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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 09:20:12 +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/t14185489719czq6cdrkos8ol1.htm/, Retrieved Thu, 16 May 2024 08:29:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267348, Retrieved Thu, 16 May 2024 08:29:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 09:20:12] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.1 0 1 22 23 48 23 12 41
2.7 0 1 22 22 50 16 45 146
2.1 0 1 22 21 150 33 37 182
2.1 0 1 20 25 154 32 37 192
2.1 1 0 19 30 109 37 108 263
2.1 1 1 20 17 68 14 10 35
2.1 0 1 22 27 194 52 68 439
2.1 0 0 21 23 158 75 72 214
2.1 0 1 21 23 159 72 143 341
2.1 0 0 21 18 67 15 9 58
2.4 0 0 21 18 147 29 55 292
1.95 0 1 21 23 39 13 17 85
2.1 0 1 21 19 100 40 37 200
2.1 0 1 21 15 111 19 27 158
1.95 0 1 22 20 138 24 37 199
2.1 0 1 24 16 101 121 58 297
2.4 1 1 21 24 131 93 66 227
2.1 0 1 22 25 101 36 21 108
2.25 0 1 20 25 114 23 19 86
2.4 0 0 21 19 165 85 78 302
2.25 0 1 24 19 114 41 35 148
2.55 0 1 25 16 111 46 48 178
1.95 0 1 22 19 75 18 27 120
2.4 0 1 21 19 82 35 43 207
2.1 0 1 21 23 121 17 30 157
2.1 0 1 22 21 32 4 25 128
2.4 0 0 23 22 150 28 69 296
2.1 0 1 24 19 117 44 72 323
2.1 1 1 20 20 71 10 23 79
2.25 0 1 22 20 165 38 13 70
2.25 0 1 25 3 154 57 61 146
2.4 0 1 22 23 126 23 43 246
2.1 0 0 22 14 138 26 22 145
2.1 0 0 21 23 149 36 51 196
2.4 0 0 21 20 145 22 67 199
2.1 0 1 21 15 120 40 36 127
1.95 0 0 22 13 138 18 21 91
2.1 0 0 22 16 109 31 44 153
2.25 0 0 22 7 132 11 45 299
2.25 0 1 21 24 172 38 34 228
2.4 0 0 22 17 169 24 36 190
2.25 0 1 23 24 114 37 72 180
2.25 0 1 21 24 156 37 39 212
2.1 0 0 21 19 172 22 43 269
2.1 1 1 21 25 68 15 25 130
2.1 1 1 19 20 89 2 56 179
2.7 0 1 21 28 167 43 80 243
2.1 0 0 21 23 113 31 40 190
2.1 1 0 19 27 115 29 73 299
2.25 1 0 18 18 78 45 34 121
2.7 1 0 19 28 118 25 72 137
2.4 1 1 21 21 87 4 42 305
2.1 0 0 22 19 173 31 61 157
2.1 0 1 22 23 2 -4 23 96
2.4 1 0 19 27 162 66 74 183
1.95 1 1 20 22 49 61 16 52
2.7 1 0 19 28 122 32 66 238
2.1 1 1 21 25 96 31 9 40
2.25 1 0 19 21 100 39 41 226
2.1 1 0 20 22 82 19 57 190
2.7 1 1 21 28 100 31 48 214
2.1 1 0 19 20 115 36 51 145
2.1 1 1 21 29 141 42 53 119
1.65 0 1 21 25 165 21 29 222
1.65 0 1 21 25 165 21 29 222
2.1 1 1 19 20 110 25 55 159
2.1 0 1 25 20 118 32 54 165
2.1 0 0 21 16 158 26 43 249
2.1 1 1 20 20 146 28 51 125
2.1 0 0 25 20 49 32 20 122
2.4 1 0 19 23 90 41 79 186
2.4 1 0 20 18 121 29 39 148
2.1 0 1 22 25 155 33 61 274
2.25 1 0 19 18 104 17 55 172
2.4 1 1 20 19 147 13 30 84
2.1 1 0 19 25 110 32 55 168
2.1 1 0 19 25 108 30 22 102
2.4 1 0 18 25 113 34 37 106
2.4 1 0 19 24 115 59 2 2
2.1 1 1 21 19 61 13 38 139
2.1 1 1 19 26 60 23 27 95
2.4 1 1 20 10 109 10 56 130
2.1 1 1 20 17 68 5 25 72
2.7 1 0 19 13 111 31 39 141
2.1 1 0 19 17 77 19 33 113
2.1 1 1 22 30 73 32 43 206
2.25 0 0 21 25 151 30 57 268
2.1 1 0 19 4 89 25 43 175
2.4 1 0 19 16 78 48 23 77
2.25 1 0 19 21 110 35 44 125
2.25 0 1 23 23 220 67 54 255
2.1 1 1 19 22 65 15 28 111
2.1 0 0 20 17 141 22 36 132
2.4 1 0 19 20 117 18 39 211
2.25 0 1 22 20 122 33 16 92
2.1 1 0 19 22 63 46 23 76
2.1 0 1 25 16 44 24 40 171
1.65 1 1 19 23 52 14 24 83
1.65 1 1 20 16 62 23 29 119
2.7 1 0 19 0 131 12 78 266
2.1 1 1 19 18 101 38 57 186
1.95 1 1 20 25 42 12 37 50
2.25 0 1 20 23 152 28 27 117
2.4 0 0 21 12 107 41 61 219
1.95 1 0 19 18 77 12 27 246
2.1 0 0 21 24 154 31 69 279
2.4 0 1 23 11 103 33 34 148
2.1 1 1 19 18 96 34 44 137
2.1 0 0 21 14 154 41 21 130
2.4 0 1 22 23 175 21 34 181
2.4 1 1 20 24 57 20 39 98
2.4 1 0 18 29 112 44 51 226
2.25 0 0 21 18 143 52 34 234
2.4 1 0 20 15 49 7 31 138
2.1 0 1 21 29 110 29 13 85
2.1 0 1 21 16 131 11 12 66
1.8 0 0 21 19 167 26 51 236
2.7 1 0 19 22 56 24 24 106
2.1 0 0 21 16 137 7 19 135
2.1 1 1 19 23 86 60 30 122
2.4 0 1 21 23 121 13 81 218
2.55 0 0 21 19 149 20 42 199
2.55 0 0 22 4 168 52 22 112
2.1 0 0 21 20 140 28 85 278
2.1 1 1 22 24 88 25 27 94
2.1 0 1 22 20 168 39 25 113
2.25 0 1 22 4 94 9 22 84
2.25 0 1 22 24 51 19 19 86
2.1 1 0 21 22 48 13 14 62
2.1 0 1 22 16 145 60 45 222
1.95 0 1 23 3 66 19 45 167
2.4 1 1 19 15 85 34 28 82
2.1 0 0 22 24 109 14 51 207
2.4 1 0 21 17 63 17 41 184
2.4 1 1 19 20 102 45 31 83
2.4 1 0 19 27 162 66 74 183
2.25 0 1 20 23 128 24 24 85
1.95 1 1 20 26 86 48 19 89
2.1 1 1 18 23 114 29 51 225
2.1 0 0 21 17 164 -2 73 237
2.55 0 1 21 20 119 51 24 102
2.1 0 0 20 22 126 2 61 221
2.1 0 1 20 19 132 24 23 128
2.1 0 1 21 24 142 40 14 91
1.95 0 0 21 19 83 20 54 198
2.25 1 1 19 23 94 19 51 204
2.4 1 0 19 15 81 16 62 158
1.95 0 1 21 27 166 20 36 138
2.1 1 0 19 26 110 40 59 226
2.1 1 1 19 22 64 27 24 44
1.95 0 0 24 22 93 25 26 196
2.1 1 0 19 18 104 49 54 83
2.1 1 1 19 15 105 39 39 79
1.95 1 1 20 22 49 61 16 52
2.1 1 0 19 27 88 19 36 105
1.95 1 1 19 10 95 67 31 116
2.4 1 1 19 20 102 45 31 83
2.4 1 0 19 17 99 30 42 196
2.4 1 1 19 23 63 8 39 153
1.95 1 0 19 19 76 19 25 157
2.7 1 0 20 13 109 52 31 75
2.1 1 1 20 27 117 22 38 106
1.95 1 1 19 23 57 17 31 58
2.1 1 0 21 16 120 33 17 75
1.95 1 1 19 25 73 34 22 74
2.1 1 0 19 2 91 22 55 185
2.25 1 0 19 26 108 30 62 265
2.7 1 1 21 20 105 25 51 131
2.1 0 0 22 23 117 38 30 139
2.4 1 0 19 22 119 26 49 196
1.35 1 1 19 24 31 13 16 78

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.70694 + 0.0987104programma[t] -0.0588083gender[t] + 0.0180295age[t] -0.00370097NUMERACYTOT[t] + 0.00102718LFM[t] + 0.000427927PRH[t] + 0.00282267CH[t] -0.000431281Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.70694 +  0.0987104programma[t] -0.0588083gender[t] +  0.0180295age[t] -0.00370097NUMERACYTOT[t] +  0.00102718LFM[t] +  0.000427927PRH[t] +  0.00282267CH[t] -0.000431281Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.70694 +  0.0987104programma[t] -0.0588083gender[t] +  0.0180295age[t] -0.00370097NUMERACYTOT[t] +  0.00102718LFM[t] +  0.000427927PRH[t] +  0.00282267CH[t] -0.000431281Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&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
PA[t] = + 1.70694 + 0.0987104programma[t] -0.0588083gender[t] + 0.0180295age[t] -0.00370097NUMERACYTOT[t] + 0.00102718LFM[t] + 0.000427927PRH[t] + 0.00282267CH[t] -0.000431281Blogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.706940.3864714.4171.82541e-059.12705e-06
programma0.09871040.05569061.7720.07819490.0390975
gender-0.05880830.0362502-1.6220.1066850.0533427
age0.01802950.01680271.0730.2848630.142431
NUMERACYTOT-0.003700970.00306746-1.2070.2293750.114688
LFM0.001027180.0005734371.7910.07511750.0375587
PRH0.0004279270.001000540.42770.669440.33472
CH0.002822670.001240122.2760.02414780.0120739
Blogs-0.0004312810.000365559-1.180.2398150.119908

\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) & 1.70694 & 0.386471 & 4.417 & 1.82541e-05 & 9.12705e-06 \tabularnewline
programma & 0.0987104 & 0.0556906 & 1.772 & 0.0781949 & 0.0390975 \tabularnewline
gender & -0.0588083 & 0.0362502 & -1.622 & 0.106685 & 0.0533427 \tabularnewline
age & 0.0180295 & 0.0168027 & 1.073 & 0.284863 & 0.142431 \tabularnewline
NUMERACYTOT & -0.00370097 & 0.00306746 & -1.207 & 0.229375 & 0.114688 \tabularnewline
LFM & 0.00102718 & 0.000573437 & 1.791 & 0.0751175 & 0.0375587 \tabularnewline
PRH & 0.000427927 & 0.00100054 & 0.4277 & 0.66944 & 0.33472 \tabularnewline
CH & 0.00282267 & 0.00124012 & 2.276 & 0.0241478 & 0.0120739 \tabularnewline
Blogs & -0.000431281 & 0.000365559 & -1.18 & 0.239815 & 0.119908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&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]1.70694[/C][C]0.386471[/C][C]4.417[/C][C]1.82541e-05[/C][C]9.12705e-06[/C][/ROW]
[ROW][C]programma[/C][C]0.0987104[/C][C]0.0556906[/C][C]1.772[/C][C]0.0781949[/C][C]0.0390975[/C][/ROW]
[ROW][C]gender[/C][C]-0.0588083[/C][C]0.0362502[/C][C]-1.622[/C][C]0.106685[/C][C]0.0533427[/C][/ROW]
[ROW][C]age[/C][C]0.0180295[/C][C]0.0168027[/C][C]1.073[/C][C]0.284863[/C][C]0.142431[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00370097[/C][C]0.00306746[/C][C]-1.207[/C][C]0.229375[/C][C]0.114688[/C][/ROW]
[ROW][C]LFM[/C][C]0.00102718[/C][C]0.000573437[/C][C]1.791[/C][C]0.0751175[/C][C]0.0375587[/C][/ROW]
