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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 14 Dec 2014 15:14:52 +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/t1418570226s0jag7gi5qetmvn.htm/, Retrieved Thu, 16 May 2024 06:22:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267676, Retrieved Thu, 16 May 2024 06:22:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multipleregression] [2014-12-14 15:14:52] [21b927ddce509724d48ffb8407994bd0] [Current]
-   PD    [Multiple Regression] [regersvgl] [2014-12-17 20:55:38] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
-   PD    [Multiple Regression] [multi ] [2014-12-18 08:37:51] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
- RMPD    [Histogram] [histo] [2014-12-18 08:55:44] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
- RMPD    [Notched Boxplots] [box] [2014-12-18 09:08:24] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
- RM D    [Two-Way ANOVA] [2wayanova] [2014-12-18 10:16:28] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
- RM D    [Two-Way ANOVA] [twoway] [2014-12-18 10:18:36] [7c87d1c7801dd67c2a0c90c2b6a05b3c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0 1 26 0 13 12 21 149 18 68 96 12.9
0 1 37 0 14 11 22 148 39 55 88 12.8
0 1 67 1 16 13 18 158 46 39 114 7.4
0 1 43 1 14 11 23 128 31 32 69 6.7
0 1 52 1 13 10 12 224 67 62 176 12.6
0 1 52 0 15 7 20 159 35 33 114 14.8
0 1 43 1 13 10 22 105 52 52 121 13.3
0 1 84 1 20 15 21 159 77 62 110 11.1
0 1 67 1 17 12 19 167 37 77 158 8.2
0 1 49 1 15 12 22 165 32 76 116 11.4
0 1 70 1 16 10 15 159 36 41 181 6.4
0 1 58 0 17 14 19 176 69 48 141 12
0 1 68 0 11 6 18 54 21 63 35 6.3
0 0 62 0 16 12 15 91 26 30 80 11.3
0 1 43 1 16 14 20 163 54 78 152 11.9
0 1 56 0 15 11 21 124 36 19 97 9.3
0 1 74 0 14 12 15 121 23 31 84 10
0 1 63 1 16 13 23 148 112 66 101 13.8
0 1 58 0 17 11 21 221 35 35 107 10.8
0 1 63 1 15 7 25 149 47 42 112 11.7
0 1 53 1 14 11 9 244 37 45 171 10.9
0 0 57 1 14 7 30 148 109 21 137 16.1
0 1 64 1 15 12 23 150 20 25 66 9.9
0 1 53 0 17 13 16 153 22 44 93 11.5
0 1 29 0 14 9 16 94 23 69 105 8.3
0 1 54 0 16 11 19 156 32 54 131 11.7
0 1 58 1 15 12 25 132 30 74 102 9
0 1 51 1 16 12 23 105 43 80 120 10.8
0 1 54 0 8 5 10 151 16 42 77 10.4
0 0 56 1 17 13 14 131 49 61 108 12.7
0 1 47 0 10 6 26 157 43 41 168 11.8
0 1 50 1 16 6 24 162 46 46 75 13
0 1 35 1 16 12 24 163 19 39 107 10.8
0 0 30 1 16 11 18 59 23 63 62 12.3
0 1 68 0 8 6 23 187 59 34 121 11.3
0 0 56 1 14 11 23 116 32 51 97 11.6
0 1 43 1 16 12 19 148 19 42 126 10.9
0 0 67 1 19 13 21 155 22 31 104 12.1
0 1 62 1 19 14 18 125 48 39 148 13.3
0 1 57 1 14 12 27 116 23 20 146 10.1
0 1 54 1 13 14 13 138 33 49 97 14.3
0 1 61 1 15 11 28 164 34 53 118 9.3
0 1 56 0 11 10 23 162 48 31 58 12.5
0 1 41 0 9 7 21 99 18 39 63 7.6
0 1 53 0 12 7 19 186 33 54 50 9.2
0 1 46 1 13 10 17 188 67 49 94 14.5
0 1 51 0 17 12 25 177 80 34 127 12.3
0 1 37 0 7 5 14 139 32 46 128 12.6
0 1 42 0 15 10 16 162 43 55 146 13
0 0 38 1 12 12 24 108 38 42 69 12.6
0 1 66 0 15 11 20 159 29 50 186 13.2
0 1 53 1 16 12 24 110 32 13 85 7.7
0 0 49 0 14 11 22 96 35 37 54 10.5
0 0 49 0 16 12 22 87 29 25 106 10.9
0 0 59 1 13 10 20 97 12 30 34 4.3
0 0 40 0 16 9 10 127 37 28 60 10.3
0 0 63 0 10 7 22 74 51 45 62 11.4
0 0 34 1 12 9 20 114 14 35 64 5.6
0 0 32 0 14 10 22 95 20 28 98 8.8
0 0 67 0 16 12 20 121 11 41 35 9
0 0 61 1 18 14 17 130 35 6 55 9.6
0 0 60 0 12 9 18 52 8 45 54 6.4
0 0 63 0 15 12 19 118 24 73 51 11.6
1 1 52 1 16 9 23 48 23 17 41 4.35
1 1 16 1 16 11 22 50 16 40 146 12.7
1 1 46 1 16 12 21 150 33 64 182 18.1
1 1 56 1 16 12 25 154 32 37 192 17.85
1 0 52 0 12 7 30 109 37 25 263 16.6
1 0 55 1 15 12 17 68 14 65 35 12.6
1 1 50 1 14 12 27 194 52 100 439 17.1
1 1 59 0 15 12 23 158 75 28 214 19.1
1 1 60 1 16 10 23 159 72 35 341 16.1
1 1 52 0 13 15 18 67 15 56 58 13.35
1 1 44 0 10 10 18 147 29 29 292 18.4
1 1 67 1 17 15 23 39 13 43 85 14.7
1 1 52 1 15 10 19 100 40 59 200 10.6
1 1 55 1 18 15 15 111 19 52 158 12.6
1 1 37 1 16 9 20 138 24 50 199 16.2
1 1 54 1 20 15 16 101 121 3 297 13.6
1 0 72 1 16 12 24 131 93 59 227 18.9
1 1 51 1 17 13 25 101 36 27 108 14.1
1 1 48 1 16 12 25 114 23 61 86 14.5
1 1 60 0 15 12 19 165 85 28 302 16.15
1 1 50 1 13 8 19 114 41 51 148 14.75
1 1 63 1 16 9 16 111 46 35 178 14.8
1 1 33 1 16 15 19 75 18 29 120 12.45
1 1 67 1 16 12 19 82 35 48 207 12.65
1 1 46 1 17 12 23 121 17 25 157 17.35
1 1 54 1 20 15 21 32 4 44 128 8.6
1 1 59 0 14 11 22 150 28 64 296 18.4
1 1 61 1 17 12 19 117 44 32 323 16.1
1 0 33 1 6 6 20 71 10 20 79 11.6
1 1 47 1 16 14 20 165 38 28 70 17.75
1 1 69 1 15 12 3 154 57 34 146 15.25
1 1 52 1 16 12 23 126 23 31 246 17.65
1 1 55 0 16 12 23 149 36 26 196 16.35
1 1 41 0 14 11 20 145 22 58 199 17.65
1 1 73 1 16 12 15 120 40 23 127 13.6
1 1 52 0 16 12 16 109 31 21 153 14.35
1 1 50 0 16 12 7 132 11 21 299 14.75
1 1 51 1 14 12 24 172 38 33 228 18.25
1 1 60 0 14 8 17 169 24 16 190 9.9
1 1 56 1 16 8 24 114 37 20 180 16
1 1 56 1 16 12 24 156 37 37 212 18.25
1 1 29 0 15 12 19 172 22 35 269 16.85
1 0 66 1 16 11 25 68 15 33 130 14.6
1 0 66 1 16 10 20 89 2 27 179 13.85
1 1 73 1 18 11 28 167 43 41 243 18.95
1 1 55 0 15 12 23 113 31 40 190 15.6
1 0 64 0 16 13 27 115 29 35 299 14.85
1 0 40 0 16 12 18 78 45 28 121 11.75
1 0 46 0 16 12 28 118 25 32 137 18.45
1 0 58 1 17 10 21 87 4 22 305 15.9
1 1 43 0 14 10 19 173 31 44 157 17.1
1 1 61 1 18 11 23 2 -4 27 96 16.1
1 0 51 0 9 8 27 162 66 17 183 19.9
1 0 50 1 15 12 22 49 61 12 52 10.95
1 0 52 0 14 9 28 122 32 45 238 18.45
1 0 54 1 15 12 25 96 31 37 40 15.1
1 0 66 0 13 9 21 100 39 37 226 15
1 0 61 0 16 11 22 82 19 108 190 11.35
1 0 80 1 20 15 28 100 31 10 214 15.95
1 0 51 0 14 8 20 115 36 68 145 18.1
1 0 56 1 12 8 29 141 42 72 119 14.6
1 1 56 1 15 11 25 165 21 143 222 15.4
1 1 56 1 15 11 25 165 21 9 222 15.4
1 0 53 1 15 11 20 110 25 55 159 17.6
1 1 47 1 16 13 20 118 32 17 165 13.35
1 1 25 0 11 7 16 158 26 37 249 19.1
1 0 47 1 16 12 20 146 28 27 125 15.35
1 1 46 0 7 8 20 49 32 37 122 7.6
1 0 50 0 11 8 23 90 41 58 186 13.4
1 0 39 0 9 4 18 121 29 66 148 13.9
1 1 51 1 15 11 25 155 33 21 274 19.1
1 0 58 0 16 10 18 104 17 19 172 15.25
1 0 35 1 14 7 19 147 13 78 84 12.9
1 0 58 0 15 12 25 110 32 35 168 16.1
1 0 60 0 13 11 25 108 30 48 102 17.35
1 0 62 0 13 9 25 113 34 27 106 13.15
1 0 63 0 12 10 24 115 59 43 2 12.15
1 0 53 1 16 8 19 61 13 30 139 12.6
1 0 46 1 14 8 26 60 23 25 95 10.35
1 0 67 1 16 11 10 109 10 69 130 15.4
1 0 59 1 14 12 17 68 5 72 72 9.6
1 0 64 0 15 10 13 111 31 23 141 18.2
1 0 38 0 10 10 17 77 19 13 113 13.6
1 0 50 1 16 12 30 73 32 61 206 14.85
1 1 48 0 14 8 25 151 30 43 268 14.75
1 0 48 0 16 11 4 89 25 22 175 14.1
1 0 47 0 12 8 16 78 48 51 77 14.9
1 0 66 0 16 10 21 110 35 67 125 16.25
1 0 47 1 16 14 23 220 67 36 255 19.25
1 0 63 1 15 9 22 65 15 21 111 13.6
1 1 58 0 14 9 17 141 22 44 132 13.6
1 0 44 0 16 10 20 117 18 45 211 15.65
1 1 51 1 11 13 20 122 33 34 92 12.75
1 0 43 0 15 12 22 63 46 36 76 14.6
1 1 55 1 18 13 16 44 24 72 171 9.85
1 0 38 1 13 8 23 52 14 39 83 12.65
1 0 45 0 7 3 0 131 12 43 266 19.2
1 0 50 1 7 8 18 101 38 25 186 16.6
1 0 54 1 17 12 25 42 12 56 50 11.2
1 1 57 1 18 11 23 152 28 80 117 15.25
1 1 60 0 15 9 12 107 41 40 219 11.9
1 0 55 0 8 12 18 77 12 73 246 13.2
1 1 56 0 13 12 24 154 31 34 279 16.35
1 1 49 1 13 12 11 103 33 72 148 12.4
1 0 37 1 15 10 18 96 34 42 137 15.85
1 1 59 1 18 13 23 175 21 61 181 18.15
1 0 46 1 16 9 24 57 20 23 98 11.15
1 0 51 0 14 12 29 112 44 74 226 15.65
1 1 58 0 15 11 18 143 52 16 234 17.75
1 0 64 0 19 14 15 49 7 66 138 7.65
1 1 53 1 16 11 29 110 29 9 85 12.35
1 1 48 1 12 9 16 131 11 41 66 15.6
1 1 51 0 16 12 19 167 26 57 236 19.3
1 0 47 0 11 8 22 56 24 48 106 15.2
1 1 59 0 16 15 16 137 7 51 135 17.1
1 0 62 1 15 12 23 86 60 53 122 15.6
1 1 62 1 19 14 23 121 13 29 218 18.4
1 1 51 0 15 12 19 149 20 29 199 19.05
1 1 64 0 14 9 4 168 52 55 112 18.55
1 1 52 0 14 9 20 140 28 54 278 19.1
1 0 67 1 17 13 24 88 25 43 94 13.1
1 1 50 1 16 13 20 168 39 51 113 12.85
1 1 54 1 20 15 4 94 9 20 84 9.5
1 1 58 1 16 11 24 51 19 79 86 4.5
1 0 56 0 9 7 22 48 13 39 62 11.85
1 1 63 1 13 10 16 145 60 61 222 13.6
1 1 31 1 15 11 3 66 19 55 167 11.7
1 0 65 1 19 14 15 85 34 30 82 12.4
1 1 71 0 16 14 24 109 14 55 207 13.35
1 0 50 0 17 13 17 63 17 22 184 11.4
1 0 57 1 16 12 20 102 45 37 83 14.9
1 0 47 0 9 8 27 162 66 2 183 19.9
1 0 47 1 11 13 26 86 48 38 89 11.2
1 0 57 1 14 9 23 114 29 27 225 14.6
1 1 43 0 19 12 17 164 -2 56 237 17.6
1 1 41 1 13 13 20 119 51 25 102 14.05
1 1 63 0 14 11 22 126 2 39 221 16.1
1 1 63 1 15 11 19 132 24 33 128 13.35
1 1 56 1 15 13 24 142 40 43 91 11.85
1 1 51 0 14 12 19 83 20 57 198 11.95
1 0 50 1 16 12 23 94 19 43 204 14.75
1 0 22 0 17 10 15 81 16 23 158 15.15
1 1 41 1 12 9 27 166 20 44 138 13.2
1 0 59 0 15 10 26 110 40 54 226 16.85
1 0 56 1 17 13 22 64 27 28 44 7.85
1 1 66 0 15 13 22 93 25 36 196 7.7
1 0 53 0 10 9 18 104 49 39 83 12.6
1 0 42 1 16 11 15 105 39 16 79 7.85
1 0 52 1 15 12 22 49 61 23 52 10.95
1 0 54 0 11 8 27 88 19 40 105 12.35
1 0 44 1 16 12 10 95 67 24 116 9.95
1 0 62 1 16 12 20 102 45 29 83 14.9
1 0 53 0 16 12 17 99 30 78 196 16.65
1 0 50 1 14 9 23 63 8 57 153 13.4
1 0 36 0 14 12 19 76 19 37 157 13.95
1 0 76 0 16 12 13 109 52 27 75 15.7
1 0 66 1 16 11 27 117 22 61 106 16.85
1 0 62 1 18 12 23 57 17 27 58 10.95
1 0 59 0 14 6 16 120 33 69 75 15.35
1 0 47 1 20 7 25 73 34 34 74 12.2
1 0 55 0 15 10 2 91 22 44 185 15.1
1 0 58 0 16 12 26 108 30 21 265 17.75
1 0 60 1 16 10 20 105 25 34 131 15.2
1 1 44 0 16 12 23 117 38 39 139 14.6
1 0 57 0 12 9 22 119 26 51 196 16.65
1 0 45 1 8 3 24 31 13 34 78 8.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&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]9 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=267676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 4.36864 + 3.9539year.bin[t] -1.20502group.bin[t] -0.0183933AMS.I[t] -0.70796genderbin[t] + 0.0483515CONFSTATTOT[t] + 0.0192111CONFSOFTTOT[t] + 0.0463614NUMERACYTOT[t] + 0.0378216LFM[t] + 0.012175PRH[t] -4.26382e-05CH[t] + 0.0110522BER[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  4.36864 +  3.9539year.bin[t] -1.20502group.bin[t] -0.0183933AMS.I[t] -0.70796genderbin[t] +  0.0483515CONFSTATTOT[t] +  0.0192111CONFSOFTTOT[t] +  0.0463614NUMERACYTOT[t] +  0.0378216LFM[t] +  0.012175PRH[t] -4.26382e-05CH[t] +  0.0110522BER[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  4.36864 +  3.9539year.bin[t] -1.20502group.bin[t] -0.0183933AMS.I[t] -0.70796genderbin[t] +  0.0483515CONFSTATTOT[t] +  0.0192111CONFSOFTTOT[t] +  0.0463614NUMERACYTOT[t] +  0.0378216LFM[t] +  0.012175PRH[t] -4.26382e-05CH[t] +  0.0110522BER[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&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] = + 4.36864 + 3.9539year.bin[t] -1.20502group.bin[t] -0.0183933AMS.I[t] -0.70796genderbin[t] + 0.0483515CONFSTATTOT[t] + 0.0192111CONFSOFTTOT[t] + 0.0463614NUMERACYTOT[t] + 0.0378216LFM[t] + 0.012175PRH[t] -4.26382e-05CH[t] + 0.0110522BER[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.368641.294843.3740.0008777780.000438889
year.bin3.95390.4151679.5243.45075e-181.72537e-18
