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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 15:07:48 +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/t1418915332lis9waclamtvmif.htm/, Retrieved Fri, 17 May 2024 19:27:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271044, Retrieved Fri, 17 May 2024 19:27:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

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 15:07:48] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 18 12 20 4 21 13 12 149 18 68 1.8
12.2 18 20 23 20 19 4 22 8 8 139 31 39 2.1
12.8 12 9 22 14 18 5 21 14 11 148 39 32 2.2
7.4 24 19 22 25 24 4 21 16 13 158 46 62 2.3
6.7 16 12 19 15 20 4 21 14 11 128 31 33 2.1
12.6 19 16 25 20 20 9 21 13 10 224 67 52 2.7
14.8 16 17 28 21 24 8 21 15 7 159 35 62 2.1
13.3 15 9 16 15 21 11 23 13 10 105 52 77 2.4
11.1 28 28 28 28 28 4 22 20 15 159 77 76 2.9
8.2 21 20 21 11 10 4 25 17 12 167 37 41 2.2
11.4 18 16 22 22 22 6 21 15 12 165 32 48 2.1
6.4 22 22 24 22 19 4 23 16 10 159 36 63 2.2
10.6 19 17 24 27 27 8 22 12 10 119 38 30 2.2
12 22 12 26 24 23 4 21 17 14 176 69 78 2.7
6.3 25 18 28 23 24 4 21 11 6 54 21 19 1.9
11.9 16 12 20 21 25 4 21 16 14 163 54 66 2.5
9.3 19 16 26 20 24 4 21 15 11 124 36 35 2.2
10 26 21 28 25 28 6 24 14 12 121 23 45 1.9
6.4 24 15 27 16 28 4 23 19 15 153 34 21 2.1
13.8 20 17 23 24 22 8 21 16 13 148 112 25 3.5
10.8 19 17 24 21 26 5 24 17 11 221 35 44 2.1
13.8 19 17 24 22 26 4 23 10 12 188 47 69 2.3
11.7 23 18 22 25 21 9 21 15 7 149 47 54 2.3
10.9 18 15 21 23 26 4 22 14 11 244 37 74 2.2
9.9 21 21 21 22 24 4 21 15 12 150 20 61 1.9
11.5 20 12 26 25 25 4 22 17 13 153 22 41 1.9
8.3 15 6 23 23 24 7 22 14 9 94 23 46 1.9
11.7 19 13 21 19 20 12 21 16 11 156 32 39 2.1
9 19 19 27 21 24 7 21 15 12 132 30 34 2
9.7 7 12 25 19 25 5 25 16 15 161 92 51 3.2
10.8 20 14 23 25 23 8 22 16 12 105 43 42 2.3
10.3 20 13 25 16 21 5 22 10 6 97 55 31 2.5
10.4 19 12 23 24 23 4 20 8 5 151 16 39 1.8
9.3 20 19 22 18 18 7 21 14 11 166 71 49 2.8
11.8 18 10 24 28 24 4 21 10 6 157 43 53 2.3
5.9 14 10 19 15 18 4 22 14 12 111 29 31 2
11.4 17 11 21 17 21 4 21 12 10 145 56 39 2.5
13 17 11 27 18 23 4 24 16 6 162 46 54 2.3
10.8 8 10 25 26 25 4 22 16 12 163 19 49 1.8
11.3 22 22 23 22 22 4 21 8 6 187 59 46 2.6
11.8 20 12 17 19 23 7 22 16 12 109 30 55 2
12.7 22 20 25 26 25 4 22 8 8 105 7 50 1.6
10.9 14 11 24 12 24 4 23 16 12 148 19 30 1.8
13.3 21 17 20 20 23 4 23 19 14 125 48 45 2.4
10.1 20 14 19 24 27 4 21 14 12 116 23 35 1.9
14.3 18 16 21 22 23 12 21 13 14 138 33 41 2.1
9.3 24 15 18 23 23 4 22 15 11 164 34 73 2.1
12.5 19 15 27 19 24 5 21 11 10 162 48 17 2.4
7.6 16 10 25 24 26 15 21 9 7 99 18 40 1.8
15.9 16 10 20 21 20 5 21 16 12 202 43 64 2.3
9.2 16 18 21 16 23 10 21 12 7 186 33 37 2.1
11.1 22 22 27 23 23 8 21 14 12 183 71 65 2.8
13 21 16 24 20 17 4 22 14 10 214 26 100 2
14.5 15 10 27 19 20 5 22 13 10 188 67 28 2.7
12.3 15 16 23 18 18 9 21 17 12 177 80 56 2.9
11.4 14 16 24 21 19 4 23 14 12 126 29 29 2
13 16 10 25 17 25 7 21 15 10 162 43 59 2.3
13.2 26 16 24 24 18 4 20 15 11 159 29 61 2
7.7 18 16 23 22 26 4 21 16 12 110 32 51 2.1
4.35 17 15 22 14 15 6 22 16 9 48 23 12 1
12.7 6 4 24 5 27 4 22 16 11 50 16 45 1
18.1 22 9 19 25 23 8 22 16 12 150 33 37 4
17.85 20 18 25 21 23 5 20 16 12 154 32 37 4
17.1 17 12 24 9 22 4 22 14 12 194 52 68 4
19.1 20 16 28 15 20 4 21 15 12 158 75 72 4
16.1 23 17 23 23 21 8 21 16 10 159 72 143 4
13.35 18 14 19 21 25 4 21 13 15 67 15 9 2
18.4 13 13 19 9 19 7 21 10 10 147 29 55 4
14.7 22 20 27 24 25 4 21 17 15 39 13 17 1
10.6 20 16 24 16 24 4 21 15 10 100 40 37 3
12.6 20 15 26 20 22 5 21 18 15 111 19 27 3
13.6 16 16 25 18 22 4 24 20 15 101 121 58 3
14.1 16 15 19 21 23 7 22 17 13 101 36 21 3
14.5 15 16 20 21 19 11 20 16 12 114 23 19 3
16.15 19 19 26 21 21 7 21 15 12 165 85 78 4
14.75 19 9 27 20 25 4 24 13 8 114 41 35 3
14.8 24 19 23 24 23 4 25 16 9 111 46 48 3
12.45 9 7 18 15 28 4 22 16 15 75 18 27 2
12.65 22 23 23 24 14 4 21 16 12 82 35 43 2
17.35 15 14 21 18 23 4 21 17 12 121 17 30 3
8.6 22 10 23 24 24 4 22 20 15 32 4 25 1
18.4 22 16 22 24 25 6 23 14 11 150 28 69 4
16.1 24 12 21 15 15 8 24 17 12 117 44 72 3
17.75 21 7 24 20 26 4 22 16 14 165 38 13 4
15.25 25 20 26 26 21 8 25 15 12 154 57 61 4
17.65 26 9 24 26 26 6 22 16 12 126 23 43 4
16.35 21 12 22 23 23 4 21 16 12 149 36 51 4
17.65 14 10 20 13 15 7 21 14 11 145 22 67 4
13.6 28 19 20 16 16 4 21 16 12 120 40 36 3
14.35 21 11 18 22 20 4 22 16 12 109 31 44 3
14.75 16 15 18 21 20 4 22 16 12 132 11 45 4
18.25 16 14 25 11 21 10 21 14 12 172 38 34 4
9.9 25 11 28 23 28 6 22 14 8 169 24 36 4
16 21 14 23 18 19 5 23 16 8 114 37 72 3
18.25 22 15 20 19 21 5 21 16 12 156 37 39 4
16.85 9 7 22 15 22 4 21 15 12 172 22 43 4
18.95 24 22 23 21 17 5 21 18 11 167 43 80 4
15.6 22 11 20 25 26 5 21 15 12 113 31 40 3
17.1 10 12 24 12 22 4 22 14 10 173 31 61 4
15.4 21 13 23 19 16 8 21 15 11 165 21 29 4
15.4 20 15 21 21 18 8 21 15 11 165 21 29 4
13.35 17 11 19 19 17 8 25 16 13 118 32 54 3
19.1 7 7 19 18 25 4 21 11 7 158 26 43 4
7.6 14 13 25 23 21 9 25 7 8 49 32 20 1
19.1 23 7 18 23 27 4 22 15 11 155 33 61 4
14.75 18 11 22 27 23 4 21 14 8 151 30 57 4
19.25 17 22 5 6 8 28 23 16 14 220 67 54 4
13.6 20 15 24 22 22 4 20 14 9 141 22 36 4
12.75 19 15 28 23 28 5 22 11 13 122 33 16 4
9.85 19 11 27 20 24 4 25 18 13 44 24 40 1
15.25 23 10 23 23 25 5 20 18 11 152 28 27 4
11.9 20 18 24 27 23 4 21 15 9 107 41 61 3
16.35 19 14 25 24 26 4 21 13 12 154 31 69 4
12.4 16 16 19 12 22 10 23 13 12 103 33 34 3
18.15 21 16 24 24 22 4 22 18 13 175 21 34 4
17.75 20 17 28 24 26 4 21 15 11 143 52 34 4
12.35 20 14 19 19 21 5 21 16 11 110 29 13 3
15.6 19 10 23 28 21 8 21 12 9 131 11 12 4
19.3 19 16 23 23 24 6 21 16 12 167 26 51 4
17.1 20 16 26 19 18 4 21 16 15 137 7 19 4
18.4 22 17 25 23 26 4 21 19 14 121 13 81 3
19.05 19 12 24 20 23 5 21 15 12 149 20 42 4
18.55 23 17 23 18 25 5 22 14 9 168 52 22 4
19.1 16 11 22 20 20 6 21 14 9 140 28 85 4
12.85 18 12 26 21 26 4 22 16 13 168 39 25 4
9.5 23 8 23 25 19 4 22 20 15 94 9 22 2
4.5 20 17 22 18 21 6 22 16 11 51 19 19 1
13.6 23 17 22 28 24 10 22 13 10 145 60 45 4
11.7 13 7 17 9 6 4 23 15 11 66 19 45 2
13.35 26 18 22 26 21 4 22 16 14 109 14 51 3
17.6 13 14 26 12 19 4 21 19 12 164 -2 73 4
14.05 10 13 24 12 24 14 21 13 13 119 51 24 3
16.1 21 19 27 20 21 5 20 14 11 126 2 61 4
13.35 24 15 22 25 21 5 20 15 11 132 24 23 4
11.85 21 15 23 24 26 5 21 15 13 142 40 14 4
11.95 23 8 22 23 24 5 21 14 12 83 20 54 2
13.2 16 11 20 22 23 16 21 12 9 166 20 36 4
7.7 26 17 27 28 26 7 24 15 13 93 25 26 2
