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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 computationMon, 08 Dec 2014 12:25:59 +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/08/t1418041596km9v5g566hr8v3e.htm/, Retrieved Sun, 19 May 2024 09:24:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263963, Retrieved Sun, 19 May 2024 09:24:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper2] [2014-12-08 12:25:59] [8fc8509b9f8606b50b2407d6e00dc1c6] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
4.35 52 51 6 22 16 9 23 48 1
12.7 16 56 4 22 16 11 22 50 1
18.1 46 67 8 22 16 12 21 150 4
17.85 56 69 5 20 16 12 25 154 4
16.6 52 57 4 19 12 7 30 109 3
12.6 55 56 17 20 15 12 17 68 2
17.1 50 55 4 22 14 12 27 194 4
19.1 59 63 4 21 15 12 23 158 4
16.1 60 67 8 21 16 10 23 159 4
13.35 52 65 4 21 13 15 18 67 2
18.4 44 47 7 21 10 10 18 147 4
14.7 67 76 4 21 17 15 23 39 1
10.6 52 64 4 21 15 10 19 100 3
12.6 55 68 5 21 18 15 15 111 3
16.2 37 64 7 22 16 9 20 138 4
13.6 54 65 4 24 20 15 16 101 3
18.9 72 71 4 21 16 12 24 131 4
14.1 51 63 7 22 17 13 25 101 3
14.5 48 60 11 20 16 12 25 114 3
16.15 60 68 7 21 15 12 19 165 4
14.75 50 72 4 24 13 8 19 114 3
14.8 63 70 4 25 16 9 16 111 3
12.45 33 61 4 22 16 15 19 75 2
12.65 67 61 4 21 16 12 19 82 2
17.35 46 62 4 21 17 12 23 121 3
8.6 54 71 4 22 20 15 21 32 1
18.4 59 71 6 23 14 11 22 150 4
16.1 61 51 8 24 17 12 19 117 3
11.6 33 56 23 20 6 6 20 71 2
17.75 47 70 4 22 16 14 20 165 4
15.25 69 73 8 25 15 12 3 154 4
17.65 52 76 6 22 16 12 23 126 4
16.35 55 68 4 21 16 12 23 149 4
17.65 41 48 7 21 14 11 20 145 4
13.6 73 52 4 21 16 12 15 120 3
14.35 52 60 4 22 16 12 16 109 3
14.75 50 59 4 22 16 12 7 132 4
18.25 51 57 10 21 14 12 24 172 4
9.9 60 79 6 22 14 8 17 169 4
16 56 60 5 23 16 8 24 114 3
18.25 56 60 5 21 16 12 24 156 4
16.85 29 59 4 21 15 12 19 172 4
14.6 66 62 4 21 16 11 25 68 2
13.85 66 59 5 19 16 10 20 89 2
18.95 73 61 5 21 18 11 28 167 4
15.6 55 71 5 21 15 12 23 113 3
14.85 64 57 5 19 16 13 27 115 3
11.75 40 66 4 18 16 12 18 78 2
18.45 46 63 6 19 16 12 28 118 3
15.9 58 69 4 21 17 10 21 87 2
17.1 43 58 4 22 14 10 19 173 4
16.1 61 59 4 22 18 11 23 2 1
19.9 51 48 9 19 9 8 27 162 4
10.95 50 66 18 20 15 12 22 49 1
18.45 52 73 6 19 14 9 28 122 4
15.1 54 67 5 21 15 12 25 96 3
15 66 61 4 19 13 9 21 100 3
11.35 61 68 11 20 16 11 22 82 2
15.95 80 75 4 21 20 15 28 100 3
18.1 51 62 10 19 14 8 20 115 3
14.6 56 69 6 21 12 8 29 141 4
15.4 56 58 8 21 15 11 25 165 4
15.4 56 60 8 21 15 11 25 165 4
17.6 53 74 6 19 15 11 20 110 3
13.35 47 55 8 25 16 13 20 118 3
19.1 25 62 4 21 11 7 16 158 4
15.35 47 63 4 20 16 12 20 146 4
7.6 46 69 9 25 7 8 20 49 1
13.4 50 58 9 19 11 8 23 90 2
13.9 39 58 5 20 9 4 18 121 3
19.1 51 68 4 22 15 11 25 155 4
15.25 58 72 4 19 16 10 18 104 3
12.9 35 62 15 20 14 7 19 147 4
16.1 58 62 10 19 15 12 25 110 3
17.35 60 65 9 19 13 11 25 108 3
13.15 62 69 7 18 13 9 25 113 3
12.15 63 66 9 19 12 10 24 115 3
12.6 53 72 6 21 16 8 19 61 1
10.35 46 62 4 19 14 8 26 60 1
15.4 67 75 7 20 16 11 10 109 3
9.6 59 58 4 20 14 12 17 68 2
18.2 64 66 7 19 15 10 13 111 3
13.6 38 55 4 19 10 10 17 77 2
14.85 50 47 15 22 16 12 30 73 2
14.75 48 72 4 21 14 8 25 151 4
14.1 48 62 9 19 16 11 4 89 2
14.9 47 64 4 19 12 8 16 78 2
16.25 66 64 4 19 16 10 21 110 3
19.25 47 19 28 23 16 14 23 220 4
13.6 63 50 4 19 15 9 22 65 2
13.6 58 68 4 20 14 9 17 141 4
15.65 44 70 4 19 16 10 20 117 3
12.75 51 79 5 22 11 13 20 122 4
14.6 43 69 4 19 15 12 22 63 2
9.85 55 71 4 25 18 13 16 44 1
12.65 38 48 12 19 13 8 23 52 1
19.2 45 73 4 19 7 3 0 131 4
16.6 50 74 6 19 7 8 18 101 3
11.2 54 66 6 20 17 12 25 42 1
15.25 57 71 5 20 18 11 23 152 4
11.9 60 74 4 21 15 9 12 107 3
13.2 55 78 4 19 8 12 18 77 2
16.35 56 75 4 21 13 12 24 154 4
12.4 49 53 10 23 13 12 11 103 3
15.85 37 60 7 19 15 10 18 96 3
18.15 59 70 4 22 18 13 23 175 4
11.15 46 69 7 20 16 9 24 57 1
15.65 51 65 4 18 14 12 29 112 3
17.75 58 78 4 21 15 11 18 143 4
7.65 64 78 12 20 19 14 15 49 1
12.35 53 59 5 21 16 11 29 110 3
15.6 48 72 8 21 12 9 16 131 4
19.3 51 70 6 21 16 12 19 167 4
15.2 47 63 17 19 11 8 22 56 1
17.1 59 63 4 21 16 15 16 137 4
15.6 62 71 5 19 15 12 23 86 2
18.4 62 74 4 21 19 14 23 121 3
19.05 51 67 5 21 15 12 19 149 4
18.55 64 66 5 22 14 9 4 168 4
19.1 52 62 6 21 14 9 20 140 4
13.1 67 80 4 22 17 13 24 88 2
12.85 50 73 4 22 16 13 20 168 4
9.5 54 67 4 22 20 15 4 94 2
4.5 58 61 6 22 16 11 24 51 1
11.85 56 73 8 21 9 7 22 48 1
13.6 63 74 10 22 13 10 16 145 4
11.7 31 32 4 23 15 11 3 66 2
12.4 65 69 5 19 19 14 15 85 2
13.35 71 69 4 22 16 14 24 109 3
11.4 50 84 4 21 17 13 17 63 2
14.9 57 64 4 19 16 12 20 102 3
19.9 47 58 16 19 9 8 27 162 4
11.2 47 59 7 20 11 13 26 86 2
14.6 57 78 4 18 14 9 23 114 3
17.6 43 57 4 21 19 12 17 164 4
14.05 41 60 14 21 13 13 20 119 3
16.1 63 68 5 20 14 11 22 126 4
13.35 63 68 5 20 15 11 19 132 4
11.85 56 73 5 21 15 13 24 142 4
11.95 51 69 5 21 14 12 19 83 2
14.75 50 67 7 19 16 12 23 94 2
15.15 22 60 19 19 17 10 15 81 2
13.2 41 65 16 21 12 9 27 166 4
16.85 59 66 4 19 15 10 26 110 3
7.85 56 74 4 19 17 13 22 64 2
7.7 66 81 7 24 15 13 22 93 2
12.6 53 72 9 19 10 9 18 104 3
7.85 42 55 5 19 16 11 15 105 3
10.95 52 49 14 20 15 12 22 49 1
12.35 54 74 4 19 11 8 27 88 2
9.95 44 53 16 19 16 12 10 95 2
14.9 62 64 10 19 16 12 20 102 3
16.65 53 65 5 19 16 12 17 99 3
13.4 50 57 6 19 14 9 23 63 2
13.95 36 51 4 19 14 12 19 76 2
15.7 76 80 4 20 16 12 13 109 3
16.85 66 67 4 20 16 11 27 117 3
10.95 62 70 5 19 18 12 23 57 1
15.35 59 74 4 21 14 6 16 120 3
12.2 47 75 4 19 20 7 25 73 2
15.1 55 70 5 19 15 10 2 91 2
17.75 58 69 4 19 16 12 26 108 3
15.2 60 65 4 21 16 10 20 105 3
14.6 44 55 5 22 16 12 23 117 3
16.65 57 71 8 19 12 9 22 119 3
8.1 45 65 15 19 8 3 24 31 1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 17.1069 + 0.00806272AMS.I[t] -0.0387425AMS.E[t] -0.0503419AMS.A[t] -0.347781age[t] + 0.0360525CONFSTATTOT[t] -0.031956CONFSOFTTOT[t] + 0.0365603NUMERACYTOT[t] + 0.0137322LFM[t] + 1.59136PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  17.1069 +  0.00806272AMS.I[t] -0.0387425AMS.E[t] -0.0503419AMS.A[t] -0.347781age[t] +  0.0360525CONFSTATTOT[t] -0.031956CONFSOFTTOT[t] +  0.0365603NUMERACYTOT[t] +  0.0137322LFM[t] +  1.59136PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  17.1069 +  0.00806272AMS.I[t] -0.0387425AMS.E[t] -0.0503419AMS.A[t] -0.347781age[t] +  0.0360525CONFSTATTOT[t] -0.031956CONFSOFTTOT[t] +  0.0365603NUMERACYTOT[t] +  0.0137322LFM[t] +  1.59136PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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] = + 17.1069 + 0.00806272AMS.I[t] -0.0387425AMS.E[t] -0.0503419AMS.A[t] -0.347781age[t] + 0.0360525CONFSTATTOT[t] -0.031956CONFSOFTTOT[t] + 0.0365603NUMERACYTOT[t] + 0.0137322LFM[t] + 1.59136PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.10693.141845.4451.97586e-079.87929e-08
AMS.I0.008062720.01982450.40670.6847820.342391
AMS.E-0.03874250.0227574-1.7020.09066990.045335
AMS.A-0.05034190.0509776-0.98750.3249120.162456
age-0.3477810.120362-2.8890.004408860.00220443
CONFSTATTOT0.03605250.08907910.40470.6862350.343117
CONFSOFTTOT-0.0319560.100732-0.31720.7514870.375743
NUMERACYTOT0.03656030.03205061.1410.2557410.12787
LFM0.01373220.01413470.97150.3327880.166394
PR1.591360.5415352.9390.003797670.00189883

\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) & 17.1069 & 3.14184 & 5.445 & 1.97586e-07 & 9.87929e-08 \tabularnewline
AMS.I & 0.00806272 & 0.0198245 & 0.4067 & 0.684782 & 0.342391 \tabularnewline
AMS.E & -0.0387425 & 0.0227574 & -1.702 & 0.0906699 & 0.045335 \tabularnewline
AMS.A & -0.0503419 & 0.0509776 & -0.9875 & 0.324912 & 0.162456 \tabularnewline
age & -0.347781 & 0.120362 & -2.889 & 0.00440886 & 0.00220443 \tabularnewline
CONFSTATTOT & 0.0360525 & 0.0890791 & 0.4047 & 0.686235 & 0.343117 \tabularnewline
CONFSOFTTOT & -0.031956 & 0.100732 & -0.3172 & 0.751487 & 0.375743 \tabularnewline
NUMERACYTOT & 0.0365603 & 0.0320506 & 1.141 & 0.255741 & 0.12787 \tabularnewline
LFM & 0.0137322 & 0.0141347 & 0.9715 & 0.332788 & 0.166394 \tabularnewline
