FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 04 Dec 2014 10:13:50 +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/04/t1417688072khik9jwqfzr2slt.htm/, Retrieved Thu, 16 May 2024 22:27:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263071, Retrieved Thu, 16 May 2024 22:27:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact107

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-04 10:13:50] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'4.35' 0 1 22 23 48 23 12 41 34
'12.7' 0 1 22 22 50 16 45 146 61
'18.1' 0 1 22 21 150 33 37 182 70
'17.85' 0 1 20 25 154 32 37 192 69
'16.6' 1 0 19 30 109 37 108 263 145
'12.6' 1 1 20 17 68 14 10 35 23
'17.1' 0 1 22 27 194 52 68 439 120
'19.1' 0 0 21 23 158 75 72 214 147
'16.1' 0 1 21 23 159 72 143 341 215
'13.35' 0 0 21 18 67 15 9 58 24
'18.4' 0 0 21 18 147 29 55 292 84
'14.7' 0 1 21 23 39 13 17 85 30
'10.6' 0 1 21 19 100 40 37 200 77
'12.6' 0 1 21 15 111 19 27 158 46
'16.2' 0 1 22 20 138 24 37 199 61
'13.6' 0 1 24 16 101 121 58 297 178
'18.9' 1 1 21 24 131 93 66 227 160
'14.1' 0 1 22 25 101 36 21 108 57
'14.5' 0 1 20 25 114 23 19 86 42
'16.15' 0 0 21 19 165 85 78 302 163
'14.75' 0 1 24 19 114 41 35 148 75
'14.8' 0 1 25 16 111 46 48 178 94
'12.45' 0 1 22 19 75 18 27 120 45
'12.65' 0 1 21 19 82 35 43 207 78
'17.35' 0 1 21 23 121 17 30 157 47
'8.6' 0 1 22 21 32 4 25 128 29
'18.4' 0 0 23 22 150 28 69 296 97
'16.1' 0 1 24 19 117 44 72 323 116
'11.6' 1 1 20 20 71 10 23 79 32
'17.75' 0 1 22 20 165 38 13 70 50
'15.25' 0 1 25 3 154 57 61 146 118
'17.65' 0 1 22 23 126 23 43 246 66
'15.6' 0 0 22 14 138 26 22 145 48
'16.35' 0 0 21 23 149 36 51 196 86
'17.65' 0 0 21 20 145 22 67 199 89
'13.6' 0 1 21 15 120 40 36 127 76
'11.7' 0 0 22 13 138 18 21 91 39
'14.35' 0 0 22 16 109 31 44 153 75
'14.75' 0 0 22 7 132 11 45 299 57
'18.25' 0 1 21 24 172 38 34 228 72
'9.9' 0 0 22 17 169 24 36 190 60
16 0 1 23 24 114 37 72 180 109
'18.25' 0 1 21 24 156 37 39 212 76
'16.85' 0 0 21 19 172 22 43 269 65
'14.6' 1 1 21 25 68 15 25 130 40
'13.85' 1 1 19 20 89 2 56 179 58
'18.95' 0 1 21 28 167 43 80 243 123
'15.6' 0 0 21 23 113 31 40 190 71
'14.85' 1 0 19 27 115 29 73 299 102
'11.75' 1 0 18 18 78 45 34 121 80
'18.45' 1 0 19 28 118 25 72 137 97
'15.9' 1 1 21 21 87 4 42 305 46
'17.1' 0 0 22 19 173 31 61 157 93
'16.1' 0 1 22 23 2 -4 23 96 19
'19.9' 1 0 19 27 162 66 74 183 140
'10.95' 1 1 20 22 49 61 16 52 78
'18.45' 1 0 19 28 122 32 66 238 98
'15.1' 1 1 21 25 96 31 9 40 40
15 1 0 19 21 100 39 41 226 80
'11.35' 1 0 20 22 82 19 57 190 76
'15.95' 1 1 21 28 100 31 48 214 79
'18.1' 1 0 19 20 115 36 51 145 87
'14.6' 1 1 21 29 141 42 53 119 95
'15.4' 0 1 21 25 165 21 29 222 49
'15.4' 0 1 21 25 165 21 29 222 49
'17.6' 1 1 19 20 110 25 55 159 80
'13.35' 0 1 25 20 118 32 54 165 86
'19.1' 0 0 21 16 158 26 43 249 69
'15.35' 1 1 20 20 146 28 51 125 79
'7.6' 0 0 25 20 49 32 20 122 52
'13.4' 1 0 19 23 90 41 79 186 120
'13.9' 1 0 20 18 121 29 39 148 69
'19.1' 0 1 22 25 155 33 61 274 94
'15.25' 1 0 19 18 104 17 55 172 72
'12.9' 1 1 20 19 147 13 30 84 43
'16.1' 1 0 19 25 110 32 55 168 87
'17.35' 1 0 19 25 108 30 22 102 52
'13.15' 1 0 18 25 113 34 37 106 71
'12.15' 1 0 19 24 115 59 2 2 61
'12.6' 1 1 21 19 61 13 38 139 51
'10.35' 1 1 19 26 60 23 27 95 50
'15.4' 1 1 20 10 109 10 56 130 67
'9.6' 1 1 20 17 68 5 25 72 30
'18.2' 1 0 19 13 111 31 39 141 70
'13.6' 1 0 19 17 77 19 33 113 52
'14.85' 1 1 22 30 73 32 43 206 75
'14.75' 0 0 21 25 151 30 57 268 87
'14.1' 1 0 19 4 89 25 43 175 69
'14.9' 1 0 19 16 78 48 23 77 72
'16.25' 1 0 19 21 110 35 44 125 79
'19.25' 0 1 23 23 220 67 54 255 121
'13.6' 1 1 19 22 65 15 28 111 43
'13.6' 0 0 20 17 141 22 36 132 58
'15.65' 1 0 19 20 117 18 39 211 57
'12.75' 0 1 22 20 122 33 16 92 50
'14.6' 1 0 19 22 63 46 23 76 69
'9.85' 0 1 25 16 44 24 40 171 64
'12.65' 1 1 19 23 52 14 24 83 38
'11.9' 1 1 20 16 62 23 29 119 53
'19.2' 1 0 19 0 131 12 78 266 90
'16.6' 1 1 19 18 101 38 57 186 96
'11.2' 1 1 20 25 42 12 37 50 49
'15.25' 0 1 20 23 152 28 27 117 56
'11.9' 0 0 21 12 107 41 61 219 102
'13.2' 1 0 19 18 77 12 27 246 40
'16.35' 0 0 21 24 154 31 69 279 100
'12.4' 0 1 23 11 103 33 34 148 67
'15.85' 1 1 19 18 96 34 44 137 78
'14.35' 0 0 21 14 154 41 21 130 62
'18.15' 0 1 22 23 175 21 34 181 55
'11.15' 1 1 20 24 57 20 39 98 59
'15.65' 1 0 18 29 112 44 51 226 96
'17.75' 0 0 21 18 143 52 34 234 86
'7.65' 1 0 20 15 49 7 31 138 38
'12.35' 0 1 21 29 110 29 13 85 43
'15.6' 0 1 21 16 131 11 12 66 23
'19.3' 0 0 21 19 167 26 51 236 77
'15.2' 1 0 19 22 56 24 24 106 48
'17.1' 0 0 21 16 137 7 19 135 26
'15.6' 1 1 19 23 86 60 30 122 91
'18.4' 0 1 21 23 121 13 81 218 94
'19.05' 0 0 21 19 149 20 42 199 62
'18.55' 0 0 22 4 168 52 22 112 74
'19.1' 0 0 21 20 140 28 85 278 114
'13.1' 1 1 22 24 88 25 27 94 52
'12.85' 0 1 22 20 168 39 25 113 64
'9.5' 0 1 22 4 94 9 22 84 31
'4.5' 0 1 22 24 51 19 19 86 38
'11.85' 1 0 21 22 48 13 14 62 27
'13.6' 0 1 22 16 145 60 45 222 105
'11.7' 0 1 23 3 66 19 45 167 64
'12.4' 1 1 19 15 85 34 28 82 62
'13.35' 0 0 22 24 109 14 51 207 65
'11.4' 1 0 21 17 63 17 41 184 58
'14.9' 1 1 19 20 102 45 31 83 76
'19.9' 1 0 19 27 162 66 74 183 140
'17.75' 0 1 20 23 128 24 24 85 48
'11.2' 1 1 20 26 86 48 19 89 68
'14.6' 1 1 18 23 114 29 51 225 80
'17.6' 0 0 21 17 164 -2 73 237 71
'14.05' 0 1 21 20 119 51 24 102 76
'16.1' 0 0 20 22 126 2 61 221 63
'13.35' 0 1 20 19 132 24 23 128 46
'11.85' 0 1 21 24 142 40 14 91 53
'11.95' 0 0 21 19 83 20 54 198 74
'14.75' 1 1 19 23 94 19 51 204 70
'15.15' 1 0 19 15 81 16 62 158 78
'13.2' 0 1 21 27 166 20 36 138 56
'16.85' 1 0 19 26 110 40 59 226 100
'7.85' 1 1 19 22 64 27 24 44 51
'7.7' 0 0 24 22 93 25 26 196 52
'12.6' 1 0 19 18 104 49 54 83 102
'7.85' 1 1 19 15 105 39 39 79 78
'10.95' 1 1 20 22 49 61 16 52 78
'12.35' 1 0 19 27 88 19 36 105 55
'9.95' 1 1 19 10 95 67 31 116 98
'14.9' 1 1 19 20 102 45 31 83 76
'16.65' 1 0 19 17 99 30 42 196 73
'13.4' 1 1 19 23 63 8 39 153 47
'13.95' 1 0 19 19 76 19 25 157 45
'15.7' 1 0 20 13 109 52 31 75 83
'16.85' 1 1 20 27 117 22 38 106 60
'10.95' 1 1 19 23 57 17 31 58 48
'15.35' 1 0 21 16 120 33 17 75 50
'12.2' 1 1 19 25 73 34 22 74 56
'15.1' 1 0 19 2 91 22 55 185 77
'17.75' 1 0 19 26 108 30 62 265 91
'15.2' 1 1 21 20 105 25 51 131 76
'14.6' 0 0 22 23 117 38 30 139 68
'16.65' 1 0 19 22 119 26 49 196 74
'8.1' 1 1 19 24 31 13 16 78 29

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'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&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'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.7713 + 0.685148programma[t] -0.232627gender[t] -0.19225age[t] + 0.0367228NUMERACYTOT[t] + 0.0465093LFM[t] -0.233533PRH[t] -0.203767CH[t] + 0.00619521Blogs[t] + 0.22532Hours[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.7713 +  0.685148programma[t] -0.232627gender[t] -0.19225age[t] +  0.0367228NUMERACYTOT[t] +  0.0465093LFM[t] -0.233533PRH[t] -0.203767CH[t] +  0.00619521Blogs[t] +  0.22532Hours[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.7713 +  0.685148programma[t] -0.232627gender[t] -0.19225age[t] +  0.0367228NUMERACYTOT[t] +  0.0465093LFM[t] -0.233533PRH[t] -0.203767CH[t] +  0.00619521Blogs[t] +  0.22532Hours[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.7713 + 0.685148programma[t] -0.232627gender[t] -0.19225age[t] + 0.0367228NUMERACYTOT[t] + 0.0465093LFM[t] -0.233533PRH[t] -0.203767CH[t] + 0.00619521Blogs[t] + 0.22532Hours[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.77133.943482.7310.007009960.00350498
programma0.6851480.5669561.2080.228640.11432
gender-0.2326270.369292-0.62990.5296350.264817
age-0.192250.171264-1.1230.2633050.131652
NUMERACYTOT0.03672280.03133631.1720.2429710.121486
LFM0.04650930.005860957.9353.3558e-131.6779e-13
PRH-0.2335330.360692-0.64750.5182570.259128
CH-0.2037670.358961-0.56770.5710590.285529
Blogs0.006195210.003732311.660.0988840.049442
Hours0.225320.3594080.62690.53160.2658

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.7713 & 3.94348 & 2.731 & 0.00700996 & 0.00350498 \tabularnewline
programma & 0.685148 & 0.566956 & 1.208 & 0.22864 & 0.11432 \tabularnewline
gender & -0.232627 & 0.369292 & -0.6299 & 0.529635 & 0.264817 \tabularnewline
age & -0.19225 & 0.171264 & -1.123 & 0.263305 & 0.131652 \tabularnewline
NUMERACYTOT & 0.0367228 & 0.0313363 & 1.172 & 0.242971 & 0.121486 \tabularnewline
