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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 15:09:17 +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/t1418915393frqo7e6w024q9ot.htm/, Retrieved Fri, 17 May 2024 15:17:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271046, Retrieved Fri, 17 May 2024 15:17:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

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] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:09:17] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 18 12 4 21 13 12 149 18 68 1.8
12.2 18 20 23 20 4 22 8 8 139 31 39 2.1
12.8 12 9 22 14 5 21 14 11 148 39 32 2.2
7.4 24 19 22 25 4 21 16 13 158 46 62 2.3
6.7 16 12 19 15 4 21 14 11 128 31 33 2.1
12.6 19 16 25 20 9 21 13 10 224 67 52 2.7
14.8 16 17 28 21 8 21 15 7 159 35 62 2.1
13.3 15 9 16 15 11 23 13 10 105 52 77 2.4
11.1 28 28 28 28 4 22 20 15 159 77 76 2.9
8.2 21 20 21 11 4 25 17 12 167 37 41 2.2
11.4 18 16 22 22 6 21 15 12 165 32 48 2.1
6.4 22 22 24 22 4 23 16 10 159 36 63 2.2
10.6 19 17 24 27 8 22 12 10 119 38 30 2.2
12 22 12 26 24 4 21 17 14 176 69 78 2.7
6.3 25 18 28 23 4 21 11 6 54 21 19 1.9
11.9 16 12 20 21 4 21 16 14 163 54 66 2.5
9.3 19 16 26 20 4 21 15 11 124 36 35 2.2
10 26 21 28 25 6 24 14 12 121 23 45 1.9
6.4 24 15 27 16 4 23 19 15 153 34 21 2.1
13.8 20 17 23 24 8 21 16 13 148 112 25 3.5
10.8 19 17 24 21 5 24 17 11 221 35 44 2.1
13.8 19 17 24 22 4 23 10 12 188 47 69 2.3
11.7 23 18 22 25 9 21 15 7 149 47 54 2.3
10.9 18 15 21 23 4 22 14 11 244 37 74 2.2
9.9 21 21 21 22 4 21 15 12 150 20 61 1.9
11.5 20 12 26 25 4 22 17 13 153 22 41 1.9
8.3 15 6 23 23 7 22 14 9 94 23 46 1.9
11.7 19 13 21 19 12 21 16 11 156 32 39 2.1
9 19 19 27 21 7 21 15 12 132 30 34 2
9.7 7 12 25 19 5 25 16 15 161 92 51 3.2
10.8 20 14 23 25 8 22 16 12 105 43 42 2.3
10.3 20 13 25 16 5 22 10 6 97 55 31 2.5
10.4 19 12 23 24 4 20 8 5 151 16 39 1.8
9.3 20 19 22 18 7 21 14 11 166 71 49 2.8
11.8 18 10 24 28 4 21 10 6 157 43 53 2.3
5.9 14 10 19 15 4 22 14 12 111 29 31 2
11.4 17 11 21 17 4 21 12 10 145 56 39 2.5
13 17 11 27 18 4 24 16 6 162 46 54 2.3
10.8 8 10 25 26 4 22 16 12 163 19 49 1.8
11.3 22 22 23 22 4 21 8 6 187 59 46 2.6
11.8 20 12 17 19 7 22 16 12 109 30 55 2
12.7 22 20 25 26 4 22 8 8 105 7 50 1.6
10.9 14 11 24 12 4 23 16 12 148 19 30 1.8
13.3 21 17 20 20 4 23 19 14 125 48 45 2.4
10.1 20 14 19 24 4 21 14 12 116 23 35 1.9
14.3 18 16 21 22 12 21 13 14 138 33 41 2.1
9.3 24 15 18 23 4 22 15 11 164 34 73 2.1
12.5 19 15 27 19 5 21 11 10 162 48 17 2.4
7.6 16 10 25 24 15 21 9 7 99 18 40 1.8
15.9 16 10 20 21 5 21 16 12 202 43 64 2.3
9.2 16 18 21 16 10 21 12 7 186 33 37 2.1
11.1 22 22 27 23 8 21 14 12 183 71 65 2.8
13 21 16 24 20 4 22 14 10 214 26 100 2
14.5 15 10 27 19 5 22 13 10 188 67 28 2.7
12.3 15 16 23 18 9 21 17 12 177 80 56 2.9
11.4 14 16 24 21 4 23 14 12 126 29 29 2
13 16 10 25 17 7 21 15 10 162 43 59 2.3
13.2 26 16 24 24 4 20 15 11 159 29 61 2
7.7 18 16 23 22 4 21 16 12 110 32 51 2.1
4.35 17 15 22 14 6 22 16 9 48 23 12 1
12.7 6 4 24 5 4 22 16 11 50 16 45 1
18.1 22 9 19 25 8 22 16 12 150 33 37 4
17.85 20 18 25 21 5 20 16 12 154 32 37 4
17.1 17 12 24 9 4 22 14 12 194 52 68 4
19.1 20 16 28 15 4 21 15 12 158 75 72 4
16.1 23 17 23 23 8 21 16 10 159 72 143 4
13.35 18 14 19 21 4 21 13 15 67 15 9 2
18.4 13 13 19 9 7 21 10 10 147 29 55 4
14.7 22 20 27 24 4 21 17 15 39 13 17 1
10.6 20 16 24 16 4 21 15 10 100 40 37 3
12.6 20 15 26 20 5 21 18 15 111 19 27 3
13.6 16 16 25 18 4 24 20 15 101 121 58 3
14.1 16 15 19 21 7 22 17 13 101 36 21 3
14.5 15 16 20 21 11 20 16 12 114 23 19 3
16.15 19 19 26 21 7 21 15 12 165 85 78 4
14.75 19 9 27 20 4 24 13 8 114 41 35 3
14.8 24 19 23 24 4 25 16 9 111 46 48 3
12.45 9 7 18 15 4 22 16 15 75 18 27 2
12.65 22 23 23 24 4 21 16 12 82 35 43 2
17.35 15 14 21 18 4 21 17 12 121 17 30 3
8.6 22 10 23 24 4 22 20 15 32 4 25 1
18.4 22 16 22 24 6 23 14 11 150 28 69 4
16.1 24 12 21 15 8 24 17 12 117 44 72 3
17.75 21 7 24 20 4 22 16 14 165 38 13 4
15.25 25 20 26 26 8 25 15 12 154 57 61 4
17.65 26 9 24 26 6 22 16 12 126 23 43 4
16.35 21 12 22 23 4 21 16 12 149 36 51 4
17.65 14 10 20 13 7 21 14 11 145 22 67 4
13.6 28 19 20 16 4 21 16 12 120 40 36 3
14.35 21 11 18 22 4 22 16 12 109 31 44 3
14.75 16 15 18 21 4 22 16 12 132 11 45 4
18.25 16 14 25 11 10 21 14 12 172 38 34 4
9.9 25 11 28 23 6 22 14 8 169 24 36 4
16 21 14 23 18 5 23 16 8 114 37 72 3
18.25 22 15 20 19 5 21 16 12 156 37 39 4
16.85 9 7 22 15 4 21 15 12 172 22 43 4
18.95 24 22 23 21 5 21 18 11 167 43 80 4
15.6 22 11 20 25 5 21 15 12 113 31 40 3
17.1 10 12 24 12 4 22 14 10 173 31 61 4
15.4 21 13 23 19 8 21 15 11 165 21 29 4
15.4 20 15 21 21 8 21 15 11 165 21 29 4
13.35 17 11 19 19 8 25 16 13 118 32 54 3
19.1 7 7 19 18 4 21 11 7 158 26 43 4
7.6 14 13 25 23 9 25 7 8 49 32 20 1
19.1 23 7 18 23 4 22 15 11 155 33 61 4
14.75 18 11 22 27 4 21 14 8 151 30 57 4
19.25 17 22 5 6 28 23 16 14 220 67 54 4
13.6 20 15 24 22 4 20 14 9 141 22 36 4
12.75 19 15 28 23 5 22 11 13 122 33 16 4
9.85 19 11 27 20 4 25 18 13 44 24 40 1
15.25 23 10 23 23 5 20 18 11 152 28 27 4
11.9 20 18 24 27 4 21 15 9 107 41 61 3
16.35 19 14 25 24 4 21 13 12 154 31 69 4
12.4 16 16 19 12 10 23 13 12 103 33 34 3
18.15 21 16 24 24 4 22 18 13 175 21 34 4
17.75 20 17 28 24 4 21 15 11 143 52 34 4
12.35 20 14 19 19 5 21 16 11 110 29 13 3
15.6 19 10 23 28 8 21 12 9 131 11 12 4
19.3 19 16 23 23 6 21 16 12 167 26 51 4
17.1 20 16 26 19 4 21 16 15 137 7 19 4
18.4 22 17 25 23 4 21 19 14 121 13 81 3
19.05 19 12 24 20 5 21 15 12 149 20 42 4
18.55 23 17 23 18 5 22 14 9 168 52 22 4
19.1 16 11 22 20 6 21 14 9 140 28 85 4
12.85 18 12 26 21 4 22 16 13 168 39 25 4
9.5 23 8 23 25 4 22 20 15 94 9 22 2
4.5 20 17 22 18 6 22 16 11 51 19 19 1
13.6 23 17 22 28 10 22 13 10 145 60 45 4
11.7 13 7 17 9 4 23 15 11 66 19 45 2
13.35 26 18 22 26 4 22 16 14 109 14 51 3
17.6 13 14 26 12 4 21 19 12 164 -2 73 4
14.05 10 13 24 12 14 21 13 13 119 51 24 3
16.1 21 19 27 20 5 20 14 11 126 2 61 4
13.35 24 15 22 25 5 20 15 11 132 24 23 4
11.85 21 15 23 24 5 21 15 13 142 40 14 4
11.95 23 8 22 23 5 21 14 12 83 20 54 2
13.2 16 11 20 22 16 21 12 9 166 20 36 4
7.7 26 17 27 28 7 24 15 13 93 25 26 2
