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 10:42:30 +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/t1418554139jnufz3v7gx0j9uv.htm/, Retrieved Thu, 16 May 2024 23:49:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267428, Retrieved Thu, 16 May 2024 23:49:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.4381 + 0.993516programma[t] -0.33352gender[t] -0.118998age[t] + 0.0124482LFM[t] + 0.0247748PRH[t] + 0.0257046CH[t] + 0.00794057Blogs[t] + 0.244809Calculation[t] + 0.131127Algebraic_Reasoning[t] -0.146629Graphical_Interpretation[t] + 0.156606Proportionality_and_Ratio[t] -0.174852Probability_and_Sampling[t] -0.589024Estimation[t] -0.0330091AMS.I[t] + 0.00551097AMS.E[t] + 0.00315548AMS.A[t] -0.244092CONFSTATTOT[t] + 0.192715CONFSOFTTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.4381 +  0.993516programma[t] -0.33352gender[t] -0.118998age[t] +  0.0124482LFM[t] +  0.0247748PRH[t] +  0.0257046CH[t] +  0.00794057Blogs[t] +  0.244809Calculation[t] +  0.131127Algebraic_Reasoning[t] -0.146629Graphical_Interpretation[t] +  0.156606Proportionality_and_Ratio[t] -0.174852Probability_and_Sampling[t] -0.589024Estimation[t] -0.0330091AMS.I[t] +  0.00551097AMS.E[t] +  0.00315548AMS.A[t] -0.244092CONFSTATTOT[t] +  0.192715CONFSOFTTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.4381 +  0.993516programma[t] -0.33352gender[t] -0.118998age[t] +  0.0124482LFM[t] +  0.0247748PRH[t] +  0.0257046CH[t] +  0.00794057Blogs[t] +  0.244809Calculation[t] +  0.131127Algebraic_Reasoning[t] -0.146629Graphical_Interpretation[t] +  0.156606Proportionality_and_Ratio[t] -0.174852Probability_and_Sampling[t] -0.589024Estimation[t] -0.0330091AMS.I[t] +  0.00551097AMS.E[t] +  0.00315548AMS.A[t] -0.244092CONFSTATTOT[t] +  0.192715CONFSOFTTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.4381 + 0.993516programma[t] -0.33352gender[t] -0.118998age[t] + 0.0124482LFM[t] + 0.0247748PRH[t] + 0.0257046CH[t] + 0.00794057Blogs[t] + 0.244809Calculation[t] + 0.131127Algebraic_Reasoning[t] -0.146629Graphical_Interpretation[t] + 0.156606Proportionality_and_Ratio[t] -0.174852Probability_and_Sampling[t] -0.589024Estimation[t] -0.0330091AMS.I[t] + 0.00551097AMS.E[t] + 0.00315548AMS.A[t] -0.244092CONFSTATTOT[t] + 0.192715CONFSOFTTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.43815.068312.0590.04240320.0212016
programma0.9935160.7188321.3820.1704320.0852158
gender-0.333520.555782-0.60010.5499870.274993
age-0.1189980.198671-0.5990.5507320.275366
LFM0.01244820.00850691.4630.1469470.0734736
PRH0.02477480.01312641.8870.06240220.0312011
CH0.02570460.01865171.3780.1716550.0858276
Blogs0.007940570.009010360.88130.3805720.190286
Calculation0.2448090.1900411.2880.2010570.100528
Algebraic_Reasoning0.1311270.1510990.86780.387850.193925
Graphical_Interpretation-0.1466290.206298-0.71080.479110.239555
Proportionality_and_Ratio0.1566060.2389930.65530.5139990.256999
Probability_and_Sampling-0.1748520.390827-0.44740.6556920.327846
Estimation-0.5890240.338227-1.7420.08508930.0425446
AMS.I-0.03300910.0242398-1.3620.1767450.0883726
AMS.E0.005510970.03724280.1480.8827020.441351
AMS.A0.003155480.09258110.034080.9728880.486444
CONFSTATTOT-0.2440920.116743-2.0910.03942540.0197127
CONFSOFTTOT0.1927150.132781.4510.1502280.0751139

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.4381 & 5.06831 & 2.059 & 0.0424032 & 0.0212016 \tabularnewline
programma & 0.993516 & 0.718832 & 1.382 & 0.170432 & 0.0852158 \tabularnewline
gender & -0.33352 & 0.555782 & -0.6001 & 0.549987 & 0.274993 \tabularnewline
age & -0.118998 & 0.198671 & -0.599 & 0.550732 & 0.275366 \tabularnewline
LFM & 0.0124482 & 0.0085069 & 1.463 & 0.146947 & 0.0734736 \tabularnewline
PRH & 0.0247748 & 0.0131264 & 1.887 & 0.0624022 & 0.0312011 \tabularnewline
CH & 0.0257046 & 0.0186517 & 1.378 & 0.171655 & 0.0858276 \tabularnewline
Blogs & 0.00794057 & 0.00901036 & 0.8813 & 0.380572 & 0.190286 \tabularnewline
Calculation & 0.244809 & 0.190041 & 1.288 & 0.201057 & 0.100528 \tabularnewline
Algebraic_Reasoning & 0.131127 & 0.151099 & 0.8678 & 0.38785 & 0.193925 \tabularnewline
Graphical_Interpretation & -0.146629 & 0.206298 & -0.7108 & 0.47911 & 0.239555 \tabularnewline
Proportionality_and_Ratio & 0.156606 & 0.238993 & 0.6553 & 0.513999 & 0.256999 \tabularnewline
Probability_and_Sampling & -0.174852 & 0.390827 & -0.4474 & 0.655692 & 0.327846 \tabularnewline
Estimation & -0.589024 & 0.338227 & -1.742 & 0.0850893 & 0.0425446 \tabularnewline
AMS.I & -0.0330091 & 0.0242398 & -1.362 & 0.176745 & 0.0883726 \tabularnewline
AMS.E & 0.00551097 & 0.0372428 & 0.148 & 0.882702 & 0.441351 \tabularnewline
AMS.A & 0.00315548 & 0.0925811 & 0.03408 & 0.972888 & 0.486444 \tabularnewline
CONFSTATTOT & -0.244092 & 0.116743 & -2.091 & 0.0394254 & 0.0197127 \tabularnewline
