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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, 18 Dec 2014 14:22:41 +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/18/t1418912661s7rybocruix16ii.htm/, Retrieved Fri, 17 May 2024 19:36:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270984, Retrieved Fri, 17 May 2024 19:36:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 14:22:41] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
-    D    [Multiple Regression] [] [2014-12-18 14:29:45] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 14:33:01] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 14:36:18] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 14:56:00] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:00:58] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:06:03] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:07:48] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:09:17] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:10:27] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:11:40] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:12:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:13:53] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:14:38] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:15:39] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:16:24] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:17:28] [8e3afc5508de37bed770d90d46857754]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 11 8 7 18 12 20 21 13 12 149 18 68 1.8
12.2 19 18 20 23 20 19 22 8 8 139 31 39 2.1
12.8 16 12 9 22 14 18 21 14 11 148 39 32 2.2
7.4 24 24 19 22 25 24 21 16 13 158 46 62 2.3
6.7 15 16 12 19 15 20 21 14 11 128 31 33 2.1
12.6 17 19 16 25 20 20 21 13 10 224 67 52 2.7
14.8 19 16 17 28 21 24 21 15 7 159 35 62 2.1
13.3 19 15 9 16 15 21 23 13 10 105 52 77 2.4
11.1 28 28 28 28 28 28 22 20 15 159 77 76 2.9
8.2 26 21 20 21 11 10 25 17 12 167 37 41 2.2
11.4 15 18 16 22 22 22 21 15 12 165 32 48 2.1
6.4 26 22 22 24 22 19 23 16 10 159 36 63 2.2
10.6 16 19 17 24 27 27 22 12 10 119 38 30 2.2
12 24 22 12 26 24 23 21 17 14 176 69 78 2.7
6.3 25 25 18 28 23 24 21 11 6 54 21 19 1.9
11.9 15 16 12 20 21 25 21 16 14 163 54 66 2.5
9.3 21 19 16 26 20 24 21 15 11 124 36 35 2.2
10 27 26 21 28 25 28 24 14 12 121 23 45 1.9
6.4 26 24 15 27 16 28 23 19 15 153 34 21 2.1
13.8 26 20 17 23 24 22 21 16 13 148 112 25 3.5
10.8 22 19 17 24 21 26 24 17 11 221 35 44 2.1
13.8 21 19 17 24 22 26 23 10 12 188 47 69 2.3
11.7 22 23 18 22 25 21 21 15 7 149 47 54 2.3
10.9 20 18 15 21 23 26 22 14 11 244 37 74 2.2
9.9 22 21 21 21 22 24 21 15 12 150 20 61 1.9
11.5 21 20 12 26 25 25 22 17 13 153 22 41 1.9
8.3 8 15 6 23 23 24 22 14 9 94 23 46 1.9
11.7 22 19 13 21 19 20 21 16 11 156 32 39 2.1
9 20 19 19 27 21 24 21 15 12 132 30 34 2
9.7 24 7 12 25 19 25 25 16 15 161 92 51 3.2
10.8 17 20 14 23 25 23 22 16 12 105 43 42 2.3
10.3 20 20 13 25 16 21 22 10 6 97 55 31 2.5
10.4 23 19 12 23 24 23 20 8 5 151 16 39 1.8
9.3 22 20 19 22 18 18 21 14 11 166 71 49 2.8
11.8 19 18 10 24 28 24 21 10 6 157 43 53 2.3
5.9 15 14 10 19 15 18 22 14 12 111 29 31 2
11.4 20 17 11 21 17 21 21 12 10 145 56 39 2.5
13 22 17 11 27 18 23 24 16 6 162 46 54 2.3
10.8 17 8 10 25 26 25 22 16 12 163 19 49 1.8
11.3 24 22 22 23 22 22 21 8 6 187 59 46 2.6
11.8 17 20 12 17 19 23 22 16 12 109 30 55 2
12.7 25 22 20 25 26 25 22 8 8 105 7 50 1.6
10.9 18 14 11 24 12 24 23 16 12 148 19 30 1.8
13.3 24 21 17 20 20 23 23 19 14 125 48 45 2.4
10.1 23 20 14 19 24 27 21 14 12 116 23 35 1.9
14.3 20 18 16 21 22 23 21 13 14 138 33 41 2.1
9.3 22 24 15 18 23 23 22 15 11 164 34 73 2.1
12.5 22 19 15 27 19 24 21 11 10 162 48 17 2.4
7.6 15 16 10 25 24 26 21 9 7 99 18 40 1.8
15.9 17 16 10 20 21 20 21 16 12 202 43 64 2.3
9.2 19 16 18 21 16 23 21 12 7 186 33 37 2.1
11.1 22 22 22 27 23 23 21 14 12 183 71 65 2.8
13 21 21 16 24 20 17 22 14 10 214 26 100 2
14.5 21 15 10 27 19 20 22 13 10 188 67 28 2.7
12.3 20 15 16 23 18 18 21 17 12 177 80 56 2.9
11.4 21 14 16 24 21 19 23 14 12 126 29 29 2
12.6 18 14 5 27 25 26 21 7 5 139 32 50 2.1
NA 22 19 18 24 15 14 21 13 10 78 47 3 2.3
13 16 16 10 25 17 25 21 15 10 162 43 59 2.3
13.2 24 26 16 24 24 18 20 15 11 159 29 61 2
7.7 19 18 16 23 22 26 21 16 12 110 32 51 2.1
4.35 20 17 15 22 14 15 22 16 9 48 23 12 1
12.7 6 6 4 24 5 27 22 16 11 50 16 45 1
18.1 15 22 9 19 25 23 22 16 12 150 33 37 4
17.85 18 20 18 25 21 23 20 16 12 154 32 37 4
17.1 21 17 12 24 9 22 22 14 12 194 52 68 4
19.1 23 20 16 28 15 20 21 15 12 158 75 72 4
16.1 20 23 17 23 23 21 21 16 10 159 72 143 4
13.35 20 18 14 19 21 25 21 13 15 67 15 9 2
18.4 18 13 13 19 9 19 21 10 10 147 29 55 4
14.7 25 22 20 27 24 25 21 17 15 39 13 17 1
10.6 16 20 16 24 16 24 21 15 10 100 40 37 3
12.6 20 20 15 26 20 22 21 18 15 111 19 27 3
16.2 14 13 10 21 15 28 22 16 9 138 24 37 4
13.6 22 16 16 25 18 22 24 20 15 101 121 58 3
14.1 20 16 15 19 21 23 22 17 13 101 36 21 3
14.5 17 15 16 20 21 19 20 16 12 114 23 19 3
16.15 22 19 19 26 21 21 21 15 12 165 85 78 4
14.75 22 19 9 27 20 25 24 13 8 114 41 35 3
14.8 20 24 19 23 24 23 25 16 9 111 46 48 3
12.45 17 9 7 18 15 28 22 16 15 75 18 27 2
12.65 22 22 23 23 24 14 21 16 12 82 35 43 2
17.35 17 15 14 21 18 23 21 17 12 121 17 30 3
8.6 22 22 10 23 24 24 22 20 15 32 4 25 1
18.4 21 22 16 22 24 25 23 14 11 150 28 69 4
16.1 25 24 12 21 15 15 24 17 12 117 44 72 3
17.75 19 21 7 24 20 26 22 16 14 165 38 13 4
15.25 24 25 20 26 26 21 25 15 12 154 57 61 4
17.65 17 26 9 24 26 26 22 16 12 126 23 43 4
16.35 22 21 12 22 23 23 21 16 12 149 36 51 4
17.65 17 14 10 20 13 15 21 14 11 145 22 67 4
13.6 26 28 19 20 16 16 21 16 12 120 40 36 3
14.35 20 21 11 18 22 20 22 16 12 109 31 44 3
14.75 19 16 15 18 21 20 22 16 12 132 11 45 4
18.25 21 16 14 25 11 21 21 14 12 172 38 34 4
9.9 24 25 11 28 23 28 22 14 8 169 24 36 4
16 21 21 14 23 18 19 23 16 8 114 37 72 3
18.25 19 22 15 20 19 21 21 16 12 156 37 39 4
16.85 13 9 7 22 15 22 21 15 12 172 22 43 4
18.95 27 24 22 23 21 17 21 18 11 167 43 80 4
15.6 22 22 11 20 25 26 21 15 12 113 31 40 3
17.1 21 10 12 24 12 22 22 14 10 173 31 61 4
16.1 22 22 17 18 24 17 22 18 11 2 -4 23 1
15.4 22 21 13 23 19 16 21 15 11 165 21 29 4
15.4 21 20 15 21 21 18 21 15 11 165 21 29 4
13.35 19 17 11 19 19 17 25 16 13 118 32 54 3
19.1 11 7 7 19 18 25 21 11 7 158 26 43 4
7.6 19 14 13 25 23 21 25 7 8 49 32 20 1
19.1 21 23 7 18 23 27 22 15 11 155 33 61 4
14.75 19 18 11 22 27 23 21 14 8 151 30 57 4
19.25 8 17 22 5 6 8 23 16 14 220 67 54 4
13.6 23 20 15 24 22 22 20 14 9 141 22 36 4
12.75 17 19 15 28 23 28 22 11 13 122 33 16 4
9.85 25 19 11 27 20 24 25 18 13 44 24 40 1
15.25 24 23 10 23 23 25 20 18 11 152 28 27 4
11.9 22 20 18 24 27 23 21 15 9 107 41 61 3
16.35 23 19 14 25 24 26 21 13 12 154 31 69 4
12.4 17 16 16 19 12 22 23 13 12 103 33 34 3
18.15 22 21 16 24 24 22 22 18 13 175 21 34 4
17.75 21 20 17 28 24 26 21 15 11 143 52 34 4
12.35 19 20 14 19 19 21 21 16 11 110 29 13 3
15.6 19 19 10 23 28 21 21 12 9 131 11 12 4
19.3 16 19 16 23 23 24 21 16 12 167 26 51 4
17.1 23 20 16 26 19 18 21 16 15 137 7 19 4
18.4 23 22 17 25 23 26 21 19 14 121 13 81 3
19.05 20 19 12 24 20 23 21 15 12 149 20 42 4
18.55 24 23 17 23 18 25 22 14 9 168 52 22 4
19.1 25 16 11 22 20 20 21 14 9 140 28 85 4
12.85 20 18 12 26 21 26 22 16 13 168 39 25 4
9.5 23 23 8 23 25 19 22 20 15 94 9 22 2
4.5 21 20 17 22 18 21 22 16 11 51 19 19 1