[ROW][C]PRH[/C][C]0.000427927[/C][C]0.00100054[/C][C]0.4277[/C][C]0.66944[/C][C]0.33472[/C][/ROW]
[ROW][C]CH[/C][C]0.00282267[/C][C]0.00124012[/C][C]2.276[/C][C]0.0241478[/C][C]0.0120739[/C][/ROW]
[ROW][C]Blogs[/C][C]-0.000431281[/C][C]0.000365559[/C][C]-1.18[/C][C]0.239815[/C][C]0.119908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&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)1.706940.3864714.4171.82541e-059.12705e-06
programma0.09871040.05569061.7720.07819490.0390975
gender-0.05880830.0362502-1.6220.1066850.0533427
age0.01802950.01680271.0730.2848630.142431
NUMERACYTOT-0.003700970.00306746-1.2070.2293750.114688
LFM0.001027180.0005734371.7910.07511750.0375587
PRH0.0004279270.001000540.42770.669440.33472
CH0.002822670.001240122.2760.02414780.0120739
Blogs-0.0004312810.000365559-1.180.2398150.119908






Multiple Linear Regression - Regression Statistics
Multiple R0.356174
R-squared0.12686
Adjusted R-squared0.083742
F-TEST (value)2.94216
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0.00426308
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.211908
Sum Squared Residuals7.27463

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.356174 \tabularnewline
R-squared & 0.12686 \tabularnewline
Adjusted R-squared & 0.083742 \tabularnewline
F-TEST (value) & 2.94216 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value & 0.00426308 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.211908 \tabularnewline
Sum Squared Residuals & 7.27463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.356174[/C][/ROW]
[ROW][C]R-squared[/C][C]0.12686[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.083742[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.94216[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C]0.00426308[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.211908[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.27463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.356174
R-squared0.12686
Adjusted R-squared0.083742
F-TEST (value)2.94216
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0.00426308
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.211908
Sum Squared Residuals7.27463






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.12.034990.065008
22.72.085620.614385
32.12.1612-0.0612015
42.12.10971-0.00970663
52.12.3564-0.256396
62.12.13348-0.0334837
72.12.16899-0.0689857
82.12.30576-0.205761
92.12.39233-0.292333
102.12.094570.00543114
112.42.211660.188343
121.951.99858-0.0485756
132.12.094450.00555253
142.12.10145-0.00145092
151.952.14139-0.191393
162.12.21277-0.11277
172.42.299390.100612
182.12.08410.0158982
192.252.059680.190324
202.42.311020.0889821
212.252.180130.0698743
222.552.232070.317928
231.952.08366-0.133659
242.42.087740.312264
252.12.090160.00984158
262.12.0170.0829983
272.42.273360.126642
282.12.21346-0.113456
292.12.14147-0.041469
302.252.163010.0869908
312.252.37956-0.129557
322.42.11420.285798
332.12.20421-0.104212
342.12.22831-0.128314
352.42.273190.126813
362.12.15846-0.0584557
371.952.22496-0.274956
382.12.22781-0.12781
392.252.216040.0339588
402.252.12850.1215
412.42.244210.155794
422.252.232520.0174826
432.252.132650.117349
442.12.20669-0.106688
452.12.1237-0.0237018
462.12.18853-0.0885253
472.72.234070.465927
482.12.16073-0.0607347
492.12.25592-0.155919
502.252.206720.0432765
512.72.320630.379367
522.42.125830.274174
532.12.32871-0.228707
542.11.983520.116483
552.42.372880.0271189
561.952.12518-0.175179
572.72.267240.432758
582.12.15296-0.0529622
592.252.208150.0418453
602.12.25612-0.156124
612.72.181010.518991
622.12.28914-0.18914
632.12.27921-0.179215
641.652.09881-0.448808
651.652.09881-0.448808
662.12.22574-0.125741
672.12.24101-0.14101
682.12.21375-0.113747
692.12.28541-0.185406
702.12.15152-0.0515179
712.42.315850.0841505
722.42.282570.117427
732.12.1796-0.0795998
742.252.27676-0.0267584
752.42.242120.157878
762.12.26516-0.165159
772.12.19757-0.097565
782.42.2270.173002
792.42.207540.192459
802.12.17067-0.0706746
812.12.099890.00011202
822.42.288660.111336
832.12.15602-0.056015
842.72.276650.423349
852.12.21693-0.116928
862.12.15367-0.0536678
872.252.206280.0437169
882.12.28142-0.181422
892.42.221370.178634
902.252.26874-0.0187421
912.252.27478-0.024783
922.12.11233-0.0123265
932.12.20354-0.103544
942.42.221160.178845
952.252.115680.134319
962.12.18333-0.0833277
972.12.13427-0.0342747
981.652.09563-0.445629
991.652.15228-0.502276
1002.72.393350.306649
1012.12.22346-0.123462
1021.952.14606-0.196056
1032.252.117460.132538
1042.42.246330.15367
1051.952.13594-0.185935
1062.12.24262-0.142621
1072.42.174160.225841
1082.12.20105-0.101053
1092.12.21268-0.112683
1102.42.166310.233693
1112.42.153530.246468
1122.42.203210.19679
1132.252.183130.066871
1142.42.192040.207964
1152.12.044860.0551443
1162.12.11221-0.0122079
1171.82.24008-0.440077
1182.72.156610.543393
1192.12.16547-0.0654679
1202.12.15035-0.0503542
1212.42.206090.193905
1222.552.209570.340426
1232.552.29740.252604
1242.12.28736-0.187355
1252.12.19143-0.0914265
1262.12.18185-0.0818456
1272.252.156250.0937488
1282.252.033010.216988
1292.12.17049-0.0704913
1302.12.19145-0.0914546
1311.952.18262-0.232625
1322.42.179410.220585
1332.12.1874-0.0873974
1342.42.229710.170289
1352.42.191120.208884
1362.42.372880.0271189
1372.252.096430.153569
1381.952.13533-0.185329
1392.12.16267-0.0626741
1402.12.29408-0.194083
1412.552.120540.429459
1422.12.19326-0.093256
1432.12.093980.0060243
1442.12.10117-0.00117237
1451.952.17608-0.226083
1462.252.164940.0850623
1472.42.28960.110395
1481.952.14799-0.197992
1492.12.25116-0.151158
1502.12.13404-0.0340396
1511.952.15331-0.203308
1522.12.32601-0.226013
1532.12.23444-0.134441
1541.952.12518-0.175179
1552.12.20314-0.103136
1561.952.21612-0.266117
1572.42.191120.208884
1582.42.233840.166159
1592.42.116510.283489
1601.952.16694-0.216941
1612.72.30750.392504
1622.12.19864-0.098643
1631.952.13259-0.18259
1642.12.27807-0.178074
1651.952.11659-0.166593
1662.12.31915-0.219154
1672.252.236470.013528
1682.72.257450.44255
1692.12.17964-0.0796371
1702.42.253930.146073
1711.352.0495-0.699505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.1 & 2.03499 & 0.065008 \tabularnewline
2 & 2.7 & 2.08562 & 0.614385 \tabularnewline
3 & 2.1 & 2.1612 & -0.0612015 \tabularnewline
4 & 2.1 & 2.10971 & -0.00970663 \tabularnewline
5 & 2.1 & 2.3564 & -0.256396 \tabularnewline
6 & 2.1 & 2.13348 & -0.0334837 \tabularnewline
7 & 2.1 & 2.16899 & -0.0689857 \tabularnewline
8 & 2.1 & 2.30576 & -0.205761 \tabularnewline
9 & 2.1 & 2.39233 & -0.292333 \tabularnewline
10 & 2.1 & 2.09457 & 0.00543114 \tabularnewline
11 & 2.4 & 2.21166 & 0.188343 \tabularnewline
12 & 1.95 & 1.99858 & -0.0485756 \tabularnewline
13 & 2.1 & 2.09445 & 0.00555253 \tabularnewline
14 & 2.1 & 2.10145 & -0.00145092 \tabularnewline
15 & 1.95 & 2.14139 & -0.191393 \tabularnewline
16 & 2.1 & 2.21277 & -0.11277 \tabularnewline
17 & 2.4 & 2.29939 & 0.100612 \tabularnewline
18 & 2.1 & 2.0841 & 0.0158982 \tabularnewline
19 & 2.25 & 2.05968 & 0.190324 \tabularnewline
20 & 2.4 & 2.31102 & 0.0889821 \tabularnewline
21 & 2.25 & 2.18013 & 0.0698743 \tabularnewline
22 & 2.55 & 2.23207 & 0.317928 \tabularnewline
23 & 1.95 & 2.08366 & -0.133659 \tabularnewline
24 & 2.4 & 2.08774 & 0.312264 \tabularnewline
25 & 2.1 & 2.09016 & 0.00984158 \tabularnewline
26 & 2.1 & 2.017 & 0.0829983 \tabularnewline
27 & 2.4 & 2.27336 & 0.126642 \tabularnewline
28 & 2.1 & 2.21346 & -0.113456 \tabularnewline
29 & 2.1 & 2.14147 & -0.041469 \tabularnewline
30 & 2.25 & 2.16301 & 0.0869908 \tabularnewline
31 & 2.25 & 2.37956 & -0.129557 \tabularnewline
32 & 2.4 & 2.1142 & 0.285798 \tabularnewline
33 & 2.1 & 2.20421 & -0.104212 \tabularnewline
34 & 2.1 & 2.22831 & -0.128314 \tabularnewline
35 & 2.4 & 2.27319 & 0.126813 \tabularnewline
36 & 2.1 & 2.15846 & -0.0584557 \tabularnewline
37 & 1.95 & 2.22496 & -0.274956 \tabularnewline
38 & 2.1 & 2.22781 & -0.12781 \tabularnewline
39 & 2.25 & 2.21604 & 0.0339588 \tabularnewline
40 & 2.25 & 2.1285 & 0.1215 \tabularnewline