CONFSOFTTOT & 0.192715 & 0.13278 & 1.451 & 0.150228 & 0.0751139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.4381[/C][C]5.06831[/C][C]2.059[/C][C]0.0424032[/C][C]0.0212016[/C][/ROW]
[ROW][C]programma[/C][C]0.993516[/C][C]0.718832[/C][C]1.382[/C][C]0.170432[/C][C]0.0852158[/C][/ROW]
[ROW][C]gender[/C][C]-0.33352[/C][C]0.555782[/C][C]-0.6001[/C][C]0.549987[/C][C]0.274993[/C][/ROW]
[ROW][C]age[/C][C]-0.118998[/C][C]0.198671[/C][C]-0.599[/C][C]0.550732[/C][C]0.275366[/C][/ROW]
[ROW][C]LFM[/C][C]0.0124482[/C][C]0.0085069[/C][C]1.463[/C][C]0.146947[/C][C]0.0734736[/C][/ROW]
[ROW][C]PRH[/C][C]0.0247748[/C][C]0.0131264[/C][C]1.887[/C][C]0.0624022[/C][C]0.0312011[/C][/ROW]
[ROW][C]CH[/C][C]0.0257046[/C][C]0.0186517[/C][C]1.378[/C][C]0.171655[/C][C]0.0858276[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00794057[/C][C]0.00901036[/C][C]0.8813[/C][C]0.380572[/C][C]0.190286[/C][/ROW]
[ROW][C]Calculation[/C][C]0.244809[/C][C]0.190041[/C][C]1.288[/C][C]0.201057[/C][C]0.100528[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]0.131127[/C][C]0.151099[/C][C]0.8678[/C][C]0.38785[/C][C]0.193925[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-0.146629[/C][C]0.206298[/C][C]-0.7108[/C][C]0.47911[/C][C]0.239555[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.156606[/C][C]0.238993[/C][C]0.6553[/C][C]0.513999[/C][C]0.256999[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]-0.174852[/C][C]0.390827[/C][C]-0.4474[/C][C]0.655692[/C][C]0.327846[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.589024[/C][C]0.338227[/C][C]-1.742[/C][C]0.0850893[/C][C]0.0425446[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0330091[/C][C]0.0242398[/C][C]-1.362[/C][C]0.176745[/C][C]0.0883726[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.00551097[/C][C]0.0372428[/C][C]0.148[/C][C]0.882702[/C][C]0.441351[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.00315548[/C][C]0.0925811[/C][C]0.03408[/C][C]0.972888[/C][C]0.486444[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.244092[/C][C]0.116743[/C][C]-2.091[/C][C]0.0394254[/C][C]0.0197127[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.192715[/C][C]0.13278[/C][C]1.451[/C][C]0.150228[/C][C]0.0751139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.43815.068312.0590.04240320.0212016
programma0.9935160.7188321.3820.1704320.0852158
gender-0.333520.555782-0.60010.5499870.274993
age-0.1189980.198671-0.5990.5507320.275366
LFM0.01244820.00850691.4630.1469470.0734736
PRH0.02477480.01312641.8870.06240220.0312011
CH0.02570460.01865171.3780.1716550.0858276
Blogs0.007940570.009010360.88130.3805720.190286
Calculation0.2448090.1900411.2880.2010570.100528
Algebraic_Reasoning0.1311270.1510990.86780.387850.193925
Graphical_Interpretation-0.1466290.206298-0.71080.479110.239555
Proportionality_and_Ratio0.1566060.2389930.65530.5139990.256999
Probability_and_Sampling-0.1748520.390827-0.44740.6556920.327846
Estimation-0.5890240.338227-1.7420.08508930.0425446
AMS.I-0.03300910.0242398-1.3620.1767450.0883726
AMS.E0.005510970.03724280.1480.8827020.441351
AMS.A0.003155480.09258110.034080.9728880.486444
CONFSTATTOT-0.2440920.116743-2.0910.03942540.0197127
CONFSOFTTOT0.1927150.132781.4510.1502280.0751139






Multiple Linear Regression - Regression Statistics
Multiple R0.512787
R-squared0.262951
Adjusted R-squared0.112191
F-TEST (value)1.74417
F-TEST (DF numerator)18
F-TEST (DF denominator)88
p-value0.0461622
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25273
Sum Squared Residuals446.582

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.512787 \tabularnewline
R-squared & 0.262951 \tabularnewline
Adjusted R-squared & 0.112191 \tabularnewline
F-TEST (value) & 1.74417 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 88 \tabularnewline
p-value & 0.0461622 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.25273 \tabularnewline
Sum Squared Residuals & 446.582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.512787[/C][/ROW]
[ROW][C]R-squared[/C][C]0.262951[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.112191[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.74417[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]88[/C][/ROW]
[ROW][C]p-value[/C][C]0.0461622[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.25273[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]446.582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&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 R0.512787
R-squared0.262951
Adjusted R-squared0.112191
F-TEST (value)1.74417
F-TEST (DF numerator)18
F-TEST (DF denominator)88
p-value0.0461622
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.25273
Sum Squared Residuals446.582






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.913.316-0.415983
212.210.28971.9103
312.810.8021.99801
47.411.8841-4.48406
