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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 15:06:03 +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/t1418915245b09g589p7w3144p.htm/, Retrieved Sun, 19 May 2024 19:41:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271040, Retrieved Sun, 19 May 2024 19:41:10 +0000
QR Codes:

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

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:06:03] [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
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
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
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=271040&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=271040&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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] = + 8.87621 + 0.00169472AMS.I1[t] -0.0461127AMS.I2[t] -0.0472934AMS.I3[t] -0.0170989AMS.E1[t] -0.0471703AMS.E2[t] -0.000823809AMS.E3[t] + 0.0141945AMS.A[t] -0.128313age[t] -0.0314677CONFSTATTOT[t] + 0.160288CONFSOFTTOT[t] -0.00301698LFM[t] -0.0211151PRH[t] + 0.0377351CH[t] + 2.80842PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.87621 +  0.00169472AMS.I1[t] -0.0461127AMS.I2[t] -0.0472934AMS.I3[t] -0.0170989AMS.E1[t] -0.0471703AMS.E2[t] -0.000823809AMS.E3[t] +  0.0141945AMS.A[t] -0.128313age[t] -0.0314677CONFSTATTOT[t] +  0.160288CONFSOFTTOT[t] -0.00301698LFM[t] -0.0211151PRH[t] +  0.0377351CH[t] +  2.80842PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.87621 +  0.00169472AMS.I1[t] -0.0461127AMS.I2[t] -0.0472934AMS.I3[t] -0.0170989AMS.E1[t] -0.0471703AMS.E2[t] -0.000823809AMS.E3[t] +  0.0141945AMS.A[t] -0.128313age[t] -0.0314677CONFSTATTOT[t] +  0.160288CONFSOFTTOT[t] -0.00301698LFM[t] -0.0211151PRH[t] +  0.0377351CH[t] +  2.80842PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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] = + 8.87621 + 0.00169472AMS.I1[t] -0.0461127AMS.I2[t] -0.0472934AMS.I3[t] -0.0170989AMS.E1[t] -0.0471703AMS.E2[t] -0.000823809AMS.E3[t] + 0.0141945AMS.A[t] -0.128313age[t] -0.0314677CONFSTATTOT[t] + 0.160288CONFSOFTTOT[t] -0.00301698LFM[t] -0.0211151PRH[t] + 0.0377351CH[t] + 2.80842PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.876214.53061.9590.05233710.0261686
AMS.I10.001694720.07774860.02180.9826450.491322
AMS.I2-0.04611270.0713896-0.64590.5195180.259759
AMS.I3-0.04729340.0622762-0.75940.4490460.224523
AMS.E1-0.01709890.0768762-0.22240.8243520.412176
AMS.E2-0.04717030.0568845-0.82920.4085680.204284
AMS.E3-0.0008238090.0662672-0.012430.9901010.495051
AMS.A0.01419450.07583170.18720.8518230.425911
age-0.1283130.176331-0.72770.4681790.23409
CONFSTATTOT-0.03146770.110404-0.2850.7761020.388051
CONFSOFTTOT0.1602880.1209231.3260.1874280.0937139
LFM-0.003016980.00624665-0.4830.6299650.314983
PRH-0.02111510.0112969-1.8690.06396780.0319839
CH0.03773510.0108533.4770.000699950.000349975
PR2.808420.24205311.61.58569e-217.92843e-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) & 8.87621 & 4.5306 & 1.959 & 0.0523371 & 0.0261686 \tabularnewline
AMS.I1 & 0.00169472 & 0.0777486 & 0.0218 & 0.982645 & 0.491322 \tabularnewline
AMS.I2 & -0.0461127 & 0.0713896 & -0.6459 & 0.519518 & 0.259759 \tabularnewline
AMS.I3 & -0.0472934 & 0.0622762 & -0.7594 & 0.449046 & 0.224523 \tabularnewline
AMS.E1 & -0.0170989 & 0.0768762 & -0.2224 & 0.824352 & 0.412176 \tabularnewline
AMS.E2 & -0.0471703 & 0.0568845 & -0.8292 & 0.408568 & 0.204284 \tabularnewline
AMS.E3 & -0.000823809 & 0.0662672 & -0.01243 & 0.990101 & 0.495051 \tabularnewline
AMS.A & 0.0141945 & 0.0758317 & 0.1872 & 0.851823 & 0.425911 \tabularnewline
age & -0.128313 & 0.176331 & -0.7277 & 0.468179 & 0.23409 \tabularnewline
CONFSTATTOT & -0.0314677 & 0.110404 & -0.285 & 0.776102 & 0.388051 \tabularnewline
CONFSOFTTOT & 0.160288 & 0.120923 & 1.326 & 0.187428 & 0.0937139 \tabularnewline
LFM & -0.00301698 & 0.00624665 & -0.483 & 0.629965 & 0.314983 \tabularnewline
PRH & -0.0211151 & 0.0112969 & -1.869 & 0.0639678 & 0.0319839 \tabularnewline
CH & 0.0377351 & 0.010853 & 3.477 & 0.00069995 & 0.000349975 \tabularnewline
PR & 2.80842 & 0.242053 & 11.6 & 1.58569e-21 & 7.92843e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&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]8.87621[/C][C]4.5306[/C][C]1.959[/C][C]0.0523371[/C][C]0.0261686[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.00169472[/C][C]0.0777486[/C][C]0.0218[/C][C]0.982645[/C][C]0.491322[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0461127[/C][C]0.0713896[/C][C]-0.6459[/C][C]0.519518[/C][C]0.259759[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0472934[/C][C]0.0622762[/C][C]-0.7594[/C][C]0.449046[/C][C]0.224523[/C][/ROW]
[ROW][C]AMS.E1[/C][C]-0.0170989[/C][C]0.0768762[/C][C]-0.2224[/C][C]0.824352[/C][C]0.412176[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0471703[/C][C]0.0568845[/C][C]-0.8292[/C][C]0.408568[/C][C]0.204284[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.000823809[/C][C]0.0662672[/C][C]-0.01243[/C][C]0.990101[/C][C]0.495051[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.0141945[/C][C]0.0758317[/C][C]0.1872[/C][C]0.851823[/C][C]0.425911[/C][/ROW]
[ROW][C]age[/C][C]-0.128313[/C][C]0.176331[/C][C]-0.7277[/C][C]0.468179[/C][C]0.23409[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.0314677[/C][C]0.110404[/C][C]-0.285[/C][C]0.776102[/C][C]0.388051[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.160288[/C][C]0.120923[/C][C]1.326[/C][C]0.187428[/C][C]0.0937139[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00301698[/C][C]0.00624665[/C][C]-0.483[/C][C]0.629965[/C][C]0.314983[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0211151[/C][C]0.0112969[/C][C]-1.869[/C][C]0.0639678[/C][C]0.0319839[/C][/ROW]
[ROW][C]CH[/C][C]0.0377351[/C][C]0.010853[/C][C]3.477[/C][C]0.00069995[/C][C]0.000349975[/C][/ROW]
[ROW][C]PR[/C][C]2.80842[/C][C]0.242053[/C][C]11.6[/C][C]1.58569e-21[/C][C]7.92843e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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)8.876214.53061.9590.05233710.0261686
AMS.I10.001694720.07774860.02180.9826450.491322
AMS.I2-0.04611270.0713896-0.64590.5195180.259759
