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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 14:07:51 +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/t1418911685dz2q49k7caj3dgm.htm/, Retrieved Fri, 17 May 2024 15:43:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270954, Retrieved Fri, 17 May 2024 15:43:36 +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 14:07:51] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&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'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.6725 + 0.0155288AMS.I1[t] -0.0643581AMS.I2[t] -0.0360881AMS.I3[t] -0.0480893AMS.E1[t] -0.022558AMS.E2[t] -0.0208066AMS.E3[t] + 0.00465854AMS.A[t] -0.144899age[t] + 0.00683179CONFSTATTOT[t] + 0.110682CONFSOFTTOT[t] -0.00694372LFM[t] -0.0231093PRH[t] + 0.0372025CH[t] + 2.75987PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.6725 +  0.0155288AMS.I1[t] -0.0643581AMS.I2[t] -0.0360881AMS.I3[t] -0.0480893AMS.E1[t] -0.022558AMS.E2[t] -0.0208066AMS.E3[t] +  0.00465854AMS.A[t] -0.144899age[t] +  0.00683179CONFSTATTOT[t] +  0.110682CONFSOFTTOT[t] -0.00694372LFM[t] -0.0231093PRH[t] +  0.0372025CH[t] +  2.75987PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.6725 +  0.0155288AMS.I1[t] -0.0643581AMS.I2[t] -0.0360881AMS.I3[t] -0.0480893AMS.E1[t] -0.022558AMS.E2[t] -0.0208066AMS.E3[t] +  0.00465854AMS.A[t] -0.144899age[t] +  0.00683179CONFSTATTOT[t] +  0.110682CONFSOFTTOT[t] -0.00694372LFM[t] -0.0231093PRH[t] +  0.0372025CH[t] +  2.75987PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.6725 + 0.0155288AMS.I1[t] -0.0643581AMS.I2[t] -0.0360881AMS.I3[t] -0.0480893AMS.E1[t] -0.022558AMS.E2[t] -0.0208066AMS.E3[t] + 0.00465854AMS.A[t] -0.144899age[t] + 0.00683179CONFSTATTOT[t] + 0.110682CONFSOFTTOT[t] -0.00694372LFM[t] -0.0231093PRH[t] + 0.0372025CH[t] + 2.75987PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.67254.640852.30.02309760.0115488
AMS.I10.01552880.08028440.19340.8469370.423469
AMS.I2-0.06435810.0735449-0.87510.3831790.19159
AMS.I3-0.03608810.0628634-0.57410.5669350.283467
AMS.E1-0.04808930.0781843-0.61510.5396050.269803
AMS.E2-0.0225580.0580261-0.38880.6981080.349054
AMS.E3-0.02080660.0665754-0.31250.7551530.377576
AMS.A0.004658540.07841050.059410.9527170.476359
age-0.1448990.181993-0.79620.4274130.213707
CONFSTATTOT0.006831790.1102470.061970.9506860.475343
CONFSOFTTOT0.1106820.1214180.91160.3637190.181859
LFM-0.006943720.00634435-1.0940.2758180.137909
PRH-0.02310930.0116442-1.9850.04934250.0246713
CH0.03720250.01120413.320.001173630.000586817
PR2.759870.24750611.151.49158e-207.45792e-21

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.6725 & 4.64085 & 2.3 & 0.0230976 & 0.0115488 \tabularnewline
AMS.I1 & 0.0155288 & 0.0802844 & 0.1934 & 0.846937 & 0.423469 \tabularnewline
AMS.I2 & -0.0643581 & 0.0735449 & -0.8751 & 0.383179 & 0.19159 \tabularnewline
AMS.I3 & -0.0360881 & 0.0628634 & -0.5741 & 0.566935 & 0.283467 \tabularnewline
AMS.E1 & -0.0480893 & 0.0781843 & -0.6151 & 0.539605 & 0.269803 \tabularnewline
AMS.E2 & -0.022558 & 0.0580261 & -0.3888 & 0.698108 & 0.349054 \tabularnewline
AMS.E3 & -0.0208066 & 0.0665754 & -0.3125 & 0.755153 & 0.377576 \tabularnewline
AMS.A & 0.00465854 & 0.0784105 & 0.05941 & 0.952717 & 0.476359 \tabularnewline
age & -0.144899 & 0.181993 & -0.7962 & 0.427413 & 0.213707 \tabularnewline
CONFSTATTOT & 0.00683179 & 0.110247 & 0.06197 & 0.950686 & 0.475343 \tabularnewline
CONFSOFTTOT & 0.110682 & 0.121418 & 0.9116 & 0.363719 & 0.181859 \tabularnewline
LFM & -0.00694372 & 0.00634435 & -1.094 & 0.275818 & 0.137909 \tabularnewline
PRH & -0.0231093 & 0.0116442 & -1.985 & 0.0493425 & 0.0246713 \tabularnewline
CH & 0.0372025 & 0.0112041 & 3.32 & 0.00117363 & 0.000586817 \tabularnewline
PR & 2.75987 & 0.247506 & 11.15 & 1.49158e-20 & 7.45792e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.6725[/C][C]4.64085[/C][C]2.3[/C][C]0.0230976[/C][C]0.0115488[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0155288[/C][C]0.0802844[/C][C]0.1934[/C][C]0.846937[/C][C]0.423469[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0643581[/C][C]0.0735449[/C][C]-0.8751[/C][C]0.383179[/C][C]0.19159[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0360881[/C][C]0.0628634[/C][C]-0.5741[/C][C]0.566935[/C][C]0.283467[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0480893[/C][C]0.0781843[/C][C]-0.6151[/C][C]0.539605[/C][C]0.269803[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.022558[/C][C]0.0580261[/C][C]-0.3888[/C][C]0.698108[/C][C]0.349054[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.0208066[/C][C]0.0665754[/C][C]-0.3125[/C][C]0.755153[/C][C]0.377576[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.00465854[/C][C]0.0784105[/C][C]0.05941[/C][C]0.952717[/C][C]0.476359[/C][/ROW]
[ROW][C]age[/C][C]-0.144899[/C][C]0.181993[/C][C]-0.7962[/C][C]0.427413[/C][C]0.213707[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.00683179[/C][C]0.110247[/C][C]0.06197[/C][C]0.950686[/C][C]0.475343[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.110682[/C][C]0.121418[/C][C]0.9116[/C][C]0.363719[/C][C]0.181859[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00694372[/C][C]0.00634435[/C][C]-1.094[/C][C]0.275818[/C][C]0.137909[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0231093[/C][C]0.0116442[/C][C]-1.985[/C][C]0.0493425[/C][C]0.0246713[/C][/ROW]
[ROW][C]CH[/C][C]0.0372025[/C][C]0.0112041[/C][C]3.32[/C][C]0.00117363[/C][C]0.000586817[/C][/ROW]
[ROW][C]PR[/C][C]2.75987[/C][C]0.247506[/C][C]11.15[/C][C]1.49158e-20[/C][C]7.45792e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.67254.640852.30.02309760.0115488
AMS.I10.01552880.08028440.19340.8469370.423469
AMS.I2-0.06435810.0735449-0.87510.3831790.19159
AMS.I3-0.03608810.0628634-0.57410.5669350.283467
AMS.E1-0.04808930.0781843-0.61510.5396050.269803
AMS.E2-0.0225580.0580261-0.38880.6981080.349054
AMS.E3-0.02080660.0665754-0.31250.7551530.377576
AMS.A0.004658540.07841050.059410.9527170.476359
age-0.1448990.181993-0.79620.4274130.213707
CONFSTATTOT0.006831790.1102470.061970.9506860.475343
CONFSOFTTOT0.1106820.1214180.91160.3637190.181859
LFM-0.006943720.00634435-1.0940.2758180.137909
PRH-0.02310930.0116442-1.9850.04934250.0246713
CH0.03720250.01120413.320.001173630.000586817
PR2.759870.24750611.151.49158e-207.45792e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.773113