14.6 16 12 20 15 5 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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.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=271046&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.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=271046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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] = + 8.86045 -0.0453268AMS.I2[t] -0.0467606AMS.I3[t] -0.0170421AMS.E1[t] -0.0474547AMS.E2[t] + 0.013755AMS.A[t] -0.127676age[t] -0.0313013CONFSTATTOT[t] + 0.160124CONFSOFTTOT[t] -0.00301755LFM[t] -0.0210861PRH[t] + 0.0377348CH[t] + 2.80874PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.86045 -0.0453268AMS.I2[t] -0.0467606AMS.I3[t] -0.0170421AMS.E1[t] -0.0474547AMS.E2[t] +  0.013755AMS.A[t] -0.127676age[t] -0.0313013CONFSTATTOT[t] +  0.160124CONFSOFTTOT[t] -0.00301755LFM[t] -0.0210861PRH[t] +  0.0377348CH[t] +  2.80874PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.86045 -0.0453268AMS.I2[t] -0.0467606AMS.I3[t] -0.0170421AMS.E1[t] -0.0474547AMS.E2[t] +  0.013755AMS.A[t] -0.127676age[t] -0.0313013CONFSTATTOT[t] +  0.160124CONFSOFTTOT[t] -0.00301755LFM[t] -0.0210861PRH[t] +  0.0377348CH[t] +  2.80874PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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] = + 8.86045 -0.0453268AMS.I2[t] -0.0467606AMS.I3[t] -0.0170421AMS.E1[t] -0.0474547AMS.E2[t] + 0.013755AMS.A[t] -0.127676age[t] -0.0313013CONFSTATTOT[t] + 0.160124CONFSOFTTOT[t] -0.00301755LFM[t] -0.0210861PRH[t] + 0.0377348CH[t] + 2.80874PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.860454.424252.0030.04735590.0236779
AMS.I2-0.04532680.0630473-0.71890.4735130.236757
AMS.I3-0.04676060.0583579-0.80130.4244830.212241
AMS.E1-0.01704210.0690376-0.24690.8054240.402712
AMS.E2-0.04745470.051184-0.92710.3556270.177813
AMS.A0.0137550.07148580.19240.8477270.423863
age-0.1276760.172852-0.73860.4614990.230749
CONFSTATTOT-0.03130130.108296-0.2890.7730310.386515
CONFSOFTTOT0.1601240.1172831.3650.17460.0873002
LFM-0.003017550.00618552-0.48780.626510.313255
PRH-0.02108610.0111031-1.8990.0598350.0299175
CH0.03773480.01073893.5140.0006141590.00030708
PR2.808740.23979311.716.83596e-223.41798e-22

\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) & 8.86045 & 4.42425 & 2.003 & 0.0473559 & 0.0236779 \tabularnewline
AMS.I2 & -0.0453268 & 0.0630473 & -0.7189 & 0.473513 & 0.236757 \tabularnewline
AMS.I3 & -0.0467606 & 0.0583579 & -0.8013 & 0.424483 & 0.212241 \tabularnewline
AMS.E1 & -0.0170421 & 0.0690376 & -0.2469 & 0.805424 & 0.402712 \tabularnewline
AMS.E2 & -0.0474547 & 0.051184 & -0.9271 & 0.355627 & 0.177813 \tabularnewline
AMS.A & 0.013755 & 0.0714858 & 0.1924 & 0.847727 & 0.423863 \tabularnewline
age & -0.127676 & 0.172852 & -0.7386 & 0.461499 & 0.230749 \tabularnewline
CONFSTATTOT & -0.0313013 & 0.108296 & -0.289 & 0.773031 & 0.386515 \tabularnewline
CONFSOFTTOT & 0.160124 & 0.117283 & 1.365 & 0.1746 & 0.0873002 \tabularnewline
LFM & -0.00301755 & 0.00618552 & -0.4878 & 0.62651 & 0.313255 \tabularnewline
PRH & -0.0210861 & 0.0111031 & -1.899 & 0.059835 & 0.0299175 \tabularnewline
CH & 0.0377348 & 0.0107389 & 3.514 & 0.000614159 & 0.00030708 \tabularnewline
PR & 2.80874 & 0.239793 & 11.71 & 6.83596e-22 & 3.41798e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&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]8.86045[/C][C]4.42425[/C][C]2.003[/C][C]0.0473559[/C][C]0.0236779[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0453268[/C][C]0.0630473[/C][C]-0.7189[/C][C]0.473513[/C][C]0.236757[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0467606[/C][C]0.0583579[/C][C]-0.8013[/C][C]0.424483[/C][C]0.212241[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0170421[/C][C]0.0690376[/C][C]-0.2469[/C][C]0.805424[/C][C]0.402712[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0474547[/C][C]0.051184[/C][C]-0.9271[/C][C]0.355627[/C][C]0.177813[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.013755[/C][C]0.0714858[/C][C]0.1924[/C][C]0.847727[/C][C]0.423863[/C][/ROW]
[ROW][C]age[/C][C]-0.127676[/C][C]0.172852[/C][C]-0.7386[/C][C]0.461499[/C][C]0.230749[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0313013[/C][C]0.108296[/C][C]-0.289[/C][C]0.773031[/C][C]0.386515[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.160124[/C][C]0.117283[/C][C]1.365[/C][C]0.1746[/C][C]0.0873002[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00301755[/C][C]0.00618552[/C][C]-0.4878[/C][C]0.62651[/C][C]0.313255[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0210861[/C][C]0.0111031[/C][C]-1.899[/C][C]0.059835[/C][C]0.0299175[/C][/ROW]
[ROW][C]CH[/C][C]0.0377348[/C][C]0.0107389[/C][C]3.514[/C][C]0.000614159[/C][C]0.00030708[/C][/ROW]
[ROW][C]PR[/C][C]2.80874[/C][C]0.239793[/C][C]11.71[/C][C]6.83596e-22[/C][C]3.41798e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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)8.860454.424252.0030.04735590.0236779
AMS.I2-0.04532680.0630473-0.71890.4735130.236757
AMS.I3-0.04676060.0583579-0.80130.4244830.212241
AMS.E1-0.01704210.0690376-0.24690.8054240.402712
AMS.E2-0.04745470.051184-0.92710.3556270.177813
AMS.A0.0137550.07148580.19240.8477270.423863
age-0.1276760.172852-0.73860.4614990.230749
CONFSTATTOT-0.03130130.108296-0.2890.7730310.386515
CONFSOFTTOT0.1601240.1172831.3650.17460.0873002
LFM-0.003017550.00618552-0.48780.626510.313255
PRH-0.02108610.0111031-1.8990.0598350.0299175
CH0.03773480.01073893.5140.0006141590.00030708
PR2.808740.23979311.716.83596e-223.41798e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.793292
R-squared0.629312
Adjusted R-squared0.594008
F-TEST (value)17.8257
F-TEST (DF numerator)12
F-TEST (DF denominator)126
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23262
Sum Squared Residuals628.056

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.793292 \tabularnewline
R-squared & 0.629312 \tabularnewline
Adjusted R-squared & 0.594008 \tabularnewline
F-TEST (value) & 17.8257 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 126 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.23262 \tabularnewline
Sum Squared Residuals & 628.056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.793292[/C][/ROW]
[ROW][C]R-squared[/C][C]0.629312[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.594008[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.8257[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]126[/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]2.23262[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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 R0.793292
R-squared0.629312
Adjusted R-squared0.594008
F-TEST (value)17.8257
F-TEST (DF numerator)12
F-TEST (DF denominator)126
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23262
Sum Squared Residuals628.056






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9752-0.075231
212.210.34191.85806
312.811.68491.11509
47.411.6304-4.23041
56.711.3391-4.63914
612.611.970.630036
714.810.96693.83313
813.313.4175-0.117532
911.112.4079-1.30785
108.210.7882-2.58822
1111.411.26780.132249
126.410.918-4.51804
1310.610.19170.408263
141213.3445-1.34445
156.38.75503-2.45503
1611.913.2333-1.33329
179.310.8912-1.59123
18109.722590.277414
196.410.2896-3.88957
2013.812.60351.19653
2110.810.18630.613654
2213.811.98371.81629
2311.710.5661.13397
2410.911.9183-1.01825
259.911.1146-1.2146
2611.510.51710.982878
278.311.0107-2.71065
2811.711.10080.599238
29910.3907-1.39073
309.713.919-4.21896
3110.811.2642-0.464191
3210.310.8075-0.507535
3310.49.693310.706687
349.312.29-2.99005
3511.810.94040.859627
365.911.2929-5.39289
3711.411.8855-0.485513
381310.75092.24909
3910.811.0494-0.249417
4011.310.71290.587112
4111.811.64090.159083
4212.79.461193.23881