PR & 1.59136 & 0.541535 & 2.939 & 0.00379767 & 0.00189883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&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]17.1069[/C][C]3.14184[/C][C]5.445[/C][C]1.97586e-07[/C][C]9.87929e-08[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00806272[/C][C]0.0198245[/C][C]0.4067[/C][C]0.684782[/C][C]0.342391[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0387425[/C][C]0.0227574[/C][C]-1.702[/C][C]0.0906699[/C][C]0.045335[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0503419[/C][C]0.0509776[/C][C]-0.9875[/C][C]0.324912[/C][C]0.162456[/C][/ROW]
[ROW][C]age[/C][C]-0.347781[/C][C]0.120362[/C][C]-2.889[/C][C]0.00440886[/C][C]0.00220443[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.0360525[/C][C]0.0890791[/C][C]0.4047[/C][C]0.686235[/C][C]0.343117[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]-0.031956[/C][C]0.100732[/C][C]-0.3172[/C][C]0.751487[/C][C]0.375743[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0365603[/C][C]0.0320506[/C][C]1.141[/C][C]0.255741[/C][C]0.12787[/C][/ROW]
[ROW][C]LFM[/C][C]0.0137322[/C][C]0.0141347[/C][C]0.9715[/C][C]0.332788[/C][C]0.166394[/C][/ROW]
[ROW][C]PR[/C][C]1.59136[/C][C]0.541535[/C][C]2.939[/C][C]0.00379767[/C][C]0.00189883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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)17.10693.141845.4451.97586e-079.87929e-08
AMS.I0.008062720.01982450.40670.6847820.342391
AMS.E-0.03874250.0227574-1.7020.09066990.045335
AMS.A-0.05034190.0509776-0.98750.3249120.162456
age-0.3477810.120362-2.8890.004408860.00220443
CONFSTATTOT0.03605250.08907910.40470.6862350.343117
CONFSOFTTOT-0.0319560.100732-0.31720.7514870.375743
NUMERACYTOT0.03656030.03205061.1410.2557410.12787
LFM0.01373220.01413470.97150.3327880.166394
PR1.591360.5415352.9390.003797670.00189883






Multiple Linear Regression - Regression Statistics
Multiple R0.702832
R-squared0.493972
Adjusted R-squared0.464779
F-TEST (value)16.9204
F-TEST (DF numerator)9
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22986
Sum Squared Residuals775.672

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.702832 \tabularnewline
R-squared & 0.493972 \tabularnewline
Adjusted R-squared & 0.464779 \tabularnewline
F-TEST (value) & 16.9204 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22986 \tabularnewline
Sum Squared Residuals & 775.672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.702832[/C][/ROW]
[ROW][C]R-squared[/C][C]0.493972[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.464779[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.9204[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/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]2.22986[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]775.672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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 R0.702832
R-squared0.493972
Adjusted R-squared0.464779
F-TEST (value)16.9204
F-TEST (DF numerator)9
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22986
Sum Squared Residuals775.672






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.9777-6.62768
212.710.52142.17862
318.116.21451.88548
417.8517.26540.584583
516.616.08530.514741
612.612.46470.135325
717.117.6645-0.564521
819.117.17041.92962
916.116.9358-0.835799
1013.3512.25331.09668
1118.417.06811.33192
1214.710.29924.40076
1310.614.605-4.00504
1412.614.3771-1.7771
1516.216.203-0.00304215
1613.613.46360.136391
1718.916.66712.23291
1814.114.3462-0.246239
1914.515.1069-0.606896
2016.1516.7836-0.633586
2114.7513.41971.33031
2214.813.17951.62047
2312.4512.16190.288104
2412.6512.9758-0.325803
2517.3515.0772.27304
268.69.97927-1.37927
2718.415.91372.48632
2816.114.17821.92182
2911.612.0034-0.403385
3017.7516.41321.33677
3115.2514.4850.765048
3217.6515.75841.89156
3316.3516.8569-0.506876
3417.6517.16310.486937
3513.615.3398-1.73981
3614.3514.3983-0.048274
3714.7515.9991-1.24905
3818.2517.2291.02096
399.916.1336-6.23356
401614.52141.47863
4118.2517.25720.992778
4216.8517.1295-0.279475
4314.612.98811.61194
4413.8513.887-0.0370438
4518.9517.75691.1931
4615.614.56851.03147
4714.8516.0569-1.20686
4811.7513.5163-1.76625
4918.4515.73862.71136
5015.912.8353.06495
5117.116.97490.125094
5216.110.21755.88251
5319.918.24351.65646
5410.9510.31710.632877
5518.4517.06961.38035
5615.114.55510.544888
571515.5627-0.562675
5811.3512.7933-1.44325
5915.9514.75411.19585
6018.115.33842.76163
6114.616.8186-2.21863
6215.417.3397-1.93974
6315.417.2623-1.86225
6417.614.96252.63752
6513.3513.5448-0.194838
6619.116.69462.40537
6715.3517.183-1.83299
687.68.6491-1.0491
6913.413.6025-0.202478
7013.915.2574-1.35735
7119.116.62832.47174
7215.2515.09350.156545
7312.916.6361-3.73607
7416.115.41720.68282
7517.3515.29982.05019
7613.1515.742-2.592
7712.1515.3407-3.19072
7812.610.58422.01584
7910.3511.8815-1.53148
8015.414.29521.10479
819.613.0378-3.43783
8218.215.10053.09947
8313.613.37580.224183
8414.8512.75822.09184
8514.7516.8018-2.05177
8614.112.80741.2926
8714.913.21291.68711
8816.2515.660.590029
8919.2517.69811.55193
9013.614.0013-0.40133
9113.616.9234-3.32339
9215.6515.30970.340298
9312.7515.3077-2.55766
9414.612.98061.61936
959.858.917780.932216
9612.6511.7010.948956
9719.216.15323.04684
9816.614.5492.05097
9911.211.03910.160863
10015.2517.1995-1.94947
10111.914.1543-2.25427
10213.212.52230.677651
10316.3516.5908-0.240805
10412.413.6221-1.2221
10515.8515.09210.757888
10618.1516.8611.28895
10711.1511.03730.112695
10815.6516.032-0.381999
10917.7516.22431.52565
1107.6510.0915-2.44152
11112.3515.2635-2.91349
11215.615.8927-0.292654
11319.316.74742.5526
11415.210.8874.31297
11517.116.56630.533738
11615.613.35842.2416
11718.414.74923.65076
11819.0516.63072.41927
11918.5516.19882.35117
12019.116.7552.34505
12113.112.16120.938794
12212.8516.3943-3.54434
1239.511.9555-2.45548
1244.510.6525-6.15247
12511.8510.17971.67033
12613.615.684-2.08399
12711.712.3047-0.604738
12812.413.2342-0.834159
12913.3514.4314-1.08135
13011.411.6177-0.217723
13114.915.3771-0.477076
13219.917.47152.42852
13311.213.1874-1.98742
13414.615.4807-0.880693
13517.617.28110.31893
13614.0514.2974-0.247397
13716.116.8263-0.726266
13813.3516.835-3.48503
13911.8516.4933-4.64331
14011.9512.4281-0.478145
14114.7513.46181.28815
14215.1512.53212.61785
14313.216.5875-3.38747
14416.8515.67281.1772
1457.8512.9456-5.09562
1467.711.1912-3.49125
14712.614.6171-2.01707
1487.8515.4448-7.59483
14910.9511.1932-0.243239
15012.3513.3853-1.03534
1519.9513.0412-3.09124
15214.915.1153-0.215339
15316.6515.10491.54514
15413.413.4977-0.0976769
15513.9513.65430.295652
15615.714.40281.29719
15716.8515.47951.37051
15810.9511.5157-0.565707
15915.3514.53080.819215
16012.213.3675-1.16748
16115.112.70552.39449
16217.7515.49322.25682
16315.214.77210.427931
16414.614.8429-0.242923
16516.6515.16271.48726
1668.110.6755-2.57554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.9777 & -6.62768 \tabularnewline
2 & 12.7 & 10.5214 & 2.17862 \tabularnewline
3 & 18.1 & 16.2145 & 1.88548 \tabularnewline
4 & 17.85 & 17.2654 & 0.584583 \tabularnewline
5 & 16.6 & 16.0853 & 0.514741 \tabularnewline
6 & 12.6 & 12.4647 & 0.135325 \tabularnewline
7 & 17.1 & 17.6645 & -0.564521 \tabularnewline
8 & 19.1 & 17.1704 & 1.92962 \tabularnewline
9 & 16.1 & 16.9358 & -0.835799 \tabularnewline
10 & 13.35 & 12.2533 & 1.09668 \tabularnewline
11 & 18.4 & 17.0681 & 1.33192 \tabularnewline
12 & 14.7 & 10.2992 & 4.40076 \tabularnewline
13 & 10.6 & 14.605 & -4.00504 \tabularnewline
14 & 12.6 & 14.3771 & -1.7771 \tabularnewline
15 & 16.2 & 16.203 & -0.00304215 \tabularnewline
16 & 13.6 & 13.4636 & 0.136391 \tabularnewline
17 & 18.9 & 16.6671 & 2.23291 \tabularnewline
18 & 14.1 & 14.3462 & -0.246239 \tabularnewline
19 & 14.5 & 15.1069 & -0.606896 \tabularnewline
20 & 16.15 & 16.7836 & -0.633586 \tabularnewline
21 & 14.75 & 13.4197 & 1.33031 \tabularnewline
22 & 14.8 & 13.1795 & 1.62047 \tabularnewline
23 & 12.45 & 12.1619 & 0.288104 \tabularnewline
24 & 12.65 & 12.9758 & -0.325803 \tabularnewline
25 & 17.35 & 15.077 & 2.27304 \tabularnewline
26 & 8.6 & 9.97927 & -1.37927 \tabularnewline
27 & 18.4 & 15.9137 & 2.48632 \tabularnewline
28 & 16.1 & 14.1782 & 1.92182 \tabularnewline
29 & 11.6 & 12.0034 & -0.403385 \tabularnewline
30 & 17.75 & 16.4132 & 1.33677 \tabularnewline