R-squared0.597704
Adjusted R-squared0.553356
F-TEST (value)13.4777
F-TEST (DF numerator)14
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3289
Sum Squared Residuals688.82

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.773113 \tabularnewline
R-squared & 0.597704 \tabularnewline
Adjusted R-squared & 0.553356 \tabularnewline
F-TEST (value) & 13.4777 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 127 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3289 \tabularnewline
Sum Squared Residuals & 688.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.773113[/C][/ROW]
[ROW][C]R-squared[/C][C]0.597704[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.553356[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.4777[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]127[/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]2.3289[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]688.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&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 R0.773113
R-squared0.597704
Adjusted R-squared0.553356
F-TEST (value)13.4777
F-TEST (DF numerator)14
F-TEST (DF denominator)127
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3289
Sum Squared Residuals688.82






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9631-0.063127
212.210.47081.72915
312.811.70241.09758
47.411.7117-4.31172
56.711.4816-4.7816
612.611.54391.05608
714.811.06583.73419
813.313.6044-0.304373
911.112.3094-1.20939
108.210.9598-2.75976
1111.411.26960.130418
126.411.2023-4.80228
1310.610.28130.318679
141213.1043-1.10426
156.39.24286-2.94286
1611.913.0969-1.19692
179.310.9742-1.67415
18109.687970.31203
196.49.9533-3.5533
2013.812.58671.21332
2110.89.977630.82237
2213.811.57652.22349
2311.710.93770.762335
2410.911.633-0.733039
259.911.3151-1.41505
2611.510.52810.971904
278.311.1981-2.89807
2811.711.23130.468661
29910.3983-1.3983
309.713.6659-3.96592
3110.811.47-0.669961
3210.310.903-0.603019
3310.49.976960.423042
349.312.2628-2.96281
3511.811.10690.693147
365.911.5038-5.60375
3711.411.8759-0.475912
381310.82332.17672
3910.811.24-0.439994
4011.310.69490.605122
4111.811.8897-0.0896779
4212.79.770222.92978
4310.910.47040.429596
4413.311.87461.4254
4510.110.8289-0.728863
4614.311.51412.78589
479.311.8031-2.5031
4812.510.2082.29195
497.610.446-2.84598
5015.912.44453.4555
519.210.4174-1.21744
5211.112.1611-1.0611
531312.29720.702807
5414.511.23.30004
5512.313.0063-0.706255
5611.410.66030.739662
5712.610.99541.60458
58NANA0.952417
591310.97992.02006
6013.217.1914-3.99141
617.711.2989-3.59892
624.351.931192.41881
6312.710.70741.99262
6418.116.28071.81926
6517.8517.68170.168256
6617.114.35832.74172
6719.121.6296-2.52957
6816.113.92932.17067
6913.3512.64380.706211
7018.411.83636.56365
7114.717.3644-2.6644
7210.611.8333-1.23326
7312.613.015-0.414958
7416.215.21870.981266
7513.612.97350.626465
7614.113.4170.682978
7714.514.5464-0.0463651
7816.1513.95482.19522
7914.7512.312.44001
8014.814.856-0.0559668
8112.4510.7841.666
8212.659.454163.19584
8317.3517.8436-0.49361
848.67.012081.58792
8518.416.85571.54427
8616.113.55612.54388
8717.7517.70850.0415427
8815.2513.76721.48278
8917.6517.9577-0.307739
9016.3517.1272-0.777163
9117.6517.25750.392516
9213.613.41820.181789
9314.3517.0522-2.70223
9414.7512.73582.01421
9518.2523.4133-5.16331
969.98.131391.76861
971613.90312.09693
9818.2518.9314-0.681361
9916.8514.90311.94695
10018.9517.16241.78764
10115.615.9639-0.363909
10217.110.08957.01052
10316.116.8276-0.727646
10415.416.1137-0.713749
10515.416.5232-1.12316
10613.3511.31722.03277
10719.118.71630.383685
1087.65.440342.15966
10919.120.9917-1.89167
11014.7512.29562.45443
11119.2521.8468-2.59678
11213.615.949-2.34898
11312.7511.43791.31214
1149.8510.4663-0.616278
11515.2517.1185-1.86852
11611.912.7805-0.880539
11716.3517.8348-1.48483
11812.410.17862.22144
11918.1515.47462.67543
12017.7518.4324-0.682429
12112.3512.3539-0.00386416
12215.612.89552.70454
12319.318.69020.609757
12417.114.24582.85422
12518.416.10962.29035
12619.0514.83624.21377
12718.5517.81820.731843
12819.121.6574-2.55735
12912.8514.6506-1.80064
1309.513.0375-3.53751
1314.56.02164-1.52164
13213.614.8111-1.21111
13311.712.6237-0.92375
13413.3514.5092-1.15921
13517.617.18270.417267
13614.0515.5822-1.53215
13716.118.5197-2.41969
13813.3516.5735-3.22354
13911.8511.9324-0.0824037
14011.9515.1503-3.20032
14113.215.1318-1.93175
1427.76.820030.879965
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9631 & -0.063127 \tabularnewline
2 & 12.2 & 10.4708 & 1.72915 \tabularnewline
3 & 12.8 & 11.7024 & 1.09758 \tabularnewline
4 & 7.4 & 11.7117 & -4.31172 \tabularnewline
5 & 6.7 & 11.4816 & -4.7816 \tabularnewline
6 & 12.6 & 11.5439 & 1.05608 \tabularnewline
7 & 14.8 & 11.0658 & 3.73419 \tabularnewline
8 & 13.3 & 13.6044 & -0.304373 \tabularnewline
9 & 11.1 & 12.3094 & -1.20939 \tabularnewline
10 & 8.2 & 10.9598 & -2.75976 \tabularnewline
11 & 11.4 & 11.2696 & 0.130418 \tabularnewline
12 & 6.4 & 11.2023 & -4.80228 \tabularnewline
13 & 10.6 & 10.2813 & 0.318679 \tabularnewline
14 & 12 & 13.1043 & -1.10426 \tabularnewline
15 & 6.3 & 9.24286 & -2.94286 \tabularnewline
16 & 11.9 & 13.0969 & -1.19692 \tabularnewline
17 & 9.3 & 10.9742 & -1.67415 \tabularnewline
18 & 10 & 9.68797 & 0.31203 \tabularnewline
19 & 6.4 & 9.9533 & -3.5533 \tabularnewline
20 & 13.8 & 12.5867 & 1.21332 \tabularnewline
21 & 10.8 & 9.97763 & 0.82237 \tabularnewline
22 & 13.8 & 11.5765 & 2.22349 \tabularnewline
23 & 11.7 & 10.9377 & 0.762335 \tabularnewline
24 & 10.9 & 11.633 & -0.733039 \tabularnewline
25 & 9.9 & 11.3151 & -1.41505 \tabularnewline
26 & 11.5 & 10.5281 & 0.971904 \tabularnewline
27 & 8.3 & 11.1981 & -2.89807 \tabularnewline
28 & 11.7 & 11.2313 & 0.468661 \tabularnewline
29 & 9 & 10.3983 & -1.3983 \tabularnewline
30 & 9.7 & 13.6659 & -3.96592 \tabularnewline
31 & 10.8 & 11.47 & -0.669961 \tabularnewline
32 & 10.3 & 10.903 & -0.603019 \tabularnewline
33 & 10.4 & 9.97696 & 0.423042 \tabularnewline
34 & 9.3 & 12.2628 & -2.96281 \tabularnewline
35 & 11.8 & 11.1069 & 0.693147 \tabularnewline
36 & 5.9 & 11.5038 & -5.60375 \tabularnewline
37 & 11.4 & 11.8759 & -0.475912 \tabularnewline
38 & 13 & 10.8233 & 2.17672 \tabularnewline
39 & 10.8 & 11.24 & -0.439994 \tabularnewline
40 & 11.3 & 10.6949 & 0.605122 \tabularnewline
41 & 11.8 & 11.8897 & -0.0896779 \tabularnewline
42 & 12.7 & 9.77022 & 2.92978 \tabularnewline