4310.910.61270.28727
4413.311.63891.66107
4510.110.516-0.415961
4614.311.54642.75358
479.311.6522-2.35217
4812.510.46212.03794
497.610.3538-2.75378
5015.912.49743.40264
519.210.4154-1.21542
5211.112.463-1.36296
531312.40850.591545
5414.511.46553.03449
5512.313.0557-0.7557
5611.410.3941.00602
571312.27260.72744
5813.211.00722.19277
597.711.4711-3.77107
604.357.19489-2.84489
6112.710.28042.41959
6218.116.13681.96323
6317.8516.11721.73276
6417.117.5411-0.441137
6519.116.73622.36382
6616.118.7019-2.6019
6713.3510.8772.47303
6818.417.8710.528955
6914.77.630997.06901
7010.613.2202-2.62022
7112.613.7958-1.19581
7213.612.63210.967897
7314.113.10520.994846
7414.513.42761.07235
7516.1516.4263-0.276274
7614.7512.57252.17755
7714.812.08932.71072
7812.4512.28430.165747
7912.6510.3062.34399
8017.3513.91173.43829
818.68.457960.142037
8218.416.8661.53399
8316.114.43921.66075
8417.7515.63662.11342
8515.2515.3554-0.105443
8617.6516.3051.34503
8716.3516.6263-0.276297
8817.6518.4005-0.750512
8913.612.97640.623646
9014.3513.81430.535748
9114.7517.1001-2.35009
9218.2516.66981.58019
939.915.3381-5.4381
941613.93912.06087
9518.2516.18332.06668
9616.8517.7389-0.888883
9718.9516.7842.166
9815.613.60221.9978
9917.117.6378-0.537845
10015.416.1164-0.716356
10115.416.0073-0.607336
10213.3514.2821-0.932101
10319.117.02082.07921
1047.66.835080.764916
10519.117.00362.09638
10614.7516.3882-1.63822
10719.2517.07672.17325
10813.616.0079-2.40794
10912.7515.5011-2.75114
1109.858.136251.71375
11115.2515.7848-0.534828
11211.913.308-1.408
11316.3517.3883-1.03832
11412.413.9119-1.51193
11518.1515.92392.2261
11617.7515.19852.55147
11712.3512.6953-0.345303
11815.615.36580.234178
11919.316.69692.60308
12017.116.52680.573203
12118.415.41522.98484
12219.0516.86812.18195
12318.5514.50144.04864
12419.118.13060.969374
12512.8515.7198-2.86977
1269.510.8617-1.36172
1274.57.43525-2.93525
12813.615.0727-1.47267
12911.712.3531-0.653145
13013.3513.9452-0.595161
13117.618.8415-1.24149
13214.0513.90430.145719
13316.117.5637-1.46371
13413.3515.5155-2.16549
13511.8515.1673-3.31729
13611.9511.83130.1187
13713.216.5112-3.31118
1387.79.53312-1.83312
13914.613.60590.99405

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9752 & -0.075231 \tabularnewline
2 & 12.2 & 10.3419 & 1.85806 \tabularnewline
3 & 12.8 & 11.6849 & 1.11509 \tabularnewline
4 & 7.4 & 11.6304 & -4.23041 \tabularnewline
5 & 6.7 & 11.3391 & -4.63914 \tabularnewline
6 & 12.6 & 11.97 & 0.630036 \tabularnewline
7 & 14.8 & 10.9669 & 3.83313 \tabularnewline
8 & 13.3 & 13.4175 & -0.117532 \tabularnewline
9 & 11.1 & 12.4079 & -1.30785 \tabularnewline
10 & 8.2 & 10.7882 & -2.58822 \tabularnewline
11 & 11.4 & 11.2678 & 0.132249 \tabularnewline
12 & 6.4 & 10.918 & -4.51804 \tabularnewline
13 & 10.6 & 10.1917 & 0.408263 \tabularnewline
14 & 12 & 13.3445 & -1.34445 \tabularnewline
15 & 6.3 & 8.75503 & -2.45503 \tabularnewline
16 & 11.9 & 13.2333 & -1.33329 \tabularnewline
17 & 9.3 & 10.8912 & -1.59123 \tabularnewline
18 & 10 & 9.72259 & 0.277414 \tabularnewline
19 & 6.4 & 10.2896 & -3.88957 \tabularnewline
20 & 13.8 & 12.6035 & 1.19653 \tabularnewline
21 & 10.8 & 10.1863 & 0.613654 \tabularnewline
22 & 13.8 & 11.9837 & 1.81629 \tabularnewline
23 & 11.7 & 10.566 & 1.13397 \tabularnewline
24 & 10.9 & 11.9183 & -1.01825 \tabularnewline
25 & 9.9 & 11.1146 & -1.2146 \tabularnewline
26 & 11.5 & 10.5171 & 0.982878 \tabularnewline
27 & 8.3 & 11.0107 & -2.71065 \tabularnewline
28 & 11.7 & 11.1008 & 0.599238 \tabularnewline
29 & 9 & 10.3907 & -1.39073 \tabularnewline
30 & 9.7 & 13.919 & -4.21896 \tabularnewline
31 & 10.8 & 11.2642 & -0.464191 \tabularnewline
32 & 10.3 & 10.8075 & -0.507535 \tabularnewline
33 & 10.4 & 9.69331 & 0.706687 \tabularnewline
34 & 9.3 & 12.29 & -2.99005 \tabularnewline
35 & 11.8 & 10.9404 & 0.859627 \tabularnewline
36 & 5.9 & 11.2929 & -5.39289 \tabularnewline
37 & 11.4 & 11.8855 & -0.485513 \tabularnewline
38 & 13 & 10.7509 & 2.24909 \tabularnewline
39 & 10.8 & 11.0494 & -0.249417 \tabularnewline
40 & 11.3 & 10.7129 & 0.587112 \tabularnewline
41 & 11.8 & 11.6409 & 0.159083 \tabularnewline
42 & 12.7 & 9.46119 & 3.23881 \tabularnewline
43 & 10.9 & 10.6127 & 0.28727 \tabularnewline
44 & 13.3 & 11.6389 & 1.66107 \tabularnewline
45 & 10.1 & 10.516 & -0.415961 \tabularnewline
46 & 14.3 & 11.5464 & 2.75358 \tabularnewline
47 & 9.3 & 11.6522 & -2.35217 \tabularnewline
48 & 12.5 & 10.4621 & 2.03794 \tabularnewline
49 & 7.6 & 10.3538 & -2.75378 \tabularnewline
50 & 15.9 & 12.4974 & 3.40264 \tabularnewline
51 & 9.2 & 10.4154 & -1.21542 \tabularnewline
52 & 11.1 & 12.463 & -1.36296 \tabularnewline
53 & 13 & 12.4085 & 0.591545 \tabularnewline
54 & 14.5 & 11.4655 & 3.03449 \tabularnewline
55 & 12.3 & 13.0557 & -0.7557 \tabularnewline
56 & 11.4 & 10.394 & 1.00602 \tabularnewline
57 & 13 & 12.2726 & 0.72744 \tabularnewline
58 & 13.2 & 11.0072 & 2.19277 \tabularnewline
59 & 7.7 & 11.4711 & -3.77107 \tabularnewline
60 & 4.35 & 7.19489 & -2.84489 \tabularnewline
61 & 12.7 & 10.2804 & 2.41959 \tabularnewline
62 & 18.1 & 16.1368 & 1.96323 \tabularnewline
63 & 17.85 & 16.1172 & 1.73276 \tabularnewline
64 & 17.1 & 17.5411 & -0.441137 \tabularnewline
65 & 19.1 & 16.7362 & 2.36382 \tabularnewline
66 & 16.1 & 18.7019 & -2.6019 \tabularnewline
67 & 13.35 & 10.877 & 2.47303 \tabularnewline
68 & 18.4 & 17.871 & 0.528955 \tabularnewline
69 & 14.7 & 7.63099 & 7.06901 \tabularnewline
70 & 10.6 & 13.2202 & -2.62022 \tabularnewline
71 & 12.6 & 13.7958 & -1.19581 \tabularnewline
72 & 13.6 & 12.6321 & 0.967897 \tabularnewline
73 & 14.1 & 13.1052 & 0.994846 \tabularnewline
74 & 14.5 & 13.4276 & 1.07235 \tabularnewline
75 & 16.15 & 16.4263 & -0.276274 \tabularnewline
76 & 14.75 & 12.5725 & 2.17755 \tabularnewline
77 & 14.8 & 12.0893 & 2.71072 \tabularnewline
78 & 12.45 & 12.2843 & 0.165747 \tabularnewline
79 & 12.65 & 10.306 & 2.34399 \tabularnewline
80 & 17.35 & 13.9117 & 3.43829 \tabularnewline
81 & 8.6 & 8.45796 & 0.142037 \tabularnewline
82 & 18.4 & 16.866 & 1.53399 \tabularnewline
83 & 16.1 & 14.4392 & 1.66075 \tabularnewline
84 & 17.75 & 15.6366 & 2.11342 \tabularnewline
85 & 15.25 & 15.3554 & -0.105443 \tabularnewline
86 & 17.65 & 16.305 & 1.34503 \tabularnewline
87 & 16.35 & 16.6263 & -0.276297 \tabularnewline
88 & 17.65 & 18.4005 & -0.750512 \tabularnewline
89 & 13.6 & 12.9764 & 0.623646 \tabularnewline
90 & 14.35 & 13.8143 & 0.535748 \tabularnewline
91 & 14.75 & 17.1001 & -2.35009 \tabularnewline
92 & 18.25 & 16.6698 & 1.58019 \tabularnewline
93 & 9.9 & 15.3381 & -5.4381 \tabularnewline
94 & 16 & 13.9391 & 2.06087 \tabularnewline
95 & 18.25 & 16.1833 & 2.06668 \tabularnewline
96 & 16.85 & 17.7389 & -0.888883 \tabularnewline
97 & 18.95 & 16.784 & 2.166 \tabularnewline
98 & 15.6 & 13.6022 & 1.9978 \tabularnewline
99 & 17.1 & 17.6378 & -0.537845 \tabularnewline
100 & 15.4 & 16.1164 & -0.716356 \tabularnewline
101 & 15.4 & 16.0073 & -0.607336 \tabularnewline
102 & 13.35 & 14.2821 & -0.932101 \tabularnewline
