655 & 0.174828 \tabularnewline
age & -0.166793 & 0.170542 & -0.978 & 0.329514 & 0.164757 \tabularnewline
NUMERACYTOT & 0.0390598 & 0.0312308 & 1.251 & 0.212843 & 0.106422 \tabularnewline
LFM & 0.0481977 & 0.00573242 & 8.408 & 1.98428e-14 & 9.92138e-15 \tabularnewline
PRH & -0.00899497 & 0.0101806 & -0.8835 & 0.378247 & 0.189123 \tabularnewline
CH & 0.0361013 & 0.00917593 & 3.934 & 0.000123227 & 6.16133e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&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]10.5639[/C][C]3.93962[/C][C]2.681[/C][C]0.00808454[/C][C]0.00404227[/C][/ROW]
[ROW][C]programma[/C][C]0.571557[/C][C]0.563694[/C][C]1.014[/C][C]0.312109[/C][C]0.156055[/C][/ROW]
[ROW][C]gender[/C][C]-0.343148[/C][C]0.365847[/C][C]-0.938[/C][C]0.349655[/C][C]0.174828[/C][/ROW]
[ROW][C]age[/C][C]-0.166793[/C][C]0.170542[/C][C]-0.978[/C][C]0.329514[/C][C]0.164757[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0390598[/C][C]0.0312308[/C][C]1.251[/C][C]0.212843[/C][C]0.106422[/C][/ROW]
[ROW][C]LFM[/C][C]0.0481977[/C][C]0.00573242[/C][C]8.408[/C][C]1.98428e-14[/C][C]9.92138e-15[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00899497[/C][C]0.0101806[/C][C]-0.8835[/C][C]0.378247[/C][C]0.189123[/C][/ROW]
[ROW][C]CH[/C][C]0.0361013[/C][C]0.00917593[/C][C]3.934[/C][C]0.000123227[/C][C]6.16133e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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)10.56393.939622.6810.008084540.00404227
programma0.5715570.5636941.0140.3121090.156055
gender-0.3431480.365847-0.9380.3496550.174828
age-0.1667930.170542-0.9780.3295140.164757
NUMERACYTOT0.03905980.03123081.2510.2128430.106422
LFM0.04819770.005732428.4081.98428e-149.92138e-15
PRH-0.008994970.0101806-0.88350.3782470.189123
CH0.03610130.009175933.9340.0001232276.16133e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.714445
R-squared0.510431
Adjusted R-squared0.489407
F-TEST (value)24.278
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1641
Sum Squared Residuals763.382

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.714445 \tabularnewline
R-squared & 0.510431 \tabularnewline
Adjusted R-squared & 0.489407 \tabularnewline
F-TEST (value) & 24.278 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.1641 \tabularnewline
Sum Squared Residuals & 763.382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.714445[/C][/ROW]
[ROW][C]R-squared[/C][C]0.510431[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.489407[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.278[/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[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.1641[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]763.382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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 R0.714445
R-squared0.510431
Adjusted R-squared0.489407
F-TEST (value)24.278
F-TEST (DF numerator)7
F-TEST (DF denominator)163
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1641
Sum Squared Residuals763.382






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.98952-5.63952
212.711.30121.39884
318.115.64012.45985
417.8516.33181.51824
516.617.9579-1.35788
612.611.6330.966994
717.118.9434-1.84344
819.117.49951.60045
916.119.7948-3.69478
1013.3511.18362.16643
1118.416.57411.82588
1214.79.992994.70701
1310.613.256-2.65597
1412.613.4578-0.857785
1516.215.10371.09633
1613.612.71610.883856
1718.916.08722.81284
1814.112.83011.26991
1914.513.8350.665021
2016.1517.8073-1.65735
2114.7513.34921.40084
2214.813.34491.45506
2312.4511.72110.728891
2412.6512.656.79174e-06
2517.3514.37852.97147
268.69.78045-1.18045
2718.417.05581.34422
2816.114.80251.29748
2911.612.4001-0.800076
3017.7515.41262.33736
3115.2515.28-0.0300339
3217.6514.86812.78192
3315.614.65290.947053
3416.3516.6584-0.308442
3517.6517.0520.597977
3613.614.0276-0.427582
3711.714.6497-2.94975
3814.3514.08260.267412
3914.7515.0556-0.305597
4018.2516.83121.41881
419.916.7877-6.88766
421615.0830.917018
4318.2516.24952.00047
4416.8517.4479-0.597869
4514.612.31122.28878
4613.8514.6977-0.847732
4718.9518.36210.587874
4815.614.57121.02882
4914.8516.9383-2.0883
5011.7513.4184-1.66836
5118.4517.12181.32817
5215.913.78342.1166
5317.117.8981-0.798143
5416.18.41247.6876
5519.918.90690.993124
5610.9510.70640.243607
5718.4517.0351.41495
5815.112.93922.16079
591514.73580.26422
6011.3514.498-3.14801
6115.9514.65711.29287
6218.115.80772.29232
6314.616.7539-2.15386
6415.416.5053-1.10527
6515.416.5053-1.10527
6617.615.46692.1331
6713.3514.1811-0.831098
6819.116.61992.48006
6915.3516.8638-1.51383
707.69.97116-2.37116
7113.415.6858-2.28578
7213.915.4817-1.58171
7319.116.90382.19619
7415.2515.5147-0.264699
7512.916.2498-3.34977
7616.115.94240.157621
7717.3514.67262.67737
7813.1515.586-2.43595
7912.1513.9881-1.83807
8012.612.22680.373219
8110.3512.2985-1.94852
8215.415.03230.367665
839.612.2555-2.65548
8418.214.95323.24677
8513.613.36210.237918
8614.8513.07761.77238
8714.7517.1035-2.35353
8814.113.73970.360279
8914.912.74942.15065
9016.2515.3620.887959
9119.2519.23320.0167948
9213.612.49131.10867
9313.615.7897-2.1897
9415.6515.63280.0172276
9512.7513.4934-0.743422
9614.612.27872.32126
979.8510.0248-0.174769
9812.6511.76840.881587
9911.911.9097-0.00973062
10019.216.98832.21173
10116.614.91031.68973
10211.211.6851-0.48507
10315.2515.8322-0.582208
10411.914.5205-2.62052
10513.213.2475-0.0474983
10616.3517.6333-1.28329
10712.412.7092-0.309158
10815.8514.23591.61406
10914.3515.4199-1.06988
11018.1516.92281.22716
11111.1512.3692-1.21922
11215.6516.1095-0.459462
11317.7515.41632.33369
1147.6511.8034-4.15337
11512.3513.3611-1.01106
11615.613.99121.60876
11719.317.45971.84029
11815.212.17533.02466
11917.114.91232.18774
12015.613.212.39003
12118.416.25572.14432
12219.0516.32122.72879
12318.5515.47443.07559
12419.117.40691.69311
12513.113.05160.0484295
12612.8515.9815-3.13146
1279.511.9514-2.45142
1284.510.4619-5.96186
12911.8511.19410.655895
13013.615.2498-1.6498
13111.711.13640.563591
13212.413.011-0.610965
13313.3514.8007-1.45069
13411.412.6605-1.26053
13514.914.0350.865016
13619.918.90690.993124
13717.7514.60313.14686
13811.212.8712-1.67119
13914.615.7633-1.16328
14017.618.2831-0.683087
14114.0513.64250.407477
14216.116.3445-0.24447
14313.3514.6036-1.25359
14411.8514.6452-2.79524
14511.9513.5734-1.62338
14614.7514.72250.0275217
14715.1514.55070.599323
14813.216.8933-3.69329
14916.8516.05390.796115
1507.8512.1908-4.34079
1517.712.6163-4.91634
15212.615.1908-2.59076
1537.8514.3271-6.47706
15410.9510.70640.243607
15512.3514.3912-2.04116
1569.9513.1091-3.15911
15714.914.0350.865016
15816.6514.64842.0016
15913.412.89410.505922
16013.9513.10320.846807
16115.714.21231.48766
16216.8515.32421.52583
16310.9512.2351-1.28513
16415.3514.35840.991614
16512.212.6066-0.406582
16615.114.21820.881802
16717.7516.15571.59426
16815.214.74790.452082
16914.614.17320.426796
17016.6516.09630.553659
1718.110.5155-2.41551

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 9.98952 & -5.63952 \tabularnewline
2 & 12.7 & 11.3012 & 1.39884 \tabularnewline
3 & 18.1 & 15.6401 & 2.45985 \tabularnewline
4 & 17.85 & 16.3318 & 1.51824 \tabularnewline
5 & 16.6 & 17.9579 & -1.35788 \tabularnewline
6 & 12.6 & 11.633 & 0.966994 \tabularnewline
7 & 17.1 & 18.9434 & -1.84344 \tabularnewline
8 & 19.1 & 17.4995 & 1.60045 \tabularnewline
9 & 16.1 & 19.7948 & -3.69478 \tabularnewline