43 & 10.9 & 10.4704 & 0.429596 \tabularnewline
44 & 13.3 & 11.8746 & 1.4254 \tabularnewline
45 & 10.1 & 10.8289 & -0.728863 \tabularnewline
46 & 14.3 & 11.5141 & 2.78589 \tabularnewline
47 & 9.3 & 11.8031 & -2.5031 \tabularnewline
48 & 12.5 & 10.208 & 2.29195 \tabularnewline
49 & 7.6 & 10.446 & -2.84598 \tabularnewline
50 & 15.9 & 12.4445 & 3.4555 \tabularnewline
51 & 9.2 & 10.4174 & -1.21744 \tabularnewline
52 & 11.1 & 12.1611 & -1.0611 \tabularnewline
53 & 13 & 12.2972 & 0.702807 \tabularnewline
54 & 14.5 & 11.2 & 3.30004 \tabularnewline
55 & 12.3 & 13.0063 & -0.706255 \tabularnewline
56 & 11.4 & 10.6603 & 0.739662 \tabularnewline
57 & 12.6 & 10.9954 & 1.60458 \tabularnewline
58 & NA & NA & 0.952417 \tabularnewline
59 & 13 & 10.9799 & 2.02006 \tabularnewline
60 & 13.2 & 17.1914 & -3.99141 \tabularnewline
61 & 7.7 & 11.2989 & -3.59892 \tabularnewline
62 & 4.35 & 1.93119 & 2.41881 \tabularnewline
63 & 12.7 & 10.7074 & 1.99262 \tabularnewline
64 & 18.1 & 16.2807 & 1.81926 \tabularnewline
65 & 17.85 & 17.6817 & 0.168256 \tabularnewline
66 & 17.1 & 14.3583 & 2.74172 \tabularnewline
67 & 19.1 & 21.6296 & -2.52957 \tabularnewline
68 & 16.1 & 13.9293 & 2.17067 \tabularnewline
69 & 13.35 & 12.6438 & 0.706211 \tabularnewline
70 & 18.4 & 11.8363 & 6.56365 \tabularnewline
71 & 14.7 & 17.3644 & -2.6644 \tabularnewline
72 & 10.6 & 11.8333 & -1.23326 \tabularnewline
73 & 12.6 & 13.015 & -0.414958 \tabularnewline
74 & 16.2 & 15.2187 & 0.981266 \tabularnewline
75 & 13.6 & 12.9735 & 0.626465 \tabularnewline
76 & 14.1 & 13.417 & 0.682978 \tabularnewline
77 & 14.5 & 14.5464 & -0.0463651 \tabularnewline
78 & 16.15 & 13.9548 & 2.19522 \tabularnewline
79 & 14.75 & 12.31 & 2.44001 \tabularnewline
80 & 14.8 & 14.856 & -0.0559668 \tabularnewline
81 & 12.45 & 10.784 & 1.666 \tabularnewline
82 & 12.65 & 9.45416 & 3.19584 \tabularnewline
83 & 17.35 & 17.8436 & -0.49361 \tabularnewline
84 & 8.6 & 7.01208 & 1.58792 \tabularnewline
85 & 18.4 & 16.8557 & 1.54427 \tabularnewline
86 & 16.1 & 13.5561 & 2.54388 \tabularnewline
87 & 17.75 & 17.7085 & 0.0415427 \tabularnewline
88 & 15.25 & 13.7672 & 1.48278 \tabularnewline
89 & 17.65 & 17.9577 & -0.307739 \tabularnewline
90 & 16.35 & 17.1272 & -0.777163 \tabularnewline
91 & 17.65 & 17.2575 & 0.392516 \tabularnewline
92 & 13.6 & 13.4182 & 0.181789 \tabularnewline
93 & 14.35 & 17.0522 & -2.70223 \tabularnewline
94 & 14.75 & 12.7358 & 2.01421 \tabularnewline
95 & 18.25 & 23.4133 & -5.16331 \tabularnewline
96 & 9.9 & 8.13139 & 1.76861 \tabularnewline
97 & 16 & 13.9031 & 2.09693 \tabularnewline
98 & 18.25 & 18.9314 & -0.681361 \tabularnewline
99 & 16.85 & 14.9031 & 1.94695 \tabularnewline
100 & 18.95 & 17.1624 & 1.78764 \tabularnewline
101 & 15.6 & 15.9639 & -0.363909 \tabularnewline
102 & 17.1 & 10.0895 & 7.01052 \tabularnewline
103 & 16.1 & 16.8276 & -0.727646 \tabularnewline
104 & 15.4 & 16.1137 & -0.713749 \tabularnewline
105 & 15.4 & 16.5232 & -1.12316 \tabularnewline
106 & 13.35 & 11.3172 & 2.03277 \tabularnewline
107 & 19.1 & 18.7163 & 0.383685 \tabularnewline
108 & 7.6 & 5.44034 & 2.15966 \tabularnewline
109 & 19.1 & 20.9917 & -1.89167 \tabularnewline
110 & 14.75 & 12.2956 & 2.45443 \tabularnewline
111 & 19.25 & 21.8468 & -2.59678 \tabularnewline
112 & 13.6 & 15.949 & -2.34898 \tabularnewline
113 & 12.75 & 11.4379 & 1.31214 \tabularnewline
114 & 9.85 & 10.4663 & -0.616278 \tabularnewline
115 & 15.25 & 17.1185 & -1.86852 \tabularnewline
116 & 11.9 & 12.7805 & -0.880539 \tabularnewline
117 & 16.35 & 17.8348 & -1.48483 \tabularnewline
118 & 12.4 & 10.1786 & 2.22144 \tabularnewline
119 & 18.15 & 15.4746 & 2.67543 \tabularnewline
120 & 17.75 & 18.4324 & -0.682429 \tabularnewline
121 & 12.35 & 12.3539 & -0.00386416 \tabularnewline
122 & 15.6 & 12.8955 & 2.70454 \tabularnewline
123 & 19.3 & 18.6902 & 0.609757 \tabularnewline
124 & 17.1 & 14.2458 & 2.85422 \tabularnewline
125 & 18.4 & 16.1096 & 2.29035 \tabularnewline
126 & 19.05 & 14.8362 & 4.21377 \tabularnewline
127 & 18.55 & 17.8182 & 0.731843 \tabularnewline
128 & 19.1 & 21.6574 & -2.55735 \tabularnewline
129 & 12.85 & 14.6506 & -1.80064 \tabularnewline
130 & 9.5 & 13.0375 & -3.53751 \tabularnewline
131 & 4.5 & 6.02164 & -1.52164 \tabularnewline
132 & 13.6 & 14.8111 & -1.21111 \tabularnewline
133 & 11.7 & 12.6237 & -0.92375 \tabularnewline
134 & 13.35 & 14.5092 & -1.15921 \tabularnewline
135 & 17.6 & 17.1827 & 0.417267 \tabularnewline
136 & 14.05 & 15.5822 & -1.53215 \tabularnewline
137 & 16.1 & 18.5197 & -2.41969 \tabularnewline
138 & 13.35 & 16.5735 & -3.22354 \tabularnewline
139 & 11.85 & 11.9324 & -0.0824037 \tabularnewline
140 & 11.95 & 15.1503 & -3.20032 \tabularnewline
141 & 13.2 & 15.1318 & -1.93175 \tabularnewline
142 & 7.7 & 6.82003 & 0.879965 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&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]12.9631[/C][C]-0.063127[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.4708[/C][C]1.72915[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7024[/C][C]1.09758[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.7117[/C][C]-4.31172[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.4816[/C][C]-4.7816[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.5439[/C][C]1.05608[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0658[/C][C]3.73419[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.6044[/C][C]-0.304373[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.3094[/C][C]-1.20939[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9598[/C][C]-2.75976[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2696[/C][C]0.130418[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.2023[/C][C]-4.80228[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.2813[/C][C]0.318679[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.1043[/C][C]-1.10426[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.24286[/C][C]-2.94286[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.0969[/C][C]-1.19692[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9742[/C][C]-1.67415[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.68797[/C][C]0.31203[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]9.9533[/C][C]-3.5533[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5867[/C][C]1.21332[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]9.97763[/C][C]0.82237[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