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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 computationTue, 16 Dec 2014 11:09:24 +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/16/t14187282426iupbr9zoreokxq.htm/, Retrieved Thu, 16 May 2024 08:18:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269323, Retrieved Thu, 16 May 2024 08:18:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper12] [2014-12-16 11:09:24] [f8a15a4749f25af1f83725a9fa901b6e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.35 1 0 51 6 16 23 48
12.7 1 0 56 4 16 22 50
18.1 1 0 67 8 16 21 150
17.85 1 0 69 5 16 25 154
16.6 0 1 57 4 12 30 109
12.6 1 1 56 17 15 17 68
17.1 1 0 55 4 14 27 194
19.1 0 0 63 4 15 23 158
16.1 1 0 67 8 16 23 159
13.35 0 0 65 4 13 18 67
18.4 0 0 47 7 10 18 147
14.7 1 0 76 4 17 23 39
10.6 1 0 64 4 15 19 100
12.6 1 0 68 5 18 15 111
16.2 1 0 64 7 16 20 138
13.6 1 0 65 4 20 16 101
18.9 1 1 71 4 16 24 131
14.1 1 0 63 7 17 25 101
14.5 1 0 60 11 16 25 114
16.15 0 0 68 7 15 19 165
14.75 1 0 72 4 13 19 114
14.8 1 0 70 4 16 16 111
12.45 1 0 61 4 16 19 75
12.65 1 0 61 4 16 19 82
17.35 1 0 62 4 17 23 121
8.6 1 0 71 4 20 21 32
18.4 0 0 71 6 14 22 150
16.1 1 0 51 8 17 19 117
11.6 1 1 56 23 6 20 71
17.75 1 0 70 4 16 20 165
15.25 1 0 73 8 15 3 154
17.65 1 0 76 6 16 23 126
16.35 0 0 68 4 16 23 149
17.65 0 0 48 7 14 20 145
13.6 1 0 52 4 16 15 120
14.35 0 0 60 4 16 16 109
14.75 0 0 59 4 16 7 132
18.25 1 0 57 10 14 24 172
9.9 0 0 79 6 14 17 169
16 1 0 60 5 16 24 114
18.25 1 0 60 5 16 24 156
16.85 0 0 59 4 15 19 172
14.6 1 1 62 4 16 25 68
13.85 1 1 59 5 16 20 89
18.95 1 0 61 5 18 28 167
15.6 0 0 71 5 15 23 113
14.85 0 1 57 5 16 27 115
11.75 0 1 66 4 16 18 78
18.45 0 1 63 6 16 28 118
15.9 1 1 69 4 17 21 87
17.1 0 0 58 4 14 19 173
16.1 1 0 59 4 18 23 2
19.9 0 1 48 9 9 27 162
10.95 1 1 66 18 15 22 49
18.45 0 1 73 6 14 28 122
15.1 1 1 67 5 15 25 96
15 0 1 61 4 13 21 100
11.35 0 1 68 11 16 22 82
15.95 1 1 75 4 20 28 100
18.1 0 1 62 10 14 20 115
14.6 1 1 69 6 12 29 141
15.4 1 0 58 8 15 25 165
15.4 1 0 60 8 15 25 165
17.6 1 1 74 6 15 20 110
13.35 1 0 55 8 16 20 118
19.1 0 0 62 4 11 16 158
15.35 1 1 63 4 16 20 146
7.6 0 0 69 9 7 20 49
13.4 0 1 58 9 11 23 90
13.9 0 1 58 5 9 18 121
19.1 1 0 68 4 15 25 155
15.25 0 1 72 4 16 18 104
12.9 1 1 62 15 14 19 147
16.1 0 1 62 10 15 25 110
17.35 0 1 65 9 13 25 108
13.15 0 1 69 7 13 25 113
12.15 0 1 66 9 12 24 115
12.6 1 1 72 6 16 19 61
10.35 1 1 62 4 14 26 60
15.4 1 1 75 7 16 10 109
9.6 1 1 58 4 14 17 68
18.2 0 1 66 7 15 13 111
13.6 0 1 55 4 10 17 77
14.85 1 1 47 15 16 30 73
14.75 0 0 72 4 14 25 151
14.1 0 1 62 9 16 4 89
14.9 0 1 64 4 12 16 78
16.25 0 1 64 4 16 21 110
19.25 1 0 19 28 16 23 220
13.6 1 1 50 4 15 22 65
13.6 0 0 68 4 14 17 141
15.65 0 1 70 4 16 20 117
12.75 1 0 79 5 11 20 122
14.6 0 1 69 4 15 22 63
9.85 1 0 71 4 18 16 44
12.65 1 1 48 12 13 23 52
19.2 0 1 73 4 7 0 131
16.6 1 1 74 6 7 18 101
11.2 1 1 66 6 17 25 42
15.25 1 0 71 5 18 23 152
11.9 0 0 74 4 15 12 107
13.2 0 1 78 4 8 18 77
16.35 0 0 75 4 13 24 154
12.4 1 0 53 10 13 11 103
15.85 1 1 60 7 15 18 96
18.15 1 0 70 4 18 23 175
11.15 1 1 69 7 16 24 57
15.65 0 1 65 4 14 29 112
17.75 0 0 78 4 15 18 143
7.65 0 1 78 12 19 15 49
12.35 1 0 59 5 16 29 110
15.6 1 0 72 8 12 16 131
19.3 0 0 70 6 16 19 167
15.2 0 1 63 17 11 22 56
17.1 0 0 63 4 16 16 137
15.6 1 1 71 5 15 23 86
18.4 1 0 74 4 19 23 121
19.05 0 0 67 5 15 19 149
18.55 0 0 66 5 14 4 168
19.1 0 0 62 6 14 20 140
13.1 1 1 80 4 17 24 88
12.85 1 0 73 4 16 20 168
9.5 1 0 67 4 20 4 94
4.5 1 0 61 6 16 24 51
11.85 0 1 73 8 9 22 48
13.6 1 0 74 10 13 16 145
11.7 1 0 32 4 15 3 66
12.4 1 1 69 5 19 15 85
13.35 0 0 69 4 16 24 109
11.4 0 1 84 4 17 17 63
14.9 1 1 64 4 16 20 102
19.9 0 1 58 16 9 27 162
11.2 1 1 59 7 11 26 86
14.6 1 1 78 4 14 23 114
17.6 0 0 57 4 19 17 164
14.05 1 0 60 14 13 20 119
16.1 0 0 68 5 14 22 126
13.35 1 0 68 5 15 19 132
11.85 1 0 73 5 15 24 142
11.95 0 0 69 5 14 19 83
14.75 1 1 67 7 16 23 94
15.15 0 1 60 19 17 15 81
13.2 1 0 65 16 12 27 166
16.85 0 1 66 4 15 26 110
7.85 1 1 74 4 17 22 64
7.7 0 0 81 7 15 22 93
12.6 0 1 72 9 10 18 104
7.85 1 1 55 5 16 15 105
10.95 1 1 49 14 15 22 49
12.35 0 1 74 4 11 27 88
9.95 1 1 53 16 16 10 95
14.9 1 1 64 10 16 20 102
16.65 0 1 65 5 16 17 99
13.4 1 1 57 6 14 23 63
13.95 0 1 51 4 14 19 76
15.7 0 1 80 4 16 13 109
16.85 1 1 67 4 16 27 117
10.95 1 1 70 5 18 23 57
15.35 0 1 74 4 14 16 120
12.2 1 1 75 4 20 25 73
15.1 0 1 70 5 15 2 91
17.75 0 1 69 4 16 26 108
15.2 1 1 65 4 16 20 105
14.6 0 0 55 5 16 23 117
16.65 0 1 71 8 12 22 119
8.1 1 1 65 15 8 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=269323&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=269323&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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] = + 9.34965 -0.844632gender[t] + 1.32475Course_id[t] -0.0360267AMS.E[t] -0.0947995AMS.A[t] + 0.0569569CONFSTATTOT[t] + 0.0453302NUMERACYTOT[t] + 0.0563172LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.34965 -0.844632gender[t] +  1.32475Course_id[t] -0.0360267AMS.E[t] -0.0947995AMS.A[t] +  0.0569569CONFSTATTOT[t] +  0.0453302NUMERACYTOT[t] +  0.0563172LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.34965 -0.844632gender[t] +  1.32475Course_id[t] -0.0360267AMS.E[t] -0.0947995AMS.A[t] +  0.0569569CONFSTATTOT[t] +  0.0453302NUMERACYTOT[t] +  0.0563172LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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] = + 9.34965 -0.844632gender[t] + 1.32475Course_id[t] -0.0360267AMS.E[t] -0.0947995AMS.A[t] + 0.0569569CONFSTATTOT[t] + 0.0453302NUMERACYTOT[t] + 0.0563172LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.349652.123994.4021.96739e-059.83696e-06
gender-0.8446320.388597-2.1740.03122850.0156143
Course_id1.324750.4095913.2340.001484910.000742455
AMS.E-0.03602670.0213255-1.6890.09311980.0465599
AMS.A-0.09479950.0503608-1.8820.06161950.0308097
CONFSTATTOT0.05695690.07356160.77430.4399250.219963
NUMERACYTOT0.04533020.03186281.4230.1568040.0784018
LFM0.05631720.0051603310.915.09123e-212.54561e-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) & 9.34965 & 2.12399 & 4.402 & 1.96739e-05 & 9.83696e-06 \tabularnewline
gender & -0.844632 & 0.388597 & -2.174 & 0.0312285 & 0.0156143 \tabularnewline
Course_id & 1.32475 & 0.409591 & 3.234 & 0.00148491 & 0.000742455 \tabularnewline
AMS.E & -0.0360267 & 0.0213255 & -1.689 & 0.0931198 & 0.0465599 \tabularnewline
AMS.A & -0.0947995 & 0.0503608 & -1.882 & 0.0616195 & 0.0308097 \tabularnewline
CONFSTATTOT & 0.0569569 & 0.0735616 & 0.7743 & 0.439925 & 0.219963 \tabularnewline
NUMERACYTOT & 0.0453302 & 0.0318628 & 1.423 & 0.156804 & 0.0784018 \tabularnewline
LFM & 0.0563172 & 0.00516033 & 10.91 & 5.09123e-21 & 2.54561e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&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]9.34965[/C][C]2.12399[/C][C]4.402[/C][C]1.96739e-05[/C][C]9.83696e-06[/C][/ROW]
[ROW][C]gender[/C][C]-0.844632[/C][C]0.388597[/C][C]-2.174[/C][C]0.0312285[/C][C]0.0156143[/C][/ROW]
[ROW][C]Course_id[/C][C]1.32475[/C][C]0.409591[/C][C]3.234[/C][C]0.00148491[/C][C]0.000742455[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0360267[/C][C]0.0213255[/C][C]-1.689[/C][C]0.0931198[/C][C]0.0465599[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0947995[/C][C]0.0503608[/C][C]-1.882[/C][C]0.0616195[/C][C]0.0308097[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.0569569[/C][C]0.0735616[/C][C]0.7743[/C][C]0.439925[/C][C]0.219963[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0453302[/C][C]0.0318628[/C][C]1.423[/C][C]0.156804[/C][C]0.0784018[/C][/ROW]
[ROW][C]LFM[/C][C]0.0563172[/C][C]0.00516033[/C][C]10.91[/C][C]5.09123e-21[/C][C]2.54561e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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)9.349652.123994.4021.96739e-059.83696e-06
gender-0.8446320.388597-2.1740.03122850.0156143
Course_id1.324750.4095913.2340.001484910.000742455
