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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 computationSat, 13 Dec 2014 13:17:34 +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/13/t1418476971dg3he06hpe7e4ks.htm/, Retrieved Fri, 17 May 2024 07:45:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267074, Retrieved Fri, 17 May 2024 07:45:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-13 13:17:34] [18673d63f90870b9c004059cd6229007] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
0 26 50 4 1 21 13 12 21 149 96 18 68 7.5
0 57 62 4 0 22 8 8 22 139 70 31 39 6
0 37 54 5 1 21 14 11 22 148 88 39 32 6.5
0 67 71 4 0 21 16 13 18 158 114 46 62 1
0 43 54 4 1 21 14 11 23 128 69 31 33 1
0 52 65 9 1 21 13 10 12 224 176 67 52 5.5
0 52 73 8 1 21 15 7 20 159 114 35 62 8.5
0 43 52 11 0 23 13 10 22 105 121 52 77 6.5
0 84 84 4 1 22 20 15 21 159 110 77 76 4.5
0 67 42 4 1 25 17 12 19 167 158 37 41 2
0 49 66 6 1 21 15 12 22 165 116 32 48 5
0 70 65 4 1 23 16 10 15 159 181 36 63 0.5
0 52 78 8 1 22 12 10 20 119 77 38 30 5
0 58 73 4 1 21 17 14 19 176 141 69 78 5
0 68 75 4 0 21 11 6 18 54 35 21 19 2.5
1 62 72 11 0 25 16 12 15 91 80 26 31 5
0 43 66 4 0 21 16 14 20 163 152 54 66 5.5
0 56 70 4 1 21 15 11 21 124 97 36 35 3.5
1 56 61 6 0 20 13 8 21 137 99 42 42 3
0 74 81 6 1 24 14 12 15 121 84 23 45 4
0 65 71 4 0 23 19 15 16 153 68 34 21 0.5
0 63 69 8 1 21 16 13 23 148 101 112 25 6.5
0 58 71 5 1 24 17 11 21 221 107 35 44 4.5
0 57 72 4 0 23 10 12 18 188 88 47 69 7.5
0 63 68 9 1 21 15 7 25 149 112 47 54 5.5
0 53 70 4 1 22 14 11 9 244 171 37 74 4
1 57 68 7 1 20 14 7 30 148 137 109 80 7.5
1 51 61 10 1 18 16 12 20 92 77 24 42 7
0 64 67 4 0 21 15 12 23 150 66 20 61 4
0 53 76 4 1 22 17 13 16 153 93 22 41 5.5
0 29 70 7 0 22 14 9 16 94 105 23 46 2.5
0 54 60 12 0 21 16 11 19 156 131 32 39 5.5
0 58 72 7 1 21 15 12 25 132 102 30 34 3.5
0 43 69 5 1 25 16 15 18 161 161 92 51 2.5
0 51 71 8 1 22 16 12 23 105 120 43 42 4.5
0 53 62 5 1 22 10 6 21 97 127 55 31 4.5
0 54 70 4 1 20 8 5 10 151 77 16 39 4.5
1 56 64 9 0 21 17 13 14 131 108 49 20 6
0 61 58 7 1 21 14 11 22 166 85 71 49 2.5
0 47 76 4 1 21 10 6 26 157 168 43 53 5
0 39 52 4 0 22 14 12 23 111 48 29 31 0
0 48 59 4 1 21 12 10 23 145 152 56 39 5
0 50 68 4 1 24 16 6 24 162 75 46 54 6.5
0 35 76 4 1 22 16 12 24 163 107 19 49 5
1 30 65 7 1 22 16 11 18 59 62 23 34 6
0 68 67 4 1 21 8 6 23 187 121 59 46 4.5
0 49 59 7 0 22 16 12 15 109 124 30 55 5.5
1 61 69 4 1 19 15 12 19 90 72 61 42 1
0 67 76 4 1 22 8 8 16 105 40 7 50 7.5
1 47 63 4 0 23 13 10 25 83 58 38 13 6
1 56 75 4 1 20 14 11 23 116 97 32 37 5
1 50 63 8 1 20 13 7 17 42 88 16 25 1
0 43 60 4 1 23 16 12 19 148 126 19 30 5
1 67 73 4 1 20 19 13 21 155 104 22 28 6.5
0 62 63 4 1 23 19 14 18 125 148 48 45 7
0 57 70 4 1 21 14 12 27 116 146 23 35 4.5
1 41 75 7 1 22 15 6 21 128 80 26 28 0
0 54 66 12 0 21 13 14 13 138 97 33 41 8.5
1 45 63 4 1 21 10 10 8 49 25 9 6 3.5
1 48 63 4 0 19 16 12 29 96 99 24 45 7.5
0 61 64 4 1 22 15 11 28 164 118 34 73 3.5
0 56 70 5 1 21 11 10 23 162 58 48 17 6
0 41 75 15 0 21 9 7 21 99 63 18 40 1.5
0 43 61 5 0 21 16 12 19 202 139 43 64 9
0 53 60 10 1 21 12 7 19 186 50 33 37 3.5
1 44 62 9 0 21 12 12 20 66 60 28 25 3.5
0 66 73 8 1 21 14 12 18 183 152 71 65 4
0 58 61 4 0 22 14 10 19 214 142 26 100 6.5
0 46 66 5 1 22 13 10 17 188 94 67 28 7.5
1 37 64 4 1 18 15 12 19 104 66 34 35 6
0 51 59 9 0 21 17 12 25 177 127 80 56 5
0 51 64 4 0 23 14 12 19 126 67 29 29 5.5
1 56 60 10 0 19 11 8 22 76 90 16 43 3.5
1 66 56 4 0 19 9 10 23 99 75 59 59 7.5
0 37 78 4 1 21 7 5 14 139 128 32 50 6.5
0 42 67 7 1 21 15 10 16 162 146 43 59 6.5
1 38 59 5 0 21 12 12 24 108 69 38 27 6.5
0 66 66 4 1 20 15 11 20 159 186 29 61 7
1 34 68 4 0 19 14 9 12 74 81 36 28 3.5
0 53 71 4 0 21 16 12 24 110 85 32 51 1.5
1 49 66 4 1 19 14 11 22 96 54 35 35 4
1 55 73 4 0 19 13 10 12 116 46 21 29 7.5
1 49 72 4 0 19 16 12 22 87 106 29 48 4.5
1 59 71 6 0 20 13 10 20 97 34 12 25 0
1 40 59 10 1 19 16 9 10 127 60 37 44 3.5
1 58 64 7 0 19 16 11 23 106 95 37 64 5.5
1 60 66 4 1 19 16 12 17 80 57 47 32 5
1 63 78 4 1 20 10 7 22 74 62 51 20 4.5
1 56 68 7 0 19 12 11 24 91 36 32 28 2.5
1 54 73 4 0 18 12 12 18 133 56 21 34 7.5
1 52 62 8 0 19 12 6 21 74 54 13 31 7
1 34 65 11 1 21 12 9 20 114 64 14 26 0
1 69 68 6 1 18 19 15 20 140 76 -2 58 4.5
1 32 65 14 1 18 14 10 22 95 98 20 23 3
1 48 60 5 0 19 13 11 19 98 88 24 21 1.5
1 67 71 4 1 21 16 12 20 121 35 11 21 3.5
1 58 65 8 0 20 15 12 26 126 102 23 33 2.5
1 57 68 9 1 24 12 12 23 98 61 24 16 5.5
1 42 64 4 1 22 8 11 24 95 80 14 20 8
1 64 74 4 1 21 10 9 21 110 49 52 37 1
1 58 69 5 1 21 16 11 21 70 78 15 35 5
1 66 76 4 1 19 16 12 19 102 90 23 33 4.5
1 26 68 5 0 19 10 12 8 86 45 19 27 3
1 61 72 4 1 20 18 14 17 130 55 35 41 3
1 52 67 4 1 18 12 8 20 96 96 24 40 8
1 51 63 7 1 19 16 10 11 102 43 39 35 2.5
1 55 59 10 0 19 10 9 8 100 52 29 28 7
1 50 73 4 0 20 14 10 15 94 60 13 32 0
1 60 66 5 0 21 12 9 18 52 54 8 22 1
1 56 62 4 0 18 11 10 18 98 51 18 44 3.5
1 63 69 4 0 19 15 12 19 118 51 24 27 5.5
1 61 66 4 0 19 7 11 19 99 38 19 17 5.5

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=267074&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=267074&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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
