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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 11:11:37 +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/t1418728413cj4afhkzitdqnkh.htm/, Retrieved Thu, 16 May 2024 12:07:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269326, Retrieved Thu, 16 May 2024 12:07:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper13] [2014-12-16 11:11:37] [f8a15a4749f25af1f83725a9fa901b6e] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 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=269326&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]6 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=269326&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&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 time6 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] = + 10.2475 -0.74745gender[t] + 1.28146Course_id[t] -0.0358231AMS.E[t] -0.104992AMS.A[t] + 0.0449181NUMERACYTOT[t] + 0.0560689LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.2475 -0.74745gender[t] +  1.28146Course_id[t] -0.0358231AMS.E[t] -0.104992AMS.A[t] +  0.0449181NUMERACYTOT[t] +  0.0560689LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.2475 -0.74745gender[t] +  1.28146Course_id[t] -0.0358231AMS.E[t] -0.104992AMS.A[t] +  0.0449181NUMERACYTOT[t] +  0.0560689LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.2475 -0.74745gender[t] + 1.28146Course_id[t] -0.0358231AMS.E[t] -0.104992AMS.A[t] + 0.0449181NUMERACYTOT[t] + 0.0560689LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.24751.777255.7664.11081e-082.05541e-08
gender-0.747450.367306-2.0350.04351660.0217583
Course_id1.281460.4052463.1620.001875850.000937927
AMS.E-0.03582310.021297-1.6820.09451760.0472588
AMS.A-0.1049920.0485486-2.1630.03206510.0160326
NUMERACYTOT0.04491810.03181821.4120.1599890.0799946
LFM0.05606890.0051438610.95.17782e-212.58891e-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) & 10.2475 & 1.77725 & 5.766 & 4.11081e-08 & 2.05541e-08 \tabularnewline
gender & -0.74745 & 0.367306 & -2.035 & 0.0435166 & 0.0217583 \tabularnewline
Course_id & 1.28146 & 0.405246 & 3.162 & 0.00187585 & 0.000937927 \tabularnewline
AMS.E & -0.0358231 & 0.021297 & -1.682 & 0.0945176 & 0.0472588 \tabularnewline
AMS.A & -0.104992 & 0.0485486 & -2.163 & 0.0320651 & 0.0160326 \tabularnewline
NUMERACYTOT & 0.0449181 & 0.0318182 & 1.412 & 0.159989 & 0.0799946 \tabularnewline
LFM & 0.0560689 & 0.00514386 & 10.9 & 5.17782e-21 & 2.58891e-21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.2475[/C][C]1.77725[/C][C]5.766[/C][C]4.11081e-08[/C][C]2.05541e-08[/C][/ROW]
[ROW][C]gender[/C][C]-0.74745[/C][C]0.367306[/C][C]-2.035[/C][C]0.0435166[/C][C]0.0217583[/C][/ROW]
[ROW][C]Course_id[/C][C]1.28146[/C][C]0.405246[/C][C]3.162[/C][C]0.00187585[/C][C]0.000937927[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0358231[/C][C]0.021297[/C][C]-1.682[/C][C]0.0945176[/C][C]0.0472588[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.104992[/C][C]0.0485486[/C][C]-2.163[/C][C]0.0320651[/C][C]0.0160326[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0449181[/C][C]0.0318182[/C][C]1.412[/C][C]0.159989[/C][C]0.0799946[/C][/ROW]
[ROW][C]LFM[/C][C]0.0560689[/C][C]0.00514386[/C][C]10.9[/C][C]5.17782e-21[/C][C]2.58891e-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.24751.777255.7664.11081e-082.05541e-08
gender-0.747450.367306-2.0350.04351660.0217583
Course_id1.281460.4052463.1620.001875850.000937927
AMS.E-0.03582310.021297-1.6820.09451760.0472588
AMS.A-0.1049920.0485486-2.1630.03206510.0160326
NUMERACYTOT0.04491810.03181821.4120.1599890.0799946
LFM0.05606890.0051438610.95.17782e-212.58891e-21






Multiple Linear Regression - Regression Statistics
Multiple R0.699168
R-squared0.488837
Adjusted R-squared0.469547
F-TEST (value)25.3425
F-TEST (DF numerator)6
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2199
Sum Squared Residuals783.545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.699168 \tabularnewline
R-squared & 0.488837 \tabularnewline
Adjusted R-squared & 0.469547 \tabularnewline
F-TEST (value) & 25.3425 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.2199 \tabularnewline
Sum Squared Residuals & 783.545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.699168[/C][/ROW]
[ROW][C]R-squared[/C][C]0.488837[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.469547[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.3425[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/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]2.2199[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]783.545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&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 R0.699168
R-squared0.488837
Adjusted R-squared0.469547
F-TEST (value)25.3425
F-TEST (DF numerator)6
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2199
Sum Squared Residuals783.545






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.7676-6.41757
212.710.86571.83434
318.115.61362.48639
417.8516.26091.58912
516.616.52620.0738471
612.611.56691.03313
717.119.2-2.09999
819.117.46271.6373
916.116.2081-0.108063
1013.3512.06421.2858
1118.416.87951.52045
1214.79.577365.12264
1310.613.2478-2.64776
1412.613.4366-0.836565
1516.215.10831.09168
1613.613.13330.466744
1718.916.24122.65881
1814.113.29420.805812
1914.513.71060.789415
2016.1517.1814-1.03142
2114.7513.74611.00386
2214.813.51481.28517
2312.4511.95350.496489
2412.6512.3460.304006
2517.3514.67652.67347
268.69.27416-0.674155
2718.416.47271.92733
2816.114.24671.85333
2911.611.23990.360121
3017.7516.72221.02778
3115.2514.81440.435582
3217.6514.24543.40463
3316.3516.779-0.428969
3417.6516.82140.828575
3513.614.6193-1.01935
3614.3514.5084-0.158372
3714.7515.4295-0.679516
3818.2517.13011.11988
399.917.0268-7.1268
401614.29561.70438
4118.2516.65051.59949
4216.8518.2113-1.36129
4314.613.07621.52383
4413.8514.0315-0.181506
4518.9517.41111.53888
4615.614.5481.05197
4714.8516.6228-1.77282
4811.7513.9266-2.17659
4918.4516.5161.93399
5015.913.7112.18895
5117.118.3032-1.20318
5216.18.11187.9882
5319.919.16050.739504
5410.9510.26290.687071
5518.4516.38212.06794
5615.114.3620.738006
571515.474-0.473978
5811.3513.5239-2.17395
5915.9514.53941.41057
6018.115.60432.49568
6114.616.8881-2.28813
6215.416.9567-1.55672
6315.416.8851-1.48507
6417.614.56663.03339
6513.3514.2044-0.854361
6619.117.18411.9159
6715.3517.1891-1.83913
687.610.4765-2.87654
6913.414.5856-1.18563
7013.916.5191-2.61915
7119.116.45782.64223
7215.2515.16940.0805551
7312.916.0812-3.18119
7416.115.54860.551437
7517.3515.43391.91605
7613.1515.781-2.63098
7712.1515.7457-3.59569
7812.611.8460.754032
7910.3512.6725-2.32254
8015.413.92051.47945
819.612.8601-3.26012
8218.215.23732.9627
8313.614.2197-0.61966
8414.8512.96351.88646
8514.7516.8377-2.08765
8614.113.53280.567171
8714.913.90840.991597
8816.2515.92720.322803
8919.2519.24790.00206897
9013.613.20310.396911
9113.616.0609-2.46091
9215.6516.0598-0.409823
9312.7513.8839-1.13386
9414.613.15781.44224
959.859.722390.127609
9612.6511.75080.899178
9719.215.8393.36104
9816.613.97222.62784
9911.211.2651-0.0651061
10015.2515.9873-0.737264
10111.913.715-1.81504
10213.213.4406-0.240647
10316.3516.8535-0.50347
10412.412.8207-0.420727
10515.8514.08831.76165
10618.1517.41770.732336
10711.1511.8488-0.69876
10815.6516.3629-0.712857
10917.7515.85971.89027
1107.6510.896-3.24603
11112.3514.3318-1.98176
11215.614.14461.45541
