FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSun, 14 Dec 2014 09:03:45 +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/14/t1418548048y7pkgj7cm0fwjdr.htm/, Retrieved Thu, 16 May 2024 23:26:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267343, Retrieved Thu, 16 May 2024 23:26:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 09:03:45] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 0 0 21 149 18 68 96 9 5 4 2 0 1
12.2 0 1 22 139 31 39 70 8 5 5 1 1 2
12.8 0 0 21 148 39 32 88 8 4 6 2 0 2
7.4 0 1 21 158 46 62 114 8 5 5 0 0 0
6.7 0 1 21 128 31 33 69 8 7 4 0 2 2
12.6 0 1 21 224 67 52 176 7 3 0 0 1 1
14.8 0 0 21 159 35 62 114 8 4 5 2 1 0
13.3 0 1 23 105 52 77 121 9 4 3 2 2 2
11.1 0 1 22 159 77 76 110 8 7 5 0 1 0
8.2 0 1 25 167 37 41 158 7 6 2 2 1 1
11.4 0 1 21 165 32 48 116 9 6 3 3 0 1
6.4 0 1 23 159 36 63 181 7 2 4 0 1 1
10.6 0 1 22 119 38 30 77 8 4 6 0 1 1
12.0 0 0 21 176 69 78 141 8 4 3 2 0 2
6.3 0 0 21 54 21 19 35 8 5 4 1 0 0
11.3 1 0 25 91 26 31 80 8 3 1 2 0 1
11.9 0 1 21 163 54 66 152 8 4 5 1 1 1
9.3 0 0 21 124 36 35 97 6 7 4 1 1 2
9.6 1 1 20 137 42 42 99 9 5 4 1 0 2
10.0 0 0 24 121 23 45 84 7 2 4 0 0 2
13.8 0 1 21 148 112 25 101 8 6 6 1 0 2
10.8 0 0 24 221 35 44 107 7 6 5 1 0 2
11.7 0 1 21 149 47 54 112 8 7 6 2 2 0
10.9 0 1 22 244 37 74 171 3 2 4 0 0 0
16.1 1 1 20 148 109 80 137 9 10 6 1 2 2
13.4 1 0 18 92 24 42 77 8 4 5 2 0 1
9.9 0 1 21 150 20 61 66 8 4 6 3 0 2
11.5 0 0 22 153 22 41 93 6 2 5 2 0 1
8.3 0 0 22 94 23 46 105 5 4 4 1 0 2
11.7 0 0 21 156 32 39 131 8 4 4 0 1 2
9.0 0 1 21 132 30 34 102 8 7 6 2 1 1
9.7 0 1 25 161 92 51 161 9 2 4 2 0 1
10.8 0 1 22 105 43 42 120 7 6 6 3 0 1
10.3 0 1 22 97 55 31 127 7 3 6 3 1 1
10.4 0 0 20 151 16 39 77 3 3 3 1 0 0
12.7 1 1 21 131 49 20 108 7 2 4 0 1 0
9.3 0 1 21 166 71 49 85 8 5 5 2 1 1
11.8 0 0 21 157 43 53 168 8 7 6 2 1 2
5.9 0 1 22 111 29 31 48 7 6 6 2 1 1
11.4 0 1 21 145 56 39 152 8 4 6 2 1 2
13.0 0 1 24 162 46 54 75 8 6 6 1 1 2
10.8 0 1 22 163 19 49 107 9 4 6 3 0 2
12.3 1 1 22 59 23 34 62 6 3 5 2 0 2
11.3 0 0 21 187 59 46 121 9 5 5 2 1 1
11.8 0 1 22 109 30 55 124 8 2 3 0 0 2
7.9 1 1 19 90 61 42 72 8 3 5 0 1 2
12.7 0 0 22 105 7 50 40 8 5 1 0 0 2
12.3 1 1 23 83 38 13 58 7 7 5 3 1 2
11.6 1 1 20 116 32 37 97 8 4 6 2 2 1
6.7 1 1 20 42 16 25 88 7 3 6 0 0 1
10.9 0 1 23 148 19 30 126 7 2 4 2 2 2
12.1 1 1 20 155 22 28 104 9 5 6 0 0 1
13.3 0 1 23 125 48 45 148 7 4 6 0 1 0
10.1 0 1 21 116 23 35 146 9 6 6 2 2 2
5.7 1 0 22 128 26 28 80 7 4 5 3 0 2
14.3 0 1 21 138 33 41 97 6 4 2 0 0 1
8.0 1 0 21 49 9 6 25 3 2 2 1 0 0
13.3 1 1 19 96 24 45 99 9 9 6 2 1 2
9.3 0 1 22 164 34 73 118 9 8 6 2 2 1
12.5 0 0 21 162 48 17 58 7 8 5 0 1 2
7.6 0 0 21 99 18 40 63 6 3 6 3 1 2
15.9 0 1 21 202 43 64 139 9 2 5 2 0 1
9.2 0 0 21 186 33 37 50 8 4 4 0 1 2
9.1 1 1 21 66 28 25 60 8 2 5 3 0 2
11.1 0 0 21 183 71 65 152 7 2 4 2 1 2
13.0 0 1 22 214 26 100 142 9 1 5 2 0 2
14.5 0 1 22 188 67 28 94 5 4 4 3 1 0
12.2 1 0 18 104 34 35 66 6 5 6 0 1 1
12.3 0 0 21 177 80 56 127 8 8 5 1 1 2
11.4 0 0 23 126 29 29 67 8 4 4 2 0 1
14.6 1 1 19 99 59 59 75 8 5 5 2 1 2
12.6 0 0 21 139 32 50 128 7 3 4 0 0 0
13.0 0 0 21 162 43 59 146 9 4 2 0 1 0
12.6 1 1 21 108 38 27 69 9 6 5 2 1 1
13.2 0 0 20 159 29 61 186 8 4 6 1 0 1
9.9 1 0 19 74 36 28 81 4 3 5 0 0 0
7.7 0 1 21 110 32 51 85 7 8 5 0 2 2
10.5 1 0 19 96 35 35 54 8 6 3 1 2 2
13.4 1 0 19 116 21 29 46 6 3 3 0 0 0
10.9 1 0 19 87 29 48 106 7 5 5 2 1 2
4.3 1 1 20 97 12 25 34 7 4 6 1 0 2
10.3 1 0 19 127 37 44 60 3 3 2 2 0 0
11.8 1 1 19 106 37 64 95 8 7 6 1 0 1
11.2 1 1 19 80 47 32 57 8 2 4 1 1 1
11.4 1 0 20 74 51 20 62 8 4 5 3 0 2
8.6 1 0 19 91 32 28 36 8 6 6 2 1 1
13.2 1 0 18 133 21 34 56 5 6 5 0 0 2
12.6 1 1 19 74 13 31 54 6 6 5 2 1 1
5.6 1 1 21 114 14 26 64 6 4 6 1 1 2
9.9 1 1 18 140 -2 58 76 7 6 5 0 0 2
8.8 1 0 18 95 20 23 98 7 5 6 1 1 2
7.7 1 1 19 98 24 21 88 7 5 5 0 0 2
9.0 1 0 21 121 11 21 35 8 6 4 0 0 2
7.3 1 1 20 126 23 33 102 9 8 5 1 1 2
11.4 1 1 24 98 24 16 61 8 5 5 2 2 1
13.6 1 1 22 95 14 20 80 8 6 5 2 1 2
7.9 1 1 21 110 52 37 49 7 4 5 2 1 2
10.7 1 1 21 70 15 35 78 9 3 4 2 1 2
10.3 1 0 19 102 23 33 90 7 3 5 3 0 1
9.6 1 1 20 130 35 41 55 7 4 5 0 0 1
14.2 1 1 18 96 24 40 96 8 5 6 0 0 1
8.5 1 0 19 102 39 35 43 6 3 1 0 1 0
13.5 1 0 19 100 29 28 52 2 4 1 0 1 0
6.4 1 0 21 52 8 22 54 8 3 3 2 0 2
9.6 1 0 18 98 18 44 51 6 5 6 0 0 1
11.6 1 0 19 118 24 27 51 8 4 4 2 0 1
11.1 1 1 19 99 19 17 38 6 4 5 2 0 2

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 11.1795 + 0.587643programma[t] -0.216487gender[t] -0.216149age[t] + 0.0108203LFM[t] + 0.0234457PRH[t] + 0.0157582CH[t] + 0.00678419Blogs[t] + 0.171972Calculation[t] + 0.0882847Algebraic_Reasoning[t] -0.195894Graphical_Interpretation[t] + 0.269851Proportionality_and_Ratio[t] -0.140467Probability_and_Sampling[t] -0.403627Estimation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.1795 +  0.587643programma[t] -0.216487gender[t] -0.216149age[t] +  0.0108203LFM[t] +  0.0234457PRH[t] +  0.0157582CH[t] +  0.00678419Blogs[t] +  0.171972Calculation[t] +  0.0882847Algebraic_Reasoning[t] -0.195894Graphical_Interpretation[t] +  0.269851Proportionality_and_Ratio[t] -0.140467Probability_and_Sampling[t] -0.403627Estimation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.1795 +  