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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 10 Dec 2014 12:38:05 +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/10/t1418215475zcsv53gx45e9xkw.htm/, Retrieved Sun, 19 May 2024 16:11:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265049, Retrieved Sun, 19 May 2024 16:11:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper1] [2014-12-10 12:38:05] [8fc8509b9f8606b50b2407d6e00dc1c6] [Current]
- RMPD    [Skewness and Kurtosis Test] [gfhfh] [2014-12-18 16:49:58] [e220e74fe92df18c70a4846ba6a6c667]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.35 1 0 52 51 6 16 9 23 48
12.7 1 0 16 56 4 16 11 22 50
18.1 1 0 46 67 8 16 12 21 150
17.85 1 0 56 69 5 16 12 25 154
16.6 0 1 52 57 4 12 7 30 109
12.6 1 1 55 56 17 15 12 17 68
17.1 1 0 50 55 4 14 12 27 194
19.1 0 0 59 63 4 15 12 23 158
16.1 1 0 60 67 8 16 10 23 159
13.35 0 0 52 65 4 13 15 18 67
18.4 0 0 44 47 7 10 10 18 147
14.7 1 0 67 76 4 17 15 23 39
10.6 1 0 52 64 4 15 10 19 100
12.6 1 0 55 68 5 18 15 15 111
16.2 1 0 37 64 7 16 9 20 138
13.6 1 0 54 65 4 20 15 16 101
18.9 1 1 72 71 4 16 12 24 131
14.1 1 0 51 63 7 17 13 25 101
14.5 1 0 48 60 11 16 12 25 114
16.15 0 0 60 68 7 15 12 19 165
14.75 1 0 50 72 4 13 8 19 114
14.8 1 0 63 70 4 16 9 16 111
12.45 1 0 33 61 4 16 15 19 75
12.65 1 0 67 61 4 16 12 19 82
17.35 1 0 46 62 4 17 12 23 121
8.6 1 0 54 71 4 20 15 21 32
18.4 0 0 59 71 6 14 11 22 150
16.1 1 0 61 51 8 17 12 19 117
11.6 1 1 33 56 23 6 6 20 71
17.75 1 0 47 70 4 16 14 20 165
15.25 1 0 69 73 8 15 12 3 154
17.65 1 0 52 76 6 16 12 23 126
16.35 0 0 55 68 4 16 12 23 149
17.65 0 0 41 48 7 14 11 20 145
13.6 1 0 73 52 4 16 12 15 120
14.35 0 0 52 60 4 16 12 16 109
14.75 0 0 50 59 4 16 12 7 132
18.25 1 0 51 57 10 14 12 24 172
9.9 0 0 60 79 6 14 8 17 169
16 1 0 56 60 5 16 8 24 114
18.25 1 0 56 60 5 16 12 24 156
16.85 0 0 29 59 4 15 12 19 172
14.6 1 1 66 62 4 16 11 25 68
13.85 1 1 66 59 5 16 10 20 89
18.95 1 0 73 61 5 18 11 28 167
15.6 0 0 55 71 5 15 12 23 113
14.85 0 1 64 57 5 16 13 27 115
11.75 0 1 40 66 4 16 12 18 78
18.45 0 1 46 63 6 16 12 28 118
15.9 1 1 58 69 4 17 10 21 87
17.1 0 0 43 58 4 14 10 19 173
16.1 1 0 61 59 4 18 11 23 2
19.9 0 1 51 48 9 9 8 27 162
10.95 1 1 50 66 18 15 12 22 49
18.45 0 1 52 73 6 14 9 28 122
15.1 1 1 54 67 5 15 12 25 96
15 0 1 66 61 4 13 9 21 100
11.35 0 1 61 68 11 16 11 22 82
15.95 1 1 80 75 4 20 15 28 100
18.1 0 1 51 62 10 14 8 20 115
14.6 1 1 56 69 6 12 8 29 141
15.4 1 0 56 58 8 15 11 25 165
15.4 1 0 56 60 8 15 11 25 165
17.6 1 1 53 74 6 15 11 20 110
13.35 1 0 47 55 8 16 13 20 118
19.1 0 0 25 62 4 11 7 16 158
15.35 1 1 47 63 4 16 12 20 146
7.6 0 0 46 69 9 7 8 20 49
13.4 0 1 50 58 9 11 8 23 90
13.9 0 1 39 58 5 9 4 18 121
19.1 1 0 51 68 4 15 11 25 155
15.25 0 1 58 72 4 16 10 18 104
12.9 1 1 35 62 15 14 7 19 147
16.1 0 1 58 62 10 15 12 25 110
17.35 0 1 60 65 9 13 11 25 108
13.15 0 1 62 69 7 13 9 25 113
12.15 0 1 63 66 9 12 10 24 115
12.6 1 1 53 72 6 16 8 19 61
10.35 1 1 46 62 4 14 8 26 60
15.4 1 1 67 75 7 16 11 10 109
9.6 1 1 59 58 4 14 12 17 68
18.2 0 1 64 66 7 15 10 13 111
13.6 0 1 38 55 4 10 10 17 77
14.85 1 1 50 47 15 16 12 30 73
14.75 0 0 48 72 4 14 8 25 151
14.1 0 1 48 62 9 16 11 4 89
14.9 0 1 47 64 4 12 8 16 78
16.25 0 1 66 64 4 16 10 21 110
19.25 1 0 47 19 28 16 14 23 220
13.6 1 1 63 50 4 15 9 22 65
13.6 0 0 58 68 4 14 9 17 141
15.65 0 1 44 70 4 16 10 20 117
12.75 1 0 51 79 5 11 13 20 122
14.6 0 1 43 69 4 15 12 22 63
9.85 1 0 55 71 4 18 13 16 44
12.65 1 1 38 48 12 13 8 23 52
19.2 0 1 45 73 4 7 3 0 131
16.6 1 1 50 74 6 7 8 18 101
11.2 1 1 54 66 6 17 12 25 42
15.25 1 0 57 71 5 18 11 23 152
11.9 0 0 60 74 4 15 9 12 107
13.2 0 1 55 78 4 8 12 18 77
16.35 0 0 56 75 4 13 12 24 154
12.4 1 0 49 53 10 13 12 11 103
15.85 1 1 37 60 7 15 10 18 96
18.15 1 0 59 70 4 18 13 23 175
11.15 1 1 46 69 7 16 9 24 57
15.65 0 1 51 65 4 14 12 29 112
17.75 0 0 58 78 4 15 11 18 143
7.65 0 1 64 78 12 19 14 15 49
12.35 1 0 53 59 5 16 11 29 110
15.6 1 0 48 72 8 12 9 16 131
19.3 0 0 51 70 6 16 12 19 167
15.2 0 1 47 63 17 11 8 22 56
17.1 0 0 59 63 4 16 15 16 137
15.6 1 1 62 71 5 15 12 23 86
18.4 1 0 62 74 4 19 14 23 121
19.05 0 0 51 67 5 15 12 19 149
18.55 0 0 64 66 5 14 9 4 168
19.1 0 0 52 62 6 14 9 20 140
13.1 1 1 67 80 4 17 13 24 88
12.85 1 0 50 73 4 16 13 20 168
9.5 1 0 54 67 4 20 15 4 94
4.5 1 0 58 61 6 16 11 24 51
11.85 0 1 56 73 8 9 7 22 48
13.6 1 0 63 74 10 13 10 16 145
11.7 1 0 31 32 4 15 11 3 66
12.4 1 1 65 69 5 19 14 15 85
13.35 0 0 71 69 4 16 14 24 109
11.4 0 1 50 84 4 17 13 17 63
14.9 1 1 57 64 4 16 12 20 102
19.9 0 1 47 58 16 9 8 27 162
11.2 1 1 47 59 7 11 13 26 86
14.6 1 1 57 78 4 14 9 23 114
17.6 0 0 43 57 4 19 12 17 164
14.05 1 0 41 60 14 13 13 20 119
16.1 0 0 63 68 5 14 11 22 126
13.35 1 0 63 68 5 15 11 19 132
11.85 1 0 56 73 5 15 13 24 142
11.95 0 0 51 69 5 14 12 19 83
14.75 1 1 50 67 7 16 12 23 94
15.15 0 1 22 60 19 17 10 15 81
13.2 1 0 41 65 16 12 9 27 166
16.85 0 1 59 66 4 15 10 26 110
7.85 1 1 56 74 4 17 13 22 64
7.7 0 0 66 81 7 15 13 22 93
12.6 0 1 53 72 9 10 9 18 104
7.85 1 1 42 55 5 16 11 15 105
10.95 1 1 52 49 14 15 12 22 49
12.35 0 1 54 74 4 11 8 27 88
9.95 1 1 44 53 16 16 12 10 95
14.9 1 1 62 64 10 16 12 20 102
16.65 0 1 53 65 5 16 12 17 99
13.4 1 1 50 57 6 14 9 23 63
13.95 0 1 36 51 4 14 12 19 76
15.7 0 1 76 80 4 16 12 13 109
16.85 1 1 66 67 4 16 11 27 117
10.95 1 1 62 70 5 18 12 23 57
15.35 0 1 59 74 4 14 6 16 120
12.2 1 1 47 75 4 20 7 25 73
15.1 0 1 55 70 5 15 10 2 91
17.75 0 1 58 69 4 16 12 26 108
15.2 1 1 60 65 4 16 10 20 105
14.6 0 0 44 55 5 16 12 23 117
16.65 0 1 57 71 8 12 9 22 119
8.1 1 1 45 65 15 8 3 24 31

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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 9.30861 -0.845385gender[t] + 1.32478Course_id[t] + 0.00251655AMS.I[t] -0.0369964AMS.E[t] -0.0944081AMS.A[t] + 0.051321CONFSTATTOT[t] + 0.0061009CONFSOFTTOT[t] + 0.0448654NUMERACYTOT[t] + 0.0562658LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.30861 -0.845385gender[t] +  1.32478Course_id[t] +  0.00251655AMS.I[t] -0.0369964AMS.E[t] -0.0944081AMS.A[t] +  0.051321CONFSTATTOT[t] +  0.0061009CONFSOFTTOT[t] +  0.0448654NUMERACYTOT[t] +  0.0562658LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.30861 -0.845385gender[t] +  1.32478Course_id[t] +  0.00251655AMS.I[t] -0.0369964AMS.E[t] -0.0944081AMS.A[t] +  0.051321CONFSTATTOT[t] +  0.0061009CONFSOFTTOT[t] +  0.0448654NUMERACYTOT[t] +  0.0562658LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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] = + 9.30861 -0.845385gender[t] + 1.32478Course_id[t] + 0.00251655AMS.I[t] -0.0369964AMS.E[t] -0.0944081AMS.A[t] + 0.051321CONFSTATTOT[t] + 0.0061009CONFSOFTTOT[t] + 0.0448654NUMERACYTOT[t] + 0.0562658LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.308612.17034.2893.13395e-051.56697e-05
gender-0.8453850.391904-2.1570.03252730.0162636
Course_id1.324780.4314233.0710.002519730.00125987
AMS.I0.002516550.02010910.12510.900570.450285
AMS.E-0.03699640.0227723-1.6250.1062620.0531308
AMS.A-0.09440810.0510195-1.850.06614260.0330713
CONFSTATTOT0.0513210.09218660.55670.5785250.289262
CONFSOFTTOT0.00610090.1033090.059050.9529840.476492
NUMERACYTOT0.04486540.03222051.3920.1657690.0828844
LFM0.05626580.0052483610.721.94006e-209.70029e-21

\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) & 9.30861 & 2.1703 & 4.289 & 3.13395e-05 & 1.56697e-05 \tabularnewline
gender & -0.845385 & 0.391904 & -2.157 & 0.0325273 & 0.0162636 \tabularnewline
Course_id & 1.32478 & 0.431423 & 3.071 & 0.00251973 & 0.00125987 \tabularnewline
AMS.I & 0.00251655 & 0.0201091 & 0.1251 & 0.90057 & 0.450285 \tabularnewline
AMS.E & -0.0369964 & 0.0227723 & -1.625 & 0.106262 & 0.0531308 \tabularnewline
AMS.A & -0.0944081 & 0.0510195 & -1.85 & 0.0661426 & 0.0330713 \tabularnewline
CONFSTATTOT & 0.051321 & 0.0921866 & 0.5567 & 0.578525 & 0.289262 \tabularnewline
CONFSOFTTOT & 0.0061009 & 0.103309 & 0.05905 & 0.952984 & 0.476492 \tabularnewline
NUMERACYTOT & 0.0448654 & 0.0322205 & 1.392 & 0.165769 & 0.0828844 \tabularnewline