10 & 13.35 & 11.1836 & 2.16643 \tabularnewline
11 & 18.4 & 16.5741 & 1.82588 \tabularnewline
12 & 14.7 & 9.99299 & 4.70701 \tabularnewline
13 & 10.6 & 13.256 & -2.65597 \tabularnewline
14 & 12.6 & 13.4578 & -0.857785 \tabularnewline
15 & 16.2 & 15.1037 & 1.09633 \tabularnewline
16 & 13.6 & 12.7161 & 0.883856 \tabularnewline
17 & 18.9 & 16.0872 & 2.81284 \tabularnewline
18 & 14.1 & 12.8301 & 1.26991 \tabularnewline
19 & 14.5 & 13.835 & 0.665021 \tabularnewline
20 & 16.15 & 17.8073 & -1.65735 \tabularnewline
21 & 14.75 & 13.3492 & 1.40084 \tabularnewline
22 & 14.8 & 13.3449 & 1.45506 \tabularnewline
23 & 12.45 & 11.7211 & 0.728891 \tabularnewline
24 & 12.65 & 12.65 & 6.79174e-06 \tabularnewline
25 & 17.35 & 14.3785 & 2.97147 \tabularnewline
26 & 8.6 & 9.78045 & -1.18045 \tabularnewline
27 & 18.4 & 17.0558 & 1.34422 \tabularnewline
28 & 16.1 & 14.8025 & 1.29748 \tabularnewline
29 & 11.6 & 12.4001 & -0.800076 \tabularnewline
30 & 17.75 & 15.4126 & 2.33736 \tabularnewline
31 & 15.25 & 15.28 & -0.0300339 \tabularnewline
32 & 17.65 & 14.8681 & 2.78192 \tabularnewline
33 & 15.6 & 14.6529 & 0.947053 \tabularnewline
34 & 16.35 & 16.6584 & -0.308442 \tabularnewline
35 & 17.65 & 17.052 & 0.597977 \tabularnewline
36 & 13.6 & 14.0276 & -0.427582 \tabularnewline
37 & 11.7 & 14.6497 & -2.94975 \tabularnewline
38 & 14.35 & 14.0826 & 0.267412 \tabularnewline
39 & 14.75 & 15.0556 & -0.305597 \tabularnewline
40 & 18.25 & 16.8312 & 1.41881 \tabularnewline
41 & 9.9 & 16.7877 & -6.88766 \tabularnewline
42 & 16 & 15.083 & 0.917018 \tabularnewline
43 & 18.25 & 16.2495 & 2.00047 \tabularnewline
44 & 16.85 & 17.4479 & -0.597869 \tabularnewline
45 & 14.6 & 12.3112 & 2.28878 \tabularnewline
46 & 13.85 & 14.6977 & -0.847732 \tabularnewline
47 & 18.95 & 18.3621 & 0.587874 \tabularnewline
48 & 15.6 & 14.5712 & 1.02882 \tabularnewline
49 & 14.85 & 16.9383 & -2.0883 \tabularnewline
50 & 11.75 & 13.4184 & -1.66836 \tabularnewline
51 & 18.45 & 17.1218 & 1.32817 \tabularnewline
52 & 15.9 & 13.7834 & 2.1166 \tabularnewline
53 & 17.1 & 17.8981 & -0.798143 \tabularnewline
54 & 16.1 & 8.4124 & 7.6876 \tabularnewline
55 & 19.9 & 18.9069 & 0.993124 \tabularnewline
56 & 10.95 & 10.7064 & 0.243607 \tabularnewline
57 & 18.45 & 17.035 & 1.41495 \tabularnewline
58 & 15.1 & 12.9392 & 2.16079 \tabularnewline
59 & 15 & 14.7358 & 0.26422 \tabularnewline
60 & 11.35 & 14.498 & -3.14801 \tabularnewline
61 & 15.95 & 14.6571 & 1.29287 \tabularnewline
62 & 18.1 & 15.8077 & 2.29232 \tabularnewline
63 & 14.6 & 16.7539 & -2.15386 \tabularnewline
64 & 15.4 & 16.5053 & -1.10527 \tabularnewline
65 & 15.4 & 16.5053 & -1.10527 \tabularnewline
66 & 17.6 & 15.4669 & 2.1331 \tabularnewline
67 & 13.35 & 14.1811 & -0.831098 \tabularnewline
68 & 19.1 & 16.6199 & 2.48006 \tabularnewline
69 & 15.35 & 16.8638 & -1.51383 \tabularnewline
70 & 7.6 & 9.97116 & -2.37116 \tabularnewline
71 & 13.4 & 15.6858 & -2.28578 \tabularnewline
72 & 13.9 & 15.4817 & -1.58171 \tabularnewline
73 & 19.1 & 16.9038 & 2.19619 \tabularnewline
74 & 15.25 & 15.5147 & -0.264699 \tabularnewline
75 & 12.9 & 16.2498 & -3.34977 \tabularnewline
76 & 16.1 & 15.9424 & 0.157621 \tabularnewline
77 & 17.35 & 14.6726 & 2.67737 \tabularnewline
78 & 13.15 & 15.586 & -2.43595 \tabularnewline
79 & 12.15 & 13.9881 & -1.83807 \tabularnewline
80 & 12.6 & 12.2268 & 0.373219 \tabularnewline
81 & 10.35 & 12.2985 & -1.94852 \tabularnewline
82 & 15.4 & 15.0323 & 0.367665 \tabularnewline
83 & 9.6 & 12.2555 & -2.65548 \tabularnewline
84 & 18.2 & 14.9532 & 3.24677 \tabularnewline
85 & 13.6 & 13.3621 & 0.237918 \tabularnewline
86 & 14.85 & 13.0776 & 1.77238 \tabularnewline
87 & 14.75 & 17.1035 & -2.35353 \tabularnewline
88 & 14.1 & 13.7397 & 0.360279 \tabularnewline
89 & 14.9 & 12.7494 & 2.15065 \tabularnewline
90 & 16.25 & 15.362 & 0.887959 \tabularnewline
91 & 19.25 & 19.2332 & 0.0167948 \tabularnewline
92 & 13.6 & 12.4913 & 1.10867 \tabularnewline
93 & 13.6 & 15.7897 & -2.1897 \tabularnewline
94 & 15.65 & 15.6328 & 0.0172276 \tabularnewline
95 & 12.75 & 13.4934 & -0.743422 \tabularnewline
96 & 14.6 & 12.2787 & 2.32126 \tabularnewline
97 & 9.85 & 10.0248 & -0.174769 \tabularnewline
98 & 12.65 & 11.7684 & 0.881587 \tabularnewline
99 & 11.9 & 11.9097 & -0.00973062 \tabularnewline
100 & 19.2 & 16.9883 & 2.21173 \tabularnewline
101 & 16.6 & 14.9103 & 1.68973 \tabularnewline
102 & 11.2 & 11.6851 & -0.48507 \tabularnewline
103 & 15.25 & 15.8322 & -0.582208 \tabularnewline
104 & 11.9 & 14.5205 & -2.62052 \tabularnewline
105 & 13.2 & 13.2475 & -0.0474983 \tabularnewline
106 & 16.35 & 17.6333 & -1.28329 \tabularnewline
107 & 12.4 & 12.7092 & -0.309158 \tabularnewline
108 & 15.85 & 14.2359 & 1.61406 \tabularnewline
109 & 14.35 & 15.4199 & -1.06988 \tabularnewline
110 & 18.15 & 16.9228 & 1.22716 \tabularnewline
111 & 11.15 & 12.3692 & -1.21922 \tabularnewline
112 & 15.65 & 16.1095 & -0.459462 \tabularnewline
113 & 17.75 & 15.4163 & 2.33369 \tabularnewline
114 & 7.65 & 11.8034 & -4.15337 \tabularnewline
115 & 12.35 & 13.3611 & -1.01106 \tabularnewline
116 & 15.6 & 13.9912 & 1.60876 \tabularnewline
117 & 19.3 & 17.4597 & 1.84029 \tabularnewline
118 & 15.2 & 12.1753 & 3.02466 \tabularnewline
119 & 17.1 & 14.9123 & 2.18774 \tabularnewline
120 & 15.6 & 13.21 & 2.39003 \tabularnewline
121 & 18.4 & 16.2557 & 2.14432 \tabularnewline
122 & 19.05 & 16.3212 & 2.72879 \tabularnewline
123 & 18.55 & 15.4744 & 3.07559 \tabularnewline
124 & 19.1 & 17.4069 & 1.69311 \tabularnewline
125 & 13.1 & 13.0516 & 0.0484295 \tabularnewline
126 & 12.85 & 15.9815 & -3.13146 \tabularnewline
127 & 9.5 & 11.9514 & -2.45142 \tabularnewline
128 & 4.5 & 10.4619 & -5.96186 \tabularnewline
129 & 11.85 & 11.1941 & 0.655895 \tabularnewline
130 & 13.6 & 15.2498 & -1.6498 \tabularnewline
131 & 11.7 & 11.1364 & 0.563591 \tabularnewline
132 & 12.4 & 13.011 & -0.610965 \tabularnewline
133 & 13.35 & 14.8007 & -1.45069 \tabularnewline
134 & 11.4 & 12.6605 & -1.26053 \tabularnewline
135 & 14.9 & 14.035 & 0.865016 \tabularnewline
136 & 19.9 & 18.9069 & 0.993124 \tabularnewline
137 & 17.75 & 14.6031 & 3.14686 \tabularnewline
138 & 11.2 & 12.8712 & -1.67119 \tabularnewline
139 & 14.6 & 15.7633 & -1.16328 \tabularnewline
140 & 17.6 & 18.2831 & -0.683087 \tabularnewline
141 & 14.05 & 13.6425 & 0.407477 \tabularnewline
142 & 16.1 & 16.3445 & -0.24447 \tabularnewline
143 & 13.35 & 14.6036 & -1.25359 \tabularnewline
144 & 11.85 & 14.6452 & -2.79524 \tabularnewline
145 & 11.95 & 13.5734 & -1.62338 \tabularnewline
146 & 14.75 & 14.7225 & 0.0275217 \tabularnewline
147 & 15.15 & 14.5507 & 0.599323 \tabularnewline
148 & 13.2 & 16.8933 & -3.69329 \tabularnewline
149 & 16.85 & 16.0539 & 0.796115 \tabularnewline
150 & 7.85 & 12.1908 & -4.34079 \tabularnewline
151 & 7.7 & 12.6163 & -4.91634 \tabularnewline
152 & 12.6 & 15.1908 & -2.59076 \tabularnewline
153 & 7.85 & 14.3271 & -6.47706 \tabularnewline
154 & 10.95 & 10.7064 & 0.243607 \tabularnewline
155 & 12.35 & 14.3912 & -2.04116 \tabularnewline
156 & 9.95 & 13.1091 & -3.15911 \tabularnewline
157 & 14.9 & 14.035 & 0.865016 \tabularnewline
158 & 16.65 & 14.6484 & 2.0016 \tabularnewline
159 & 13.4 & 12.8941 & 0.505922 \tabularnewline
160 & 13.95 & 13.1032 & 0.846807 \tabularnewline
161 & 15.7 & 14.2123 & 1.48766 \tabularnewline
162 & 16.85 & 15.3242 & 1.52583 \tabularnewline
163 & 10.95 & 12.2351 & -1.28513 \tabularnewline