LFM & 0.0465093 & 0.00586095 & 7.935 & 3.3558e-13 & 1.6779e-13 \tabularnewline
PRH & -0.233533 & 0.360692 & -0.6475 & 0.518257 & 0.259128 \tabularnewline
CH & -0.203767 & 0.358961 & -0.5677 & 0.571059 & 0.285529 \tabularnewline
Blogs & 0.00619521 & 0.00373231 & 1.66 & 0.098884 & 0.049442 \tabularnewline
Hours & 0.22532 & 0.359408 & 0.6269 & 0.5316 & 0.2658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.7713[/C][C]3.94348[/C][C]2.731[/C][C]0.00700996[/C][C]0.00350498[/C][/ROW]
[ROW][C]programma[/C][C]0.685148[/C][C]0.566956[/C][C]1.208[/C][C]0.22864[/C][C]0.11432[/C][/ROW]
[ROW][C]gender[/C][C]-0.232627[/C][C]0.369292[/C][C]-0.6299[/C][C]0.529635[/C][C]0.264817[/C][/ROW]
[ROW][C]age[/C][C]-0.19225[/C][C]0.171264[/C][C]-1.123[/C][C]0.263305[/C][C]0.131652[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0367228[/C][C]0.0313363[/C][C]1.172[/C][C]0.242971[/C][C]0.121486[/C][/ROW]
[ROW][C]LFM[/C][C]0.0465093[/C][C]0.00586095[/C][C]7.935[/C][C]3.3558e-13[/C][C]1.6779e-13[/C][/ROW]
[ROW][C]PRH[/C][C]-0.233533[/C][C]0.360692[/C][C]-0.6475[/C][C]0.518257[/C][C]0.259128[/C][/ROW]
[ROW][C]CH[/C][C]-0.203767[/C][C]0.358961[/C][C]-0.5677[/C][C]0.571059[/C][C]0.285529[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00619521[/C][C]0.00373231[/C][C]1.66[/C][C]0.098884[/C][C]0.049442[/C][/ROW]
[ROW][C]Hours[/C][C]0.22532[/C][C]0.359408[/C][C]0.6269[/C][C]0.5316[/C][C]0.2658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.77133.943482.7310.007009960.00350498
programma0.6851480.5669561.2080.228640.11432
gender-0.2326270.369292-0.62990.5296350.264817
age-0.192250.171264-1.1230.2633050.131652
NUMERACYTOT0.03672280.03133631.1720.2429710.121486
LFM0.04650930.005860957.9353.3558e-131.6779e-13
PRH-0.2335330.360692-0.64750.5182570.259128
CH-0.2037670.358961-0.56770.5710590.285529
Blogs0.006195210.003732311.660.0988840.049442
Hours0.225320.3594080.62690.53160.2658






Multiple Linear Regression - Regression Statistics
Multiple R0.72141
R-squared0.520433
Adjusted R-squared0.493625
F-TEST (value)19.4133
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.15514
Sum Squared Residuals747.786

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.72141 \tabularnewline
R-squared & 0.520433 \tabularnewline
Adjusted R-squared & 0.493625 \tabularnewline
F-TEST (value) & 19.4133 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.15514 \tabularnewline
Sum Squared Residuals & 747.786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.72141[/C][/ROW]
[ROW][C]R-squared[/C][C]0.520433[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.493625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.4133[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]161[/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]2.15514[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]747.786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&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 R0.72141
R-squared0.520433
Adjusted R-squared0.493625
F-TEST (value)19.4133
F-TEST (DF numerator)9
F-TEST (DF denominator)161
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.15514
Sum Squared Residuals747.786






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.48464-5.13464
212.711.18551.5145
318.115.71072.38933
417.8516.49831.35173
516.617.628-1.02805
612.611.25781.34222
717.120.0817-2.98167
819.117.18871.91131
916.119.3443-3.24427
1013.3510.94132.40873
1118.416.98811.41186
1214.79.946124.75388
1310.613.558-2.95803
1412.613.6195-1.0195
1516.215.29510.904921
1613.613.08050.519464
1718.916.45092.44912
1814.112.75071.34933
1914.513.66720.832828
2016.1517.9597-1.80973
2114.7513.03361.71638
2214.813.2421.55803
2312.4511.67260.777396
2412.6512.9346-0.284617
2517.3514.45332.89673
268.69.8676-1.2676
2718.417.22481.17521
2816.115.25540.844552
2911.612.0931-0.493113
3017.7514.89412.85594
3115.2514.75610.493938
3217.6515.27582.37416
3315.614.63310.966894
3416.3516.3010.0490002
3517.6516.70850.94146
3613.613.8675-0.267521
3711.714.306-2.606
3814.3513.84040.509558
3914.7515.8953-1.1453
4018.2517.21551.03445
419.916.782-6.88202
421614.66341.33663
4318.2516.48831.76175
4416.8517.844-0.99396
4514.612.48832.11174
4613.8514.7443-0.89433
4718.9518.17320.776811
4815.614.61880.9812
4914.8517.3313-2.48131
5011.7513.6228-1.8728
5118.4516.51521.93476
5215.914.7661.13404
5317.117.5437-0.443705
5416.18.370127.72988
5519.918.51621.38375
5610.9510.8570.0930489
5718.4517.14021.30982
5815.112.75672.34332
591515.1893-0.189254
6011.3514.4827-3.13265
6115.9514.97140.978577
6218.115.58852.51147
6314.616.3439-1.74389
6415.416.6961-1.29608
6515.416.6961-1.29608
6617.615.38672.21335
6713.3513.8782-0.528197
6819.116.92592.1741
6915.3516.5472-1.19724
707.69.90249-2.30249
7113.415.3524-1.95239
7213.915.6447-1.74465
7319.117.17731.92266
7415.2515.413-0.163026
7512.915.9736-3.07362
7616.115.80120.298846
7717.3514.60442.74557
7813.1515.3444-2.19445
7912.1513.6045-1.45449
8012.612.29470.305267
8110.3512.298-1.94797
8215.414.97110.428919
839.612.1095-2.50954
8418.214.90313.29692
8513.613.26440.335559
8614.8513.43131.41867
8714.7517.3174-2.56744
8814.114.1208-0.0208262
8914.912.82282.07721
9016.2515.12611.12386
9119.2519.3869-0.136899
9213.612.571.02998
9313.615.5214-1.92136
9415.6515.9796-0.329643
9512.7513.5868-0.836826
9614.612.13042.4696
979.8510.0908-0.240759
9812.6511.75070.899346
9911.912.2486-0.348625
10019.217.12692.0731
10116.615.22351.37646
10211.211.2589-0.0589316
10315.2515.9098-0.659805
10411.914.4859-2.58593
10513.214.2786-1.07862
10616.3517.7388-1.38881
10712.412.69-0.289961
10815.8514.21481.63523
10914.3515.3318-0.981817
11018.1516.97461.17544
11111.1512.1946-1.04461
11215.6516.6332-0.983179
11317.7515.80121.94875
1147.6511.9068-4.25682
11512.3513.4763-1.1263
11615.613.75891.84114
11719.317.54651.75346
11815.212.19293.00705
11917.114.88172.21826
12015.613.55042.04961
12118.415.96322.43677
12219.0516.33552.71454
12318.5515.24323.30684
12419.117.52941.57059
12513.112.92740.172586
12612.8515.7757-2.92573
1279.511.7486-2.24857
1284.510.3487-5.84872
12911.8511.03860.811395
13013.615.493-1.89297
13111.711.14510.554879
13212.412.9074-0.507419
13313.3514.7593-1.40927
13411.412.8575-1.45752
13514.913.86221.03781
13619.918.51621.38375
13717.7514.33823.41179
13811.213.1253-1.92535
13914.616.1649-1.56492
14017.618.0439-0.443914
14114.0513.72610.323922
14216.116.2618-0.161812
14313.3514.3969-1.04688
14411.8514.2987-2.44872
14511.9513.5183-1.56829
14614.7514.9945-0.244527
14715.1514.30550.844504
14813.216.68-3.48004
14916.8516.4430.40698
1507.8511.9237-4.07365
1517.713.0852-5.38517
15212.614.3519-1.75194
1537.8514.015-6.16503
15410.9510.8570.0930489
15512.3514.1584-1.80837
1569.9513.1931-3.24314
15714.913.86221.03781
15816.6515.13081.51921
15913.413.06850.331501
16013.9513.61690.333138
16115.713.8641.83598
16216.8515.10691.74308
16310.9511.9546-1.00455
16415.3514.14791.20214
16512.212.5377-0.337656
16615.114.26030.839691
16717.7516.28781.46223
16815.214.50990.690074
16914.614.02360.576396
17016.6515.97770.672321
1718.110.6155-2.5155

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 9.48464 & -5.13464 \tabularnewline
2 & 12.7 & 11.1855 & 1.5145 \tabularnewline
3 & 18.1 & 15.7107 & 2.38933 \tabularnewline
4 & 17.85 & 16.4983 & 1.35173 \tabularnewline
5 & 16.6 & 17.628 & -1.02805 \tabularnewline
6 & 12.6 & 11.2578 & 1.34222 \tabularnewline
7 & 17.1 & 20.0817 & -2.98167 \tabularnewline
8 & 19.1 & 17.1887 & 1.91131 \tabularnewline
9 & 16.1 & 19.3443 & -3.24427 \tabularnewline
10 & 13.35 & 10.9413 & 2.40873 \tabularnewline
11 & 18.4 & 16.9881 & 1.41186 \tabularnewline
12 & 14.7 & 9.94612 & 4.75388 \tabularnewline
13 & 10.6 & 13.558 & -2.95803 \tabularnewline
14 & 12.6 & 13.6195 & -1.0195 \tabularnewline
15 & 16.2 & 15.2951 & 0.904921 \tabularnewline
16 & 13.6 & 13.0805 & 0.519464 \tabularnewline
17 & 18.9 & 16.4509 & 2.44912 \tabularnewline
18 & 14.1 & 12.7507 & 1.34933 \tabularnewline
19 & 14.5 & 13.6672 & 0.832828 \tabularnewline
20 & 16.15 & 17.9597 & -1.80973 \tabularnewline
21 & 14.75 & 13.0336 & 1.71638 \tabularnewline
22 & 14.8 & 13.242 & 1.55803 \tabularnewline
23 & 12.45 & 11.6726 & 0.777396 \tabularnewline
24 & 12.65 & 12.9346 & -0.284617 \tabularnewline
25 & 17.35 & 14.4533 & 2.89673 \tabularnewline
26 & 8.6 & 9.8676 & -1.2676 \tabularnewline
27 & 18.4 & 17.2248 & 1.17521 \tabularnewline