LFM & 0.0562658 & 0.00524836 & 10.72 & 1.94006e-20 & 9.70029e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&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]9.30861[/C][C]2.1703[/C][C]4.289[/C][C]3.13395e-05[/C][C]1.56697e-05[/C][/ROW]
[ROW][C]gender[/C][C]-0.845385[/C][C]0.391904[/C][C]-2.157[/C][C]0.0325273[/C][C]0.0162636[/C][/ROW]
[ROW][C]Course_id[/C][C]1.32478[/C][C]0.431423[/C][C]3.071[/C][C]0.00251973[/C][C]0.00125987[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00251655[/C][C]0.0201091[/C][C]0.1251[/C][C]0.90057[/C][C]0.450285[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0369964[/C][C]0.0227723[/C][C]-1.625[/C][C]0.106262[/C][C]0.0531308[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0944081[/C][C]0.0510195[/C][C]-1.85[/C][C]0.0661426[/C][C]0.0330713[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.051321[/C][C]0.0921866[/C][C]0.5567[/C][C]0.578525[/C][C]0.289262[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.0061009[/C][C]0.103309[/C][C]0.05905[/C][C]0.952984[/C][C]0.476492[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0448654[/C][C]0.0322205[/C][C]1.392[/C][C]0.165769[/C][C]0.0828844[/C][/ROW]
[ROW][C]LFM[/C][C]0.0562658[/C][C]0.00524836[/C][C]10.72[/C][C]1.94006e-20[/C][C]9.70029e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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)9.308612.17034.2893.13395e-051.56697e-05
gender-0.8453850.391904-2.1570.03252730.0162636
Course_id1.324780.4314233.0710.002519730.00125987
AMS.I0.002516550.02010910.12510.900570.450285
AMS.E-0.03699640.0227723-1.6250.1062620.0531308
AMS.A-0.09440810.0510195-1.850.06614260.0330713
CONFSTATTOT0.0513210.09218660.55670.5785250.289262
CONFSOFTTOT0.00610090.1033090.059050.9529840.476492
NUMERACYTOT0.04486540.03222051.3920.1657690.0828844
LFM0.05626580.0052483610.721.94006e-209.70029e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.700599
R-squared0.490839
Adjusted R-squared0.461464
F-TEST (value)16.7096
F-TEST (DF numerator)9
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23675
Sum Squared Residuals780.475

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.700599 \tabularnewline
R-squared & 0.490839 \tabularnewline
Adjusted R-squared & 0.461464 \tabularnewline
F-TEST (value) & 16.7096 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.23675 \tabularnewline
Sum Squared Residuals & 780.475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.700599[/C][/ROW]
[ROW][C]R-squared[/C][C]0.490839[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.461464[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.7096[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/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]2.23675[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]780.475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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 R0.700599
R-squared0.490839
Adjusted R-squared0.461464
F-TEST (value)16.7096
F-TEST (DF numerator)9
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23675
Sum Squared Residuals780.475






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.7495-6.39953
212.710.74261.95736
318.115.62142.47865
417.8516.26031.58972
516.616.41530.184681
612.611.68150.918505
717.119.0953-1.99525
819.117.51361.58639
916.116.2405-0.140506
1013.3511.99311.35685
1118.416.67251.72748
1214.79.632725.06728
1310.613.1585-2.55853
1412.613.5476-0.947617
1516.215.06571.13426
1613.613.33530.264652
1718.916.30682.59324
1814.113.35620.74381
1914.513.7560.743969
2016.1517.2623-1.11232
2114.7513.53041.2196
2214.813.49381.30622
2312.4511.89690.553112
2412.6512.3580.291992
2517.3514.69332.65669
268.69.45536-0.855357
2718.416.47641.92359
2816.114.35591.74414
2911.610.86460.735418
3017.7516.70181.04816
3115.2514.82340.426579
3217.6514.23173.41835
3316.3516.8635-0.513488
3417.6516.81660.833442
3513.614.6647-1.06471
3614.3514.5872-0.237221
3714.7515.5095-0.759509
3818.2517.08491.16511
399.917.0094-7.10937
401614.27331.72666
4118.2516.66091.58909
4216.8518.1944-1.34436
4314.613.11861.48135
4413.8514.0864-0.236383
4518.9517.56161.38838
4615.614.58121.0188
4714.8516.796-1.946
4811.7514.0053-2.25532
4918.4516.64191.80812
5015.913.77432.12565
5117.118.2593-1.15933
5216.18.191647.90836
5319.918.97340.926642
5410.9510.35980.590183
5518.4516.39112.05887
5615.114.33930.760719
571515.4559-0.455908
5811.3513.7217-2.37174
5915.9514.83771.11229
6018.115.65912.44094
6114.616.709-2.10897
6215.416.9455-1.54551
6315.416.8715-1.47152
6417.614.54073.05933
6513.3514.2286-0.878558
6619.116.91522.1848
6715.3517.2043-1.85434
687.610.0843-2.48434
6913.414.4729-1.07292
7013.916.2157-2.31574
7119.116.37792.72206
7215.2515.2793-0.0293457
7312.916.0509-3.1509
7416.115.69540.404606
7517.3515.46261.88743
7613.1515.7776-2.62756
7712.1515.7247-3.5747
7812.611.84580.754203
7910.3512.5421-2.19211
8015.413.99091.4091
819.612.7936-3.19355
8218.215.35142.84859
8313.613.986-0.385986
8414.8513.10661.7434
8514.7516.7731-2.0231
8614.113.68610.413897
8714.913.77751.12249
8816.2516.06760.18236
8919.2519.5521-0.302073
9013.613.18810.411861
9113.616.0308-2.43077
9215.6516.1393-0.489293
9312.7513.6024-0.852392
9414.613.1861.41397
959.859.793890.0561077
9612.6511.64861.00138
9719.215.41663.78336
9816.613.50813.09187
9911.211.3462-0.146159
10015.2516.0831-0.833078
10111.913.7278-1.82779
10213.213.13230.067725
10316.3516.7793-0.429261
10412.412.7109-0.310928
10515.8514.04041.8096
10618.1517.52580.62417
10711.1511.8501-0.700127
10815.6516.3739-0.72391
10917.7515.88171.86827
1107.6511.2664-3.61635
11112.3514.3204-1.97036
11215.613.92441.67556
11319.317.42391.87606
11415.211.56723.63278
11517.116.08761.01241
11615.613.5592.04096
11718.414.40453.99554
11819.0516.56522.48477
11918.5516.96141.58861
12019.116.12722.97283
12113.113.5992-0.499202
12212.8516.7611-3.91109
1239.512.3291-2.82911
1244.510.6205-6.12053
12511.8511.51070.339289
12613.614.5445-0.944523
12711.711.66480.0352121
12812.413.4429-1.04288
12913.3514.6732-1.32319
13011.412.5331-1.13312
13114.914.71680.183183
13219.917.93251.96753
13311.213.7118-2.51184
13414.614.8877-0.287708
13517.617.969-0.369008
13614.0513.36430.68567
13716.115.34150.758509
13813.3514.7504-1.40043
13911.8515.347-3.49701
14011.9512.7264-0.776372
14114.7513.98950.760543
14215.1512.83922.3108
14313.215.8734-2.67336
14416.8516.1490.700963
1457.8512.3534-4.50339
1467.712.886-5.18602
14712.614.4807-1.8807
1487.8514.856-7.006
14910.9511.3714-0.421422
15012.3514.43-2.08002
1519.9513.1157-3.16565
15214.914.1630.737049
15316.6515.11731.53266
15413.412.58860.811355
15513.9514.3799-0.429889
15615.715.09790.602124
15716.8515.78041.06958
15810.9512.1183-1.16829
15915.3515.8913-0.541346
16012.213.0521-0.852089
16115.113.75081.34924
16217.7515.98651.76348
16315.214.8440.356035
16414.615.4218-0.821847
16516.6515.74830.901744
1668.19.33025-1.23025

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.7495 & -6.39953 \tabularnewline
2 & 12.7 & 10.7426 & 1.95736 \tabularnewline
3 & 18.1 & 15.6214 & 2.47865 \tabularnewline
4 & 17.85 & 16.2603 & 1.58972 \tabularnewline
5 & 16.6 & 16.4153 & 0.184681 \tabularnewline
6 & 12.6 & 11.6815 & 0.918505 \tabularnewline
7 & 17.1 & 19.0953 & -1.99525 \tabularnewline
8 & 19.1 & 17.5136 & 1.58639 \tabularnewline
9 & 16.1 & 16.2405 & -0.140506 \tabularnewline
10 & 13.35 & 11.9931 & 1.35685 \tabularnewline
11 & 18.4 & 16.6725 & 1.72748 \tabularnewline
12 & 14.7 & 9.63272 & 5.06728 \tabularnewline
13 & 10.6 & 13.1585 & -2.55853 \tabularnewline
14 & 12.6 & 13.5476 & -0.947617 \tabularnewline
15 & 16.2 & 15.0657 & 1.13426 \tabularnewline
16 & 13.6 & 13.3353 & 0.264652 \tabularnewline
17 & 18.9 & 16.3068 & 2.59324 \tabularnewline
18 & 14.1 & 13.3562 & 0.74381 \tabularnewline
19 & 14.5 & 13.756 & 0.743969 \tabularnewline
20 & 16.15 & 17.2623 & -1.11232 \tabularnewline
21 & 14.75 & 13.5304 & 1.2196 \tabularnewline
22 & 14.8 & 13.4938 & 1.30622 \tabularnewline
23 & 12.45 & 11.8969 & 0.553112 \tabularnewline
24 & 12.65 & 12.358 & 0.291992 \tabularnewline
25 & 17.35 & 14.6933 & 2.65669 \tabularnewline