14.6 16 12 20 15 20 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 time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.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=271044&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]'Sir Maurice George Kendall' @ kendall.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=271044&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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'Sir Maurice George Kendall' @ kendall.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] = + 8.87177 -0.0454166AMS.I2[t] -0.0469579AMS.I3[t] -0.0166631AMS.E1[t] -0.0471059AMS.E2[t] -0.00096747AMS.E3[t] + 0.013689AMS.A[t] -0.127728age[t] -0.0315385CONFSTATTOT[t] + 0.160475CONFSOFTTOT[t] -0.00301278LFM[t] -0.0210822PRH[t] + 0.0377254CH[t] + 2.80859PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.87177 -0.0454166AMS.I2[t] -0.0469579AMS.I3[t] -0.0166631AMS.E1[t] -0.0471059AMS.E2[t] -0.00096747AMS.E3[t] +  0.013689AMS.A[t] -0.127728age[t] -0.0315385CONFSTATTOT[t] +  0.160475CONFSOFTTOT[t] -0.00301278LFM[t] -0.0210822PRH[t] +  0.0377254CH[t] +  2.80859PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.87177 -0.0454166AMS.I2[t] -0.0469579AMS.I3[t] -0.0166631AMS.E1[t] -0.0471059AMS.E2[t] -0.00096747AMS.E3[t] +  0.013689AMS.A[t] -0.127728age[t] -0.0315385CONFSTATTOT[t] +  0.160475CONFSOFTTOT[t] -0.00301278LFM[t] -0.0210822PRH[t] +  0.0377254CH[t] +  2.80859PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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] = + 8.87177 -0.0454166AMS.I2[t] -0.0469579AMS.I3[t] -0.0166631AMS.E1[t] -0.0471059AMS.E2[t] -0.00096747AMS.E3[t] + 0.013689AMS.A[t] -0.127728age[t] -0.0315385CONFSTATTOT[t] + 0.160475CONFSOFTTOT[t] -0.00301278LFM[t] -0.0210822PRH[t] + 0.0377254CH[t] + 2.80859PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.871774.50791.9680.05127590.0256379
AMS.I2-0.04541660.0635917-0.71420.4764410.238221
AMS.I3-0.04695790.060102-0.78130.4361020.218051
AMS.E1-0.01666310.073934-0.22540.8220530.411027
AMS.E2-0.04710590.0565802-0.83260.4066850.203343
AMS.E3-0.000967470.0656745-0.014730.988270.494135
AMS.A0.0136890.07191050.19040.8493340.424667
age-0.1277280.173578-0.73590.4631990.2316
CONFSTATTOT-0.03153850.109914-0.28690.7746350.387318
CONFSOFTTOT0.1604750.1201371.3360.1840550.0920274
LFM-0.003012780.00621867-0.48450.6288980.314449
PRH-0.02108220.0111505-1.8910.06098150.0304908
CH0.03772540.01080043.4930.0006611770.000330588
PR2.808590.24095911.661.05334e-215.2667e-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) & 8.87177 & 4.5079 & 1.968 & 0.0512759 & 0.0256379 \tabularnewline
AMS.I2 & -0.0454166 & 0.0635917 & -0.7142 & 0.476441 & 0.238221 \tabularnewline
AMS.I3 & -0.0469579 & 0.060102 & -0.7813 & 0.436102 & 0.218051 \tabularnewline
AMS.E1 & -0.0166631 & 0.073934 & -0.2254 & 0.822053 & 0.411027 \tabularnewline
AMS.E2 & -0.0471059 & 0.0565802 & -0.8326 & 0.406685 & 0.203343 \tabularnewline
AMS.E3 & -0.00096747 & 0.0656745 & -0.01473 & 0.98827 & 0.494135 \tabularnewline
AMS.A & 0.013689 & 0.0719105 & 0.1904 & 0.849334 & 0.424667 \tabularnewline
age & -0.127728 & 0.173578 & -0.7359 & 0.463199 & 0.2316 \tabularnewline
CONFSTATTOT & -0.0315385 & 0.109914 & -0.2869 & 0.774635 & 0.387318 \tabularnewline
CONFSOFTTOT & 0.160475 & 0.120137 & 1.336 & 0.184055 & 0.0920274 \tabularnewline
LFM & -0.00301278 & 0.00621867 & -0.4845 & 0.628898 & 0.314449 \tabularnewline
PRH & -0.0210822 & 0.0111505 & -1.891 & 0.0609815 & 0.0304908 \tabularnewline
CH & 0.0377254 & 0.0108004 & 3.493 & 0.000661177 & 0.000330588 \tabularnewline
PR & 2.80859 & 0.240959 & 11.66 & 1.05334e-21 & 5.2667e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&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]8.87177[/C][C]4.5079[/C][C]1.968[/C][C]0.0512759[/C][C]0.0256379[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0454166[/C][C]0.0635917[/C][C]-0.7142[/C][C]0.476441[/C][C]0.238221[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0469579[/C][C]0.060102[/C][C]-0.7813[/C][C]0.436102[/C][C]0.218051[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0166631[/C][C]0.073934[/C][C]-0.2254[/C][C]0.822053[/C][C]0.411027[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0471059[/C][C]0.0565802[/C][C]-0.8326[/C][C]0.406685[/C][C]0.203343[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.00096747[/C][C]0.0656745[/C][C]-0.01473[/C][C]0.98827[/C][C]0.494135[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.013689[/C][C]0.0719105[/C][C]0.1904[/C][C]0.849334[/C][C]0.424667[/C][/ROW]
[ROW][C]age[/C][C]-0.127728[/C][C]0.173578[/C][C]-0.7359[/C][C]0.463199[/C][C]0.2316[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0315385[/C][C]0.109914[/C][C]-0.2869[/C][C]0.774635[/C][C]0.387318[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.160475[/C][C]0.120137[/C][C]1.336[/C][C]0.184055[/C][C]0.0920274[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00301278[/C][C]0.00621867[/C][C]-0.4845[/C][C]0.628898[/C][C]0.314449[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0210822[/C][C]0.0111505[/C][C]-1.891[/C][C]0.0609815[/C][C]0.0304908[/C][/ROW]
[ROW][C]CH[/C][C]0.0377254[/C][C]0.0108004[/C][C]3.493[/C][C]0.000661177[/C][C]0.000330588[/C][/ROW]
[ROW][C]PR[/C][C]2.80859[/C][C]0.240959[/C][C]11.66[/C][C]1.05334e-21[/C][C]5.2667e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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)8.871774.50791.9680.05127590.0256379
AMS.I2-0.04541660.0635917-0.71420.4764410.238221
AMS.I3-0.04695790.060102-0.78130.4361020.218051
AMS.E1-0.01666310.073934-0.22540.8220530.411027
AMS.E2-0.04710590.0565802-0.83260.4066850.203343
AMS.E3-0.000967470.0656745-0.014730.988270.494135
AMS.A0.0136890.07191050.19040.8493340.424667
age-0.1277280.173578-0.73590.4631990.2316
CONFSTATTOT-0.03153850.109914-0.28690.7746350.387318
CONFSOFTTOT0.1604750.1201371.3360.1840550.0920274
LFM-0.003012780.00621867-0.48450.6288980.314449
PRH-0.02108220.0111505-1.8910.06098150.0304908
CH0.03772540.01080043.4930.0006611770.000330588
PR2.808590.24095911.661.05334e-215.2667e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.793292
R-squared0.629312
Adjusted R-squared0.590761
F-TEST (value)16.3239
F-TEST (DF numerator)13
F-TEST (DF denominator)125
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.24153
Sum Squared Residuals628.055

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.793292 \tabularnewline
R-squared & 0.629312 \tabularnewline
Adjusted R-squared & 0.590761 \tabularnewline
F-TEST (value) & 16.3239 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 125 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.24153 \tabularnewline
Sum Squared Residuals & 628.055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&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]0.590761[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.3239[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]125[/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]2.24153[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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-squared0.590761
F-TEST (value)16.3239
F-TEST (DF numerator)13
F-TEST (DF denominator)125
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.24153
Sum Squared Residuals628.055






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9758-0.0757595
212.210.34461.85539
312.811.68851.11147
47.411.6291-4.22909
56.711.339-4.63903
612.611.97270.627293
714.810.96543.8346
813.313.4148-0.114841