11319.317.32691.97309
11415.211.61533.58468
11517.115.97081.12917
11615.613.56822.03182
11718.414.24674.15335
11819.0516.53012.51987
11918.5516.95751.59251
12019.116.14452.95545
12113.113.5078-0.407817
12212.8516.783-3.93296
1239.512.1301-2.63011
1244.510.6225-6.12246
12511.8511.75350.096531
12613.614.6479-1.04793
12711.711.7691-0.0690716
12812.413.2244-0.824409
12913.3514.5453-1.19531
13011.412.3958-0.995826
13114.914.68630.213722
13219.918.06731.83268
13311.213.9228-2.72282
13414.614.9923-0.392336
13517.617.7445-0.144547
13614.0513.45140.598637
13716.115.33950.760526
13813.3514.7937-1.44368
13911.8515.3998-3.54985
14011.9512.7579-0.807935
14114.7513.950.799964
14215.1512.60012.5499
14313.216.0119-2.81193
14416.8516.08010.769858
1457.8512.2873-4.43727
1467.712.8135-5.11352
14712.614.6445-2.04448
1487.8514.8473-6.99731
14910.9511.2919-0.34189
15012.3514.605-2.25496
1519.9512.9788-3.02876
15214.914.05630.843674
15316.6514.991.66005
15413.412.67510.724876
15513.9514.3967-0.446719
15615.714.93860.761386
15716.8515.73431.11573
15810.9511.978-1.028
15915.3515.9051-0.555065
16012.212.8908-0.690817
16115.113.68851.41149
16217.7515.86051.88947
16315.214.81870.381338
16414.615.3455-0.745473
16516.6515.8060.843995
1668.19.69432-1.59432

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.7676 & -6.41757 \tabularnewline
2 & 12.7 & 10.8657 & 1.83434 \tabularnewline
3 & 18.1 & 15.6136 & 2.48639 \tabularnewline
4 & 17.85 & 16.2609 & 1.58912 \tabularnewline
5 & 16.6 & 16.5262 & 0.0738471 \tabularnewline
6 & 12.6 & 11.5669 & 1.03313 \tabularnewline
7 & 17.1 & 19.2 & -2.09999 \tabularnewline
8 & 19.1 & 17.4627 & 1.6373 \tabularnewline
9 & 16.1 & 16.2081 & -0.108063 \tabularnewline
10 & 13.35 & 12.0642 & 1.2858 \tabularnewline
11 & 18.4 & 16.8795 & 1.52045 \tabularnewline
12 & 14.7 & 9.57736 & 5.12264 \tabularnewline
13 & 10.6 & 13.2478 & -2.64776 \tabularnewline
14 & 12.6 & 13.4366 & -0.836565 \tabularnewline
15 & 16.2 & 15.1083 & 1.09168 \tabularnewline
16 & 13.6 & 13.1333 & 0.466744 \tabularnewline
17 & 18.9 & 16.2412 & 2.65881 \tabularnewline
18 & 14.1 & 13.2942 & 0.805812 \tabularnewline
19 & 14.5 & 13.7106 & 0.789415 \tabularnewline
20 & 16.15 & 17.1814 & -1.03142 \tabularnewline
21 & 14.75 & 13.7461 & 1.00386 \tabularnewline
22 & 14.8 & 13.5148 & 1.28517 \tabularnewline
23 & 12.45 & 11.9535 & 0.496489 \tabularnewline
24 & 12.65 & 12.346 & 0.304006 \tabularnewline
25 & 17.35 & 14.6765 & 2.67347 \tabularnewline
26 & 8.6 & 9.27416 & -0.674155 \tabularnewline
27 & 18.4 & 16.4727 & 1.92733 \tabularnewline
28 & 16.1 & 14.2467 & 1.85333 \tabularnewline
29 & 11.6 & 11.2399 & 0.360121 \tabularnewline
30 & 17.75 & 16.7222 & 1.02778 \tabularnewline
31 & 15.25 & 14.8144 & 0.435582 \tabularnewline
32 & 17.65 & 14.2454 & 3.40463 \tabularnewline
33 & 16.35 & 16.779 & -0.428969 \tabularnewline
34 & 17.65 & 16.8214 & 0.828575 \tabularnewline
35 & 13.6 & 14.6193 & -1.01935 \tabularnewline
36 & 14.35 & 14.5084 & -0.158372 \tabularnewline
37 & 14.75 & 15.4295 & -0.679516 \tabularnewline
38 & 18.25 & 17.1301 & 1.11988 \tabularnewline
39 & 9.9 & 17.0268 & -7.1268 \tabularnewline
40 & 16 & 14.2956 & 1.70438 \tabularnewline
41 & 18.25 & 16.6505 & 1.59949 \tabularnewline
42 & 16.85 & 18.2113 & -1.36129 \tabularnewline
43 & 14.6 & 13.0762 & 1.52383 \tabularnewline
44 & 13.85 & 14.0315 & -0.181506 \tabularnewline
45 & 18.95 & 17.4111 & 1.53888 \tabularnewline
46 & 15.6 & 14.548 & 1.05197 \tabularnewline
47 & 14.85 & 16.6228 & -1.77282 \tabularnewline
48 & 11.75 & 13.9266 & -2.17659 \tabularnewline
49 & 18.45 & 16.516 & 1.93399 \tabularnewline
50 & 15.9 & 13.711 & 2.18895 \tabularnewline
51 & 17.1 & 18.3032 & -1.20318 \tabularnewline
52 & 16.1 & 8.1118 & 7.9882 \tabularnewline
53 & 19.9 & 19.1605 & 0.739504 \tabularnewline
54 & 10.95 & 10.2629 & 0.687071 \tabularnewline
55 & 18.45 & 16.3821 & 2.06794 \tabularnewline
56 & 15.1 & 14.362 & 0.738006 \tabularnewline
57 & 15 & 15.474 & -0.473978 \tabularnewline
58 & 11.35 & 13.5239 & -2.17395 \tabularnewline
59 & 15.95 & 14.5394 & 1.41057 \tabularnewline
60 & 18.1 & 15.6043 & 2.49568 \tabularnewline
61 & 14.6 & 16.8881 & -2.28813 \tabularnewline
62 & 15.4 & 16.9567 & -1.55672 \tabularnewline
63 & 15.4 & 16.8851 & -1.48507 \tabularnewline
64 & 17.6 & 14.5666 & 3.03339 \tabularnewline
65 & 13.35 & 14.2044 & -0.854361 \tabularnewline
66 & 19.1 & 17.1841 & 1.9159 \tabularnewline
67 & 15.35 & 17.1891 & -1.83913 \tabularnewline
68 & 7.6 & 10.4765 & -2.87654 \tabularnewline
69 & 13.4 & 14.5856 & -1.18563 \tabularnewline
70 & 13.9 & 16.5191 & -2.61915 \tabularnewline
71 & 19.1 & 16.4578 & 2.64223 \tabularnewline
72 & 15.25 & 15.1694 & 0.0805551 \tabularnewline
73 & 12.9 & 16.0812 & -3.18119 \tabularnewline
74 & 16.1 & 15.5486 & 0.551437 \tabularnewline
75 & 17.35 & 15.4339 & 1.91605 \tabularnewline
76 & 13.15 & 15.781 & -2.63098 \tabularnewline
77 & 12.15 & 15.7457 & -3.59569 \tabularnewline
78 & 12.6 & 11.846 & 0.754032 \tabularnewline
79 & 10.35 & 12.6725 & -2.32254 \tabularnewline
80 & 15.4 & 13.9205 & 1.47945 \tabularnewline
81 & 9.6 & 12.8601 & -3.26012 \tabularnewline
82 & 18.2 & 15.2373 & 2.9627 \tabularnewline
83 & 13.6 & 14.2197 & -0.61966 \tabularnewline
84 & 14.85 & 12.9635 & 1.88646 \tabularnewline
85 & 14.75 & 16.8377 & -2.08765 \tabularnewline
86 & 14.1 & 13.5328 & 0.567171 \tabularnewline
87 & 14.9 & 13.9084 & 0.991597 \tabularnewline
88 & 16.25 & 15.9272 & 0.322803 \tabularnewline
89 & 19.25 & 19.2479 & 0.00206897 \tabularnewline
90 & 13.6 & 13.2031 & 0.396911 \tabularnewline
91 & 13.6 & 16.0609 & -2.46091 \tabularnewline
92 & 15.65 & 16.0598 & -0.409823 \tabularnewline
93 & 12.75 & 13.8839 & -1.13386 \tabularnewline
94 & 14.6 & 13.1578 & 1.44224 \tabularnewline
95 & 9.85 & 9.72239 & 0.127609 \tabularnewline
96 & 12.65 & 11.7508 & 0.899178 \tabularnewline
97 & 19.2 & 15.839 & 3.36104 \tabularnewline
98 & 16.6 & 13.9722 & 2.62784 \tabularnewline
99 & 11.2 & 11.2651 & -0.0651061 \tabularnewline
100 & 15.25 & 15.9873 & -0.737264 \tabularnewline
101 & 11.9 & 13.715 & -1.81504 \tabularnewline
102 & 13.2 & 13.4406 & -0.240647 \tabularnewline
103 & 16.35 & 16.8535 & -0.50347 \tabularnewline
104 & 12.4 & 12.8207 & -0.420727 \tabularnewline
105 & 15.85 & 14.0883 & 1.76165 \tabularnewline
106 & 18.15 & 17.4177 & 0.732336 \tabularnewline
107 & 11.15 & 11.8488 & -0.69876 \tabularnewline
108 & 15.65 & 16.3629 & -0.712857 \tabularnewline
109 & 17.75 & 15.8597 & 1.89027 \tabularnewline
110 & 7.65 & 10.896 & -3.24603 \tabularnewline
111 & 12.35 & 14.3318 & -1.98176 \tabularnewline
112 & 15.6 & 14.1446 & 1.45541 \tabularnewline
113 & 19.3 & 17.3269 & 1.97309 \tabularnewline
114 & 15.2 & 11.6153 & 3.58468 \tabularnewline
115 & 17.1 & 15.9708 & 1.12917 \tabularnewline
116 & 15.6 & 13.5682 & 2.03182 \tabularnewline
117 & 18.4 & 14.2467 & 4.15335 \tabularnewline
118 & 19.05 & 16.5301 & 2.51987 \tabularnewline
119 & 18.55 & 16.9575 & 1.59251 \tabularnewline
120 & 19.1 & 16.1445 & 2.95545 \tabularnewline
121 & 13.1 & 13.5078 & -0.407817 \tabularnewline
122 & 12.85 & 16.783 & -3.93296 \tabularnewline
123 & 9.5 & 12.1301 & -2.63011 \tabularnewline
124 & 4.5 & 10.6225 & -6.12246 \tabularnewline
125 & 11.85 & 11.7535 & 0.096531 \tabularnewline
126 & 13.6 & 14.6479 & -1.04793 \tabularnewline
127 & 11.7 & 11.7691 & -0.0690716 \tabularnewline
128 & 12.4 & 13.2244 & -0.824409 \tabularnewline
129 & 13.35 & 14.5453 & -1.19531 \tabularnewline
130 & 11.4 & 12.3958 & -0.995826 \tabularnewline