group.bin-1.205020.348916-3.4540.0006650150.000332507
AMS.I-0.01839330.0146592-1.2550.2109260.105463
genderbin-0.707960.32401-2.1850.029960.01498
CONFSTATTOT0.04835150.0780180.61970.5360740.268037
CONFSOFTTOT0.01921110.08546530.22480.822360.41118
NUMERACYTOT0.04636140.0285551.6240.1059170.0529583
LFM0.03782160.004925527.6795.43228e-132.71614e-13
PRH0.0121750.008608051.4140.1586870.0793434
CH-4.26382e-050.00770208-0.0055360.9955880.497794
BER0.01105220.002645154.1784.25746e-052.12873e-05

\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) & 4.36864 & 1.29484 & 3.374 & 0.000877778 & 0.000438889 \tabularnewline
year.bin & 3.9539 & 0.415167 & 9.524 & 3.45075e-18 & 1.72537e-18 \tabularnewline
group.bin & -1.20502 & 0.348916 & -3.454 & 0.000665015 & 0.000332507 \tabularnewline
AMS.I & -0.0183933 & 0.0146592 & -1.255 & 0.210926 & 0.105463 \tabularnewline
genderbin & -0.70796 & 0.32401 & -2.185 & 0.02996 & 0.01498 \tabularnewline
CONFSTATTOT & 0.0483515 & 0.078018 & 0.6197 & 0.536074 & 0.268037 \tabularnewline
CONFSOFTTOT & 0.0192111 & 0.0854653 & 0.2248 & 0.82236 & 0.41118 \tabularnewline
NUMERACYTOT & 0.0463614 & 0.028555 & 1.624 & 0.105917 & 0.0529583 \tabularnewline
LFM & 0.0378216 & 0.00492552 & 7.679 & 5.43228e-13 & 2.71614e-13 \tabularnewline
PRH & 0.012175 & 0.00860805 & 1.414 & 0.158687 & 0.0793434 \tabularnewline
CH & -4.26382e-05 & 0.00770208 & -0.005536 & 0.995588 & 0.497794 \tabularnewline
BER & 0.0110522 & 0.00264515 & 4.178 & 4.25746e-05 & 2.12873e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&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]4.36864[/C][C]1.29484[/C][C]3.374[/C][C]0.000877778[/C][C]0.000438889[/C][/ROW]
[ROW][C]year.bin[/C][C]3.9539[/C][C]0.415167[/C][C]9.524[/C][C]3.45075e-18[/C][C]1.72537e-18[/C][/ROW]
[ROW][C]group.bin[/C][C]-1.20502[/C][C]0.348916[/C][C]-3.454[/C][C]0.000665015[/C][C]0.000332507[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0183933[/C][C]0.0146592[/C][C]-1.255[/C][C]0.210926[/C][C]0.105463[/C][/ROW]
[ROW][C]genderbin[/C][C]-0.70796[/C][C]0.32401[/C][C]-2.185[/C][C]0.02996[/C][C]0.01498[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.0483515[/C][C]0.078018[/C][C]0.6197[/C][C]0.536074[/C][C]0.268037[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.0192111[/C][C]0.0854653[/C][C]0.2248[/C][C]0.82236[/C][C]0.41118[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0463614[/C][C]0.028555[/C][C]1.624[/C][C]0.105917[/C][C]0.0529583[/C][/ROW]
[ROW][C]LFM[/C][C]0.0378216[/C][C]0.00492552[/C][C]7.679[/C][C]5.43228e-13[/C][C]2.71614e-13[/C][/ROW]
[ROW][C]PRH[/C][C]0.012175[/C][C]0.00860805[/C][C]1.414[/C][C]0.158687[/C][C]0.0793434[/C][/ROW]
[ROW][C]CH[/C][C]-4.26382e-05[/C][C]0.00770208[/C][C]-0.005536[/C][C]0.995588[/C][C]0.497794[/C][/ROW]
[ROW][C]BER[/C][C]0.0110522[/C][C]0.00264515[/C][C]4.178[/C][C]4.25746e-05[/C][C]2.12873e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&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)4.368641.294843.3740.0008777780.000438889
year.bin3.95390.4151679.5243.45075e-181.72537e-18
group.bin-1.205020.348916-3.4540.0006650150.000332507
AMS.I-0.01839330.0146592-1.2550.2109260.105463
genderbin-0.707960.32401-2.1850.029960.01498
CONFSTATTOT0.04835150.0780180.61970.5360740.268037
CONFSOFTTOT0.01921110.08546530.22480.822360.41118
NUMERACYTOT0.04636140.0285551.6240.1059170.0529583
LFM0.03782160.004925527.6795.43228e-132.71614e-13
PRH0.0121750.008608051.4140.1586870.0793434
CH-4.26382e-050.00770208-0.0055360.9955880.497794
BER0.01105220.002645154.1784.25746e-052.12873e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.763184
R-squared0.58245
Adjusted R-squared0.561284
F-TEST (value)27.5181
F-TEST (DF numerator)11
F-TEST (DF denominator)217
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20254
Sum Squared Residuals1052.71

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.763184 \tabularnewline
R-squared & 0.58245 \tabularnewline
Adjusted R-squared & 0.561284 \tabularnewline
F-TEST (value) & 27.5181 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 217 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20254 \tabularnewline
Sum Squared Residuals & 1052.71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.763184[/C][/ROW]
[ROW][C]R-squared[/C][C]0.58245[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.561284[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.5181[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]217[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.20254[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1052.71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.763184
R-squared0.58245
Adjusted R-squared0.561284
F-TEST (value)27.5181
F-TEST (DF numerator)11
F-TEST (DF denominator)217
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20254
Sum Squared Residuals1052.71






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.43081.46923
212.811.43391.36606
37.410.8753-3.47534
46.79.59913-2.89913
512.614.1065-1.50654
614.811.69253.10755
713.39.444853.85515
811.111.3036-0.203616
98.211.6663-3.46634
1011.411.4391-0.0391273
116.411.2799-4.87992
121213.1216-1.1216
136.36.07670.223303
1411.39.569051.73095
1511.912.1335-0.233539
169.310.2432-0.943214
17109.188890.811105
1813.811.46122.33878
1910.814.0695-3.26949
2011.710.75940.940631
2110.914.3533-3.45327
2216.113.25242.84757
239.99.94573-0.0457278
2411.511.08280.417186
258.39.21462-0.914617
2611.711.7715-0.0715039
2799.98556-0.985563
2810.89.405721.39428
2910.49.871960.528043
3012.711.09371.60632
3111.812.4199-0.619856
321311.05171.94834
3310.811.5059-0.705898
3412.38.122384.17762
3511.312.6081-1.3081
3611.610.4321.16799
3710.910.76950.130515
3812.111.84860.251352
3913.310.28363.01642
4010.19.846560.253442
4114.39.653784.64622
429.311.487-2.18698
4312.511.27511.22491
447.68.61084-1.01084
459.211.7712-2.57124
4614.512.18142.3186
4712.313.5077-1.20771
4812.610.62621.97383
491312.31220.687828
5012.610.19342.40665
5113.212.23380.966223
527.79.08651-1.38651
5310.510.02780.472197
5410.910.30550.594499
554.38.51268-4.21268
5610.310.9588-0.658803
5711.48.950852.44915
585.69.90362-4.30362
598.810.5875-1.78751
60910.1631-1.16309
619.610.4167-0.816663
626.47.51169-1.11169
6311.610.36221.23777
644.3510.0138-5.66383
6512.711.8180.882031
6618.115.6252.47499
6717.8515.87731.97269
6816.616.9503-0.350329
6912.611.47331.12672
7017.120.4673-3.36727
7119.117.30741.79265
7216.117.9956-1.89556
7313.3511.26762.08237
7418.416.95721.44278
7514.79.924594.77541
7610.613.7285-3.12845
7712.613.4254-0.825404
7816.215.31160.888394
7913.615.9888-2.38884
8018.917.00031.89966
8114.113.1530.94697
8214.513.22951.27055
8316.1518.4626-2.31261
8414.7513.59741.15258
8514.813.66311.13686
8612.4512.1260.323957
8712.6512.8755-0.225496
8817.3514.19983.15018
898.610.3169-1.71691
9018.417.22341.17662
9116.115.75030.34972
9211.612.0196-0.419565
9317.7514.99062.75944
9415.2514.3660.88401
9517.6515.28672.36335
9616.3516.4152-0.0652071
9717.6516.12781.52223
9813.613.19470.405327
9914.3514.09710.252914
10014.7515.9566-1.20664
10118.2516.97811.2719
1029.916.416-6.51597
1031614.17021.82979
10418.2516.18852.06149
10516.8518.1655-1.31551
10614.612.73451.86552
10713.8513.66130.188742
10818.9516.97031.97971
10915.614.87750.722507
11014.8517.4262-2.57618
11111.7514.2596-2.50956
11218.4516.05882.39116
11315.915.24460.65538
11417.116.73040.369605
11516.18.522347.57766
11619.918.17761.72243
11710.9511.8408-0.890809
11818.4517.14641.30362
11915.113.1851.91501
1201515.6369-0.636854
12111.3514.6335-3.28346
12215.9515.22080.729232
12318.115.52982.57022
12414.615.8193-1.21928
12515.416.4189-1.0189
12615.416.4246-1.02462
12717.614.72332.87673
12813.3514.1711-0.821116
12919.117.10861.99139
13015.3515.9247-0.574713
1317.611.2805-3.68046
13213.415.1111-1.71111
13313.915.5141-1.61412
13419.116.85872.24133
13515.2515.09660.153434
13612.915.3062-2.40619
13716.115.77580.324171
13817.3514.79312.55687
13913.1515.0008-1.85084
14012.1514.1368-1.98684
14112.612.44830.151709
14210.3512.4027-2.05271
14315.413.50891.89106
1449.611.6504-2.05041
14518.214.79853.40154
14613.613.47930.120697
14714.8514.51470.335348
14814.7517.2608-2.51077
14914.114.2138-0.113755
15014.913.31711.58292
15116.2515.01311.23692
15219.2520.8122-1.56225
15313.612.24091.35914
15413.614.7464-1.14639
15515.6516.3806-0.73058
15612.7513.2117-0.461708
15714.613.28861.31137
1589.8511.103-1.253
15912.6511.81710.832946
16019.215.92973.27027
16116.614.35892.24115
16211.211.11770.0822816
16315.2514.88860.361412
16411.914.4333-2.53325
16513.214.5369-1.33686
16616.3517.3433-0.993349
16712.412.8074-0.407429
16815.8514.24311.60689
16918.1516.3831.76695
17011.1512.3092-1.15916
17115.6516.9028-1.25279
17217.7516.44891.30106
1737.6512.4893-4.8393
17412.3513.2167-0.866651
17515.612.83782.76216
17619.317.30311.99686
17715.212.74332.45674
17817.114.59262.50745
17915.613.82561.77442
18018.414.6663.73404
18119.0516.09322.95679
18218.5515.19823.35175
18319.116.64432.45574
18413.113.2364-0.136375
18512.8515.5161-2.66607
1869.511.4493-1.94931
1874.510.5477-6.04772
18811.8511.53940.310603
18913.615.4789-1.87887
19011.711.4860.214021
19112.412.8359-0.43586
19213.3514.5453-1.19532
19311.413.8851-2.48515
19414.913.8191.08102
19519.918.25181.64821
19611.213.5562-2.35619
19714.615.6326-1.03264
19817.617.05940.540648
19914.0513.70890.341065
20016.115.09771.00231
20113.3513.7662-0.416175
20211.8514.3288-2.47881
20311.9513.5364-1.58639
20414.7514.8048-0.0547641
20515.1514.6310.51898
20613.215.7055-2.50553
20716.8516.5030.347006
2087.8511.9106-4.06064
2097.713.885-6.18502
21012.614.2843-1.68431
2117.8513.841-5.99097
21210.9511.8036-0.853553
21312.3513.985-1.63502
2149.9513.9629-4.01286
21514.913.72741.17264
21616.6515.41251.2375
21713.412.77980.620227
21813.9514.2881-0.338094
21915.714.11491.58508
22016.8514.49922.35076
22110.9511.6441-0.694053
22215.3514.53760.812352
22312.213.002-0.801981
22415.114.07341.02658
22517.7516.84320.906787
22615.214.1261.07402
22714.614.8011-0.201061
22816.6516.02860.621424
2298.110.5354-2.43537

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.4308 & 1.46923 \tabularnewline
2 & 12.8 & 11.4339 & 1.36606 \tabularnewline
3 & 7.4 & 10.8753 & -3.47534 \tabularnewline
4 & 6.7 & 9.59913 & -2.89913 \tabularnewline
5 & 12.6 & 14.1065 & -1.50654 \tabularnewline
6 & 14.8 & 11.6925 & 3.10755 \tabularnewline
7 & 13.3 & 9.44485 & 3.85515 \tabularnewline
8 & 11.1 & 11.3036 & -0.203616 \tabularnewline
9 & 8.2 & 11.6663 & -3.46634 \tabularnewline
10 & 11.4 & 11.4391 & -0.0391273 \tabularnewline
11 & 6.4 & 11.2799 & -4.87992 \tabularnewline
12 & 12 & 13.1216 & -1.1216 \tabularnewline
13 & 6.3 & 6.0767 & 0.223303 \tabularnewline
14 & 11.3 & 9.56905 & 1.73095 \tabularnewline
15 & 11.9 & 12.1335 & -0.233539 \tabularnewline
16 & 9.3 & 10.2432 & -0.943214 \tabularnewline
17 & 10 & 9.18889 & 0.811105 \tabularnewline
18 & 13.8 & 11.4612 & 2.33878 \tabularnewline
19 & 10.8 & 14.0695 & -3.26949 \tabularnewline
20 & 11.7 & 10.7594 & 0.940631 \tabularnewline