103 & 19.1 & 17.0208 & 2.07921 \tabularnewline
104 & 7.6 & 6.83508 & 0.764916 \tabularnewline
105 & 19.1 & 17.0036 & 2.09638 \tabularnewline
106 & 14.75 & 16.3882 & -1.63822 \tabularnewline
107 & 19.25 & 17.0767 & 2.17325 \tabularnewline
108 & 13.6 & 16.0079 & -2.40794 \tabularnewline
109 & 12.75 & 15.5011 & -2.75114 \tabularnewline
110 & 9.85 & 8.13625 & 1.71375 \tabularnewline
111 & 15.25 & 15.7848 & -0.534828 \tabularnewline
112 & 11.9 & 13.308 & -1.408 \tabularnewline
113 & 16.35 & 17.3883 & -1.03832 \tabularnewline
114 & 12.4 & 13.9119 & -1.51193 \tabularnewline
115 & 18.15 & 15.9239 & 2.2261 \tabularnewline
116 & 17.75 & 15.1985 & 2.55147 \tabularnewline
117 & 12.35 & 12.6953 & -0.345303 \tabularnewline
118 & 15.6 & 15.3658 & 0.234178 \tabularnewline
119 & 19.3 & 16.6969 & 2.60308 \tabularnewline
120 & 17.1 & 16.5268 & 0.573203 \tabularnewline
121 & 18.4 & 15.4152 & 2.98484 \tabularnewline
122 & 19.05 & 16.8681 & 2.18195 \tabularnewline
123 & 18.55 & 14.5014 & 4.04864 \tabularnewline
124 & 19.1 & 18.1306 & 0.969374 \tabularnewline
125 & 12.85 & 15.7198 & -2.86977 \tabularnewline
126 & 9.5 & 10.8617 & -1.36172 \tabularnewline
127 & 4.5 & 7.43525 & -2.93525 \tabularnewline
128 & 13.6 & 15.0727 & -1.47267 \tabularnewline
129 & 11.7 & 12.3531 & -0.653145 \tabularnewline
130 & 13.35 & 13.9452 & -0.595161 \tabularnewline
131 & 17.6 & 18.8415 & -1.24149 \tabularnewline
132 & 14.05 & 13.9043 & 0.145719 \tabularnewline
133 & 16.1 & 17.5637 & -1.46371 \tabularnewline
134 & 13.35 & 15.5155 & -2.16549 \tabularnewline
135 & 11.85 & 15.1673 & -3.31729 \tabularnewline
136 & 11.95 & 11.8313 & 0.1187 \tabularnewline
137 & 13.2 & 16.5112 & -3.31118 \tabularnewline
138 & 7.7 & 9.53312 & -1.83312 \tabularnewline
139 & 14.6 & 13.6059 & 0.99405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&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]12.9752[/C][C]-0.075231[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.3419[/C][C]1.85806[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.6849[/C][C]1.11509[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6304[/C][C]-4.23041[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.3391[/C][C]-4.63914[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.97[/C][C]0.630036[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.9669[/C][C]3.83313[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.4175[/C][C]-0.117532[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4079[/C][C]-1.30785[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.7882[/C][C]-2.58822[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2678[/C][C]0.132249[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.918[/C][C]-4.51804[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1917[/C][C]0.408263[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3445[/C][C]-1.34445[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.75503[/C][C]-2.45503[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2333[/C][C]-1.33329[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.8912[/C][C]-1.59123[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.72259[/C][C]0.277414[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.2896[/C][C]-3.88957[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.6035[/C][C]1.19653[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.1863[/C][C]0.613654[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.9837[/C][C]1.81629[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.566[/C][C]1.13397[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9183[/C][C]-1.01825[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1146[/C][C]-1.2146[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5171[/C][C]0.982878[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.0107[/C][C]-2.71065[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1008[/C][C]0.599238[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3907[/C][C]-1.39073[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.919[/C][C]-4.21896[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2642[/C][C]-0.464191[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8075[/C][C]-0.507535[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.69331[/C][C]0.706687[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.29[/C][C]-2.99005[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9404[/C][C]0.859627[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2929[/C][C]-5.39289[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8855[/C][C]-0.485513[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7509[/C][C]2.24909[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.0494[/C][C]-0.249417[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7129[/C][C]0.587112[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.6409[/C][C]0.159083[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.46119[/C][C]3.23881[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.6127[/C][C]0.28727[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6389[/C][C]1.66107[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.516[/C][C]-0.415961[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5464[/C][C]2.75358[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6522[/C][C]-2.35217[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4621[/C][C]2.03794[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.3538[/C][C]-2.75378[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4974[/C][C]3.40264[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4154[/C][C]-1.21542[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.463[/C][C]-1.36296[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.4085[/C][C]0.591545[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.4655[/C][C]3.03449[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0557[/C][C]-0.7557[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.394[/C][C]1.00602[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2726[/C][C]0.72744[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0072[/C][C]2.19277[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4711[/C][C]-3.77107[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.19489[/C][C]-2.84489[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2804[/C][C]2.41959[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1368[/C][C]1.96323[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1172[/C][C]1.73276[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5411[/C][C]-0.441137[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7362[/C][C]2.36382[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.7019[/C][C]-2.6019[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.877[/C][C]2.47303[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.871[/C][C]0.528955[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.63099[/C][C]7.06901[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.2202