AMS.I3-0.04729340.0622762-0.75940.4490460.224523
AMS.E1-0.01709890.0768762-0.22240.8243520.412176
AMS.E2-0.04717030.0568845-0.82920.4085680.204284
AMS.E3-0.0008238090.0662672-0.012430.9901010.495051
AMS.A0.01419450.07583170.18720.8518230.425911
age-0.1283130.176331-0.72770.4681790.23409
CONFSTATTOT-0.03146770.110404-0.2850.7761020.388051
CONFSOFTTOT0.1602880.1209231.3260.1874280.0937139
LFM-0.003016980.00624665-0.4830.6299650.314983
PRH-0.02111510.0112969-1.8690.06396780.0319839
CH0.03773510.0108533.4770.000699950.000349975
PR2.808420.24205311.61.58569e-217.92843e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.793293
R-squared0.629314
Adjusted R-squared0.587462
F-TEST (value)15.0368
F-TEST (DF numerator)14
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25054
Sum Squared Residuals628.053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.793293 \tabularnewline
R-squared & 0.629314 \tabularnewline
Adjusted R-squared & 0.587462 \tabularnewline
F-TEST (value) & 15.0368 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 124 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.25054 \tabularnewline
Sum Squared Residuals & 628.053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.793293[/C][/ROW]
[ROW][C]R-squared[/C][C]0.629314[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.587462[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.0368[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]124[/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]2.25054[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]628.053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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 R0.793293
R-squared0.629314
Adjusted R-squared0.587462
F-TEST (value)15.0368
F-TEST (DF numerator)14
F-TEST (DF denominator)124
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25054
Sum Squared Residuals628.053






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9727-0.072734
212.210.34011.85993
312.811.6881.11198
47.411.6298-4.22985
56.711.3344-4.63444
612.611.96630.63373
714.810.9653.83497
813.313.4224-0.122442
911.112.4013-1.30129
108.210.7979-2.59789
1111.411.26040.139606
126.410.9226-4.52255
1310.610.18210.417883
141213.3487-1.34873
156.38.75545-2.45545
1611.913.2255-1.32551
179.310.8896-1.58959
18109.720740.279263
196.410.2873-3.88734
2013.812.61131.18875
2110.810.18290.617065
2213.811.981.82001
2311.710.56721.1328
2410.911.9153-1.01531
259.911.112-1.21202
2611.510.51730.982692
278.310.9975-2.69746
2811.711.10920.590771
29910.3878-1.38775
309.713.9294-4.22939
3110.811.2593-0.459346
3210.310.8056-0.505553
3310.49.700860.699135
349.312.2926-2.99263
3511.810.94150.858484
365.911.2921-5.39208
3711.411.888-0.48799
381310.75222.24779
3910.811.0538-0.25379
4011.310.71280.587247
4111.811.63580.164189
4212.79.463843.23616
4310.910.60980.290204
4413.311.63961.66042
4510.110.5179-0.417855
4614.311.552.74998
479.311.6505-2.35045
4812.510.46292.03715
497.610.3529-2.75289
5015.912.49873.40132
519.210.4143-1.21429
5211.112.46-1.36001
531312.41090.589081
5414.511.47213.02789
5512.313.0606-0.760624
5611.410.40010.999892
571312.26730.732668
5813.211.01232.1877
597.711.4661-3.76606
604.357.20005-2.85005
6112.710.26352.43654
6218.116.12961.97037
6317.8516.11051.73953
6417.117.5405-0.440546
6519.116.73742.3626
6616.118.6983-2.59825
6713.3510.8772.47303
6818.417.87370.526336
6914.77.633227.06678
7010.613.2078-2.60775
7112.613.7947-1.19471
7213.612.63080.969157
7314.113.10660.993376
7414.513.43091.06906
7516.1516.427-0.277018
7614.7512.57292.17712
7714.812.0792.72104
7812.4512.28460.165362
7912.6510.30962.34041
8017.3513.90823.44184
818.68.460610.13939
8218.416.8621.53799
8316.114.44811.65193
8417.7515.63232.11768
8515.2515.3544-0.104441
8617.6516.2951.35499
8716.3516.6283-0.278265
8817.6518.4063-0.756331
8913.612.97990.620062
9014.3513.81560.534422
9114.7517.1015-2.35151
9218.2516.67391.57607
939.915.3367-5.43666
941613.93892.06106
9518.2516.17892.07115
9616.8517.7368-0.886826
9718.9516.79082.15917
9815.613.60321.99683
9917.117.6439-0.543875
10015.416.1246-0.72456
10115.416.0126-0.612601
10213.3514.2864-0.936428
10319.117.01522.08482
1047.66.83990.7601
10519.117.00142.09863
10614.7516.389-1.63898
10719.2517.072.17996
10813.616.0121-2.41209
10912.7515.491-2.74101
1109.858.141971.70803
11115.2515.7888-0.53878
11211.913.3084-1.40841
11316.3517.3908-1.04079
11412.413.9073-1.50732
11518.1515.92442.22562
11617.7515.19392.55608
11712.3512.6933-0.343347
11815.615.3710.229013
11919.316.68852.61154
12017.116.53320.566814
12118.415.41352.98646
12219.0516.86832.1817
12318.5514.49854.05146
12419.118.14470.955284
12512.8515.7169-2.86688
1269.510.8703-1.37032
1274.57.43533-2.93533
12813.615.0742-1.47417
12911.712.3555-0.655535
13013.3513.9516-0.601602
13117.618.8391-1.23907
13214.0513.90840.14157
13316.117.5664-1.46638
13413.3515.5205-2.17052
13511.8515.1627-3.31266
13611.9511.83140.118552
13713.216.5098-3.3098
1387.79.5295-1.8295
13914.613.6020.997976

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9727 & -0.072734 \tabularnewline
2 & 12.2 & 10.3401 & 1.85993 \tabularnewline
3 & 12.8 & 11.688 & 1.11198 \tabularnewline
4 & 7.4 & 11.6298 & -4.22985 \tabularnewline
5 & 6.7 & 11.3344 & -4.63444 \tabularnewline
6 & 12.6 & 11.9663 & 0.63373 \tabularnewline
7 & 14.8 & 10.965 & 3.83497 \tabularnewline
8 & 13.3 & 13.4224 & -0.122442 \tabularnewline
9 & 11.1 & 12.4013 & -1.30129 \tabularnewline
10 & 8.2 & 10.7979 & -2.59789 \tabularnewline
11 & 11.4 & 11.2604 & 0.139606 \tabularnewline
12 & 6.4 & 10.9226 & -4.52255 \tabularnewline
13 & 10.6 & 10.1821 & 0.417883 \tabularnewline
14 & 12 & 13.3487 & -1.34873 \tabularnewline
15 & 6.3 & 8.75545 & -2.45545 \tabularnewline
16 & 11.9 & 13.2255 & -1.32551 \tabularnewline
17 & 9.3 & 10.8896 & -1.58959 \tabularnewline
18 & 10 & 9.72074 & 0.279263 \tabularnewline
19 & 6.4 & 10.2873 & -3.88734 \tabularnewline
20 & 13.8 & 12.6113 & 1.18875 \tabularnewline
21 & 10.8 & 10.1829 & 0.617065 \tabularnewline
22 & 13.8 & 11.98 & 1.82001 \tabularnewline
23 & 11.7 & 10.5672 & 1.1328 \tabularnewline
24 & 10.9 & 11.9153 & -1.01531 \tabularnewline
25 & 9.9 & 11.112 & -1.21202 \tabularnewline
26 & 11.5 & 10.5173 & 0.982692 \tabularnewline
27 & 8.3 & 10.9975 & -2.69746 \tabularnewline
