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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 computationMon, 08 Dec 2014 12:14:22 +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/08/t1418041023gs24tenyn7ih9dm.htm/, Retrieved Sun, 19 May 2024 12:40:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263958, Retrieved Sun, 19 May 2024 12:40:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regressi...] [2014-12-08 12:14:22] [d71ad52285d92a63edfc83f9fb1da7a1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1 34 20 6547 48 23
1 95 34 32898 50 16
4 96 54 24200 150 33
4 122 51 24191 154 32
3 162 33 91688 109 37
2 38 27 14547 68 14
4 175 64 75809 194 52
4 89 53 52008 158 75
4 142 43 114114 159 72
2 42 22 8013 67 15
4 135 49 45389 147 29
1 29 39 11305 39 13
3 121 44 26485 100 40
3 86 46 32326 111 19
4 79 44 26305 138 24
3 161 45 23451 101 121
4 129 39 88375 131 93
3 66 45 21554 101 36
3 55 44 15268 114 23
4 149 51 55035 165 85
3 115 47 25705 114 41
3 92 43 34919 111 46
2 60 41 31379 75 18
2 76 44 50495 82 35
3 110 47 33972 121 17
1 63 48 15063 32 4
4 148 55 58281 150 28
3 127 44 62183 117 44
2 50 29 34952 71 10
4 24 53 11925 165 38
4 112 47 39570 154 57
4 115 50 43469 126 23
4 82 41 19555 138 26
4 174 46 39753 149 36
4 103 50 57585 145 22
3 77 46 30661 120 40
4 39 53 17766 138 18
3 100 40 24850 109 31
4 79 43 51478 132 11
4 102 50 39337 172 38
4 128 46 34393 169 24
3 95 46 32642 114 37
4 114 43 56632 156 37
4 97 47 54266 172 22
2 87 35 21362 68 15
2 129 26 59433 89 2
4 142 45 73670 167 43
3 91 45 36584 113 31
3 168 33 60090 115 29
2 88 29 26024 78 45
3 69 26 44114 118 25
2 55 24 59244 87 4
4 103 55 39635 173 31
1 27 6 16365 2 -4
4 149 39 67320 162 66
1 40 26 9881 49 61
4 99 32 37807 122 32
3 73 27 3665 96 31
3 102 32 56096 100 39
2 82 29 52634 82 19
3 117 27 58907 100 31
3 88 26 51846 115 36
4 85 32 47634 141 42
4 135 45 20668 165 21
4 135 45 20668 165 21
3 90 34 52996 110 25
3 70 43 33526 118 32
4 118 52 61485 158 26
4 56 40 29774 146 28
1 20 50 8004 49 32
2 122 32 62822 90 41
3 95 30 36484 121 29
4 150 52 46802 155 33
3 59 26 51818 104 17
4 39 37 24158 147 13
3 62 26 47273 110 32
3 97 27 24399 108 30
3 64 26 29500 113 34
3 22 28 1157 115 59
1 79 31 36018 61 13
1 83 23 17325 60 23
3 64 27 37528 109 10
2 26 23 29555 68 5
3 63 29 33853 111 31
2 85 29 33374 77 19
2 80 29 52141 73 32
4 129 46 35699 151 30
2 78 27 42271 89 25
2 45 26 15568 78 48
3 93 27 36164 110 35
4 143 59 46803 220 67
2 85 28 14142 65 15
4 81 43 27130 141 22
3 105 32 44131 117 18
4 52 56 17122 122 33
2 92 27 14779 63 46
1 95 46 32640 44 24
1 53 27 16870 52 14
1 53 23 30710 62 23
4 81 32 63319 131 12
3 102 27 73112 101 38
1 26 13 30832 42 12
4 78 47 18013 152 28
3 97 47 42611 107 41
2 85 26 31887 77 12
4 148 42 63301 154 31
3 41 47 23245 103 33
3 75 27 32055 96 34
4 66 49 20807 154 41
4 109 51 37105 175 21
1 45 26 33683 57 20
3 110 29 47650 112 44
4 141 49 32828 143 52
1 67 24 21788 49 7
3 81 34 10604 110 29
4 39 44 10444 131 11
4 112 53 51413 167 26
1 47 26 17272 56 24
4 48 50 21095 137 7
2 86 26 16249 86 60
3 129 44 63283 121 13
4 122 41 35183 149 20
4 88 45 15127 168 52
4 155 44 65486 140 28
2 67 28 30434 88 25
4 123 54 17780 168 39
2 56 54 15200 94 9
1 59 32 22056 51 19
1 38 23 10999 48 13
4 68 45 25342 145 60
2 86 42 36217 66 19
2 25 28 23218 85 34
3 96 40 54169 109 14
2 77 31 20636 63 17
3 28 28 26517 102 45
4 149 39 67320 162 66
4 40 44 15436 128 24
2 74 27 12735 86 48
3 128 29 49363 114 29
4 124 42 64552 164 -2
3 83 45 11596 119 51
4 100 43 49823 126 2
4 104 44 24457 132 24
4 59 44 17842 142 40
2 86 43 40369 83 20
2 93 30 41100 94 19
2 98 28 56119 81 16
4 74 45 42787 166 20
3 95 32 50424 110 40
2 30 26 19234 64 27
2 73 44 7959 93 25