28 & 16.1 & 15.2554 & 0.844552 \tabularnewline
29 & 11.6 & 12.0931 & -0.493113 \tabularnewline
30 & 17.75 & 14.8941 & 2.85594 \tabularnewline
31 & 15.25 & 14.7561 & 0.493938 \tabularnewline
32 & 17.65 & 15.2758 & 2.37416 \tabularnewline
33 & 15.6 & 14.6331 & 0.966894 \tabularnewline
34 & 16.35 & 16.301 & 0.0490002 \tabularnewline
35 & 17.65 & 16.7085 & 0.94146 \tabularnewline
36 & 13.6 & 13.8675 & -0.267521 \tabularnewline
37 & 11.7 & 14.306 & -2.606 \tabularnewline
38 & 14.35 & 13.8404 & 0.509558 \tabularnewline
39 & 14.75 & 15.8953 & -1.1453 \tabularnewline
40 & 18.25 & 17.2155 & 1.03445 \tabularnewline
41 & 9.9 & 16.782 & -6.88202 \tabularnewline
42 & 16 & 14.6634 & 1.33663 \tabularnewline
43 & 18.25 & 16.4883 & 1.76175 \tabularnewline
44 & 16.85 & 17.844 & -0.99396 \tabularnewline
45 & 14.6 & 12.4883 & 2.11174 \tabularnewline
46 & 13.85 & 14.7443 & -0.89433 \tabularnewline
47 & 18.95 & 18.1732 & 0.776811 \tabularnewline
48 & 15.6 & 14.6188 & 0.9812 \tabularnewline
49 & 14.85 & 17.3313 & -2.48131 \tabularnewline
50 & 11.75 & 13.6228 & -1.8728 \tabularnewline
51 & 18.45 & 16.5152 & 1.93476 \tabularnewline
52 & 15.9 & 14.766 & 1.13404 \tabularnewline
53 & 17.1 & 17.5437 & -0.443705 \tabularnewline
54 & 16.1 & 8.37012 & 7.72988 \tabularnewline
55 & 19.9 & 18.5162 & 1.38375 \tabularnewline
56 & 10.95 & 10.857 & 0.0930489 \tabularnewline
57 & 18.45 & 17.1402 & 1.30982 \tabularnewline
58 & 15.1 & 12.7567 & 2.34332 \tabularnewline
59 & 15 & 15.1893 & -0.189254 \tabularnewline
60 & 11.35 & 14.4827 & -3.13265 \tabularnewline
61 & 15.95 & 14.9714 & 0.978577 \tabularnewline
62 & 18.1 & 15.5885 & 2.51147 \tabularnewline
63 & 14.6 & 16.3439 & -1.74389 \tabularnewline
64 & 15.4 & 16.6961 & -1.29608 \tabularnewline
65 & 15.4 & 16.6961 & -1.29608 \tabularnewline
66 & 17.6 & 15.3867 & 2.21335 \tabularnewline
67 & 13.35 & 13.8782 & -0.528197 \tabularnewline
68 & 19.1 & 16.9259 & 2.1741 \tabularnewline
69 & 15.35 & 16.5472 & -1.19724 \tabularnewline
70 & 7.6 & 9.90249 & -2.30249 \tabularnewline
71 & 13.4 & 15.3524 & -1.95239 \tabularnewline
72 & 13.9 & 15.6447 & -1.74465 \tabularnewline
73 & 19.1 & 17.1773 & 1.92266 \tabularnewline
74 & 15.25 & 15.413 & -0.163026 \tabularnewline
75 & 12.9 & 15.9736 & -3.07362 \tabularnewline
76 & 16.1 & 15.8012 & 0.298846 \tabularnewline
77 & 17.35 & 14.6044 & 2.74557 \tabularnewline
78 & 13.15 & 15.3444 & -2.19445 \tabularnewline
79 & 12.15 & 13.6045 & -1.45449 \tabularnewline
80 & 12.6 & 12.2947 & 0.305267 \tabularnewline
81 & 10.35 & 12.298 & -1.94797 \tabularnewline
82 & 15.4 & 14.9711 & 0.428919 \tabularnewline
83 & 9.6 & 12.1095 & -2.50954 \tabularnewline
84 & 18.2 & 14.9031 & 3.29692 \tabularnewline
85 & 13.6 & 13.2644 & 0.335559 \tabularnewline
86 & 14.85 & 13.4313 & 1.41867 \tabularnewline
87 & 14.75 & 17.3174 & -2.56744 \tabularnewline
88 & 14.1 & 14.1208 & -0.0208262 \tabularnewline
89 & 14.9 & 12.8228 & 2.07721 \tabularnewline
90 & 16.25 & 15.1261 & 1.12386 \tabularnewline
91 & 19.25 & 19.3869 & -0.136899 \tabularnewline
92 & 13.6 & 12.57 & 1.02998 \tabularnewline
93 & 13.6 & 15.5214 & -1.92136 \tabularnewline
94 & 15.65 & 15.9796 & -0.329643 \tabularnewline
95 & 12.75 & 13.5868 & -0.836826 \tabularnewline
96 & 14.6 & 12.1304 & 2.4696 \tabularnewline
97 & 9.85 & 10.0908 & -0.240759 \tabularnewline
98 & 12.65 & 11.7507 & 0.899346 \tabularnewline
99 & 11.9 & 12.2486 & -0.348625 \tabularnewline
100 & 19.2 & 17.1269 & 2.0731 \tabularnewline
101 & 16.6 & 15.2235 & 1.37646 \tabularnewline
102 & 11.2 & 11.2589 & -0.0589316 \tabularnewline
103 & 15.25 & 15.9098 & -0.659805 \tabularnewline
104 & 11.9 & 14.4859 & -2.58593 \tabularnewline
105 & 13.2 & 14.2786 & -1.07862 \tabularnewline
106 & 16.35 & 17.7388 & -1.38881 \tabularnewline
107 & 12.4 & 12.69 & -0.289961 \tabularnewline
108 & 15.85 & 14.2148 & 1.63523 \tabularnewline
109 & 14.35 & 15.3318 & -0.981817 \tabularnewline
110 & 18.15 & 16.9746 & 1.17544 \tabularnewline
111 & 11.15 & 12.1946 & -1.04461 \tabularnewline
112 & 15.65 & 16.6332 & -0.983179 \tabularnewline
113 & 17.75 & 15.8012 & 1.94875 \tabularnewline
114 & 7.65 & 11.9068 & -4.25682 \tabularnewline
115 & 12.35 & 13.4763 & -1.1263 \tabularnewline
116 & 15.6 & 13.7589 & 1.84114 \tabularnewline
117 & 19.3 & 17.5465 & 1.75346 \tabularnewline
118 & 15.2 & 12.1929 & 3.00705 \tabularnewline
119 & 17.1 & 14.8817 & 2.21826 \tabularnewline
120 & 15.6 & 13.5504 & 2.04961 \tabularnewline
121 & 18.4 & 15.9632 & 2.43677 \tabularnewline
122 & 19.05 & 16.3355 & 2.71454 \tabularnewline
123 & 18.55 & 15.2432 & 3.30684 \tabularnewline
124 & 19.1 & 17.5294 & 1.57059 \tabularnewline
125 & 13.1 & 12.9274 & 0.172586 \tabularnewline
126 & 12.85 & 15.7757 & -2.92573 \tabularnewline
127 & 9.5 & 11.7486 & -2.24857 \tabularnewline
128 & 4.5 & 10.3487 & -5.84872 \tabularnewline
129 & 11.85 & 11.0386 & 0.811395 \tabularnewline
130 & 13.6 & 15.493 & -1.89297 \tabularnewline
131 & 11.7 & 11.1451 & 0.554879 \tabularnewline
132 & 12.4 & 12.9074 & -0.507419 \tabularnewline
133 & 13.35 & 14.7593 & -1.40927 \tabularnewline
134 & 11.4 & 12.8575 & -1.45752 \tabularnewline
135 & 14.9 & 13.8622 & 1.03781 \tabularnewline
136 & 19.9 & 18.5162 & 1.38375 \tabularnewline
137 & 17.75 & 14.3382 & 3.41179 \tabularnewline
138 & 11.2 & 13.1253 & -1.92535 \tabularnewline
139 & 14.6 & 16.1649 & -1.56492 \tabularnewline
140 & 17.6 & 18.0439 & -0.443914 \tabularnewline
141 & 14.05 & 13.7261 & 0.323922 \tabularnewline
142 & 16.1 & 16.2618 & -0.161812 \tabularnewline
143 & 13.35 & 14.3969 & -1.04688 \tabularnewline
144 & 11.85 & 14.2987 & -2.44872 \tabularnewline
145 & 11.95 & 13.5183 & -1.56829 \tabularnewline
146 & 14.75 & 14.9945 & -0.244527 \tabularnewline
147 & 15.15 & 14.3055 & 0.844504 \tabularnewline
148 & 13.2 & 16.68 & -3.48004 \tabularnewline
149 & 16.85 & 16.443 & 0.40698 \tabularnewline
150 & 7.85 & 11.9237 & -4.07365 \tabularnewline
151 & 7.7 & 13.0852 & -5.38517 \tabularnewline
152 & 12.6 & 14.3519 & -1.75194 \tabularnewline
153 & 7.85 & 14.015 & -6.16503 \tabularnewline
154 & 10.95 & 10.857 & 0.0930489 \tabularnewline
155 & 12.35 & 14.1584 & -1.80837 \tabularnewline
156 & 9.95 & 13.1931 & -3.24314 \tabularnewline
157 & 14.9 & 13.8622 & 1.03781 \tabularnewline
158 & 16.65 & 15.1308 & 1.51921 \tabularnewline
159 & 13.4 & 13.0685 & 0.331501 \tabularnewline
160 & 13.95 & 13.6169 & 0.333138 \tabularnewline
161 & 15.7 & 13.864 & 1.83598 \tabularnewline
162 & 16.85 & 15.1069 & 1.74308 \tabularnewline
163 & 10.95 & 11.9546 & -1.00455 \tabularnewline
164 & 15.35 & 14.1479 & 1.20214 \tabularnewline
165 & 12.2 & 12.5377 & -0.337656 \tabularnewline
166 & 15.1 & 14.2603 & 0.839691 \tabularnewline
167 & 17.75 & 16.2878 & 1.46223 \tabularnewline
168 & 15.2 & 14.5099 & 0.690074 \tabularnewline
169 & 14.6 & 14.0236 & 0.576396 \tabularnewline
170 & 16.65 & 15.9777 & 0.672321 \tabularnewline
171 & 8.1 & 10.6155 & -2.5155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4.35[/C][C]9.48464[/C][C]-5.13464[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.1855[/C][C]1.5145[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.7107[/C][C]2.38933[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.4983[/C][C]1.35173[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.628[/C][C]-1.02805[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.2578[/C][C]1.34222[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]20.0817[/C][C]-2.98167[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.1887[/C][C]1.91131[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]19.3443[/C][C]-3.24427[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.9413[/C][C]2.40873[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.9881[/C][C]1.41186[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.94612[/C][C]4.75388[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.558[/C][C]-2.95803[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.6195[/C][C]-1.0195[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.2951[/C][C]0.904921[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.0805[/C][C]0.519464[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.4509[/C][C]2.44912[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.7507[/C][C]1.34933[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.6672[/C][C]0.832828[