164 & 15.35 & 14.3584 & 0.991614 \tabularnewline
165 & 12.2 & 12.6066 & -0.406582 \tabularnewline
166 & 15.1 & 14.2182 & 0.881802 \tabularnewline
167 & 17.75 & 16.1557 & 1.59426 \tabularnewline
168 & 15.2 & 14.7479 & 0.452082 \tabularnewline
169 & 14.6 & 14.1732 & 0.426796 \tabularnewline
170 & 16.65 & 16.0963 & 0.553659 \tabularnewline
171 & 8.1 & 10.5155 & -2.41551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&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]4.35[/C][C]9.98952[/C][C]-5.63952[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.3012[/C][C]1.39884[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6401[/C][C]2.45985[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.3318[/C][C]1.51824[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.9579[/C][C]-1.35788[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.633[/C][C]0.966994[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]18.9434[/C][C]-1.84344[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.4995[/C][C]1.60045[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]19.7948[/C][C]-3.69478[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.1836[/C][C]2.16643[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.5741[/C][C]1.82588[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.99299[/C][C]4.70701[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.256[/C][C]-2.65597[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.4578[/C][C]-0.857785[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.1037[/C][C]1.09633[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]12.7161[/C][C]0.883856[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.0872[/C][C]2.81284[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.8301[/C][C]1.26991[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.835[/C][C]0.665021[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.8073[/C][C]-1.65735[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.3492[/C][C]1.40084[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.3449[/C][C]1.45506[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.7211[/C][C]0.728891[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.65[/C][C]6.79174e-06[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.3785[/C][C]2.97147[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.78045[/C][C]-1.18045[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.0558[/C][C]1.34422[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.8025[/C][C]1.29748[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.4001[/C][C]-0.800076[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.4126[/C][C]2.33736[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]15.28[/C][C]-0.0300339[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.8681[/C][C]2.78192[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.6529[/C][C]0.947053[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.6584[/C][C]-0.308442[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]17.052[/C][C]0.597977[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]14.0276[/C][C]-0.427582[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.6497[/C][C]-2.94975[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]14.0826[/C][C]0.267412[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.0556[/C][C]-0.305597[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]16.8312[/C][C]1.41881[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.7877[/C][C]-6.88766[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.083[/C][C]0.917018[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.2495[/C][C]2.00047[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.4479[/C][C]-0.597869[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.3112[/C][C]2.28878[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.6977[/C][C]-0.847732[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.3621[/C][C]0.587874[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.5712[/C][C]1.02882[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]16.9383[/C][C]-2.0883[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.4184[/C][C]-1.66836[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]17.1218[/C][C]1.32817[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]13.7834[/C][C]2.1166[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.8981[/C][C]-0.798143[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.4124[/C][C]7.6876[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]18.9069[/C][C]0.993124[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.7064[/C][C]0.243607[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.035[/C][C]1.41495[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]12.9392[/C][C]2.16079[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.7358[/C][C]0.26422[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]14.498[/C][C]-3.14801[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.6571[/C][C]1.29287[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.8077[/C][C]2.29232[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]16.7539[/C][C]-2.15386[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.5053[/C][C]-1.10527[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.5053[/C][C]-1.10527[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.4669[/C][C]2.1331[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]14.1811[/C][C]-0.831098[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.6199[/C][C]2.48006[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.8638[/C][C]-1.51383[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.97116[/C][C]-2.37116[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.6858[/C][C]-2.28578[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.4817[/C][C]-1.58171[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]16.9038[/C][C]2.19619[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]15.5147[/C][C]-0.264699[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]16.2498[/C][C]-3.34977[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.9424[/C][C]0.157621[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.6726[/C][C]2.67737[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.586[/C][C]-2.43595[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.9881[/C][C]-1.83807[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.2268[/C][C]0.373219[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]12.2985[/C][C]-1.94852[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.0323[/C][C]0.367665[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]12.2555[/C][C]-2.65548[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.9532[/C][C]3.24677[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.3621[/C][C]0.237918[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.0776[/C][C]1.77238[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.1035[/C][C]-2.35353[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]13.7397[/C][C]0.360279[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.7494[/C][C]2.15065[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]15.362[/C][C]0.887959[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.2332[/C][C]0.0167948[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.4913[/C][C]1.10867[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.7897[/C][C]-2.1897[