28 & 11.7 & 11.1092 & 0.590771 \tabularnewline
29 & 9 & 10.3878 & -1.38775 \tabularnewline
30 & 9.7 & 13.9294 & -4.22939 \tabularnewline
31 & 10.8 & 11.2593 & -0.459346 \tabularnewline
32 & 10.3 & 10.8056 & -0.505553 \tabularnewline
33 & 10.4 & 9.70086 & 0.699135 \tabularnewline
34 & 9.3 & 12.2926 & -2.99263 \tabularnewline
35 & 11.8 & 10.9415 & 0.858484 \tabularnewline
36 & 5.9 & 11.2921 & -5.39208 \tabularnewline
37 & 11.4 & 11.888 & -0.48799 \tabularnewline
38 & 13 & 10.7522 & 2.24779 \tabularnewline
39 & 10.8 & 11.0538 & -0.25379 \tabularnewline
40 & 11.3 & 10.7128 & 0.587247 \tabularnewline
41 & 11.8 & 11.6358 & 0.164189 \tabularnewline
42 & 12.7 & 9.46384 & 3.23616 \tabularnewline
43 & 10.9 & 10.6098 & 0.290204 \tabularnewline
44 & 13.3 & 11.6396 & 1.66042 \tabularnewline
45 & 10.1 & 10.5179 & -0.417855 \tabularnewline
46 & 14.3 & 11.55 & 2.74998 \tabularnewline
47 & 9.3 & 11.6505 & -2.35045 \tabularnewline
48 & 12.5 & 10.4629 & 2.03715 \tabularnewline
49 & 7.6 & 10.3529 & -2.75289 \tabularnewline
50 & 15.9 & 12.4987 & 3.40132 \tabularnewline
51 & 9.2 & 10.4143 & -1.21429 \tabularnewline
52 & 11.1 & 12.46 & -1.36001 \tabularnewline
53 & 13 & 12.4109 & 0.589081 \tabularnewline
54 & 14.5 & 11.4721 & 3.02789 \tabularnewline
55 & 12.3 & 13.0606 & -0.760624 \tabularnewline
56 & 11.4 & 10.4001 & 0.999892 \tabularnewline
57 & 13 & 12.2673 & 0.732668 \tabularnewline
58 & 13.2 & 11.0123 & 2.1877 \tabularnewline
59 & 7.7 & 11.4661 & -3.76606 \tabularnewline
60 & 4.35 & 7.20005 & -2.85005 \tabularnewline
61 & 12.7 & 10.2635 & 2.43654 \tabularnewline
62 & 18.1 & 16.1296 & 1.97037 \tabularnewline
63 & 17.85 & 16.1105 & 1.73953 \tabularnewline
64 & 17.1 & 17.5405 & -0.440546 \tabularnewline
65 & 19.1 & 16.7374 & 2.3626 \tabularnewline
66 & 16.1 & 18.6983 & -2.59825 \tabularnewline
67 & 13.35 & 10.877 & 2.47303 \tabularnewline
68 & 18.4 & 17.8737 & 0.526336 \tabularnewline
69 & 14.7 & 7.63322 & 7.06678 \tabularnewline
70 & 10.6 & 13.2078 & -2.60775 \tabularnewline
71 & 12.6 & 13.7947 & -1.19471 \tabularnewline
72 & 13.6 & 12.6308 & 0.969157 \tabularnewline
73 & 14.1 & 13.1066 & 0.993376 \tabularnewline
74 & 14.5 & 13.4309 & 1.06906 \tabularnewline
75 & 16.15 & 16.427 & -0.277018 \tabularnewline
76 & 14.75 & 12.5729 & 2.17712 \tabularnewline
77 & 14.8 & 12.079 & 2.72104 \tabularnewline
78 & 12.45 & 12.2846 & 0.165362 \tabularnewline
79 & 12.65 & 10.3096 & 2.34041 \tabularnewline
80 & 17.35 & 13.9082 & 3.44184 \tabularnewline
81 & 8.6 & 8.46061 & 0.13939 \tabularnewline
82 & 18.4 & 16.862 & 1.53799 \tabularnewline
83 & 16.1 & 14.4481 & 1.65193 \tabularnewline
84 & 17.75 & 15.6323 & 2.11768 \tabularnewline
85 & 15.25 & 15.3544 & -0.104441 \tabularnewline
86 & 17.65 & 16.295 & 1.35499 \tabularnewline
87 & 16.35 & 16.6283 & -0.278265 \tabularnewline
88 & 17.65 & 18.4063 & -0.756331 \tabularnewline
89 & 13.6 & 12.9799 & 0.620062 \tabularnewline
90 & 14.35 & 13.8156 & 0.534422 \tabularnewline
91 & 14.75 & 17.1015 & -2.35151 \tabularnewline
92 & 18.25 & 16.6739 & 1.57607 \tabularnewline
93 & 9.9 & 15.3367 & -5.43666 \tabularnewline
94 & 16 & 13.9389 & 2.06106 \tabularnewline
95 & 18.25 & 16.1789 & 2.07115 \tabularnewline
96 & 16.85 & 17.7368 & -0.886826 \tabularnewline
97 & 18.95 & 16.7908 & 2.15917 \tabularnewline
98 & 15.6 & 13.6032 & 1.99683 \tabularnewline
99 & 17.1 & 17.6439 & -0.543875 \tabularnewline
100 & 15.4 & 16.1246 & -0.72456 \tabularnewline
101 & 15.4 & 16.0126 & -0.612601 \tabularnewline
102 & 13.35 & 14.2864 & -0.936428 \tabularnewline
103 & 19.1 & 17.0152 & 2.08482 \tabularnewline
104 & 7.6 & 6.8399 & 0.7601 \tabularnewline
105 & 19.1 & 17.0014 & 2.09863 \tabularnewline
106 & 14.75 & 16.389 & -1.63898 \tabularnewline
107 & 19.25 & 17.07 & 2.17996 \tabularnewline
108 & 13.6 & 16.0121 & -2.41209 \tabularnewline
109 & 12.75 & 15.491 & -2.74101 \tabularnewline
110 & 9.85 & 8.14197 & 1.70803 \tabularnewline
111 & 15.25 & 15.7888 & -0.53878 \tabularnewline
112 & 11.9 & 13.3084 & -1.40841 \tabularnewline
113 & 16.35 & 17.3908 & -1.04079 \tabularnewline
114 & 12.4 & 13.9073 & -1.50732 \tabularnewline
115 & 18.15 & 15.9244 & 2.22562 \tabularnewline
116 & 17.75 & 15.1939 & 2.55608 \tabularnewline
117 & 12.35 & 12.6933 & -0.343347 \tabularnewline
118 & 15.6 & 15.371 & 0.229013 \tabularnewline
119 & 19.3 & 16.6885 & 2.61154 \tabularnewline
120 & 17.1 & 16.5332 & 0.566814 \tabularnewline
121 & 18.4 & 15.4135 & 2.98646 \tabularnewline
122 & 19.05 & 16.8683 & 2.1817 \tabularnewline
123 & 18.55 & 14.4985 & 4.05146 \tabularnewline
124 & 19.1 & 18.1447 & 0.955284 \tabularnewline
125 & 12.85 & 15.7169 & -2.86688 \tabularnewline
126 & 9.5 & 10.8703 & -1.37032 \tabularnewline
127 & 4.5 & 7.43533 & -2.93533 \tabularnewline
128 & 13.6 & 15.0742 & -1.47417 \tabularnewline
129 & 11.7 & 12.3555 & -0.655535 \tabularnewline
130 & 13.35 & 13.9516 & -0.601602 \tabularnewline
131 & 17.6 & 18.8391 & -1.23907 \tabularnewline
132 & 14.05 & 13.9084 & 0.14157 \tabularnewline
133 & 16.1 & 17.5664 & -1.46638 \tabularnewline
134 & 13.35 & 15.5205 & -2.17052 \tabularnewline
135 & 11.85 & 15.1627 & -3.31266 \tabularnewline
136 & 11.95 & 11.8314 & 0.118552 \tabularnewline
137 & 13.2 & 16.5098 & -3.3098 \tabularnewline
138 & 7.7 & 9.5295 & -1.8295 \tabularnewline
139 & 14.6 & 13.602 & 0.997976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&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]12.9727[/C][C]-0.072734[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.3401[/C][C]1.85993[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.688[/C][C]1.11198[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6298[/C][C]-4.22985[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.3344[/C][C]-4.63444[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.9663[/C][C]0.63373[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]10.965[/C][C]3.83497[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.4224[/C][C]-0.122442[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4013[/C][C]-1.30129[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.7979[/C][C]-2.59789[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2604[/C][C]0.139606[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.9226[/C][C]-4.52255[