21 & 10.9 & 14.3533 & -3.45327 \tabularnewline
22 & 16.1 & 13.2524 & 2.84757 \tabularnewline
23 & 9.9 & 9.94573 & -0.0457278 \tabularnewline
24 & 11.5 & 11.0828 & 0.417186 \tabularnewline
25 & 8.3 & 9.21462 & -0.914617 \tabularnewline
26 & 11.7 & 11.7715 & -0.0715039 \tabularnewline
27 & 9 & 9.98556 & -0.985563 \tabularnewline
28 & 10.8 & 9.40572 & 1.39428 \tabularnewline
29 & 10.4 & 9.87196 & 0.528043 \tabularnewline
30 & 12.7 & 11.0937 & 1.60632 \tabularnewline
31 & 11.8 & 12.4199 & -0.619856 \tabularnewline
32 & 13 & 11.0517 & 1.94834 \tabularnewline
33 & 10.8 & 11.5059 & -0.705898 \tabularnewline
34 & 12.3 & 8.12238 & 4.17762 \tabularnewline
35 & 11.3 & 12.6081 & -1.3081 \tabularnewline
36 & 11.6 & 10.432 & 1.16799 \tabularnewline
37 & 10.9 & 10.7695 & 0.130515 \tabularnewline
38 & 12.1 & 11.8486 & 0.251352 \tabularnewline
39 & 13.3 & 10.2836 & 3.01642 \tabularnewline
40 & 10.1 & 9.84656 & 0.253442 \tabularnewline
41 & 14.3 & 9.65378 & 4.64622 \tabularnewline
42 & 9.3 & 11.487 & -2.18698 \tabularnewline
43 & 12.5 & 11.2751 & 1.22491 \tabularnewline
44 & 7.6 & 8.61084 & -1.01084 \tabularnewline
45 & 9.2 & 11.7712 & -2.57124 \tabularnewline
46 & 14.5 & 12.1814 & 2.3186 \tabularnewline
47 & 12.3 & 13.5077 & -1.20771 \tabularnewline
48 & 12.6 & 10.6262 & 1.97383 \tabularnewline
49 & 13 & 12.3122 & 0.687828 \tabularnewline
50 & 12.6 & 10.1934 & 2.40665 \tabularnewline
51 & 13.2 & 12.2338 & 0.966223 \tabularnewline
52 & 7.7 & 9.08651 & -1.38651 \tabularnewline
53 & 10.5 & 10.0278 & 0.472197 \tabularnewline
54 & 10.9 & 10.3055 & 0.594499 \tabularnewline
55 & 4.3 & 8.51268 & -4.21268 \tabularnewline
56 & 10.3 & 10.9588 & -0.658803 \tabularnewline
57 & 11.4 & 8.95085 & 2.44915 \tabularnewline
58 & 5.6 & 9.90362 & -4.30362 \tabularnewline
59 & 8.8 & 10.5875 & -1.78751 \tabularnewline
60 & 9 & 10.1631 & -1.16309 \tabularnewline
61 & 9.6 & 10.4167 & -0.816663 \tabularnewline
62 & 6.4 & 7.51169 & -1.11169 \tabularnewline
63 & 11.6 & 10.3622 & 1.23777 \tabularnewline
64 & 4.35 & 10.0138 & -5.66383 \tabularnewline
65 & 12.7 & 11.818 & 0.882031 \tabularnewline
66 & 18.1 & 15.625 & 2.47499 \tabularnewline
67 & 17.85 & 15.8773 & 1.97269 \tabularnewline
68 & 16.6 & 16.9503 & -0.350329 \tabularnewline
69 & 12.6 & 11.4733 & 1.12672 \tabularnewline
70 & 17.1 & 20.4673 & -3.36727 \tabularnewline
71 & 19.1 & 17.3074 & 1.79265 \tabularnewline
72 & 16.1 & 17.9956 & -1.89556 \tabularnewline
73 & 13.35 & 11.2676 & 2.08237 \tabularnewline
74 & 18.4 & 16.9572 & 1.44278 \tabularnewline
75 & 14.7 & 9.92459 & 4.77541 \tabularnewline
76 & 10.6 & 13.7285 & -3.12845 \tabularnewline
77 & 12.6 & 13.4254 & -0.825404 \tabularnewline
78 & 16.2 & 15.3116 & 0.888394 \tabularnewline
79 & 13.6 & 15.9888 & -2.38884 \tabularnewline
80 & 18.9 & 17.0003 & 1.89966 \tabularnewline
81 & 14.1 & 13.153 & 0.94697 \tabularnewline
82 & 14.5 & 13.2295 & 1.27055 \tabularnewline
83 & 16.15 & 18.4626 & -2.31261 \tabularnewline
84 & 14.75 & 13.5974 & 1.15258 \tabularnewline
85 & 14.8 & 13.6631 & 1.13686 \tabularnewline
86 & 12.45 & 12.126 & 0.323957 \tabularnewline
87 & 12.65 & 12.8755 & -0.225496 \tabularnewline
88 & 17.35 & 14.1998 & 3.15018 \tabularnewline
89 & 8.6 & 10.3169 & -1.71691 \tabularnewline
90 & 18.4 & 17.2234 & 1.17662 \tabularnewline
91 & 16.1 & 15.7503 & 0.34972 \tabularnewline
92 & 11.6 & 12.0196 & -0.419565 \tabularnewline
93 & 17.75 & 14.9906 & 2.75944 \tabularnewline
94 & 15.25 & 14.366 & 0.88401 \tabularnewline
95 & 17.65 & 15.2867 & 2.36335 \tabularnewline
96 & 16.35 & 16.4152 & -0.0652071 \tabularnewline
97 & 17.65 & 16.1278 & 1.52223 \tabularnewline
98 & 13.6 & 13.1947 & 0.405327 \tabularnewline
99 & 14.35 & 14.0971 & 0.252914 \tabularnewline
100 & 14.75 & 15.9566 & -1.20664 \tabularnewline
101 & 18.25 & 16.9781 & 1.2719 \tabularnewline
102 & 9.9 & 16.416 & -6.51597 \tabularnewline
103 & 16 & 14.1702 & 1.82979 \tabularnewline
104 & 18.25 & 16.1885 & 2.06149 \tabularnewline
105 & 16.85 & 18.1655 & -1.31551 \tabularnewline
106 & 14.6 & 12.7345 & 1.86552 \tabularnewline
107 & 13.85 & 13.6613 & 0.188742 \tabularnewline
108 & 18.95 & 16.9703 & 1.97971 \tabularnewline
109 & 15.6 & 14.8775 & 0.722507 \tabularnewline
110 & 14.85 & 17.4262 & -2.57618 \tabularnewline
111 & 11.75 & 14.2596 & -2.50956 \tabularnewline
112 & 18.45 & 16.0588 & 2.39116 \tabularnewline
113 & 15.9 & 15.2446 & 0.65538 \tabularnewline
114 & 17.1 & 16.7304 & 0.369605 \tabularnewline
115 & 16.1 & 8.52234 & 7.57766 \tabularnewline
116 & 19.9 & 18.1776 & 1.72243 \tabularnewline
117 & 10.95 & 11.8408 & -0.890809 \tabularnewline
118 & 18.45 & 17.1464 & 1.30362 \tabularnewline
119 & 15.1 & 13.185 & 1.91501 \tabularnewline
120 & 15 & 15.6369 & -0.636854 \tabularnewline
121 & 11.35 & 14.6335 & -3.28346 \tabularnewline
122 & 15.95 & 15.2208 & 0.729232 \tabularnewline
123 & 18.1 & 15.5298 & 2.57022 \tabularnewline
124 & 14.6 & 15.8193 & -1.21928 \tabularnewline
125 & 15.4 & 16.4189 & -1.0189 \tabularnewline
126 & 15.4 & 16.4246 & -1.02462 \tabularnewline
127 & 17.6 & 14.7233 & 2.87673 \tabularnewline
128 & 13.35 & 14.1711 & -0.821116 \tabularnewline
129 & 19.1 & 17.1086 & 1.99139 \tabularnewline
130 & 15.35 & 15.9247 & -0.574713 \tabularnewline
131 & 7.6 & 11.2805 & -3.68046 \tabularnewline
132 & 13.4 & 15.1111 & -1.71111 \tabularnewline
133 & 13.9 & 15.5141 & -1.61412 \tabularnewline
134 & 19.1 & 16.8587 & 2.24133 \tabularnewline
135 & 15.25 & 15.0966 & 0.153434 \tabularnewline
136 & 12.9 & 15.3062 & -2.40619 \tabularnewline
137 & 16.1 & 15.7758 & 0.324171 \tabularnewline
138 & 17.35 & 14.7931 & 2.55687 \tabularnewline
139 & 13.15 & 15.0008 & -1.85084 \tabularnewline
140 & 12.15 & 14.1368 & -1.98684 \tabularnewline
141 & 12.6 & 12.4483 & 0.151709 \tabularnewline
142 & 10.35 & 12.4027 & -2.05271 \tabularnewline
143 & 15.4 & 13.5089 & 1.89106 \tabularnewline
144 & 9.6 & 11.6504 & -2.05041 \tabularnewline
145 & 18.2 & 14.7985 & 3.40154 \tabularnewline
146 & 13.6 & 13.4793 & 0.120697 \tabularnewline
147 & 14.85 & 14.5147 & 0.335348 \tabularnewline
148 & 14.75 & 17.2608 & -2.51077 \tabularnewline
149 & 14.1 & 14.2138 & -0.113755 \tabularnewline
150 & 14.9 & 13.3171 & 1.58292 \tabularnewline
151 & 16.25 & 15.0131 & 1.23692 \tabularnewline
152 & 19.25 & 20.8122 & -1.56225 \tabularnewline
153 & 13.6 & 12.2409 & 1.35914 \tabularnewline
154 & 13.6 & 14.7464 & -1.14639 \tabularnewline
155 & 15.65 & 16.3806 & -0.73058 \tabularnewline
156 & 12.75 & 13.2117 & -0.461708 \tabularnewline
157 & 14.6 & 13.2886 & 1.31137 \tabularnewline
158 & 9.85 & 11.103 & -1.253 \tabularnewline
159 & 12.65 & 11.8171 & 0.832946 \tabularnewline
160 & 19.2 & 15.9297 & 3.27027 \tabularnewline
161 & 16.6 & 14.3589 & 2.24115 \tabularnewline
162 & 11.2 & 11.1177 & 0.0822816 \tabularnewline
163 & 15.25 & 14.8886 & 0.361412 \tabularnewline
164 & 11.9 & 14.4333 & -2.53325 \tabularnewline
165 & 13.2 & 14.5369 & -1.33686 \tabularnewline
166 & 16.35 & 17.3433 & -0.993349 \tabularnewline
167 & 12.4 & 12.8074 & -0.407429 \tabularnewline
168 & 15.85 & 14.2431 & 1.60689 \tabularnewline
169 & 18.15 & 16.383 & 1.76695 \tabularnewline
170 & 11.15 & 12.3092 & -1.15916 \tabularnewline
171 & 15.65 & 16.9028 & -1.25279 \tabularnewline
172 & 17.75 & 16.4489 & 1.30106 \tabularnewline
173 & 7.65 & 12.4893 & -4.8393 \tabularnewline
174 & 12.35 & 13.2167 & -0.866651 \tabularnewline
175 & 15.6 & 12.8378 & 2.76216 \tabularnewline
176 & 19.3 & 17.3031 & 1.99686 \tabularnewline
177 & 15.2 & 12.7433 & 2.45674 \tabularnewline
178 & 17.1 & 14.5926 & 2.50745 \tabularnewline
179 & 15.6 & 13.8256 & 1.77442 \tabularnewline
180 & 18.4 & 14.666 & 3.73404 \tabularnewline
181 & 19.05 & 16.0932 & 2.95679 \tabularnewline
182 & 18.55 & 15.1982 & 3.35175 \tabularnewline
183 & 19.1 & 16.6443 & 2.45574 \tabularnewline
184 & 13.1 & 13.2364 & -0.136375 \tabularnewline
185 & 12.85 & 15.5161 & -2.66607 \tabularnewline
186 & 9.5 & 11.4493 & -1.94931 \tabularnewline
187 & 4.5 & 10.5477 & -6.04772 \tabularnewline
188 & 11.85 & 11.5394 & 0.310603 \tabularnewline
189 & 13.6 & 15.4789 & -1.87887 \tabularnewline
190 & 11.7 & 11.486 & 0.214021 \tabularnewline
191 & 12.4 & 12.8359 & -0.43586 \tabularnewline
192 & 13.35 & 14.5453 & -1.19532 \tabularnewline
193 & 11.4 & 13.8851 & -2.48515 \tabularnewline
194 & 14.9 & 13.819 & 1.08102 \tabularnewline
195 & 19.9 & 18.2518 & 1.64821 \tabularnewline
196 & 11.2 & 13.5562 & -2.35619 \tabularnewline
197 & 14.6 & 15.6326 & -1.03264 \tabularnewline
198 & 17.6 & 17.0594 & 0.540648 \tabularnewline
199 & 14.05 & 13.7089 & 0.341065 \tabularnewline
200 & 16.1 & 15.0977 & 1.00231 \tabularnewline
201 & 13.35 & 13.7662 & -0.416175 \tabularnewline
202 & 11.85 & 14.3288 & -2.47881 \tabularnewline
203 & 11.95 & 13.5364 & -1.58639 \tabularnewline
204 & 14.75 & 14.8048 & -0.0547641 \tabularnewline
205 & 15.15 & 14.631 & 0.51898 \tabularnewline
206 & 13.2 & 15.7055 & -2.50553 \tabularnewline
207 & 16.85 & 16.503 & 0.347006 \tabularnewline
208 & 7.85 & 11.9106 & -4.06064 \tabularnewline
209 & 7.7 & 13.885 & -6.18502 \tabularnewline
210 & 12.6 & 14.2843 & -1.68431 \tabularnewline
211 & 7.85 & 13.841 & -5.99097 \tabularnewline
212 & 10.95 & 11.8036 & -0.853553 \tabularnewline
213 & 12.35 & 13.985 & -1.63502 \tabularnewline
214 & 9.95 & 13.9629 & -4.01286 \tabularnewline
215 & 14.9 & 13.7274 & 1.17264 \tabularnewline
216 & 16.65 & 15.4125 & 1.2375 \tabularnewline
217 & 13.4 & 12.7798 & 0.620227 \tabularnewline
218 & 13.95 & 14.2881 & -0.338094 \tabularnewline
219 & 15.7 & 14.1149 & 1.58508 \tabularnewline
220 & 16.85 & 14.4992 & 2.35076 \tabularnewline
221 & 10.95 & 11.6441 & -0.694053 \tabularnewline
222 & 15.35 & 14.5376 & 0.812352 \tabularnewline
223 & 12.2 & 13.002 & -0.801981 \tabularnewline
224 & 15.1 & 14.0734 & 1.02658 \tabularnewline
225 & 17.75 & 16.8432 & 0.906787 \tabularnewline
226 & 15.2 & 14.126 & 1.07402 \tabularnewline
227 & 14.6 & 14.8011 & -0.201061 \tabularnewline
228 & 16.65 & 16.0286 & 0.621424 \tabularnewline
