FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 14 Dec 2014 08:31:32 +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/t14185461910qq9e6izokvfzhm.htm/, Retrieved Thu, 16 May 2024 12:09:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267330, Retrieved Thu, 16 May 2024 12:09:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

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 08:31:32] [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
12.7 0 1 22 22 50 16 45 146
18.1 0 1 22 21 150 33 37 182
17.85 0 1 20 25 154 32 37 192
16.6 1 0 19 30 109 37 108 263
12.6 1 1 20 17 68 14 10 35
17.1 0 1 22 27 194 52 68 439
19.1 0 0 21 23 158 75 72 214
16.1 0 1 21 23 159 72 143 341
13.35 0 0 21 18 67 15 9 58
18.4 0 0 21 18 147 29 55 292
14.7 0 1 21 23 39 13 17 85
10.6 0 1 21 19 100 40 37 200
12.6 0 1 21 15 111 19 27 158
16.2 0 1 22 20 138 24 37 199
13.6 0 1 24 16 101 121 58 297
18.9 1 1 21 24 131 93 66 227
14.1 0 1 22 25 101 36 21 108
14.5 0 1 20 25 114 23 19 86
16.15 0 0 21 19 165 85 78 302
14.75 0 1 24 19 114 41 35 148
14.8 0 1 25 16 111 46 48 178
12.45 0 1 22 19 75 18 27 120
12.65 0 1 21 19 82 35 43 207
17.35 0 1 21 23 121 17 30 157
8.6 0 1 22 21 32 4 25 128
18.4 0 0 23 22 150 28 69 296
16.1 0 1 24 19 117 44 72 323
11.6 1 1 20 20 71 10 23 79
17.75 0 1 22 20 165 38 13 70
15.25 0 1 25 3 154 57 61 146
17.65 0 1 22 23 126 23 43 246
15.6 0 0 22 14 138 26 22 145
16.35 0 0 21 23 149 36 51 196
17.65 0 0 21 20 145 22 67 199
13.6 0 1 21 15 120 40 36 127
11.7 0 0 22 13 138 18 21 91
14.35 0 0 22 16 109 31 44 153
14.75 0 0 22 7 132 11 45 299
18.25 0 1 21 24 172 38 34 228
9.9 0 0 22 17 169 24 36 190
16 0 1 23 24 114 37 72 180
18.25 0 1 21 24 156 37 39 212
16.85 0 0 21 19 172 22 43 269
14.6 1 1 21 25 68 15 25 130
13.85 1 1 19 20 89 2 56 179
18.95 0 1 21 28 167 43 80 243
15.6 0 0 21 23 113 31 40 190
14.85 1 0 19 27 115 29 73 299
11.75 1 0 18 18 78 45 34 121
18.45 1 0 19 28 118 25 72 137
15.9 1 1 21 21 87 4 42 305
17.1 0 0 22 19 173 31 61 157
16.1 0 1 22 23 2 -4 23 96
19.9 1 0 19 27 162 66 74 183
10.95 1 1 20 22 49 61 16 52
18.45 1 0 19 28 122 32 66 238
15.1 1 1 21 25 96 31 9 40
15 1 0 19 21 100 39 41 226
11.35 1 0 20 22 82 19 57 190
15.95 1 1 21 28 100 31 48 214
18.1 1 0 19 20 115 36 51 145
14.6 1 1 21 29 141 42 53 119
15.4 0 1 21 25 165 21 29 222
15.4 0 1 21 25 165 21 29 222
17.6 1 1 19 20 110 25 55 159
13.35 0 1 25 20 118 32 54 165
19.1 0 0 21 16 158 26 43 249
15.35 1 1 20 20 146 28 51 125
7.6 0 0 25 20 49 32 20 122
13.4 1 0 19 23 90 41 79 186
13.9 1 0 20 18 121 29 39 148
19.1 0 1 22 25 155 33 61 274
15.25 1 0 19 18 104 17 55 172
12.9 1 1 20 19 147 13 30 84
16.1 1 0 19 25 110 32 55 168
17.35 1 0 19 25 108 30 22 102
13.15 1 0 18 25 113 34 37 106
12.15 1 0 19 24 115 59 2 2
12.6 1 1 21 19 61 13 38 139
10.35 1 1 19 26 60 23 27 95
15.4 1 1 20 10 109 10 56 130
9.6 1 1 20 17 68 5 25 72
18.2 1 0 19 13 111 31 39 141
13.6 1 0 19 17 77 19 33 113
14.85 1 1 22 30 73 32 43 206
14.75 0 0 21 25 151 30 57 268
14.1 1 0 19 4 89 25 43 175
14.9 1 0 19 16 78 48 23 77
16.25 1 0 19 21 110 35 44 125
19.25 0 1 23 23 220 67 54 255
13.6 1 1 19 22 65 15 28 111
13.6 0 0 20 17 141 22 36 132
15.65 1 0 19 20 117 18 39 211
12.75 0 1 22 20 122 33 16 92
14.6 1 0 19 22 63 46 23 76
9.85 0 1 25 16 44 24 40 171
12.65 1 1 19 23 52 14 24 83
11.9 1 1 20 16 62 23 29 119
19.2 1 0 19 0 131 12 78 266
16.6 1 1 19 18 101 38 57 186
11.2 1 1 20 25 42 12 37 50
15.25 0 1 20 23 152 28 27 117
11.9 0 0 21 12 107 41 61 219
13.2 1 0 19 18 77 12 27 246
16.35 0 0 21 24 154 31 69 279
12.4 0 1 23 11 103 33 34 148
15.85 1 1 19 18 96 34 44 137
14.35 0 0 21 14 154 41 21 130
18.15 0 1 22 23 175 21 34 181
11.15 1 1 20 24 57 20 39 98
15.65 1 0 18 29 112 44 51 226
17.75 0 0 21 18 143 52 34 234
7.65 1 0 20 15 49 7 31 138
12.35 0 1 21 29 110 29 13 85
15.6 0 1 21 16 131 11 12 66
19.3 0 0 21 19 167 26 51 236
15.2 1 0 19 22 56 24 24 106
17.1 0 0 21 16 137 7 19 135
15.6 1 1 19 23 86 60 30 122
18.4 0 1 21 23 121 13 81 218
19.05 0 0 21 19 149 20 42 199
18.55 0 0 22 4 168 52 22 112
19.1 0 0 21 20 140 28 85 278
13.1 1 1 22 24 88 25 27 94
12.85 0 1 22 20 168 39 25 113
9.5 0 1 22 4 94 9 22 84
4.5 0 1 22 24 51 19 19 86
11.85 1 0 21 22 48 13 14 62
13.6 0 1 22 16 145 60 45 222
11.7 0 1 23 3 66 19 45 167
12.4 1 1 19 15 85 34 28 82
13.35 0 0 22 24 109 14 51 207
11.4 1 0 21 17 63 17 41 184
14.9 1 1 19 20 102 45 31 83
19.9 1 0 19 27 162 66 74 183
17.75 0 1 20 23 128 24 24 85
11.2 1 1 20 26 86 48 19 89
14.6 1 1 18 23 114 29 51 225
17.6 0 0 21 17 164 -2 73 237
14.05 0 1 21 20 119 51 24 102
16.1 0 0 20 22 126 2 61 221
13.35 0 1 20 19 132 24 23 128
11.85 0 1 21 24 142 40 14 91
11.95 0 0 21 19 83 20 54 198
14.75 1 1 19 23 94 19 51 204
15.15 1 0 19 15 81 16 62 158
13.2 0 1 21 27 166 20 36 138
16.85 1 0 19 26 110 40 59 226
7.85 1 1 19 22 64 27 24 44
7.7 0 0 24 22 93 25 26 196
12.6 1 0 19 18 104 49 54 83
7.85 1 1 19 15 105 39 39 79
10.95 1 1 20 22 49 61 16 52
12.35 1 0 19 27 88 19 36 105
9.95 1 1 19 10 95 67 31 116
14.9 1 1 19 20 102 45 31 83
16.65 1 0 19 17 99 30 42 196
13.4 1 1 19 23 63 8 39 153
13.95 1 0 19 19 76 19 25 157
15.7 1 0 20 13 109 52 31 75
16.85 1 1 20 27 117 22 38 106
10.95 1 1 19 23 57 17 31 58
15.35 1 0 21 16 120 33 17 75
12.2 1 1 19 25 73 34 22 74
15.1 1 0 19 2 91 22 55 185
17.75 1 0 19 26 108 30 62 265
15.2 1 1 21 20 105 25 51 131
14.6 0 0 22 23 117 38 30 139
16.65 1 0 19 22 119 26 49 196
8.1 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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.972 + 0.701136programma[t] -0.24606gender[t] -0.199374age[t] + 0.0348699NUMERACYTOT[t] + 0.0461442LFM[t] -0.00749876PRH[t] + 0.0211339CH[t] + 0.00640139Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.972 +  0.701136programma[t] -0.24606gender[t] -0.199374age[t] +  0.0348699NUMERACYTOT[t] +  0.0461442LFM[t] -0.00749876PRH[t] +  0.0211339CH[t] +  0.00640139Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.972 +  0.701136programma[t] -0.24606gender[t] -0.199374age[t] +  0.0348699NUMERACYTOT[t] +  0.0461442LFM[t] -0.00749876PRH[t] +  0.0211339CH[t] +  0.00640139Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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] = + 10.972 + 0.701136programma[t] -0.24606gender[t] -0.199374age[t] + 0.0348699NUMERACYTOT[t] + 0.0461442LFM[t] -0.00749876PRH[t] + 0.0211339CH[t] + 0.00640139Blogs[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.9723.92312.7970.005786860.00289343
programma0.7011360.5653211.240.2166780.108339
gender-0.246060.367979-0.66870.504650.252325
age-0.1993740.170566-1.1690.2441630.122081
NUMERACYTOT0.03486990.03113811.120.2644360.132218
LFM0.04614420.005821017.9273.43308e-131.71654e-13
PRH-0.007498760.0101566-0.73830.4613910.230695
CH0.02113390.01258851.6790.09511530.0475577
Blogs0.006401390.003710821.7250.08642410.043212

\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) & 10.972 & 3.9231 & 2.797 & 0.00578686 & 0.00289343 \tabularnewline
programma & 0.701136 & 0.565321 & 1.24 & 0.216678 & 0.108339 \tabularnewline
gender & -0.24606 & 0.367979 & -0.6687 & 0.50465 & 0.252325 \tabularnewline
age & -0.199374 & 0.170566 & -1.169 & 0.244163 & 0.122081 \tabularnewline
NUMERACYTOT & 0.0348699 & 0.0311381 & 1.12 & 0.264436 & 0.132218 \tabularnewline
LFM & 0.0461442 & 0.00582101 & 7.927 & 3.43308e-13 & 1.71654e-13 \tabularnewline
PRH & -0.00749876 & 0.0101566 & -0.7383 & 0.461391 & 0.230695 \tabularnewline
CH & 0.0211339 & 0.0125885 & 1.679 & 0.0951153 & 0.0475577 \tabularnewline
Blogs & 0.00640139 & 0.00371082 & 1.725 & 0.0864241 & 0.043212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&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]10.972[/C][C]3.9231[/C][C]2.797[/C][C]0.00578686[/C][C]0.00289343[/C][/ROW]
[ROW][C]programma[/C][C]0.701136[/C][C]0.565321[/C][C]1.24[/C][C]0.216678[/C][C]0.108339[/C][/ROW]
[ROW][C]gender[/C][C]-0.24606[/C][C]0.367979[/C][C]-0.6687[/C][C]0.50465[/C][C]0.252325[/C][/ROW]
[ROW][C]age[/C][C]-0.199374[/C][C]0.170566[/C][C]-1.169[/C][C]0.244163[/C][C]0.122081[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0348699[/C][C]0.0311381[/C][C]1.12[/C][C]0.264436[/C][C]0.132218[/C][/ROW]
[ROW][C]LFM[/C][C]0.0461442[/C][C]0.00582101[/C][C]7.927[/C][C]3.43308e-13[/C][C]1.71654e-13[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00749876[/C][C]0.0101566[/C][C]-0.7383[/C][C]0.461391[/C][C]0.230695[/C][/ROW]