[/C][C]-2.62022[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7958[/C][C]-1.19581[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.6321[/C][C]0.967897[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.1052[/C][C]0.994846[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.4276[/C][C]1.07235[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.4263[/C][C]-0.276274[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.5725[/C][C]2.17755[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.0893[/C][C]2.71072[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.2843[/C][C]0.165747[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.306[/C][C]2.34399[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9117[/C][C]3.43829[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.45796[/C][C]0.142037[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.866[/C][C]1.53399[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.4392[/C][C]1.66075[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6366[/C][C]2.11342[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3554[/C][C]-0.105443[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.305[/C][C]1.34503[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6263[/C][C]-0.276297[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.4005[/C][C]-0.750512[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.9764[/C][C]0.623646[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.8143[/C][C]0.535748[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1001[/C][C]-2.35009[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.6698[/C][C]1.58019[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3381[/C][C]-5.4381[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.9391[/C][C]2.06087[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1833[/C][C]2.06668[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7389[/C][C]-0.888883[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.784[/C][C]2.166[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.6022[/C][C]1.9978[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.6378[/C][C]-0.537845[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1164[/C][C]-0.716356[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0073[/C][C]-0.607336[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.2821[/C][C]-0.932101[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0208[/C][C]2.07921[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.83508[/C][C]0.764916[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]17.0036[/C][C]2.09638[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.3882[/C][C]-1.63822[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]17.0767[/C][C]2.17325[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0079[/C][C]-2.40794[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.5011[/C][C]-2.75114[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.13625[/C][C]1.71375[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.7848[/C][C]-0.534828[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.308[/C][C]-1.408[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3883[/C][C]-1.03832[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.9119[/C][C]-1.51193[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]15.9239[/C][C]2.2261[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.1985[/C][C]2.55147[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.6953[/C][C]-0.345303[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3658[/C][C]0.234178[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.6969[/C][C]2.60308[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5268[/C][C]0.573203[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.4152[/C][C]2.98484[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8681[/C][C]2.18195[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.5014[/C][C]4.04864[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.1306[/C][C]0.969374[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7198[/C][C]-2.86977[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.8617[/C][C]-1.36172[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.43525[/C][C]-2.93525[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.0727[/C][C]-1.47267[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3531[/C][C]-0.653145[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.9452[/C][C]-0.595161[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.8415[/C][C]-1.24149[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.9043[/C][C]0.145719[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5637[/C][C]-1.46371[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5155[/C][C]-2.16549[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1673[/C][C]-3.31729[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.8313[/C][C]0.1187[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.5112[/C][C]-3.31118[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.53312[/C][C]-1.83312[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.6059[/C][C]0.99405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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
112.912.9752-0.075231
212.210.34191.85806
312.811.68491.11509
47.411.6304-4.23041
56.711.3391-4.63914
612.611.970.630036
714.810.96693.83313
813.313.4175-0.117532
911.112.4079-1.30785
108.210.7882-2.58822
1111.411.26780.132249
126.410.918-4.51804
1310.610.19170.408263
141213.3445-1.34445
156.38.75503-2.45503
1611.913.2333-1.33329
179.310.8912-1.59123
18109.722590.277414
196.410.2896-3.88957
2013.812.60351.19653
2110.810.18630.613654
2213.811.98371.81629
2311.710.5661.13397
2410.911.9183-1.01825
259.911.1146-1.2146
2611.510.51710.982878
278.311.0107-2.71065
2811.711.10080.599238
29910.3907-1.39073
309.713.919-4.21896
3110.811.2642-0.464191
3210.310.8075-0.507535
3310.49.693310.706687
349.312.29-2.99005
3511.810.94040.859627
365.911.2929-5.39289
3711.411.8855-0.485513
381310.75092.24909
3910.811.0494-0.249417
4011.310.71290.587112
4111.811.64090.159083
4212.79.461193.23881
4310.910.61270.28727
4413.311.63891.66107
4510.110.516-0.415961
4614.311.54642.75358
479.311.6522-2.35217
4812.510.46212.03794
497.610.3538-2.75378
5015.912.49743.40264
519.210.4154-1.21542
5211.112.463-1.36296
531312.40850.591545
5414.511.46553.03449
5512.313.0557-0.7557
5611.410.3941.00602
571312.27260.72744
5813.211.00722.19277
597.711.4711-3.77107
604.357.19489-2.84489
6112.710.28042.41959
6218.116.13681.96323
6317.8516.11721.73276
6417.117.5411-0.441137
6519.116.73622.36382
6616.118.7019-2.6019
6713.3510.8772.47303
6818.417.8710.528955
6914.77.630997.06901
7010.613.2202-2.62022
7112.613.7958-1.19581
7213.612.63210.967897
7314.113.10520.994846
7414.513.42761.07235
7516.1516.4263-0.276274
7614.7512.57252.17755
7714.812.08932.71072
7812.4512.28430.165747
7912.6510.3062.34399
8017.3513.91173.43829
818.68.457960.142037
8218.416.8661.53399