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1821[/C][C]0.417883[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.3487[/C][C]-1.34873[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.75545[/C][C]-2.45545[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2255[/C][C]-1.32551[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.8896[/C][C]-1.58959[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.72074[/C][C]0.279263[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.2873[/C][C]-3.88734[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.6113[/C][C]1.18875[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.1829[/C][C]0.617065[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.98[/C][C]1.82001[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.5672[/C][C]1.1328[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9153[/C][C]-1.01531[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.112[/C][C]-1.21202[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5173[/C][C]0.982692[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.9975[/C][C]-2.69746[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1092[/C][C]0.590771[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3878[/C][C]-1.38775[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9294[/C][C]-4.22939[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.2593[/C][C]-0.459346[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.8056[/C][C]-0.505553[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.70086[/C][C]0.699135[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2926[/C][C]-2.99263[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9415[/C][C]0.858484[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2921[/C][C]-5.39208[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.888[/C][C]-0.48799[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.7522[/C][C]2.24779[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.0538[/C][C]-0.25379[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7128[/C][C]0.587247[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.6358[/C][C]0.164189[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.46384[/C][C]3.23616[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.6098[/C][C]0.290204[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6396[/C][C]1.66042[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.5179[/C][C]-0.417855[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.55[/C][C]2.74998[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6505[/C][C]-2.35045[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4629[/C][C]2.03715[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.3529[/C][C]-2.75289[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.4987[/C][C]3.40132[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4143[/C][C]-1.21429[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.46[/C][C]-1.36001[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.4109[/C][C]0.589081[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.4721[/C][C]3.02789[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.0606[/C][C]-0.760624[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.4001[/C][C]0.999892[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2673[/C][C]0.732668[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0123[/C][C]2.1877[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4661[/C][C]-3.76606[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.20005[/C][C]-2.85005[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2635[/C][C]2.43654[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1296[/C][C]1.97037[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1105[/C][C]1.73953[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.5405[/C][C]-0.440546[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7374[/C][C]2.3626[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.6983[/C][C]-2.59825[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.877[/C][C]2.47303[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.8737[/C][C]0.526336[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.63322[/C][C]7.06678[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.2078[/C][C]-2.60775[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7947[/C][C]-1.19471[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.6308[/C][C]0.969157[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.1066[/C][C]0.993376[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.4309[/C][C]1.06906[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.427[/C][C]-0.277018[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.5729[/C][C]2.17712[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.079[/C][C]2.72104[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.2846[/C][C]0.165362[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.3096[/C][C]2.34041[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9082[/C][C]3.44184[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.46061[/C][C]0.13939[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.862[/C][C]1.53799[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.4481[/C][C]1.65193[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.6323[/C][C]2.11768[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3544[/C][C]-0.104441[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.295[/C][C]1.35499[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6283[/C][C]-0.278265[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.4063[/C][C]-0.756331[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.9799[/C][C]0.620062[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.8156[/C][C]0.534422[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1015[/C][C]-2.35151[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.6739[/C][C]1.57607[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.3367[/C][C]-5.43666[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.9389[/C][C]2.06106[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1789[/C][C]2.07115[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.7368[/C][C]-0.886826[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.7908[/C][C]2.15917[