[ROW][C]CH[/C][C]0.0211339[/C][C]0.0125885[/C][C]1.679[/C][C]0.0951153[/C][C]0.0475577[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00640139[/C][C]0.00371082[/C][C]1.725[/C][C]0.0864241[/C][C]0.043212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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)10.9723.92312.7970.005786860.00289343
programma0.7011360.5653211.240.2166780.108339
gender-0.246060.367979-0.66870.504650.252325
age-0.1993740.170566-1.1690.2441630.122081
NUMERACYTOT0.03486990.03113811.120.2644360.132218
LFM0.04614420.005821017.9273.43308e-131.71654e-13
PRH-0.007498760.0101566-0.73830.4613910.230695
CH0.02113390.01258851.6790.09511530.0475577
Blogs0.006401390.003710821.7250.08642410.043212






Multiple Linear Regression - Regression Statistics
Multiple R0.720598
R-squared0.519262
Adjusted R-squared0.495522
F-TEST (value)21.8728
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1511
Sum Squared Residuals749.612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.720598 \tabularnewline
R-squared & 0.519262 \tabularnewline
Adjusted R-squared & 0.495522 \tabularnewline
F-TEST (value) & 21.8728 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.1511 \tabularnewline
Sum Squared Residuals & 749.612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.720598[/C][/ROW]
[ROW][C]R-squared[/C][C]0.519262[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.495522[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.8728[/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[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.1511[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]749.612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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 R0.720598
R-squared0.519262
Adjusted R-squared0.495522
F-TEST (value)21.8728
F-TEST (DF numerator)8
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1511
Sum Squared Residuals749.612






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.359.70023-5.35023
212.711.17971.52029
318.115.69322.40685
417.8516.48751.36253
516.617.6494-1.04941
612.611.50061.09941
717.120.0905-2.99055
819.117.20711.89294
916.119.3431-3.24312
1013.3510.95352.39653
1118.417.01011.3899
1214.79.946634.75337
1310.613.5783-2.97831
1412.613.6237-1.0237
1516.215.28090.919134
1613.613.37910.220929
1718.916.27262.62745
1814.112.73721.36277
1914.513.65020.849764
2016.1518.0057-1.85573
2114.7513.24361.50643
2214.813.23041.56956
2312.4511.66690.783143
2412.6512.9568-0.306824
2517.3514.43612.9139
268.69.86632-1.26632
2718.417.21821.18175
2816.115.26170.838298
2911.612.33-0.730032
3017.7515.08882.66121
3115.2514.74870.501258
3217.6515.26692.38309
3315.614.640.959973
3416.3516.5252-0.175181
3517.6516.69830.951677
3613.613.8733-0.273281
3711.714.2983-2.59834
3814.3513.85020.499752
3914.7515.7034-0.953446
4018.2517.20591.04412
419.916.774-6.87404
421614.63411.36591
4318.2516.47831.77168
4416.8517.8502-1.00023
4514.612.49782.10218
4613.8514.7575-0.907548
4718.9518.14530.804679
4815.614.63060.969396
4914.8517.3724-2.52242
5011.7513.467-1.71698
5118.4516.51761.93244
5215.914.79711.10292
5317.117.293-0.192966
5416.18.364627.73538
5519.918.54231.35768
5610.9510.68140.268611
5718.4517.16941.28062
5815.112.75562.34439
591515.2525-0.252467
6011.3514.515-3.16503
6115.9514.98290.967141
6218.115.62512.47492
6314.616.3247-1.72469
6415.416.9011-1.50114
6515.416.9011-1.50114
6617.615.40492.19506
6713.3513.8415-0.491499
6819.116.94162.15842
6915.3516.5421-1.19208
707.69.9098-2.3098
7113.415.3928-1.9928
7213.915.4509-1.55092
7319.117.15951.94051
7415.2515.4476-0.197606
7512.915.9596-3.05957
7616.115.83050.269526
7717.3514.63332.71673
7813.1515.376-2.22599
7912.1513.6411-1.49113
8012.612.31290.287059
8110.3512.3205-1.97051
8215.414.75870.6413
839.612.1219-2.52194
8418.214.95473.2453
8513.613.30920.290781
8614.8513.4431.40705
8714.7517.3199-2.5699
8814.113.97290.127128
8914.912.66122.23876
9016.2515.16081.08923
9119.2519.3652-0.115231
9213.612.59531.0047
9313.615.5245-1.92447
9415.6516.0212-0.371234
9512.7513.3463-0.596311
9614.612.18692.41311
979.8510.0899-0.239874
9812.6511.7740.875979
9911.912.0606-0.16063
10019.217.19112.00886
10116.615.03751.56247
10211.211.2614-0.061437
10315.2515.664-0.413995
10411.914.5246-2.62463
10513.214.1212-0.921161
10616.3517.74-1.38999
10712.412.6953-0.295255
10815.8514.24841.60161
10914.3515.3481-0.998072
11018.1516.93671.21332
11111.1512.2083-1.05827
11215.6516.4584-0.808376
11317.7515.8381.91204
1147.6511.9558-4.30582
11512.3513.2276-0.877568
11615.613.73551.8645
11719.317.54731.75266
11815.212.2422.95797
11917.114.87812.22194
12015.613.37442.22556
12118.415.93442.4656
12219.0516.33472.71532
12318.5515.26943.28056
12419.117.30871.79127
12513.112.92330.17671
12612.8515.7486-2.89858
1279.511.7519-2.25192
1284.510.3395-5.83953
12911.8511.06360.786382
13013.615.5107-1.91074
13111.711.1680.531957
13212.412.946-0.545978
13313.3514.7503-1.4003
13411.412.903-1.50302
13514.913.89211.0079
13619.918.54231.35768
13717.7514.31833.43172
13811.212.9259-1.72594
13914.616.2015-1.60146
14017.618.0205-0.420479
14114.0513.50540.54464
14216.116.2547-0.1547
14313.3514.6175-1.26751
14411.8514.5069-2.65689
14511.9513.5364-1.58637
14614.7515.0198-0.269765
14715.1514.34750.802504
14813.216.6347-3.43474
14916.8516.26120.588831
1507.8511.9457-4.09574
1517.712.8623-5.16225
15212.614.6168-2.01679
1537.8514.0446-6.19464
15410.9510.68140.268611
15512.3514.1777-1.82769
1569.9513.2667-3.31666
15714.913.89211.0079
15816.6514.96341.68658
15913.413.09170.308295
16013.9513.44540.504595
16115.713.9141.786
16216.8515.09661.75339
16310.9511.9701-1.02015
16415.3514.17341.17658
16512.212.5629-0.362933
16615.114.33550.764463
16717.7516.55691.19308
16815.214.51170.688301
16914.614.02550.574494
17016.6516.23860.41141
1718.110.6463-2.54628

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 9.70023 & -5.35023 \tabularnewline
2 & 12.7 & 11.1797 & 1.52029 \tabularnewline
3 & 18.1 & 15.6932 & 2.40685 \tabularnewline
4 & 17.85 & 16.4875 & 1.36253 \tabularnewline
5 & 16.6 & 17.6494 & -1.04941 \tabularnewline
6 & 12.6 & 11.5006 & 1.09941 \tabularnewline
7 & 17.1 & 20.0905 & -2.99055 \tabularnewline
8 & 19.1 & 17.2071 & 1.89294 \tabularnewline
9 & 16.1 & 19.3431 & -3.24312 \tabularnewline
10 & 13.35 & 10.9535 & 2.39653 \tabularnewline
11 & 18.4 & 17.0101 & 1.3899 \tabularnewline
12 & 14.7 & 9.94663 & 4.75337 \tabularnewline
13 & 10.6 & 13.5783 & -2.97831 \tabularnewline
14 & 12.6 & 13.6237 & -1.0237 \tabularnewline
15 & 16.2 & 15.2809 & 0.919134 \tabularnewline
16 & 13.6 & 13.3791 & 0.220929 \tabularnewline
17 & 18.9 & 16.2726 & 2.62745 \tabularnewline
18 & 14.1 & 12.7372 & 1.36277 \tabularnewline
19 & 14.5 & 13.6502 & 0.849764 \tabularnewline
20 & 16.15 & 18.0057 & -1.85573 \tabularnewline
21 & 14.75 & 13.2436 & 1.50643 \tabularnewline
22 & 14.8 & 13.2304 & 1.56956 \tabularnewline
23 & 12.45 & 11.6669 & 0.783143 \tabularnewline
24 & 12.65 & 12.9568 & -0.306824 \tabularnewline
25 & 17.35 & 14.4361 & 2.9139 \tabularnewline
26 & 8.6 & 9.86632 & -1.26632 \tabularnewline
27 & 18.4 & 17.2182 & 1.18175 \tabularnewline
28 & 16.1 & 15.2617 & 0.838298 \tabularnewline
29 & 11.6 & 12.33 & -0.730032 \tabularnewline
30 & 17.75 & 15.0888 & 2.66121 \tabularnewline
31 & 15.25 & 14.7487 & 0.501258 \tabularnewline
32 & 17.65 & 15.2669 & 2.38309 \tabularnewline
33 & 15.6 & 14.64 & 0.959973 \tabularnewline
34 & 16.35 & 16.5252 & -0.175181 \tabularnewline
35 & 17.65 & 16.6983 & 0.951677 \tabularnewline
36 & 13.6 & 13.8733 & -0.273281 \tabularnewline
37 & 11.7 & 14.2983 & -2.59834 \tabularnewline
38 & 14.35 & 13.8502 & 0.499752 \tabularnewline
39 & 14.75 & 15.7034 & -0.953446 \tabularnewline
40 & 18.25 & 17.2059 & 1.04412 \tabularnewline
41 & 9.9 & 16.774 & -6.87404 \tabularnewline
42 & 16 & 14.6341 & 1.36591 \tabularnewline
43 & 18.25 & 16.4783 & 1.77168 \tabularnewline
44 & 16.85 & 17.8502 & -1.00023 \tabularnewline
45 & 14.6 & 12.4978 & 2.10218 \tabularnewline
46 & 13.85 & 14.7575 & -0.907548 \tabularnewline
47 & 18.95 & 18.1453 & 0.804679 \tabularnewline
48 & 15.6 & 14.6306 & 0.969396 \tabularnewline