13.6 23 23 17 22 28 24 22 13 10 145 60 45 4
11.7 11 13 7 17 9 6 23 15 11 66 19 45 2
13.35 27 26 18 22 26 21 22 16 14 109 14 51 3
17.6 16 13 14 26 12 19 21 19 12 164 -2 73 4
14.05 18 10 13 24 12 24 21 13 13 119 51 24 3
16.1 23 21 19 27 20 21 20 14 11 126 2 61 4
13.35 24 24 15 22 25 21 20 15 11 132 24 23 4
11.85 20 21 15 23 24 26 21 15 13 142 40 14 4
11.95 20 23 8 22 23 24 21 14 12 83 20 54 2
13.2 14 16 11 20 22 23 21 12 9 166 20 36 4
7.7 23 26 17 27 28 26 24 15 13 93 25 26 2
14.6 16 16 12 20 15 20 22 16 12 117 38 30 3

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.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=270984&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]'Gertrude Mary Cox' @ cox.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=270984&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&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'Gertrude Mary Cox' @ cox.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
TOT[t] = + 10.7255 + 0.0140558AMS.I1[t] -0.0640375AMS.I2[t] -0.0352355AMS.I3[t] -0.0491968AMS.E1[t] -0.0225199AMS.E2[t] -0.0211464AMS.E3[t] -0.143908age[t] + 0.00594465CONFSTATTOT[t] + 0.110765CONFSOFTTOT[t] -0.00692369LFM[t] -0.0229751PRH[t] + 0.0371168CH[t] + 2.7615PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.7255 +  0.0140558AMS.I1[t] -0.0640375AMS.I2[t] -0.0352355AMS.I3[t] -0.0491968AMS.E1[t] -0.0225199AMS.E2[t] -0.0211464AMS.E3[t] -0.143908age[t] +  0.00594465CONFSTATTOT[t] +  0.110765CONFSOFTTOT[t] -0.00692369LFM[t] -0.0229751PRH[t] +  0.0371168CH[t] +  2.7615PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.7255 +  0.0140558AMS.I1[t] -0.0640375AMS.I2[t] -0.0352355AMS.I3[t] -0.0491968AMS.E1[t] -0.0225199AMS.E2[t] -0.0211464AMS.E3[t] -0.143908age[t] +  0.00594465CONFSTATTOT[t] +  0.110765CONFSOFTTOT[t] -0.00692369LFM[t] -0.0229751PRH[t] +  0.0371168CH[t] +  2.7615PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.7255 + 0.0140558AMS.I1[t] -0.0640375AMS.I2[t] -0.0352355AMS.I3[t] -0.0491968AMS.E1[t] -0.0225199AMS.E2[t] -0.0211464AMS.E3[t] -0.143908age[t] + 0.00594465CONFSTATTOT[t] + 0.110765CONFSOFTTOT[t] -0.00692369LFM[t] -0.0229751PRH[t] + 0.0371168CH[t] + 2.7615PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.72554.536552.3640.01957050.00978525
AMS.I10.01405580.07606220.18480.8536840.426842
AMS.I2-0.06403750.0730607-0.87650.3824010.191201
AMS.I3-0.03523550.0609649-0.5780.5643040.282152
AMS.E1-0.04919680.0756336-0.65050.516560.25828
AMS.E2-0.02251990.0577963-0.38960.6974480.348724
AMS.E3-0.02114640.0660705-0.32010.7494460.374723
age-0.1439080.18052-0.79720.4268190.213409
CONFSTATTOT0.005944650.1088050.054640.9565140.478257
CONFSOFTTOT0.1107650.1209370.91590.3614480.180724
LFM-0.006923690.00631067-1.0970.274640.13732
PRH-0.02297510.0113783-2.0190.04555570.0227778
CH0.03711680.01106763.3540.001049420.000524711
PR2.76150.2450311.276.86972e-213.43486e-21

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.7255 & 4.53655 & 2.364 & 0.0195705 & 0.00978525 \tabularnewline
AMS.I1 & 0.0140558 & 0.0760622 & 0.1848 & 0.853684 & 0.426842 \tabularnewline
AMS.I2 & -0.0640375 & 0.0730607 & -0.8765 & 0.382401 & 0.191201 \tabularnewline
AMS.I3 & -0.0352355 & 0.0609649 & -0.578 & 0.564304 & 0.282152 \tabularnewline
AMS.E1 & -0.0491968 & 0.0756336 & -0.6505 & 0.51656 & 0.25828 \tabularnewline
AMS.E2 & -0.0225199 & 0.0577963 & -0.3896 & 0.697448 & 0.348724 \tabularnewline
AMS.E3 & -0.0211464 & 0.0660705 & -0.3201 & 0.749446 & 0.374723 \tabularnewline
age & -0.143908 & 0.18052 & -0.7972 & 0.426819 & 0.213409 \tabularnewline
CONFSTATTOT & 0.00594465 & 0.108805 & 0.05464 & 0.956514 & 0.478257 \tabularnewline
CONFSOFTTOT & 0.110765 & 0.120937 & 0.9159 & 0.361448 & 0.180724 \tabularnewline
LFM & -0.00692369 & 0.00631067 & -1.097 & 0.27464 & 0.13732 \tabularnewline
PRH & -0.0229751 & 0.0113783 & -2.019 & 0.0455557 & 0.0227778 \tabularnewline
CH & 0.0371168 & 0.0110676 & 3.354 & 0.00104942 & 0.000524711 \tabularnewline
PR & 2.7615 & 0.24503 & 11.27 & 6.86972e-21 & 3.43486e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.7255[/C][C]4.53655[/C][C]2.364[/C][C]0.0195705[/C][C]0.00978525[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0140558[/C][C]0.0760622[/C][C]0.1848[/C][C]0.853684[/C][C]0.426842[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0640375[/C][C]0.0730607[/C][C]-0.8765[/C][C]0.382401[/C][C]0.191201[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0352355[/C][C]0.0609649[/C][C]-0.578[/C][C]0.564304[/C][C]0.282152[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0491968[/C][C]0.0756336[/C][C]-0.6505[/C][C]0.51656[/C][C]0.25828[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0225199[/C][C]0.0577963[/C][C]-0.3896[/C][C]0.697448[/C][C]0.348724[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0211464[/C][C]0.0660705[/C][C]-0.3201[/C][C]0.749446[/C][C]0.374723[/C][/ROW]
[ROW][C]age[/C][C]-0.143908[/C][C]0.18052[/C][C]-0.7972[/C][C]0.426819[/C][C]0.213409[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.00594465[/C][C]0.108805[/C][C]0.05464[/C][C]0.956514[/C][C]0.478257[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.110765[/C][C]0.120937[/C][C]0.9159[/C][C]0.361448[/C][C]0.180724[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00692369[/C][C]0.00631067[/C][C]-1.097[/C][C]0.27464[/C][C]0.13732[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0229751[/C][C]0.0113783[/C][C]-2.019[/C][C]0.0455557[/C][C]0.0227778[/C][/ROW]
[ROW][C]CH[/C][C]0.0371168[/C][C]0.0110676[/C][C]3.354[/C][C]0.00104942[/C][C]0.000524711[/C][/ROW]
[ROW][C]PR[/C][C]2.7615[/C][C]0.24503[/C][C]11.27[/C][C]6.86972e-21[/C][C]3.43486e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.72554.536552.3640.01957050.00978525
AMS.I10.01405580.07606220.18480.8536840.426842
AMS.I2-0.06403750.0730607-0.87650.3824010.191201
AMS.I3-0.03523550.0609649-0.5780.5643040.282152
AMS.E1-0.04919680.0756336-0.65050.516560.25828
AMS.E2-0.02251990.0577963-0.38960.6974480.348724
AMS.E3-0.02114640.0660705-0.32010.7494460.374723
age-0.1439080.18052-0.79720.4268190.213409
CONFSTATTOT0.005944650.1088050.054640.9565140.478257
CONFSOFTTOT0.1107650.1209370.91590.3614480.180724
LFM-0.006923690.00631067-1.0970.274640.13732
PRH-0.02297510.0113783-2.0190.04555570.0227778
CH0.03711680.01106763.3540.001049420.000524711
PR2.76150.2450311.276.86972e-213.43486e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.773106
R-squared0.597692
Adjusted R-squared0.556833
F-TEST (value)14.628
F-TEST (DF numerator)13
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.31982
Sum Squared Residuals688.839

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.773106 \tabularnewline
R-squared & 0.597692 \tabularnewline
Adjusted R-squared & 0.556833 \tabularnewline
F-TEST (value) & 14.628 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 128 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.31982 \tabularnewline
Sum Squared Residuals & 688.839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.773106[/C][/ROW]
[ROW][C]R-squared[/C][C]0.597692[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.556833[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.628[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]128[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.31982[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]688.839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&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 R0.773106
