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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 computationThu, 10 Dec 2015 17:30:32 +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/2015/Dec/10/t1449769136rb8t73e61hypyih.htm/, Retrieved Thu, 16 May 2024 12:51:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285827, Retrieved Thu, 16 May 2024 12:51:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2015-12-10 17:30:32] [18106851e169be8e7181c7a62bb5da83] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
6 19 20 18 23 20 19 8 22 139 31 39
5.5 17 16 19 25 20 20 10 12 224 67 52
5 16 17 19 24 27 27 10 20 119 38 30
5 24 12 22 26 24 23 14 19 176 69 78
5.5 15 12 16 20 21 25 14 20 163 54 66
3 22 16 18 21 19 21 8 21 137 42 42
6.5 26 17 20 23 24 22 13 23 148 112 25
4 22 21 21 21 22 24 12 23 150 20 61
5.5 21 12 20 26 25 25 13 16 153 22 41
2.5 8 6 15 23 23 24 9 16 94 23 46
4.5 20 13 20 25 16 21 6 21 97 55 31
2.5 22 19 20 22 18 18 11 22 166 71 49
6 14 7 9 25 18 22 11 18 59 23 34
1 23 18 20 28 17 24 12 19 90 61 42
3.5 22 15 24 18 23 23 11 28 164 34 73
6 22 15 19 27 19 24 10 23 162 48 17
9 17 10 16 20 21 20 12 19 202 43 64
3.5 20 10 14 23 17 22 12 20 66 28 25
6 16 7 14 24 18 22 12 19 104 34 35
5 20 16 15 23 18 18 12 25 177 80 56
7.5 23 22 21 23 17 16 10 23 99 59 59
6.5 18 5 14 27 25 26 5 14 139 32 50
6.5 17 8 13 19 17 23 12 24 108 38 27
8 21 12 17 24 9 22 12 27 194 52 68
7 20 17 23 23 23 21 10 23 159 72 143
8.5 20 14 18 19 21 25 15 18 67 15 9
7 17 16 15 20 21 19 12 25 114 23 19
2.5 22 10 22 23 24 24 15 21 32 4 25
9 17 9 26 24 26 26 12 23 126 23 43
8 22 12 21 22 23 23 12 23 149 36 51
5.5 26 19 28 20 16 16 12 15 120 40 36
7 20 11 21 18 22 20 12 16 109 31 44
9 21 14 16 25 11 21 12 24 172 38 34
9 19 15 22 20 19 21 12 24 156 37 39
10 27 22 24 23 21 17 11 28 167 43 80
8.5 23 17 18 26 18 25 10 21 87 4 42
6 19 11 17 19 19 17 13 20 118 32 54
7 17 13 17 23 16 24 12 20 146 28 51
8.5 17 16 17 17 13 17 12 30 73 32 43
8 22 20 21 19 17 14 9 22 65 15 28
7.5 24 10 23 23 23 25 11 23 152 28 27
7 22 11 22 27 25 26 12 18 77 12 27
8 18 12 21 21 24 20 12 29 112 44 51
6.5 19 10 19 23 28 21 9 16 131 11 12
8.5 18 11 18 21 21 21 8 22 56 24 24
10 23 17 22 25 23 26 14 23 121 13 81
9.5 20 12 19 24 20 23 12 19 149 20 42
9 24 17 23 23 18 25 9 4 168 52 22
5 21 23 21 25 22 22 14 15 85 34 28
8 22 16 19 26 24 28 9 23 114 29 51
5.5 18 13 10 24 12 24 13 20 119 51 24
3.5 20 15 21 23 24 26 13 24 142 40 14
3 17 19 20 25 25 24 13 22 64 27 24
8 22 18 20 21 21 23 10 20 105 25 51
7.5 11 7 8 18 12 20 12 21 149 18 68
6.5 16 9 12 22 14 18 11 22 148 39 32
1 24 19 24 22 25 24 13 18 158 46 62
1 15 12 16 19 15 20 11 23 128 31 33
8.5 19 17 16 28 21 24 7 20 159 35 62
6.5 19 9 15 16 15 21 10 22 105 52 77
4.5 28 28 28 28 28 28 15 21 159 77 76
2 26 20 21 21 11 10 12 19 167 37 41
5 15 16 18 22 22 22 12 22 165 32 48
0.5 26 22 22 24 22 19 10 15 159 36 63
5 22 20 20 24 24 24 12 15 91 26 31
4 27 21 26 28 25 28 12 15 121 23 45
0.5 26 15 24 27 16 28 15 16 153 34 21
4.5 22 17 19 24 21 26 11 21 221 35 44
7.5 21 17 19 24 22 26 12 18 188 47 69
5.5 22 18 23 22 25 21 7 25 149 47 54
7 20 13 18 20 21 20 12 20 92 24 42
5.5 22 13 19 21 19 20 11 19 156 32 39
3.5 20 19 19 27 21 24 12 25 132 30 34
2.5 24 12 7 25 19 25 15 18 161 92 51
4.5 17 14 20 23 25 23 12 23 105 43 42
6 20 17 19 19 24 21 13 14 131 49 20
5 19 10 18 24 28 24 6 26 157 43 53
0 15 10 14 19 15 18 12 23 111 29 31
5 20 11 17 21 17 21 10 23 145 56 39
6.5 22 11 17 27 18 23 6 24 162 46 54
4.5 24 22 22 23 22 22 6 23 187 59 46
1 20 14 16 21 21 21 7 17 42 16 25
6.5 23 20 24 28 20 25 13 21 155 22 28
7 24 17 21 20 20 23 14 18 125 48 45
0 13 8 20 24 24 27 6 21 128 26 28
7.5 19 10 19 23 20 20 12 29 96 24 45
1.5 15 10 16 25 24 26 7 21 99 18 40
4 22 22 22 27 23 23 12 18 183 71 65
6.5 21 16 21 24 20 17 10 19 214 26 100
3.5 13 8 13 25 21 22 9 12 74 36 28
5.5 23 20 18 23 21 22 11 19 99 19 17
0.5 20 15 17 22 14 15 9 23 48 23 12
7.5 6 4 6 24 5 27 11 22 50 16 45
9 15 9 22 19 25 23 12 21 150 33 37
7 22 17 16 18 20 18 12 17 68 14 10
10 23 16 20 28 15 20 12 23 158 75 72
9 18 13 13 19 9 19 10 18 147 29 55
9.5 25 20 22 27 24 25 15 23 39 13 17
4 16 16 20 24 16 24 10 19 100 40 37
6 20 15 20 26 20 22 15 15 111 19 27
8 14 10 13 21 15 28 9 20 138 24 37
9.5 26 21 25 28 22 21 12 24 131 93 66
7.5 20 15 16 19 21 23 13 25 101 36 21
7.5 22 19 19 26 21 21 12 19 165 85 78
8 22 9 19 27 20 25 8 19 114 41 35
7 20 19 24 23 24 23 9 16 111 46 48
7 17 7 9 18 15 28 15 19 75 18 27
6 22 23 22 23 24 14 12 19 82 35 43
10 17 14 15 21 18 23 12 23 121 17 30
9 21 16 22 22 24 25 11 22 150 28 69
6 11 10 12 14 19 23 6 20 71 10 23
8.5 19 7 21 24 20 26 14 20 165 38 13
6 24 20 25 26 26 21 12 3 154 57 61
9 17 10 14 20 13 15 11 20 145 22 67
5.5 19 15 16 18 21 20 12 7 132 11 45
2 24 11 25 28 23 28 8 17 169 24 36
8.5 21 14 21 23 18 19 8 24 114 37 72
7.5 28 19 19 24 15 20 10 20 89 2 56
8 21 12 10 24 12 22 10 19 173 31 61
7 19 16 21 24 23 22 8 29 141 42 53
7.5 22 13 21 23 19 16 11 25 165 21 29
9.5 20 15 18 24 23 27 11 20 110 25 55
7 17 8 14 21 18 19 4 18 121 29 39
8 23 21 22 27 18 19 10 21 110 35 44
8 15 9 20 23 22 25 10 20 117 18 39
9 21 14 8 25 19 25 12 22 63 46 23
7.5 21 14 19 24 18 24 12 25 42 12 37
8 23 14 19 25 24 26 12 24 154 31 69
8.5 15 11 11 19 16 25 10 18 96 34 44
3.5 20 23 21 26 26 26 14 15 49 7 31
6.5 19 14 20 19 19 21 11 29 110 29 13
10 22 19 21 25 23 23 12 23 86 60 30
7.5 25 19 23 27 28 25 13 24 88 25 27
4.5 20 12 18 26 21 26 13 20 168 39 25
4.5 23 8 23 23 25 19 15 4 94 9 22
6.5 23 13 20 26 24 23 7 22 48 13 14
4.5 23 17 23 22 28 24 10 16 145 60 45
8.5 16 14 13 26 12 19 12 17 164 -2 73
7 23 19 21 27 20 21 11 22 126 2 61
5 24 15 24 22 25 21 11 19 132 24 23
8.5 4 7 11 15 20 25 10 15 81 16 62

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
EX[t] = + 3.65353 + 0.0769451`AMS,L1`[t] -0.0488041`AMS,L3`[t] -0.03357`AMS,L2`[t] -0.0438495`AMS,E1`[t] -0.037543`AMS,E2`[t] + 0.0358927`AMS,E3`[t] + 0.0833837CONFSOFTTOT[t] + 0.0915184NUMERACYTOT[t] -0.00172771LFM[t] -0.0178339PRH[t] + 0.0304676CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EX[t] =  +  3.65353 +  0.0769451`AMS,L1`[t] -0.0488041`AMS,L3`[t] -0.03357`AMS,L2`[t] -0.0438495`AMS,E1`[t] -0.037543`AMS,E2`[t] +  0.0358927`AMS,E3`[t] +  0.0833837CONFSOFTTOT[t] +  0.0915184NUMERACYTOT[t] -0.00172771LFM[t] -0.0178339PRH[t] +  0.0304676CH[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EX[t] =  +  3.65353 +  0.0769451`AMS,L1`[t] -0.0488041`AMS,L3`[t] -0.03357`AMS,L2`[t] -0.0438495`AMS,E1`[t] -0.037543`AMS,E2`[t] +  0.0358927`AMS,E3`[t] +  0.0833837CONFSOFTTOT[t] +  0.0915184NUMERACYTOT[t] -0.00172771LFM[t] -0.0178339PRH[t] +  0.0304676CH[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&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] = + 3.65353 + 0.0769451`AMS,L1`[t] -0.0488041`AMS,L3`[t] -0.03357`AMS,L2`[t] -0.0438495`AMS,E1`[t] -0.037543`AMS,E2`[t] + 0.0358927`AMS,E3`[t] + 0.0833837CONFSOFTTOT[t] + 0.0915184NUMERACYTOT[t] -0.00172771LFM[t] -0.0178339PRH[t] + 0.0304676CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.654 2.447+1.4930e+00 0.1378 0.0689