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]15.6328[/C][C]0.0172276[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.4934[/C][C]-0.743422[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]12.2787[/C][C]2.32126[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]10.0248[/C][C]-0.174769[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.7684[/C][C]0.881587[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]11.9097[/C][C]-0.00973062[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]16.9883[/C][C]2.21173[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]14.9103[/C][C]1.68973[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.6851[/C][C]-0.48507[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.8322[/C][C]-0.582208[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.5205[/C][C]-2.62052[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.2475[/C][C]-0.0474983[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.6333[/C][C]-1.28329[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]12.7092[/C][C]-0.309158[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]14.2359[/C][C]1.61406[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.4199[/C][C]-1.06988[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]16.9228[/C][C]1.22716[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]12.3692[/C][C]-1.21922[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.1095[/C][C]-0.459462[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.4163[/C][C]2.33369[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]11.8034[/C][C]-4.15337[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.3611[/C][C]-1.01106[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.9912[/C][C]1.60876[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.4597[/C][C]1.84029[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]12.1753[/C][C]3.02466[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.9123[/C][C]2.18774[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.21[/C][C]2.39003[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]16.2557[/C][C]2.14432[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.3212[/C][C]2.72879[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.4744[/C][C]3.07559[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.4069[/C][C]1.69311[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.0516[/C][C]0.0484295[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]15.9815[/C][C]-3.13146[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.9514[/C][C]-2.45142[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.4619[/C][C]-5.96186[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]11.1941[/C][C]0.655895[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.2498[/C][C]-1.6498[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.1364[/C][C]0.563591[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]13.011[/C][C]-0.610965[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]14.8007[/C][C]-1.45069[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.6605[/C][C]-1.26053[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]14.035[/C][C]0.865016[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]18.9069[/C][C]0.993124[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.6031[/C][C]3.14686[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]12.8712[/C][C]-1.67119[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]15.7633[/C][C]-1.16328[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]18.2831[/C][C]-0.683087[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.6425[/C][C]0.407477[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]16.3445[/C][C]-0.24447[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.6036[/C][C]-1.25359[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.6452[/C][C]-2.79524[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]13.5734[/C][C]-1.62338[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]14.7225[/C][C]0.0275217[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.5507[/C][C]0.599323[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.8933[/C][C]-3.69329[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.0539[/C][C]0.796115[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]12.1908[/C][C]-4.34079[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]12.6163[/C][C]-4.91634[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]15.1908[/C][C]-2.59076[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]14.3271[/C][C]-6.47706[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.7064[/C][C]0.243607[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]14.3912[/C][C]-2.04116[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.1091[/C][C]-3.15911[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]14.035[/C][C]0.865016[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]14.6484[/C][C]2.0016[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]12.8941[/C][C]0.505922[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.1032[/C][C]0.846807[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]14.2123[/C][C]1.48766[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.3242[/C][C]1.52583[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]12.2351[/C][C]-1.28513[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]14.3584[/C][C]0.991614[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.6066[/C][C]-0.406582[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.2182[/C][C]0.881802[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.1557[/C][C]1.59426[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.7479[/C][C]0.452082[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.1732[/C][C]0.426796[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]16.0963[/C][C]0.553659[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]10.5155[/C][C]-2.41551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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
14.359.98952-5.63952
212.711.30121.39884
318.115.64012.45985
417.8516.33181.51824
516.617.9579-1.35788
612.611.6330.966994
717.118.9434-1.84344
819.117.49951.60045
916.119.7948-3.69478
1013.3511.18362.16643
1118.416.57411.82588
1214.79.992994.70701
1310.613.256-2.65597
1412.613.4578-0.857785
1516.215.10371.09633
1613.612.71610.883856
1718.916.08722.81284
1814.112.83011.26991
1914.513.8350.665021
2016.1517.8073-1.65735
2114.7513.34921.40084
2214.813.34491.45506
2312.4511.72110.728891
2412.6512.656.79174e-06
2517.3514.37852.97147
268.69.78045-1.18045
2718.417.05581.34422
2816.114.80251.29748
2911.612.4001-0.800076
3017.7515.41262.33736
3115.2515.28-0.0300339
3217.6514.86812.78192
3315.614.65290.947053
3416.3516.6584-0.308442
3517.6517.0520.597977
3613.614.0276-0.427582
3711.714.6497-2.94975
3814.3514.08260.267412
3914.7515.0556-0.305597
4018.2516.83121.41881
419.916.7877-6.88766
421615.0830.917018
4318.2516.24952.00047
4416.8517.4479-0.597869
4514.612.31122.28878
4613.8514.6977-0.847732
4718.9518.36210.587874
4815.614.57121.02882
4914.8516.9383-2.0883
5011.7513.4184-1.66836
5118.4517.12181.32817
5215.913.78342.1166
5317.117.8981-0.798143
5416.18.41247.6876
5519.918.90690.993124
5610.9510.70640.243607
5718.4517.0351.41495
5815.112.93922.16079
591514.73580.26422
6011.3514.498-3.14801
6115.9514.65711.29287
6218.115.80772.29232
6314.616.7539-2.15386