.5765[/C][C]2.22349[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.9377[/C][C]0.762335[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.633[/C][C]-0.733039[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3151[/C][C]-1.41505[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5281[/C][C]0.971904[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.1981[/C][C]-2.89807[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.2313[/C][C]0.468661[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3983[/C][C]-1.3983[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.6659[/C][C]-3.96592[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.47[/C][C]-0.669961[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.903[/C][C]-0.603019[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.97696[/C][C]0.423042[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2628[/C][C]-2.96281[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]11.1069[/C][C]0.693147[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.5038[/C][C]-5.60375[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8759[/C][C]-0.475912[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.8233[/C][C]2.17672[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.24[/C][C]-0.439994[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.6949[/C][C]0.605122[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.8897[/C][C]-0.0896779[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.77022[/C][C]2.92978[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.4704[/C][C]0.429596[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.8746[/C][C]1.4254[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.8289[/C][C]-0.728863[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.5141[/C][C]2.78589[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.8031[/C][C]-2.5031[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.208[/C][C]2.29195[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.446[/C][C]-2.84598[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4445[/C][C]3.4555[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4174[/C][C]-1.21744[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.1611[/C][C]-1.0611[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.2972[/C][C]0.702807[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.2[/C][C]3.30004[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0063[/C][C]-0.706255[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.6603[/C][C]0.739662[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]10.9954[/C][C]1.60458[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]0.952417[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.9799[/C][C]2.02006[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]17.1914[/C][C]-3.99141[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.2989[/C][C]-3.59892[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.93119[/C][C]2.41881[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.7074[/C][C]1.99262[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.2807[/C][C]1.81926[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.6817[/C][C]0.168256[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.3583[/C][C]2.74172[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.6296[/C][C]-2.52957[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.9293[/C][C]2.17067[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]12.6438[/C][C]0.706211[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]11.8363[/C][C]6.56365[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]17.3644[/C][C]-2.6644[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.8333[/C][C]-1.23326[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]13.015[/C][C]-0.414958[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]15.2187[/C][C]0.981266[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.9735[/C][C]0.626465[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]13.417[/C][C]0.682978[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5464[/C][C]-0.0463651[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]13.9548[/C][C]2.19522[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]12.31[/C][C]2.44001[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.856[/C][C]-0.0559668[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]10.784[/C][C]1.666[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]9.45416[/C][C]3.19584[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]17.8436[/C][C]-0.49361[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]7.01208[/C][C]1.58792[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.8557[/C][C]1.54427[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]13.5561[/C][C]2.54388[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]17.7085[/C][C]0.0415427[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.7672[/C][C]1.48278[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.9577[/C][C]-0.307739[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.1272[/C][C]-0.777163[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.2575[/C][C]0.392516[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.4182[/C][C]0.181789[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]17.0522[/C][C]-2.70223[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]12.7358[/C][C]2.01421[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]23.4133[/C][C]-5.16331[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]8.13139[/C][C]1.76861[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.9031[/C][C]2.09693[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]18.9314[/C][C]-0.681361[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.9031[/C][C]1.94695[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]17.1624[/C][C]1.78764[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]15.9639[/C][C]-0.363909[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.0895[/C][C]7.01052[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]16.8276[/C][C]-0.727646[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]16.1137[/C][C]-0.713749[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]16.5232[/C][C]-1.12316[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]11.3172[/C][C]2.03277[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]18.7163