31 & 15.25 & 14.485 & 0.765048 \tabularnewline
32 & 17.65 & 15.7584 & 1.89156 \tabularnewline
33 & 16.35 & 16.8569 & -0.506876 \tabularnewline
34 & 17.65 & 17.1631 & 0.486937 \tabularnewline
35 & 13.6 & 15.3398 & -1.73981 \tabularnewline
36 & 14.35 & 14.3983 & -0.048274 \tabularnewline
37 & 14.75 & 15.9991 & -1.24905 \tabularnewline
38 & 18.25 & 17.229 & 1.02096 \tabularnewline
39 & 9.9 & 16.1336 & -6.23356 \tabularnewline
40 & 16 & 14.5214 & 1.47863 \tabularnewline
41 & 18.25 & 17.2572 & 0.992778 \tabularnewline
42 & 16.85 & 17.1295 & -0.279475 \tabularnewline
43 & 14.6 & 12.9881 & 1.61194 \tabularnewline
44 & 13.85 & 13.887 & -0.0370438 \tabularnewline
45 & 18.95 & 17.7569 & 1.1931 \tabularnewline
46 & 15.6 & 14.5685 & 1.03147 \tabularnewline
47 & 14.85 & 16.0569 & -1.20686 \tabularnewline
48 & 11.75 & 13.5163 & -1.76625 \tabularnewline
49 & 18.45 & 15.7386 & 2.71136 \tabularnewline
50 & 15.9 & 12.835 & 3.06495 \tabularnewline
51 & 17.1 & 16.9749 & 0.125094 \tabularnewline
52 & 16.1 & 10.2175 & 5.88251 \tabularnewline
53 & 19.9 & 18.2435 & 1.65646 \tabularnewline
54 & 10.95 & 10.3171 & 0.632877 \tabularnewline
55 & 18.45 & 17.0696 & 1.38035 \tabularnewline
56 & 15.1 & 14.5551 & 0.544888 \tabularnewline
57 & 15 & 15.5627 & -0.562675 \tabularnewline
58 & 11.35 & 12.7933 & -1.44325 \tabularnewline
59 & 15.95 & 14.7541 & 1.19585 \tabularnewline
60 & 18.1 & 15.3384 & 2.76163 \tabularnewline
61 & 14.6 & 16.8186 & -2.21863 \tabularnewline
62 & 15.4 & 17.3397 & -1.93974 \tabularnewline
63 & 15.4 & 17.2623 & -1.86225 \tabularnewline
64 & 17.6 & 14.9625 & 2.63752 \tabularnewline
65 & 13.35 & 13.5448 & -0.194838 \tabularnewline
66 & 19.1 & 16.6946 & 2.40537 \tabularnewline
67 & 15.35 & 17.183 & -1.83299 \tabularnewline
68 & 7.6 & 8.6491 & -1.0491 \tabularnewline
69 & 13.4 & 13.6025 & -0.202478 \tabularnewline
70 & 13.9 & 15.2574 & -1.35735 \tabularnewline
71 & 19.1 & 16.6283 & 2.47174 \tabularnewline
72 & 15.25 & 15.0935 & 0.156545 \tabularnewline
73 & 12.9 & 16.6361 & -3.73607 \tabularnewline
74 & 16.1 & 15.4172 & 0.68282 \tabularnewline
75 & 17.35 & 15.2998 & 2.05019 \tabularnewline
76 & 13.15 & 15.742 & -2.592 \tabularnewline
77 & 12.15 & 15.3407 & -3.19072 \tabularnewline
78 & 12.6 & 10.5842 & 2.01584 \tabularnewline
79 & 10.35 & 11.8815 & -1.53148 \tabularnewline
80 & 15.4 & 14.2952 & 1.10479 \tabularnewline
81 & 9.6 & 13.0378 & -3.43783 \tabularnewline
82 & 18.2 & 15.1005 & 3.09947 \tabularnewline
83 & 13.6 & 13.3758 & 0.224183 \tabularnewline
84 & 14.85 & 12.7582 & 2.09184 \tabularnewline
85 & 14.75 & 16.8018 & -2.05177 \tabularnewline
86 & 14.1 & 12.8074 & 1.2926 \tabularnewline
87 & 14.9 & 13.2129 & 1.68711 \tabularnewline
88 & 16.25 & 15.66 & 0.590029 \tabularnewline
89 & 19.25 & 17.6981 & 1.55193 \tabularnewline
90 & 13.6 & 14.0013 & -0.40133 \tabularnewline
91 & 13.6 & 16.9234 & -3.32339 \tabularnewline
92 & 15.65 & 15.3097 & 0.340298 \tabularnewline
93 & 12.75 & 15.3077 & -2.55766 \tabularnewline
94 & 14.6 & 12.9806 & 1.61936 \tabularnewline
95 & 9.85 & 8.91778 & 0.932216 \tabularnewline
96 & 12.65 & 11.701 & 0.948956 \tabularnewline
97 & 19.2 & 16.1532 & 3.04684 \tabularnewline
98 & 16.6 & 14.549 & 2.05097 \tabularnewline
99 & 11.2 & 11.0391 & 0.160863 \tabularnewline
100 & 15.25 & 17.1995 & -1.94947 \tabularnewline
101 & 11.9 & 14.1543 & -2.25427 \tabularnewline
102 & 13.2 & 12.5223 & 0.677651 \tabularnewline
103 & 16.35 & 16.5908 & -0.240805 \tabularnewline
104 & 12.4 & 13.6221 & -1.2221 \tabularnewline
105 & 15.85 & 15.0921 & 0.757888 \tabularnewline
106 & 18.15 & 16.861 & 1.28895 \tabularnewline
107 & 11.15 & 11.0373 & 0.112695 \tabularnewline
108 & 15.65 & 16.032 & -0.381999 \tabularnewline
109 & 17.75 & 16.2243 & 1.52565 \tabularnewline
110 & 7.65 & 10.0915 & -2.44152 \tabularnewline
111 & 12.35 & 15.2635 & -2.91349 \tabularnewline
112 & 15.6 & 15.8927 & -0.292654 \tabularnewline
113 & 19.3 & 16.7474 & 2.5526 \tabularnewline
114 & 15.2 & 10.887 & 4.31297 \tabularnewline
115 & 17.1 & 16.5663 & 0.533738 \tabularnewline
116 & 15.6 & 13.3584 & 2.2416 \tabularnewline
117 & 18.4 & 14.7492 & 3.65076 \tabularnewline
118 & 19.05 & 16.6307 & 2.41927 \tabularnewline
119 & 18.55 & 16.1988 & 2.35117 \tabularnewline
120 & 19.1 & 16.755 & 2.34505 \tabularnewline
121 & 13.1 & 12.1612 & 0.938794 \tabularnewline
122 & 12.85 & 16.3943 & -3.54434 \tabularnewline
123 & 9.5 & 11.9555 & -2.45548 \tabularnewline
124 & 4.5 & 10.6525 & -6.15247 \tabularnewline
125 & 11.85 & 10.1797 & 1.67033 \tabularnewline
126 & 13.6 & 15.684 & -2.08399 \tabularnewline
127 & 11.7 & 12.3047 & -0.604738 \tabularnewline
128 & 12.4 & 13.2342 & -0.834159 \tabularnewline
129 & 13.35 & 14.4314 & -1.08135 \tabularnewline
130 & 11.4 & 11.6177 & -0.217723 \tabularnewline
131 & 14.9 & 15.3771 & -0.477076 \tabularnewline
132 & 19.9 & 17.4715 & 2.42852 \tabularnewline
133 & 11.2 & 13.1874 & -1.98742 \tabularnewline
134 & 14.6 & 15.4807 & -0.880693 \tabularnewline
135 & 17.6 & 17.2811 & 0.31893 \tabularnewline
136 & 14.05 & 14.2974 & -0.247397 \tabularnewline
137 & 16.1 & 16.8263 & -0.726266 \tabularnewline
138 & 13.35 & 16.835 & -3.48503 \tabularnewline
139 & 11.85 & 16.4933 & -4.64331 \tabularnewline
140 & 11.95 & 12.4281 & -0.478145 \tabularnewline
141 & 14.75 & 13.4618 & 1.28815 \tabularnewline
142 & 15.15 & 12.5321 & 2.61785 \tabularnewline
143 & 13.2 & 16.5875 & -3.38747 \tabularnewline
144 & 16.85 & 15.6728 & 1.1772 \tabularnewline
145 & 7.85 & 12.9456 & -5.09562 \tabularnewline
146 & 7.7 & 11.1912 & -3.49125 \tabularnewline
147 & 12.6 & 14.6171 & -2.01707 \tabularnewline
148 & 7.85 & 15.4448 & -7.59483 \tabularnewline
149 & 10.95 & 11.1932 & -0.243239 \tabularnewline
150 & 12.35 & 13.3853 & -1.03534 \tabularnewline
151 & 9.95 & 13.0412 & -3.09124 \tabularnewline
152 & 14.9 & 15.1153 & -0.215339 \tabularnewline
153 & 16.65 & 15.1049 & 1.54514 \tabularnewline
154 & 13.4 & 13.4977 & -0.0976769 \tabularnewline
155 & 13.95 & 13.6543 & 0.295652 \tabularnewline
156 & 15.7 & 14.4028 & 1.29719 \tabularnewline
157 & 16.85 & 15.4795 & 1.37051 \tabularnewline
158 & 10.95 & 11.5157 & -0.565707 \tabularnewline
159 & 15.35 & 14.5308 & 0.819215 \tabularnewline
160 & 12.2 & 13.3675 & -1.16748 \tabularnewline
161 & 15.1 & 12.7055 & 2.39449 \tabularnewline
162 & 17.75 & 15.4932 & 2.25682 \tabularnewline
163 & 15.2 & 14.7721 & 0.427931 \tabularnewline
164 & 14.6 & 14.8429 & -0.242923 \tabularnewline
165 & 16.65 & 15.1627 & 1.48726 \tabularnewline
166 & 8.1 & 10.6755 & -2.57554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&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]4.35[/C][C]10.9777[/C][C]-6.62768[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.5214[/C][C]2.17862[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]16.2145[/C][C]1.88548[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.2654[/C][C]0.584583[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.0853[/C][C]0.514741[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.4647[/C][C]0.135325[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]17.6645[/C][C]-0.564521[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.1704[/C][C]1.92962[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.9358[/C][C]-0.835799[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]12.2533[/C][C]1.09668[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.0681[/C][C]1.33192[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.2992[/C][C]4.40076[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]14.605[/C][C]-4.00504[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]14.3771[/C][C]-1.7771[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]16.203[/C][C]-0.00304215[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.4636[/C][C]0.136391[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.6671[/C][C]2.23291[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]14.3462[/C][C]-0.246239[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]15.1069[/C][C]-0.606896[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]16.7836[/C][C]-0.633586[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.4197[/C][C]1.33031[