`AMS,L1`+0.07694 0.08053+9.5550e-01 0.3411 0.1706
`AMS,L3`-0.0488 0.06332-7.7080e-01 0.4422 0.2211
`AMS,L2`-0.03357 0.07888-4.2560e-01 0.6711 0.3356
`AMS,E1`-0.04385 0.08386-5.2290e-01 0.6019 0.301
`AMS,E2`-0.03754 0.06529-5.7500e-01 0.5663 0.2831
`AMS,E3`+0.03589 0.07673+4.6780e-01 0.6407 0.3204
CONFSOFTTOT+0.08338 0.09345+8.9230e-01 0.3739 0.187
NUMERACYTOT+0.09152 0.04694+1.9500e+00 0.0534 0.0267
LFM-0.001728 0.00633-2.7290e-01 0.7853 0.3927
PRH-0.01783 0.01232-1.4470e+00 0.1503 0.07515
CH+0.03047 0.01191+2.5590e+00 0.01164 0.005822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +3.654 &  2.447 & +1.4930e+00 &  0.1378 &  0.0689 \tabularnewline
`AMS,L1` & +0.07694 &  0.08053 & +9.5550e-01 &  0.3411 &  0.1706 \tabularnewline
`AMS,L3` & -0.0488 &  0.06332 & -7.7080e-01 &  0.4422 &  0.2211 \tabularnewline
`AMS,L2` & -0.03357 &  0.07888 & -4.2560e-01 &  0.6711 &  0.3356 \tabularnewline
`AMS,E1` & -0.04385 &  0.08386 & -5.2290e-01 &  0.6019 &  0.301 \tabularnewline
`AMS,E2` & -0.03754 &  0.06529 & -5.7500e-01 &  0.5663 &  0.2831 \tabularnewline
`AMS,E3` & +0.03589 &  0.07673 & +4.6780e-01 &  0.6407 &  0.3204 \tabularnewline
CONFSOFTTOT & +0.08338 &  0.09345 & +8.9230e-01 &  0.3739 &  0.187 \tabularnewline
NUMERACYTOT & +0.09152 &  0.04694 & +1.9500e+00 &  0.0534 &  0.0267 \tabularnewline
LFM & -0.001728 &  0.00633 & -2.7290e-01 &  0.7853 &  0.3927 \tabularnewline
PRH & -0.01783 &  0.01232 & -1.4470e+00 &  0.1503 &  0.07515 \tabularnewline
CH & +0.03047 &  0.01191 & +2.5590e+00 &  0.01164 &  0.005822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+3.654[/C][C] 2.447[/C][C]+1.4930e+00[/C][C] 0.1378[/C][C] 0.0689[/C][/ROW]
[ROW][C]`AMS,L1`[/C][C]+0.07694[/C][C] 0.08053[/C][C]+9.5550e-01[/C][C] 0.3411[/C][C] 0.1706[/C][/ROW]
[ROW][C]`AMS,L3`[/C][C]-0.0488[/C][C] 0.06332[/C][C]-7.7080e-01[/C][C] 0.4422[/C][C] 0.2211[/C][/ROW]
[ROW][C]`AMS,L2`[/C][C]-0.03357[/C][C] 0.07888[/C][C]-4.2560e-01[/C][C] 0.6711[/C][C] 0.3356[/C][/ROW]
[ROW][C]`AMS,E1`[/C][C]-0.04385[/C][C] 0.08386[/C][C]-5.2290e-01[/C][C] 0.6019[/C][C] 0.301[/C][/ROW]
[ROW][C]`AMS,E2`[/C][C]-0.03754[/C][C] 0.06529[/C][C]-5.7500e-01[/C][C] 0.5663[/C][C] 0.2831[/C][/ROW]
[ROW][C]`AMS,E3`[/C][C]+0.03589[/C][C] 0.07673[/C][C]+4.6780e-01[/C][C] 0.6407[/C][C] 0.3204[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]+0.08338[/C][C] 0.09345[/C][C]+8.9230e-01[/C][C] 0.3739[/C][C] 0.187[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]+0.09152[/C][C] 0.04694[/C][C]+1.9500e+00[/C][C] 0.0534[/C][C] 0.0267[/C][/ROW]
[ROW][C]LFM[/C][C]-0.001728[/C][C] 0.00633[/C][C]-2.7290e-01[/C][C] 0.7853[/C][C] 0.3927[/C][/ROW]
[ROW][C]PRH[/C][C]-0.01783[/C][C] 0.01232[/C][C]-1.4470e+00[/C][C] 0.1503[/C][C] 0.07515[/C][/ROW]
[ROW][C]CH[/C][C]+0.03047[/C][C] 0.01191[/C][C]+2.5590e+00[/C][C] 0.01164[/C][C] 0.005822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.654 2.447+1.4930e+00 0.1378 0.0689
`AMS,L1`+0.07694 0.08053+9.5550e-01 0.3411 0.1706
`AMS,L3`-0.0488 0.06332-7.7080e-01 0.4422 0.2211
`AMS,L2`-0.03357 0.07888-4.2560e-01 0.6711 0.3356
`AMS,E1`-0.04385 0.08386-5.2290e-01 0.6019 0.301
`AMS,E2`-0.03754 0.06529-5.7500e-01 0.5663 0.2831
`AMS,E3`+0.03589 0.07673+4.6780e-01 0.6407 0.3204
CONFSOFTTOT+0.08338 0.09345+8.9230e-01 0.3739 0.187
NUMERACYTOT+0.09152 0.04694+1.9500e+00 0.0534 0.0267
LFM-0.001728 0.00633-2.7290e-01 0.7853 0.3927
PRH-0.01783 0.01232-1.4470e+00 0.1503 0.07515
CH+0.03047 0.01191+2.5590e+00 0.01164 0.005822






Multiple Linear Regression - Regression Statistics
Multiple R 0.3293
R-squared 0.1085
Adjusted R-squared 0.03243
F-TEST (value) 1.427
F-TEST (DF numerator)11
F-TEST (DF denominator)129
p-value 0.1685
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.401
Sum Squared Residuals 743.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3293 \tabularnewline
R-squared &  0.1085 \tabularnewline
Adjusted R-squared &  0.03243 \tabularnewline
F-TEST (value) &  1.427 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 129 \tabularnewline
p-value &  0.1685 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.401 \tabularnewline
Sum Squared Residuals &  743.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3293[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.1085[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.03243[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.427[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]129[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1685[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.401[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 743.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&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 R 0.3293
R-squared 0.1085
Adjusted R-squared 0.03243
F-TEST (value) 1.427
F-TEST (DF numerator)11
F-TEST (DF denominator)129
p-value 0.1685
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.401
Sum Squared Residuals 743.7






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 6 5.533 0.4666
2 5.5 4.348 1.152
3 5 5.015-0.01515
4 5 6.709-1.709
5 5.5 6.681-1.181
6 3 5.964-2.964
7 6.5 4.731 1.769
8 4 7.079-3.079
9 5.5 5.972-0.4718
10 2.5 5.506-3.006
11 4.5 5.162-0.6616
12 2.5 5.624-3.124
13 6 6.193-0.193
14 1 5.645-4.645
15 3.5 7.796-4.296
16 6 5.261 0.7386
17 9 6.562 2.438
18 3.5 6.357-2.857
19 6 6.154-0.1543
20 5 6.132-1.132
21 7.5 6.085 1.415
22 6.5 5.546 0.954
23 6.5 6.644-0.1443
24 8 7.793 0.2067
25 7 8.209-1.209
26 8.5 5.97 2.53
27 7 5.954 1.046
28 2.5 6.879-4.379
29 9 6.342 2.658
30 8 6.813 1.187
31 5.5 5.433 0.067
32 7 6.118 0.8825
33 9 6.552 2.448
34 9 6.264 2.736
35 10 7.527 2.473
36 8.5 6.885 1.615
37 6 6.857-0.8567
38 7 6.642 0.3581
39 8.5 7.346 1.154
40 8 5.934 2.066
41 7.5 6.348 1.152
42 7 6.005 0.9948
43 8 6.874 1.126
44 6.5 4.841 1.659
45 8.5 5.829 2.671
46 10 8.128 1.872
47 9.5 6.396 3.104
48 9 3.681 5.319
49 5 4.949 0.0506
50 8 6.587 1.413
51 5.5 5.957-0.4574
52 3.5 5.527-2.027
53 3 5.426-2.426
54 8 6.504 1.496
55 7.5 7.784-0.2836