Ex[t] = + 5.91378 + 0.387706ID_bin[t] -0.0145032AMS.I[t] -0.00419385AMS.E[t] + 0.00901541AMS.A[t] + 0.210623gender[t] -0.12555age[t] -0.227284CONFSTATTOT[t] + 0.1674CONFSOFTTOT[t] + 0.0277855NUMERACYTOT[t] + 0.00916567LFM[t] + 0.00573364Blogs[t] + 0.00432575PRH[t] + 0.0228097CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  5.91378 +  0.387706ID_bin[t] -0.0145032AMS.I[t] -0.00419385AMS.E[t] +  0.00901541AMS.A[t] +  0.210623gender[t] -0.12555age[t] -0.227284CONFSTATTOT[t] +  0.1674CONFSOFTTOT[t] +  0.0277855NUMERACYTOT[t] +  0.00916567LFM[t] +  0.00573364Blogs[t] +  0.00432575PRH[t] +  0.0228097CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  5.91378 +  0.387706ID_bin[t] -0.0145032AMS.I[t] -0.00419385AMS.E[t] +  0.00901541AMS.A[t] +  0.210623gender[t] -0.12555age[t] -0.227284CONFSTATTOT[t] +  0.1674CONFSOFTTOT[t] +  0.0277855NUMERACYTOT[t] +  0.00916567LFM[t] +  0.00573364Blogs[t] +  0.00432575PRH[t] +  0.0228097CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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
Ex[t] = + 5.91378 + 0.387706ID_bin[t] -0.0145032AMS.I[t] -0.00419385AMS.E[t] + 0.00901541AMS.A[t] + 0.210623gender[t] -0.12555age[t] -0.227284CONFSTATTOT[t] + 0.1674CONFSOFTTOT[t] + 0.0277855NUMERACYTOT[t] + 0.00916567LFM[t] + 0.00573364Blogs[t] + 0.00432575PRH[t] + 0.0228097CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.913784.52951.3060.194740.0973702
ID_bin0.3877060.6613230.58630.559050.279525
AMS.I-0.01450320.0214954-0.67470.5014480.250724
AMS.E-0.004193850.0333401-0.12580.9001560.450078
AMS.A0.009015410.08696160.10370.9176420.458821
gender0.2106230.474780.44360.6582940.329147
age-0.125550.164632-0.76260.4475280.223764
CONFSTATTOT-0.2272840.104478-2.1750.03200240.0160012
CONFSOFTTOT0.16740.1220751.3710.1734150.0867075
NUMERACYTOT0.02778550.04933710.56320.5746020.287301
LFM0.009165670.008131771.1270.2624340.131217
Blogs0.005733640.008368720.68510.4948810.247441
PRH0.004325750.01240720.34860.7281010.36405
CH0.02280970.01738251.3120.192510.0962551

\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) & 5.91378 & 4.5295 & 1.306 & 0.19474 & 0.0973702 \tabularnewline
ID_bin & 0.387706 & 0.661323 & 0.5863 & 0.55905 & 0.279525 \tabularnewline
AMS.I & -0.0145032 & 0.0214954 & -0.6747 & 0.501448 & 0.250724 \tabularnewline
AMS.E & -0.00419385 & 0.0333401 & -0.1258 & 0.900156 & 0.450078 \tabularnewline
AMS.A & 0.00901541 & 0.0869616 & 0.1037 & 0.917642 & 0.458821 \tabularnewline
gender & 0.210623 & 0.47478 & 0.4436 & 0.658294 & 0.329147 \tabularnewline
age & -0.12555 & 0.164632 & -0.7626 & 0.447528 & 0.223764 \tabularnewline
CONFSTATTOT & -0.227284 & 0.104478 & -2.175 & 0.0320024 & 0.0160012 \tabularnewline
CONFSOFTTOT & 0.1674 & 0.122075 & 1.371 & 0.173415 & 0.0867075 \tabularnewline
NUMERACYTOT & 0.0277855 & 0.0493371 & 0.5632 & 0.574602 & 0.287301 \tabularnewline
LFM & 0.00916567 & 0.00813177 & 1.127 & 0.262434 & 0.131217 \tabularnewline
Blogs & 0.00573364 & 0.00836872 & 0.6851 & 0.494881 & 0.247441 \tabularnewline
PRH & 0.00432575 & 0.0124072 & 0.3486 & 0.728101 & 0.36405 \tabularnewline
CH & 0.0228097 & 0.0173825 & 1.312 & 0.19251 & 0.0962551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&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]5.91378[/C][C]4.5295[/C][C]1.306[/C][C]0.19474[/C][C]0.0973702[/C][/ROW]
[ROW][C]ID_bin[/C][C]0.387706[/C][C]0.661323[/C][C]0.5863[/C][C]0.55905[/C][C]0.279525[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0145032[/C][C]0.0214954[/C][C]-0.6747[/C][C]0.501448[/C][C]0.250724[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.00419385[/C][C]0.0333401[/C][C]-0.1258[/C][C]0.900156[/C][C]0.450078[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.00901541[/C][C]0.0869616[/C][C]0.1037[/C][C]0.917642[/C][C]0.458821[/C][/ROW]
[ROW][C]gender[/C][C]0.210623[/C][C]0.47478[/C][C]0.4436[/C][C]0.658294[/C][C]0.329147[/C][/ROW]
[ROW][C]age[/C][C]-0.12555[/C][C]0.164632[/C][C]-0.7626[/C][C]0.447528[/C][C]0.223764[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.227284[/C][C]0.104478[/C][C]-2.175[/C][C]0.0320024[/C][C]0.0160012[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.1674[/C][C]0.122075[/C][C]1.371[/C][C]0.173415[/C][C]0.0867075[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0277855[/C][C]0.0493371[/C][C]0.5632[/C][C]0.574602[/C][C]0.287301[/C][/ROW]
[ROW][C]LFM[/C][C]0.00916567[/C][C]0.00813177[/C][C]1.127[/C][C]0.262434[/C][C]0.131217[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00573364[/C][C]0.00836872[/C][C]0.6851[/C][C]0.494881[/C][C]0.247441[/C][/ROW]
[ROW][C]PRH[/C][C]0.00432575[/C][C]0.0124072[/C][C]0.3486[/C][C]0.728101[/C][C]0.36405[/C][/ROW]
[ROW][C]CH[/C][C]0.0228097[/C][C]0.0173825[/C][C]1.312[/C][C]0.19251[/C][C]0.0962551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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)5.913784.52951.3060.194740.0973702
ID_bin0.3877060.6613230.58630.559050.279525
AMS.I-0.01450320.0214954-0.67470.5014480.250724
AMS.E-0.004193850.0333401-0.12580.9001560.450078
AMS.A0.009015410.08696160.10370.9176420.458821
gender0.2106230.474780.44360.6582940.329147
age-0.125550.164632-0.76260.4475280.223764
CONFSTATTOT-0.2272840.104478-2.1750.03200240.0160012
CONFSOFTTOT0.16740.1220751.3710.1734150.0867075
NUMERACYTOT0.02778550.04933710.56320.5746020.287301
LFM0.009165670.008131771.1270.2624340.131217
Blogs0.005733640.008368720.68510.4948810.247441
PRH0.004325750.01240720.34860.7281010.36405
CH0.02280970.01738251.3120.192510.0962551






Multiple Linear Regression - Regression Statistics
Multiple R0.363304
R-squared0.13199
Adjusted R-squared0.0168456
F-TEST (value)1.1463
F-TEST (DF numerator)13
F-TEST (DF denominator)98
p-value0.330885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18977
Sum Squared Residuals469.917