26 & 8.6 & 9.45536 & -0.855357 \tabularnewline
27 & 18.4 & 16.4764 & 1.92359 \tabularnewline
28 & 16.1 & 14.3559 & 1.74414 \tabularnewline
29 & 11.6 & 10.8646 & 0.735418 \tabularnewline
30 & 17.75 & 16.7018 & 1.04816 \tabularnewline
31 & 15.25 & 14.8234 & 0.426579 \tabularnewline
32 & 17.65 & 14.2317 & 3.41835 \tabularnewline
33 & 16.35 & 16.8635 & -0.513488 \tabularnewline
34 & 17.65 & 16.8166 & 0.833442 \tabularnewline
35 & 13.6 & 14.6647 & -1.06471 \tabularnewline
36 & 14.35 & 14.5872 & -0.237221 \tabularnewline
37 & 14.75 & 15.5095 & -0.759509 \tabularnewline
38 & 18.25 & 17.0849 & 1.16511 \tabularnewline
39 & 9.9 & 17.0094 & -7.10937 \tabularnewline
40 & 16 & 14.2733 & 1.72666 \tabularnewline
41 & 18.25 & 16.6609 & 1.58909 \tabularnewline
42 & 16.85 & 18.1944 & -1.34436 \tabularnewline
43 & 14.6 & 13.1186 & 1.48135 \tabularnewline
44 & 13.85 & 14.0864 & -0.236383 \tabularnewline
45 & 18.95 & 17.5616 & 1.38838 \tabularnewline
46 & 15.6 & 14.5812 & 1.0188 \tabularnewline
47 & 14.85 & 16.796 & -1.946 \tabularnewline
48 & 11.75 & 14.0053 & -2.25532 \tabularnewline
49 & 18.45 & 16.6419 & 1.80812 \tabularnewline
50 & 15.9 & 13.7743 & 2.12565 \tabularnewline
51 & 17.1 & 18.2593 & -1.15933 \tabularnewline
52 & 16.1 & 8.19164 & 7.90836 \tabularnewline
53 & 19.9 & 18.9734 & 0.926642 \tabularnewline
54 & 10.95 & 10.3598 & 0.590183 \tabularnewline
55 & 18.45 & 16.3911 & 2.05887 \tabularnewline
56 & 15.1 & 14.3393 & 0.760719 \tabularnewline
57 & 15 & 15.4559 & -0.455908 \tabularnewline
58 & 11.35 & 13.7217 & -2.37174 \tabularnewline
59 & 15.95 & 14.8377 & 1.11229 \tabularnewline
60 & 18.1 & 15.6591 & 2.44094 \tabularnewline
61 & 14.6 & 16.709 & -2.10897 \tabularnewline
62 & 15.4 & 16.9455 & -1.54551 \tabularnewline
63 & 15.4 & 16.8715 & -1.47152 \tabularnewline
64 & 17.6 & 14.5407 & 3.05933 \tabularnewline
65 & 13.35 & 14.2286 & -0.878558 \tabularnewline
66 & 19.1 & 16.9152 & 2.1848 \tabularnewline
67 & 15.35 & 17.2043 & -1.85434 \tabularnewline
68 & 7.6 & 10.0843 & -2.48434 \tabularnewline
69 & 13.4 & 14.4729 & -1.07292 \tabularnewline
70 & 13.9 & 16.2157 & -2.31574 \tabularnewline
71 & 19.1 & 16.3779 & 2.72206 \tabularnewline
72 & 15.25 & 15.2793 & -0.0293457 \tabularnewline
73 & 12.9 & 16.0509 & -3.1509 \tabularnewline
74 & 16.1 & 15.6954 & 0.404606 \tabularnewline
75 & 17.35 & 15.4626 & 1.88743 \tabularnewline
76 & 13.15 & 15.7776 & -2.62756 \tabularnewline
77 & 12.15 & 15.7247 & -3.5747 \tabularnewline
78 & 12.6 & 11.8458 & 0.754203 \tabularnewline
79 & 10.35 & 12.5421 & -2.19211 \tabularnewline
80 & 15.4 & 13.9909 & 1.4091 \tabularnewline
81 & 9.6 & 12.7936 & -3.19355 \tabularnewline
82 & 18.2 & 15.3514 & 2.84859 \tabularnewline
83 & 13.6 & 13.986 & -0.385986 \tabularnewline
84 & 14.85 & 13.1066 & 1.7434 \tabularnewline
85 & 14.75 & 16.7731 & -2.0231 \tabularnewline
86 & 14.1 & 13.6861 & 0.413897 \tabularnewline
87 & 14.9 & 13.7775 & 1.12249 \tabularnewline
88 & 16.25 & 16.0676 & 0.18236 \tabularnewline
89 & 19.25 & 19.5521 & -0.302073 \tabularnewline
90 & 13.6 & 13.1881 & 0.411861 \tabularnewline
91 & 13.6 & 16.0308 & -2.43077 \tabularnewline
92 & 15.65 & 16.1393 & -0.489293 \tabularnewline
93 & 12.75 & 13.6024 & -0.852392 \tabularnewline
94 & 14.6 & 13.186 & 1.41397 \tabularnewline
95 & 9.85 & 9.79389 & 0.0561077 \tabularnewline
96 & 12.65 & 11.6486 & 1.00138 \tabularnewline
97 & 19.2 & 15.4166 & 3.78336 \tabularnewline
98 & 16.6 & 13.5081 & 3.09187 \tabularnewline
99 & 11.2 & 11.3462 & -0.146159 \tabularnewline
100 & 15.25 & 16.0831 & -0.833078 \tabularnewline
101 & 11.9 & 13.7278 & -1.82779 \tabularnewline
102 & 13.2 & 13.1323 & 0.067725 \tabularnewline
103 & 16.35 & 16.7793 & -0.429261 \tabularnewline
104 & 12.4 & 12.7109 & -0.310928 \tabularnewline
105 & 15.85 & 14.0404 & 1.8096 \tabularnewline
106 & 18.15 & 17.5258 & 0.62417 \tabularnewline
107 & 11.15 & 11.8501 & -0.700127 \tabularnewline
108 & 15.65 & 16.3739 & -0.72391 \tabularnewline
109 & 17.75 & 15.8817 & 1.86827 \tabularnewline
110 & 7.65 & 11.2664 & -3.61635 \tabularnewline
111 & 12.35 & 14.3204 & -1.97036 \tabularnewline
112 & 15.6 & 13.9244 & 1.67556 \tabularnewline
113 & 19.3 & 17.4239 & 1.87606 \tabularnewline
114 & 15.2 & 11.5672 & 3.63278 \tabularnewline
115 & 17.1 & 16.0876 & 1.01241 \tabularnewline
116 & 15.6 & 13.559 & 2.04096 \tabularnewline
117 & 18.4 & 14.4045 & 3.99554 \tabularnewline
118 & 19.05 & 16.5652 & 2.48477 \tabularnewline
119 & 18.55 & 16.9614 & 1.58861 \tabularnewline
120 & 19.1 & 16.1272 & 2.97283 \tabularnewline
121 & 13.1 & 13.5992 & -0.499202 \tabularnewline
122 & 12.85 & 16.7611 & -3.91109 \tabularnewline
123 & 9.5 & 12.3291 & -2.82911 \tabularnewline
124 & 4.5 & 10.6205 & -6.12053 \tabularnewline
125 & 11.85 & 11.5107 & 0.339289 \tabularnewline
126 & 13.6 & 14.5445 & -0.944523 \tabularnewline
127 & 11.7 & 11.6648 & 0.0352121 \tabularnewline
128 & 12.4 & 13.4429 & -1.04288 \tabularnewline
129 & 13.35 & 14.6732 & -1.32319 \tabularnewline
130 & 11.4 & 12.5331 & -1.13312 \tabularnewline
131 & 14.9 & 14.7168 & 0.183183 \tabularnewline
132 & 19.9 & 17.9325 & 1.96753 \tabularnewline
133 & 11.2 & 13.7118 & -2.51184 \tabularnewline
134 & 14.6 & 14.8877 & -0.287708 \tabularnewline
135 & 17.6 & 17.969 & -0.369008 \tabularnewline
136 & 14.05 & 13.3643 & 0.68567 \tabularnewline
137 & 16.1 & 15.3415 & 0.758509 \tabularnewline
138 & 13.35 & 14.7504 & -1.40043 \tabularnewline
139 & 11.85 & 15.347 & -3.49701 \tabularnewline
140 & 11.95 & 12.7264 & -0.776372 \tabularnewline
141 & 14.75 & 13.9895 & 0.760543 \tabularnewline
142 & 15.15 & 12.8392 & 2.3108 \tabularnewline
143 & 13.2 & 15.8734 & -2.67336 \tabularnewline
144 & 16.85 & 16.149 & 0.700963 \tabularnewline
145 & 7.85 & 12.3534 & -4.50339 \tabularnewline
146 & 7.7 & 12.886 & -5.18602 \tabularnewline
147 & 12.6 & 14.4807 & -1.8807 \tabularnewline
148 & 7.85 & 14.856 & -7.006 \tabularnewline
149 & 10.95 & 11.3714 & -0.421422 \tabularnewline
150 & 12.35 & 14.43 & -2.08002 \tabularnewline
151 & 9.95 & 13.1157 & -3.16565 \tabularnewline
152 & 14.9 & 14.163 & 0.737049 \tabularnewline
153 & 16.65 & 15.1173 & 1.53266 \tabularnewline
154 & 13.4 & 12.5886 & 0.811355 \tabularnewline
155 & 13.95 & 14.3799 & -0.429889 \tabularnewline
156 & 15.7 & 15.0979 & 0.602124 \tabularnewline
157 & 16.85 & 15.7804 & 1.06958 \tabularnewline
158 & 10.95 & 12.1183 & -1.16829 \tabularnewline
159 & 15.35 & 15.8913 & -0.541346 \tabularnewline
160 & 12.2 & 13.0521 & -0.852089 \tabularnewline
161 & 15.1 & 13.7508 & 1.34924 \tabularnewline
162 & 17.75 & 15.9865 & 1.76348 \tabularnewline
163 & 15.2 & 14.844 & 0.356035 \tabularnewline
164 & 14.6 & 15.4218 & -0.821847 \tabularnewline
165 & 16.65 & 15.7483 & 0.901744 \tabularnewline
166 & 8.1 & 9.33025 & -1.23025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&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]10.7495[/C][C]-6.39953[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.7426[/C][C]1.95736[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6214[/C][C]2.47865[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.2603[/C][C]1.58972[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.4153[/C][C]0.184681[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.6815[/C][C]0.918505[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.0953[/C][C]-1.99525[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.5136[/C][C]1.58639[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.2405[/C][C]-0.140506[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.9931[/C][C]1.35685[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.6725[/C][C]1.72748[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.63272[/C][C]5.06728[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.1585[/C][C]-2.55853[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.5476[/C][C]-0.947617[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.0657[/C][C]1.13426[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3353[/C][C]0.264652[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.3068[/C][C]2.59324[