0.587643programma[t] -0.216487gender[t] -0.216149age[t] +  0.0108203LFM[t] +  0.0234457PRH[t] +  0.0157582CH[t] +  0.00678419Blogs[t] +  0.171972Calculation[t] +  0.0882847Algebraic_Reasoning[t] -0.195894Graphical_Interpretation[t] +  0.269851Proportionality_and_Ratio[t] -0.140467Probability_and_Sampling[t] -0.403627Estimation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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] = + 11.1795 + 0.587643programma[t] -0.216487gender[t] -0.216149age[t] + 0.0108203LFM[t] + 0.0234457PRH[t] + 0.0157582CH[t] + 0.00678419Blogs[t] + 0.171972Calculation[t] + 0.0882847Algebraic_Reasoning[t] -0.195894Graphical_Interpretation[t] + 0.269851Proportionality_and_Ratio[t] -0.140467Probability_and_Sampling[t] -0.403627Estimation[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.17954.616582.4220.01739220.00869612
programma0.5876430.6985810.84120.4023960.201198
gender-0.2164870.517982-0.41790.6769530.338477
age-0.2161490.19346-1.1170.2667530.133376
LFM0.01082030.008487371.2750.2055290.102765
PRH0.02344570.01307911.7930.07628560.0381428
CH0.01575820.0183280.85980.3921150.196058
Blogs0.006784190.008860930.76560.4458360.222918
Calculation0.1719720.1857940.92560.3570480.178524
Algebraic_Reasoning0.08828470.1503150.58730.5584040.279202
Graphical_Interpretation-0.1958940.193386-1.0130.3137050.156852
Proportionality_and_Ratio0.2698510.2334961.1560.2507670.125383
Probability_and_Sampling-0.1404670.382706-0.3670.7144270.357213
Estimation-0.4036270.330622-1.2210.2252440.112622

\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) & 11.1795 & 4.61658 & 2.422 & 0.0173922 & 0.00869612 \tabularnewline
programma & 0.587643 & 0.698581 & 0.8412 & 0.402396 & 0.201198 \tabularnewline
gender & -0.216487 & 0.517982 & -0.4179 & 0.676953 & 0.338477 \tabularnewline
age & -0.216149 & 0.19346 & -1.117 & 0.266753 & 0.133376 \tabularnewline
LFM & 0.0108203 & 0.00848737 & 1.275 & 0.205529 & 0.102765 \tabularnewline
PRH & 0.0234457 & 0.0130791 & 1.793 & 0.0762856 & 0.0381428 \tabularnewline
CH & 0.0157582 & 0.018328 & 0.8598 & 0.392115 & 0.196058 \tabularnewline
Blogs & 0.00678419 & 0.00886093 & 0.7656 & 0.445836 & 0.222918 \tabularnewline
Calculation & 0.171972 & 0.185794 & 0.9256 & 0.357048 & 0.178524 \tabularnewline
Algebraic_Reasoning & 0.0882847 & 0.150315 & 0.5873 & 0.558404 & 0.279202 \tabularnewline
Graphical_Interpretation & -0.195894 & 0.193386 & -1.013 & 0.313705 & 0.156852 \tabularnewline
Proportionality_and_Ratio & 0.269851 & 0.233496 & 1.156 & 0.250767 & 0.125383 \tabularnewline
Probability_and_Sampling & -0.140467 & 0.382706 & -0.367 & 0.714427 & 0.357213 \tabularnewline
Estimation & -0.403627 & 0.330622 & -1.221 & 0.225244 & 0.112622 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&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]11.1795[/C][C]4.61658[/C][C]2.422[/C][C]0.0173922[/C][C]0.00869612[/C][/ROW]
[ROW][C]programma[/C][C]0.587643[/C][C]0.698581[/C][C]0.8412[/C][C]0.402396[/C][C]0.201198[/C][/ROW]
[ROW][C]gender[/C][C]-0.216487[/C][C]0.517982[/C][C]-0.4179[/C][C]0.676953[/C][C]0.338477[/C][/ROW]
[ROW][C]age[/C][C]-0.216149[/C][C]0.19346[/C][C]-1.117[/C][C]0.266753[/C][C]0.133376[/C][/ROW]
[ROW][C]LFM[/C][C]0.0108203[/C][C]0.00848737[/C][C]1.275[/C][C]0.205529[/C][C]0.102765[/C][/ROW]
[ROW][C]PRH[/C][C]0.0234457[/C][C]0.0130791[/C][C]1.793[/C][C]0.0762856[/C][C]0.0381428[/C][/ROW]
[ROW][C]CH[/C][C]0.0157582[/C][C]0.018328[/C][C]0.8598[/C][C]0.392115[/C][C]0.196058[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00678419[/C][C]0.00886093[/C][C]0.7656[/C][C]0.445836[/C][C]0.222918[/C][/ROW]
[ROW][C]Calculation[/C][C]0.171972[/C][C]0.185794[/C][C]0.9256[/C][C]0.357048[/C][C]0.178524[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]0.0882847[/C][C]0.150315[/C][C]0.5873[/C][C]0.558404[/C][C]0.279202[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-0.195894[/C][C]0.193386[/C][C]-1.013[/C][C]0.313705[/C][C]0.156852[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.269851[/C][C]0.233496[/C][C]1.156[/C][C]0.250767[/C][C]0.125383[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]-0.140467[/C][C]0.382706[/C][C]-0.367[/C][C]0.714427[/C][C]0.357213[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.403627[/C][C]0.330622[/C][C]-1.221[/C][C]0.225244[/C][C]0.112622[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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)11.17954.616582.4220.01739220.00869612
programma0.5876430.6985810.84120.4023960.201198
gender-0.2164870.517982-0.41790.6769530.338477
age-0.2161490.19346-1.1170.2667530.133376
LFM0.01082030.008487371.2750.2055290.102765
PRH0.02344570.01307911.7930.07628560.0381428
CH0.01575820.0183280.85980.3921150.196058
Blogs0.006784190.008860930.76560.4458360.222918
Calculation0.1719720.1857940.92560.3570480.178524
Algebraic_Reasoning0.08828470.1503150.58730.5584040.279202
Graphical_Interpretation-0.1958940.193386-1.0130.3137050.156852
Proportionality_and_Ratio0.2698510.2334961.1560.2507670.125383
Probability_and_Sampling-0.1404670.382706-0.3670.7144270.357213
Estimation-0.4036270.330622-1.2210.2252440.112622






Multiple Linear Regression - Regression Statistics
Multiple R0.457051
R-squared0.208896
Adjusted R-squared0.0983112