3 74 27 26338 104 49
3 44 26 25615 105 39
1 40 26 9881 49 61
2 93 27 27041 88 19
2 46 26 15407 95 67
3 28 28 26517 102 45
3 106 28 34968 99 30
2 83 31 34298 63 8
2 53 28 31721 76 19
3 22 28 19056 109 52
3 103 32 29988 117 22
1 64 22 14395 57 17
3 40 31 20200 120 33
2 26 32 11208 73 34
2 100 27 33908 91 22
3 122 28 45592 108 30
3 67 27 23456 105 25
3 108 42 23198 117 38
3 103 33 47682 119 26
1 67 22 12636 31 13

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'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
PR[t] = + 0.20417 -0.00129765blogged_computations[t] -0.00114125compendiums_reviewed[t] + 8.09717e-07totrevisions[t] + 0.0257678LFM[t] -0.00106751PRH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PR[t] =  +  0.20417 -0.00129765blogged_computations[t] -0.00114125compendiums_reviewed[t] +  8.09717e-07totrevisions[t] +  0.0257678LFM[t] -0.00106751PRH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PR[t] =  +  0.20417 -0.00129765blogged_computations[t] -0.00114125compendiums_reviewed[t] +  8.09717e-07totrevisions[t] +  0.0257678LFM[t] -0.00106751PRH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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
PR[t] = + 0.20417 -0.00129765blogged_computations[t] -0.00114125compendiums_reviewed[t] + 8.09717e-07totrevisions[t] + 0.0257678LFM[t] -0.00106751PRH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.204170.1079371.8920.06030050.0301502
blogged_computations-0.001297650.00111361-1.1650.2455940.122797
compendiums_reviewed-0.001141250.00374391-0.30480.7608810.380441
totrevisions8.09717e-071.94908e-060.41540.6783630.339181
LFM0.02576780.0011061923.294.82502e-542.41251e-54
PRH-0.001067510.00162147-0.65840.5112240.255612

\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) & 0.20417 & 0.107937 & 1.892 & 0.0603005 & 0.0301502 \tabularnewline
blogged_computations & -0.00129765 & 0.00111361 & -1.165 & 0.245594 & 0.122797 \tabularnewline
compendiums_reviewed & -0.00114125 & 0.00374391 & -0.3048 & 0.760881 & 0.380441 \tabularnewline
totrevisions & 8.09717e-07 & 1.94908e-06 & 0.4154 & 0.678363 & 0.339181 \tabularnewline
LFM & 0.0257678 & 0.00110619 & 23.29 & 4.82502e-54 & 2.41251e-54 \tabularnewline
PRH & -0.00106751 & 0.00162147 & -0.6584 & 0.511224 & 0.255612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&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]0.20417[/C][C]0.107937[/C][C]1.892[/C][C]0.0603005[/C][C]0.0301502[/C][/ROW]
[ROW][C]blogged_computations[/C][C]-0.00129765[/C][C]0.00111361[/C][C]-1.165[/C][C]0.245594[/C][C]0.122797[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]-0.00114125[/C][C]0.00374391[/C][C]-0.3048[/C][C]0.760881[/C][C]0.380441[/C][/ROW]
[ROW][C]totrevisions[/C][C]8.09717e-07[/C][C]1.94908e-06[/C][C]0.4154[/C][C]0.678363[/C][C]0.339181[/C][/ROW]
[ROW][C]LFM[/C][C]0.0257678[/C][C]0.00110619[/C][C]23.29[/C][C]4.82502e-54[/C][C]2.41251e-54[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00106751[/C][C]0.00162147[/C][C]-0.6584[/C][C]0.511224[/C][C]0.255612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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)0.204170.1079371.8920.06030050.0301502
blogged_computations-0.001297650.00111361-1.1650.2455940.122797
compendiums_reviewed-0.001141250.00374391-0.30480.7608810.380441
totrevisions8.09717e-071.94908e-060.41540.6783630.339181
LFM0.02576780.0011061923.294.82502e-542.41251e-54
PRH-0.001067510.00162147-0.65840.5112240.255612






Multiple Linear Regression - Regression Statistics
Multiple R0.939705
R-squared0.883045
Adjusted R-squared0.879501
F-TEST (value)249.16
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.353625