56.79.71865-3.01865
612.613.5554-0.955363
714.811.57133.22868
813.311.63221.66776
911.111.8806-0.780586
108.210.3856-2.18564
1111.412.4547-1.05466
126.410.0028-3.60281
1310.69.867960.732035
141213.1912-1.19116
156.38.73473-2.43473
1611.310.38710.912856
1711.912.768-0.868043
189.39.65681-0.356807
199.611.4559-1.85586
20108.611211.38879
2113.811.43712.36289
2210.810.38960.410373
2311.710.87350.826499
2410.912.4623-1.56227
2516.114.11581.98419
2613.411.28592.11411
279.99.875860.0241427
2811.59.821981.67802
298.39.4846-1.1846
3011.710.06051.63946
31910.3774-1.3774
329.713.5675-3.86754
3310.810.53310.266924
3410.310.11860.181437
3510.410.4194-0.0193792
3612.710.91841.78164
379.311.8265-2.52655
3811.812.2075-0.407479
395.99.84195-3.94195
4011.411.32230.0776607
41139.045363.95464
4210.810.9186-0.118598
4312.39.019353.28065
4411.312.8878-1.58778
4511.89.724792.07521
467.910.4164-2.51638
4712.710.00172.69826
4812.39.708162.59184
4911.610.93390.666072
506.78.51478-1.81478
5110.98.958191.94181
5212.110.19921.90082
5313.310.00183.29819
5410.19.986310.113689
555.79.77989-4.07989
5614.311.22023.07984
5789.339-1.339
5813.311.2162.08397
599.311.7412-2.44115
6012.510.51431.98572
617.69.50474-1.90474
6215.912.73053.16945
639.29.99637-0.796366
649.110.314-1.21397
6511.112.3479-1.24788
661312.17330.826749
6714.512.0252.47502
6812.211.12641.07363
6912.312.4835-0.183512
7011.410.46330.936654
7114.612.35762.24244
7212.612.7605-0.160453
731313.2273-0.227348
7412.612.40320.196801
7513.211.62781.57218
769.910.5683-0.668305
777.79.31691-1.61691
7810.510.9569-0.456929
7913.411.02132.37866
8010.910.84320.0568221
814.38.68909-4.38909
8210.311.2887-0.98874
8311.811.71160.0884161
8411.210.10241.09759
8511.410.55530.84466
868.611.2557-2.65571
8713.210.89972.30032
8812.69.255453.34455
895.69.53483-3.93483
909.99.701940.198057
918.810.4863-1.6863
927.710.057-2.35699
9399.1092-0.109202
947.310.9576-3.65761
9511.410.11291.28714
9613.611.27742.32262
977.910.6636-2.76359
9810.79.027521.67248
9910.310.4878-0.187752
1009.610.3104-0.710353
10114.210.95953.24046
1028.510.8959-2.39594
10313.510.79392.70606
1046.49.02387-2.62387
1059.610.8951-1.29511
10611.610.91830.681711
10711.110.44830.651659

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 13.316 & -0.415983 \tabularnewline
2 & 12.2 & 10.2897 & 1.9103 \tabularnewline
3 & 12.8 & 10.802 & 1.99801 \tabularnewline
4 & 7.4 & 11.8841 & -4.48406 \tabularnewline
5 & 6.7 & 9.71865 & -3.01865 \tabularnewline
6 & 12.6 & 13.5554 & -0.955363 \tabularnewline
7 & 14.8 & 11.5713 & 3.22868 \tabularnewline
8 & 13.3 & 11.6322 & 1.66776 \tabularnewline
9 & 11.1 & 11.8806 & -0.780586 \tabularnewline
10 & 8.2 & 10.3856 & -2.18564 \tabularnewline
11 & 11.4 & 12.4547 & -1.05466 \tabularnewline
12 & 6.4 & 10.0028 & -3.60281 \tabularnewline
13 & 10.6 & 9.86796 & 0.732035 \tabularnewline
14 & 12 & 13.1912 & -1.19116 \tabularnewline
15 & 6.3 & 8.73473 & -2.43473 \tabularnewline
16 & 11.3 & 10.3871 & 0.912856 \tabularnewline
17 & 11.9 & 12.768 & -0.868043 \tabularnewline
18 & 9.3 & 9.65681 & -0.356807 \tabularnewline
19 & 9.6 & 11.4559 & -1.85586 \tabularnewline
20 & 10 & 8.61121 & 1.38879 \tabularnewline
21 & 13.8 & 11.4371 & 2.36289 \tabularnewline
22 & 10.8 & 10.3896 & 0.410373 \tabularnewline
23 & 11.7 & 10.8735 & 0.826499 \tabularnewline
24 & 10.9 & 12.4623 & -1.56227 \tabularnewline
25 & 16.1 & 14.1158 & 1.98419 \tabularnewline
26 & 13.4 & 11.2859 & 2.11411 \tabularnewline
27 & 9.9 & 9.87586 & 0.0241427 \tabularnewline
28 & 11.5 & 9.82198 & 1.67802 \tabularnewline
29 & 8.3 & 9.4846 & -1.1846 \tabularnewline
30 & 11.7 & 10.0605 & 1.63946 \tabularnewline
31 & 9 & 10.3774 & -1.3774 \tabularnewline
32 & 9.7 & 13.5675 & -3.86754 \tabularnewline
33 & 10.8 & 10.5331 & 0.266924 \tabularnewline
34 & 10.3 & 10.1186 & 0.181437 \tabularnewline
35 & 10.4 & 10.4194 & -0.0193792 \tabularnewline
36 & 12.7 & 10.9184 & 1.78164 \tabularnewline
37 & 9.3 & 11.8265 & -2.52655 \tabularnewline
38 & 11.8 & 12.2075 & -0.407479 \tabularnewline
39 & 5.9 & 9.84195 & -3.94195 \tabularnewline
40 & 11.4 & 11.3223 & 0.0776607 \tabularnewline
41 & 13 & 9.04536 & 3.95464 \tabularnewline
42 & 10.8 & 10.9186 & -0.118598 \tabularnewline
43 & 12.3 & 9.01935 & 3.28065 \tabularnewline
44 & 11.3 & 12.8878 & -1.58778 \tabularnewline
45 & 11.8 & 9.72479 & 2.07521 \tabularnewline
46 & 7.9 & 10.4164 & -2.51638 \tabularnewline
47 & 12.7 & 10.0017 & 2.69826 \tabularnewline
48 & 12.3 & 9.70816 & 2.59184 \tabularnewline
49 & 11.6 & 10.9339 & 0.666072 \tabularnewline
50 & 6.7 & 8.51478 & -1.81478 \tabularnewline
51 & 10.9 & 8.95819 & 1.94181 \tabularnewline
52 & 12.1 & 10.1992 & 1.90082 \tabularnewline
53 & 13.3 & 10.0018 & 3.29819 \tabularnewline
54 & 10.1 & 9.98631 & 0.113689 \tabularnewline
55 & 5.7 & 9.77989 & -4.07989 \tabularnewline