49 & 14.85 & 17.3724 & -2.52242 \tabularnewline
50 & 11.75 & 13.467 & -1.71698 \tabularnewline
51 & 18.45 & 16.5176 & 1.93244 \tabularnewline
52 & 15.9 & 14.7971 & 1.10292 \tabularnewline
53 & 17.1 & 17.293 & -0.192966 \tabularnewline
54 & 16.1 & 8.36462 & 7.73538 \tabularnewline
55 & 19.9 & 18.5423 & 1.35768 \tabularnewline
56 & 10.95 & 10.6814 & 0.268611 \tabularnewline
57 & 18.45 & 17.1694 & 1.28062 \tabularnewline
58 & 15.1 & 12.7556 & 2.34439 \tabularnewline
59 & 15 & 15.2525 & -0.252467 \tabularnewline
60 & 11.35 & 14.515 & -3.16503 \tabularnewline
61 & 15.95 & 14.9829 & 0.967141 \tabularnewline
62 & 18.1 & 15.6251 & 2.47492 \tabularnewline
63 & 14.6 & 16.3247 & -1.72469 \tabularnewline
64 & 15.4 & 16.9011 & -1.50114 \tabularnewline
65 & 15.4 & 16.9011 & -1.50114 \tabularnewline
66 & 17.6 & 15.4049 & 2.19506 \tabularnewline
67 & 13.35 & 13.8415 & -0.491499 \tabularnewline
68 & 19.1 & 16.9416 & 2.15842 \tabularnewline
69 & 15.35 & 16.5421 & -1.19208 \tabularnewline
70 & 7.6 & 9.9098 & -2.3098 \tabularnewline
71 & 13.4 & 15.3928 & -1.9928 \tabularnewline
72 & 13.9 & 15.4509 & -1.55092 \tabularnewline
73 & 19.1 & 17.1595 & 1.94051 \tabularnewline
74 & 15.25 & 15.4476 & -0.197606 \tabularnewline
75 & 12.9 & 15.9596 & -3.05957 \tabularnewline
76 & 16.1 & 15.8305 & 0.269526 \tabularnewline
77 & 17.35 & 14.6333 & 2.71673 \tabularnewline
78 & 13.15 & 15.376 & -2.22599 \tabularnewline
79 & 12.15 & 13.6411 & -1.49113 \tabularnewline
80 & 12.6 & 12.3129 & 0.287059 \tabularnewline
81 & 10.35 & 12.3205 & -1.97051 \tabularnewline
82 & 15.4 & 14.7587 & 0.6413 \tabularnewline
83 & 9.6 & 12.1219 & -2.52194 \tabularnewline
84 & 18.2 & 14.9547 & 3.2453 \tabularnewline
85 & 13.6 & 13.3092 & 0.290781 \tabularnewline
86 & 14.85 & 13.443 & 1.40705 \tabularnewline
87 & 14.75 & 17.3199 & -2.5699 \tabularnewline
88 & 14.1 & 13.9729 & 0.127128 \tabularnewline
89 & 14.9 & 12.6612 & 2.23876 \tabularnewline
90 & 16.25 & 15.1608 & 1.08923 \tabularnewline
91 & 19.25 & 19.3652 & -0.115231 \tabularnewline
92 & 13.6 & 12.5953 & 1.0047 \tabularnewline
93 & 13.6 & 15.5245 & -1.92447 \tabularnewline
94 & 15.65 & 16.0212 & -0.371234 \tabularnewline
95 & 12.75 & 13.3463 & -0.596311 \tabularnewline
96 & 14.6 & 12.1869 & 2.41311 \tabularnewline
97 & 9.85 & 10.0899 & -0.239874 \tabularnewline
98 & 12.65 & 11.774 & 0.875979 \tabularnewline
99 & 11.9 & 12.0606 & -0.16063 \tabularnewline
100 & 19.2 & 17.1911 & 2.00886 \tabularnewline
101 & 16.6 & 15.0375 & 1.56247 \tabularnewline
102 & 11.2 & 11.2614 & -0.061437 \tabularnewline
103 & 15.25 & 15.664 & -0.413995 \tabularnewline
104 & 11.9 & 14.5246 & -2.62463 \tabularnewline
105 & 13.2 & 14.1212 & -0.921161 \tabularnewline
106 & 16.35 & 17.74 & -1.38999 \tabularnewline
107 & 12.4 & 12.6953 & -0.295255 \tabularnewline
108 & 15.85 & 14.2484 & 1.60161 \tabularnewline
109 & 14.35 & 15.3481 & -0.998072 \tabularnewline
110 & 18.15 & 16.9367 & 1.21332 \tabularnewline
111 & 11.15 & 12.2083 & -1.05827 \tabularnewline
112 & 15.65 & 16.4584 & -0.808376 \tabularnewline
113 & 17.75 & 15.838 & 1.91204 \tabularnewline
114 & 7.65 & 11.9558 & -4.30582 \tabularnewline
115 & 12.35 & 13.2276 & -0.877568 \tabularnewline
116 & 15.6 & 13.7355 & 1.8645 \tabularnewline
117 & 19.3 & 17.5473 & 1.75266 \tabularnewline
118 & 15.2 & 12.242 & 2.95797 \tabularnewline
119 & 17.1 & 14.8781 & 2.22194 \tabularnewline
120 & 15.6 & 13.3744 & 2.22556 \tabularnewline
121 & 18.4 & 15.9344 & 2.4656 \tabularnewline
122 & 19.05 & 16.3347 & 2.71532 \tabularnewline
123 & 18.55 & 15.2694 & 3.28056 \tabularnewline
124 & 19.1 & 17.3087 & 1.79127 \tabularnewline
125 & 13.1 & 12.9233 & 0.17671 \tabularnewline
126 & 12.85 & 15.7486 & -2.89858 \tabularnewline
127 & 9.5 & 11.7519 & -2.25192 \tabularnewline
128 & 4.5 & 10.3395 & -5.83953 \tabularnewline
129 & 11.85 & 11.0636 & 0.786382 \tabularnewline
130 & 13.6 & 15.5107 & -1.91074 \tabularnewline
131 & 11.7 & 11.168 & 0.531957 \tabularnewline
132 & 12.4 & 12.946 & -0.545978 \tabularnewline
133 & 13.35 & 14.7503 & -1.4003 \tabularnewline
134 & 11.4 & 12.903 & -1.50302 \tabularnewline
135 & 14.9 & 13.8921 & 1.0079 \tabularnewline
136 & 19.9 & 18.5423 & 1.35768 \tabularnewline
137 & 17.75 & 14.3183 & 3.43172 \tabularnewline
138 & 11.2 & 12.9259 & -1.72594 \tabularnewline
139 & 14.6 & 16.2015 & -1.60146 \tabularnewline
140 & 17.6 & 18.0205 & -0.420479 \tabularnewline
141 & 14.05 & 13.5054 & 0.54464 \tabularnewline
142 & 16.1 & 16.2547 & -0.1547 \tabularnewline
143 & 13.35 & 14.6175 & -1.26751 \tabularnewline
144 & 11.85 & 14.5069 & -2.65689 \tabularnewline
145 & 11.95 & 13.5364 & -1.58637 \tabularnewline
146 & 14.75 & 15.0198 & -0.269765 \tabularnewline
147 & 15.15 & 14.3475 & 0.802504 \tabularnewline
148 & 13.2 & 16.6347 & -3.43474 \tabularnewline
149 & 16.85 & 16.2612 & 0.588831 \tabularnewline
150 & 7.85 & 11.9457 & -4.09574 \tabularnewline
151 & 7.7 & 12.8623 & -5.16225 \tabularnewline
152 & 12.6 & 14.6168 & -2.01679 \tabularnewline
153 & 7.85 & 14.0446 & -6.19464 \tabularnewline
154 & 10.95 & 10.6814 & 0.268611 \tabularnewline
155 & 12.35 & 14.1777 & -1.82769 \tabularnewline
156 & 9.95 & 13.2667 & -3.31666 \tabularnewline
157 & 14.9 & 13.8921 & 1.0079 \tabularnewline
158 & 16.65 & 14.9634 & 1.68658 \tabularnewline
159 & 13.4 & 13.0917 & 0.308295 \tabularnewline
160 & 13.95 & 13.4454 & 0.504595 \tabularnewline
161 & 15.7 & 13.914 & 1.786 \tabularnewline
162 & 16.85 & 15.0966 & 1.75339 \tabularnewline
163 & 10.95 & 11.9701 & -1.02015 \tabularnewline
164 & 15.35 & 14.1734 & 1.17658 \tabularnewline
165 & 12.2 & 12.5629 & -0.362933 \tabularnewline
166 & 15.1 & 14.3355 & 0.764463 \tabularnewline
167 & 17.75 & 16.5569 & 1.19308 \tabularnewline
168 & 15.2 & 14.5117 & 0.688301 \tabularnewline
169 & 14.6 & 14.0255 & 0.574494 \tabularnewline
170 & 16.65 & 16.2386 & 0.41141 \tabularnewline
171 & 8.1 & 10.6463 & -2.54628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&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]9.70023[/C][C]-5.35023[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]11.1797[/C][C]1.52029[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6932[/C][C]2.40685[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.4875[/C][C]1.36253[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.6494[/C][C]-1.04941[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.5006[/C][C]1.09941[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]20.0905[/C][C]-2.99055[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.2071[/C][C]1.89294[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]19.3431[/C][C]-3.24312[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.9535[/C][C]2.39653[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.0101[/C][C]1.3899[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.94663[/C][C]4.75337[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.5783[/C][C]-2.97831[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.6237[/C][C]-1.0237[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.2809[/C][C]0.919134[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3791[/C][C]0.220929[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.2726[/C][C]2.62745[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.7372[/C][C]1.36277[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.6502[/C][C]0.849764[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]18.0057[/C][C]-1.85573[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.2436[/C][C]1.50643[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.2304[/C][C]1.56956[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.6669[/C][C]0.783143[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9568[/C][C]-0.306824[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.4361[/C][C]2.9139[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.86632[/C][C]-1.26632[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.2182[/C][C]1.18175[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.2617[/C][C]0.838298[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]12.33[/C][C]-0.730032[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.0888[/C][C]2.66121[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.7487[/C][C]0.501258[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