AMS.E-0.03602670.0213255-1.6890.09311980.0465599
AMS.A-0.09479950.0503608-1.8820.06161950.0308097
CONFSTATTOT0.05695690.07356160.77430.4399250.219963
NUMERACYTOT0.04533020.03186281.4230.1568040.0784018
LFM0.05631720.0051603310.915.09123e-212.54561e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.700549
R-squared0.490769
Adjusted R-squared0.468208
F-TEST (value)21.7531
F-TEST (DF numerator)7
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2227
Sum Squared Residuals780.583

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.700549 \tabularnewline
R-squared & 0.490769 \tabularnewline
Adjusted R-squared & 0.468208 \tabularnewline
F-TEST (value) & 21.7531 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.2227 \tabularnewline
Sum Squared Residuals & 780.583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.700549[/C][/ROW]
[ROW][C]R-squared[/C][C]0.490769[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.468208[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.7531[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/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]2.2227[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]780.583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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 R0.700549
R-squared0.490769
Adjusted R-squared0.468208
F-TEST (value)21.7531
F-TEST (DF numerator)7
F-TEST (DF denominator)158
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2227
Sum Squared Residuals780.583






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.756-6.40599
212.710.83281.86724
318.115.64372.45634
417.8516.26261.5874
516.616.42360.176354
612.611.65520.944781
717.119.0912-1.99121
819.117.49581.60416
916.116.2412-0.141178
1013.3511.95841.39165
1118.416.65691.74305
1214.79.595025.10498
1310.613.1675-2.56746
1412.613.5376-0.937595
1516.215.12541.07459
1613.613.33650.263454
1718.916.26952.63054
1814.113.36130.738698
1914.513.76540.734648
2016.1517.2442-1.09421
2114.7513.55381.19622
2214.813.49181.30824
2312.4511.92460.525432
2412.6512.31880.331211
2517.3514.71742.63259
268.69.46115-0.861148
2718.416.46521.9348
2816.114.32791.77208
2911.610.87880.721248
3017.7516.71421.03579
3115.2514.77990.47013
3217.6514.24813.40193
3316.3516.8658-0.515809
3417.6516.82680.823228
3513.614.6018-1.00176
3614.3514.584-0.234023
3714.7515.5074-0.757374
3818.2517.07541.17461
399.917.0204-7.12037
401614.28881.71118
4118.2516.65411.59586
4216.8518.2471-1.39707
4314.613.09111.50895
4413.8514.0603-0.210342
4518.9517.53281.41716
4615.614.57861.02145
4714.8516.7586-1.90859
4811.7514.0374-2.28744
4918.4516.66191.78809
5015.913.78452.11547
5117.118.2825-1.18246
5216.18.18077.9193
5319.918.95180.948159
5410.9510.35680.593225
5518.4516.4132.037
5615.114.3360.763958
571515.4217-0.421669
5811.3513.7084-2.35838
5915.9514.78871.16133
6018.115.67322.42677
6114.616.7139-2.11391
6215.416.937-1.53703
6315.416.865-1.46497
6417.614.55083.04915
6513.3514.2285-0.878502
6619.116.98672.11327
6715.3517.2211-1.87112
687.610.0755-2.47546
6913.414.4693-1.06933
7013.916.2538-2.35379
7119.116.39282.70722
7215.2515.2855-0.0355238
7312.916.1114-3.21142
7416.115.67530.424748
7517.3515.43541.91458
7613.1515.7625-2.6125
7712.1515.6913-3.54133
7812.611.8750.725018
7910.3512.5719-2.22193
8015.413.96741.43264
819.612.7586-3.1586
8218.215.32792.8721
8313.613.9903-0.390342
8414.8513.09691.75311
8514.7516.8111-2.06108
8614.113.69240.407587
8714.913.7911.109
8816.2516.04760.202368
8919.2519.5098-0.259823
9013.613.16150.438528
9113.616.0294-2.42938
9215.6516.1804-0.530362
9312.7513.5887-0.838743
9414.613.2091.39104
959.859.796390.0536099
9612.6511.67440.975578
9719.215.44153.75849
9816.613.49773.10232
9911.211.3501-0.150053
10015.2516.1012-0.851163
10111.913.7287-1.82874
10213.213.09310.106857
10316.3516.7697-0.419667
10412.412.6874-0.287355
10515.8514.08131.76868
10618.1517.52730.622715
10711.1511.8896-0.739645
10815.6516.373-0.722967
10917.7515.8841.86597
1107.6511.2484-3.5984
11112.3514.3262-1.97623
11215.613.9391.66098
11319.317.43651.86345
11415.211.57073.62932
11517.116.05281.04718
11615.613.53812.0619
11718.414.3994.001
11819.0516.56882.48124
11918.5516.93791.6121
12019.116.13562.96439
12113.113.5805-0.48054
12212.8516.7751-3.92508
1239.512.3263-2.82631
1244.510.61-6.11001
12511.8511.49920.350843
12613.614.5228-0.922768
12711.711.68020.019751
12812.413.419-1.01903
12913.3514.6224-1.27242
13011.412.5558-1.15582
13114.914.70710.192869
13219.917.9281.97202
13311.213.689-2.48899
13414.614.9006-0.30064
13517.618.0058-0.405751
13614.0513.3650.684982
13716.115.31650.783531
13813.3514.7307-1.38071
13911.8515.3404-3.4904
14011.9512.7228-0.772811
14114.7514.00010.749894
14215.1512.92152.22848
14313.215.9025-2.70255
14416.8516.14530.704728
1457.8512.3544-4.50443
1467.712.857-5.15701
14712.614.4698-1.86978
1487.8514.8789-7.02887
14910.9511.3484-0.398428
15012.3514.4356-2.08558
1519.9513.1183-3.16831
15214.914.13830.761665
15316.6515.1161.534
15413.412.59540.804575
15513.9514.3966-0.44662
15615.715.05220.647755
15716.8515.76111.08888
15810.9512.1118-1.1618
15915.3515.91-0.559972
16012.213.1321-0.932116
16115.113.74841.35159
16217.7515.98151.76849
16315.214.84010.359944
16414.615.4372-0.837206
16516.6515.74060.909396
1668.19.35545-1.25545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.756 & -6.40599 \tabularnewline
2 & 12.7 & 10.8328 & 1.86724 \tabularnewline
3 & 18.1 & 15.6437 & 2.45634 \tabularnewline
4 & 17.85 & 16.2626 & 1.5874 \tabularnewline
5 & 16.6 & 16.4236 & 0.176354 \tabularnewline
6 & 12.6 & 11.6552 & 0.944781 \tabularnewline
7 & 17.1 & 19.0912 & -1.99121 \tabularnewline
8 & 19.1 & 17.4958 & 1.60416 \tabularnewline
9 & 16.1 & 16.2412 & -0.141178 \tabularnewline
10 & 13.35 & 11.9584 & 1.39165 \tabularnewline
11 & 18.4 & 16.6569 & 1.74305 \tabularnewline
12 & 14.7 & 9.59502 & 5.10498 \tabularnewline
13 & 10.6 & 13.1675 & -2.56746 \tabularnewline
14 & 12.6 & 13.5376 & -0.937595 \tabularnewline
15 & 16.2 & 15.1254 & 1.07459 \tabularnewline
16 & 13.6 & 13.3365 & 0.263454 \tabularnewline
17 & 18.9 & 16.2695 & 2.63054 \tabularnewline
18 & 14.1 & 13.3613 & 0.738698 \tabularnewline
19 & 14.5 & 13.7654 & 0.734648 \tabularnewline
20 & 16.15 & 17.2442 & -1.09421 \tabularnewline
21 & 14.75 & 13.5538 & 1.19622 \tabularnewline
22 & 14.8 & 13.4918 & 1.30824 \tabularnewline
23 & 12.45 & 11.9246 & 0.525432 \tabularnewline
24 & 12.65 & 12.3188 & 0.331211 \tabularnewline
25 & 17.35 & 14.7174 & 2.63259 \tabularnewline
26 & 8.6 & 9.46115 & -0.861148 \tabularnewline
27 & 18.4 & 16.4652 & 1.9348 \tabularnewline
28 & 16.1 & 14.3279 & 1.77208 \tabularnewline
29 & 11.6 & 10.8788 & 0.721248 \tabularnewline
30 & 17.75 & 16.7142 & 1.03579 \tabularnewline
31 & 15.25 & 14.7799 & 0.47013 \tabularnewline
32 & 17.65 & 14.2481 & 3.40193 \tabularnewline
33 & 16.35 & 16.8658 & -0.515809 \tabularnewline
34 & 17.65 & 16.8268 & 0.823228 \tabularnewline
35 & 13.6 & 14.6018 & -1.00176 \tabularnewline
36 & 14.35 & 14.584 & -0.234023 \tabularnewline
37 & 14.75 & 15.5074 & -0.757374 \tabularnewline
38 & 18.25 & 17.0754 & 1.17461 \tabularnewline
39 & 9.9 & 17.0204 & -7.12037 \tabularnewline
40 & 16 & 14.2888 & 1.71118 \tabularnewline
41 & 18.25 & 16.6541 & 1.59586 \tabularnewline
42 & 16.85 & 18.2471 & -1.39707 \tabularnewline
43 & 14.6 & 13.0911 & 1.50895 \tabularnewline
44 & 13.85 & 14.0603 & -0.210342 \tabularnewline
45 & 18.95 & 17.5328 & 1.41716 \tabularnewline
46 & 15.6 & 14.5786 & 1.02145 \tabularnewline
47 & 14.85 & 16.7586 & -1.90859 \tabularnewline
48 & 11.75 & 14.0374 & -2.28744 \tabularnewline
49 & 18.45 & 16.6619 & 1.78809 \tabularnewline
50 & 15.9 & 13.7845 & 2.11547 \tabularnewline
51 & 17.1 & 18.2825 & -1.18246 \tabularnewline
52 & 16.1 & 8.1807 & 7.9193 \tabularnewline
53 & 19.9 & 18.9518 & 0.948159 \tabularnewline
54 & 10.95 & 10.3568 & 0.593225 \tabularnewline
55 & 18.45 & 16.413 & 2.037 \tabularnewline
56 & 15.1 & 14.336 & 0.763958 \tabularnewline
57 & 15 & 15.4217 & -0.421669 \tabularnewline