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.3562[/C][C]0.74381[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.756[/C][C]0.743969[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.2623[/C][C]-1.11232[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.5304[/C][C]1.2196[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.4938[/C][C]1.30622[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.8969[/C][C]0.553112[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.358[/C][C]0.291992[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.6933[/C][C]2.65669[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.45536[/C][C]-0.855357[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]16.4764[/C][C]1.92359[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.3559[/C][C]1.74414[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]10.8646[/C][C]0.735418[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.7018[/C][C]1.04816[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.8234[/C][C]0.426579[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.2317[/C][C]3.41835[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.8635[/C][C]-0.513488[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.8166[/C][C]0.833442[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.6647[/C][C]-1.06471[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.5872[/C][C]-0.237221[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.5095[/C][C]-0.759509[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.0849[/C][C]1.16511[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]17.0094[/C][C]-7.10937[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2733[/C][C]1.72666[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.6609[/C][C]1.58909[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.1944[/C][C]-1.34436[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.1186[/C][C]1.48135[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.0864[/C][C]-0.236383[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.5616[/C][C]1.38838[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.5812[/C][C]1.0188[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.796[/C][C]-1.946[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]14.0053[/C][C]-2.25532[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.6419[/C][C]1.80812[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]13.7743[/C][C]2.12565[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.2593[/C][C]-1.15933[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]8.19164[/C][C]7.90836[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.9734[/C][C]0.926642[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.3598[/C][C]0.590183[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.3911[/C][C]2.05887[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.3393[/C][C]0.760719[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.4559[/C][C]-0.455908[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]13.7217[/C][C]-2.37174[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.8377[/C][C]1.11229[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6591[/C][C]2.44094[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.709[/C][C]-2.10897[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.9455[/C][C]-1.54551[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.8715[/C][C]-1.47152[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.5407[/C][C]3.05933[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.2286[/C][C]-0.878558[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.9152[/C][C]2.1848[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.2043[/C][C]-1.85434[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.0843[/C][C]-2.48434[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.4729[/C][C]-1.07292[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]16.2157[/C][C]-2.31574[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.3779[/C][C]2.72206[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.2793[/C][C]-0.0293457[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.0509[/C][C]-3.1509[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.6954[/C][C]0.404606[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.4626[/C][C]1.88743[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.7776[/C][C]-2.62756[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.7247[/C][C]-3.5747[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.8458[/C][C]0.754203[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.5421[/C][C]-2.19211[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]13.9909[/C][C]1.4091[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.7936[/C][C]-3.19355[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.3514[/C][C]2.84859[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.986[/C][C]-0.385986[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.1066[/C][C]1.7434[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.7731[/C][C]-2.0231[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]13.6861[/C][C]0.413897[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.7775[/C][C]1.12249[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]16.0676[/C][C]0.18236[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.5521[/C][C]-0.302073[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]13.1881[/C][C]0.411861[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.0308[/C][C]-2.43077[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.1393[/C][C]-0.489293[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.6024[/C][C]-0.852392[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.186[/C][C]1.41397[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.79389[/C][C]0.0561077[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.6486[/C][C]1.00138[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]15.4166[/C][C]3.78336[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]13.5081[/C][C]3.09187[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.3462[/C][C]-0.146159[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]16.0831[/C][C]-0.833078[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]13.7278[/C][C]-1.82779[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.1323[/C][C]0.067725[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.7793[/C][C]-0.429261[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]12.7109[/C][C]-0.310928[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0404[/C][C]1.8096[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.5258[/C][C]0.62417[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.8501[/C][C]-0.700127[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.3739[/C][C]-0.72391[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.8817[/C][C]1.86827[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.2664[/C][C]-3.61635[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]14.3204[/C][C]-1.97036[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]13.9244[/C][C]1.67556[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.4239[/C][C]1.87606[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5672[/C][C]3.63278[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]16.0876[/C][C]1.01241[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.559[/C][C]2.04096[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.4045[/C][C]3.99554[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.5652[/C][C]2.48477[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]16.9614[/C][C]1.58861[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.1272[/C][C]2.97283[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.5992[/C][C]-0.499202[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.7611[/C][C]-3.91109[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]12.3291[/C][C]-2.82911[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.6205[/C][C]-6.12053[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.5107[/C][C]0.339289[