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.1795[/C][C]1.62047[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.1619[/C][C]0.288104[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9758[/C][C]-0.325803[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]15.077[/C][C]2.27304[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.97927[/C][C]-1.37927[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]15.9137[/C][C]2.48632[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.1782[/C][C]1.92182[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.0034[/C][C]-0.403385[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.4132[/C][C]1.33677[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.485[/C][C]0.765048[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.7584[/C][C]1.89156[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.8569[/C][C]-0.506876[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]17.1631[/C][C]0.486937[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]15.3398[/C][C]-1.73981[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.3983[/C][C]-0.048274[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.9991[/C][C]-1.24905[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.229[/C][C]1.02096[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]16.1336[/C][C]-6.23356[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5214[/C][C]1.47863[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]17.2572[/C][C]0.992778[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]17.1295[/C][C]-0.279475[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]12.9881[/C][C]1.61194[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]13.887[/C][C]-0.0370438[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.7569[/C][C]1.1931[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.5685[/C][C]1.03147[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.0569[/C][C]-1.20686[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]13.5163[/C][C]-1.76625[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]15.7386[/C][C]2.71136[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.835[/C][C]3.06495[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]16.9749[/C][C]0.125094[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]10.2175[/C][C]5.88251[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.2435[/C][C]1.65646[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.3171[/C][C]0.632877[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]17.0696[/C][C]1.38035[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.5551[/C][C]0.544888[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.5627[/C][C]-0.562675[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]12.7933[/C][C]-1.44325[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.7541[/C][C]1.19585[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.3384[/C][C]2.76163[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.8186[/C][C]-2.21863[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]17.3397[/C][C]-1.93974[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]17.2623[/C][C]-1.86225[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.9625[/C][C]2.63752[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]13.5448[/C][C]-0.194838[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.6946[/C][C]2.40537[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.183[/C][C]-1.83299[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]8.6491[/C][C]-1.0491[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]13.6025[/C][C]-0.202478[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]15.2574[/C][C]-1.35735[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.6283[/C][C]2.47174[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.0935[/C][C]0.156545[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.6361[/C][C]-3.73607[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.4172[/C][C]0.68282[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.2998[/C][C]2.05019[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.742[/C][C]-2.592[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.3407[/C][C]-3.19072[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.5842[/C][C]2.01584[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]11.8815[/C][C]-1.53148[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]14.2952[/C][C]1.10479[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]13.0378[/C][C]-3.43783[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.1005[/C][C]3.09947[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.3758[/C][C]0.224183[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]12.7582[/C][C]2.09184[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.8018[/C][C]-2.05177[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]12.8074[/C][C]1.2926[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.2129[/C][C]1.68711[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.66[/C][C]0.590029[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]17.6981[/C][C]1.55193[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]14.0013[/C][C]-0.40133[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.9234[/C][C]-3.32339[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]15.3097[/C][C]0.340298[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]15.3077[/C][C]-2.55766[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]12.9806[/C][C]1.61936[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]8.91778[/C][C]0.932216[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.701[/C][C]0.948956[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]16.1532[/C][C]3.04684[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]14.549[/C][C]2.05097[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.0391[/C][C]0.160863[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]17.1995[/C][C]-1.94947[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]14.1543[/C][C]-2.25427[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]12.5223[/C][C]0.677651[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.5908[/C][C]-0.240805[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]13.6221[/C][C]-1.2221[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]15.0921[/C][C]0.757888[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]16.861[/C][C]1.28895[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.0373[/C][C]0.112695[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.032[/C][C]-0.381999[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]16.2243[/C][C]1.52565[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]10.0915[/C][C]-2.44152[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]15.2635[/C][C]-2.91349[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]15.8927[/C][C]-0.292654[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]16.7474[/C][C]2.5526[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]10.887[/C][C]4.31297[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]16.5663[/C][C]0.533738[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.3584[/C][C]2.2416[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.7492[/C][C]3.65076[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.6307[/C][C]2.41927[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]16.1988[/C][C]2.35117[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.755[/C][C]2.34505[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]12.1612[/C][C]0.938794[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.3943[/C][C]-3.54434[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]11.9555[/C][C]-2.45548[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.6525[/C][C]-6.15247[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]10.1797[/C][C]1.67033[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]15.684[/C][C]-2.08399[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]12.3047[/C][C]-0.604738[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.2342[/C][C]-0.834159[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.4314[/C][C]-1.08135[