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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 11:15:22 +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/t1418728654kyanhnexvfpb4ht.htm/, Retrieved Thu, 16 May 2024 17:29:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269333, Retrieved Thu, 16 May 2024 17:29:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact49

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper14] [2014-12-16 11:15:22] [f8a15a4749f25af1f83725a9fa901b6e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.35 1 0 51 6 48
12.7 1 0 56 4 50
18.1 1 0 67 8 150
17.85 1 0 69 5 154
16.6 0 1 57 4 109
12.6 1 1 56 17 68
17.1 1 0 55 4 194
19.1 0 0 63 4 158
16.1 1 0 67 8 159
13.35 0 0 65 4 67
18.4 0 0 47 7 147
14.7 1 0 76 4 39
10.6 1 0 64 4 100
12.6 1 0 68 5 111
16.2 1 0 64 7 138
13.6 1 0 65 4 101
18.9 1 1 71 4 131
14.1 1 0 63 7 101
14.5 1 0 60 11 114
16.15 0 0 68 7 165
14.75 1 0 72 4 114
14.8 1 0 70 4 111
12.45 1 0 61 4 75
12.65 1 0 61 4 82
17.35 1 0 62 4 121
8.6 1 0 71 4 32
18.4 0 0 71 6 150
16.1 1 0 51 8 117
11.6 1 1 56 23 71
17.75 1 0 70 4 165
15.25 1 0 73 8 154
17.65 1 0 76 6 126
16.35 0 0 68 4 149
17.65 0 0 48 7 145
13.6 1 0 52 4 120
14.35 0 0 60 4 109
14.75 0 0 59 4 132
18.25 1 0 57 10 172
9.9 0 0 79 6 169
16 1 0 60 5 114
18.25 1 0 60 5 156
16.85 0 0 59 4 172
14.6 1 1 62 4 68
13.85 1 1 59 5 89
18.95 1 0 61 5 167
15.6 0 0 71 5 113
14.85 0 1 57 5 115
11.75 0 1 66 4 78
18.45 0 1 63 6 118
15.9 1 1 69 4 87
17.1 0 0 58 4 173
16.1 1 0 59 4 2
19.9 0 1 48 9 162
10.95 1 1 66 18 49
18.45 0 1 73 6 122
15.1 1 1 67 5 96
15 0 1 61 4 100
11.35 0 1 68 11 82
15.95 1 1 75 4 100
18.1 0 1 62 10 115
14.6 1 1 69 6 141
15.4 1 0 58 8 165
15.4 1 0 60 8 165
17.6 1 1 74 6 110
13.35 1 0 55 8 118
19.1 0 0 62 4 158
15.35 1 1 63 4 146
7.6 0 0 69 9 49
13.4 0 1 58 9 90
13.9 0 1 58 5 121
19.1 1 0 68 4 155
15.25 0 1 72 4 104
12.9 1 1 62 15 147
16.1 0 1 62 10 110
17.35 0 1 65 9 108
13.15 0 1 69 7 113
12.15 0 1 66 9 115
12.6 1 1 72 6 61
10.35 1 1 62 4 60
15.4 1 1 75 7 109
9.6 1 1 58 4 68
18.2 0 1 66 7 111
13.6 0 1 55 4 77
14.85 1 1 47 15 73
14.75 0 0 72 4 151
14.1 0 1 62 9 89
14.9 0 1 64 4 78
16.25 0 1 64 4 110
19.25 1 0 19 28 220
13.6 1 1 50 4 65
13.6 0 0 68 4 141
15.65 0 1 70 4 117
12.75 1 0 79 5 122
14.6 0 1 69 4 63
9.85 1 0 71 4 44
12.65 1 1 48 12 52
19.2 0 1 73 4 131
16.6 1 1 74 6 101
11.2 1 1 66 6 42
15.25 1 0 71 5 152
11.9 0 0 74 4 107
13.2 0 1 78 4 77
16.35 0 0 75 4 154
12.4 1 0 53 10 103
15.85 1 1 60 7 96
18.15 1 0 70 4 175
11.15 1 1 69 7 57
15.65 0 1 65 4 112
17.75 0 0 78 4 143
7.65 0 1 78 12 49
12.35 1 0 59 5 110
15.6 1 0 72 8 131
19.3 0 0 70 6 167
15.2 0 1 63 17 56
17.1 0 0 63 4 137
15.6 1 1 71 5 86
18.4 1 0 74 4 121
19.05 0 0 67 5 149
18.55 0 0 66 5 168
19.1 0 0 62 6 140
13.1 1 1 80 4 88
12.85 1 0 73 4 168
9.5 1 0 67 4 94
4.5 1 0 61 6 51
11.85 0 1 73 8 48
13.6 1 0 74 10 145
11.7 1 0 32 4 66
12.4 1 1 69 5 85
13.35 0 0 69 4 109
11.4 0 1 84 4 63
14.9 1 1 64 4 102
19.9 0 1 58 16 162
11.2 1 1 59 7 86
14.6 1 1 78 4 114
17.6 0 0 57 4 164
14.05 1 0 60 14 119
16.1 0 0 68 5 126
13.35 1 0 68 5 132
11.85 1 0 73 5 142
11.95 0 0 69 5 83
14.75 1 1 67 7 94
15.15 0 1 60 19 81
13.2 1 0 65 16 166
16.85 0 1 66 4 110
7.85 1 1 74 4 64
7.7 0 0 81 7 93
12.6 0 1 72 9 104
7.85 1 1 55 5 105
10.95 1 1 49 14 49
12.35 0 1 74 4 88
9.95 1 1 53 16 95
14.9 1 1 64 10 102
16.65 0 1 65 5 99
13.4 1 1 57 6 63
13.95 0 1 51 4 76
15.7 0 1 80 4 109
16.85 1 1 67 4 117
10.95 1 1 70 5 57
15.35 0 1 74 4 120
12.2 1 1 75 4 73
15.1 0 1 70 5 91
17.75 0 1 69 4 108
15.2 1 1 65 4 105
14.6 0 0 55 5 117
16.65 0 1 71 8 119
8.1 1 1 65 15 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=269333&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=269333&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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] = + 11.0068 -0.665432gender[t] + 1.3661Course_id[t] -0.0361899AMS.E[t] -0.104885AMS.A[t] + 0.0568655LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.0068 -0.665432gender[t] +  1.3661Course_id[t] -0.0361899AMS.E[t] -0.104885AMS.A[t] +  0.0568655LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.0068 -0.665432gender[t] +  1.3661Course_id[t] -0.0361899AMS.E[t] -0.104885AMS.A[t] +  0.0568655LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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] = + 11.0068 -0.665432gender[t] + 1.3661Course_id[t] -0.0361899AMS.E[t] -0.104885AMS.A[t] + 0.0568655LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.00681.699176.4781.09363e-095.46814e-10
gender-0.6654320.363805-1.8290.06924810.0346241
Course_id1.36610.4020273.3980.0008565740.000428287
AMS.E-0.03618990.0213614-1.6940.0921780.046089
AMS.A-0.1048850.048699-2.1540.03275840.0163792
LFM0.05686550.0051286511.091.48231e-217.41153e-22

\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) & 11.0068 & 1.69917 & 6.478 & 1.09363e-09 & 5.46814e-10 \tabularnewline
gender & -0.665432 & 0.363805 & -1.829 & 0.0692481 & 0.0346241 \tabularnewline
Course_id & 1.3661 & 0.402027 & 3.398 & 0.000856574 & 0.000428287 \tabularnewline
AMS.E & -0.0361899 & 0.0213614 & -1.694 & 0.092178 & 0.046089 \tabularnewline
AMS.A & -0.104885 & 0.048699 & -2.154 & 0.0327584 & 0.0163792 \tabularnewline
LFM & 0.0568655 & 0.00512865 & 11.09 & 1.48231e-21 & 7.41153e-22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&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]11.0068[/C][C]1.69917[/C][C]6.478[/C][C]1.09363e-09[/C][C]5.46814e-10[/C][/ROW]
[ROW][C]gender[/C][C]-0.665432[/C][C]0.363805[/C][C]-1.829[/C][C]0.0692481[/C][C]0.0346241[/C][/ROW]
[ROW][C]Course_id[/C][C]1.3661[/C][C]0.402027[/C][C]3.398[/C][C]0.000856574[/C][C]0.000428287[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0361899[/C][C]0.0213614[/C][C]-1.694[/C][C]0.092178[/C][C]0.046089[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.104885[/C][C]0.048699[/C][C]-2.154[/C][C]0.0327584[/C][C]0.0163792[/C][/ROW]
[ROW][C]LFM[/C][C]0.0568655[/C][C]0.00512865[/C][C]11.09[/C][C]1.48231e-21[/C][C]7.41153e-22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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)11.00681.699176.4781.09363e-095.46814e-10
gender-0.6654320.363805-1.8290.06924810.0346241
Course_id1.36610.4020273.3980.0008565740.000428287
AMS.E-0.03618990.0213614-1.6940.0921780.046089
AMS.A-0.1048850.048699-2.1540.03275840.0163792
LFM0.05686550.0051286511.091.48231e-217.41153e-22






Multiple Linear Regression - Regression Statistics
Multiple R0.694571
R-squared0.48243
Adjusted R-squared0.466255
F-TEST (value)29.8273
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22678
Sum Squared Residuals793.366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.694571 \tabularnewline
R-squared & 0.48243 \tabularnewline
Adjusted R-squared & 0.466255 \tabularnewline
F-TEST (value) & 29.8273 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22678 \tabularnewline
Sum Squared Residuals & 793.366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.694571[/C][/ROW]
[ROW][C]R-squared[/C][C]0.48243[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.466255[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.8273[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/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]2.22678[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]793.366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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 R0.694571
R-squared0.48243
Adjusted R-squared0.466255
F-TEST (value)29.8273
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22678
Sum Squared Residuals793.366






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.5959-6.2459
212.710.73841.96155
318.115.60742.49263
417.8516.07711.7729
516.616.08890.511142
612.611.76460.835366
717.118.9633-1.86327
819.117.2921.80798
916.116.1192-0.0191581
1013.3512.04491.30511