58 & 11.35 & 13.7084 & -2.35838 \tabularnewline
59 & 15.95 & 14.7887 & 1.16133 \tabularnewline
60 & 18.1 & 15.6732 & 2.42677 \tabularnewline
61 & 14.6 & 16.7139 & -2.11391 \tabularnewline
62 & 15.4 & 16.937 & -1.53703 \tabularnewline
63 & 15.4 & 16.865 & -1.46497 \tabularnewline
64 & 17.6 & 14.5508 & 3.04915 \tabularnewline
65 & 13.35 & 14.2285 & -0.878502 \tabularnewline
66 & 19.1 & 16.9867 & 2.11327 \tabularnewline
67 & 15.35 & 17.2211 & -1.87112 \tabularnewline
68 & 7.6 & 10.0755 & -2.47546 \tabularnewline
69 & 13.4 & 14.4693 & -1.06933 \tabularnewline
70 & 13.9 & 16.2538 & -2.35379 \tabularnewline
71 & 19.1 & 16.3928 & 2.70722 \tabularnewline
72 & 15.25 & 15.2855 & -0.0355238 \tabularnewline
73 & 12.9 & 16.1114 & -3.21142 \tabularnewline
74 & 16.1 & 15.6753 & 0.424748 \tabularnewline
75 & 17.35 & 15.4354 & 1.91458 \tabularnewline
76 & 13.15 & 15.7625 & -2.6125 \tabularnewline
77 & 12.15 & 15.6913 & -3.54133 \tabularnewline
78 & 12.6 & 11.875 & 0.725018 \tabularnewline
79 & 10.35 & 12.5719 & -2.22193 \tabularnewline
80 & 15.4 & 13.9674 & 1.43264 \tabularnewline
81 & 9.6 & 12.7586 & -3.1586 \tabularnewline
82 & 18.2 & 15.3279 & 2.8721 \tabularnewline
83 & 13.6 & 13.9903 & -0.390342 \tabularnewline
84 & 14.85 & 13.0969 & 1.75311 \tabularnewline
85 & 14.75 & 16.8111 & -2.06108 \tabularnewline
86 & 14.1 & 13.6924 & 0.407587 \tabularnewline
87 & 14.9 & 13.791 & 1.109 \tabularnewline
88 & 16.25 & 16.0476 & 0.202368 \tabularnewline
89 & 19.25 & 19.5098 & -0.259823 \tabularnewline
90 & 13.6 & 13.1615 & 0.438528 \tabularnewline
91 & 13.6 & 16.0294 & -2.42938 \tabularnewline
92 & 15.65 & 16.1804 & -0.530362 \tabularnewline
93 & 12.75 & 13.5887 & -0.838743 \tabularnewline
94 & 14.6 & 13.209 & 1.39104 \tabularnewline
95 & 9.85 & 9.79639 & 0.0536099 \tabularnewline
96 & 12.65 & 11.6744 & 0.975578 \tabularnewline
97 & 19.2 & 15.4415 & 3.75849 \tabularnewline
98 & 16.6 & 13.4977 & 3.10232 \tabularnewline
99 & 11.2 & 11.3501 & -0.150053 \tabularnewline
100 & 15.25 & 16.1012 & -0.851163 \tabularnewline
101 & 11.9 & 13.7287 & -1.82874 \tabularnewline
102 & 13.2 & 13.0931 & 0.106857 \tabularnewline
103 & 16.35 & 16.7697 & -0.419667 \tabularnewline
104 & 12.4 & 12.6874 & -0.287355 \tabularnewline
105 & 15.85 & 14.0813 & 1.76868 \tabularnewline
106 & 18.15 & 17.5273 & 0.622715 \tabularnewline
107 & 11.15 & 11.8896 & -0.739645 \tabularnewline
108 & 15.65 & 16.373 & -0.722967 \tabularnewline
109 & 17.75 & 15.884 & 1.86597 \tabularnewline
110 & 7.65 & 11.2484 & -3.5984 \tabularnewline
111 & 12.35 & 14.3262 & -1.97623 \tabularnewline
112 & 15.6 & 13.939 & 1.66098 \tabularnewline
113 & 19.3 & 17.4365 & 1.86345 \tabularnewline
114 & 15.2 & 11.5707 & 3.62932 \tabularnewline
115 & 17.1 & 16.0528 & 1.04718 \tabularnewline
116 & 15.6 & 13.5381 & 2.0619 \tabularnewline
117 & 18.4 & 14.399 & 4.001 \tabularnewline
118 & 19.05 & 16.5688 & 2.48124 \tabularnewline
119 & 18.55 & 16.9379 & 1.6121 \tabularnewline
120 & 19.1 & 16.1356 & 2.96439 \tabularnewline
121 & 13.1 & 13.5805 & -0.48054 \tabularnewline
122 & 12.85 & 16.7751 & -3.92508 \tabularnewline
123 & 9.5 & 12.3263 & -2.82631 \tabularnewline
124 & 4.5 & 10.61 & -6.11001 \tabularnewline
125 & 11.85 & 11.4992 & 0.350843 \tabularnewline
126 & 13.6 & 14.5228 & -0.922768 \tabularnewline
127 & 11.7 & 11.6802 & 0.019751 \tabularnewline
128 & 12.4 & 13.419 & -1.01903 \tabularnewline
129 & 13.35 & 14.6224 & -1.27242 \tabularnewline
130 & 11.4 & 12.5558 & -1.15582 \tabularnewline
131 & 14.9 & 14.7071 & 0.192869 \tabularnewline
132 & 19.9 & 17.928 & 1.97202 \tabularnewline
133 & 11.2 & 13.689 & -2.48899 \tabularnewline
134 & 14.6 & 14.9006 & -0.30064 \tabularnewline
135 & 17.6 & 18.0058 & -0.405751 \tabularnewline
136 & 14.05 & 13.365 & 0.684982 \tabularnewline
137 & 16.1 & 15.3165 & 0.783531 \tabularnewline
138 & 13.35 & 14.7307 & -1.38071 \tabularnewline
139 & 11.85 & 15.3404 & -3.4904 \tabularnewline
140 & 11.95 & 12.7228 & -0.772811 \tabularnewline
141 & 14.75 & 14.0001 & 0.749894 \tabularnewline
142 & 15.15 & 12.9215 & 2.22848 \tabularnewline
143 & 13.2 & 15.9025 & -2.70255 \tabularnewline
144 & 16.85 & 16.1453 & 0.704728 \tabularnewline
145 & 7.85 & 12.3544 & -4.50443 \tabularnewline
146 & 7.7 & 12.857 & -5.15701 \tabularnewline
147 & 12.6 & 14.4698 & -1.86978 \tabularnewline
148 & 7.85 & 14.8789 & -7.02887 \tabularnewline
149 & 10.95 & 11.3484 & -0.398428 \tabularnewline
150 & 12.35 & 14.4356 & -2.08558 \tabularnewline
151 & 9.95 & 13.1183 & -3.16831 \tabularnewline
152 & 14.9 & 14.1383 & 0.761665 \tabularnewline
153 & 16.65 & 15.116 & 1.534 \tabularnewline
154 & 13.4 & 12.5954 & 0.804575 \tabularnewline
155 & 13.95 & 14.3966 & -0.44662 \tabularnewline
156 & 15.7 & 15.0522 & 0.647755 \tabularnewline
157 & 16.85 & 15.7611 & 1.08888 \tabularnewline
158 & 10.95 & 12.1118 & -1.1618 \tabularnewline
159 & 15.35 & 15.91 & -0.559972 \tabularnewline
160 & 12.2 & 13.1321 & -0.932116 \tabularnewline
161 & 15.1 & 13.7484 & 1.35159 \tabularnewline
162 & 17.75 & 15.9815 & 1.76849 \tabularnewline
163 & 15.2 & 14.8401 & 0.359944 \tabularnewline
164 & 14.6 & 15.4372 & -0.837206 \tabularnewline
165 & 16.65 & 15.7406 & 0.909396 \tabularnewline
166 & 8.1 & 9.35545 & -1.25545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&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]10.756[/C][C]-6.40599[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.8328[/C][C]1.86724[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6437[/C][C]2.45634[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.2626[/C][C]1.5874[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.4236[/C][C]0.176354[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.6552[/C][C]0.944781[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.0912[/C][C]-1.99121[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.4958[/C][C]1.60416[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.2412[/C][C]-0.141178[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.9584[/C][C]1.39165[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.6569[/C][C]1.74305[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.59502[/C][C]5.10498[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.1675[/C][C]-2.56746[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.5376[/C][C]-0.937595[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.1254[/C][C]1.07459[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3365[/C][C]0.263454[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.2695[/C][C]2.63054[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.3613[/C][C]0.738698[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.7654[/C][C]0.734648[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.2442[/C][C]-1.09421[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.5538[/C][C]1.19622[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.4918[/C][C]1.30824[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.9246[/C][C]0.525432[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.3188[/C][C]0.331211[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.7174[/C][C]2.63259[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.46115[/C][C]-0.861148[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]16.4652[/C][C]1.9348[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.3279[/C][C]1.77208[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]10.8788[/C][C]0.721248[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.7142[/C][C]1.03579[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.7799[/C][C]0.47013[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.2481[/C][C]3.40193[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.8658[/C][C]-0.515809[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.8268[/C][C]0.823228[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.6018[/C][C]-1.00176[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.584[/C][C]-0.234023[