[/C][C]0.383685[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]5.44034[/C][C]2.15966[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]20.9917[/C][C]-1.89167[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]12.2956[/C][C]2.45443[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.8468[/C][C]-2.59678[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]15.949[/C][C]-2.34898[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.4379[/C][C]1.31214[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]10.4663[/C][C]-0.616278[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.1185[/C][C]-1.86852[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.7805[/C][C]-0.880539[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]17.8348[/C][C]-1.48483[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]10.1786[/C][C]2.22144[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]15.4746[/C][C]2.67543[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]18.4324[/C][C]-0.682429[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.3539[/C][C]-0.00386416[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]12.8955[/C][C]2.70454[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.6902[/C][C]0.609757[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.2458[/C][C]2.85422[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.1096[/C][C]2.29035[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]14.8362[/C][C]4.21377[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.8182[/C][C]0.731843[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6574[/C][C]-2.55735[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]14.6506[/C][C]-1.80064[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]13.0375[/C][C]-3.53751[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]6.02164[/C][C]-1.52164[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]14.8111[/C][C]-1.21111[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]12.6237[/C][C]-0.92375[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]14.5092[/C][C]-1.15921[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.1827[/C][C]0.417267[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.5822[/C][C]-1.53215[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.5197[/C][C]-2.41969[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.5735[/C][C]-3.22354[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.9324[/C][C]-0.0824037[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]15.1503[/C][C]-3.20032[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.1318[/C][C]-1.93175[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]6.82003[/C][C]0.879965[/C][/ROW]
[ROW][C]143[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&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
112.912.9631-0.063127
212.210.47081.72915
312.811.70241.09758
47.411.7117-4.31172
56.711.4816-4.7816
612.611.54391.05608
714.811.06583.73419
813.313.6044-0.304373
911.112.3094-1.20939
108.210.9598-2.75976
1111.411.26960.130418
126.411.2023-4.80228
1310.610.28130.318679
141213.1043-1.10426
156.39.24286-2.94286
1611.913.0969-1.19692
179.310.9742-1.67415
18109.687970.31203
196.49.9533-3.5533
2013.812.58671.21332
2110.89.977630.82237
2213.811.57652.22349
2311.710.93770.762335
2410.911.633-0.733039
259.911.3151-1.41505
2611.510.52810.971904
278.311.1981-2.89807
2811.711.23130.468661
29910.3983-1.3983
309.713.6659-3.96592
3110.811.47-0.669961
3210.310.903-0.603019
3310.49.976960.423042
349.312.2628-2.96281
3511.811.10690.693147
365.911.5038-5.60375
3711.411.8759-0.475912
381310.82332.17672
3910.811.24-0.439994
4011.310.69490.605122
4111.811.8897-0.0896779
4212.79.770222.92978
4310.910.47040.429596
4413.311.87461.4254
4510.110.8289-0.728863
4614.311.51412.78589
479.311.8031-2.5031
4812.510.2082.29195
497.610.446-2.84598
5015.912.44453.4555
519.210.4174-1.21744
5211.112.1611-1.0611
531312.29720.702807
5414.511.23.30004
5512.313.0063-0.706255
5611.410.66030.739662
5712.610.99541.60458
58NANA0.952417
591310.97992.02006
6013.217.1914-3.99141
617.711.2989-3.59892
624.351.931192.41881
6312.710.70741.99262
6418.116.28071.81926
6517.8517.68170.168256
6617.114.35832.74172
6719.121.6296-2.52957
6816.113.92932.17067
6913.3512.64380.706211
7018.411.83636.56365
7114.717.3644-2.6644
7210.611.8333-1.23326
7312.613.015-0.414958
7416.215.21870.981266
7513.612.97350.626465
7614.113.4170.682978
7714.514.5464-0.0463651
7816.1513.95482.19522
7914.7512.312.44001
8014.814.856-0.0559668
8112.4510.7841.666
8212.659.454163.19584
8317.3517.8436-0.49361
848.67.012081.58792
8518.416.85571.54427
8616.113.55612.54388
8717.7517.70850.0415427
8815.2513.76721.48278
8917.6517.9577-0.307739
9016.3517.1272-0.777163
9117.6517.25750.392516
9213.613.41820.181789
9314.3517.0522-2.70223
9414.7512.73582.01421
9518.2523.4133-5.16331
969.98.131391.76861
971613.90312.09693
9818.2518.9314-0.681361
9916.8514.90311.94695
10018.9517.16241.78764
10115.615.9639-0.363909
10217.110.08957.01052
10316.116.8276-0.727646
10415.416.1137-0.713749
10515.416.5232-1.12316
10613.3511.31722.03277
10719.118.71630.383685
1087.65.440342.15966
10919.120.9917-1.89167
11014.7512.29562.45443
11119.2521.8468-2.59678
11213.615.949-2.34898
11312.7511.43791.31214
1149.8510.4663-0.616278
11515.2517.1185-1.86852
11611.912.7805-0.880539
11716.3517.8348-1.48483
11812.410.17862.22144
11918.1515.47462.67543
12017.7518.4324-0.682429
12112.3512.3539-0.00386416
12215.612.89552.70454
12319.318.69020.609757
12417.114.24582.85422
12518.416.10962.29035
12619.0514.83624.21377
12718.5517.81820.731843
12819.121.6574-2.55735
12912.8514.6506-1.80064
1309.513.0375-3.53751
1314.56.02164-1.52164
13213.614.8111-1.21111
13311.712.6237-0.92375
13413.3514.5092-1.15921
13517.617.18270.417267
13614.0515.5822-1.53215
13716.118.5197-2.41969
13813.3516.5735-3.22354
13911.8511.9324-0.0824037
14011.9515.1503-3.20032
14113.215.1318-1.93175
1427.76.820030.879965
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.4158950.8317910.584105
190.2703030.5406070.729697
200.1567350.3134690.843265
210.1220130.2440260.877987
220.07740980.154820.92259
230.08815750.1763150.911842
240.08341620.1668320.916584
250.0718810.1437620.928119
260.07001730.1400350.929983
270.05703450.1140690.942966
280.03557410.07114820.964426
290.09717270.1943450.902827
300.3159380.6318750.684062
310.2689920.5379850.731008
320.2182870.4365730.781713