56 & 14.3 & 11.2202 & 3.07984 \tabularnewline
57 & 8 & 9.339 & -1.339 \tabularnewline
58 & 13.3 & 11.216 & 2.08397 \tabularnewline
59 & 9.3 & 11.7412 & -2.44115 \tabularnewline
60 & 12.5 & 10.5143 & 1.98572 \tabularnewline
61 & 7.6 & 9.50474 & -1.90474 \tabularnewline
62 & 15.9 & 12.7305 & 3.16945 \tabularnewline
63 & 9.2 & 9.99637 & -0.796366 \tabularnewline
64 & 9.1 & 10.314 & -1.21397 \tabularnewline
65 & 11.1 & 12.3479 & -1.24788 \tabularnewline
66 & 13 & 12.1733 & 0.826749 \tabularnewline
67 & 14.5 & 12.025 & 2.47502 \tabularnewline
68 & 12.2 & 11.1264 & 1.07363 \tabularnewline
69 & 12.3 & 12.4835 & -0.183512 \tabularnewline
70 & 11.4 & 10.4633 & 0.936654 \tabularnewline
71 & 14.6 & 12.3576 & 2.24244 \tabularnewline
72 & 12.6 & 12.7605 & -0.160453 \tabularnewline
73 & 13 & 13.2273 & -0.227348 \tabularnewline
74 & 12.6 & 12.4032 & 0.196801 \tabularnewline
75 & 13.2 & 11.6278 & 1.57218 \tabularnewline
76 & 9.9 & 10.5683 & -0.668305 \tabularnewline
77 & 7.7 & 9.31691 & -1.61691 \tabularnewline
78 & 10.5 & 10.9569 & -0.456929 \tabularnewline
79 & 13.4 & 11.0213 & 2.37866 \tabularnewline
80 & 10.9 & 10.8432 & 0.0568221 \tabularnewline
81 & 4.3 & 8.68909 & -4.38909 \tabularnewline
82 & 10.3 & 11.2887 & -0.98874 \tabularnewline
83 & 11.8 & 11.7116 & 0.0884161 \tabularnewline
84 & 11.2 & 10.1024 & 1.09759 \tabularnewline
85 & 11.4 & 10.5553 & 0.84466 \tabularnewline
86 & 8.6 & 11.2557 & -2.65571 \tabularnewline
87 & 13.2 & 10.8997 & 2.30032 \tabularnewline
88 & 12.6 & 9.25545 & 3.34455 \tabularnewline
89 & 5.6 & 9.53483 & -3.93483 \tabularnewline
90 & 9.9 & 9.70194 & 0.198057 \tabularnewline
91 & 8.8 & 10.4863 & -1.6863 \tabularnewline
92 & 7.7 & 10.057 & -2.35699 \tabularnewline
93 & 9 & 9.1092 & -0.109202 \tabularnewline
94 & 7.3 & 10.9576 & -3.65761 \tabularnewline
95 & 11.4 & 10.1129 & 1.28714 \tabularnewline
96 & 13.6 & 11.2774 & 2.32262 \tabularnewline
97 & 7.9 & 10.6636 & -2.76359 \tabularnewline
98 & 10.7 & 9.02752 & 1.67248 \tabularnewline
99 & 10.3 & 10.4878 & -0.187752 \tabularnewline
100 & 9.6 & 10.3104 & -0.710353 \tabularnewline
101 & 14.2 & 10.9595 & 3.24046 \tabularnewline
102 & 8.5 & 10.8959 & -2.39594 \tabularnewline
103 & 13.5 & 10.7939 & 2.70606 \tabularnewline
104 & 6.4 & 9.02387 & -2.62387 \tabularnewline
105 & 9.6 & 10.8951 & -1.29511 \tabularnewline
106 & 11.6 & 10.9183 & 0.681711 \tabularnewline
107 & 11.1 & 10.4483 & 0.651659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&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]13.316[/C][C]-0.415983[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.2897[/C][C]1.9103[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.802[/C][C]1.99801[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.8841[/C][C]-4.48406[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]9.71865[/C][C]-3.01865[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]13.5554[/C][C]-0.955363[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.5713[/C][C]3.22868[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.6322[/C][C]1.66776[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.8806[/C][C]-0.780586[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.3856[/C][C]-2.18564[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]12.4547[/C][C]-1.05466[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.0028[/C][C]-3.60281[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.86796[/C][C]0.732035[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.1912[/C][C]-1.19116[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.73473[/C][C]-2.43473[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.3871[/C][C]0.912856[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]12.768[/C][C]-0.868043[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]9.65681[/C][C]-0.356807[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.4559[/C][C]-1.85586[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]8.61121[/C][C]1.38879[/C][/ROW]
[ROW][C]21[/C][C]13.8[/C][C]11.4371[/C][C]2.36289[/C][/ROW]
[ROW][C]22[/C][C]10.8[/C][C]10.3896[/C][C]0.410373[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.8735[/C][C]0.826499[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.4623[/C][C]-1.56227[/C][/ROW]
[ROW][C]25[/C][C]16.1[/C][C]14.1158[/C][C]1.98419[/C][/ROW]
[ROW][C]26[/C][C]13.4[/C][C]11.2859[/C][C]2.11411[/C][/ROW]
[ROW][C]27[/C][C]9.9[/C][C]9.87586[/C][C]0.0241427[/C][/ROW]
[ROW][C]28[/C][C]11.5[/C][C]9.82198[/C][C]1.67802[/C][/ROW]
[ROW][C]29[/C][C]8.3[/C][C]9.4846[/C][C]-1.1846[/C][/ROW]
[ROW][C]30[/C][C]11.7[/C][C]10.0605[/C][C]1.63946[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]10.3774[/C][C]-1.3774[/C][/ROW]
[ROW][C]32[/C][C]9.7[/C][C]13.5675[/C][C]-3.86754[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]10.5331[/C][C]0.266924[/C][/ROW]