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.5074[/C][C]-0.757374[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.0754[/C][C]1.17461[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]17.0204[/C][C]-7.12037[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2888[/C][C]1.71118[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.6541[/C][C]1.59586[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.2471[/C][C]-1.39707[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.0911[/C][C]1.50895[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.0603[/C][C]-0.210342[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.5328[/C][C]1.41716[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.5786[/C][C]1.02145[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.7586[/C][C]-1.90859[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]14.0374[/C][C]-2.28744[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.6619[/C][C]1.78809[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]13.7845[/C][C]2.11547[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.2825[/C][C]-1.18246[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]8.1807[/C][C]7.9193[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.9518[/C][C]0.948159[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.3568[/C][C]0.593225[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.413[/C][C]2.037[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.336[/C][C]0.763958[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.4217[/C][C]-0.421669[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]13.7084[/C][C]-2.35838[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.7887[/C][C]1.16133[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6732[/C][C]2.42677[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.7139[/C][C]-2.11391[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.937[/C][C]-1.53703[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.865[/C][C]-1.46497[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.5508[/C][C]3.04915[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.2285[/C][C]-0.878502[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.9867[/C][C]2.11327[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.2211[/C][C]-1.87112[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.0755[/C][C]-2.47546[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.4693[/C][C]-1.06933[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]16.2538[/C][C]-2.35379[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.3928[/C][C]2.70722[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.2855[/C][C]-0.0355238[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.1114[/C][C]-3.21142[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.6753[/C][C]0.424748[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.4354[/C][C]1.91458[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.7625[/C][C]-2.6125[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.6913[/C][C]-3.54133[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.875[/C][C]0.725018[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.5719[/C][C]-2.22193[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]13.9674[/C][C]1.43264[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.7586[/C][C]-3.1586[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.3279[/C][C]2.8721[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]13.9903[/C][C]-0.390342[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.0969[/C][C]1.75311[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.8111[/C][C]-2.06108[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]13.6924[/C][C]0.407587[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.791[/C][C]1.109[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]16.0476[/C][C]0.202368[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.5098[/C][C]-0.259823[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]13.1615[/C][C]0.438528[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.0294[/C][C]-2.42938[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.1804[/C][C]-0.530362[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.5887[/C][C]-0.838743[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.209[/C][C]1.39104[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.79639[/C][C]0.0536099[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.6744[/C][C]0.975578[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]15.4415[/C][C]3.75849[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]13.4977[/C][C]3.10232[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.3501[/C][C]-0.150053[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]16.1012[/C][C]-0.851163[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]13.7287[/C][C]-1.82874[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.0931[/C][C]0.106857[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.7697[/C][C]-0.419667[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]12.6874[/C][C]-0.287355[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0813[/C][C]1.76868[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.5273[/C][C]0.622715[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.8896[/C][C]-0.739645[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.373[/C][C]-0.722967[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.884[/C][C]1.86597[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.2484[/C][C]-3.5984[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]14.3262[/C][C]-1.97623[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]13.939[/C][C]1.66098[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.4365[/C][C]1.86345[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5707[/C][C]3.62932[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]16.0528[/C][C]1.04718[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.5381[/C][C]2.0619[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.399[/C][C]4.001[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.5688[/C][C]2.48124[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]16.9379[/C][C]1.6121[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.1356[/C][C]2.96439[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.5805[/C][C]-0.48054[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.7751[/C][C]-3.92508[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]12.3263[/C][C]-2.82631[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.61[/C][C]-6.11001[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.4992[/C][C]0.350843[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.5228[/C][C]-0.922768[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.6802[/C][C]0.019751[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.419[/C][C]-1.01903[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.6224[/C][C]-1.27242[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.5558[/C][C]-1.15582[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.7071[/C][C]0.192869[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.928[/C][C]1.97202[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.689[/C][C]-2.48899[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]14.9006[/C][C]-0.30064[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]18.0058[/C][C]-0.405751[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.365[/C][C]0.684982[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.3165[/C][C]0.783531[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.7307[/C][C]-1.38071[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]15.3404[/C][C]-3.4904[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.7228[/C][C]-0.772811[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]14.0001[/C][C]0.749894[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.9215[/C][C]2.22848[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