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.9597[/C][C]-1.80973[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.0336[/C][C]1.71638[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.242[/C][C]1.55803[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.6726[/C][C]0.777396[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9346[/C][C]-0.284617[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.4533[/C][C]2.89673[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.8676[/C][C]-1.2676[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.2248[/C][C]1.17521[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.2554[/C][C]0.844552[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.0931[/C][C]-0.493113[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]14.8941[/C][C]2.85594[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.7561[/C][C]0.493938[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.2758[/C][C]2.37416[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.6331[/C][C]0.966894[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.301[/C][C]0.0490002[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]16.7085[/C][C]0.94146[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.8675[/C][C]-0.267521[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.306[/C][C]-2.606[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]13.8404[/C][C]0.509558[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.8953[/C][C]-1.1453[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]17.2155[/C][C]1.03445[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.782[/C][C]-6.88202[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.6634[/C][C]1.33663[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.4883[/C][C]1.76175[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.844[/C][C]-0.99396[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.4883[/C][C]2.11174[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.7443[/C][C]-0.89433[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.1732[/C][C]0.776811[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.6188[/C][C]0.9812[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]17.3313[/C][C]-2.48131[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.6228[/C][C]-1.8728[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]16.5152[/C][C]1.93476[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]14.766[/C][C]1.13404[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.5437[/C][C]-0.443705[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.37012[/C][C]7.72988[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]18.5162[/C][C]1.38375[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.857[/C][C]0.0930489[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.1402[/C][C]1.30982[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]12.7567[/C][C]2.34332[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1893[/C][C]-0.189254[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]14.4827[/C][C]-3.13265[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.9714[/C][C]0.978577[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.5885[/C][C]2.51147[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]16.3439[/C][C]-1.74389[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.6961[/C][C]-1.29608[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.6961[/C][C]-1.29608[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.3867[/C][C]2.21335[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]13.8782[/C][C]-0.528197[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.9259[/C][C]2.1741[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.5472[/C][C]-1.19724[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.90249[/C][C]-2.30249[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.3524[/C][C]-1.95239[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.6447[/C][C]-1.74465[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.1773[/C][C]1.92266[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]15.413[/C][C]-0.163026[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]15.9736[/C][C]-3.07362[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.8012[/C][C]0.298846[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.6044[/C][C]2.74557[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.3444[/C][C]-2.19445[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.6045[/C][C]-1.45449[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.2947[/C][C]0.305267[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]12.298[/C][C]-1.94797[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]14.9711[/C][C]0.428919[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]12.1095[/C][C]-2.50954[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.9031[/C][C]3.29692[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.2644[/C][C]0.335559[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.4313[/C][C]1.41867[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.3174[/C][C]-2.56744[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]14.1208[/C][C]-0.0208262[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.8228[/C][C]2.07721[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]15.1261[/C][C]1.12386[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.3869[/C][C]-0.136899[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.57[/C][C]1.02998[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.5214[/C][C]-1.92136[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]15.9796[/C][C]-0.329643[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.5868[/C][C]-0.836826[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]12.1304[/C][C]2.4696[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]10.0908[/C][C]-0.240759[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.7507[/C][C]0.899346[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.2486[/C][C]-0.348625[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.1269[/C][C]2.0731[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.2235[/C][C]1.37646[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.2589[/C][C]-0.0589316[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.9098[/C][C]-0.659805[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.4859[/C][C]-2.58593[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]14.2786[/C][C]-1.07862[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.7388[/C][C]-1.38881[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]12.69[/C][C]-0.289961[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]14.2148[/C][C]1.63523[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.3318[/C][C]-0.981817[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]16.9746[/C][C]1.17544[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]12.1946[/C][C]-1.04461[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.6332[/C][C]-0.983179[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.8012[/C][C]1.94875[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]11.9068[/C][C]-4.25682[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.4763[/C][C]-1.1263[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.7589[/C][C]1.84114[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.5465[/C][C]1.75346[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]12.1929[/C][C]3.00705[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.8817[/C][C]2.21826[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.5504[/C][C]2.04961[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.9632[/C][C]2.43677[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.3355[/C][C]2.71454[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.2432[/C][C]3.30684[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.5294[/C][C]1.57059[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]12.9274[/C][C]0.172586[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]15.7757[/C][C]-2.92573[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.7486[/C][C]-2.24857[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.3487[/C][C]-5.84872[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]11.0386[/C][C]0.811395[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.493[/C][C]-1.89297[