131 & 14.9 & 14.6863 & 0.213722 \tabularnewline
132 & 19.9 & 18.0673 & 1.83268 \tabularnewline
133 & 11.2 & 13.9228 & -2.72282 \tabularnewline
134 & 14.6 & 14.9923 & -0.392336 \tabularnewline
135 & 17.6 & 17.7445 & -0.144547 \tabularnewline
136 & 14.05 & 13.4514 & 0.598637 \tabularnewline
137 & 16.1 & 15.3395 & 0.760526 \tabularnewline
138 & 13.35 & 14.7937 & -1.44368 \tabularnewline
139 & 11.85 & 15.3998 & -3.54985 \tabularnewline
140 & 11.95 & 12.7579 & -0.807935 \tabularnewline
141 & 14.75 & 13.95 & 0.799964 \tabularnewline
142 & 15.15 & 12.6001 & 2.5499 \tabularnewline
143 & 13.2 & 16.0119 & -2.81193 \tabularnewline
144 & 16.85 & 16.0801 & 0.769858 \tabularnewline
145 & 7.85 & 12.2873 & -4.43727 \tabularnewline
146 & 7.7 & 12.8135 & -5.11352 \tabularnewline
147 & 12.6 & 14.6445 & -2.04448 \tabularnewline
148 & 7.85 & 14.8473 & -6.99731 \tabularnewline
149 & 10.95 & 11.2919 & -0.34189 \tabularnewline
150 & 12.35 & 14.605 & -2.25496 \tabularnewline
151 & 9.95 & 12.9788 & -3.02876 \tabularnewline
152 & 14.9 & 14.0563 & 0.843674 \tabularnewline
153 & 16.65 & 14.99 & 1.66005 \tabularnewline
154 & 13.4 & 12.6751 & 0.724876 \tabularnewline
155 & 13.95 & 14.3967 & -0.446719 \tabularnewline
156 & 15.7 & 14.9386 & 0.761386 \tabularnewline
157 & 16.85 & 15.7343 & 1.11573 \tabularnewline
158 & 10.95 & 11.978 & -1.028 \tabularnewline
159 & 15.35 & 15.9051 & -0.555065 \tabularnewline
160 & 12.2 & 12.8908 & -0.690817 \tabularnewline
161 & 15.1 & 13.6885 & 1.41149 \tabularnewline
162 & 17.75 & 15.8605 & 1.88947 \tabularnewline
163 & 15.2 & 14.8187 & 0.381338 \tabularnewline
164 & 14.6 & 15.3455 & -0.745473 \tabularnewline
165 & 16.65 & 15.806 & 0.843995 \tabularnewline
166 & 8.1 & 9.69432 & -1.59432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&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]10.7676[/C][C]-6.41757[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.8657[/C][C]1.83434[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6136[/C][C]2.48639[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.2609[/C][C]1.58912[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.5262[/C][C]0.0738471[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.5669[/C][C]1.03313[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.2[/C][C]-2.09999[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.4627[/C][C]1.6373[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.2081[/C][C]-0.108063[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]12.0642[/C][C]1.2858[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.8795[/C][C]1.52045[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.57736[/C][C]5.12264[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.2478[/C][C]-2.64776[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.4366[/C][C]-0.836565[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.1083[/C][C]1.09168[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.1333[/C][C]0.466744[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.2412[/C][C]2.65881[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.2942[/C][C]0.805812[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.7106[/C][C]0.789415[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.1814[/C][C]-1.03142[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.7461[/C][C]1.00386[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.5148[/C][C]1.28517[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.9535[/C][C]0.496489[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.346[/C][C]0.304006[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.6765[/C][C]2.67347[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.27416[/C][C]-0.674155[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]16.4727[/C][C]1.92733[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.2467[/C][C]1.85333[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.2399[/C][C]0.360121[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.7222[/C][C]1.02778[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.8144[/C][C]0.435582[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.2454[/C][C]3.40463[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.779[/C][C]-0.428969[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.8214[/C][C]0.828575[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.6193[/C][C]-1.01935[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.5084[/C][C]-0.158372[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.4295[/C][C]-0.679516[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.1301[/C][C]1.11988[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]17.0268[/C][C]-7.1268[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2956[/C][C]1.70438[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.6505[/C][C]1.59949[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.2113[/C][C]-1.36129[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]13.0762[/C][C]1.52383[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.0315[/C][C]-0.181506[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.4111[/C][C]1.53888[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.548[/C][C]1.05197[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.6228[/C][C]-1.77282[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]13.9266[/C][C]-2.17659[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.516[/C][C]1.93399[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]13.711[/C][C]2.18895[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.3032[/C][C]-1.20318[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]8.1118[/C][C]7.9882[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]19.1605[/C][C]0.739504[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.2629[/C][C]0.687071[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.3821[/C][C]2.06794[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.362[/C][C]0.738006[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.474[/C][C]-0.473978[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]13.5239[/C][C]-2.17395[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.5394[/C][C]1.41057[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6043[/C][C]2.49568[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.8881[/C][C]-2.28813[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.9567[/C][C]-1.55672[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.8851[/C][C]-1.48507[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.5666[/C][C]3.03339[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.2044[/C][C]-0.854361[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]17.1841[/C][C]1.9159[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.1891[/C][C]-1.83913[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.4765[/C][C]-2.87654[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.5856[/C][C]-1.18563[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]16.5191[/C][C]-2.61915[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.4578[/C][C]2.64223[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.1694[/C][C]0.0805551[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.0812[/C][C]-3.18119[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.5486[/C][C]0.551437[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.4339[/C][C]1.91605[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.781[/C][C]-2.63