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.6032[/C][C]1.99683[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.6439[/C][C]-0.543875[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1246[/C][C]-0.72456[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0126[/C][C]-0.612601[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.2864[/C][C]-0.936428[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0152[/C][C]2.08482[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.8399[/C][C]0.7601[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]17.0014[/C][C]2.09863[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.389[/C][C]-1.63898[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]17.07[/C][C]2.17996[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0121[/C][C]-2.41209[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.491[/C][C]-2.74101[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.14197[/C][C]1.70803[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.7888[/C][C]-0.53878[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.3084[/C][C]-1.40841[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.3908[/C][C]-1.04079[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.9073[/C][C]-1.50732[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]15.9244[/C][C]2.22562[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.1939[/C][C]2.55608[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.6933[/C][C]-0.343347[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.371[/C][C]0.229013[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.6885[/C][C]2.61154[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5332[/C][C]0.566814[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.4135[/C][C]2.98646[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8683[/C][C]2.1817[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.4985[/C][C]4.05146[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.1447[/C][C]0.955284[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.7169[/C][C]-2.86688[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.8703[/C][C]-1.37032[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.43533[/C][C]-2.93533[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]15.0742[/C][C]-1.47417[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.3555[/C][C]-0.655535[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.9516[/C][C]-0.601602[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]18.8391[/C][C]-1.23907[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.9084[/C][C]0.14157[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5664[/C][C]-1.46638[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.5205[/C][C]-2.17052[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1627[/C][C]-3.31266[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.8314[/C][C]0.118552[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.5098[/C][C]-3.3098[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.5295[/C][C]-1.8295[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.602[/C][C]0.997976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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
112.912.9727-0.072734
212.210.34011.85993
312.811.6881.11198
47.411.6298-4.22985
56.711.3344-4.63444
612.611.96630.63373
714.810.9653.83497
813.313.4224-0.122442
911.112.4013-1.30129
108.210.7979-2.59789
1111.411.26040.139606
126.410.9226-4.52255
1310.610.18210.417883
141213.3487-1.34873
156.38.75545-2.45545
1611.913.2255-1.32551
179.310.8896-1.58959
18109.720740.279263
196.410.2873-3.88734
2013.812.61131.18875
2110.810.18290.617065
2213.811.981.82001
2311.710.56721.1328
2410.911.9153-1.01531
259.911.112-1.21202
2611.510.51730.982692
278.310.9975-2.69746
2811.711.10920.590771
29910.3878-1.38775
309.713.9294-4.22939
3110.811.2593-0.459346
3210.310.8056-0.505553
3310.49.700860.699135
349.312.2926-2.99263
3511.810.94150.858484
365.911.2921-5.39208
3711.411.888-0.48799
381310.75222.24779
3910.811.0538-0.25379
4011.310.71280.587247
4111.811.63580.164189
4212.79.463843.23616
4310.910.60980.290204
4413.311.63961.66042
4510.110.5179-0.417855
4614.311.552.74998
479.311.6505-2.35045
4812.510.46292.03715
497.610.3529-2.75289
5015.912.49873.40132
519.210.4143-1.21429
5211.112.46-1.36001
531312.41090.589081
5414.511.47213.02789
5512.313.0606-0.760624
5611.410.40010.999892
571312.26730.732668
5813.211.01232.1877
597.711.4661-3.76606
604.357.20005-2.85005
6112.710.26352.43654
6218.116.12961.97037
6317.8516.11051.73953
6417.117.5405-0.440546
6519.116.73742.3626
6616.118.6983-2.59825
6713.3510.8772.47303
6818.417.87370.526336
6914.77.633227.06678
7010.613.2078-2.60775
7112.613.7947-1.19471
7213.612.63080.969157
7314.113.10660.993376
7414.513.43091.06906
7516.1516.427-0.277018
7614.7512.57292.17712
7714.812.0792.72104
7812.4512.28460.165362
7912.6510.30962.34041
8017.3513.90823.44184
818.68.460610.13939
8218.416.8621.53799
8316.114.44811.65193
8417.7515.63232.11768
8515.2515.3544-0.104441
8617.6516.2951.35499
8716.3516.6283-0.278265
8817.6518.4063-0.756331
8913.612.97990.620062
9014.3513.81560.534422
9114.7517.1015-2.35151
9218.2516.67391.57607
939.915.3367-5.43666
941613.93892.06106
9518.2516.17892.07115
9616.8517.7368-0.886826
9718.9516.79082.15917
9815.613.60321.99683
9917.117.6439-0.543875
10015.416.1246-0.72456
10115.416.0126-0.612601
10213.3514.2864-0.936428
10319.117.01522.08482
1047.66.83990.7601
10519.117.00142.09863
10614.7516.389-1.63898
10719.2517.072.17996
10813.616.0121-2.41209
10912.7515.491-2.74101
1109.858.141971.70803
11115.2515.7888-0.53878
11211.913.3084-1.40841
11316.3517.3908-1.04079
11412.413.9073-1.50732
11518.1515.92442.22562
11617.7515.19392.55608
11712.3512.6933-0.343347
11815.615.3710.229013
11919.316.68852.61154
12017.116.53320.566814
12118.415.41352.98646
12219.0516.86832.1817
12318.5514.49854.05146
12419.118.14470.955284
12512.8515.7169-2.86688
1269.510.8703-1.37032
1274.57.43533-2.93533
12813.615.0742-1.47417
12911.712.3555-0.655535
13013.3513.9516-0.601602
13117.618.8391-1.23907
13214.0513.90840.14157
13316.117.5664-1.46638
13413.3515.5205-2.17052
13511.8515.1627-3.31266
13611.9511.83140.118552
13713.216.5098-3.3098
1387.79.5295-1.8295