911.112.4035-1.30346
108.210.7947-2.59468
1111.411.26830.131657
126.410.9197-4.51967
1310.610.18950.410512
141213.3469-1.34691
156.38.75399-2.45399
1611.913.2313-1.33128
179.310.8903-1.59032
18109.718880.281119
196.410.2842-3.88415
2013.812.6051.19502
2110.810.18260.61741
2213.811.98211.81794
2311.710.56571.13427
2410.911.9152-1.01525
259.911.1115-1.21154
2611.510.51790.982062
278.311.011-2.71102
2811.711.10140.598577
29910.3902-1.39017
309.713.9192-4.21922
3110.811.2648-0.464817
3210.310.8075-0.507509
3310.49.69410.705901
349.312.2922-2.99221
3511.810.94220.857796
365.911.2955-5.39553
3711.411.8862-0.486189
381310.74972.2503
3910.811.0515-0.251536
4011.310.71220.587843
4111.811.63750.162472
4212.79.460273.23973
4310.910.60990.290127
4413.311.63631.66373
4510.110.5117-0.411742
4614.311.54642.75362
479.311.6498-2.34978
4812.510.46232.03772
497.610.3525-2.75252
5015.912.53.39999
519.210.4113-1.21126
5211.112.4634-1.36337
531312.4130.586971
5414.511.47053.02953
5512.313.0588-0.758795
5611.410.39851.0015
571312.27010.729866
5813.211.01212.18789
597.711.4678-3.76777
604.357.19831-2.84831
6112.710.27352.42649
6218.116.13671.96335
6317.8516.11671.73327
6417.117.5389-0.438897
6519.116.73812.36188
6616.118.7014-2.60143
6713.3510.87512.47493
6818.417.86990.530117
6914.77.630617.06939
7010.613.2165-2.61648
7112.613.7974-1.1974
7213.612.63230.967726
7314.113.10330.996743
7414.513.42961.07035
7516.1516.4279-0.277899
7614.7512.57152.17854
7714.812.08712.71285
7812.4512.27830.171713
7912.6510.31322.33675
8017.3513.90963.44036
818.68.45810.1419
8218.416.86331.53675
8316.114.44271.65726
8417.7515.63552.11452
8515.2515.3578-0.107798
8617.6516.30381.34623
8716.3516.6263-0.276315
8817.6518.4047-0.754749
8913.612.97810.6219
9014.3513.81550.534545
9114.7517.1005-2.35048
9218.2516.66911.58088
939.915.3345-5.43447
941613.93952.06052
9518.2516.18252.06748
9616.8517.7395-0.889509
9718.9516.78622.16379
9815.613.59962.00041
9917.117.6365-0.536462
10015.416.1218-0.721778
10115.416.0105-0.610459
10213.3514.2858-0.935783
10319.117.01772.08226
1047.66.839040.760963
10519.116.99882.10119
10614.7516.3891-1.6391
10719.2517.07672.17326
10813.616.0084-2.40836
10912.7515.4998-2.74979
1109.858.136191.71381
11115.2515.7829-0.532873
11211.913.3081-1.40814
11316.3517.3873-1.03725
11412.413.9079-1.50789
11518.1515.92532.22474
11617.7515.19742.55255
11712.3512.6943-0.344291
11815.615.37030.229716
11919.316.69572.60434
12017.116.53230.567746
12118.415.4122.98803
12219.0516.86822.18185
12318.5514.49664.05336
12419.118.1320.967956
12512.8515.7186-2.86862
1269.510.8675-1.36755
1274.57.43424-2.93424
12813.615.072-1.47199
12911.712.3642-0.664207
13013.3513.947-0.597048
13117.618.8424-1.24239
13214.0513.90180.148238
13316.117.5649-1.46491
13413.3515.5173-2.16729
13511.8515.1654-3.31539
13611.9511.83130.118742
13713.216.51-3.31
1387.79.53289-1.83289
13914.613.60590.994149

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9758 & -0.0757595 \tabularnewline
2 & 12.2 & 10.3446 & 1.85539 \tabularnewline
3 & 12.8 & 11.6885 & 1.11147 \tabularnewline
4 & 7.4 & 11.6291 & -4.22909 \tabularnewline
5 & 6.7 & 11.339 & -4.63903 \tabularnewline
6 & 12.6 & 11.9727 & 0.627293 \tabularnewline
7 & 14.8 & 10.9654 & 3.8346 \tabularnewline
8 & 13.3 & 13.4148 & -0.114841 \tabularnewline
9 & 11.1 & 12.4035 & -1.30346 \tabularnewline
10 & 8.2 & 10.7947 & -2.59468 \tabularnewline
11 & 11.4 & 11.2683 & 0.131657 \tabularnewline
12 & 6.4 & 10.9197 & -4.51967 \tabularnewline
13 & 10.6 & 10.1895 & 0.410512 \tabularnewline
14 & 12 & 13.3469 & -1.34691 \tabularnewline
15 & 6.3 & 8.75399 & -2.45399 \tabularnewline
16 & 11.9 & 13.2313 & -1.33128 \tabularnewline
17 & 9.3 & 10.8903 & -1.59032 \tabularnewline
18 & 10 & 9.71888 & 0.281119 \tabularnewline
19 & 6.4 & 10.2842 & -3.88415 \tabularnewline
20 & 13.8 & 12.605 & 1.19502 \tabularnewline
21 & 10.8 & 10.1826 & 0.61741 \tabularnewline
22 & 13.8 & 11.9821 & 1.81794 \tabularnewline
23 & 11.7 & 10.5657 & 1.13427 \tabularnewline
24 & 10.9 & 11.9152 & -1.01525 \tabularnewline
25 & 9.9 & 11.1115 & -1.21154 \tabularnewline
26 & 11.5 & 10.5179 & 0.982062 \tabularnewline
27 & 8.3 & 11.011 & -2.71102 \tabularnewline
28 & 11.7 & 11.1014 & 0.598577 \tabularnewline
29 & 9 & 10.3902 & -1.39017 \tabularnewline
30 & 9.7 & 13.9192 & -4.21922 \tabularnewline
31 & 10.8 & 11.2648 & -0.464817 \tabularnewline
32 & 10.3 & 10.8075 & -0.507509 \tabularnewline
33 & 10.4 & 9.6941 & 0.705901 \tabularnewline
34 & 9.3 & 12.2922 & -2.99221 \tabularnewline
35 & 11.8 & 10.9422 & 0.857796 \tabularnewline
36 & 5.9 & 11.2955 & -5.39553 \tabularnewline
37 & 11.4 & 11.8862 & -0.486189 \tabularnewline
38 & 13 & 10.7497 & 2.2503 \tabularnewline
39 & 10.8 & 11.0515 & -0.251536 \tabularnewline
40 & 11.3 & 10.7122 & 0.587843 \tabularnewline
41 & 11.8 & 11.6375 & 0.162472 \tabularnewline
42 & 12.7 & 9.46027 & 3.23973 \tabularnewline
43 & 10.9 & 10.6099 & 0.290127 \tabularnewline
44 & 13.3 & 11.6363 & 1.66373 \tabularnewline
45 & 10.1 & 10.5117 & -0.411742 \tabularnewline
46 & 14.3 & 11.5464 & 2.75362 \tabularnewline
47 & 9.3 & 11.6498 & -2.34978 \tabularnewline
48 & 12.5 & 10.4623 & 2.03772 \tabularnewline
49 & 7.6 & 10.3525 & -2.75252 \tabularnewline
50 & 15.9 & 12.5 & 3.39999 \tabularnewline
51 & 9.2 & 10.4113 & -1.21126 \tabularnewline
52 & 11.1 & 12.4634 & -1.36337 \tabularnewline
53 & 13 & 12.413 & 0.586971 \tabularnewline
54 & 14.5 & 11.4705 & 3.02953 \tabularnewline
55 & 12.3 & 13.0588 & -0.758795 \tabularnewline
56 & 11.4 & 10.3985 & 1.0015 \tabularnewline
57 & 13 & 12.2701 & 0.729866 \tabularnewline
58 & 13.2 & 11.0121 & 2.18789 \tabularnewline
59 & 7.7 & 11.4678 & -3.76777 \tabularnewline
60 & 4.35 & 7.19831 & -2.84831 \tabularnewline
61 & 12.7 & 10.2735 & 2.42649 \tabularnewline
62 & 18.1 & 16.1367 & 1.96335 \tabularnewline
63 & 17.85 & 16.1167 & 1.73327 \tabularnewline
64 & 17.1 & 17.5389 & -0.438897 \tabularnewline
65 & 19.1 & 16.7381 & 2.36188 \tabularnewline
66 & 16.1 & 18.7014 & -2.60143 \tabularnewline
67 & 13.35 & 10.8751 & 2.47493 \tabularnewline
68 & 18.4 & 17.8699 & 0.530117 \tabularnewline
69 & 14.7 & 7.63061 & 7.06939 \tabularnewline
70 & 10.6 & 13.2165 & -2.61648 \tabularnewline
71 & 12.6 & 13.7974 & -1.1974 \tabularnewline
72 & 13.6 & 12.6323 & 0.967726 \tabularnewline
73 & 14.1 & 13.1033 & 0.996743 \tabularnewline
74 & 14.5 & 13.4296 & 1.07035 \tabularnewline
75 & 16.15 & 16.4279 & -0.277899 \tabularnewline
76 & 14.75 & 12.5715 & 2.17854 \tabularnewline
77 & 14.8 & 12.0871 & 2.71285 \tabularnewline
78 & 12.45 & 12.2783 & 0.171713 \tabularnewline
79 & 12.65 & 10.3132 & 2.33675 \tabularnewline
80 & 17.35 & 13.9096 & 3.44036 \tabularnewline
81 & 8.6 & 8.4581 & 0.1419 \tabularnewline
82 & 18.4 & 16.8633 & 1.53675 \tabularnewline
83 & 16.1 & 14.4427 & 1.65726 \tabularnewline
84 & 17.75 & 15.6355 & 2.11452 \tabularnewline
85 & 15.25 & 15.3578 & -0.107798 \tabularnewline
86 & 17.65 & 16.3038 & 1.34623 \tabularnewline
87 & 16.35 & 16.6263 & -0.276315 \tabularnewline
88 & 17.65 & 18.4047 & -0.754749 \tabularnewline