F-TEST (value)1.88901
F-TEST (DF numerator)13
F-TEST (DF denominator)93
p-value0.0412043
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27027
Sum Squared Residuals479.334

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.457051 \tabularnewline
R-squared & 0.208896 \tabularnewline
Adjusted R-squared & 0.0983112 \tabularnewline
F-TEST (value) & 1.88901 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 93 \tabularnewline
p-value & 0.0412043 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.27027 \tabularnewline
Sum Squared Residuals & 479.334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.457051[/C][/ROW]
[ROW][C]R-squared[/C][C]0.208896[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0983112[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.88901[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]93[/C][/ROW]
[ROW][C]p-value[/C][C]0.0412043[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.27027[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]479.334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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 R0.457051
R-squared0.208896
Adjusted R-squared0.0983112
F-TEST (value)1.88901
F-TEST (DF numerator)13
F-TEST (DF denominator)93
p-value0.0412043
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27027
Sum Squared Residuals479.334






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.73911.16085
212.29.687912.51209
312.810.54342.25655
47.411.8001-4.40015
56.79.64585-2.94585
612.613.3565-0.756524
714.812.08052.7195
813.311.14592.1541
911.112.5512-1.45122
108.211.2891-3.08911
1111.412.3986-0.998573
126.410.8295-4.42949
1310.69.390891.20911
141213.2219-1.22191
156.39.55737-3.25737
1611.310.56970.730284
1711.912.0001-0.100121
189.310.2241-0.924133
199.611.6965-2.09647
20108.908981.09102
2113.811.92311.8769
2210.810.8397-0.0397394
2311.711.826-0.126022
2410.911.9505-1.05052
2516.114.01172.08833
2613.411.50441.89561
279.910.4807-0.580717
2811.510.23761.26243
298.39.30983-1.00983
3011.710.57951.12046
31910.5973-1.59733
329.712.4311-2.73112
3310.810.79210.00793088
3410.310.4557-0.155684
3510.410.4654-0.0654249
3612.711.0821.61797
379.312.0669-2.76686
3811.811.73270.067343
395.99.45663-3.55663
4011.411.09710.302895
411310.01892.98114
4210.810.64280.157188
4312.38.97913.3209
4411.312.5982-1.29816
4511.89.955881.84412
467.910.7116-2.8116
4712.79.597823.10218
4812.39.670762.62924
4911.610.88290.717077
506.78.9379-2.2379
5110.99.414341.48566
5212.110.97761.12239
5313.310.42382.87617
5410.110.1139-0.013942
555.710.5702-4.8702
5614.310.58453.71548
5788.80393-0.803933
5813.311.1852.11503
599.311.6641-2.36412
6012.510.16292.33706
617.69.1745-1.5745
6215.912.45023.44978
639.210.3466-1.14656
649.19.75834-0.658341
6511.112.5294-1.42941
661312.06110.938925
6714.511.99032.50971
6812.210.5521.64797
6912.312.6-0.300007
7011.410.24431.15569
7114.611.76662.83339
7212.611.2361.36396
731312.69030.309699
7412.611.05821.54175
7513.211.89981.30022
769.910.2691-0.369096
777.79.58715-1.88715
7810.510.937-0.437025
7913.410.88592.51409
8010.911.0149-0.114875
814.39.0274-4.7274
8210.311.9311-1.63109
8311.811.79590.00406303
8411.210.79690.403099
8511.410.92810.471862
868.610.8064-2.20643
8713.210.32652.87352
8812.69.981862.61814
895.68.94894-3.34894
909.910.5043-0.604305
918.810.1926-1.3926
927.79.85336-2.15336
9399.67821-0.678212
947.310.939-3.639
9511.49.345022.05498
9613.69.527464.07254
977.910.5059-2.60589
9810.79.822360.877642
9910.311.329-1.02896
1009.610.648-1.048
10114.210.78133.41874
1028.511.482-2.98196
10313.510.5772.92299
1046.49.47667-3.07667
1059.610.2925-0.692508
10611.611.35270.247301
10711.19.624161.47584

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.7391 & 1.16085 \tabularnewline
2 & 12.2 & 9.68791 & 2.51209 \tabularnewline
3 & 12.8 & 10.5434 & 2.25655 \tabularnewline
4 & 7.4 & 11.8001 & -4.40015 \tabularnewline
5 & 6.7 & 9.64585 & -2.94585 \tabularnewline
6 & 12.6 & 13.3565 & -0.756524 \tabularnewline
7 & 14.8 & 12.0805 & 2.7195 \tabularnewline
8 & 13.3 & 11.1459 & 2.1541 \tabularnewline
9 & 11.1 & 12.5512 & -1.45122 \tabularnewline
10 & 8.2 & 11.2891 & -3.08911 \tabularnewline
11 & 11.4 & 12.3986 & -0.998573 \tabularnewline
12 & 6.4 & 10.8295 & -4.42949 \tabularnewline
13 & 10.6 & 9.39089 & 1.20911 \tabularnewline
14 & 12 & 13.2219 & -1.22191 \tabularnewline
15 & 6.3 & 9.55737 & -3.25737 \tabularnewline
16 & 11.3 & 10.5697 & 0.730284 \tabularnewline
17 & 11.9 & 12.0001 & -0.100121 \tabularnewline
18 & 9.3 & 10.2241 & -0.924133 \tabularnewline
19 & 9.6 & 11.6965 & -2.09647 \tabularnewline
20 & 10 & 8.90898 & 1.09102 \tabularnewline
21 & 13.8 & 11.9231 & 1.8769 \tabularnewline
22 & 10.8 & 10.8397 & -0.0397394 \tabularnewline
23 & 11.7 & 11.826 & -0.126022 \tabularnewline
24 & 10.9 & 11.9505 & -1.05052 \tabularnewline
25 & 16.1 & 14.0117 & 2.08833 \tabularnewline
26 & 13.4 & 11.5044 & 1.89561 \tabularnewline
27 & 9.9 & 10.4807 & -0.580717 \tabularnewline
28 & 11.5 & 10.2376 & 1.26243 \tabularnewline
29 & 8.3 & 9.30983 & -1.00983 \tabularnewline
30 & 11.7 & 10.5795 & 1.12046 \tabularnewline
31 & 9 & 10.5973 & -1.59733 \tabularnewline
32 & 9.7 & 12.4311 & -2.73112 \tabularnewline
33 & 10.8 & 10.7921 & 0.00793088 \tabularnewline