6415.416.5053-1.10527
6515.416.5053-1.10527
6617.615.46692.1331
6713.3514.1811-0.831098
6819.116.61992.48006
6915.3516.8638-1.51383
707.69.97116-2.37116
7113.415.6858-2.28578
7213.915.4817-1.58171
7319.116.90382.19619
7415.2515.5147-0.264699
7512.916.2498-3.34977
7616.115.94240.157621
7717.3514.67262.67737
7813.1515.586-2.43595
7912.1513.9881-1.83807
8012.612.22680.373219
8110.3512.2985-1.94852
8215.415.03230.367665
839.612.2555-2.65548
8418.214.95323.24677
8513.613.36210.237918
8614.8513.07761.77238
8714.7517.1035-2.35353
8814.113.73970.360279
8914.912.74942.15065
9016.2515.3620.887959
9119.2519.23320.0167948
9213.612.49131.10867
9313.615.7897-2.1897
9415.6515.63280.0172276
9512.7513.4934-0.743422
9614.612.27872.32126
979.8510.0248-0.174769
9812.6511.76840.881587
9911.911.9097-0.00973062
10019.216.98832.21173
10116.614.91031.68973
10211.211.6851-0.48507
10315.2515.8322-0.582208
10411.914.5205-2.62052
10513.213.2475-0.0474983
10616.3517.6333-1.28329
10712.412.7092-0.309158
10815.8514.23591.61406
10914.3515.4199-1.06988
11018.1516.92281.22716
11111.1512.3692-1.21922
11215.6516.1095-0.459462
11317.7515.41632.33369
1147.6511.8034-4.15337
11512.3513.3611-1.01106
11615.613.99121.60876
11719.317.45971.84029
11815.212.17533.02466
11917.114.91232.18774
12015.613.212.39003
12118.416.25572.14432
12219.0516.32122.72879
12318.5515.47443.07559
12419.117.40691.69311
12513.113.05160.0484295
12612.8515.9815-3.13146
1279.511.9514-2.45142
1284.510.4619-5.96186
12911.8511.19410.655895
13013.615.2498-1.6498
13111.711.13640.563591
13212.413.011-0.610965
13313.3514.8007-1.45069
13411.412.6605-1.26053
13514.914.0350.865016
13619.918.90690.993124
13717.7514.60313.14686
13811.212.8712-1.67119
13914.615.7633-1.16328
14017.618.2831-0.683087
14114.0513.64250.407477
14216.116.3445-0.24447
14313.3514.6036-1.25359
14411.8514.6452-2.79524
14511.9513.5734-1.62338
14614.7514.72250.0275217
14715.1514.55070.599323
14813.216.8933-3.69329
14916.8516.05390.796115
1507.8512.1908-4.34079
1517.712.6163-4.91634
15212.615.1908-2.59076
1537.8514.3271-6.47706
15410.9510.70640.243607
15512.3514.3912-2.04116
1569.9513.1091-3.15911
15714.914.0350.865016
15816.6514.64842.0016
15913.412.89410.505922
16013.9513.10320.846807
16115.714.21231.48766
16216.8515.32421.52583
16310.9512.2351-1.28513
16415.3514.35840.991614
16512.212.6066-0.406582
16615.114.21820.881802
16717.7516.15571.59426
16815.214.74790.452082
16914.614.17320.426796
17016.6516.09630.553659
1718.110.5155-2.41551






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9430190.1139620.056981
120.9857480.02850360.0142518
130.9869290.02614150.0130708
140.9835240.03295110.0164756
150.9710550.05789070.0289454
160.9770480.04590460.0229523
170.9761280.0477440.023872
180.9621580.07568420.0378421
190.941620.116760.0583799
200.93820.1235990.0617997
210.9124990.1750020.0875009
220.8810020.2379950.118998
230.8408180.3183650.159182
240.7936460.4127080.206354
250.803210.393580.19679
260.7672150.465570.232785
270.7141940.5716120.285806
280.6733910.6532190.326609
290.6507090.6985830.349291
300.6046750.7906510.395325
310.5441040.9117930.455896
320.546350.9073010.45365
330.4992940.9985870.500706
340.4591490.9182980.540851
350.4018330.8036660.598167
360.345190.690380.65481
370.4729130.9458270.527087
380.4156790.8313580.584321
390.361890.7237790.63811
400.3164870.6329750.683513
410.8187850.362430.181215
420.7841150.4317710.215885
430.7641660.4716670.235834
440.7224670.5550660.277533
450.6978180.6043640.302182
460.6537060.6925880.346294
470.6061170.7877670.393883
480.5644380.8711240.435562
490.5540.8920.446
500.5164850.967030.483515
510.490460.980920.50954
520.472240.9444810.52776
530.4263740.8527470.573626
540.8570270.2859460.142973
550.8375460.3249080.162454
560.8143340.3713320.185666
570.7904260.4191470.209574
580.7782170.4435670.221783
590.7420750.515850.257925
600.7923310.4153380.207669
610.7684810.4630370.231519
620.7888660.4222690.211134
630.8215990.3568020.178401
640.805510.388980.19449
650.7855940.4288130.214406
660.7933860.4132280.206614
670.7745010.4509980.225499
680.7979750.4040490.202025
690.7819950.4360110.218005
700.8047050.390590.195295
710.8112620.3774750.188738
720.7985140.4029710.201486
730.7997310.4005380.200269
740.7689210.4621580.231079
750.8261630.3476750.173837
760.7960230.4079540.203977
770.8065260.3869480.193474
780.8242810.3514380.175719
790.8242620.3514750.175738
800.7964020.4071950.203598
810.7975750.404850.202425
820.7689540.4620920.231046
830.7847030.4305940.215297
840.8315580.3368840.168442
850.8020990.3958030.197901
860.7980240.4039520.201976
870.8043130.3913740.195687
880.7772730.4454530.222727
890.7753820.4492360.224618
900.7445070.5109860.255493
910.7070770.5858450.292923
920.6830520.6338960.316948
930.6874120.6251760.312588
940.6494710.7010590.350529
950.6143690.7712630.385631
960.6275160.7449680.372484
970.6223240.7553520.377676
980.59950.8009990.4005
990.5601830.8796340.439817
1000.5567490.8865020.443251
1010.537480.9250410.46252
1020.5064780.9870440.493522
1030.464920.9298410.53508
1040.4789890.9579780.521011
1050.4326540.8653090.567346
1060.4095070.8190140.590493
1070.3732090.7464170.626791
1080.3569590.7139170.643041
1090.3382270.6764540.661773
1100.3112330.6224650.688767
1110.2839080.5678150.716092
1120.2547620.5095230.745238
1130.2505870.5011750.749413
1140.3557870.7115740.644213
1150.3227060.6454130.677294
1160.3183620.6367250.681638
1170.2977170.5954340.702283
1180.3447270.6894540.655273
1190.3499280.6998570.650072
1200.3954460.7908920.604554
1210.4459580.8919150.554042
1220.4782730.9565460.521727
1230.5277030.9445930.472297
1240.5236060.9527890.476394
1250.4868540.9737080.513146
1260.4982350.9964690.501765
1270.4777270.9554550.522273
1280.6396320.7207360.360368
1290.6031980.7936030.396802
1300.5593880.8812230.440612
1310.5989120.8021750.401088
1320.5462910.9074180.453709
1330.4936310.9872620.506369
1340.4403790.8807570.559621
1350.4078150.815630.592185
1360.3533040.7066080.646696
1370.5527010.8945990.447299
1380.5045060.9909870.495494
1390.4522380.9044750.547762
1400.3909250.781850.609075
1410.4596090.9192180.540391
1420.4018360.8036710.598164
1430.3984530.7969050.601547
1440.3643650.7287310.635635
1450.3244380.6488750.675562
1460.2713120.5426240.728688
1470.2180340.4360680.781966
1480.1848480.3696960.815152
1490.139050.2780990.86095
1500.1822690.3645370.817731
1510.4116090.8232170.588391
1520.4743150.948630.525685
1530.9155090.1689820.0844908
1540.9276560.1446880.072344
1550.9798050.04039080.0201954
1560.9949620.01007610.00503803
1570.9858460.02830840.0141542
1580.975790.04841920.0242096
1590.96950.06100080.0305004
1600.9669230.06615390.0330769

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.943019 & 0.113962 & 0.056981 \tabularnewline
12 & 0.985748 & 0.0285036 & 0.0142518 \tabularnewline
13 & 0.986929 & 0.0261415 & 0.0130708 \tabularnewline
14 & 0.983524 & 0.0329511 & 0.0164756 \tabularnewline
15 & 0.971055 & 0.0578907 & 0.0289454 \tabularnewline
16 & 0.977048 & 0.0459046 & 0.0229523 \tabularnewline
17 & 0.976128 & 0.047744 & 0.023872 \tabularnewline
18 & 0.962158 & 0.0756842 & 0.0378421 \tabularnewline
19 & 0.94162 & 0.11676 & 0.0583799 \tabularnewline
20 & 0.9382 & 0.123599 & 0.0617997 \tabularnewline
21 & 0.912499 & 0.175002 & 0.0875009 \tabularnewline
22 & 0.881002 & 0.237995 & 0.118998 \tabularnewline
23 & 0.840818 & 0.318365 & 0.159182 \tabularnewline