41 & 2.4 & 2.24421 & 0.155794 \tabularnewline
42 & 2.25 & 2.23252 & 0.0174826 \tabularnewline
43 & 2.25 & 2.13265 & 0.117349 \tabularnewline
44 & 2.1 & 2.20669 & -0.106688 \tabularnewline
45 & 2.1 & 2.1237 & -0.0237018 \tabularnewline
46 & 2.1 & 2.18853 & -0.0885253 \tabularnewline
47 & 2.7 & 2.23407 & 0.465927 \tabularnewline
48 & 2.1 & 2.16073 & -0.0607347 \tabularnewline
49 & 2.1 & 2.25592 & -0.155919 \tabularnewline
50 & 2.25 & 2.20672 & 0.0432765 \tabularnewline
51 & 2.7 & 2.32063 & 0.379367 \tabularnewline
52 & 2.4 & 2.12583 & 0.274174 \tabularnewline
53 & 2.1 & 2.32871 & -0.228707 \tabularnewline
54 & 2.1 & 1.98352 & 0.116483 \tabularnewline
55 & 2.4 & 2.37288 & 0.0271189 \tabularnewline
56 & 1.95 & 2.12518 & -0.175179 \tabularnewline
57 & 2.7 & 2.26724 & 0.432758 \tabularnewline
58 & 2.1 & 2.15296 & -0.0529622 \tabularnewline
59 & 2.25 & 2.20815 & 0.0418453 \tabularnewline
60 & 2.1 & 2.25612 & -0.156124 \tabularnewline
61 & 2.7 & 2.18101 & 0.518991 \tabularnewline
62 & 2.1 & 2.28914 & -0.18914 \tabularnewline
63 & 2.1 & 2.27921 & -0.179215 \tabularnewline
64 & 1.65 & 2.09881 & -0.448808 \tabularnewline
65 & 1.65 & 2.09881 & -0.448808 \tabularnewline
66 & 2.1 & 2.22574 & -0.125741 \tabularnewline
67 & 2.1 & 2.24101 & -0.14101 \tabularnewline
68 & 2.1 & 2.21375 & -0.113747 \tabularnewline
69 & 2.1 & 2.28541 & -0.185406 \tabularnewline
70 & 2.1 & 2.15152 & -0.0515179 \tabularnewline
71 & 2.4 & 2.31585 & 0.0841505 \tabularnewline
72 & 2.4 & 2.28257 & 0.117427 \tabularnewline
73 & 2.1 & 2.1796 & -0.0795998 \tabularnewline
74 & 2.25 & 2.27676 & -0.0267584 \tabularnewline
75 & 2.4 & 2.24212 & 0.157878 \tabularnewline
76 & 2.1 & 2.26516 & -0.165159 \tabularnewline
77 & 2.1 & 2.19757 & -0.097565 \tabularnewline
78 & 2.4 & 2.227 & 0.173002 \tabularnewline
79 & 2.4 & 2.20754 & 0.192459 \tabularnewline
80 & 2.1 & 2.17067 & -0.0706746 \tabularnewline
81 & 2.1 & 2.09989 & 0.00011202 \tabularnewline
82 & 2.4 & 2.28866 & 0.111336 \tabularnewline
83 & 2.1 & 2.15602 & -0.056015 \tabularnewline
84 & 2.7 & 2.27665 & 0.423349 \tabularnewline
85 & 2.1 & 2.21693 & -0.116928 \tabularnewline
86 & 2.1 & 2.15367 & -0.0536678 \tabularnewline
87 & 2.25 & 2.20628 & 0.0437169 \tabularnewline
88 & 2.1 & 2.28142 & -0.181422 \tabularnewline
89 & 2.4 & 2.22137 & 0.178634 \tabularnewline
90 & 2.25 & 2.26874 & -0.0187421 \tabularnewline
91 & 2.25 & 2.27478 & -0.024783 \tabularnewline
92 & 2.1 & 2.11233 & -0.0123265 \tabularnewline
93 & 2.1 & 2.20354 & -0.103544 \tabularnewline
94 & 2.4 & 2.22116 & 0.178845 \tabularnewline
95 & 2.25 & 2.11568 & 0.134319 \tabularnewline
96 & 2.1 & 2.18333 & -0.0833277 \tabularnewline
97 & 2.1 & 2.13427 & -0.0342747 \tabularnewline
98 & 1.65 & 2.09563 & -0.445629 \tabularnewline
99 & 1.65 & 2.15228 & -0.502276 \tabularnewline
100 & 2.7 & 2.39335 & 0.306649 \tabularnewline
101 & 2.1 & 2.22346 & -0.123462 \tabularnewline
102 & 1.95 & 2.14606 & -0.196056 \tabularnewline
103 & 2.25 & 2.11746 & 0.132538 \tabularnewline
104 & 2.4 & 2.24633 & 0.15367 \tabularnewline
105 & 1.95 & 2.13594 & -0.185935 \tabularnewline
106 & 2.1 & 2.24262 & -0.142621 \tabularnewline
107 & 2.4 & 2.17416 & 0.225841 \tabularnewline
108 & 2.1 & 2.20105 & -0.101053 \tabularnewline
109 & 2.1 & 2.21268 & -0.112683 \tabularnewline
110 & 2.4 & 2.16631 & 0.233693 \tabularnewline
111 & 2.4 & 2.15353 & 0.246468 \tabularnewline
112 & 2.4 & 2.20321 & 0.19679 \tabularnewline
113 & 2.25 & 2.18313 & 0.066871 \tabularnewline
114 & 2.4 & 2.19204 & 0.207964 \tabularnewline
115 & 2.1 & 2.04486 & 0.0551443 \tabularnewline
116 & 2.1 & 2.11221 & -0.0122079 \tabularnewline
117 & 1.8 & 2.24008 & -0.440077 \tabularnewline
118 & 2.7 & 2.15661 & 0.543393 \tabularnewline
119 & 2.1 & 2.16547 & -0.0654679 \tabularnewline
120 & 2.1 & 2.15035 & -0.0503542 \tabularnewline
121 & 2.4 & 2.20609 & 0.193905 \tabularnewline
122 & 2.55 & 2.20957 & 0.340426 \tabularnewline
123 & 2.55 & 2.2974 & 0.252604 \tabularnewline
124 & 2.1 & 2.28736 & -0.187355 \tabularnewline
125 & 2.1 & 2.19143 & -0.0914265 \tabularnewline
126 & 2.1 & 2.18185 & -0.0818456 \tabularnewline
127 & 2.25 & 2.15625 & 0.0937488 \tabularnewline
128 & 2.25 & 2.03301 & 0.216988 \tabularnewline
129 & 2.1 & 2.17049 & -0.0704913 \tabularnewline
130 & 2.1 & 2.19145 & -0.0914546 \tabularnewline
131 & 1.95 & 2.18262 & -0.232625 \tabularnewline
132 & 2.4 & 2.17941 & 0.220585 \tabularnewline
133 & 2.1 & 2.1874 & -0.0873974 \tabularnewline
134 & 2.4 & 2.22971 & 0.170289 \tabularnewline
135 & 2.4 & 2.19112 & 0.208884 \tabularnewline
136 & 2.4 & 2.37288 & 0.0271189 \tabularnewline
137 & 2.25 & 2.09643 & 0.153569 \tabularnewline
138 & 1.95 & 2.13533 & -0.185329 \tabularnewline
139 & 2.1 & 2.16267 & -0.0626741 \tabularnewline
140 & 2.1 & 2.29408 & -0.194083 \tabularnewline
141 & 2.55 & 2.12054 & 0.429459 \tabularnewline
142 & 2.1 & 2.19326 & -0.093256 \tabularnewline
143 & 2.1 & 2.09398 & 0.0060243 \tabularnewline
144 & 2.1 & 2.10117 & -0.00117237 \tabularnewline
145 & 1.95 & 2.17608 & -0.226083 \tabularnewline
146 & 2.25 & 2.16494 & 0.0850623 \tabularnewline
147 & 2.4 & 2.2896 & 0.110395 \tabularnewline
148 & 1.95 & 2.14799 & -0.197992 \tabularnewline
149 & 2.1 & 2.25116 & -0.151158 \tabularnewline
150 & 2.1 & 2.13404 & -0.0340396 \tabularnewline
151 & 1.95 & 2.15331 & -0.203308 \tabularnewline
152 & 2.1 & 2.32601 & -0.226013 \tabularnewline
153 & 2.1 & 2.23444 & -0.134441 \tabularnewline
154 & 1.95 & 2.12518 & -0.175179 \tabularnewline
155 & 2.1 & 2.20314 & -0.103136 \tabularnewline
156 & 1.95 & 2.21612 & -0.266117 \tabularnewline
157 & 2.4 & 2.19112 & 0.208884 \tabularnewline
158 & 2.4 & 2.23384 & 0.166159 \tabularnewline
159 & 2.4 & 2.11651 & 0.283489 \tabularnewline
160 & 1.95 & 2.16694 & -0.216941 \tabularnewline
161 & 2.7 & 2.3075 & 0.392504 \tabularnewline
162 & 2.1 & 2.19864 & -0.098643 \tabularnewline
163 & 1.95 & 2.13259 & -0.18259 \tabularnewline
164 & 2.1 & 2.27807 & -0.178074 \tabularnewline
165 & 1.95 & 2.11659 & -0.166593 \tabularnewline
166 & 2.1 & 2.31915 & -0.219154 \tabularnewline
167 & 2.25 & 2.23647 & 0.013528 \tabularnewline
168 & 2.7 & 2.25745 & 0.44255 \tabularnewline
169 & 2.1 & 2.17964 & -0.0796371 \tabularnewline
170 & 2.4 & 2.25393 & 0.146073 \tabularnewline
171 & 1.35 & 2.0495 & -0.699505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&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]2.1[/C][C]2.03499[/C][C]0.065008[/C][/ROW]
[ROW][C]2[/C][C]2.7[/C][C]2.08562[/C][C]0.614385[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.1612[/C][C]-0.0612015[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]2.10971[/C][C]-0.00970663[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]2.3564[/C][C]-0.256396[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.13348[/C][C]-0.0334837[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.16899[/C][C]-0.0689857[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.30576[/C][C]-0.205761[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.39233[/C][C]-0.292333[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.09457[/C][C]0.00543114[/C][/ROW]
[ROW][C]11[/C][C]2.4[/C][C]2.21166[/C][C]0.188343[/C][/ROW]
[ROW][C]12[/C][C]1.95[/C][C]1.99858[/C][C]-0.0485756[/C][/ROW]
[ROW][C]13[/C][C]2.1[/C][C]2.09445[/C][C]0.00555253[/C][/ROW]
[ROW][C]14[/C][C]2.1[/C][C]2.10145[/C][C]-0.00145092[/C][/ROW]
[ROW][C]15[/C][C]1.95[/C][C]2.14139[/C][C]-0.191393[/C][/ROW]
[ROW][C]16[/C][C]2.1[/C][C]2.21277[/C][C]-0.11277[/C][/ROW]
[ROW][C]17[/C][C]2.4[/C][C]2.29939[/C][C]0.100612[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.0841[/C][C]0.0158982[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]2.05968[/C][C]0.190324[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.31102[/C][C]0.0889821[/C][/ROW]
[ROW][C]21[/C][C]2.25[/C][C]2.18013[/C][C]0.0698743[/C][/ROW]
[ROW][C]22[/C][C]2.55[/C][C]2.23207[/C][C]0.317928[/C][/ROW]
[ROW][C]23[/C][C]1.95[/C][C]2.08366[/C][C]-0.133659[/C][/ROW]
[ROW][C]24[/C][C]2.4[/C][C]2.08774[/C][C]0.312264[/C][/ROW]
[ROW][C]25[/C][C]2.1[/C][C]2.09016[/C][C]0.00984158[/C][/ROW]