098[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.7457[/C][C]-3.59569[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.846[/C][C]0.754032[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.6725[/C][C]-2.32254[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]13.9205[/C][C]1.47945[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.8601[/C][C]-3.26012[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.2373[/C][C]2.9627[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]14.2197[/C][C]-0.61966[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]12.9635[/C][C]1.88646[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.8377[/C][C]-2.08765[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]13.5328[/C][C]0.567171[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]13.9084[/C][C]0.991597[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.9272[/C][C]0.322803[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.2479[/C][C]0.00206897[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]13.2031[/C][C]0.396911[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.0609[/C][C]-2.46091[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.0598[/C][C]-0.409823[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.8839[/C][C]-1.13386[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.1578[/C][C]1.44224[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.72239[/C][C]0.127609[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.7508[/C][C]0.899178[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]15.839[/C][C]3.36104[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]13.9722[/C][C]2.62784[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.2651[/C][C]-0.0651061[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]15.9873[/C][C]-0.737264[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]13.715[/C][C]-1.81504[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.4406[/C][C]-0.240647[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.8535[/C][C]-0.50347[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]12.8207[/C][C]-0.420727[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.0883[/C][C]1.76165[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.4177[/C][C]0.732336[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.8488[/C][C]-0.69876[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]16.3629[/C][C]-0.712857[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.8597[/C][C]1.89027[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]10.896[/C][C]-3.24603[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]14.3318[/C][C]-1.98176[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]14.1446[/C][C]1.45541[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.3269[/C][C]1.97309[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.6153[/C][C]3.58468[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]15.9708[/C][C]1.12917[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.5682[/C][C]2.03182[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.2467[/C][C]4.15335[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.5301[/C][C]2.51987[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]16.9575[/C][C]1.59251[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.1445[/C][C]2.95545[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.5078[/C][C]-0.407817[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.783[/C][C]-3.93296[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]12.1301[/C][C]-2.63011[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.6225[/C][C]-6.12246[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.7535[/C][C]0.096531[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.6479[/C][C]-1.04793[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]11.7691[/C][C]-0.0690716[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.2244[/C][C]-0.824409[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.5453[/C][C]-1.19531[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.3958[/C][C]-0.995826[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.6863[/C][C]0.213722[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]18.0673[/C][C]1.83268[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.9228[/C][C]-2.72282[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]14.9923[/C][C]-0.392336[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.7445[/C][C]-0.144547[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.4514[/C][C]0.598637[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.3395[/C][C]0.760526[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.7937[/C][C]-1.44368[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]15.3998[/C][C]-3.54985[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.7579[/C][C]-0.807935[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]13.95[/C][C]0.799964[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.6001[/C][C]2.5499[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]16.0119[/C][C]-2.81193[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]16.0801[/C][C]0.769858[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.2873[/C][C]-4.43727[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.8135[/C][C]-5.11352[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.6445[/C][C]-2.04448[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]14.8473[/C][C]-6.99731[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.2919[/C][C]-0.34189[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.605[/C][C]-2.25496[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]12.9788[/C][C]-3.02876[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]14.0563[/C][C]0.843674[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]14.99[/C][C]1.66005[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.6751[/C][C]0.724876[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.3967[/C][C]-0.446719[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]14.9386[/C][C]0.761386[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.7343[/C][C]1.11573[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]11.978[/C][C]-1.028[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]15.9051[/C][C]-0.555065[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]12.8908[/C][C]-0.690817[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]13.6885[/C][C]1.41149[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.8605[/C][C]1.88947[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.8187[/C][C]0.381338[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]15.3455[/C][C]-0.745473[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.806[/C][C]0.843995[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.69432[/C][C]-1.59432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&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
14.3510.7676-6.41757
212.710.86571.83434
318.115.61362.48639
417.8516.26091.58912
516.616.52620.0738471
612.611.56691.03313
717.119.2-2.09999
819.117.46271.6373
916.116.2081-0.108063
1013.3512.06421.2858
1118.416.87951.52045
1214.79.577365.12264
1310.613.2478-2.64776
1412.613.4366-0.836565
1516.215.10831.09168
1613.613.13330.466744
1718.916.24122.65881
1814.113.29420.805812
1914.513.71060.789415
2016.1517.1814-1.03142
2114.7513.74611.00386
2214.813.51481.28517
2312.4511.95350.496489
2412.6512.3460.304006
2517.3514.67652.67347