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.1451[/C][C]0.554879[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.9074[/C][C]-0.507419[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]14.7593[/C][C]-1.40927[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.8575[/C][C]-1.45752[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.8622[/C][C]1.03781[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]18.5162[/C][C]1.38375[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.3382[/C][C]3.41179[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]13.1253[/C][C]-1.92535[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]16.1649[/C][C]-1.56492[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]18.0439[/C][C]-0.443914[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.7261[/C][C]0.323922[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]16.2618[/C][C]-0.161812[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.3969[/C][C]-1.04688[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.2987[/C][C]-2.44872[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]13.5183[/C][C]-1.56829[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]14.9945[/C][C]-0.244527[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.3055[/C][C]0.844504[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.68[/C][C]-3.48004[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.443[/C][C]0.40698[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]11.9237[/C][C]-4.07365[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]13.0852[/C][C]-5.38517[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]14.3519[/C][C]-1.75194[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]14.015[/C][C]-6.16503[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.857[/C][C]0.0930489[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]14.1584[/C][C]-1.80837[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.1931[/C][C]-3.24314[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.8622[/C][C]1.03781[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]15.1308[/C][C]1.51921[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]13.0685[/C][C]0.331501[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.6169[/C][C]0.333138[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.864[/C][C]1.83598[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.1069[/C][C]1.74308[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]11.9546[/C][C]-1.00455[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]14.1479[/C][C]1.20214[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.5377[/C][C]-0.337656[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.2603[/C][C]0.839691[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.2878[/C][C]1.46223[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.5099[/C][C]0.690074[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.0236[/C][C]0.576396[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]15.9777[/C][C]0.672321[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]10.6155[/C][C]-2.5155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.48464-5.13464
212.711.18551.5145
318.115.71072.38933
417.8516.49831.35173
516.617.628-1.02805
612.611.25781.34222
717.120.0817-2.98167
819.117.18871.91131
916.119.3443-3.24427
1013.3510.94132.40873
1118.416.98811.41186
1214.79.946124.75388
1310.613.558-2.95803
1412.613.6195-1.0195
1516.215.29510.904921
1613.613.08050.519464
1718.916.45092.44912
1814.112.75071.34933
1914.513.66720.832828
2016.1517.9597-1.80973
2114.7513.03361.71638
2214.813.2421.55803
2312.4511.67260.777396
2412.6512.9346-0.284617
2517.3514.45332.89673
268.69.8676-1.2676
2718.417.22481.17521
2816.115.25540.844552
2911.612.0931-0.493113
3017.7514.89412.85594
3115.2514.75610.493938
3217.6515.27582.37416
3315.614.63310.966894
3416.3516.3010.0490002
3517.6516.70850.94146
3613.613.8675-0.267521
3711.714.306-2.606
3814.3513.84040.509558
3914.7515.8953-1.1453
4018.2517.21551.03445
419.916.782-6.88202
421614.66341.33663
4318.2516.48831.76175
4416.8517.844-0.99396
4514.612.48832.11174
4613.8514.7443-0.89433
4718.9518.17320.776811
4815.614.61880.9812
4914.8517.3313-2.48131
5011.7513.6228-1.8728
5118.4516.51521.93476
5215.914.7661.13404
5317.117.5437-0.443705
5416.18.370127.72988
5519.918.51621.38375
5610.9510.8570.0930489
5718.4517.14021.30982
5815.112.75672.34332
591515.1893-0.189254
6011.3514.4827-3.13265
6115.9514.97140.978577
6218.115.58852.51147
6314.616.3439-1.74389
6415.416.6961-1.29608
6515.416.6961-1.29608
6617.615.38672.21335
6713.3513.8782-0.528197
6819.116.92592.1741
6915.3516.5472-1.19724
707.69.90249-2.30249
7113.415.3524-1.95239
7213.915.6447-1.74465
7319.117.17731.92266
7415.2515.413-0.163026
7512.915.9736-3.07362
7616.115.80120.298846
7717.3514.60442.74557
7813.1515.3444-2.19445
7912.1513.6045-1.45449
8012.612.29470.305267
8110.3512.298-1.94797
8215.414.97110.428919
839.612.1095-2.50954
8418.214.90313.29692
8513.613.26440.335559
8614.8513.43131.41867
8714.7517.3174-2.56744
8814.114.1208-0.0208262
8914.912.82282.07721
9016.2515.12611.12386
9119.2519.3869-0.136899
9213.612.571.02998
9313.615.5214-1.92136
9415.6515.9796-0.329643
9512.7513.5868-0.836826
9614.612.13042.4696
979.8510.0908-0.240759
9812.6511.75070.899346
9911.912.2486-0.348625
10019.217.12692.0731
10116.615.22351.37646
10211.211.2589-0.0589316
10315.2515.9098-0.659805
10411.914.4859-2.58593
10513.214.2786-1.07862
10616.3517.7388-1.38881
10712.412.69-0.289961
10815.8514.21481.63523
10914.3515.3318-0.981817
11018.1516.97461.17544
11111.1512.1946-1.04461
11215.6516.6332-0.983179
11317.7515.80121.94875
1147.6511.9068-4.25682
11512.3513.4763-1.1263
11615.613.75891.84114
11719.317.54651.75346
11815.212.19293.00705
11917.114.88172.21826
12015.613.55042.04961
12118.415.96322.43677
12219.0516.33552.71454
12318.5515.24323.30684
12419.117.52941.57059
12513.112.92740.172586
12612.8515.7757-2.92573
1279.511.7486-2.24857
1284.510.3487-5.84872
12911.8511.03860.811395
13013.615.493-1.89297
13111.711.14510.554879
13212.412.9074-0.507419
13313.3514.7593-1.40927
13411.412.8575-1.45752
13514.913.86221.03781
13619.918.51621.38375
13717.7514.33823.41179
13811.213.1253-1.92535
13914.616.1649-1.56492
14017.618.0439-0.443914
14114.0513.72610.323922
14216.116.2618-0.161812
14313.3514.3969-1.04688
14411.8514.2987-2.44872
14511.9513.5183-1.56829
14614.7514.9945-0.244527
14715.1514.30550.844504
14813.216.68-3.48004
14916.8516.4430.40698
1507.8511.9237-4.07365
1517.713.0852-5.38517
15212.614.3519-1.75194
1537.8514.015-6.16503
15410.9510.8570.0930489
15512.3514.1584-1.80837
1569.9513.1931-3.24314
15714.913.86221.03781
15816.6515.13081.51921
15913.413.06850.331501
16013.9513.61690.333138
16115.713.8641.83598
16216.8515.10691.74308
16310.9511.9546-1.00455
16415.3514.14791.20214
16512.212.5377-0.337656
16615.114.26030.839691
16717.7516.28781.46223
16815.214.50990.690074
16914.614.02360.576396
17016.6515.97770.672321
1718.110.6155-2.5155






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.9321090.1357820.067891
140.9401660.1196680.0598341
150.898770.202460.10123
160.9131970.1736060.0868028
170.9519620.0960770.0480385
180.9228370.1543270.0771635
190.8834180.2331650.116582
200.8757830.2484340.124217
210.8429980.3140030.157002
220.812880.374240.18712
230.7564670.4870660.243533
240.6900920.6198150.309908
250.7012410.5975190.298759
260.6766090.6467830.323391
270.6100860.7798280.389914
280.5530670.8938660.446933
290.4827560.9655120.517244
300.4579990.9159970.542001
310.4397040.8794080.560296
320.434050.8680990.56595
330.3990090.7980180.600991
340.3389150.677830.661085
350.2853210.5706410.714679
360.2368820.4737640.763118
370.4134960.8269920.586504
380.3566120.7132250.643388
390.3098210.6196420.690179
400.2625590.5251180.737441
410.8052090.3895830.194791
420.7695380.4609240.230462
430.7421250.515750.257875
440.6988880.6022240.301112
450.6661610.6676790.333839
460.6183490.7633020.381651
470.5688140.8623720.431186
480.522970.9540590.47703
490.516930.966140.48307
500.5120790.9758420.487921
510.4903110.9806220.509689
520.4615650.923130.538435
530.4331160.8662310.566884
540.8595440.2809120.140456
550.8420650.3158710.157935
560.8385660.3228680.161434
570.8169340.3661320.183066
580.8092520.3814950.190748
590.7771670.4456660.222833
600.8203080.3593840.179692