[ROW][C]26[/C][C]2.1[/C][C]2.017[/C][C]0.0829983[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.27336[/C][C]0.126642[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.21346[/C][C]-0.113456[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]2.14147[/C][C]-0.041469[/C][/ROW]
[ROW][C]30[/C][C]2.25[/C][C]2.16301[/C][C]0.0869908[/C][/ROW]
[ROW][C]31[/C][C]2.25[/C][C]2.37956[/C][C]-0.129557[/C][/ROW]
[ROW][C]32[/C][C]2.4[/C][C]2.1142[/C][C]0.285798[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.20421[/C][C]-0.104212[/C][/ROW]
[ROW][C]34[/C][C]2.1[/C][C]2.22831[/C][C]-0.128314[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]2.27319[/C][C]0.126813[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.15846[/C][C]-0.0584557[/C][/ROW]
[ROW][C]37[/C][C]1.95[/C][C]2.22496[/C][C]-0.274956[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.22781[/C][C]-0.12781[/C][/ROW]
[ROW][C]39[/C][C]2.25[/C][C]2.21604[/C][C]0.0339588[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.1285[/C][C]0.1215[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.24421[/C][C]0.155794[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.23252[/C][C]0.0174826[/C][/ROW]
[ROW][C]43[/C][C]2.25[/C][C]2.13265[/C][C]0.117349[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.20669[/C][C]-0.106688[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]2.1237[/C][C]-0.0237018[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.18853[/C][C]-0.0885253[/C][/ROW]
[ROW][C]47[/C][C]2.7[/C][C]2.23407[/C][C]0.465927[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]2.16073[/C][C]-0.0607347[/C][/ROW]
[ROW][C]49[/C][C]2.1[/C][C]2.25592[/C][C]-0.155919[/C][/ROW]
[ROW][C]50[/C][C]2.25[/C][C]2.20672[/C][C]0.0432765[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.32063[/C][C]0.379367[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.12583[/C][C]0.274174[/C][/ROW]
[ROW][C]53[/C][C]2.1[/C][C]2.32871[/C][C]-0.228707[/C][/ROW]
[ROW][C]54[/C][C]2.1[/C][C]1.98352[/C][C]0.116483[/C][/ROW]
[ROW][C]55[/C][C]2.4[/C][C]2.37288[/C][C]0.0271189[/C][/ROW]
[ROW][C]56[/C][C]1.95[/C][C]2.12518[/C][C]-0.175179[/C][/ROW]
[ROW][C]57[/C][C]2.7[/C][C]2.26724[/C][C]0.432758[/C][/ROW]
[ROW][C]58[/C][C]2.1[/C][C]2.15296[/C][C]-0.0529622[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.20815[/C][C]0.0418453[/C][/ROW]
[ROW][C]60[/C][C]2.1[/C][C]2.25612[/C][C]-0.156124[/C][/ROW]
[ROW][C]61[/C][C]2.7[/C][C]2.18101[/C][C]0.518991[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]2.28914[/C][C]-0.18914[/C][/ROW]
[ROW][C]63[/C][C]2.1[/C][C]2.27921[/C][C]-0.179215[/C][/ROW]
[ROW][C]64[/C][C]1.65[/C][C]2.09881[/C][C]-0.448808[/C][/ROW]
[ROW][C]65[/C][C]1.65[/C][C]2.09881[/C][C]-0.448808[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.22574[/C][C]-0.125741[/C][/ROW]
[ROW][C]67[/C][C]2.1[/C][C]2.24101[/C][C]-0.14101[/C][/ROW]
[ROW][C]68[/C][C]2.1[/C][C]2.21375[/C][C]-0.113747[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.28541[/C][C]-0.185406[/C][/ROW]
[ROW][C]70[/C][C]2.1[/C][C]2.15152[/C][C]-0.0515179[/C][/ROW]
[ROW][C]71[/C][C]2.4[/C][C]2.31585[/C][C]0.0841505[/C][/ROW]
[ROW][C]72[/C][C]2.4[/C][C]2.28257[/C][C]0.117427[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.1796[/C][C]-0.0795998[/C][/ROW]
[ROW][C]74[/C][C]2.25[/C][C]2.27676[/C][C]-0.0267584[/C][/ROW]
[ROW][C]75[/C][C]2.4[/C][C]2.24212[/C][C]0.157878[/C][/ROW]
[ROW][C]76[/C][C]2.1[/C][C]2.26516[/C][C]-0.165159[/C][/ROW]
[ROW][C]77[/C][C]2.1[/C][C]2.19757[/C][C]-0.097565[/C][/ROW]
[ROW][C]78[/C][C]2.4[/C][C]2.227[/C][C]0.173002[/C][/ROW]
[ROW][C]79[/C][C]2.4[/C][C]2.20754[/C][C]0.192459[/C][/ROW]
[ROW][C]80[/C][C]2.1[/C][C]2.17067[/C][C]-0.0706746[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.09989[/C][C]0.00011202[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.28866[/C][C]0.111336[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]2.15602[/C][C]-0.056015[/C][/ROW]
[ROW][C]84[/C][C]2.7[/C][C]2.27665[/C][C]0.423349[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.21693[/C][C]-0.116928[/C][/ROW]
[ROW][C]86[/C][C]2.1[/C][C]2.15367[/C][C]-0.0536678[/C][/ROW]
[ROW][C]87[/C][C]2.25[/C][C]2.20628[/C][C]0.0437169[/C][/ROW]
[ROW][C]88[/C][C]2.1[/C][C]2.28142[/C][C]-0.181422[/C][/ROW]
[ROW][C]89[/C][C]2.4[/C][C]2.22137[/C][C]0.178634[/C][/ROW]
[ROW][C]90[/C][C]2.25[/C][C]2.26874[/C][C]-0.0187421[/C][/ROW]
[ROW][C]91[/C][C]2.25[/C][C]2.27478[/C][C]-0.024783[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]2.11233[/C][C]-0.0123265[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]2.20354[/C][C]-0.103544[/C][/ROW]
[ROW][C]94[/C][C]2.4[/C][C]2.22116[/C][C]0.178845[/C][/ROW]
[ROW][C]95[/C][C]2.25[/C][C]2.11568[/C][C]0.134319[/C][/ROW]
[ROW][C]96[/C][C]2.1[/C][C]2.18333[/C][C]-0.0833277[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]2.13427[/C][C]-0.0342747[/C][/ROW]
[ROW][C]98[/C][C]1.65[/C][C]2.09563[/C][C]-0.445629[/C][/ROW]
[ROW][C]99[/C][C]1.65[/C][C]2.15228[/C][C]-0.502276[/C][/ROW]
[ROW][C]100[/C][C]2.7[/C][C]2.39335[/C][C]0.306649[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]2.22346[/C][C]-0.123462[/C][/ROW]
[ROW][C]102[/C][C]1.95[/C][C]2.14606[/C][C]-0.196056[/C][/ROW]
[ROW][C]103[/C][C]2.25[/C][C]2.11746[/C][C]0.132538[/C][/ROW]
[ROW][C]104[/C][C]2.4[/C][C]2.24633[/C][C]0.15367[/C][/ROW]
[ROW][C]105[/C][C]1.95[/C][C]2.13594[/C][C]-0.185935[/C][/ROW]
[ROW][C]106[/C][C]2.1[/C][C]2.24262[/C][C]-0.142621[/C][/ROW]
[ROW][C]107[/C][C]2.4[/C][C]2.17416[/C][C]0.225841[/C][/ROW]
[ROW][C]108[/C][C]2.1[/C][C]2.20105[/C][C]-0.101053[/C][/ROW]
[ROW][C]109[/C][C]2.1[/C][C]2.21268[/C][C]-0.112683[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.16631[/C][C]0.233693[/C][/ROW]
[ROW][C]111[/C][C]2.4[/C][C]2.15353[/C][C]0.246468[/C][/ROW]
[ROW][C]112[/C][C]2.4[/C][C]2.20321[/C][C]0.19679[/C][/ROW]
[ROW][C]113[/C][C]2.25[/C][C]2.18313[/C][C]0.066871[/C][/ROW]
[ROW][C]114[/C][C]2.4[/C][C]2.19204[/C][C]0.207964[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]2.04486[/C][C]0.0551443[/C][/ROW]
[ROW][C]116[/C][C]2.1[/C][C]2.11221[/C][C]-0.0122079[/C][/ROW]
[ROW][C]117[/C][C]1.8[/C][C]2.24008[/C][C]-0.440077[/C][/ROW]
[ROW][C]118[/C][C]2.7[/C][C]2.15661[/C][C]0.543393[/C][/ROW]
[ROW][C]119[/C][C]2.1[/C][C]2.16547[/C][C]-0.0654679[/C][/ROW]
[ROW][C]120[/C][C]2.1[/C][C]2.15035[/C][C]-0.0503542[/C][/ROW]
[ROW][C]121[/C][C]2.4[/C][C]2.20609[/C][C]0.193905[/C][/ROW]
[ROW][C]122[/C][C]2.55[/C][C]2.20957[/C][C]0.340426[/C][/ROW]
[ROW][C]123[/C][C]2.55[/C][C]2.2974[/C][C]0.252604[/C][/ROW]
[ROW][C]124[/C][C]2.1[/C][C]2.28736[/C][C]-0.187355[/C][/ROW]
[ROW][C]125[/C][C]2.1[/C][C]2.19143[/C][C]-0.0914265[/C][/ROW]
[ROW][C]126[/C][C]2.1[/C][C]2.18185[/C][C]-0.0818456[/C][/ROW]
[ROW][C]127[/C][C]2.25[/C][C]2.15625[/C][C]0.0937488[/C][/ROW]
[ROW][C]128[/C][C]2.25[/C][C]2.03301[/C][C]0.216988[/C][/ROW]
[ROW][C]129[/C][C]2.1[/C][C]2.17049[/C][C]-0.0704913[/C][/ROW]
[ROW][C]130[/C][C]2.1[/C][C]2.19145[/C][C]-0.0914546[/C][/ROW]
[ROW][C]131[/C][C]1.95[/C][C]2.18262[/C][C]-0.232625[/C][/ROW]
[ROW][C]132[/C][C]2.4[/C][C]2.17941[/C][C]0.220585[/C][/ROW]
[ROW][C]133[/C][C]2.1[/C][C]2.1874[/C][C]-0.0873974[/C][/ROW]
[ROW][C]134[/C][C]2.4[/C][C]2.22971[/C][C]0.170289[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]2.19112[/C][C]0.208884[/C][/ROW]
[ROW][C]136[/C][C]2.4[/C][C]2.37288[/C][C]0.0271189[/C][/ROW]
[ROW][C]137[/C][C]2.25[/C][C]2.09643[/C][C]0.153569[/C][/ROW]
[ROW][C]138[/C][C]1.95[/C][C]2.13533[/C][C]-0.185329[/C][/ROW]
[ROW][C]139[/C][C]2.1[/C][C]2.16267[/C][C]-0.0626741[/C][/ROW]
[ROW][C]140[/C][C]2.1[/C][C]2.29408[/C][C]-0.194083[/C][/ROW]
[ROW][C]141[/C][C]2.55[/C][C]2.12054[/C][C]0.429459[/C][/ROW]
[ROW][C]142[/C][C]2.1[/C][C]2.19326[/C][C]-0.093256[/C][/ROW]
[ROW][C]143[/C][C]2.1[/C][C]2.09398[/C][C]0.0060243[/C][/ROW]
[ROW][C]144[/C][C]2.1[/C][C]2.10117[/C][C]-0.00117237[/C][/ROW]
[ROW][C]145[/C][C]1.95[/C][C]2.17608[/C][C]-0.226083[/C][/ROW]
[ROW][C]146[/C][C]2.25[/C][C]2.16494[/C][C]0.0850623[/C][/ROW]
[ROW][C]147[/C][C]2.4[/C][C]2.2896[/C][C]0.110395[/C][/ROW]
[ROW][C]148[/C][C]1.95[/C][C]2.14799[/C][C]-0.197992[/C][/ROW]