1118.416.93091.46911
1214.79.389135.31087
1310.613.2922-2.6922
1412.613.6681-1.06808
1516.215.13841.06156
1613.613.31290.287121
1718.916.16782.73219
1814.113.07061.02939
1914.513.49891.00111
2016.1517.1945-1.04448
2114.7513.79880.951199
2214.813.70061.09942
2312.4511.97910.470863
2412.6512.37720.272805
2517.3514.55882.79124
268.69.17202-0.572023
2718.416.33782.06219
2816.114.30981.79015
2911.611.30590.294077
3017.7516.77130.978681
3115.2515.6177-0.367692
3217.6514.12673.52334
3316.3516.5993-0.249284
3417.6516.7810.869034
3513.614.8638-1.26379
3614.3514.6142-0.264184
3714.7515.9583-1.20828
3818.2517.01051.23946
399.917.1287-7.22874
401614.12821.8718
4118.2516.51651.73346
4216.8518.2329-1.3829
4314.612.9111.68901
4413.8514.1089-0.258853
4518.9517.10591.84413
4615.614.33871.26133
4714.8516.3252-1.47517
4811.7514.0003-2.25032
4918.4516.17372.27626
5015.913.73812.16189
5117.118.326-1.22595
5216.17.900348.19966
5319.918.9040.995986
5410.9510.21740.732593
5518.4516.03932.4107
5615.114.21740.882608
571515.4323-0.43231
5811.3513.4212-2.07121
5915.9514.26021.68978
6018.115.61982.48021
6114.616.5991-1.99907
6215.416.7861-1.38606
6315.416.7137-1.31368
6417.614.65532.94471
6513.3514.222-0.871953
6619.117.32821.77179
6715.3517.3103-1.96031
687.610.3521-2.75213
6913.414.4478-1.0478
7013.916.6302-2.73017
7119.116.2752.82496
7215.2515.2617-0.011683
7312.916.2496-3.34963
7416.115.33550.764533
7517.3515.21812.13195
7613.1515.5674-2.41739
7712.1515.5799-3.42992
7812.611.94130.658733
7910.3512.4561-2.10607
8015.414.45740.942645
819.613.0558-3.45575
8218.215.56222.63777
8313.614.3415-0.741543
8414.8512.58442.26556
8514.7516.5683-1.81826
8614.114.2462-0.146177
8714.914.07270.8273
8816.2515.89240.357605
8919.2519.22740.0226253
9013.613.17470.425325
9113.616.1444-2.54436
9215.6516.0733-0.423314
9312.7513.8955-1.14551
9414.613.03881.56123
959.859.85441-0.00440879
9612.6511.66870.981272
9719.216.76092.43914
9816.614.14352.45649
9911.211.0780.122037
10015.2515.891-0.640994
10111.913.9938-2.0938
10213.213.5092-0.309176
10316.3516.6303-0.280282
10412.413.2316-0.831582
10515.8514.2611.58905
10618.1517.340.810026
10711.1511.7175-0.56749
10815.6515.9699-0.319936
10917.7515.89621.85381
1107.6511.0779-3.42787
11112.3513.9369-1.58692
11215.614.3461.25402
11319.317.34071.95929
11415.211.49443.70565
11517.116.09781.00215
11615.613.5042.09602
11718.414.12454.27552
11819.0516.53062.51941
11918.5517.64720.902777
12019.116.09493.00514
12113.113.3969-0.296885
12212.8516.8333-3.98335
1239.512.8424-3.34244
1244.510.4046-5.9046
12511.8511.62150.228511
12613.614.8599-1.25994
12711.712.5169-0.816854
12812.413.5195-1.11949
12913.3514.2885-0.938475
13011.412.4959-1.09592
13114.914.7720.127961
13219.917.80792.09208
13311.213.7285-2.52849
13414.614.9478-0.347766
13517.617.8504-0.250354
13614.0513.46860.581438
13716.115.18650.913506
13813.3514.8623-1.51225
13911.8515.25-3.39996
14011.9512.7051-0.755089
14114.7513.89390.856108
14215.1512.81482.33521
14313.215.7505-2.55052
14416.8515.821.02999
1457.8512.2493-4.39925
1467.712.6297-4.9297
14712.614.7373-2.13726
1487.8515.1635-7.31346
14910.9511.2522-0.302173
15012.3514.2795-1.92946
1519.9513.5135-3.56346
15214.914.14270.757268
15316.6515.12581.5242
15413.412.59780.802154
15513.9514.4294-0.479437
15615.715.25650.443509
15716.8515.51651.33355
15810.9511.8911-0.94107
15915.3516.0992-0.749151
16012.212.7249-0.524852
16115.114.48990.610073
16217.7515.59772.15229
16315.214.90640.293554
16414.615.1452-0.545173
16516.6515.73130.918683
1668.19.54467-1.44467

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.5959 & -6.2459 \tabularnewline
2 & 12.7 & 10.7384 & 1.96155 \tabularnewline
3 & 18.1 & 15.6074 & 2.49263 \tabularnewline
4 & 17.85 & 16.0771 & 1.7729 \tabularnewline
5 & 16.6 & 16.0889 & 0.511142 \tabularnewline
6 & 12.6 & 11.7646 & 0.835366 \tabularnewline
7 & 17.1 & 18.9633 & -1.86327 \tabularnewline
8 & 19.1 & 17.292 & 1.80798 \tabularnewline
9 & 16.1 & 16.1192 & -0.0191581 \tabularnewline
10 & 13.35 & 12.0449 & 1.30511 \tabularnewline
11 & 18.4 & 16.9309 & 1.46911 \tabularnewline
12 & 14.7 & 9.38913 & 5.31087 \tabularnewline
13 & 10.6 & 13.2922 & -2.6922 \tabularnewline
14 & 12.6 & 13.6681 & -1.06808 \tabularnewline
15 & 16.2 & 15.1384 & 1.06156 \tabularnewline
16 & 13.6 & 13.3129 & 0.287121 \tabularnewline
17 & 18.9 & 16.1678 & 2.73219 \tabularnewline
18 & 14.1 & 13.0706 & 1.02939 \tabularnewline
19 & 14.5 & 13.4989 & 1.00111 \tabularnewline
20 & 16.15 & 17.1945 & -1.04448 \tabularnewline
21 & 14.75 & 13.7988 & 0.951199 \tabularnewline
22 & 14.8 & 13.7006 & 1.09942 \tabularnewline
23 & 12.45 & 11.9791 & 0.470863 \tabularnewline
24 & 12.65 & 12.3772 & 0.272805 \tabularnewline
25 & 17.35 & 14.5588 & 2.79124 \tabularnewline
26 & 8.6 & 9.17202 & -0.572023 \tabularnewline
27 & 18.4 & 16.3378 & 2.06219 \tabularnewline
28 & 16.1 & 14.3098 & 1.79015 \tabularnewline
29 & 11.6 & 11.3059 & 0.294077 \tabularnewline
30 & 17.75 & 16.7713 & 0.978681 \tabularnewline
31 & 15.25 & 15.6177 & -0.367692 \tabularnewline
32 & 17.65 & 14.1267 & 3.52334 \tabularnewline
33 & 16.35 & 16.5993 & -0.249284 \tabularnewline
34 & 17.65 & 16.781 & 0.869034 \tabularnewline
35 & 13.6 & 14.8638 & -1.26379 \tabularnewline
36 & 14.35 & 14.6142 & -0.264184 \tabularnewline
37 & 14.75 & 15.9583 & -1.20828 \tabularnewline
38 & 18.25 & 17.0105 & 1.23946 \tabularnewline
39 & 9.9 & 17.1287 & -7.22874 \tabularnewline
40 & 16 & 14.1282 & 1.8718 \tabularnewline
41 & 18.25 & 16.5165 & 1.73346 \tabularnewline
42 & 16.85 & 18.2329 & -1.3829 \tabularnewline
43 & 14.6 & 12.911 & 1.68901 \tabularnewline
44 & 13.85 & 14.1089 & -0.258853 \tabularnewline
45 & 18.95 & 17.1059 & 1.84413 \tabularnewline
46 & 15.6 & 14.3387 & 1.26133 \tabularnewline
47 & 14.85 & 16.3252 & -1.47517 \tabularnewline
48 & 11.75 & 14.0003 & -2.25032 \tabularnewline
49 & 18.45 & 16.1737 & 2.27626 \tabularnewline
50 & 15.9 & 13.7381 & 2.16189 \tabularnewline
51 & 17.1 & 18.326 & -1.22595 \tabularnewline
52 & 16.1 & 7.90034 & 8.19966 \tabularnewline
53 & 19.9 & 18.904 & 0.995986 \tabularnewline
54 & 10.95 & 10.2174 & 0.732593 \tabularnewline
55 & 18.45 & 16.0393 & 2.4107 \tabularnewline
56 & 15.1 & 14.2174 & 0.882608 \tabularnewline
57 & 15 & 15.4323 & -0.43231 \tabularnewline
58 & 11.35 & 13.4212 & -2.07121 \tabularnewline
59 & 15.95 & 14.2602 & 1.68978 \tabularnewline
60 & 18.1 & 15.6198 & 2.48021 \tabularnewline
61 & 14.6 & 16.5991 & -1.99907 \tabularnewline
62 & 15.4 & 16.7861 & -1.38606 \tabularnewline
63 & 15.4 & 16.7137 & -1.31368 \tabularnewline
64 & 17.6 & 14.6553 & 2.94471 \tabularnewline
65 & 13.35 & 14.222 & -0.871953 \tabularnewline
66 & 19.1 & 17.3282 & 1.77179 \tabularnewline
67 & 15.35 & 17.3103 & -1.96031 \tabularnewline
68 & 7.6 & 10.3521 & -2.75213 \tabularnewline
69 & 13.4 & 14.4478 & -1.0478 \tabularnewline
70 & 13.9 & 16.6302 & -2.73017 \tabularnewline
71 & 19.1 & 16.275 & 2.82496 \tabularnewline
72 & 15.25 & 15.2617 & -0.011683 \tabularnewline
73 & 12.9 & 16.2496 & -3.34963 \tabularnewline
74 & 16.1 & 15.3355 & 0.764533 \tabularnewline
75 & 17.35 & 15.2181 & 2.13195 \tabularnewline
76 & 13.15 & 15.5674 & -2.41739 \tabularnewline
77 & 12.15 & 15.5799 & -3.42992 \tabularnewline
78 & 12.6 & 11.9413 & 0.658733 \tabularnewline
79 & 10.35 & 12.4561 & -2.10607 \tabularnewline
80 & 15.4 & 14.4574 & 0.942645 \tabularnewline
81 & 9.6 & 13.0558 & -3.45575 \tabularnewline
82 & 18.2 & 15.5622 & 2.63777 \tabularnewline
83 & 13.6 & 14.3415 & -0.741543 \tabularnewline
84 & 14.85 & 12.5844 & 2.26556 \tabularnewline
85 & 14.75 & 16.5683 & -1.81826 \tabularnewline
86 & 14.1 & 14.2462 & -0.146177 \tabularnewline
87 & 14.9 & 14.0727 & 0.8273 \tabularnewline
88 & 16.25 & 15.8924 & 0.357605 \tabularnewline
89 & 19.25 & 19.2274 & 0.0226253 \tabularnewline
90 & 13.6 & 13.1747 & 0.425325 \tabularnewline
91 & 13.6 & 16.1444 & -2.54436 \tabularnewline