56 6.5 6.153 0.3473
57 1 6.252-5.252
58 1 6.26-5.26
59 8.5 5.998 2.502
60 6.5 7.746-1.246
61 4.5 6.068-1.568
62 2 6.038-4.038
63 5 6.042-1.042
64 0.5 5.855-5.355
65 5 5.304-0.3038
66 4 5.797-1.797
67 0.5 5.821-5.321
68 4.5 6.145-1.645
69 7.5 6.444 1.056
70 5.5 5.968-0.4675
71 7 6.53 0.4702
72 5.5 6.162-0.6618
73 3.5 6.078-2.578
74 2.5 6.3-3.8
75 4.5 5.922-1.422
76 6 4.619 1.381
77 5 6.237-1.237
78 0 6.441-6.441
79 5 6.157-1.157
80 6.5 6.446 0.05365
81 4.5 5.275-0.7746
82 1 5.56-4.56
83 6.5 5.759 0.741
84 7 6.277 0.723
85 0 5.198-5.198
86 7.5 7.38 0.1201
87 1.5 5.951-4.451
88 4 5.358-1.358
89 6.5 7.377-0.8765
90 3.5 4.664-1.164
91 5.5 5.5 0.0002429
92 0.5 5.666-5.166
93 7.5 7.378 0.1225
94 9 5.886 3.114
95 7 5.579 1.421
96 10 6.587 3.413
97 9 6.864 2.136
98 9.5 6.249 3.251
99 4 5.455-1.455
100 6 5.603 0.3968
101 8 6.369 1.631
102 9.5 5.813 3.687
103 7.5 6.322 1.178
104 7.5 5.921 1.579
105 8 5.776 2.224
106 7 5.04 1.96
107 7 7.332-0.332
108 6 5.362 0.6381
109 10 6.511 3.489
110 9 7.056 1.944
111 6 5.838 0.1622
112 8.5 5.63 2.87
113 6 4.174 1.826
114 9 7.323 1.677
115 5.5 5.575-0.07451
116 2 5.638-3.638
117 8.5 7.079 1.421
118 7.5 7.526-0.02616
119 8 7.214 0.7858
120 7 6.447 0.5533
121 7.5 6.288 1.212
122 9.5 6.697 2.803
123 7 5.629 1.371
124 8 5.764 2.236
125 8 6.089 1.911
126 9 6.191 2.809
127 7.5 7.211 0.2892
128 8 7.519 0.4815
129 8.5 6.415 2.085
130 3.5 5.457-1.957
131 6.5 6.228 0.2718
132 10 5.381 4.619
133 7.5 6.045 1.455
134 4.5 5.698-1.198
135 4.5 4.96-0.4598
136 6.5 5.611 0.8893
137 4.5 5.016-0.5156
138 8.5 7.389 1.111
139 7 7.146-0.1458
140 5 5.514-0.5137
141 8.5 6.409 2.091

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6 &  5.533 &  0.4666 \tabularnewline
2 &  5.5 &  4.348 &  1.152 \tabularnewline
3 &  5 &  5.015 & -0.01515 \tabularnewline
4 &  5 &  6.709 & -1.709 \tabularnewline
5 &  5.5 &  6.681 & -1.181 \tabularnewline
6 &  3 &  5.964 & -2.964 \tabularnewline
7 &  6.5 &  4.731 &  1.769 \tabularnewline
8 &  4 &  7.079 & -3.079 \tabularnewline
9 &  5.5 &  5.972 & -0.4718 \tabularnewline
10 &  2.5 &  5.506 & -3.006 \tabularnewline
11 &  4.5 &  5.162 & -0.6616 \tabularnewline
12 &  2.5 &  5.624 & -3.124 \tabularnewline
13 &  6 &  6.193 & -0.193 \tabularnewline
14 &  1 &  5.645 & -4.645 \tabularnewline
15 &  3.5 &  7.796 & -4.296 \tabularnewline
16 &  6 &  5.261 &  0.7386 \tabularnewline
17 &  9 &  6.562 &  2.438 \tabularnewline
18 &  3.5 &  6.357 & -2.857 \tabularnewline
19 &  6 &  6.154 & -0.1543 \tabularnewline
20 &  5 &  6.132 & -1.132 \tabularnewline
21 &  7.5 &  6.085 &  1.415 \tabularnewline
22 &  6.5 &  5.546 &  0.954 \tabularnewline
23 &  6.5 &  6.644 & -0.1443 \tabularnewline
24 &  8 &  7.793 &  0.2067 \tabularnewline
25 &  7 &  8.209 & -1.209 \tabularnewline
26 &  8.5 &  5.97 &  2.53 \tabularnewline
27 &  7 &  5.954 &  1.046 \tabularnewline
28 &  2.5 &  6.879 & -4.379 \tabularnewline
29 &  9 &  6.342 &  2.658 \tabularnewline
30 &  8 &  6.813 &  1.187 \tabularnewline
31 &  5.5 &  5.433 &  0.067 \tabularnewline
32 &  7 &  6.118 &  0.8825 \tabularnewline
33 &  9 &  6.552 &  2.448 \tabularnewline
34 &  9 &  6.264 &  2.736 \tabularnewline
35 &  10 &  7.527 &  2.473 \tabularnewline
36 &  8.5 &  6.885 &  1.615 \tabularnewline
37 &  6 &  6.857 & -0.8567 \tabularnewline
38 &  7 &  6.642 &  0.3581 \tabularnewline
39 &  8.5 &  7.346 &  1.154 \tabularnewline
40 &  8 &  5.934 &  2.066 \tabularnewline
41 &  7.5 &  6.348 &  1.152 \tabularnewline
42 &  7 &  6.005 &  0.9948 \tabularnewline
43 &  8 &  6.874 &  1.126 \tabularnewline
44 &  6.5 &  4.841 &  1.659 \tabularnewline
45 &  8.5 &  5.829 &  2.671 \tabularnewline
46 &  10 &  8.128 &  1.872 \tabularnewline
47 &  9.5 &  6.396 &  3.104 \tabularnewline
48 &  9 &  3.681 &  5.319 \tabularnewline
49 &  5 &  4.949 &  0.0506 \tabularnewline
50 &  8 &  6.587 &  1.413 \tabularnewline
51 &  5.5 &  5.957 & -0.4574 \tabularnewline
52 &  3.5 &  5.527 & -2.027 \tabularnewline
53 &  3 &  5.426 & -2.426 \tabularnewline
54 &  8 &  6.504 &  1.496 \tabularnewline
55 &  7.5 &  7.784 & -0.2836 \tabularnewline
56 &  6.5 &  6.153 &  0.3473 \tabularnewline
57 &  1 &  6.252 & -5.252 \tabularnewline
58 &  1 &  6.26 & -5.26 \tabularnewline
59 &  8.5 &  5.998 &  2.502 \tabularnewline
60 &  6.5 &  7.746 & -1.246 \tabularnewline
61 &  4.5 &  6.068 & -1.568 \tabularnewline
62 &  2 &  6.038 & -4.038 \tabularnewline
63 &  5 &  6.042 & -1.042 \tabularnewline
64 &  0.5 &  5.855 & -5.355 \tabularnewline
65 &  5 &  5.304 & -0.3038 \tabularnewline
66 &  4 &  5.797 & -1.797 \tabularnewline
67 &  0.5 &  5.821 & -5.321 \tabularnewline
68 &  4.5 &  6.145 & -1.645 \tabularnewline
69 &  7.5 &  6.444 &  1.056 \tabularnewline
70 &  5.5 &  5.968 & -0.4675 \tabularnewline
71 &  7 &  6.53 &  0.4702 \tabularnewline
72 &  5.5 &  6.162 & -0.6618 \tabularnewline
73 &  3.5 &  6.078 & -2.578 \tabularnewline
74 &  2.5 &  6.3 & -3.8 \tabularnewline
75 &  4.5 &  5.922 & -1.422 \tabularnewline
76 &  6 &  4.619 &  1.381 \tabularnewline
77 &  5 &  6.237 & -1.237 \tabularnewline
78 &  0 &  6.441 & -6.441 \tabularnewline
79 &  5 &  6.157 & -1.157 \tabularnewline
80 &  6.5 &  6.446 &  0.05365 \tabularnewline
81 &  4.5 &  5.275 & -0.7746 \tabularnewline
82 &  1 &  5.56 & -4.56 \tabularnewline
83 &  6.5 &  5.759 &  0.741 \tabularnewline
84 &  7 &  6.277 &  0.723 \tabularnewline
85 &  0 &  5.198 & -5.198 \tabularnewline
86 &  7.5 &  7.38 &  0.1201 \tabularnewline
87 &  1.5 &  5.951 & -4.451 \tabularnewline
88 &  4 &  5.358 & -1.358 \tabularnewline
89 &  6.5 &  7.377 & -0.8765 \tabularnewline
90 &  3.5 &  4.664 & -1.164 \tabularnewline
91 &  5.5 &  5.5 &  0.0002429 \tabularnewline
92 &  0.5 &  5.666 & -5.166 \tabularnewline
93 &  7.5 &  7.378 &  0.1225 \tabularnewline
94 &  9 &  5.886 &  3.114 \tabularnewline
95 &  7 &  5.579 &  1.421 \tabularnewline
96 &  10 &  6.587 &  3.413 \tabularnewline
97 &  9 &  6.864 &  2.136 \tabularnewline
98 &  9.5 &  6.249 &  3.251 \tabularnewline
99 &  4 &  5.455 & -1.455 \tabularnewline
100 &  6 &  5.603 &  0.3968 \tabularnewline
101 &  8 &  6.369 &  1.631 \tabularnewline
102 &  9.5 &  5.813 &  3.687 \tabularnewline
103 &  7.5 &  6.322 &  1.178 \tabularnewline
104 &  7.5 &  5.921 &  1.579 \tabularnewline
105 &  8 &  5.776 &  2.224 \tabularnewline
106 &  7 &  5.04 &  1.96 \tabularnewline
107 &  7 &  7.332 & -0.332 \tabularnewline
108 &  6 &  5.362 &  0.6381 \tabularnewline
109 &  10 &  6.511 &  3.489 \tabularnewline
110 &  9 &  7.056 &  1.944 \tabularnewline
111 &  6 &  5.838 &  0.1622 \tabularnewline
112 &  8.5 &  5.63 &  2.87 \tabularnewline