Sum Squared Residuals20.6333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939705 \tabularnewline
R-squared & 0.883045 \tabularnewline
Adjusted R-squared & 0.879501 \tabularnewline
F-TEST (value) & 249.16 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.353625 \tabularnewline
Sum Squared Residuals & 20.6333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939705[/C][/ROW]
[ROW][C]R-squared[/C][C]0.883045[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.879501[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]249.16[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/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]0.353625[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]20.6333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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 R0.939705
R-squared0.883045
Adjusted R-squared0.879501
F-TEST (value)249.16
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.353625
Sum Squared Residuals20.6333






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.35483-0.35483
211.34004-0.340041
343.867510.13249
443.941330.0586731
532.799730.200273
621.873090.126908
744.90887-0.908874
844.06156-0.061559
944.08345-0.0834548
1021.841480.158518
1143.766730.233268
1211.12225-0.122252
1332.552470.447532
1432.90620.0938035
1543.603080.396919
1632.436260.563737
1743.340130.659869
1832.648740.351258
1933.00793-0.0079274
2044.15813-0.158133
2132.915880.0841194
2232.875110.124889
2322.0183-0.0183005
2422.17182-0.17182
2533.13506-0.135058
2610.9001360.099864
2743.831830.168175
2833.00737-0.00737035
2921.953330.0466662
3044.33332-0.333323
3143.944630.0553668
3243.255270.74473
3343.595010.404989
3443.759050.240953
3543.772930.227072
3633.12602-0.12602
3743.644210.355793
3832.824480.175522
3943.483880.516124
4044.4381-0.438101
4144.34257-0.342566
4232.952860.0471382
4344.0333-0.0333042
4444.47718-0.477181
4521.804830.195172
4622.34643-0.346427
4744.28552-0.285525
4832.943020.0569772
4932.92950.0704972
5022.03981-0.0398059
5133.1346-0.134596
5222.39091-0.390912
5344.46458-0.464579
5410.2313430.768657
5544.12475-0.124755
5611.3281-0.328098
5743.179310.820689
5832.522210.477785
5932.615860.384138
6022.19997-0.199965
6132.612920.38708
6233.02716-0.0271555
6343.684350.315651
6444.22364-0.223641
6544.22364-0.223641
6632.899260.100735
6733.09785-0.0978514
6844.08505-0.0850502
6943.842170.157827
7011.3561-0.356099
7122.33554-0.335542
7233.16315-0.163148
7343.946860.0531397
7432.80160.198399
7543.904890.0951096
7632.932620.0673776
7732.818140.181859
7832.99080.00919554
7933.04492-0.0449212
8011.6534-0.653402
8111.60576-0.605762
8232.918710.0812874
8321.914990.0850111
8432.943870.0561301
8522.05164-0.0516378
8621.956370.043627
8743.87210.127901
8822.37302-0.373017
8922.08736-0.0873596
9032.879060.120944
9145.58657-1.58657
9221.732260.267737
9343.681730.318266
9433.06275-0.0627519
9543.195090.804906
9621.640210.359792
9711.16299-0.16299
9811.44322-0.443223
9911.70707-0.707065
10043.476590.523413
10132.662180.337818
10211.25-0.249999
10343.950720.0492796
10432.772550.227447
10522.06133-0.0613301
10643.950590.0494052
10732.735010.264991
10832.53940.460595
10944.00393-0.00393
11044.52152-0.52152
11111.59079-0.590794
11232.905940.0940577
11343.621150.378849
11411.36263-0.362631
11532.872350.127652
11643.475650.524353
11744.31545-0.31545
11811.54487-0.544872
11943.624620.375378
12022.22804-0.22804
12133.14183-0.14183
12243.845610.154389
12344.32435-0.324355
12443.583450.416549
12522.3508-0.350797
12644.28469-0.284691
12722.49475-0.494751
12811.40282-0.402825
12911.3605-0.360495
13043.757380.242621
13121.754360.24564
13222.31254-0.312544
13332.871560.128444
13421.690810.309192
13532.737630.262367