.9025[/C][C]-2.70255[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.1453[/C][C]0.704728[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.3544[/C][C]-4.50443[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.857[/C][C]-5.15701[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.4698[/C][C]-1.86978[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]14.8789[/C][C]-7.02887[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.3484[/C][C]-0.398428[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.4356[/C][C]-2.08558[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.1183[/C][C]-3.16831[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]14.1383[/C][C]0.761665[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.116[/C][C]1.534[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.5954[/C][C]0.804575[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.3966[/C][C]-0.44662[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]15.0522[/C][C]0.647755[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.7611[/C][C]1.08888[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]12.1118[/C][C]-1.1618[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]15.91[/C][C]-0.559972[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]13.1321[/C][C]-0.932116[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]13.7484[/C][C]1.35159[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.9815[/C][C]1.76849[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.8401[/C][C]0.359944[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]15.4372[/C][C]-0.837206[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.7406[/C][C]0.909396[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.35545[/C][C]-1.25545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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
14.3510.756-6.40599
212.710.83281.86724
318.115.64372.45634
417.8516.26261.5874
516.616.42360.176354
612.611.65520.944781
717.119.0912-1.99121
819.117.49581.60416
916.116.2412-0.141178
1013.3511.95841.39165
1118.416.65691.74305
1214.79.595025.10498
1310.613.1675-2.56746
1412.613.5376-0.937595
1516.215.12541.07459
1613.613.33650.263454
1718.916.26952.63054
1814.113.36130.738698
1914.513.76540.734648
2016.1517.2442-1.09421
2114.7513.55381.19622
2214.813.49181.30824
2312.4511.92460.525432
2412.6512.31880.331211
2517.3514.71742.63259
268.69.46115-0.861148
2718.416.46521.9348
2816.114.32791.77208
2911.610.87880.721248
3017.7516.71421.03579
3115.2514.77990.47013
3217.6514.24813.40193
3316.3516.8658-0.515809
3417.6516.82680.823228
3513.614.6018-1.00176
3614.3514.584-0.234023
3714.7515.5074-0.757374
3818.2517.07541.17461
399.917.0204-7.12037
401614.28881.71118
4118.2516.65411.59586
4216.8518.2471-1.39707
4314.613.09111.50895
4413.8514.0603-0.210342
4518.9517.53281.41716
4615.614.57861.02145
4714.8516.7586-1.90859
4811.7514.0374-2.28744
4918.4516.66191.78809
5015.913.78452.11547
5117.118.2825-1.18246
5216.18.18077.9193
5319.918.95180.948159
5410.9510.35680.593225
5518.4516.4132.037
5615.114.3360.763958
571515.4217-0.421669
5811.3513.7084-2.35838
5915.9514.78871.16133
6018.115.67322.42677
6114.616.7139-2.11391
6215.416.937-1.53703
6315.416.865-1.46497
6417.614.55083.04915
6513.3514.2285-0.878502
6619.116.98672.11327
6715.3517.2211-1.87112
687.610.0755-2.47546
6913.414.4693-1.06933
7013.916.2538-2.35379
7119.116.39282.70722
7215.2515.2855-0.0355238
7312.916.1114-3.21142
7416.115.67530.424748
7517.3515.43541.91458
7613.1515.7625-2.6125
7712.1515.6913-3.54133
7812.611.8750.725018
7910.3512.5719-2.22193
8015.413.96741.43264
819.612.7586-3.1586
8218.215.32792.8721
8313.613.9903-0.390342
8414.8513.09691.75311
8514.7516.8111-2.06108
8614.113.69240.407587
8714.913.7911.109
8816.2516.04760.202368
8919.2519.5098-0.259823
9013.613.16150.438528
9113.616.0294-2.42938
9215.6516.1804-0.530362
9312.7513.5887-0.838743
9414.613.2091.39104
959.859.796390.0536099
9612.6511.67440.975578
9719.215.44153.75849
9816.613.49773.10232
9911.211.3501-0.150053
10015.2516.1012-0.851163
10111.913.7287-1.82874
10213.213.09310.106857
10316.3516.7697-0.419667
10412.412.6874-0.287355
10515.8514.08131.76868
10618.1517.52730.622715
10711.1511.8896-0.739645
10815.6516.373-0.722967
10917.7515.8841.86597
1107.6511.2484-3.5984
11112.3514.3262-1.97623
11215.613.9391.66098
11319.317.43651.86345
11415.211.57073.62932
11517.116.05281.04718
11615.613.53812.0619
11718.414.3994.001
11819.0516.56882.48124
11918.5516.93791.6121
12019.116.13562.96439
12113.113.5805-0.48054
12212.8516.7751-3.92508
1239.512.3263-2.82631
1244.510.61-6.11001
12511.8511.49920.350843
12613.614.5228-0.922768
12711.711.68020.019751
12812.413.419-1.01903
12913.3514.6224-1.27242
13011.412.5558-1.15582
13114.914.70710.192869
13219.917.9281.97202
13311.213.689-2.48899
13414.614.9006-0.30064
13517.618.0058-0.405751
13614.0513.3650.684982
13716.115.31650.783531
13813.3514.7307-1.38071
13911.8515.3404-3.4904
14011.9512.7228-0.772811
14114.7514.00010.749894
14215.1512.92152.22848
14313.215.9025-2.70255
14416.8516.14530.704728
1457.8512.3544-4.50443
1467.712.857-5.15701
14712.614.4698-1.86978
1487.8514.8789-7.02887
14910.9511.3484-0.398428
15012.3514.4356-2.08558
1519.9513.1183-3.16831
15214.914.13830.761665
15316.6515.1161.534
15413.412.59540.804575
15513.9514.3966-0.44662
15615.715.05220.647755
15716.8515.76111.08888
15810.9512.1118-1.1618
15915.3515.91-0.559972
16012.213.1321-0.932116
16115.113.74841.35159
16217.7515.98151.76849
16315.214.84010.359944
16414.615.4372-0.837206
16516.6515.74060.909396
1668.19.35545-1.25545






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5909440.8181110.409056
120.7443370.5113260.255663
130.9334930.1330130.0665067
140.8878140.2243730.112186
150.8349280.3301440.165072
160.8142050.3715910.185795
170.7657130.4685740.234287
180.6940980.6118040.305902
190.6116630.7766750.388337
200.7082640.5834730.291736
210.6658240.6683510.334176
220.5933930.8132140.406607
230.5270710.9458590.472929
240.4559420.9118830.544058
250.5097480.9805040.490252
260.4850880.9701760.514912
270.4237720.8475440.576228
280.4920540.9841070.507946
290.4391870.8783750.560813
300.3776310.7552620.622369
310.3253320.6506640.674668
320.3117450.623490.688255
330.2905530.5811050.709447
340.2675570.5351140.732443
350.2194890.4389790.780511
360.1774810.3549620.822519
370.1417350.283470.858265
380.1159730.2319450.884027
390.730570.538860.26943
400.6993130.6013740.300687
410.6645110.6709780.335489
420.6239290.7521410.376071
430.5792380.8415240.420762
440.5347120.9305750.465288
450.4929820.9859630.507018
460.4475970.8951940.552403
470.4348040.8696080.565196
480.41970.8393990.5803
490.4026120.8052230.597388
500.3816360.7632720.618364
510.3389110.6778210.661089
520.8222720.3554550.177728
530.7971740.4056520.202826
540.7693770.4612460.230623
550.7497150.500570.250285
560.7167240.5665520.283276
570.6755150.648970.324485
580.6811130.6377740.318887
590.6503290.6993410.349671
600.6766640.6466720.323336
610.713660.572680.28634
620.699990.6000190.30001
630.6812460.6375070.318754
640.7089080.5821850.291092
650.6752510.6494970.324749
660.6778920.6442160.322108
670.6665160.6669670.333484
680.7062370.5875260.293763
690.6757950.648410.324205
700.6876730.6246530.312327
710.7126360.5747280.287364
720.6718620.6562770.328138
730.7225880.5548250.277412
740.683790.632420.31621
750.6701080.6597840.329892
760.7013210.5973590.298679
770.7803720.4392550.219628
780.7548550.490290.245145
790.7655380.4689240.234462
800.7494070.5011850.250593
810.7807980.4384050.219202
820.8097120.3805760.190288
830.7859090.4281810.214091
840.779820.4403610.22018
850.7789380.4421240.221062
860.7471990.5056030.252801
870.7193850.5612290.280615
880.679540.640920.32046
890.6445730.7108540.355427
900.604710.7905790.39529
910.6187530.7624950.381247
920.5817090.8365830.418291
930.5390460.9219080.460954
940.5123170.9753660.487683
950.5207970.9584050.479203
960.4911340.9822670.508866
970.5634590.8730810.436541