89 & 13.6 & 12.9781 & 0.6219 \tabularnewline
90 & 14.35 & 13.8155 & 0.534545 \tabularnewline
91 & 14.75 & 17.1005 & -2.35048 \tabularnewline
92 & 18.25 & 16.6691 & 1.58088 \tabularnewline
93 & 9.9 & 15.3345 & -5.43447 \tabularnewline
94 & 16 & 13.9395 & 2.06052 \tabularnewline
95 & 18.25 & 16.1825 & 2.06748 \tabularnewline
96 & 16.85 & 17.7395 & -0.889509 \tabularnewline
97 & 18.95 & 16.7862 & 2.16379 \tabularnewline
98 & 15.6 & 13.5996 & 2.00041 \tabularnewline
99 & 17.1 & 17.6365 & -0.536462 \tabularnewline
100 & 15.4 & 16.1218 & -0.721778 \tabularnewline
101 & 15.4 & 16.0105 & -0.610459 \tabularnewline
102 & 13.35 & 14.2858 & -0.935783 \tabularnewline
103 & 19.1 & 17.0177 & 2.08226 \tabularnewline
104 & 7.6 & 6.83904 & 0.760963 \tabularnewline
105 & 19.1 & 16.9988 & 2.10119 \tabularnewline
106 & 14.75 & 16.3891 & -1.6391 \tabularnewline
107 & 19.25 & 17.0767 & 2.17326 \tabularnewline
108 & 13.6 & 16.0084 & -2.40836 \tabularnewline
109 & 12.75 & 15.4998 & -2.74979 \tabularnewline
110 & 9.85 & 8.13619 & 1.71381 \tabularnewline
111 & 15.25 & 15.7829 & -0.532873 \tabularnewline
112 & 11.9 & 13.3081 & -1.40814 \tabularnewline
113 & 16.35 & 17.3873 & -1.03725 \tabularnewline
114 & 12.4 & 13.9079 & -1.50789 \tabularnewline
115 & 18.15 & 15.9253 & 2.22474 \tabularnewline
116 & 17.75 & 15.1974 & 2.55255 \tabularnewline
117 & 12.35 & 12.6943 & -0.344291 \tabularnewline
118 & 15.6 & 15.3703 & 0.229716 \tabularnewline
119 & 19.3 & 16.6957 & 2.60434 \tabularnewline
120 & 17.1 & 16.5323 & 0.567746 \tabularnewline
121 & 18.4 & 15.412 & 2.98803 \tabularnewline
122 & 19.05 & 16.8682 & 2.18185 \tabularnewline
123 & 18.55 & 14.4966 & 4.05336 \tabularnewline
124 & 19.1 & 18.132 & 0.967956 \tabularnewline
125 & 12.85 & 15.7186 & -2.86862 \tabularnewline
126 & 9.5 & 10.8675 & -1.36755 \tabularnewline
127 & 4.5 & 7.43424 & -2.93424 \tabularnewline
128 & 13.6 & 15.072 & -1.47199 \tabularnewline
129 & 11.7 & 12.3642 & -0.664207 \tabularnewline
130 & 13.35 & 13.947 & -0.597048 \tabularnewline
131 & 17.6 & 18.8424 & -1.24239 \tabularnewline
132 & 14.05 & 13.9018 & 0.148238 \tabularnewline
133 & 16.1 & 17.5649 & -1.46491 \tabularnewline
134 & 13.35 & 15.5173 & -2.16729 \tabularnewline
135 & 11.85 & 15.1654 & -3.31539 \tabularnewline
136 & 11.95 & 11.8313 & 0.118742 \tabularnewline
137 & 13.2 & 16.51 & -3.31 \tabularnewline
138 & 7.7 & 9.53289 & -1.83289 \tabularnewline
139 & 14.6 & 13.6059 & 0.994149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&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]12.9758[/C][C]-0.0757595[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.3446[/C][C]1.85539[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.6885[/C][C]1.11147[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6291[/C][C]-4.22909[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.339[/C][C]-4.63903[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9727[/C][C]0.627293[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.9654[/C][C]3.8346[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.4148[/C][C]-0.114841[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4035[/C][C]-1.30346[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.7947[/C][C]-2.59468[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2683[/C][C]0.131657[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.9197[/C][C]-4.51967[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1895[/C][C]0.410512[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3469[/C][C]-1.34691[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.75399[/C][C]-2.45399[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2313[/C][C]-1.33128[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.8903[/C][C]-1.59032[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.71888[/C][C]0.281119[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.2842[/C][C]-3.88415[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.605[/C][C]1.19502[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.1826[/C][C]0.61741[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.9821[/C][C]1.81794[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.5657[/C][C]1.13427[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9152[/C][C]-1.01525[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1115[/C][C]-1.21154[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5179[/C][C]0.982062[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.011[/C][C]-2.71102[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1014[/C][C]0.598577[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3902[/C][C]-1.39017[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9192[/C][C]-4.21922[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2648[/C][C]-0.464817[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8075[/C][C]-0.507509[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.6941[/C][C]0.705901[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2922[/C][C]-2.99221[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9422[/C][C]0.857796[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2955[/C][C]-5.39553[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8862[/C][C]-0.486189[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7497[/C][C]2.2503[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.0515[/C][C]-0.251536[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7122[/C][C]0.587843[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.6375[/C][C]0.162472[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.46027[/C][C]3.23973[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.6099[/C][C]0.290127[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6363[/C][C]1.66373[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.5117[/C][C]-0.411742[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5464[/C][C]2.75362[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6498[/C][C]-2.34978[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4623[/C][C]2.03772[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.3525[/C][C]-2.75252[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.5[/C][C]3.39999[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4113[/C][C]-1.21126[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.4634[/C][C]-1.36337[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.413[/C][C]0.586971[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.4705[/C][C]3.02953[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0588[/C][C]-0.758795[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.3985[/C][C]1.0015[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2701[/C][C]0.729866[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0121[/C][C]2.18789[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4678[/C][C]-3.76777[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.19831[/C][C]-2.84831[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2735[/C][C]2.42649[