113 &  6 &  4.174 &  1.826 \tabularnewline
114 &  9 &  7.323 &  1.677 \tabularnewline
115 &  5.5 &  5.575 & -0.07451 \tabularnewline
116 &  2 &  5.638 & -3.638 \tabularnewline
117 &  8.5 &  7.079 &  1.421 \tabularnewline
118 &  7.5 &  7.526 & -0.02616 \tabularnewline
119 &  8 &  7.214 &  0.7858 \tabularnewline
120 &  7 &  6.447 &  0.5533 \tabularnewline
121 &  7.5 &  6.288 &  1.212 \tabularnewline
122 &  9.5 &  6.697 &  2.803 \tabularnewline
123 &  7 &  5.629 &  1.371 \tabularnewline
124 &  8 &  5.764 &  2.236 \tabularnewline
125 &  8 &  6.089 &  1.911 \tabularnewline
126 &  9 &  6.191 &  2.809 \tabularnewline
127 &  7.5 &  7.211 &  0.2892 \tabularnewline
128 &  8 &  7.519 &  0.4815 \tabularnewline
129 &  8.5 &  6.415 &  2.085 \tabularnewline
130 &  3.5 &  5.457 & -1.957 \tabularnewline
131 &  6.5 &  6.228 &  0.2718 \tabularnewline
132 &  10 &  5.381 &  4.619 \tabularnewline
133 &  7.5 &  6.045 &  1.455 \tabularnewline
134 &  4.5 &  5.698 & -1.198 \tabularnewline
135 &  4.5 &  4.96 & -0.4598 \tabularnewline
136 &  6.5 &  5.611 &  0.8893 \tabularnewline
137 &  4.5 &  5.016 & -0.5156 \tabularnewline
138 &  8.5 &  7.389 &  1.111 \tabularnewline
139 &  7 &  7.146 & -0.1458 \tabularnewline
140 &  5 &  5.514 & -0.5137 \tabularnewline
141 &  8.5 &  6.409 &  2.091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&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] 6[/C][C] 5.533[/C][C] 0.4666[/C][/ROW]
[ROW][C]2[/C][C] 5.5[/C][C] 4.348[/C][C] 1.152[/C][/ROW]
[ROW][C]3[/C][C] 5[/C][C] 5.015[/C][C]-0.01515[/C][/ROW]
[ROW][C]4[/C][C] 5[/C][C] 6.709[/C][C]-1.709[/C][/ROW]
[ROW][C]5[/C][C] 5.5[/C][C] 6.681[/C][C]-1.181[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 5.964[/C][C]-2.964[/C][/ROW]
[ROW][C]7[/C][C] 6.5[/C][C] 4.731[/C][C] 1.769[/C][/ROW]
[ROW][C]8[/C][C] 4[/C][C] 7.079[/C][C]-3.079[/C][/ROW]
[ROW][C]9[/C][C] 5.5[/C][C] 5.972[/C][C]-0.4718[/C][/ROW]
[ROW][C]10[/C][C] 2.5[/C][C] 5.506[/C][C]-3.006[/C][/ROW]
[ROW][C]11[/C][C] 4.5[/C][C] 5.162[/C][C]-0.6616[/C][/ROW]
[ROW][C]12[/C][C] 2.5[/C][C] 5.624[/C][C]-3.124[/C][/ROW]
[ROW][C]13[/C][C] 6[/C][C] 6.193[/C][C]-0.193[/C][/ROW]
[ROW][C]14[/C][C] 1[/C][C] 5.645[/C][C]-4.645[/C][/ROW]
[ROW][C]15[/C][C] 3.5[/C][C] 7.796[/C][C]-4.296[/C][/ROW]
[ROW][C]16[/C][C] 6[/C][C] 5.261[/C][C] 0.7386[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 6.562[/C][C] 2.438[/C][/ROW]
[ROW][C]18[/C][C] 3.5[/C][C] 6.357[/C][C]-2.857[/C][/ROW]
[ROW][C]19[/C][C] 6[/C][C] 6.154[/C][C]-0.1543[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.132[/C][C]-1.132[/C][/ROW]
[ROW][C]21[/C][C] 7.5[/C][C] 6.085[/C][C] 1.415[/C][/ROW]
[ROW][C]22[/C][C] 6.5[/C][C] 5.546[/C][C] 0.954[/C][/ROW]
[ROW][C]23[/C][C] 6.5[/C][C] 6.644[/C][C]-0.1443[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 7.793[/C][C] 0.2067[/C][/ROW]
[ROW][C]25[/C][C] 7[/C][C] 8.209[/C][C]-1.209[/C][/ROW]
[ROW][C]26[/C][C] 8.5[/C][C] 5.97[/C][C] 2.53[/C][/ROW]
[ROW][C]27[/C][C] 7[/C][C] 5.954[/C][C] 1.046[/C][/ROW]
[ROW][C]28[/C][C] 2.5[/C][C] 6.879[/C][C]-4.379[/C][/ROW]
[ROW][C]29[/C][C] 9[/C][C] 6.342[/C][C] 2.658[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 6.813[/C][C] 1.187[/C][/ROW]
[ROW][C]31[/C][C] 5.5[/C][C] 5.433[/C][C] 0.067[/C][/ROW]
[ROW][C]32[/C][C] 7[/C][C] 6.118[/C][C] 0.8825[/C][/ROW]
[ROW][C]33[/C][C] 9[/C][C] 6.552[/C][C] 2.448[/C][/ROW]
[ROW][C]34[/C][C] 9[/C][C] 6.264[/C][C] 2.736[/C][/ROW]
[ROW][C]35[/C][C] 10[/C][C] 7.527[/C][C] 2.473[/C][/ROW]
[ROW][C]36[/C][C] 8.5[/C][C] 6.885[/C][C] 1.615[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 6.857[/C][C]-0.8567[/C][/ROW]
[ROW][C]38[/C][C] 7[/C][C] 6.642[/C][C] 0.3581[/C][/ROW]
[ROW][C]39[/C][C] 8.5[/C][C] 7.346[/C][C] 1.154[/C][/ROW]
[ROW][C]40[/C][C] 8[/C][C] 5.934[/C][C] 2.066[/C][/ROW]
[ROW][C]41[/C][C] 7.5[/C][C] 6.348[/C][C] 1.152[/C][/ROW]
[ROW][C]42[/C][C] 7[/C][C] 6.005[/C][C] 0.9948[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 6.874[/C][C] 1.126[/C][/ROW]
[ROW][C]44[/C][C] 6.5[/C][C] 4.841[/C][C] 1.659[/C][/ROW]
[ROW][C]45[/C][C] 8.5[/C][C] 5.829[/C][C] 2.671[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 8.128[/C][C] 1.872[/C][/ROW]
[ROW][C]47[/C][C] 9.5[/C][C] 6.396[/C][C] 3.104[/C][/ROW]
[ROW][C]48[/C][C] 9[/C][C] 3.681[/C][C] 5.319[/C][/ROW]
[ROW][C]49[/C][C] 5[/C][C] 4.949[/C][C] 0.0506[/C][/ROW]
[ROW][C]50[/C][C] 8[/C][C] 6.587[/C][C] 1.413[/C][/ROW]
[ROW][C]51[/C][C] 5.5[/C][C] 5.957[/C][C]-0.4574[/C][/ROW]
[ROW][C]52[/C][C] 3.5[/C][C] 5.527[/C][C]-2.027[/C][/ROW]
[ROW][C]53[/C][C] 3[/C][C] 5.426[/C][C]-2.426[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 6.504[/C][C] 1.496[/C][/ROW]
[ROW][C]55[/C][C] 7.5[/C][C] 7.784[/C][C]-0.2836[/C][/ROW]
[ROW][C]56[/C][C] 6.5[/C][C] 6.153[/C][C] 0.3473[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 6.252[/C][C]-5.252[/C][/ROW]
[ROW][C]58[/C][C] 1[/C][C] 6.26[/C][C]-5.26[/C][/ROW]
[ROW][C]59[/C][C] 8.5[/C][C] 5.998[/C][C] 2.502[/C][/ROW]
[ROW][C]60[/C][C] 6.5[/C][C] 7.746[/C][C]-1.246[/C][/ROW]
[ROW][C]61[/C][C] 4.5[/C][C] 6.068[/C][C]-1.568[/C][/ROW]
[ROW][C]62[/C][C] 2[/C][C] 6.038[/C][C]-4.038[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 6.042[/C][C]-1.042[/C][/ROW]
[ROW][C]64[/C][C] 0.5[/C][C] 5.855[/C][C]-5.355[/C][/ROW]
[ROW][C]65[/C][C] 5[/C][C] 5.304[/C][C]-0.3038[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 5.797[/C][C]-1.797[/C][/ROW]
[ROW][C]67[/C][C] 0.5[/C][C] 5.821[/C][C]-5.321[/C][/ROW]
[ROW][C]68[/C][C] 4.5[/C][C] 6.145[/C][C]-1.645[/C][/ROW]
[ROW][C]69[/C][C] 7.5[/C][C] 6.444[/C][C] 1.056[/C][/ROW]
[ROW][C]70[/C][C] 5.5[/C][C] 5.968[/C][C]-0.4675[/C][/ROW]
[ROW][C]71[/C][C] 7[/C][C] 6.53[/C][C] 0.4702[/C][/ROW]
[ROW][C]72[/C][C] 5.5[/C][C] 6.162[/C][C]-0.6618[/C][/ROW]
[ROW][C]73[/C][C] 3.5[/C][C] 6.078[/C][C]-2.578[/C][/ROW]
[ROW][C]74[/C][C] 2.5[/C][C] 6.3[/C][C]-3.8[/C][/ROW]
[ROW][C]75[/C][C] 4.5[/C][C] 5.922[/C][C]-1.422[/C][/ROW]
[ROW][C]76[/C][C] 6[/C][C] 4.619[/C][C] 1.381[/C][/ROW]
[ROW][C]77[/C][C] 5[/C][C] 6.237[/C][C]-1.237[/C][/ROW]
[ROW][C]78[/C][C] 0[/C][C] 6.441[/C][C]-6.441[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 6.157[/C][C]-1.157[/C][/ROW]
[ROW][C]80[/C][C] 6.5[/C][C] 6.446[/C][C] 0.05365[/C][/ROW]
[ROW][C]81[/C][C] 4.5[/C][C] 5.275[/C][C]-0.7746[/C][/ROW]
[ROW][C]82[/C][C] 1[/C][C] 5.56[/C][C]-4.56[/C][/ROW]
[ROW][C]83[/C][C] 6.5[/C][C] 5.759[/C][C] 0.741[/C][/ROW]
[ROW][C]84[/C][C] 7[/C][C] 6.277[/C][C] 0.723[/C][/ROW]
[ROW][C]85[/C][C] 0[/C][C] 5.198[/C][C]-5.198[/C][/ROW]
[ROW][C]86[/C][C] 7.5[/C][C] 7.38[/C][C] 0.1201[/C][/ROW]