8316.114.43921.66075
8417.7515.63662.11342
8515.2515.3554-0.105443
8617.6516.3051.34503
8716.3516.6263-0.276297
8817.6518.4005-0.750512
8913.612.97640.623646
9014.3513.81430.535748
9114.7517.1001-2.35009
9218.2516.66981.58019
939.915.3381-5.4381
941613.93912.06087
9518.2516.18332.06668
9616.8517.7389-0.888883
9718.9516.7842.166
9815.613.60221.9978
9917.117.6378-0.537845
10015.416.1164-0.716356
10115.416.0073-0.607336
10213.3514.2821-0.932101
10319.117.02082.07921
1047.66.835080.764916
10519.117.00362.09638
10614.7516.3882-1.63822
10719.2517.07672.17325
10813.616.0079-2.40794
10912.7515.5011-2.75114
1109.858.136251.71375
11115.2515.7848-0.534828
11211.913.308-1.408
11316.3517.3883-1.03832
11412.413.9119-1.51193
11518.1515.92392.2261
11617.7515.19852.55147
11712.3512.6953-0.345303
11815.615.36580.234178
11919.316.69692.60308
12017.116.52680.573203
12118.415.41522.98484
12219.0516.86812.18195
12318.5514.50144.04864
12419.118.13060.969374
12512.8515.7198-2.86977
1269.510.8617-1.36172
1274.57.43525-2.93525
12813.615.0727-1.47267
12911.712.3531-0.653145
13013.3513.9452-0.595161
13117.618.8415-1.24149
13214.0513.90430.145719
13316.117.5637-1.46371
13413.3515.5155-2.16549
13511.8515.1673-3.31729
13611.9511.83130.1187
13713.216.5112-3.31118
1387.79.53312-1.83312
13914.613.60590.99405






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.5050080.9899850.494992
170.3575780.7151570.642422
180.224760.4495210.77524
190.1657520.3315050.834248
200.09725460.1945090.902745
210.06380890.1276180.936191
220.04255370.08510740.957446
230.06385270.1277050.936147
240.04447610.08895210.955524
250.0407780.08155610.959222
260.0565650.113130.943435
270.08397750.1679550.916023
280.05922620.1184520.940774
290.1370.2740010.863
300.302820.605640.69718
310.2633820.5267630.736618
320.2117040.4234080.788296
330.164820.329640.83518
340.1973370.3946740.802663
350.1575090.3150170.842491
360.2745710.5491420.725429
370.237170.474340.76283
380.2513550.5027110.748645
390.211110.422220.78889
400.1680930.3361850.831907
410.1733580.3467160.826642
420.1910440.3820880.808956
430.1670830.3341650.832917
440.402550.8050990.59745
450.355440.7108810.64456
460.3583730.7167450.641627
470.3709980.7419960.629002
480.3686320.7372650.631368
490.5748670.8502650.425133
500.6599020.6801960.340098
510.6549420.6901160.345058
520.6316030.7367950.368397
530.5904570.8190850.409543
540.5912530.8174940.408747
550.554490.891020.44551
560.5137220.9725560.486278
570.472590.945180.52741
580.4536410.9072820.546359
590.5382280.9235440.461772
600.6055750.788850.394425
610.6483710.7032580.351629
620.6950990.6098020.304901
630.6628840.6742330.337116
640.6436210.7127580.356379
650.6312060.7375870.368794
660.6617730.6764550.338227
670.6872640.6254720.312736
680.6416530.7166940.358347
690.9483770.1032460.0516231
700.9530570.09388570.0469428
710.9447640.1104710.0552357
720.9488010.1023970.0511987
730.9380270.1239460.0619729
740.9309650.138070.0690351
750.9291560.1416890.0708444
760.9266040.1467920.0733961
770.9340720.1318560.0659278
780.9158310.1683390.0841693
790.9115350.176930.0884649
800.939170.121660.0608298
810.9233890.1532210.0766107
820.908750.1825010.0912504
830.8952330.2095330.104767
840.8790570.2418860.120943
850.8540920.2918150.145908
860.8564660.2870680.143534
870.8281610.3436780.171839
880.7966210.4067580.203379
890.7619470.4761060.238053
900.715340.5693210.28466
910.7137490.5725010.286251
920.6949690.6100630.305031
930.8931020.2137960.106898
940.8747680.2504640.125232
950.8579470.2841070.142053
960.8352570.3294850.164743
970.8067550.386490.193245
980.8022190.3955620.197781
990.8090130.3819730.190987
1000.7710890.4578230.228911
1010.7221390.5557220.277861
1020.6880480.6239040.311952
1030.6722310.6555370.327769
1040.6956290.6087420.304371
1050.6440840.7118320.355916
1060.5996450.8007090.400355
1070.5453120.9093770.454688
1080.5292160.9415680.470784
1090.5072520.9854950.492748
1100.4852930.9705860.514707
1110.4327220.8654450.567278
1120.3755890.7511780.624411
1130.3707660.7415320.629234
1140.2990130.5980270.700987
1150.2667370.5334750.733263
1160.2436930.4873870.756307
1170.1786580.3573160.821342
1180.5032880.9934250.496712
1190.6409820.7180350.359018
1200.6272360.7455290.372764
1210.5599420.8801170.440058
1220.7345290.5309420.265471
1230.7183270.5633450.281673

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.505008 & 0.989985 & 0.494992 \tabularnewline
17 & 0.357578 & 0.715157 & 0.642422 \tabularnewline
18 & 0.22476 & 0.449521 & 0.77524 \tabularnewline
19 & 0.165752 & 0.331505 & 0.834248 \tabularnewline
20 & 0.0972546 & 0.194509 & 0.902745 \tabularnewline
21 & 0.0638089 & 0.127618 & 0.936191 \tabularnewline
22 & 0.0425537 & 0.0851074 & 0.957446 \tabularnewline
23 & 0.0638527 & 0.127705 & 0.936147 \tabularnewline
24 & 0.0444761 & 0.0889521 & 0.955524 \tabularnewline
25 & 0.040778 & 0.0815561 & 0.959222 \tabularnewline
26 & 0.056565 & 0.11313 & 0.943435 \tabularnewline
27 & 0.0839775 & 0.167955 & 0.916023 \tabularnewline
28 & 0.0592262 & 0.118452 & 0.940774 \tabularnewline
29 & 0.137 & 0.274001 & 0.863 \tabularnewline
30 & 0.30282 & 0.60564 & 0.69718 \tabularnewline
31 & 0.263382 & 0.526763 & 0.736618 \tabularnewline
32 & 0.211704 & 0.423408 & 0.788296 \tabularnewline
33 & 0.16482 & 0.32964 & 0.83518 \tabularnewline
34 & 0.197337 & 0.394674 & 0.802663 \tabularnewline
35 & 0.157509 & 0.315017 & 0.842491 \tabularnewline
36 & 0.274571 & 0.549142 & 0.725429 \tabularnewline
37 & 0.23717 & 0.47434 & 0.76283 \tabularnewline
38 & 0.251355 & 0.502711 & 0.748645 \tabularnewline
39 & 0.21111 & 0.42222 & 0.78889 \tabularnewline
40 & 0.168093 & 0.336185 & 0.831907 \tabularnewline
41 & 0.173358 & 0.346716 & 0.826642 \tabularnewline
42 & 0.191044 & 0.382088 & 0.808956 \tabularnewline
43 & 0.167083 & 0.334165 & 0.832917 \tabularnewline
44 & 0.40255 & 0.805099 & 0.59745 \tabularnewline
45 & 0.35544 & 0.710881 & 0.64456 \tabularnewline
46 & 0.358373 & 0.716745 & 0.641627 \tabularnewline
47 & 0.370998 & 0.741996 & 0.629002 \tabularnewline
48 & 0.368632 & 0.737265 & 0.631368 \tabularnewline
49 & 0.574867 & 0.850265 & 0.425133 \tabularnewline
50 & 0.659902 & 0.680196 & 0.340098 \tabularnewline
51 & 0.654942 & 0.690116 & 0.345058 \tabularnewline
52 & 0.631603 & 0.736795 & 0.368397 \tabularnewline
53 & 0.590457 & 0.819085 & 0.409543 \tabularnewline
54 & 0.591253 & 0.817494 & 0.408747 \tabularnewline
55 & 0.55449 & 0.89102 & 0.44551 \tabularnewline
56 & 0.513722 & 0.972556 & 0.486278 \tabularnewline
57 & 0.47259 & 0.94518 & 0.52741 \tabularnewline
58 & 0.453641 & 0.907282 & 0.546359 \tabularnewline
59 & 0.538228 & 0.923544 & 0.461772 \tabularnewline
60 & 0.605575 & 0.78885 & 0.394425 \tabularnewline
61 & 0.648371 & 0.703258 & 0.351629 \tabularnewline
62 & 0.695099 & 0.609802 & 0.304901 \tabularnewline
63 & 0.662884 & 0.674233 & 0.337116 \tabularnewline
64 & 0.643621 & 0.712758 & 0.356379 \tabularnewline
65 & 0.631206 & 0.737587 & 0.368794 \tabularnewline