330.1670510.3341010.832949
340.1916860.3833720.808314
350.1488450.297690.851155
360.2482620.4965250.751738
370.2097290.4194590.790271
380.2154090.4308180.784591
390.1758470.3516930.824153
400.1367990.2735980.863201
410.1358810.2717630.864119
420.1413590.2827180.858641
430.1170760.2341520.882924
440.2886970.5773950.711303
450.2449250.489850.755075
460.2393690.4787380.760631
470.2467960.4935910.753204
480.2366010.4732010.763399
490.4394820.8789640.560518
500.5332960.9334070.466704
510.5353850.929230.464615
520.5002630.9994740.499737
530.4523510.9047020.547649
540.4551370.9102750.544863
550.4107190.8214380.589281
560.3648560.7297120.635144
570.3348680.6697360.665132
580.3037320.6074640.696268
590.2856330.5712660.714367
600.3441130.6882260.655887
610.4233050.8466090.576695
620.4838320.9676640.516168
630.5391360.9217290.460864
640.5041590.9916820.495841
650.4652130.9304270.534787
660.4718990.9437980.528101
670.4817590.9635180.518241
680.4996050.999210.500395
690.4503570.9007130.549643
700.8038010.3923990.196199
710.8186530.3626950.181347
720.8002190.3995610.199781
730.7702430.4595130.229757
740.7764470.4471050.223553
750.7442050.5115890.255795
760.7056480.5887030.294352
770.6634160.6731680.336584
780.6551980.6896030.344802
790.6644510.6710980.335549
800.6233060.7533870.376694
810.6004030.7991940.399597
820.6182470.7635070.381753
830.572610.8547810.42739
840.5314680.9370630.468532
850.4945940.9891890.505406
860.4982830.9965650.501717
870.4470760.8941530.552924
880.4364370.8728740.563563
890.3885480.7770950.611452
900.3433560.6867110.656644
910.299950.59990.70005
920.254040.508080.74596
930.3255490.6510980.674451
940.349990.6999790.65001
950.4946480.9892960.505352
960.4592850.918570.540715
970.433740.867480.56626
980.3806120.7612230.619388
990.3459270.6918540.654073
1000.3106550.6213090.689345
1010.2686540.5373080.731346
1020.8114760.3770480.188524
1030.7737930.4524150.226207
1040.7262530.5474940.273747
1050.6971660.6056680.302834
1060.6787690.6424620.321231
1070.6796280.6407430.320372
1080.626620.746760.37338
1090.5794720.8410570.420528
1100.5256380.9487250.474362
1110.5117560.9764890.488244
1120.4649330.9298670.535067
1130.4365570.8731140.563443
1140.375710.7514210.62429
1150.3195690.6391380.680431
1160.3126610.6253220.687339
1170.2465660.4931320.753434
1180.1976490.3952970.802351
1190.1807010.3614030.819299
1200.1222580.2445160.877742
1210.3766040.7532090.623396
1220.7956350.4087290.204365
1230.8756060.2487880.124394
1240.8126670.3746670.187333
1250.9408170.1183650.0591826

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.415895 & 0.831791 & 0.584105 \tabularnewline
19 & 0.270303 & 0.540607 & 0.729697 \tabularnewline
20 & 0.156735 & 0.313469 & 0.843265 \tabularnewline
21 & 0.122013 & 0.244026 & 0.877987 \tabularnewline
22 & 0.0774098 & 0.15482 & 0.92259 \tabularnewline
23 & 0.0881575 & 0.176315 & 0.911842 \tabularnewline
24 & 0.0834162 & 0.166832 & 0.916584 \tabularnewline
25 & 0.071881 & 0.143762 & 0.928119 \tabularnewline
26 & 0.0700173 & 0.140035 & 0.929983 \tabularnewline
27 & 0.0570345 & 0.114069 & 0.942966 \tabularnewline
28 & 0.0355741 & 0.0711482 & 0.964426 \tabularnewline
29 & 0.0971727 & 0.194345 & 0.902827 \tabularnewline
30 & 0.315938 & 0.631875 & 0.684062 \tabularnewline
31 & 0.268992 & 0.537985 & 0.731008 \tabularnewline
32 & 0.218287 & 0.436573 & 0.781713 \tabularnewline
33 & 0.167051 & 0.334101 & 0.832949 \tabularnewline
34 & 0.191686 & 0.383372 & 0.808314 \tabularnewline
35 & 0.148845 & 0.29769 & 0.851155 \tabularnewline
36 & 0.248262 & 0.496525 & 0.751738 \tabularnewline
37 & 0.209729 & 0.419459 & 0.790271 \tabularnewline
38 & 0.215409 & 0.430818 & 0.784591 \tabularnewline
39 & 0.175847 & 0.351693 & 0.824153 \tabularnewline
40 & 0.136799 & 0.273598 & 0.863201 \tabularnewline
41 & 0.135881 & 0.271763 & 0.864119 \tabularnewline
42 & 0.141359 & 0.282718 & 0.858641 \tabularnewline
43 & 0.117076 & 0.234152 & 0.882924 \tabularnewline
44 & 0.288697 & 0.577395 & 0.711303 \tabularnewline
45 & 0.244925 & 0.48985 & 0.755075 \tabularnewline
46 & 0.239369 & 0.478738 & 0.760631 \tabularnewline
47 & 0.246796 & 0.493591 & 0.753204 \tabularnewline
48 & 0.236601 & 0.473201 & 0.763399 \tabularnewline
49 & 0.439482 & 0.878964 & 0.560518 \tabularnewline
50 & 0.533296 & 0.933407 & 0.466704 \tabularnewline
51 & 0.535385 & 0.92923 & 0.464615 \tabularnewline
52 & 0.500263 & 0.999474 & 0.499737 \tabularnewline
53 & 0.452351 & 0.904702 & 0.547649 \tabularnewline
54 & 0.455137 & 0.910275 & 0.544863 \tabularnewline
55 & 0.410719 & 0.821438 & 0.589281 \tabularnewline
56 & 0.364856 & 0.729712 & 0.635144 \tabularnewline
57 & 0.334868 & 0.669736 & 0.665132 \tabularnewline
58 & 0.303732 & 0.607464 & 0.696268 \tabularnewline
59 & 0.285633 & 0.571266 & 0.714367 \tabularnewline
60 & 0.344113 & 0.688226 & 0.655887 \tabularnewline
61 & 0.423305 & 0.846609 & 0.576695 \tabularnewline
62 & 0.483832 & 0.967664 & 0.516168 \tabularnewline
63 & 0.539136 & 0.921729 & 0.460864 \tabularnewline
64 & 0.504159 & 0.991682 & 0.495841 \tabularnewline
65 & 0.465213 & 0.930427 & 0.534787 \tabularnewline
66 & 0.471899 & 0.943798 & 0.528101 \tabularnewline
67 & 0.481759 & 0.963518 & 0.518241 \tabularnewline
68 & 0.499605 & 0.99921 & 0.500395 \tabularnewline
69 & 0.450357 & 0.900713 & 0.549643 \tabularnewline
70 & 0.803801 & 0.392399 & 0.196199 \tabularnewline
71 & 0.818653 & 0.362695 & 0.181347 \tabularnewline
72 & 0.800219 & 0.399561 & 0.199781 \tabularnewline
73 & 0.770243 & 0.459513 & 0.229757 \tabularnewline
74 & 0.776447 & 0.447105 & 0.223553 \tabularnewline
75 & 0.744205 & 0.511589 & 0.255795 \tabularnewline
76 & 0.705648 & 0.588703 & 0.294352 \tabularnewline
77 & 0.663416 & 0.673168 & 0.336584 \tabularnewline
78 & 0.655198 & 0.689603 & 0.344802 \tabularnewline
79 & 0.664451 & 0.671098 & 0.335549 \tabularnewline
80 & 0.623306 & 0.753387 & 0.376694 \tabularnewline
81 & 0.600403 & 0.799194 & 0.399597 \tabularnewline
82 & 0.618247 & 0.763507 & 0.381753 \tabularnewline
83 & 0.57261 & 0.854781 & 0.42739 \tabularnewline
84 & 0.531468 & 0.937063 & 0.468532 \tabularnewline
85 & 0.494594 & 0.989189 & 0.505406 \tabularnewline
86 & 0.498283 & 0.996565 & 0.501717 \tabularnewline
87 & 0.447076 & 0.894153 & 0.552924 \tabularnewline
88 & 0.436437 & 0.872874 & 0.563563 \tabularnewline
89 & 0.388548 & 0.777095 & 0.611452 \tabularnewline
90 & 0.343356 & 0.686711 & 0.656644 \tabularnewline