[ROW][C]87[/C][C] 1.5[/C][C] 5.951[/C][C]-4.451[/C][/ROW]
[ROW][C]88[/C][C] 4[/C][C] 5.358[/C][C]-1.358[/C][/ROW]
[ROW][C]89[/C][C] 6.5[/C][C] 7.377[/C][C]-0.8765[/C][/ROW]
[ROW][C]90[/C][C] 3.5[/C][C] 4.664[/C][C]-1.164[/C][/ROW]
[ROW][C]91[/C][C] 5.5[/C][C] 5.5[/C][C] 0.0002429[/C][/ROW]
[ROW][C]92[/C][C] 0.5[/C][C] 5.666[/C][C]-5.166[/C][/ROW]
[ROW][C]93[/C][C] 7.5[/C][C] 7.378[/C][C] 0.1225[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 5.886[/C][C] 3.114[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 5.579[/C][C] 1.421[/C][/ROW]
[ROW][C]96[/C][C] 10[/C][C] 6.587[/C][C] 3.413[/C][/ROW]
[ROW][C]97[/C][C] 9[/C][C] 6.864[/C][C] 2.136[/C][/ROW]
[ROW][C]98[/C][C] 9.5[/C][C] 6.249[/C][C] 3.251[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 5.455[/C][C]-1.455[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 5.603[/C][C] 0.3968[/C][/ROW]
[ROW][C]101[/C][C] 8[/C][C] 6.369[/C][C] 1.631[/C][/ROW]
[ROW][C]102[/C][C] 9.5[/C][C] 5.813[/C][C] 3.687[/C][/ROW]
[ROW][C]103[/C][C] 7.5[/C][C] 6.322[/C][C] 1.178[/C][/ROW]
[ROW][C]104[/C][C] 7.5[/C][C] 5.921[/C][C] 1.579[/C][/ROW]
[ROW][C]105[/C][C] 8[/C][C] 5.776[/C][C] 2.224[/C][/ROW]
[ROW][C]106[/C][C] 7[/C][C] 5.04[/C][C] 1.96[/C][/ROW]
[ROW][C]107[/C][C] 7[/C][C] 7.332[/C][C]-0.332[/C][/ROW]
[ROW][C]108[/C][C] 6[/C][C] 5.362[/C][C] 0.6381[/C][/ROW]
[ROW][C]109[/C][C] 10[/C][C] 6.511[/C][C] 3.489[/C][/ROW]
[ROW][C]110[/C][C] 9[/C][C] 7.056[/C][C] 1.944[/C][/ROW]
[ROW][C]111[/C][C] 6[/C][C] 5.838[/C][C] 0.1622[/C][/ROW]
[ROW][C]112[/C][C] 8.5[/C][C] 5.63[/C][C] 2.87[/C][/ROW]
[ROW][C]113[/C][C] 6[/C][C] 4.174[/C][C] 1.826[/C][/ROW]
[ROW][C]114[/C][C] 9[/C][C] 7.323[/C][C] 1.677[/C][/ROW]
[ROW][C]115[/C][C] 5.5[/C][C] 5.575[/C][C]-0.07451[/C][/ROW]
[ROW][C]116[/C][C] 2[/C][C] 5.638[/C][C]-3.638[/C][/ROW]
[ROW][C]117[/C][C] 8.5[/C][C] 7.079[/C][C] 1.421[/C][/ROW]
[ROW][C]118[/C][C] 7.5[/C][C] 7.526[/C][C]-0.02616[/C][/ROW]
[ROW][C]119[/C][C] 8[/C][C] 7.214[/C][C] 0.7858[/C][/ROW]
[ROW][C]120[/C][C] 7[/C][C] 6.447[/C][C] 0.5533[/C][/ROW]
[ROW][C]121[/C][C] 7.5[/C][C] 6.288[/C][C] 1.212[/C][/ROW]
[ROW][C]122[/C][C] 9.5[/C][C] 6.697[/C][C] 2.803[/C][/ROW]
[ROW][C]123[/C][C] 7[/C][C] 5.629[/C][C] 1.371[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 5.764[/C][C] 2.236[/C][/ROW]
[ROW][C]125[/C][C] 8[/C][C] 6.089[/C][C] 1.911[/C][/ROW]
[ROW][C]126[/C][C] 9[/C][C] 6.191[/C][C] 2.809[/C][/ROW]
[ROW][C]127[/C][C] 7.5[/C][C] 7.211[/C][C] 0.2892[/C][/ROW]
[ROW][C]128[/C][C] 8[/C][C] 7.519[/C][C] 0.4815[/C][/ROW]
[ROW][C]129[/C][C] 8.5[/C][C] 6.415[/C][C] 2.085[/C][/ROW]
[ROW][C]130[/C][C] 3.5[/C][C] 5.457[/C][C]-1.957[/C][/ROW]
[ROW][C]131[/C][C] 6.5[/C][C] 6.228[/C][C] 0.2718[/C][/ROW]
[ROW][C]132[/C][C] 10[/C][C] 5.381[/C][C] 4.619[/C][/ROW]
[ROW][C]133[/C][C] 7.5[/C][C] 6.045[/C][C] 1.455[/C][/ROW]
[ROW][C]134[/C][C] 4.5[/C][C] 5.698[/C][C]-1.198[/C][/ROW]
[ROW][C]135[/C][C] 4.5[/C][C] 4.96[/C][C]-0.4598[/C][/ROW]
[ROW][C]136[/C][C] 6.5[/C][C] 5.611[/C][C] 0.8893[/C][/ROW]
[ROW][C]137[/C][C] 4.5[/C][C] 5.016[/C][C]-0.5156[/C][/ROW]
[ROW][C]138[/C][C] 8.5[/C][C] 7.389[/C][C] 1.111[/C][/ROW]
[ROW][C]139[/C][C] 7[/C][C] 7.146[/C][C]-0.1458[/C][/ROW]
[ROW][C]140[/C][C] 5[/C][C] 5.514[/C][C]-0.5137[/C][/ROW]
[ROW][C]141[/C][C] 8.5[/C][C] 6.409[/C][C] 2.091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&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
1 6 5.533 0.4666
2 5.5 4.348 1.152
3 5 5.015-0.01515
4 5 6.709-1.709
5 5.5 6.681-1.181
6 3 5.964-2.964
7 6.5 4.731 1.769
8 4 7.079-3.079
9 5.5 5.972-0.4718
10 2.5 5.506-3.006
11 4.5 5.162-0.6616
12 2.5 5.624-3.124
13 6 6.193-0.193
14 1 5.645-4.645
15 3.5 7.796-4.296
16 6 5.261 0.7386
17 9 6.562 2.438
18 3.5 6.357-2.857
19 6 6.154-0.1543
20 5 6.132-1.132
21 7.5 6.085 1.415
22 6.5 5.546 0.954
23 6.5 6.644-0.1443
24 8 7.793 0.2067
25 7 8.209-1.209
26 8.5 5.97 2.53
27 7 5.954 1.046
28 2.5 6.879-4.379
29 9 6.342 2.658
30 8 6.813 1.187
31 5.5 5.433 0.067
32 7 6.118 0.8825
33 9 6.552 2.448
34 9 6.264 2.736
35 10 7.527 2.473
36 8.5 6.885 1.615
37 6 6.857-0.8567
38 7 6.642 0.3581
39 8.5 7.346 1.154
40 8 5.934 2.066
41 7.5 6.348 1.152
42 7 6.005 0.9948
43 8 6.874 1.126
44 6.5 4.841 1.659
45 8.5 5.829 2.671
46 10 8.128 1.872
47 9.5 6.396 3.104
48 9 3.681 5.319
49 5 4.949 0.0506
50 8 6.587 1.413
51 5.5 5.957-0.4574
52 3.5 5.527-2.027
53 3 5.426-2.426
54 8 6.504 1.496
55 7.5 7.784-0.2836
56 6.5 6.153 0.3473
57 1 6.252-5.252
58 1 6.26-5.26
59 8.5 5.998 2.502
60 6.5 7.746-1.246
61 4.5 6.068-1.568
62 2 6.038-4.038
63 5 6.042-1.042
64 0.5 5.855-5.355
65 5 5.304-0.3038
66 4 5.797-1.797
67 0.5 5.821-5.321
68 4.5 6.145-1.645
69 7.5 6.444 1.056
70 5.5 5.968-0.4675
71 7 6.53 0.4702
72 5.5 6.162-0.6618
73 3.5 6.078-2.578
74 2.5 6.3-3.8
75 4.5 5.922-1.422
76 6 4.619 1.381
77 5 6.237-1.237
78 0 6.441-6.441
79 5 6.157-1.157
80 6.5 6.446 0.05365
81 4.5 5.275-0.7746
82 1 5.56-4.56
83 6.5 5.759 0.741
84 7 6.277 0.723
85 0 5.198-5.198
86 7.5 7.38 0.1201
87 1.5 5.951-4.451
88 4 5.358-1.358
89 6.5 7.377-0.8765
90 3.5 4.664-1.164
91 5.5 5.5 0.0002429
92 0.5 5.666-5.166
93 7.5 7.378 0.1225
94 9 5.886 3.114
95 7 5.579 1.421
96 10 6.587 3.413
97 9 6.864 2.136
98 9.5 6.249 3.251
99 4 5.455-1.455
100 6 5.603 0.3968
101 8 6.369 1.631
102 9.5 5.813 3.687
103 7.5 6.322 1.178
104 7.5 5.921 1.579
105 8 5.776 2.224
106 7 5.04 1.96
107 7 7.332-0.332
108 6 5.362 0.6381
109 10 6.511 3.489
110 9 7.056 1.944
111 6 5.838 0.1622
112 8.5 5.63 2.87
113 6 4.174 1.826
114 9 7.323 1.677
115 5.5 5.575-0.07451
116 2 5.638-3.638
117 8.5 7.079 1.421
118 7.5 7.526-0.02616
119 8 7.214 0.7858
120 7 6.447 0.5533
121 7.5 6.288 1.212
122 9.5 6.697 2.803
123 7 5.629 1.371
124 8 5.764 2.236
125 8 6.089 1.911
126 9 6.191 2.809
127 7.5 7.211 0.2892
128 8 7.519 0.4815
129 8.5 6.415 2.085
130 3.5 5.457-1.957
131 6.5 6.228 0.2718
132 10 5.381 4.619
133 7.5 6.045 1.455
134 4.5 5.698-1.198
135 4.5 4.96-0.4598
136 6.5 5.611 0.8893
137 4.5 5.016-0.5156
138 8.5 7.389 1.111
139 7 7.146-0.1458
140 5 5.514-0.5137
141 8.5 6.409 2.091






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
15 0.1815 0.363 0.8185
16 0.3177 0.6354 0.6823
17 0.2885 0.577 0.7115
18 0.1852 0.3704 0.8148
19 0.1218 0.2437 0.8782
20 0.1228 0.2456 0.8772
21 0.3756 0.7512 0.6244
22 0.2922 0.5844 0.7078
23 0.2462 0.4923 0.7538
24 0.2601 0.5202 0.7399
25 0.2301 0.4603 0.7699
26 0.4533 0.9067 0.5467
27 0.3839 0.7678 0.6161
28 0.3774 0.7548 0.6226
29 0.5081 0.9838 0.4919
30 0.4647 0.9293 0.5353
31 0.3968 0.7937 0.6032