R-squared0.597692
Adjusted R-squared0.556833
F-TEST (value)14.628
F-TEST (DF numerator)13
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.31982
Sum Squared Residuals688.839






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9763-0.0763236
212.210.49131.70873
312.811.70851.09153
47.411.7185-4.31852
56.711.4986-4.79858
612.611.53951.06052
714.811.04893.75112
813.313.5848-0.284818
911.112.3129-1.21293
108.210.9724-2.77245
1111.411.27630.123654
126.411.2076-4.80758
1310.610.27980.32018
141213.1022-1.10225
156.39.24238-2.94238
1611.913.1114-1.21141
179.310.9774-1.67744
18109.677020.322982
196.49.94746-3.54746
2013.812.58221.21777
2110.89.979290.820714
2213.811.58882.21123
2311.710.9250.775005
2410.911.6427-0.742673
259.911.3231-1.42312
2611.510.52520.974775
278.311.1981-2.89809
2811.711.19890.501103
29910.3898-1.38977
309.713.6655-3.96553
3110.811.4633-0.663282
3210.310.9107-0.610712
3310.49.979290.42071
349.312.2661-2.96612
3511.811.11440.685587
365.911.5195-5.61954
3711.411.888-0.48804
381310.82172.17834
3910.811.2384-0.438364
4011.310.71380.586245
4111.811.8891-0.0891027
4212.79.773412.92659
4310.910.47330.42669
4413.311.88171.41828
4510.110.8333-0.733281
4614.311.48852.81154
479.311.8131-2.51309
4812.510.21092.28909
497.610.4024-2.80236
5015.912.45043.44964
519.210.405-1.20503
5211.112.155-1.05496
531312.30210.697924
5414.511.20193.29806
5512.312.9963-0.69634
5611.410.66820.731783
5712.610.99551.60453
58NANA0.96144
591310.98172.01832
6013.217.1969-3.99688
617.711.2968-3.59682
624.351.935932.41407
6312.710.70721.99279
6418.116.28831.81168
6517.8517.68910.160935
6617.114.36372.73635
6719.121.6205-2.52052
6816.113.93962.16043
6913.3512.64720.702843
7018.411.83026.56982
7114.717.3785-2.67853
7210.611.8306-1.23062
7312.613.0129-0.412892
7416.215.23040.969555
7513.612.97210.627944
7614.113.39960.70038
7714.514.5445-0.0445407
7816.1513.95552.19454
7914.7512.32692.42309
8014.814.859-0.0589743
8112.4510.79681.65318
8212.659.463573.18643
8317.3517.8344-0.484399
848.67.013581.58642
8518.416.84141.55857
8616.113.56312.53689
8717.7517.70340.0465621
8815.2513.76581.48416
8917.6517.9629-0.312918
9016.3517.1247-0.774697
9117.6517.26890.381075
9213.613.42940.170612
9314.3517.066-2.71597
9414.7512.71552.03455
9518.2523.4008-5.15082
969.98.132811.76719
971613.91492.08507
9818.2518.9419-0.691877
9916.8514.90351.94649
10018.9517.16241.78757
10115.615.9663-0.366295
10217.110.09417.00591
10316.116.8172-0.717159
10415.416.1077-0.707731
10515.416.5182-1.11821
10613.3511.33592.01414
10719.118.70540.394553
1087.65.44712.1529
10919.120.9998-1.89978
11014.7512.24762.50243
11119.2521.8506-2.60059
11213.615.958-2.35801
11312.7511.42581.32424
1149.8510.4592-0.609195
11515.2517.1244-1.87442
11611.912.7816-0.881553
11716.3517.8276-1.47762
11812.410.18462.21542
11918.1515.48182.66817
12017.7518.4424-0.69243
12112.3512.34520.00482986
12215.612.90052.6995
12319.318.6930.606988
12417.114.23942.86061
12518.416.10972.29035
12619.0514.84434.20574
12718.5517.80540.744592
12819.121.6631-2.56308
12912.8514.6441-1.7941
1309.513.0338-3.53381
1314.56.0093-1.5093
13213.614.8326-1.23256
13311.712.6249-0.924852
13413.3514.5095-1.15953
13517.617.14640.453583
13614.0515.577-1.52701
13716.118.5218-2.4218
13813.3516.582-3.23201
13911.8511.9276-0.0775577
14011.9515.1139-3.16388
14113.215.1212-1.92122
1427.76.831510.868486
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9763 & -0.0763236 \tabularnewline
2 & 12.2 & 10.4913 & 1.70873 \tabularnewline
3 & 12.8 & 11.7085 & 1.09153 \tabularnewline
4 & 7.4 & 11.7185 & -4.31852 \tabularnewline
5 & 6.7 & 11.4986 & -4.79858 \tabularnewline
6 & 12.6 & 11.5395 & 1.06052 \tabularnewline
7 & 14.8 & 11.0489 & 3.75112 \tabularnewline
8 & 13.3 & 13.5848 & -0.284818 \tabularnewline
9 & 11.1 & 12.3129 & -1.21293 \tabularnewline
10 & 8.2 & 10.9724 & -2.77245 \tabularnewline
11 & 11.4 & 11.2763 & 0.123654 \tabularnewline
12 & 6.4 & 11.2076 & -4.80758 \tabularnewline
13 & 10.6 & 10.2798 & 0.32018 \tabularnewline
14 & 12 & 13.1022 & -1.10225 \tabularnewline
15 & 6.3 & 9.24238 & -2.94238 \tabularnewline
16 & 11.9 & 13.1114 & -1.21141 \tabularnewline
17 & 9.3 & 10.9774 & -1.67744 \tabularnewline
18 & 10 & 9.67702 & 0.322982 \tabularnewline
19 & 6.4 & 9.94746 & -3.54746 \tabularnewline
20 & 13.8 & 12.5822 & 1.21777 \tabularnewline
21 & 10.8 & 9.97929 & 0.820714 \tabularnewline
22 & 13.8 & 11.5888 & 2.21123 \tabularnewline
23 & 11.7 & 10.925 & 0.775005 \tabularnewline
24 & 10.9 & 11.6427 & -0.742673 \tabularnewline
25 & 9.9 & 11.3231 & -1.42312 \tabularnewline
26 & 11.5 & 10.5252 & 0.974775 \tabularnewline
27 & 8.3 & 11.1981 & -2.89809 \tabularnewline
28 & 11.7 & 11.1989 & 0.501103 \tabularnewline
29 & 9 & 10.3898 & -1.38977 \tabularnewline
30 & 9.7 & 13.6655 & -3.96553 \tabularnewline
31 & 10.8 & 11.4633 & -0.663282 \tabularnewline
32 & 10.3 & 10.9107 & -0.610712 \tabularnewline
33 & 10.4 & 9.97929 & 0.42071 \tabularnewline
34 & 9.3 & 12.2661 & -2.96612 \tabularnewline
35 & 11.8 & 11.1144 & 0.685587 \tabularnewline
36 & 5.9 & 11.5195 & -5.61954 \tabularnewline
37 & 11.4 & 11.888 & -0.48804 \tabularnewline
38 & 13 & 10.8217 & 2.17834 \tabularnewline
39 & 10.8 & 11.2384 & -0.438364 \tabularnewline
40 & 11.3 & 10.7138 & 0.586245 \tabularnewline
41 & 11.8 & 11.8891 & -0.0891027 \tabularnewline
42 & 12.7 & 9.77341 & 2.92659 \tabularnewline
43 & 10.9 & 10.4733 & 0.42669 \tabularnewline
44 & 13.3 & 11.8817 & 1.41828 \tabularnewline
45 & 10.1 & 10.8333 & -0.733281 \tabularnewline
46 & 14.3 & 11.4885 & 2.81154 \tabularnewline
47 & 9.3 & 11.8131 & -2.51309 \tabularnewline
48 & 12.5 & 10.2109 & 2.28909 \tabularnewline
49 & 7.6 & 10.4024 & -2.80236 \tabularnewline
50 & 15.9 & 12.4504 & 3.44964 \tabularnewline
51 & 9.2 & 10.405 & -1.20503 \tabularnewline
52 & 11.1 & 12.155 & -1.05496 \tabularnewline
53 & 13 & 12.3021 & 0.697924 \tabularnewline
54 & 14.5 & 11.2019 & 3.29806 \tabularnewline
55 & 12.3 & 12.9963 & -0.69634 \tabularnewline
56 & 11.4 & 10.6682 & 0.731783 \tabularnewline
57 & 12.6 & 10.9955 & 1.60453 \tabularnewline
58 & NA & NA & 0.96144 \tabularnewline
59 & 13 & 10.9817 & 2.01832 \tabularnewline
60 & 13.2 & 17.1969 & -3.99688 \tabularnewline
61 & 7.7 & 11.2968 & -3.59682 \tabularnewline
62 & 4.35 & 1.93593 & 2.41407 \tabularnewline
63 & 12.7 & 10.7072 & 1.99279 \tabularnewline
64 & 18.1 & 16.2883 & 1.81168 \tabularnewline
65 & 17.85 & 17.6891 & 0.160935 \tabularnewline
66 & 17.1 & 14.3637 & 2.73635 \tabularnewline
67 & 19.1 & 21.6205 & -2.52052 \tabularnewline
68 & 16.1 & 13.9396 & 2.16043 \tabularnewline
69 & 13.35 & 12.6472 & 0.702843 \tabularnewline
70 & 18.4 & 11.8302 & 6.56982 \tabularnewline
71 & 14.7 & 17.3785 & -2.67853 \tabularnewline
72 & 10.6 & 11.8306 & -1.23062 \tabularnewline
73 & 12.6 & 13.0129 & -0.412892 \tabularnewline
74 & 16.2 & 15.2304 & 0.969555 \tabularnewline
75 & 13.6 & 12.9721 & 0.627944 \tabularnewline
76 & 14.1 & 13.3996 & 0.70038 \tabularnewline
77 & 14.5 & 14.5445 & -0.0445407 \tabularnewline
78 & 16.15 & 13.9555 & 2.19454 \tabularnewline
79 & 14.75 & 12.3269 & 2.42309 \tabularnewline
80 & 14.8 & 14.859 & -0.0589743 \tabularnewline