229 & 8.1 & 10.5354 & -2.43537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&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]11.4308[/C][C]1.46923[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]11.4339[/C][C]1.36606[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]10.8753[/C][C]-3.47534[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]9.59913[/C][C]-2.89913[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]14.1065[/C][C]-1.50654[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]11.6925[/C][C]3.10755[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]9.44485[/C][C]3.85515[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]11.3036[/C][C]-0.203616[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]11.6663[/C][C]-3.46634[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]11.4391[/C][C]-0.0391273[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]11.2799[/C][C]-4.87992[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.1216[/C][C]-1.1216[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]6.0767[/C][C]0.223303[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]9.56905[/C][C]1.73095[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]12.1335[/C][C]-0.233539[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]10.2432[/C][C]-0.943214[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]9.18889[/C][C]0.811105[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]11.4612[/C][C]2.33878[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]14.0695[/C][C]-3.26949[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]10.7594[/C][C]0.940631[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]14.3533[/C][C]-3.45327[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]13.2524[/C][C]2.84757[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]9.94573[/C][C]-0.0457278[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]11.0828[/C][C]0.417186[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]9.21462[/C][C]-0.914617[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.7715[/C][C]-0.0715039[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.98556[/C][C]-0.985563[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]9.40572[/C][C]1.39428[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]9.87196[/C][C]0.528043[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]11.0937[/C][C]1.60632[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]12.4199[/C][C]-0.619856[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]11.0517[/C][C]1.94834[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]11.5059[/C][C]-0.705898[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]8.12238[/C][C]4.17762[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]12.6081[/C][C]-1.3081[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]10.432[/C][C]1.16799[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]10.7695[/C][C]0.130515[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]11.8486[/C][C]0.251352[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]10.2836[/C][C]3.01642[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]9.84656[/C][C]0.253442[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]9.65378[/C][C]4.64622[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]11.487[/C][C]-2.18698[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]11.2751[/C][C]1.22491[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]8.61084[/C][C]-1.01084[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]11.7712[/C][C]-2.57124[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]12.1814[/C][C]2.3186[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]13.5077[/C][C]-1.20771[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]10.6262[/C][C]1.97383[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]12.3122[/C][C]0.687828[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]10.1934[/C][C]2.40665[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]12.2338[/C][C]0.966223[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]9.08651[/C][C]-1.38651[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]10.0278[/C][C]0.472197[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.3055[/C][C]0.594499[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]8.51268[/C][C]-4.21268[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]10.9588[/C][C]-0.658803[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]8.95085[/C][C]2.44915[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]9.90362[/C][C]-4.30362[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]10.5875[/C][C]-1.78751[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]10.1631[/C][C]-1.16309[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]10.4167[/C][C]-0.816663[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]7.51169[/C][C]-1.11169[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]10.3622[/C][C]1.23777[/C][/ROW]
[ROW][C]64[/C][C]4.35[/C][C]10.0138[/C][C]-5.66383[/C][/ROW]
[ROW][C]65[/C][C]12.7[/C][C]11.818[/C][C]0.882031[/C][/ROW]
[ROW][C]66[/C][C]18.1[/C][C]15.625[/C][C]2.47499[/C][/ROW]
[ROW][C]67[/C][C]17.85[/C][C]15.8773[/C][C]1.97269[/C][/ROW]
[ROW][C]68[/C][C]16.6[/C][C]16.9503[/C][C]-0.350329[/C][/ROW]
[ROW][C]69[/C][C]12.6[/C][C]11.4733[/C][C]1.12672[/C][/ROW]
[ROW][C]70[/C][C]17.1[/C][C]20.4673[/C][C]-3.36727[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]17.3074[/C][C]1.79265[/C][/ROW]
[ROW][C]72[/C][C]16.1[/C][C]17.9956[/C][C]-1.89556[/C][/ROW]
[ROW][C]73[/C][C]13.35[/C][C]11.2676[/C][C]2.08237[/C][/ROW]
[ROW][C]74[/C][C]18.4[/C][C]16.9572[/C][C]1.44278[/C][/ROW]
[ROW][C]75[/C][C]14.7[/C][C]9.92459[/C][C]4.77541[/C][/ROW]
[ROW][C]76[/C][C]10.6[/C][C]13.7285[/C][C]-3.12845[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]13.4254[/C][C]-0.825404[/C][/ROW]
[ROW][C]78[/C][C]16.2[/C][C]15.3116[/C][C]0.888394[/C][/ROW]
[ROW][C]79[/C][C]13.6[/C][C]15.9888[/C][C]-2.38884[/C][/ROW]
[ROW][C]80[/C][C]18.9[/C][C]17.0003[/C][C]1.89966[/C][/ROW]
[ROW][C]81[/C][C]14.1[/C][C]13.153[/C][C]0.94697[/C][/ROW]
[ROW][C]82[/C][C]14.5[/C][C]13.2295[/C][C]1.27055[/C][/ROW]
[ROW][C]83[/C][C]16.15[/C][C]18.4626[/C][C]-2.31261[/C][/ROW]
[ROW][C]84[/C][C]14.75[/C][C]13.5974[/C][C]1.15258[/C][/ROW]
[ROW][C]85[/C][C]14.8[/C][C]13.6631[/C][C]1.13686[/C][/ROW]
[ROW][C]86[/C][C]12.45[/C][C]12.126[/C][C]0.323957[/C][/ROW]
[ROW][C]87[/C][C]12.65[/C][C]12.8755[/C][C]-0.225496[/C][/ROW]
[ROW][C]88[/C][C]17.35[/C][C]14.1998[/C][C]3.15018[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.3169[/C][C]-1.71691[/C][/ROW]
[ROW][C]90[/C][C]18.4[/C][C]17.2234[/C][C]1.17662[/C][/ROW]
[ROW][C]91[/C][C]16.1[/C][C]15.7503[/C][C]0.34972[/C][/ROW]
[ROW][C]92[/C][C]11.6[/C][C]12.0196[/C][C]-0.419565[/C][/ROW]
[ROW][C]93[/C][C]17.75[/C][C]14.9906[/C][C]2.75944[/C][/ROW]
[ROW][C]94[/C][C]15.25[/C][C]14.366[/C][C]0.88401[/C][/ROW]
[ROW][C]95[/C][C]17.65[/C][C]15.2867[/C][C]2.36335[/C][/ROW]
[ROW][C]96[/C][C]16.35[/C][C]16.4152[/C][C]-0.0652071[/C][/ROW]
[ROW][C]97[/C][C]17.65[/C][C]16.1278[/C][C]1.52223[/C][/ROW]
[ROW][C]98[/C][C]13.6[/C][C]13.1947[/C][C]0.405327[/C][/ROW]
[ROW][C]99[/C][C]14.35[/C][C]14.0971[/C][C]0.252914[/C][/ROW]
[ROW][C]100[/C][C]14.75[/C][C]15.9566[/C][C]-1.20664[/C][/ROW]
[ROW][C]101[/C][C]18.25[/C][C]16.9781[/C][C]1.2719[/C][/ROW]
[ROW][C]102[/C][C]9.9[/C][C]16.416[/C][C]-6.51597[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.1702[/C][C]1.82979[/C][/ROW]
[ROW][C]104[/C][C]18.25[/C][C]16.1885[/C][C]2.06149[/C][/ROW]
[ROW][C]105[/C][C]16.85[/C][C]18.1655[/C][C]-1.31551[/C][/ROW]
[ROW][C]106[/C][C]14.6[/C][C]12.7345[/C][C]1.86552[/C][/ROW]
[ROW][C]107[/C][C]13.85[/C][C]13.6613[/C][C]0.188742[/C][/ROW]
[ROW][C]108[/C][C]18.95[/C][C]16.9703[/C][C]1.97971[/C][/ROW]
[ROW][C]109[/C][C]15.6[/C][C]14.8775[/C][C]0.722507[/C][/ROW]
[ROW][C]110[/C][C]14.85[/C][C]17.4262[/C][C]-2.57618[/C][/ROW]
[ROW][C]111[/C][C]11.75[/C][C]14.2596[/C][C]-2.50956[/C][/ROW]
[ROW][C]112[/C][C]18.45[/C][C]16.0588[/C][C]2.39116[/C][/ROW]
[ROW][C]113[/C][C]15.9[/C][C]15.2446[/C][C]0.65538[/C][/ROW]
[ROW][C]114[/C][C]17.1[/C][C]16.7304[/C][C]0.369605[/C][/ROW]
[ROW][C]115[/C][C]16.1[/C][C]8.52234[/C][C]7.57766[/C][/ROW]
[ROW][C]116[/C][C]19.9[/C][C]18.1776[/C][C]1.72243[/C][/ROW]
[ROW][C]117[/C][C]10.95[/C][C]11.8408[/C][C]-0.890809[/C][/ROW]
[ROW][C]118[/C][C]18.45[/C][C]17.1464[/C][C]1.30362[/C][/ROW]
[ROW][C]119[/C][C]15.1[/C][C]13.185[/C][C]1.91501[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.6369[/C][C]-0.636854[/C][/ROW]
[ROW][C]121[/C][C]11.35[/C][C]14.6335[/C][C]-3.28346[/C][/ROW]
[ROW][C]122[/C][C]15.95[/C][C]15.2208[/C][C]0.729232[/C][/ROW]
[ROW][C]123[/C][C]18.1[/C][C]15.5298[/C][C]2.57022[/C][/ROW]
[ROW][C]124[/C][C]14.6[/C][C]15.8193[/C][C]-1.21928[/C][/ROW]
[ROW][C]125[/C][C]15.4[/C][C]16.4189[/C][C]-1.0189[/C][/ROW]
[ROW][C]126[/C][C]15.4[/C][C]16.4246[/C][C]-1.02462[/C][/ROW]
[ROW][C]127[/C][C]17.6[/C][C]14.7233[/C][C]2.87673[/C][/ROW]
[ROW][C]128[/C][C]13.35[/C][C]14.1711[/C][C]-0.821116[/C][/ROW]
[ROW][C]129[/C][C]19.1[/C][C]17.1086[/C][C]1.99139[/C][/ROW]
[ROW][C]130[/C][C]15.35[/C][C]15.9247[/C][C]-0.574713[/C][/ROW]
[ROW][C]131[/C][C]7.6[/C][C]11.2805[/C][C]-3.68046[/C][/ROW]
[ROW][C]132[/C][C]13.4[/C][C]15.1111[/C][C]-1.71111[/C][/ROW]
[ROW][C]133[/C][C]13.9[/C][C]15.5141[/C][C]-1.61412[/C][/ROW]
[ROW][C]134[/C][C]19.1[/C][C]16.8587[/C][C]2.24133[/C][/ROW]
[ROW][C]135[/C][C]15.25[/C][C]15.0966[/C][C]0.153434[/C][/ROW]
[ROW][C]136[/C][C]12.9[/C][C]15.3062[/C][C]-2.40619[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.7758[/C][C]0.324171[/C][/ROW]
[ROW][C]138[/C][C]17.35[/C][C]14.7931[/C][C]2.55687[/C][/ROW]
[ROW][C]139[/C][C]13.15[/C][C]15.0008[/C][C]-1.85084[/C][/ROW]
[ROW][C]140[/C][C]12.15[/C][C]14.1368[/C][C]-1.98684[/C][/ROW]
[ROW][C]141[/C][C]12.6[/C][C]12.4483[/C][C]0.151709[/C][/ROW]
[ROW][C]142[/C][C]10.35[/C][C]12.4027[/C][C]-2.05271[/C][/ROW]
[ROW][C]143[/C][C]15.4[/C][C]13.5089[/C][C]1.89106[/C][/ROW]
[ROW][C]144[/C][C]9.6[/C][C]11.6504[/C][C]-2.05041[/C][/ROW]
[ROW][C]145[/C][C]18.2[/C][C]14.7985[/C][C]3.40154[/C][/ROW]
[ROW][C]146[/C][C]13.6[/C][C]13.4793[/C][C]0.120697[/C][/ROW]
[ROW][C]147[/C][C]14.85[/C][C]14.5147[/C][C]0.335348[/C][/ROW]
[ROW][C]148[/C][C]14.75[/C][C]17.2608[/C][C]-2.51077[/C][/ROW]
[ROW][C]149[/C][C]14.1[/C][C]14.2138[/C][C]-0.113755[/C][/ROW]
[ROW][C]150[/C][C]14.9[/C][C]13.3171[/C][C]1.58292[/C][/ROW]
[ROW][C]151[/C][C]16.25[/C][C]15.0131[/C][C]1.23692[/C][/ROW]
[ROW][C]152[/C][C]19.25[/C][C]20.8122[/C][C]-1.56225[/C][/ROW]
[ROW][C]153[/C][C]13.6[/C][C]12.2409[/C][C]1.35914[/C][/ROW]
[ROW][C]154[/C][C]13.6[/C][C]14.7464[/C][C]-1.14639[/C][/ROW]
[ROW][C]155[/C][C]15.65[/C][C]16.3806[/C][C]-0.73058[/C][/ROW]
[ROW][C]156[/C][C]12.75[/C][C]13.2117[/C][C]-0.461708[/C][/ROW]
[ROW][C]157[/C][C]14.6[/C][C]13.2886[/C][C]1.31137[/C][/ROW]
[ROW][C]158[/C][C]9.85[/C][C]11.103[/C][C]-1.253[/C][/ROW]
[ROW][C]159[/C][C]12.65[/C][C]11.8171[/C][C]0.832946[/C][/ROW]
[ROW][C]160[/C][C]19.2[/C][C]15.9297[/C][C]3.27027[/C][/ROW]
[ROW][C]161[/C][C]16.6[/C][C]14.3589[/C][C]2.24115[/C][/ROW]
[ROW][C]162[/C][C]11.2[/C][C]11.1177[/C][C]0.0822816[/C][/ROW]
[ROW][C]163[/C][C]15.25[/C][C]14.8886[/C][C]0.361412[/C][/ROW]
[ROW][C]164[/C][C]11.9[/C][C]14.4333[/C][C]-2.53325[/C][/ROW]
[ROW][C]165[/C][C]13.2[/C][C]14.5369[/C][C]-1.33686[/C][/ROW]
[ROW][C]166[/C][C]16.35[/C][C]17.3433[/C][C]-0.993349[/C][/ROW]
[ROW][C]167[/C][C]12.4[/C][C]12.8074[/C][C]-0.407429[/C][/ROW]
[ROW][C]168[/C][C]15.85[/C][C]14.2431[/C][C]1.60689[/C][/ROW]
[ROW][C]169[/C][C]18.15[/C][C]16.383[/C][C]1.76695[/C][/ROW]
[ROW][C]170[/C][C]11.15[/C][C]12.3092[/C][C]-1.15916[/C][/ROW]
[ROW][C]171[/C][C]15.65[/C][C]16.9028[/C][C]-1.25279[/C][/ROW]
[ROW][C]172[/C][C]17.75[/C][C]16.4489[/C][C]1.30106[/C][/ROW]
[ROW][C]173[/C][C]7.65[/C][C]12.4893[/C][C]-4.8393[/C][/ROW]
[ROW][C]174[/C][C]12.35[/C][C]13.2167[/C][C]-0.866651[/C][/ROW]
[ROW][C]175[/C][C]15.6[/C][C]12.8378[/C][C]2.76216[/C][/ROW]
[ROW][C]176[/C][C]19.3[/C][C]17.3031[/C][C]1.99686[/C][/ROW]
[ROW][C]177[/C][C]15.2[/C][C]12.7433[/C][C]2.45674[/C][/ROW]
[ROW][C]178[/C][C]17.1[/C][C]14.5926[/C][C]2.50745[/C][/ROW]