.2669[/C][C]2.38309[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.64[/C][C]0.959973[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.5252[/C][C]-0.175181[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]16.6983[/C][C]0.951677[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.8733[/C][C]-0.273281[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.2983[/C][C]-2.59834[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]13.8502[/C][C]0.499752[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.7034[/C][C]-0.953446[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]17.2059[/C][C]1.04412[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.774[/C][C]-6.87404[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.6341[/C][C]1.36591[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.4783[/C][C]1.77168[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.8502[/C][C]-1.00023[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.4978[/C][C]2.10218[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.7575[/C][C]-0.907548[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.1453[/C][C]0.804679[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.6306[/C][C]0.969396[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]17.3724[/C][C]-2.52242[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.467[/C][C]-1.71698[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]16.5176[/C][C]1.93244[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]14.7971[/C][C]1.10292[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.293[/C][C]-0.192966[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.36462[/C][C]7.73538[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]18.5423[/C][C]1.35768[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.6814[/C][C]0.268611[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.1694[/C][C]1.28062[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]12.7556[/C][C]2.34439[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2525[/C][C]-0.252467[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]14.515[/C][C]-3.16503[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.9829[/C][C]0.967141[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.6251[/C][C]2.47492[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]16.3247[/C][C]-1.72469[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.9011[/C][C]-1.50114[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.9011[/C][C]-1.50114[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.4049[/C][C]2.19506[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]13.8415[/C][C]-0.491499[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.9416[/C][C]2.15842[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.5421[/C][C]-1.19208[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.9098[/C][C]-2.3098[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.3928[/C][C]-1.9928[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.4509[/C][C]-1.55092[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.1595[/C][C]1.94051[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]15.4476[/C][C]-0.197606[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]15.9596[/C][C]-3.05957[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.8305[/C][C]0.269526[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.6333[/C][C]2.71673[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.376[/C][C]-2.22599[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.6411[/C][C]-1.49113[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.3129[/C][C]0.287059[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]12.3205[/C][C]-1.97051[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]14.7587[/C][C]0.6413[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]12.1219[/C][C]-2.52194[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.9547[/C][C]3.2453[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.3092[/C][C]0.290781[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.443[/C][C]1.40705[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.3199[/C][C]-2.5699[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]13.9729[/C][C]0.127128[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.6612[/C][C]2.23876[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]15.1608[/C][C]1.08923[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.3652[/C][C]-0.115231[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.5953[/C][C]1.0047[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.5245[/C][C]-1.92447[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]16.0212[/C][C]-0.371234[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.3463[/C][C]-0.596311[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]12.1869[/C][C]2.41311[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]10.0899[/C][C]-0.239874[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.774[/C][C]0.875979[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.0606[/C][C]-0.16063[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.1911[/C][C]2.00886[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.0375[/C][C]1.56247[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.2614[/C][C]-0.061437[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.664[/C][C]-0.413995[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.5246[/C][C]-2.62463[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]14.1212[/C][C]-0.921161[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.74[/C][C]-1.38999[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]12.6953[/C][C]-0.295255[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]14.2484[/C][C]1.60161[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.3481[/C][C]-0.998072[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]16.9367[/C][C]1.21332[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]12.2083[/C][C]-1.05827[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.4584[/C][C]-0.808376[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.838[/C][C]1.91204[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]11.9558[/C][C]-4.30582[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.2276[/C][C]-0.877568[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.7355[/C][C]1.8645[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.5473[/C][C]1.75266[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]12.242[/C][C]2.95797[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.8781[/C][C]2.22194[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.3744[/C][C]2.22556[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.9344[/C][C]2.4656[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.3347[/C][C]2.71532[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.2694[/C][C]3.28056[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.3087[/C][C]1.79127[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]12.9233[/C][C]0.17671[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]15.7486[/C][C]-2.89858[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.7519[/C][C]-2.25192[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.3395[/C][C]-5.83953[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]11.0636[/C][C]0.786382[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.5107[/C][C]-1.91074[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.168[/C][C]0.531957[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.946[/C][C]-0.545978[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]14.7503[/C][C]-1.4003[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.903[/C][C]-1.50302[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.8921[/C][C]1.0079[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]18.5423[/C][C]1.35768[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.3183[/C][C]3.43172[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]12.9259[/C][C]-1.72594[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]16.2015[/C][C]-1.60146[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]18.0205[/C][C]-0.420479[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.5054[/C][C]0.54464[