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.5445[/C][C]-0.944523[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.6648[/C][C]0.0352121[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.4429[/C][C]-1.04288[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.6732[/C][C]-1.32319[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.5331[/C][C]-1.13312[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.7168[/C][C]0.183183[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.9325[/C][C]1.96753[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.7118[/C][C]-2.51184[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]14.8877[/C][C]-0.287708[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.969[/C][C]-0.369008[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.3643[/C][C]0.68567[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.3415[/C][C]0.758509[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.7504[/C][C]-1.40043[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]15.347[/C][C]-3.49701[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.7264[/C][C]-0.776372[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]13.9895[/C][C]0.760543[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.8392[/C][C]2.3108[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.8734[/C][C]-2.67336[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.149[/C][C]0.700963[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.3534[/C][C]-4.50339[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.886[/C][C]-5.18602[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.4807[/C][C]-1.8807[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]14.856[/C][C]-7.006[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.3714[/C][C]-0.421422[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.43[/C][C]-2.08002[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.1157[/C][C]-3.16565[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]14.163[/C][C]0.737049[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.1173[/C][C]1.53266[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.5886[/C][C]0.811355[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.3799[/C][C]-0.429889[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]15.0979[/C][C]0.602124[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.7804[/C][C]1.06958[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]12.1183[/C][C]-1.16829[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]15.8913[/C][C]-0.541346[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]13.0521[/C][C]-0.852089[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]13.7508[/C][C]1.34924[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.9865[/C][C]1.76348[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.844[/C][C]0.356035[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]15.4218[/C][C]-0.821847[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.7483[/C][C]0.901744[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.33025[/C][C]-1.23025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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
14.3510.7495-6.39953
212.710.74261.95736
318.115.62142.47865
417.8516.26031.58972
516.616.41530.184681
612.611.68150.918505
717.119.0953-1.99525
819.117.51361.58639
916.116.2405-0.140506
1013.3511.99311.35685
1118.416.67251.72748
1214.79.632725.06728
1310.613.1585-2.55853
1412.613.5476-0.947617
1516.215.06571.13426
1613.613.33530.264652
1718.916.30682.59324
1814.113.35620.74381
1914.513.7560.743969
2016.1517.2623-1.11232
2114.7513.53041.2196
2214.813.49381.30622
2312.4511.89690.553112
2412.6512.3580.291992
2517.3514.69332.65669
268.69.45536-0.855357
2718.416.47641.92359
2816.114.35591.74414
2911.610.86460.735418
3017.7516.70181.04816
3115.2514.82340.426579
3217.6514.23173.41835
3316.3516.8635-0.513488
3417.6516.81660.833442
3513.614.6647-1.06471
3614.3514.5872-0.237221
3714.7515.5095-0.759509
3818.2517.08491.16511
399.917.0094-7.10937
401614.27331.72666
4118.2516.66091.58909
4216.8518.1944-1.34436
4314.613.11861.48135
4413.8514.0864-0.236383
4518.9517.56161.38838
4615.614.58121.0188
4714.8516.796-1.946
4811.7514.0053-2.25532
4918.4516.64191.80812
5015.913.77432.12565
5117.118.2593-1.15933
5216.18.191647.90836
5319.918.97340.926642
5410.9510.35980.590183
5518.4516.39112.05887
5615.114.33930.760719
571515.4559-0.455908
5811.3513.7217-2.37174
5915.9514.83771.11229
6018.115.65912.44094
6114.616.709-2.10897
6215.416.9455-1.54551
6315.416.8715-1.47152
6417.614.54073.05933
6513.3514.2286-0.878558
6619.116.91522.1848
6715.3517.2043-1.85434
687.610.0843-2.48434
6913.414.4729-1.07292
7013.916.2157-2.31574
7119.116.37792.72206
7215.2515.2793-0.0293457
7312.916.0509-3.1509
7416.115.69540.404606
7517.3515.46261.88743
7613.1515.7776-2.62756
7712.1515.7247-3.5747
7812.611.84580.754203
7910.3512.5421-2.19211
8015.413.99091.4091
819.612.7936-3.19355
8218.215.35142.84859
8313.613.986-0.385986
8414.8513.10661.7434
8514.7516.7731-2.0231
8614.113.68610.413897
8714.913.77751.12249
8816.2516.06760.18236
8919.2519.5521-0.302073
9013.613.18810.411861
9113.616.0308-2.43077
9215.6516.1393-0.489293
9312.7513.6024-0.852392
9414.613.1861.41397
959.859.793890.0561077
9612.6511.64861.00138
9719.215.41663.78336
9816.613.50813.09187
9911.211.3462-0.146159
10015.2516.0831-0.833078
10111.913.7278-1.82779
10213.213.13230.067725
10316.3516.7793-0.429261
10412.412.7109-0.310928
10515.8514.04041.8096
10618.1517.52580.62417
10711.1511.8501-0.700127
10815.6516.3739-0.72391
10917.7515.88171.86827
1107.6511.2664-3.61635
11112.3514.3204-1.97036
11215.613.92441.67556
11319.317.42391.87606
11415.211.56723.63278
11517.116.08761.01241
11615.613.5592.04096
11718.414.40453.99554
11819.0516.56522.48477
11918.5516.96141.58861
12019.116.12722.97283
12113.113.5992-0.499202
12212.8516.7611-3.91109
1239.512.3291-2.82911
1244.510.6205-6.12053
12511.8511.51070.339289
12613.614.5445-0.944523
12711.711.66480.0352121
12812.413.4429-1.04288
12913.3514.6732-1.32319
13011.412.5331-1.13312
13114.914.71680.183183
13219.917.93251.96753
13311.213.7118-2.51184
13414.614.8877-0.287708
13517.617.969-0.369008
13614.0513.36430.68567
13716.115.34150.758509
13813.3514.7504-1.40043
13911.8515.347-3.49701
14011.9512.7264-0.776372
14114.7513.98950.760543
14215.1512.83922.3108
14313.215.8734-2.67336
14416.8516.1490.700963
1457.8512.3534-4.50339
1467.712.886-5.18602
14712.614.4807-1.8807
1487.8514.856-7.006
14910.9511.3714-0.421422
15012.3514.43-2.08002
1519.9513.1157-3.16565
15214.914.1630.737049
15316.6515.11731.53266
15413.412.58860.811355
15513.9514.3799-0.429889
15615.715.09790.602124
15716.8515.78041.06958
15810.9512.1183-1.16829
15915.3515.8913-0.541346
16012.213.0521-0.852089
16115.113.75081.34924
16217.7515.98651.76348
16315.214.8440.356035
16414.615.4218-0.821847
16516.6515.74830.901744
1668.19.33025-1.23025






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7580110.4839780.241989
140.6445610.7108770.355439
150.5054310.9891380.494569
160.5307910.9384180.469209
170.5191480.9617030.480852
180.4093790.8187580.590621
190.3130940.6261880.686906
200.3952420.7904850.604758
210.307890.615780.69211
220.3732830.7465670.626717
230.330360.6607210.66964
240.3294220.6588430.670578
250.3754940.7509880.624506
260.3518850.7037710.648115
270.2895560.5791120.710444
280.4595150.919030.540485
290.4171160.8342330.582884
300.3739560.7479110.626044
310.3138660.6277310.686134
320.2967260.5934530.703274
330.2697680.5395370.730232
340.2453060.4906120.754694
350.2005290.4010570.799471
360.1585260.3170520.841474
370.1239330.2478660.876067
380.09740250.1948050.902598
390.5398780.9202450.460122
400.5725420.8549160.427458
410.5288170.9423670.471183
420.4919820.9839650.508018
430.4420720.8841440.557928
440.3903730.7807460.609627
450.3539410.7078820.646059
460.3110050.622010.688995
470.3254660.6509330.674534
480.3056170.6112340.694383
490.290160.5803210.70984
500.2852540.5705070.714746
510.2449060.4898120.755094
520.7946050.410790.205395
530.7630020.4739960.236998