34 & 10.3 & 10.4557 & -0.155684 \tabularnewline
35 & 10.4 & 10.4654 & -0.0654249 \tabularnewline
36 & 12.7 & 11.082 & 1.61797 \tabularnewline
37 & 9.3 & 12.0669 & -2.76686 \tabularnewline
38 & 11.8 & 11.7327 & 0.067343 \tabularnewline
39 & 5.9 & 9.45663 & -3.55663 \tabularnewline
40 & 11.4 & 11.0971 & 0.302895 \tabularnewline
41 & 13 & 10.0189 & 2.98114 \tabularnewline
42 & 10.8 & 10.6428 & 0.157188 \tabularnewline
43 & 12.3 & 8.9791 & 3.3209 \tabularnewline
44 & 11.3 & 12.5982 & -1.29816 \tabularnewline
45 & 11.8 & 9.95588 & 1.84412 \tabularnewline
46 & 7.9 & 10.7116 & -2.8116 \tabularnewline
47 & 12.7 & 9.59782 & 3.10218 \tabularnewline
48 & 12.3 & 9.67076 & 2.62924 \tabularnewline
49 & 11.6 & 10.8829 & 0.717077 \tabularnewline
50 & 6.7 & 8.9379 & -2.2379 \tabularnewline
51 & 10.9 & 9.41434 & 1.48566 \tabularnewline
52 & 12.1 & 10.9776 & 1.12239 \tabularnewline
53 & 13.3 & 10.4238 & 2.87617 \tabularnewline
54 & 10.1 & 10.1139 & -0.013942 \tabularnewline
55 & 5.7 & 10.5702 & -4.8702 \tabularnewline
56 & 14.3 & 10.5845 & 3.71548 \tabularnewline
57 & 8 & 8.80393 & -0.803933 \tabularnewline
58 & 13.3 & 11.185 & 2.11503 \tabularnewline
59 & 9.3 & 11.6641 & -2.36412 \tabularnewline
60 & 12.5 & 10.1629 & 2.33706 \tabularnewline
61 & 7.6 & 9.1745 & -1.5745 \tabularnewline
62 & 15.9 & 12.4502 & 3.44978 \tabularnewline
63 & 9.2 & 10.3466 & -1.14656 \tabularnewline
64 & 9.1 & 9.75834 & -0.658341 \tabularnewline
65 & 11.1 & 12.5294 & -1.42941 \tabularnewline
66 & 13 & 12.0611 & 0.938925 \tabularnewline
67 & 14.5 & 11.9903 & 2.50971 \tabularnewline
68 & 12.2 & 10.552 & 1.64797 \tabularnewline
69 & 12.3 & 12.6 & -0.300007 \tabularnewline
70 & 11.4 & 10.2443 & 1.15569 \tabularnewline
71 & 14.6 & 11.7666 & 2.83339 \tabularnewline
72 & 12.6 & 11.236 & 1.36396 \tabularnewline
73 & 13 & 12.6903 & 0.309699 \tabularnewline
74 & 12.6 & 11.0582 & 1.54175 \tabularnewline
75 & 13.2 & 11.8998 & 1.30022 \tabularnewline
76 & 9.9 & 10.2691 & -0.369096 \tabularnewline
77 & 7.7 & 9.58715 & -1.88715 \tabularnewline
78 & 10.5 & 10.937 & -0.437025 \tabularnewline
79 & 13.4 & 10.8859 & 2.51409 \tabularnewline
80 & 10.9 & 11.0149 & -0.114875 \tabularnewline
81 & 4.3 & 9.0274 & -4.7274 \tabularnewline
82 & 10.3 & 11.9311 & -1.63109 \tabularnewline
83 & 11.8 & 11.7959 & 0.00406303 \tabularnewline
84 & 11.2 & 10.7969 & 0.403099 \tabularnewline
85 & 11.4 & 10.9281 & 0.471862 \tabularnewline
86 & 8.6 & 10.8064 & -2.20643 \tabularnewline
87 & 13.2 & 10.3265 & 2.87352 \tabularnewline
88 & 12.6 & 9.98186 & 2.61814 \tabularnewline
89 & 5.6 & 8.94894 & -3.34894 \tabularnewline
90 & 9.9 & 10.5043 & -0.604305 \tabularnewline
91 & 8.8 & 10.1926 & -1.3926 \tabularnewline
92 & 7.7 & 9.85336 & -2.15336 \tabularnewline
93 & 9 & 9.67821 & -0.678212 \tabularnewline
94 & 7.3 & 10.939 & -3.639 \tabularnewline
95 & 11.4 & 9.34502 & 2.05498 \tabularnewline
96 & 13.6 & 9.52746 & 4.07254 \tabularnewline
97 & 7.9 & 10.5059 & -2.60589 \tabularnewline
98 & 10.7 & 9.82236 & 0.877642 \tabularnewline
99 & 10.3 & 11.329 & -1.02896 \tabularnewline
100 & 9.6 & 10.648 & -1.048 \tabularnewline
101 & 14.2 & 10.7813 & 3.41874 \tabularnewline
102 & 8.5 & 11.482 & -2.98196 \tabularnewline
103 & 13.5 & 10.577 & 2.92299 \tabularnewline
104 & 6.4 & 9.47667 & -3.07667 \tabularnewline
105 & 9.6 & 10.2925 & -0.692508 \tabularnewline
106 & 11.6 & 11.3527 & 0.247301 \tabularnewline
107 & 11.1 & 9.62416 & 1.47584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&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]11.7391[/C][C]1.16085[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]9.68791[/C][C]2.51209[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.5434[/C][C]2.25655[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.8001[/C][C]-4.40015[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]9.64585[/C][C]-2.94585[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]13.3565[/C][C]-0.756524[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]12.0805[/C][C]2.7195[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.1459[/C][C]2.1541[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.5512[/C][C]-1.45122[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.2891[/C][C]-3.08911[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]12.3986[/C][C]-0.998573[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.8295[/C][C]-4.42949[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.39089[/C][C]1.20911[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.2219[/C][C]-1.22191[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.55737[/C][C]-3.25737[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.5697[/C][C]0.730284[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]12.0001[/C][C]-0.100121[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.2241[/C][C]-0.924133[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.6965[/C][C]-2.09647[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]8.90898[/C][C]1.09102[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]11.9231[/C][C]1.8769[/C][/ROW]
[ROW][C]22[/C][C]10.8[/C][C]10.8397[/C][C]-0.0397394[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]11.826[/C][C]-0.126022[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.9505[/C][C]-1.05052[/C][/ROW]