13644.12475-0.124755
13743.387210.61279
13822.25244-0.252435
13932.951520.04848
14044.27566-0.275658
14133.06643-0.0664275
14243.310290.689714
14343.414540.585463
14443.708170.291827
14522.19357-0.193566
14622.48442-0.484424
14722.1606-0.1606
14844.34754-0.347543
14932.876960.123036
15021.771460.228539
15122.43539-0.435392
15232.72620.273797
15332.802130.197869
15411.3281-0.328098
15522.32186-0.321857
15622.5037-0.503702
15732.737630.262367
15832.581970.418031
15921.703690.296308
16022.0672-0.0671975
16132.912280.08772
16233.04963-0.0496253
16311.55829-0.558288
16433.19015-0.190154
16521.987740.0122569
16622.39244-0.392435
16732.801720.198281
16832.784340.215659
16933.00915-0.00914642
17033.11008-0.110077
17110.8872770.112723

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.35483 & -0.35483 \tabularnewline
2 & 1 & 1.34004 & -0.340041 \tabularnewline
3 & 4 & 3.86751 & 0.13249 \tabularnewline
4 & 4 & 3.94133 & 0.0586731 \tabularnewline
5 & 3 & 2.79973 & 0.200273 \tabularnewline
6 & 2 & 1.87309 & 0.126908 \tabularnewline
7 & 4 & 4.90887 & -0.908874 \tabularnewline
8 & 4 & 4.06156 & -0.061559 \tabularnewline
9 & 4 & 4.08345 & -0.0834548 \tabularnewline
10 & 2 & 1.84148 & 0.158518 \tabularnewline
11 & 4 & 3.76673 & 0.233268 \tabularnewline
12 & 1 & 1.12225 & -0.122252 \tabularnewline
13 & 3 & 2.55247 & 0.447532 \tabularnewline
14 & 3 & 2.9062 & 0.0938035 \tabularnewline
15 & 4 & 3.60308 & 0.396919 \tabularnewline
16 & 3 & 2.43626 & 0.563737 \tabularnewline
17 & 4 & 3.34013 & 0.659869 \tabularnewline
18 & 3 & 2.64874 & 0.351258 \tabularnewline
19 & 3 & 3.00793 & -0.0079274 \tabularnewline
20 & 4 & 4.15813 & -0.158133 \tabularnewline
21 & 3 & 2.91588 & 0.0841194 \tabularnewline
22 & 3 & 2.87511 & 0.124889 \tabularnewline
23 & 2 & 2.0183 & -0.0183005 \tabularnewline
24 & 2 & 2.17182 & -0.17182 \tabularnewline
25 & 3 & 3.13506 & -0.135058 \tabularnewline
26 & 1 & 0.900136 & 0.099864 \tabularnewline
27 & 4 & 3.83183 & 0.168175 \tabularnewline
28 & 3 & 3.00737 & -0.00737035 \tabularnewline
29 & 2 & 1.95333 & 0.0466662 \tabularnewline
30 & 4 & 4.33332 & -0.333323 \tabularnewline
31 & 4 & 3.94463 & 0.0553668 \tabularnewline
32 & 4 & 3.25527 & 0.74473 \tabularnewline
33 & 4 & 3.59501 & 0.404989 \tabularnewline
34 & 4 & 3.75905 & 0.240953 \tabularnewline
35 & 4 & 3.77293 & 0.227072 \tabularnewline
36 & 3 & 3.12602 & -0.12602 \tabularnewline
37 & 4 & 3.64421 & 0.355793 \tabularnewline
38 & 3 & 2.82448 & 0.175522 \tabularnewline
39 & 4 & 3.48388 & 0.516124 \tabularnewline
40 & 4 & 4.4381 & -0.438101 \tabularnewline
41 & 4 & 4.34257 & -0.342566 \tabularnewline
42 & 3 & 2.95286 & 0.0471382 \tabularnewline
43 & 4 & 4.0333 & -0.0333042 \tabularnewline
44 & 4 & 4.47718 & -0.477181 \tabularnewline
45 & 2 & 1.80483 & 0.195172 \tabularnewline
46 & 2 & 2.34643 & -0.346427 \tabularnewline
47 & 4 & 4.28552 & -0.285525 \tabularnewline
48 & 3 & 2.94302 & 0.0569772 \tabularnewline
49 & 3 & 2.9295 & 0.0704972 \tabularnewline
50 & 2 & 2.03981 & -0.0398059 \tabularnewline
51 & 3 & 3.1346 & -0.134596 \tabularnewline
52 & 2 & 2.39091 & -0.390912 \tabularnewline
53 & 4 & 4.46458 & -0.464579 \tabularnewline
54 & 1 & 0.231343 & 0.768657 \tabularnewline
55 & 4 & 4.12475 & -0.124755 \tabularnewline
56 & 1 & 1.3281 & -0.328098 \tabularnewline
57 & 4 & 3.17931 & 0.820689 \tabularnewline
58 & 3 & 2.52221 & 0.477785 \tabularnewline
59 & 3 & 2.61586 & 0.384138 \tabularnewline
60 & 2 & 2.19997 & -0.199965 \tabularnewline
61 & 3 & 2.61292 & 0.38708 \tabularnewline
62 & 3 & 3.02716 & -0.0271555 \tabularnewline
63 & 4 & 3.68435 & 0.315651 \tabularnewline
64 & 4 & 4.22364 & -0.223641 \tabularnewline
65 & 4 & 4.22364 & -0.223641 \tabularnewline
66 & 3 & 2.89926 & 0.100735 \tabularnewline