540.7334640.5330710.266536
550.7194190.5611620.280581
560.6881440.6237120.311856
570.6443260.7113480.355674
580.6441050.711790.355895
590.6154340.7691320.384566
600.668590.6628210.33141
610.7020910.5958180.297909
620.6888480.6223030.311152
630.6700490.6599020.329951
640.6968760.6062470.303124
650.6654420.6691160.334558
660.6774050.6451910.322595
670.6673850.665230.332615
680.7098650.5802690.290135
690.6784080.6431830.321592
700.688640.6227190.31136
710.7116020.5767970.288398
720.6707470.6585060.329253
730.7173730.5652540.282627
740.6769790.6460420.323021
750.6604570.6790870.339543
760.690490.619020.30951
770.7735740.4528520.226426
780.7443270.5113470.255673
790.7522730.4954540.247727
800.7338910.5322180.266109
810.7742330.4515330.225767
820.8042510.3914980.195749
830.7762240.4475510.223776
840.7694950.461010.230505
850.7684440.4631130.231556
860.7361590.5276810.263841
870.7085520.5828950.291448
880.6683280.6633440.331672
890.6290970.7418070.370903
900.5867840.8264320.413216
910.6036350.792730.396365
920.5671810.8656380.432819
930.5320670.9358650.467933
940.5064860.9870280.493514
950.5149440.9701120.485056
960.4809930.9619860.519007
970.5565760.8868480.443424
980.605630.7887410.39437
990.5816570.8366860.418343
1000.539120.9217610.46088
1010.5200050.959990.479995
1020.484520.969040.51548
1030.4372510.8745020.562749
1040.3971730.7943460.602827
1050.3957720.7915440.604228
1060.355050.7100990.64495
1070.320950.6418990.67905
1080.2857750.5715490.714225
1090.2764920.5529840.723508
1100.3494620.6989250.650538
1110.3252580.6505160.674742
1120.3744820.7489630.625518
1130.3578920.7157850.642108
1140.4205790.8411580.579421
1150.3807040.7614080.619296
1160.4235080.8470160.576492
1170.7177990.5644020.282201
1180.7582790.4834430.241721
1190.7243270.5513450.275673
1200.7747450.4505110.225255
1210.752280.495440.24772
1220.7609480.4781030.239052
1230.7397890.5204210.260211
1240.8776560.2446870.122344
1250.8499590.3000820.150041
1260.8228280.3543440.177172
1270.8191070.3617850.180893
1280.7792660.4414690.220734
1290.7434220.5131560.256578
1300.6978580.6042840.302142
1310.6619690.6760610.338031
1320.6083160.7833690.391684
1330.5646110.8707770.435389
1340.5415340.9169330.458466
1350.4800570.9601140.519943
1360.6219230.7561550.378077
1370.567730.864540.43227
1380.5373420.9253150.462658
1390.5147810.9704390.485219
1400.5591970.8816060.440803
1410.6439090.7121820.356091
1420.633070.733860.36693
1430.7376650.524670.262335
1440.7374040.5251920.262596
1450.6837220.6325550.316278
1460.7026630.5946740.297337
1470.6133010.7733980.386699
1480.923120.153760.0768802
1490.8769940.2460120.123006
1500.9376840.1246310.0623156
1510.9553340.08933190.0446659
1520.9472730.1054550.0527275
1530.9749540.05009190.025046

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.758011 & 0.483978 & 0.241989 \tabularnewline
14 & 0.644561 & 0.710877 & 0.355439 \tabularnewline
15 & 0.505431 & 0.989138 & 0.494569 \tabularnewline
16 & 0.530791 & 0.938418 & 0.469209 \tabularnewline
17 & 0.519148 & 0.961703 & 0.480852 \tabularnewline
18 & 0.409379 & 0.818758 & 0.590621 \tabularnewline
19 & 0.313094 & 0.626188 & 0.686906 \tabularnewline
20 & 0.395242 & 0.790485 & 0.604758 \tabularnewline
21 & 0.30789 & 0.61578 & 0.69211 \tabularnewline
22 & 0.373283 & 0.746567 & 0.626717 \tabularnewline
23 & 0.33036 & 0.660721 & 0.66964 \tabularnewline
24 & 0.329422 & 0.658843 & 0.670578 \tabularnewline
25 & 0.375494 & 0.750988 & 0.624506 \tabularnewline
26 & 0.351885 & 0.703771 & 0.648115 \tabularnewline
27 & 0.289556 & 0.579112 & 0.710444 \tabularnewline
28 & 0.459515 & 0.91903 & 0.540485 \tabularnewline
29 & 0.417116 & 0.834233 & 0.582884 \tabularnewline
30 & 0.373956 & 0.747911 & 0.626044 \tabularnewline
31 & 0.313866 & 0.627731 & 0.686134 \tabularnewline
32 & 0.296726 & 0.593453 & 0.703274 \tabularnewline
33 & 0.269768 & 0.539537 & 0.730232 \tabularnewline
34 & 0.245306 & 0.490612 & 0.754694 \tabularnewline
35 & 0.200529 & 0.401057 & 0.799471 \tabularnewline
36 & 0.158526 & 0.317052 & 0.841474 \tabularnewline
37 & 0.123933 & 0.247866 & 0.876067 \tabularnewline
38 & 0.0974025 & 0.194805 & 0.902598 \tabularnewline
39 & 0.539878 & 0.920245 & 0.460122 \tabularnewline
40 & 0.572542 & 0.854916 & 0.427458 \tabularnewline
41 & 0.528817 & 0.942367 & 0.471183 \tabularnewline
42 & 0.491982 & 0.983965 & 0.508018 \tabularnewline
43 & 0.442072 & 0.884144 & 0.557928 \tabularnewline
44 & 0.390373 & 0.780746 & 0.609627 \tabularnewline
45 & 0.353941 & 0.707882 & 0.646059 \tabularnewline
46 & 0.311005 & 0.62201 & 0.688995 \tabularnewline
47 & 0.325466 & 0.650933 & 0.674534 \tabularnewline
48 & 0.305617 & 0.611234 & 0.694383 \tabularnewline
49 & 0.29016 & 0.580321 & 0.70984 \tabularnewline
50 & 0.285254 & 0.570507 & 0.714746 \tabularnewline
51 & 0.244906 & 0.489812 & 0.755094 \tabularnewline
52 & 0.794605 & 0.41079 & 0.205395 \tabularnewline
53 & 0.763002 & 0.473996 & 0.236998 \tabularnewline
54 & 0.733464 & 0.533071 & 0.266536 \tabularnewline
55 & 0.719419 & 0.561162 & 0.280581 \tabularnewline
56 & 0.688144 & 0.623712 & 0.311856 \tabularnewline
57 & 0.644326 & 0.711348 & 0.355674 \tabularnewline
58 & 0.644105 & 0.71179 & 0.355895 \tabularnewline
59 & 0.615434 & 0.769132 & 0.384566 \tabularnewline
60 & 0.66859 & 0.662821 & 0.33141 \tabularnewline
61 & 0.702091 & 0.595818 & 0.297909 \tabularnewline
62 & 0.688848 & 0.622303 & 0.311152 \tabularnewline
63 & 0.670049 & 0.659902 & 0.329951 \tabularnewline
64 & 0.696876 & 0.606247 & 0.303124 \tabularnewline
65 & 0.665442 & 0.669116 & 0.334558 \tabularnewline
66 & 0.677405 & 0.645191 & 0.322595 \tabularnewline
67 & 0.667385 & 0.66523 & 0.332615 \tabularnewline
68 & 0.709865 & 0.580269 & 0.290135 \tabularnewline
69 & 0.678408 & 0.643183 & 0.321592 \tabularnewline
70 & 0.68864 & 0.622719 & 0.31136 \tabularnewline
71 & 0.711602 & 0.576797 & 0.288398 \tabularnewline
72 & 0.670747 & 0.658506 & 0.329253 \tabularnewline
73 & 0.717373 & 0.565254 & 0.282627 \tabularnewline
74 & 0.676979 & 0.646042 & 0.323021 \tabularnewline
75 & 0.660457 & 0.679087 & 0.339543 \tabularnewline
76 & 0.69049 & 0.61902 & 0.30951 \tabularnewline
77 & 0.773574 & 0.452852 & 0.226426 \tabularnewline
78 & 0.744327 & 0.511347 & 0.255673 \tabularnewline
79 & 0.752273 & 0.495454 & 0.247727 \tabularnewline
80 & 0.733891 & 0.532218 & 0.266109 \tabularnewline
81 & 0.774233 & 0.451533 & 0.225767 \tabularnewline
82 & 0.804251 & 0.391498 & 0.195749 \tabularnewline
83 & 0.776224 & 0.447551 & 0.223776 \tabularnewline
84 & 0.769495 & 0.46101 & 0.230505 \tabularnewline
85 & 0.768444 & 0.463113 & 0.231556 \tabularnewline
86 & 0.736159 & 0.527681 & 0.263841 \tabularnewline
87 & 0.708552 & 0.582895 & 0.291448 \tabularnewline
88 & 0.668328 & 0.663344 & 0.331672 \tabularnewline
89 & 0.629097 & 0.741807 & 0.370903 \tabularnewline
90 & 0.586784 & 0.826432 & 0.413216 \tabularnewline
91 & 0.603635 & 0.79273 & 0.396365 \tabularnewline
92 & 0.567181 & 0.865638 & 0.432819 \tabularnewline
93 & 0.532067 & 0.935865 & 0.467933 \tabularnewline
94 & 0.506486 & 0.987028 & 0.493514 \tabularnewline
95 & 0.514944 & 0.970112 & 0.485056 \tabularnewline
96 & 0.480993 & 0.961986 & 0.519007 \tabularnewline
97 & 0.556576 & 0.886848 & 0.443424 \tabularnewline
98 & 0.60563 & 0.788741 & 0.39437 \tabularnewline
99 & 0.581657 & 0.836686 & 0.418343 \tabularnewline
100 & 0.53912 & 0.921761 & 0.46088 \tabularnewline
101 & 0.520005 & 0.95999 & 0.479995 \tabularnewline
102 & 0.48452 & 0.96904 & 0.51548 \tabularnewline
103 & 0.437251 & 0.874502 & 0.562749 \tabularnewline
104 & 0.397173 & 0.794346 & 0.602827 \tabularnewline
105 & 0.395772 & 0.791544 & 0.604228 \tabularnewline
106 & 0.35505 & 0.710099 & 0.64495 \tabularnewline
107 & 0.32095 & 0.641899 & 0.67905 \tabularnewline
108 & 0.285775 & 0.571549 & 0.714225 \tabularnewline
109 & 0.276492 & 0.552984 & 0.723508 \tabularnewline
110 & 0.349462 & 0.698925 & 0.650538 \tabularnewline
111 & 0.325258 & 0.650516 & 0.674742 \tabularnewline
112 & 0.374482 & 0.748963 & 0.625518 \tabularnewline
113 & 0.357892 & 0.715785 & 0.642108 \tabularnewline