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]16.2547[/C][C]-0.1547[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.6175[/C][C]-1.26751[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.5069[/C][C]-2.65689[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]13.5364[/C][C]-1.58637[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]15.0198[/C][C]-0.269765[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.3475[/C][C]0.802504[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.6347[/C][C]-3.43474[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.2612[/C][C]0.588831[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]11.9457[/C][C]-4.09574[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]12.8623[/C][C]-5.16225[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]14.6168[/C][C]-2.01679[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]14.0446[/C][C]-6.19464[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.6814[/C][C]0.268611[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]14.1777[/C][C]-1.82769[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.2667[/C][C]-3.31666[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.8921[/C][C]1.0079[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]14.9634[/C][C]1.68658[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]13.0917[/C][C]0.308295[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.4454[/C][C]0.504595[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.914[/C][C]1.786[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.0966[/C][C]1.75339[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]11.9701[/C][C]-1.02015[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]14.1734[/C][C]1.17658[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.5629[/C][C]-0.362933[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.3355[/C][C]0.764463[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.5569[/C][C]1.19308[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.5117[/C][C]0.688301[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.0255[/C][C]0.574494[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]16.2386[/C][C]0.41141[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]10.6463[/C][C]-2.54628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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
14.359.70023-5.35023
212.711.17971.52029
318.115.69322.40685
417.8516.48751.36253
516.617.6494-1.04941
612.611.50061.09941
717.120.0905-2.99055
819.117.20711.89294
916.119.3431-3.24312
1013.3510.95352.39653
1118.417.01011.3899
1214.79.946634.75337
1310.613.5783-2.97831
1412.613.6237-1.0237
1516.215.28090.919134
1613.613.37910.220929
1718.916.27262.62745
1814.112.73721.36277
1914.513.65020.849764
2016.1518.0057-1.85573
2114.7513.24361.50643
2214.813.23041.56956
2312.4511.66690.783143
2412.6512.9568-0.306824
2517.3514.43612.9139
268.69.86632-1.26632
2718.417.21821.18175
2816.115.26170.838298
2911.612.33-0.730032
3017.7515.08882.66121
3115.2514.74870.501258
3217.6515.26692.38309
3315.614.640.959973
3416.3516.5252-0.175181
3517.6516.69830.951677
3613.613.8733-0.273281
3711.714.2983-2.59834
3814.3513.85020.499752
3914.7515.7034-0.953446
4018.2517.20591.04412
419.916.774-6.87404
421614.63411.36591
4318.2516.47831.77168
4416.8517.8502-1.00023
4514.612.49782.10218
4613.8514.7575-0.907548
4718.9518.14530.804679
4815.614.63060.969396
4914.8517.3724-2.52242
5011.7513.467-1.71698
5118.4516.51761.93244
5215.914.79711.10292
5317.117.293-0.192966
5416.18.364627.73538
5519.918.54231.35768
5610.9510.68140.268611
5718.4517.16941.28062
5815.112.75562.34439
591515.2525-0.252467
6011.3514.515-3.16503
6115.9514.98290.967141
6218.115.62512.47492
6314.616.3247-1.72469
6415.416.9011-1.50114
6515.416.9011-1.50114
6617.615.40492.19506
6713.3513.8415-0.491499
6819.116.94162.15842
6915.3516.5421-1.19208
707.69.9098-2.3098
7113.415.3928-1.9928
7213.915.4509-1.55092
7319.117.15951.94051
7415.2515.4476-0.197606
7512.915.9596-3.05957
7616.115.83050.269526
7717.3514.63332.71673
7813.1515.376-2.22599
7912.1513.6411-1.49113
8012.612.31290.287059
8110.3512.3205-1.97051
8215.414.75870.6413
839.612.1219-2.52194
8418.214.95473.2453
8513.613.30920.290781
8614.8513.4431.40705
8714.7517.3199-2.5699
8814.113.97290.127128
8914.912.66122.23876
9016.2515.16081.08923
9119.2519.3652-0.115231
9213.612.59531.0047
9313.615.5245-1.92447
9415.6516.0212-0.371234
9512.7513.3463-0.596311
9614.612.18692.41311
979.8510.0899-0.239874
9812.6511.7740.875979
9911.912.0606-0.16063
10019.217.19112.00886
10116.615.03751.56247
10211.211.2614-0.061437
10315.2515.664-0.413995
10411.914.5246-2.62463
10513.214.1212-0.921161
10616.3517.74-1.38999
10712.412.6953-0.295255
10815.8514.24841.60161
10914.3515.3481-0.998072
11018.1516.93671.21332
11111.1512.2083-1.05827
11215.6516.4584-0.808376
11317.7515.8381.91204
1147.6511.9558-4.30582
11512.3513.2276-0.877568
11615.613.73551.8645
11719.317.54731.75266
11815.212.2422.95797
11917.114.87812.22194
12015.613.37442.22556
12118.415.93442.4656
12219.0516.33472.71532
12318.5515.26943.28056
12419.117.30871.79127
12513.112.92330.17671
12612.8515.7486-2.89858
1279.511.7519-2.25192
1284.510.3395-5.83953
12911.8511.06360.786382
13013.615.5107-1.91074
13111.711.1680.531957
13212.412.946-0.545978
13313.3514.7503-1.4003
13411.412.903-1.50302
13514.913.89211.0079
13619.918.54231.35768
13717.7514.31833.43172
13811.212.9259-1.72594
13914.616.2015-1.60146
14017.618.0205-0.420479
14114.0513.50540.54464
14216.116.2547-0.1547
14313.3514.6175-1.26751
14411.8514.5069-2.65689
14511.9513.5364-1.58637
14614.7515.0198-0.269765
14715.1514.34750.802504
14813.216.6347-3.43474
14916.8516.26120.588831
1507.8511.9457-4.09574
1517.712.8623-5.16225
15212.614.6168-2.01679
1537.8514.0446-6.19464
15410.9510.68140.268611
15512.3514.1777-1.82769
1569.9513.2667-3.31666
15714.913.89211.0079
15816.6514.96341.68658
15913.413.09170.308295
16013.9513.44540.504595
16115.713.9141.786
16216.8515.09661.75339
16310.9511.9701-1.02015
16415.3514.17341.17658
16512.212.5629-0.362933
16615.114.33550.764463
16717.7516.55691.19308
16815.214.51170.688301
16914.614.02550.574494
17016.6516.23860.41141
1718.110.6463-2.54628






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9940820.01183590.00591795
130.9934780.01304330.00652164
140.9911010.01779730.00889864
150.9822760.03544780.0177239
160.9879530.02409410.012047
170.9868770.0262470.0131235
180.9778320.04433660.0221683
190.9639430.07211340.0360567
200.9614510.07709750.0385488
210.9425340.1149320.0574661
220.9181630.1636740.0818368
230.8864250.227150.113575
240.8486380.3027240.151362
250.8548280.2903430.145172
260.8209650.3580690.179035
270.7736540.4526930.226346
280.7379650.524070.262035
290.7156670.5686670.284333
300.6757530.6484940.324247
310.6204540.7590920.379546
320.6171140.7657720.382886
330.5706950.8586090.429305
340.5297190.9405620.470281
350.4723180.9446360.527682
360.41250.8249990.5875
370.5356940.9286110.464306
380.4780460.9560920.521954
390.4217580.8435170.578242
400.3697350.7394690.630265
410.853090.293820.14691
420.8251780.3496430.174822
430.8041670.3916650.195833
440.7683650.463270.231635
450.7427180.5145650.257282
460.7016760.5966480.298324
470.6572420.6855160.342758
480.6159480.7681040.384052
490.616430.767140.38357
500.5804660.8390690.419534
510.5621410.8757190.437859
520.5282240.9435520.471776
530.4787420.9574840.521258
540.8904670.2190660.109533
550.8756030.2487940.124397
560.8556410.2887180.144359
570.8339520.3320970.166048
580.8262790.3474410.173721
590.7955190.4089620.204481
600.8397010.3205990.160299
610.8143720.3712550.185628
620.8358910.3282190.164109
630.8558160.2883670.144184
640.8462360.3075270.153764
650.8342020.3315970.165798
660.8420960.3158080.157904
670.8254660.3490690.174534
680.837130.325740.16287
690.8176550.3646890.182345
700.8375890.3248220.162411
710.8347470.3305060.165253
720.8223710.3552580.177629
730.8166140.3667730.183386
740.7864980.4270040.213502
750.8320840.3358320.167916
760.80220.3955990.1978
770.8136790.3726420.186321
780.8250540.3498930.174946
790.8177130.3645750.182287
800.7886030.4227940.211397
810.790230.4195390.20977
820.7627180.4745650.237282
830.7755860.4488280.224414
840.8227920.3544160.177208
850.792140.4157190.20786