13914.613.6020.997976






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.4454820.8909640.554518
190.30220.6044010.6978
200.1815230.3630470.818477
210.1451410.2902830.854859
220.09464760.1892950.905352
230.1096440.2192870.890356
240.1066310.2132620.893369
250.09398140.1879630.906019
260.09298250.1859650.907018
270.07720050.1544010.922799
280.04993730.09987460.950063
290.1313820.2627640.868618
300.4035850.8071710.596415
310.3524540.7049090.647546
320.2948930.5897860.705107
330.2355370.4710740.764463
340.271780.5435590.72822
350.2207450.441490.779255
360.3519360.7038720.648064
370.307090.6141790.69291
380.3194170.6388330.680583
390.2705060.5410110.729494
400.2194810.4389620.780519
410.218760.4375210.78124
420.237850.4756990.76215
430.2053180.4106350.794682
440.43040.86080.5696
450.3764880.7529750.623512
460.3723410.7446830.627659
470.3839130.7678260.616087
480.371890.743780.62811
490.5948610.8102770.405139
500.6847370.6305260.315263
510.684950.63010.31505
520.660410.679180.33959
530.617680.7646410.38232
540.6168740.7662520.383126
550.5773410.8453180.422659
560.5296670.9406660.470333
570.4922310.9844610.507769
580.4628410.9256820.537159
590.549940.900120.45006
600.6063630.7872740.393637
610.6630920.6738160.336908
620.7134270.5731470.286573
630.6827730.6344540.317227
640.6654890.6690220.334511
650.6553060.6893890.344694
660.680040.6399190.31996
670.6990420.6019160.300958
680.6527480.6945050.347252
690.9435390.1129210.0564606
700.949380.1012410.0506203
710.9412260.1175490.0587744
720.9455820.1088360.0544181
730.9334080.1331840.066592
740.9262950.147410.0737052
750.9229090.1541820.0770912
760.9194560.1610880.080544
770.9277380.1445240.0722618
780.9075260.1849470.0924737
790.9020760.1958470.0979236
800.9284550.1430890.0715447
810.9097440.1805120.0902561
820.8916640.2166720.108336
830.8718060.2563880.128194
840.8522780.2954430.147722
850.8231690.3536630.176831
860.8239530.3520930.176047
870.7923910.4152170.207609
880.7593620.4812760.240638
890.72050.5590.2795
900.6688560.6622880.331144
910.6698020.6603960.330198
920.6445820.7108370.355418
930.8595120.2809760.140488
940.836030.327940.16397
950.8162410.3675180.183759
960.7873550.4252890.212645
970.7533340.4933320.246666
980.7416130.5167740.258387
990.7580210.4839580.241979
1000.7151580.5696830.284842
1010.6620790.6758430.337921
1020.6296520.7406960.370348
1030.6114260.7771480.388574
1040.6129540.7740910.387046
1050.5582480.8835050.441752
1060.508150.98370.49185
1070.4502240.9004490.549776
1080.4371940.8743880.562806
1090.4005280.8010570.599472
1100.3767390.7534780.623261
1110.3192780.6385560.680722
1120.265860.531720.73414
1130.2615630.5231270.738437
1140.2027510.4055030.797249
1150.1607740.3215480.839226
1160.1469090.2938170.853091
1170.09671860.1934370.903281
1180.3340080.6680170.665992
1190.7712590.4574810.228741
1200.8614920.2770170.138508
1210.7973240.4053510.202676

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.445482 & 0.890964 & 0.554518 \tabularnewline
19 & 0.3022 & 0.604401 & 0.6978 \tabularnewline
20 & 0.181523 & 0.363047 & 0.818477 \tabularnewline
21 & 0.145141 & 0.290283 & 0.854859 \tabularnewline
22 & 0.0946476 & 0.189295 & 0.905352 \tabularnewline
23 & 0.109644 & 0.219287 & 0.890356 \tabularnewline
24 & 0.106631 & 0.213262 & 0.893369 \tabularnewline
25 & 0.0939814 & 0.187963 & 0.906019 \tabularnewline
26 & 0.0929825 & 0.185965 & 0.907018 \tabularnewline
27 & 0.0772005 & 0.154401 & 0.922799 \tabularnewline
28 & 0.0499373 & 0.0998746 & 0.950063 \tabularnewline
29 & 0.131382 & 0.262764 & 0.868618 \tabularnewline
30 & 0.403585 & 0.807171 & 0.596415 \tabularnewline
31 & 0.352454 & 0.704909 & 0.647546 \tabularnewline
32 & 0.294893 & 0.589786 & 0.705107 \tabularnewline
33 & 0.235537 & 0.471074 & 0.764463 \tabularnewline
34 & 0.27178 & 0.543559 & 0.72822 \tabularnewline
35 & 0.220745 & 0.44149 & 0.779255 \tabularnewline
36 & 0.351936 & 0.703872 & 0.648064 \tabularnewline
37 & 0.30709 & 0.614179 & 0.69291 \tabularnewline
38 & 0.319417 & 0.638833 & 0.680583 \tabularnewline
39 & 0.270506 & 0.541011 & 0.729494 \tabularnewline
40 & 0.219481 & 0.438962 & 0.780519 \tabularnewline
41 & 0.21876 & 0.437521 & 0.78124 \tabularnewline
42 & 0.23785 & 0.475699 & 0.76215 \tabularnewline
43 & 0.205318 & 0.410635 & 0.794682 \tabularnewline
44 & 0.4304 & 0.8608 & 0.5696 \tabularnewline
45 & 0.376488 & 0.752975 & 0.623512 \tabularnewline
46 & 0.372341 & 0.744683 & 0.627659 \tabularnewline
47 & 0.383913 & 0.767826 & 0.616087 \tabularnewline
48 & 0.37189 & 0.74378 & 0.62811 \tabularnewline
49 & 0.594861 & 0.810277 & 0.405139 \tabularnewline
50 & 0.684737 & 0.630526 & 0.315263 \tabularnewline
51 & 0.68495 & 0.6301 & 0.31505 \tabularnewline
52 & 0.66041 & 0.67918 & 0.33959 \tabularnewline
53 & 0.61768 & 0.764641 & 0.38232 \tabularnewline
54 & 0.616874 & 0.766252 & 0.383126 \tabularnewline
55 & 0.577341 & 0.845318 & 0.422659 \tabularnewline
56 & 0.529667 & 0.940666 & 0.470333 \tabularnewline
57 & 0.492231 & 0.984461 & 0.507769 \tabularnewline
58 & 0.462841 & 0.925682 & 0.537159 \tabularnewline
59 & 0.54994 & 0.90012 & 0.45006 \tabularnewline
60 & 0.606363 & 0.787274 & 0.393637 \tabularnewline
61 & 0.663092 & 0.673816 & 0.336908 \tabularnewline
62 & 0.713427 & 0.573147 & 0.286573 \tabularnewline
63 & 0.682773 & 0.634454 & 0.317227 \tabularnewline
64 & 0.665489 & 0.669022 & 0.334511 \tabularnewline
65 & 0.655306 & 0.689389 & 0.344694 \tabularnewline
66 & 0.68004 & 0.639919 & 0.31996 \tabularnewline
67 & 0.699042 & 0.601916 & 0.300958 \tabularnewline
68 & 0.652748 & 0.694505 & 0.347252 \tabularnewline
69 & 0.943539 & 0.112921 & 0.0564606 \tabularnewline
70 & 0.94938 & 0.101241 & 0.0506203 \tabularnewline
71 & 0.941226 & 0.117549 & 0.0587744 \tabularnewline
72 & 0.945582 & 0.108836 & 0.0544181 \tabularnewline
73 & 0.933408 & 0.133184 & 0.066592 \tabularnewline
74 & 0.926295 & 0.14741 & 0.0737052 \tabularnewline
75 & 0.922909 & 0.154182 & 0.0770912 \tabularnewline
76 & 0.919456 & 0.161088 & 0.080544 \tabularnewline
77 & 0.927738 & 0.144524 & 0.0722618 \tabularnewline
78 & 0.907526 & 0.184947 & 0.0924737 \tabularnewline
79 & 0.902076 & 0.195847 & 0.0979236 \tabularnewline
80 & 0.928455 & 0.143089 & 0.0715447 \tabularnewline
81 & 0.909744 & 0.180512 & 0.0902561 \tabularnewline