114 & 0.420579 & 0.841158 & 0.579421 \tabularnewline
115 & 0.380704 & 0.761408 & 0.619296 \tabularnewline
116 & 0.423508 & 0.847016 & 0.576492 \tabularnewline
117 & 0.717799 & 0.564402 & 0.282201 \tabularnewline
118 & 0.758279 & 0.483443 & 0.241721 \tabularnewline
119 & 0.724327 & 0.551345 & 0.275673 \tabularnewline
120 & 0.774745 & 0.450511 & 0.225255 \tabularnewline
121 & 0.75228 & 0.49544 & 0.24772 \tabularnewline
122 & 0.760948 & 0.478103 & 0.239052 \tabularnewline
123 & 0.739789 & 0.520421 & 0.260211 \tabularnewline
124 & 0.877656 & 0.244687 & 0.122344 \tabularnewline
125 & 0.849959 & 0.300082 & 0.150041 \tabularnewline
126 & 0.822828 & 0.354344 & 0.177172 \tabularnewline
127 & 0.819107 & 0.361785 & 0.180893 \tabularnewline
128 & 0.779266 & 0.441469 & 0.220734 \tabularnewline
129 & 0.743422 & 0.513156 & 0.256578 \tabularnewline
130 & 0.697858 & 0.604284 & 0.302142 \tabularnewline
131 & 0.661969 & 0.676061 & 0.338031 \tabularnewline
132 & 0.608316 & 0.783369 & 0.391684 \tabularnewline
133 & 0.564611 & 0.870777 & 0.435389 \tabularnewline
134 & 0.541534 & 0.916933 & 0.458466 \tabularnewline
135 & 0.480057 & 0.960114 & 0.519943 \tabularnewline
136 & 0.621923 & 0.756155 & 0.378077 \tabularnewline
137 & 0.56773 & 0.86454 & 0.43227 \tabularnewline
138 & 0.537342 & 0.925315 & 0.462658 \tabularnewline
139 & 0.514781 & 0.970439 & 0.485219 \tabularnewline
140 & 0.559197 & 0.881606 & 0.440803 \tabularnewline
141 & 0.643909 & 0.712182 & 0.356091 \tabularnewline
142 & 0.63307 & 0.73386 & 0.36693 \tabularnewline
143 & 0.737665 & 0.52467 & 0.262335 \tabularnewline
144 & 0.737404 & 0.525192 & 0.262596 \tabularnewline
145 & 0.683722 & 0.632555 & 0.316278 \tabularnewline
146 & 0.702663 & 0.594674 & 0.297337 \tabularnewline
147 & 0.613301 & 0.773398 & 0.386699 \tabularnewline
148 & 0.92312 & 0.15376 & 0.0768802 \tabularnewline
149 & 0.876994 & 0.246012 & 0.123006 \tabularnewline
150 & 0.937684 & 0.124631 & 0.0623156 \tabularnewline
151 & 0.955334 & 0.0893319 & 0.0446659 \tabularnewline
152 & 0.947273 & 0.105455 & 0.0527275 \tabularnewline
153 & 0.974954 & 0.0500919 & 0.025046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&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]0.758011[/C][C]0.483978[/C][C]0.241989[/C][/ROW]
[ROW][C]14[/C][C]0.644561[/C][C]0.710877[/C][C]0.355439[/C][/ROW]
[ROW][C]15[/C][C]0.505431[/C][C]0.989138[/C][C]0.494569[/C][/ROW]
[ROW][C]16[/C][C]0.530791[/C][C]0.938418[/C][C]0.469209[/C][/ROW]
[ROW][C]17[/C][C]0.519148[/C][C]0.961703[/C][C]0.480852[/C][/ROW]
[ROW][C]18[/C][C]0.409379[/C][C]0.818758[/C][C]0.590621[/C][/ROW]
[ROW][C]19[/C][C]0.313094[/C][C]0.626188[/C][C]0.686906[/C][/ROW]
[ROW][C]20[/C][C]0.395242[/C][C]0.790485[/C][C]0.604758[/C][/ROW]
[ROW][C]21[/C][C]0.30789[/C][C]0.61578[/C][C]0.69211[/C][/ROW]
[ROW][C]22[/C][C]0.373283[/C][C]0.746567[/C][C]0.626717[/C][/ROW]
[ROW][C]23[/C][C]0.33036[/C][C]0.660721[/C][C]0.66964[/C][/ROW]
[ROW][C]24[/C][C]0.329422[/C][C]0.658843[/C][C]0.670578[/C][/ROW]
[ROW][C]25[/C][C]0.375494[/C][C]0.750988[/C][C]0.624506[/C][/ROW]
[ROW][C]26[/C][C]0.351885[/C][C]0.703771[/C][C]0.648115[/C][/ROW]
[ROW][C]27[/C][C]0.289556[/C][C]0.579112[/C][C]0.710444[/C][/ROW]
[ROW][C]28[/C][C]0.459515[/C][C]0.91903[/C][C]0.540485[/C][/ROW]
[ROW][C]29[/C][C]0.417116[/C][C]0.834233[/C][C]0.582884[/C][/ROW]
[ROW][C]30[/C][C]0.373956[/C][C]0.747911[/C][C]0.626044[/C][/ROW]
[ROW][C]31[/C][C]0.313866[/C][C]0.627731[/C][C]0.686134[/C][/ROW]
[ROW][C]32[/C][C]0.296726[/C][C]0.593453[/C][C]0.703274[/C][/ROW]
[ROW][C]33[/C][C]0.269768[/C][C]0.539537[/C][C]0.730232[/C][/ROW]
[ROW][C]34[/C][C]0.245306[/C][C]0.490612[/C][C]0.754694[/C][/ROW]
[ROW][C]35[/C][C]0.200529[/C][C]0.401057[/C][C]0.799471[/C][/ROW]
[ROW][C]36[/C][C]0.158526[/C][C]0.317052[/C][C]0.841474[/C][/ROW]
[ROW][C]37[/C][C]0.123933[/C][C]0.247866[/C][C]0.876067[/C][/ROW]
[ROW][C]38[/C][C]0.0974025[/C][C]0.194805[/C][C]0.902598[/C][/ROW]
[ROW][C]39[/C][C]0.539878[/C][C]0.920245[/C][C]0.460122[/C][/ROW]
[ROW][C]40[/C][C]0.572542[/C][C]0.854916[/C][C]0.427458[/C][/ROW]
[ROW][C]41[/C][C]0.528817[/C][C]0.942367[/C][C]0.471183[/C][/ROW]
[ROW][C]42[/C][C]0.491982[/C][C]0.983965[/C][C]0.508018[/C][/ROW]
[ROW][C]43[/C][C]0.442072[/C][C]0.884144[/C][C]0.557928[/C][/ROW]
[ROW][C]44[/C][C]0.390373[/C][C]0.780746[/C][C]0.609627[/C][/ROW]
[ROW][C]45[/C][C]0.353941[/C][C]0.707882[/C][C]0.646059[/C][/ROW]
[ROW][C]46[/C][C]0.311005[/C][C]0.62201[/C][C]0.688995[/C][/ROW]
[ROW][C]47[/C][C]0.325466[/C][C]0.650933[/C][C]0.674534[/C][/ROW]
[ROW][C]48[/C][C]0.305617[/C][C]0.611234[/C][C]0.694383[/C][/ROW]
[ROW][C]49[/C][C]0.29016[/C][C]0.580321[/C][C]0.70984[/C][/ROW]
[ROW][C]50[/C][C]0.285254[/C][C]0.570507[/C][C]0.714746[/C][/ROW]
[ROW][C]51[/C][C]0.244906[/C][C]0.489812[/C][C]0.755094[/C][/ROW]
[ROW][C]52[/C][C]0.794605[/C][C]0.41079[/C][C]0.205395[/C][/ROW]
[ROW][C]53[/C][C]0.763002[/C][C]0.473996[/C][C]0.236998[/C][/ROW]
[ROW][C]54[/C][C]0.733464[/C][C]0.533071[/C][C]0.266536[/C][/ROW]
[ROW][C]55[/C][C]0.719419[/C][C]0.561162[/C][C]0.280581[/C][/ROW]
[ROW][C]56[/C][C]0.688144[/C][C]0.623712[/C][C]0.311856[/C][/ROW]
[ROW][C]57[/C][C]0.644326[/C][C]0.711348[/C][C]0.355674[/C][/ROW]
[ROW][C]58[/C][C]0.644105[/C][C]0.71179[/C][C]0.355895[/C][/ROW]
[ROW][C]59[/C][C]0.615434[/C][C]0.769132[/C][C]0.384566[/C][/ROW]
[ROW][C]60[/C][C]0.66859[/C][C]0.662821[/C][C]0.33141[/C][/ROW]
[ROW][C]61[/C][C]0.702091[/C][C]0.595818[/C][C]0.297909[/C][/ROW]
[ROW][C]62[/C][C]0.688848[/C][C]0.622303[/C][C]0.311152[/C][/ROW]
[ROW][C]63[/C][C]0.670049[/C][C]0.659902[/C][C]0.329951[/C][/ROW]
[ROW][C]64[/C][C]0.696876[/C][C]0.606247[/C][C]0.303124[/C][/ROW]
[ROW][C]65[/C][C]0.665442[/C][C]0.669116[/C][C]0.334558[/C][/ROW]
[ROW][C]66[/C][C]0.677405[/C][C]0.645191[/C][C]0.322595[/C][/ROW]
[ROW][C]67[/C][C]0.667385[/C][C]0.66523[/C][C]0.332615[/C][/ROW]
[ROW][C]68[/C][C]0.709865[/C][C]0.580269[/C][C]0.290135[/C][/ROW]
[ROW][C]69[/C][C]0.678408[/C][C]0.643183[/C][C]0.321592[/C][/ROW]
[ROW][C]70[/C][C]0.68864[/C][C]0.622719[/C][C]0.31136[/C][/ROW]
[ROW][C]71[/C][C]0.711602[/C][C]0.576797[/C][C]0.288398[/C][/ROW]
[ROW][C]72[/C][C]0.670747[/C][C]0.658506[/C][C]0.329253[/C][/ROW]
[ROW][C]73[/C][C]0.717373[/C][C]0.565254[/C][C]0.282627[/C][/ROW]
[ROW][C]74[/C][C]0.676979[/C][C]0.646042[/C][C]0.323021[/C][/ROW]
[ROW][C]75[/C][C]0.660457[/C][C]0.679087[/C][C]0.339543[/C][/ROW]
[ROW][C]76[/C][C]0.69049[/C][C]0.61902[/C][C]0.30951[/C][/ROW]
[ROW][C]77[/C][C]0.773574[/C][C]0.452852[/C][C]0.226426[/C][/ROW]
[ROW][C]78[/C][C]0.744327[/C][C]0.511347[/C][C]0.255673[/C][/ROW]
[ROW][C]79[/C][C]0.752273[/C][C]0.495454[/C][C]0.247727[/C][/ROW]
[ROW][C]80[/C][C]0.733891[/C][C]0.532218[/C][C]0.266109[/C][/ROW]
[ROW][C]81[/C][C]0.774233[/C][C]0.451533[/C][C]0.225767[/C][/ROW]
[ROW][C]82[/C][C]0.804251[/C][C]0.391498[/C][C]0.195749[/C][/ROW]
[ROW][C]83[/C][C]0.776224[/C][C]0.447551[/C][C]0.223776[/C][/ROW]
[ROW][C]84[/C][C]0.769495[/C][C]0.46101[/C][C]0.230505[/C][/ROW]
[ROW][C]85[/C][C]0.768444[/C][C]0.463113[/C][C]0.231556[/C][/ROW]
[ROW][C]86[/C][C]0.736159[/C][C]0.527681[/C][C]0.263841[/C][/ROW]
[ROW][C]87[/C][C]0.708552[/C][C]0.582895[/C][C]0.291448[/C][/ROW]
[ROW][C]88[/C][C]0.668328[/C][C]0.663344[/C][C]0.331672[/C][/ROW]
[ROW][C]89[/C][C]0.629097[/C][C]0.741807[/C][C]0.370903[/C][/ROW]
[ROW][C]90[/C][C]0.586784[/C][C]0.826432[/C][C]0.413216[/C][/ROW]
[ROW][C]91[/C][C]0.603635[/C][C]0.79273[/C][C]0.396365[/C][/ROW]
[ROW][C]92[/C][C]0.567181[/C][C]0.865638[/C][C]0.432819[/C][/ROW]
[ROW][C]93[/C][C]0.532067[/C][C]0.935865[/C][C]0.467933[/C][/ROW]
[ROW][C]94[/C][C]0.506486[/C][C]0.987028[/C][C]0.493514[/C][/ROW]
[ROW][C]95[/C][C]0.514944[/C][C]0.970112[/C][C]0.485056[/C][/ROW]
[ROW][C]96[/C][C]0.480993[/C][C]0.961986[/C][C]0.519007[/C][/ROW]
[ROW][C]97[/C][C]0.556576[/C][C]0.886848[/C][C]0.443424[/C][/ROW]
[ROW][C]98[/C][C]0.60563[/C][C]0.788741[/C][C]0.39437[/C][/ROW]
[ROW][C]99[/C][C]0.581657[/C][C]0.836686[/C][C]0.418343[/C][/ROW]
[ROW][C]100[/C][C]0.53912[/C][C]0.921761[/C][C]0.46088[/C][/ROW]
[ROW][C]101[/C][C]0.520005[/C][C]0.95999[/C][C]0.479995[/C][/ROW]