[ROW][C]25[/C][C]16.1[/C][C]14.0117[/C][C]2.08833[/C][/ROW]
[ROW][C]26[/C][C]13.4[/C][C]11.5044[/C][C]1.89561[/C][/ROW]
[ROW][C]27[/C][C]9.9[/C][C]10.4807[/C][C]-0.580717[/C][/ROW]
[ROW][C]28[/C][C]11.5[/C][C]10.2376[/C][C]1.26243[/C][/ROW]
[ROW][C]29[/C][C]8.3[/C][C]9.30983[/C][C]-1.00983[/C][/ROW]
[ROW][C]30[/C][C]11.7[/C][C]10.5795[/C][C]1.12046[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]10.5973[/C][C]-1.59733[/C][/ROW]
[ROW][C]32[/C][C]9.7[/C][C]12.4311[/C][C]-2.73112[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]10.7921[/C][C]0.00793088[/C][/ROW]
[ROW][C]34[/C][C]10.3[/C][C]10.4557[/C][C]-0.155684[/C][/ROW]
[ROW][C]35[/C][C]10.4[/C][C]10.4654[/C][C]-0.0654249[/C][/ROW]
[ROW][C]36[/C][C]12.7[/C][C]11.082[/C][C]1.61797[/C][/ROW]
[ROW][C]37[/C][C]9.3[/C][C]12.0669[/C][C]-2.76686[/C][/ROW]
[ROW][C]38[/C][C]11.8[/C][C]11.7327[/C][C]0.067343[/C][/ROW]
[ROW][C]39[/C][C]5.9[/C][C]9.45663[/C][C]-3.55663[/C][/ROW]
[ROW][C]40[/C][C]11.4[/C][C]11.0971[/C][C]0.302895[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]10.0189[/C][C]2.98114[/C][/ROW]
[ROW][C]42[/C][C]10.8[/C][C]10.6428[/C][C]0.157188[/C][/ROW]
[ROW][C]43[/C][C]12.3[/C][C]8.9791[/C][C]3.3209[/C][/ROW]
[ROW][C]44[/C][C]11.3[/C][C]12.5982[/C][C]-1.29816[/C][/ROW]
[ROW][C]45[/C][C]11.8[/C][C]9.95588[/C][C]1.84412[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]10.7116[/C][C]-2.8116[/C][/ROW]
[ROW][C]47[/C][C]12.7[/C][C]9.59782[/C][C]3.10218[/C][/ROW]
[ROW][C]48[/C][C]12.3[/C][C]9.67076[/C][C]2.62924[/C][/ROW]
[ROW][C]49[/C][C]11.6[/C][C]10.8829[/C][C]0.717077[/C][/ROW]
[ROW][C]50[/C][C]6.7[/C][C]8.9379[/C][C]-2.2379[/C][/ROW]
[ROW][C]51[/C][C]10.9[/C][C]9.41434[/C][C]1.48566[/C][/ROW]
[ROW][C]52[/C][C]12.1[/C][C]10.9776[/C][C]1.12239[/C][/ROW]
[ROW][C]53[/C][C]13.3[/C][C]10.4238[/C][C]2.87617[/C][/ROW]
[ROW][C]54[/C][C]10.1[/C][C]10.1139[/C][C]-0.013942[/C][/ROW]
[ROW][C]55[/C][C]5.7[/C][C]10.5702[/C][C]-4.8702[/C][/ROW]
[ROW][C]56[/C][C]14.3[/C][C]10.5845[/C][C]3.71548[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]8.80393[/C][C]-0.803933[/C][/ROW]
[ROW][C]58[/C][C]13.3[/C][C]11.185[/C][C]2.11503[/C][/ROW]
[ROW][C]59[/C][C]9.3[/C][C]11.6641[/C][C]-2.36412[/C][/ROW]
[ROW][C]60[/C][C]12.5[/C][C]10.1629[/C][C]2.33706[/C][/ROW]
[ROW][C]61[/C][C]7.6[/C][C]9.1745[/C][C]-1.5745[/C][/ROW]
[ROW][C]62[/C][C]15.9[/C][C]12.4502[/C][C]3.44978[/C][/ROW]
[ROW][C]63[/C][C]9.2[/C][C]10.3466[/C][C]-1.14656[/C][/ROW]
[ROW][C]64[/C][C]9.1[/C][C]9.75834[/C][C]-0.658341[/C][/ROW]
[ROW][C]65[/C][C]11.1[/C][C]12.5294[/C][C]-1.42941[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.0611[/C][C]0.938925[/C][/ROW]
[ROW][C]67[/C][C]14.5[/C][C]11.9903[/C][C]2.50971[/C][/ROW]
[ROW][C]68[/C][C]12.2[/C][C]10.552[/C][C]1.64797[/C][/ROW]
[ROW][C]69[/C][C]12.3[/C][C]12.6[/C][C]-0.300007[/C][/ROW]
[ROW][C]70[/C][C]11.4[/C][C]10.2443[/C][C]1.15569[/C][/ROW]
[ROW][C]71[/C][C]14.6[/C][C]11.7666[/C][C]2.83339[/C][/ROW]
[ROW][C]72[/C][C]12.6[/C][C]11.236[/C][C]1.36396[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.6903[/C][C]0.309699[/C][/ROW]
[ROW][C]74[/C][C]12.6[/C][C]11.0582[/C][C]1.54175[/C][/ROW]
[ROW][C]75[/C][C]13.2[/C][C]11.8998[/C][C]1.30022[/C][/ROW]
[ROW][C]76[/C][C]9.9[/C][C]10.2691[/C][C]-0.369096[/C][/ROW]
[ROW][C]77[/C][C]7.7[/C][C]9.58715[/C][C]-1.88715[/C][/ROW]
[ROW][C]78[/C][C]10.5[/C][C]10.937[/C][C]-0.437025[/C][/ROW]
[ROW][C]79[/C][C]13.4[/C][C]10.8859[/C][C]2.51409[/C][/ROW]
[ROW][C]80[/C][C]10.9[/C][C]11.0149[/C][C]-0.114875[/C][/ROW]
[ROW][C]81[/C][C]4.3[/C][C]9.0274[/C][C]-4.7274[/C][/ROW]
[ROW][C]82[/C][C]10.3[/C][C]11.9311[/C][C]-1.63109[/C][/ROW]
[ROW][C]83[/C][C]11.8[/C][C]11.7959[/C][C]0.00406303[/C][/ROW]
[ROW][C]84[/C][C]11.2[/C][C]10.7969[/C][C]0.403099[/C][/ROW]
[ROW][C]85[/C][C]11.4[/C][C]10.9281[/C][C]0.471862[/C][/ROW]
[ROW][C]86[/C][C]8.6[/C][C]10.8064[/C][C]-2.20643[/C][/ROW]
[ROW][C]87[/C][C]13.2[/C][C]10.3265[/C][C]2.87352[/C][/ROW]
[ROW][C]88[/C][C]12.6[/C][C]9.98186[/C][C]2.61814[/C][/ROW]
[ROW][C]89[/C][C]5.6[/C][C]8.94894[/C][C]-3.34894[/C][/ROW]
[ROW][C]90[/C][C]9.9[/C][C]10.5043[/C][C]-0.604305[/C][/ROW]
[ROW][C]91[/C][C]8.8[/C][C]10.1926[/C][C]-1.3926[/C][/ROW]
[ROW][C]92[/C][C]7.7[/C][C]9.85336[/C][C]-2.15336[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.67821[/C][C]-0.678212[/C][/ROW]
[ROW][C]94[/C][C]7.3[/C][C]10.939[/C][C]-3.639[/C][/ROW]
[ROW][C]95[/C][C]11.4[/C][C]9.34502[/C][C]2.05498[/C][/ROW]
[ROW][C]96[/C][C]13.6[/C][C]9.52746[/C][C]4.07254[/C][/ROW]
[ROW][C]97[/C][C]7.9[/C][C]10.5059[/C][C]-2.60589[/C][/ROW]
[ROW][C]98[/C][C]10.7[/C][C]9.82236[/C][C]0.877642[/C][/ROW]
[ROW][C]99[/C][C]10.3[/C][C]11.329[/C][C]-1.02896[/C][/ROW]
[ROW][C]100[/C][C]9.6[/C][C]10.648[/C][C]-1.048[/C][/ROW]
[ROW][C]101[/C][C]14.2[/C][C]10.7813[/C][C]3.41874[/C][/ROW]
[ROW][C]102[/C][C]8.5[/C][C]11.482[/C][C]-2.98196[/C][/ROW]
[ROW][C]103[/C][C]13.5[/C][C]10.577[/C][C]2.92299[/C][/ROW]
[ROW][C]104[/C][C]6.4[/C][C]9.47667[/C][C]-3.07667[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]10.2925[/C][C]-0.692508[/C][/ROW]
[ROW][C]106[/C][C]11.6[/C][C]11.3527[/C][C]0.247301[/C][/ROW]
[ROW][C]107[/C][C]11.1[/C][C]9.62416[/C][C]1.47584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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