92 & 15.65 & 16.0733 & -0.423314 \tabularnewline
93 & 12.75 & 13.8955 & -1.14551 \tabularnewline
94 & 14.6 & 13.0388 & 1.56123 \tabularnewline
95 & 9.85 & 9.85441 & -0.00440879 \tabularnewline
96 & 12.65 & 11.6687 & 0.981272 \tabularnewline
97 & 19.2 & 16.7609 & 2.43914 \tabularnewline
98 & 16.6 & 14.1435 & 2.45649 \tabularnewline
99 & 11.2 & 11.078 & 0.122037 \tabularnewline
100 & 15.25 & 15.891 & -0.640994 \tabularnewline
101 & 11.9 & 13.9938 & -2.0938 \tabularnewline
102 & 13.2 & 13.5092 & -0.309176 \tabularnewline
103 & 16.35 & 16.6303 & -0.280282 \tabularnewline
104 & 12.4 & 13.2316 & -0.831582 \tabularnewline
105 & 15.85 & 14.261 & 1.58905 \tabularnewline
106 & 18.15 & 17.34 & 0.810026 \tabularnewline
107 & 11.15 & 11.7175 & -0.56749 \tabularnewline
108 & 15.65 & 15.9699 & -0.319936 \tabularnewline
109 & 17.75 & 15.8962 & 1.85381 \tabularnewline
110 & 7.65 & 11.0779 & -3.42787 \tabularnewline
111 & 12.35 & 13.9369 & -1.58692 \tabularnewline
112 & 15.6 & 14.346 & 1.25402 \tabularnewline
113 & 19.3 & 17.3407 & 1.95929 \tabularnewline
114 & 15.2 & 11.4944 & 3.70565 \tabularnewline
115 & 17.1 & 16.0978 & 1.00215 \tabularnewline
116 & 15.6 & 13.504 & 2.09602 \tabularnewline
117 & 18.4 & 14.1245 & 4.27552 \tabularnewline
118 & 19.05 & 16.5306 & 2.51941 \tabularnewline
119 & 18.55 & 17.6472 & 0.902777 \tabularnewline
120 & 19.1 & 16.0949 & 3.00514 \tabularnewline
121 & 13.1 & 13.3969 & -0.296885 \tabularnewline
122 & 12.85 & 16.8333 & -3.98335 \tabularnewline
123 & 9.5 & 12.8424 & -3.34244 \tabularnewline
124 & 4.5 & 10.4046 & -5.9046 \tabularnewline
125 & 11.85 & 11.6215 & 0.228511 \tabularnewline
126 & 13.6 & 14.8599 & -1.25994 \tabularnewline
127 & 11.7 & 12.5169 & -0.816854 \tabularnewline
128 & 12.4 & 13.5195 & -1.11949 \tabularnewline
129 & 13.35 & 14.2885 & -0.938475 \tabularnewline
130 & 11.4 & 12.4959 & -1.09592 \tabularnewline
131 & 14.9 & 14.772 & 0.127961 \tabularnewline
132 & 19.9 & 17.8079 & 2.09208 \tabularnewline
133 & 11.2 & 13.7285 & -2.52849 \tabularnewline
134 & 14.6 & 14.9478 & -0.347766 \tabularnewline
135 & 17.6 & 17.8504 & -0.250354 \tabularnewline
136 & 14.05 & 13.4686 & 0.581438 \tabularnewline
137 & 16.1 & 15.1865 & 0.913506 \tabularnewline
138 & 13.35 & 14.8623 & -1.51225 \tabularnewline
139 & 11.85 & 15.25 & -3.39996 \tabularnewline
140 & 11.95 & 12.7051 & -0.755089 \tabularnewline
141 & 14.75 & 13.8939 & 0.856108 \tabularnewline
142 & 15.15 & 12.8148 & 2.33521 \tabularnewline
143 & 13.2 & 15.7505 & -2.55052 \tabularnewline
144 & 16.85 & 15.82 & 1.02999 \tabularnewline
145 & 7.85 & 12.2493 & -4.39925 \tabularnewline
146 & 7.7 & 12.6297 & -4.9297 \tabularnewline
147 & 12.6 & 14.7373 & -2.13726 \tabularnewline
148 & 7.85 & 15.1635 & -7.31346 \tabularnewline
149 & 10.95 & 11.2522 & -0.302173 \tabularnewline
150 & 12.35 & 14.2795 & -1.92946 \tabularnewline
151 & 9.95 & 13.5135 & -3.56346 \tabularnewline
152 & 14.9 & 14.1427 & 0.757268 \tabularnewline
153 & 16.65 & 15.1258 & 1.5242 \tabularnewline
154 & 13.4 & 12.5978 & 0.802154 \tabularnewline
155 & 13.95 & 14.4294 & -0.479437 \tabularnewline
156 & 15.7 & 15.2565 & 0.443509 \tabularnewline
157 & 16.85 & 15.5165 & 1.33355 \tabularnewline
158 & 10.95 & 11.8911 & -0.94107 \tabularnewline
159 & 15.35 & 16.0992 & -0.749151 \tabularnewline
160 & 12.2 & 12.7249 & -0.524852 \tabularnewline
161 & 15.1 & 14.4899 & 0.610073 \tabularnewline
162 & 17.75 & 15.5977 & 2.15229 \tabularnewline
163 & 15.2 & 14.9064 & 0.293554 \tabularnewline
164 & 14.6 & 15.1452 & -0.545173 \tabularnewline
165 & 16.65 & 15.7313 & 0.918683 \tabularnewline
166 & 8.1 & 9.54467 & -1.44467 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&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]10.5959[/C][C]-6.2459[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]10.7384[/C][C]1.96155[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.6074[/C][C]2.49263[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.0771[/C][C]1.7729[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.0889[/C][C]0.511142[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.7646[/C][C]0.835366[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]18.9633[/C][C]-1.86327[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.292[/C][C]1.80798[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]16.1192[/C][C]-0.0191581[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]12.0449[/C][C]1.30511[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.9309[/C][C]1.46911[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.38913[/C][C]5.31087[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.2922[/C][C]-2.6922[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]13.6681[/C][C]-1.06808[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.1384[/C][C]1.06156[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.3129[/C][C]0.287121[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.1678[/C][C]2.73219[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.0706[/C][C]1.02939[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.4989[/C][C]1.00111[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.1945[/C][C]-1.04448[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.7988[/C][C]0.951199[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]13.7006[/C][C]1.09942[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]11.9791[/C][C]0.470863[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.3772[/C][C]0.272805[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.5588[/C][C]2.79124[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.17202[/C][C]-0.572023[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]16.3378[/C][C]2.06219[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.3098[/C][C]1.79015[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.3059[/C][C]0.294077[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]16.7713[/C][C]0.978681[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]15.6177[/C][C]-0.367692[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]14.1267[/C][C]3.52334[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.5993[/C][C]-0.249284[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]16.781[/C][C]0.869034[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.8638[/C][C]-1.26379[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.6142[/C][C]-0.264184[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]15.9583[/C][C]-1.20828[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.0105[/C][C]1.23946[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]17.1287[/C][C]-7.22874[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.1282[/C][C]1.8718[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.5165[/C][C]1.73346[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]18.2329[/C][C]-1.3829[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]12.911[/C][C]1.68901[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.1089[/C][C]-0.258853[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]17.1059[/C][C]1.84413[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.3387[/C][C]1.26133[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.3252[/C][C]-1.47517[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]14.0003[/C][C]-2.25032[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]16.1737[/C][C]2.27626[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]13.7381[/C][C]2.16189[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.326[/C][C]-1.22595[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]7.90034[/C][C]8.19966[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]18.904[/C][C]0.995986[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.2174[/C][C]0.732593[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.0393[/C][C]2.4107[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]14