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1367[/C][C]1.96335[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1167[/C][C]1.73327[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5389[/C][C]-0.438897[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7381[/C][C]2.36188[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.7014[/C][C]-2.60143[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.8751[/C][C]2.47493[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.8699[/C][C]0.530117[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.63061[/C][C]7.06939[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.2165[/C][C]-2.61648[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7974[/C][C]-1.1974[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.6323[/C][C]0.967726[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.1033[/C][C]0.996743[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.4296[/C][C]1.07035[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.4279[/C][C]-0.277899[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.5715[/C][C]2.17854[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.0871[/C][C]2.71285[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.2783[/C][C]0.171713[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3132[/C][C]2.33675[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9096[/C][C]3.44036[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.4581[/C][C]0.1419[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.8633[/C][C]1.53675[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.4427[/C][C]1.65726[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6355[/C][C]2.11452[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3578[/C][C]-0.107798[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.3038[/C][C]1.34623[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6263[/C][C]-0.276315[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.4047[/C][C]-0.754749[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.9781[/C][C]0.6219[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.8155[/C][C]0.534545[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1005[/C][C]-2.35048[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.6691[/C][C]1.58088[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3345[/C][C]-5.43447[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.9395[/C][C]2.06052[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1825[/C][C]2.06748[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7395[/C][C]-0.889509[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.7862[/C][C]2.16379[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.5996[/C][C]2.00041[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.6365[/C][C]-0.536462[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1218[/C][C]-0.721778[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0105[/C][C]-0.610459[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.2858[/C][C]-0.935783[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0177[/C][C]2.08226[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.83904[/C][C]0.760963[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.9988[/C][C]2.10119[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.3891[/C][C]-1.6391[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]17.0767[/C][C]2.17326[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0084[/C][C]-2.40836[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.4998[/C][C]-2.74979[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.13619[/C][C]1.71381[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.7829[/C][C]-0.532873[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.3081[/C][C]-1.40814[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3873[/C][C]-1.03725[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.9079[/C][C]-1.50789[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]15.9253[/C][C]2.22474[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.1974[/C][C]2.55255[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.6943[/C][C]-0.344291[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3703[/C][C]0.229716[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.6957[/C][C]2.60434[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5323[/C][C]0.567746[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.412[/C][C]2.98803[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8682[/C][C]2.18185[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.4966[/C][C]4.05336[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.132[/C][C]0.967956[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7186[/C][C]-2.86862[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.8675[/C][C]-1.36755[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.43424[/C][C]-2.93424[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.072[/C][C]-1.47199[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3642[/C][C]-0.664207[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.947[/C][C]-0.597048[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.8424[/C][C]-1.24239[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.9018[/C][C]0.148238[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5649[/C][C]-1.46491[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5173[/C][C]-2.16729[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1654[/C][C]-3.31539[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.8313[/C][C]0.118742[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.51[/C][C]-3.31[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.53289[/C][C]-1.83289[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.6059[/C][C]0.994149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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
112.912.9758-0.0757595
212.210.34461.85539
312.811.68851.11147
47.411.6291-4.22909
56.711.339-4.63903
612.611.97270.627293
714.810.96543.8346
813.313.4148-0.114841
911.112.4035-1.30346
108.210.7947-2.59468
1111.411.26830.131657
126.410.9197-4.51967
1310.610.18950.410512
141213.3469-1.34691
156.38.75399-2.45399
1611.913.2313-1.33128
179.310.8903-1.59032
18109.718880.281119
196.410.2842-3.88415
2013.812.6051.19502
2110.810.18260.61741
2213.811.98211.81794
2311.710.56571.13427
2410.911.9152-1.01525
259.911.1115-1.21154
2611.510.51790.982062
278.311.011-2.71102
2811.711.10140.598577
29910.3902-1.39017
309.713.9192-4.21922
3110.811.2648-0.464817
3210.310.8075-0.507509
3310.49.69410.705901
349.312.2922-2.99221
3511.810.94220.857796
365.911.2955-5.39553
3711.411.8862-0.486189
381310.74972.2503
3910.811.0515-0.251536
4011.310.71220.587843
4111.811.63750.162472
4212.79.460273.23973
4310.910.60990.290127
4413.311.63631.66373
4510.110.5117-0.411742
4614.311.54642.75362
479.311.6498-2.34978
4812.510.46232.03772
497.610.3525-2.75252
5015.912.53.39999
519.210.4113-1.21126
5211.112.4634-1.36337
531312.4130.586971
5414.511.47053.02953