112.911.73911.16085
212.29.687912.51209
312.810.54342.25655
47.411.8001-4.40015
56.79.64585-2.94585
612.613.3565-0.756524
714.812.08052.7195
813.311.14592.1541
911.112.5512-1.45122
108.211.2891-3.08911
1111.412.3986-0.998573
126.410.8295-4.42949
1310.69.390891.20911
141213.2219-1.22191
156.39.55737-3.25737
1611.310.56970.730284
1711.912.0001-0.100121
189.310.2241-0.924133
199.611.6965-2.09647
20108.908981.09102
2113.811.92311.8769
2210.810.8397-0.0397394
2311.711.826-0.126022
2410.911.9505-1.05052
2516.114.01172.08833
2613.411.50441.89561
279.910.4807-0.580717
2811.510.23761.26243
298.39.30983-1.00983
3011.710.57951.12046
31910.5973-1.59733
329.712.4311-2.73112
3310.810.79210.00793088
3410.310.4557-0.155684
3510.410.4654-0.0654249
3612.711.0821.61797
379.312.0669-2.76686
3811.811.73270.067343
395.99.45663-3.55663
4011.411.09710.302895
411310.01892.98114
4210.810.64280.157188
4312.38.97913.3209
4411.312.5982-1.29816
4511.89.955881.84412
467.910.7116-2.8116
4712.79.597823.10218
4812.39.670762.62924
4911.610.88290.717077
506.78.9379-2.2379
5110.99.414341.48566
5212.110.97761.12239
5313.310.42382.87617
5410.110.1139-0.013942
555.710.5702-4.8702
5614.310.58453.71548
5788.80393-0.803933
5813.311.1852.11503
599.311.6641-2.36412
6012.510.16292.33706
617.69.1745-1.5745
6215.912.45023.44978
639.210.3466-1.14656
649.19.75834-0.658341
6511.112.5294-1.42941
661312.06110.938925
6714.511.99032.50971
6812.210.5521.64797
6912.312.6-0.300007
7011.410.24431.15569
7114.611.76662.83339
7212.611.2361.36396
731312.69030.309699
7412.611.05821.54175
7513.211.89981.30022
769.910.2691-0.369096
777.79.58715-1.88715
7810.510.937-0.437025
7913.410.88592.51409
8010.911.0149-0.114875
814.39.0274-4.7274
8210.311.9311-1.63109
8311.811.79590.00406303
8411.210.79690.403099
8511.410.92810.471862
868.610.8064-2.20643
8713.210.32652.87352
8812.69.981862.61814
895.68.94894-3.34894
909.910.5043-0.604305
918.810.1926-1.3926
927.79.85336-2.15336
9399.67821-0.678212
947.310.939-3.639
9511.49.345022.05498
9613.69.527464.07254
977.910.5059-2.60589
9810.79.822360.877642
9910.311.329-1.02896
1009.610.648-1.048
10114.210.78133.41874
1028.511.482-2.98196
10313.510.5772.92299
1046.49.47667-3.07667
1059.610.2925-0.692508
10611.611.35270.247301
10711.19.624161.47584






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2851750.570350.714825
180.4983550.9967110.501645
190.3615670.7231330.638433
200.2591420.5182850.740858
210.229680.459360.77032
220.1679610.3359230.832039
230.1380290.2760570.861971
240.1039920.2079840.896008
250.129970.259940.87003
260.09696410.1939280.903036
270.1667560.3335110.833244
280.1329140.2658290.867086
290.1117760.2235520.888224
300.08834130.1766830.911659
310.06364290.1272860.936357
320.1082850.2165710.891715
330.1234590.2469190.876541
340.09955410.1991080.900446
350.0717850.143570.928215
360.05278340.1055670.947217
370.1477340.2954670.852266
380.1115130.2230260.888487
390.1957010.3914020.804299
400.1517730.3035460.848227
410.1846880.3693760.815312
420.1444990.2889980.855501
430.1733640.3467280.826636
440.1856030.3712070.814397
450.2102670.4205340.789733
460.3568650.713730.643135
470.438570.877140.56143
480.4306530.8613070.569347
490.3704320.7408640.629568
500.360670.721340.63933
510.3121890.6243780.687811
520.2832950.566590.716705
530.3622650.724530.637735
540.313520.6270390.68648
550.5740860.8518280.425914
560.6539040.6921920.346096
570.5982680.8034630.401732
580.5706860.8586290.429314
590.606640.786720.39336
600.604710.790580.39529
610.5695840.8608320.430416
620.6011010.7977970.398899
630.558180.8836390.44182
640.498030.9960610.50197
650.4466650.8933310.553335
660.405910.8118190.59409
670.3859150.771830.614085
680.3605210.7210420.639479
690.2986740.5973470.701326
700.2580160.5160330.741984
710.3301850.6603690.669815
720.2850640.5701280.714936
730.2286410.4572810.771359
740.1832960.3665930.816704
750.1641210.3282430.835879
760.1326860.2653720.867314
770.1017070.2034150.898293
780.07755990.155120.92244
790.07522370.1504470.924776
800.05231530.1046310.947685
810.1389470.2778940.861053
820.1215210.2430420.878479
830.08369730.1673950.916303
840.07646360.1529270.923536
850.05666640.1133330.943334
860.04359080.08718170.956409
870.09884040.1976810.90116
880.09501460.1900290.904985
890.1389180.2778360.861082
900.0859480.1718960.914052

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.285175 & 0.57035 & 0.714825 \tabularnewline
18 & 0.498355 & 0.996711 & 0.501645 \tabularnewline
19 & 0.361567 & 0.723133 & 0.638433 \tabularnewline
20 & 0.259142 & 0.518285 & 0.740858 \tabularnewline
21 & 0.22968 & 0.45936 & 0.77032 \tabularnewline
22 & 0.167961 & 0.335923 & 0.832039 \tabularnewline
23 & 0.138029 & 0.276057 & 0.861971 \tabularnewline
24 & 0.103992 & 0.207984 & 0.896008 \tabularnewline
25 & 0.12997 & 0.25994 & 0.87003 \tabularnewline
26 & 0.0969641 & 0.193928 & 0.903036 \tabularnewline
27 & 0.166756 & 0.333511 & 0.833244 \tabularnewline
28 & 0.132914 & 0.265829 & 0.867086 \tabularnewline
29 & 0.111776 & 0.223552 & 0.888224 \tabularnewline
30 & 0.0883413 & 0.176683 & 0.911659 \tabularnewline
31 & 0.0636429 & 0.127286 & 0.936357 \tabularnewline
32 & 0.108285 & 0.216571 & 0.891715 \tabularnewline