81 & 12.45 & 10.7968 & 1.65318 \tabularnewline
82 & 12.65 & 9.46357 & 3.18643 \tabularnewline
83 & 17.35 & 17.8344 & -0.484399 \tabularnewline
84 & 8.6 & 7.01358 & 1.58642 \tabularnewline
85 & 18.4 & 16.8414 & 1.55857 \tabularnewline
86 & 16.1 & 13.5631 & 2.53689 \tabularnewline
87 & 17.75 & 17.7034 & 0.0465621 \tabularnewline
88 & 15.25 & 13.7658 & 1.48416 \tabularnewline
89 & 17.65 & 17.9629 & -0.312918 \tabularnewline
90 & 16.35 & 17.1247 & -0.774697 \tabularnewline
91 & 17.65 & 17.2689 & 0.381075 \tabularnewline
92 & 13.6 & 13.4294 & 0.170612 \tabularnewline
93 & 14.35 & 17.066 & -2.71597 \tabularnewline
94 & 14.75 & 12.7155 & 2.03455 \tabularnewline
95 & 18.25 & 23.4008 & -5.15082 \tabularnewline
96 & 9.9 & 8.13281 & 1.76719 \tabularnewline
97 & 16 & 13.9149 & 2.08507 \tabularnewline
98 & 18.25 & 18.9419 & -0.691877 \tabularnewline
99 & 16.85 & 14.9035 & 1.94649 \tabularnewline
100 & 18.95 & 17.1624 & 1.78757 \tabularnewline
101 & 15.6 & 15.9663 & -0.366295 \tabularnewline
102 & 17.1 & 10.0941 & 7.00591 \tabularnewline
103 & 16.1 & 16.8172 & -0.717159 \tabularnewline
104 & 15.4 & 16.1077 & -0.707731 \tabularnewline
105 & 15.4 & 16.5182 & -1.11821 \tabularnewline
106 & 13.35 & 11.3359 & 2.01414 \tabularnewline
107 & 19.1 & 18.7054 & 0.394553 \tabularnewline
108 & 7.6 & 5.4471 & 2.1529 \tabularnewline
109 & 19.1 & 20.9998 & -1.89978 \tabularnewline
110 & 14.75 & 12.2476 & 2.50243 \tabularnewline
111 & 19.25 & 21.8506 & -2.60059 \tabularnewline
112 & 13.6 & 15.958 & -2.35801 \tabularnewline
113 & 12.75 & 11.4258 & 1.32424 \tabularnewline
114 & 9.85 & 10.4592 & -0.609195 \tabularnewline
115 & 15.25 & 17.1244 & -1.87442 \tabularnewline
116 & 11.9 & 12.7816 & -0.881553 \tabularnewline
117 & 16.35 & 17.8276 & -1.47762 \tabularnewline
118 & 12.4 & 10.1846 & 2.21542 \tabularnewline
119 & 18.15 & 15.4818 & 2.66817 \tabularnewline
120 & 17.75 & 18.4424 & -0.69243 \tabularnewline
121 & 12.35 & 12.3452 & 0.00482986 \tabularnewline
122 & 15.6 & 12.9005 & 2.6995 \tabularnewline
123 & 19.3 & 18.693 & 0.606988 \tabularnewline
124 & 17.1 & 14.2394 & 2.86061 \tabularnewline
125 & 18.4 & 16.1097 & 2.29035 \tabularnewline
126 & 19.05 & 14.8443 & 4.20574 \tabularnewline
127 & 18.55 & 17.8054 & 0.744592 \tabularnewline
128 & 19.1 & 21.6631 & -2.56308 \tabularnewline
129 & 12.85 & 14.6441 & -1.7941 \tabularnewline
130 & 9.5 & 13.0338 & -3.53381 \tabularnewline
131 & 4.5 & 6.0093 & -1.5093 \tabularnewline
132 & 13.6 & 14.8326 & -1.23256 \tabularnewline
133 & 11.7 & 12.6249 & -0.924852 \tabularnewline
134 & 13.35 & 14.5095 & -1.15953 \tabularnewline
135 & 17.6 & 17.1464 & 0.453583 \tabularnewline
136 & 14.05 & 15.577 & -1.52701 \tabularnewline
137 & 16.1 & 18.5218 & -2.4218 \tabularnewline
138 & 13.35 & 16.582 & -3.23201 \tabularnewline
139 & 11.85 & 11.9276 & -0.0775577 \tabularnewline
140 & 11.95 & 15.1139 & -3.16388 \tabularnewline
141 & 13.2 & 15.1212 & -1.92122 \tabularnewline
142 & 7.7 & 6.83151 & 0.868486 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&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]12.9[/C][C]12.9763[/C][C]-0.0763236[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.4913[/C][C]1.70873[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7085[/C][C]1.09153[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7185[/C][C]-4.31852[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.4986[/C][C]-4.79858[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.5395[/C][C]1.06052[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0489[/C][C]3.75112[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.5848[/C][C]-0.284818[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.3129[/C][C]-1.21293[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9724[/C][C]-2.77245[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2763[/C][C]0.123654[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.2076[/C][C]-4.80758[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.2798[/C][C]0.32018[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.1022[/C][C]-1.10225[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.24238[/C][C]-2.94238[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.1114[/C][C]-1.21141[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9774[/C][C]-1.67744[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.67702[/C][C]0.322982[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.94746[/C][C]-3.54746[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5822[/C][C]1.21777[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]9.97929[/C][C]0.820714[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.5888[/C][C]2.21123[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.925[/C][C]0.775005[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.6427[/C][C]-0.742673[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3231[/C][C]-1.42312[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5252[/C][C]0.974775[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.1981[/C][C]-2.89809[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1989[/C][C]0.501103[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3898[/C][C]-1.38977[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.6655[/C][C]-3.96553[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.4633[/C][C]-0.663282[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.9107[/C][C]-0.610712[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.97929[/C][C]0.42071[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2661[/C][C]-2.96612[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.1144[/C][C]0.685587[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.5195[/C][C]-5.61954[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.888[/C][C]-0.48804[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.8217[/C][C]2.17834[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.2384[/C][C]-0.438364[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7138[/C][C]0.586245[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.8891[/C][C]-0.0891027[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.77341[/C][C]2.92659[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.4733[/C][C]0.42669[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8817[/C][C]1.41828[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.8333[/C][C]-0.733281[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.4885[/C][C]2.81154[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.8131[/C][C]-2.51309[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.2109[/C][C]2.28909[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.4024[/C][C]-2.80236[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4504[/C][C]3.44964[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.405[/C][C]-1.20503[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.155[/C][C]-1.05496[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.3021[/C][C]0.697924[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.2019[/C][C]3.29806[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.9963[/C][C]-0.69634[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.6682[/C][C]0.731783[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]10.9955[/C][C]1.60453[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]0.96144[