32 0.3366 0.6732 0.6634
33 0.3439 0.6878 0.6561
34 0.3328 0.6655 0.6672
35 0.3515 0.7029 0.6485
36 0.3984 0.7967 0.6016
37 0.3469 0.6938 0.6531
38 0.2942 0.5884 0.7058
39 0.2645 0.529 0.7355
40 0.2361 0.4723 0.7639
41 0.1932 0.3864 0.8068
42 0.1644 0.3288 0.8356
43 0.131 0.262 0.869
44 0.1082 0.2165 0.8918
45 0.1157 0.2314 0.8843
46 0.1282 0.2565 0.8718
47 0.1352 0.2704 0.8648
48 0.2878 0.5755 0.7122
49 0.2409 0.4818 0.7591
50 0.2185 0.437 0.7815
51 0.1789 0.3578 0.8211
52 0.1804 0.3608 0.8196
53 0.1699 0.3397 0.8301
54 0.1468 0.2935 0.8532
55 0.1184 0.2368 0.8816
56 0.09405 0.1881 0.9059
57 0.2242 0.4484 0.7758
58 0.4233 0.8467 0.5767
59 0.4212 0.8424 0.5788
60 0.4051 0.8103 0.5949
61 0.3986 0.7971 0.6014
62 0.5482 0.9036 0.4518
63 0.5057 0.9887 0.4943
64 0.714 0.572 0.286
65 0.6681 0.6639 0.3319
66 0.6491 0.7019 0.3509
67 0.8286 0.3427 0.1714
68 0.8125 0.375 0.1875
69 0.7856 0.4289 0.2144
70 0.7538 0.4923 0.2462
71 0.7135 0.573 0.2865
72 0.674 0.6519 0.326
73 0.6749 0.6502 0.3251
74 0.7847 0.4307 0.2153
75 0.7716 0.4569 0.2284
76 0.7417 0.5166 0.2583
77 0.7201 0.5598 0.2799
78 0.9545 0.09106 0.04553
79 0.9553 0.08941 0.0447
80 0.9414 0.1171 0.05857
81 0.9275 0.1451 0.07253
82 0.9628 0.07446 0.03723
83 0.953 0.09402 0.04701
84 0.951 0.0981 0.04905
85 0.981 0.0381 0.01905
86 0.9773 0.04547 0.02273
87 0.9917 0.01662 0.008309
88 0.9938 0.01243 0.006215
89 0.9939 0.01214 0.006072
90 0.9929 0.01422 0.00711
91 0.9894 0.02115 0.01057
92 0.9996 0.000859 0.0004295
93 0.9996 0.0007859 0.000393
94 0.9996 0.0007507 0.0003753
95 0.9994 0.001143 0.0005713
96 0.9993 0.001344 0.000672
97 0.9991 0.001741 0.0008704
98 0.9995 0.0009851 0.0004925
99 0.9998 0.0004158 0.0002079
100 0.9996 0.0007331 0.0003665
101 0.9994 0.00118 0.0005898
102 0.9992 0.001575 0.0007876
103 0.9986 0.00272 0.00136
104 0.9988 0.002326 0.001163
105 0.9983 0.003484 0.001742
106 0.9973 0.005386 0.002693
107 0.9974 0.005288 0.002644
108 0.9968 0.006325 0.003163
109 0.9983 0.003346 0.001673
110 0.9984 0.003286 0.001643
111 0.9969 0.006267 0.003134
112 0.9986 0.002845 0.001422
113 0.9977 0.004661 0.002331
114 0.9964 0.007119 0.00356
115 0.9936 0.0128 0.006402
116 0.9914 0.0172 0.008598
117 0.9898 0.02034 0.01017
118 0.9805 0.03898 0.01949
119 0.9639 0.07211 0.03605
120 0.9704 0.05924 0.02962
121 0.9445 0.1109 0.05547
122 0.977 0.04599 0.02299
123 0.9606 0.0788 0.0394
124 0.9174 0.1652 0.08259
125 0.8764 0.2471 0.1236
126 0.9291 0.1418 0.07091

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 &  0.1815 &  0.363 &  0.8185 \tabularnewline
16 &  0.3177 &  0.6354 &  0.6823 \tabularnewline
17 &  0.2885 &  0.577 &  0.7115 \tabularnewline
18 &  0.1852 &  0.3704 &  0.8148 \tabularnewline
19 &  0.1218 &  0.2437 &  0.8782 \tabularnewline
20 &  0.1228 &  0.2456 &  0.8772 \tabularnewline
21 &  0.3756 &  0.7512 &  0.6244 \tabularnewline
22 &  0.2922 &  0.5844 &  0.7078 \tabularnewline
23 &  0.2462 &  0.4923 &  0.7538 \tabularnewline
24 &  0.2601 &  0.5202 &  0.7399 \tabularnewline
25 &  0.2301 &  0.4603 &  0.7699 \tabularnewline
26 &  0.4533 &  0.9067 &  0.5467 \tabularnewline
27 &  0.3839 &  0.7678 &  0.6161 \tabularnewline
28 &  0.3774 &  0.7548 &  0.6226 \tabularnewline
29 &  0.5081 &  0.9838 &  0.4919 \tabularnewline
30 &  0.4647 &  0.9293 &  0.5353 \tabularnewline
31 &  0.3968 &  0.7937 &  0.6032 \tabularnewline
32 &  0.3366 &  0.6732 &  0.6634 \tabularnewline
33 &  0.3439 &  0.6878 &  0.6561 \tabularnewline
34 &  0.3328 &  0.6655 &  0.6672 \tabularnewline
35 &  0.3515 &  0.7029 &  0.6485 \tabularnewline
36 &  0.3984 &  0.7967 &  0.6016 \tabularnewline
37 &  0.3469 &  0.6938 &  0.6531 \tabularnewline
38 &  0.2942 &  0.5884 &  0.7058 \tabularnewline
39 &  0.2645 &  0.529 &  0.7355 \tabularnewline
40 &  0.2361 &  0.4723 &  0.7639 \tabularnewline
41 &  0.1932 &  0.3864 &  0.8068 \tabularnewline
42 &  0.1644 &  0.3288 &  0.8356 \tabularnewline
43 &  0.131 &  0.262 &  0.869 \tabularnewline
44 &  0.1082 &  0.2165 &  0.8918 \tabularnewline
45 &  0.1157 &  0.2314 &  0.8843 \tabularnewline
46 &  0.1282 &  0.2565 &  0.8718 \tabularnewline
47 &  0.1352 &  0.2704 &  0.8648 \tabularnewline
48 &  0.2878 &  0.5755 &  0.7122 \tabularnewline
49 &  0.2409 &  0.4818 &  0.7591 \tabularnewline
50 &  0.2185 &  0.437 &  0.7815 \tabularnewline
51 &  0.1789 &  0.3578 &  0.8211 \tabularnewline
52 &  0.1804 &  0.3608 &  0.8196 \tabularnewline
53 &  0.1699 &  0.3397 &  0.8301 \tabularnewline
54 &  0.1468 &  0.2935 &  0.8532 \tabularnewline
55 &  0.1184 &  0.2368 &  0.8816 \tabularnewline
56 &  0.09405 &  0.1881 &  0.9059 \tabularnewline
57 &  0.2242 &  0.4484 &  0.7758 \tabularnewline
58 &  0.4233 &  0.8467 &  0.5767 \tabularnewline
59 &  0.4212 &  0.8424 &  0.5788 \tabularnewline
60 &  0.4051 &  0.8103 &  0.5949 \tabularnewline
61 &  0.3986 &  0.7971 &  0.6014 \tabularnewline
62 &  0.5482 &  0.9036 &  0.4518 \tabularnewline
63 &  0.5057 &  0.9887 &  0.4943 \tabularnewline
64 &  0.714 &  0.572 &  0.286 \tabularnewline
65 &  0.6681 &  0.6639 &  0.3319 \tabularnewline
66 &  0.6491 &  0.7019 &  0.3509 \tabularnewline
67 &  0.8286 &  0.3427 &  0.1714 \tabularnewline
68 &  0.8125 &  0.375 &  0.1875 \tabularnewline
69 &  0.7856 &  0.4289 &  0.2144 \tabularnewline
70 &  0.7538 &  0.4923 &  0.2462 \tabularnewline
71 &  0.7135 &  0.573 &  0.2865 \tabularnewline
72 &  0.674 &  0.6519 &  0.326 \tabularnewline
73 &  0.6749 &  0.6502 &  0.3251 \tabularnewline
74 &  0.7847 &  0.4307 &  0.2153 \tabularnewline
75 &  0.7716 &  0.4569 &  0.2284 \tabularnewline
76 &  0.7417 &  0.5166 &  0.2583 \tabularnewline
77 &  0.7201 &  0.5598 &  0.2799 \tabularnewline
78 &  0.9545 &  0.09106 &  0.04553 \tabularnewline
79 &  0.9553 &  0.08941 &  0.0447 \tabularnewline
80 &  0.9414 &  0.1171 &  0.05857 \tabularnewline
81 &  0.9275 &  0.1451 &  0.07253 \tabularnewline
82 &  0.9628 &  0.07446 &  0.03723 \tabularnewline
83 &  0.953 &  0.09402 &  0.04701 \tabularnewline
84 &  0.951 &  0.0981 &  0.04905 \tabularnewline
85 &  0.981 &  0.0381 &  0.01905 \tabularnewline
86 &  0.9773 &  0.04547 &  0.02273 \tabularnewline
87 &  0.9917 &  0.01662 &  0.008309 \tabularnewline
88 &  0.9938 &  0.01243 &  0.006215 \tabularnewline
89 &  0.9939 &  0.01214 &  0.006072 \tabularnewline
90 &  0.9929 &  0.01422 &  0.00711 \tabularnewline
91 &  0.9894 &  0.02115 &  0.01057 \tabularnewline
92 &  0.9996 &  0.000859 &  0.0004295 \tabularnewline
93 &  0.9996 &  0.0007859 &  0.000393 \tabularnewline
94 &  0.9996 &  0.0007507 &  0.0003753 \tabularnewline
95 &  0.9994 &  0.001143 &  0.0005713 \tabularnewline