.2174[/C][C]0.882608[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]15.4323[/C][C]-0.43231[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]13.4212[/C][C]-2.07121[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.2602[/C][C]1.68978[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]15.6198[/C][C]2.48021[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.5991[/C][C]-1.99907[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.7861[/C][C]-1.38606[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.7137[/C][C]-1.31368[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]14.6553[/C][C]2.94471[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]14.222[/C][C]-0.871953[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]17.3282[/C][C]1.77179[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]17.3103[/C][C]-1.96031[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]10.3521[/C][C]-2.75213[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.4478[/C][C]-1.0478[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]16.6302[/C][C]-2.73017[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]16.275[/C][C]2.82496[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]15.2617[/C][C]-0.011683[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]16.2496[/C][C]-3.34963[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]15.3355[/C][C]0.764533[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]15.2181[/C][C]2.13195[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]15.5674[/C][C]-2.41739[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]15.5799[/C][C]-3.42992[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]11.9413[/C][C]0.658733[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]12.4561[/C][C]-2.10607[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]14.4574[/C][C]0.942645[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]13.0558[/C][C]-3.45575[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]15.5622[/C][C]2.63777[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]14.3415[/C][C]-0.741543[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]12.5844[/C][C]2.26556[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]16.5683[/C][C]-1.81826[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]14.2462[/C][C]-0.146177[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]14.0727[/C][C]0.8273[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]15.8924[/C][C]0.357605[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.2274[/C][C]0.0226253[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]13.1747[/C][C]0.425325[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]16.1444[/C][C]-2.54436[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]16.0733[/C][C]-0.423314[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]13.8955[/C][C]-1.14551[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]13.0388[/C][C]1.56123[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]9.85441[/C][C]-0.00440879[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.6687[/C][C]0.981272[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]16.7609[/C][C]2.43914[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]14.1435[/C][C]2.45649[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.078[/C][C]0.122037[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]15.891[/C][C]-0.640994[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]13.9938[/C][C]-2.0938[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.5092[/C][C]-0.309176[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]16.6303[/C][C]-0.280282[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]13.2316[/C][C]-0.831582[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]14.261[/C][C]1.58905[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.34[/C][C]0.810026[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]11.7175[/C][C]-0.56749[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]15.9699[/C][C]-0.319936[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.8962[/C][C]1.85381[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.0779[/C][C]-3.42787[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]13.9369[/C][C]-1.58692[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]14.346[/C][C]1.25402[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.3407[/C][C]1.95929[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.4944[/C][C]3.70565[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]16.0978[/C][C]1.00215[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.504[/C][C]2.09602[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]14.1245[/C][C]4.27552[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.5306[/C][C]2.51941[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]17.6472[/C][C]0.902777[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]16.0949[/C][C]3.00514[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]13.3969[/C][C]-0.296885[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.8333[/C][C]-3.98335[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]12.8424[/C][C]-3.34244[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]10.4046[/C][C]-5.9046[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]11.6215[/C][C]0.228511[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]14.8599[/C][C]-1.25994[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]12.5169[/C][C]-0.816854[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]13.5195[/C][C]-1.11949[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]14.2885[/C][C]-0.938475[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.4959[/C][C]-1.09592[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]14.772[/C][C]0.127961[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.8079[/C][C]2.09208[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]13.7285[/C][C]-2.52849[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]14.9478[/C][C]-0.347766[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]17.8504[/C][C]-0.250354[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]13.4686[/C][C]0.581438[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]15.1865[/C][C]0.913506[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.8623[/C][C]-1.51225[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]15.25[/C][C]-3.39996[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]12.7051[/C][C]-0.755089[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]13.8939[/C][C]0.856108[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]12.8148[/C][C]2.33521[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]15.7505[/C][C]-2.55052[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]15.82[/C][C]1.02999[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]12.2493[/C][C]-4.39925[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]12.6297[/C][C]-4.9297[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]14.7373[/C][C]-2.13726[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]15.1635[/C][C]-7.31346[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]11.2522[/C][C]-0.302173[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]14.2795[/C][C]-1.92946[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.5135[/C][C]-3.56346[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]14.1427[/C][C]0.757268[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]15.1258[/C][C]1.5242[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.5978[/C][C]0.802154[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]14.4294[/C][C]-0.479437[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]15.2565[/C][C]0.443509[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]15.5165[/C][C]1.33355[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]11.8911[/C][C]-0.94107[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]16.0992[/C][C]-0.749151[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]12.7249[/C][C]-0.524852[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]14.4899[/C][C]0.610073[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.5977[/C][C]2.15229[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.9064[/C][C]0.293554[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]15.1452[/C][C]-0.545173[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]15.7313[/C][C]0.918683[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]9.54467[/C][C]-1.44467[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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