[ROW][C]179[/C][C]15.6[/C][C]13.8256[/C][C]1.77442[/C][/ROW]
[ROW][C]180[/C][C]18.4[/C][C]14.666[/C][C]3.73404[/C][/ROW]
[ROW][C]181[/C][C]19.05[/C][C]16.0932[/C][C]2.95679[/C][/ROW]
[ROW][C]182[/C][C]18.55[/C][C]15.1982[/C][C]3.35175[/C][/ROW]
[ROW][C]183[/C][C]19.1[/C][C]16.6443[/C][C]2.45574[/C][/ROW]
[ROW][C]184[/C][C]13.1[/C][C]13.2364[/C][C]-0.136375[/C][/ROW]
[ROW][C]185[/C][C]12.85[/C][C]15.5161[/C][C]-2.66607[/C][/ROW]
[ROW][C]186[/C][C]9.5[/C][C]11.4493[/C][C]-1.94931[/C][/ROW]
[ROW][C]187[/C][C]4.5[/C][C]10.5477[/C][C]-6.04772[/C][/ROW]
[ROW][C]188[/C][C]11.85[/C][C]11.5394[/C][C]0.310603[/C][/ROW]
[ROW][C]189[/C][C]13.6[/C][C]15.4789[/C][C]-1.87887[/C][/ROW]
[ROW][C]190[/C][C]11.7[/C][C]11.486[/C][C]0.214021[/C][/ROW]
[ROW][C]191[/C][C]12.4[/C][C]12.8359[/C][C]-0.43586[/C][/ROW]
[ROW][C]192[/C][C]13.35[/C][C]14.5453[/C][C]-1.19532[/C][/ROW]
[ROW][C]193[/C][C]11.4[/C][C]13.8851[/C][C]-2.48515[/C][/ROW]
[ROW][C]194[/C][C]14.9[/C][C]13.819[/C][C]1.08102[/C][/ROW]
[ROW][C]195[/C][C]19.9[/C][C]18.2518[/C][C]1.64821[/C][/ROW]
[ROW][C]196[/C][C]11.2[/C][C]13.5562[/C][C]-2.35619[/C][/ROW]
[ROW][C]197[/C][C]14.6[/C][C]15.6326[/C][C]-1.03264[/C][/ROW]
[ROW][C]198[/C][C]17.6[/C][C]17.0594[/C][C]0.540648[/C][/ROW]
[ROW][C]199[/C][C]14.05[/C][C]13.7089[/C][C]0.341065[/C][/ROW]
[ROW][C]200[/C][C]16.1[/C][C]15.0977[/C][C]1.00231[/C][/ROW]
[ROW][C]201[/C][C]13.35[/C][C]13.7662[/C][C]-0.416175[/C][/ROW]
[ROW][C]202[/C][C]11.85[/C][C]14.3288[/C][C]-2.47881[/C][/ROW]
[ROW][C]203[/C][C]11.95[/C][C]13.5364[/C][C]-1.58639[/C][/ROW]
[ROW][C]204[/C][C]14.75[/C][C]14.8048[/C][C]-0.0547641[/C][/ROW]
[ROW][C]205[/C][C]15.15[/C][C]14.631[/C][C]0.51898[/C][/ROW]
[ROW][C]206[/C][C]13.2[/C][C]15.7055[/C][C]-2.50553[/C][/ROW]
[ROW][C]207[/C][C]16.85[/C][C]16.503[/C][C]0.347006[/C][/ROW]
[ROW][C]208[/C][C]7.85[/C][C]11.9106[/C][C]-4.06064[/C][/ROW]
[ROW][C]209[/C][C]7.7[/C][C]13.885[/C][C]-6.18502[/C][/ROW]
[ROW][C]210[/C][C]12.6[/C][C]14.2843[/C][C]-1.68431[/C][/ROW]
[ROW][C]211[/C][C]7.85[/C][C]13.841[/C][C]-5.99097[/C][/ROW]
[ROW][C]212[/C][C]10.95[/C][C]11.8036[/C][C]-0.853553[/C][/ROW]
[ROW][C]213[/C][C]12.35[/C][C]13.985[/C][C]-1.63502[/C][/ROW]
[ROW][C]214[/C][C]9.95[/C][C]13.9629[/C][C]-4.01286[/C][/ROW]
[ROW][C]215[/C][C]14.9[/C][C]13.7274[/C][C]1.17264[/C][/ROW]
[ROW][C]216[/C][C]16.65[/C][C]15.4125[/C][C]1.2375[/C][/ROW]
[ROW][C]217[/C][C]13.4[/C][C]12.7798[/C][C]0.620227[/C][/ROW]
[ROW][C]218[/C][C]13.95[/C][C]14.2881[/C][C]-0.338094[/C][/ROW]
[ROW][C]219[/C][C]15.7[/C][C]14.1149[/C][C]1.58508[/C][/ROW]
[ROW][C]220[/C][C]16.85[/C][C]14.4992[/C][C]2.35076[/C][/ROW]
[ROW][C]221[/C][C]10.95[/C][C]11.6441[/C][C]-0.694053[/C][/ROW]
[ROW][C]222[/C][C]15.35[/C][C]14.5376[/C][C]0.812352[/C][/ROW]
[ROW][C]223[/C][C]12.2[/C][C]13.002[/C][C]-0.801981[/C][/ROW]
[ROW][C]224[/C][C]15.1[/C][C]14.0734[/C][C]1.02658[/C][/ROW]
[ROW][C]225[/C][C]17.75[/C][C]16.8432[/C][C]0.906787[/C][/ROW]
[ROW][C]226[/C][C]15.2[/C][C]14.126[/C][C]1.07402[/C][/ROW]
[ROW][C]227[/C][C]14.6[/C][C]14.8011[/C][C]-0.201061[/C][/ROW]
[ROW][C]228[/C][C]16.65[/C][C]16.0286[/C][C]0.621424[/C][/ROW]
[ROW][C]229[/C][C]8.1[/C][C]10.5354[/C][C]-2.43537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&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
112.911.43081.46923
212.811.43391.36606
37.410.8753-3.47534
46.79.59913-2.89913
512.614.1065-1.50654
614.811.69253.10755
713.39.444853.85515
811.111.3036-0.203616
98.211.6663-3.46634
1011.411.4391-0.0391273
116.411.2799-4.87992
121213.1216-1.1216
136.36.07670.223303
1411.39.569051.73095
1511.912.1335-0.233539
169.310.2432-0.943214
17109.188890.811105
1813.811.46122.33878
1910.814.0695-3.26949
2011.710.75940.940631
2110.914.3533-3.45327
2216.113.25242.84757
239.99.94573-0.0457278
2411.511.08280.417186
258.39.21462-0.914617
2611.711.7715-0.0715039
2799.98556-0.985563
2810.89.405721.39428
2910.49.871960.528043
3012.711.09371.60632
3111.812.4199-0.619856
321311.05171.94834
3310.811.5059-0.705898
3412.38.122384.17762
3511.312.6081-1.3081
3611.610.4321.16799
3710.910.76950.130515
3812.111.84860.251352
3913.310.28363.01642
4010.19.846560.253442
4114.39.653784.64622
429.311.487-2.18698
4312.511.27511.22491
447.68.61084-1.01084
459.211.7712-2.57124
4614.512.18142.3186
4712.313.5077-1.20771
4812.610.62621.97383
491312.31220.687828
5012.610.19342.40665
5113.212.23380.966223
527.79.08651-1.38651
5310.510.02780.472197
5410.910.30550.594499
554.38.51268-4.21268
5610.310.9588-0.658803
5711.48.950852.44915
585.69.90362-4.30362
598.810.5875-1.78751
60910.1631-1.16309
619.610.4167-0.816663
626.47.51169-1.11169
6311.610.36221.23777
644.3510.0138-5.66383
6512.711.8180.882031
6618.115.6252.47499
6717.8515.87731.97269
6816.616.9503-0.350329
6912.611.47331.12672
7017.120.4673-3.36727
7119.117.30741.79265
7216.117.9956-1.89556
7313.3511.26762.08237
7418.416.95721.44278
7514.79.924594.77541
7610.613.7285-3.12845
7712.613.4254-0.825404
7816.215.31160.888394
7913.615.9888-2.38884
8018.917.00031.89966
8114.113.1530.94697
8214.513.22951.27055
8316.1518.4626-2.31261
8414.7513.59741.15258
8514.813.66311.13686
8612.4512.1260.323957
8712.6512.8755-0.225496
8817.3514.19983.15018
898.610.3169-1.71691
9018.417.22341.17662
9116.115.75030.34972
9211.612.0196-0.419565
9317.7514.99062.75944
9415.2514.3660.88401
9517.6515.28672.36335
9616.3516.4152-0.0652071
9717.6516.12781.52223
9813.613.19470.405327
9914.3514.09710.252914
10014.7515.9566-1.20664
10118.2516.97811.2719
1029.916.416-6.51597
1031614.17021.82979
10418.2516.18852.06149
10516.8518.1655-1.31551
10614.612.73451.86552
10713.8513.66130.188742
10818.9516.97031.97971
10915.614.87750.722507
11014.8517.4262-2.57618
11111.7514.2596-2.50956
11218.4516.05882.39116
11315.915.24460.65538
11417.116.73040.369605
11516.18.522347.57766
11619.918.17761.72243
11710.9511.8408-0.890809
11818.4517.14641.30362
11915.113.1851.91501
1201515.6369-0.636854
12111.3514.6335-3.28346
12215.9515.22080.729232
12318.115.52982.57022
12414.615.8193-1.21928
12515.416.4189-1.0189
12615.416.4246-1.02462
12717.614.72332.87673
12813.3514.1711-0.821116
12919.117.10861.99139
13015.3515.9247-0.574713
1317.611.2805-3.68046
13213.415.1111-1.71111
13313.915.5141-1.61412
13419.116.85872.24133
13515.2515.09660.153434
13612.915.3062-2.40619
13716.115.77580.324171
13817.3514.79312.55687
13913.1515.0008-1.85084
14012.1514.1368-1.98684
14112.612.44830.151709
14210.3512.4027-2.05271
14315.413.50891.89106
1449.611.6504-2.05041
14518.214.79853.40154
14613.613.47930.120697
14714.8514.51470.335348
14814.7517.2608-2.51077
14914.114.2138-0.113755
15014.913.31711.58292
15116.2515.01311.23692
15219.2520.8122-1.56225
15313.612.24091.35914
15413.614.7464-1.14639
15515.6516.3806-0.73058
15612.7513.2117-0.461708
15714.613.28861.31137
1589.8511.103-1.253
15912.6511.81710.832946
16019.215.92973.27027
16116.614.35892.24115
16211.211.11770.0822816
16315.2514.88860.361412
16411.914.4333-2.53325
16513.214.5369-1.33686
16616.3517.3433-0.993349
16712.412.8074-0.407429
16815.8514.24311.60689
16918.1516.3831.76695
17011.1512.3092-1.15916
17115.6516.9028-1.25279
17217.7516.44891.30106
1737.6512.4893-4.8393
17412.3513.2167-0.866651
17515.612.83782.76216
17619.317.30311.99686
17715.212.74332.45674
17817.114.59262.50745
17915.613.82561.77442
18018.414.6663.73404
18119.0516.09322.95679
18218.5515.19823.35175
18319.116.64432.45574
18413.113.2364-0.136375
18512.8515.5161-2.66607
1869.511.4493-1.94931
1874.510.5477-6.04772
18811.8511.53940.310603
18913.615.4789-1.87887
19011.711.4860.214021
19112.412.8359-0.43586
19213.3514.5453-1.19532
19311.413.8851-2.48515
19414.913.8191.08102
19519.918.25181.64821
19611.213.5562-2.35619
19714.615.6326-1.03264
19817.617.05940.540648
19914.0513.70890.341065
20016.115.09771.00231
20113.3513.7662-0.416175
20211.8514.3288-2.47881
20311.9513.5364-1.58639
20414.7514.8048-0.0547641
20515.1514.6310.51898
20613.215.7055-2.50553
20716.8516.5030.347006
2087.8511.9106-4.06064
2097.713.885-6.18502
21012.614.2843-1.68431
2117.8513.841-5.99097
21210.9511.8036-0.853553
21312.3513.985-1.63502
2149.9513.9629-4.01286
21514.913.72741.17264
21616.6515.41251.2375
21713.412.77980.620227
21813.9514.2881-0.338094
21915.714.11491.58508
22016.8514.49922.35076
22110.9511.6441-0.694053
22215.3514.53760.812352
22312.213.002-0.801981
22415.114.07341.02658
22517.7516.84320.906787
22615.214.1261.07402
22714.614.8011-0.201061
22816.6516.02860.621424
2298.110.5354-2.43537






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.6099060.7801890.390094
160.450730.9014590.54927
170.8455310.3089370.154469
180.7754320.4491360.224568
190.72640.54720.2736
200.6455060.7089880.354494
210.6016450.796710.398355
220.5490450.9019110.450955
230.5882680.8234640.411732
240.5213650.9572710.478635
250.5469280.9061440.453072
260.4703530.9407060.529647
270.4009350.801870.599065
280.3524130.7048260.647587
290.2885420.5770830.711458
300.2409790.4819590.759021
310.1954330.3908650.804567
320.1595780.3191570.840422
330.1214010.2428020.878599
340.1034270.2068540.896573
350.08193680.1638740.918063
360.05995380.1199080.940046
370.0509280.1018560.949072
380.03737080.07474160.962629
390.08438940.1687790.915611
400.06898480.137970.931015
410.1989930.3979860.801007
420.1692540.3385080.830746
430.137490.2749810.86251
440.15210.3041990.8479
450.1498410.2996810.850159
460.1350990.2701980.864901
470.1281850.2563690.871815
480.1153980.2307970.884602
490.09594610.1918920.904054
500.08595850.1719170.914042
510.1140970.2281940.885903
520.1173380.2346760.882662
530.1126050.2252090.887395
540.09939890.1987980.900601
550.2187650.4375290.781235
560.2160140.4320280.783986
570.2080330.4160670.791967
580.3421970.6843940.657803
590.3480510.6961020.651949
600.3089190.6178390.691081
610.2717470.5434940.728253
620.2562990.5125980.743701
630.2346450.4692910.765355
640.2571260.5142510.742874
650.3167010.6334020.683299
660.4516210.9032420.548379
670.4883710.9767410.511629
680.4534620.9069230.546538
690.4192480.8384960.580752
700.4958140.9916280.504186
710.4738650.9477290.526135
720.4493530.8987060.550647
730.4287920.8575830.571208
740.4140.8280.586
750.5493590.9012830.450641
760.606490.7870190.39351
770.5699690.8600620.430031
780.5569820.8860370.443018
790.6125060.7749870.387494
800.5934120.8131750.406588
810.5594670.8810670.440533
820.5276330.9447350.472367
830.5246160.9507680.475384
840.4942040.9884070.505796
850.4763780.9527560.523622
860.4419560.8839130.558044
870.4016090.8032170.598391
880.4718820.9437630.528118
890.4568890.9137780.543111
900.43750.8750010.5625
910.4124340.8248680.587566
920.3819040.7638070.618096
930.3936620.7873230.606338
940.360360.7207210.63964
950.3874230.7748460.612577
960.3493280.6986560.650672
970.328610.6572190.67139
980.293980.587960.70602
990.2612850.5225690.738715
1000.2382840.4765670.761716
1010.2189260.4378520.781074
1020.5176540.9646920.482346
1030.5183940.9632120.481606
1040.5177920.9644160.482208
1050.4877570.9755130.512243
1060.4710750.942150.528925
1070.4406410.8812830.559359
1080.4379860.8759720.562014
1090.4045310.8090630.595469