67 & 3 & 3.09785 & -0.0978514 \tabularnewline
68 & 4 & 4.08505 & -0.0850502 \tabularnewline
69 & 4 & 3.84217 & 0.157827 \tabularnewline
70 & 1 & 1.3561 & -0.356099 \tabularnewline
71 & 2 & 2.33554 & -0.335542 \tabularnewline
72 & 3 & 3.16315 & -0.163148 \tabularnewline
73 & 4 & 3.94686 & 0.0531397 \tabularnewline
74 & 3 & 2.8016 & 0.198399 \tabularnewline
75 & 4 & 3.90489 & 0.0951096 \tabularnewline
76 & 3 & 2.93262 & 0.0673776 \tabularnewline
77 & 3 & 2.81814 & 0.181859 \tabularnewline
78 & 3 & 2.9908 & 0.00919554 \tabularnewline
79 & 3 & 3.04492 & -0.0449212 \tabularnewline
80 & 1 & 1.6534 & -0.653402 \tabularnewline
81 & 1 & 1.60576 & -0.605762 \tabularnewline
82 & 3 & 2.91871 & 0.0812874 \tabularnewline
83 & 2 & 1.91499 & 0.0850111 \tabularnewline
84 & 3 & 2.94387 & 0.0561301 \tabularnewline
85 & 2 & 2.05164 & -0.0516378 \tabularnewline
86 & 2 & 1.95637 & 0.043627 \tabularnewline
87 & 4 & 3.8721 & 0.127901 \tabularnewline
88 & 2 & 2.37302 & -0.373017 \tabularnewline
89 & 2 & 2.08736 & -0.0873596 \tabularnewline
90 & 3 & 2.87906 & 0.120944 \tabularnewline
91 & 4 & 5.58657 & -1.58657 \tabularnewline
92 & 2 & 1.73226 & 0.267737 \tabularnewline
93 & 4 & 3.68173 & 0.318266 \tabularnewline
94 & 3 & 3.06275 & -0.0627519 \tabularnewline
95 & 4 & 3.19509 & 0.804906 \tabularnewline
96 & 2 & 1.64021 & 0.359792 \tabularnewline
97 & 1 & 1.16299 & -0.16299 \tabularnewline
98 & 1 & 1.44322 & -0.443223 \tabularnewline
99 & 1 & 1.70707 & -0.707065 \tabularnewline
100 & 4 & 3.47659 & 0.523413 \tabularnewline
101 & 3 & 2.66218 & 0.337818 \tabularnewline
102 & 1 & 1.25 & -0.249999 \tabularnewline
103 & 4 & 3.95072 & 0.0492796 \tabularnewline
104 & 3 & 2.77255 & 0.227447 \tabularnewline
105 & 2 & 2.06133 & -0.0613301 \tabularnewline
106 & 4 & 3.95059 & 0.0494052 \tabularnewline
107 & 3 & 2.73501 & 0.264991 \tabularnewline
108 & 3 & 2.5394 & 0.460595 \tabularnewline
109 & 4 & 4.00393 & -0.00393 \tabularnewline
110 & 4 & 4.52152 & -0.52152 \tabularnewline
111 & 1 & 1.59079 & -0.590794 \tabularnewline
112 & 3 & 2.90594 & 0.0940577 \tabularnewline
113 & 4 & 3.62115 & 0.378849 \tabularnewline
114 & 1 & 1.36263 & -0.362631 \tabularnewline
115 & 3 & 2.87235 & 0.127652 \tabularnewline
116 & 4 & 3.47565 & 0.524353 \tabularnewline
117 & 4 & 4.31545 & -0.31545 \tabularnewline
118 & 1 & 1.54487 & -0.544872 \tabularnewline
119 & 4 & 3.62462 & 0.375378 \tabularnewline
120 & 2 & 2.22804 & -0.22804 \tabularnewline
121 & 3 & 3.14183 & -0.14183 \tabularnewline
122 & 4 & 3.84561 & 0.154389 \tabularnewline
123 & 4 & 4.32435 & -0.324355 \tabularnewline
124 & 4 & 3.58345 & 0.416549 \tabularnewline
125 & 2 & 2.3508 & -0.350797 \tabularnewline
126 & 4 & 4.28469 & -0.284691 \tabularnewline
127 & 2 & 2.49475 & -0.494751 \tabularnewline
128 & 1 & 1.40282 & -0.402825 \tabularnewline
129 & 1 & 1.3605 & -0.360495 \tabularnewline
130 & 4 & 3.75738 & 0.242621 \tabularnewline
131 & 2 & 1.75436 & 0.24564 \tabularnewline
132 & 2 & 2.31254 & -0.312544 \tabularnewline
133 & 3 & 2.87156 & 0.128444 \tabularnewline
134 & 2 & 1.69081 & 0.309192 \tabularnewline
135 & 3 & 2.73763 & 0.262367 \tabularnewline
136 & 4 & 4.12475 & -0.124755 \tabularnewline
137 & 4 & 3.38721 & 0.61279 \tabularnewline
138 & 2 & 2.25244 & -0.252435 \tabularnewline
139 & 3 & 2.95152 & 0.04848 \tabularnewline
140 & 4 & 4.27566 & -0.275658 \tabularnewline
141 & 3 & 3.06643 & -0.0664275 \tabularnewline
142 & 4 & 3.31029 & 0.689714 \tabularnewline
143 & 4 & 3.41454 & 0.585463 \tabularnewline
144 & 4 & 3.70817 & 0.291827 \tabularnewline
145 & 2 & 2.19357 & -0.193566 \tabularnewline
146 & 2 & 2.48442 & -0.484424 \tabularnewline
147 & 2 & 2.1606 & -0.1606 \tabularnewline