5512.313.0588-0.758795
5611.410.39851.0015
571312.27010.729866
5813.211.01212.18789
597.711.4678-3.76777
604.357.19831-2.84831
6112.710.27352.42649
6218.116.13671.96335
6317.8516.11671.73327
6417.117.5389-0.438897
6519.116.73812.36188
6616.118.7014-2.60143
6713.3510.87512.47493
6818.417.86990.530117
6914.77.630617.06939
7010.613.2165-2.61648
7112.613.7974-1.1974
7213.612.63230.967726
7314.113.10330.996743
7414.513.42961.07035
7516.1516.4279-0.277899
7614.7512.57152.17854
7714.812.08712.71285
7812.4512.27830.171713
7912.6510.31322.33675
8017.3513.90963.44036
818.68.45810.1419
8218.416.86331.53675
8316.114.44271.65726
8417.7515.63552.11452
8515.2515.3578-0.107798
8617.6516.30381.34623
8716.3516.6263-0.276315
8817.6518.4047-0.754749
8913.612.97810.6219
9014.3513.81550.534545
9114.7517.1005-2.35048
9218.2516.66911.58088
939.915.3345-5.43447
941613.93952.06052
9518.2516.18252.06748
9616.8517.7395-0.889509
9718.9516.78622.16379
9815.613.59962.00041
9917.117.6365-0.536462
10015.416.1218-0.721778
10115.416.0105-0.610459
10213.3514.2858-0.935783
10319.117.01772.08226
1047.66.839040.760963
10519.116.99882.10119
10614.7516.3891-1.6391
10719.2517.07672.17326
10813.616.0084-2.40836
10912.7515.4998-2.74979
1109.858.136191.71381
11115.2515.7829-0.532873
11211.913.3081-1.40814
11316.3517.3873-1.03725
11412.413.9079-1.50789
11518.1515.92532.22474
11617.7515.19742.55255
11712.3512.6943-0.344291
11815.615.37030.229716
11919.316.69572.60434
12017.116.53230.567746
12118.415.4122.98803
12219.0516.86822.18185
12318.5514.49664.05336
12419.118.1320.967956
12512.8515.7186-2.86862
1269.510.8675-1.36755
1274.57.43424-2.93424
12813.615.072-1.47199
12911.712.3642-0.664207
13013.3513.947-0.597048
13117.618.8424-1.24239
13214.0513.90180.148238
13316.117.5649-1.46491
13413.3515.5173-2.16729
13511.8515.1654-3.31539
13611.9511.83130.118742
13713.216.51-3.31
1387.79.53289-1.83289
13914.613.60590.994149






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.4623590.9247170.537641
180.3170590.6341180.682941
190.2074390.4148780.792561
200.120690.241380.87931
210.1070540.2141080.892946
220.06884470.1376890.931155
230.0917090.1834180.908291
240.06971490.139430.930285
250.06398780.1279760.936012
260.06953990.139080.93046
270.1092570.2185130.890743
280.07573680.1514740.924263
290.1754780.3509570.824522
300.3506530.7013070.649347
310.3010620.6021240.698938
320.2481610.4963210.751839
330.1954930.3909870.804507
340.2305450.4610910.769455
350.1851180.3702360.814882
360.3121060.6242130.687894
370.2711470.5422940.728853
380.2837740.5675480.716226
390.2383980.4767960.761602
400.1917220.3834450.808278
410.1924230.3848450.807577
420.2123330.4246660.787667
430.1825510.3651010.817449
440.4073990.8147980.592601
450.3562050.7124090.643795
460.3601590.7203180.639841
470.3729840.7459690.627016
480.3636790.7273590.636321
490.5832570.8334850.416743
500.676020.647960.32398
510.6758180.6483650.324182
520.6535920.6928160.346408
530.6117660.7764680.388234
540.6133050.7733890.386695
550.5741980.8516040.425802
560.5277550.944490.472245
570.4899790.9799590.510021
580.4644250.928850.535575
590.5524350.895130.447565
600.6167730.7664540.383227
610.6615830.6768350.338417
620.7066690.5866620.293331
630.6741670.6516660.325833
640.6559910.6880180.344009
650.6461380.7077250.353862
660.6774510.6450970.322549
670.699490.6010210.30051
680.6535320.6929360.346468
690.9479460.1041080.0520541
700.9540150.09196940.0459847
710.9461430.1077140.053857
720.9489550.102090.0510449
730.9379070.1241850.0620927
740.9312280.1375440.0687718
750.9266470.1467060.0733529
760.9235540.1528920.0764462
770.9300620.1398760.0699378
780.911730.176540.08827
790.9078140.1843720.0921861
800.9339830.1320340.0660169
810.9164220.1671560.0835782
820.8997640.2004720.100236
830.8821280.2357440.117872
840.8642750.2714510.135725
850.837540.324920.16246
860.8392940.3214120.160706
870.8087360.3825270.191264
880.7770230.4459540.222977
890.7399880.5200230.260012
900.6905890.6188230.309411
910.6875150.624970.312485
920.6674760.6650490.332524
930.8758530.2482940.124147
940.8543910.2912190.145609
950.8358320.3283350.164168
960.8098730.3802530.190127
970.7793490.4413020.220651
980.7707940.4584120.229206
990.7766290.4467430.223371
1000.7345490.5309020.265451
1010.6815280.6369440.318472
1020.6453420.7093170.354658
1030.6314770.7370460.368523
1040.6509280.6981440.349072
1050.5992310.8015390.400769
1060.5519010.8961970.448099
1070.495320.990640.50468
1080.4769860.9539730.523014
1090.4515990.9031980.548401
1100.4341720.8683430.565828
1110.3759020.7518040.624098
1120.3210010.6420010.678999
1130.3149010.6298020.685099
1140.25190.5037990.7481
1150.2101650.4203290.789835
1160.1868370.3736730.813163
1170.1296370.2592740.870363
1180.420870.8417410.57913
1190.6138850.772230.386115
1200.7436270.5127470.256373
1210.6129660.7740680.387034
1220.6979640.6040710.302036

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.462359 & 0.924717 & 0.537641 \tabularnewline
18 & 0.317059 & 0.634118 & 0.682941 \tabularnewline
19 & 0.207439 & 0.414878 & 0.792561 \tabularnewline
20 & 0.12069 & 0.24138 & 0.87931 \tabularnewline
21 & 0.107054 & 0.214108 & 0.892946 \tabularnewline
22 & 0.0688447 & 0.137689 & 0.931155 \tabularnewline
23 & 0.091709 & 0.183418 & 0.908291 \tabularnewline
24 & 0.0697149 & 0.13943 & 0.930285 \tabularnewline
25 & 0.0639878 & 0.127976 & 0.936012 \tabularnewline
26 & 0.0695399 & 0.13908 & 0.93046 \tabularnewline
27 & 0.109257 & 0.218513 & 0.890743 \tabularnewline
28 & 0.0757368 & 0.151474 & 0.924263 \tabularnewline
29 & 0.175478 & 0.350957 & 0.824522 \tabularnewline
30 & 0.350653 & 0.701307 & 0.649347 \tabularnewline
31 & 0.301062 & 0.602124 & 0.698938 \tabularnewline
32 & 0.248161 & 0.496321 & 0.751839 \tabularnewline
33 & 0.195493 & 0.390987 & 0.804507 \tabularnewline
34 & 0.230545 & 0.461091 & 0.769455 \tabularnewline
35 & 0.185118 & 0.370236 & 0.814882 \tabularnewline
36 & 0.312106 & 0.624213 & 0.687894 \tabularnewline
37 & 0.271147 & 0.542294 & 0.728853 \tabularnewline
38 & 0.283774 & 0.567548 & 0.716226 \tabularnewline
39 & 0.238398 & 0.476796 & 0.761602 \tabularnewline
40 & 0.191722 & 0.383445 & 0.808278 \tabularnewline
41 & 0.192423 & 0.384845 & 0.807577 \tabularnewline
42 & 0.212333 & 0.424666 & 0.787667 \tabularnewline
43 & 0.182551 & 0.365101 & 0.817449 \tabularnewline
44 & 0.407399 & 0.814798 & 0.592601 \tabularnewline
45 & 0.356205 & 0.712409 & 0.643795 \tabularnewline
46 & 0.360159 & 0.720318 & 0.639841 \tabularnewline
47 & 0.372984 & 0.745969 & 0.627016 \tabularnewline
48 & 0.363679 & 0.727359 & 0.636321 \tabularnewline
49 & 0.583257 & 0.833485 & 0.416743 \tabularnewline
50 & 0.67602 & 0.64796 & 0.32398 \tabularnewline
51 & 0.675818 & 0.648365 & 0.324182 \tabularnewline
52 & 0.653592 & 0.692816 & 0.346408 \tabularnewline
53 & 0.611766 & 0.776468 & 0.388234 \tabularnewline
54 & 0.613305 & 0.773389 & 0.386695 \tabularnewline
55 & 0.574198 & 0.851604 & 0.425802 \tabularnewline
56 & 0.527755 & 0.94449 & 0.472245 \tabularnewline