1100.4243010.8486010.575699
1110.4507190.9014390.549281
1120.4516760.9033520.548324
1130.4350290.8700570.564971
1140.3969840.7939670.603016
1150.837470.3250590.16253
1160.8202790.3594410.179721
1170.8163920.3672170.183608
1180.7955030.4089930.204497
1190.7919540.4160920.208046
1200.7693430.4613130.230657
1210.8203180.3593640.179682
1220.7946170.4107660.205383
1230.8019250.396150.198075
1240.7928980.4142030.207102
1250.7715190.4569610.228481
1260.754790.490420.24521
1270.774030.451940.22597
1280.7481510.5036990.251849
1290.7468920.5062160.253108
1300.7213810.5572390.278619
1310.7735190.4529620.226481
1320.7615430.4769130.238457
1330.7572920.4854150.242708
1340.7606860.4786280.239314
1350.7310830.5378340.268917
1360.7681450.463710.231855
1370.7363390.5273220.263661
1380.7435290.5129410.256471
1390.7464940.5070120.253506
1400.7499850.5000310.250015
1410.7165020.5669970.283498
1420.7100260.5799480.289974
1430.6913150.617370.308685
1440.6922050.615590.307795
1450.7251760.5496490.274824
1460.6893740.6212510.310626
1470.6688340.6623330.331166
1480.689520.620960.31048
1490.6533090.6933810.346691
1500.6374140.7251720.362586
1510.6045460.7909070.395454
1520.6287010.7425990.371299
1530.608830.7823410.39117
1540.5907450.8185110.409255
1550.5692840.8614310.430716
1560.5306380.9387230.469362
1570.5516460.8967070.448354
1580.5419110.9161790.458089
1590.5232790.9534410.476721
1600.533310.9333810.46669
1610.5230250.953950.476975
1620.5120040.9759920.487996
1630.4675940.9351870.532406
1640.4795230.9590460.520477
1650.4475670.8951330.552433
1660.4269630.8539270.573037
1670.3883950.7767890.611605
1680.3794570.7589150.620543
1690.3494990.6989980.650501
1700.3153130.6306260.684687
1710.2826510.5653010.717349
1720.2512420.5024840.748758
1730.376740.7534790.62326
1740.3458040.6916070.654196
1750.3906830.7813670.609317
1760.3606650.7213310.639335
1770.4218730.8437460.578127
1780.4346120.8692230.565388
1790.4666060.9332120.533394
1800.6284730.7430540.371527
1810.6779870.6440260.322013
1820.6961960.6076080.303804
1830.7198190.5603610.280181
1840.6753940.6492110.324606
1850.6704810.6590380.329519
1860.6295410.7409190.370459
1870.7519740.4960530.248026
1880.7260540.5478920.273946
1890.7145680.5708630.285432
1900.7328620.5342750.267138
1910.6831820.6336360.316818
1920.6319770.7360450.368023
1930.6050990.7898010.394901
1940.5781920.8436170.421808
1950.5725980.8548040.427402
1960.5232260.9535490.476774
1970.4658910.9317830.534109
1980.4022660.8045320.597734
1990.5835450.832910.416455
2000.5485040.9029920.451496
2010.5450030.9099950.454997
2020.4824910.9649810.517509
2030.4207130.8414270.579287
2040.3441560.6883120.655844
2050.3261460.6522930.673854
2060.2706860.5413720.729314
2070.2373490.4746990.762651
2080.2768270.5536540.723173
2090.9078390.1843210.0921606
2100.848140.3037210.15186
2110.9085930.1828150.0914074
2120.9007270.1985450.0992726
2130.8907920.2184170.109208
2140.9741950.05161030.0258052

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.609906 & 0.780189 & 0.390094 \tabularnewline
16 & 0.45073 & 0.901459 & 0.54927 \tabularnewline
17 & 0.845531 & 0.308937 & 0.154469 \tabularnewline
18 & 0.775432 & 0.449136 & 0.224568 \tabularnewline
19 & 0.7264 & 0.5472 & 0.2736 \tabularnewline
20 & 0.645506 & 0.708988 & 0.354494 \tabularnewline
21 & 0.601645 & 0.79671 & 0.398355 \tabularnewline
22 & 0.549045 & 0.901911 & 0.450955 \tabularnewline
23 & 0.588268 & 0.823464 & 0.411732 \tabularnewline
24 & 0.521365 & 0.957271 & 0.478635 \tabularnewline
25 & 0.546928 & 0.906144 & 0.453072 \tabularnewline
26 & 0.470353 & 0.940706 & 0.529647 \tabularnewline
27 & 0.400935 & 0.80187 & 0.599065 \tabularnewline
28 & 0.352413 & 0.704826 & 0.647587 \tabularnewline
29 & 0.288542 & 0.577083 & 0.711458 \tabularnewline
30 & 0.240979 & 0.481959 & 0.759021 \tabularnewline
31 & 0.195433 & 0.390865 & 0.804567 \tabularnewline
32 & 0.159578 & 0.319157 & 0.840422 \tabularnewline
33 & 0.121401 & 0.242802 & 0.878599 \tabularnewline
34 & 0.103427 & 0.206854 & 0.896573 \tabularnewline
35 & 0.0819368 & 0.163874 & 0.918063 \tabularnewline
36 & 0.0599538 & 0.119908 & 0.940046 \tabularnewline
37 & 0.050928 & 0.101856 & 0.949072 \tabularnewline
38 & 0.0373708 & 0.0747416 & 0.962629 \tabularnewline
39 & 0.0843894 & 0.168779 & 0.915611 \tabularnewline
40 & 0.0689848 & 0.13797 & 0.931015 \tabularnewline
41 & 0.198993 & 0.397986 & 0.801007 \tabularnewline
42 & 0.169254 & 0.338508 & 0.830746 \tabularnewline
43 & 0.13749 & 0.274981 & 0.86251 \tabularnewline
44 & 0.1521 & 0.304199 & 0.8479 \tabularnewline
45 & 0.149841 & 0.299681 & 0.850159 \tabularnewline
46 & 0.135099 & 0.270198 & 0.864901 \tabularnewline
47 & 0.128185 & 0.256369 & 0.871815 \tabularnewline
48 & 0.115398 & 0.230797 & 0.884602 \tabularnewline
49 & 0.0959461 & 0.191892 & 0.904054 \tabularnewline
50 & 0.0859585 & 0.171917 & 0.914042 \tabularnewline
51 & 0.114097 & 0.228194 & 0.885903 \tabularnewline
52 & 0.117338 & 0.234676 & 0.882662 \tabularnewline
53 & 0.112605 & 0.225209 & 0.887395 \tabularnewline
54 & 0.0993989 & 0.198798 & 0.900601 \tabularnewline
55 & 0.218765 & 0.437529 & 0.781235 \tabularnewline
56 & 0.216014 & 0.432028 & 0.783986 \tabularnewline
57 & 0.208033 & 0.416067 & 0.791967 \tabularnewline
58 & 0.342197 & 0.684394 & 0.657803 \tabularnewline
59 & 0.348051 & 0.696102 & 0.651949 \tabularnewline
60 & 0.308919 & 0.617839 & 0.691081 \tabularnewline
61 & 0.271747 & 0.543494 & 0.728253 \tabularnewline
62 & 0.256299 & 0.512598 & 0.743701 \tabularnewline
63 & 0.234645 & 0.469291 & 0.765355 \tabularnewline
64 & 0.257126 & 0.514251 & 0.742874 \tabularnewline
65 & 0.316701 & 0.633402 & 0.683299 \tabularnewline
66 & 0.451621 & 0.903242 & 0.548379 \tabularnewline
67 & 0.488371 & 0.976741 & 0.511629 \tabularnewline
68 & 0.453462 & 0.906923 & 0.546538 \tabularnewline
69 & 0.419248 & 0.838496 & 0.580752 \tabularnewline
70 & 0.495814 & 0.991628 & 0.504186 \tabularnewline
71 & 0.473865 & 0.947729 & 0.526135 \tabularnewline
72 & 0.449353 & 0.898706 & 0.550647 \tabularnewline
73 & 0.428792 & 0.857583 & 0.571208 \tabularnewline
74 & 0.414 & 0.828 & 0.586 \tabularnewline
75 & 0.549359 & 0.901283 & 0.450641 \tabularnewline
76 & 0.60649 & 0.787019 & 0.39351 \tabularnewline
77 & 0.569969 & 0.860062 & 0.430031 \tabularnewline
78 & 0.556982 & 0.886037 & 0.443018 \tabularnewline
79 & 0.612506 & 0.774987 & 0.387494 \tabularnewline
80 & 0.593412 & 0.813175 & 0.406588 \tabularnewline
81 & 0.559467 & 0.881067 & 0.440533 \tabularnewline
82 & 0.527633 & 0.944735 & 0.472367 \tabularnewline
83 & 0.524616 & 0.950768 & 0.475384 \tabularnewline
84 & 0.494204 & 0.988407 & 0.505796 \tabularnewline
85 & 0.476378 & 0.952756 & 0.523622 \tabularnewline
86 & 0.441956 & 0.883913 & 0.558044 \tabularnewline
87 & 0.401609 & 0.803217 & 0.598391 \tabularnewline
88 & 0.471882 & 0.943763 & 0.528118 \tabularnewline
89 & 0.456889 & 0.913778 & 0.543111 \tabularnewline
90 & 0.4375 & 0.875001 & 0.5625 \tabularnewline
91 & 0.412434 & 0.824868 & 0.587566 \tabularnewline
92 & 0.381904 & 0.763807 & 0.618096 \tabularnewline
93 & 0.393662 & 0.787323 & 0.606338 \tabularnewline
94 & 0.36036 & 0.720721 & 0.63964 \tabularnewline
95 & 0.387423 & 0.774846 & 0.612577 \tabularnewline
96 & 0.349328 & 0.698656 & 0.650672 \tabularnewline
97 & 0.32861 & 0.657219 & 0.67139 \tabularnewline
98 & 0.29398 & 0.58796 & 0.70602 \tabularnewline
99 & 0.261285 & 0.522569 & 0.738715 \tabularnewline
100 & 0.238284 & 0.476567 & 0.761716 \tabularnewline
101 & 0.218926 & 0.437852 & 0.781074 \tabularnewline
102 & 0.517654 & 0.964692 & 0.482346 \tabularnewline
103 & 0.518394 & 0.963212 & 0.481606 \tabularnewline
104 & 0.517792 & 0.964416 & 0.482208 \tabularnewline
105 & 0.487757 & 0.975513 & 0.512243 \tabularnewline
106 & 0.471075 & 0.94215 & 0.528925 \tabularnewline
107 & 0.440641 & 0.881283 & 0.559359 \tabularnewline
108 & 0.437986 & 0.875972 & 0.562014 \tabularnewline
109 & 0.404531 & 0.809063 & 0.595469 \tabularnewline
110 & 0.424301 & 0.848601 & 0.575699 \tabularnewline
111 & 0.450719 & 0.901439 & 0.549281 \tabularnewline
112 & 0.451676 & 0.903352 & 0.548324 \tabularnewline
113 & 0.435029 & 0.870057 & 0.564971 \tabularnewline
114 & 0.396984 & 0.793967 & 0.603016 \tabularnewline
115 & 0.83747 & 0.325059 & 0.16253 \tabularnewline
116 & 0.820279 & 0.359441 & 0.179721 \tabularnewline
117 & 0.816392 & 0.367217 & 0.183608 \tabularnewline
118 & 0.795503 & 0.408993 & 0.204497 \tabularnewline
119 & 0.791954 & 0.416092 & 0.208046 \tabularnewline
120 & 0.769343 & 0.461313 & 0.230657 \tabularnewline
121 & 0.820318 & 0.359364 & 0.179682 \tabularnewline
122 & 0.794617 & 0.410766 & 0.205383 \tabularnewline
123 & 0.801925 & 0.39615 & 0.198075 \tabularnewline
124 & 0.792898 & 0.414203 & 0.207102 \tabularnewline
125 & 0.771519 & 0.456961 & 0.228481 \tabularnewline
126 & 0.75479 & 0.49042 & 0.24521 \tabularnewline
127 & 0.77403 & 0.45194 & 0.22597 \tabularnewline
128 & 0.748151 & 0.503699 & 0.251849 \tabularnewline
129 & 0.746892 & 0.506216 & 0.253108 \tabularnewline
130 & 0.721381 & 0.557239 & 0.278619 \tabularnewline
131 & 0.773519 & 0.452962 & 0.226481 \tabularnewline
132 & 0.761543 & 0.476913 & 0.238457 \tabularnewline
133 & 0.757292 & 0.485415 & 0.242708 \tabularnewline
134 & 0.760686 & 0.478628 & 0.239314 \tabularnewline
135 & 0.731083 & 0.537834 & 0.268917 \tabularnewline
136 & 0.768145 & 0.46371 & 0.231855 \tabularnewline
137 & 0.736339 & 0.527322 & 0.263661 \tabularnewline
138 & 0.743529 & 0.512941 & 0.256471 \tabularnewline
139 & 0.746494 & 0.507012 & 0.253506 \tabularnewline
140 & 0.749985 & 0.500031 & 0.250015 \tabularnewline
141 & 0.716502 & 0.566997 & 0.283498 \tabularnewline
142 & 0.710026 & 0.579948 & 0.289974 \tabularnewline
143 & 0.691315 & 0.61737 & 0.308685 \tabularnewline
144 & 0.692205 & 0.61559 & 0.307795 \tabularnewline
145 & 0.725176 & 0.549649 & 0.274824 \tabularnewline
146 & 0.689374 & 0.621251 & 0.310626 \tabularnewline
147 & 0.668834 & 0.662333 & 0.331166 \tabularnewline
148 & 0.68952 & 0.62096 & 0.31048 \tabularnewline
149 & 0.653309 & 0.693381 & 0.346691 \tabularnewline
150 & 0.637414 & 0.725172 & 0.362586 \tabularnewline
151 & 0.604546 & 0.790907 & 0.395454 \tabularnewline
152 & 0.628701 & 0.742599 & 0.371299 \tabularnewline
153 & 0.60883 & 0.782341 & 0.39117 \tabularnewline
154 & 0.590745 & 0.818511 & 0.409255 \tabularnewline
155 & 0.569284 & 0.861431 & 0.430716 \tabularnewline
156 & 0.530638 & 0.938723 & 0.469362 \tabularnewline
157 & 0.551646 & 0.896707 & 0.448354 \tabularnewline
158 & 0.541911 & 0.916179 & 0.458089 \tabularnewline
159 & 0.523279 & 0.953441 & 0.476721 \tabularnewline
160 & 0.53331 & 0.933381 & 0.46669 \tabularnewline
161 & 0.523025 & 0.95395 & 0.476975 \tabularnewline
162 & 0.512004 & 0.975992 & 0.487996 \tabularnewline
163 & 0.467594 & 0.935187 & 0.532406 \tabularnewline
164 & 0.479523 & 0.959046 & 0.520477 \tabularnewline
165 & 0.447567 & 0.895133 & 0.552433 \tabularnewline
166 & 0.426963 & 0.853927 & 0.573037 \tabularnewline
167 & 0.388395 & 0.776789 & 0.611605 \tabularnewline