96 &  0.9993 &  0.001344 &  0.000672 \tabularnewline
97 &  0.9991 &  0.001741 &  0.0008704 \tabularnewline
98 &  0.9995 &  0.0009851 &  0.0004925 \tabularnewline
99 &  0.9998 &  0.0004158 &  0.0002079 \tabularnewline
100 &  0.9996 &  0.0007331 &  0.0003665 \tabularnewline
101 &  0.9994 &  0.00118 &  0.0005898 \tabularnewline
102 &  0.9992 &  0.001575 &  0.0007876 \tabularnewline
103 &  0.9986 &  0.00272 &  0.00136 \tabularnewline
104 &  0.9988 &  0.002326 &  0.001163 \tabularnewline
105 &  0.9983 &  0.003484 &  0.001742 \tabularnewline
106 &  0.9973 &  0.005386 &  0.002693 \tabularnewline
107 &  0.9974 &  0.005288 &  0.002644 \tabularnewline
108 &  0.9968 &  0.006325 &  0.003163 \tabularnewline
109 &  0.9983 &  0.003346 &  0.001673 \tabularnewline
110 &  0.9984 &  0.003286 &  0.001643 \tabularnewline
111 &  0.9969 &  0.006267 &  0.003134 \tabularnewline
112 &  0.9986 &  0.002845 &  0.001422 \tabularnewline
113 &  0.9977 &  0.004661 &  0.002331 \tabularnewline
114 &  0.9964 &  0.007119 &  0.00356 \tabularnewline
115 &  0.9936 &  0.0128 &  0.006402 \tabularnewline
116 &  0.9914 &  0.0172 &  0.008598 \tabularnewline
117 &  0.9898 &  0.02034 &  0.01017 \tabularnewline
118 &  0.9805 &  0.03898 &  0.01949 \tabularnewline
119 &  0.9639 &  0.07211 &  0.03605 \tabularnewline
120 &  0.9704 &  0.05924 &  0.02962 \tabularnewline
121 &  0.9445 &  0.1109 &  0.05547 \tabularnewline
122 &  0.977 &  0.04599 &  0.02299 \tabularnewline
123 &  0.9606 &  0.0788 &  0.0394 \tabularnewline
124 &  0.9174 &  0.1652 &  0.08259 \tabularnewline
125 &  0.8764 &  0.2471 &  0.1236 \tabularnewline
126 &  0.9291 &  0.1418 &  0.07091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&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]15[/C][C] 0.1815[/C][C] 0.363[/C][C] 0.8185[/C][/ROW]
[ROW][C]16[/C][C] 0.3177[/C][C] 0.6354[/C][C] 0.6823[/C][/ROW]
[ROW][C]17[/C][C] 0.2885[/C][C] 0.577[/C][C] 0.7115[/C][/ROW]
[ROW][C]18[/C][C] 0.1852[/C][C] 0.3704[/C][C] 0.8148[/C][/ROW]
[ROW][C]19[/C][C] 0.1218[/C][C] 0.2437[/C][C] 0.8782[/C][/ROW]
[ROW][C]20[/C][C] 0.1228[/C][C] 0.2456[/C][C] 0.8772[/C][/ROW]
[ROW][C]21[/C][C] 0.3756[/C][C] 0.7512[/C][C] 0.6244[/C][/ROW]
[ROW][C]22[/C][C] 0.2922[/C][C] 0.5844[/C][C] 0.7078[/C][/ROW]
[ROW][C]23[/C][C] 0.2462[/C][C] 0.4923[/C][C] 0.7538[/C][/ROW]
[ROW][C]24[/C][C] 0.2601[/C][C] 0.5202[/C][C] 0.7399[/C][/ROW]
[ROW][C]25[/C][C] 0.2301[/C][C] 0.4603[/C][C] 0.7699[/C][/ROW]
[ROW][C]26[/C][C] 0.4533[/C][C] 0.9067[/C][C] 0.5467[/C][/ROW]
[ROW][C]27[/C][C] 0.3839[/C][C] 0.7678[/C][C] 0.6161[/C][/ROW]
[ROW][C]28[/C][C] 0.3774[/C][C] 0.7548[/C][C] 0.6226[/C][/ROW]
[ROW][C]29[/C][C] 0.5081[/C][C] 0.9838[/C][C] 0.4919[/C][/ROW]
[ROW][C]30[/C][C] 0.4647[/C][C] 0.9293[/C][C] 0.5353[/C][/ROW]
[ROW][C]31[/C][C] 0.3968[/C][C] 0.7937[/C][C] 0.6032[/C][/ROW]
[ROW][C]32[/C][C] 0.3366[/C][C] 0.6732[/C][C] 0.6634[/C][/ROW]
[ROW][C]33[/C][C] 0.3439[/C][C] 0.6878[/C][C] 0.6561[/C][/ROW]
[ROW][C]34[/C][C] 0.3328[/C][C] 0.6655[/C][C] 0.6672[/C][/ROW]
[ROW][C]35[/C][C] 0.3515[/C][C] 0.7029[/C][C] 0.6485[/C][/ROW]
[ROW][C]36[/C][C] 0.3984[/C][C] 0.7967[/C][C] 0.6016[/C][/ROW]
[ROW][C]37[/C][C] 0.3469[/C][C] 0.6938[/C][C] 0.6531[/C][/ROW]
[ROW][C]38[/C][C] 0.2942[/C][C] 0.5884[/C][C] 0.7058[/C][/ROW]
[ROW][C]39[/C][C] 0.2645[/C][C] 0.529[/C][C] 0.7355[/C][/ROW]
[ROW][C]40[/C][C] 0.2361[/C][C] 0.4723[/C][C] 0.7639[/C][/ROW]
[ROW][C]41[/C][C] 0.1932[/C][C] 0.3864[/C][C] 0.8068[/C][/ROW]
[ROW][C]42[/C][C] 0.1644[/C][C] 0.3288[/C][C] 0.8356[/C][/ROW]
[ROW][C]43[/C][C] 0.131[/C][C] 0.262[/C][C] 0.869[/C][/ROW]
[ROW][C]44[/C][C] 0.1082[/C][C] 0.2165[/C][C] 0.8918[/C][/ROW]
[ROW][C]45[/C][C] 0.1157[/C][C] 0.2314[/C][C] 0.8843[/C][/ROW]
[ROW][C]46[/C][C] 0.1282[/C][C] 0.2565[/C][C] 0.8718[/C][/ROW]
[ROW][C]47[/C][C] 0.1352[/C][C] 0.2704[/C][C] 0.8648[/C][/ROW]
[ROW][C]48[/C][C] 0.2878[/C][C] 0.5755[/C][C] 0.7122[/C][/ROW]
[ROW][C]49[/C][C] 0.2409[/C][C] 0.4818[/C][C] 0.7591[/C][/ROW]
[ROW][C]50[/C][C] 0.2185[/C][C] 0.437[/C][C] 0.7815[/C][/ROW]
[ROW][C]51[/C][C] 0.1789[/C][C] 0.3578[/C][C] 0.8211[/C][/ROW]
[ROW][C]52[/C][C] 0.1804[/C][C] 0.3608[/C][C] 0.8196[/C][/ROW]
[ROW][C]53[/C][C] 0.1699[/C][C] 0.3397[/C][C] 0.8301[/C][/ROW]
[ROW][C]54[/C][C] 0.1468[/C][C] 0.2935[/C][C] 0.8532[/C][/ROW]
[ROW][C]55[/C][C] 0.1184[/C][C] 0.2368[/C][C] 0.8816[/C][/ROW]
[ROW][C]56[/C][C] 0.09405[/C][C] 0.1881[/C][C] 0.9059[/C][/ROW]
[ROW][C]57[/C][C] 0.2242[/C][C] 0.4484[/C][C] 0.7758[/C][/ROW]
[ROW][C]58[/C][C] 0.4233[/C][C] 0.8467[/C][C] 0.5767[/C][/ROW]
[ROW][C]59[/C][C] 0.4212[/C][C] 0.8424[/C][C] 0.5788[/C][/ROW]
[ROW][C]60[/C][C] 0.4051[/C][C] 0.8103[/C][C] 0.5949[/C][/ROW]
[ROW][C]61[/C][C] 0.3986[/C][C] 0.7971[/C][C] 0.6014[/C][/ROW]
[ROW][C]62[/C][C] 0.5482[/C][C] 0.9036[/C][C] 0.4518[/C][/ROW]
[ROW][C]63[/C][C] 0.5057[/C][C] 0.9887[/C][C] 0.4943[/C][/ROW]
[ROW][C]64[/C][C] 0.714[/C][C] 0.572[/C][C] 0.286[/C][/ROW]
[ROW][C]65[/C][C] 0.6681[/C][C] 0.6639[/C][C] 0.3319[/C][/ROW]
[ROW][C]66[/C][C] 0.6491[/C][C] 0.7019[/C][C] 0.3509[/C][/ROW]
[ROW][C]67[/C][C] 0.8286[/C][C] 0.3427[/C][C] 0.1714[/C][/ROW]
[ROW][C]68[/C][C] 0.8125[/C][C] 0.375[/C][C] 0.1875[/C][/ROW]
[ROW][C]69[/C][C] 0.7856[/C][C] 0.4289[/C][C] 0.2144[/C][/ROW]
[ROW][C]70[/C][C] 0.7538[/C][C] 0.4923[/C][C] 0.2462[/C][/ROW]
[ROW][C]71[/C][C] 0.7135[/C][C] 0.573[/C][C] 0.2865[/C][/ROW]
[ROW][C]72[/C][C] 0.674[/C][C] 0.6519[/C][C] 0.326[/C][/ROW]
[ROW][C]73[/C][C] 0.6749[/C][C] 0.6502[/C][C] 0.3251[/C][/ROW]
[ROW][C]74[/C][C] 0.7847[/C][C] 0.4307[/C][C] 0.2153[/C][/ROW]
[ROW][C]75[/C][C] 0.7716[/C][C] 0.4569[/C][C] 0.2284[/C][/ROW]
[ROW][C]76[/C][C] 0.7417[/C][C] 0.5166[/C][C] 0.2583[/C][/ROW]
[ROW][C]77[/C][C] 0.7201[/C][C] 0.5598[/C][C] 0.2799[/C][/ROW]
[ROW][C]78[/C][C] 0.9545[/C][C] 0.09106[/C][C] 0.04553[/C][/ROW]
[ROW][C]79[/C][C] 0.9553[/C][C] 0.08941[/C][C] 0.0447[/C][/ROW]
[ROW][C]80[/C][C] 0.9414[/C][C] 0.1171[/C][C] 0.05857[/C][/ROW]
[ROW][C]81[/C][C] 0.9275[/C][C] 0.1451[/C][C] 0.07253[/C][/ROW]
[ROW][C]82[/C][C] 0.9628[/C][C] 0.07446[/C][C] 0.03723[/C][/ROW]
[ROW][C]83[/C][C] 0.953[/C][C] 0.09402[/C][C] 0.04701[/C][/ROW]
[ROW][C]84[/C][C] 0.951[/C][C] 0.0981[/C][C] 0.04905[/C][/ROW]
[ROW][C]85[/C][C] 0.981[/C][C] 0.0381[/C][C] 0.01905[/C][/ROW]
[ROW][C]86[/C][C] 0.9773[/C][C] 0.04547[/C][C] 0.02273[/C][/ROW]
[ROW][C]87[/C][C] 0.9917[/C][C] 0.01662[/C][C] 0.008309[/C][/ROW]
[ROW][C]88[/C][C] 0.9938[/C][C] 0.01243[/C][C] 0.006215[/C][/ROW]
[ROW][C]89[/C][C] 0.9939[/C][C] 0.01214[/C][C] 0.006072[/C][/ROW]