57 & 0.489979 & 0.979959 & 0.510021 \tabularnewline
58 & 0.464425 & 0.92885 & 0.535575 \tabularnewline
59 & 0.552435 & 0.89513 & 0.447565 \tabularnewline
60 & 0.616773 & 0.766454 & 0.383227 \tabularnewline
61 & 0.661583 & 0.676835 & 0.338417 \tabularnewline
62 & 0.706669 & 0.586662 & 0.293331 \tabularnewline
63 & 0.674167 & 0.651666 & 0.325833 \tabularnewline
64 & 0.655991 & 0.688018 & 0.344009 \tabularnewline
65 & 0.646138 & 0.707725 & 0.353862 \tabularnewline
66 & 0.677451 & 0.645097 & 0.322549 \tabularnewline
67 & 0.69949 & 0.601021 & 0.30051 \tabularnewline
68 & 0.653532 & 0.692936 & 0.346468 \tabularnewline
69 & 0.947946 & 0.104108 & 0.0520541 \tabularnewline
70 & 0.954015 & 0.0919694 & 0.0459847 \tabularnewline
71 & 0.946143 & 0.107714 & 0.053857 \tabularnewline
72 & 0.948955 & 0.10209 & 0.0510449 \tabularnewline
73 & 0.937907 & 0.124185 & 0.0620927 \tabularnewline
74 & 0.931228 & 0.137544 & 0.0687718 \tabularnewline
75 & 0.926647 & 0.146706 & 0.0733529 \tabularnewline
76 & 0.923554 & 0.152892 & 0.0764462 \tabularnewline
77 & 0.930062 & 0.139876 & 0.0699378 \tabularnewline
78 & 0.91173 & 0.17654 & 0.08827 \tabularnewline
79 & 0.907814 & 0.184372 & 0.0921861 \tabularnewline
80 & 0.933983 & 0.132034 & 0.0660169 \tabularnewline
81 & 0.916422 & 0.167156 & 0.0835782 \tabularnewline
82 & 0.899764 & 0.200472 & 0.100236 \tabularnewline
83 & 0.882128 & 0.235744 & 0.117872 \tabularnewline
84 & 0.864275 & 0.271451 & 0.135725 \tabularnewline
85 & 0.83754 & 0.32492 & 0.16246 \tabularnewline
86 & 0.839294 & 0.321412 & 0.160706 \tabularnewline
87 & 0.808736 & 0.382527 & 0.191264 \tabularnewline
88 & 0.777023 & 0.445954 & 0.222977 \tabularnewline
89 & 0.739988 & 0.520023 & 0.260012 \tabularnewline
90 & 0.690589 & 0.618823 & 0.309411 \tabularnewline
91 & 0.687515 & 0.62497 & 0.312485 \tabularnewline
92 & 0.667476 & 0.665049 & 0.332524 \tabularnewline
93 & 0.875853 & 0.248294 & 0.124147 \tabularnewline
94 & 0.854391 & 0.291219 & 0.145609 \tabularnewline
95 & 0.835832 & 0.328335 & 0.164168 \tabularnewline
96 & 0.809873 & 0.380253 & 0.190127 \tabularnewline
97 & 0.779349 & 0.441302 & 0.220651 \tabularnewline
98 & 0.770794 & 0.458412 & 0.229206 \tabularnewline
99 & 0.776629 & 0.446743 & 0.223371 \tabularnewline
100 & 0.734549 & 0.530902 & 0.265451 \tabularnewline
101 & 0.681528 & 0.636944 & 0.318472 \tabularnewline
102 & 0.645342 & 0.709317 & 0.354658 \tabularnewline
103 & 0.631477 & 0.737046 & 0.368523 \tabularnewline
104 & 0.650928 & 0.698144 & 0.349072 \tabularnewline
105 & 0.599231 & 0.801539 & 0.400769 \tabularnewline
106 & 0.551901 & 0.896197 & 0.448099 \tabularnewline
107 & 0.49532 & 0.99064 & 0.50468 \tabularnewline
108 & 0.476986 & 0.953973 & 0.523014 \tabularnewline
109 & 0.451599 & 0.903198 & 0.548401 \tabularnewline
110 & 0.434172 & 0.868343 & 0.565828 \tabularnewline
111 & 0.375902 & 0.751804 & 0.624098 \tabularnewline
112 & 0.321001 & 0.642001 & 0.678999 \tabularnewline
113 & 0.314901 & 0.629802 & 0.685099 \tabularnewline
114 & 0.2519 & 0.503799 & 0.7481 \tabularnewline
115 & 0.210165 & 0.420329 & 0.789835 \tabularnewline
116 & 0.186837 & 0.373673 & 0.813163 \tabularnewline
117 & 0.129637 & 0.259274 & 0.870363 \tabularnewline
118 & 0.42087 & 0.841741 & 0.57913 \tabularnewline
119 & 0.613885 & 0.77223 & 0.386115 \tabularnewline
120 & 0.743627 & 0.512747 & 0.256373 \tabularnewline
121 & 0.612966 & 0.774068 & 0.387034 \tabularnewline
122 & 0.697964 & 0.604071 & 0.302036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&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]0.462359[/C][C]0.924717[/C][C]0.537641[/C][/ROW]
[ROW][C]18[/C][C]0.317059[/C][C]0.634118[/C][C]0.682941[/C][/ROW]
[ROW][C]19[/C][C]0.207439[/C][C]0.414878[/C][C]0.792561[/C][/ROW]
[ROW][C]20[/C][C]0.12069[/C][C]0.24138[/C][C]0.87931[/C][/ROW]
[ROW][C]21[/C][C]0.107054[/C][C]0.214108[/C][C]0.892946[/C][/ROW]
[ROW][C]22[/C][C]0.0688447[/C][C]0.137689[/C][C]0.931155[/C][/ROW]
[ROW][C]23[/C][C]0.091709[/C][C]0.183418[/C][C]0.908291[/C][/ROW]
[ROW][C]24[/C][C]0.0697149[/C][C]0.13943[/C][C]0.930285[/C][/ROW]
[ROW][C]25[/C][C]0.0639878[/C][C]0.127976[/C][C]0.936012[/C][/ROW]
[ROW][C]26[/C][C]0.0695399[/C][C]0.13908[/C][C]0.93046[/C][/ROW]
[ROW][C]27[/C][C]0.109257[/C][C]0.218513[/C][C]0.890743[/C][/ROW]
[ROW][C]28[/C][C]0.0757368[/C][C]0.151474[/C][C]0.924263[/C][/ROW]
[ROW][C]29[/C][C]0.175478[/C][C]0.350957[/C][C]0.824522[/C][/ROW]
[ROW][C]30[/C][C]0.350653[/C][C]0.701307[/C][C]0.649347[/C][/ROW]
[ROW][C]31[/C][C]0.301062[/C][C]0.602124[/C][C]0.698938[/C][/ROW]
[ROW][C]32[/C][C]0.248161[/C][C]0.496321[/C][C]0.751839[/C][/ROW]
[ROW][C]33[/C][C]0.195493[/C][C]0.390987[/C][C]0.804507[/C][/ROW]
[ROW][C]34[/C][C]0.230545[/C][C]0.461091[/C][C]0.769455[/C][/ROW]
[ROW][C]35[/C][C]0.185118[/C][C]0.370236[/C][C]0.814882[/C][/ROW]
[ROW][C]36[/C][C]0.312106[/C][C]0.624213[/C][C]0.687894[/C][/ROW]
[ROW][C]37[/C][C]0.271147[/C][C]0.542294[/C][C]0.728853[/C][/ROW]
[ROW][C]38[/C][C]0.283774[/C][C]0.567548[/C][C]0.716226[/C][/ROW]
[ROW][C]39[/C][C]0.238398[/C][C]0.476796[/C][C]0.761602[/C][/ROW]
[ROW][C]40[/C][C]0.191722[/C][C]0.383445[/C][C]0.808278[/C][/ROW]
[ROW][C]41[/C][C]0.192423[/C][C]0.384845[/C][C]0.807577[/C][/ROW]
[ROW][C]42[/C][C]0.212333[/C][C]0.424666[/C][C]0.787667[/C][/ROW]
[ROW][C]43[/C][C]0.182551[/C][C]0.365101[/C][C]0.817449[/C][/ROW]
[ROW][C]44[/C][C]0.407399[/C][C]0.814798[/C][C]0.592601[/C][/ROW]
[ROW][C]45[/C][C]0.356205[/C][C]0.712409[/C][C]0.643795[/C][/ROW]
[ROW][C]46[/C][C]0.360159[/C][C]0.720318[/C][C]0.639841[/C][/ROW]
[ROW][C]47[/C][C]0.372984[/C][C]0.745969[/C][C]0.627016[/C][/ROW]
[ROW][C]48[/C][C]0.363679[/C][C]0.727359[/C][C]0.636321[/C][/ROW]
[ROW][C]49[/C][C]0.583257[/C][C]0.833485[/C][C]0.416743[/C][/ROW]
[ROW][C]50[/C][C]0.67602[/C][C]0.64796[/C][C]0.32398[/C][/ROW]
[ROW][C]51[/C][C]0.675818[/C][C]0.648365[/C][C]0.324182[/C][/ROW]
[ROW][C]52[/C][C]0.653592[/C][C]0.692816[/C][C]0.346408[/C][/ROW]
[ROW][C]53[/C][C]0.611766[/C][C]0.776468[/C][C]0.388234[/C][/ROW]
[ROW][C]54[/C][C]0.613305[/C][C]0.773389[/C][C]0.386695[/C][/ROW]
[ROW][C]55[/C][C]0.574198[/C][C]0.851604[/C][C]0.425802[/C][/ROW]
[ROW][C]56[/C][C]0.527755[/C][C]0.94449[/C][C]0.472245[/C][/ROW]
[ROW][C]57[/C][C]0.489979[/C][C]0.979959[/C][C]0.510021[/C][/ROW]
[ROW][C]58[/C][C]0.464425[/C][C]0.92885[/C][C]0.535575[/C][/ROW]
[ROW][C]59[/C][C]0.552435[/C][C]0.89513[/C][C]0.447565[/C][/ROW]
[ROW][C]60[/C][C]0.616773[/C][C]0.766454[/C][C]0.383227[/C][/ROW]
[ROW][C]61[/C][C]0.661583[/C][C]0.676835[/C][C]0.338417[/C][/ROW]
[ROW][C]62[/C][C]0.706669[/C][C]0.586662[/C][C]0.293331[/C][/ROW]
[ROW][C]63[/C][C]0.674167[/C][C]0.651666[/C][C]0.325833[/C][/ROW]
[ROW][C]64[/C][C]0.655991[/C][C]0.688018[/C][C]0.344009[/C][/ROW]
[ROW][C]65[/C][C]0.646138[/C][C]0.707725[/C][C]0.353862[/C][/ROW]
[ROW][C]66[/C][C]0.677451[/C][C]0.645097[/C][C]0.322549[/C][/ROW]
[ROW][C]67[/C][C]0.69949[/C][C]0.601021[/C][C]0.30051[/C][/ROW]
[ROW][C]68[/C][C]0.653532[/C][C]0.692936[/C][C]0.346468[/C][/ROW]
[ROW][C]69[/C][C]0.947946[/C][C]0.104108[/C][C]0.0520541[/C][/ROW]