[ROW][C]34[/C][C]10.3[/C][C]10.1186[/C][C]0.181437[/C][/ROW]
[ROW][C]35[/C][C]10.4[/C][C]10.4194[/C][C]-0.0193792[/C][/ROW]
[ROW][C]36[/C][C]12.7[/C][C]10.9184[/C][C]1.78164[/C][/ROW]
[ROW][C]37[/C][C]9.3[/C][C]11.8265[/C][C]-2.52655[/C][/ROW]
[ROW][C]38[/C][C]11.8[/C][C]12.2075[/C][C]-0.407479[/C][/ROW]
[ROW][C]39[/C][C]5.9[/C][C]9.84195[/C][C]-3.94195[/C][/ROW]
[ROW][C]40[/C][C]11.4[/C][C]11.3223[/C][C]0.0776607[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]9.04536[/C][C]3.95464[/C][/ROW]
[ROW][C]42[/C][C]10.8[/C][C]10.9186[/C][C]-0.118598[/C][/ROW]
[ROW][C]43[/C][C]12.3[/C][C]9.01935[/C][C]3.28065[/C][/ROW]
[ROW][C]44[/C][C]11.3[/C][C]12.8878[/C][C]-1.58778[/C][/ROW]
[ROW][C]45[/C][C]11.8[/C][C]9.72479[/C][C]2.07521[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]10.4164[/C][C]-2.51638[/C][/ROW]
[ROW][C]47[/C][C]12.7[/C][C]10.0017[/C][C]2.69826[/C][/ROW]
[ROW][C]48[/C][C]12.3[/C][C]9.70816[/C][C]2.59184[/C][/ROW]
[ROW][C]49[/C][C]11.6[/C][C]10.9339[/C][C]0.666072[/C][/ROW]
[ROW][C]50[/C][C]6.7[/C][C]8.51478[/C][C]-1.81478[/C][/ROW]
[ROW][C]51[/C][C]10.9[/C][C]8.95819[/C][C]1.94181[/C][/ROW]
[ROW][C]52[/C][C]12.1[/C][C]10.1992[/C][C]1.90082[/C][/ROW]
[ROW][C]53[/C][C]13.3[/C][C]10.0018[/C][C]3.29819[/C][/ROW]
[ROW][C]54[/C][C]10.1[/C][C]9.98631[/C][C]0.113689[/C][/ROW]
[ROW][C]55[/C][C]5.7[/C][C]9.77989[/C][C]-4.07989[/C][/ROW]
[ROW][C]56[/C][C]14.3[/C][C]11.2202[/C][C]3.07984[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]9.339[/C][C]-1.339[/C][/ROW]
[ROW][C]58[/C][C]13.3[/C][C]11.216[/C][C]2.08397[/C][/ROW]
[ROW][C]59[/C][C]9.3[/C][C]11.7412[/C][C]-2.44115[/C][/ROW]
[ROW][C]60[/C][C]12.5[/C][C]10.5143[/C][C]1.98572[/C][/ROW]
[ROW][C]61[/C][C]7.6[/C][C]9.50474[/C][C]-1.90474[/C][/ROW]
[ROW][C]62[/C][C]15.9[/C][C]12.7305[/C][C]3.16945[/C][/ROW]
[ROW][C]63[/C][C]9.2[/C][C]9.99637[/C][C]-0.796366[/C][/ROW]
[ROW][C]64[/C][C]9.1[/C][C]10.314[/C][C]-1.21397[/C][/ROW]
[ROW][C]65[/C][C]11.1[/C][C]12.3479[/C][C]-1.24788[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.1733[/C][C]0.826749[/C][/ROW]
[ROW][C]67[/C][C]14.5[/C][C]12.025[/C][C]2.47502[/C][/ROW]
[ROW][C]68[/C][C]12.2[/C][C]11.1264[/C][C]1.07363[/C][/ROW]
[ROW][C]69[/C][C]12.3[/C][C]12.4835[/C][C]-0.183512[/C][/ROW]
[ROW][C]70[/C][C]11.4[/C][C]10.4633[/C][C]0.936654[/C][/ROW]
[ROW][C]71[/C][C]14.6[/C][C]12.3576[/C][C]2.24244[/C][/ROW]
[ROW][C]72[/C][C]12.6[/C][C]12.7605[/C][C]-0.160453[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]13.2273[/C][C]-0.227348[/C][/ROW]
[ROW][C]74[/C][C]12.6[/C][C]12.4032[/C][C]0.196801[/C][/ROW]
[ROW][C]75[/C][C]13.2[/C][C]11.6278[/C][C]1.57218[/C][/ROW]
[ROW][C]76[/C][C]9.9[/C][C]10.5683[/C][C]-0.668305[/C][/ROW]
[ROW][C]77[/C][C]7.7[/C][C]9.31691[/C][C]-1.61691[/C][/ROW]
[ROW][C]78[/C][C]10.5[/C][C]10.9569[/C][C]-0.456929[/C][/ROW]
[ROW][C]79[/C][C]13.4[/C][C]11.0213[/C][C]2.37866[/C][/ROW]
[ROW][C]80[/C][C]10.9[/C][C]10.8432[/C][C]0.0568221[/C][/ROW]
[ROW][C]81[/C][C]4.3[/C][C]8.68909[/C][C]-4.38909[/C][/ROW]
[ROW][C]82[/C][C]10.3[/C][C]11.2887[/C][C]-0.98874[/C][/ROW]
[ROW][C]83[/C][C]11.8[/C][C]11.7116[/C][C]0.0884161[/C][/ROW]
[ROW][C]84[/C][C]11.2[/C][C]10.1024[/C][C]1.09759[/C][/ROW]
[ROW][C]85[/C][C]11.4[/C][C]10.5553[/C][C]0.84466[/C][/ROW]
[ROW][C]86[/C][C]8.6[/C][C]11.2557[/C][C]-2.65571[/C][/ROW]
[ROW][C]87[/C][C]13.2[/C][C]10.8997[/C][C]2.30032[/C][/ROW]
[ROW][C]88[/C][C]12.6[/C][C]9.25545[/C][C]3.34455[/C][/ROW]
[ROW][C]89[/C][C]5.6[/C][C]9.53483[/C][C]-3.93483[/C][/ROW]
[ROW][C]90[/C][C]9.9[/C][C]9.70194[/C][C]0.198057[/C][/ROW]
[ROW][C]91[/C][C]8.8[/C][C]10.4863[/C][C]-1.6863[/C][/ROW]
[ROW][C]92[/C][C]7.7[/C][C]10.057[/C][C]-2.35699[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.1092[/C][C]-0.109202[/C][/ROW]
[ROW][C]94[/C][C]7.3[/C][C]10.9576[/C][C]-3.65761[/C][/ROW]
[ROW][C]95[/C][C]11.4[/C][C]10.1129[/C][C]1.28714[/C][/ROW]
[ROW][C]96[/C][C]13.6[/C][C]11.2774[/C][C]2.32262[/C][/ROW]
[ROW][C]97[/C][C]7.9[/C][C]10.6636[/C][C]-2.76359[/C][/ROW]
[ROW][C]98[/C][C]10.7[/C][C]9.02752[/C][C]1.67248[/C][/ROW]
[ROW][C]99[/C][C]10.3[/C][C]10.4878[/C][C]-0.187752[/C][/ROW]
[ROW][C]100[/C][C]9.6[/C][C]10.3104[/C][C]-0.710353[/C][/ROW]
[ROW][C]101[/C][C]14.2[/C][C]10.9595[/C][C]3.24046[/C][/ROW]
[ROW][C]102[/C][C]8.5[/C][C]10.8959[/C][C]-2.39594[/C][/ROW]
[ROW][C]103[/C][C]13.5[/C][C]10.7939[/C][C]2.70606[/C][/ROW]
[ROW][C]104[/C][C]6.4[/C][C]9.02387[/C][C]-2.62387[/C][/ROW]
[ROW][C]105[/C][C]9.6[/C][C]10.8951[/C][C]-1.29511[/C][/ROW]
[ROW][C]106[/C][C]11.6[/C][C]10.9183[/C][C]0.681711[/C][/ROW]
[ROW][C]107[/C][C]11.1[/C][C]10.4483[/C][C]0.651659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&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
112.913.316-0.415983
212.210.28971.9103
312.810.8021.99801
47.411.8841-4.48406
56.79.71865-3.01865
612.613.5554-0.955363