91 & 0.29995 & 0.5999 & 0.70005 \tabularnewline
92 & 0.25404 & 0.50808 & 0.74596 \tabularnewline
93 & 0.325549 & 0.651098 & 0.674451 \tabularnewline
94 & 0.34999 & 0.699979 & 0.65001 \tabularnewline
95 & 0.494648 & 0.989296 & 0.505352 \tabularnewline
96 & 0.459285 & 0.91857 & 0.540715 \tabularnewline
97 & 0.43374 & 0.86748 & 0.56626 \tabularnewline
98 & 0.380612 & 0.761223 & 0.619388 \tabularnewline
99 & 0.345927 & 0.691854 & 0.654073 \tabularnewline
100 & 0.310655 & 0.621309 & 0.689345 \tabularnewline
101 & 0.268654 & 0.537308 & 0.731346 \tabularnewline
102 & 0.811476 & 0.377048 & 0.188524 \tabularnewline
103 & 0.773793 & 0.452415 & 0.226207 \tabularnewline
104 & 0.726253 & 0.547494 & 0.273747 \tabularnewline
105 & 0.697166 & 0.605668 & 0.302834 \tabularnewline
106 & 0.678769 & 0.642462 & 0.321231 \tabularnewline
107 & 0.679628 & 0.640743 & 0.320372 \tabularnewline
108 & 0.62662 & 0.74676 & 0.37338 \tabularnewline
109 & 0.579472 & 0.841057 & 0.420528 \tabularnewline
110 & 0.525638 & 0.948725 & 0.474362 \tabularnewline
111 & 0.511756 & 0.976489 & 0.488244 \tabularnewline
112 & 0.464933 & 0.929867 & 0.535067 \tabularnewline
113 & 0.436557 & 0.873114 & 0.563443 \tabularnewline
114 & 0.37571 & 0.751421 & 0.62429 \tabularnewline
115 & 0.319569 & 0.639138 & 0.680431 \tabularnewline
116 & 0.312661 & 0.625322 & 0.687339 \tabularnewline
117 & 0.246566 & 0.493132 & 0.753434 \tabularnewline
118 & 0.197649 & 0.395297 & 0.802351 \tabularnewline
119 & 0.180701 & 0.361403 & 0.819299 \tabularnewline
120 & 0.122258 & 0.244516 & 0.877742 \tabularnewline
121 & 0.376604 & 0.753209 & 0.623396 \tabularnewline
122 & 0.795635 & 0.408729 & 0.204365 \tabularnewline
123 & 0.875606 & 0.248788 & 0.124394 \tabularnewline
124 & 0.812667 & 0.374667 & 0.187333 \tabularnewline
125 & 0.940817 & 0.118365 & 0.0591826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&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]18[/C][C]0.415895[/C][C]0.831791[/C][C]0.584105[/C][/ROW]
[ROW][C]19[/C][C]0.270303[/C][C]0.540607[/C][C]0.729697[/C][/ROW]
[ROW][C]20[/C][C]0.156735[/C][C]0.313469[/C][C]0.843265[/C][/ROW]
[ROW][C]21[/C][C]0.122013[/C][C]0.244026[/C][C]0.877987[/C][/ROW]
[ROW][C]22[/C][C]0.0774098[/C][C]0.15482[/C][C]0.92259[/C][/ROW]
[ROW][C]23[/C][C]0.0881575[/C][C]0.176315[/C][C]0.911842[/C][/ROW]
[ROW][C]24[/C][C]0.0834162[/C][C]0.166832[/C][C]0.916584[/C][/ROW]
[ROW][C]25[/C][C]0.071881[/C][C]0.143762[/C][C]0.928119[/C][/ROW]
[ROW][C]26[/C][C]0.0700173[/C][C]0.140035[/C][C]0.929983[/C][/ROW]
[ROW][C]27[/C][C]0.0570345[/C][C]0.114069[/C][C]0.942966[/C][/ROW]
[ROW][C]28[/C][C]0.0355741[/C][C]0.0711482[/C][C]0.964426[/C][/ROW]
[ROW][C]29[/C][C]0.0971727[/C][C]0.194345[/C][C]0.902827[/C][/ROW]
[ROW][C]30[/C][C]0.315938[/C][C]0.631875[/C][C]0.684062[/C][/ROW]
[ROW][C]31[/C][C]0.268992[/C][C]0.537985[/C][C]0.731008[/C][/ROW]
[ROW][C]32[/C][C]0.218287[/C][C]0.436573[/C][C]0.781713[/C][/ROW]
[ROW][C]33[/C][C]0.167051[/C][C]0.334101[/C][C]0.832949[/C][/ROW]
[ROW][C]34[/C][C]0.191686[/C][C]0.383372[/C][C]0.808314[/C][/ROW]
[ROW][C]35[/C][C]0.148845[/C][C]0.29769[/C][C]0.851155[/C][/ROW]
[ROW][C]36[/C][C]0.248262[/C][C]0.496525[/C][C]0.751738[/C][/ROW]
[ROW][C]37[/C][C]0.209729[/C][C]0.419459[/C][C]0.790271[/C][/ROW]
[ROW][C]38[/C][C]0.215409[/C][C]0.430818[/C][C]0.784591[/C][/ROW]
[ROW][C]39[/C][C]0.175847[/C][C]0.351693[/C][C]0.824153[/C][/ROW]
[ROW][C]40[/C][C]0.136799[/C][C]0.273598[/C][C]0.863201[/C][/ROW]
[ROW][C]41[/C][C]0.135881[/C][C]0.271763[/C][C]0.864119[/C][/ROW]
[ROW][C]42[/C][C]0.141359[/C][C]0.282718[/C][C]0.858641[/C][/ROW]
[ROW][C]43[/C][C]0.117076[/C][C]0.234152[/C][C]0.882924[/C][/ROW]
[ROW][C]44[/C][C]0.288697[/C][C]0.577395[/C][C]0.711303[/C][/ROW]
[ROW][C]45[/C][C]0.244925[/C][C]0.48985[/C][C]0.755075[/C][/ROW]
[ROW][C]46[/C][C]0.239369[/C][C]0.478738[/C][C]0.760631[/C][/ROW]
[ROW][C]47[/C][C]0.246796[/C][C]0.493591[/C][C]0.753204[/C][/ROW]
[ROW][C]48[/C][C]0.236601[/C][C]0.473201[/C][C]0.763399[/C][/ROW]
[ROW][C]49[/C][C]0.439482[/C][C]0.878964[/C][C]0.560518[/C][/ROW]
[ROW][C]50[/C][C]0.533296[/C][C]0.933407[/C][C]0.466704[/C][/ROW]
[ROW][C]51[/C][C]0.535385[/C][C]0.92923[/C][C]0.464615[/C][/ROW]
[ROW][C]52[/C][C]0.500263[/C][C]0.999474[/C][C]0.499737[/C][/ROW]
[ROW][C]53[/C][C]0.452351[/C][C]0.904702[/C][C]0.547649[/C][/ROW]
[ROW][C]54[/C][C]0.455137[/C][C]0.910275[/C][C]0.544863[/C][/ROW]
[ROW][C]55[/C][C]0.410719[/C][C]0.821438[/C][C]0.589281[/C][/ROW]
[ROW][C]56[/C][C]0.364856[/C][C]0.729712[/C][C]0.635144[/C][/ROW]
[ROW][C]57[/C][C]0.334868[/C][C]0.669736[/C][C]0.665132[/C][/ROW]
[ROW][C]58[/C][C]0.303732[/C][C]0.607464[/C][C]0.696268[/C][/ROW]
[ROW][C]59[/C][C]0.285633[/C][C]0.571266[/C][C]0.714367[/C][/ROW]
[ROW][C]60[/C][C]0.344113[/C][C]0.688226[/C][C]0.655887[/C][/ROW]
[ROW][C]61[/C][C]0.423305[/C][C]0.846609[/C][C]0.576695[/C][/ROW]
[ROW][C]62[/C][C]0.483832[/C][C]0.967664[/C][C]0.516168[/C][/ROW]
[ROW][C]63[/C][C]0.539136[/C][C]0.921729[/C][C]0.460864[/C][/ROW]
[ROW][C]64[/C][C]0.504159[/C][C]0.991682[/C][C]0.495841[/C][/ROW]
[ROW][C]65[/C][C]0.465213[/C][C]0.930427[/C][C]0.534787[/C][/ROW]
[ROW][C]66[/C][C]0.471899[/C][C]0.943798[/C][C]0.528101[/C][/ROW]
[ROW][C]67[/C][C]0.481759[/C][C]0.963518[/C][C]0.518241[/C][/ROW]
[ROW][C]68[/C][C]0.499605[/C][C]0.99921[/C][C]0.500395[/C][/ROW]
[ROW][C]69[/C][C]0.450357[/C][C]0.900713[/C][C]0.549643[/C][/ROW]
[ROW][C]70[/C][C]0.803801[/C][C]0.392399[/C][C]0.196199[/C][/ROW]
[ROW][C]71[/C][C]0.818653[/C][C]0.362695[/C][C]0.181347[/C][/ROW]
[ROW][C]72[/C][C]0.800219[/C][C]0.399561[/C][C]0.199781[/C][/ROW]
[ROW][C]73[/C][C]0.770243[/C][C]0.459513[/C][C]0.229757[/C][/ROW]
[ROW][C]74[/C][C]0.776447[/C][C]0.447105[/C][C]0.223553[/C][/ROW]
[ROW][C]75[/C][C]0.744205[/C][C]0.511589[/C][C]0.255795[/C][/ROW]
[ROW][C]76[/C][C]0.705648[/C][C]0.588703[/C][C]0.294352[/C][/ROW]
[ROW][C]77[/C][C]0.663416[/C][C]0.673168[/C][C]0.336584[/C][/ROW]
[ROW][C]78[/C][C]0.655198[/C][C]0.689603[/C][C]0.344802[/C][/ROW]
[ROW][C]79[/C][C]0.664451[/C][C]0.671098[/C][C]0.335549[/C][/ROW]
[ROW][C]80[/C][C]0.623306[/C][C]0.753387[/C][C]0.376694[/C][/ROW]
[ROW][C]81[/C][C]0.600403[/C][C]0.799194[/C][C]0.399597[/C][/ROW]
[ROW][C]82[/C][C]0.618247[/C][C]0.763507[/C][C]0.381753[/C][/ROW]
[ROW][C]83[/C][C]0.57261[/C][C]0.854781[/C][C]0.42739[/C][/ROW]
[ROW][C]84[/C][C]0.531468[/C][C]0.937063[/C][C]0.468532[/C][/ROW]