82 & 0.891664 & 0.216672 & 0.108336 \tabularnewline
83 & 0.871806 & 0.256388 & 0.128194 \tabularnewline
84 & 0.852278 & 0.295443 & 0.147722 \tabularnewline
85 & 0.823169 & 0.353663 & 0.176831 \tabularnewline
86 & 0.823953 & 0.352093 & 0.176047 \tabularnewline
87 & 0.792391 & 0.415217 & 0.207609 \tabularnewline
88 & 0.759362 & 0.481276 & 0.240638 \tabularnewline
89 & 0.7205 & 0.559 & 0.2795 \tabularnewline
90 & 0.668856 & 0.662288 & 0.331144 \tabularnewline
91 & 0.669802 & 0.660396 & 0.330198 \tabularnewline
92 & 0.644582 & 0.710837 & 0.355418 \tabularnewline
93 & 0.859512 & 0.280976 & 0.140488 \tabularnewline
94 & 0.83603 & 0.32794 & 0.16397 \tabularnewline
95 & 0.816241 & 0.367518 & 0.183759 \tabularnewline
96 & 0.787355 & 0.425289 & 0.212645 \tabularnewline
97 & 0.753334 & 0.493332 & 0.246666 \tabularnewline
98 & 0.741613 & 0.516774 & 0.258387 \tabularnewline
99 & 0.758021 & 0.483958 & 0.241979 \tabularnewline
100 & 0.715158 & 0.569683 & 0.284842 \tabularnewline
101 & 0.662079 & 0.675843 & 0.337921 \tabularnewline
102 & 0.629652 & 0.740696 & 0.370348 \tabularnewline
103 & 0.611426 & 0.777148 & 0.388574 \tabularnewline
104 & 0.612954 & 0.774091 & 0.387046 \tabularnewline
105 & 0.558248 & 0.883505 & 0.441752 \tabularnewline
106 & 0.50815 & 0.9837 & 0.49185 \tabularnewline
107 & 0.450224 & 0.900449 & 0.549776 \tabularnewline
108 & 0.437194 & 0.874388 & 0.562806 \tabularnewline
109 & 0.400528 & 0.801057 & 0.599472 \tabularnewline
110 & 0.376739 & 0.753478 & 0.623261 \tabularnewline
111 & 0.319278 & 0.638556 & 0.680722 \tabularnewline
112 & 0.26586 & 0.53172 & 0.73414 \tabularnewline
113 & 0.261563 & 0.523127 & 0.738437 \tabularnewline
114 & 0.202751 & 0.405503 & 0.797249 \tabularnewline
115 & 0.160774 & 0.321548 & 0.839226 \tabularnewline
116 & 0.146909 & 0.293817 & 0.853091 \tabularnewline
117 & 0.0967186 & 0.193437 & 0.903281 \tabularnewline
118 & 0.334008 & 0.668017 & 0.665992 \tabularnewline
119 & 0.771259 & 0.457481 & 0.228741 \tabularnewline
120 & 0.861492 & 0.277017 & 0.138508 \tabularnewline
121 & 0.797324 & 0.405351 & 0.202676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&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]0.445482[/C][C]0.890964[/C][C]0.554518[/C][/ROW]
[ROW][C]19[/C][C]0.3022[/C][C]0.604401[/C][C]0.6978[/C][/ROW]
[ROW][C]20[/C][C]0.181523[/C][C]0.363047[/C][C]0.818477[/C][/ROW]
[ROW][C]21[/C][C]0.145141[/C][C]0.290283[/C][C]0.854859[/C][/ROW]
[ROW][C]22[/C][C]0.0946476[/C][C]0.189295[/C][C]0.905352[/C][/ROW]
[ROW][C]23[/C][C]0.109644[/C][C]0.219287[/C][C]0.890356[/C][/ROW]
[ROW][C]24[/C][C]0.106631[/C][C]0.213262[/C][C]0.893369[/C][/ROW]
[ROW][C]25[/C][C]0.0939814[/C][C]0.187963[/C][C]0.906019[/C][/ROW]
[ROW][C]26[/C][C]0.0929825[/C][C]0.185965[/C][C]0.907018[/C][/ROW]
[ROW][C]27[/C][C]0.0772005[/C][C]0.154401[/C][C]0.922799[/C][/ROW]
[ROW][C]28[/C][C]0.0499373[/C][C]0.0998746[/C][C]0.950063[/C][/ROW]
[ROW][C]29[/C][C]0.131382[/C][C]0.262764[/C][C]0.868618[/C][/ROW]
[ROW][C]30[/C][C]0.403585[/C][C]0.807171[/C][C]0.596415[/C][/ROW]
[ROW][C]31[/C][C]0.352454[/C][C]0.704909[/C][C]0.647546[/C][/ROW]
[ROW][C]32[/C][C]0.294893[/C][C]0.589786[/C][C]0.705107[/C][/ROW]
[ROW][C]33[/C][C]0.235537[/C][C]0.471074[/C][C]0.764463[/C][/ROW]
[ROW][C]34[/C][C]0.27178[/C][C]0.543559[/C][C]0.72822[/C][/ROW]
[ROW][C]35[/C][C]0.220745[/C][C]0.44149[/C][C]0.779255[/C][/ROW]
[ROW][C]36[/C][C]0.351936[/C][C]0.703872[/C][C]0.648064[/C][/ROW]
[ROW][C]37[/C][C]0.30709[/C][C]0.614179[/C][C]0.69291[/C][/ROW]
[ROW][C]38[/C][C]0.319417[/C][C]0.638833[/C][C]0.680583[/C][/ROW]
[ROW][C]39[/C][C]0.270506[/C][C]0.541011[/C][C]0.729494[/C][/ROW]
[ROW][C]40[/C][C]0.219481[/C][C]0.438962[/C][C]0.780519[/C][/ROW]
[ROW][C]41[/C][C]0.21876[/C][C]0.437521[/C][C]0.78124[/C][/ROW]
[ROW][C]42[/C][C]0.23785[/C][C]0.475699[/C][C]0.76215[/C][/ROW]
[ROW][C]43[/C][C]0.205318[/C][C]0.410635[/C][C]0.794682[/C][/ROW]
[ROW][C]44[/C][C]0.4304[/C][C]0.8608[/C][C]0.5696[/C][/ROW]
[ROW][C]45[/C][C]0.376488[/C][C]0.752975[/C][C]0.623512[/C][/ROW]
[ROW][C]46[/C][C]0.372341[/C][C]0.744683[/C][C]0.627659[/C][/ROW]
[ROW][C]47[/C][C]0.383913[/C][C]0.767826[/C][C]0.616087[/C][/ROW]
[ROW][C]48[/C][C]0.37189[/C][C]0.74378[/C][C]0.62811[/C][/ROW]
[ROW][C]49[/C][C]0.594861[/C][C]0.810277[/C][C]0.405139[/C][/ROW]
[ROW][C]50[/C][C]0.684737[/C][C]0.630526[/C][C]0.315263[/C][/ROW]
[ROW][C]51[/C][C]0.68495[/C][C]0.6301[/C][C]0.31505[/C][/ROW]
[ROW][C]52[/C][C]0.66041[/C][C]0.67918[/C][C]0.33959[/C][/ROW]
[ROW][C]53[/C][C]0.61768[/C][C]0.764641[/C][C]0.38232[/C][/ROW]
[ROW][C]54[/C][C]0.616874[/C][C]0.766252[/C][C]0.383126[/C][/ROW]
[ROW][C]55[/C][C]0.577341[/C][C]0.845318[/C][C]0.422659[/C][/ROW]
[ROW][C]56[/C][C]0.529667[/C][C]0.940666[/C][C]0.470333[/C][/ROW]
[ROW][C]57[/C][C]0.492231[/C][C]0.984461[/C][C]0.507769[/C][/ROW]
[ROW][C]58[/C][C]0.462841[/C][C]0.925682[/C][C]0.537159[/C][/ROW]
[ROW][C]59[/C][C]0.54994[/C][C]0.90012[/C][C]0.45006[/C][/ROW]
[ROW][C]60[/C][C]0.606363[/C][C]0.787274[/C][C]0.393637[/C][/ROW]
[ROW][C]61[/C][C]0.663092[/C][C]0.673816[/C][C]0.336908[/C][/ROW]
[ROW][C]62[/C][C]0.713427[/C][C]0.573147[/C][C]0.286573[/C][/ROW]
[ROW][C]63[/C][C]0.682773[/C][C]0.634454[/C][C]0.317227[/C][/ROW]
[ROW][C]64[/C][C]0.665489[/C][C]0.669022[/C][C]0.334511[/C][/ROW]
[ROW][C]65[/C][C]0.655306[/C][C]0.689389[/C][C]0.344694[/C][/ROW]
[ROW][C]66[/C][C]0.68004[/C][C]0.639919[/C][C]0.31996[/C][/ROW]
[ROW][C]67[/C][C]0.699042[/C][C]0.601916[/C][C]0.300958[/C][/ROW]
[ROW][C]68[/C][C]0.652748[/C][C]0.694505[/C][C]0.347252[/C][/ROW]
[ROW][C]69[/C][C]0.943539[/C][C]0.112921[/C][C]0.0564606[/C][/ROW]
[ROW][C]70[/C][C]0.94938[/C][C]0.101241[/C][C]0.0506203[/C][/ROW]
[ROW][C]71[/C][C]0.941226[/C][C]0.117549[/C][C]0.0587744[/C][/ROW]
[ROW][C]72[/C][C]0.945582[/C][C]0.108836[/C][C]0.0544181[/C][/ROW]
[ROW][C]73[/C][C]0.933408[/C][C]0.133184[/C][C]0.066592[/C][/ROW]
[ROW][C]74[/C][C]0.926295[/C][C]0.14741[/C][C]0.0737052[/C][/ROW]
[ROW][C]75[/C][C]0.922909[/C][C]0.154182[/C][C]0.0770912[/C][/ROW]
[ROW][C]76[/C][C]0.919456[/C][C]0.161088[/C][C]0.080544[/C][/ROW]
[ROW][C]77[/C][C]0.927738[/C][C]0.144524[/C][C]0.0722618[/C][/ROW]
[ROW][C]78[/C][C]0.907526[/C][C]0.184947[/C][C]0.0924737[/C][/ROW]
[ROW][C]79[/C][C]0.902076[/C][C]0.195847[/C][C]0.0979236[/C][/ROW]