714.811.57133.22868
813.311.63221.66776
911.111.8806-0.780586
108.210.3856-2.18564
1111.412.4547-1.05466
126.410.0028-3.60281
1310.69.867960.732035
141213.1912-1.19116
156.38.73473-2.43473
1611.310.38710.912856
1711.912.768-0.868043
189.39.65681-0.356807
199.611.4559-1.85586
20108.611211.38879
2113.811.43712.36289
2210.810.38960.410373
2311.710.87350.826499
2410.912.4623-1.56227
2516.114.11581.98419
2613.411.28592.11411
279.99.875860.0241427
2811.59.821981.67802
298.39.4846-1.1846
3011.710.06051.63946
31910.3774-1.3774
329.713.5675-3.86754
3310.810.53310.266924
3410.310.11860.181437
3510.410.4194-0.0193792
3612.710.91841.78164
379.311.8265-2.52655
3811.812.2075-0.407479
395.99.84195-3.94195
4011.411.32230.0776607
41139.045363.95464
4210.810.9186-0.118598
4312.39.019353.28065
4411.312.8878-1.58778
4511.89.724792.07521
467.910.4164-2.51638
4712.710.00172.69826
4812.39.708162.59184
4911.610.93390.666072
506.78.51478-1.81478
5110.98.958191.94181
5212.110.19921.90082
5313.310.00183.29819
5410.19.986310.113689
555.79.77989-4.07989
5614.311.22023.07984
5789.339-1.339
5813.311.2162.08397
599.311.7412-2.44115
6012.510.51431.98572
617.69.50474-1.90474
6215.912.73053.16945
639.29.99637-0.796366
649.110.314-1.21397
6511.112.3479-1.24788
661312.17330.826749
6714.512.0252.47502
6812.211.12641.07363
6912.312.4835-0.183512
7011.410.46330.936654
7114.612.35762.24244
7212.612.7605-0.160453
731313.2273-0.227348
7412.612.40320.196801
7513.211.62781.57218
769.910.5683-0.668305
777.79.31691-1.61691
7810.510.9569-0.456929
7913.411.02132.37866
8010.910.84320.0568221
814.38.68909-4.38909
8210.311.2887-0.98874
8311.811.71160.0884161
8411.210.10241.09759
8511.410.55530.84466
868.611.2557-2.65571
8713.210.89972.30032
8812.69.255453.34455
895.69.53483-3.93483
909.99.701940.198057
918.810.4863-1.6863
927.710.057-2.35699
9399.1092-0.109202
947.310.9576-3.65761
9511.410.11291.28714
9613.611.27742.32262
977.910.6636-2.76359
9810.79.027521.67248
9910.310.4878-0.187752
1009.610.3104-0.710353
10114.210.95953.24046
1028.510.8959-2.39594
10313.510.79392.70606
1046.49.02387-2.62387
1059.610.8951-1.29511
10611.610.91830.681711
10711.110.44830.651659






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.2088360.4176720.791164
230.2929970.5859950.707003
240.1811840.3623670.818816
250.1037770.2075540.896223
260.06125880.1225180.938741
270.03133730.06267460.968663
280.0169080.0338160.983092
290.009877660.01975530.990122
300.004727010.009454030.995273
310.005588110.01117620.994412
320.03285920.06571830.967141
330.02263130.04526250.977369
340.01356980.02713970.98643
350.00997220.01994440.990028
360.008839520.0176790.99116
370.07394110.1478820.926059
380.05995040.1199010.94005
390.1302760.2605530.869724
400.09949170.1989830.900508
410.2133030.4266060.786697
420.1679810.3359620.832019
430.210810.4216210.78919
440.2144810.4289620.785519
450.2540270.5080530.745973
460.2743980.5487960.725602
470.3856920.7713850.614308
480.4387510.8775030.561249
490.378380.7567610.62162
500.3590260.7180520.640974
510.3236420.6472840.676358
520.3455650.691130.654435
530.5774930.8450140.422507
540.5247580.9504830.475242
550.7239650.5520710.276035
560.7299970.5400060.270003
570.6994030.6011930.300597
580.6802090.6395820.319791
590.7685570.4628860.231443
600.7470280.5059440.252972
610.7954640.4090720.204536
620.818880.3622410.18112
630.8000280.3999440.199972
640.7637770.4724460.236223
650.7078480.5843040.292152
660.6536970.6926060.346303
670.6243130.7513740.375687
680.5704090.8591820.429591
690.5545340.8909320.445466
700.6023920.7952160.397608
710.6045310.7909370.395469
720.5640660.8718670.435934
730.4901750.980350.509825
740.4172850.8345710.582715
750.3792630.7585260.620737
760.3854830.7709660.614517
770.3102520.6205040.689748
780.2328840.4657680.767116
790.18350.3670010.8165
800.1495510.2991030.850449
810.252330.504660.74767
820.2167480.4334960.783252
830.2701310.5402630.729869
840.2075410.4150820.792459
850.1962370.3924750.803763

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.208836 & 0.417672 & 0.791164 \tabularnewline
23 & 0.292997 & 0.585995 & 0.707003 \tabularnewline
24 & 0.181184 & 0.362367 & 0.818816 \tabularnewline
25 & 0.103777 & 0.207554 & 0.896223 \tabularnewline
26 & 0.0612588 & 0.122518 & 0.938741 \tabularnewline
27 & 0.0313373 & 0.0626746 & 0.968663 \tabularnewline
28 & 0.016908 & 0.033816 & 0.983092 \tabularnewline
29 & 0.00987766 & 0.0197553 & 0.990122 \tabularnewline
30 & 0.00472701 & 0.00945403 & 0.995273 \tabularnewline
31 & 0.00558811 & 0.0111762 & 0.994412 \tabularnewline
32 & 0.0328592 & 0.0657183 & 0.967141 \tabularnewline
33 & 0.0226313 & 0.0452625 & 0.977369 \tabularnewline
34 & 0.0135698 & 0.0271397 & 0.98643 \tabularnewline
35 & 0.0099722 & 0.0199444 & 0.990028 \tabularnewline
36 & 0.00883952 & 0.017679 & 0.99116 \tabularnewline