[ROW][C]85[/C][C]0.494594[/C][C]0.989189[/C][C]0.505406[/C][/ROW]
[ROW][C]86[/C][C]0.498283[/C][C]0.996565[/C][C]0.501717[/C][/ROW]
[ROW][C]87[/C][C]0.447076[/C][C]0.894153[/C][C]0.552924[/C][/ROW]
[ROW][C]88[/C][C]0.436437[/C][C]0.872874[/C][C]0.563563[/C][/ROW]
[ROW][C]89[/C][C]0.388548[/C][C]0.777095[/C][C]0.611452[/C][/ROW]
[ROW][C]90[/C][C]0.343356[/C][C]0.686711[/C][C]0.656644[/C][/ROW]
[ROW][C]91[/C][C]0.29995[/C][C]0.5999[/C][C]0.70005[/C][/ROW]
[ROW][C]92[/C][C]0.25404[/C][C]0.50808[/C][C]0.74596[/C][/ROW]
[ROW][C]93[/C][C]0.325549[/C][C]0.651098[/C][C]0.674451[/C][/ROW]
[ROW][C]94[/C][C]0.34999[/C][C]0.699979[/C][C]0.65001[/C][/ROW]
[ROW][C]95[/C][C]0.494648[/C][C]0.989296[/C][C]0.505352[/C][/ROW]
[ROW][C]96[/C][C]0.459285[/C][C]0.91857[/C][C]0.540715[/C][/ROW]
[ROW][C]97[/C][C]0.43374[/C][C]0.86748[/C][C]0.56626[/C][/ROW]
[ROW][C]98[/C][C]0.380612[/C][C]0.761223[/C][C]0.619388[/C][/ROW]
[ROW][C]99[/C][C]0.345927[/C][C]0.691854[/C][C]0.654073[/C][/ROW]
[ROW][C]100[/C][C]0.310655[/C][C]0.621309[/C][C]0.689345[/C][/ROW]
[ROW][C]101[/C][C]0.268654[/C][C]0.537308[/C][C]0.731346[/C][/ROW]
[ROW][C]102[/C][C]0.811476[/C][C]0.377048[/C][C]0.188524[/C][/ROW]
[ROW][C]103[/C][C]0.773793[/C][C]0.452415[/C][C]0.226207[/C][/ROW]
[ROW][C]104[/C][C]0.726253[/C][C]0.547494[/C][C]0.273747[/C][/ROW]
[ROW][C]105[/C][C]0.697166[/C][C]0.605668[/C][C]0.302834[/C][/ROW]
[ROW][C]106[/C][C]0.678769[/C][C]0.642462[/C][C]0.321231[/C][/ROW]
[ROW][C]107[/C][C]0.679628[/C][C]0.640743[/C][C]0.320372[/C][/ROW]
[ROW][C]108[/C][C]0.62662[/C][C]0.74676[/C][C]0.37338[/C][/ROW]
[ROW][C]109[/C][C]0.579472[/C][C]0.841057[/C][C]0.420528[/C][/ROW]
[ROW][C]110[/C][C]0.525638[/C][C]0.948725[/C][C]0.474362[/C][/ROW]
[ROW][C]111[/C][C]0.511756[/C][C]0.976489[/C][C]0.488244[/C][/ROW]
[ROW][C]112[/C][C]0.464933[/C][C]0.929867[/C][C]0.535067[/C][/ROW]
[ROW][C]113[/C][C]0.436557[/C][C]0.873114[/C][C]0.563443[/C][/ROW]
[ROW][C]114[/C][C]0.37571[/C][C]0.751421[/C][C]0.62429[/C][/ROW]
[ROW][C]115[/C][C]0.319569[/C][C]0.639138[/C][C]0.680431[/C][/ROW]
[ROW][C]116[/C][C]0.312661[/C][C]0.625322[/C][C]0.687339[/C][/ROW]
[ROW][C]117[/C][C]0.246566[/C][C]0.493132[/C][C]0.753434[/C][/ROW]
[ROW][C]118[/C][C]0.197649[/C][C]0.395297[/C][C]0.802351[/C][/ROW]
[ROW][C]119[/C][C]0.180701[/C][C]0.361403[/C][C]0.819299[/C][/ROW]
[ROW][C]120[/C][C]0.122258[/C][C]0.244516[/C][C]0.877742[/C][/ROW]
[ROW][C]121[/C][C]0.376604[/C][C]0.753209[/C][C]0.623396[/C][/ROW]
[ROW][C]122[/C][C]0.795635[/C][C]0.408729[/C][C]0.204365[/C][/ROW]
[ROW][C]123[/C][C]0.875606[/C][C]0.248788[/C][C]0.124394[/C][/ROW]
[ROW][C]124[/C][C]0.812667[/C][C]0.374667[/C][C]0.187333[/C][/ROW]
[ROW][C]125[/C][C]0.940817[/C][C]0.118365[/C][C]0.0591826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&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
180.4158950.8317910.584105
190.2703030.5406070.729697
200.1567350.3134690.843265
210.1220130.2440260.877987
220.07740980.154820.92259
230.08815750.1763150.911842
240.08341620.1668320.916584
250.0718810.1437620.928119
260.07001730.1400350.929983
270.05703450.1140690.942966
280.03557410.07114820.964426
290.09717270.1943450.902827
300.3159380.6318750.684062
310.2689920.5379850.731008
320.2182870.4365730.781713
330.1670510.3341010.832949
340.1916860.3833720.808314
350.1488450.297690.851155
360.2482620.4965250.751738
370.2097290.4194590.790271
380.2154090.4308180.784591
390.1758470.3516930.824153
400.1367990.2735980.863201
410.1358810.2717630.864119
420.1413590.2827180.858641
430.1170760.2341520.882924
440.2886970.5773950.711303
450.2449250.489850.755075
460.2393690.4787380.760631
470.2467960.4935910.753204
480.2366010.4732010.763399
490.4394820.8789640.560518
500.5332960.9334070.466704
510.5353850.929230.464615
520.5002630.9994740.499737
530.4523510.9047020.547649
540.4551370.9102750.544863
550.4107190.8214380.589281
560.3648560.7297120.635144
570.3348680.6697360.665132
580.3037320.6074640.696268
590.2856330.5712660.714367
600.3441130.6882260.655887
610.4233050.8466090.576695
620.4838320.9676640.516168
630.5391360.9217290.460864
640.5041590.9916820.495841
650.4652130.9304270.534787
660.4718990.9437980.528101
670.4817590.9635180.518241
680.4996050.999210.500395
690.4503570.9007130.549643
700.8038010.3923990.196199
710.8186530.3626950.181347
720.8002190.3995610.199781
730.7702430.4595130.229757
740.7764470.4471050.223553
750.7442050.5115890.255795
760.7056480.5887030.294352
770.6634160.6731680.336584
780.6551980.6896030.344802
790.6644510.6710980.335549
800.6233060.7533870.376694
810.6004030.7991940.399597
820.6182470.7635070.381753
830.572610.8547810.42739
840.5314680.9370630.468532
850.4945940.9891890.505406
860.4982830.9965650.501717
870.4470760.8941530.552924
880.4364370.8728740.563563
890.3885480.7770950.611452
900.3433560.6867110.656644
910.299950.59990.70005
920.254040.508080.74596
930.3255490.6510980.674451
940.349990.6999790.65001
950.4946480.9892960.505352
960.4592850.918570.540715
970.433740.867480.56626
980.3806120.7612230.619388
990.3459270.6918540.654073
1000.3106550.6213090.689345
1010.2686540.5373080.731346
1020.8114760.3770480.188524
1030.7737930.4524150.226207
1040.7262530.5474940.273747
1050.6971660.6056680.302834
1060.6787690.6424620.321231
1070.6796280.6407430.320372
1080.626620.746760.37338
1090.5794720.8410570.420528
1100.5256380.9487250.474362
1110.5117560.9764890.488244
1120.4649330.9298670.535067
1130.4365570.8731140.563443
1140.375710.7514210.62429
1150.3195690.6391380.680431
1160.3126610.6253220.687339
1170.2465660.4931320.753434
1180.1976490.3952970.802351
1190.1807010.3614030.819299
1200.1222580.2445160.877742
1210.3766040.7532090.623396
1220.7956350.4087290.204365
1230.8756060.2487880.124394
1240.8126670.3746670.187333
1250.9408170.1183650.0591826






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 level10.00925926OK

\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 & 0.00925926 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270954&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]0.00925926[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270954&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270954&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00925926OK


Figures (Output of Computation)

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Input Parameters & R Code

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