980.6103120.7793760.389688
990.5855770.8288460.414423
1000.5454570.9090850.454543
1010.522030.9559390.47797
1020.4747510.9495030.525249
1030.4305320.8610630.569468
1040.3882510.7765020.611749
1050.3796140.7592280.620386
1060.3409660.6819320.659034
1070.3115040.6230080.688496
1080.2825140.5650290.717486
1090.2725360.5450710.727464
1100.337090.674180.66291
1110.3136170.6272330.686383
1120.3424860.6849720.657514
1130.322340.644680.67766
1140.3945560.7891110.605444
1150.3565980.7131960.643402
1160.3949630.7899260.605037
1170.6842040.6315930.315796
1180.7109980.5780050.289002
1190.6906330.6187340.309367
1200.7654750.4690490.234525
1210.738770.522460.26123
1220.76430.47140.2357
1230.7443220.5113550.255678
1240.8565640.2868720.143436
1250.8307180.3385640.169282
1260.8093680.3812650.190632
1270.8100660.3798690.189934
1280.7703740.4592520.229626
1290.7261770.5476460.273823
1300.6816960.6366070.318304
1310.6436530.7126940.356347
1320.5922090.8155830.407791
1330.5631860.8736270.436814
1340.5168040.9663910.483196
1350.4550860.9101710.544914
1360.501950.99610.49805
1370.4957960.9915930.504204
1380.5099120.9801760.490088
1390.4692780.9385570.530722
1400.4833240.9666470.516676
1410.4505140.9010290.549486
1420.3840410.7680810.615959
1430.3381430.6762860.661857
1440.2751360.5502730.724864
1450.3496490.6992970.650351
1460.4495240.8990470.550476
1470.4111140.8222280.588886
1480.9408760.1182480.0591238
1490.9146060.1707870.0853935
1500.9696150.06076970.0303849
1510.9884110.02317790.0115889
1520.9816660.03666770.0183338
1530.9545010.0909980.045499
1540.9589670.08206540.0410327
1550.9287570.1424870.0712433

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.590944 & 0.818111 & 0.409056 \tabularnewline
12 & 0.744337 & 0.511326 & 0.255663 \tabularnewline
13 & 0.933493 & 0.133013 & 0.0665067 \tabularnewline
14 & 0.887814 & 0.224373 & 0.112186 \tabularnewline
15 & 0.834928 & 0.330144 & 0.165072 \tabularnewline
16 & 0.814205 & 0.371591 & 0.185795 \tabularnewline
17 & 0.765713 & 0.468574 & 0.234287 \tabularnewline
18 & 0.694098 & 0.611804 & 0.305902 \tabularnewline
19 & 0.611663 & 0.776675 & 0.388337 \tabularnewline
20 & 0.708264 & 0.583473 & 0.291736 \tabularnewline
21 & 0.665824 & 0.668351 & 0.334176 \tabularnewline
22 & 0.593393 & 0.813214 & 0.406607 \tabularnewline
23 & 0.527071 & 0.945859 & 0.472929 \tabularnewline
24 & 0.455942 & 0.911883 & 0.544058 \tabularnewline
25 & 0.509748 & 0.980504 & 0.490252 \tabularnewline
26 & 0.485088 & 0.970176 & 0.514912 \tabularnewline
27 & 0.423772 & 0.847544 & 0.576228 \tabularnewline
28 & 0.492054 & 0.984107 & 0.507946 \tabularnewline
29 & 0.439187 & 0.878375 & 0.560813 \tabularnewline
30 & 0.377631 & 0.755262 & 0.622369 \tabularnewline
31 & 0.325332 & 0.650664 & 0.674668 \tabularnewline
32 & 0.311745 & 0.62349 & 0.688255 \tabularnewline
33 & 0.290553 & 0.581105 & 0.709447 \tabularnewline
34 & 0.267557 & 0.535114 & 0.732443 \tabularnewline
35 & 0.219489 & 0.438979 & 0.780511 \tabularnewline
36 & 0.177481 & 0.354962 & 0.822519 \tabularnewline
37 & 0.141735 & 0.28347 & 0.858265 \tabularnewline
38 & 0.115973 & 0.231945 & 0.884027 \tabularnewline
39 & 0.73057 & 0.53886 & 0.26943 \tabularnewline
40 & 0.699313 & 0.601374 & 0.300687 \tabularnewline
41 & 0.664511 & 0.670978 & 0.335489 \tabularnewline
42 & 0.623929 & 0.752141 & 0.376071 \tabularnewline
43 & 0.579238 & 0.841524 & 0.420762 \tabularnewline
44 & 0.534712 & 0.930575 & 0.465288 \tabularnewline
45 & 0.492982 & 0.985963 & 0.507018 \tabularnewline
46 & 0.447597 & 0.895194 & 0.552403 \tabularnewline
47 & 0.434804 & 0.869608 & 0.565196 \tabularnewline
48 & 0.4197 & 0.839399 & 0.5803 \tabularnewline
49 & 0.402612 & 0.805223 & 0.597388 \tabularnewline
50 & 0.381636 & 0.763272 & 0.618364 \tabularnewline
51 & 0.338911 & 0.677821 & 0.661089 \tabularnewline
52 & 0.822272 & 0.355455 & 0.177728 \tabularnewline
53 & 0.797174 & 0.405652 & 0.202826 \tabularnewline
54 & 0.769377 & 0.461246 & 0.230623 \tabularnewline
55 & 0.749715 & 0.50057 & 0.250285 \tabularnewline
56 & 0.716724 & 0.566552 & 0.283276 \tabularnewline
57 & 0.675515 & 0.64897 & 0.324485 \tabularnewline
58 & 0.681113 & 0.637774 & 0.318887 \tabularnewline
59 & 0.650329 & 0.699341 & 0.349671 \tabularnewline
60 & 0.676664 & 0.646672 & 0.323336 \tabularnewline
61 & 0.71366 & 0.57268 & 0.28634 \tabularnewline
62 & 0.69999 & 0.600019 & 0.30001 \tabularnewline
63 & 0.681246 & 0.637507 & 0.318754 \tabularnewline
64 & 0.708908 & 0.582185 & 0.291092 \tabularnewline
65 & 0.675251 & 0.649497 & 0.324749 \tabularnewline
66 & 0.677892 & 0.644216 & 0.322108 \tabularnewline
67 & 0.666516 & 0.666967 & 0.333484 \tabularnewline
68 & 0.706237 & 0.587526 & 0.293763 \tabularnewline
69 & 0.675795 & 0.64841 & 0.324205 \tabularnewline
70 & 0.687673 & 0.624653 & 0.312327 \tabularnewline
71 & 0.712636 & 0.574728 & 0.287364 \tabularnewline
72 & 0.671862 & 0.656277 & 0.328138 \tabularnewline
73 & 0.722588 & 0.554825 & 0.277412 \tabularnewline
74 & 0.68379 & 0.63242 & 0.31621 \tabularnewline
75 & 0.670108 & 0.659784 & 0.329892 \tabularnewline
76 & 0.701321 & 0.597359 & 0.298679 \tabularnewline
77 & 0.780372 & 0.439255 & 0.219628 \tabularnewline
78 & 0.754855 & 0.49029 & 0.245145 \tabularnewline
79 & 0.765538 & 0.468924 & 0.234462 \tabularnewline
80 & 0.749407 & 0.501185 & 0.250593 \tabularnewline
81 & 0.780798 & 0.438405 & 0.219202 \tabularnewline
82 & 0.809712 & 0.380576 & 0.190288 \tabularnewline
83 & 0.785909 & 0.428181 & 0.214091 \tabularnewline
84 & 0.77982 & 0.440361 & 0.22018 \tabularnewline
85 & 0.778938 & 0.442124 & 0.221062 \tabularnewline
86 & 0.747199 & 0.505603 & 0.252801 \tabularnewline
87 & 0.719385 & 0.561229 & 0.280615 \tabularnewline
88 & 0.67954 & 0.64092 & 0.32046 \tabularnewline
89 & 0.644573 & 0.710854 & 0.355427 \tabularnewline
90 & 0.60471 & 0.790579 & 0.39529 \tabularnewline
91 & 0.618753 & 0.762495 & 0.381247 \tabularnewline
92 & 0.581709 & 0.836583 & 0.418291 \tabularnewline
93 & 0.539046 & 0.921908 & 0.460954 \tabularnewline
94 & 0.512317 & 0.975366 & 0.487683 \tabularnewline
95 & 0.520797 & 0.958405 & 0.479203 \tabularnewline
96 & 0.491134 & 0.982267 & 0.508866 \tabularnewline
97 & 0.563459 & 0.873081 & 0.436541 \tabularnewline
98 & 0.610312 & 0.779376 & 0.389688 \tabularnewline
99 & 0.585577 & 0.828846 & 0.414423 \tabularnewline
100 & 0.545457 & 0.909085 & 0.454543 \tabularnewline
101 & 0.52203 & 0.955939 & 0.47797 \tabularnewline
102 & 0.474751 & 0.949503 & 0.525249 \tabularnewline
103 & 0.430532 & 0.861063 & 0.569468 \tabularnewline
104 & 0.388251 & 0.776502 & 0.611749 \tabularnewline
105 & 0.379614 & 0.759228 & 0.620386 \tabularnewline
106 & 0.340966 & 0.681932 & 0.659034 \tabularnewline
107 & 0.311504 & 0.623008 & 0.688496 \tabularnewline
108 & 0.282514 & 0.565029 & 0.717486 \tabularnewline
109 & 0.272536 & 0.545071 & 0.727464 \tabularnewline
110 & 0.33709 & 0.67418 & 0.66291 \tabularnewline
111 & 0.313617 & 0.627233 & 0.686383 \tabularnewline
112 & 0.342486 & 0.684972 & 0.657514 \tabularnewline
113 & 0.32234 & 0.64468 & 0.67766 \tabularnewline
114 & 0.394556 & 0.789111 & 0.605444 \tabularnewline
115 & 0.356598 & 0.713196 & 0.643402 \tabularnewline
116 & 0.394963 & 0.789926 & 0.605037 \tabularnewline
117 & 0.684204 & 0.631593 & 0.315796 \tabularnewline
118 & 0.710998 & 0.578005 & 0.289002 \tabularnewline
119 & 0.690633 & 0.618734 & 0.309367 \tabularnewline
120 & 0.765475 & 0.469049 & 0.234525 \tabularnewline
121 & 0.73877 & 0.52246 & 0.26123 \tabularnewline
122 & 0.7643 & 0.4714 & 0.2357 \tabularnewline
123 & 0.744322 & 0.511355 & 0.255678 \tabularnewline
124 & 0.856564 & 0.286872 & 0.143436 \tabularnewline
125 & 0.830718 & 0.338564 & 0.169282 \tabularnewline
126 & 0.809368 & 0.381265 & 0.190632 \tabularnewline
127 & 0.810066 & 0.379869 & 0.189934 \tabularnewline
128 & 0.770374 & 0.459252 & 0.229626 \tabularnewline
129 & 0.726177 & 0.547646 & 0.273823 \tabularnewline
130 & 0.681696 & 0.636607 & 0.318304 \tabularnewline