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]11.6177[/C][C]-0.217723[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]15.3771[/C][C]-0.477076[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.4715[/C][C]2.42852[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.1874[/C][C]-1.98742[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]15.4807[/C][C]-0.880693[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.2811[/C][C]0.31893[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]14.2974[/C][C]-0.247397[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]16.8263[/C][C]-0.726266[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]16.835[/C][C]-3.48503[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]16.4933[/C][C]-4.64331[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.4281[/C][C]-0.478145[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]13.4618[/C][C]1.28815[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.5321[/C][C]2.61785[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]16.5875[/C][C]-3.38747[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]15.6728[/C][C]1.1772[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.9456[/C][C]-5.09562[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]11.1912[/C][C]-3.49125[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.6171[/C][C]-2.01707[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]15.4448[/C][C]-7.59483[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.1932[/C][C]-0.243239[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]13.3853[/C][C]-1.03534[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.0412[/C][C]-3.09124[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]15.1153[/C][C]-0.215339[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.1049[/C][C]1.54514[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]13.4977[/C][C]-0.0976769[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]13.6543[/C][C]0.295652[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]14.4028[/C][C]1.29719[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.4795[/C][C]1.37051[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]11.5157[/C][C]-0.565707[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]14.5308[/C][C]0.819215[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]13.3675[/C][C]-1.16748[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]12.7055[/C][C]2.39449[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.4932[/C][C]2.25682[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.7721[/C][C]0.427931[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]14.8429[/C][C]-0.242923[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.1627[/C][C]1.48726[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]10.6755[/C][C]-2.57554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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
14.3510.9777-6.62768
212.710.52142.17862
318.116.21451.88548
417.8517.26540.584583
516.616.08530.514741
612.612.46470.135325
717.117.6645-0.564521
819.117.17041.92962
916.116.9358-0.835799
1013.3512.25331.09668
1118.417.06811.33192
1214.710.29924.40076
1310.614.605-4.00504
1412.614.3771-1.7771
1516.216.203-0.00304215
1613.613.46360.136391
1718.916.66712.23291
1814.114.3462-0.246239
1914.515.1069-0.606896
2016.1516.7836-0.633586
2114.7513.41971.33031
2214.813.17951.62047
2312.4512.16190.288104
2412.6512.9758-0.325803
2517.3515.0772.27304
268.69.97927-1.37927
2718.415.91372.48632
2816.114.17821.92182
2911.612.0034-0.403385
3017.7516.41321.33677
3115.2514.4850.765048
3217.6515.75841.89156
3316.3516.8569-0.506876
3417.6517.16310.486937
3513.615.3398-1.73981
3614.3514.3983-0.048274
3714.7515.9991-1.24905
3818.2517.2291.02096
399.916.1336-6.23356
401614.52141.47863
4118.2517.25720.992778
4216.8517.1295-0.279475
4314.612.98811.61194
4413.8513.887-0.0370438
4518.9517.75691.1931
4615.614.56851.03147
4714.8516.0569-1.20686
4811.7513.5163-1.76625
4918.4515.73862.71136
5015.912.8353.06495
5117.116.97490.125094
5216.110.21755.88251
5319.918.24351.65646
5410.9510.31710.632877
5518.4517.06961.38035
5615.114.55510.544888
571515.5627-0.562675
5811.3512.7933-1.44325
5915.9514.75411.19585
6018.115.33842.76163
6114.616.8186-2.21863
6215.417.3397-1.93974
6315.417.2623-1.86225
6417.614.96252.63752
6513.3513.5448-0.194838
6619.116.69462.40537
6715.3517.183-1.83299
687.68.6491-1.0491
6913.413.6025-0.202478
7013.915.2574-1.35735
7119.116.62832.47174
7215.2515.09350.156545
7312.916.6361-3.73607
7416.115.41720.68282
7517.3515.29982.05019
7613.1515.742-2.592
7712.1515.3407-3.19072
7812.610.58422.01584
7910.3511.8815-1.53148
8015.414.29521.10479
819.613.0378-3.43783
8218.215.10053.09947
8313.613.37580.224183
8414.8512.75822.09184
8514.7516.8018-2.05177
8614.112.80741.2926
8714.913.21291.68711
8816.2515.660.590029
8919.2517.69811.55193
9013.614.0013-0.40133
9113.616.9234-3.32339
9215.6515.30970.340298
9312.7515.3077-2.55766
9414.612.98061.61936
959.858.917780.932216
9612.6511.7010.948956
9719.216.15323.04684
9816.614.5492.05097
9911.211.03910.160863
10015.2517.1995-1.94947
10111.914.1543-2.25427
10213.212.52230.677651
10316.3516.5908-0.240805
10412.413.6221-1.2221
10515.8515.09210.757888
10618.1516.8611.28895
10711.1511.03730.112695
10815.6516.032-0.381999
10917.7516.22431.52565
1107.6510.0915-2.44152
11112.3515.2635-2.91349
11215.615.8927-0.292654
11319.316.74742.5526
11415.210.8874.31297
11517.116.56630.533738
11615.613.35842.2416
11718.414.74923.65076
11819.0516.63072.41927
11918.5516.19882.35117
12019.116.7552.34505
12113.112.16120.938794
12212.8516.3943-3.54434
1239.511.9555-2.45548
1244.510.6525-6.15247
12511.8510.17971.67033
12613.615.684-2.08399
12711.712.3047-0.604738
12812.413.2342-0.834159
12913.3514.4314-1.08135
13011.411.6177-0.217723
13114.915.3771-0.477076
13219.917.47152.42852
13311.213.1874-1.98742
13414.615.4807-0.880693
13517.617.28110.31893
13614.0514.2974-0.247397
13716.116.8263-0.726266
13813.3516.835-3.48503
13911.8516.4933-4.64331
14011.9512.4281-0.478145
14114.7513.46181.28815
14215.1512.53212.61785
14313.216.5875-3.38747
14416.8515.67281.1772
1457.8512.9456-5.09562
1467.711.1912-3.49125
14712.614.6171-2.01707
1487.8515.4448-7.59483
14910.9511.1932-0.243239
15012.3513.3853-1.03534
1519.9513.0412-3.09124
15214.915.1153-0.215339
15316.6515.10491.54514
15413.413.4977-0.0976769
15513.9513.65430.295652
15615.714.40281.29719
15716.8515.47951.37051
15810.9511.5157-0.565707
15915.3514.53080.819215
16012.213.3675-1.16748
16115.112.70552.39449
16217.7515.49322.25682
16315.214.77210.427931
16414.614.8429-0.242923
16516.6515.16271.48726
1668.110.6755-2.57554






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8606490.2787020.139351
140.7605970.4788050.239403
150.6429050.714190.357095
160.5432090.9135820.456791
170.4291960.8583920.570804
180.6262190.7475620.373781
190.5743440.8513120.425656
200.4878540.9757080.512146
210.4001370.8002750.599863
220.4420530.8841050.557947
230.3897520.7795050.610248
240.3858080.7716150.614192
250.4525390.9050780.547461
260.4157140.8314280.584286
270.356110.7122190.64389
280.4320930.8641870.567907
290.397440.794880.60256
300.3394530.6789070.660547
310.2829630.5659260.717037
320.241560.4831190.75844
330.2072480.4144950.792752
340.1714320.3428650.828568
350.1378260.2756520.862174
360.1060450.212090.893955
370.08014220.1602840.919858
380.06017930.1203590.939821
390.2058570.4117140.794143
400.1993470.3986940.800653
410.1639920.3279840.836008
420.1326060.2652120.867394
430.1170050.2340090.882995
440.1531580.3063160.846842
450.1304360.2608710.869564
460.1050680.2101360.894932
470.09675960.1935190.90324
480.08087560.1617510.919124
490.09365840.1873170.906342
500.171240.3424790.82876
510.1411560.2823120.858844
520.3229430.6458870.677057
530.2943210.5886410.705679
540.2585760.5171520.741424
550.2302370.4604740.769763
560.2149490.4298970.785051
570.1802290.3604570.819771
580.1563580.3127170.843642
590.1454590.2909190.854541
600.2215620.4431240.778438
610.2721670.5443340.727833
620.2687740.5375490.731226
630.2600010.5200010.739999
640.2898670.5797330.710133
650.2608220.5216440.739178
660.2795470.5590950.720453