14.3510.5959-6.2459
212.710.73841.96155
318.115.60742.49263
417.8516.07711.7729
516.616.08890.511142
612.611.76460.835366
717.118.9633-1.86327
819.117.2921.80798
916.116.1192-0.0191581
1013.3512.04491.30511
1118.416.93091.46911
1214.79.389135.31087
1310.613.2922-2.6922
1412.613.6681-1.06808
1516.215.13841.06156
1613.613.31290.287121
1718.916.16782.73219
1814.113.07061.02939
1914.513.49891.00111
2016.1517.1945-1.04448
2114.7513.79880.951199
2214.813.70061.09942
2312.4511.97910.470863
2412.6512.37720.272805
2517.3514.55882.79124
268.69.17202-0.572023
2718.416.33782.06219
2816.114.30981.79015
2911.611.30590.294077
3017.7516.77130.978681
3115.2515.6177-0.367692
3217.6514.12673.52334
3316.3516.5993-0.249284
3417.6516.7810.869034
3513.614.8638-1.26379
3614.3514.6142-0.264184
3714.7515.9583-1.20828
3818.2517.01051.23946
399.917.1287-7.22874
401614.12821.8718
4118.2516.51651.73346
4216.8518.2329-1.3829
4314.612.9111.68901
4413.8514.1089-0.258853
4518.9517.10591.84413
4615.614.33871.26133
4714.8516.3252-1.47517
4811.7514.0003-2.25032
4918.4516.17372.27626
5015.913.73812.16189
5117.118.326-1.22595
5216.17.900348.19966
5319.918.9040.995986
5410.9510.21740.732593
5518.4516.03932.4107
5615.114.21740.882608
571515.4323-0.43231
5811.3513.4212-2.07121
5915.9514.26021.68978
6018.115.61982.48021
6114.616.5991-1.99907
6215.416.7861-1.38606
6315.416.7137-1.31368
6417.614.65532.94471
6513.3514.222-0.871953
6619.117.32821.77179
6715.3517.3103-1.96031
687.610.3521-2.75213
6913.414.4478-1.0478
7013.916.6302-2.73017
7119.116.2752.82496
7215.2515.2617-0.011683
7312.916.2496-3.34963
7416.115.33550.764533
7517.3515.21812.13195
7613.1515.5674-2.41739
7712.1515.5799-3.42992
7812.611.94130.658733
7910.3512.4561-2.10607
8015.414.45740.942645
819.613.0558-3.45575
8218.215.56222.63777
8313.614.3415-0.741543
8414.8512.58442.26556
8514.7516.5683-1.81826
8614.114.2462-0.146177
8714.914.07270.8273
8816.2515.89240.357605
8919.2519.22740.0226253
9013.613.17470.425325
9113.616.1444-2.54436
9215.6516.0733-0.423314
9312.7513.8955-1.14551
9414.613.03881.56123
959.859.85441-0.00440879
9612.6511.66870.981272
9719.216.76092.43914
9816.614.14352.45649
9911.211.0780.122037
10015.2515.891-0.640994
10111.913.9938-2.0938
10213.213.5092-0.309176
10316.3516.6303-0.280282
10412.413.2316-0.831582
10515.8514.2611.58905
10618.1517.340.810026
10711.1511.7175-0.56749
10815.6515.9699-0.319936
10917.7515.89621.85381
1107.6511.0779-3.42787
11112.3513.9369-1.58692
11215.614.3461.25402
11319.317.34071.95929
11415.211.49443.70565
11517.116.09781.00215
11615.613.5042.09602
11718.414.12454.27552
11819.0516.53062.51941
11918.5517.64720.902777
12019.116.09493.00514
12113.113.3969-0.296885
12212.8516.8333-3.98335
1239.512.8424-3.34244
1244.510.4046-5.9046
12511.8511.62150.228511
12613.614.8599-1.25994
12711.712.5169-0.816854
12812.413.5195-1.11949
12913.3514.2885-0.938475
13011.412.4959-1.09592
13114.914.7720.127961
13219.917.80792.09208
13311.213.7285-2.52849
13414.614.9478-0.347766
13517.617.8504-0.250354
13614.0513.46860.581438
13716.115.18650.913506
13813.3514.8623-1.51225
13911.8515.25-3.39996
14011.9512.7051-0.755089
14114.7513.89390.856108
14215.1512.81482.33521
14313.215.7505-2.55052
14416.8515.821.02999
1457.8512.2493-4.39925
1467.712.6297-4.9297
14712.614.7373-2.13726
1487.8515.1635-7.31346
14910.9511.2522-0.302173
15012.3514.2795-1.92946
1519.9513.5135-3.56346
15214.914.14270.757268
15316.6515.12581.5242
15413.412.59780.802154
15513.9514.4294-0.479437
15615.715.25650.443509
15716.8515.51651.33355
15810.9511.8911-0.94107
15915.3516.0992-0.749151
16012.212.7249-0.524852
16115.114.48990.610073
16217.7515.59772.15229
16315.214.90640.293554
16414.615.1452-0.545173
16516.6515.73130.918683
1668.19.54467-1.44467






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8569010.2861980.143099
100.7617930.4764140.238207
110.8107750.378450.189225
120.829080.341840.17092
130.8577510.2844970.142249
140.8390930.3218130.160907
150.7778740.4442530.222126
160.7025430.5949140.297457
170.6455750.7088510.354425
180.5712380.8575250.428762
190.4933480.9866960.506652
200.6497440.7005120.350256
210.5817760.8364470.418224
220.5082360.9835280.491764
230.4407140.8814270.559286
240.3721880.7443770.627812
250.4223410.8446820.577659
260.4103140.8206290.589686
270.3537310.7074620.646269
280.3907660.7815330.609234
290.335430.670860.66457
300.2818740.5637470.718126
310.2655990.5311980.734401
320.2693360.5386730.730664
330.2454080.4908160.754592
340.2192770.4385540.780723
350.18090.36180.8191
360.1484370.2968740.851563
370.1318780.2637560.868122
380.1125140.2250290.887486
390.72420.55160.2758
400.7009430.5981150.299057
410.672820.654360.32718
420.6350860.7298290.364914
430.5935330.8129330.406467
440.5619290.8761430.438071
450.5360640.9278720.463936
460.5024750.995050.497525
470.4759670.9519340.524033
480.4767180.9534350.523282
490.4824080.9648160.517592
500.4559780.9119560.544022
510.4144420.8288840.585558
520.8922340.2155330.107766
530.8785140.2429720.121486
540.8578880.2842230.142112
550.8548520.2902970.145148
560.830830.3383410.16917
570.8025670.3948650.197433
580.8053080.3893840.194692
590.7845380.4309250.215462
600.7908540.4182930.209146
610.8031040.3937910.196896
620.7826690.4346620.217331
630.7588420.4823150.241158
640.7719270.4561470.228073
650.7434970.5130050.256503
660.7360680.5278650.263932
670.7402070.5195860.259793
680.7667310.4665390.233269
690.7404950.519010.259505
700.7693010.4613980.230699
710.7964930.4070140.203507
720.76290.4741990.2371
730.8140610.3718770.185939
740.7873310.4253390.212669
750.782560.4348810.21744
760.8010910.3978170.198909
770.8578990.2842010.142101
780.839370.3212610.16063
790.8454670.3090650.154533
800.8218680.3562630.178132
810.8651490.2697030.134851
820.8721880.2556230.127812
830.8514780.2970440.148522
840.8587110.2825780.141289
850.8513820.2972350.148618
860.8247710.3504570.175229
870.7967510.4064990.203249
880.7644860.4710270.235514
890.7412670.5174660.258733
900.7075850.5848290.292415
910.7228090.5543810.277191
920.690630.6187390.30937
930.66320.6735990.3368
940.6427490.7145020.357251
950.6476770.7046450.352323
960.6218920.7562170.378108
970.6161620.7676760.383838
980.6388610.7222780.361139
990.6181830.7636330.381817
1000.5772660.8454670.422734
1010.5614640.8770710.438536
1020.5154050.969190.484595
1030.470880.941760.52912
1040.4311680.8623360.568832
1050.4183370.8366730.581663
1060.3822430.7644860.617757
1070.351440.702880.64856
1080.315050.63010.68495
1090.3044180.6088370.695582
1100.3608950.721790.639105
1110.3313720.6627440.668628
1120.3331820.6663640.666818
1130.3186990.6373970.681301
1140.3967670.7935340.603233
1150.3596810.7193620.640319
1160.3954460.7908920.604554
1170.7258420.5483170.274158
1180.7544310.4911380.245569
1190.72030.55940.2797
1200.7924220.4151550.207578
1210.7711560.4576880.228844
1220.7988670.4022650.201133
1230.7916860.4166270.208314
1240.8762260.2475480.123774
1250.8480980.3038040.151902
1260.8202210.3595570.179779
1270.7881640.4236720.211836
1280.748780.5024390.25122
1290.7028920.5942160.297108
1300.656040.6879210.34396
1310.6138140.7723720.386186
1320.5677050.864590.432295
1330.5493420.9013160.450658