610.794120.4117610.20588
620.8231740.3536520.176826
630.8496560.3006870.150344
640.8288260.3423480.171174
650.8050380.3899240.194962
660.8171460.3657090.182854
670.8030030.3939950.196997
680.8180990.3638030.181901
690.7974830.4050330.202517
700.822430.355140.17757
710.8163240.3673520.183676
720.8154480.3691040.184552
730.8091470.3817050.190853
740.7789390.4421220.221061
750.8290350.3419310.170965
760.7989370.4021260.201063
770.8119350.376130.188065
780.8224380.3551240.177562
790.8146590.3706830.185341
800.7856770.4286460.214323
810.7876820.4246350.212318
820.7565030.4869930.243497
830.7685590.4628820.231441
840.825160.3496810.17484
850.7951770.4096460.204823
860.7885460.4229080.211454
870.7984860.4030280.201514
880.7711940.4576110.228806
890.7573190.4853630.242681
900.7280160.5439680.271984
910.6891860.6216270.310814
920.6645960.6708080.335404
930.6621490.6757030.337851
940.6230610.7538780.376939
950.5953530.8092940.404647
960.6216910.7566180.378309
970.6208730.7582550.379127
980.6012260.7975490.398774
990.5604740.8790520.439526
1000.5591860.8816280.440814
1010.5233460.9533070.476654
1020.4925950.9851890.507405
1030.4585780.9171560.541422
1040.4665890.9331790.533411
1050.4365220.8730430.563478
1060.4135660.8271320.586434
1070.3767320.7534640.623268
1080.3600090.7200180.639991
1090.3391480.6782960.660852
1100.3077370.6154730.692263
1110.2794960.5589930.720504
1120.2690870.5381750.730913
1130.2590090.5180170.740991
1140.3701530.7403060.629847
1150.3360240.6720480.663976
1160.3295890.6591780.670411
1170.3045160.6090320.695484
1180.3612830.7225670.638717
1190.3590550.718110.640945
1200.3728230.7456450.627177
1210.4383690.8767370.561631
1220.4630530.9261060.536947
1230.5145620.9708770.485438
1240.4920330.9840660.507967
1250.4592990.9185970.540701
1260.4673950.934790.532605
1270.4416720.8833450.558328
1280.5839830.8320340.416017
1290.555290.889420.44471
1300.5147390.9705230.485261
1310.5647010.8705990.435299
1320.5094420.9811160.490558
1330.4547790.9095590.545221
1340.4008020.8016050.599198
1350.369670.7393410.63033
1360.3173410.6346820.682659
1370.5287890.9424220.471211
1380.4892480.9784960.510752
1390.4661850.932370.533815
1400.4012340.8024670.598766
1410.4459780.8919560.554022
1420.3944750.788950.605525
1430.376540.753080.62346
1440.3365760.6731520.663424
1450.3098670.6197330.690133
1460.2469670.4939350.753033
1470.2092140.4184290.790786
1480.1727940.3455890.827206
1490.134950.26990.86505
1500.144980.2899590.85502
1510.5582280.8835450.441772
1520.4649910.9299830.535009
1530.8934220.2131560.106578
1540.8698020.2603960.130198
1550.9795180.04096430.0204822
1560.9949750.01005040.00502522
1570.9822390.03552250.0177613
1580.9462340.1075310.0537657

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.932109 & 0.135782 & 0.067891 \tabularnewline
14 & 0.940166 & 0.119668 & 0.0598341 \tabularnewline
15 & 0.89877 & 0.20246 & 0.10123 \tabularnewline
16 & 0.913197 & 0.173606 & 0.0868028 \tabularnewline
17 & 0.951962 & 0.096077 & 0.0480385 \tabularnewline
18 & 0.922837 & 0.154327 & 0.0771635 \tabularnewline
19 & 0.883418 & 0.233165 & 0.116582 \tabularnewline
20 & 0.875783 & 0.248434 & 0.124217 \tabularnewline
21 & 0.842998 & 0.314003 & 0.157002 \tabularnewline
22 & 0.81288 & 0.37424 & 0.18712 \tabularnewline
23 & 0.756467 & 0.487066 & 0.243533 \tabularnewline
24 & 0.690092 & 0.619815 & 0.309908 \tabularnewline
25 & 0.701241 & 0.597519 & 0.298759 \tabularnewline
26 & 0.676609 & 0.646783 & 0.323391 \tabularnewline
27 & 0.610086 & 0.779828 & 0.389914 \tabularnewline
28 & 0.553067 & 0.893866 & 0.446933 \tabularnewline
29 & 0.482756 & 0.965512 & 0.517244 \tabularnewline
30 & 0.457999 & 0.915997 & 0.542001 \tabularnewline
31 & 0.439704 & 0.879408 & 0.560296 \tabularnewline
32 & 0.43405 & 0.868099 & 0.56595 \tabularnewline
33 & 0.399009 & 0.798018 & 0.600991 \tabularnewline
34 & 0.338915 & 0.67783 & 0.661085 \tabularnewline
35 & 0.285321 & 0.570641 & 0.714679 \tabularnewline
36 & 0.236882 & 0.473764 & 0.763118 \tabularnewline
37 & 0.413496 & 0.826992 & 0.586504 \tabularnewline
38 & 0.356612 & 0.713225 & 0.643388 \tabularnewline
39 & 0.309821 & 0.619642 & 0.690179 \tabularnewline
40 & 0.262559 & 0.525118 & 0.737441 \tabularnewline
41 & 0.805209 & 0.389583 & 0.194791 \tabularnewline
42 & 0.769538 & 0.460924 & 0.230462 \tabularnewline
43 & 0.742125 & 0.51575 & 0.257875 \tabularnewline
44 & 0.698888 & 0.602224 & 0.301112 \tabularnewline
45 & 0.666161 & 0.667679 & 0.333839 \tabularnewline
46 & 0.618349 & 0.763302 & 0.381651 \tabularnewline
47 & 0.568814 & 0.862372 & 0.431186 \tabularnewline
48 & 0.52297 & 0.954059 & 0.47703 \tabularnewline
49 & 0.51693 & 0.96614 & 0.48307 \tabularnewline
50 & 0.512079 & 0.975842 & 0.487921 \tabularnewline
51 & 0.490311 & 0.980622 & 0.509689 \tabularnewline
52 & 0.461565 & 0.92313 & 0.538435 \tabularnewline
53 & 0.433116 & 0.866231 & 0.566884 \tabularnewline
54 & 0.859544 & 0.280912 & 0.140456 \tabularnewline
55 & 0.842065 & 0.315871 & 0.157935 \tabularnewline
56 & 0.838566 & 0.322868 & 0.161434 \tabularnewline
57 & 0.816934 & 0.366132 & 0.183066 \tabularnewline
58 & 0.809252 & 0.381495 & 0.190748 \tabularnewline
59 & 0.777167 & 0.445666 & 0.222833 \tabularnewline
60 & 0.820308 & 0.359384 & 0.179692 \tabularnewline
61 & 0.79412 & 0.411761 & 0.20588 \tabularnewline
62 & 0.823174 & 0.353652 & 0.176826 \tabularnewline
63 & 0.849656 & 0.300687 & 0.150344 \tabularnewline
64 & 0.828826 & 0.342348 & 0.171174 \tabularnewline
65 & 0.805038 & 0.389924 & 0.194962 \tabularnewline
66 & 0.817146 & 0.365709 & 0.182854 \tabularnewline
67 & 0.803003 & 0.393995 & 0.196997 \tabularnewline
68 & 0.818099 & 0.363803 & 0.181901 \tabularnewline
69 & 0.797483 & 0.405033 & 0.202517 \tabularnewline
70 & 0.82243 & 0.35514 & 0.17757 \tabularnewline
71 & 0.816324 & 0.367352 & 0.183676 \tabularnewline
72 & 0.815448 & 0.369104 & 0.184552 \tabularnewline
73 & 0.809147 & 0.381705 & 0.190853 \tabularnewline
74 & 0.778939 & 0.442122 & 0.221061 \tabularnewline
75 & 0.829035 & 0.341931 & 0.170965 \tabularnewline
76 & 0.798937 & 0.402126 & 0.201063 \tabularnewline
77 & 0.811935 & 0.37613 & 0.188065 \tabularnewline
78 & 0.822438 & 0.355124 & 0.177562 \tabularnewline
79 & 0.814659 & 0.370683 & 0.185341 \tabularnewline
80 & 0.785677 & 0.428646 & 0.214323 \tabularnewline
81 & 0.787682 & 0.424635 & 0.212318 \tabularnewline
82 & 0.756503 & 0.486993 & 0.243497 \tabularnewline
83 & 0.768559 & 0.462882 & 0.231441 \tabularnewline
84 & 0.82516 & 0.349681 & 0.17484 \tabularnewline
85 & 0.795177 & 0.409646 & 0.204823 \tabularnewline
86 & 0.788546 & 0.422908 & 0.211454 \tabularnewline
87 & 0.798486 & 0.403028 & 0.201514 \tabularnewline
88 & 0.771194 & 0.457611 & 0.228806 \tabularnewline
89 & 0.757319 & 0.485363 & 0.242681 \tabularnewline
90 & 0.728016 & 0.543968 & 0.271984 \tabularnewline
91 & 0.689186 & 0.621627 & 0.310814 \tabularnewline
92 & 0.664596 & 0.670808 & 0.335404 \tabularnewline
93 & 0.662149 & 0.675703 & 0.337851 \tabularnewline
94 & 0.623061 & 0.753878 & 0.376939 \tabularnewline
95 & 0.595353 & 0.809294 & 0.404647 \tabularnewline
96 & 0.621691 & 0.756618 & 0.378309 \tabularnewline
97 & 0.620873 & 0.758255 & 0.379127 \tabularnewline
98 & 0.601226 & 0.797549 & 0.398774 \tabularnewline
99 & 0.560474 & 0.879052 & 0.439526 \tabularnewline
100 & 0.559186 & 0.881628 & 0.440814 \tabularnewline
101 & 0.523346 & 0.953307 & 0.476654 \tabularnewline
102 & 0.492595 & 0.985189 & 0.507405 \tabularnewline
103 & 0.458578 & 0.917156 & 0.541422 \tabularnewline
104 & 0.466589 & 0.933179 & 0.533411 \tabularnewline
105 & 0.436522 & 0.873043 & 0.563478 \tabularnewline
106 & 0.413566 & 0.827132 & 0.586434 \tabularnewline
107 & 0.376732 & 0.753464 & 0.623268 \tabularnewline
108 & 0.360009 & 0.720018 & 0.639991 \tabularnewline
109 & 0.339148 & 0.678296 & 0.660852 \tabularnewline
110 & 0.307737 & 0.615473 & 0.692263 \tabularnewline
111 & 0.279496 & 0.558993 & 0.720504 \tabularnewline
112 & 0.269087 & 0.538175 & 0.730913 \tabularnewline
113 & 0.259009 & 0.518017 & 0.740991 \tabularnewline
114 & 0.370153 & 0.740306 & 0.629847 \tabularnewline
115 & 0.336024 & 0.672048 & 0.663976 \tabularnewline
116 & 0.329589 & 0.659178 & 0.670411 \tabularnewline
117 & 0.304516 & 0.609032 & 0.695484 \tabularnewline
118 & 0.361283 & 0.722567 & 0.638717 \tabularnewline
119 & 0.359055 & 0.71811 & 0.640945 \tabularnewline