24 & 0.793646 & 0.412708 & 0.206354 \tabularnewline
25 & 0.80321 & 0.39358 & 0.19679 \tabularnewline
26 & 0.767215 & 0.46557 & 0.232785 \tabularnewline
27 & 0.714194 & 0.571612 & 0.285806 \tabularnewline
28 & 0.673391 & 0.653219 & 0.326609 \tabularnewline
29 & 0.650709 & 0.698583 & 0.349291 \tabularnewline
30 & 0.604675 & 0.790651 & 0.395325 \tabularnewline
31 & 0.544104 & 0.911793 & 0.455896 \tabularnewline
32 & 0.54635 & 0.907301 & 0.45365 \tabularnewline
33 & 0.499294 & 0.998587 & 0.500706 \tabularnewline
34 & 0.459149 & 0.918298 & 0.540851 \tabularnewline
35 & 0.401833 & 0.803666 & 0.598167 \tabularnewline
36 & 0.34519 & 0.69038 & 0.65481 \tabularnewline
37 & 0.472913 & 0.945827 & 0.527087 \tabularnewline
38 & 0.415679 & 0.831358 & 0.584321 \tabularnewline
39 & 0.36189 & 0.723779 & 0.63811 \tabularnewline
40 & 0.316487 & 0.632975 & 0.683513 \tabularnewline
41 & 0.818785 & 0.36243 & 0.181215 \tabularnewline
42 & 0.784115 & 0.431771 & 0.215885 \tabularnewline
43 & 0.764166 & 0.471667 & 0.235834 \tabularnewline
44 & 0.722467 & 0.555066 & 0.277533 \tabularnewline
45 & 0.697818 & 0.604364 & 0.302182 \tabularnewline
46 & 0.653706 & 0.692588 & 0.346294 \tabularnewline
47 & 0.606117 & 0.787767 & 0.393883 \tabularnewline
48 & 0.564438 & 0.871124 & 0.435562 \tabularnewline
49 & 0.554 & 0.892 & 0.446 \tabularnewline
50 & 0.516485 & 0.96703 & 0.483515 \tabularnewline
51 & 0.49046 & 0.98092 & 0.50954 \tabularnewline
52 & 0.47224 & 0.944481 & 0.52776 \tabularnewline
53 & 0.426374 & 0.852747 & 0.573626 \tabularnewline
54 & 0.857027 & 0.285946 & 0.142973 \tabularnewline
55 & 0.837546 & 0.324908 & 0.162454 \tabularnewline
56 & 0.814334 & 0.371332 & 0.185666 \tabularnewline
57 & 0.790426 & 0.419147 & 0.209574 \tabularnewline
58 & 0.778217 & 0.443567 & 0.221783 \tabularnewline
59 & 0.742075 & 0.51585 & 0.257925 \tabularnewline
60 & 0.792331 & 0.415338 & 0.207669 \tabularnewline
61 & 0.768481 & 0.463037 & 0.231519 \tabularnewline
62 & 0.788866 & 0.422269 & 0.211134 \tabularnewline
63 & 0.821599 & 0.356802 & 0.178401 \tabularnewline
64 & 0.80551 & 0.38898 & 0.19449 \tabularnewline
65 & 0.785594 & 0.428813 & 0.214406 \tabularnewline
66 & 0.793386 & 0.413228 & 0.206614 \tabularnewline
67 & 0.774501 & 0.450998 & 0.225499 \tabularnewline
68 & 0.797975 & 0.404049 & 0.202025 \tabularnewline
69 & 0.781995 & 0.436011 & 0.218005 \tabularnewline
70 & 0.804705 & 0.39059 & 0.195295 \tabularnewline
71 & 0.811262 & 0.377475 & 0.188738 \tabularnewline
72 & 0.798514 & 0.402971 & 0.201486 \tabularnewline
73 & 0.799731 & 0.400538 & 0.200269 \tabularnewline
74 & 0.768921 & 0.462158 & 0.231079 \tabularnewline
75 & 0.826163 & 0.347675 & 0.173837 \tabularnewline
76 & 0.796023 & 0.407954 & 0.203977 \tabularnewline
77 & 0.806526 & 0.386948 & 0.193474 \tabularnewline
78 & 0.824281 & 0.351438 & 0.175719 \tabularnewline
79 & 0.824262 & 0.351475 & 0.175738 \tabularnewline
80 & 0.796402 & 0.407195 & 0.203598 \tabularnewline
81 & 0.797575 & 0.40485 & 0.202425 \tabularnewline
82 & 0.768954 & 0.462092 & 0.231046 \tabularnewline
83 & 0.784703 & 0.430594 & 0.215297 \tabularnewline
84 & 0.831558 & 0.336884 & 0.168442 \tabularnewline
85 & 0.802099 & 0.395803 & 0.197901 \tabularnewline
86 & 0.798024 & 0.403952 & 0.201976 \tabularnewline
87 & 0.804313 & 0.391374 & 0.195687 \tabularnewline
88 & 0.777273 & 0.445453 & 0.222727 \tabularnewline
89 & 0.775382 & 0.449236 & 0.224618 \tabularnewline
90 & 0.744507 & 0.510986 & 0.255493 \tabularnewline
91 & 0.707077 & 0.585845 & 0.292923 \tabularnewline
92 & 0.683052 & 0.633896 & 0.316948 \tabularnewline
93 & 0.687412 & 0.625176 & 0.312588 \tabularnewline
94 & 0.649471 & 0.701059 & 0.350529 \tabularnewline
95 & 0.614369 & 0.771263 & 0.385631 \tabularnewline
96 & 0.627516 & 0.744968 & 0.372484 \tabularnewline
97 & 0.622324 & 0.755352 & 0.377676 \tabularnewline
98 & 0.5995 & 0.800999 & 0.4005 \tabularnewline
99 & 0.560183 & 0.879634 & 0.439817 \tabularnewline
100 & 0.556749 & 0.886502 & 0.443251 \tabularnewline
101 & 0.53748 & 0.925041 & 0.46252 \tabularnewline
102 & 0.506478 & 0.987044 & 0.493522 \tabularnewline
103 & 0.46492 & 0.929841 & 0.53508 \tabularnewline
104 & 0.478989 & 0.957978 & 0.521011 \tabularnewline
105 & 0.432654 & 0.865309 & 0.567346 \tabularnewline
106 & 0.409507 & 0.819014 & 0.590493 \tabularnewline
107 & 0.373209 & 0.746417 & 0.626791 \tabularnewline
108 & 0.356959 & 0.713917 & 0.643041 \tabularnewline
109 & 0.338227 & 0.676454 & 0.661773 \tabularnewline
110 & 0.311233 & 0.622465 & 0.688767 \tabularnewline
111 & 0.283908 & 0.567815 & 0.716092 \tabularnewline
112 & 0.254762 & 0.509523 & 0.745238 \tabularnewline
113 & 0.250587 & 0.501175 & 0.749413 \tabularnewline
114 & 0.355787 & 0.711574 & 0.644213 \tabularnewline
115 & 0.322706 & 0.645413 & 0.677294 \tabularnewline
116 & 0.318362 & 0.636725 & 0.681638 \tabularnewline
117 & 0.297717 & 0.595434 & 0.702283 \tabularnewline
118 & 0.344727 & 0.689454 & 0.655273 \tabularnewline
119 & 0.349928 & 0.699857 & 0.650072 \tabularnewline
120 & 0.395446 & 0.790892 & 0.604554 \tabularnewline
121 & 0.445958 & 0.891915 & 0.554042 \tabularnewline
122 & 0.478273 & 0.956546 & 0.521727 \tabularnewline
123 & 0.527703 & 0.944593 & 0.472297 \tabularnewline
124 & 0.523606 & 0.952789 & 0.476394 \tabularnewline
125 & 0.486854 & 0.973708 & 0.513146 \tabularnewline
126 & 0.498235 & 0.996469 & 0.501765 \tabularnewline
127 & 0.477727 & 0.955455 & 0.522273 \tabularnewline
128 & 0.639632 & 0.720736 & 0.360368 \tabularnewline
129 & 0.603198 & 0.793603 & 0.396802 \tabularnewline
130 & 0.559388 & 0.881223 & 0.440612 \tabularnewline
131 & 0.598912 & 0.802175 & 0.401088 \tabularnewline
132 & 0.546291 & 0.907418 & 0.453709 \tabularnewline
133 & 0.493631 & 0.987262 & 0.506369 \tabularnewline
134 & 0.440379 & 0.880757 & 0.559621 \tabularnewline
135 & 0.407815 & 0.81563 & 0.592185 \tabularnewline
136 & 0.353304 & 0.706608 & 0.646696 \tabularnewline
137 & 0.552701 & 0.894599 & 0.447299 \tabularnewline
138 & 0.504506 & 0.990987 & 0.495494 \tabularnewline
139 & 0.452238 & 0.904475 & 0.547762 \tabularnewline
140 & 0.390925 & 0.78185 & 0.609075 \tabularnewline
141 & 0.459609 & 0.919218 & 0.540391 \tabularnewline
142 & 0.401836 & 0.803671 & 0.598164 \tabularnewline
143 & 0.398453 & 0.796905 & 0.601547 \tabularnewline
144 & 0.364365 & 0.728731 & 0.635635 \tabularnewline
145 & 0.324438 & 0.648875 & 0.675562 \tabularnewline
146 & 0.271312 & 0.542624 & 0.728688 \tabularnewline
147 & 0.218034 & 0.436068 & 0.781966 \tabularnewline
148 & 0.184848 & 0.369696 & 0.815152 \tabularnewline
149 & 0.13905 & 0.278099 & 0.86095 \tabularnewline
150 & 0.182269 & 0.364537 & 0.817731 \tabularnewline
151 & 0.411609 & 0.823217 & 0.588391 \tabularnewline
152 & 0.474315 & 0.94863 & 0.525685 \tabularnewline
153 & 0.915509 & 0.168982 & 0.0844908 \tabularnewline
154 & 0.927656 & 0.144688 & 0.072344 \tabularnewline
155 & 0.979805 & 0.0403908 & 0.0201954 \tabularnewline
156 & 0.994962 & 0.0100761 & 0.00503803 \tabularnewline
157 & 0.985846 & 0.0283084 & 0.0141542 \tabularnewline
158 & 0.97579 & 0.0484192 & 0.0242096 \tabularnewline
159 & 0.9695 & 0.0610008 & 0.0305004 \tabularnewline
160 & 0.966923 & 0.0661539 & 0.0330769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&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]0.943019[/C][C]0.113962[/C][C]0.056981[/C][/ROW]
[ROW][C]12[/C][C]0.985748[/C][C]0.0285036[/C][C]0.0142518[/C][/ROW]