148 & 4 & 4.34754 & -0.347543 \tabularnewline
149 & 3 & 2.87696 & 0.123036 \tabularnewline
150 & 2 & 1.77146 & 0.228539 \tabularnewline
151 & 2 & 2.43539 & -0.435392 \tabularnewline
152 & 3 & 2.7262 & 0.273797 \tabularnewline
153 & 3 & 2.80213 & 0.197869 \tabularnewline
154 & 1 & 1.3281 & -0.328098 \tabularnewline
155 & 2 & 2.32186 & -0.321857 \tabularnewline
156 & 2 & 2.5037 & -0.503702 \tabularnewline
157 & 3 & 2.73763 & 0.262367 \tabularnewline
158 & 3 & 2.58197 & 0.418031 \tabularnewline
159 & 2 & 1.70369 & 0.296308 \tabularnewline
160 & 2 & 2.0672 & -0.0671975 \tabularnewline
161 & 3 & 2.91228 & 0.08772 \tabularnewline
162 & 3 & 3.04963 & -0.0496253 \tabularnewline
163 & 1 & 1.55829 & -0.558288 \tabularnewline
164 & 3 & 3.19015 & -0.190154 \tabularnewline
165 & 2 & 1.98774 & 0.0122569 \tabularnewline
166 & 2 & 2.39244 & -0.392435 \tabularnewline
167 & 3 & 2.80172 & 0.198281 \tabularnewline
168 & 3 & 2.78434 & 0.215659 \tabularnewline
169 & 3 & 3.00915 & -0.00914642 \tabularnewline
170 & 3 & 3.11008 & -0.110077 \tabularnewline
171 & 1 & 0.887277 & 0.112723 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&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]1[/C][C]1.35483[/C][C]-0.35483[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.34004[/C][C]-0.340041[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.86751[/C][C]0.13249[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.94133[/C][C]0.0586731[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.79973[/C][C]0.200273[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.87309[/C][C]0.126908[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]4.90887[/C][C]-0.908874[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.06156[/C][C]-0.061559[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.08345[/C][C]-0.0834548[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.84148[/C][C]0.158518[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]3.76673[/C][C]0.233268[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.12225[/C][C]-0.122252[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]2.55247[/C][C]0.447532[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]2.9062[/C][C]0.0938035[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]3.60308[/C][C]0.396919[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.43626[/C][C]0.563737[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.34013[/C][C]0.659869[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]2.64874[/C][C]0.351258[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]3.00793[/C][C]-0.0079274[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]4.15813[/C][C]-0.158133[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.91588[/C][C]0.0841194[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]2.87511[/C][C]0.124889[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.0183[/C][C]-0.0183005[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.17182[/C][C]-0.17182[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.13506[/C][C]-0.135058[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.900136[/C][C]0.099864[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.83183[/C][C]0.168175[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.00737[/C][C]-0.00737035[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.95333[/C][C]0.0466662[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.33332[/C][C]-0.333323[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.94463[/C][C]0.0553668[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.25527[/C][C]0.74473[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.59501[/C][C]0.404989[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]3.