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.363304 \tabularnewline
R-squared & 0.13199 \tabularnewline
Adjusted R-squared & 0.0168456 \tabularnewline
F-TEST (value) & 1.1463 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 98 \tabularnewline
p-value & 0.330885 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.18977 \tabularnewline
Sum Squared Residuals & 469.917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.363304[/C][/ROW]
[ROW][C]R-squared[/C][C]0.13199[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0168456[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.1463[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]98[/C][/ROW]
[ROW][C]p-value[/C][C]0.330885[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.18977[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]469.917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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 R0.363304
R-squared0.13199
Adjusted R-squared0.0168456
F-TEST (value)1.1463
F-TEST (DF numerator)13
F-TEST (DF denominator)98
p-value0.330885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18977
Sum Squared Residuals469.917






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.56.119781.38022
264.932311.06769
36.54.800241.69976
414.79861-3.79861
514.42795-3.42795
65.56.13312-0.633121
78.54.494494.00551
86.55.251621.24838
94.54.53129-0.0312916
1024.07859-2.07859
1155.17604-0.176045
120.54.5271-4.0271
1354.236320.763682
1455.88351-0.883511
152.52.23680.263201
1652.962132.03787
175.55.78018-0.280177
183.54.08057-0.580575
1934.70759-1.70759
2043.712570.287433
210.52.87721-2.37721
226.54.525971.97403
234.54.372540.12746
247.56.174541.32546
255.54.270171.22983
2646.31047-2.31047
277.56.214051.28595
2874.62142.3786
2944.57389-0.573885
305.54.033841.46616
312.53.88216-1.38216
325.54.292351.20765
333.54.402-0.901999
342.55.45237-2.95237
354.54.202770.29723
364.54.256010.243993
374.54.6536-0.153603
3863.854952.14505
392.55.12739-2.62739
4055.7747-0.774698
4103.99508-3.99508
4255.49834-0.498339
436.53.51722.9828
4454.91850.0815024
4563.58172.4183
464.55.79412-1.29412
475.54.140071.35993
4814.57573-3.57573
497.54.435923.06408
5063.472192.52781
5154.810710.189292
5213.30229-2.30229
5354.143170.856834
546.53.951422.54858
5573.863113.13689
564.54.77886-0.278857
5703.46562-3.46562
588.54.974543.52546
593.53.386460.113541
607.54.747562.75244
613.55.44732-1.94732
6264.652921.34708
631.54.4719-2.9719
6495.63733.3627
653.54.50823-1.00823
663.54.32534-0.825336
6745.96217-1.96217
686.56.279750.220247
697.54.845712.65429
7064.887781.11222
7155.18478-0.184778
725.53.734831.76517
733.54.65539-1.15539
747.55.965961.53404
756.55.582030.91797
766.55.223881.27612
776.55.025451.47455
7875.443951.55605
793.53.76894-0.26894
801.54.08327-2.58327
8144.58524-0.585237
827.53.980293.51971
834.54.54851-0.0485132
8403.67231-3.67231
853.54.2089-0.708897
865.54.849610.650394
8753.853641.14636
884.54.017140.482857
892.54.48021-1.98021
907.55.176352.32365
9173.585673.41433
9204.60937-4.60937
934.54.80198-0.301976
9434.7888-1.7888
951.54.41358-2.91358
963.53.406420.0935798
972.54.66271-2.16271
985.54.105411.39459
9985.44472.5553
10014.8484-3.8484
10153.530471.46953
1024.54.09010.409904
10335.00141-2.00141
10434.16885-1.16885
10584.867163.13284
1062.53.67748-1.17748
10774.395962.60404
10803.69578-3.69578
10913.16501-2.16501
1103.54.95161-1.45161
1115.53.970131.52987
1125.55.164180.335818

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 6.11978 & 1.38022 \tabularnewline
2 & 6 & 4.93231 & 1.06769 \tabularnewline
3 & 6.5 & 4.80024 & 1.69976 \tabularnewline
4 & 1 & 4.79861 & -3.79861 \tabularnewline
5 & 1 & 4.42795 & -3.42795 \tabularnewline
6 & 5.5 & 6.13312 & -0.633121 \tabularnewline
7 & 8.5 & 4.49449 & 4.00551 \tabularnewline
8 & 6.5 & 5.25162 & 1.24838 \tabularnewline
9 & 4.5 & 4.53129 & -0.0312916 \tabularnewline
10 & 2 & 4.07859 & -2.07859 \tabularnewline
11 & 5 & 5.17604 & -0.176045 \tabularnewline
12 & 0.5 & 4.5271 & -4.0271 \tabularnewline
13 & 5 & 4.23632 & 0.763682 \tabularnewline
14 & 5 & 5.88351 & -0.883511 \tabularnewline
15 & 2.5 & 2.2368 & 0.263201 \tabularnewline
16 & 5 & 2.96213 & 2.03787 \tabularnewline
17 & 5.5 & 5.78018 & -0.280177 \tabularnewline
18 & 3.5 & 4.08057 & -0.580575 \tabularnewline
19 & 3 & 4.70759 & -1.70759 \tabularnewline
20 & 4 & 3.71257 & 0.287433 \tabularnewline
21 & 0.5 & 2.87721 & -2.37721 \tabularnewline
22 & 6.5 & 4.52597 & 1.97403 \tabularnewline
23 & 4.5 & 4.37254 & 0.12746 \tabularnewline
24 & 7.5 & 6.17454 & 1.32546 \tabularnewline
25 & 5.5 & 4.27017 & 1.22983 \tabularnewline
26 & 4 & 6.31047 & -2.31047 \tabularnewline
27 & 7.5 & 6.21405 & 1.28595 \tabularnewline
28 & 7 & 4.6214 & 2.3786 \tabularnewline
29 & 4 & 4.57389 & -0.573885 \tabularnewline
30 & 5.5 & 4.03384 & 1.46616 \tabularnewline
31 & 2.5 & 3.88216 & -1.38216 \tabularnewline
32 & 5.5 & 4.29235 & 1.20765 \tabularnewline
33 & 3.5 & 4.402 & -0.901999 \tabularnewline
34 & 2.5 & 5.45237 & -2.95237 \tabularnewline
35 & 4.5 & 4.20277 & 0.29723 \tabularnewline
36 & 4.5 & 4.25601 & 0.243993 \tabularnewline
37 & 4.5 & 4.6536 & -0.153603 \tabularnewline
38 & 6 & 3.85495 & 2.14505 \tabularnewline
39 & 2.5 & 5.12739 & -2.62739 \tabularnewline
40 & 5 & 5.7747 & -0.774698 \tabularnewline
41 & 0 & 3.99508 & -3.99508 \tabularnewline
42 & 5 & 5.49834 & -0.498339 \tabularnewline
43 & 6.5 & 3.5172 & 2.9828 \tabularnewline
44 & 5 & 4.9185 & 0.0815024 \tabularnewline
45 & 6 & 3.5817 & 2.4183 \tabularnewline
46 & 4.5 & 5.79412 & -1.29412 \tabularnewline