[ROW][C]90[/C][C] 0.9929[/C][C] 0.01422[/C][C] 0.00711[/C][/ROW]
[ROW][C]91[/C][C] 0.9894[/C][C] 0.02115[/C][C] 0.01057[/C][/ROW]
[ROW][C]92[/C][C] 0.9996[/C][C] 0.000859[/C][C] 0.0004295[/C][/ROW]
[ROW][C]93[/C][C] 0.9996[/C][C] 0.0007859[/C][C] 0.000393[/C][/ROW]
[ROW][C]94[/C][C] 0.9996[/C][C] 0.0007507[/C][C] 0.0003753[/C][/ROW]
[ROW][C]95[/C][C] 0.9994[/C][C] 0.001143[/C][C] 0.0005713[/C][/ROW]
[ROW][C]96[/C][C] 0.9993[/C][C] 0.001344[/C][C] 0.000672[/C][/ROW]
[ROW][C]97[/C][C] 0.9991[/C][C] 0.001741[/C][C] 0.0008704[/C][/ROW]
[ROW][C]98[/C][C] 0.9995[/C][C] 0.0009851[/C][C] 0.0004925[/C][/ROW]
[ROW][C]99[/C][C] 0.9998[/C][C] 0.0004158[/C][C] 0.0002079[/C][/ROW]
[ROW][C]100[/C][C] 0.9996[/C][C] 0.0007331[/C][C] 0.0003665[/C][/ROW]
[ROW][C]101[/C][C] 0.9994[/C][C] 0.00118[/C][C] 0.0005898[/C][/ROW]
[ROW][C]102[/C][C] 0.9992[/C][C] 0.001575[/C][C] 0.0007876[/C][/ROW]
[ROW][C]103[/C][C] 0.9986[/C][C] 0.00272[/C][C] 0.00136[/C][/ROW]
[ROW][C]104[/C][C] 0.9988[/C][C] 0.002326[/C][C] 0.001163[/C][/ROW]
[ROW][C]105[/C][C] 0.9983[/C][C] 0.003484[/C][C] 0.001742[/C][/ROW]
[ROW][C]106[/C][C] 0.9973[/C][C] 0.005386[/C][C] 0.002693[/C][/ROW]
[ROW][C]107[/C][C] 0.9974[/C][C] 0.005288[/C][C] 0.002644[/C][/ROW]
[ROW][C]108[/C][C] 0.9968[/C][C] 0.006325[/C][C] 0.003163[/C][/ROW]
[ROW][C]109[/C][C] 0.9983[/C][C] 0.003346[/C][C] 0.001673[/C][/ROW]
[ROW][C]110[/C][C] 0.9984[/C][C] 0.003286[/C][C] 0.001643[/C][/ROW]
[ROW][C]111[/C][C] 0.9969[/C][C] 0.006267[/C][C] 0.003134[/C][/ROW]
[ROW][C]112[/C][C] 0.9986[/C][C] 0.002845[/C][C] 0.001422[/C][/ROW]
[ROW][C]113[/C][C] 0.9977[/C][C] 0.004661[/C][C] 0.002331[/C][/ROW]
[ROW][C]114[/C][C] 0.9964[/C][C] 0.007119[/C][C] 0.00356[/C][/ROW]
[ROW][C]115[/C][C] 0.9936[/C][C] 0.0128[/C][C] 0.006402[/C][/ROW]
[ROW][C]116[/C][C] 0.9914[/C][C] 0.0172[/C][C] 0.008598[/C][/ROW]
[ROW][C]117[/C][C] 0.9898[/C][C] 0.02034[/C][C] 0.01017[/C][/ROW]
[ROW][C]118[/C][C] 0.9805[/C][C] 0.03898[/C][C] 0.01949[/C][/ROW]
[ROW][C]119[/C][C] 0.9639[/C][C] 0.07211[/C][C] 0.03605[/C][/ROW]
[ROW][C]120[/C][C] 0.9704[/C][C] 0.05924[/C][C] 0.02962[/C][/ROW]
[ROW][C]121[/C][C] 0.9445[/C][C] 0.1109[/C][C] 0.05547[/C][/ROW]
[ROW][C]122[/C][C] 0.977[/C][C] 0.04599[/C][C] 0.02299[/C][/ROW]
[ROW][C]123[/C][C] 0.9606[/C][C] 0.0788[/C][C] 0.0394[/C][/ROW]
[ROW][C]124[/C][C] 0.9174[/C][C] 0.1652[/C][C] 0.08259[/C][/ROW]
[ROW][C]125[/C][C] 0.8764[/C][C] 0.2471[/C][C] 0.1236[/C][/ROW]
[ROW][C]126[/C][C] 0.9291[/C][C] 0.1418[/C][C] 0.07091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&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
15 0.1815 0.363 0.8185
16 0.3177 0.6354 0.6823
17 0.2885 0.577 0.7115
18 0.1852 0.3704 0.8148
19 0.1218 0.2437 0.8782
20 0.1228 0.2456 0.8772
21 0.3756 0.7512 0.6244
22 0.2922 0.5844 0.7078
23 0.2462 0.4923 0.7538
24 0.2601 0.5202 0.7399
25 0.2301 0.4603 0.7699
26 0.4533 0.9067 0.5467
27 0.3839 0.7678 0.6161
28 0.3774 0.7548 0.6226
29 0.5081 0.9838 0.4919
30 0.4647 0.9293 0.5353
31 0.3968 0.7937 0.6032
32 0.3366 0.6732 0.6634
33 0.3439 0.6878 0.6561
34 0.3328 0.6655 0.6672
35 0.3515 0.7029 0.6485
36 0.3984 0.7967 0.6016
37 0.3469 0.6938 0.6531
38 0.2942 0.5884 0.7058
39 0.2645 0.529 0.7355
40 0.2361 0.4723 0.7639
41 0.1932 0.3864 0.8068
42 0.1644 0.3288 0.8356
43 0.131 0.262 0.869
44 0.1082 0.2165 0.8918
45 0.1157 0.2314 0.8843
46 0.1282 0.2565 0.8718
47 0.1352 0.2704 0.8648
48 0.2878 0.5755 0.7122
49 0.2409 0.4818 0.7591
50 0.2185 0.437 0.7815
51 0.1789 0.3578 0.8211
52 0.1804 0.3608 0.8196
53 0.1699 0.3397 0.8301
54 0.1468 0.2935 0.8532
55 0.1184 0.2368 0.8816
56 0.09405 0.1881 0.9059
57 0.2242 0.4484 0.7758
58 0.4233 0.8467 0.5767
59 0.4212 0.8424 0.5788
60 0.4051 0.8103 0.5949
61 0.3986 0.7971 0.6014
62 0.5482 0.9036 0.4518
63 0.5057 0.9887 0.4943
64 0.714 0.572 0.286
65 0.6681 0.6639 0.3319
66 0.6491 0.7019 0.3509
67 0.8286 0.3427 0.1714
68 0.8125 0.375 0.1875
69 0.7856 0.4289 0.2144
70 0.7538 0.4923 0.2462
71 0.7135 0.573 0.2865
72 0.674 0.6519 0.326
73 0.6749 0.6502 0.3251
74 0.7847 0.4307 0.2153
75 0.7716 0.4569 0.2284
76 0.7417 0.5166 0.2583
77 0.7201 0.5598 0.2799
78 0.9545 0.09106 0.04553
79 0.9553 0.08941 0.0447
80 0.9414 0.1171 0.05857
81 0.9275 0.1451 0.07253
82 0.9628 0.07446 0.03723
83 0.953 0.09402 0.04701
84 0.951 0.0981 0.04905
85 0.981 0.0381 0.01905
86 0.9773 0.04547 0.02273
87 0.9917 0.01662 0.008309
88 0.9938 0.01243 0.006215
89 0.9939 0.01214 0.006072
90 0.9929 0.01422 0.00711
91 0.9894 0.02115 0.01057
92 0.9996 0.000859 0.0004295
93 0.9996 0.0007859 0.000393
94 0.9996 0.0007507 0.0003753
95 0.9994 0.001143 0.0005713
96 0.9993 0.001344 0.000672
97 0.9991 0.001741 0.0008704
98 0.9995 0.0009851 0.0004925
99 0.9998 0.0004158 0.0002079
100 0.9996 0.0007331 0.0003665
101 0.9994 0.00118 0.0005898
102 0.9992 0.001575 0.0007876
103 0.9986 0.00272 0.00136
104 0.9988 0.002326 0.001163
105 0.9983 0.003484 0.001742
106 0.9973 0.005386 0.002693
107 0.9974 0.005288 0.002644
108 0.9968 0.006325 0.003163
109 0.9983 0.003346 0.001673
110 0.9984 0.003286 0.001643
111 0.9969 0.006267 0.003134
112 0.9986 0.002845 0.001422
113 0.9977 0.004661 0.002331
114 0.9964 0.007119 0.00356
115 0.9936 0.0128 0.006402
116 0.9914 0.0172 0.008598
117 0.9898 0.02034 0.01017
118 0.9805 0.03898 0.01949
119 0.9639 0.07211 0.03605
120 0.9704 0.05924 0.02962
121 0.9445 0.1109 0.05547
122 0.977 0.04599 0.02299
123 0.9606 0.0788 0.0394
124 0.9174 0.1652 0.08259
125 0.8764 0.2471 0.1236
126 0.9291 0.1418 0.07091






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level23 0.2054NOK
5% type I error level350.3125NOK
10% type I error level430.383929NOK

\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 & 23 &  0.2054 & NOK \tabularnewline
5% type I error level & 35 & 0.3125 & NOK \tabularnewline
10% type I error level & 43 & 0.383929 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285827&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]23[/C][C] 0.2054[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.3125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.383929[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285827&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285827&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 level23 0.2054NOK
5% type I error level350.3125NOK
10% type I error level430.383929NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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 ; par4 = ; par5 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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')
}
}