75905[/C][C]0.240953[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]3.77293[/C][C]0.227072[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]3.12602[/C][C]-0.12602[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]3.64421[/C][C]0.355793[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]2.82448[/C][C]0.175522[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.48388[/C][C]0.516124[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.4381[/C][C]-0.438101[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]4.34257[/C][C]-0.342566[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]2.95286[/C][C]0.0471382[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]4.0333[/C][C]-0.0333042[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]4.47718[/C][C]-0.477181[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.80483[/C][C]0.195172[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]2.34643[/C][C]-0.346427[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.28552[/C][C]-0.285525[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.94302[/C][C]0.0569772[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]2.9295[/C][C]0.0704972[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.03981[/C][C]-0.0398059[/C][/ROW]
[ROW][C]51[/C][C]3[/C][C]3.1346[/C][C]-0.134596[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]2.39091[/C][C]-0.390912[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]4.46458[/C][C]-0.464579[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.231343[/C][C]0.768657[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.12475[/C][C]-0.124755[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.3281[/C][C]-0.328098[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.17931[/C][C]0.820689[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.52221[/C][C]0.477785[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.61586[/C][C]0.384138[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.19997[/C][C]-0.199965[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]2.61292[/C][C]0.38708[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]3.02716[/C][C]-0.0271555[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.68435[/C][C]0.315651[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.22364[/C][C]-0.223641[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]4.22364[/C][C]-0.223641[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]2.89926[/C][C]0.100735[/C][/ROW]
[ROW][C]67[/C][C]3[/C][C]3.09785[/C][C]-0.0978514[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]4.08505[/C][C]-0.0850502[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]3.84217[/C][C]0.157827[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.3561[/C][C]-0.356099[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]2.33554[/C][C]-0.335542[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.16315[/C][C]-0.163148[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.94686[/C][C]0.0531397[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.8016[/C][C]0.198399[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]3.90489[/C][C]0.0951096[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.93262[/C][C]0.0673776[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]2.81814[/C][C]0.181859[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]2.9908[/C][C]0.00919554[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]3.04492[/C][C]-0.0449212[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.6534[/C][C]-0.653402[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.60576[/C][C]-0.605762[/C][/ROW]