268.69.27416-0.674155
2718.416.47271.92733
2816.114.24671.85333
2911.611.23990.360121
3017.7516.72221.02778
3115.2514.81440.435582
3217.6514.24543.40463
3316.3516.779-0.428969
3417.6516.82140.828575
3513.614.6193-1.01935
3614.3514.5084-0.158372
3714.7515.4295-0.679516
3818.2517.13011.11988
399.917.0268-7.1268
401614.29561.70438
4118.2516.65051.59949
4216.8518.2113-1.36129
4314.613.07621.52383
4413.8514.0315-0.181506
4518.9517.41111.53888
4615.614.5481.05197
4714.8516.6228-1.77282
4811.7513.9266-2.17659
4918.4516.5161.93399
5015.913.7112.18895
5117.118.3032-1.20318
5216.18.11187.9882
5319.919.16050.739504
5410.9510.26290.687071
5518.4516.38212.06794
5615.114.3620.738006
571515.474-0.473978
5811.3513.5239-2.17395
5915.9514.53941.41057
6018.115.60432.49568
6114.616.8881-2.28813
6215.416.9567-1.55672
6315.416.8851-1.48507
6417.614.56663.03339
6513.3514.2044-0.854361
6619.117.18411.9159
6715.3517.1891-1.83913
687.610.4765-2.87654
6913.414.5856-1.18563
7013.916.5191-2.61915
7119.116.45782.64223
7215.2515.16940.0805551
7312.916.0812-3.18119
7416.115.54860.551437
7517.3515.43391.91605
7613.1515.781-2.63098
7712.1515.7457-3.59569
7812.611.8460.754032
7910.3512.6725-2.32254
8015.413.92051.47945
819.612.8601-3.26012
8218.215.23732.9627
8313.614.2197-0.61966
8414.8512.96351.88646
8514.7516.8377-2.08765
8614.113.53280.567171
8714.913.90840.991597
8816.2515.92720.322803
8919.2519.24790.00206897
9013.613.20310.396911
9113.616.0609-2.46091
9215.6516.0598-0.409823
9312.7513.8839-1.13386
9414.613.15781.44224
959.859.722390.127609
9612.6511.75080.899178
9719.215.8393.36104
9816.613.97222.62784
9911.211.2651-0.0651061
10015.2515.9873-0.737264
10111.913.715-1.81504
10213.213.4406-0.240647
10316.3516.8535-0.50347
10412.412.8207-0.420727
10515.8514.08831.76165
10618.1517.41770.732336
10711.1511.8488-0.69876
10815.6516.3629-0.712857
10917.7515.85971.89027
1107.6510.896-3.24603
11112.3514.3318-1.98176
11215.614.14461.45541
11319.317.32691.97309
11415.211.61533.58468
11517.115.97081.12917
11615.613.56822.03182
11718.414.24674.15335
11819.0516.53012.51987
11918.5516.95751.59251
12019.116.14452.95545
12113.113.5078-0.407817
12212.8516.783-3.93296
1239.512.1301-2.63011
1244.510.6225-6.12246
12511.8511.75350.096531
12613.614.6479-1.04793
12711.711.7691-0.0690716
12812.413.2244-0.824409
12913.3514.5453-1.19531
13011.412.3958-0.995826
13114.914.68630.213722
13219.918.06731.83268
13311.213.9228-2.72282
13414.614.9923-0.392336
13517.617.7445-0.144547
13614.0513.45140.598637
13716.115.33950.760526
13813.3514.7937-1.44368
13911.8515.3998-3.54985
14011.9512.7579-0.807935
14114.7513.950.799964
14215.1512.60012.5499
14313.216.0119-2.81193
14416.8516.08010.769858
1457.8512.2873-4.43727
1467.712.8135-5.11352
14712.614.6445-2.04448
1487.8514.8473-6.99731
14910.9511.2919-0.34189
15012.3514.605-2.25496
1519.9512.9788-3.02876
15214.914.05630.843674
15316.6514.991.66005
15413.412.67510.724876
15513.9514.3967-0.446719
15615.714.93860.761386
15716.8515.73431.11573
15810.9511.978-1.028
15915.3515.9051-0.555065
16012.212.8908-0.690817
16115.113.68851.41149
16217.7515.86051.88947
16315.214.81870.381338
16414.615.3455-0.745473
16516.6515.8060.843995
1668.19.69432-1.59432






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5372090.9255810.462791
110.4940050.9880090.505995
120.6276090.7447830.372391
130.9096110.1807770.0903887
140.86870.26260.1313
150.8136960.3726070.186304
160.7569070.4861860.243093
170.714730.5705410.28527
180.6358680.7282630.364132
190.5516410.8967170.448359
200.6900880.6198240.309912
210.6194460.7611070.380554
220.5482130.9035740.451787
230.4834190.9668380.516581
240.414710.8294210.58529
250.4566220.9132430.543378
260.4556570.9113140.544343
270.3962820.7925650.603718
280.4436190.8872370.556381
290.3861760.7723520.613824
300.3280540.6561080.671946
310.2796730.5593460.720327
320.268230.536460.73177
330.2508910.5017830.749109
340.2293270.4586530.770673
350.1860350.3720690.813965
360.1488890.2977790.851111
370.1176430.2352850.882357
380.0953180.1906360.904682
390.6971810.6056370.302819
400.6648850.670230.335115
410.6293540.7412910.370646
420.5876790.8246420.412321
430.5428360.9143280.457164
440.4984320.9968640.501568
450.4588930.9177870.541107
460.4148260.8296520.585174
470.4023030.8046060.597697
480.387620.775240.61238
490.3712180.7424370.628782
500.3524220.7048440.647578
510.3113690.6227370.688631
520.807870.3842590.19213
530.7816520.4366970.218348
540.7539020.4921970.246098
550.7343350.5313290.265665
560.7004590.5990830.299541
570.6586540.6826930.341346
580.6668290.6663420.333171
590.6391340.7217320.360866
600.6644780.6710440.335522
610.7026870.5946270.297313
620.6890410.6219190.310959
630.6704020.6591970.329598
640.6988690.6022620.301131
650.6651180.6697630.334882
660.6700090.6599830.329991
670.6595060.6809880.340494
680.7050750.589850.294925
690.6765340.6469320.323466
700.6954010.6091980.304599
710.7182510.5634990.281749
720.6784780.6430440.321522
730.7265660.5468690.273434
740.6897580.6204850.310242
750.6767760.6464490.323224
760.7094590.5810810.290541
770.7891880.4216250.210812
780.7642710.4714590.235729
790.7814840.4370320.218516
800.7657680.4684650.234232
810.8007360.3985280.199264
820.832830.3343390.16717
830.807450.38510.19255
840.8049240.3901520.195076
850.8051460.3897080.194854
860.7815850.436830.218415
870.7526230.4947540.247377
880.716850.5663010.28315
890.692990.614020.30701
900.6558680.6882640.344132
910.6687790.6624420.331221
920.6340250.7319510.365975
930.6052520.7894960.394748
940.5809510.8380980.419049
950.585250.82950.41475
960.5566360.8867290.443364
970.5960.8080.404
980.6266060.7467890.373394
990.6030940.7938120.396906
1000.5623380.8753230.437662
1010.5389050.922190.461095
1020.4922310.9844620.507769
1030.4489520.8979030.551048
1040.4065980.8131960.593402
1050.3979490.7958970.602051
1060.3605960.7211920.639404
1070.3305740.6611490.669426
1080.3010140.6020290.698986
1090.2916170.5832350.708383
1100.3357440.6714890.664256
1110.3126980.6253970.687302
1120.3208790.6417570.679121
1130.3060860.6121730.693914
1140.3769110.7538220.623089
1150.3416540.6833070.658346
1160.3733170.7466340.626683
1170.6956180.6087640.304382
1180.7242490.5515020.275751
1190.6997990.6004020.300201
1200.7714110.4571770.228589
1210.7479270.5041460.252073
1220.7752960.4494070.224704
1230.7543220.4913550.245678
1240.8601830.2796350.139817
1250.8284870.3430260.171513
1260.8017710.3964570.198229
1270.7811890.4376230.218811
1280.7418030.5163950.258197
1290.6953490.6093020.304651
1300.6455060.7089890.354494
1310.6055380.7889230.394462
1320.5536760.8926480.446324
1330.5525530.8948940.447447
1340.5005540.9988920.499446
1350.4383290.8766580.561671
1360.4670070.9340150.532993
1370.4574130.9148260.542587
1380.4523850.904770.547615
1390.4134990.8269980.586501
1400.4078810.8157620.592119
1410.3828960.7657910.617104
1420.3709170.7418330.629083
1430.320350.6406990.67965
1440.2572440.5144880.742756
1450.3234360.6468720.676564
1460.4094960.8189920.590504
1470.4159240.8318480.584076
1480.9638370.07232610.0361631
1490.9481160.1037690.0518845
1500.9815060.03698730.0184937
1510.9934780.01304480.00652242
1520.9839510.03209860.0160493
1530.9727540.05449240.0272462
1540.9584280.08314350.0415717
1550.9491140.1017710.0508855
1560.9100810.1798380.0899188

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.537209 & 0.925581 & 0.462791 \tabularnewline
11 & 0.494005 & 0.988009 & 0.505995 \tabularnewline
12 & 0.627609 & 0.744783 & 0.372391 \tabularnewline