37 & 0.0739411 & 0.147882 & 0.926059 \tabularnewline
38 & 0.0599504 & 0.119901 & 0.94005 \tabularnewline
39 & 0.130276 & 0.260553 & 0.869724 \tabularnewline
40 & 0.0994917 & 0.198983 & 0.900508 \tabularnewline
41 & 0.213303 & 0.426606 & 0.786697 \tabularnewline
42 & 0.167981 & 0.335962 & 0.832019 \tabularnewline
43 & 0.21081 & 0.421621 & 0.78919 \tabularnewline
44 & 0.214481 & 0.428962 & 0.785519 \tabularnewline
45 & 0.254027 & 0.508053 & 0.745973 \tabularnewline
46 & 0.274398 & 0.548796 & 0.725602 \tabularnewline
47 & 0.385692 & 0.771385 & 0.614308 \tabularnewline
48 & 0.438751 & 0.877503 & 0.561249 \tabularnewline
49 & 0.37838 & 0.756761 & 0.62162 \tabularnewline
50 & 0.359026 & 0.718052 & 0.640974 \tabularnewline
51 & 0.323642 & 0.647284 & 0.676358 \tabularnewline
52 & 0.345565 & 0.69113 & 0.654435 \tabularnewline
53 & 0.577493 & 0.845014 & 0.422507 \tabularnewline
54 & 0.524758 & 0.950483 & 0.475242 \tabularnewline
55 & 0.723965 & 0.552071 & 0.276035 \tabularnewline
56 & 0.729997 & 0.540006 & 0.270003 \tabularnewline
57 & 0.699403 & 0.601193 & 0.300597 \tabularnewline
58 & 0.680209 & 0.639582 & 0.319791 \tabularnewline
59 & 0.768557 & 0.462886 & 0.231443 \tabularnewline
60 & 0.747028 & 0.505944 & 0.252972 \tabularnewline
61 & 0.795464 & 0.409072 & 0.204536 \tabularnewline
62 & 0.81888 & 0.362241 & 0.18112 \tabularnewline
63 & 0.800028 & 0.399944 & 0.199972 \tabularnewline
64 & 0.763777 & 0.472446 & 0.236223 \tabularnewline
65 & 0.707848 & 0.584304 & 0.292152 \tabularnewline
66 & 0.653697 & 0.692606 & 0.346303 \tabularnewline
67 & 0.624313 & 0.751374 & 0.375687 \tabularnewline
68 & 0.570409 & 0.859182 & 0.429591 \tabularnewline
69 & 0.554534 & 0.890932 & 0.445466 \tabularnewline
70 & 0.602392 & 0.795216 & 0.397608 \tabularnewline
71 & 0.604531 & 0.790937 & 0.395469 \tabularnewline
72 & 0.564066 & 0.871867 & 0.435934 \tabularnewline
73 & 0.490175 & 0.98035 & 0.509825 \tabularnewline
74 & 0.417285 & 0.834571 & 0.582715 \tabularnewline
75 & 0.379263 & 0.758526 & 0.620737 \tabularnewline
76 & 0.385483 & 0.770966 & 0.614517 \tabularnewline
77 & 0.310252 & 0.620504 & 0.689748 \tabularnewline
78 & 0.232884 & 0.465768 & 0.767116 \tabularnewline
79 & 0.1835 & 0.367001 & 0.8165 \tabularnewline
80 & 0.149551 & 0.299103 & 0.850449 \tabularnewline
81 & 0.25233 & 0.50466 & 0.74767 \tabularnewline
82 & 0.216748 & 0.433496 & 0.783252 \tabularnewline
83 & 0.270131 & 0.540263 & 0.729869 \tabularnewline
84 & 0.207541 & 0.415082 & 0.792459 \tabularnewline
85 & 0.196237 & 0.392475 & 0.803763 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&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]22[/C][C]0.208836[/C][C]0.417672[/C][C]0.791164[/C][/ROW]
[ROW][C]23[/C][C]0.292997[/C][C]0.585995[/C][C]0.707003[/C][/ROW]
[ROW][C]24[/C][C]0.181184[/C][C]0.362367[/C][C]0.818816[/C][/ROW]
[ROW][C]25[/C][C]0.103777[/C][C]0.207554[/C][C]0.896223[/C][/ROW]
[ROW][C]26[/C][C]0.0612588[/C][C]0.122518[/C][C]0.938741[/C][/ROW]
[ROW][C]27[/C][C]0.0313373[/C][C]0.0626746[/C][C]0.968663[/C][/ROW]
[ROW][C]28[/C][C]0.016908[/C][C]0.033816[/C][C]0.983092[/C][/ROW]
[ROW][C]29[/C][C]0.00987766[/C][C]0.0197553[/C][C]0.990122[/C][/ROW]
[ROW][C]30[/C][C]0.00472701[/C][C]0.00945403[/C][C]0.995273[/C][/ROW]
[ROW][C]31[/C][C]0.00558811[/C][C]0.0111762[/C][C]0.994412[/C][/ROW]
[ROW][C]32[/C][C]0.0328592[/C][C]0.0657183[/C][C]0.967141[/C][/ROW]
[ROW][C]33[/C][C]0.0226313[/C][C]0.0452625[/C][C]0.977369[/C][/ROW]
[ROW][C]34[/C][C]0.0135698[/C][C]0.0271397[/C][C]0.98643[/C][/ROW]
[ROW][C]35[/C][C]0.0099722[/C][C]0.0199444[/C][C]0.990028[/C][/ROW]
[ROW][C]36[/C][C]0.00883952[/C][C]0.017679[/C][C]0.99116[/C][/ROW]
[ROW][C]37[/C][C]0.0739411[/C][C]0.147882[/C][C]0.926059[/C][/ROW]
[ROW][C]38[/C][C]0.0599504[/C][C]0.119901[/C][C]0.94005[/C][/ROW]
[ROW][C]39[/C][C]0.130276[/C][C]0.260553[/C][C]0.869724[/C][/ROW]
[ROW][C]40[/C][C]0.0994917[/C][C]0.198983[/C][C]0.900508[/C][/ROW]
[ROW][C]41[/C][C]0.213303[/C][C]0.426606[/C][C]0.786697[/C][/ROW]
[ROW][C]42[/C][C]0.167981[/C][C]0.335962[/C][C]0.832019[/C][/ROW]
[ROW][C]43[/C][C]0.21081[/C][C]0.421621[/C][C]0.78919[/C][/ROW]
[ROW][C]44[/C][C]0.214481[/C][C]0.428962[/C][C]0.785519[/C][/ROW]
[ROW][C]45[/C][C]0.254027[/C][C]0.508053[/C][C]0.745973[/C][/ROW]
[ROW][C]46[/C][C]0.274398[/C][C]0.548796[/C][C]0.725602[/C][/ROW]
[ROW][C]47[/C][C]0.385692[/C][C]0.771385[/C][C]0.614308[/C][/ROW]
[ROW][C]48[/C][C]0.438751[/C][C]0.877503[/C][C]0.561249[/C][/ROW]
[ROW][C]49[/C][C]0.37838[/C][C]0.756761[/C][C]0.62162[/C][/ROW]
[ROW][C]50[/C][C]0.359026[/C][C]0.718052[/C][C]0.640974[/C][/ROW]
[ROW][C]51[/C][C]0.323642[/C][C]0.647284[/C][C]0.676358[/C][/ROW]
[ROW][C]52[/C][C]0.345565[/C][C]0.69113[/C][C]0.654435[/C][/ROW]
[ROW][C]53[/C][C]0.577493[/C][C]0.845014[/C][C]0.422507[/C][/ROW]
[ROW][C]54[/C][C]0.524758[/C][C]0.950483[/C][C]0.475242[/C][/ROW]