120 & 0.372823 & 0.745645 & 0.627177 \tabularnewline
121 & 0.438369 & 0.876737 & 0.561631 \tabularnewline
122 & 0.463053 & 0.926106 & 0.536947 \tabularnewline
123 & 0.514562 & 0.970877 & 0.485438 \tabularnewline
124 & 0.492033 & 0.984066 & 0.507967 \tabularnewline
125 & 0.459299 & 0.918597 & 0.540701 \tabularnewline
126 & 0.467395 & 0.93479 & 0.532605 \tabularnewline
127 & 0.441672 & 0.883345 & 0.558328 \tabularnewline
128 & 0.583983 & 0.832034 & 0.416017 \tabularnewline
129 & 0.55529 & 0.88942 & 0.44471 \tabularnewline
130 & 0.514739 & 0.970523 & 0.485261 \tabularnewline
131 & 0.564701 & 0.870599 & 0.435299 \tabularnewline
132 & 0.509442 & 0.981116 & 0.490558 \tabularnewline
133 & 0.454779 & 0.909559 & 0.545221 \tabularnewline
134 & 0.400802 & 0.801605 & 0.599198 \tabularnewline
135 & 0.36967 & 0.739341 & 0.63033 \tabularnewline
136 & 0.317341 & 0.634682 & 0.682659 \tabularnewline
137 & 0.528789 & 0.942422 & 0.471211 \tabularnewline
138 & 0.489248 & 0.978496 & 0.510752 \tabularnewline
139 & 0.466185 & 0.93237 & 0.533815 \tabularnewline
140 & 0.401234 & 0.802467 & 0.598766 \tabularnewline
141 & 0.445978 & 0.891956 & 0.554022 \tabularnewline
142 & 0.394475 & 0.78895 & 0.605525 \tabularnewline
143 & 0.37654 & 0.75308 & 0.62346 \tabularnewline
144 & 0.336576 & 0.673152 & 0.663424 \tabularnewline
145 & 0.309867 & 0.619733 & 0.690133 \tabularnewline
146 & 0.246967 & 0.493935 & 0.753033 \tabularnewline
147 & 0.209214 & 0.418429 & 0.790786 \tabularnewline
148 & 0.172794 & 0.345589 & 0.827206 \tabularnewline
149 & 0.13495 & 0.2699 & 0.86505 \tabularnewline
150 & 0.14498 & 0.289959 & 0.85502 \tabularnewline
151 & 0.558228 & 0.883545 & 0.441772 \tabularnewline
152 & 0.464991 & 0.929983 & 0.535009 \tabularnewline
153 & 0.893422 & 0.213156 & 0.106578 \tabularnewline
154 & 0.869802 & 0.260396 & 0.130198 \tabularnewline
155 & 0.979518 & 0.0409643 & 0.0204822 \tabularnewline
156 & 0.994975 & 0.0100504 & 0.00502522 \tabularnewline
157 & 0.982239 & 0.0355225 & 0.0177613 \tabularnewline
158 & 0.946234 & 0.107531 & 0.0537657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]13[/C][C]0.932109[/C][C]0.135782[/C][C]0.067891[/C][/ROW]
[ROW][C]14[/C][C]0.940166[/C][C]0.119668[/C][C]0.0598341[/C][/ROW]
[ROW][C]15[/C][C]0.89877[/C][C]0.20246[/C][C]0.10123[/C][/ROW]
[ROW][C]16[/C][C]0.913197[/C][C]0.173606[/C][C]0.0868028[/C][/ROW]
[ROW][C]17[/C][C]0.951962[/C][C]0.096077[/C][C]0.0480385[/C][/ROW]
[ROW][C]18[/C][C]0.922837[/C][C]0.154327[/C][C]0.0771635[/C][/ROW]
[ROW][C]19[/C][C]0.883418[/C][C]0.233165[/C][C]0.116582[/C][/ROW]
[ROW][C]20[/C][C]0.875783[/C][C]0.248434[/C][C]0.124217[/C][/ROW]
[ROW][C]21[/C][C]0.842998[/C][C]0.314003[/C][C]0.157002[/C][/ROW]
[ROW][C]22[/C][C]0.81288[/C][C]0.37424[/C][C]0.18712[/C][/ROW]
[ROW][C]23[/C][C]0.756467[/C][C]0.487066[/C][C]0.243533[/C][/ROW]
[ROW][C]24[/C][C]0.690092[/C][C]0.619815[/C][C]0.309908[/C][/ROW]
[ROW][C]25[/C][C]0.701241[/C][C]0.597519[/C][C]0.298759[/C][/ROW]
[ROW][C]26[/C][C]0.676609[/C][C]0.646783[/C][C]0.323391[/C][/ROW]
[ROW][C]27[/C][C]0.610086[/C][C]0.779828[/C][C]0.389914[/C][/ROW]
[ROW][C]28[/C][C]0.553067[/C][C]0.893866[/C][C]0.446933[/C][/ROW]
[ROW][C]29[/C][C]0.482756[/C][C]0.965512[/C][C]0.517244[/C][/ROW]
[ROW][C]30[/C][C]0.457999[/C][C]0.915997[/C][C]0.542001[/C][/ROW]
[ROW][C]31[/C][C]0.439704[/C][C]0.879408[/C][C]0.560296[/C][/ROW]
[ROW][C]32[/C][C]0.43405[/C][C]0.868099[/C][C]0.56595[/C][/ROW]
[ROW][C]33[/C][C]0.399009[/C][C]0.798018[/C][C]0.600991[/C][/ROW]
[ROW][C]34[/C][C]0.338915[/C][C]0.67783[/C][C]0.661085[/C][/ROW]
[ROW][C]35[/C][C]0.285321[/C][C]0.570641[/C][C]0.714679[/C][/ROW]
[ROW][C]36[/C][C]0.236882[/C][C]0.473764[/C][C]0.763118[/C][/ROW]
[ROW][C]37[/C][C]0.413496[/C][C]0.826992[/C][C]0.586504[/C][/ROW]
[ROW][C]38[/C][C]0.356612[/C][C]0.713225[/C][C]0.643388[/C][/ROW]
[ROW][C]39[/C][C]0.309821[/C][C]0.619642[/C][C]0.690179[/C][/ROW]
[ROW][C]40[/C][C]0.262559[/C][C]0.525118[/C][C]0.737441[/C][/ROW]
[ROW][C]41[/C][C]0.805209[/C][C]0.389583[/C][C]0.194791[/C][/ROW]
[ROW][C]42[/C][C]0.769538[/C][C]0.460924[/C][C]0.230462[/C][/ROW]
[ROW][C]43[/C][C]0.742125[/C][C]0.51575[/C][C]0.257875[/C][/ROW]
[ROW][C]44[/C][C]0.698888[/C][C]0.602224[/C][C]0.301112[/C][/ROW]
[ROW][C]45[/C][C]0.666161[/C][C]0.667679[/C][C]0.333839[/C][/ROW]
[ROW][C]46[/C][C]0.618349[/C][C]0.763302[/C][C]0.381651[/C][/ROW]
[ROW][C]47[/C][C]0.568814[/C][C]0.862372[/C][C]0.431186[/C][/ROW]
[ROW][C]48[/C][C]0.52297[/C][C]0.954059[/C][C]0.47703[/C][/ROW]
[ROW][C]49[/C][C]0.51693[/C][C]0.96614[/C][C]0.48307[/C][/ROW]
[ROW][C]50[/C][C]0.512079[/C][C]0.975842[/C][C]0.487921[/C][/ROW]
[ROW][C]51[/C][C]0.490311[/C][C]0.980622[/C][C]0.509689[/C][/ROW]
[ROW][C]52[/C][C]0.461565[/C][C]0.92313[/C][C]0.538435[/C][/ROW]
[ROW][C]53[/C][C]0.433116[/C][C]0.866231[/C][C]0.566884[/C][/ROW]
[ROW][C]54[/C][C]0.859544[/C][C]0.280912[/C][C]0.140456[/C][/ROW]
[ROW][C]55[/C][C]0.842065[/C][C]0.315871[/C][C]0.157935[/C][/ROW]
[ROW][C]56[/C][C]0.838566[/C][C]0.322868[/C][C]0.161434[/C][/ROW]
[ROW][C]57[/C][C]0.816934[/C][C]0.366132[/C][C]0.183066[/C][/ROW]
[ROW][C]58[/C][C]0.809252[/C][C]0.381495[/C][C]0.190748[/C][/ROW]
[ROW][C]59[/C][C]0.777167[/C][C]0.445666[/C][C]0.222833[/C][/ROW]
[ROW][C]60[/C][C]0.820308[/C][C]0.359384[/C][C]0.179692[/C][/ROW]
[ROW][C]61[/C][C]0.79412[/C][C]0.411761[/C][C]0.20588[/C][/ROW]
[ROW][C]62[/C][C]0.823174[/C][C]0.353652[/C][C]0.176826[/C][/ROW]
[ROW][C]63[/C][C]0.849656[/C][C]0.300687[/C][C]0.150344[/C][/ROW]
[ROW][C]64[/C][C]0.828826[/C][C]0.342348[/C][C]0.171174[/C][/ROW]
[ROW][C]65[/C][C]0.805038[/C][C]0.389924[/C][C]0.194962[/C][/ROW]
[ROW][C]66[/C][C]0.817146[/C][C]0.365709[/C][C]0.182854[/C][/ROW]
[ROW][C]67[/C][C]0.803003[/C][C]0.393995[/C][C]0.196997[/C][/ROW]
[ROW][C]68[/C][C]0.818099[/C][C]0.363803[/C][C]0.181901[/C][/ROW]
[ROW][C]69[/C][C]0.797483[/C][C]0.405033[/C][C]0.202517[/C][/ROW]
[ROW][C]70[/C][C]0.82243[/C][C]0.35514[/C][C]0.17757[/C][/ROW]
[ROW][C]71[/C][C]0.816324[/C][C]0.367352[/C][C]0.183676[/C][/ROW]
[ROW][C]72[/C][C]0.815448[/C][C]0.369104[/C][C]0.184552[/C][/ROW]
[ROW][C]73[/C][C]0.809147[/C][C]0.381705[/C][C]0.190853[/C][/ROW]
[ROW][C]74[/C][C]0.778939[/C][C]0.442122[/C][C]0.221061[/C][/ROW]
[ROW][C]75[/C][C]0.829035[/C][C]0.341931[/C][C]0.170965[/C][/ROW]
[ROW][C]76[/C][C]0.798937[/C][C]0.402126[/C][C]0.201063[/C][/ROW]
[ROW][C]77[/C][C]0.811935[/C][C]0.37613[/C][C]0.188065[/C][/ROW]
[ROW][C]78[/C][C]0.822438[/C][C]0.355124[/C][C]0.177562[/C][/ROW]
[ROW][C]79[/C][C]0.814659[/C][C]0.370683[/C][C]0.185341[/C][/ROW]
[ROW][C]80[/C][C]0.785677[/C][C]0.428646[/C][C]0.214323[/C][/ROW]
[ROW][C]81[/C][C]0.787682[/C][C]0.424635[/C][C]0.212318[/C][/ROW]
[ROW][C]82[/C][C]0.756503[/C][C]0.486993[/C][C]0.243497[/C][/ROW]
[ROW][C]83[/C][C]0.768559[/C][C]0.462882[/C][C]0.231441[/C][/ROW]
[ROW][C]84[/C][C]0.82516[/C][C]0.349681[/C][C]0.17484[/C][/ROW]
[ROW][C]85[/C][C]0.795177[/C][C]0.409646[/C][C]0.204823[/C][/ROW]
[ROW][C]86[/C][C]0.788546[/C][C]0.422908[/C][C]0.211454[/C][/ROW]
[ROW][C]87[/C][C]0.798486[/C][C]0.403028[/C][C]0.201514[/C][/ROW]
[ROW][C]88[/C][C]0.771194[/C][C]0.457611[/C][C]0.228806[/C][/ROW]
[ROW][C]89[/C][C]0.757319[/C][C]0.485363[/C][C]0.242681[/C][/ROW]
[ROW][C]90[/C][C]0.728016[/C][C]0.543968[/C][C]0.271984[/C][/ROW]
[ROW][C]91[/C][C]0.689186[/C][C]0.621627[/C][C]0.310814[/C][/ROW]
[ROW][C]92[/C][C]0.664596[/C][C]0.670808[/C][C]0.335404[/C][/ROW]
[ROW][C]93[/C][C]0.662149[/C][C]0.675703[/C][C]0.337851[/C][/ROW]
[ROW][C]94[/C][C]0.623061[/C][C]0.753878[/C][C]0.376939[/C][/ROW]
[ROW][C]95[/C][C]0.595353[/C][C]0.809294[/C][C]0.404647[/C][/ROW]
[ROW][C]96[/C][C]0.621691[/C][C]0.756618[/C][C]0.378309[/C][/ROW]
[ROW][C]97[/C][C]0.620873[/C][C]0.758255[/C][C]0.379127[/C][/ROW]
[ROW][C]98[/C][C]0.601226[/C][C]0.797549[/C][C]0.398774[/C][/ROW]
[ROW][C]99[/C][C]0.560474[/C][C]0.879052[/C][C]0.439526[/C][/ROW]
[ROW][C]100[/C][C]0.559186[/C][C]0.881628[/C][C]0.440814[/C][/ROW]
[ROW][C]101[/C][C]0.523346[/C][C]0.953307[/C][C]0.476654[/C][/ROW]
[ROW][C]102[/C][C]0.492595[/C][C]0.985189[/C][C]0.507405[/C][/ROW]
[ROW][C]103[/C][C]0.458578[/C][C]0.917156[/C][C]0.541422[/C][/ROW]
[ROW][C]104[/C][C]0.466589[/C][C]0.933179[/C][C]0.533411[/C][/ROW]
[ROW][C]105[/C][C]0.436522[/C][C]0.873043[/C][C]0.563478[/C][/ROW]
[ROW][C]106[/C][C]0.413566[/C][C]0.827132[/C][C]0.586434[/C][/ROW]