13 & 0.909611 & 0.180777 & 0.0903887 \tabularnewline
14 & 0.8687 & 0.2626 & 0.1313 \tabularnewline
15 & 0.813696 & 0.372607 & 0.186304 \tabularnewline
16 & 0.756907 & 0.486186 & 0.243093 \tabularnewline
17 & 0.71473 & 0.570541 & 0.28527 \tabularnewline
18 & 0.635868 & 0.728263 & 0.364132 \tabularnewline
19 & 0.551641 & 0.896717 & 0.448359 \tabularnewline
20 & 0.690088 & 0.619824 & 0.309912 \tabularnewline
21 & 0.619446 & 0.761107 & 0.380554 \tabularnewline
22 & 0.548213 & 0.903574 & 0.451787 \tabularnewline
23 & 0.483419 & 0.966838 & 0.516581 \tabularnewline
24 & 0.41471 & 0.829421 & 0.58529 \tabularnewline
25 & 0.456622 & 0.913243 & 0.543378 \tabularnewline
26 & 0.455657 & 0.911314 & 0.544343 \tabularnewline
27 & 0.396282 & 0.792565 & 0.603718 \tabularnewline
28 & 0.443619 & 0.887237 & 0.556381 \tabularnewline
29 & 0.386176 & 0.772352 & 0.613824 \tabularnewline
30 & 0.328054 & 0.656108 & 0.671946 \tabularnewline
31 & 0.279673 & 0.559346 & 0.720327 \tabularnewline
32 & 0.26823 & 0.53646 & 0.73177 \tabularnewline
33 & 0.250891 & 0.501783 & 0.749109 \tabularnewline
34 & 0.229327 & 0.458653 & 0.770673 \tabularnewline
35 & 0.186035 & 0.372069 & 0.813965 \tabularnewline
36 & 0.148889 & 0.297779 & 0.851111 \tabularnewline
37 & 0.117643 & 0.235285 & 0.882357 \tabularnewline
38 & 0.095318 & 0.190636 & 0.904682 \tabularnewline
39 & 0.697181 & 0.605637 & 0.302819 \tabularnewline
40 & 0.664885 & 0.67023 & 0.335115 \tabularnewline
41 & 0.629354 & 0.741291 & 0.370646 \tabularnewline
42 & 0.587679 & 0.824642 & 0.412321 \tabularnewline
43 & 0.542836 & 0.914328 & 0.457164 \tabularnewline
44 & 0.498432 & 0.996864 & 0.501568 \tabularnewline
45 & 0.458893 & 0.917787 & 0.541107 \tabularnewline
46 & 0.414826 & 0.829652 & 0.585174 \tabularnewline
47 & 0.402303 & 0.804606 & 0.597697 \tabularnewline
48 & 0.38762 & 0.77524 & 0.61238 \tabularnewline
49 & 0.371218 & 0.742437 & 0.628782 \tabularnewline
50 & 0.352422 & 0.704844 & 0.647578 \tabularnewline
51 & 0.311369 & 0.622737 & 0.688631 \tabularnewline
52 & 0.80787 & 0.384259 & 0.19213 \tabularnewline
53 & 0.781652 & 0.436697 & 0.218348 \tabularnewline
54 & 0.753902 & 0.492197 & 0.246098 \tabularnewline
55 & 0.734335 & 0.531329 & 0.265665 \tabularnewline
56 & 0.700459 & 0.599083 & 0.299541 \tabularnewline
57 & 0.658654 & 0.682693 & 0.341346 \tabularnewline
58 & 0.666829 & 0.666342 & 0.333171 \tabularnewline
59 & 0.639134 & 0.721732 & 0.360866 \tabularnewline
60 & 0.664478 & 0.671044 & 0.335522 \tabularnewline
61 & 0.702687 & 0.594627 & 0.297313 \tabularnewline
62 & 0.689041 & 0.621919 & 0.310959 \tabularnewline
63 & 0.670402 & 0.659197 & 0.329598 \tabularnewline
64 & 0.698869 & 0.602262 & 0.301131 \tabularnewline
65 & 0.665118 & 0.669763 & 0.334882 \tabularnewline
66 & 0.670009 & 0.659983 & 0.329991 \tabularnewline
67 & 0.659506 & 0.680988 & 0.340494 \tabularnewline
68 & 0.705075 & 0.58985 & 0.294925 \tabularnewline
69 & 0.676534 & 0.646932 & 0.323466 \tabularnewline
70 & 0.695401 & 0.609198 & 0.304599 \tabularnewline
71 & 0.718251 & 0.563499 & 0.281749 \tabularnewline
72 & 0.678478 & 0.643044 & 0.321522 \tabularnewline
73 & 0.726566 & 0.546869 & 0.273434 \tabularnewline
74 & 0.689758 & 0.620485 & 0.310242 \tabularnewline
75 & 0.676776 & 0.646449 & 0.323224 \tabularnewline
76 & 0.709459 & 0.581081 & 0.290541 \tabularnewline
77 & 0.789188 & 0.421625 & 0.210812 \tabularnewline
78 & 0.764271 & 0.471459 & 0.235729 \tabularnewline
79 & 0.781484 & 0.437032 & 0.218516 \tabularnewline
80 & 0.765768 & 0.468465 & 0.234232 \tabularnewline
81 & 0.800736 & 0.398528 & 0.199264 \tabularnewline
82 & 0.83283 & 0.334339 & 0.16717 \tabularnewline
83 & 0.80745 & 0.3851 & 0.19255 \tabularnewline
84 & 0.804924 & 0.390152 & 0.195076 \tabularnewline
85 & 0.805146 & 0.389708 & 0.194854 \tabularnewline
86 & 0.781585 & 0.43683 & 0.218415 \tabularnewline
87 & 0.752623 & 0.494754 & 0.247377 \tabularnewline
88 & 0.71685 & 0.566301 & 0.28315 \tabularnewline
89 & 0.69299 & 0.61402 & 0.30701 \tabularnewline
90 & 0.655868 & 0.688264 & 0.344132 \tabularnewline
91 & 0.668779 & 0.662442 & 0.331221 \tabularnewline
92 & 0.634025 & 0.731951 & 0.365975 \tabularnewline
93 & 0.605252 & 0.789496 & 0.394748 \tabularnewline
94 & 0.580951 & 0.838098 & 0.419049 \tabularnewline
95 & 0.58525 & 0.8295 & 0.41475 \tabularnewline
96 & 0.556636 & 0.886729 & 0.443364 \tabularnewline
97 & 0.596 & 0.808 & 0.404 \tabularnewline
98 & 0.626606 & 0.746789 & 0.373394 \tabularnewline
99 & 0.603094 & 0.793812 & 0.396906 \tabularnewline
100 & 0.562338 & 0.875323 & 0.437662 \tabularnewline
101 & 0.538905 & 0.92219 & 0.461095 \tabularnewline
102 & 0.492231 & 0.984462 & 0.507769 \tabularnewline
103 & 0.448952 & 0.897903 & 0.551048 \tabularnewline
104 & 0.406598 & 0.813196 & 0.593402 \tabularnewline
105 & 0.397949 & 0.795897 & 0.602051 \tabularnewline
106 & 0.360596 & 0.721192 & 0.639404 \tabularnewline
107 & 0.330574 & 0.661149 & 0.669426 \tabularnewline
108 & 0.301014 & 0.602029 & 0.698986 \tabularnewline
109 & 0.291617 & 0.583235 & 0.708383 \tabularnewline
110 & 0.335744 & 0.671489 & 0.664256 \tabularnewline
111 & 0.312698 & 0.625397 & 0.687302 \tabularnewline
112 & 0.320879 & 0.641757 & 0.679121 \tabularnewline
113 & 0.306086 & 0.612173 & 0.693914 \tabularnewline
114 & 0.376911 & 0.753822 & 0.623089 \tabularnewline
115 & 0.341654 & 0.683307 & 0.658346 \tabularnewline
116 & 0.373317 & 0.746634 & 0.626683 \tabularnewline
117 & 0.695618 & 0.608764 & 0.304382 \tabularnewline
118 & 0.724249 & 0.551502 & 0.275751 \tabularnewline
119 & 0.699799 & 0.600402 & 0.300201 \tabularnewline
120 & 0.771411 & 0.457177 & 0.228589 \tabularnewline
121 & 0.747927 & 0.504146 & 0.252073 \tabularnewline
122 & 0.775296 & 0.449407 & 0.224704 \tabularnewline
123 & 0.754322 & 0.491355 & 0.245678 \tabularnewline
124 & 0.860183 & 0.279635 & 0.139817 \tabularnewline
125 & 0.828487 & 0.343026 & 0.171513 \tabularnewline
126 & 0.801771 & 0.396457 & 0.198229 \tabularnewline
127 & 0.781189 & 0.437623 & 0.218811 \tabularnewline
128 & 0.741803 & 0.516395 & 0.258197 \tabularnewline
129 & 0.695349 & 0.609302 & 0.304651 \tabularnewline
130 & 0.645506 & 0.708989 & 0.354494 \tabularnewline
131 & 0.605538 & 0.788923 & 0.394462 \tabularnewline
132 & 0.553676 & 0.892648 & 0.446324 \tabularnewline
133 & 0.552553 & 0.894894 & 0.447447 \tabularnewline
134 & 0.500554 & 0.998892 & 0.499446 \tabularnewline
135 & 0.438329 & 0.876658 & 0.561671 \tabularnewline
136 & 0.467007 & 0.934015 & 0.532993 \tabularnewline
137 & 0.457413 & 0.914826 & 0.542587 \tabularnewline
138 & 0.452385 & 0.90477 & 0.547615 \tabularnewline
139 & 0.413499 & 0.826998 & 0.586501 \tabularnewline
140 & 0.407881 & 0.815762 & 0.592119 \tabularnewline
141 & 0.382896 & 0.765791 & 0.617104 \tabularnewline
142 & 0.370917 & 0.741833 & 0.629083 \tabularnewline
143 & 0.32035 & 0.640699 & 0.67965 \tabularnewline
144 & 0.257244 & 0.514488 & 0.742756 \tabularnewline
145 & 0.323436 & 0.646872 & 0.676564 \tabularnewline
146 & 0.409496 & 0.818992 & 0.590504 \tabularnewline
147 & 0.415924 & 0.831848 & 0.584076 \tabularnewline
148 & 0.963837 & 0.0723261 & 0.0361631 \tabularnewline
149 & 0.948116 & 0.103769 & 0.0518845 \tabularnewline
150 & 0.981506 & 0.0369873 & 0.0184937 \tabularnewline
151 & 0.993478 & 0.0130448 & 0.00652242 \tabularnewline
152 & 0.983951 & 0.0320986 & 0.0160493 \tabularnewline
153 & 0.972754 & 0.0544924 & 0.0272462 \tabularnewline
154 & 0.958428 & 0.0831435 & 0.0415717 \tabularnewline