47 & 5.5 & 4.14007 & 1.35993 \tabularnewline
48 & 1 & 4.57573 & -3.57573 \tabularnewline
49 & 7.5 & 4.43592 & 3.06408 \tabularnewline
50 & 6 & 3.47219 & 2.52781 \tabularnewline
51 & 5 & 4.81071 & 0.189292 \tabularnewline
52 & 1 & 3.30229 & -2.30229 \tabularnewline
53 & 5 & 4.14317 & 0.856834 \tabularnewline
54 & 6.5 & 3.95142 & 2.54858 \tabularnewline
55 & 7 & 3.86311 & 3.13689 \tabularnewline
56 & 4.5 & 4.77886 & -0.278857 \tabularnewline
57 & 0 & 3.46562 & -3.46562 \tabularnewline
58 & 8.5 & 4.97454 & 3.52546 \tabularnewline
59 & 3.5 & 3.38646 & 0.113541 \tabularnewline
60 & 7.5 & 4.74756 & 2.75244 \tabularnewline
61 & 3.5 & 5.44732 & -1.94732 \tabularnewline
62 & 6 & 4.65292 & 1.34708 \tabularnewline
63 & 1.5 & 4.4719 & -2.9719 \tabularnewline
64 & 9 & 5.6373 & 3.3627 \tabularnewline
65 & 3.5 & 4.50823 & -1.00823 \tabularnewline
66 & 3.5 & 4.32534 & -0.825336 \tabularnewline
67 & 4 & 5.96217 & -1.96217 \tabularnewline
68 & 6.5 & 6.27975 & 0.220247 \tabularnewline
69 & 7.5 & 4.84571 & 2.65429 \tabularnewline
70 & 6 & 4.88778 & 1.11222 \tabularnewline
71 & 5 & 5.18478 & -0.184778 \tabularnewline
72 & 5.5 & 3.73483 & 1.76517 \tabularnewline
73 & 3.5 & 4.65539 & -1.15539 \tabularnewline
74 & 7.5 & 5.96596 & 1.53404 \tabularnewline
75 & 6.5 & 5.58203 & 0.91797 \tabularnewline
76 & 6.5 & 5.22388 & 1.27612 \tabularnewline
77 & 6.5 & 5.02545 & 1.47455 \tabularnewline
78 & 7 & 5.44395 & 1.55605 \tabularnewline
79 & 3.5 & 3.76894 & -0.26894 \tabularnewline
80 & 1.5 & 4.08327 & -2.58327 \tabularnewline
81 & 4 & 4.58524 & -0.585237 \tabularnewline
82 & 7.5 & 3.98029 & 3.51971 \tabularnewline
83 & 4.5 & 4.54851 & -0.0485132 \tabularnewline
84 & 0 & 3.67231 & -3.67231 \tabularnewline
85 & 3.5 & 4.2089 & -0.708897 \tabularnewline
86 & 5.5 & 4.84961 & 0.650394 \tabularnewline
87 & 5 & 3.85364 & 1.14636 \tabularnewline
88 & 4.5 & 4.01714 & 0.482857 \tabularnewline
89 & 2.5 & 4.48021 & -1.98021 \tabularnewline
90 & 7.5 & 5.17635 & 2.32365 \tabularnewline
91 & 7 & 3.58567 & 3.41433 \tabularnewline
92 & 0 & 4.60937 & -4.60937 \tabularnewline
93 & 4.5 & 4.80198 & -0.301976 \tabularnewline
94 & 3 & 4.7888 & -1.7888 \tabularnewline
95 & 1.5 & 4.41358 & -2.91358 \tabularnewline
96 & 3.5 & 3.40642 & 0.0935798 \tabularnewline
97 & 2.5 & 4.66271 & -2.16271 \tabularnewline
98 & 5.5 & 4.10541 & 1.39459 \tabularnewline
99 & 8 & 5.4447 & 2.5553 \tabularnewline
100 & 1 & 4.8484 & -3.8484 \tabularnewline
101 & 5 & 3.53047 & 1.46953 \tabularnewline
102 & 4.5 & 4.0901 & 0.409904 \tabularnewline
103 & 3 & 5.00141 & -2.00141 \tabularnewline
104 & 3 & 4.16885 & -1.16885 \tabularnewline
105 & 8 & 4.86716 & 3.13284 \tabularnewline
106 & 2.5 & 3.67748 & -1.17748 \tabularnewline
107 & 7 & 4.39596 & 2.60404 \tabularnewline
108 & 0 & 3.69578 & -3.69578 \tabularnewline
109 & 1 & 3.16501 & -2.16501 \tabularnewline
110 & 3.5 & 4.95161 & -1.45161 \tabularnewline
111 & 5.5 & 3.97013 & 1.52987 \tabularnewline
112 & 5.5 & 5.16418 & 0.335818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&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]7.5[/C][C]6.11978[/C][C]1.38022[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]4.93231[/C][C]1.06769[/C][/ROW]
[ROW][C]3[/C][C]6.5[/C][C]4.80024[/C][C]1.69976[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.79861[/C][C]-3.79861[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]4.42795[/C][C]-3.42795[/C][/ROW]
[ROW][C]6[/C][C]5.5[/C][C]6.13312[/C][C]-0.633121[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]4.49449[/C][C]4.00551[/C][/ROW]
[ROW][C]8[/C][C]6.5[/C][C]5.25162[/C][C]1.24838[/C][/ROW]
[ROW][C]9[/C][C]4.5[/C][C]4.53129[/C][C]-0.0312916[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]4.07859[/C][C]-2.07859[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]5.17604[/C][C]-0.176045[/C][/ROW]
[ROW][C]12[/C][C]0.5[/C][C]4.5271[/C][C]-4.0271[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]4.23632[/C][C]0.763682[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.88351[/C][C]-0.883511[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]2.2368[/C][C]0.263201[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]2.96213[/C][C]2.03787[/C][/ROW]
[ROW][C]17[/C][C]5.5[/C][C]5.78018[/C][C]-0.280177[/C][/ROW]
[ROW][C]18[/C][C]3.5[/C][C]4.08057[/C][C]-0.580575[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]4.70759[/C][C]-1.70759[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.71257[/C][C]0.287433[/C][/ROW]
[ROW][C]21[/C][C]0.5[/C][C]2.87721[/C][C]-2.37721[/C][/ROW]
[ROW][C]22[/C][C]6.5[/C][C]4.52597[/C][C]1.97403[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]4.37254[/C][C]0.12746[/C][/ROW]
[ROW][C]24[/C][C]7.5[/C][C]6.17454[/C][C]1.32546[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]4.27017[/C][C]1.22983[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]6.31047[/C][C]-2.31047[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]6.21405[/C][C]1.28595[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]4.6214[/C][C]2.3786[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.57389[/C][C]-0.573885[/C][/ROW]
[ROW][C]30[/C][C]5.5[/C][C]4.03384[/C][C]1.46616[/C][/ROW]
[ROW][C]31[/C][C]2.5[/C][C]3.88216[/C][C]-1.38216[/C][/ROW]
[ROW][C]32[/C][C]5.5[/C][C]4.29235[/C][C]1.20765[/C][/ROW]
[ROW][C]33[/C][C]3.5[/C][C]4.402[/C][C]-0.901999[/C][/ROW]
[ROW][C]34[/C][C]2.5[/C][C]5.45237[/C][C]-2.95237[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]4.20277[/C][C]0.29723[/C][/ROW]