131 & 0.643653 & 0.712694 & 0.356347 \tabularnewline
132 & 0.592209 & 0.815583 & 0.407791 \tabularnewline
133 & 0.563186 & 0.873627 & 0.436814 \tabularnewline
134 & 0.516804 & 0.966391 & 0.483196 \tabularnewline
135 & 0.455086 & 0.910171 & 0.544914 \tabularnewline
136 & 0.50195 & 0.9961 & 0.49805 \tabularnewline
137 & 0.495796 & 0.991593 & 0.504204 \tabularnewline
138 & 0.509912 & 0.980176 & 0.490088 \tabularnewline
139 & 0.469278 & 0.938557 & 0.530722 \tabularnewline
140 & 0.483324 & 0.966647 & 0.516676 \tabularnewline
141 & 0.450514 & 0.901029 & 0.549486 \tabularnewline
142 & 0.384041 & 0.768081 & 0.615959 \tabularnewline
143 & 0.338143 & 0.676286 & 0.661857 \tabularnewline
144 & 0.275136 & 0.550273 & 0.724864 \tabularnewline
145 & 0.349649 & 0.699297 & 0.650351 \tabularnewline
146 & 0.449524 & 0.899047 & 0.550476 \tabularnewline
147 & 0.411114 & 0.822228 & 0.588886 \tabularnewline
148 & 0.940876 & 0.118248 & 0.0591238 \tabularnewline
149 & 0.914606 & 0.170787 & 0.0853935 \tabularnewline
150 & 0.969615 & 0.0607697 & 0.0303849 \tabularnewline
151 & 0.988411 & 0.0231779 & 0.0115889 \tabularnewline
152 & 0.981666 & 0.0366677 & 0.0183338 \tabularnewline
153 & 0.954501 & 0.090998 & 0.045499 \tabularnewline
154 & 0.958967 & 0.0820654 & 0.0410327 \tabularnewline
155 & 0.928757 & 0.142487 & 0.0712433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&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]11[/C][C]0.590944[/C][C]0.818111[/C][C]0.409056[/C][/ROW]
[ROW][C]12[/C][C]0.744337[/C][C]0.511326[/C][C]0.255663[/C][/ROW]
[ROW][C]13[/C][C]0.933493[/C][C]0.133013[/C][C]0.0665067[/C][/ROW]
[ROW][C]14[/C][C]0.887814[/C][C]0.224373[/C][C]0.112186[/C][/ROW]
[ROW][C]15[/C][C]0.834928[/C][C]0.330144[/C][C]0.165072[/C][/ROW]
[ROW][C]16[/C][C]0.814205[/C][C]0.371591[/C][C]0.185795[/C][/ROW]
[ROW][C]17[/C][C]0.765713[/C][C]0.468574[/C][C]0.234287[/C][/ROW]
[ROW][C]18[/C][C]0.694098[/C][C]0.611804[/C][C]0.305902[/C][/ROW]
[ROW][C]19[/C][C]0.611663[/C][C]0.776675[/C][C]0.388337[/C][/ROW]
[ROW][C]20[/C][C]0.708264[/C][C]0.583473[/C][C]0.291736[/C][/ROW]
[ROW][C]21[/C][C]0.665824[/C][C]0.668351[/C][C]0.334176[/C][/ROW]
[ROW][C]22[/C][C]0.593393[/C][C]0.813214[/C][C]0.406607[/C][/ROW]
[ROW][C]23[/C][C]0.527071[/C][C]0.945859[/C][C]0.472929[/C][/ROW]
[ROW][C]24[/C][C]0.455942[/C][C]0.911883[/C][C]0.544058[/C][/ROW]
[ROW][C]25[/C][C]0.509748[/C][C]0.980504[/C][C]0.490252[/C][/ROW]
[ROW][C]26[/C][C]0.485088[/C][C]0.970176[/C][C]0.514912[/C][/ROW]
[ROW][C]27[/C][C]0.423772[/C][C]0.847544[/C][C]0.576228[/C][/ROW]
[ROW][C]28[/C][C]0.492054[/C][C]0.984107[/C][C]0.507946[/C][/ROW]
[ROW][C]29[/C][C]0.439187[/C][C]0.878375[/C][C]0.560813[/C][/ROW]
[ROW][C]30[/C][C]0.377631[/C][C]0.755262[/C][C]0.622369[/C][/ROW]
[ROW][C]31[/C][C]0.325332[/C][C]0.650664[/C][C]0.674668[/C][/ROW]
[ROW][C]32[/C][C]0.311745[/C][C]0.62349[/C][C]0.688255[/C][/ROW]
[ROW][C]33[/C][C]0.290553[/C][C]0.581105[/C][C]0.709447[/C][/ROW]
[ROW][C]34[/C][C]0.267557[/C][C]0.535114[/C][C]0.732443[/C][/ROW]
[ROW][C]35[/C][C]0.219489[/C][C]0.438979[/C][C]0.780511[/C][/ROW]
[ROW][C]36[/C][C]0.177481[/C][C]0.354962[/C][C]0.822519[/C][/ROW]
[ROW][C]37[/C][C]0.141735[/C][C]0.28347[/C][C]0.858265[/C][/ROW]
[ROW][C]38[/C][C]0.115973[/C][C]0.231945[/C][C]0.884027[/C][/ROW]
[ROW][C]39[/C][C]0.73057[/C][C]0.53886[/C][C]0.26943[/C][/ROW]
[ROW][C]40[/C][C]0.699313[/C][C]0.601374[/C][C]0.300687[/C][/ROW]
[ROW][C]41[/C][C]0.664511[/C][C]0.670978[/C][C]0.335489[/C][/ROW]
[ROW][C]42[/C][C]0.623929[/C][C]0.752141[/C][C]0.376071[/C][/ROW]
[ROW][C]43[/C][C]0.579238[/C][C]0.841524[/C][C]0.420762[/C][/ROW]
[ROW][C]44[/C][C]0.534712[/C][C]0.930575[/C][C]0.465288[/C][/ROW]
[ROW][C]45[/C][C]0.492982[/C][C]0.985963[/C][C]0.507018[/C][/ROW]
[ROW][C]46[/C][C]0.447597[/C][C]0.895194[/C][C]0.552403[/C][/ROW]
[ROW][C]47[/C][C]0.434804[/C][C]0.869608[/C][C]0.565196[/C][/ROW]
[ROW][C]48[/C][C]0.4197[/C][C]0.839399[/C][C]0.5803[/C][/ROW]
[ROW][C]49[/C][C]0.402612[/C][C]0.805223[/C][C]0.597388[/C][/ROW]
[ROW][C]50[/C][C]0.381636[/C][C]0.763272[/C][C]0.618364[/C][/ROW]
[ROW][C]51[/C][C]0.338911[/C][C]0.677821[/C][C]0.661089[/C][/ROW]
[ROW][C]52[/C][C]0.822272[/C][C]0.355455[/C][C]0.177728[/C][/ROW]
[ROW][C]53[/C][C]0.797174[/C][C]0.405652[/C][C]0.202826[/C][/ROW]
[ROW][C]54[/C][C]0.769377[/C][C]0.461246[/C][C]0.230623[/C][/ROW]
[ROW][C]55[/C][C]0.749715[/C][C]0.50057[/C][C]0.250285[/C][/ROW]
[ROW][C]56[/C][C]0.716724[/C][C]0.566552[/C][C]0.283276[/C][/ROW]
[ROW][C]57[/C][C]0.675515[/C][C]0.64897[/C][C]0.324485[/C][/ROW]
[ROW][C]58[/C][C]0.681113[/C][C]0.637774[/C][C]0.318887[/C][/ROW]
[ROW][C]59[/C][C]0.650329[/C][C]0.699341[/C][C]0.349671[/C][/ROW]
[ROW][C]60[/C][C]0.676664[/C][C]0.646672[/C][C]0.323336[/C][/ROW]
[ROW][C]61[/C][C]0.71366[/C][C]0.57268[/C][C]0.28634[/C][/ROW]
[ROW][C]62[/C][C]0.69999[/C][C]0.600019[/C][C]0.30001[/C][/ROW]
[ROW][C]63[/C][C]0.681246[/C][C]0.637507[/C][C]0.318754[/C][/ROW]
[ROW][C]64[/C][C]0.708908[/C][C]0.582185[/C][C]0.291092[/C][/ROW]
[ROW][C]65[/C][C]0.675251[/C][C]0.649497[/C][C]0.324749[/C][/ROW]
[ROW][C]66[/C][C]0.677892[/C][C]0.644216[/C][C]0.322108[/C][/ROW]
[ROW][C]67[/C][C]0.666516[/C][C]0.666967[/C][C]0.333484[/C][/ROW]
[ROW][C]68[/C][C]0.706237[/C][C]0.587526[/C][C]0.293763[/C][/ROW]
[ROW][C]69[/C][C]0.675795[/C][C]0.64841[/C][C]0.324205[/C][/ROW]
[ROW][C]70[/C][C]0.687673[/C][C]0.624653[/C][C]0.312327[/C][/ROW]
[ROW][C]71[/C][C]0.712636[/C][C]0.574728[/C][C]0.287364[/C][/ROW]
[ROW][C]72[/C][C]0.671862[/C][C]0.656277[/C][C]0.328138[/C][/ROW]
[ROW][C]73[/C][C]0.722588[/C][C]0.554825[/C][C]0.277412[/C][/ROW]
[ROW][C]74[/C][C]0.68379[/C][C]0.63242[/C][C]0.31621[/C][/ROW]
[ROW][C]75[/C][C]0.670108[/C][C]0.659784[/C][C]0.329892[/C][/ROW]
[ROW][C]76[/C][C]0.701321[/C][C]0.597359[/C][C]0.298679[/C][/ROW]
[ROW][C]77[/C][C]0.780372[/C][C]0.439255[/C][C]0.219628[/C][/ROW]
[ROW][C]78[/C][C]0.754855[/C][C]0.49029[/C][C]0.245145[/C][/ROW]
[ROW][C]79[/C][C]0.765538[/C][C]0.468924[/C][C]0.234462[/C][/ROW]
[ROW][C]80[/C][C]0.749407[/C][C]0.501185[/C][C]0.250593[/C][/ROW]
[ROW][C]81[/C][C]0.780798[/C][C]0.438405[/C][C]0.219202[/C][/ROW]
[ROW][C]82[/C][C]0.809712[/C][C]0.380576[/C][C]0.190288[/C][/ROW]
[ROW][C]83[/C][C]0.785909[/C][C]0.428181[/C][C]0.214091[/C][/ROW]
[ROW][C]84[/C][C]0.77982[/C][C]0.440361[/C][C]0.22018[/C][/ROW]
[ROW][C]85[/C][C]0.778938[/C][C]0.442124[/C][C]0.221062[/C][/ROW]
[ROW][C]86[/C][C]0.747199[/C][C]0.505603[/C][C]0.252801[/C][/ROW]
[ROW][C]87[/C][C]0.719385[/C][C]0.561229[/C][C]0.280615[/C][/ROW]
[ROW][C]88[/C][C]0.67954[/C][C]0.64092[/C][C]0.32046[/C][/ROW]
[ROW][C]89[/C][C]0.644573[/C][C]0.710854[/C][C]0.355427[/C][/ROW]
[ROW][C]90[/C][C]0.60471[/C][C]0.790579[/C][C]0.39529[/C][/ROW]
[ROW][C]91[/C][C]0.618753[/C][C]0.762495[/C][C]0.381247[/C][/ROW]
[ROW][C]92[/C][C]0.581709[/C][C]0.836583[/C][C]0.418291[/C][/ROW]
[ROW][C]93[/C][C]0.539046[/C][C]0.921908[/C][C]0.460954[/C][/ROW]
[ROW][C]94[/C][C]0.512317[/C][C]0.975366[/C][C]0.487683[/C][/ROW]
[ROW][C]95[/C][C]0.520797[/C][C]0.958405[/C][C]0.479203[/C][/ROW]
[ROW][C]96[/C][C]0.491134[/C][C]0.982267[/C][C]0.508866[/C][/ROW]
[ROW][C]97[/C][C]0.563459[/C][C]0.873081[/C][C]0.436541[/C][/ROW]
[ROW][C]98[/C][C]0.610312[/C][C]0.779376[/C][C]0.389688[/C][/ROW]
[ROW][C]99[/C][C]0.585577[/C][C]0.828846[/C][C]0.414423[/C][/ROW]
[ROW][C]100[/C][C]0.545457[/C][C]0.909085[/C][C]0.454543[/C][/ROW]
[ROW][C]101[/C][C]0.52203[/C][C]0.955939[/C][C]0.47797[/C][/ROW]
[ROW][C]102[/C][C]0.474751[/C][C]0.949503[/C][C]0.525249[/C][/ROW]
[ROW][C]103[/C][C]0.430532[/C][C]0.861063[/C][C]0.569468[/C][/ROW]
[ROW][C]104[/C][C]0.388251[/C][C]0.776502[/C][C]0.611749[/C][/ROW]
[ROW][C]105[/C][C]0.379614[/C][C]0.759228[/C][C]0.620386[/C][/ROW]
[ROW][C]106[/C][C]0.340966[/C][C]0.681932[/C][C]0.659034[/C][/ROW]
[ROW][C]107[/C][C]0.311504[/C][C]0.623008[/C][C]0.688496[/C][/ROW]
[ROW][C]108[/C][C]0.282514[/C][C]0.565029[/C][C]0.717486[/C][/ROW]
[ROW][C]109[/C][C]0.272536[/C][C]0.545071[/C][C]0.727464[/C][/ROW]