155 & 0.949114 & 0.101771 & 0.0508855 \tabularnewline
156 & 0.910081 & 0.179838 & 0.0899188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&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]10[/C][C]0.537209[/C][C]0.925581[/C][C]0.462791[/C][/ROW]
[ROW][C]11[/C][C]0.494005[/C][C]0.988009[/C][C]0.505995[/C][/ROW]
[ROW][C]12[/C][C]0.627609[/C][C]0.744783[/C][C]0.372391[/C][/ROW]
[ROW][C]13[/C][C]0.909611[/C][C]0.180777[/C][C]0.0903887[/C][/ROW]
[ROW][C]14[/C][C]0.8687[/C][C]0.2626[/C][C]0.1313[/C][/ROW]
[ROW][C]15[/C][C]0.813696[/C][C]0.372607[/C][C]0.186304[/C][/ROW]
[ROW][C]16[/C][C]0.756907[/C][C]0.486186[/C][C]0.243093[/C][/ROW]
[ROW][C]17[/C][C]0.71473[/C][C]0.570541[/C][C]0.28527[/C][/ROW]
[ROW][C]18[/C][C]0.635868[/C][C]0.728263[/C][C]0.364132[/C][/ROW]
[ROW][C]19[/C][C]0.551641[/C][C]0.896717[/C][C]0.448359[/C][/ROW]
[ROW][C]20[/C][C]0.690088[/C][C]0.619824[/C][C]0.309912[/C][/ROW]
[ROW][C]21[/C][C]0.619446[/C][C]0.761107[/C][C]0.380554[/C][/ROW]
[ROW][C]22[/C][C]0.548213[/C][C]0.903574[/C][C]0.451787[/C][/ROW]
[ROW][C]23[/C][C]0.483419[/C][C]0.966838[/C][C]0.516581[/C][/ROW]
[ROW][C]24[/C][C]0.41471[/C][C]0.829421[/C][C]0.58529[/C][/ROW]
[ROW][C]25[/C][C]0.456622[/C][C]0.913243[/C][C]0.543378[/C][/ROW]
[ROW][C]26[/C][C]0.455657[/C][C]0.911314[/C][C]0.544343[/C][/ROW]
[ROW][C]27[/C][C]0.396282[/C][C]0.792565[/C][C]0.603718[/C][/ROW]
[ROW][C]28[/C][C]0.443619[/C][C]0.887237[/C][C]0.556381[/C][/ROW]
[ROW][C]29[/C][C]0.386176[/C][C]0.772352[/C][C]0.613824[/C][/ROW]
[ROW][C]30[/C][C]0.328054[/C][C]0.656108[/C][C]0.671946[/C][/ROW]
[ROW][C]31[/C][C]0.279673[/C][C]0.559346[/C][C]0.720327[/C][/ROW]
[ROW][C]32[/C][C]0.26823[/C][C]0.53646[/C][C]0.73177[/C][/ROW]
[ROW][C]33[/C][C]0.250891[/C][C]0.501783[/C][C]0.749109[/C][/ROW]
[ROW][C]34[/C][C]0.229327[/C][C]0.458653[/C][C]0.770673[/C][/ROW]
[ROW][C]35[/C][C]0.186035[/C][C]0.372069[/C][C]0.813965[/C][/ROW]
[ROW][C]36[/C][C]0.148889[/C][C]0.297779[/C][C]0.851111[/C][/ROW]
[ROW][C]37[/C][C]0.117643[/C][C]0.235285[/C][C]0.882357[/C][/ROW]
[ROW][C]38[/C][C]0.095318[/C][C]0.190636[/C][C]0.904682[/C][/ROW]
[ROW][C]39[/C][C]0.697181[/C][C]0.605637[/C][C]0.302819[/C][/ROW]
[ROW][C]40[/C][C]0.664885[/C][C]0.67023[/C][C]0.335115[/C][/ROW]
[ROW][C]41[/C][C]0.629354[/C][C]0.741291[/C][C]0.370646[/C][/ROW]
[ROW][C]42[/C][C]0.587679[/C][C]0.824642[/C][C]0.412321[/C][/ROW]
[ROW][C]43[/C][C]0.542836[/C][C]0.914328[/C][C]0.457164[/C][/ROW]
[ROW][C]44[/C][C]0.498432[/C][C]0.996864[/C][C]0.501568[/C][/ROW]
[ROW][C]45[/C][C]0.458893[/C][C]0.917787[/C][C]0.541107[/C][/ROW]
[ROW][C]46[/C][C]0.414826[/C][C]0.829652[/C][C]0.585174[/C][/ROW]
[ROW][C]47[/C][C]0.402303[/C][C]0.804606[/C][C]0.597697[/C][/ROW]
[ROW][C]48[/C][C]0.38762[/C][C]0.77524[/C][C]0.61238[/C][/ROW]
[ROW][C]49[/C][C]0.371218[/C][C]0.742437[/C][C]0.628782[/C][/ROW]
[ROW][C]50[/C][C]0.352422[/C][C]0.704844[/C][C]0.647578[/C][/ROW]
[ROW][C]51[/C][C]0.311369[/C][C]0.622737[/C][C]0.688631[/C][/ROW]
[ROW][C]52[/C][C]0.80787[/C][C]0.384259[/C][C]0.19213[/C][/ROW]
[ROW][C]53[/C][C]0.781652[/C][C]0.436697[/C][C]0.218348[/C][/ROW]
[ROW][C]54[/C][C]0.753902[/C][C]0.492197[/C][C]0.246098[/C][/ROW]
[ROW][C]55[/C][C]0.734335[/C][C]0.531329[/C][C]0.265665[/C][/ROW]
[ROW][C]56[/C][C]0.700459[/C][C]0.599083[/C][C]0.299541[/C][/ROW]
[ROW][C]57[/C][C]0.658654[/C][C]0.682693[/C][C]0.341346[/C][/ROW]
[ROW][C]58[/C][C]0.666829[/C][C]0.666342[/C][C]0.333171[/C][/ROW]
[ROW][C]59[/C][C]0.639134[/C][C]0.721732[/C][C]0.360866[/C][/ROW]
[ROW][C]60[/C][C]0.664478[/C][C]0.671044[/C][C]0.335522[/C][/ROW]
[ROW][C]61[/C][C]0.702687[/C][C]0.594627[/C][C]0.297313[/C][/ROW]
[ROW][C]62[/C][C]0.689041[/C][C]0.621919[/C][C]0.310959[/C][/ROW]
[ROW][C]63[/C][C]0.670402[/C][C]0.659197[/C][C]0.329598[/C][/ROW]
[ROW][C]64[/C][C]0.698869[/C][C]0.602262[/C][C]0.301131[/C][/ROW]
[ROW][C]65[/C][C]0.665118[/C][C]0.669763[/C][C]0.334882[/C][/ROW]
[ROW][C]66[/C][C]0.670009[/C][C]0.659983[/C][C]0.329991[/C][/ROW]
[ROW][C]67[/C][C]0.659506[/C][C]0.680988[/C][C]0.340494[/C][/ROW]
[ROW][C]68[/C][C]0.705075[/C][C]0.58985[/C][C]0.294925[/C][/ROW]
[ROW][C]69[/C][C]0.676534[/C][C]0.646932[/C][C]0.323466[/C][/ROW]
[ROW][C]70[/C][C]0.695401[/C][C]0.609198[/C][C]0.304599[/C][/ROW]
[ROW][C]71[/C][C]0.718251[/C][C]0.563499[/C][C]0.281749[/C][/ROW]
[ROW][C]72[/C][C]0.678478[/C][C]0.643044[/C][C]0.321522[/C][/ROW]
[ROW][C]73[/C][C]0.726566[/C][C]0.546869[/C][C]0.273434[/C][/ROW]
[ROW][C]74[/C][C]0.689758[/C][C]0.620485[/C][C]0.310242[/C][/ROW]
[ROW][C]75[/C][C]0.676776[/C][C]0.646449[/C][C]0.323224[/C][/ROW]
[ROW][C]76[/C][C]0.709459[/C][C]0.581081[/C][C]0.290541[/C][/ROW]
[ROW][C]77[/C][C]0.789188[/C][C]0.421625[/C][C]0.210812[/C][/ROW]
[ROW][C]78[/C][C]0.764271[/C][C]0.471459[/C][C]0.235729[/C][/ROW]
[ROW][C]79[/C][C]0.781484[/C][C]0.437032[/C][C]0.218516[/C][/ROW]
[ROW][C]80[/C][C]0.765768[/C][C]0.468465[/C][C]0.234232[/C][/ROW]
[ROW][C]81[/C][C]0.800736[/C][C]0.398528[/C][C]0.199264[/C][/ROW]
[ROW][C]82[/C][C]0.83283[/C][C]0.334339[/C][C]0.16717[/C][/ROW]
[ROW][C]83[/C][C]0.80745[/C][C]0.3851[/C][C]0.19255[/C][/ROW]
[ROW][C]84[/C][C]0.804924[/C][C]0.390152[/C][C]0.195076[/C][/ROW]
[ROW][C]85[/C][C]0.805146[/C][C]0.389708[/C][C]0.194854[/C][/ROW]
[ROW][C]86[/C][C]0.781585[/C][C]0.43683[/C][C]0.218415[/C][/ROW]
[ROW][C]87[/C][C]0.752623[/C][C]0.494754[/C][C]0.247377[/C][/ROW]
[ROW][C]88[/C][C]0.71685[/C][C]0.566301[/C][C]0.28315[/C][/ROW]
[ROW][C]89[/C][C]0.69299[/C][C]0.61402[/C][C]0.30701[/C][/ROW]
[ROW][C]90[/C][C]0.655868[/C][C]0.688264[/C][C]0.344132[/C][/ROW]
[ROW][C]91[/C][C]0.668779[/C][C]0.662442[/C][C]0.331221[/C][/ROW]
[ROW][C]92[/C][C]0.634025[/C][C]0.731951[/C][C]0.365975[/C][/ROW]
[ROW][C]93[/C][C]0.605252[/C][C]0.789496[/C][C]0.394748[/C][/ROW]
[ROW][C]94[/C][C]0.580951[/C][C]0.838098[/C][C]0.419049[/C][/ROW]
[ROW][C]95[/C][C]0.58525[/C][C]0.8295[/C][C]0.41475[/C][/ROW]
[ROW][C]96[/C][C]0.556636[/C][C]0.886729[/C][C]0.443364[/C][/ROW]
[ROW][C]97[/C][C]0.596[/C][C]0.808[/C][C]0.404[/C][/ROW]
[ROW][C]98[/C][C]0.626606[/C][C]0.746789[/C][C]0.373394[/C][/ROW]
[ROW][C]99[/C][C]0.603094[/C][C]0.793812[/C][C]0.396906[/C][/ROW]
[ROW][C]100[/C][C]0.562338[/C][C]0.875323[/C][C]0.437662[/C][/ROW]
[ROW][C]101[/C][C]0.538905[/C][C]0.92219[/C][C]0.461095[/C][/ROW]
[ROW][C]102[/C][C]0.492231[/C][C]0.984462[/C][C]0.507769[/C][/ROW]
[ROW][C]103[/C][C]0.448952[/C][C]0.897903[/C][C]0.551048[/C][/ROW]
[ROW][C]104[/C][C]0.406598[/C][C]0.813196[/C][C]0.593402[/C][/ROW]
[ROW][C]105[/C][C]0.397949[/C][C]0.795897[/C][C]0.602051[/C][/ROW]
[ROW][C]106[/C][C]0.360596[/C][C]0.721192[/C][C]0.639404[/C][/ROW]
[ROW][C]107[/C][C]0.330574[/C][C]0.661149[/C][C]0.669426[/C][/ROW]
[ROW][C]108[/C][C]0.301014[/C][C]0.602029[/C][C]0.698986[/C][/ROW]
[ROW][C]109[/C][C]0.291617[/C][C]0.583235[/C][C]0.708383[/C][/ROW]
[ROW][C]110[/C][C]0.335744[/C][C]0.671489[/C][C]0.664256[/C][/ROW]
[ROW][C]111[/C][C]0.312698[/C][C]0.625397[/C][C]0.687302[/C][/ROW]
[ROW][C]112[/C][C]0.320879[/C][C]0.641757[/C][C]0.679121[/C][/ROW]
[ROW][C]113[/C][C]0.306086[/C][C]0.612173[/C][C]0.693914[/C][/ROW]
[ROW][C]114[/C][C]0.376911[/C][C]0.753822[/C][C]0.623089[/C][/ROW]
[ROW][C]115[/C][C]0.341654[/C][C]0.683307[/C][C]0.658346[/C][/ROW]
[ROW][C]116[/C][C]0.373317[/C][C]0.746634[/C][C]0.626683[/C][/ROW]
[ROW][C]117[/C][C]0.695618[/C][C]0.608764[/C][C]0.304382[/C][/ROW]
[ROW][C]118[/C][C]0.724249[/C][C]0.551502[/C][C]0.275751[/C][/ROW]