[ROW][C]36[/C][C]4.5[/C][C]4.25601[/C][C]0.243993[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]4.6536[/C][C]-0.153603[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]3.85495[/C][C]2.14505[/C][/ROW]
[ROW][C]39[/C][C]2.5[/C][C]5.12739[/C][C]-2.62739[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]5.7747[/C][C]-0.774698[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]3.99508[/C][C]-3.99508[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]5.49834[/C][C]-0.498339[/C][/ROW]
[ROW][C]43[/C][C]6.5[/C][C]3.5172[/C][C]2.9828[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.9185[/C][C]0.0815024[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]3.5817[/C][C]2.4183[/C][/ROW]
[ROW][C]46[/C][C]4.5[/C][C]5.79412[/C][C]-1.29412[/C][/ROW]
[ROW][C]47[/C][C]5.5[/C][C]4.14007[/C][C]1.35993[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]4.57573[/C][C]-3.57573[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]4.43592[/C][C]3.06408[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]3.47219[/C][C]2.52781[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]4.81071[/C][C]0.189292[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]3.30229[/C][C]-2.30229[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.14317[/C][C]0.856834[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]3.95142[/C][C]2.54858[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]3.86311[/C][C]3.13689[/C][/ROW]
[ROW][C]56[/C][C]4.5[/C][C]4.77886[/C][C]-0.278857[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]3.46562[/C][C]-3.46562[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]4.97454[/C][C]3.52546[/C][/ROW]
[ROW][C]59[/C][C]3.5[/C][C]3.38646[/C][C]0.113541[/C][/ROW]
[ROW][C]60[/C][C]7.5[/C][C]4.74756[/C][C]2.75244[/C][/ROW]
[ROW][C]61[/C][C]3.5[/C][C]5.44732[/C][C]-1.94732[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]4.65292[/C][C]1.34708[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]4.4719[/C][C]-2.9719[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]5.6373[/C][C]3.3627[/C][/ROW]
[ROW][C]65[/C][C]3.5[/C][C]4.50823[/C][C]-1.00823[/C][/ROW]
[ROW][C]66[/C][C]3.5[/C][C]4.32534[/C][C]-0.825336[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]5.96217[/C][C]-1.96217[/C][/ROW]
[ROW][C]68[/C][C]6.5[/C][C]6.27975[/C][C]0.220247[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]4.84571[/C][C]2.65429[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]4.88778[/C][C]1.11222[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.18478[/C][C]-0.184778[/C][/ROW]
[ROW][C]72[/C][C]5.5[/C][C]3.73483[/C][C]1.76517[/C][/ROW]
[ROW][C]73[/C][C]3.5[/C][C]4.65539[/C][C]-1.15539[/C][/ROW]
[ROW][C]74[/C][C]7.5[/C][C]5.96596[/C][C]1.53404[/C][/ROW]
[ROW][C]75[/C][C]6.5[/C][C]5.58203[/C][C]0.91797[/C][/ROW]
[ROW][C]76[/C][C]6.5[/C][C]5.22388[/C][C]1.27612[/C][/ROW]
[ROW][C]77[/C][C]6.5[/C][C]5.02545[/C][C]1.47455[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]5.44395[/C][C]1.55605[/C][/ROW]
[ROW][C]79[/C][C]3.5[/C][C]3.76894[/C][C]-0.26894[/C][/ROW]
[ROW][C]80[/C][C]1.5[/C][C]4.08327[/C][C]-2.58327[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.58524[/C][C]-0.585237[/C][/ROW]
[ROW][C]82[/C][C]7.5[/C][C]3.98029[/C][C]3.51971[/C][/ROW]
[ROW][C]83[/C][C]4.5[/C][C]4.54851[/C][C]-0.0485132[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]3.67231[/C][C]-3.67231[/C][/ROW]
[ROW][C]85[/C][C]3.5[/C][C]4.2089[/C][C]-0.708897[/C][/ROW]
[ROW][C]86[/C][C]5.5[/C][C]4.84961[/C][C]0.650394[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]3.85364[/C][C]1.14636[/C][/ROW]
[ROW][C]88[/C][C]4.5[/C][C]4.01714[/C][C]0.482857[/C][/ROW]
[ROW][C]89[/C][C]2.5[/C][C]4.48021[/C][C]-1.98021[/C][/ROW]
[ROW][C]90[/C][C]7.5[/C][C]5.17635[/C][C]2.32365[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]3.58567[/C][C]3.41433[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]4.60937[/C][C]-4.60937[/C][/ROW]
[ROW][C]93[/C][C]4.5[/C][C]4.80198[/C][C]-0.301976[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]4.7888[/C][C]-1.7888[/C][/ROW]
[ROW][C]95[/C][C]1.5[/C][C]4.41358[/C][C]-2.91358[/C][/ROW]
[ROW][C]96[/C][C]3.5[/C][C]3.40642[/C][C]0.0935798[/C][/ROW]
[ROW][C]97[/C][C]2.5[/C][C]4.66271[/C][C]-2.16271[/C][/ROW]
[ROW][C]98[/C][C]5.5[/C][C]4.10541[/C][C]1.39459[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]5.4447[/C][C]2.5553[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]4.8484[/C][C]-3.8484[/C][/ROW]
[ROW][C]101[/C][C]5[/C][C]3.53047[/C][C]1.46953[/C][/ROW]
[ROW][C]102[/C][C]4.5[/C][C]4.0901[/C][C]0.409904[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]5.00141[/C][C]-2.00141[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.16885[/C][C]-1.16885[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]4.86716[/C][C]3.13284[/C][/ROW]
[ROW][C]106[/C][C]2.5[/C][C]3.67748[/C][C]-1.17748[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]4.39596[/C][C]2.60404[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]3.69578[/C][C]-3.69578[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]3.16501[/C][C]-2.16501[/C][/ROW]
[ROW][C]110[/C][C]3.5[/C][C]4.95161[/C][C]-1.45161[/C][/ROW]
[ROW][C]111[/C][C]5.5[/C][C]3.97013[/C][C]1.52987[/C][/ROW]
[ROW][C]112[/C][C]5.5[/C][C]5.16418[/C][C]0.335818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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
17.56.119781.38022
264.932311.06769
36.54.800241.69976
414.79861-3.79861
514.42795-3.42795
65.56.13312-0.633121
78.54.494494.00551
86.55.251621.24838
94.54.53129-0.0312916
1024.07859-2.07859
1155.17604-0.176045