670.2679960.5359930.732004
680.2551180.5102360.744882
690.2193020.4386050.780698
700.1968110.3936220.803189
710.2022110.4044220.797789
720.1706090.3412180.829391
730.2086520.4173040.791348
740.179530.359060.82047
750.1720580.3441160.827942
760.1893180.3786360.810682
770.2337910.4675820.766209
780.2479270.4958530.752073
790.2316220.4632440.768378
800.2152120.4304230.784788
810.2649150.5298290.735085
820.3223170.6446330.677683
830.2825870.5651740.717413
840.3145260.6290520.685474
850.3126710.6253420.687329
860.2970990.5941980.702901
870.2769010.5538030.723099
880.2414940.4829880.758506
890.22990.4598010.7701
900.1967090.3934180.803291
910.2366670.4733340.763333
920.2025350.4050710.797465
930.2044360.4088710.795564
940.1952660.3905330.804734
950.2052890.4105780.794711
960.1777660.3555320.822234
970.2050860.4101730.794914
980.1947410.3894810.805259
990.1720940.3441880.827906
1000.1664160.3328310.833584
1010.1651210.3302430.834879
1020.1386180.2772360.861382
1030.1138710.2277410.886129
1040.09850130.1970030.901499
1050.08434540.1686910.915655
1060.07071690.1414340.929283
1070.05612850.1122570.943871
1080.04418540.08837080.955815
1090.03942880.07885760.960571
1100.03837480.07674960.961625
1110.04034040.08068080.95966
1120.03134320.06268640.968657
1130.03204940.06409880.967951
1140.06178630.1235730.938214
1150.05124010.102480.94876
1160.04993520.09987040.950065
1170.08863740.1772750.911363
1180.1031650.206330.896835
1190.09448740.1889750.905513
1200.1106720.2213440.889328
1210.11110.22220.8889
1220.1267050.2534110.873295
1230.1205350.2410710.879465
1240.2947780.5895550.705222
1250.2899060.5798130.710094
1260.2590460.5180930.740954
1270.2218480.4436970.778152
1280.1915840.3831680.808416
1290.1576520.3153040.842348
1300.1651360.3302720.834864
1310.1313820.2627630.868618
1320.1242480.2484960.875752
1330.1019820.2039640.898018
1340.08062550.1612510.919375
1350.05956070.1191210.940439
1360.05305390.1061080.946946
1370.04141820.08283630.958582
1380.04255750.0851150.957442
1390.05546170.1109230.944538
1400.04125380.08250760.958746
1410.03124270.06248540.968757
1420.2643930.5287860.735607
1430.2207290.4414570.779271
1440.16660.3331990.8334
1450.2131730.4263460.786827
1460.252410.504820.74759
1470.2266730.4533450.773327
1480.9714550.05709070.0285454
1490.9861280.02774440.0138722
1500.9983240.003352690.00167634
1510.9962060.007588620.00379431
1520.9935570.01288650.00644323
1530.9718090.05638110.0281905

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.860649 & 0.278702 & 0.139351 \tabularnewline
14 & 0.760597 & 0.478805 & 0.239403 \tabularnewline
15 & 0.642905 & 0.71419 & 0.357095 \tabularnewline
16 & 0.543209 & 0.913582 & 0.456791 \tabularnewline
17 & 0.429196 & 0.858392 & 0.570804 \tabularnewline
18 & 0.626219 & 0.747562 & 0.373781 \tabularnewline
19 & 0.574344 & 0.851312 & 0.425656 \tabularnewline
20 & 0.487854 & 0.975708 & 0.512146 \tabularnewline
21 & 0.400137 & 0.800275 & 0.599863 \tabularnewline
22 & 0.442053 & 0.884105 & 0.557947 \tabularnewline
23 & 0.389752 & 0.779505 & 0.610248 \tabularnewline
24 & 0.385808 & 0.771615 & 0.614192 \tabularnewline
25 & 0.452539 & 0.905078 & 0.547461 \tabularnewline
26 & 0.415714 & 0.831428 & 0.584286 \tabularnewline
27 & 0.35611 & 0.712219 & 0.64389 \tabularnewline
28 & 0.432093 & 0.864187 & 0.567907 \tabularnewline
29 & 0.39744 & 0.79488 & 0.60256 \tabularnewline
30 & 0.339453 & 0.678907 & 0.660547 \tabularnewline
31 & 0.282963 & 0.565926 & 0.717037 \tabularnewline
32 & 0.24156 & 0.483119 & 0.75844 \tabularnewline
33 & 0.207248 & 0.414495 & 0.792752 \tabularnewline
34 & 0.171432 & 0.342865 & 0.828568 \tabularnewline
35 & 0.137826 & 0.275652 & 0.862174 \tabularnewline
36 & 0.106045 & 0.21209 & 0.893955 \tabularnewline
37 & 0.0801422 & 0.160284 & 0.919858 \tabularnewline
38 & 0.0601793 & 0.120359 & 0.939821 \tabularnewline
39 & 0.205857 & 0.411714 & 0.794143 \tabularnewline
40 & 0.199347 & 0.398694 & 0.800653 \tabularnewline
41 & 0.163992 & 0.327984 & 0.836008 \tabularnewline
42 & 0.132606 & 0.265212 & 0.867394 \tabularnewline
43 & 0.117005 & 0.234009 & 0.882995 \tabularnewline
44 & 0.153158 & 0.306316 & 0.846842 \tabularnewline
45 & 0.130436 & 0.260871 & 0.869564 \tabularnewline
46 & 0.105068 & 0.210136 & 0.894932 \tabularnewline
47 & 0.0967596 & 0.193519 & 0.90324 \tabularnewline
48 & 0.0808756 & 0.161751 & 0.919124 \tabularnewline
49 & 0.0936584 & 0.187317 & 0.906342 \tabularnewline
50 & 0.17124 & 0.342479 & 0.82876 \tabularnewline
51 & 0.141156 & 0.282312 & 0.858844 \tabularnewline
52 & 0.322943 & 0.645887 & 0.677057 \tabularnewline
53 & 0.294321 & 0.588641 & 0.705679 \tabularnewline
54 & 0.258576 & 0.517152 & 0.741424 \tabularnewline
55 & 0.230237 & 0.460474 & 0.769763 \tabularnewline
56 & 0.214949 & 0.429897 & 0.785051 \tabularnewline
57 & 0.180229 & 0.360457 & 0.819771 \tabularnewline
58 & 0.156358 & 0.312717 & 0.843642 \tabularnewline
59 & 0.145459 & 0.290919 & 0.854541 \tabularnewline
60 & 0.221562 & 0.443124 & 0.778438 \tabularnewline
61 & 0.272167 & 0.544334 & 0.727833 \tabularnewline
62 & 0.268774 & 0.537549 & 0.731226 \tabularnewline
63 & 0.260001 & 0.520001 & 0.739999 \tabularnewline
64 & 0.289867 & 0.579733 & 0.710133 \tabularnewline
65 & 0.260822 & 0.521644 & 0.739178 \tabularnewline
66 & 0.279547 & 0.559095 & 0.720453 \tabularnewline
67 & 0.267996 & 0.535993 & 0.732004 \tabularnewline
68 & 0.255118 & 0.510236 & 0.744882 \tabularnewline
69 & 0.219302 & 0.438605 & 0.780698 \tabularnewline
70 & 0.196811 & 0.393622 & 0.803189 \tabularnewline
71 & 0.202211 & 0.404422 & 0.797789 \tabularnewline
72 & 0.170609 & 0.341218 & 0.829391 \tabularnewline
73 & 0.208652 & 0.417304 & 0.791348 \tabularnewline
74 & 0.17953 & 0.35906 & 0.82047 \tabularnewline
75 & 0.172058 & 0.344116 & 0.827942 \tabularnewline
76 & 0.189318 & 0.378636 & 0.810682 \tabularnewline
77 & 0.233791 & 0.467582 & 0.766209 \tabularnewline
78 & 0.247927 & 0.495853 & 0.752073 \tabularnewline
79 & 0.231622 & 0.463244 & 0.768378 \tabularnewline
80 & 0.215212 & 0.430423 & 0.784788 \tabularnewline
81 & 0.264915 & 0.529829 & 0.735085 \tabularnewline
82 & 0.322317 & 0.644633 & 0.677683 \tabularnewline
83 & 0.282587 & 0.565174 & 0.717413 \tabularnewline
84 & 0.314526 & 0.629052 & 0.685474 \tabularnewline
85 & 0.312671 & 0.625342 & 0.687329 \tabularnewline
86 & 0.297099 & 0.594198 & 0.702901 \tabularnewline
87 & 0.276901 & 0.553803 & 0.723099 \tabularnewline
88 & 0.241494 & 0.482988 & 0.758506 \tabularnewline
89 & 0.2299 & 0.459801 & 0.7701 \tabularnewline
90 & 0.196709 & 0.393418 & 0.803291 \tabularnewline
91 & 0.236667 & 0.473334 & 0.763333 \tabularnewline
92 & 0.202535 & 0.405071 & 0.797465 \tabularnewline
93 & 0.204436 & 0.408871 & 0.795564 \tabularnewline
94 & 0.195266 & 0.390533 & 0.804734 \tabularnewline
95 & 0.205289 & 0.410578 & 0.794711 \tabularnewline
96 & 0.177766 & 0.355532 & 0.822234 \tabularnewline
97 & 0.205086 & 0.410173 & 0.794914 \tabularnewline
98 & 0.194741 & 0.389481 & 0.805259 \tabularnewline
99 & 0.172094 & 0.344188 & 0.827906 \tabularnewline
100 & 0.166416 & 0.332831 & 0.833584 \tabularnewline
101 & 0.165121 & 0.330243 & 0.834879 \tabularnewline
102 & 0.138618 & 0.277236 & 0.861382 \tabularnewline
103 & 0.113871 & 0.227741 & 0.886129 \tabularnewline
104 & 0.0985013 & 0.197003 & 0.901499 \tabularnewline
105 & 0.0843454 & 0.168691 & 0.915655 \tabularnewline
106 & 0.0707169 & 0.141434 & 0.929283 \tabularnewline
107 & 0.0561285 & 0.112257 & 0.943871 \tabularnewline
108 & 0.0441854 & 0.0883708 & 0.955815 \tabularnewline
109 & 0.0394288 & 0.0788576 & 0.960571 \tabularnewline
110 & 0.0383748 & 0.0767496 & 0.961625 \tabularnewline
111 & 0.0403404 & 0.0806808 & 0.95966 \tabularnewline
112 & 0.0313432 & 0.0626864 & 0.968657 \tabularnewline
113 & 0.0320494 & 0.0640988 & 0.967951 \tabularnewline
114 & 0.0617863 & 0.123573 & 0.938214 \tabularnewline
115 & 0.0512401 & 0.10248 & 0.94876 \tabularnewline
116 & 0.0499352 & 0.0998704 & 0.950065 \tabularnewline
117 & 0.0886374 & 0.177275 & 0.911363 \tabularnewline
118 & 0.103165 & 0.20633 & 0.896835 \tabularnewline
119 & 0.0944874 & 0.188975 & 0.905513 \tabularnewline
120 & 0.110672 & 0.221344 & 0.889328 \tabularnewline