[ROW][C]110[/C][C]0.33709[/C][C]0.67418[/C][C]0.66291[/C][/ROW]
[ROW][C]111[/C][C]0.313617[/C][C]0.627233[/C][C]0.686383[/C][/ROW]
[ROW][C]112[/C][C]0.342486[/C][C]0.684972[/C][C]0.657514[/C][/ROW]
[ROW][C]113[/C][C]0.32234[/C][C]0.64468[/C][C]0.67766[/C][/ROW]
[ROW][C]114[/C][C]0.394556[/C][C]0.789111[/C][C]0.605444[/C][/ROW]
[ROW][C]115[/C][C]0.356598[/C][C]0.713196[/C][C]0.643402[/C][/ROW]
[ROW][C]116[/C][C]0.394963[/C][C]0.789926[/C][C]0.605037[/C][/ROW]
[ROW][C]117[/C][C]0.684204[/C][C]0.631593[/C][C]0.315796[/C][/ROW]
[ROW][C]118[/C][C]0.710998[/C][C]0.578005[/C][C]0.289002[/C][/ROW]
[ROW][C]119[/C][C]0.690633[/C][C]0.618734[/C][C]0.309367[/C][/ROW]
[ROW][C]120[/C][C]0.765475[/C][C]0.469049[/C][C]0.234525[/C][/ROW]
[ROW][C]121[/C][C]0.73877[/C][C]0.52246[/C][C]0.26123[/C][/ROW]
[ROW][C]122[/C][C]0.7643[/C][C]0.4714[/C][C]0.2357[/C][/ROW]
[ROW][C]123[/C][C]0.744322[/C][C]0.511355[/C][C]0.255678[/C][/ROW]
[ROW][C]124[/C][C]0.856564[/C][C]0.286872[/C][C]0.143436[/C][/ROW]
[ROW][C]125[/C][C]0.830718[/C][C]0.338564[/C][C]0.169282[/C][/ROW]
[ROW][C]126[/C][C]0.809368[/C][C]0.381265[/C][C]0.190632[/C][/ROW]
[ROW][C]127[/C][C]0.810066[/C][C]0.379869[/C][C]0.189934[/C][/ROW]
[ROW][C]128[/C][C]0.770374[/C][C]0.459252[/C][C]0.229626[/C][/ROW]
[ROW][C]129[/C][C]0.726177[/C][C]0.547646[/C][C]0.273823[/C][/ROW]
[ROW][C]130[/C][C]0.681696[/C][C]0.636607[/C][C]0.318304[/C][/ROW]
[ROW][C]131[/C][C]0.643653[/C][C]0.712694[/C][C]0.356347[/C][/ROW]
[ROW][C]132[/C][C]0.592209[/C][C]0.815583[/C][C]0.407791[/C][/ROW]
[ROW][C]133[/C][C]0.563186[/C][C]0.873627[/C][C]0.436814[/C][/ROW]
[ROW][C]134[/C][C]0.516804[/C][C]0.966391[/C][C]0.483196[/C][/ROW]
[ROW][C]135[/C][C]0.455086[/C][C]0.910171[/C][C]0.544914[/C][/ROW]
[ROW][C]136[/C][C]0.50195[/C][C]0.9961[/C][C]0.49805[/C][/ROW]
[ROW][C]137[/C][C]0.495796[/C][C]0.991593[/C][C]0.504204[/C][/ROW]
[ROW][C]138[/C][C]0.509912[/C][C]0.980176[/C][C]0.490088[/C][/ROW]
[ROW][C]139[/C][C]0.469278[/C][C]0.938557[/C][C]0.530722[/C][/ROW]
[ROW][C]140[/C][C]0.483324[/C][C]0.966647[/C][C]0.516676[/C][/ROW]
[ROW][C]141[/C][C]0.450514[/C][C]0.901029[/C][C]0.549486[/C][/ROW]
[ROW][C]142[/C][C]0.384041[/C][C]0.768081[/C][C]0.615959[/C][/ROW]
[ROW][C]143[/C][C]0.338143[/C][C]0.676286[/C][C]0.661857[/C][/ROW]
[ROW][C]144[/C][C]0.275136[/C][C]0.550273[/C][C]0.724864[/C][/ROW]
[ROW][C]145[/C][C]0.349649[/C][C]0.699297[/C][C]0.650351[/C][/ROW]
[ROW][C]146[/C][C]0.449524[/C][C]0.899047[/C][C]0.550476[/C][/ROW]
[ROW][C]147[/C][C]0.411114[/C][C]0.822228[/C][C]0.588886[/C][/ROW]
[ROW][C]148[/C][C]0.940876[/C][C]0.118248[/C][C]0.0591238[/C][/ROW]
[ROW][C]149[/C][C]0.914606[/C][C]0.170787[/C][C]0.0853935[/C][/ROW]
[ROW][C]150[/C][C]0.969615[/C][C]0.0607697[/C][C]0.0303849[/C][/ROW]
[ROW][C]151[/C][C]0.988411[/C][C]0.0231779[/C][C]0.0115889[/C][/ROW]
[ROW][C]152[/C][C]0.981666[/C][C]0.0366677[/C][C]0.0183338[/C][/ROW]
[ROW][C]153[/C][C]0.954501[/C][C]0.090998[/C][C]0.045499[/C][/ROW]
[ROW][C]154[/C][C]0.958967[/C][C]0.0820654[/C][C]0.0410327[/C][/ROW]
[ROW][C]155[/C][C]0.928757[/C][C]0.142487[/C][C]0.0712433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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
110.5909440.8181110.409056
120.7443370.5113260.255663
130.9334930.1330130.0665067
140.8878140.2243730.112186
150.8349280.3301440.165072
160.8142050.3715910.185795
170.7657130.4685740.234287
180.6940980.6118040.305902
190.6116630.7766750.388337
200.7082640.5834730.291736
210.6658240.6683510.334176
220.5933930.8132140.406607
230.5270710.9458590.472929
240.4559420.9118830.544058
250.5097480.9805040.490252
260.4850880.9701760.514912
270.4237720.8475440.576228
280.4920540.9841070.507946
290.4391870.8783750.560813
300.3776310.7552620.622369
310.3253320.6506640.674668
320.3117450.623490.688255
330.2905530.5811050.709447
340.2675570.5351140.732443
350.2194890.4389790.780511
360.1774810.3549620.822519
370.1417350.283470.858265
380.1159730.2319450.884027
390.730570.538860.26943
400.6993130.6013740.300687
410.6645110.6709780.335489
420.6239290.7521410.376071
430.5792380.8415240.420762
440.5347120.9305750.465288
450.4929820.9859630.507018
460.4475970.8951940.552403
470.4348040.8696080.565196
480.41970.8393990.5803
490.4026120.8052230.597388
500.3816360.7632720.618364
510.3389110.6778210.661089
520.8222720.3554550.177728
530.7971740.4056520.202826
540.7693770.4612460.230623
550.7497150.500570.250285
560.7167240.5665520.283276
570.6755150.648970.324485
580.6811130.6377740.318887
590.6503290.6993410.349671
600.6766640.6466720.323336
610.713660.572680.28634
620.699990.6000190.30001
630.6812460.6375070.318754
640.7089080.5821850.291092
650.6752510.6494970.324749
660.6778920.6442160.322108
670.6665160.6669670.333484
680.7062370.5875260.293763
690.6757950.648410.324205
700.6876730.6246530.312327
710.7126360.5747280.287364
720.6718620.6562770.328138
730.7225880.5548250.277412
740.683790.632420.31621
750.6701080.6597840.329892
760.7013210.5973590.298679
770.7803720.4392550.219628
780.7548550.490290.245145
790.7655380.4689240.234462
800.7494070.5011850.250593
810.7807980.4384050.219202
820.8097120.3805760.190288
830.7859090.4281810.214091
840.779820.4403610.22018
850.7789380.4421240.221062
860.7471990.5056030.252801
870.7193850.5612290.280615
880.679540.640920.32046
890.6445730.7108540.355427
900.604710.7905790.39529
910.6187530.7624950.381247
920.5817090.8365830.418291
930.5390460.9219080.460954
940.5123170.9753660.487683
950.5207970.9584050.479203
960.4911340.9822670.508866
970.5634590.8730810.436541
980.6103120.7793760.389688
990.5855770.8288460.414423
1000.5454570.9090850.454543
1010.522030.9559390.47797
1020.4747510.9495030.525249
1030.4305320.8610630.569468
1040.3882510.7765020.611749
1050.3796140.7592280.620386
1060.3409660.6819320.659034
1070.3115040.6230080.688496
1080.2825140.5650290.717486
1090.2725360.5450710.727464
1100.337090.674180.66291
1110.3136170.6272330.686383
1120.3424860.6849720.657514
1130.322340.644680.67766
1140.3945560.7891110.605444
1150.3565980.7131960.643402
1160.3949630.7899260.605037
1170.6842040.6315930.315796
1180.7109980.5780050.289002
1190.6906330.6187340.309367
1200.7654750.4690490.234525
1210.738770.522460.26123
1220.76430.47140.2357
1230.7443220.5113550.255678
1240.8565640.2868720.143436
1250.8307180.3385640.169282
1260.8093680.3812650.190632
1270.8100660.3798690.189934
1280.7703740.4592520.229626
1290.7261770.5476460.273823
1300.6816960.6366070.318304
1310.6436530.7126940.356347
1320.5922090.8155830.407791
1330.5631860.8736270.436814
1340.5168040.9663910.483196
1350.4550860.9101710.544914
1360.501950.99610.49805
1370.4957960.9915930.504204
1380.5099120.9801760.490088
1390.4692780.9385570.530722
1400.4833240.9666470.516676
1410.4505140.9010290.549486
1420.3840410.7680810.615959
1430.3381430.6762860.661857
1440.2751360.5502730.724864
1450.3496490.6992970.650351
1460.4495240.8990470.550476
1470.4111140.8222280.588886
1480.9408760.1182480.0591238
1490.9146060.1707870.0853935
1500.9696150.06076970.0303849
1510.9884110.02317790.0115889
1520.9816660.03666770.0183338
1530.9545010.0909980.045499
1540.9589670.08206540.0410327
1550.9287570.1424870.0712433






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0137931OK
10% type I error level50.0344828OK

\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 & 2 & 0.0137931 & OK \tabularnewline
10% type I error level & 5 & 0.0344828 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269323&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]2[/C][C]0.0137931[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0344828[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269323&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269323&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 level20.0137931OK
10% type I error level50.0344828OK


Figures (Output of Computation)

Figure 1
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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')
}