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.9817[/C][C]2.01832[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.1969[/C][C]-3.99688[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.2968[/C][C]-3.59682[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.93593[/C][C]2.41407[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.7072[/C][C]1.99279[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.2883[/C][C]1.81168[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.6891[/C][C]0.160935[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.3637[/C][C]2.73635[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.6205[/C][C]-2.52052[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.9396[/C][C]2.16043[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.6472[/C][C]0.702843[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8302[/C][C]6.56982[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.3785[/C][C]-2.67853[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.8306[/C][C]-1.23062[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]13.0129[/C][C]-0.412892[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]15.2304[/C][C]0.969555[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.9721[/C][C]0.627944[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.3996[/C][C]0.70038[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5445[/C][C]-0.0445407[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]13.9555[/C][C]2.19454[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.3269[/C][C]2.42309[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.859[/C][C]-0.0589743[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.7968[/C][C]1.65318[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.46357[/C][C]3.18643[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.8344[/C][C]-0.484399[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.01358[/C][C]1.58642[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.8414[/C][C]1.55857[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.5631[/C][C]2.53689[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]17.7034[/C][C]0.0465621[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.7658[/C][C]1.48416[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.9629[/C][C]-0.312918[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.1247[/C][C]-0.774697[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.2689[/C][C]0.381075[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.4294[/C][C]0.170612[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.066[/C][C]-2.71597[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.7155[/C][C]2.03455[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.4008[/C][C]-5.15082[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]8.13281[/C][C]1.76719[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.9149[/C][C]2.08507[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]18.9419[/C][C]-0.691877[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.9035[/C][C]1.94649[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]17.1624[/C][C]1.78757[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]15.9663[/C][C]-0.366295[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.0941[/C][C]7.00591[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.8172[/C][C]-0.717159[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.1077[/C][C]-0.707731[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.5182[/C][C]-1.11821[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3359[/C][C]2.01414[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.7054[/C][C]0.394553[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.4471[/C][C]2.1529[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]20.9998[/C][C]-1.89978[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]12.2476[/C][C]2.50243[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.8506[/C][C]-2.60059[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]15.958[/C][C]-2.35801[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.4258[/C][C]1.32424[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.4592[/C][C]-0.609195[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.1244[/C][C]-1.87442[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.7816[/C][C]-0.881553[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.8276[/C][C]-1.47762[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.1846[/C][C]2.21542[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.4818[/C][C]2.66817[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.4424[/C][C]-0.69243[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.3452[/C][C]0.00482986[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]12.9005[/C][C]2.6995[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.693[/C][C]0.606988[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.2394[/C][C]2.86061[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.1097[/C][C]2.29035[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.8443[/C][C]4.20574[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.8054[/C][C]0.744592[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6631[/C][C]-2.56308[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.6441[/C][C]-1.7941[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]13.0338[/C][C]-3.53381[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]6.0093[/C][C]-1.5093[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]14.8326[/C][C]-1.23256[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.6249[/C][C]-0.924852[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.5095[/C][C]-1.15953[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.1464[/C][C]0.453583[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.577[/C][C]-1.52701[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.5218[/C][C]-2.4218[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.582[/C][C]-3.23201[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.9276[/C][C]-0.0775577[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.1139[/C][C]-3.16388[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.1212[/C][C]-1.92122[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.83151[/C][C]0.868486[/C][/ROW]
[ROW][C]143[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&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
112.912.9763-0.0763236
212.210.49131.70873
312.811.70851.09153
47.411.7185-4.31852
56.711.4986-4.79858
612.611.53951.06052
714.811.04893.75112
813.313.5848-0.284818
911.112.3129-1.21293
108.210.9724-2.77245
1111.411.27630.123654
126.411.2076-4.80758
1310.610.27980.32018
141213.1022-1.10225
156.39.24238-2.94238
1611.913.1114-1.21141
179.310.9774-1.67744
18109.677020.322982
196.49.94746-3.54746
2013.812.58221.21777
2110.89.979290.820714
2213.811.58882.21123
2311.710.9250.775005
2410.911.6427-0.742673
259.911.3231-1.42312
2611.510.52520.974775
278.311.1981-2.89809
2811.711.19890.501103
29910.3898-1.38977
309.713.6655-3.96553
3110.811.4633-0.663282
3210.310.9107-0.610712
3310.49.979290.42071
349.312.2661-2.96612
3511.811.11440.685587
365.911.5195-5.61954
3711.411.888-0.48804
381310.82172.17834
3910.811.2384-0.438364
4011.310.71380.586245
4111.811.8891-0.0891027
4212.79.773412.92659
4310.910.47330.42669