33 & 0.123459 & 0.246919 & 0.876541 \tabularnewline
34 & 0.0995541 & 0.199108 & 0.900446 \tabularnewline
35 & 0.071785 & 0.14357 & 0.928215 \tabularnewline
36 & 0.0527834 & 0.105567 & 0.947217 \tabularnewline
37 & 0.147734 & 0.295467 & 0.852266 \tabularnewline
38 & 0.111513 & 0.223026 & 0.888487 \tabularnewline
39 & 0.195701 & 0.391402 & 0.804299 \tabularnewline
40 & 0.151773 & 0.303546 & 0.848227 \tabularnewline
41 & 0.184688 & 0.369376 & 0.815312 \tabularnewline
42 & 0.144499 & 0.288998 & 0.855501 \tabularnewline
43 & 0.173364 & 0.346728 & 0.826636 \tabularnewline
44 & 0.185603 & 0.371207 & 0.814397 \tabularnewline
45 & 0.210267 & 0.420534 & 0.789733 \tabularnewline
46 & 0.356865 & 0.71373 & 0.643135 \tabularnewline
47 & 0.43857 & 0.87714 & 0.56143 \tabularnewline
48 & 0.430653 & 0.861307 & 0.569347 \tabularnewline
49 & 0.370432 & 0.740864 & 0.629568 \tabularnewline
50 & 0.36067 & 0.72134 & 0.63933 \tabularnewline
51 & 0.312189 & 0.624378 & 0.687811 \tabularnewline
52 & 0.283295 & 0.56659 & 0.716705 \tabularnewline
53 & 0.362265 & 0.72453 & 0.637735 \tabularnewline
54 & 0.31352 & 0.627039 & 0.68648 \tabularnewline
55 & 0.574086 & 0.851828 & 0.425914 \tabularnewline
56 & 0.653904 & 0.692192 & 0.346096 \tabularnewline
57 & 0.598268 & 0.803463 & 0.401732 \tabularnewline
58 & 0.570686 & 0.858629 & 0.429314 \tabularnewline
59 & 0.60664 & 0.78672 & 0.39336 \tabularnewline
60 & 0.60471 & 0.79058 & 0.39529 \tabularnewline
61 & 0.569584 & 0.860832 & 0.430416 \tabularnewline
62 & 0.601101 & 0.797797 & 0.398899 \tabularnewline
63 & 0.55818 & 0.883639 & 0.44182 \tabularnewline
64 & 0.49803 & 0.996061 & 0.50197 \tabularnewline
65 & 0.446665 & 0.893331 & 0.553335 \tabularnewline
66 & 0.40591 & 0.811819 & 0.59409 \tabularnewline
67 & 0.385915 & 0.77183 & 0.614085 \tabularnewline
68 & 0.360521 & 0.721042 & 0.639479 \tabularnewline
69 & 0.298674 & 0.597347 & 0.701326 \tabularnewline
70 & 0.258016 & 0.516033 & 0.741984 \tabularnewline
71 & 0.330185 & 0.660369 & 0.669815 \tabularnewline
72 & 0.285064 & 0.570128 & 0.714936 \tabularnewline
73 & 0.228641 & 0.457281 & 0.771359 \tabularnewline
74 & 0.183296 & 0.366593 & 0.816704 \tabularnewline
75 & 0.164121 & 0.328243 & 0.835879 \tabularnewline
76 & 0.132686 & 0.265372 & 0.867314 \tabularnewline
77 & 0.101707 & 0.203415 & 0.898293 \tabularnewline
78 & 0.0775599 & 0.15512 & 0.92244 \tabularnewline
79 & 0.0752237 & 0.150447 & 0.924776 \tabularnewline
80 & 0.0523153 & 0.104631 & 0.947685 \tabularnewline
81 & 0.138947 & 0.277894 & 0.861053 \tabularnewline
82 & 0.121521 & 0.243042 & 0.878479 \tabularnewline
83 & 0.0836973 & 0.167395 & 0.916303 \tabularnewline
84 & 0.0764636 & 0.152927 & 0.923536 \tabularnewline
85 & 0.0566664 & 0.113333 & 0.943334 \tabularnewline
86 & 0.0435908 & 0.0871817 & 0.956409 \tabularnewline
87 & 0.0988404 & 0.197681 & 0.90116 \tabularnewline
88 & 0.0950146 & 0.190029 & 0.904985 \tabularnewline
89 & 0.138918 & 0.277836 & 0.861082 \tabularnewline
90 & 0.085948 & 0.171896 & 0.914052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.285175[/C][C]0.57035[/C][C]0.714825[/C][/ROW]
[ROW][C]18[/C][C]0.498355[/C][C]0.996711[/C][C]0.501645[/C][/ROW]
[ROW][C]19[/C][C]0.361567[/C][C]0.723133[/C][C]0.638433[/C][/ROW]
[ROW][C]20[/C][C]0.259142[/C][C]0.518285[/C][C]0.740858[/C][/ROW]
[ROW][C]21[/C][C]0.22968[/C][C]0.45936[/C][C]0.77032[/C][/ROW]
[ROW][C]22[/C][C]0.167961[/C][C]0.335923[/C][C]0.832039[/C][/ROW]
[ROW][C]23[/C][C]0.138029[/C][C]0.276057[/C][C]0.861971[/C][/ROW]
[ROW][C]24[/C][C]0.103992[/C][C]0.207984[/C][C]0.896008[/C][/ROW]
[ROW][C]25[/C][C]0.12997[/C][C]0.25994[/C][C]0.87003[/C][/ROW]
[ROW][C]26[/C][C]0.0969641[/C][C]0.193928[/C][C]0.903036[/C][/ROW]
[ROW][C]27[/C][C]0.166756[/C][C]0.333511[/C][C]0.833244[/C][/ROW]
[ROW][C]28[/C][C]0.132914[/C][C]0.265829[/C][C]0.867086[/C][/ROW]
[ROW][C]29[/C][C]0.111776[/C][C]0.223552[/C][C]0.888224[/C][/ROW]
[ROW][C]30[/C][C]0.0883413[/C][C]0.176683[/C][C]0.911659[/C][/ROW]
[ROW][C]31[/C][C]0.0636429[/C][C]0.127286[/C][C]0.936357[/C][/ROW]
[ROW][C]32[/C][C]0.108285[/C][C]0.216571[/C][C]0.891715[/C][/ROW]
[ROW][C]33[/C][C]0.123459[/C][C]0.246919[/C][C]0.876541[/C][/ROW]
[ROW][C]34[/C][C]0.0995541[/C][C]0.199108[/C][C]0.900446[/C][/ROW]
[ROW][C]35[/C][C]0.071785[/C][C]0.14357[/C][C]0.928215[/C][/ROW]
[ROW][C]36[/C][C]0.0527834[/C][C]0.105567[/C][C]0.947217[/C][/ROW]
[ROW][C]37[/C][C]0.147734[/C][C]0.295467[/C][C]0.852266[/C][/ROW]
[ROW][C]38[/C][C]0.111513[/C][C]0.223026[/C][C]0.888487[/C][/ROW]
[ROW][C]39[/C][C]0.195701[/C][C]0.391402[/C][C]0.804299[/C][/ROW]
[ROW][C]40[/C][C]0.151773[/C][C]0.303546[/C][C]0.848227[/C][/ROW]
[ROW][C]41[/C][C]0.184688[/C][C]0.369376[/C][C]0.815312[/C][/ROW]
[ROW][C]42[/C][C]0.144499[/C][C]0.288998[/C][C]0.855501[/C][/ROW]
[ROW][C]43[/C][C]0.173364[/C][C]0.346728[/C][C]0.826636[/C][/ROW]
[ROW][C]44[/C][C]0.185603[/C][C]0.371207[/C][C]0.814397[/C][/ROW]
[ROW][C]45[/C][C]0.210267[/C][C]0.420534[/C][C]0.789733[/C][/ROW]
[ROW][C]46[/C][C]0.356865[/C][C]0.71373[/C][C]0.643135[/C][/ROW]
[ROW][C]47[/C][C]0.43857[/C][C]0.87714[/C][C]0.56143[/C][/ROW]
[ROW][C]48[/C][C]0.430653[/C][C]0.861307[/C][C]0.569347[/C][/ROW]
[ROW][C]49[/C][C]0.370432[/C][C]0.740864[/C][C]0.629568[/C][/ROW]