[ROW][C]119[/C][C]0.699799[/C][C]0.600402[/C][C]0.300201[/C][/ROW]
[ROW][C]120[/C][C]0.771411[/C][C]0.457177[/C][C]0.228589[/C][/ROW]
[ROW][C]121[/C][C]0.747927[/C][C]0.504146[/C][C]0.252073[/C][/ROW]
[ROW][C]122[/C][C]0.775296[/C][C]0.449407[/C][C]0.224704[/C][/ROW]
[ROW][C]123[/C][C]0.754322[/C][C]0.491355[/C][C]0.245678[/C][/ROW]
[ROW][C]124[/C][C]0.860183[/C][C]0.279635[/C][C]0.139817[/C][/ROW]
[ROW][C]125[/C][C]0.828487[/C][C]0.343026[/C][C]0.171513[/C][/ROW]
[ROW][C]126[/C][C]0.801771[/C][C]0.396457[/C][C]0.198229[/C][/ROW]
[ROW][C]127[/C][C]0.781189[/C][C]0.437623[/C][C]0.218811[/C][/ROW]
[ROW][C]128[/C][C]0.741803[/C][C]0.516395[/C][C]0.258197[/C][/ROW]
[ROW][C]129[/C][C]0.695349[/C][C]0.609302[/C][C]0.304651[/C][/ROW]
[ROW][C]130[/C][C]0.645506[/C][C]0.708989[/C][C]0.354494[/C][/ROW]
[ROW][C]131[/C][C]0.605538[/C][C]0.788923[/C][C]0.394462[/C][/ROW]
[ROW][C]132[/C][C]0.553676[/C][C]0.892648[/C][C]0.446324[/C][/ROW]
[ROW][C]133[/C][C]0.552553[/C][C]0.894894[/C][C]0.447447[/C][/ROW]
[ROW][C]134[/C][C]0.500554[/C][C]0.998892[/C][C]0.499446[/C][/ROW]
[ROW][C]135[/C][C]0.438329[/C][C]0.876658[/C][C]0.561671[/C][/ROW]
[ROW][C]136[/C][C]0.467007[/C][C]0.934015[/C][C]0.532993[/C][/ROW]
[ROW][C]137[/C][C]0.457413[/C][C]0.914826[/C][C]0.542587[/C][/ROW]
[ROW][C]138[/C][C]0.452385[/C][C]0.90477[/C][C]0.547615[/C][/ROW]
[ROW][C]139[/C][C]0.413499[/C][C]0.826998[/C][C]0.586501[/C][/ROW]
[ROW][C]140[/C][C]0.407881[/C][C]0.815762[/C][C]0.592119[/C][/ROW]
[ROW][C]141[/C][C]0.382896[/C][C]0.765791[/C][C]0.617104[/C][/ROW]
[ROW][C]142[/C][C]0.370917[/C][C]0.741833[/C][C]0.629083[/C][/ROW]
[ROW][C]143[/C][C]0.32035[/C][C]0.640699[/C][C]0.67965[/C][/ROW]
[ROW][C]144[/C][C]0.257244[/C][C]0.514488[/C][C]0.742756[/C][/ROW]
[ROW][C]145[/C][C]0.323436[/C][C]0.646872[/C][C]0.676564[/C][/ROW]
[ROW][C]146[/C][C]0.409496[/C][C]0.818992[/C][C]0.590504[/C][/ROW]
[ROW][C]147[/C][C]0.415924[/C][C]0.831848[/C][C]0.584076[/C][/ROW]
[ROW][C]148[/C][C]0.963837[/C][C]0.0723261[/C][C]0.0361631[/C][/ROW]
[ROW][C]149[/C][C]0.948116[/C][C]0.103769[/C][C]0.0518845[/C][/ROW]
[ROW][C]150[/C][C]0.981506[/C][C]0.0369873[/C][C]0.0184937[/C][/ROW]
[ROW][C]151[/C][C]0.993478[/C][C]0.0130448[/C][C]0.00652242[/C][/ROW]
[ROW][C]152[/C][C]0.983951[/C][C]0.0320986[/C][C]0.0160493[/C][/ROW]
[ROW][C]153[/C][C]0.972754[/C][C]0.0544924[/C][C]0.0272462[/C][/ROW]
[ROW][C]154[/C][C]0.958428[/C][C]0.0831435[/C][C]0.0415717[/C][/ROW]
[ROW][C]155[/C][C]0.949114[/C][C]0.101771[/C][C]0.0508855[/C][/ROW]
[ROW][C]156[/C][C]0.910081[/C][C]0.179838[/C][C]0.0899188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&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
100.5372090.9255810.462791
110.4940050.9880090.505995
120.6276090.7447830.372391
130.9096110.1807770.0903887
140.86870.26260.1313
150.8136960.3726070.186304
160.7569070.4861860.243093
170.714730.5705410.28527
180.6358680.7282630.364132
190.5516410.8967170.448359
200.6900880.6198240.309912
210.6194460.7611070.380554
220.5482130.9035740.451787
230.4834190.9668380.516581
240.414710.8294210.58529
250.4566220.9132430.543378
260.4556570.9113140.544343
270.3962820.7925650.603718
280.4436190.8872370.556381
290.3861760.7723520.613824
300.3280540.6561080.671946
310.2796730.5593460.720327
320.268230.536460.73177
330.2508910.5017830.749109
340.2293270.4586530.770673
350.1860350.3720690.813965
360.1488890.2977790.851111
370.1176430.2352850.882357
380.0953180.1906360.904682
390.6971810.6056370.302819
400.6648850.670230.335115
410.6293540.7412910.370646
420.5876790.8246420.412321
430.5428360.9143280.457164
440.4984320.9968640.501568
450.4588930.9177870.541107
460.4148260.8296520.585174
470.4023030.8046060.597697
480.387620.775240.61238
490.3712180.7424370.628782
500.3524220.7048440.647578
510.3113690.6227370.688631
520.807870.3842590.19213
530.7816520.4366970.218348
540.7539020.4921970.246098
550.7343350.5313290.265665
560.7004590.5990830.299541
570.6586540.6826930.341346
580.6668290.6663420.333171
590.6391340.7217320.360866
600.6644780.6710440.335522
610.7026870.5946270.297313
620.6890410.6219190.310959
630.6704020.6591970.329598
640.6988690.6022620.301131
650.6651180.6697630.334882
660.6700090.6599830.329991
670.6595060.6809880.340494
680.7050750.589850.294925
690.6765340.6469320.323466
700.6954010.6091980.304599
710.7182510.5634990.281749
720.6784780.6430440.321522
730.7265660.5468690.273434
740.6897580.6204850.310242
750.6767760.6464490.323224
760.7094590.5810810.290541
770.7891880.4216250.210812
780.7642710.4714590.235729
790.7814840.4370320.218516
800.7657680.4684650.234232
810.8007360.3985280.199264
820.832830.3343390.16717
830.807450.38510.19255
840.8049240.3901520.195076
850.8051460.3897080.194854
860.7815850.436830.218415
870.7526230.4947540.247377
880.716850.5663010.28315
890.692990.614020.30701
900.6558680.6882640.344132
910.6687790.6624420.331221
920.6340250.7319510.365975
930.6052520.7894960.394748
940.5809510.8380980.419049
950.585250.82950.41475
960.5566360.8867290.443364
970.5960.8080.404
980.6266060.7467890.373394
990.6030940.7938120.396906
1000.5623380.8753230.437662
1010.5389050.922190.461095
1020.4922310.9844620.507769
1030.4489520.8979030.551048
1040.4065980.8131960.593402
1050.3979490.7958970.602051
1060.3605960.7211920.639404
1070.3305740.6611490.669426
1080.3010140.6020290.698986
1090.2916170.5832350.708383
1100.3357440.6714890.664256
1110.3126980.6253970.687302
1120.3208790.6417570.679121
1130.3060860.6121730.693914
1140.3769110.7538220.623089
1150.3416540.6833070.658346
1160.3733170.7466340.626683
1170.6956180.6087640.304382
1180.7242490.5515020.275751
1190.6997990.6004020.300201
1200.7714110.4571770.228589
1210.7479270.5041460.252073
1220.7752960.4494070.224704
1230.7543220.4913550.245678
1240.8601830.2796350.139817
1250.8284870.3430260.171513
1260.8017710.3964570.198229
1270.7811890.4376230.218811
1280.7418030.5163950.258197
1290.6953490.6093020.304651
1300.6455060.7089890.354494
1310.6055380.7889230.394462
1320.5536760.8926480.446324
1330.5525530.8948940.447447
1340.5005540.9988920.499446
1350.4383290.8766580.561671
1360.4670070.9340150.532993
1370.4574130.9148260.542587
1380.4523850.904770.547615
1390.4134990.8269980.586501
1400.4078810.8157620.592119
1410.3828960.7657910.617104
1420.3709170.7418330.629083
1430.320350.6406990.67965
1440.2572440.5144880.742756
1450.3234360.6468720.676564
1460.4094960.8189920.590504
1470.4159240.8318480.584076
1480.9638370.07232610.0361631
1490.9481160.1037690.0518845
1500.9815060.03698730.0184937
1510.9934780.01304480.00652242
1520.9839510.03209860.0160493
1530.9727540.05449240.0272462
1540.9584280.08314350.0415717
1550.9491140.1017710.0508855
1560.9100810.1798380.0899188






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0204082OK
10% type I error level60.0408163OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0204082 & OK \tabularnewline
10% type I error level & 6 & 0.0408163 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269326&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0204082[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0408163[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269326&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269326&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0204082OK
10% type I error level60.0408163OK


Figures (Output of Computation)

Figure 1
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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')
}