[ROW][C]80[/C][C]0.928455[/C][C]0.143089[/C][C]0.0715447[/C][/ROW]
[ROW][C]81[/C][C]0.909744[/C][C]0.180512[/C][C]0.0902561[/C][/ROW]
[ROW][C]82[/C][C]0.891664[/C][C]0.216672[/C][C]0.108336[/C][/ROW]
[ROW][C]83[/C][C]0.871806[/C][C]0.256388[/C][C]0.128194[/C][/ROW]
[ROW][C]84[/C][C]0.852278[/C][C]0.295443[/C][C]0.147722[/C][/ROW]
[ROW][C]85[/C][C]0.823169[/C][C]0.353663[/C][C]0.176831[/C][/ROW]
[ROW][C]86[/C][C]0.823953[/C][C]0.352093[/C][C]0.176047[/C][/ROW]
[ROW][C]87[/C][C]0.792391[/C][C]0.415217[/C][C]0.207609[/C][/ROW]
[ROW][C]88[/C][C]0.759362[/C][C]0.481276[/C][C]0.240638[/C][/ROW]
[ROW][C]89[/C][C]0.7205[/C][C]0.559[/C][C]0.2795[/C][/ROW]
[ROW][C]90[/C][C]0.668856[/C][C]0.662288[/C][C]0.331144[/C][/ROW]
[ROW][C]91[/C][C]0.669802[/C][C]0.660396[/C][C]0.330198[/C][/ROW]
[ROW][C]92[/C][C]0.644582[/C][C]0.710837[/C][C]0.355418[/C][/ROW]
[ROW][C]93[/C][C]0.859512[/C][C]0.280976[/C][C]0.140488[/C][/ROW]
[ROW][C]94[/C][C]0.83603[/C][C]0.32794[/C][C]0.16397[/C][/ROW]
[ROW][C]95[/C][C]0.816241[/C][C]0.367518[/C][C]0.183759[/C][/ROW]
[ROW][C]96[/C][C]0.787355[/C][C]0.425289[/C][C]0.212645[/C][/ROW]
[ROW][C]97[/C][C]0.753334[/C][C]0.493332[/C][C]0.246666[/C][/ROW]
[ROW][C]98[/C][C]0.741613[/C][C]0.516774[/C][C]0.258387[/C][/ROW]
[ROW][C]99[/C][C]0.758021[/C][C]0.483958[/C][C]0.241979[/C][/ROW]
[ROW][C]100[/C][C]0.715158[/C][C]0.569683[/C][C]0.284842[/C][/ROW]
[ROW][C]101[/C][C]0.662079[/C][C]0.675843[/C][C]0.337921[/C][/ROW]
[ROW][C]102[/C][C]0.629652[/C][C]0.740696[/C][C]0.370348[/C][/ROW]
[ROW][C]103[/C][C]0.611426[/C][C]0.777148[/C][C]0.388574[/C][/ROW]
[ROW][C]104[/C][C]0.612954[/C][C]0.774091[/C][C]0.387046[/C][/ROW]
[ROW][C]105[/C][C]0.558248[/C][C]0.883505[/C][C]0.441752[/C][/ROW]
[ROW][C]106[/C][C]0.50815[/C][C]0.9837[/C][C]0.49185[/C][/ROW]
[ROW][C]107[/C][C]0.450224[/C][C]0.900449[/C][C]0.549776[/C][/ROW]
[ROW][C]108[/C][C]0.437194[/C][C]0.874388[/C][C]0.562806[/C][/ROW]
[ROW][C]109[/C][C]0.400528[/C][C]0.801057[/C][C]0.599472[/C][/ROW]
[ROW][C]110[/C][C]0.376739[/C][C]0.753478[/C][C]0.623261[/C][/ROW]
[ROW][C]111[/C][C]0.319278[/C][C]0.638556[/C][C]0.680722[/C][/ROW]
[ROW][C]112[/C][C]0.26586[/C][C]0.53172[/C][C]0.73414[/C][/ROW]
[ROW][C]113[/C][C]0.261563[/C][C]0.523127[/C][C]0.738437[/C][/ROW]
[ROW][C]114[/C][C]0.202751[/C][C]0.405503[/C][C]0.797249[/C][/ROW]
[ROW][C]115[/C][C]0.160774[/C][C]0.321548[/C][C]0.839226[/C][/ROW]
[ROW][C]116[/C][C]0.146909[/C][C]0.293817[/C][C]0.853091[/C][/ROW]
[ROW][C]117[/C][C]0.0967186[/C][C]0.193437[/C][C]0.903281[/C][/ROW]
[ROW][C]118[/C][C]0.334008[/C][C]0.668017[/C][C]0.665992[/C][/ROW]
[ROW][C]119[/C][C]0.771259[/C][C]0.457481[/C][C]0.228741[/C][/ROW]
[ROW][C]120[/C][C]0.861492[/C][C]0.277017[/C][C]0.138508[/C][/ROW]
[ROW][C]121[/C][C]0.797324[/C][C]0.405351[/C][C]0.202676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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
180.4454820.8909640.554518
190.30220.6044010.6978
200.1815230.3630470.818477
210.1451410.2902830.854859
220.09464760.1892950.905352
230.1096440.2192870.890356
240.1066310.2132620.893369
250.09398140.1879630.906019
260.09298250.1859650.907018
270.07720050.1544010.922799
280.04993730.09987460.950063
290.1313820.2627640.868618
300.4035850.8071710.596415
310.3524540.7049090.647546
320.2948930.5897860.705107
330.2355370.4710740.764463
340.271780.5435590.72822
350.2207450.441490.779255
360.3519360.7038720.648064
370.307090.6141790.69291
380.3194170.6388330.680583
390.2705060.5410110.729494
400.2194810.4389620.780519
410.218760.4375210.78124
420.237850.4756990.76215
430.2053180.4106350.794682
440.43040.86080.5696
450.3764880.7529750.623512
460.3723410.7446830.627659
470.3839130.7678260.616087
480.371890.743780.62811
490.5948610.8102770.405139
500.6847370.6305260.315263
510.684950.63010.31505
520.660410.679180.33959
530.617680.7646410.38232
540.6168740.7662520.383126
550.5773410.8453180.422659
560.5296670.9406660.470333
570.4922310.9844610.507769
580.4628410.9256820.537159
590.549940.900120.45006
600.6063630.7872740.393637
610.6630920.6738160.336908
620.7134270.5731470.286573
630.6827730.6344540.317227
640.6654890.6690220.334511
650.6553060.6893890.344694
660.680040.6399190.31996
670.6990420.6019160.300958
680.6527480.6945050.347252
690.9435390.1129210.0564606
700.949380.1012410.0506203
710.9412260.1175490.0587744
720.9455820.1088360.0544181
730.9334080.1331840.066592
740.9262950.147410.0737052
750.9229090.1541820.0770912
760.9194560.1610880.080544
770.9277380.1445240.0722618
780.9075260.1849470.0924737
790.9020760.1958470.0979236
800.9284550.1430890.0715447
810.9097440.1805120.0902561
820.8916640.2166720.108336
830.8718060.2563880.128194
840.8522780.2954430.147722
850.8231690.3536630.176831
860.8239530.3520930.176047
870.7923910.4152170.207609
880.7593620.4812760.240638
890.72050.5590.2795
900.6688560.6622880.331144
910.6698020.6603960.330198
920.6445820.7108370.355418
930.8595120.2809760.140488
940.836030.327940.16397
950.8162410.3675180.183759
960.7873550.4252890.212645
970.7533340.4933320.246666
980.7416130.5167740.258387
990.7580210.4839580.241979
1000.7151580.5696830.284842
1010.6620790.6758430.337921
1020.6296520.7406960.370348
1030.6114260.7771480.388574
1040.6129540.7740910.387046
1050.5582480.8835050.441752
1060.508150.98370.49185
1070.4502240.9004490.549776
1080.4371940.8743880.562806
1090.4005280.8010570.599472
1100.3767390.7534780.623261
1110.3192780.6385560.680722
1120.265860.531720.73414
1130.2615630.5231270.738437
1140.2027510.4055030.797249
1150.1607740.3215480.839226
1160.1469090.2938170.853091
1170.09671860.1934370.903281
1180.3340080.6680170.665992
1190.7712590.4574810.228741
1200.8614920.2770170.138508
1210.7973240.4053510.202676






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 level10.00961538OK

\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 & 0.00961538 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271040&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]0.00961538[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271040&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271040&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 level10.00961538OK


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')
}