860.7815040.4369920.218496
870.7931530.4136950.206847
880.7633620.4732750.236638
890.7637730.4724550.236227
900.7337010.5325970.266299
910.6965980.6068040.303402
920.6702510.6594980.329749
930.6663520.6672960.333648
940.6289450.7421090.371055
950.5908820.8182360.409118
960.6103030.7793930.389697
970.6007830.7984350.399217
980.5774730.8450540.422527
990.5367840.9264310.463216
1000.5276840.9446320.472316
1010.5044660.9910690.495534
1020.4744850.9489710.525515
1030.430930.8618610.56907
1040.4447150.8894290.555285
1050.4073410.8146810.592659
1060.3881970.7763950.611803
1070.3505130.7010260.649487
1080.3332050.666410.666795
1090.313470.6269390.68653
1100.28440.5688010.7156
1110.2571050.5142090.742895
1120.234650.46930.76535
1130.219020.4380390.78098
1140.3275290.6550570.672471
1150.2939230.5878470.706077
1160.295770.591540.70423
1170.2723860.5447730.727614
1180.3172790.6345590.682721
1190.3209290.6418580.679071
1200.3560780.7121550.643922
1210.4232220.8464440.576778
1220.4492850.898570.550715
1230.5018010.9963980.498199
1240.5013720.9972570.498628
1250.4656660.9313310.534334
1260.4720720.9441450.527928
1270.4462850.8925690.553715
1280.5979590.8040830.402041
1290.5643320.8713350.435668
1300.5271810.9456370.472819
1310.5628610.8742780.437139
1320.5087420.9825160.491258
1330.4551470.9102940.544853
1340.4049880.8099770.595012
1350.3750750.750150.624925
1360.3243230.6486460.675677
1370.5584110.8831780.441589
1380.5122640.9754720.487736
1390.4876470.9752950.512353
1400.4234080.8468160.576592
1410.4965290.9930580.503471
1420.4458030.8916070.554197
1430.4297130.8594260.570287
1440.3881930.7763870.611807
1450.3608410.7216820.639159
1460.2946670.5893340.705333
1470.2576750.515350.742325
1480.2166140.4332270.783386
1490.1665340.3330670.833466
1500.1832570.3665140.816743
1510.608810.7823810.39119
1520.5234330.9531340.476567
1530.8878870.2242270.112113
1540.8959040.2081920.104096
1550.9623530.07529390.037647
1560.9923640.01527180.00763592
1570.9799560.04008710.0200436
1580.9524650.09507040.0475352
1590.9208030.1583930.0791967

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.994082 & 0.0118359 & 0.00591795 \tabularnewline
13 & 0.993478 & 0.0130433 & 0.00652164 \tabularnewline
14 & 0.991101 & 0.0177973 & 0.00889864 \tabularnewline
15 & 0.982276 & 0.0354478 & 0.0177239 \tabularnewline
16 & 0.987953 & 0.0240941 & 0.012047 \tabularnewline
17 & 0.986877 & 0.026247 & 0.0131235 \tabularnewline
18 & 0.977832 & 0.0443366 & 0.0221683 \tabularnewline
19 & 0.963943 & 0.0721134 & 0.0360567 \tabularnewline
20 & 0.961451 & 0.0770975 & 0.0385488 \tabularnewline
21 & 0.942534 & 0.114932 & 0.0574661 \tabularnewline
22 & 0.918163 & 0.163674 & 0.0818368 \tabularnewline
23 & 0.886425 & 0.22715 & 0.113575 \tabularnewline
24 & 0.848638 & 0.302724 & 0.151362 \tabularnewline
25 & 0.854828 & 0.290343 & 0.145172 \tabularnewline
26 & 0.820965 & 0.358069 & 0.179035 \tabularnewline
27 & 0.773654 & 0.452693 & 0.226346 \tabularnewline
28 & 0.737965 & 0.52407 & 0.262035 \tabularnewline
29 & 0.715667 & 0.568667 & 0.284333 \tabularnewline
30 & 0.675753 & 0.648494 & 0.324247 \tabularnewline
31 & 0.620454 & 0.759092 & 0.379546 \tabularnewline
32 & 0.617114 & 0.765772 & 0.382886 \tabularnewline
33 & 0.570695 & 0.858609 & 0.429305 \tabularnewline
34 & 0.529719 & 0.940562 & 0.470281 \tabularnewline
35 & 0.472318 & 0.944636 & 0.527682 \tabularnewline
36 & 0.4125 & 0.824999 & 0.5875 \tabularnewline
37 & 0.535694 & 0.928611 & 0.464306 \tabularnewline
38 & 0.478046 & 0.956092 & 0.521954 \tabularnewline
39 & 0.421758 & 0.843517 & 0.578242 \tabularnewline
40 & 0.369735 & 0.739469 & 0.630265 \tabularnewline
41 & 0.85309 & 0.29382 & 0.14691 \tabularnewline
42 & 0.825178 & 0.349643 & 0.174822 \tabularnewline
43 & 0.804167 & 0.391665 & 0.195833 \tabularnewline
44 & 0.768365 & 0.46327 & 0.231635 \tabularnewline
45 & 0.742718 & 0.514565 & 0.257282 \tabularnewline
46 & 0.701676 & 0.596648 & 0.298324 \tabularnewline
47 & 0.657242 & 0.685516 & 0.342758 \tabularnewline
48 & 0.615948 & 0.768104 & 0.384052 \tabularnewline
49 & 0.61643 & 0.76714 & 0.38357 \tabularnewline
50 & 0.580466 & 0.839069 & 0.419534 \tabularnewline
51 & 0.562141 & 0.875719 & 0.437859 \tabularnewline
52 & 0.528224 & 0.943552 & 0.471776 \tabularnewline
53 & 0.478742 & 0.957484 & 0.521258 \tabularnewline
54 & 0.890467 & 0.219066 & 0.109533 \tabularnewline
55 & 0.875603 & 0.248794 & 0.124397 \tabularnewline
56 & 0.855641 & 0.288718 & 0.144359 \tabularnewline
57 & 0.833952 & 0.332097 & 0.166048 \tabularnewline
58 & 0.826279 & 0.347441 & 0.173721 \tabularnewline
59 & 0.795519 & 0.408962 & 0.204481 \tabularnewline
60 & 0.839701 & 0.320599 & 0.160299 \tabularnewline
61 & 0.814372 & 0.371255 & 0.185628 \tabularnewline
62 & 0.835891 & 0.328219 & 0.164109 \tabularnewline
63 & 0.855816 & 0.288367 & 0.144184 \tabularnewline
64 & 0.846236 & 0.307527 & 0.153764 \tabularnewline
65 & 0.834202 & 0.331597 & 0.165798 \tabularnewline
66 & 0.842096 & 0.315808 & 0.157904 \tabularnewline
67 & 0.825466 & 0.349069 & 0.174534 \tabularnewline
68 & 0.83713 & 0.32574 & 0.16287 \tabularnewline
69 & 0.817655 & 0.364689 & 0.182345 \tabularnewline
70 & 0.837589 & 0.324822 & 0.162411 \tabularnewline
71 & 0.834747 & 0.330506 & 0.165253 \tabularnewline
72 & 0.822371 & 0.355258 & 0.177629 \tabularnewline
73 & 0.816614 & 0.366773 & 0.183386 \tabularnewline
74 & 0.786498 & 0.427004 & 0.213502 \tabularnewline
75 & 0.832084 & 0.335832 & 0.167916 \tabularnewline
76 & 0.8022 & 0.395599 & 0.1978 \tabularnewline
77 & 0.813679 & 0.372642 & 0.186321 \tabularnewline
78 & 0.825054 & 0.349893 & 0.174946 \tabularnewline
79 & 0.817713 & 0.364575 & 0.182287 \tabularnewline
80 & 0.788603 & 0.422794 & 0.211397 \tabularnewline
81 & 0.79023 & 0.419539 & 0.20977 \tabularnewline
82 & 0.762718 & 0.474565 & 0.237282 \tabularnewline
83 & 0.775586 & 0.448828 & 0.224414 \tabularnewline
84 & 0.822792 & 0.354416 & 0.177208 \tabularnewline
85 & 0.79214 & 0.415719 & 0.20786 \tabularnewline
86 & 0.781504 & 0.436992 & 0.218496 \tabularnewline
87 & 0.793153 & 0.413695 & 0.206847 \tabularnewline
88 & 0.763362 & 0.473275 & 0.236638 \tabularnewline
89 & 0.763773 & 0.472455 & 0.236227 \tabularnewline
90 & 0.733701 & 0.532597 & 0.266299 \tabularnewline
91 & 0.696598 & 0.606804 & 0.303402 \tabularnewline
92 & 0.670251 & 0.659498 & 0.329749 \tabularnewline
93 & 0.666352 & 0.667296 & 0.333648 \tabularnewline
94 & 0.628945 & 0.742109 & 0.371055 \tabularnewline
95 & 0.590882 & 0.818236 & 0.409118 \tabularnewline
96 & 0.610303 & 0.779393 & 0.389697 \tabularnewline
97 & 0.600783 & 0.798435 & 0.399217 \tabularnewline
98 & 0.577473 & 0.845054 & 0.422527 \tabularnewline
99 & 0.536784 & 0.926431 & 0.463216 \tabularnewline
100 & 0.527684 & 0.944632 & 0.472316 \tabularnewline
101 & 0.504466 & 0.991069 & 0.495534 \tabularnewline
102 & 0.474485 & 0.948971 & 0.525515 \tabularnewline
103 & 0.43093 & 0.861861 & 0.56907 \tabularnewline
104 & 0.444715 & 0.889429 & 0.555285 \tabularnewline
105 & 0.407341 & 0.814681 & 0.592659 \tabularnewline
106 & 0.388197 & 0.776395 & 0.611803 \tabularnewline
107 & 0.350513 & 0.701026 & 0.649487 \tabularnewline
108 & 0.333205 & 0.66641 & 0.666795 \tabularnewline
109 & 0.31347 & 0.626939 & 0.68653 \tabularnewline
110 & 0.2844 & 0.568801 & 0.7156 \tabularnewline
111 & 0.257105 & 0.514209 & 0.742895 \tabularnewline
112 & 0.23465 & 0.4693 & 0.76535 \tabularnewline
113 & 0.21902 & 0.438039 & 0.78098 \tabularnewline
114 & 0.327529 & 0.655057 & 0.672471 \tabularnewline
115 & 0.293923 & 0.587847 & 0.706077 \tabularnewline
116 & 0.29577 & 0.59154 & 0.70423 \tabularnewline
117 & 0.272386 & 0.544773 & 0.727614 \tabularnewline
118 & 0.317279 & 0.634559 & 0.682721 \tabularnewline
119 & 0.320929 & 0.641858 & 0.679071 \tabularnewline
120 & 0.356078 & 0.712155 & 0.643922 \tabularnewline
121 & 0.423222 & 0.846444 & 0.576778 \tabularnewline
122 & 0.449285 & 0.89857 & 0.550715 \tabularnewline
123 & 0.501801 & 0.996398 & 0.498199 \tabularnewline
124 & 0.501372 & 0.997257 & 0.498628 \tabularnewline
125 & 0.465666 & 0.931331 & 0.534334 \tabularnewline
126 & 0.472072 & 0.944145 & 0.527928 \tabularnewline
127 & 0.446285 & 0.892569 & 0.553715 \tabularnewline
128 & 0.597959 & 0.804083 & 0.402041 \tabularnewline