[ROW][C]102[/C][C]0.48452[/C][C]0.96904[/C][C]0.51548[/C][/ROW]
[ROW][C]103[/C][C]0.437251[/C][C]0.874502[/C][C]0.562749[/C][/ROW]
[ROW][C]104[/C][C]0.397173[/C][C]0.794346[/C][C]0.602827[/C][/ROW]
[ROW][C]105[/C][C]0.395772[/C][C]0.791544[/C][C]0.604228[/C][/ROW]
[ROW][C]106[/C][C]0.35505[/C][C]0.710099[/C][C]0.64495[/C][/ROW]
[ROW][C]107[/C][C]0.32095[/C][C]0.641899[/C][C]0.67905[/C][/ROW]
[ROW][C]108[/C][C]0.285775[/C][C]0.571549[/C][C]0.714225[/C][/ROW]
[ROW][C]109[/C][C]0.276492[/C][C]0.552984[/C][C]0.723508[/C][/ROW]
[ROW][C]110[/C][C]0.349462[/C][C]0.698925[/C][C]0.650538[/C][/ROW]
[ROW][C]111[/C][C]0.325258[/C][C]0.650516[/C][C]0.674742[/C][/ROW]
[ROW][C]112[/C][C]0.374482[/C][C]0.748963[/C][C]0.625518[/C][/ROW]
[ROW][C]113[/C][C]0.357892[/C][C]0.715785[/C][C]0.642108[/C][/ROW]
[ROW][C]114[/C][C]0.420579[/C][C]0.841158[/C][C]0.579421[/C][/ROW]
[ROW][C]115[/C][C]0.380704[/C][C]0.761408[/C][C]0.619296[/C][/ROW]
[ROW][C]116[/C][C]0.423508[/C][C]0.847016[/C][C]0.576492[/C][/ROW]
[ROW][C]117[/C][C]0.717799[/C][C]0.564402[/C][C]0.282201[/C][/ROW]
[ROW][C]118[/C][C]0.758279[/C][C]0.483443[/C][C]0.241721[/C][/ROW]
[ROW][C]119[/C][C]0.724327[/C][C]0.551345[/C][C]0.275673[/C][/ROW]
[ROW][C]120[/C][C]0.774745[/C][C]0.450511[/C][C]0.225255[/C][/ROW]
[ROW][C]121[/C][C]0.75228[/C][C]0.49544[/C][C]0.24772[/C][/ROW]
[ROW][C]122[/C][C]0.760948[/C][C]0.478103[/C][C]0.239052[/C][/ROW]
[ROW][C]123[/C][C]0.739789[/C][C]0.520421[/C][C]0.260211[/C][/ROW]
[ROW][C]124[/C][C]0.877656[/C][C]0.244687[/C][C]0.122344[/C][/ROW]
[ROW][C]125[/C][C]0.849959[/C][C]0.300082[/C][C]0.150041[/C][/ROW]
[ROW][C]126[/C][C]0.822828[/C][C]0.354344[/C][C]0.177172[/C][/ROW]
[ROW][C]127[/C][C]0.819107[/C][C]0.361785[/C][C]0.180893[/C][/ROW]
[ROW][C]128[/C][C]0.779266[/C][C]0.441469[/C][C]0.220734[/C][/ROW]
[ROW][C]129[/C][C]0.743422[/C][C]0.513156[/C][C]0.256578[/C][/ROW]
[ROW][C]130[/C][C]0.697858[/C][C]0.604284[/C][C]0.302142[/C][/ROW]
[ROW][C]131[/C][C]0.661969[/C][C]0.676061[/C][C]0.338031[/C][/ROW]
[ROW][C]132[/C][C]0.608316[/C][C]0.783369[/C][C]0.391684[/C][/ROW]
[ROW][C]133[/C][C]0.564611[/C][C]0.870777[/C][C]0.435389[/C][/ROW]
[ROW][C]134[/C][C]0.541534[/C][C]0.916933[/C][C]0.458466[/C][/ROW]
[ROW][C]135[/C][C]0.480057[/C][C]0.960114[/C][C]0.519943[/C][/ROW]
[ROW][C]136[/C][C]0.621923[/C][C]0.756155[/C][C]0.378077[/C][/ROW]
[ROW][C]137[/C][C]0.56773[/C][C]0.86454[/C][C]0.43227[/C][/ROW]
[ROW][C]138[/C][C]0.537342[/C][C]0.925315[/C][C]0.462658[/C][/ROW]
[ROW][C]139[/C][C]0.514781[/C][C]0.970439[/C][C]0.485219[/C][/ROW]
[ROW][C]140[/C][C]0.559197[/C][C]0.881606[/C][C]0.440803[/C][/ROW]
[ROW][C]141[/C][C]0.643909[/C][C]0.712182[/C][C]0.356091[/C][/ROW]
[ROW][C]142[/C][C]0.63307[/C][C]0.73386[/C][C]0.36693[/C][/ROW]
[ROW][C]143[/C][C]0.737665[/C][C]0.52467[/C][C]0.262335[/C][/ROW]
[ROW][C]144[/C][C]0.737404[/C][C]0.525192[/C][C]0.262596[/C][/ROW]
[ROW][C]145[/C][C]0.683722[/C][C]0.632555[/C][C]0.316278[/C][/ROW]
[ROW][C]146[/C][C]0.702663[/C][C]0.594674[/C][C]0.297337[/C][/ROW]
[ROW][C]147[/C][C]0.613301[/C][C]0.773398[/C][C]0.386699[/C][/ROW]
[ROW][C]148[/C][C]0.92312[/C][C]0.15376[/C][C]0.0768802[/C][/ROW]
[ROW][C]149[/C][C]0.876994[/C][C]0.246012[/C][C]0.123006[/C][/ROW]
[ROW][C]150[/C][C]0.937684[/C][C]0.124631[/C][C]0.0623156[/C][/ROW]
[ROW][C]151[/C][C]0.955334[/C][C]0.0893319[/C][C]0.0446659[/C][/ROW]
[ROW][C]152[/C][C]0.947273[/C][C]0.105455[/C][C]0.0527275[/C][/ROW]
[ROW][C]153[/C][C]0.974954[/C][C]0.0500919[/C][C]0.025046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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
130.7580110.4839780.241989
140.6445610.7108770.355439
150.5054310.9891380.494569
160.5307910.9384180.469209
170.5191480.9617030.480852
180.4093790.8187580.590621
190.3130940.6261880.686906
200.3952420.7904850.604758
210.307890.615780.69211
220.3732830.7465670.626717
230.330360.6607210.66964
240.3294220.6588430.670578
250.3754940.7509880.624506
260.3518850.7037710.648115
270.2895560.5791120.710444
280.4595150.919030.540485
290.4171160.8342330.582884
300.3739560.7479110.626044
310.3138660.6277310.686134
320.2967260.5934530.703274
330.2697680.5395370.730232
340.2453060.4906120.754694
350.2005290.4010570.799471
360.1585260.3170520.841474
370.1239330.2478660.876067
380.09740250.1948050.902598
390.5398780.9202450.460122
400.5725420.8549160.427458
410.5288170.9423670.471183
420.4919820.9839650.508018
430.4420720.8841440.557928
440.3903730.7807460.609627
450.3539410.7078820.646059
460.3110050.622010.688995
470.3254660.6509330.674534
480.3056170.6112340.694383
490.290160.5803210.70984
500.2852540.5705070.714746
510.2449060.4898120.755094
520.7946050.410790.205395
530.7630020.4739960.236998
540.7334640.5330710.266536
550.7194190.5611620.280581
560.6881440.6237120.311856
570.6443260.7113480.355674
580.6441050.711790.355895
590.6154340.7691320.384566
600.668590.6628210.33141
610.7020910.5958180.297909
620.6888480.6223030.311152
630.6700490.6599020.329951
640.6968760.6062470.303124
650.6654420.6691160.334558
660.6774050.6451910.322595
670.6673850.665230.332615
680.7098650.5802690.290135
690.6784080.6431830.321592
700.688640.6227190.31136
710.7116020.5767970.288398
720.6707470.6585060.329253
730.7173730.5652540.282627
740.6769790.6460420.323021
750.6604570.6790870.339543
760.690490.619020.30951
770.7735740.4528520.226426
780.7443270.5113470.255673
790.7522730.4954540.247727
800.7338910.5322180.266109
810.7742330.4515330.225767
820.8042510.3914980.195749
830.7762240.4475510.223776
840.7694950.461010.230505
850.7684440.4631130.231556
860.7361590.5276810.263841
870.7085520.5828950.291448
880.6683280.6633440.331672
890.6290970.7418070.370903
900.5867840.8264320.413216
910.6036350.792730.396365
920.5671810.8656380.432819
930.5320670.9358650.467933
940.5064860.9870280.493514
950.5149440.9701120.485056
960.4809930.9619860.519007
970.5565760.8868480.443424
980.605630.7887410.39437
990.5816570.8366860.418343
1000.539120.9217610.46088
1010.5200050.959990.479995
1020.484520.969040.51548
1030.4372510.8745020.562749
1040.3971730.7943460.602827
1050.3957720.7915440.604228
1060.355050.7100990.64495
1070.320950.6418990.67905
1080.2857750.5715490.714225
1090.2764920.5529840.723508
1100.3494620.6989250.650538
1110.3252580.6505160.674742
1120.3744820.7489630.625518
1130.3578920.7157850.642108
1140.4205790.8411580.579421
1150.3807040.7614080.619296
1160.4235080.8470160.576492
1170.7177990.5644020.282201
1180.7582790.4834430.241721
1190.7243270.5513450.275673
1200.7747450.4505110.225255
1210.752280.495440.24772
1220.7609480.4781030.239052
1230.7397890.5204210.260211
1240.8776560.2446870.122344
1250.8499590.3000820.150041
1260.8228280.3543440.177172
1270.8191070.3617850.180893
1280.7792660.4414690.220734
1290.7434220.5131560.256578
1300.6978580.6042840.302142
1310.6619690.6760610.338031
1320.6083160.7833690.391684
1330.5646110.8707770.435389
1340.5415340.9169330.458466
1350.4800570.9601140.519943
1360.6219230.7561550.378077
1370.567730.864540.43227
1380.5373420.9253150.462658
1390.5147810.9704390.485219
1400.5591970.8816060.440803
1410.6439090.7121820.356091
1420.633070.733860.36693
1430.7376650.524670.262335
1440.7374040.5251920.262596
1450.6837220.6325550.316278
1460.7026630.5946740.297337
1470.6133010.7733980.386699
1480.923120.153760.0768802
1490.8769940.2460120.123006
1500.9376840.1246310.0623156
1510.9553340.08933190.0446659
1520.9472730.1054550.0527275
1530.9749540.05009190.025046






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0141844OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0141844 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265049&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0141844[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265049&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265049&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 level00OK
10% type I error level20.0141844OK


Figures (Output of Computation)

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

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