4413.311.88171.41828
4510.110.8333-0.733281
4614.311.48852.81154
479.311.8131-2.51309
4812.510.21092.28909
497.610.4024-2.80236
5015.912.45043.44964
519.210.405-1.20503
5211.112.155-1.05496
531312.30210.697924
5414.511.20193.29806
5512.312.9963-0.69634
5611.410.66820.731783
5712.610.99551.60453
58NANA0.96144
591310.98172.01832
6013.217.1969-3.99688
617.711.2968-3.59682
624.351.935932.41407
6312.710.70721.99279
6418.116.28831.81168
6517.8517.68910.160935
6617.114.36372.73635
6719.121.6205-2.52052
6816.113.93962.16043
6913.3512.64720.702843
7018.411.83026.56982
7114.717.3785-2.67853
7210.611.8306-1.23062
7312.613.0129-0.412892
7416.215.23040.969555
7513.612.97210.627944
7614.113.39960.70038
7714.514.5445-0.0445407
7816.1513.95552.19454
7914.7512.32692.42309
8014.814.859-0.0589743
8112.4510.79681.65318
8212.659.463573.18643
8317.3517.8344-0.484399
848.67.013581.58642
8518.416.84141.55857
8616.113.56312.53689
8717.7517.70340.0465621
8815.2513.76581.48416
8917.6517.9629-0.312918
9016.3517.1247-0.774697
9117.6517.26890.381075
9213.613.42940.170612
9314.3517.066-2.71597
9414.7512.71552.03455
9518.2523.4008-5.15082
969.98.132811.76719
971613.91492.08507
9818.2518.9419-0.691877
9916.8514.90351.94649
10018.9517.16241.78757
10115.615.9663-0.366295
10217.110.09417.00591
10316.116.8172-0.717159
10415.416.1077-0.707731
10515.416.5182-1.11821
10613.3511.33592.01414
10719.118.70540.394553
1087.65.44712.1529
10919.120.9998-1.89978
11014.7512.24762.50243
11119.2521.8506-2.60059
11213.615.958-2.35801
11312.7511.42581.32424
1149.8510.4592-0.609195
11515.2517.1244-1.87442
11611.912.7816-0.881553
11716.3517.8276-1.47762
11812.410.18462.21542
11918.1515.48182.66817
12017.7518.4424-0.69243
12112.3512.34520.00482986
12215.612.90052.6995
12319.318.6930.606988
12417.114.23942.86061
12518.416.10972.29035
12619.0514.84434.20574
12718.5517.80540.744592
12819.121.6631-2.56308
12912.8514.6441-1.7941
1309.513.0338-3.53381
1314.56.0093-1.5093
13213.614.8326-1.23256
13311.712.6249-0.924852
13413.3514.5095-1.15953
13517.617.14640.453583
13614.0515.577-1.52701
13716.118.5218-2.4218
13813.3516.582-3.23201
13911.8511.9276-0.0775577
14011.9515.1139-3.16388
14113.215.1212-1.92122
1427.76.831510.868486
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.4200850.840170.579915
180.3158830.6317660.684117
190.2026250.4052510.797375
200.118410.236820.88159
210.07017330.1403470.929827
220.04714230.09428460.952858
230.05519520.110390.944805
240.06265080.1253020.937349
250.08257350.1651470.917426
260.09161060.1832210.908389
270.1147090.2294180.885291
280.1550310.3100630.844969
290.1836810.3673610.816319
300.4011710.8023430.598829
310.3775290.7550580.622471
320.3088360.6176720.691164
330.2568490.5136980.743151
340.2636120.5272230.736388
350.2097040.4194090.790296
360.4021740.8043480.597826
370.3412630.6825260.658737
380.3023080.6046170.697692
390.2663940.5327880.733606
400.2215790.4431580.778421
410.2065550.4131110.793445
420.2159380.4318750.784062
430.1790310.3580630.820969
440.314370.6287390.68563
450.2677720.5355440.732228
460.355240.7104810.64476
470.3666440.7332870.633356
480.3640030.7280060.635997
490.4262470.8524930.573753
500.5239720.9520560.476028
510.5120550.975890.487945
520.4743330.9486660.525667
530.4249780.8499560.575022
540.4313160.8626310.568684
550.3881560.7763120.611844
560.341150.68230.65885
570.3152310.6304630.684769
580.2879070.5758140.712093
590.2752840.5505670.724716
600.3263750.6527510.673625
610.4273390.8546780.572661
620.4706830.9413670.529317
630.5237110.9525780.476289
640.489440.9788810.51056
650.447460.894920.55254
660.4579040.9158080.542096
670.4688680.9377360.531132
680.4885180.9770360.511482
690.4414130.8828270.558587
700.7985040.4029920.201496
710.8127720.3744560.187228
720.7943860.4112280.205614
730.7685560.4628890.231444
740.7719050.4561890.228095
750.7408390.5183230.259161
760.7001980.5996040.299802
770.658010.683980.34199
780.6489950.702010.351005
790.6571250.6857490.342875
800.6144160.7711690.385584
810.5962260.8075470.403774
820.618340.763320.38166
830.5764970.8470060.423503
840.537320.925360.46268
850.5010970.9978060.498903
860.5092170.9815670.490783
870.4580930.9161870.541907
880.4396220.8792450.560378
890.392490.784980.60751
900.3480210.6960410.651979
910.3016090.6032180.698391
920.2550390.5100790.744961
930.3207260.6414510.679274
940.3363650.672730.663635
950.5007440.9985110.499256
960.4634580.9269150.536542
970.4457510.8915010.554249
980.3925850.7851710.607415
990.3611930.7223860.638807
1000.3284740.6569470.671526
1010.2819880.5639770.718012
1020.7998560.4002870.200144
1030.7631720.4736560.236828
1040.7141740.5716520.285826
1050.6908510.6182970.309149
1060.7075560.5848880.292444
1070.7212740.5574520.278726
1080.6733230.6533550.326677
1090.627320.745360.37268
1100.5775860.8448280.422414
1110.5594070.8811860.440593
1120.5100250.9799490.489975
1130.4806360.9612720.519364
1140.4328210.8656420.567179
1150.3703030.7406050.629697
1160.31320.62640.6868
1170.2433080.4866170.756692
1180.2220220.4440440.777978
1190.2177940.4355880.782206
1200.153710.3074190.84629
1210.4818980.9637960.518102
1220.5949110.8101780.405089
1230.5603380.8793240.439662
1240.5822080.8355830.417792
1250.6488190.7023620.351181
1260.5297990.9404010.470201

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.420085 & 0.84017 & 0.579915 \tabularnewline
18 & 0.315883 & 0.631766 & 0.684117 \tabularnewline
19 & 0.202625 & 0.405251 & 0.797375 \tabularnewline
20 & 0.11841 & 0.23682 & 0.88159 \tabularnewline
21 & 0.0701733 & 0.140347 & 0.929827 \tabularnewline
22 & 0.0471423 & 0.0942846 & 0.952858 \tabularnewline
23 & 0.0551952 & 0.11039 & 0.944805 \tabularnewline
24 & 0.0626508 & 0.125302 & 0.937349 \tabularnewline
25 & 0.0825735 & 0.165147 & 0.917426 \tabularnewline
26 & 0.0916106 & 0.183221 & 0.908389 \tabularnewline
27 & 0.114709 & 0.229418 & 0.885291 \tabularnewline
28 & 0.155031 & 0.310063 & 0.844969 \tabularnewline
29 & 0.183681 & 0.367361 & 0.816319 \tabularnewline
30 & 0.401171 & 0.802343 & 0.598829 \tabularnewline
31 & 0.377529 & 0.755058 & 0.622471 \tabularnewline
32 & 0.308836 & 0.617672 & 0.691164 \tabularnewline
33 & 0.256849 & 0.513698 & 0.743151 \tabularnewline
34 & 0.263612 & 0.527223 & 0.736388 \tabularnewline
35 & 0.209704 & 0.419409 & 0.790296 \tabularnewline
36 & 0.402174 & 0.804348 & 0.597826 \tabularnewline
37 & 0.341263 & 0.682526 & 0.658737 \tabularnewline
38 & 0.302308 & 0.604617 & 0.697692 \tabularnewline
39 & 0.266394 & 0.532788 & 0.733606 \tabularnewline
40 & 0.221579 & 0.443158 & 0.778421 \tabularnewline
41 & 0.206555 & 0.413111 & 0.793445 \tabularnewline
42 & 0.215938 & 0.431875 & 0.784062 \tabularnewline
43 & 0.179031 & 0.358063 & 0.820969 \tabularnewline
44 & 0.31437 & 0.628739 & 0.68563 \tabularnewline
45 & 0.267772 & 0.535544 & 0.732228 \tabularnewline
46 & 0.35524 & 0.710481 & 0.64476 \tabularnewline
47 & 0.366644 & 0.733287 & 0.633356 \tabularnewline
48 & 0.364003 & 0.728006 & 0.635997 \tabularnewline
49 & 0.426247 & 0.852493 & 0.573753 \tabularnewline
50 & 0.523972 & 0.952056 & 0.476028 \tabularnewline
51 & 0.512055 & 0.97589 & 0.487945 \tabularnewline
52 & 0.474333 & 0.948666 & 0.525667 \tabularnewline
53 & 0.424978 & 0.849956 & 0.575022 \tabularnewline
54 & 0.431316 & 0.862631 & 0.568684 \tabularnewline
55 & 0.388156 & 0.776312 & 0.611844 \tabularnewline