[ROW][C]13[/C][C]0.986929[/C][C]0.0261415[/C][C]0.0130708[/C][/ROW]
[ROW][C]14[/C][C]0.983524[/C][C]0.0329511[/C][C]0.0164756[/C][/ROW]
[ROW][C]15[/C][C]0.971055[/C][C]0.0578907[/C][C]0.0289454[/C][/ROW]
[ROW][C]16[/C][C]0.977048[/C][C]0.0459046[/C][C]0.0229523[/C][/ROW]
[ROW][C]17[/C][C]0.976128[/C][C]0.047744[/C][C]0.023872[/C][/ROW]
[ROW][C]18[/C][C]0.962158[/C][C]0.0756842[/C][C]0.0378421[/C][/ROW]
[ROW][C]19[/C][C]0.94162[/C][C]0.11676[/C][C]0.0583799[/C][/ROW]
[ROW][C]20[/C][C]0.9382[/C][C]0.123599[/C][C]0.0617997[/C][/ROW]
[ROW][C]21[/C][C]0.912499[/C][C]0.175002[/C][C]0.0875009[/C][/ROW]
[ROW][C]22[/C][C]0.881002[/C][C]0.237995[/C][C]0.118998[/C][/ROW]
[ROW][C]23[/C][C]0.840818[/C][C]0.318365[/C][C]0.159182[/C][/ROW]
[ROW][C]24[/C][C]0.793646[/C][C]0.412708[/C][C]0.206354[/C][/ROW]
[ROW][C]25[/C][C]0.80321[/C][C]0.39358[/C][C]0.19679[/C][/ROW]
[ROW][C]26[/C][C]0.767215[/C][C]0.46557[/C][C]0.232785[/C][/ROW]
[ROW][C]27[/C][C]0.714194[/C][C]0.571612[/C][C]0.285806[/C][/ROW]
[ROW][C]28[/C][C]0.673391[/C][C]0.653219[/C][C]0.326609[/C][/ROW]
[ROW][C]29[/C][C]0.650709[/C][C]0.698583[/C][C]0.349291[/C][/ROW]
[ROW][C]30[/C][C]0.604675[/C][C]0.790651[/C][C]0.395325[/C][/ROW]
[ROW][C]31[/C][C]0.544104[/C][C]0.911793[/C][C]0.455896[/C][/ROW]
[ROW][C]32[/C][C]0.54635[/C][C]0.907301[/C][C]0.45365[/C][/ROW]
[ROW][C]33[/C][C]0.499294[/C][C]0.998587[/C][C]0.500706[/C][/ROW]
[ROW][C]34[/C][C]0.459149[/C][C]0.918298[/C][C]0.540851[/C][/ROW]
[ROW][C]35[/C][C]0.401833[/C][C]0.803666[/C][C]0.598167[/C][/ROW]
[ROW][C]36[/C][C]0.34519[/C][C]0.69038[/C][C]0.65481[/C][/ROW]
[ROW][C]37[/C][C]0.472913[/C][C]0.945827[/C][C]0.527087[/C][/ROW]
[ROW][C]38[/C][C]0.415679[/C][C]0.831358[/C][C]0.584321[/C][/ROW]
[ROW][C]39[/C][C]0.36189[/C][C]0.723779[/C][C]0.63811[/C][/ROW]
[ROW][C]40[/C][C]0.316487[/C][C]0.632975[/C][C]0.683513[/C][/ROW]
[ROW][C]41[/C][C]0.818785[/C][C]0.36243[/C][C]0.181215[/C][/ROW]
[ROW][C]42[/C][C]0.784115[/C][C]0.431771[/C][C]0.215885[/C][/ROW]
[ROW][C]43[/C][C]0.764166[/C][C]0.471667[/C][C]0.235834[/C][/ROW]
[ROW][C]44[/C][C]0.722467[/C][C]0.555066[/C][C]0.277533[/C][/ROW]
[ROW][C]45[/C][C]0.697818[/C][C]0.604364[/C][C]0.302182[/C][/ROW]
[ROW][C]46[/C][C]0.653706[/C][C]0.692588[/C][C]0.346294[/C][/ROW]
[ROW][C]47[/C][C]0.606117[/C][C]0.787767[/C][C]0.393883[/C][/ROW]
[ROW][C]48[/C][C]0.564438[/C][C]0.871124[/C][C]0.435562[/C][/ROW]
[ROW][C]49[/C][C]0.554[/C][C]0.892[/C][C]0.446[/C][/ROW]
[ROW][C]50[/C][C]0.516485[/C][C]0.96703[/C][C]0.483515[/C][/ROW]
[ROW][C]51[/C][C]0.49046[/C][C]0.98092[/C][C]0.50954[/C][/ROW]
[ROW][C]52[/C][C]0.47224[/C][C]0.944481[/C][C]0.52776[/C][/ROW]
[ROW][C]53[/C][C]0.426374[/C][C]0.852747[/C][C]0.573626[/C][/ROW]
[ROW][C]54[/C][C]0.857027[/C][C]0.285946[/C][C]0.142973[/C][/ROW]
[ROW][C]55[/C][C]0.837546[/C][C]0.324908[/C][C]0.162454[/C][/ROW]
[ROW][C]56[/C][C]0.814334[/C][C]0.371332[/C][C]0.185666[/C][/ROW]
[ROW][C]57[/C][C]0.790426[/C][C]0.419147[/C][C]0.209574[/C][/ROW]
[ROW][C]58[/C][C]0.778217[/C][C]0.443567[/C][C]0.221783[/C][/ROW]
[ROW][C]59[/C][C]0.742075[/C][C]0.51585[/C][C]0.257925[/C][/ROW]
[ROW][C]60[/C][C]0.792331[/C][C]0.415338[/C][C]0.207669[/C][/ROW]
[ROW][C]61[/C][C]0.768481[/C][C]0.463037[/C][C]0.231519[/C][/ROW]
[ROW][C]62[/C][C]0.788866[/C][C]0.422269[/C][C]0.211134[/C][/ROW]
[ROW][C]63[/C][C]0.821599[/C][C]0.356802[/C][C]0.178401[/C][/ROW]
[ROW][C]64[/C][C]0.80551[/C][C]0.38898[/C][C]0.19449[/C][/ROW]
[ROW][C]65[/C][C]0.785594[/C][C]0.428813[/C][C]0.214406[/C][/ROW]
[ROW][C]66[/C][C]0.793386[/C][C]0.413228[/C][C]0.206614[/C][/ROW]
[ROW][C]67[/C][C]0.774501[/C][C]0.450998[/C][C]0.225499[/C][/ROW]
[ROW][C]68[/C][C]0.797975[/C][C]0.404049[/C][C]0.202025[/C][/ROW]
[ROW][C]69[/C][C]0.781995[/C][C]0.436011[/C][C]0.218005[/C][/ROW]
[ROW][C]70[/C][C]0.804705[/C][C]0.39059[/C][C]0.195295[/C][/ROW]
[ROW][C]71[/C][C]0.811262[/C][C]0.377475[/C][C]0.188738[/C][/ROW]
[ROW][C]72[/C][C]0.798514[/C][C]0.402971[/C][C]0.201486[/C][/ROW]
[ROW][C]73[/C][C]0.799731[/C][C]0.400538[/C][C]0.200269[/C][/ROW]
[ROW][C]74[/C][C]0.768921[/C][C]0.462158[/C][C]0.231079[/C][/ROW]
[ROW][C]75[/C][C]0.826163[/C][C]0.347675[/C][C]0.173837[/C][/ROW]
[ROW][C]76[/C][C]0.796023[/C][C]0.407954[/C][C]0.203977[/C][/ROW]
[ROW][C]77[/C][C]0.806526[/C][C]0.386948[/C][C]0.193474[/C][/ROW]
[ROW][C]78[/C][C]0.824281[/C][C]0.351438[/C][C]0.175719[/C][/ROW]
[ROW][C]79[/C][C]0.824262[/C][C]0.351475[/C][C]0.175738[/C][/ROW]
[ROW][C]80[/C][C]0.796402[/C][C]0.407195[/C][C]0.203598[/C][/ROW]
[ROW][C]81[/C][C]0.797575[/C][C]0.40485[/C][C]0.202425[/C][/ROW]
[ROW][C]82[/C][C]0.768954[/C][C]0.462092[/C][C]0.231046[/C][/ROW]
[ROW][C]83[/C][C]0.784703[/C][C]0.430594[/C][C]0.215297[/C][/ROW]
[ROW][C]84[/C][C]0.831558[/C][C]0.336884[/C][C]0.168442[/C][/ROW]
[ROW][C]85[/C][C]0.802099[/C][C]0.395803[/C][C]0.197901[/C][/ROW]
[ROW][C]86[/C][C]0.798024[/C][C]0.403952[/C][C]0.201976[/C][/ROW]
[ROW][C]87[/C][C]0.804313[/C][C]0.391374[/C][C]0.195687[/C][/ROW]
[ROW][C]88[/C][C]0.777273[/C][C]0.445453[/C][C]0.222727[/C][/ROW]
[ROW][C]89[/C][C]0.775382[/C][C]0.449236[/C][C]0.224618[/C][/ROW]
[ROW][C]90[/C][C]0.744507[/C][C]0.510986[/C][C]0.255493[/C][/ROW]
[ROW][C]91[/C][C]0.707077[/C][C]0.585845[/C][C]0.292923[/C][/ROW]
[ROW][C]92[/C][C]0.683052[/C][C]0.633896[/C][C]0.316948[/C][/ROW]
[ROW][C]93[/C][C]0.687412[/C][C]0.625176[/C][C]0.312588[/C][/ROW]
[ROW][C]94[/C][C]0.649471[/C][C]0.701059[/C][C]0.350529[/C][/ROW]
[ROW][C]95[/C][C]0.614369[/C][C]0.771263[/C][C]0.385631[/C][/ROW]
[ROW][C]96[/C][C]0.627516[/C][C]0.744968[/C][C]0.372484[/C][/ROW]
[ROW][C]97[/C][C]0.622324[/C][C]0.755352[/C][C]0.377676[/C][/ROW]
[ROW][C]98[/C][C]0.5995[/C][C]0.800999[/C][C]0.4005[/C][/ROW]
[ROW][C]99[/C][C]0.560183[/C][C]0.879634[/C][C]0.439817[/C][/ROW]
[ROW][C]100[/C][C]0.556749[/C][C]0.886502[/C][C]0.443251[/C][/ROW]
[ROW][C]101[/C][C]0.53748[/C][C]0.925041[/C][C]0.46252[/C][/ROW]
[ROW][C]102[/C][C]0.506478[/C][C]0.987044[/C][C]0.493522[/C][/ROW]
[ROW][C]103[/C][C]0.46492[/C][C]0.929841[/C][C]0.53508[/C][/ROW]
[ROW][C]104[/C][C]0.478989[/C][C]0.957978[/C][C]0.521011[/C][/ROW]
[ROW][C]105[/C][C]0.432654[/C][C]0.865309[/C][C]0.567346[/C][/ROW]
[ROW][C]106[/C][C]0.409507[/C][C]0.819014[/C][C]0.590493[/C][/ROW]
[ROW][C]107[/C][C]0.373209[/C][C]0.746417[/C][C]0.626791[/C][/ROW]
[ROW][C]108[/C][C]0.356959[/C][C]0.713917[/C][C]0.643041[/C][/ROW]
[ROW][C]109[/C][C]0.338227[/C][C]0.676454[/C][C]0.661773[/C][/ROW]
[ROW][C]110[/C][C]0.311233[/C][C]0.622465[/C][C]0.688767[/C][/ROW]
[ROW][C]111[/C][C]0.283908[/C][C]0.567815[/C][C]0.716092[/C][/ROW]
[ROW][C]112[/C][C]0.254762[/C][C]0.509523[/C][C]0.745238[/C][/ROW]
[ROW][C]113[/C][C]0.250587[/C][C]0.501175[/C][C]0.749413[/C][/ROW]
[ROW][C]114[/C][C]0.355787[/C][C]0.711574[/C][C]0.644213[/C][/ROW]
[ROW][C]115[/C][C]0.322706[/C][C]0.645413[/C][C]0.677294[/C][/ROW]
[ROW][C]116[/C][C]0.318362[/C][C]0.636725[/C][C]0.681638[/C][/ROW]
[ROW][C]117[/C][C]0.297717[/C][C]0.595434[/C][C]0.702283[/C][/ROW]
[ROW][C]118[/C][C]0.344727[/C][C]0.689454[/C][C]0.655273[/C][/ROW]
[ROW][C]119[/C][C]0.349928[/C][C]0.699857[/C][C]0.650072[/C][/ROW]
[ROW][C]120[/C][C]0.395446[/C][C]0.790892[/C][C]0.604554[/C][/ROW]
[ROW][C]121[/C][C]0.445958[/C][C]0.891915[/C][C]0.554042[/C][/ROW]
[ROW][C]122[/C][C]0.478273[/C][C]0.956546[/C][C]0.521727[/C][/ROW]
[ROW][C]123[/C][C]0.527703[/C][C]0.944593[/C][C]0.472297[/C][/ROW]