[ROW][C]50[/C][C]0.36067[/C][C]0.72134[/C][C]0.63933[/C][/ROW]
[ROW][C]51[/C][C]0.312189[/C][C]0.624378[/C][C]0.687811[/C][/ROW]
[ROW][C]52[/C][C]0.283295[/C][C]0.56659[/C][C]0.716705[/C][/ROW]
[ROW][C]53[/C][C]0.362265[/C][C]0.72453[/C][C]0.637735[/C][/ROW]
[ROW][C]54[/C][C]0.31352[/C][C]0.627039[/C][C]0.68648[/C][/ROW]
[ROW][C]55[/C][C]0.574086[/C][C]0.851828[/C][C]0.425914[/C][/ROW]
[ROW][C]56[/C][C]0.653904[/C][C]0.692192[/C][C]0.346096[/C][/ROW]
[ROW][C]57[/C][C]0.598268[/C][C]0.803463[/C][C]0.401732[/C][/ROW]
[ROW][C]58[/C][C]0.570686[/C][C]0.858629[/C][C]0.429314[/C][/ROW]
[ROW][C]59[/C][C]0.60664[/C][C]0.78672[/C][C]0.39336[/C][/ROW]
[ROW][C]60[/C][C]0.60471[/C][C]0.79058[/C][C]0.39529[/C][/ROW]
[ROW][C]61[/C][C]0.569584[/C][C]0.860832[/C][C]0.430416[/C][/ROW]
[ROW][C]62[/C][C]0.601101[/C][C]0.797797[/C][C]0.398899[/C][/ROW]
[ROW][C]63[/C][C]0.55818[/C][C]0.883639[/C][C]0.44182[/C][/ROW]
[ROW][C]64[/C][C]0.49803[/C][C]0.996061[/C][C]0.50197[/C][/ROW]
[ROW][C]65[/C][C]0.446665[/C][C]0.893331[/C][C]0.553335[/C][/ROW]
[ROW][C]66[/C][C]0.40591[/C][C]0.811819[/C][C]0.59409[/C][/ROW]
[ROW][C]67[/C][C]0.385915[/C][C]0.77183[/C][C]0.614085[/C][/ROW]
[ROW][C]68[/C][C]0.360521[/C][C]0.721042[/C][C]0.639479[/C][/ROW]
[ROW][C]69[/C][C]0.298674[/C][C]0.597347[/C][C]0.701326[/C][/ROW]
[ROW][C]70[/C][C]0.258016[/C][C]0.516033[/C][C]0.741984[/C][/ROW]
[ROW][C]71[/C][C]0.330185[/C][C]0.660369[/C][C]0.669815[/C][/ROW]
[ROW][C]72[/C][C]0.285064[/C][C]0.570128[/C][C]0.714936[/C][/ROW]
[ROW][C]73[/C][C]0.228641[/C][C]0.457281[/C][C]0.771359[/C][/ROW]
[ROW][C]74[/C][C]0.183296[/C][C]0.366593[/C][C]0.816704[/C][/ROW]
[ROW][C]75[/C][C]0.164121[/C][C]0.328243[/C][C]0.835879[/C][/ROW]
[ROW][C]76[/C][C]0.132686[/C][C]0.265372[/C][C]0.867314[/C][/ROW]
[ROW][C]77[/C][C]0.101707[/C][C]0.203415[/C][C]0.898293[/C][/ROW]
[ROW][C]78[/C][C]0.0775599[/C][C]0.15512[/C][C]0.92244[/C][/ROW]
[ROW][C]79[/C][C]0.0752237[/C][C]0.150447[/C][C]0.924776[/C][/ROW]
[ROW][C]80[/C][C]0.0523153[/C][C]0.104631[/C][C]0.947685[/C][/ROW]
[ROW][C]81[/C][C]0.138947[/C][C]0.277894[/C][C]0.861053[/C][/ROW]
[ROW][C]82[/C][C]0.121521[/C][C]0.243042[/C][C]0.878479[/C][/ROW]
[ROW][C]83[/C][C]0.0836973[/C][C]0.167395[/C][C]0.916303[/C][/ROW]
[ROW][C]84[/C][C]0.0764636[/C][C]0.152927[/C][C]0.923536[/C][/ROW]
[ROW][C]85[/C][C]0.0566664[/C][C]0.113333[/C][C]0.943334[/C][/ROW]
[ROW][C]86[/C][C]0.0435908[/C][C]0.0871817[/C][C]0.956409[/C][/ROW]
[ROW][C]87[/C][C]0.0988404[/C][C]0.197681[/C][C]0.90116[/C][/ROW]
[ROW][C]88[/C][C]0.0950146[/C][C]0.190029[/C][C]0.904985[/C][/ROW]
[ROW][C]89[/C][C]0.138918[/C][C]0.277836[/C][C]0.861082[/C][/ROW]
[ROW][C]90[/C][C]0.085948[/C][C]0.171896[/C][C]0.914052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2851750.570350.714825
180.4983550.9967110.501645
190.3615670.7231330.638433
200.2591420.5182850.740858
210.229680.459360.77032
220.1679610.3359230.832039
230.1380290.2760570.861971
240.1039920.2079840.896008
250.129970.259940.87003
260.09696410.1939280.903036
270.1667560.3335110.833244
280.1329140.2658290.867086
290.1117760.2235520.888224
300.08834130.1766830.911659
310.06364290.1272860.936357
320.1082850.2165710.891715
330.1234590.2469190.876541
340.09955410.1991080.900446
350.0717850.143570.928215
360.05278340.1055670.947217
370.1477340.2954670.852266
380.1115130.2230260.888487
390.1957010.3914020.804299
400.1517730.3035460.848227
410.1846880.3693760.815312
420.1444990.2889980.855501
430.1733640.3467280.826636
440.1856030.3712070.814397
450.2102670.4205340.789733
460.3568650.713730.643135
470.438570.877140.56143
480.4306530.8613070.569347
490.3704320.7408640.629568
500.360670.721340.63933
510.3121890.6243780.687811
520.2832950.566590.716705
530.3622650.724530.637735
540.313520.6270390.68648
550.5740860.8518280.425914
560.6539040.6921920.346096
570.5982680.8034630.401732
580.5706860.8586290.429314
590.606640.786720.39336
600.604710.790580.39529
610.5695840.8608320.430416
620.6011010.7977970.398899
630.558180.8836390.44182
640.498030.9960610.50197
650.4466650.8933310.553335
660.405910.8118190.59409
670.3859150.771830.614085
680.3605210.7210420.639479
690.2986740.5973470.701326
700.2580160.5160330.741984
710.3301850.6603690.669815
720.2850640.5701280.714936
730.2286410.4572810.771359
740.1832960.3665930.816704
750.1641210.3282430.835879
760.1326860.2653720.867314
770.1017070.2034150.898293
780.07755990.155120.92244
790.07522370.1504470.924776
800.05231530.1046310.947685
810.1389470.2778940.861053
820.1215210.2430420.878479
830.08369730.1673950.916303
840.07646360.1529270.923536
850.05666640.1133330.943334
860.04359080.08718170.956409
870.09884040.1976810.90116
880.09501460.1900290.904985
890.1389180.2778360.861082
900.0859480.1718960.914052






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 level10.0135135OK

\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 & 0.0135135 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267343&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]0.0135135[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267343&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267343&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 level10.0135135OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}