1340.4987050.997410.501295
1350.4371740.8743480.562826
1360.4524770.9049540.547523
1370.4467780.8935570.553222
1380.4164740.8329480.583526
1390.3775150.7550290.622485
1400.3472820.6945650.652718
1410.3300570.6601130.669943
1420.3193370.6386730.680663
1430.281880.563760.71812
1440.229590.459180.77041
1450.3075880.6151760.692412
1460.4144980.8289960.585502
1470.4109830.8219660.589017
1480.9768460.04630750.0231537
1490.9679150.06417060.0320853
1500.9811280.03774480.0188724
1510.9972910.005417320.00270866
1520.992970.01405950.00702974
1530.9880990.02380110.0119005
1540.9815060.03698820.0184941
1550.9737810.05243860.0262193
1560.944670.110660.0553301
1570.8862030.2275930.113797

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.856901 & 0.286198 & 0.143099 \tabularnewline
10 & 0.761793 & 0.476414 & 0.238207 \tabularnewline
11 & 0.810775 & 0.37845 & 0.189225 \tabularnewline
12 & 0.82908 & 0.34184 & 0.17092 \tabularnewline
13 & 0.857751 & 0.284497 & 0.142249 \tabularnewline
14 & 0.839093 & 0.321813 & 0.160907 \tabularnewline
15 & 0.777874 & 0.444253 & 0.222126 \tabularnewline
16 & 0.702543 & 0.594914 & 0.297457 \tabularnewline
17 & 0.645575 & 0.708851 & 0.354425 \tabularnewline
18 & 0.571238 & 0.857525 & 0.428762 \tabularnewline
19 & 0.493348 & 0.986696 & 0.506652 \tabularnewline
20 & 0.649744 & 0.700512 & 0.350256 \tabularnewline
21 & 0.581776 & 0.836447 & 0.418224 \tabularnewline
22 & 0.508236 & 0.983528 & 0.491764 \tabularnewline
23 & 0.440714 & 0.881427 & 0.559286 \tabularnewline
24 & 0.372188 & 0.744377 & 0.627812 \tabularnewline
25 & 0.422341 & 0.844682 & 0.577659 \tabularnewline
26 & 0.410314 & 0.820629 & 0.589686 \tabularnewline
27 & 0.353731 & 0.707462 & 0.646269 \tabularnewline
28 & 0.390766 & 0.781533 & 0.609234 \tabularnewline
29 & 0.33543 & 0.67086 & 0.66457 \tabularnewline
30 & 0.281874 & 0.563747 & 0.718126 \tabularnewline
31 & 0.265599 & 0.531198 & 0.734401 \tabularnewline
32 & 0.269336 & 0.538673 & 0.730664 \tabularnewline
33 & 0.245408 & 0.490816 & 0.754592 \tabularnewline
34 & 0.219277 & 0.438554 & 0.780723 \tabularnewline
35 & 0.1809 & 0.3618 & 0.8191 \tabularnewline
36 & 0.148437 & 0.296874 & 0.851563 \tabularnewline
37 & 0.131878 & 0.263756 & 0.868122 \tabularnewline
38 & 0.112514 & 0.225029 & 0.887486 \tabularnewline
39 & 0.7242 & 0.5516 & 0.2758 \tabularnewline
40 & 0.700943 & 0.598115 & 0.299057 \tabularnewline
41 & 0.67282 & 0.65436 & 0.32718 \tabularnewline
42 & 0.635086 & 0.729829 & 0.364914 \tabularnewline
43 & 0.593533 & 0.812933 & 0.406467 \tabularnewline
44 & 0.561929 & 0.876143 & 0.438071 \tabularnewline
45 & 0.536064 & 0.927872 & 0.463936 \tabularnewline
46 & 0.502475 & 0.99505 & 0.497525 \tabularnewline
47 & 0.475967 & 0.951934 & 0.524033 \tabularnewline
48 & 0.476718 & 0.953435 & 0.523282 \tabularnewline
49 & 0.482408 & 0.964816 & 0.517592 \tabularnewline
50 & 0.455978 & 0.911956 & 0.544022 \tabularnewline
51 & 0.414442 & 0.828884 & 0.585558 \tabularnewline
52 & 0.892234 & 0.215533 & 0.107766 \tabularnewline
53 & 0.878514 & 0.242972 & 0.121486 \tabularnewline
54 & 0.857888 & 0.284223 & 0.142112 \tabularnewline
55 & 0.854852 & 0.290297 & 0.145148 \tabularnewline
56 & 0.83083 & 0.338341 & 0.16917 \tabularnewline
57 & 0.802567 & 0.394865 & 0.197433 \tabularnewline
58 & 0.805308 & 0.389384 & 0.194692 \tabularnewline
59 & 0.784538 & 0.430925 & 0.215462 \tabularnewline
60 & 0.790854 & 0.418293 & 0.209146 \tabularnewline
61 & 0.803104 & 0.393791 & 0.196896 \tabularnewline
62 & 0.782669 & 0.434662 & 0.217331 \tabularnewline
63 & 0.758842 & 0.482315 & 0.241158 \tabularnewline
64 & 0.771927 & 0.456147 & 0.228073 \tabularnewline
65 & 0.743497 & 0.513005 & 0.256503 \tabularnewline
66 & 0.736068 & 0.527865 & 0.263932 \tabularnewline
67 & 0.740207 & 0.519586 & 0.259793 \tabularnewline
68 & 0.766731 & 0.466539 & 0.233269 \tabularnewline
69 & 0.740495 & 0.51901 & 0.259505 \tabularnewline
70 & 0.769301 & 0.461398 & 0.230699 \tabularnewline
71 & 0.796493 & 0.407014 & 0.203507 \tabularnewline
72 & 0.7629 & 0.474199 & 0.2371 \tabularnewline
73 & 0.814061 & 0.371877 & 0.185939 \tabularnewline
74 & 0.787331 & 0.425339 & 0.212669 \tabularnewline
75 & 0.78256 & 0.434881 & 0.21744 \tabularnewline
76 & 0.801091 & 0.397817 & 0.198909 \tabularnewline
77 & 0.857899 & 0.284201 & 0.142101 \tabularnewline
78 & 0.83937 & 0.321261 & 0.16063 \tabularnewline
79 & 0.845467 & 0.309065 & 0.154533 \tabularnewline
80 & 0.821868 & 0.356263 & 0.178132 \tabularnewline
81 & 0.865149 & 0.269703 & 0.134851 \tabularnewline
82 & 0.872188 & 0.255623 & 0.127812 \tabularnewline
83 & 0.851478 & 0.297044 & 0.148522 \tabularnewline
84 & 0.858711 & 0.282578 & 0.141289 \tabularnewline
85 & 0.851382 & 0.297235 & 0.148618 \tabularnewline
86 & 0.824771 & 0.350457 & 0.175229 \tabularnewline
87 & 0.796751 & 0.406499 & 0.203249 \tabularnewline
88 & 0.764486 & 0.471027 & 0.235514 \tabularnewline
89 & 0.741267 & 0.517466 & 0.258733 \tabularnewline
90 & 0.707585 & 0.584829 & 0.292415 \tabularnewline
91 & 0.722809 & 0.554381 & 0.277191 \tabularnewline
92 & 0.69063 & 0.618739 & 0.30937 \tabularnewline
93 & 0.6632 & 0.673599 & 0.3368 \tabularnewline
94 & 0.642749 & 0.714502 & 0.357251 \tabularnewline
95 & 0.647677 & 0.704645 & 0.352323 \tabularnewline
96 & 0.621892 & 0.756217 & 0.378108 \tabularnewline
97 & 0.616162 & 0.767676 & 0.383838 \tabularnewline
98 & 0.638861 & 0.722278 & 0.361139 \tabularnewline
99 & 0.618183 & 0.763633 & 0.381817 \tabularnewline
100 & 0.577266 & 0.845467 & 0.422734 \tabularnewline
101 & 0.561464 & 0.877071 & 0.438536 \tabularnewline
102 & 0.515405 & 0.96919 & 0.484595 \tabularnewline
103 & 0.47088 & 0.94176 & 0.52912 \tabularnewline
104 & 0.431168 & 0.862336 & 0.568832 \tabularnewline
105 & 0.418337 & 0.836673 & 0.581663 \tabularnewline
106 & 0.382243 & 0.764486 & 0.617757 \tabularnewline
107 & 0.35144 & 0.70288 & 0.64856 \tabularnewline
108 & 0.31505 & 0.6301 & 0.68495 \tabularnewline
109 & 0.304418 & 0.608837 & 0.695582 \tabularnewline
110 & 0.360895 & 0.72179 & 0.639105 \tabularnewline
111 & 0.331372 & 0.662744 & 0.668628 \tabularnewline
112 & 0.333182 & 0.666364 & 0.666818 \tabularnewline
113 & 0.318699 & 0.637397 & 0.681301 \tabularnewline
114 & 0.396767 & 0.793534 & 0.603233 \tabularnewline
115 & 0.359681 & 0.719362 & 0.640319 \tabularnewline
116 & 0.395446 & 0.790892 & 0.604554 \tabularnewline
117 & 0.725842 & 0.548317 & 0.274158 \tabularnewline
118 & 0.754431 & 0.491138 & 0.245569 \tabularnewline
119 & 0.7203 & 0.5594 & 0.2797 \tabularnewline
120 & 0.792422 & 0.415155 & 0.207578 \tabularnewline
121 & 0.771156 & 0.457688 & 0.228844 \tabularnewline
122 & 0.798867 & 0.402265 & 0.201133 \tabularnewline
123 & 0.791686 & 0.416627 & 0.208314 \tabularnewline
124 & 0.876226 & 0.247548 & 0.123774 \tabularnewline
125 & 0.848098 & 0.303804 & 0.151902 \tabularnewline
126 & 0.820221 & 0.359557 & 0.179779 \tabularnewline
127 & 0.788164 & 0.423672 & 0.211836 \tabularnewline
128 & 0.74878 & 0.502439 & 0.25122 \tabularnewline
129 & 0.702892 & 0.594216 & 0.297108 \tabularnewline
130 & 0.65604 & 0.687921 & 0.34396 \tabularnewline
131 & 0.613814 & 0.772372 & 0.386186 \tabularnewline
132 & 0.567705 & 0.86459 & 0.432295 \tabularnewline
133 & 0.549342 & 0.901316 & 0.450658 \tabularnewline
134 & 0.498705 & 0.99741 & 0.501295 \tabularnewline
135 & 0.437174 & 0.874348 & 0.562826 \tabularnewline
136 & 0.452477 & 0.904954 & 0.547523 \tabularnewline
137 & 0.446778 & 0.893557 & 0.553222 \tabularnewline
138 & 0.416474 & 0.832948 & 0.583526 \tabularnewline
139 & 0.377515 & 0.755029 & 0.622485 \tabularnewline
140 & 0.347282 & 0.694565 & 0.652718 \tabularnewline
141 & 0.330057 & 0.660113 & 0.669943 \tabularnewline
142 & 0.319337 & 0.638673 & 0.680663 \tabularnewline
143 & 0.28188 & 0.56376 & 0.71812 \tabularnewline
144 & 0.22959 & 0.45918 & 0.77041 \tabularnewline
145 & 0.307588 & 0.615176 & 0.692412 \tabularnewline