56 & 0.34115 & 0.6823 & 0.65885 \tabularnewline
57 & 0.315231 & 0.630463 & 0.684769 \tabularnewline
58 & 0.287907 & 0.575814 & 0.712093 \tabularnewline
59 & 0.275284 & 0.550567 & 0.724716 \tabularnewline
60 & 0.326375 & 0.652751 & 0.673625 \tabularnewline
61 & 0.427339 & 0.854678 & 0.572661 \tabularnewline
62 & 0.470683 & 0.941367 & 0.529317 \tabularnewline
63 & 0.523711 & 0.952578 & 0.476289 \tabularnewline
64 & 0.48944 & 0.978881 & 0.51056 \tabularnewline
65 & 0.44746 & 0.89492 & 0.55254 \tabularnewline
66 & 0.457904 & 0.915808 & 0.542096 \tabularnewline
67 & 0.468868 & 0.937736 & 0.531132 \tabularnewline
68 & 0.488518 & 0.977036 & 0.511482 \tabularnewline
69 & 0.441413 & 0.882827 & 0.558587 \tabularnewline
70 & 0.798504 & 0.402992 & 0.201496 \tabularnewline
71 & 0.812772 & 0.374456 & 0.187228 \tabularnewline
72 & 0.794386 & 0.411228 & 0.205614 \tabularnewline
73 & 0.768556 & 0.462889 & 0.231444 \tabularnewline
74 & 0.771905 & 0.456189 & 0.228095 \tabularnewline
75 & 0.740839 & 0.518323 & 0.259161 \tabularnewline
76 & 0.700198 & 0.599604 & 0.299802 \tabularnewline
77 & 0.65801 & 0.68398 & 0.34199 \tabularnewline
78 & 0.648995 & 0.70201 & 0.351005 \tabularnewline
79 & 0.657125 & 0.685749 & 0.342875 \tabularnewline
80 & 0.614416 & 0.771169 & 0.385584 \tabularnewline
81 & 0.596226 & 0.807547 & 0.403774 \tabularnewline
82 & 0.61834 & 0.76332 & 0.38166 \tabularnewline
83 & 0.576497 & 0.847006 & 0.423503 \tabularnewline
84 & 0.53732 & 0.92536 & 0.46268 \tabularnewline
85 & 0.501097 & 0.997806 & 0.498903 \tabularnewline
86 & 0.509217 & 0.981567 & 0.490783 \tabularnewline
87 & 0.458093 & 0.916187 & 0.541907 \tabularnewline
88 & 0.439622 & 0.879245 & 0.560378 \tabularnewline
89 & 0.39249 & 0.78498 & 0.60751 \tabularnewline
90 & 0.348021 & 0.696041 & 0.651979 \tabularnewline
91 & 0.301609 & 0.603218 & 0.698391 \tabularnewline
92 & 0.255039 & 0.510079 & 0.744961 \tabularnewline
93 & 0.320726 & 0.641451 & 0.679274 \tabularnewline
94 & 0.336365 & 0.67273 & 0.663635 \tabularnewline
95 & 0.500744 & 0.998511 & 0.499256 \tabularnewline
96 & 0.463458 & 0.926915 & 0.536542 \tabularnewline
97 & 0.445751 & 0.891501 & 0.554249 \tabularnewline
98 & 0.392585 & 0.785171 & 0.607415 \tabularnewline
99 & 0.361193 & 0.722386 & 0.638807 \tabularnewline
100 & 0.328474 & 0.656947 & 0.671526 \tabularnewline
101 & 0.281988 & 0.563977 & 0.718012 \tabularnewline
102 & 0.799856 & 0.400287 & 0.200144 \tabularnewline
103 & 0.763172 & 0.473656 & 0.236828 \tabularnewline
104 & 0.714174 & 0.571652 & 0.285826 \tabularnewline
105 & 0.690851 & 0.618297 & 0.309149 \tabularnewline
106 & 0.707556 & 0.584888 & 0.292444 \tabularnewline
107 & 0.721274 & 0.557452 & 0.278726 \tabularnewline
108 & 0.673323 & 0.653355 & 0.326677 \tabularnewline
109 & 0.62732 & 0.74536 & 0.37268 \tabularnewline
110 & 0.577586 & 0.844828 & 0.422414 \tabularnewline
111 & 0.559407 & 0.881186 & 0.440593 \tabularnewline
112 & 0.510025 & 0.979949 & 0.489975 \tabularnewline
113 & 0.480636 & 0.961272 & 0.519364 \tabularnewline
114 & 0.432821 & 0.865642 & 0.567179 \tabularnewline
115 & 0.370303 & 0.740605 & 0.629697 \tabularnewline
116 & 0.3132 & 0.6264 & 0.6868 \tabularnewline
117 & 0.243308 & 0.486617 & 0.756692 \tabularnewline
118 & 0.222022 & 0.444044 & 0.777978 \tabularnewline
119 & 0.217794 & 0.435588 & 0.782206 \tabularnewline
120 & 0.15371 & 0.307419 & 0.84629 \tabularnewline
121 & 0.481898 & 0.963796 & 0.518102 \tabularnewline
122 & 0.594911 & 0.810178 & 0.405089 \tabularnewline
123 & 0.560338 & 0.879324 & 0.439662 \tabularnewline
124 & 0.582208 & 0.835583 & 0.417792 \tabularnewline
125 & 0.648819 & 0.702362 & 0.351181 \tabularnewline
126 & 0.529799 & 0.940401 & 0.470201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&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]0.420085[/C][C]0.84017[/C][C]0.579915[/C][/ROW]
[ROW][C]18[/C][C]0.315883[/C][C]0.631766[/C][C]0.684117[/C][/ROW]
[ROW][C]19[/C][C]0.202625[/C][C]0.405251[/C][C]0.797375[/C][/ROW]
[ROW][C]20[/C][C]0.11841[/C][C]0.23682[/C][C]0.88159[/C][/ROW]
[ROW][C]21[/C][C]0.0701733[/C][C]0.140347[/C][C]0.929827[/C][/ROW]
[ROW][C]22[/C][C]0.0471423[/C][C]0.0942846[/C][C]0.952858[/C][/ROW]
[ROW][C]23[/C][C]0.0551952[/C][C]0.11039[/C][C]0.944805[/C][/ROW]
[ROW][C]24[/C][C]0.0626508[/C][C]0.125302[/C][C]0.937349[/C][/ROW]
[ROW][C]25[/C][C]0.0825735[/C][C]0.165147[/C][C]0.917426[/C][/ROW]
[ROW][C]26[/C][C]0.0916106[/C][C]0.183221[/C][C]0.908389[/C][/ROW]
[ROW][C]27[/C][C]0.114709[/C][C]0.229418[/C][C]0.885291[/C][/ROW]
[ROW][C]28[/C][C]0.155031[/C][C]0.310063[/C][C]0.844969[/C][/ROW]
[ROW][C]29[/C][C]0.183681[/C][C]0.367361[/C][C]0.816319[/C][/ROW]
[ROW][C]30[/C][C]0.401171[/C][C]0.802343[/C][C]0.598829[/C][/ROW]
[ROW][C]31[/C][C]0.377529[/C][C]0.755058[/C][C]0.622471[/C][/ROW]
[ROW][C]32[/C][C]0.308836[/C][C]0.617672[/C][C]0.691164[/C][/ROW]
[ROW][C]33[/C][C]0.256849[/C][C]0.513698[/C][C]0.743151[/C][/ROW]
[ROW][C]34[/C][C]0.263612[/C][C]0.527223[/C][C]0.736388[/C][/ROW]
[ROW][C]35[/C][C]0.209704[/C][C]0.419409[/C][C]0.790296[/C][/ROW]
[ROW][C]36[/C][C]0.402174[/C][C]0.804348[/C][C]0.597826[/C][/ROW]
[ROW][C]37[/C][C]0.341263[/C][C]0.682526[/C][C]0.658737[/C][/ROW]
[ROW][C]38[/C][C]0.302308[/C][C]0.604617[/C][C]0.697692[/C][/ROW]
[ROW][C]39[/C][C]0.266394[/C][C]0.532788[/C][C]0.733606[/C][/ROW]
[ROW][C]40[/C][C]0.221579[/C][C]0.443158[/C][C]0.778421[/C][/ROW]
[ROW][C]41[/C][C]0.206555[/C][C]0.413111[/C][C]0.793445[/C][/ROW]
[ROW][C]42[/C][C]0.215938[/C][C]0.431875[/C][C]0.784062[/C][/ROW]
[ROW][C]43[/C][C]0.179031[/C][C]0.358063[/C][C]0.820969[/C][/ROW]
[ROW][C]44[/C][C]0.31437[/C][C]0.628739[/C][C]0.68563[/C][/ROW]
[ROW][C]45[/C][C]0.267772[/C][C]0.535544[/C][C]0.732228[/C][/ROW]
[ROW][C]46[/C][C]0.35524[/C][C]0.710481[/C][C]0.64476[/C][/ROW]
[ROW][C]47[/C][C]0.366644[/C][C]0.733287[/C][C]0.633356[/C][/ROW]
[ROW][C]48[/C][C]0.364003[/C][C]0.728006[/C][C]0.635997[/C][/ROW]
[ROW][C]49[/C][C]0.426247[/C][C]0.852493[/C][C]0.573753[/C][/ROW]
[ROW][C]50[/C][C]0.523972[/C][C]0.952056[/C][C]0.476028[/C][/ROW]
[ROW][C]51[/C][C]0.512055[/C][C]0.97589[/C][C]0.487945[/C][/ROW]
[ROW][C]52[/C][C]0.474333[/C][C]0.948666[/C][C]0.525667[/C][/ROW]
[ROW][C]53[/C][C]0.424978[/C][C]0.849956[/C][C]0.575022[/C][/ROW]
[ROW][C]54[/C][C]0.431316[/C][C]0.862631[/C][C]0.568684[/C][/ROW]
[ROW][C]55[/C][C]0.388156[/C][C]0.776312[/C][C]0.611844[/C][/ROW]
[ROW][C]56[/C][C]0.34115[/C][C]0.6823[/C][C]0.65885[/C][/ROW]
[ROW][C]57[/C][C]0.315231[/C][C]0.630463[/C][C]0.684769[/C][/ROW]
[ROW][C]58[/C][C]0.287907[/C][C]0.575814[/C][C]0.712093[/C][/ROW]
[ROW][C]59[/C][C]0.275284[/C][C]0.550567[/C][C]0.724716[/C][/ROW]
[ROW][C]60[/C][C]0.326375[/C][C]0.652751[/C][C]0.673625[/C][/ROW]
[ROW][C]61[/C][C]0.427339[/C][C]0.854678[/C][C]0.572661[/C][/ROW]
[ROW][C]62[/C][C]0.470683[/C][C]0.941367[/C][C]0.529317[/C][/ROW]
[ROW][C]63[/C][C]0.523711[/C][C]0.952578[/C][C]0.476289[/C][/ROW]
[ROW][C]64[/C][C]0.48944[/C][C]0.978881[/C][C]0.51056[/C][/ROW]
[ROW][C]65[/C][C]0.44746[/C][C]0.89492[/C][C]0.55254[/C][/ROW]
[ROW][C]66[/C][C]0.457904[/C][C]0.915808[/C][C]0.542096[/C][/ROW]
[ROW][C]67[/C][C]0.468868[/C][C]0.937736[/C][C]0.531132[/C][/ROW]
[ROW][C]68[/C][C]0.488518[/C][C]0.977036[/C][C]0.511482[/C][/ROW]
[ROW][C]69[/C][C]0.441413[/C][C]0.882827[/C][C]0.558587[/C][/ROW]