[ROW][C]149[/C][C]2.1[/C][C]2.25116[/C][C]-0.151158[/C][/ROW]
[ROW][C]150[/C][C]2.1[/C][C]2.13404[/C][C]-0.0340396[/C][/ROW]
[ROW][C]151[/C][C]1.95[/C][C]2.15331[/C][C]-0.203308[/C][/ROW]
[ROW][C]152[/C][C]2.1[/C][C]2.32601[/C][C]-0.226013[/C][/ROW]
[ROW][C]153[/C][C]2.1[/C][C]2.23444[/C][C]-0.134441[/C][/ROW]
[ROW][C]154[/C][C]1.95[/C][C]2.12518[/C][C]-0.175179[/C][/ROW]
[ROW][C]155[/C][C]2.1[/C][C]2.20314[/C][C]-0.103136[/C][/ROW]
[ROW][C]156[/C][C]1.95[/C][C]2.21612[/C][C]-0.266117[/C][/ROW]
[ROW][C]157[/C][C]2.4[/C][C]2.19112[/C][C]0.208884[/C][/ROW]
[ROW][C]158[/C][C]2.4[/C][C]2.23384[/C][C]0.166159[/C][/ROW]
[ROW][C]159[/C][C]2.4[/C][C]2.11651[/C][C]0.283489[/C][/ROW]
[ROW][C]160[/C][C]1.95[/C][C]2.16694[/C][C]-0.216941[/C][/ROW]
[ROW][C]161[/C][C]2.7[/C][C]2.3075[/C][C]0.392504[/C][/ROW]
[ROW][C]162[/C][C]2.1[/C][C]2.19864[/C][C]-0.098643[/C][/ROW]
[ROW][C]163[/C][C]1.95[/C][C]2.13259[/C][C]-0.18259[/C][/ROW]
[ROW][C]164[/C][C]2.1[/C][C]2.27807[/C][C]-0.178074[/C][/ROW]
[ROW][C]165[/C][C]1.95[/C][C]2.11659[/C][C]-0.166593[/C][/ROW]
[ROW][C]166[/C][C]2.1[/C][C]2.31915[/C][C]-0.219154[/C][/ROW]
[ROW][C]167[/C][C]2.25[/C][C]2.23647[/C][C]0.013528[/C][/ROW]
[ROW][C]168[/C][C]2.7[/C][C]2.25745[/C][C]0.44255[/C][/ROW]
[ROW][C]169[/C][C]2.1[/C][C]2.17964[/C][C]-0.0796371[/C][/ROW]
[ROW][C]170[/C][C]2.4[/C][C]2.25393[/C][C]0.146073[/C][/ROW]
[ROW][C]171[/C][C]1.35[/C][C]2.0495[/C][C]-0.699505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&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
12.12.034990.065008
22.72.085620.614385
32.12.1612-0.0612015
42.12.10971-0.00970663
52.12.3564-0.256396
62.12.13348-0.0334837
72.12.16899-0.0689857
82.12.30576-0.205761
92.12.39233-0.292333
102.12.094570.00543114
112.42.211660.188343
121.951.99858-0.0485756
132.12.094450.00555253
142.12.10145-0.00145092
151.952.14139-0.191393
162.12.21277-0.11277
172.42.299390.100612
182.12.08410.0158982
192.252.059680.190324
202.42.311020.0889821
212.252.180130.0698743
222.552.232070.317928
231.952.08366-0.133659
242.42.087740.312264
252.12.090160.00984158
262.12.0170.0829983
272.42.273360.126642
282.12.21346-0.113456
292.12.14147-0.041469
302.252.163010.0869908
312.252.37956-0.129557
322.42.11420.285798
332.12.20421-0.104212
342.12.22831-0.128314
352.42.273190.126813
362.12.15846-0.0584557
371.952.22496-0.274956
382.12.22781-0.12781
392.252.216040.0339588
402.252.12850.1215
412.42.244210.155794
422.252.232520.0174826
432.252.132650.117349
442.12.20669-0.106688
452.12.1237-0.0237018
462.12.18853-0.0885253
472.72.234070.465927
482.12.16073-0.0607347
492.12.25592-0.155919
502.252.206720.0432765
512.72.320630.379367
522.42.125830.274174
532.12.32871-0.228707
542.11.983520.116483
552.42.372880.0271189
561.952.12518-0.175179
572.72.267240.432758
582.12.15296-0.0529622
592.252.208150.0418453
602.12.25612-0.156124
612.72.181010.518991
622.12.28914-0.18914
632.12.27921-0.179215
641.652.09881-0.448808
651.652.09881-0.448808
662.12.22574-0.125741
672.12.24101-0.14101
682.12.21375-0.113747
692.12.28541-0.185406
702.12.15152-0.0515179
712.42.315850.0841505
722.42.282570.117427
732.12.1796-0.0795998
742.252.27676-0.0267584
752.42.242120.157878
762.12.26516-0.165159
772.12.19757-0.097565
782.42.2270.173002
792.42.207540.192459
802.12.17067-0.0706746
812.12.099890.00011202
822.42.288660.111336
832.12.15602-0.056015
842.72.276650.423349
852.12.21693-0.116928
862.12.15367-0.0536678
872.252.206280.0437169
882.12.28142-0.181422
892.42.221370.178634
902.252.26874-0.0187421
912.252.27478-0.024783
922.12.11233-0.0123265
932.12.20354-0.103544
942.42.221160.178845
952.252.115680.134319
962.12.18333-0.0833277
972.12.13427-0.0342747
981.652.09563-0.445629
991.652.15228-0.502276
1002.72.393350.306649
1012.12.22346-0.123462
1021.952.14606-0.196056
1032.252.117460.132538
1042.42.246330.15367
1051.952.13594-0.185935
1062.12.24262-0.142621
1072.42.174160.225841
1082.12.20105-0.101053
1092.12.21268-0.112683
1102.42.166310.233693
1112.42.153530.246468
1122.42.203210.19679
1132.252.183130.066871
1142.42.192040.207964
1152.12.044860.0551443
1162.12.11221-0.0122079
1171.82.24008-0.440077
1182.72.156610.543393
1192.12.16547-0.0654679
1202.12.15035-0.0503542
1212.42.206090.193905
1222.552.209570.340426
1232.552.29740.252604
1242.12.28736-0.187355
1252.12.19143-0.0914265
1262.12.18185-0.0818456
1272.252.156250.0937488
1282.252.033010.216988
1292.12.17049-0.0704913
1302.12.19145-0.0914546
1311.952.18262-0.232625
1322.42.179410.220585
1332.12.1874-0.0873974
1342.42.229710.170289
1352.42.191120.208884
1362.42.372880.0271189
1372.252.096430.153569
1381.952.13533-0.185329
1392.12.16267-0.0626741
1402.12.29408-0.194083
1412.552.120540.429459
1422.12.19326-0.093256
1432.12.093980.0060243
1442.12.10117-0.00117237
1451.952.17608-0.226083
1462.252.164940.0850623
1472.42.28960.110395
1481.952.14799-0.197992
1492.12.25116-0.151158
1502.12.13404-0.0340396
1511.952.15331-0.203308
1522.12.32601-0.226013
1532.12.23444-0.134441
1541.952.12518-0.175179
1552.12.20314-0.103136
1561.952.21612-0.266117
1572.42.191120.208884
1582.42.233840.166159
1592.42.116510.283489
1601.952.16694-0.216941
1612.72.30750.392504
1622.12.19864-0.098643
1631.952.13259-0.18259
1642.12.27807-0.178074
1651.952.11659-0.166593
1662.12.31915-0.219154
1672.252.236470.013528
1682.72.257450.44255
1692.12.17964-0.0796371
1702.42.253930.146073
1711.352.0495-0.699505






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.858610.2827790.14139
130.755630.4887410.24437
140.6670280.6659440.332972
150.700570.598860.29943
160.5968790.8062420.403121
170.7407920.5184150.259208
180.6535440.6929120.346456
190.6553240.6893530.344676
200.6476720.7046560.352328
210.5731190.8537620.426881
220.579050.84190.42095
230.5885080.8229830.411492
240.6186920.7626150.381308
250.5442360.9115280.455764
260.4914390.9828780.508561
270.4256380.8512760.574362
280.4192110.8384220.580789
290.3553520.7107050.644648
300.3047990.6095990.695201
310.2625450.525090.737455
320.2787110.5574230.721289
330.2476350.495270.752365
340.2120420.4240850.787958
350.1982360.3964720.801764
360.1579650.3159290.842035
370.1774970.3549940.822503
380.1506220.3012440.849378
390.1195370.2390740.880463
400.09999440.1999890.900006
410.0954790.1909580.904521
420.07287110.1457420.927129
430.05855610.1171120.941444
440.04752830.09505670.952472
450.03677090.07354180.963229
460.02701670.05403350.972983
470.08800820.1760160.911992
480.07055640.1411130.929444
490.05719580.1143920.942804
500.05583740.1116750.944163
510.1197510.2395020.880249
520.1288590.2577190.871141
530.1296750.2593510.870325
540.1128360.2256720.887164
550.09374780.1874960.906252
560.08823770.1764750.911762
570.1618040.3236080.838196
580.1401120.2802250.859888
590.1153930.2307850.884607
600.1085930.2171850.891407
610.2278120.4556240.772188
620.2152390.4304780.784761
630.2261860.4523710.773814
640.4111420.8222850.588858
650.5741920.8516160.425808
660.5368020.9263960.463198
670.5224820.9550360.477518
680.483590.967180.51641
690.4665670.9331340.533433
700.4380710.8761430.561929
710.3982420.7964830.601758
720.3733670.7467340.626633
730.3375470.6750950.662453
740.2965990.5931990.703401
750.2879980.5759970.712002
760.2763030.5526070.723697
770.2470840.4941680.752916
780.2359820.4719640.764018
790.2311270.4622540.768873
800.2036040.4072090.796396
810.1756040.3512080.824396
820.1592190.3184380.840781
830.1350430.2700850.864957
840.2227120.4454250.777288
850.2015760.4031520.798424
860.1771070.3542140.822893
870.1501780.3003560.849822
880.1424480.2848960.857552
890.1335130.2670250.866487
900.1107070.2214130.889293
910.09337730.1867550.906623
920.07668670.1533730.923313
930.06459830.1291970.935402
940.06000460.1200090.939995
950.05231830.1046370.947682
960.04325210.08650420.956748
970.03422120.06844240.965779
980.07257570.1451510.927424
990.1621960.3243920.837804
1000.1923060.3846130.807694
1010.170560.3411190.82944
1020.1654880.3309760.834512
1030.1485230.2970460.851477
1040.1364680.2729370.863532
1050.1269070.2538140.873093
1060.1126340.2252690.887366
1070.1147540.2295080.885246