168 & 0.379457 & 0.758915 & 0.620543 \tabularnewline
169 & 0.349499 & 0.698998 & 0.650501 \tabularnewline
170 & 0.315313 & 0.630626 & 0.684687 \tabularnewline
171 & 0.282651 & 0.565301 & 0.717349 \tabularnewline
172 & 0.251242 & 0.502484 & 0.748758 \tabularnewline
173 & 0.37674 & 0.753479 & 0.62326 \tabularnewline
174 & 0.345804 & 0.691607 & 0.654196 \tabularnewline
175 & 0.390683 & 0.781367 & 0.609317 \tabularnewline
176 & 0.360665 & 0.721331 & 0.639335 \tabularnewline
177 & 0.421873 & 0.843746 & 0.578127 \tabularnewline
178 & 0.434612 & 0.869223 & 0.565388 \tabularnewline
179 & 0.466606 & 0.933212 & 0.533394 \tabularnewline
180 & 0.628473 & 0.743054 & 0.371527 \tabularnewline
181 & 0.677987 & 0.644026 & 0.322013 \tabularnewline
182 & 0.696196 & 0.607608 & 0.303804 \tabularnewline
183 & 0.719819 & 0.560361 & 0.280181 \tabularnewline
184 & 0.675394 & 0.649211 & 0.324606 \tabularnewline
185 & 0.670481 & 0.659038 & 0.329519 \tabularnewline
186 & 0.629541 & 0.740919 & 0.370459 \tabularnewline
187 & 0.751974 & 0.496053 & 0.248026 \tabularnewline
188 & 0.726054 & 0.547892 & 0.273946 \tabularnewline
189 & 0.714568 & 0.570863 & 0.285432 \tabularnewline
190 & 0.732862 & 0.534275 & 0.267138 \tabularnewline
191 & 0.683182 & 0.633636 & 0.316818 \tabularnewline
192 & 0.631977 & 0.736045 & 0.368023 \tabularnewline
193 & 0.605099 & 0.789801 & 0.394901 \tabularnewline
194 & 0.578192 & 0.843617 & 0.421808 \tabularnewline
195 & 0.572598 & 0.854804 & 0.427402 \tabularnewline
196 & 0.523226 & 0.953549 & 0.476774 \tabularnewline
197 & 0.465891 & 0.931783 & 0.534109 \tabularnewline
198 & 0.402266 & 0.804532 & 0.597734 \tabularnewline
199 & 0.583545 & 0.83291 & 0.416455 \tabularnewline
200 & 0.548504 & 0.902992 & 0.451496 \tabularnewline
201 & 0.545003 & 0.909995 & 0.454997 \tabularnewline
202 & 0.482491 & 0.964981 & 0.517509 \tabularnewline
203 & 0.420713 & 0.841427 & 0.579287 \tabularnewline
204 & 0.344156 & 0.688312 & 0.655844 \tabularnewline
205 & 0.326146 & 0.652293 & 0.673854 \tabularnewline
206 & 0.270686 & 0.541372 & 0.729314 \tabularnewline
207 & 0.237349 & 0.474699 & 0.762651 \tabularnewline
208 & 0.276827 & 0.553654 & 0.723173 \tabularnewline
209 & 0.907839 & 0.184321 & 0.0921606 \tabularnewline
210 & 0.84814 & 0.303721 & 0.15186 \tabularnewline
211 & 0.908593 & 0.182815 & 0.0914074 \tabularnewline
212 & 0.900727 & 0.198545 & 0.0992726 \tabularnewline
213 & 0.890792 & 0.218417 & 0.109208 \tabularnewline
214 & 0.974195 & 0.0516103 & 0.0258052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&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]15[/C][C]0.609906[/C][C]0.780189[/C][C]0.390094[/C][/ROW]
[ROW][C]16[/C][C]0.45073[/C][C]0.901459[/C][C]0.54927[/C][/ROW]
[ROW][C]17[/C][C]0.845531[/C][C]0.308937[/C][C]0.154469[/C][/ROW]
[ROW][C]18[/C][C]0.775432[/C][C]0.449136[/C][C]0.224568[/C][/ROW]
[ROW][C]19[/C][C]0.7264[/C][C]0.5472[/C][C]0.2736[/C][/ROW]
[ROW][C]20[/C][C]0.645506[/C][C]0.708988[/C][C]0.354494[/C][/ROW]
[ROW][C]21[/C][C]0.601645[/C][C]0.79671[/C][C]0.398355[/C][/ROW]
[ROW][C]22[/C][C]0.549045[/C][C]0.901911[/C][C]0.450955[/C][/ROW]
[ROW][C]23[/C][C]0.588268[/C][C]0.823464[/C][C]0.411732[/C][/ROW]
[ROW][C]24[/C][C]0.521365[/C][C]0.957271[/C][C]0.478635[/C][/ROW]
[ROW][C]25[/C][C]0.546928[/C][C]0.906144[/C][C]0.453072[/C][/ROW]
[ROW][C]26[/C][C]0.470353[/C][C]0.940706[/C][C]0.529647[/C][/ROW]
[ROW][C]27[/C][C]0.400935[/C][C]0.80187[/C][C]0.599065[/C][/ROW]
[ROW][C]28[/C][C]0.352413[/C][C]0.704826[/C][C]0.647587[/C][/ROW]
[ROW][C]29[/C][C]0.288542[/C][C]0.577083[/C][C]0.711458[/C][/ROW]
[ROW][C]30[/C][C]0.240979[/C][C]0.481959[/C][C]0.759021[/C][/ROW]
[ROW][C]31[/C][C]0.195433[/C][C]0.390865[/C][C]0.804567[/C][/ROW]
[ROW][C]32[/C][C]0.159578[/C][C]0.319157[/C][C]0.840422[/C][/ROW]
[ROW][C]33[/C][C]0.121401[/C][C]0.242802[/C][C]0.878599[/C][/ROW]
[ROW][C]34[/C][C]0.103427[/C][C]0.206854[/C][C]0.896573[/C][/ROW]
[ROW][C]35[/C][C]0.0819368[/C][C]0.163874[/C][C]0.918063[/C][/ROW]
[ROW][C]36[/C][C]0.0599538[/C][C]0.119908[/C][C]0.940046[/C][/ROW]
[ROW][C]37[/C][C]0.050928[/C][C]0.101856[/C][C]0.949072[/C][/ROW]
[ROW][C]38[/C][C]0.0373708[/C][C]0.0747416[/C][C]0.962629[/C][/ROW]
[ROW][C]39[/C][C]0.0843894[/C][C]0.168779[/C][C]0.915611[/C][/ROW]
[ROW][C]40[/C][C]0.0689848[/C][C]0.13797[/C][C]0.931015[/C][/ROW]
[ROW][C]41[/C][C]0.198993[/C][C]0.397986[/C][C]0.801007[/C][/ROW]
[ROW][C]42[/C][C]0.169254[/C][C]0.338508[/C][C]0.830746[/C][/ROW]
[ROW][C]43[/C][C]0.13749[/C][C]0.274981[/C][C]0.86251[/C][/ROW]
[ROW][C]44[/C][C]0.1521[/C][C]0.304199[/C][C]0.8479[/C][/ROW]
[ROW][C]45[/C][C]0.149841[/C][C]0.299681[/C][C]0.850159[/C][/ROW]
[ROW][C]46[/C][C]0.135099[/C][C]0.270198[/C][C]0.864901[/C][/ROW]
[ROW][C]47[/C][C]0.128185[/C][C]0.256369[/C][C]0.871815[/C][/ROW]
[ROW][C]48[/C][C]0.115398[/C][C]0.230797[/C][C]0.884602[/C][/ROW]
[ROW][C]49[/C][C]0.0959461[/C][C]0.191892[/C][C]0.904054[/C][/ROW]
[ROW][C]50[/C][C]0.0859585[/C][C]0.171917[/C][C]0.914042[/C][/ROW]
[ROW][C]51[/C][C]0.114097[/C][C]0.228194[/C][C]0.885903[/C][/ROW]
[ROW][C]52[/C][C]0.117338[/C][C]0.234676[/C][C]0.882662[/C][/ROW]
[ROW][C]53[/C][C]0.112605[/C][C]0.225209[/C][C]0.887395[/C][/ROW]
[ROW][C]54[/C][C]0.0993989[/C][C]0.198798[/C][C]0.900601[/C][/ROW]
[ROW][C]55[/C][C]0.218765[/C][C]0.437529[/C][C]0.781235[/C][/ROW]
[ROW][C]56[/C][C]0.216014[/C][C]0.432028[/C][C]0.783986[/C][/ROW]
[ROW][C]57[/C][C]0.208033[/C][C]0.416067[/C][C]0.791967[/C][/ROW]
[ROW][C]58[/C][C]0.342197[/C][C]0.684394[/C][C]0.657803[/C][/ROW]
[ROW][C]59[/C][C]0.348051[/C][C]0.696102[/C][C]0.651949[/C][/ROW]
[ROW][C]60[/C][C]0.308919[/C][C]0.617839[/C][C]0.691081[/C][/ROW]
[ROW][C]61[/C][C]0.271747[/C][C]0.543494[/C][C]0.728253[/C][/ROW]
[ROW][C]62[/C][C]0.256299[/C][C]0.512598[/C][C]0.743701[/C][/ROW]
[ROW][C]63[/C][C]0.234645[/C][C]0.469291[/C][C]0.765355[/C][/ROW]
[ROW][C]64[/C][C]0.257126[/C][C]0.514251[/C][C]0.742874[/C][/ROW]
[ROW][C]65[/C][C]0.316701[/C][C]0.633402[/C][C]0.683299[/C][/ROW]
[ROW][C]66[/C][C]0.451621[/C][C]0.903242[/C][C]0.548379[/C][/ROW]
[ROW][C]67[/C][C]0.488371[/C][C]0.976741[/C][C]0.511629[/C][/ROW]
[ROW][C]68[/C][C]0.453462[/C][C]0.906923[/C][C]0.546538[/C][/ROW]
[ROW][C]69[/C][C]0.419248[/C][C]0.838496[/C][C]0.580752[/C][/ROW]
[ROW][C]70[/C][C]0.495814[/C][C]0.991628[/C][C]0.504186[/C][/ROW]
[ROW][C]71[/C][C]0.473865[/C][C]0.947729[/C][C]0.526135[/C][/ROW]
[ROW][C]72[/C][C]0.449353[/C][C]0.898706[/C][C]0.550647[/C][/ROW]
[ROW][C]73[/C][C]0.428792[/C][C]0.857583[/C][C]0.571208[/C][/ROW]
[ROW][C]74[/C][C]0.414[/C][C]0.828[/C][C]0.586[/C][/ROW]
[ROW][C]75[/C][C]0.549359[/C][C]0.901283[/C][C]0.450641[/C][/ROW]
[ROW][C]76[/C][C]0.60649[/C][C]0.787019[/C][C]0.39351[/C][/ROW]
[ROW][C]77[/C][C]0.569969[/C][C]0.860062[/C][C]0.430031[/C][/ROW]
[ROW][C]78[/C][C]0.556982[/C][C]0.886037[/C][C]0.443018[/C][/ROW]
[ROW][C]79[/C][C]0.612506[/C][C]0.774987[/C][C]0.387494[/C][/ROW]
[ROW][C]80[/C][C]0.593412[/C][C]0.813175[/C][C]0.406588[/C][/ROW]
[ROW][C]81[/C][C]0.559467[/C][C]0.881067[/C][C]0.440533[/C][/ROW]
[ROW][C]82[/C][C]0.527633[/C][C]0.944735[/C][C]0.472367[/C][/ROW]
[ROW][C]83[/C][C]0.524616[/C][C]0.950768[/C][C]0.475384[/C][/ROW]
[ROW][C]84[/C][C]0.494204[/C][C]0.988407[/C][C]0.505796[/C][/ROW]
[ROW][C]85[/C][C]0.476378[/C][C]0.952756[/C][C]0.523622[/C][/ROW]
[ROW][C]86[/C][C]0.441956[/C][C]0.883913[/C][C]0.558044[/C][/ROW]
[ROW][C]87[/C][C]0.401609[/C][C]0.803217[/C][C]0.598391[/C][/ROW]
[ROW][C]88[/C][C]0.471882[/C][C]0.943763[/C][C]0.528118[/C][/ROW]
[ROW][C]89[/C][C]0.456889[/C][C]0.913778[/C][C]0.543111[/C][/ROW]
[ROW][C]90[/C][C]0.4375[/C][C]0.875001[/C][C]0.5625[/C][/ROW]
[ROW][C]91[/C][C]0.412434[/C][C]0.824868[/C][C]0.587566[/C][/ROW]
[ROW][C]92[/C][C]0.381904[/C][C]0.763807[/C][C]0.618096[/C][/ROW]
[ROW][C]93[/C][C]0.393662[/C][C]0.787323[/C][C]0.606338[/C][/ROW]
[ROW][C]94[/C][C]0.36036[/C][C]0.720721[/C][C]0.63964[/C][/ROW]
[ROW][C]95[/C][C]0.387423[/C][C]0.774846[/C][C]0.612577[/C][/ROW]
[ROW][C]96[/C][C]0.349328[/C][C]0.698656[/C][C]0.650672[/C][/ROW]
[ROW][C]97[/C][C]0.32861[/C][C]0.657219[/C][C]0.67139[/C][/ROW]
[ROW][C]98[/C][C]0.29398[/C][C]0.58796[/C][C]0.70602[/C][/ROW]
[ROW][C]99[/C][C]0.261285[/C][C]0.522569[/C][C]0.738715[/C][/ROW]
[ROW][C]100[/C][C]0.238284[/C][C]0.476567[/C][C]0.761716[/C][/ROW]
[ROW][C]101[/C][C]0.218926[/C][C]0.437852[/C][C]0.781074[/C][/ROW]
[ROW][C]102[/C][C]0.517654[/C][C]0.964692[/C][C]0.482346[/C][/ROW]
[ROW][C]103[/C][C]0.518394[/C][C]0.963212[/C][C]0.481606[/C][/ROW]
[ROW][C]104[/C][C]0.517792[/C][C]0.964416[/C][C]0.482208[/C][/ROW]
[ROW][C]105[/C][C]0.487757[/C][C]0.975513[/C][C]0.512243[/C][/ROW]
[ROW][C]106[/C][C]0.471075[/C][C]0.94215[/C][C]0.528925[/C][/ROW]
[ROW][C]107[/C][C]0.440641[/C][C]0.881283[/C][C]0.559359[/C][/ROW]
[ROW][C]108[/C][C]0.437986[/C][C]0.875972[/C][C]0.562014[/C][/ROW]
[ROW][C]109[/C][C]0.404531[/C][C]0.809063[/C][C]0.595469[/C][/ROW]
[ROW][C]110[/C][C]0.424301[/C][C]0.848601[/C][C]0.575699[/C][/ROW]
[ROW][C]111[/C][C]0.450719[/C][C]0.901439[/C][C]0.549281[/C][/ROW]
[ROW][C]112[/C][C]0.451676[/C][C]0.903352[/C][C]0.548324[/C][/ROW]
[ROW][C]113[/C][C]0.435029[/C][C]0.870057[/C][C]0.564971[/C][/ROW]
[ROW][C]114[/C][C]0.396984[/C][C]0.793967[/C][C]0.603016[/C][/ROW]
[ROW][C]115[/C][C]0.83747[/C][C]0.325059[/C][C]0.16253[/C][/ROW]
[ROW][C]116[/C][C]0.820279[/C][C]0.359441[/C][C]0.179721[/C][/ROW]
[ROW][C]117[/C][C]0.816392[/C][C]0.367217[/C][C]0.183608[/C][/ROW]
[ROW][C]118[/C][C]0.795503[/C][C]0.408993[/C][C]0.204497[/C][/ROW]
[ROW][C]119[/C][C]0.791954[/C][C]0.416092[/C][C]0.208046[/C][/ROW]
[ROW][C]120[/C][C]0.769343[/C][C]0.461313[/C][C]0.230657[/C][/ROW]
[ROW][C]121[/C][C]0.820318[/C][C]0.359364[/C][C]0.179682[/C][/ROW]
[ROW][C]122[/C][C]0.794617[/C][C]0.410766[/C][C]0.205383[/C][/ROW]
[ROW][C]123[/C][C]0.801925[/C][C]0.39615[/C][C]0.198075[/C][/ROW]
[ROW][C]124[/C][C]0.792898[/C][C]0.414203[/C][C]0.207102[/C][/ROW]
[ROW][C]125[/C][C]0.771519[/C][C]0.456961[/C][C]0.228481[/C][/ROW]
[ROW][C]126[/C][C]0.75479[/C][C]0.49042[/C][C]0.24521[/C][/ROW]
[ROW][C]127[/C][C]0.77403[/C][C]0.45194[/C][C]0.22597[/C][/ROW]
[ROW][C]128[/C][C]0.748151[/C][C]0.503699[/C][C]0.251849[/C][/ROW]
[ROW][C]129[/C][C]0.746892[/C][C]0.506216[/C][C]0.253108[/C][/ROW]
[ROW][C]130[/C][C]0.721381[/C][C]0.557239[/C][C]0.278619[/C][/ROW]
[ROW][C]131[/C][C]0.773519[/C][C]0.452962[/C][C]0.226481[/C][/ROW]
[ROW][C]132[/C][C]0.761543[/C][C]0.476913[/C][C]0.238457[/C][/ROW]
[ROW][C]133[/C][C]0.757292[/C][C]0.485415[/C][C]0.242708[/C][/ROW]
[ROW][C]134[/C][C]0.760686[/C][C]0.478628[/C][C]0.239314[/C][/ROW]
[ROW][C]135[/C][C]0.731083[/C][C]0.537834[/C][C]0.268917[/C][/ROW]
[ROW][C]136[/C][C]0.768145[/C][C]0.46371[/C][C]0.231855[/C][/ROW]
[ROW][C]137[/C][C]0.736339[/C][C]0.527322[/C][C]0.263661[/C][/ROW]
[ROW][C]138[/C][C]0.743529[/C][C]0.512941[/C][C]0.256471[/C][/ROW]
[ROW][C]139[/C][C]0.746494[/C][C]0.507012[/C][C]0.253506[/C][/ROW]
[ROW][C]140[/C][C]0.749985[/C][C]0.500031[/C][C]0.250015[/C][/ROW]
[ROW][C]141[/C][C]0.716502[/C][C]0.566997[/C][C]0.283498[/C][/ROW]