120.54.5271-4.0271
1354.236320.763682
1455.88351-0.883511
152.52.23680.263201
1652.962132.03787
175.55.78018-0.280177
183.54.08057-0.580575
1934.70759-1.70759
2043.712570.287433
210.52.87721-2.37721
226.54.525971.97403
234.54.372540.12746
247.56.174541.32546
255.54.270171.22983
2646.31047-2.31047
277.56.214051.28595
2874.62142.3786
2944.57389-0.573885
305.54.033841.46616
312.53.88216-1.38216
325.54.292351.20765
333.54.402-0.901999
342.55.45237-2.95237
354.54.202770.29723
364.54.256010.243993
374.54.6536-0.153603
3863.854952.14505
392.55.12739-2.62739
4055.7747-0.774698
4103.99508-3.99508
4255.49834-0.498339
436.53.51722.9828
4454.91850.0815024
4563.58172.4183
464.55.79412-1.29412
475.54.140071.35993
4814.57573-3.57573
497.54.435923.06408
5063.472192.52781
5154.810710.189292
5213.30229-2.30229
5354.143170.856834
546.53.951422.54858
5573.863113.13689
564.54.77886-0.278857
5703.46562-3.46562
588.54.974543.52546
593.53.386460.113541
607.54.747562.75244
613.55.44732-1.94732
6264.652921.34708
631.54.4719-2.9719
6495.63733.3627
653.54.50823-1.00823
663.54.32534-0.825336
6745.96217-1.96217
686.56.279750.220247
697.54.845712.65429
7064.887781.11222
7155.18478-0.184778
725.53.734831.76517
733.54.65539-1.15539
747.55.965961.53404
756.55.582030.91797
766.55.223881.27612
776.55.025451.47455
7875.443951.55605
793.53.76894-0.26894
801.54.08327-2.58327
8144.58524-0.585237
827.53.980293.51971
834.54.54851-0.0485132
8403.67231-3.67231
853.54.2089-0.708897
865.54.849610.650394
8753.853641.14636
884.54.017140.482857
892.54.48021-1.98021
907.55.176352.32365
9173.585673.41433
9204.60937-4.60937
934.54.80198-0.301976
9434.7888-1.7888
951.54.41358-2.91358
963.53.406420.0935798
972.54.66271-2.16271
985.54.105411.39459
9985.44472.5553
10014.8484-3.8484
10153.530471.46953
1024.54.09010.409904
10335.00141-2.00141
10434.16885-1.16885
10584.867163.13284
1062.53.67748-1.17748
10774.395962.60404
10803.69578-3.69578
10913.16501-2.16501
1103.54.95161-1.45161
1115.53.970131.52987
1125.55.164180.335818






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.6547720.6904560.345228
180.5449570.9100850.455043
190.4024010.8048020.597599
200.2847290.5694580.715271
210.2808680.5617370.719132
220.2336730.4673450.766327
230.3126330.6252660.687367
240.2305340.4610680.769466
250.1637210.3274410.836279
260.1340050.2680090.865995
270.1295550.2591110.870445
280.1593830.3187670.840617
290.112430.2248590.88757
300.1020240.2040470.897976
310.1007640.2015270.899236
320.07298540.1459710.927015
330.05994440.1198890.940056
340.06447970.1289590.93552
350.04423510.08847030.955765
360.03609590.07219180.963904
370.02527670.05055340.974723
380.03177910.06355820.968221
390.07101680.1420340.928983
400.0507160.1014320.949284
410.1100280.2200560.889972
420.09627170.1925430.903728
430.1132010.2264020.886799
440.08529250.1705850.914708
450.08332270.1666450.916677
460.07676110.1535220.923239
470.07977840.1595570.920222
480.1214740.2429480.878526
490.1706560.3413110.829344
500.2009610.4019210.799039
510.160580.3211610.83942
520.2001920.4003840.799808
530.1923550.384710.807645
540.2228010.4456030.777199
550.3594270.7188550.640573
560.3227410.6454820.677259
570.4734480.9468960.526552
580.5613170.8773660.438683
590.5030930.9938130.496907
600.5386380.9227240.461362
610.5408030.9183940.459197
620.4850540.9701070.514946
630.6288890.7422220.371111
640.6721610.6556790.327839
650.6353860.7292280.364614
660.5933460.8133080.406654
670.572220.8555590.42778
680.5117320.9765360.488268
690.4893040.9786070.510696
700.4354440.8708870.564556
710.3725160.7450310.627484
720.359740.719480.64026
730.3153040.6306080.684696
740.2702330.5404660.729767
750.2232190.4464380.776781
760.1973940.3947870.802606
770.1922730.3845460.807727
780.1706010.3412020.829399
790.1308560.2617130.869144
800.1086910.2173820.891309
810.0803060.1606120.919694
820.139590.2791810.86041
830.1092710.2185420.890729
840.1216410.2432810.878359
850.08883010.177660.91117
860.082690.165380.91731
870.06115450.1223090.938846
880.03884190.07768380.961158
890.02558740.05117480.974413
900.04572490.09144980.954275
910.1834980.3669970.816502
920.2045370.4090730.795463
930.2403240.4806490.759676
940.1846410.3692820.815359
950.1995040.3990070.800496

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.654772 & 0.690456 & 0.345228 \tabularnewline
18 & 0.544957 & 0.910085 & 0.455043 \tabularnewline
19 & 0.402401 & 0.804802 & 0.597599 \tabularnewline
20 & 0.284729 & 0.569458 & 0.715271 \tabularnewline
21 & 0.280868 & 0.561737 & 0.719132 \tabularnewline
22 & 0.233673 & 0.467345 & 0.766327 \tabularnewline
23 & 0.312633 & 0.625266 & 0.687367 \tabularnewline
24 & 0.230534 & 0.461068 & 0.769466 \tabularnewline
25 & 0.163721 & 0.327441 & 0.836279 \tabularnewline
26 & 0.134005 & 0.268009 & 0.865995 \tabularnewline
27 & 0.129555 & 0.259111 & 0.870445 \tabularnewline
28 & 0.159383 & 0.318767 & 0.840617 \tabularnewline
29 & 0.11243 & 0.224859 & 0.88757 \tabularnewline
30 & 0.102024 & 0.204047 & 0.897976 \tabularnewline
31 & 0.100764 & 0.201527 & 0.899236 \tabularnewline
32 & 0.0729854 & 0.145971 & 0.927015 \tabularnewline
33 & 0.0599444 & 0.119889 & 0.940056 \tabularnewline
34 & 0.0644797 & 0.128959 & 0.93552 \tabularnewline
35 & 0.0442351 & 0.0884703 & 0.955765 \tabularnewline
36 & 0.0360959 & 0.0721918 & 0.963904 \tabularnewline
37 & 0.0252767 & 0.0505534 & 0.974723 \tabularnewline
38 & 0.0317791 & 0.0635582 & 0.968221 \tabularnewline
39 & 0.0710168 & 0.142034 & 0.928983 \tabularnewline
40 & 0.050716 & 0.101432 & 0.949284 \tabularnewline