[ROW][C]55[/C][C]0.723965[/C][C]0.552071[/C][C]0.276035[/C][/ROW]
[ROW][C]56[/C][C]0.729997[/C][C]0.540006[/C][C]0.270003[/C][/ROW]
[ROW][C]57[/C][C]0.699403[/C][C]0.601193[/C][C]0.300597[/C][/ROW]
[ROW][C]58[/C][C]0.680209[/C][C]0.639582[/C][C]0.319791[/C][/ROW]
[ROW][C]59[/C][C]0.768557[/C][C]0.462886[/C][C]0.231443[/C][/ROW]
[ROW][C]60[/C][C]0.747028[/C][C]0.505944[/C][C]0.252972[/C][/ROW]
[ROW][C]61[/C][C]0.795464[/C][C]0.409072[/C][C]0.204536[/C][/ROW]
[ROW][C]62[/C][C]0.81888[/C][C]0.362241[/C][C]0.18112[/C][/ROW]
[ROW][C]63[/C][C]0.800028[/C][C]0.399944[/C][C]0.199972[/C][/ROW]
[ROW][C]64[/C][C]0.763777[/C][C]0.472446[/C][C]0.236223[/C][/ROW]
[ROW][C]65[/C][C]0.707848[/C][C]0.584304[/C][C]0.292152[/C][/ROW]
[ROW][C]66[/C][C]0.653697[/C][C]0.692606[/C][C]0.346303[/C][/ROW]
[ROW][C]67[/C][C]0.624313[/C][C]0.751374[/C][C]0.375687[/C][/ROW]
[ROW][C]68[/C][C]0.570409[/C][C]0.859182[/C][C]0.429591[/C][/ROW]
[ROW][C]69[/C][C]0.554534[/C][C]0.890932[/C][C]0.445466[/C][/ROW]
[ROW][C]70[/C][C]0.602392[/C][C]0.795216[/C][C]0.397608[/C][/ROW]
[ROW][C]71[/C][C]0.604531[/C][C]0.790937[/C][C]0.395469[/C][/ROW]
[ROW][C]72[/C][C]0.564066[/C][C]0.871867[/C][C]0.435934[/C][/ROW]
[ROW][C]73[/C][C]0.490175[/C][C]0.98035[/C][C]0.509825[/C][/ROW]
[ROW][C]74[/C][C]0.417285[/C][C]0.834571[/C][C]0.582715[/C][/ROW]
[ROW][C]75[/C][C]0.379263[/C][C]0.758526[/C][C]0.620737[/C][/ROW]
[ROW][C]76[/C][C]0.385483[/C][C]0.770966[/C][C]0.614517[/C][/ROW]
[ROW][C]77[/C][C]0.310252[/C][C]0.620504[/C][C]0.689748[/C][/ROW]
[ROW][C]78[/C][C]0.232884[/C][C]0.465768[/C][C]0.767116[/C][/ROW]
[ROW][C]79[/C][C]0.1835[/C][C]0.367001[/C][C]0.8165[/C][/ROW]
[ROW][C]80[/C][C]0.149551[/C][C]0.299103[/C][C]0.850449[/C][/ROW]
[ROW][C]81[/C][C]0.25233[/C][C]0.50466[/C][C]0.74767[/C][/ROW]
[ROW][C]82[/C][C]0.216748[/C][C]0.433496[/C][C]0.783252[/C][/ROW]
[ROW][C]83[/C][C]0.270131[/C][C]0.540263[/C][C]0.729869[/C][/ROW]
[ROW][C]84[/C][C]0.207541[/C][C]0.415082[/C][C]0.792459[/C][/ROW]
[ROW][C]85[/C][C]0.196237[/C][C]0.392475[/C][C]0.803763[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&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
220.2088360.4176720.791164
230.2929970.5859950.707003
240.1811840.3623670.818816
250.1037770.2075540.896223
260.06125880.1225180.938741
270.03133730.06267460.968663
280.0169080.0338160.983092
290.009877660.01975530.990122
300.004727010.009454030.995273
310.005588110.01117620.994412
320.03285920.06571830.967141
330.02263130.04526250.977369
340.01356980.02713970.98643
350.00997220.01994440.990028
360.008839520.0176790.99116
370.07394110.1478820.926059
380.05995040.1199010.94005
390.1302760.2605530.869724
400.09949170.1989830.900508
410.2133030.4266060.786697
420.1679810.3359620.832019
430.210810.4216210.78919
440.2144810.4289620.785519
450.2540270.5080530.745973
460.2743980.5487960.725602
470.3856920.7713850.614308
480.4387510.8775030.561249
490.378380.7567610.62162
500.3590260.7180520.640974
510.3236420.6472840.676358
520.3455650.691130.654435
530.5774930.8450140.422507
540.5247580.9504830.475242
550.7239650.5520710.276035
560.7299970.5400060.270003
570.6994030.6011930.300597
580.6802090.6395820.319791
590.7685570.4628860.231443
600.7470280.5059440.252972
610.7954640.4090720.204536
620.818880.3622410.18112
630.8000280.3999440.199972
640.7637770.4724460.236223
650.7078480.5843040.292152
660.6536970.6926060.346303
670.6243130.7513740.375687
680.5704090.8591820.429591
690.5545340.8909320.445466
700.6023920.7952160.397608
710.6045310.7909370.395469
720.5640660.8718670.435934
730.4901750.980350.509825
740.4172850.8345710.582715
750.3792630.7585260.620737
760.3854830.7709660.614517
770.3102520.6205040.689748
780.2328840.4657680.767116
790.18350.3670010.8165
800.1495510.2991030.850449
810.252330.504660.74767
820.2167480.4334960.783252
830.2701310.5402630.729869
840.2075410.4150820.792459
850.1962370.3924750.803763






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.015625NOK
5% type I error level80.125NOK
10% type I error level100.15625NOK

\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 & 1 & 0.015625 & NOK \tabularnewline
5% type I error level & 8 & 0.125 & NOK \tabularnewline
10% type I error level & 10 & 0.15625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267428&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]1[/C][C]0.015625[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.15625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267428&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267428&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 level10.015625NOK
5% type I error level80.125NOK
10% type I error level100.15625NOK


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):
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')
}