[ROW][C]142[/C][C]0.710026[/C][C]0.579948[/C][C]0.289974[/C][/ROW]
[ROW][C]143[/C][C]0.691315[/C][C]0.61737[/C][C]0.308685[/C][/ROW]
[ROW][C]144[/C][C]0.692205[/C][C]0.61559[/C][C]0.307795[/C][/ROW]
[ROW][C]145[/C][C]0.725176[/C][C]0.549649[/C][C]0.274824[/C][/ROW]
[ROW][C]146[/C][C]0.689374[/C][C]0.621251[/C][C]0.310626[/C][/ROW]
[ROW][C]147[/C][C]0.668834[/C][C]0.662333[/C][C]0.331166[/C][/ROW]
[ROW][C]148[/C][C]0.68952[/C][C]0.62096[/C][C]0.31048[/C][/ROW]
[ROW][C]149[/C][C]0.653309[/C][C]0.693381[/C][C]0.346691[/C][/ROW]
[ROW][C]150[/C][C]0.637414[/C][C]0.725172[/C][C]0.362586[/C][/ROW]
[ROW][C]151[/C][C]0.604546[/C][C]0.790907[/C][C]0.395454[/C][/ROW]
[ROW][C]152[/C][C]0.628701[/C][C]0.742599[/C][C]0.371299[/C][/ROW]
[ROW][C]153[/C][C]0.60883[/C][C]0.782341[/C][C]0.39117[/C][/ROW]
[ROW][C]154[/C][C]0.590745[/C][C]0.818511[/C][C]0.409255[/C][/ROW]
[ROW][C]155[/C][C]0.569284[/C][C]0.861431[/C][C]0.430716[/C][/ROW]
[ROW][C]156[/C][C]0.530638[/C][C]0.938723[/C][C]0.469362[/C][/ROW]
[ROW][C]157[/C][C]0.551646[/C][C]0.896707[/C][C]0.448354[/C][/ROW]
[ROW][C]158[/C][C]0.541911[/C][C]0.916179[/C][C]0.458089[/C][/ROW]
[ROW][C]159[/C][C]0.523279[/C][C]0.953441[/C][C]0.476721[/C][/ROW]
[ROW][C]160[/C][C]0.53331[/C][C]0.933381[/C][C]0.46669[/C][/ROW]
[ROW][C]161[/C][C]0.523025[/C][C]0.95395[/C][C]0.476975[/C][/ROW]
[ROW][C]162[/C][C]0.512004[/C][C]0.975992[/C][C]0.487996[/C][/ROW]
[ROW][C]163[/C][C]0.467594[/C][C]0.935187[/C][C]0.532406[/C][/ROW]
[ROW][C]164[/C][C]0.479523[/C][C]0.959046[/C][C]0.520477[/C][/ROW]
[ROW][C]165[/C][C]0.447567[/C][C]0.895133[/C][C]0.552433[/C][/ROW]
[ROW][C]166[/C][C]0.426963[/C][C]0.853927[/C][C]0.573037[/C][/ROW]
[ROW][C]167[/C][C]0.388395[/C][C]0.776789[/C][C]0.611605[/C][/ROW]
[ROW][C]168[/C][C]0.379457[/C][C]0.758915[/C][C]0.620543[/C][/ROW]
[ROW][C]169[/C][C]0.349499[/C][C]0.698998[/C][C]0.650501[/C][/ROW]
[ROW][C]170[/C][C]0.315313[/C][C]0.630626[/C][C]0.684687[/C][/ROW]
[ROW][C]171[/C][C]0.282651[/C][C]0.565301[/C][C]0.717349[/C][/ROW]
[ROW][C]172[/C][C]0.251242[/C][C]0.502484[/C][C]0.748758[/C][/ROW]
[ROW][C]173[/C][C]0.37674[/C][C]0.753479[/C][C]0.62326[/C][/ROW]
[ROW][C]174[/C][C]0.345804[/C][C]0.691607[/C][C]0.654196[/C][/ROW]
[ROW][C]175[/C][C]0.390683[/C][C]0.781367[/C][C]0.609317[/C][/ROW]
[ROW][C]176[/C][C]0.360665[/C][C]0.721331[/C][C]0.639335[/C][/ROW]
[ROW][C]177[/C][C]0.421873[/C][C]0.843746[/C][C]0.578127[/C][/ROW]
[ROW][C]178[/C][C]0.434612[/C][C]0.869223[/C][C]0.565388[/C][/ROW]
[ROW][C]179[/C][C]0.466606[/C][C]0.933212[/C][C]0.533394[/C][/ROW]
[ROW][C]180[/C][C]0.628473[/C][C]0.743054[/C][C]0.371527[/C][/ROW]
[ROW][C]181[/C][C]0.677987[/C][C]0.644026[/C][C]0.322013[/C][/ROW]
[ROW][C]182[/C][C]0.696196[/C][C]0.607608[/C][C]0.303804[/C][/ROW]
[ROW][C]183[/C][C]0.719819[/C][C]0.560361[/C][C]0.280181[/C][/ROW]
[ROW][C]184[/C][C]0.675394[/C][C]0.649211[/C][C]0.324606[/C][/ROW]
[ROW][C]185[/C][C]0.670481[/C][C]0.659038[/C][C]0.329519[/C][/ROW]
[ROW][C]186[/C][C]0.629541[/C][C]0.740919[/C][C]0.370459[/C][/ROW]
[ROW][C]187[/C][C]0.751974[/C][C]0.496053[/C][C]0.248026[/C][/ROW]
[ROW][C]188[/C][C]0.726054[/C][C]0.547892[/C][C]0.273946[/C][/ROW]
[ROW][C]189[/C][C]0.714568[/C][C]0.570863[/C][C]0.285432[/C][/ROW]
[ROW][C]190[/C][C]0.732862[/C][C]0.534275[/C][C]0.267138[/C][/ROW]
[ROW][C]191[/C][C]0.683182[/C][C]0.633636[/C][C]0.316818[/C][/ROW]
[ROW][C]192[/C][C]0.631977[/C][C]0.736045[/C][C]0.368023[/C][/ROW]
[ROW][C]193[/C][C]0.605099[/C][C]0.789801[/C][C]0.394901[/C][/ROW]
[ROW][C]194[/C][C]0.578192[/C][C]0.843617[/C][C]0.421808[/C][/ROW]
[ROW][C]195[/C][C]0.572598[/C][C]0.854804[/C][C]0.427402[/C][/ROW]
[ROW][C]196[/C][C]0.523226[/C][C]0.953549[/C][C]0.476774[/C][/ROW]
[ROW][C]197[/C][C]0.465891[/C][C]0.931783[/C][C]0.534109[/C][/ROW]
[ROW][C]198[/C][C]0.402266[/C][C]0.804532[/C][C]0.597734[/C][/ROW]
[ROW][C]199[/C][C]0.583545[/C][C]0.83291[/C][C]0.416455[/C][/ROW]
[ROW][C]200[/C][C]0.548504[/C][C]0.902992[/C][C]0.451496[/C][/ROW]
[ROW][C]201[/C][C]0.545003[/C][C]0.909995[/C][C]0.454997[/C][/ROW]
[ROW][C]202[/C][C]0.482491[/C][C]0.964981[/C][C]0.517509[/C][/ROW]
[ROW][C]203[/C][C]0.420713[/C][C]0.841427[/C][C]0.579287[/C][/ROW]
[ROW][C]204[/C][C]0.344156[/C][C]0.688312[/C][C]0.655844[/C][/ROW]
[ROW][C]205[/C][C]0.326146[/C][C]0.652293[/C][C]0.673854[/C][/ROW]
[ROW][C]206[/C][C]0.270686[/C][C]0.541372[/C][C]0.729314[/C][/ROW]
[ROW][C]207[/C][C]0.237349[/C][C]0.474699[/C][C]0.762651[/C][/ROW]
[ROW][C]208[/C][C]0.276827[/C][C]0.553654[/C][C]0.723173[/C][/ROW]
[ROW][C]209[/C][C]0.907839[/C][C]0.184321[/C][C]0.0921606[/C][/ROW]
[ROW][C]210[/C][C]0.84814[/C][C]0.303721[/C][C]0.15186[/C][/ROW]
[ROW][C]211[/C][C]0.908593[/C][C]0.182815[/C][C]0.0914074[/C][/ROW]
[ROW][C]212[/C][C]0.900727[/C][C]0.198545[/C][C]0.0992726[/C][/ROW]
[ROW][C]213[/C][C]0.890792[/C][C]0.218417[/C][C]0.109208[/C][/ROW]
[ROW][C]214[/C][C]0.974195[/C][C]0.0516103[/C][C]0.0258052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&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
150.6099060.7801890.390094
160.450730.9014590.54927
170.8455310.3089370.154469
180.7754320.4491360.224568
190.72640.54720.2736
200.6455060.7089880.354494
210.6016450.796710.398355
220.5490450.9019110.450955
230.5882680.8234640.411732
240.5213650.9572710.478635
250.5469280.9061440.453072
260.4703530.9407060.529647
270.4009350.801870.599065
280.3524130.7048260.647587
290.2885420.5770830.711458
300.2409790.4819590.759021
310.1954330.3908650.804567
320.1595780.3191570.840422
330.1214010.2428020.878599
340.1034270.2068540.896573
350.08193680.1638740.918063
360.05995380.1199080.940046
370.0509280.1018560.949072
380.03737080.07474160.962629
390.08438940.1687790.915611
400.06898480.137970.931015
410.1989930.3979860.801007
420.1692540.3385080.830746
430.137490.2749810.86251
440.15210.3041990.8479
450.1498410.2996810.850159
460.1350990.2701980.864901
470.1281850.2563690.871815
480.1153980.2307970.884602
490.09594610.1918920.904054
500.08595850.1719170.914042
510.1140970.2281940.885903
520.1173380.2346760.882662
530.1126050.2252090.887395
540.09939890.1987980.900601
550.2187650.4375290.781235
560.2160140.4320280.783986
570.2080330.4160670.791967
580.3421970.6843940.657803
590.3480510.6961020.651949
600.3089190.6178390.691081
610.2717470.5434940.728253
620.2562990.5125980.743701
630.2346450.4692910.765355
640.2571260.5142510.742874
650.3167010.6334020.683299
660.4516210.9032420.548379
670.4883710.9767410.511629
680.4534620.9069230.546538
690.4192480.8384960.580752
700.4958140.9916280.504186
710.4738650.9477290.526135
720.4493530.8987060.550647
730.4287920.8575830.571208
740.4140.8280.586
750.5493590.9012830.450641
760.606490.7870190.39351
770.5699690.8600620.430031
780.5569820.8860370.443018
790.6125060.7749870.387494
800.5934120.8131750.406588
810.5594670.8810670.440533
820.5276330.9447350.472367
830.5246160.9507680.475384
840.4942040.9884070.505796
850.4763780.9527560.523622
860.4419560.8839130.558044
870.4016090.8032170.598391
880.4718820.9437630.528118
890.4568890.9137780.543111
900.43750.8750010.5625
910.4124340.8248680.587566
920.3819040.7638070.618096
930.3936620.7873230.606338
940.360360.7207210.63964
950.3874230.7748460.612577
960.3493280.6986560.650672
970.328610.6572190.67139
980.293980.587960.70602
990.2612850.5225690.738715
1000.2382840.4765670.761716
1010.2189260.4378520.781074
1020.5176540.9646920.482346
1030.5183940.9632120.481606
1040.5177920.9644160.482208
1050.4877570.9755130.512243
1060.4710750.942150.528925
1070.4406410.8812830.559359
1080.4379860.8759720.562014
1090.4045310.8090630.595469
1100.4243010.8486010.575699
1110.4507190.9014390.549281
1120.4516760.9033520.548324
1130.4350290.8700570.564971
1140.3969840.7939670.603016
1150.837470.3250590.16253
1160.8202790.3594410.179721
1170.8163920.3672170.183608
1180.7955030.4089930.204497
1190.7919540.4160920.208046
1200.7693430.4613130.230657
1210.8203180.3593640.179682
1220.7946170.4107660.205383
1230.8019250.396150.198075
1240.7928980.4142030.207102
1250.7715190.4569610.228481
1260.754790.490420.24521
1270.774030.451940.22597
1280.7481510.5036990.251849
1290.7468920.5062160.253108
1300.7213810.5572390.278619
1310.7735190.4529620.226481
1320.7615430.4769130.238457
1330.7572920.4854150.242708
1340.7606860.4786280.239314
1350.7310830.5378340.268917
1360.7681450.463710.231855
1370.7363390.5273220.263661
1380.7435290.5129410.256471
1390.7464940.5070120.253506
1400.7499850.5000310.250015
1410.7165020.5669970.283498
1420.7100260.5799480.289974
1430.6913150.617370.308685
1440.6922050.615590.307795
1450.7251760.5496490.274824
1460.6893740.6212510.310626
1470.6688340.6623330.331166
1480.689520.620960.31048
1490.6533090.6933810.346691
1500.6374140.7251720.362586
1510.6045460.7909070.395454
1520.6287010.7425990.371299
1530.608830.7823410.39117
1540.5907450.8185110.409255
1550.5692840.8614310.430716
1560.5306380.9387230.469362
1570.5516460.8967070.448354
1580.5419110.9161790.458089
1590.5232790.9534410.476721
1600.533310.9333810.46669
1610.5230250.953950.476975
1620.5120040.9759920.487996
1630.4675940.9351870.532406
1640.4795230.9590460.520477
1650.4475670.8951330.552433
1660.4269630.8539270.573037
1670.3883950.7767890.611605
1680.3794570.7589150.620543
1690.3494990.6989980.650501
1700.3153130.6306260.684687
1710.2826510.5653010.717349
1720.2512420.5024840.748758
1730.376740.7534790.62326
1740.3458040.6916070.654196
1750.3906830.7813670.609317
1760.3606650.7213310.639335
1770.4218730.8437460.578127
1780.4346120.8692230.565388
1790.4666060.9332120.533394
1800.6284730.7430540.371527
1810.6779870.6440260.322013
1820.6961960.6076080.303804
1830.7198190.5603610.280181
1840.6753940.6492110.324606
1850.6704810.6590380.329519
1860.6295410.7409190.370459
1870.7519740.4960530.248026
1880.7260540.5478920.273946
1890.7145680.5708630.285432
1900.7328620.5342750.267138
1910.6831820.6336360.316818
1920.6319770.7360450.368023
1930.6050990.7898010.394901
1940.5781920.8436170.421808
1950.5725980.8548040.427402
1960.5232260.9535490.476774
1970.4658910.9317830.534109
1980.4022660.8045320.597734
1990.5835450.832910.416455
2000.5485040.9029920.451496
2010.5450030.9099950.454997
2020.4824910.9649810.517509
2030.4207130.8414270.579287
2040.3441560.6883120.655844
2050.3261460.6522930.673854
2060.2706860.5413720.729314
2070.2373490.4746990.762651
2080.2768270.5536540.723173
2090.9078390.1843210.0921606
2100.848140.3037210.15186
2110.9085930.1828150.0914074
2120.9007270.1985450.0992726
2130.8907920.2184170.109208
2140.9741950.05161030.0258052






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 level20.01OK

\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 & 2 & 0.01 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267676&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]2[/C][C]0.01[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267676&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267676&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.01OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 12 ; 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')
}