41 & 0.110028 & 0.220056 & 0.889972 \tabularnewline
42 & 0.0962717 & 0.192543 & 0.903728 \tabularnewline
43 & 0.113201 & 0.226402 & 0.886799 \tabularnewline
44 & 0.0852925 & 0.170585 & 0.914708 \tabularnewline
45 & 0.0833227 & 0.166645 & 0.916677 \tabularnewline
46 & 0.0767611 & 0.153522 & 0.923239 \tabularnewline
47 & 0.0797784 & 0.159557 & 0.920222 \tabularnewline
48 & 0.121474 & 0.242948 & 0.878526 \tabularnewline
49 & 0.170656 & 0.341311 & 0.829344 \tabularnewline
50 & 0.200961 & 0.401921 & 0.799039 \tabularnewline
51 & 0.16058 & 0.321161 & 0.83942 \tabularnewline
52 & 0.200192 & 0.400384 & 0.799808 \tabularnewline
53 & 0.192355 & 0.38471 & 0.807645 \tabularnewline
54 & 0.222801 & 0.445603 & 0.777199 \tabularnewline
55 & 0.359427 & 0.718855 & 0.640573 \tabularnewline
56 & 0.322741 & 0.645482 & 0.677259 \tabularnewline
57 & 0.473448 & 0.946896 & 0.526552 \tabularnewline
58 & 0.561317 & 0.877366 & 0.438683 \tabularnewline
59 & 0.503093 & 0.993813 & 0.496907 \tabularnewline
60 & 0.538638 & 0.922724 & 0.461362 \tabularnewline
61 & 0.540803 & 0.918394 & 0.459197 \tabularnewline
62 & 0.485054 & 0.970107 & 0.514946 \tabularnewline
63 & 0.628889 & 0.742222 & 0.371111 \tabularnewline
64 & 0.672161 & 0.655679 & 0.327839 \tabularnewline
65 & 0.635386 & 0.729228 & 0.364614 \tabularnewline
66 & 0.593346 & 0.813308 & 0.406654 \tabularnewline
67 & 0.57222 & 0.855559 & 0.42778 \tabularnewline
68 & 0.511732 & 0.976536 & 0.488268 \tabularnewline
69 & 0.489304 & 0.978607 & 0.510696 \tabularnewline
70 & 0.435444 & 0.870887 & 0.564556 \tabularnewline
71 & 0.372516 & 0.745031 & 0.627484 \tabularnewline
72 & 0.35974 & 0.71948 & 0.64026 \tabularnewline
73 & 0.315304 & 0.630608 & 0.684696 \tabularnewline
74 & 0.270233 & 0.540466 & 0.729767 \tabularnewline
75 & 0.223219 & 0.446438 & 0.776781 \tabularnewline
76 & 0.197394 & 0.394787 & 0.802606 \tabularnewline
77 & 0.192273 & 0.384546 & 0.807727 \tabularnewline
78 & 0.170601 & 0.341202 & 0.829399 \tabularnewline
79 & 0.130856 & 0.261713 & 0.869144 \tabularnewline
80 & 0.108691 & 0.217382 & 0.891309 \tabularnewline
81 & 0.080306 & 0.160612 & 0.919694 \tabularnewline
82 & 0.13959 & 0.279181 & 0.86041 \tabularnewline
83 & 0.109271 & 0.218542 & 0.890729 \tabularnewline
84 & 0.121641 & 0.243281 & 0.878359 \tabularnewline
85 & 0.0888301 & 0.17766 & 0.91117 \tabularnewline
86 & 0.08269 & 0.16538 & 0.91731 \tabularnewline
87 & 0.0611545 & 0.122309 & 0.938846 \tabularnewline
88 & 0.0388419 & 0.0776838 & 0.961158 \tabularnewline
89 & 0.0255874 & 0.0511748 & 0.974413 \tabularnewline
90 & 0.0457249 & 0.0914498 & 0.954275 \tabularnewline
91 & 0.183498 & 0.366997 & 0.816502 \tabularnewline
92 & 0.204537 & 0.409073 & 0.795463 \tabularnewline
93 & 0.240324 & 0.480649 & 0.759676 \tabularnewline
94 & 0.184641 & 0.369282 & 0.815359 \tabularnewline
95 & 0.199504 & 0.399007 & 0.800496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&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]17[/C][C]0.654772[/C][C]0.690456[/C][C]0.345228[/C][/ROW]
[ROW][C]18[/C][C]0.544957[/C][C]0.910085[/C][C]0.455043[/C][/ROW]
[ROW][C]19[/C][C]0.402401[/C][C]0.804802[/C][C]0.597599[/C][/ROW]
[ROW][C]20[/C][C]0.284729[/C][C]0.569458[/C][C]0.715271[/C][/ROW]
[ROW][C]21[/C][C]0.280868[/C][C]0.561737[/C][C]0.719132[/C][/ROW]
[ROW][C]22[/C][C]0.233673[/C][C]0.467345[/C][C]0.766327[/C][/ROW]
[ROW][C]23[/C][C]0.312633[/C][C]0.625266[/C][C]0.687367[/C][/ROW]
[ROW][C]24[/C][C]0.230534[/C][C]0.461068[/C][C]0.769466[/C][/ROW]
[ROW][C]25[/C][C]0.163721[/C][C]0.327441[/C][C]0.836279[/C][/ROW]
[ROW][C]26[/C][C]0.134005[/C][C]0.268009[/C][C]0.865995[/C][/ROW]
[ROW][C]27[/C][C]0.129555[/C][C]0.259111[/C][C]0.870445[/C][/ROW]
[ROW][C]28[/C][C]0.159383[/C][C]0.318767[/C][C]0.840617[/C][/ROW]
[ROW][C]29[/C][C]0.11243[/C][C]0.224859[/C][C]0.88757[/C][/ROW]
[ROW][C]30[/C][C]0.102024[/C][C]0.204047[/C][C]0.897976[/C][/ROW]
[ROW][C]31[/C][C]0.100764[/C][C]0.201527[/C][C]0.899236[/C][/ROW]
[ROW][C]32[/C][C]0.0729854[/C][C]0.145971[/C][C]0.927015[/C][/ROW]
[ROW][C]33[/C][C]0.0599444[/C][C]0.119889[/C][C]0.940056[/C][/ROW]
[ROW][C]34[/C][C]0.0644797[/C][C]0.128959[/C][C]0.93552[/C][/ROW]
[ROW][C]35[/C][C]0.0442351[/C][C]0.0884703[/C][C]0.955765[/C][/ROW]
[ROW][C]36[/C][C]0.0360959[/C][C]0.0721918[/C][C]0.963904[/C][/ROW]
[ROW][C]37[/C][C]0.0252767[/C][C]0.0505534[/C][C]0.974723[/C][/ROW]
[ROW][C]38[/C][C]0.0317791[/C][C]0.0635582[/C][C]0.968221[/C][/ROW]
[ROW][C]39[/C][C]0.0710168[/C][C]0.142034[/C][C]0.928983[/C][/ROW]
[ROW][C]40[/C][C]0.050716[/C][C]0.101432[/C][C]0.949284[/C][/ROW]
[ROW][C]41[/C][C]0.110028[/C][C]0.220056[/C][C]0.889972[/C][/ROW]
[ROW][C]42[/C][C]0.0962717[/C][C]0.192543[/C][C]0.903728[/C][/ROW]
[ROW][C]43[/C][C]0.113201[/C][C]0.226402[/C][C]0.886799[/C][/ROW]
[ROW][C]44[/C][C]0.0852925[/C][C]0.170585[/C][C]0.914708[/C][/ROW]
[ROW][C]45[/C][C]0.0833227[/C][C]0.166645[/C][C]0.916677[/C][/ROW]
[ROW][C]46[/C][C]0.0767611[/C][C]0.153522[/C][C]0.923239[/C][/ROW]
[ROW][C]47[/C][C]0.0797784[/C][C]0.159557[/C][C]0.920222[/C][/ROW]
[ROW][C]48[/C][C]0.121474[/C][C]0.242948[/C][C]0.878526[/C][/ROW]
[ROW][C]49[/C][C]0.170656[/C][C]0.341311[/C][C]0.829344[/C][/ROW]
[ROW][C]50[/C][C]0.200961[/C][C]0.401921[/C][C]0.799039[/C][/ROW]
[ROW][C]51[/C][C]0.16058[/C][C]0.321161[/C][C]0.83942[/C][/ROW]
[ROW][C]52[/C][C]0.200192[/C][C]0.400384[/C][C]0.799808[/C][/ROW]
[ROW][C]53[/C][C]0.192355[/C][C]0.38471[/C][C]0.807645[/C][/ROW]
[ROW][C]54[/C][C]0.222801[/C][C]0.445603[/C][C]0.777199[/C][/ROW]
[ROW][C]55[/C][C]0.359427[/C][C]0.718855[/C][C]0.640573[/C][/ROW]