129 & 0.564332 & 0.871335 & 0.435668 \tabularnewline
130 & 0.527181 & 0.945637 & 0.472819 \tabularnewline
131 & 0.562861 & 0.874278 & 0.437139 \tabularnewline
132 & 0.508742 & 0.982516 & 0.491258 \tabularnewline
133 & 0.455147 & 0.910294 & 0.544853 \tabularnewline
134 & 0.404988 & 0.809977 & 0.595012 \tabularnewline
135 & 0.375075 & 0.75015 & 0.624925 \tabularnewline
136 & 0.324323 & 0.648646 & 0.675677 \tabularnewline
137 & 0.558411 & 0.883178 & 0.441589 \tabularnewline
138 & 0.512264 & 0.975472 & 0.487736 \tabularnewline
139 & 0.487647 & 0.975295 & 0.512353 \tabularnewline
140 & 0.423408 & 0.846816 & 0.576592 \tabularnewline
141 & 0.496529 & 0.993058 & 0.503471 \tabularnewline
142 & 0.445803 & 0.891607 & 0.554197 \tabularnewline
143 & 0.429713 & 0.859426 & 0.570287 \tabularnewline
144 & 0.388193 & 0.776387 & 0.611807 \tabularnewline
145 & 0.360841 & 0.721682 & 0.639159 \tabularnewline
146 & 0.294667 & 0.589334 & 0.705333 \tabularnewline
147 & 0.257675 & 0.51535 & 0.742325 \tabularnewline
148 & 0.216614 & 0.433227 & 0.783386 \tabularnewline
149 & 0.166534 & 0.333067 & 0.833466 \tabularnewline
150 & 0.183257 & 0.366514 & 0.816743 \tabularnewline
151 & 0.60881 & 0.782381 & 0.39119 \tabularnewline
152 & 0.523433 & 0.953134 & 0.476567 \tabularnewline
153 & 0.887887 & 0.224227 & 0.112113 \tabularnewline
154 & 0.895904 & 0.208192 & 0.104096 \tabularnewline
155 & 0.962353 & 0.0752939 & 0.037647 \tabularnewline
156 & 0.992364 & 0.0152718 & 0.00763592 \tabularnewline
157 & 0.979956 & 0.0400871 & 0.0200436 \tabularnewline
158 & 0.952465 & 0.0950704 & 0.0475352 \tabularnewline
159 & 0.920803 & 0.158393 & 0.0791967 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&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]0.994082[/C][C]0.0118359[/C][C]0.00591795[/C][/ROW]
[ROW][C]13[/C][C]0.993478[/C][C]0.0130433[/C][C]0.00652164[/C][/ROW]
[ROW][C]14[/C][C]0.991101[/C][C]0.0177973[/C][C]0.00889864[/C][/ROW]
[ROW][C]15[/C][C]0.982276[/C][C]0.0354478[/C][C]0.0177239[/C][/ROW]
[ROW][C]16[/C][C]0.987953[/C][C]0.0240941[/C][C]0.012047[/C][/ROW]
[ROW][C]17[/C][C]0.986877[/C][C]0.026247[/C][C]0.0131235[/C][/ROW]
[ROW][C]18[/C][C]0.977832[/C][C]0.0443366[/C][C]0.0221683[/C][/ROW]
[ROW][C]19[/C][C]0.963943[/C][C]0.0721134[/C][C]0.0360567[/C][/ROW]
[ROW][C]20[/C][C]0.961451[/C][C]0.0770975[/C][C]0.0385488[/C][/ROW]
[ROW][C]21[/C][C]0.942534[/C][C]0.114932[/C][C]0.0574661[/C][/ROW]
[ROW][C]22[/C][C]0.918163[/C][C]0.163674[/C][C]0.0818368[/C][/ROW]
[ROW][C]23[/C][C]0.886425[/C][C]0.22715[/C][C]0.113575[/C][/ROW]
[ROW][C]24[/C][C]0.848638[/C][C]0.302724[/C][C]0.151362[/C][/ROW]
[ROW][C]25[/C][C]0.854828[/C][C]0.290343[/C][C]0.145172[/C][/ROW]
[ROW][C]26[/C][C]0.820965[/C][C]0.358069[/C][C]0.179035[/C][/ROW]
[ROW][C]27[/C][C]0.773654[/C][C]0.452693[/C][C]0.226346[/C][/ROW]
[ROW][C]28[/C][C]0.737965[/C][C]0.52407[/C][C]0.262035[/C][/ROW]
[ROW][C]29[/C][C]0.715667[/C][C]0.568667[/C][C]0.284333[/C][/ROW]
[ROW][C]30[/C][C]0.675753[/C][C]0.648494[/C][C]0.324247[/C][/ROW]
[ROW][C]31[/C][C]0.620454[/C][C]0.759092[/C][C]0.379546[/C][/ROW]
[ROW][C]32[/C][C]0.617114[/C][C]0.765772[/C][C]0.382886[/C][/ROW]
[ROW][C]33[/C][C]0.570695[/C][C]0.858609[/C][C]0.429305[/C][/ROW]
[ROW][C]34[/C][C]0.529719[/C][C]0.940562[/C][C]0.470281[/C][/ROW]
[ROW][C]35[/C][C]0.472318[/C][C]0.944636[/C][C]0.527682[/C][/ROW]
[ROW][C]36[/C][C]0.4125[/C][C]0.824999[/C][C]0.5875[/C][/ROW]
[ROW][C]37[/C][C]0.535694[/C][C]0.928611[/C][C]0.464306[/C][/ROW]
[ROW][C]38[/C][C]0.478046[/C][C]0.956092[/C][C]0.521954[/C][/ROW]
[ROW][C]39[/C][C]0.421758[/C][C]0.843517[/C][C]0.578242[/C][/ROW]
[ROW][C]40[/C][C]0.369735[/C][C]0.739469[/C][C]0.630265[/C][/ROW]
[ROW][C]41[/C][C]0.85309[/C][C]0.29382[/C][C]0.14691[/C][/ROW]
[ROW][C]42[/C][C]0.825178[/C][C]0.349643[/C][C]0.174822[/C][/ROW]
[ROW][C]43[/C][C]0.804167[/C][C]0.391665[/C][C]0.195833[/C][/ROW]
[ROW][C]44[/C][C]0.768365[/C][C]0.46327[/C][C]0.231635[/C][/ROW]
[ROW][C]45[/C][C]0.742718[/C][C]0.514565[/C][C]0.257282[/C][/ROW]
[ROW][C]46[/C][C]0.701676[/C][C]0.596648[/C][C]0.298324[/C][/ROW]
[ROW][C]47[/C][C]0.657242[/C][C]0.685516[/C][C]0.342758[/C][/ROW]
[ROW][C]48[/C][C]0.615948[/C][C]0.768104[/C][C]0.384052[/C][/ROW]
[ROW][C]49[/C][C]0.61643[/C][C]0.76714[/C][C]0.38357[/C][/ROW]
[ROW][C]50[/C][C]0.580466[/C][C]0.839069[/C][C]0.419534[/C][/ROW]
[ROW][C]51[/C][C]0.562141[/C][C]0.875719[/C][C]0.437859[/C][/ROW]
[ROW][C]52[/C][C]0.528224[/C][C]0.943552[/C][C]0.471776[/C][/ROW]
[ROW][C]53[/C][C]0.478742[/C][C]0.957484[/C][C]0.521258[/C][/ROW]
[ROW][C]54[/C][C]0.890467[/C][C]0.219066[/C][C]0.109533[/C][/ROW]
[ROW][C]55[/C][C]0.875603[/C][C]0.248794[/C][C]0.124397[/C][/ROW]
[ROW][C]56[/C][C]0.855641[/C][C]0.288718[/C][C]0.144359[/C][/ROW]
[ROW][C]57[/C][C]0.833952[/C][C]0.332097[/C][C]0.166048[/C][/ROW]
[ROW][C]58[/C][C]0.826279[/C][C]0.347441[/C][C]0.173721[/C][/ROW]
[ROW][C]59[/C][C]0.795519[/C][C]0.408962[/C][C]0.204481[/C][/ROW]
[ROW][C]60[/C][C]0.839701[/C][C]0.320599[/C][C]0.160299[/C][/ROW]
[ROW][C]61[/C][C]0.814372[/C][C]0.371255[/C][C]0.185628[/C][/ROW]
[ROW][C]62[/C][C]0.835891[/C][C]0.328219[/C][C]0.164109[/C][/ROW]
[ROW][C]63[/C][C]0.855816[/C][C]0.288367[/C][C]0.144184[/C][/ROW]
[ROW][C]64[/C][C]0.846236[/C][C]0.307527[/C][C]0.153764[/C][/ROW]
[ROW][C]65[/C][C]0.834202[/C][C]0.331597[/C][C]0.165798[/C][/ROW]
[ROW][C]66[/C][C]0.842096[/C][C]0.315808[/C][C]0.157904[/C][/ROW]
[ROW][C]67[/C][C]0.825466[/C][C]0.349069[/C][C]0.174534[/C][/ROW]
[ROW][C]68[/C][C]0.83713[/C][C]0.32574[/C][C]0.16287[/C][/ROW]
[ROW][C]69[/C][C]0.817655[/C][C]0.364689[/C][C]0.182345[/C][/ROW]
[ROW][C]70[/C][C]0.837589[/C][C]0.324822[/C][C]0.162411[/C][/ROW]
[ROW][C]71[/C][C]0.834747[/C][C]0.330506[/C][C]0.165253[/C][/ROW]
[ROW][C]72[/C][C]0.822371[/C][C]0.355258[/C][C]0.177629[/C][/ROW]
[ROW][C]73[/C][C]0.816614[/C][C]0.366773[/C][C]0.183386[/C][/ROW]
[ROW][C]74[/C][C]0.786498[/C][C]0.427004[/C][C]0.213502[/C][/ROW]
[ROW][C]75[/C][C]0.832084[/C][C]0.335832[/C][C]0.167916[/C][/ROW]
[ROW][C]76[/C][C]0.8022[/C][C]0.395599[/C][C]0.1978[/C][/ROW]
[ROW][C]77[/C][C]0.813679[/C][C]0.372642[/C][C]0.186321[/C][/ROW]
[ROW][C]78[/C][C]0.825054[/C][C]0.349893[/C][C]0.174946[/C][/ROW]
[ROW][C]79[/C][C]0.817713[/C][C]0.364575[/C][C]0.182287[/C][/ROW]
[ROW][C]80[/C][C]0.788603[/C][C]0.422794[/C][C]0.211397[/C][/ROW]
[ROW][C]81[/C][C]0.79023[/C][C]0.419539[/C][C]0.20977[/C][/ROW]
[ROW][C]82[/C][C]0.762718[/C][C]0.474565[/C][C]0.237282[/C][/ROW]
[ROW][C]83[/C][C]0.775586[/C][C]0.448828[/C][C]0.224414[/C][/ROW]
[ROW][C]84[/C][C]0.822792[/C][C]0.354416[/C][C]0.177208[/C][/ROW]
[ROW][C]85[/C][C]0.79214[/C][C]0.415719[/C][C]0.20786[/C][/ROW]
[ROW][C]86[/C][C]0.781504[/C][C]0.436992[/C][C]0.218496[/C][/ROW]
[ROW][C]87[/C][C]0.793153[/C][C]0.413695[/C][C]0.206847[/C][/ROW]
[ROW][C]88[/C][C]0.763362[/C][C]0.473275[/C][C]0.236638[/C][/ROW]
[ROW][C]89[/C][C]0.763773[/C][C]0.472455[/C][C]0.236227[/C][/ROW]
[ROW][C]90[/C][C]0.733701[/C][C]0.532597[/C][C]0.266299[/C][/ROW]
[ROW][C]91[/C][C]0.696598[/C][C]0.606804[/C][C]0.303402[/C][/ROW]
[ROW][C]92[/C][C]0.670251[/C][C]0.659498[/C][C]0.329749[/C][/ROW]
[ROW][C]93[/C][C]0.666352[/C][C]0.667296[/C][C]0.333648[/C][/ROW]
[ROW][C]94[/C][C]0.628945[/C][C]0.742109[/C][C]0.371055[/C][/ROW]
[ROW][C]95[/C][C]0.590882[/C][C]0.818236[/C][C]0.409118[/C][/ROW]
[ROW][C]96[/C][C]0.610303[/C][C]0.779393[/C][C]0.389697[/C][/ROW]
[ROW][C]97[/C][C]0.600783[/C][C]0.798435[/C][C]0.399217[/C][/ROW]
[ROW][C]98[/C][C]0.577473[/C][C]0.845054[/C][C]0.422527[/C][/ROW]
[ROW][C]99[/C][C]0.536784[/C][C]0.926431[/C][C]0.463216[/C][/ROW]
[ROW][C]100[/C][C]0.527684[/C][C]0.944632[/C][C]0.472316[/C][/ROW]
[ROW][C]101[/C][C]0.504466[/C][C]0.991069[/C][C]0.495534[/C][/ROW]
[ROW][C]102[/C][C]0.474485[/C][C]0.948971[/C][C]0.525515[/C][/ROW]
[ROW][C]103[/C][C]0.43093[/C][C]0.861861[/C][C]0.56907[/C][/ROW]
[ROW][C]104[/C][C]0.444715[/C][C]0.889429[/C][C]0.555285[/C][/ROW]
[ROW][C]105[/C][C]0.407341[/C][C]0.814681[/C][C]0.592659[/C][/ROW]
[ROW][C]106[/C][C]0.388197[/C][C]0.776395[/C][C]0.611803[/C][/ROW]
[ROW][C]107[/C][C]0.350513[/C][C]0.701026[/C][C]0.649487[/C][/ROW]
[ROW][C]108[/C][C]0.333205[/C][C]0.66641[/C][C]0.666795[/C][/ROW]
[ROW][C]109[/C][C]0.31347[/C][C]0.626939[/C][C]0.68653[/C][/ROW]