146 & 0.414498 & 0.828996 & 0.585502 \tabularnewline
147 & 0.410983 & 0.821966 & 0.589017 \tabularnewline
148 & 0.976846 & 0.0463075 & 0.0231537 \tabularnewline
149 & 0.967915 & 0.0641706 & 0.0320853 \tabularnewline
150 & 0.981128 & 0.0377448 & 0.0188724 \tabularnewline
151 & 0.997291 & 0.00541732 & 0.00270866 \tabularnewline
152 & 0.99297 & 0.0140595 & 0.00702974 \tabularnewline
153 & 0.988099 & 0.0238011 & 0.0119005 \tabularnewline
154 & 0.981506 & 0.0369882 & 0.0184941 \tabularnewline
155 & 0.973781 & 0.0524386 & 0.0262193 \tabularnewline
156 & 0.94467 & 0.11066 & 0.0553301 \tabularnewline
157 & 0.886203 & 0.227593 & 0.113797 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&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]9[/C][C]0.856901[/C][C]0.286198[/C][C]0.143099[/C][/ROW]
[ROW][C]10[/C][C]0.761793[/C][C]0.476414[/C][C]0.238207[/C][/ROW]
[ROW][C]11[/C][C]0.810775[/C][C]0.37845[/C][C]0.189225[/C][/ROW]
[ROW][C]12[/C][C]0.82908[/C][C]0.34184[/C][C]0.17092[/C][/ROW]
[ROW][C]13[/C][C]0.857751[/C][C]0.284497[/C][C]0.142249[/C][/ROW]
[ROW][C]14[/C][C]0.839093[/C][C]0.321813[/C][C]0.160907[/C][/ROW]
[ROW][C]15[/C][C]0.777874[/C][C]0.444253[/C][C]0.222126[/C][/ROW]
[ROW][C]16[/C][C]0.702543[/C][C]0.594914[/C][C]0.297457[/C][/ROW]
[ROW][C]17[/C][C]0.645575[/C][C]0.708851[/C][C]0.354425[/C][/ROW]
[ROW][C]18[/C][C]0.571238[/C][C]0.857525[/C][C]0.428762[/C][/ROW]
[ROW][C]19[/C][C]0.493348[/C][C]0.986696[/C][C]0.506652[/C][/ROW]
[ROW][C]20[/C][C]0.649744[/C][C]0.700512[/C][C]0.350256[/C][/ROW]
[ROW][C]21[/C][C]0.581776[/C][C]0.836447[/C][C]0.418224[/C][/ROW]
[ROW][C]22[/C][C]0.508236[/C][C]0.983528[/C][C]0.491764[/C][/ROW]
[ROW][C]23[/C][C]0.440714[/C][C]0.881427[/C][C]0.559286[/C][/ROW]
[ROW][C]24[/C][C]0.372188[/C][C]0.744377[/C][C]0.627812[/C][/ROW]
[ROW][C]25[/C][C]0.422341[/C][C]0.844682[/C][C]0.577659[/C][/ROW]
[ROW][C]26[/C][C]0.410314[/C][C]0.820629[/C][C]0.589686[/C][/ROW]
[ROW][C]27[/C][C]0.353731[/C][C]0.707462[/C][C]0.646269[/C][/ROW]
[ROW][C]28[/C][C]0.390766[/C][C]0.781533[/C][C]0.609234[/C][/ROW]
[ROW][C]29[/C][C]0.33543[/C][C]0.67086[/C][C]0.66457[/C][/ROW]
[ROW][C]30[/C][C]0.281874[/C][C]0.563747[/C][C]0.718126[/C][/ROW]
[ROW][C]31[/C][C]0.265599[/C][C]0.531198[/C][C]0.734401[/C][/ROW]
[ROW][C]32[/C][C]0.269336[/C][C]0.538673[/C][C]0.730664[/C][/ROW]
[ROW][C]33[/C][C]0.245408[/C][C]0.490816[/C][C]0.754592[/C][/ROW]
[ROW][C]34[/C][C]0.219277[/C][C]0.438554[/C][C]0.780723[/C][/ROW]
[ROW][C]35[/C][C]0.1809[/C][C]0.3618[/C][C]0.8191[/C][/ROW]
[ROW][C]36[/C][C]0.148437[/C][C]0.296874[/C][C]0.851563[/C][/ROW]
[ROW][C]37[/C][C]0.131878[/C][C]0.263756[/C][C]0.868122[/C][/ROW]
[ROW][C]38[/C][C]0.112514[/C][C]0.225029[/C][C]0.887486[/C][/ROW]
[ROW][C]39[/C][C]0.7242[/C][C]0.5516[/C][C]0.2758[/C][/ROW]
[ROW][C]40[/C][C]0.700943[/C][C]0.598115[/C][C]0.299057[/C][/ROW]
[ROW][C]41[/C][C]0.67282[/C][C]0.65436[/C][C]0.32718[/C][/ROW]
[ROW][C]42[/C][C]0.635086[/C][C]0.729829[/C][C]0.364914[/C][/ROW]
[ROW][C]43[/C][C]0.593533[/C][C]0.812933[/C][C]0.406467[/C][/ROW]
[ROW][C]44[/C][C]0.561929[/C][C]0.876143[/C][C]0.438071[/C][/ROW]
[ROW][C]45[/C][C]0.536064[/C][C]0.927872[/C][C]0.463936[/C][/ROW]
[ROW][C]46[/C][C]0.502475[/C][C]0.99505[/C][C]0.497525[/C][/ROW]
[ROW][C]47[/C][C]0.475967[/C][C]0.951934[/C][C]0.524033[/C][/ROW]
[ROW][C]48[/C][C]0.476718[/C][C]0.953435[/C][C]0.523282[/C][/ROW]
[ROW][C]49[/C][C]0.482408[/C][C]0.964816[/C][C]0.517592[/C][/ROW]
[ROW][C]50[/C][C]0.455978[/C][C]0.911956[/C][C]0.544022[/C][/ROW]
[ROW][C]51[/C][C]0.414442[/C][C]0.828884[/C][C]0.585558[/C][/ROW]
[ROW][C]52[/C][C]0.892234[/C][C]0.215533[/C][C]0.107766[/C][/ROW]
[ROW][C]53[/C][C]0.878514[/C][C]0.242972[/C][C]0.121486[/C][/ROW]
[ROW][C]54[/C][C]0.857888[/C][C]0.284223[/C][C]0.142112[/C][/ROW]
[ROW][C]55[/C][C]0.854852[/C][C]0.290297[/C][C]0.145148[/C][/ROW]
[ROW][C]56[/C][C]0.83083[/C][C]0.338341[/C][C]0.16917[/C][/ROW]
[ROW][C]57[/C][C]0.802567[/C][C]0.394865[/C][C]0.197433[/C][/ROW]
[ROW][C]58[/C][C]0.805308[/C][C]0.389384[/C][C]0.194692[/C][/ROW]
[ROW][C]59[/C][C]0.784538[/C][C]0.430925[/C][C]0.215462[/C][/ROW]
[ROW][C]60[/C][C]0.790854[/C][C]0.418293[/C][C]0.209146[/C][/ROW]
[ROW][C]61[/C][C]0.803104[/C][C]0.393791[/C][C]0.196896[/C][/ROW]
[ROW][C]62[/C][C]0.782669[/C][C]0.434662[/C][C]0.217331[/C][/ROW]
[ROW][C]63[/C][C]0.758842[/C][C]0.482315[/C][C]0.241158[/C][/ROW]
[ROW][C]64[/C][C]0.771927[/C][C]0.456147[/C][C]0.228073[/C][/ROW]
[ROW][C]65[/C][C]0.743497[/C][C]0.513005[/C][C]0.256503[/C][/ROW]
[ROW][C]66[/C][C]0.736068[/C][C]0.527865[/C][C]0.263932[/C][/ROW]
[ROW][C]67[/C][C]0.740207[/C][C]0.519586[/C][C]0.259793[/C][/ROW]
[ROW][C]68[/C][C]0.766731[/C][C]0.466539[/C][C]0.233269[/C][/ROW]
[ROW][C]69[/C][C]0.740495[/C][C]0.51901[/C][C]0.259505[/C][/ROW]
[ROW][C]70[/C][C]0.769301[/C][C]0.461398[/C][C]0.230699[/C][/ROW]
[ROW][C]71[/C][C]0.796493[/C][C]0.407014[/C][C]0.203507[/C][/ROW]
[ROW][C]72[/C][C]0.7629[/C][C]0.474199[/C][C]0.2371[/C][/ROW]
[ROW][C]73[/C][C]0.814061[/C][C]0.371877[/C][C]0.185939[/C][/ROW]
[ROW][C]74[/C][C]0.787331[/C][C]0.425339[/C][C]0.212669[/C][/ROW]
[ROW][C]75[/C][C]0.78256[/C][C]0.434881[/C][C]0.21744[/C][/ROW]
[ROW][C]76[/C][C]0.801091[/C][C]0.397817[/C][C]0.198909[/C][/ROW]
[ROW][C]77[/C][C]0.857899[/C][C]0.284201[/C][C]0.142101[/C][/ROW]
[ROW][C]78[/C][C]0.83937[/C][C]0.321261[/C][C]0.16063[/C][/ROW]
[ROW][C]79[/C][C]0.845467[/C][C]0.309065[/C][C]0.154533[/C][/ROW]
[ROW][C]80[/C][C]0.821868[/C][C]0.356263[/C][C]0.178132[/C][/ROW]
[ROW][C]81[/C][C]0.865149[/C][C]0.269703[/C][C]0.134851[/C][/ROW]
[ROW][C]82[/C][C]0.872188[/C][C]0.255623[/C][C]0.127812[/C][/ROW]
[ROW][C]83[/C][C]0.851478[/C][C]0.297044[/C][C]0.148522[/C][/ROW]
[ROW][C]84[/C][C]0.858711[/C][C]0.282578[/C][C]0.141289[/C][/ROW]
[ROW][C]85[/C][C]0.851382[/C][C]0.297235[/C][C]0.148618[/C][/ROW]
[ROW][C]86[/C][C]0.824771[/C][C]0.350457[/C][C]0.175229[/C][/ROW]
[ROW][C]87[/C][C]0.796751[/C][C]0.406499[/C][C]0.203249[/C][/ROW]
[ROW][C]88[/C][C]0.764486[/C][C]0.471027[/C][C]0.235514[/C][/ROW]
[ROW][C]89[/C][C]0.741267[/C][C]0.517466[/C][C]0.258733[/C][/ROW]
[ROW][C]90[/C][C]0.707585[/C][C]0.584829[/C][C]0.292415[/C][/ROW]
[ROW][C]91[/C][C]0.722809[/C][C]0.554381[/C][C]0.277191[/C][/ROW]
[ROW][C]92[/C][C]0.69063[/C][C]0.618739[/C][C]0.30937[/C][/ROW]
[ROW][C]93[/C][C]0.6632[/C][C]0.673599[/C][C]0.3368[/C][/ROW]
[ROW][C]94[/C][C]0.642749[/C][C]0.714502[/C][C]0.357251[/C][/ROW]
[ROW][C]95[/C][C]0.647677[/C][C]0.704645[/C][C]0.352323[/C][/ROW]
[ROW][C]96[/C][C]0.621892[/C][C]0.756217[/C][C]0.378108[/C][/ROW]
[ROW][C]97[/C][C]0.616162[/C][C]0.767676[/C][C]0.383838[/C][/ROW]
[ROW][C]98[/C][C]0.638861[/C][C]0.722278[/C][C]0.361139[/C][/ROW]
[ROW][C]99[/C][C]0.618183[/C][C]0.763633[/C][C]0.381817[/C][/ROW]
[ROW][C]100[/C][C]0.577266[/C][C]0.845467[/C][C]0.422734[/C][/ROW]
[ROW][C]101[/C][C]0.561464[/C][C]0.877071[/C][C]0.438536[/C][/ROW]
[ROW][C]102[/C][C]0.515405[/C][C]0.96919[/C][C]0.484595[/C][/ROW]
[ROW][C]103[/C][C]0.47088[/C][C]0.94176[/C][C]0.52912[/C][/ROW]
[ROW][C]104[/C][C]0.431168[/C][C]0.862336[/C][C]0.568832[/C][/ROW]
[ROW][C]105[/C][C]0.418337[/C][C]0.836673[/C][C]0.581663[/C][/ROW]
[ROW][C]106[/C][C]0.382243[/C][C]0.764486[/C][C]0.617757[/C][/ROW]
[ROW][C]107[/C][C]0.35144[/C][C]0.70288[/C][C]0.64856[/C][/ROW]
[ROW][C]108[/C][C]0.31505[/C][C]0.6301[/C][C]0.68495[/C][/ROW]
[ROW][C]109[/C][C]0.304418[/C][C]0.608837[/C][C]0.695582[/C][/ROW]
[ROW][C]110[/C][C]0.360895[/C][C]0.72179[/C][C]0.639105[/C][/ROW]
[ROW][C]111[/C][C]0.331372[/C][C]0.662744[/C][C]0.668628[/C][/ROW]
[ROW][C]112[/C][C]0.333182[/C][C]0.666364[/C][C]0.666818[/C][/ROW]
[ROW][C]113[/C][C]0.318699[/C][C]0.637397[/C][C]0.681301[/C][/ROW]
[ROW][C]114[/C][C]0.396767[/C][C]0.793534[/C][C]0.603233[/C][/ROW]
[ROW][C]115[/C][C]0.359681[/C][C]0.719362[/C][C]0.640319[/C][/ROW]
[ROW][C]116[/C][C]0.395446[/C][C]0.790892[/C][C]0.604554[/C][/ROW]