1080.09780020.19560.9022
1090.08372560.1674510.916274
1100.08584520.171690.914155
1110.09185030.1837010.90815
1120.08919920.1783980.910801
1130.07442010.148840.92558
1140.07430070.1486010.925699
1150.06005230.1201050.939948
1160.04700750.09401510.952992
1170.09290910.1858180.907091
1180.2876260.5752520.712374
1190.2498430.4996850.750157
1200.2121290.4242580.787871
1210.20790.41580.7921
1220.2643890.5287790.735611
1230.2648370.5296750.735163
1240.2469950.4939910.753005
1250.2185950.4371910.781405
1260.195610.391220.80439
1270.1680670.3361340.831933
1280.214330.4286590.78567
1290.1812780.3625560.818722
1300.1607620.3215240.839238
1310.1646130.3292260.835387
1320.1647040.3294080.835296
1330.1338430.2676870.866157
1340.1392260.2784510.860774
1350.1315040.2630070.868496
1360.1118170.2236350.888183
1370.1101390.2202770.889861
1380.09971960.1994390.90028
1390.08088990.161780.91911
1400.08906290.1781260.910937
1410.2351590.4703180.764841
1420.1891890.3783780.810811
1430.1749860.3499730.825014
1440.1752970.3505940.824703
1450.1419890.2839780.858011
1460.1069990.2139970.893001
1470.08331970.1666390.91668
1480.07162680.1432540.928373
1490.07172880.1434580.928271
1500.06078080.1215620.939219
1510.0501590.1003180.949841
1520.06746060.1349210.932539
1530.05491220.1098240.945088
1540.03502560.07005110.964974
1550.0241820.04836390.975818
1560.03789240.07578490.962108
1570.02073990.04147980.97926
1580.01370970.02741940.98629
1590.1966270.3932540.803373

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.85861 & 0.282779 & 0.14139 \tabularnewline
13 & 0.75563 & 0.488741 & 0.24437 \tabularnewline
14 & 0.667028 & 0.665944 & 0.332972 \tabularnewline
15 & 0.70057 & 0.59886 & 0.29943 \tabularnewline
16 & 0.596879 & 0.806242 & 0.403121 \tabularnewline
17 & 0.740792 & 0.518415 & 0.259208 \tabularnewline
18 & 0.653544 & 0.692912 & 0.346456 \tabularnewline
19 & 0.655324 & 0.689353 & 0.344676 \tabularnewline
20 & 0.647672 & 0.704656 & 0.352328 \tabularnewline
21 & 0.573119 & 0.853762 & 0.426881 \tabularnewline
22 & 0.57905 & 0.8419 & 0.42095 \tabularnewline
23 & 0.588508 & 0.822983 & 0.411492 \tabularnewline
24 & 0.618692 & 0.762615 & 0.381308 \tabularnewline
25 & 0.544236 & 0.911528 & 0.455764 \tabularnewline
26 & 0.491439 & 0.982878 & 0.508561 \tabularnewline
27 & 0.425638 & 0.851276 & 0.574362 \tabularnewline
28 & 0.419211 & 0.838422 & 0.580789 \tabularnewline
29 & 0.355352 & 0.710705 & 0.644648 \tabularnewline
30 & 0.304799 & 0.609599 & 0.695201 \tabularnewline
31 & 0.262545 & 0.52509 & 0.737455 \tabularnewline
32 & 0.278711 & 0.557423 & 0.721289 \tabularnewline
33 & 0.247635 & 0.49527 & 0.752365 \tabularnewline
34 & 0.212042 & 0.424085 & 0.787958 \tabularnewline
35 & 0.198236 & 0.396472 & 0.801764 \tabularnewline
36 & 0.157965 & 0.315929 & 0.842035 \tabularnewline
37 & 0.177497 & 0.354994 & 0.822503 \tabularnewline
38 & 0.150622 & 0.301244 & 0.849378 \tabularnewline
39 & 0.119537 & 0.239074 & 0.880463 \tabularnewline
40 & 0.0999944 & 0.199989 & 0.900006 \tabularnewline
41 & 0.095479 & 0.190958 & 0.904521 \tabularnewline
42 & 0.0728711 & 0.145742 & 0.927129 \tabularnewline
43 & 0.0585561 & 0.117112 & 0.941444 \tabularnewline
44 & 0.0475283 & 0.0950567 & 0.952472 \tabularnewline
45 & 0.0367709 & 0.0735418 & 0.963229 \tabularnewline
46 & 0.0270167 & 0.0540335 & 0.972983 \tabularnewline
47 & 0.0880082 & 0.176016 & 0.911992 \tabularnewline
48 & 0.0705564 & 0.141113 & 0.929444 \tabularnewline
49 & 0.0571958 & 0.114392 & 0.942804 \tabularnewline
50 & 0.0558374 & 0.111675 & 0.944163 \tabularnewline
51 & 0.119751 & 0.239502 & 0.880249 \tabularnewline
52 & 0.128859 & 0.257719 & 0.871141 \tabularnewline
53 & 0.129675 & 0.259351 & 0.870325 \tabularnewline
54 & 0.112836 & 0.225672 & 0.887164 \tabularnewline
55 & 0.0937478 & 0.187496 & 0.906252 \tabularnewline
56 & 0.0882377 & 0.176475 & 0.911762 \tabularnewline
57 & 0.161804 & 0.323608 & 0.838196 \tabularnewline
58 & 0.140112 & 0.280225 & 0.859888 \tabularnewline
59 & 0.115393 & 0.230785 & 0.884607 \tabularnewline
60 & 0.108593 & 0.217185 & 0.891407 \tabularnewline
61 & 0.227812 & 0.455624 & 0.772188 \tabularnewline
62 & 0.215239 & 0.430478 & 0.784761 \tabularnewline
63 & 0.226186 & 0.452371 & 0.773814 \tabularnewline
64 & 0.411142 & 0.822285 & 0.588858 \tabularnewline
65 & 0.574192 & 0.851616 & 0.425808 \tabularnewline
66 & 0.536802 & 0.926396 & 0.463198 \tabularnewline
67 & 0.522482 & 0.955036 & 0.477518 \tabularnewline
68 & 0.48359 & 0.96718 & 0.51641 \tabularnewline
69 & 0.466567 & 0.933134 & 0.533433 \tabularnewline
70 & 0.438071 & 0.876143 & 0.561929 \tabularnewline
71 & 0.398242 & 0.796483 & 0.601758 \tabularnewline
72 & 0.373367 & 0.746734 & 0.626633 \tabularnewline
73 & 0.337547 & 0.675095 & 0.662453 \tabularnewline
74 & 0.296599 & 0.593199 & 0.703401 \tabularnewline
75 & 0.287998 & 0.575997 & 0.712002 \tabularnewline
76 & 0.276303 & 0.552607 & 0.723697 \tabularnewline
77 & 0.247084 & 0.494168 & 0.752916 \tabularnewline
78 & 0.235982 & 0.471964 & 0.764018 \tabularnewline
79 & 0.231127 & 0.462254 & 0.768873 \tabularnewline
80 & 0.203604 & 0.407209 & 0.796396 \tabularnewline
81 & 0.175604 & 0.351208 & 0.824396 \tabularnewline
82 & 0.159219 & 0.318438 & 0.840781 \tabularnewline
83 & 0.135043 & 0.270085 & 0.864957 \tabularnewline
84 & 0.222712 & 0.445425 & 0.777288 \tabularnewline
85 & 0.201576 & 0.403152 & 0.798424 \tabularnewline
86 & 0.177107 & 0.354214 & 0.822893 \tabularnewline
87 & 0.150178 & 0.300356 & 0.849822 \tabularnewline
88 & 0.142448 & 0.284896 & 0.857552 \tabularnewline
89 & 0.133513 & 0.267025 & 0.866487 \tabularnewline
90 & 0.110707 & 0.221413 & 0.889293 \tabularnewline
91 & 0.0933773 & 0.186755 & 0.906623 \tabularnewline
92 & 0.0766867 & 0.153373 & 0.923313 \tabularnewline
93 & 0.0645983 & 0.129197 & 0.935402 \tabularnewline
94 & 0.0600046 & 0.120009 & 0.939995 \tabularnewline
95 & 0.0523183 & 0.104637 & 0.947682 \tabularnewline
96 & 0.0432521 & 0.0865042 & 0.956748 \tabularnewline
97 & 0.0342212 & 0.0684424 & 0.965779 \tabularnewline
98 & 0.0725757 & 0.145151 & 0.927424 \tabularnewline
99 & 0.162196 & 0.324392 & 0.837804 \tabularnewline
100 & 0.192306 & 0.384613 & 0.807694 \tabularnewline
101 & 0.17056 & 0.341119 & 0.82944 \tabularnewline
102 & 0.165488 & 0.330976 & 0.834512 \tabularnewline
103 & 0.148523 & 0.297046 & 0.851477 \tabularnewline
104 & 0.136468 & 0.272937 & 0.863532 \tabularnewline
105 & 0.126907 & 0.253814 & 0.873093 \tabularnewline
106 & 0.112634 & 0.225269 & 0.887366 \tabularnewline
107 & 0.114754 & 0.229508 & 0.885246 \tabularnewline
108 & 0.0978002 & 0.1956 & 0.9022 \tabularnewline
109 & 0.0837256 & 0.167451 & 0.916274 \tabularnewline
110 & 0.0858452 & 0.17169 & 0.914155 \tabularnewline
111 & 0.0918503 & 0.183701 & 0.90815 \tabularnewline
112 & 0.0891992 & 0.178398 & 0.910801 \tabularnewline
113 & 0.0744201 & 0.14884 & 0.92558 \tabularnewline
114 & 0.0743007 & 0.148601 & 0.925699 \tabularnewline
115 & 0.0600523 & 0.120105 & 0.939948 \tabularnewline
116 & 0.0470075 & 0.0940151 & 0.952992 \tabularnewline
117 & 0.0929091 & 0.185818 & 0.907091 \tabularnewline
118 & 0.287626 & 0.575252 & 0.712374 \tabularnewline
119 & 0.249843 & 0.499685 & 0.750157 \tabularnewline
120 & 0.212129 & 0.424258 & 0.787871 \tabularnewline
121 & 0.2079 & 0.4158 & 0.7921 \tabularnewline
122 & 0.264389 & 0.528779 & 0.735611 \tabularnewline
123 & 0.264837 & 0.529675 & 0.735163 \tabularnewline
124 & 0.246995 & 0.493991 & 0.753005 \tabularnewline
125 & 0.218595 & 0.437191 & 0.781405 \tabularnewline
126 & 0.19561 & 0.39122 & 0.80439 \tabularnewline
127 & 0.168067 & 0.336134 & 0.831933 \tabularnewline
128 & 0.21433 & 0.428659 & 0.78567 \tabularnewline
129 & 0.181278 & 0.362556 & 0.818722 \tabularnewline
130 & 0.160762 & 0.321524 & 0.839238 \tabularnewline
131 & 0.164613 & 0.329226 & 0.835387 \tabularnewline
132 & 0.164704 & 0.329408 & 0.835296 \tabularnewline
133 & 0.133843 & 0.267687 & 0.866157 \tabularnewline
134 & 0.139226 & 0.278451 & 0.860774 \tabularnewline
135 & 0.131504 & 0.263007 & 0.868496 \tabularnewline
136 & 0.111817 & 0.223635 & 0.888183 \tabularnewline