[ROW][C]110[/C][C]0.2844[/C][C]0.568801[/C][C]0.7156[/C][/ROW]
[ROW][C]111[/C][C]0.257105[/C][C]0.514209[/C][C]0.742895[/C][/ROW]
[ROW][C]112[/C][C]0.23465[/C][C]0.4693[/C][C]0.76535[/C][/ROW]
[ROW][C]113[/C][C]0.21902[/C][C]0.438039[/C][C]0.78098[/C][/ROW]
[ROW][C]114[/C][C]0.327529[/C][C]0.655057[/C][C]0.672471[/C][/ROW]
[ROW][C]115[/C][C]0.293923[/C][C]0.587847[/C][C]0.706077[/C][/ROW]
[ROW][C]116[/C][C]0.29577[/C][C]0.59154[/C][C]0.70423[/C][/ROW]
[ROW][C]117[/C][C]0.272386[/C][C]0.544773[/C][C]0.727614[/C][/ROW]
[ROW][C]118[/C][C]0.317279[/C][C]0.634559[/C][C]0.682721[/C][/ROW]
[ROW][C]119[/C][C]0.320929[/C][C]0.641858[/C][C]0.679071[/C][/ROW]
[ROW][C]120[/C][C]0.356078[/C][C]0.712155[/C][C]0.643922[/C][/ROW]
[ROW][C]121[/C][C]0.423222[/C][C]0.846444[/C][C]0.576778[/C][/ROW]
[ROW][C]122[/C][C]0.449285[/C][C]0.89857[/C][C]0.550715[/C][/ROW]
[ROW][C]123[/C][C]0.501801[/C][C]0.996398[/C][C]0.498199[/C][/ROW]
[ROW][C]124[/C][C]0.501372[/C][C]0.997257[/C][C]0.498628[/C][/ROW]
[ROW][C]125[/C][C]0.465666[/C][C]0.931331[/C][C]0.534334[/C][/ROW]
[ROW][C]126[/C][C]0.472072[/C][C]0.944145[/C][C]0.527928[/C][/ROW]
[ROW][C]127[/C][C]0.446285[/C][C]0.892569[/C][C]0.553715[/C][/ROW]
[ROW][C]128[/C][C]0.597959[/C][C]0.804083[/C][C]0.402041[/C][/ROW]
[ROW][C]129[/C][C]0.564332[/C][C]0.871335[/C][C]0.435668[/C][/ROW]
[ROW][C]130[/C][C]0.527181[/C][C]0.945637[/C][C]0.472819[/C][/ROW]
[ROW][C]131[/C][C]0.562861[/C][C]0.874278[/C][C]0.437139[/C][/ROW]
[ROW][C]132[/C][C]0.508742[/C][C]0.982516[/C][C]0.491258[/C][/ROW]
[ROW][C]133[/C][C]0.455147[/C][C]0.910294[/C][C]0.544853[/C][/ROW]
[ROW][C]134[/C][C]0.404988[/C][C]0.809977[/C][C]0.595012[/C][/ROW]
[ROW][C]135[/C][C]0.375075[/C][C]0.75015[/C][C]0.624925[/C][/ROW]
[ROW][C]136[/C][C]0.324323[/C][C]0.648646[/C][C]0.675677[/C][/ROW]
[ROW][C]137[/C][C]0.558411[/C][C]0.883178[/C][C]0.441589[/C][/ROW]
[ROW][C]138[/C][C]0.512264[/C][C]0.975472[/C][C]0.487736[/C][/ROW]
[ROW][C]139[/C][C]0.487647[/C][C]0.975295[/C][C]0.512353[/C][/ROW]
[ROW][C]140[/C][C]0.423408[/C][C]0.846816[/C][C]0.576592[/C][/ROW]
[ROW][C]141[/C][C]0.496529[/C][C]0.993058[/C][C]0.503471[/C][/ROW]
[ROW][C]142[/C][C]0.445803[/C][C]0.891607[/C][C]0.554197[/C][/ROW]
[ROW][C]143[/C][C]0.429713[/C][C]0.859426[/C][C]0.570287[/C][/ROW]
[ROW][C]144[/C][C]0.388193[/C][C]0.776387[/C][C]0.611807[/C][/ROW]
[ROW][C]145[/C][C]0.360841[/C][C]0.721682[/C][C]0.639159[/C][/ROW]
[ROW][C]146[/C][C]0.294667[/C][C]0.589334[/C][C]0.705333[/C][/ROW]
[ROW][C]147[/C][C]0.257675[/C][C]0.51535[/C][C]0.742325[/C][/ROW]
[ROW][C]148[/C][C]0.216614[/C][C]0.433227[/C][C]0.783386[/C][/ROW]
[ROW][C]149[/C][C]0.166534[/C][C]0.333067[/C][C]0.833466[/C][/ROW]
[ROW][C]150[/C][C]0.183257[/C][C]0.366514[/C][C]0.816743[/C][/ROW]
[ROW][C]151[/C][C]0.60881[/C][C]0.782381[/C][C]0.39119[/C][/ROW]
[ROW][C]152[/C][C]0.523433[/C][C]0.953134[/C][C]0.476567[/C][/ROW]
[ROW][C]153[/C][C]0.887887[/C][C]0.224227[/C][C]0.112113[/C][/ROW]
[ROW][C]154[/C][C]0.895904[/C][C]0.208192[/C][C]0.104096[/C][/ROW]
[ROW][C]155[/C][C]0.962353[/C][C]0.0752939[/C][C]0.037647[/C][/ROW]
[ROW][C]156[/C][C]0.992364[/C][C]0.0152718[/C][C]0.00763592[/C][/ROW]
[ROW][C]157[/C][C]0.979956[/C][C]0.0400871[/C][C]0.0200436[/C][/ROW]
[ROW][C]158[/C][C]0.952465[/C][C]0.0950704[/C][C]0.0475352[/C][/ROW]
[ROW][C]159[/C][C]0.920803[/C][C]0.158393[/C][C]0.0791967[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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
120.9940820.01183590.00591795
130.9934780.01304330.00652164
140.9911010.01779730.00889864
150.9822760.03544780.0177239
160.9879530.02409410.012047
170.9868770.0262470.0131235
180.9778320.04433660.0221683
190.9639430.07211340.0360567
200.9614510.07709750.0385488
210.9425340.1149320.0574661
220.9181630.1636740.0818368
230.8864250.227150.113575
240.8486380.3027240.151362
250.8548280.2903430.145172
260.8209650.3580690.179035
270.7736540.4526930.226346
280.7379650.524070.262035
290.7156670.5686670.284333
300.6757530.6484940.324247
310.6204540.7590920.379546
320.6171140.7657720.382886
330.5706950.8586090.429305
340.5297190.9405620.470281
350.4723180.9446360.527682
360.41250.8249990.5875
370.5356940.9286110.464306
380.4780460.9560920.521954
390.4217580.8435170.578242
400.3697350.7394690.630265
410.853090.293820.14691
420.8251780.3496430.174822
430.8041670.3916650.195833
440.7683650.463270.231635
450.7427180.5145650.257282
460.7016760.5966480.298324
470.6572420.6855160.342758
480.6159480.7681040.384052
490.616430.767140.38357
500.5804660.8390690.419534
510.5621410.8757190.437859
520.5282240.9435520.471776
530.4787420.9574840.521258
540.8904670.2190660.109533
550.8756030.2487940.124397
560.8556410.2887180.144359
570.8339520.3320970.166048
580.8262790.3474410.173721
590.7955190.4089620.204481
600.8397010.3205990.160299
610.8143720.3712550.185628
620.8358910.3282190.164109
630.8558160.2883670.144184
640.8462360.3075270.153764
650.8342020.3315970.165798
660.8420960.3158080.157904
670.8254660.3490690.174534
680.837130.325740.16287
690.8176550.3646890.182345
700.8375890.3248220.162411
710.8347470.3305060.165253
720.8223710.3552580.177629
730.8166140.3667730.183386
740.7864980.4270040.213502
750.8320840.3358320.167916
760.80220.3955990.1978
770.8136790.3726420.186321
780.8250540.3498930.174946
790.8177130.3645750.182287
800.7886030.4227940.211397
810.790230.4195390.20977
820.7627180.4745650.237282
830.7755860.4488280.224414
840.8227920.3544160.177208
850.792140.4157190.20786
860.7815040.4369920.218496
870.7931530.4136950.206847
880.7633620.4732750.236638
890.7637730.4724550.236227
900.7337010.5325970.266299
910.6965980.6068040.303402
920.6702510.6594980.329749
930.6663520.6672960.333648
940.6289450.7421090.371055
950.5908820.8182360.409118
960.6103030.7793930.389697
970.6007830.7984350.399217
980.5774730.8450540.422527
990.5367840.9264310.463216
1000.5276840.9446320.472316
1010.5044660.9910690.495534
1020.4744850.9489710.525515
1030.430930.8618610.56907
1040.4447150.8894290.555285
1050.4073410.8146810.592659
1060.3881970.7763950.611803
1070.3505130.7010260.649487
1080.3332050.666410.666795
1090.313470.6269390.68653
1100.28440.5688010.7156
1110.2571050.5142090.742895
1120.234650.46930.76535
1130.219020.4380390.78098
1140.3275290.6550570.672471
1150.2939230.5878470.706077
1160.295770.591540.70423
1170.2723860.5447730.727614
1180.3172790.6345590.682721
1190.3209290.6418580.679071
1200.3560780.7121550.643922
1210.4232220.8464440.576778
1220.4492850.898570.550715
1230.5018010.9963980.498199
1240.5013720.9972570.498628
1250.4656660.9313310.534334
1260.4720720.9441450.527928
1270.4462850.8925690.553715
1280.5979590.8040830.402041
1290.5643320.8713350.435668
1300.5271810.9456370.472819
1310.5628610.8742780.437139
1320.5087420.9825160.491258
1330.4551470.9102940.544853
1340.4049880.8099770.595012
1350.3750750.750150.624925
1360.3243230.6486460.675677
1370.5584110.8831780.441589
1380.5122640.9754720.487736
1390.4876470.9752950.512353
1400.4234080.8468160.576592
1410.4965290.9930580.503471
1420.4458030.8916070.554197
1430.4297130.8594260.570287
1440.3881930.7763870.611807
1450.3608410.7216820.639159
1460.2946670.5893340.705333
1470.2576750.515350.742325
1480.2166140.4332270.783386
1490.1665340.3330670.833466
1500.1832570.3665140.816743
1510.608810.7823810.39119
1520.5234330.9531340.476567
1530.8878870.2242270.112113
1540.8959040.2081920.104096
1550.9623530.07529390.037647
1560.9923640.01527180.00763592
1570.9799560.04008710.0200436
1580.9524650.09507040.0475352
1590.9208030.1583930.0791967






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0608108NOK
10% type I error level130.0878378OK

\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 & 9 & 0.0608108 & NOK \tabularnewline
10% type I error level & 13 & 0.0878378 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267330&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]9[/C][C]0.0608108[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0878378[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267330&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267330&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 level90.0608108NOK
10% type I error level130.0878378OK


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')
}