[ROW][C]70[/C][C]0.954015[/C][C]0.0919694[/C][C]0.0459847[/C][/ROW]
[ROW][C]71[/C][C]0.946143[/C][C]0.107714[/C][C]0.053857[/C][/ROW]
[ROW][C]72[/C][C]0.948955[/C][C]0.10209[/C][C]0.0510449[/C][/ROW]
[ROW][C]73[/C][C]0.937907[/C][C]0.124185[/C][C]0.0620927[/C][/ROW]
[ROW][C]74[/C][C]0.931228[/C][C]0.137544[/C][C]0.0687718[/C][/ROW]
[ROW][C]75[/C][C]0.926647[/C][C]0.146706[/C][C]0.0733529[/C][/ROW]
[ROW][C]76[/C][C]0.923554[/C][C]0.152892[/C][C]0.0764462[/C][/ROW]
[ROW][C]77[/C][C]0.930062[/C][C]0.139876[/C][C]0.0699378[/C][/ROW]
[ROW][C]78[/C][C]0.91173[/C][C]0.17654[/C][C]0.08827[/C][/ROW]
[ROW][C]79[/C][C]0.907814[/C][C]0.184372[/C][C]0.0921861[/C][/ROW]
[ROW][C]80[/C][C]0.933983[/C][C]0.132034[/C][C]0.0660169[/C][/ROW]
[ROW][C]81[/C][C]0.916422[/C][C]0.167156[/C][C]0.0835782[/C][/ROW]
[ROW][C]82[/C][C]0.899764[/C][C]0.200472[/C][C]0.100236[/C][/ROW]
[ROW][C]83[/C][C]0.882128[/C][C]0.235744[/C][C]0.117872[/C][/ROW]
[ROW][C]84[/C][C]0.864275[/C][C]0.271451[/C][C]0.135725[/C][/ROW]
[ROW][C]85[/C][C]0.83754[/C][C]0.32492[/C][C]0.16246[/C][/ROW]
[ROW][C]86[/C][C]0.839294[/C][C]0.321412[/C][C]0.160706[/C][/ROW]
[ROW][C]87[/C][C]0.808736[/C][C]0.382527[/C][C]0.191264[/C][/ROW]
[ROW][C]88[/C][C]0.777023[/C][C]0.445954[/C][C]0.222977[/C][/ROW]
[ROW][C]89[/C][C]0.739988[/C][C]0.520023[/C][C]0.260012[/C][/ROW]
[ROW][C]90[/C][C]0.690589[/C][C]0.618823[/C][C]0.309411[/C][/ROW]
[ROW][C]91[/C][C]0.687515[/C][C]0.62497[/C][C]0.312485[/C][/ROW]
[ROW][C]92[/C][C]0.667476[/C][C]0.665049[/C][C]0.332524[/C][/ROW]
[ROW][C]93[/C][C]0.875853[/C][C]0.248294[/C][C]0.124147[/C][/ROW]
[ROW][C]94[/C][C]0.854391[/C][C]0.291219[/C][C]0.145609[/C][/ROW]
[ROW][C]95[/C][C]0.835832[/C][C]0.328335[/C][C]0.164168[/C][/ROW]
[ROW][C]96[/C][C]0.809873[/C][C]0.380253[/C][C]0.190127[/C][/ROW]
[ROW][C]97[/C][C]0.779349[/C][C]0.441302[/C][C]0.220651[/C][/ROW]
[ROW][C]98[/C][C]0.770794[/C][C]0.458412[/C][C]0.229206[/C][/ROW]
[ROW][C]99[/C][C]0.776629[/C][C]0.446743[/C][C]0.223371[/C][/ROW]
[ROW][C]100[/C][C]0.734549[/C][C]0.530902[/C][C]0.265451[/C][/ROW]
[ROW][C]101[/C][C]0.681528[/C][C]0.636944[/C][C]0.318472[/C][/ROW]
[ROW][C]102[/C][C]0.645342[/C][C]0.709317[/C][C]0.354658[/C][/ROW]
[ROW][C]103[/C][C]0.631477[/C][C]0.737046[/C][C]0.368523[/C][/ROW]
[ROW][C]104[/C][C]0.650928[/C][C]0.698144[/C][C]0.349072[/C][/ROW]
[ROW][C]105[/C][C]0.599231[/C][C]0.801539[/C][C]0.400769[/C][/ROW]
[ROW][C]106[/C][C]0.551901[/C][C]0.896197[/C][C]0.448099[/C][/ROW]
[ROW][C]107[/C][C]0.49532[/C][C]0.99064[/C][C]0.50468[/C][/ROW]
[ROW][C]108[/C][C]0.476986[/C][C]0.953973[/C][C]0.523014[/C][/ROW]
[ROW][C]109[/C][C]0.451599[/C][C]0.903198[/C][C]0.548401[/C][/ROW]
[ROW][C]110[/C][C]0.434172[/C][C]0.868343[/C][C]0.565828[/C][/ROW]
[ROW][C]111[/C][C]0.375902[/C][C]0.751804[/C][C]0.624098[/C][/ROW]
[ROW][C]112[/C][C]0.321001[/C][C]0.642001[/C][C]0.678999[/C][/ROW]
[ROW][C]113[/C][C]0.314901[/C][C]0.629802[/C][C]0.685099[/C][/ROW]
[ROW][C]114[/C][C]0.2519[/C][C]0.503799[/C][C]0.7481[/C][/ROW]
[ROW][C]115[/C][C]0.210165[/C][C]0.420329[/C][C]0.789835[/C][/ROW]
[ROW][C]116[/C][C]0.186837[/C][C]0.373673[/C][C]0.813163[/C][/ROW]
[ROW][C]117[/C][C]0.129637[/C][C]0.259274[/C][C]0.870363[/C][/ROW]
[ROW][C]118[/C][C]0.42087[/C][C]0.841741[/C][C]0.57913[/C][/ROW]
[ROW][C]119[/C][C]0.613885[/C][C]0.77223[/C][C]0.386115[/C][/ROW]
[ROW][C]120[/C][C]0.743627[/C][C]0.512747[/C][C]0.256373[/C][/ROW]
[ROW][C]121[/C][C]0.612966[/C][C]0.774068[/C][C]0.387034[/C][/ROW]
[ROW][C]122[/C][C]0.697964[/C][C]0.604071[/C][C]0.302036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.4623590.9247170.537641
180.3170590.6341180.682941
190.2074390.4148780.792561
200.120690.241380.87931
210.1070540.2141080.892946
220.06884470.1376890.931155
230.0917090.1834180.908291
240.06971490.139430.930285
250.06398780.1279760.936012
260.06953990.139080.93046
270.1092570.2185130.890743
280.07573680.1514740.924263
290.1754780.3509570.824522
300.3506530.7013070.649347
310.3010620.6021240.698938
320.2481610.4963210.751839
330.1954930.3909870.804507
340.2305450.4610910.769455
350.1851180.3702360.814882
360.3121060.6242130.687894
370.2711470.5422940.728853
380.2837740.5675480.716226
390.2383980.4767960.761602
400.1917220.3834450.808278
410.1924230.3848450.807577
420.2123330.4246660.787667
430.1825510.3651010.817449
440.4073990.8147980.592601
450.3562050.7124090.643795
460.3601590.7203180.639841
470.3729840.7459690.627016
480.3636790.7273590.636321
490.5832570.8334850.416743
500.676020.647960.32398
510.6758180.6483650.324182
520.6535920.6928160.346408
530.6117660.7764680.388234
540.6133050.7733890.386695
550.5741980.8516040.425802
560.5277550.944490.472245
570.4899790.9799590.510021
580.4644250.928850.535575
590.5524350.895130.447565
600.6167730.7664540.383227
610.6615830.6768350.338417
620.7066690.5866620.293331
630.6741670.6516660.325833
640.6559910.6880180.344009
650.6461380.7077250.353862
660.6774510.6450970.322549
670.699490.6010210.30051
680.6535320.6929360.346468
690.9479460.1041080.0520541
700.9540150.09196940.0459847
710.9461430.1077140.053857
720.9489550.102090.0510449
730.9379070.1241850.0620927
740.9312280.1375440.0687718
750.9266470.1467060.0733529
760.9235540.1528920.0764462
770.9300620.1398760.0699378
780.911730.176540.08827
790.9078140.1843720.0921861
800.9339830.1320340.0660169
810.9164220.1671560.0835782
820.8997640.2004720.100236
830.8821280.2357440.117872
840.8642750.2714510.135725
850.837540.324920.16246
860.8392940.3214120.160706
870.8087360.3825270.191264
880.7770230.4459540.222977
890.7399880.5200230.260012
900.6905890.6188230.309411
910.6875150.624970.312485
920.6674760.6650490.332524
930.8758530.2482940.124147
940.8543910.2912190.145609
950.8358320.3283350.164168
960.8098730.3802530.190127
970.7793490.4413020.220651
980.7707940.4584120.229206
990.7766290.4467430.223371
1000.7345490.5309020.265451
1010.6815280.6369440.318472
1020.6453420.7093170.354658
1030.6314770.7370460.368523
1040.6509280.6981440.349072
1050.5992310.8015390.400769
1060.5519010.8961970.448099
1070.495320.990640.50468
1080.4769860.9539730.523014
1090.4515990.9031980.548401
1100.4341720.8683430.565828
1110.3759020.7518040.624098
1120.3210010.6420010.678999
1130.3149010.6298020.685099
1140.25190.5037990.7481
1150.2101650.4203290.789835
1160.1868370.3736730.813163
1170.1296370.2592740.870363
1180.420870.8417410.57913
1190.6138850.772230.386115
1200.7436270.5127470.256373
1210.6129660.7740680.387034
1220.6979640.6040710.302036






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 level10.00943396OK

\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 & 0.00943396 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271044&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]0.00943396[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271044&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271044&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 level10.00943396OK


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')
}