[ROW][C]124[/C][C]0.523606[/C][C]0.952789[/C][C]0.476394[/C][/ROW]
[ROW][C]125[/C][C]0.486854[/C][C]0.973708[/C][C]0.513146[/C][/ROW]
[ROW][C]126[/C][C]0.498235[/C][C]0.996469[/C][C]0.501765[/C][/ROW]
[ROW][C]127[/C][C]0.477727[/C][C]0.955455[/C][C]0.522273[/C][/ROW]
[ROW][C]128[/C][C]0.639632[/C][C]0.720736[/C][C]0.360368[/C][/ROW]
[ROW][C]129[/C][C]0.603198[/C][C]0.793603[/C][C]0.396802[/C][/ROW]
[ROW][C]130[/C][C]0.559388[/C][C]0.881223[/C][C]0.440612[/C][/ROW]
[ROW][C]131[/C][C]0.598912[/C][C]0.802175[/C][C]0.401088[/C][/ROW]
[ROW][C]132[/C][C]0.546291[/C][C]0.907418[/C][C]0.453709[/C][/ROW]
[ROW][C]133[/C][C]0.493631[/C][C]0.987262[/C][C]0.506369[/C][/ROW]
[ROW][C]134[/C][C]0.440379[/C][C]0.880757[/C][C]0.559621[/C][/ROW]
[ROW][C]135[/C][C]0.407815[/C][C]0.81563[/C][C]0.592185[/C][/ROW]
[ROW][C]136[/C][C]0.353304[/C][C]0.706608[/C][C]0.646696[/C][/ROW]
[ROW][C]137[/C][C]0.552701[/C][C]0.894599[/C][C]0.447299[/C][/ROW]
[ROW][C]138[/C][C]0.504506[/C][C]0.990987[/C][C]0.495494[/C][/ROW]
[ROW][C]139[/C][C]0.452238[/C][C]0.904475[/C][C]0.547762[/C][/ROW]
[ROW][C]140[/C][C]0.390925[/C][C]0.78185[/C][C]0.609075[/C][/ROW]
[ROW][C]141[/C][C]0.459609[/C][C]0.919218[/C][C]0.540391[/C][/ROW]
[ROW][C]142[/C][C]0.401836[/C][C]0.803671[/C][C]0.598164[/C][/ROW]
[ROW][C]143[/C][C]0.398453[/C][C]0.796905[/C][C]0.601547[/C][/ROW]
[ROW][C]144[/C][C]0.364365[/C][C]0.728731[/C][C]0.635635[/C][/ROW]
[ROW][C]145[/C][C]0.324438[/C][C]0.648875[/C][C]0.675562[/C][/ROW]
[ROW][C]146[/C][C]0.271312[/C][C]0.542624[/C][C]0.728688[/C][/ROW]
[ROW][C]147[/C][C]0.218034[/C][C]0.436068[/C][C]0.781966[/C][/ROW]
[ROW][C]148[/C][C]0.184848[/C][C]0.369696[/C][C]0.815152[/C][/ROW]
[ROW][C]149[/C][C]0.13905[/C][C]0.278099[/C][C]0.86095[/C][/ROW]
[ROW][C]150[/C][C]0.182269[/C][C]0.364537[/C][C]0.817731[/C][/ROW]
[ROW][C]151[/C][C]0.411609[/C][C]0.823217[/C][C]0.588391[/C][/ROW]
[ROW][C]152[/C][C]0.474315[/C][C]0.94863[/C][C]0.525685[/C][/ROW]
[ROW][C]153[/C][C]0.915509[/C][C]0.168982[/C][C]0.0844908[/C][/ROW]
[ROW][C]154[/C][C]0.927656[/C][C]0.144688[/C][C]0.072344[/C][/ROW]
[ROW][C]155[/C][C]0.979805[/C][C]0.0403908[/C][C]0.0201954[/C][/ROW]
[ROW][C]156[/C][C]0.994962[/C][C]0.0100761[/C][C]0.00503803[/C][/ROW]
[ROW][C]157[/C][C]0.985846[/C][C]0.0283084[/C][C]0.0141542[/C][/ROW]
[ROW][C]158[/C][C]0.97579[/C][C]0.0484192[/C][C]0.0242096[/C][/ROW]
[ROW][C]159[/C][C]0.9695[/C][C]0.0610008[/C][C]0.0305004[/C][/ROW]
[ROW][C]160[/C][C]0.966923[/C][C]0.0661539[/C][C]0.0330769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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
110.9430190.1139620.056981
120.9857480.02850360.0142518
130.9869290.02614150.0130708
140.9835240.03295110.0164756
150.9710550.05789070.0289454
160.9770480.04590460.0229523
170.9761280.0477440.023872
180.9621580.07568420.0378421
190.941620.116760.0583799
200.93820.1235990.0617997
210.9124990.1750020.0875009
220.8810020.2379950.118998
230.8408180.3183650.159182
240.7936460.4127080.206354
250.803210.393580.19679
260.7672150.465570.232785
270.7141940.5716120.285806
280.6733910.6532190.326609
290.6507090.6985830.349291
300.6046750.7906510.395325
310.5441040.9117930.455896
320.546350.9073010.45365
330.4992940.9985870.500706
340.4591490.9182980.540851
350.4018330.8036660.598167
360.345190.690380.65481
370.4729130.9458270.527087
380.4156790.8313580.584321
390.361890.7237790.63811
400.3164870.6329750.683513
410.8187850.362430.181215
420.7841150.4317710.215885
430.7641660.4716670.235834
440.7224670.5550660.277533
450.6978180.6043640.302182
460.6537060.6925880.346294
470.6061170.7877670.393883
480.5644380.8711240.435562
490.5540.8920.446
500.5164850.967030.483515
510.490460.980920.50954
520.472240.9444810.52776
530.4263740.8527470.573626
540.8570270.2859460.142973
550.8375460.3249080.162454
560.8143340.3713320.185666
570.7904260.4191470.209574
580.7782170.4435670.221783
590.7420750.515850.257925
600.7923310.4153380.207669
610.7684810.4630370.231519
620.7888660.4222690.211134
630.8215990.3568020.178401
640.805510.388980.19449
650.7855940.4288130.214406
660.7933860.4132280.206614
670.7745010.4509980.225499
680.7979750.4040490.202025
690.7819950.4360110.218005
700.8047050.390590.195295
710.8112620.3774750.188738
720.7985140.4029710.201486
730.7997310.4005380.200269
740.7689210.4621580.231079
750.8261630.3476750.173837
760.7960230.4079540.203977
770.8065260.3869480.193474
780.8242810.3514380.175719
790.8242620.3514750.175738
800.7964020.4071950.203598
810.7975750.404850.202425
820.7689540.4620920.231046
830.7847030.4305940.215297
840.8315580.3368840.168442
850.8020990.3958030.197901
860.7980240.4039520.201976
870.8043130.3913740.195687
880.7772730.4454530.222727
890.7753820.4492360.224618
900.7445070.5109860.255493
910.7070770.5858450.292923
920.6830520.6338960.316948
930.6874120.6251760.312588
940.6494710.7010590.350529
950.6143690.7712630.385631
960.6275160.7449680.372484
970.6223240.7553520.377676
980.59950.8009990.4005
990.5601830.8796340.439817
1000.5567490.8865020.443251
1010.537480.9250410.46252
1020.5064780.9870440.493522
1030.464920.9298410.53508
1040.4789890.9579780.521011
1050.4326540.8653090.567346
1060.4095070.8190140.590493
1070.3732090.7464170.626791
1080.3569590.7139170.643041
1090.3382270.6764540.661773
1100.3112330.6224650.688767
1110.2839080.5678150.716092
1120.2547620.5095230.745238
1130.2505870.5011750.749413
1140.3557870.7115740.644213
1150.3227060.6454130.677294
1160.3183620.6367250.681638
1170.2977170.5954340.702283
1180.3447270.6894540.655273
1190.3499280.6998570.650072
1200.3954460.7908920.604554
1210.4459580.8919150.554042
1220.4782730.9565460.521727
1230.5277030.9445930.472297
1240.5236060.9527890.476394
1250.4868540.9737080.513146
1260.4982350.9964690.501765
1270.4777270.9554550.522273
1280.6396320.7207360.360368
1290.6031980.7936030.396802
1300.5593880.8812230.440612
1310.5989120.8021750.401088
1320.5462910.9074180.453709
1330.4936310.9872620.506369
1340.4403790.8807570.559621
1350.4078150.815630.592185
1360.3533040.7066080.646696
1370.5527010.8945990.447299
1380.5045060.9909870.495494
1390.4522380.9044750.547762
1400.3909250.781850.609075
1410.4596090.9192180.540391
1420.4018360.8036710.598164
1430.3984530.7969050.601547
1440.3643650.7287310.635635
1450.3244380.6488750.675562
1460.2713120.5426240.728688
1470.2180340.4360680.781966
1480.1848480.3696960.815152
1490.139050.2780990.86095
1500.1822690.3645370.817731
1510.4116090.8232170.588391
1520.4743150.948630.525685
1530.9155090.1689820.0844908
1540.9276560.1446880.072344
1550.9798050.04039080.0201954
1560.9949620.01007610.00503803
1570.9858460.02830840.0141542
1580.975790.04841920.0242096
1590.96950.06100080.0305004
1600.9669230.06615390.0330769






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.06NOK
10% type I error level130.0866667OK

\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 & 9 & 0.06 & NOK \tabularnewline
10% type I error level & 13 & 0.0866667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263030&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]9[/C][C]0.06[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0866667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263030&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263030&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 level90.06NOK
10% type I error level130.0866667OK


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')
}