66 & 0.661773 & 0.676455 & 0.338227 \tabularnewline
67 & 0.687264 & 0.625472 & 0.312736 \tabularnewline
68 & 0.641653 & 0.716694 & 0.358347 \tabularnewline
69 & 0.948377 & 0.103246 & 0.0516231 \tabularnewline
70 & 0.953057 & 0.0938857 & 0.0469428 \tabularnewline
71 & 0.944764 & 0.110471 & 0.0552357 \tabularnewline
72 & 0.948801 & 0.102397 & 0.0511987 \tabularnewline
73 & 0.938027 & 0.123946 & 0.0619729 \tabularnewline
74 & 0.930965 & 0.13807 & 0.0690351 \tabularnewline
75 & 0.929156 & 0.141689 & 0.0708444 \tabularnewline
76 & 0.926604 & 0.146792 & 0.0733961 \tabularnewline
77 & 0.934072 & 0.131856 & 0.0659278 \tabularnewline
78 & 0.915831 & 0.168339 & 0.0841693 \tabularnewline
79 & 0.911535 & 0.17693 & 0.0884649 \tabularnewline
80 & 0.93917 & 0.12166 & 0.0608298 \tabularnewline
81 & 0.923389 & 0.153221 & 0.0766107 \tabularnewline
82 & 0.90875 & 0.182501 & 0.0912504 \tabularnewline
83 & 0.895233 & 0.209533 & 0.104767 \tabularnewline
84 & 0.879057 & 0.241886 & 0.120943 \tabularnewline
85 & 0.854092 & 0.291815 & 0.145908 \tabularnewline
86 & 0.856466 & 0.287068 & 0.143534 \tabularnewline
87 & 0.828161 & 0.343678 & 0.171839 \tabularnewline
88 & 0.796621 & 0.406758 & 0.203379 \tabularnewline
89 & 0.761947 & 0.476106 & 0.238053 \tabularnewline
90 & 0.71534 & 0.569321 & 0.28466 \tabularnewline
91 & 0.713749 & 0.572501 & 0.286251 \tabularnewline
92 & 0.694969 & 0.610063 & 0.305031 \tabularnewline
93 & 0.893102 & 0.213796 & 0.106898 \tabularnewline
94 & 0.874768 & 0.250464 & 0.125232 \tabularnewline
95 & 0.857947 & 0.284107 & 0.142053 \tabularnewline
96 & 0.835257 & 0.329485 & 0.164743 \tabularnewline
97 & 0.806755 & 0.38649 & 0.193245 \tabularnewline
98 & 0.802219 & 0.395562 & 0.197781 \tabularnewline
99 & 0.809013 & 0.381973 & 0.190987 \tabularnewline
100 & 0.771089 & 0.457823 & 0.228911 \tabularnewline
101 & 0.722139 & 0.555722 & 0.277861 \tabularnewline
102 & 0.688048 & 0.623904 & 0.311952 \tabularnewline
103 & 0.672231 & 0.655537 & 0.327769 \tabularnewline
104 & 0.695629 & 0.608742 & 0.304371 \tabularnewline
105 & 0.644084 & 0.711832 & 0.355916 \tabularnewline
106 & 0.599645 & 0.800709 & 0.400355 \tabularnewline
107 & 0.545312 & 0.909377 & 0.454688 \tabularnewline
108 & 0.529216 & 0.941568 & 0.470784 \tabularnewline
109 & 0.507252 & 0.985495 & 0.492748 \tabularnewline
110 & 0.485293 & 0.970586 & 0.514707 \tabularnewline
111 & 0.432722 & 0.865445 & 0.567278 \tabularnewline
112 & 0.375589 & 0.751178 & 0.624411 \tabularnewline
113 & 0.370766 & 0.741532 & 0.629234 \tabularnewline
114 & 0.299013 & 0.598027 & 0.700987 \tabularnewline
115 & 0.266737 & 0.533475 & 0.733263 \tabularnewline
116 & 0.243693 & 0.487387 & 0.756307 \tabularnewline
117 & 0.178658 & 0.357316 & 0.821342 \tabularnewline
118 & 0.503288 & 0.993425 & 0.496712 \tabularnewline
119 & 0.640982 & 0.718035 & 0.359018 \tabularnewline
120 & 0.627236 & 0.745529 & 0.372764 \tabularnewline
121 & 0.559942 & 0.880117 & 0.440058 \tabularnewline
122 & 0.734529 & 0.530942 & 0.265471 \tabularnewline
123 & 0.718327 & 0.563345 & 0.281673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&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]16[/C][C]0.505008[/C][C]0.989985[/C][C]0.494992[/C][/ROW]
[ROW][C]17[/C][C]0.357578[/C][C]0.715157[/C][C]0.642422[/C][/ROW]
[ROW][C]18[/C][C]0.22476[/C][C]0.449521[/C][C]0.77524[/C][/ROW]
[ROW][C]19[/C][C]0.165752[/C][C]0.331505[/C][C]0.834248[/C][/ROW]
[ROW][C]20[/C][C]0.0972546[/C][C]0.194509[/C][C]0.902745[/C][/ROW]
[ROW][C]21[/C][C]0.0638089[/C][C]0.127618[/C][C]0.936191[/C][/ROW]
[ROW][C]22[/C][C]0.0425537[/C][C]0.0851074[/C][C]0.957446[/C][/ROW]
[ROW][C]23[/C][C]0.0638527[/C][C]0.127705[/C][C]0.936147[/C][/ROW]
[ROW][C]24[/C][C]0.0444761[/C][C]0.0889521[/C][C]0.955524[/C][/ROW]
[ROW][C]25[/C][C]0.040778[/C][C]0.0815561[/C][C]0.959222[/C][/ROW]
[ROW][C]26[/C][C]0.056565[/C][C]0.11313[/C][C]0.943435[/C][/ROW]
[ROW][C]27[/C][C]0.0839775[/C][C]0.167955[/C][C]0.916023[/C][/ROW]
[ROW][C]28[/C][C]0.0592262[/C][C]0.118452[/C][C]0.940774[/C][/ROW]
[ROW][C]29[/C][C]0.137[/C][C]0.274001[/C][C]0.863[/C][/ROW]
[ROW][C]30[/C][C]0.30282[/C][C]0.60564[/C][C]0.69718[/C][/ROW]
[ROW][C]31[/C][C]0.263382[/C][C]0.526763[/C][C]0.736618[/C][/ROW]
[ROW][C]32[/C][C]0.211704[/C][C]0.423408[/C][C]0.788296[/C][/ROW]
[ROW][C]33[/C][C]0.16482[/C][C]0.32964[/C][C]0.83518[/C][/ROW]
[ROW][C]34[/C][C]0.197337[/C][C]0.394674[/C][C]0.802663[/C][/ROW]
[ROW][C]35[/C][C]0.157509[/C][C]0.315017[/C][C]0.842491[/C][/ROW]
[ROW][C]36[/C][C]0.274571[/C][C]0.549142[/C][C]0.725429[/C][/ROW]
[ROW][C]37[/C][C]0.23717[/C][C]0.47434[/C][C]0.76283[/C][/ROW]
[ROW][C]38[/C][C]0.251355[/C][C]0.502711[/C][C]0.748645[/C][/ROW]
[ROW][C]39[/C][C]0.21111[/C][C]0.42222[/C][C]0.78889[/C][/ROW]
[ROW][C]40[/C][C]0.168093[/C][C]0.336185[/C][C]0.831907[/C][/ROW]
[ROW][C]41[/C][C]0.173358[/C][C]0.346716[/C][C]0.826642[/C][/ROW]
[ROW][C]42[/C][C]0.191044[/C][C]0.382088[/C][C]0.808956[/C][/ROW]
[ROW][C]43[/C][C]0.167083[/C][C]0.334165[/C][C]0.832917[/C][/ROW]
[ROW][C]44[/C][C]0.40255[/C][C]0.805099[/C][C]0.59745[/C][/ROW]
[ROW][C]45[/C][C]0.35544[/C][C]0.710881[/C][C]0.64456[/C][/ROW]
[ROW][C]46[/C][C]0.358373[/C][C]0.716745[/C][C]0.641627[/C][/ROW]
[ROW][C]47[/C][C]0.370998[/C][C]0.741996[/C][C]0.629002[/C][/ROW]
[ROW][C]48[/C][C]0.368632[/C][C]0.737265[/C][C]0.631368[/C][/ROW]
[ROW][C]49[/C][C]0.574867[/C][C]0.850265[/C][C]0.425133[/C][/ROW]
[ROW][C]50[/C][C]0.659902[/C][C]0.680196[/C][C]0.340098[/C][/ROW]
[ROW][C]51[/C][C]0.654942[/C][C]0.690116[/C][C]0.345058[/C][/ROW]
[ROW][C]52[/C][C]0.631603[/C][C]0.736795[/C][C]0.368397[/C][/ROW]
[ROW][C]53[/C][C]0.590457[/C][C]0.819085[/C][C]0.409543[/C][/ROW]
[ROW][C]54[/C][C]0.591253[/C][C]0.817494[/C][C]0.408747[/C][/ROW]
[ROW][C]55[/C][C]0.55449[/C][C]0.89102[/C][C]0.44551[/C][/ROW]
[ROW][C]56[/C][C]0.513722[/C][C]0.972556[/C][C]0.486278[/C][/ROW]
[ROW][C]57[/C][C]0.47259[/C][C]0.94518[/C][C]0.52741[/C][/ROW]
[ROW][C]58[/C][C]0.453641[/C][C]0.907282[/C][C]0.546359[/C][/ROW]
[ROW][C]59[/C][C]0.538228[/C][C]0.923544[/C][C]0.461772[/C][/ROW]
[ROW][C]60[/C][C]0.605575[/C][C]0.78885[/C][C]0.394425[/C][/ROW]
[ROW][C]61[/C][C]0.648371[/C][C]0.703258[/C][C]0.351629[/C][/ROW]
[ROW][C]62[/C][C]0.695099[/C][C]0.609802[/C][C]0.304901[/C][/ROW]
[ROW][C]63[/C][C]0.662884[/C][C]0.674233[/C][C]0.337116[/C][/ROW]
[ROW][C]64[/C][C]0.643621[/C][C]0.712758[/C][C]0.356379[/C][/ROW]
[ROW][C]65[/C][C]0.631206[/C][C]0.737587[/C][C]0.368794[/C][/ROW]
[ROW][C]66[/C][C]0.661773[/C][C]0.676455[/C][C]0.338227[/C][/ROW]
[ROW][C]67[/C][C]0.687264[/C][C]0.625472[/C][C]0.312736[/C][/ROW]
[ROW][C]68[/C][C]0.641653[/C][C]0.716694[/C][C]0.358347[/C][/ROW]
[ROW][C]69[/C][C]0.948377[/C][C]0.103246[/C][C]0.0516231[/C][/ROW]
[ROW][C]70[/C][C]0.953057[/C][C]0.0938857[/C][C]0.0469428[/C][/ROW]
[ROW][C]71[/C][C]0.944764[/C][C]0.110471[/C][C]0.0552357[/C][/ROW]
[ROW][C]72[/C][C]0.948801[/C][C]0.102397[/C][C]0.0511987[/C][/ROW]
[ROW][C]73[/C][C]0.938027[/C][C]0.123946[/C][C]0.0619729[/C][/ROW]