121 & 0.1111 & 0.2222 & 0.8889 \tabularnewline
122 & 0.126705 & 0.253411 & 0.873295 \tabularnewline
123 & 0.120535 & 0.241071 & 0.879465 \tabularnewline
124 & 0.294778 & 0.589555 & 0.705222 \tabularnewline
125 & 0.289906 & 0.579813 & 0.710094 \tabularnewline
126 & 0.259046 & 0.518093 & 0.740954 \tabularnewline
127 & 0.221848 & 0.443697 & 0.778152 \tabularnewline
128 & 0.191584 & 0.383168 & 0.808416 \tabularnewline
129 & 0.157652 & 0.315304 & 0.842348 \tabularnewline
130 & 0.165136 & 0.330272 & 0.834864 \tabularnewline
131 & 0.131382 & 0.262763 & 0.868618 \tabularnewline
132 & 0.124248 & 0.248496 & 0.875752 \tabularnewline
133 & 0.101982 & 0.203964 & 0.898018 \tabularnewline
134 & 0.0806255 & 0.161251 & 0.919375 \tabularnewline
135 & 0.0595607 & 0.119121 & 0.940439 \tabularnewline
136 & 0.0530539 & 0.106108 & 0.946946 \tabularnewline
137 & 0.0414182 & 0.0828363 & 0.958582 \tabularnewline
138 & 0.0425575 & 0.085115 & 0.957442 \tabularnewline
139 & 0.0554617 & 0.110923 & 0.944538 \tabularnewline
140 & 0.0412538 & 0.0825076 & 0.958746 \tabularnewline
141 & 0.0312427 & 0.0624854 & 0.968757 \tabularnewline
142 & 0.264393 & 0.528786 & 0.735607 \tabularnewline
143 & 0.220729 & 0.441457 & 0.779271 \tabularnewline
144 & 0.1666 & 0.333199 & 0.8334 \tabularnewline
145 & 0.213173 & 0.426346 & 0.786827 \tabularnewline
146 & 0.25241 & 0.50482 & 0.74759 \tabularnewline
147 & 0.226673 & 0.453345 & 0.773327 \tabularnewline
148 & 0.971455 & 0.0570907 & 0.0285454 \tabularnewline
149 & 0.986128 & 0.0277444 & 0.0138722 \tabularnewline
150 & 0.998324 & 0.00335269 & 0.00167634 \tabularnewline
151 & 0.996206 & 0.00758862 & 0.00379431 \tabularnewline
152 & 0.993557 & 0.0128865 & 0.00644323 \tabularnewline
153 & 0.971809 & 0.0563811 & 0.0281905 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&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]13[/C][C]0.860649[/C][C]0.278702[/C][C]0.139351[/C][/ROW]
[ROW][C]14[/C][C]0.760597[/C][C]0.478805[/C][C]0.239403[/C][/ROW]
[ROW][C]15[/C][C]0.642905[/C][C]0.71419[/C][C]0.357095[/C][/ROW]
[ROW][C]16[/C][C]0.543209[/C][C]0.913582[/C][C]0.456791[/C][/ROW]
[ROW][C]17[/C][C]0.429196[/C][C]0.858392[/C][C]0.570804[/C][/ROW]
[ROW][C]18[/C][C]0.626219[/C][C]0.747562[/C][C]0.373781[/C][/ROW]
[ROW][C]19[/C][C]0.574344[/C][C]0.851312[/C][C]0.425656[/C][/ROW]
[ROW][C]20[/C][C]0.487854[/C][C]0.975708[/C][C]0.512146[/C][/ROW]
[ROW][C]21[/C][C]0.400137[/C][C]0.800275[/C][C]0.599863[/C][/ROW]
[ROW][C]22[/C][C]0.442053[/C][C]0.884105[/C][C]0.557947[/C][/ROW]
[ROW][C]23[/C][C]0.389752[/C][C]0.779505[/C][C]0.610248[/C][/ROW]
[ROW][C]24[/C][C]0.385808[/C][C]0.771615[/C][C]0.614192[/C][/ROW]
[ROW][C]25[/C][C]0.452539[/C][C]0.905078[/C][C]0.547461[/C][/ROW]
[ROW][C]26[/C][C]0.415714[/C][C]0.831428[/C][C]0.584286[/C][/ROW]
[ROW][C]27[/C][C]0.35611[/C][C]0.712219[/C][C]0.64389[/C][/ROW]
[ROW][C]28[/C][C]0.432093[/C][C]0.864187[/C][C]0.567907[/C][/ROW]
[ROW][C]29[/C][C]0.39744[/C][C]0.79488[/C][C]0.60256[/C][/ROW]
[ROW][C]30[/C][C]0.339453[/C][C]0.678907[/C][C]0.660547[/C][/ROW]
[ROW][C]31[/C][C]0.282963[/C][C]0.565926[/C][C]0.717037[/C][/ROW]
[ROW][C]32[/C][C]0.24156[/C][C]0.483119[/C][C]0.75844[/C][/ROW]
[ROW][C]33[/C][C]0.207248[/C][C]0.414495[/C][C]0.792752[/C][/ROW]
[ROW][C]34[/C][C]0.171432[/C][C]0.342865[/C][C]0.828568[/C][/ROW]
[ROW][C]35[/C][C]0.137826[/C][C]0.275652[/C][C]0.862174[/C][/ROW]
[ROW][C]36[/C][C]0.106045[/C][C]0.21209[/C][C]0.893955[/C][/ROW]
[ROW][C]37[/C][C]0.0801422[/C][C]0.160284[/C][C]0.919858[/C][/ROW]
[ROW][C]38[/C][C]0.0601793[/C][C]0.120359[/C][C]0.939821[/C][/ROW]
[ROW][C]39[/C][C]0.205857[/C][C]0.411714[/C][C]0.794143[/C][/ROW]
[ROW][C]40[/C][C]0.199347[/C][C]0.398694[/C][C]0.800653[/C][/ROW]
[ROW][C]41[/C][C]0.163992[/C][C]0.327984[/C][C]0.836008[/C][/ROW]
[ROW][C]42[/C][C]0.132606[/C][C]0.265212[/C][C]0.867394[/C][/ROW]
[ROW][C]43[/C][C]0.117005[/C][C]0.234009[/C][C]0.882995[/C][/ROW]
[ROW][C]44[/C][C]0.153158[/C][C]0.306316[/C][C]0.846842[/C][/ROW]
[ROW][C]45[/C][C]0.130436[/C][C]0.260871[/C][C]0.869564[/C][/ROW]
[ROW][C]46[/C][C]0.105068[/C][C]0.210136[/C][C]0.894932[/C][/ROW]
[ROW][C]47[/C][C]0.0967596[/C][C]0.193519[/C][C]0.90324[/C][/ROW]
[ROW][C]48[/C][C]0.0808756[/C][C]0.161751[/C][C]0.919124[/C][/ROW]
[ROW][C]49[/C][C]0.0936584[/C][C]0.187317[/C][C]0.906342[/C][/ROW]
[ROW][C]50[/C][C]0.17124[/C][C]0.342479[/C][C]0.82876[/C][/ROW]
[ROW][C]51[/C][C]0.141156[/C][C]0.282312[/C][C]0.858844[/C][/ROW]
[ROW][C]52[/C][C]0.322943[/C][C]0.645887[/C][C]0.677057[/C][/ROW]
[ROW][C]53[/C][C]0.294321[/C][C]0.588641[/C][C]0.705679[/C][/ROW]
[ROW][C]54[/C][C]0.258576[/C][C]0.517152[/C][C]0.741424[/C][/ROW]
[ROW][C]55[/C][C]0.230237[/C][C]0.460474[/C][C]0.769763[/C][/ROW]
[ROW][C]56[/C][C]0.214949[/C][C]0.429897[/C][C]0.785051[/C][/ROW]
[ROW][C]57[/C][C]0.180229[/C][C]0.360457[/C][C]0.819771[/C][/ROW]
[ROW][C]58[/C][C]0.156358[/C][C]0.312717[/C][C]0.843642[/C][/ROW]
[ROW][C]59[/C][C]0.145459[/C][C]0.290919[/C][C]0.854541[/C][/ROW]
[ROW][C]60[/C][C]0.221562[/C][C]0.443124[/C][C]0.778438[/C][/ROW]
[ROW][C]61[/C][C]0.272167[/C][C]0.544334[/C][C]0.727833[/C][/ROW]
[ROW][C]62[/C][C]0.268774[/C][C]0.537549[/C][C]0.731226[/C][/ROW]
[ROW][C]63[/C][C]0.260001[/C][C]0.520001[/C][C]0.739999[/C][/ROW]
[ROW][C]64[/C][C]0.289867[/C][C]0.579733[/C][C]0.710133[/C][/ROW]
[ROW][C]65[/C][C]0.260822[/C][C]0.521644[/C][C]0.739178[/C][/ROW]
[ROW][C]66[/C][C]0.279547[/C][C]0.559095[/C][C]0.720453[/C][/ROW]
[ROW][C]67[/C][C]0.267996[/C][C]0.535993[/C][C]0.732004[/C][/ROW]
[ROW][C]68[/C][C]0.255118[/C][C]0.510236[/C][C]0.744882[/C][/ROW]
[ROW][C]69[/C][C]0.219302[/C][C]0.438605[/C][C]0.780698[/C][/ROW]
[ROW][C]70[/C][C]0.196811[/C][C]0.393622[/C][C]0.803189[/C][/ROW]
[ROW][C]71[/C][C]0.202211[/C][C]0.404422[/C][C]0.797789[/C][/ROW]
[ROW][C]72[/C][C]0.170609[/C][C]0.341218[/C][C]0.829391[/C][/ROW]
[ROW][C]73[/C][C]0.208652[/C][C]0.417304[/C][C]0.791348[/C][/ROW]
[ROW][C]74[/C][C]0.17953[/C][C]0.35906[/C][C]0.82047[/C][/ROW]
[ROW][C]75[/C][C]0.172058[/C][C]0.344116[/C][C]0.827942[/C][/ROW]
[ROW][C]76[/C][C]0.189318[/C][C]0.378636[/C][C]0.810682[/C][/ROW]
[ROW][C]77[/C][C]0.233791[/C][C]0.467582[/C][C]0.766209[/C][/ROW]
[ROW][C]78[/C][C]0.247927[/C][C]0.495853[/C][C]0.752073[/C][/ROW]
[ROW][C]79[/C][C]0.231622[/C][C]0.463244[/C][C]0.768378[/C][/ROW]
[ROW][C]80[/C][C]0.215212[/C][C]0.430423[/C][C]0.784788[/C][/ROW]
[ROW][C]81[/C][C]0.264915[/C][C]0.529829[/C][C]0.735085[/C][/ROW]
[ROW][C]82[/C][C]0.322317[/C][C]0.644633[/C][C]0.677683[/C][/ROW]
[ROW][C]83[/C][C]0.282587[/C][C]0.565174[/C][C]0.717413[/C][/ROW]
[ROW][C]84[/C][C]0.314526[/C][C]0.629052[/C][C]0.685474[/C][/ROW]
[ROW][C]85[/C][C]0.312671[/C][C]0.625342[/C][C]0.687329[/C][/ROW]
[ROW][C]86[/C][C]0.297099[/C][C]0.594198[/C][C]0.702901[/C][/ROW]
[ROW][C]87[/C][C]0.276901[/C][C]0.553803[/C][C]0.723099[/C][/ROW]
[ROW][C]88[/C][C]0.241494[/C][C]0.482988[/C][C]0.758506[/C][/ROW]
[ROW][C]89[/C][C]0.2299[/C][C]0.459801[/C][C]0.7701[/C][/ROW]
[ROW][C]90[/C][C]0.196709[/C][C]0.393418[/C][C]0.803291[/C][/ROW]
[ROW][C]91[/C][C]0.236667[/C][C]0.473334[/C][C]0.763333[/C][/ROW]
[ROW][C]92[/C][C]0.202535[/C][C]0.405071[/C][C]0.797465[/C][/ROW]
[ROW][C]93[/C][C]0.204436[/C][C]0.408871[/C][C]0.795564[/C][/ROW]
[ROW][C]94[/C][C]0.195266[/C][C]0.390533[/C][C]0.804734[/C][/ROW]
[ROW][C]95[/C][C]0.205289[/C][C]0.410578[/C][C]0.794711[/C][/ROW]
[ROW][C]96[/C][C]0.177766[/C][C]0.355532[/C][C]0.822234[/C][/ROW]
[ROW][C]97[/C][C]0.205086[/C][C]0.410173[/C][C]0.794914[/C][/ROW]
[ROW][C]98[/C][C]0.194741[/C][C]0.389481[/C][C]0.805259[/C][/ROW]
[ROW][C]99[/C][C]0.172094[/C][C]0.344188[/C][C]0.827906[/C][/ROW]
[ROW][C]100[/C][C]0.166416[/C][C]0.332831[/C][C]0.833584[/C][/ROW]
[ROW][C]101[/C][C]0.165121[/C][C]0.330243[/C][C]0.834879[/C][/ROW]
[ROW][C]102[/C][C]0.138618[/C][C]0.277236[/C][C]0.861382[/C][/ROW]
[ROW][C]103[/C][C]0.113871[/C][C]0.227741[/C][C]0.886129[/C][/ROW]
[ROW][C]104[/C][C]0.0985013[/C][C]0.197003[/C][C]0.901499[/C][/ROW]
[ROW][C]105[/C][C]0.0843454[/C][C]0.168691[/C][C]0.915655[/C][/ROW]
[ROW][C]106[/C][C]0.0707169[/C][C]0.141434[/C][C]0.929283[/C][/ROW]
[ROW][C]107[/C][C]0.0561285[/C][C]0.112257[/C][C]0.943871[/C][/ROW]