[ROW][C]117[/C][C]0.725842[/C][C]0.548317[/C][C]0.274158[/C][/ROW]
[ROW][C]118[/C][C]0.754431[/C][C]0.491138[/C][C]0.245569[/C][/ROW]
[ROW][C]119[/C][C]0.7203[/C][C]0.5594[/C][C]0.2797[/C][/ROW]
[ROW][C]120[/C][C]0.792422[/C][C]0.415155[/C][C]0.207578[/C][/ROW]
[ROW][C]121[/C][C]0.771156[/C][C]0.457688[/C][C]0.228844[/C][/ROW]
[ROW][C]122[/C][C]0.798867[/C][C]0.402265[/C][C]0.201133[/C][/ROW]
[ROW][C]123[/C][C]0.791686[/C][C]0.416627[/C][C]0.208314[/C][/ROW]
[ROW][C]124[/C][C]0.876226[/C][C]0.247548[/C][C]0.123774[/C][/ROW]
[ROW][C]125[/C][C]0.848098[/C][C]0.303804[/C][C]0.151902[/C][/ROW]
[ROW][C]126[/C][C]0.820221[/C][C]0.359557[/C][C]0.179779[/C][/ROW]
[ROW][C]127[/C][C]0.788164[/C][C]0.423672[/C][C]0.211836[/C][/ROW]
[ROW][C]128[/C][C]0.74878[/C][C]0.502439[/C][C]0.25122[/C][/ROW]
[ROW][C]129[/C][C]0.702892[/C][C]0.594216[/C][C]0.297108[/C][/ROW]
[ROW][C]130[/C][C]0.65604[/C][C]0.687921[/C][C]0.34396[/C][/ROW]
[ROW][C]131[/C][C]0.613814[/C][C]0.772372[/C][C]0.386186[/C][/ROW]
[ROW][C]132[/C][C]0.567705[/C][C]0.86459[/C][C]0.432295[/C][/ROW]
[ROW][C]133[/C][C]0.549342[/C][C]0.901316[/C][C]0.450658[/C][/ROW]
[ROW][C]134[/C][C]0.498705[/C][C]0.99741[/C][C]0.501295[/C][/ROW]
[ROW][C]135[/C][C]0.437174[/C][C]0.874348[/C][C]0.562826[/C][/ROW]
[ROW][C]136[/C][C]0.452477[/C][C]0.904954[/C][C]0.547523[/C][/ROW]
[ROW][C]137[/C][C]0.446778[/C][C]0.893557[/C][C]0.553222[/C][/ROW]
[ROW][C]138[/C][C]0.416474[/C][C]0.832948[/C][C]0.583526[/C][/ROW]
[ROW][C]139[/C][C]0.377515[/C][C]0.755029[/C][C]0.622485[/C][/ROW]
[ROW][C]140[/C][C]0.347282[/C][C]0.694565[/C][C]0.652718[/C][/ROW]
[ROW][C]141[/C][C]0.330057[/C][C]0.660113[/C][C]0.669943[/C][/ROW]
[ROW][C]142[/C][C]0.319337[/C][C]0.638673[/C][C]0.680663[/C][/ROW]
[ROW][C]143[/C][C]0.28188[/C][C]0.56376[/C][C]0.71812[/C][/ROW]
[ROW][C]144[/C][C]0.22959[/C][C]0.45918[/C][C]0.77041[/C][/ROW]
[ROW][C]145[/C][C]0.307588[/C][C]0.615176[/C][C]0.692412[/C][/ROW]
[ROW][C]146[/C][C]0.414498[/C][C]0.828996[/C][C]0.585502[/C][/ROW]
[ROW][C]147[/C][C]0.410983[/C][C]0.821966[/C][C]0.589017[/C][/ROW]
[ROW][C]148[/C][C]0.976846[/C][C]0.0463075[/C][C]0.0231537[/C][/ROW]
[ROW][C]149[/C][C]0.967915[/C][C]0.0641706[/C][C]0.0320853[/C][/ROW]
[ROW][C]150[/C][C]0.981128[/C][C]0.0377448[/C][C]0.0188724[/C][/ROW]
[ROW][C]151[/C][C]0.997291[/C][C]0.00541732[/C][C]0.00270866[/C][/ROW]
[ROW][C]152[/C][C]0.99297[/C][C]0.0140595[/C][C]0.00702974[/C][/ROW]
[ROW][C]153[/C][C]0.988099[/C][C]0.0238011[/C][C]0.0119005[/C][/ROW]
[ROW][C]154[/C][C]0.981506[/C][C]0.0369882[/C][C]0.0184941[/C][/ROW]
[ROW][C]155[/C][C]0.973781[/C][C]0.0524386[/C][C]0.0262193[/C][/ROW]
[ROW][C]156[/C][C]0.94467[/C][C]0.11066[/C][C]0.0553301[/C][/ROW]
[ROW][C]157[/C][C]0.886203[/C][C]0.227593[/C][C]0.113797[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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
90.8569010.2861980.143099
100.7617930.4764140.238207
110.8107750.378450.189225
120.829080.341840.17092
130.8577510.2844970.142249
140.8390930.3218130.160907
150.7778740.4442530.222126
160.7025430.5949140.297457
170.6455750.7088510.354425
180.5712380.8575250.428762
190.4933480.9866960.506652
200.6497440.7005120.350256
210.5817760.8364470.418224
220.5082360.9835280.491764
230.4407140.8814270.559286
240.3721880.7443770.627812
250.4223410.8446820.577659
260.4103140.8206290.589686
270.3537310.7074620.646269
280.3907660.7815330.609234
290.335430.670860.66457
300.2818740.5637470.718126
310.2655990.5311980.734401
320.2693360.5386730.730664
330.2454080.4908160.754592
340.2192770.4385540.780723
350.18090.36180.8191
360.1484370.2968740.851563
370.1318780.2637560.868122
380.1125140.2250290.887486
390.72420.55160.2758
400.7009430.5981150.299057
410.672820.654360.32718
420.6350860.7298290.364914
430.5935330.8129330.406467
440.5619290.8761430.438071
450.5360640.9278720.463936
460.5024750.995050.497525
470.4759670.9519340.524033
480.4767180.9534350.523282
490.4824080.9648160.517592
500.4559780.9119560.544022
510.4144420.8288840.585558
520.8922340.2155330.107766
530.8785140.2429720.121486
540.8578880.2842230.142112
550.8548520.2902970.145148
560.830830.3383410.16917
570.8025670.3948650.197433
580.8053080.3893840.194692
590.7845380.4309250.215462
600.7908540.4182930.209146
610.8031040.3937910.196896
620.7826690.4346620.217331
630.7588420.4823150.241158
640.7719270.4561470.228073
650.7434970.5130050.256503
660.7360680.5278650.263932
670.7402070.5195860.259793
680.7667310.4665390.233269
690.7404950.519010.259505
700.7693010.4613980.230699
710.7964930.4070140.203507
720.76290.4741990.2371
730.8140610.3718770.185939
740.7873310.4253390.212669
750.782560.4348810.21744
760.8010910.3978170.198909
770.8578990.2842010.142101
780.839370.3212610.16063
790.8454670.3090650.154533
800.8218680.3562630.178132
810.8651490.2697030.134851
820.8721880.2556230.127812
830.8514780.2970440.148522
840.8587110.2825780.141289
850.8513820.2972350.148618
860.8247710.3504570.175229
870.7967510.4064990.203249
880.7644860.4710270.235514
890.7412670.5174660.258733
900.7075850.5848290.292415
910.7228090.5543810.277191
920.690630.6187390.30937
930.66320.6735990.3368
940.6427490.7145020.357251
950.6476770.7046450.352323
960.6218920.7562170.378108
970.6161620.7676760.383838
980.6388610.7222780.361139
990.6181830.7636330.381817
1000.5772660.8454670.422734
1010.5614640.8770710.438536
1020.5154050.969190.484595
1030.470880.941760.52912
1040.4311680.8623360.568832
1050.4183370.8366730.581663
1060.3822430.7644860.617757
1070.351440.702880.64856
1080.315050.63010.68495
1090.3044180.6088370.695582
1100.3608950.721790.639105
1110.3313720.6627440.668628
1120.3331820.6663640.666818
1130.3186990.6373970.681301
1140.3967670.7935340.603233
1150.3596810.7193620.640319
1160.3954460.7908920.604554
1170.7258420.5483170.274158
1180.7544310.4911380.245569
1190.72030.55940.2797
1200.7924220.4151550.207578
1210.7711560.4576880.228844
1220.7988670.4022650.201133
1230.7916860.4166270.208314
1240.8762260.2475480.123774
1250.8480980.3038040.151902
1260.8202210.3595570.179779
1270.7881640.4236720.211836
1280.748780.5024390.25122
1290.7028920.5942160.297108
1300.656040.6879210.34396
1310.6138140.7723720.386186
1320.5677050.864590.432295
1330.5493420.9013160.450658
1340.4987050.997410.501295
1350.4371740.8743480.562826
1360.4524770.9049540.547523
1370.4467780.8935570.553222
1380.4164740.8329480.583526
1390.3775150.7550290.622485
1400.3472820.6945650.652718
1410.3300570.6601130.669943
1420.3193370.6386730.680663
1430.281880.563760.71812
1440.229590.459180.77041
1450.3075880.6151760.692412
1460.4144980.8289960.585502
1470.4109830.8219660.589017
1480.9768460.04630750.0231537
1490.9679150.06417060.0320853
1500.9811280.03774480.0188724
1510.9972910.005417320.00270866
1520.992970.01405950.00702974
1530.9880990.02380110.0119005
1540.9815060.03698820.0184941
1550.9737810.05243860.0262193
1560.944670.110660.0553301
1570.8862030.2275930.113797






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00671141OK
5% type I error level60.0402685OK
10% type I error level80.0536913OK

\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 & 1 & 0.00671141 & OK \tabularnewline
5% type I error level & 6 & 0.0402685 & OK \tabularnewline
10% type I error level & 8 & 0.0536913 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269333&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]1[/C][C]0.00671141[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.0402685[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0536913[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269333&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269333&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 level10.00671141OK
5% type I error level60.0402685OK
10% type I error level80.0536913OK


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')
}