[ROW][C]74[/C][C]0.930965[/C][C]0.13807[/C][C]0.0690351[/C][/ROW]
[ROW][C]75[/C][C]0.929156[/C][C]0.141689[/C][C]0.0708444[/C][/ROW]
[ROW][C]76[/C][C]0.926604[/C][C]0.146792[/C][C]0.0733961[/C][/ROW]
[ROW][C]77[/C][C]0.934072[/C][C]0.131856[/C][C]0.0659278[/C][/ROW]
[ROW][C]78[/C][C]0.915831[/C][C]0.168339[/C][C]0.0841693[/C][/ROW]
[ROW][C]79[/C][C]0.911535[/C][C]0.17693[/C][C]0.0884649[/C][/ROW]
[ROW][C]80[/C][C]0.93917[/C][C]0.12166[/C][C]0.0608298[/C][/ROW]
[ROW][C]81[/C][C]0.923389[/C][C]0.153221[/C][C]0.0766107[/C][/ROW]
[ROW][C]82[/C][C]0.90875[/C][C]0.182501[/C][C]0.0912504[/C][/ROW]
[ROW][C]83[/C][C]0.895233[/C][C]0.209533[/C][C]0.104767[/C][/ROW]
[ROW][C]84[/C][C]0.879057[/C][C]0.241886[/C][C]0.120943[/C][/ROW]
[ROW][C]85[/C][C]0.854092[/C][C]0.291815[/C][C]0.145908[/C][/ROW]
[ROW][C]86[/C][C]0.856466[/C][C]0.287068[/C][C]0.143534[/C][/ROW]
[ROW][C]87[/C][C]0.828161[/C][C]0.343678[/C][C]0.171839[/C][/ROW]
[ROW][C]88[/C][C]0.796621[/C][C]0.406758[/C][C]0.203379[/C][/ROW]
[ROW][C]89[/C][C]0.761947[/C][C]0.476106[/C][C]0.238053[/C][/ROW]
[ROW][C]90[/C][C]0.71534[/C][C]0.569321[/C][C]0.28466[/C][/ROW]
[ROW][C]91[/C][C]0.713749[/C][C]0.572501[/C][C]0.286251[/C][/ROW]
[ROW][C]92[/C][C]0.694969[/C][C]0.610063[/C][C]0.305031[/C][/ROW]
[ROW][C]93[/C][C]0.893102[/C][C]0.213796[/C][C]0.106898[/C][/ROW]
[ROW][C]94[/C][C]0.874768[/C][C]0.250464[/C][C]0.125232[/C][/ROW]
[ROW][C]95[/C][C]0.857947[/C][C]0.284107[/C][C]0.142053[/C][/ROW]
[ROW][C]96[/C][C]0.835257[/C][C]0.329485[/C][C]0.164743[/C][/ROW]
[ROW][C]97[/C][C]0.806755[/C][C]0.38649[/C][C]0.193245[/C][/ROW]
[ROW][C]98[/C][C]0.802219[/C][C]0.395562[/C][C]0.197781[/C][/ROW]
[ROW][C]99[/C][C]0.809013[/C][C]0.381973[/C][C]0.190987[/C][/ROW]
[ROW][C]100[/C][C]0.771089[/C][C]0.457823[/C][C]0.228911[/C][/ROW]
[ROW][C]101[/C][C]0.722139[/C][C]0.555722[/C][C]0.277861[/C][/ROW]
[ROW][C]102[/C][C]0.688048[/C][C]0.623904[/C][C]0.311952[/C][/ROW]
[ROW][C]103[/C][C]0.672231[/C][C]0.655537[/C][C]0.327769[/C][/ROW]
[ROW][C]104[/C][C]0.695629[/C][C]0.608742[/C][C]0.304371[/C][/ROW]
[ROW][C]105[/C][C]0.644084[/C][C]0.711832[/C][C]0.355916[/C][/ROW]
[ROW][C]106[/C][C]0.599645[/C][C]0.800709[/C][C]0.400355[/C][/ROW]
[ROW][C]107[/C][C]0.545312[/C][C]0.909377[/C][C]0.454688[/C][/ROW]
[ROW][C]108[/C][C]0.529216[/C][C]0.941568[/C][C]0.470784[/C][/ROW]
[ROW][C]109[/C][C]0.507252[/C][C]0.985495[/C][C]0.492748[/C][/ROW]
[ROW][C]110[/C][C]0.485293[/C][C]0.970586[/C][C]0.514707[/C][/ROW]
[ROW][C]111[/C][C]0.432722[/C][C]0.865445[/C][C]0.567278[/C][/ROW]
[ROW][C]112[/C][C]0.375589[/C][C]0.751178[/C][C]0.624411[/C][/ROW]
[ROW][C]113[/C][C]0.370766[/C][C]0.741532[/C][C]0.629234[/C][/ROW]
[ROW][C]114[/C][C]0.299013[/C][C]0.598027[/C][C]0.700987[/C][/ROW]
[ROW][C]115[/C][C]0.266737[/C][C]0.533475[/C][C]0.733263[/C][/ROW]
[ROW][C]116[/C][C]0.243693[/C][C]0.487387[/C][C]0.756307[/C][/ROW]
[ROW][C]117[/C][C]0.178658[/C][C]0.357316[/C][C]0.821342[/C][/ROW]
[ROW][C]118[/C][C]0.503288[/C][C]0.993425[/C][C]0.496712[/C][/ROW]
[ROW][C]119[/C][C]0.640982[/C][C]0.718035[/C][C]0.359018[/C][/ROW]
[ROW][C]120[/C][C]0.627236[/C][C]0.745529[/C][C]0.372764[/C][/ROW]
[ROW][C]121[/C][C]0.559942[/C][C]0.880117[/C][C]0.440058[/C][/ROW]
[ROW][C]122[/C][C]0.734529[/C][C]0.530942[/C][C]0.265471[/C][/ROW]
[ROW][C]123[/C][C]0.718327[/C][C]0.563345[/C][C]0.281673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&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
160.5050080.9899850.494992
170.3575780.7151570.642422
180.224760.4495210.77524
190.1657520.3315050.834248
200.09725460.1945090.902745
210.06380890.1276180.936191
220.04255370.08510740.957446
230.06385270.1277050.936147
240.04447610.08895210.955524
250.0407780.08155610.959222
260.0565650.113130.943435
270.08397750.1679550.916023
280.05922620.1184520.940774
290.1370.2740010.863
300.302820.605640.69718
310.2633820.5267630.736618
320.2117040.4234080.788296
330.164820.329640.83518
340.1973370.3946740.802663
350.1575090.3150170.842491
360.2745710.5491420.725429
370.237170.474340.76283
380.2513550.5027110.748645
390.211110.422220.78889
400.1680930.3361850.831907
410.1733580.3467160.826642
420.1910440.3820880.808956
430.1670830.3341650.832917
440.402550.8050990.59745
450.355440.7108810.64456
460.3583730.7167450.641627
470.3709980.7419960.629002
480.3686320.7372650.631368
490.5748670.8502650.425133
500.6599020.6801960.340098
510.6549420.6901160.345058
520.6316030.7367950.368397
530.5904570.8190850.409543
540.5912530.8174940.408747
550.554490.891020.44551
560.5137220.9725560.486278
570.472590.945180.52741
580.4536410.9072820.546359
590.5382280.9235440.461772
600.6055750.788850.394425
610.6483710.7032580.351629
620.6950990.6098020.304901
630.6628840.6742330.337116
640.6436210.7127580.356379
650.6312060.7375870.368794
660.6617730.6764550.338227
670.6872640.6254720.312736
680.6416530.7166940.358347
690.9483770.1032460.0516231
700.9530570.09388570.0469428
710.9447640.1104710.0552357
720.9488010.1023970.0511987
730.9380270.1239460.0619729
740.9309650.138070.0690351
750.9291560.1416890.0708444
760.9266040.1467920.0733961
770.9340720.1318560.0659278
780.9158310.1683390.0841693
790.9115350.176930.0884649
800.939170.121660.0608298
810.9233890.1532210.0766107
820.908750.1825010.0912504
830.8952330.2095330.104767
840.8790570.2418860.120943
850.8540920.2918150.145908
860.8564660.2870680.143534
870.8281610.3436780.171839
880.7966210.4067580.203379
890.7619470.4761060.238053
900.715340.5693210.28466
910.7137490.5725010.286251
920.6949690.6100630.305031
930.8931020.2137960.106898
940.8747680.2504640.125232
950.8579470.2841070.142053
960.8352570.3294850.164743
970.8067550.386490.193245
980.8022190.3955620.197781
990.8090130.3819730.190987
1000.7710890.4578230.228911
1010.7221390.5557220.277861
1020.6880480.6239040.311952
1030.6722310.6555370.327769
1040.6956290.6087420.304371
1050.6440840.7118320.355916
1060.5996450.8007090.400355
1070.5453120.9093770.454688
1080.5292160.9415680.470784
1090.5072520.9854950.492748
1100.4852930.9705860.514707
1110.4327220.8654450.567278
1120.3755890.7511780.624411
1130.3707660.7415320.629234
1140.2990130.5980270.700987
1150.2667370.5334750.733263
1160.2436930.4873870.756307
1170.1786580.3573160.821342
1180.5032880.9934250.496712
1190.6409820.7180350.359018
1200.6272360.7455290.372764
1210.5599420.8801170.440058
1220.7345290.5309420.265471
1230.7183270.5633450.281673






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 level40.037037OK

\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 & 4 & 0.037037 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271046&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]4[/C][C]0.037037[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271046&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271046&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.037037OK


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 ;
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')
}