[ROW][C]56[/C][C]0.322741[/C][C]0.645482[/C][C]0.677259[/C][/ROW]
[ROW][C]57[/C][C]0.473448[/C][C]0.946896[/C][C]0.526552[/C][/ROW]
[ROW][C]58[/C][C]0.561317[/C][C]0.877366[/C][C]0.438683[/C][/ROW]
[ROW][C]59[/C][C]0.503093[/C][C]0.993813[/C][C]0.496907[/C][/ROW]
[ROW][C]60[/C][C]0.538638[/C][C]0.922724[/C][C]0.461362[/C][/ROW]
[ROW][C]61[/C][C]0.540803[/C][C]0.918394[/C][C]0.459197[/C][/ROW]
[ROW][C]62[/C][C]0.485054[/C][C]0.970107[/C][C]0.514946[/C][/ROW]
[ROW][C]63[/C][C]0.628889[/C][C]0.742222[/C][C]0.371111[/C][/ROW]
[ROW][C]64[/C][C]0.672161[/C][C]0.655679[/C][C]0.327839[/C][/ROW]
[ROW][C]65[/C][C]0.635386[/C][C]0.729228[/C][C]0.364614[/C][/ROW]
[ROW][C]66[/C][C]0.593346[/C][C]0.813308[/C][C]0.406654[/C][/ROW]
[ROW][C]67[/C][C]0.57222[/C][C]0.855559[/C][C]0.42778[/C][/ROW]
[ROW][C]68[/C][C]0.511732[/C][C]0.976536[/C][C]0.488268[/C][/ROW]
[ROW][C]69[/C][C]0.489304[/C][C]0.978607[/C][C]0.510696[/C][/ROW]
[ROW][C]70[/C][C]0.435444[/C][C]0.870887[/C][C]0.564556[/C][/ROW]
[ROW][C]71[/C][C]0.372516[/C][C]0.745031[/C][C]0.627484[/C][/ROW]
[ROW][C]72[/C][C]0.35974[/C][C]0.71948[/C][C]0.64026[/C][/ROW]
[ROW][C]73[/C][C]0.315304[/C][C]0.630608[/C][C]0.684696[/C][/ROW]
[ROW][C]74[/C][C]0.270233[/C][C]0.540466[/C][C]0.729767[/C][/ROW]
[ROW][C]75[/C][C]0.223219[/C][C]0.446438[/C][C]0.776781[/C][/ROW]
[ROW][C]76[/C][C]0.197394[/C][C]0.394787[/C][C]0.802606[/C][/ROW]
[ROW][C]77[/C][C]0.192273[/C][C]0.384546[/C][C]0.807727[/C][/ROW]
[ROW][C]78[/C][C]0.170601[/C][C]0.341202[/C][C]0.829399[/C][/ROW]
[ROW][C]79[/C][C]0.130856[/C][C]0.261713[/C][C]0.869144[/C][/ROW]
[ROW][C]80[/C][C]0.108691[/C][C]0.217382[/C][C]0.891309[/C][/ROW]
[ROW][C]81[/C][C]0.080306[/C][C]0.160612[/C][C]0.919694[/C][/ROW]
[ROW][C]82[/C][C]0.13959[/C][C]0.279181[/C][C]0.86041[/C][/ROW]
[ROW][C]83[/C][C]0.109271[/C][C]0.218542[/C][C]0.890729[/C][/ROW]
[ROW][C]84[/C][C]0.121641[/C][C]0.243281[/C][C]0.878359[/C][/ROW]
[ROW][C]85[/C][C]0.0888301[/C][C]0.17766[/C][C]0.91117[/C][/ROW]
[ROW][C]86[/C][C]0.08269[/C][C]0.16538[/C][C]0.91731[/C][/ROW]
[ROW][C]87[/C][C]0.0611545[/C][C]0.122309[/C][C]0.938846[/C][/ROW]
[ROW][C]88[/C][C]0.0388419[/C][C]0.0776838[/C][C]0.961158[/C][/ROW]
[ROW][C]89[/C][C]0.0255874[/C][C]0.0511748[/C][C]0.974413[/C][/ROW]
[ROW][C]90[/C][C]0.0457249[/C][C]0.0914498[/C][C]0.954275[/C][/ROW]
[ROW][C]91[/C][C]0.183498[/C][C]0.366997[/C][C]0.816502[/C][/ROW]
[ROW][C]92[/C][C]0.204537[/C][C]0.409073[/C][C]0.795463[/C][/ROW]
[ROW][C]93[/C][C]0.240324[/C][C]0.480649[/C][C]0.759676[/C][/ROW]
[ROW][C]94[/C][C]0.184641[/C][C]0.369282[/C][C]0.815359[/C][/ROW]
[ROW][C]95[/C][C]0.199504[/C][C]0.399007[/C][C]0.800496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&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
170.6547720.6904560.345228
180.5449570.9100850.455043
190.4024010.8048020.597599
200.2847290.5694580.715271
210.2808680.5617370.719132
220.2336730.4673450.766327
230.3126330.6252660.687367
240.2305340.4610680.769466
250.1637210.3274410.836279
260.1340050.2680090.865995
270.1295550.2591110.870445
280.1593830.3187670.840617
290.112430.2248590.88757
300.1020240.2040470.897976
310.1007640.2015270.899236
320.07298540.1459710.927015
330.05994440.1198890.940056
340.06447970.1289590.93552
350.04423510.08847030.955765
360.03609590.07219180.963904
370.02527670.05055340.974723
380.03177910.06355820.968221
390.07101680.1420340.928983
400.0507160.1014320.949284
410.1100280.2200560.889972
420.09627170.1925430.903728
430.1132010.2264020.886799
440.08529250.1705850.914708
450.08332270.1666450.916677
460.07676110.1535220.923239
470.07977840.1595570.920222
480.1214740.2429480.878526
490.1706560.3413110.829344
500.2009610.4019210.799039
510.160580.3211610.83942
520.2001920.4003840.799808
530.1923550.384710.807645
540.2228010.4456030.777199
550.3594270.7188550.640573
560.3227410.6454820.677259
570.4734480.9468960.526552
580.5613170.8773660.438683
590.5030930.9938130.496907
600.5386380.9227240.461362
610.5408030.9183940.459197
620.4850540.9701070.514946
630.6288890.7422220.371111
640.6721610.6556790.327839
650.6353860.7292280.364614
660.5933460.8133080.406654
670.572220.8555590.42778
680.5117320.9765360.488268
690.4893040.9786070.510696
700.4354440.8708870.564556
710.3725160.7450310.627484
720.359740.719480.64026
730.3153040.6306080.684696
740.2702330.5404660.729767
750.2232190.4464380.776781
760.1973940.3947870.802606
770.1922730.3845460.807727
780.1706010.3412020.829399
790.1308560.2617130.869144
800.1086910.2173820.891309
810.0803060.1606120.919694
820.139590.2791810.86041
830.1092710.2185420.890729
840.1216410.2432810.878359
850.08883010.177660.91117
860.082690.165380.91731
870.06115450.1223090.938846
880.03884190.07768380.961158
890.02558740.05117480.974413
900.04572490.09144980.954275
910.1834980.3669970.816502
920.2045370.4090730.795463
930.2403240.4806490.759676
940.1846410.3692820.815359
950.1995040.3990070.800496






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0886076OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0886076 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267074&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0886076[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267074&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267074&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level70.0886076OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 14 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 14 ; 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')
}