[ROW][C]70[/C][C]0.798504[/C][C]0.402992[/C][C]0.201496[/C][/ROW]
[ROW][C]71[/C][C]0.812772[/C][C]0.374456[/C][C]0.187228[/C][/ROW]
[ROW][C]72[/C][C]0.794386[/C][C]0.411228[/C][C]0.205614[/C][/ROW]
[ROW][C]73[/C][C]0.768556[/C][C]0.462889[/C][C]0.231444[/C][/ROW]
[ROW][C]74[/C][C]0.771905[/C][C]0.456189[/C][C]0.228095[/C][/ROW]
[ROW][C]75[/C][C]0.740839[/C][C]0.518323[/C][C]0.259161[/C][/ROW]
[ROW][C]76[/C][C]0.700198[/C][C]0.599604[/C][C]0.299802[/C][/ROW]
[ROW][C]77[/C][C]0.65801[/C][C]0.68398[/C][C]0.34199[/C][/ROW]
[ROW][C]78[/C][C]0.648995[/C][C]0.70201[/C][C]0.351005[/C][/ROW]
[ROW][C]79[/C][C]0.657125[/C][C]0.685749[/C][C]0.342875[/C][/ROW]
[ROW][C]80[/C][C]0.614416[/C][C]0.771169[/C][C]0.385584[/C][/ROW]
[ROW][C]81[/C][C]0.596226[/C][C]0.807547[/C][C]0.403774[/C][/ROW]
[ROW][C]82[/C][C]0.61834[/C][C]0.76332[/C][C]0.38166[/C][/ROW]
[ROW][C]83[/C][C]0.576497[/C][C]0.847006[/C][C]0.423503[/C][/ROW]
[ROW][C]84[/C][C]0.53732[/C][C]0.92536[/C][C]0.46268[/C][/ROW]
[ROW][C]85[/C][C]0.501097[/C][C]0.997806[/C][C]0.498903[/C][/ROW]
[ROW][C]86[/C][C]0.509217[/C][C]0.981567[/C][C]0.490783[/C][/ROW]
[ROW][C]87[/C][C]0.458093[/C][C]0.916187[/C][C]0.541907[/C][/ROW]
[ROW][C]88[/C][C]0.439622[/C][C]0.879245[/C][C]0.560378[/C][/ROW]
[ROW][C]89[/C][C]0.39249[/C][C]0.78498[/C][C]0.60751[/C][/ROW]
[ROW][C]90[/C][C]0.348021[/C][C]0.696041[/C][C]0.651979[/C][/ROW]
[ROW][C]91[/C][C]0.301609[/C][C]0.603218[/C][C]0.698391[/C][/ROW]
[ROW][C]92[/C][C]0.255039[/C][C]0.510079[/C][C]0.744961[/C][/ROW]
[ROW][C]93[/C][C]0.320726[/C][C]0.641451[/C][C]0.679274[/C][/ROW]
[ROW][C]94[/C][C]0.336365[/C][C]0.67273[/C][C]0.663635[/C][/ROW]
[ROW][C]95[/C][C]0.500744[/C][C]0.998511[/C][C]0.499256[/C][/ROW]
[ROW][C]96[/C][C]0.463458[/C][C]0.926915[/C][C]0.536542[/C][/ROW]
[ROW][C]97[/C][C]0.445751[/C][C]0.891501[/C][C]0.554249[/C][/ROW]
[ROW][C]98[/C][C]0.392585[/C][C]0.785171[/C][C]0.607415[/C][/ROW]
[ROW][C]99[/C][C]0.361193[/C][C]0.722386[/C][C]0.638807[/C][/ROW]
[ROW][C]100[/C][C]0.328474[/C][C]0.656947[/C][C]0.671526[/C][/ROW]
[ROW][C]101[/C][C]0.281988[/C][C]0.563977[/C][C]0.718012[/C][/ROW]
[ROW][C]102[/C][C]0.799856[/C][C]0.400287[/C][C]0.200144[/C][/ROW]
[ROW][C]103[/C][C]0.763172[/C][C]0.473656[/C][C]0.236828[/C][/ROW]
[ROW][C]104[/C][C]0.714174[/C][C]0.571652[/C][C]0.285826[/C][/ROW]
[ROW][C]105[/C][C]0.690851[/C][C]0.618297[/C][C]0.309149[/C][/ROW]
[ROW][C]106[/C][C]0.707556[/C][C]0.584888[/C][C]0.292444[/C][/ROW]
[ROW][C]107[/C][C]0.721274[/C][C]0.557452[/C][C]0.278726[/C][/ROW]
[ROW][C]108[/C][C]0.673323[/C][C]0.653355[/C][C]0.326677[/C][/ROW]
[ROW][C]109[/C][C]0.62732[/C][C]0.74536[/C][C]0.37268[/C][/ROW]
[ROW][C]110[/C][C]0.577586[/C][C]0.844828[/C][C]0.422414[/C][/ROW]
[ROW][C]111[/C][C]0.559407[/C][C]0.881186[/C][C]0.440593[/C][/ROW]
[ROW][C]112[/C][C]0.510025[/C][C]0.979949[/C][C]0.489975[/C][/ROW]
[ROW][C]113[/C][C]0.480636[/C][C]0.961272[/C][C]0.519364[/C][/ROW]
[ROW][C]114[/C][C]0.432821[/C][C]0.865642[/C][C]0.567179[/C][/ROW]
[ROW][C]115[/C][C]0.370303[/C][C]0.740605[/C][C]0.629697[/C][/ROW]
[ROW][C]116[/C][C]0.3132[/C][C]0.6264[/C][C]0.6868[/C][/ROW]
[ROW][C]117[/C][C]0.243308[/C][C]0.486617[/C][C]0.756692[/C][/ROW]
[ROW][C]118[/C][C]0.222022[/C][C]0.444044[/C][C]0.777978[/C][/ROW]
[ROW][C]119[/C][C]0.217794[/C][C]0.435588[/C][C]0.782206[/C][/ROW]
[ROW][C]120[/C][C]0.15371[/C][C]0.307419[/C][C]0.84629[/C][/ROW]
[ROW][C]121[/C][C]0.481898[/C][C]0.963796[/C][C]0.518102[/C][/ROW]
[ROW][C]122[/C][C]0.594911[/C][C]0.810178[/C][C]0.405089[/C][/ROW]
[ROW][C]123[/C][C]0.560338[/C][C]0.879324[/C][C]0.439662[/C][/ROW]
[ROW][C]124[/C][C]0.582208[/C][C]0.835583[/C][C]0.417792[/C][/ROW]
[ROW][C]125[/C][C]0.648819[/C][C]0.702362[/C][C]0.351181[/C][/ROW]
[ROW][C]126[/C][C]0.529799[/C][C]0.940401[/C][C]0.470201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.4200850.840170.579915
180.3158830.6317660.684117
190.2026250.4052510.797375
200.118410.236820.88159
210.07017330.1403470.929827
220.04714230.09428460.952858
230.05519520.110390.944805
240.06265080.1253020.937349
250.08257350.1651470.917426
260.09161060.1832210.908389
270.1147090.2294180.885291
280.1550310.3100630.844969
290.1836810.3673610.816319
300.4011710.8023430.598829
310.3775290.7550580.622471
320.3088360.6176720.691164
330.2568490.5136980.743151
340.2636120.5272230.736388
350.2097040.4194090.790296
360.4021740.8043480.597826
370.3412630.6825260.658737
380.3023080.6046170.697692
390.2663940.5327880.733606
400.2215790.4431580.778421
410.2065550.4131110.793445
420.2159380.4318750.784062
430.1790310.3580630.820969
440.314370.6287390.68563
450.2677720.5355440.732228
460.355240.7104810.64476
470.3666440.7332870.633356
480.3640030.7280060.635997
490.4262470.8524930.573753
500.5239720.9520560.476028
510.5120550.975890.487945
520.4743330.9486660.525667
530.4249780.8499560.575022
540.4313160.8626310.568684
550.3881560.7763120.611844
560.341150.68230.65885
570.3152310.6304630.684769
580.2879070.5758140.712093
590.2752840.5505670.724716
600.3263750.6527510.673625
610.4273390.8546780.572661
620.4706830.9413670.529317
630.5237110.9525780.476289
640.489440.9788810.51056
650.447460.894920.55254
660.4579040.9158080.542096
670.4688680.9377360.531132
680.4885180.9770360.511482
690.4414130.8828270.558587
700.7985040.4029920.201496
710.8127720.3744560.187228
720.7943860.4112280.205614
730.7685560.4628890.231444
740.7719050.4561890.228095
750.7408390.5183230.259161
760.7001980.5996040.299802
770.658010.683980.34199
780.6489950.702010.351005
790.6571250.6857490.342875
800.6144160.7711690.385584
810.5962260.8075470.403774
820.618340.763320.38166
830.5764970.8470060.423503
840.537320.925360.46268
850.5010970.9978060.498903
860.5092170.9815670.490783
870.4580930.9161870.541907
880.4396220.8792450.560378
890.392490.784980.60751
900.3480210.6960410.651979
910.3016090.6032180.698391
920.2550390.5100790.744961
930.3207260.6414510.679274
940.3363650.672730.663635
950.5007440.9985110.499256
960.4634580.9269150.536542
970.4457510.8915010.554249
980.3925850.7851710.607415
990.3611930.7223860.638807
1000.3284740.6569470.671526
1010.2819880.5639770.718012
1020.7998560.4002870.200144
1030.7631720.4736560.236828
1040.7141740.5716520.285826
1050.6908510.6182970.309149
1060.7075560.5848880.292444
1070.7212740.5574520.278726
1080.6733230.6533550.326677
1090.627320.745360.37268
1100.5775860.8448280.422414
1110.5594070.8811860.440593
1120.5100250.9799490.489975
1130.4806360.9612720.519364
1140.4328210.8656420.567179
1150.3703030.7406050.629697
1160.31320.62640.6868
1170.2433080.4866170.756692
1180.2220220.4440440.777978
1190.2177940.4355880.782206
1200.153710.3074190.84629
1210.4818980.9637960.518102
1220.5949110.8101780.405089
1230.5603380.8793240.439662
1240.5822080.8355830.417792
1250.6488190.7023620.351181
1260.5297990.9404010.470201






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 level10.00909091OK

\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 & 1 & 0.00909091 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270984&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]1[/C][C]0.00909091[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270984&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270984&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 level10.00909091OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}