137 & 0.110139 & 0.220277 & 0.889861 \tabularnewline
138 & 0.0997196 & 0.199439 & 0.90028 \tabularnewline
139 & 0.0808899 & 0.16178 & 0.91911 \tabularnewline
140 & 0.0890629 & 0.178126 & 0.910937 \tabularnewline
141 & 0.235159 & 0.470318 & 0.764841 \tabularnewline
142 & 0.189189 & 0.378378 & 0.810811 \tabularnewline
143 & 0.174986 & 0.349973 & 0.825014 \tabularnewline
144 & 0.175297 & 0.350594 & 0.824703 \tabularnewline
145 & 0.141989 & 0.283978 & 0.858011 \tabularnewline
146 & 0.106999 & 0.213997 & 0.893001 \tabularnewline
147 & 0.0833197 & 0.166639 & 0.91668 \tabularnewline
148 & 0.0716268 & 0.143254 & 0.928373 \tabularnewline
149 & 0.0717288 & 0.143458 & 0.928271 \tabularnewline
150 & 0.0607808 & 0.121562 & 0.939219 \tabularnewline
151 & 0.050159 & 0.100318 & 0.949841 \tabularnewline
152 & 0.0674606 & 0.134921 & 0.932539 \tabularnewline
153 & 0.0549122 & 0.109824 & 0.945088 \tabularnewline
154 & 0.0350256 & 0.0700511 & 0.964974 \tabularnewline
155 & 0.024182 & 0.0483639 & 0.975818 \tabularnewline
156 & 0.0378924 & 0.0757849 & 0.962108 \tabularnewline
157 & 0.0207399 & 0.0414798 & 0.97926 \tabularnewline
158 & 0.0137097 & 0.0274194 & 0.98629 \tabularnewline
159 & 0.196627 & 0.393254 & 0.803373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&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]12[/C][C]0.85861[/C][C]0.282779[/C][C]0.14139[/C][/ROW]
[ROW][C]13[/C][C]0.75563[/C][C]0.488741[/C][C]0.24437[/C][/ROW]
[ROW][C]14[/C][C]0.667028[/C][C]0.665944[/C][C]0.332972[/C][/ROW]
[ROW][C]15[/C][C]0.70057[/C][C]0.59886[/C][C]0.29943[/C][/ROW]
[ROW][C]16[/C][C]0.596879[/C][C]0.806242[/C][C]0.403121[/C][/ROW]
[ROW][C]17[/C][C]0.740792[/C][C]0.518415[/C][C]0.259208[/C][/ROW]
[ROW][C]18[/C][C]0.653544[/C][C]0.692912[/C][C]0.346456[/C][/ROW]
[ROW][C]19[/C][C]0.655324[/C][C]0.689353[/C][C]0.344676[/C][/ROW]
[ROW][C]20[/C][C]0.647672[/C][C]0.704656[/C][C]0.352328[/C][/ROW]
[ROW][C]21[/C][C]0.573119[/C][C]0.853762[/C][C]0.426881[/C][/ROW]
[ROW][C]22[/C][C]0.57905[/C][C]0.8419[/C][C]0.42095[/C][/ROW]
[ROW][C]23[/C][C]0.588508[/C][C]0.822983[/C][C]0.411492[/C][/ROW]
[ROW][C]24[/C][C]0.618692[/C][C]0.762615[/C][C]0.381308[/C][/ROW]
[ROW][C]25[/C][C]0.544236[/C][C]0.911528[/C][C]0.455764[/C][/ROW]
[ROW][C]26[/C][C]0.491439[/C][C]0.982878[/C][C]0.508561[/C][/ROW]
[ROW][C]27[/C][C]0.425638[/C][C]0.851276[/C][C]0.574362[/C][/ROW]
[ROW][C]28[/C][C]0.419211[/C][C]0.838422[/C][C]0.580789[/C][/ROW]
[ROW][C]29[/C][C]0.355352[/C][C]0.710705[/C][C]0.644648[/C][/ROW]
[ROW][C]30[/C][C]0.304799[/C][C]0.609599[/C][C]0.695201[/C][/ROW]
[ROW][C]31[/C][C]0.262545[/C][C]0.52509[/C][C]0.737455[/C][/ROW]
[ROW][C]32[/C][C]0.278711[/C][C]0.557423[/C][C]0.721289[/C][/ROW]
[ROW][C]33[/C][C]0.247635[/C][C]0.49527[/C][C]0.752365[/C][/ROW]
[ROW][C]34[/C][C]0.212042[/C][C]0.424085[/C][C]0.787958[/C][/ROW]
[ROW][C]35[/C][C]0.198236[/C][C]0.396472[/C][C]0.801764[/C][/ROW]
[ROW][C]36[/C][C]0.157965[/C][C]0.315929[/C][C]0.842035[/C][/ROW]
[ROW][C]37[/C][C]0.177497[/C][C]0.354994[/C][C]0.822503[/C][/ROW]
[ROW][C]38[/C][C]0.150622[/C][C]0.301244[/C][C]0.849378[/C][/ROW]
[ROW][C]39[/C][C]0.119537[/C][C]0.239074[/C][C]0.880463[/C][/ROW]
[ROW][C]40[/C][C]0.0999944[/C][C]0.199989[/C][C]0.900006[/C][/ROW]
[ROW][C]41[/C][C]0.095479[/C][C]0.190958[/C][C]0.904521[/C][/ROW]
[ROW][C]42[/C][C]0.0728711[/C][C]0.145742[/C][C]0.927129[/C][/ROW]
[ROW][C]43[/C][C]0.0585561[/C][C]0.117112[/C][C]0.941444[/C][/ROW]
[ROW][C]44[/C][C]0.0475283[/C][C]0.0950567[/C][C]0.952472[/C][/ROW]
[ROW][C]45[/C][C]0.0367709[/C][C]0.0735418[/C][C]0.963229[/C][/ROW]
[ROW][C]46[/C][C]0.0270167[/C][C]0.0540335[/C][C]0.972983[/C][/ROW]
[ROW][C]47[/C][C]0.0880082[/C][C]0.176016[/C][C]0.911992[/C][/ROW]
[ROW][C]48[/C][C]0.0705564[/C][C]0.141113[/C][C]0.929444[/C][/ROW]
[ROW][C]49[/C][C]0.0571958[/C][C]0.114392[/C][C]0.942804[/C][/ROW]
[ROW][C]50[/C][C]0.0558374[/C][C]0.111675[/C][C]0.944163[/C][/ROW]
[ROW][C]51[/C][C]0.119751[/C][C]0.239502[/C][C]0.880249[/C][/ROW]
[ROW][C]52[/C][C]0.128859[/C][C]0.257719[/C][C]0.871141[/C][/ROW]
[ROW][C]53[/C][C]0.129675[/C][C]0.259351[/C][C]0.870325[/C][/ROW]
[ROW][C]54[/C][C]0.112836[/C][C]0.225672[/C][C]0.887164[/C][/ROW]
[ROW][C]55[/C][C]0.0937478[/C][C]0.187496[/C][C]0.906252[/C][/ROW]
[ROW][C]56[/C][C]0.0882377[/C][C]0.176475[/C][C]0.911762[/C][/ROW]
[ROW][C]57[/C][C]0.161804[/C][C]0.323608[/C][C]0.838196[/C][/ROW]
[ROW][C]58[/C][C]0.140112[/C][C]0.280225[/C][C]0.859888[/C][/ROW]
[ROW][C]59[/C][C]0.115393[/C][C]0.230785[/C][C]0.884607[/C][/ROW]
[ROW][C]60[/C][C]0.108593[/C][C]0.217185[/C][C]0.891407[/C][/ROW]
[ROW][C]61[/C][C]0.227812[/C][C]0.455624[/C][C]0.772188[/C][/ROW]
[ROW][C]62[/C][C]0.215239[/C][C]0.430478[/C][C]0.784761[/C][/ROW]
[ROW][C]63[/C][C]0.226186[/C][C]0.452371[/C][C]0.773814[/C][/ROW]
[ROW][C]64[/C][C]0.411142[/C][C]0.822285[/C][C]0.588858[/C][/ROW]
[ROW][C]65[/C][C]0.574192[/C][C]0.851616[/C][C]0.425808[/C][/ROW]
[ROW][C]66[/C][C]0.536802[/C][C]0.926396[/C][C]0.463198[/C][/ROW]
[ROW][C]67[/C][C]0.522482[/C][C]0.955036[/C][C]0.477518[/C][/ROW]
[ROW][C]68[/C][C]0.48359[/C][C]0.96718[/C][C]0.51641[/C][/ROW]
[ROW][C]69[/C][C]0.466567[/C][C]0.933134[/C][C]0.533433[/C][/ROW]
[ROW][C]70[/C][C]0.438071[/C][C]0.876143[/C][C]0.561929[/C][/ROW]
[ROW][C]71[/C][C]0.398242[/C][C]0.796483[/C][C]0.601758[/C][/ROW]
[ROW][C]72[/C][C]0.373367[/C][C]0.746734[/C][C]0.626633[/C][/ROW]
[ROW][C]73[/C][C]0.337547[/C][C]0.675095[/C][C]0.662453[/C][/ROW]
[ROW][C]74[/C][C]0.296599[/C][C]0.593199[/C][C]0.703401[/C][/ROW]
[ROW][C]75[/C][C]0.287998[/C][C]0.575997[/C][C]0.712002[/C][/ROW]
[ROW][C]76[/C][C]0.276303[/C][C]0.552607[/C][C]0.723697[/C][/ROW]
[ROW][C]77[/C][C]0.247084[/C][C]0.494168[/C][C]0.752916[/C][/ROW]
[ROW][C]78[/C][C]0.235982[/C][C]0.471964[/C][C]0.764018[/C][/ROW]
[ROW][C]79[/C][C]0.231127[/C][C]0.462254[/C][C]0.768873[/C][/ROW]
[ROW][C]80[/C][C]0.203604[/C][C]0.407209[/C][C]0.796396[/C][/ROW]
[ROW][C]81[/C][C]0.175604[/C][C]0.351208[/C][C]0.824396[/C][/ROW]
[ROW][C]82[/C][C]0.159219[/C][C]0.318438[/C][C]0.840781[/C][/ROW]
[ROW][C]83[/C][C]0.135043[/C][C]0.270085[/C][C]0.864957[/C][/ROW]
[ROW][C]84[/C][C]0.222712[/C][C]0.445425[/C][C]0.777288[/C][/ROW]
[ROW][C]85[/C][C]0.201576[/C][C]0.403152[/C][C]0.798424[/C][/ROW]
[ROW][C]86[/C][C]0.177107[/C][C]0.354214[/C][C]0.822893[/C][/ROW]
[ROW][C]87[/C][C]0.150178[/C][C]0.300356[/C][C]0.849822[/C][/ROW]
[ROW][C]88[/C][C]0.142448[/C][C]0.284896[/C][C]0.857552[/C][/ROW]
[ROW][C]89[/C][C]0.133513[/C][C]0.267025[/C][C]0.866487[/C][/ROW]
[ROW][C]90[/C][C]0.110707[/C][C]0.221413[/C][C]0.889293[/C][/ROW]
[ROW][C]91[/C][C]0.0933773[/C][C]0.186755[/C][C]0.906623[/C][/ROW]
[ROW][C]92[/C][C]0.0766867[/C][C]0.153373[/C][C]0.923313[/C][/ROW]
[ROW][C]93[/C][C]0.0645983[/C][C]0.129197[/C][C]0.935402[/C][/ROW]
[ROW][C]94[/C][C]0.0600046[/C][C]0.120009[/C][C]0.939995[/C][/ROW]
[ROW][C]95[/C][C]0.0523183[/C][C]0.104637[/C][C]0.947682[/C][/ROW]
[ROW][C]96[/C][C]0.0432521[/C][C]0.0865042[/C][C]0.956748[/C][/ROW]
[ROW][C]97[/C][C]0.0342212[/C][C]0.0684424[/C][C]0.965779[/C][/ROW]
[ROW][C]98[/C][C]0.0725757[/C][C]0.145151[/C][C]0.927424[/C][/ROW]
[ROW][C]99[/C][C]0.162196[/C][C]0.324392[/C][C]0.837804[/C][/ROW]
[ROW][C]100[/C][C]0.192306[/C][C]0.384613[/C][C]0.807694[/C][/ROW]
[ROW][C]101[/C][C]0.17056[/C][C]0.341119[/C][C]0.82944[/C][/ROW]
[ROW][C]102[/C][C]0.165488[/C][C]0.330976[/C][C]0.834512[/C][/ROW]
[ROW][C]103[/C][C]0.148523[/C][C]0.297046[/C][C]0.851477[/C][/ROW]
[ROW][C]104[/C][C]0.136468[/C][C]0.272937[/C][C]0.863532[/C][/ROW]
[ROW][C]105[/C][C]0.126907[/C][C]0.253814[/C][C]0.873093[/C][/ROW]
[ROW][C]106[/C][C]0.112634[/C][C]0.225269[/C][C]0.887366[/C][/ROW]
[ROW][C]107[/C][C]0.114754[/C][C]0.229508[/C][C]0.885246[/C][/ROW]
[ROW][C]108[/C][C]0.0978002[/C][C]0.1956[/C][C]0.9022[/C][/ROW]
[ROW][C]109[/C][C]0.0837256[/C][C]0.167451[/C][C]0.916274[/C][/ROW]
[ROW][C]110[/C][C]0.0858452[/C][C]0.17169[/C][C]0.914155[/C][/ROW]
[ROW][C]111[/C][C]0.0918503[/C][C]0.183701[/C][C]0.90815[/C][/ROW]
[ROW][C]112[/C][C]0.0891992[/C][C]0.178398[/C][C]0.910801[/C][/ROW]