[ROW][C]107[/C][C]0.376732[/C][C]0.753464[/C][C]0.623268[/C][/ROW]
[ROW][C]108[/C][C]0.360009[/C][C]0.720018[/C][C]0.639991[/C][/ROW]
[ROW][C]109[/C][C]0.339148[/C][C]0.678296[/C][C]0.660852[/C][/ROW]
[ROW][C]110[/C][C]0.307737[/C][C]0.615473[/C][C]0.692263[/C][/ROW]
[ROW][C]111[/C][C]0.279496[/C][C]0.558993[/C][C]0.720504[/C][/ROW]
[ROW][C]112[/C][C]0.269087[/C][C]0.538175[/C][C]0.730913[/C][/ROW]
[ROW][C]113[/C][C]0.259009[/C][C]0.518017[/C][C]0.740991[/C][/ROW]
[ROW][C]114[/C][C]0.370153[/C][C]0.740306[/C][C]0.629847[/C][/ROW]
[ROW][C]115[/C][C]0.336024[/C][C]0.672048[/C][C]0.663976[/C][/ROW]
[ROW][C]116[/C][C]0.329589[/C][C]0.659178[/C][C]0.670411[/C][/ROW]
[ROW][C]117[/C][C]0.304516[/C][C]0.609032[/C][C]0.695484[/C][/ROW]
[ROW][C]118[/C][C]0.361283[/C][C]0.722567[/C][C]0.638717[/C][/ROW]
[ROW][C]119[/C][C]0.359055[/C][C]0.71811[/C][C]0.640945[/C][/ROW]
[ROW][C]120[/C][C]0.372823[/C][C]0.745645[/C][C]0.627177[/C][/ROW]
[ROW][C]121[/C][C]0.438369[/C][C]0.876737[/C][C]0.561631[/C][/ROW]
[ROW][C]122[/C][C]0.463053[/C][C]0.926106[/C][C]0.536947[/C][/ROW]
[ROW][C]123[/C][C]0.514562[/C][C]0.970877[/C][C]0.485438[/C][/ROW]
[ROW][C]124[/C][C]0.492033[/C][C]0.984066[/C][C]0.507967[/C][/ROW]
[ROW][C]125[/C][C]0.459299[/C][C]0.918597[/C][C]0.540701[/C][/ROW]
[ROW][C]126[/C][C]0.467395[/C][C]0.93479[/C][C]0.532605[/C][/ROW]
[ROW][C]127[/C][C]0.441672[/C][C]0.883345[/C][C]0.558328[/C][/ROW]
[ROW][C]128[/C][C]0.583983[/C][C]0.832034[/C][C]0.416017[/C][/ROW]
[ROW][C]129[/C][C]0.55529[/C][C]0.88942[/C][C]0.44471[/C][/ROW]
[ROW][C]130[/C][C]0.514739[/C][C]0.970523[/C][C]0.485261[/C][/ROW]
[ROW][C]131[/C][C]0.564701[/C][C]0.870599[/C][C]0.435299[/C][/ROW]
[ROW][C]132[/C][C]0.509442[/C][C]0.981116[/C][C]0.490558[/C][/ROW]
[ROW][C]133[/C][C]0.454779[/C][C]0.909559[/C][C]0.545221[/C][/ROW]
[ROW][C]134[/C][C]0.400802[/C][C]0.801605[/C][C]0.599198[/C][/ROW]
[ROW][C]135[/C][C]0.36967[/C][C]0.739341[/C][C]0.63033[/C][/ROW]
[ROW][C]136[/C][C]0.317341[/C][C]0.634682[/C][C]0.682659[/C][/ROW]
[ROW][C]137[/C][C]0.528789[/C][C]0.942422[/C][C]0.471211[/C][/ROW]
[ROW][C]138[/C][C]0.489248[/C][C]0.978496[/C][C]0.510752[/C][/ROW]
[ROW][C]139[/C][C]0.466185[/C][C]0.93237[/C][C]0.533815[/C][/ROW]
[ROW][C]140[/C][C]0.401234[/C][C]0.802467[/C][C]0.598766[/C][/ROW]
[ROW][C]141[/C][C]0.445978[/C][C]0.891956[/C][C]0.554022[/C][/ROW]
[ROW][C]142[/C][C]0.394475[/C][C]0.78895[/C][C]0.605525[/C][/ROW]
[ROW][C]143[/C][C]0.37654[/C][C]0.75308[/C][C]0.62346[/C][/ROW]
[ROW][C]144[/C][C]0.336576[/C][C]0.673152[/C][C]0.663424[/C][/ROW]
[ROW][C]145[/C][C]0.309867[/C][C]0.619733[/C][C]0.690133[/C][/ROW]
[ROW][C]146[/C][C]0.246967[/C][C]0.493935[/C][C]0.753033[/C][/ROW]
[ROW][C]147[/C][C]0.209214[/C][C]0.418429[/C][C]0.790786[/C][/ROW]
[ROW][C]148[/C][C]0.172794[/C][C]0.345589[/C][C]0.827206[/C][/ROW]
[ROW][C]149[/C][C]0.13495[/C][C]0.2699[/C][C]0.86505[/C][/ROW]
[ROW][C]150[/C][C]0.14498[/C][C]0.289959[/C][C]0.85502[/C][/ROW]
[ROW][C]151[/C][C]0.558228[/C][C]0.883545[/C][C]0.441772[/C][/ROW]
[ROW][C]152[/C][C]0.464991[/C][C]0.929983[/C][C]0.535009[/C][/ROW]
[ROW][C]153[/C][C]0.893422[/C][C]0.213156[/C][C]0.106578[/C][/ROW]
[ROW][C]154[/C][C]0.869802[/C][C]0.260396[/C][C]0.130198[/C][/ROW]
[ROW][C]155[/C][C]0.979518[/C][C]0.0409643[/C][C]0.0204822[/C][/ROW]
[ROW][C]156[/C][C]0.994975[/C][C]0.0100504[/C][C]0.00502522[/C][/ROW]
[ROW][C]157[/C][C]0.982239[/C][C]0.0355225[/C][C]0.0177613[/C][/ROW]
[ROW][C]158[/C][C]0.946234[/C][C]0.107531[/C][C]0.0537657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.9321090.1357820.067891
140.9401660.1196680.0598341
150.898770.202460.10123
160.9131970.1736060.0868028
170.9519620.0960770.0480385
180.9228370.1543270.0771635
190.8834180.2331650.116582
200.8757830.2484340.124217
210.8429980.3140030.157002
220.812880.374240.18712
230.7564670.4870660.243533
240.6900920.6198150.309908
250.7012410.5975190.298759
260.6766090.6467830.323391
270.6100860.7798280.389914
280.5530670.8938660.446933
290.4827560.9655120.517244
300.4579990.9159970.542001
310.4397040.8794080.560296
320.434050.8680990.56595
330.3990090.7980180.600991
340.3389150.677830.661085
350.2853210.5706410.714679
360.2368820.4737640.763118
370.4134960.8269920.586504
380.3566120.7132250.643388
390.3098210.6196420.690179
400.2625590.5251180.737441
410.8052090.3895830.194791
420.7695380.4609240.230462
430.7421250.515750.257875
440.6988880.6022240.301112
450.6661610.6676790.333839
460.6183490.7633020.381651
470.5688140.8623720.431186
480.522970.9540590.47703
490.516930.966140.48307
500.5120790.9758420.487921
510.4903110.9806220.509689
520.4615650.923130.538435
530.4331160.8662310.566884
540.8595440.2809120.140456
550.8420650.3158710.157935
560.8385660.3228680.161434
570.8169340.3661320.183066
580.8092520.3814950.190748
590.7771670.4456660.222833
600.8203080.3593840.179692
610.794120.4117610.20588
620.8231740.3536520.176826
630.8496560.3006870.150344
640.8288260.3423480.171174
650.8050380.3899240.194962
660.8171460.3657090.182854
670.8030030.3939950.196997
680.8180990.3638030.181901
690.7974830.4050330.202517
700.822430.355140.17757
710.8163240.3673520.183676
720.8154480.3691040.184552
730.8091470.3817050.190853
740.7789390.4421220.221061
750.8290350.3419310.170965
760.7989370.4021260.201063
770.8119350.376130.188065
780.8224380.3551240.177562
790.8146590.3706830.185341
800.7856770.4286460.214323
810.7876820.4246350.212318
820.7565030.4869930.243497
830.7685590.4628820.231441
840.825160.3496810.17484
850.7951770.4096460.204823
860.7885460.4229080.211454
870.7984860.4030280.201514
880.7711940.4576110.228806
890.7573190.4853630.242681
900.7280160.5439680.271984
910.6891860.6216270.310814
920.6645960.6708080.335404
930.6621490.6757030.337851
940.6230610.7538780.376939
950.5953530.8092940.404647
960.6216910.7566180.378309
970.6208730.7582550.379127
980.6012260.7975490.398774
990.5604740.8790520.439526
1000.5591860.8816280.440814
1010.5233460.9533070.476654
1020.4925950.9851890.507405
1030.4585780.9171560.541422
1040.4665890.9331790.533411
1050.4365220.8730430.563478
1060.4135660.8271320.586434
1070.3767320.7534640.623268
1080.3600090.7200180.639991
1090.3391480.6782960.660852
1100.3077370.6154730.692263
1110.2794960.5589930.720504
1120.2690870.5381750.730913
1130.2590090.5180170.740991
1140.3701530.7403060.629847
1150.3360240.6720480.663976
1160.3295890.6591780.670411
1170.3045160.6090320.695484
1180.3612830.7225670.638717
1190.3590550.718110.640945
1200.3728230.7456450.627177
1210.4383690.8767370.561631
1220.4630530.9261060.536947
1230.5145620.9708770.485438
1240.4920330.9840660.507967
1250.4592990.9185970.540701
1260.4673950.934790.532605
1270.4416720.8833450.558328
1280.5839830.8320340.416017
1290.555290.889420.44471
1300.5147390.9705230.485261
1310.5647010.8705990.435299
1320.5094420.9811160.490558
1330.4547790.9095590.545221
1340.4008020.8016050.599198
1350.369670.7393410.63033
1360.3173410.6346820.682659
1370.5287890.9424220.471211
1380.4892480.9784960.510752
1390.4661850.932370.533815
1400.4012340.8024670.598766
1410.4459780.8919560.554022
1420.3944750.788950.605525
1430.376540.753080.62346
1440.3365760.6731520.663424
1450.3098670.6197330.690133
1460.2469670.4939350.753033
1470.2092140.4184290.790786
1480.1727940.3455890.827206
1490.134950.26990.86505
1500.144980.2899590.85502
1510.5582280.8835450.441772
1520.4649910.9299830.535009
1530.8934220.2131560.106578
1540.8698020.2603960.130198
1550.9795180.04096430.0204822
1560.9949750.01005040.00502522
1570.9822390.03552250.0177613
1580.9462340.1075310.0537657






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0205479OK
10% type I error level40.0273973OK

\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 & 0.0205479 & OK \tabularnewline
10% type I error level & 4 & 0.0273973 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263071&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]0.0205479[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0273973[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263071&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263071&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 level30.0205479OK
10% type I error level40.0273973OK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

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