[ROW][C]108[/C][C]0.0441854[/C][C]0.0883708[/C][C]0.955815[/C][/ROW]
[ROW][C]109[/C][C]0.0394288[/C][C]0.0788576[/C][C]0.960571[/C][/ROW]
[ROW][C]110[/C][C]0.0383748[/C][C]0.0767496[/C][C]0.961625[/C][/ROW]
[ROW][C]111[/C][C]0.0403404[/C][C]0.0806808[/C][C]0.95966[/C][/ROW]
[ROW][C]112[/C][C]0.0313432[/C][C]0.0626864[/C][C]0.968657[/C][/ROW]
[ROW][C]113[/C][C]0.0320494[/C][C]0.0640988[/C][C]0.967951[/C][/ROW]
[ROW][C]114[/C][C]0.0617863[/C][C]0.123573[/C][C]0.938214[/C][/ROW]
[ROW][C]115[/C][C]0.0512401[/C][C]0.10248[/C][C]0.94876[/C][/ROW]
[ROW][C]116[/C][C]0.0499352[/C][C]0.0998704[/C][C]0.950065[/C][/ROW]
[ROW][C]117[/C][C]0.0886374[/C][C]0.177275[/C][C]0.911363[/C][/ROW]
[ROW][C]118[/C][C]0.103165[/C][C]0.20633[/C][C]0.896835[/C][/ROW]
[ROW][C]119[/C][C]0.0944874[/C][C]0.188975[/C][C]0.905513[/C][/ROW]
[ROW][C]120[/C][C]0.110672[/C][C]0.221344[/C][C]0.889328[/C][/ROW]
[ROW][C]121[/C][C]0.1111[/C][C]0.2222[/C][C]0.8889[/C][/ROW]
[ROW][C]122[/C][C]0.126705[/C][C]0.253411[/C][C]0.873295[/C][/ROW]
[ROW][C]123[/C][C]0.120535[/C][C]0.241071[/C][C]0.879465[/C][/ROW]
[ROW][C]124[/C][C]0.294778[/C][C]0.589555[/C][C]0.705222[/C][/ROW]
[ROW][C]125[/C][C]0.289906[/C][C]0.579813[/C][C]0.710094[/C][/ROW]
[ROW][C]126[/C][C]0.259046[/C][C]0.518093[/C][C]0.740954[/C][/ROW]
[ROW][C]127[/C][C]0.221848[/C][C]0.443697[/C][C]0.778152[/C][/ROW]
[ROW][C]128[/C][C]0.191584[/C][C]0.383168[/C][C]0.808416[/C][/ROW]
[ROW][C]129[/C][C]0.157652[/C][C]0.315304[/C][C]0.842348[/C][/ROW]
[ROW][C]130[/C][C]0.165136[/C][C]0.330272[/C][C]0.834864[/C][/ROW]
[ROW][C]131[/C][C]0.131382[/C][C]0.262763[/C][C]0.868618[/C][/ROW]
[ROW][C]132[/C][C]0.124248[/C][C]0.248496[/C][C]0.875752[/C][/ROW]
[ROW][C]133[/C][C]0.101982[/C][C]0.203964[/C][C]0.898018[/C][/ROW]
[ROW][C]134[/C][C]0.0806255[/C][C]0.161251[/C][C]0.919375[/C][/ROW]
[ROW][C]135[/C][C]0.0595607[/C][C]0.119121[/C][C]0.940439[/C][/ROW]
[ROW][C]136[/C][C]0.0530539[/C][C]0.106108[/C][C]0.946946[/C][/ROW]
[ROW][C]137[/C][C]0.0414182[/C][C]0.0828363[/C][C]0.958582[/C][/ROW]
[ROW][C]138[/C][C]0.0425575[/C][C]0.085115[/C][C]0.957442[/C][/ROW]
[ROW][C]139[/C][C]0.0554617[/C][C]0.110923[/C][C]0.944538[/C][/ROW]
[ROW][C]140[/C][C]0.0412538[/C][C]0.0825076[/C][C]0.958746[/C][/ROW]
[ROW][C]141[/C][C]0.0312427[/C][C]0.0624854[/C][C]0.968757[/C][/ROW]
[ROW][C]142[/C][C]0.264393[/C][C]0.528786[/C][C]0.735607[/C][/ROW]
[ROW][C]143[/C][C]0.220729[/C][C]0.441457[/C][C]0.779271[/C][/ROW]
[ROW][C]144[/C][C]0.1666[/C][C]0.333199[/C][C]0.8334[/C][/ROW]
[ROW][C]145[/C][C]0.213173[/C][C]0.426346[/C][C]0.786827[/C][/ROW]
[ROW][C]146[/C][C]0.25241[/C][C]0.50482[/C][C]0.74759[/C][/ROW]
[ROW][C]147[/C][C]0.226673[/C][C]0.453345[/C][C]0.773327[/C][/ROW]
[ROW][C]148[/C][C]0.971455[/C][C]0.0570907[/C][C]0.0285454[/C][/ROW]
[ROW][C]149[/C][C]0.986128[/C][C]0.0277444[/C][C]0.0138722[/C][/ROW]
[ROW][C]150[/C][C]0.998324[/C][C]0.00335269[/C][C]0.00167634[/C][/ROW]
[ROW][C]151[/C][C]0.996206[/C][C]0.00758862[/C][C]0.00379431[/C][/ROW]
[ROW][C]152[/C][C]0.993557[/C][C]0.0128865[/C][C]0.00644323[/C][/ROW]
[ROW][C]153[/C][C]0.971809[/C][C]0.0563811[/C][C]0.0281905[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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
130.8606490.2787020.139351
140.7605970.4788050.239403
150.6429050.714190.357095
160.5432090.9135820.456791
170.4291960.8583920.570804
180.6262190.7475620.373781
190.5743440.8513120.425656
200.4878540.9757080.512146
210.4001370.8002750.599863
220.4420530.8841050.557947
230.3897520.7795050.610248
240.3858080.7716150.614192
250.4525390.9050780.547461
260.4157140.8314280.584286
270.356110.7122190.64389
280.4320930.8641870.567907
290.397440.794880.60256
300.3394530.6789070.660547
310.2829630.5659260.717037
320.241560.4831190.75844
330.2072480.4144950.792752
340.1714320.3428650.828568
350.1378260.2756520.862174
360.1060450.212090.893955
370.08014220.1602840.919858
380.06017930.1203590.939821
390.2058570.4117140.794143
400.1993470.3986940.800653
410.1639920.3279840.836008
420.1326060.2652120.867394
430.1170050.2340090.882995
440.1531580.3063160.846842
450.1304360.2608710.869564
460.1050680.2101360.894932
470.09675960.1935190.90324
480.08087560.1617510.919124
490.09365840.1873170.906342
500.171240.3424790.82876
510.1411560.2823120.858844
520.3229430.6458870.677057
530.2943210.5886410.705679
540.2585760.5171520.741424
550.2302370.4604740.769763
560.2149490.4298970.785051
570.1802290.3604570.819771
580.1563580.3127170.843642
590.1454590.2909190.854541
600.2215620.4431240.778438
610.2721670.5443340.727833
620.2687740.5375490.731226
630.2600010.5200010.739999
640.2898670.5797330.710133
650.2608220.5216440.739178
660.2795470.5590950.720453
670.2679960.5359930.732004
680.2551180.5102360.744882
690.2193020.4386050.780698
700.1968110.3936220.803189
710.2022110.4044220.797789
720.1706090.3412180.829391
730.2086520.4173040.791348
740.179530.359060.82047
750.1720580.3441160.827942
760.1893180.3786360.810682
770.2337910.4675820.766209
780.2479270.4958530.752073
790.2316220.4632440.768378
800.2152120.4304230.784788
810.2649150.5298290.735085
820.3223170.6446330.677683
830.2825870.5651740.717413
840.3145260.6290520.685474
850.3126710.6253420.687329
860.2970990.5941980.702901
870.2769010.5538030.723099
880.2414940.4829880.758506
890.22990.4598010.7701
900.1967090.3934180.803291
910.2366670.4733340.763333
920.2025350.4050710.797465
930.2044360.4088710.795564
940.1952660.3905330.804734
950.2052890.4105780.794711
960.1777660.3555320.822234
970.2050860.4101730.794914
980.1947410.3894810.805259
990.1720940.3441880.827906
1000.1664160.3328310.833584
1010.1651210.3302430.834879
1020.1386180.2772360.861382
1030.1138710.2277410.886129
1040.09850130.1970030.901499
1050.08434540.1686910.915655
1060.07071690.1414340.929283
1070.05612850.1122570.943871
1080.04418540.08837080.955815
1090.03942880.07885760.960571
1100.03837480.07674960.961625
1110.04034040.08068080.95966
1120.03134320.06268640.968657
1130.03204940.06409880.967951
1140.06178630.1235730.938214
1150.05124010.102480.94876
1160.04993520.09987040.950065
1170.08863740.1772750.911363
1180.1031650.206330.896835
1190.09448740.1889750.905513
1200.1106720.2213440.889328
1210.11110.22220.8889
1220.1267050.2534110.873295
1230.1205350.2410710.879465
1240.2947780.5895550.705222
1250.2899060.5798130.710094
1260.2590460.5180930.740954
1270.2218480.4436970.778152
1280.1915840.3831680.808416
1290.1576520.3153040.842348
1300.1651360.3302720.834864
1310.1313820.2627630.868618
1320.1242480.2484960.875752
1330.1019820.2039640.898018
1340.08062550.1612510.919375
1350.05956070.1191210.940439
1360.05305390.1061080.946946
1370.04141820.08283630.958582
1380.04255750.0851150.957442
1390.05546170.1109230.944538
1400.04125380.08250760.958746
1410.03124270.06248540.968757
1420.2643930.5287860.735607
1430.2207290.4414570.779271
1440.16660.3331990.8334
1450.2131730.4263460.786827
1460.252410.504820.74759
1470.2266730.4533450.773327
1480.9714550.05709070.0285454
1490.9861280.02774440.0138722
1500.9983240.003352690.00167634
1510.9962060.007588620.00379431
1520.9935570.01288650.00644323
1530.9718090.05638110.0281905






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0141844NOK
5% type I error level40.0283688OK
10% type I error level170.120567NOK

\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 & 2 & 0.0141844 & NOK \tabularnewline
5% type I error level & 4 & 0.0283688 & OK \tabularnewline
10% type I error level & 17 & 0.120567 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263963&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]2[/C][C]0.0141844[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0283688[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.120567[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263963&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263963&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 level20.0141844NOK
5% type I error level40.0283688OK
10% type I error level170.120567NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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):
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')
}