[ROW][C]113[/C][C]0.0744201[/C][C]0.14884[/C][C]0.92558[/C][/ROW]
[ROW][C]114[/C][C]0.0743007[/C][C]0.148601[/C][C]0.925699[/C][/ROW]
[ROW][C]115[/C][C]0.0600523[/C][C]0.120105[/C][C]0.939948[/C][/ROW]
[ROW][C]116[/C][C]0.0470075[/C][C]0.0940151[/C][C]0.952992[/C][/ROW]
[ROW][C]117[/C][C]0.0929091[/C][C]0.185818[/C][C]0.907091[/C][/ROW]
[ROW][C]118[/C][C]0.287626[/C][C]0.575252[/C][C]0.712374[/C][/ROW]
[ROW][C]119[/C][C]0.249843[/C][C]0.499685[/C][C]0.750157[/C][/ROW]
[ROW][C]120[/C][C]0.212129[/C][C]0.424258[/C][C]0.787871[/C][/ROW]
[ROW][C]121[/C][C]0.2079[/C][C]0.4158[/C][C]0.7921[/C][/ROW]
[ROW][C]122[/C][C]0.264389[/C][C]0.528779[/C][C]0.735611[/C][/ROW]
[ROW][C]123[/C][C]0.264837[/C][C]0.529675[/C][C]0.735163[/C][/ROW]
[ROW][C]124[/C][C]0.246995[/C][C]0.493991[/C][C]0.753005[/C][/ROW]
[ROW][C]125[/C][C]0.218595[/C][C]0.437191[/C][C]0.781405[/C][/ROW]
[ROW][C]126[/C][C]0.19561[/C][C]0.39122[/C][C]0.80439[/C][/ROW]
[ROW][C]127[/C][C]0.168067[/C][C]0.336134[/C][C]0.831933[/C][/ROW]
[ROW][C]128[/C][C]0.21433[/C][C]0.428659[/C][C]0.78567[/C][/ROW]
[ROW][C]129[/C][C]0.181278[/C][C]0.362556[/C][C]0.818722[/C][/ROW]
[ROW][C]130[/C][C]0.160762[/C][C]0.321524[/C][C]0.839238[/C][/ROW]
[ROW][C]131[/C][C]0.164613[/C][C]0.329226[/C][C]0.835387[/C][/ROW]
[ROW][C]132[/C][C]0.164704[/C][C]0.329408[/C][C]0.835296[/C][/ROW]
[ROW][C]133[/C][C]0.133843[/C][C]0.267687[/C][C]0.866157[/C][/ROW]
[ROW][C]134[/C][C]0.139226[/C][C]0.278451[/C][C]0.860774[/C][/ROW]
[ROW][C]135[/C][C]0.131504[/C][C]0.263007[/C][C]0.868496[/C][/ROW]
[ROW][C]136[/C][C]0.111817[/C][C]0.223635[/C][C]0.888183[/C][/ROW]
[ROW][C]137[/C][C]0.110139[/C][C]0.220277[/C][C]0.889861[/C][/ROW]
[ROW][C]138[/C][C]0.0997196[/C][C]0.199439[/C][C]0.90028[/C][/ROW]
[ROW][C]139[/C][C]0.0808899[/C][C]0.16178[/C][C]0.91911[/C][/ROW]
[ROW][C]140[/C][C]0.0890629[/C][C]0.178126[/C][C]0.910937[/C][/ROW]
[ROW][C]141[/C][C]0.235159[/C][C]0.470318[/C][C]0.764841[/C][/ROW]
[ROW][C]142[/C][C]0.189189[/C][C]0.378378[/C][C]0.810811[/C][/ROW]
[ROW][C]143[/C][C]0.174986[/C][C]0.349973[/C][C]0.825014[/C][/ROW]
[ROW][C]144[/C][C]0.175297[/C][C]0.350594[/C][C]0.824703[/C][/ROW]
[ROW][C]145[/C][C]0.141989[/C][C]0.283978[/C][C]0.858011[/C][/ROW]
[ROW][C]146[/C][C]0.106999[/C][C]0.213997[/C][C]0.893001[/C][/ROW]
[ROW][C]147[/C][C]0.0833197[/C][C]0.166639[/C][C]0.91668[/C][/ROW]
[ROW][C]148[/C][C]0.0716268[/C][C]0.143254[/C][C]0.928373[/C][/ROW]
[ROW][C]149[/C][C]0.0717288[/C][C]0.143458[/C][C]0.928271[/C][/ROW]
[ROW][C]150[/C][C]0.0607808[/C][C]0.121562[/C][C]0.939219[/C][/ROW]
[ROW][C]151[/C][C]0.050159[/C][C]0.100318[/C][C]0.949841[/C][/ROW]
[ROW][C]152[/C][C]0.0674606[/C][C]0.134921[/C][C]0.932539[/C][/ROW]
[ROW][C]153[/C][C]0.0549122[/C][C]0.109824[/C][C]0.945088[/C][/ROW]
[ROW][C]154[/C][C]0.0350256[/C][C]0.0700511[/C][C]0.964974[/C][/ROW]
[ROW][C]155[/C][C]0.024182[/C][C]0.0483639[/C][C]0.975818[/C][/ROW]
[ROW][C]156[/C][C]0.0378924[/C][C]0.0757849[/C][C]0.962108[/C][/ROW]
[ROW][C]157[/C][C]0.0207399[/C][C]0.0414798[/C][C]0.97926[/C][/ROW]
[ROW][C]158[/C][C]0.0137097[/C][C]0.0274194[/C][C]0.98629[/C][/ROW]
[ROW][C]159[/C][C]0.196627[/C][C]0.393254[/C][C]0.803373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&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
120.858610.2827790.14139
130.755630.4887410.24437
140.6670280.6659440.332972
150.700570.598860.29943
160.5968790.8062420.403121
170.7407920.5184150.259208
180.6535440.6929120.346456
190.6553240.6893530.344676
200.6476720.7046560.352328
210.5731190.8537620.426881
220.579050.84190.42095
230.5885080.8229830.411492
240.6186920.7626150.381308
250.5442360.9115280.455764
260.4914390.9828780.508561
270.4256380.8512760.574362
280.4192110.8384220.580789
290.3553520.7107050.644648
300.3047990.6095990.695201
310.2625450.525090.737455
320.2787110.5574230.721289
330.2476350.495270.752365
340.2120420.4240850.787958
350.1982360.3964720.801764
360.1579650.3159290.842035
370.1774970.3549940.822503
380.1506220.3012440.849378
390.1195370.2390740.880463
400.09999440.1999890.900006
410.0954790.1909580.904521
420.07287110.1457420.927129
430.05855610.1171120.941444
440.04752830.09505670.952472
450.03677090.07354180.963229
460.02701670.05403350.972983
470.08800820.1760160.911992
480.07055640.1411130.929444
490.05719580.1143920.942804
500.05583740.1116750.944163
510.1197510.2395020.880249
520.1288590.2577190.871141
530.1296750.2593510.870325
540.1128360.2256720.887164
550.09374780.1874960.906252
560.08823770.1764750.911762
570.1618040.3236080.838196
580.1401120.2802250.859888
590.1153930.2307850.884607
600.1085930.2171850.891407
610.2278120.4556240.772188
620.2152390.4304780.784761
630.2261860.4523710.773814
640.4111420.8222850.588858
650.5741920.8516160.425808
660.5368020.9263960.463198
670.5224820.9550360.477518
680.483590.967180.51641
690.4665670.9331340.533433
700.4380710.8761430.561929
710.3982420.7964830.601758
720.3733670.7467340.626633
730.3375470.6750950.662453
740.2965990.5931990.703401
750.2879980.5759970.712002
760.2763030.5526070.723697
770.2470840.4941680.752916
780.2359820.4719640.764018
790.2311270.4622540.768873
800.2036040.4072090.796396
810.1756040.3512080.824396
820.1592190.3184380.840781
830.1350430.2700850.864957
840.2227120.4454250.777288
850.2015760.4031520.798424
860.1771070.3542140.822893
870.1501780.3003560.849822
880.1424480.2848960.857552
890.1335130.2670250.866487
900.1107070.2214130.889293
910.09337730.1867550.906623
920.07668670.1533730.923313
930.06459830.1291970.935402
940.06000460.1200090.939995
950.05231830.1046370.947682
960.04325210.08650420.956748
970.03422120.06844240.965779
980.07257570.1451510.927424
990.1621960.3243920.837804
1000.1923060.3846130.807694
1010.170560.3411190.82944
1020.1654880.3309760.834512
1030.1485230.2970460.851477
1040.1364680.2729370.863532
1050.1269070.2538140.873093
1060.1126340.2252690.887366
1070.1147540.2295080.885246
1080.09780020.19560.9022
1090.08372560.1674510.916274
1100.08584520.171690.914155
1110.09185030.1837010.90815
1120.08919920.1783980.910801
1130.07442010.148840.92558
1140.07430070.1486010.925699
1150.06005230.1201050.939948
1160.04700750.09401510.952992
1170.09290910.1858180.907091
1180.2876260.5752520.712374
1190.2498430.4996850.750157
1200.2121290.4242580.787871
1210.20790.41580.7921
1220.2643890.5287790.735611
1230.2648370.5296750.735163
1240.2469950.4939910.753005
1250.2185950.4371910.781405
1260.195610.391220.80439
1270.1680670.3361340.831933
1280.214330.4286590.78567
1290.1812780.3625560.818722
1300.1607620.3215240.839238
1310.1646130.3292260.835387
1320.1647040.3294080.835296
1330.1338430.2676870.866157
1340.1392260.2784510.860774
1350.1315040.2630070.868496
1360.1118170.2236350.888183
1370.1101390.2202770.889861
1380.09971960.1994390.90028
1390.08088990.161780.91911
1400.08906290.1781260.910937
1410.2351590.4703180.764841
1420.1891890.3783780.810811
1430.1749860.3499730.825014
1440.1752970.3505940.824703
1450.1419890.2839780.858011
1460.1069990.2139970.893001
1470.08331970.1666390.91668
1480.07162680.1432540.928373
1490.07172880.1434580.928271
1500.06078080.1215620.939219
1510.0501590.1003180.949841
1520.06746060.1349210.932539
1530.05491220.1098240.945088
1540.03502560.07005110.964974
1550.0241820.04836390.975818
1560.03789240.07578490.962108
1570.02073990.04147980.97926
1580.01370970.02741940.98629
1590.1966270.3932540.803373






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0202703OK
10% type I error level110.0743243OK

\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 & 3 & 0.0202703 & OK \tabularnewline
10% type I error level & 11 & 0.0743243 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267348&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]3[/C][C]0.0202703[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0743243[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267348&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267348&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0202703OK
10% type I error level110.0743243OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}