[ROW][C]82[/C][C]3[/C][C]2.91871[/C][C]0.0812874[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.91499[/C][C]0.0850111[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.94387[/C][C]0.0561301[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]2.05164[/C][C]-0.0516378[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.95637[/C][C]0.043627[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.8721[/C][C]0.127901[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]2.37302[/C][C]-0.373017[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]2.08736[/C][C]-0.0873596[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.87906[/C][C]0.120944[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]5.58657[/C][C]-1.58657[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.73226[/C][C]0.267737[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]3.68173[/C][C]0.318266[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]3.06275[/C][C]-0.0627519[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]3.19509[/C][C]0.804906[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.64021[/C][C]0.359792[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.16299[/C][C]-0.16299[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.44322[/C][C]-0.443223[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.70707[/C][C]-0.707065[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.47659[/C][C]0.523413[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.66218[/C][C]0.337818[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.25[/C][C]-0.249999[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]3.95072[/C][C]0.0492796[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.77255[/C][C]0.227447[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]2.06133[/C][C]-0.0613301[/C][/ROW]
[ROW][C]106[/C][C]4[/C][C]3.95059[/C][C]0.0494052[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]2.73501[/C][C]0.264991[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]2.5394[/C][C]0.460595[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.00393[/C][C]-0.00393[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]4.52152[/C][C]-0.52152[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.59079[/C][C]-0.590794[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]2.90594[/C][C]0.0940577[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]3.62115[/C][C]0.378849[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.36263[/C][C]-0.362631[/C][/ROW]
[ROW][C]115[/C][C]3[/C][C]2.87235[/C][C]0.127652[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]3.47565[/C][C]0.524353[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]4.31545[/C][C]-0.31545[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.54487[/C][C]-0.544872[/C][/ROW]
[ROW][C]119[/C][C]4[/C][C]3.62462[/C][C]0.375378[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.22804[/C][C]-0.22804[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.14183[/C][C]-0.14183[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.84561[/C][C]0.154389[/C][/ROW]
[ROW][C]123[/C][C]4[/C][C]4.32435[/C][C]-0.324355[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]3.58345[/C][C]0.416549[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]2.3508[/C][C]-0.350797[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]4.28469[/C][C]-0.284691[/C][/ROW]
[ROW][C]127[/C][C]2[/C][C]2.49475[/C][C]-0.494751[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.40282[/C][C]-0.402825[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.3605[/C][C]-0.360495[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]3.75738[/C][C]0.242621[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.75436[/C][C]0.24564[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]2.31254[/C][C]-0.312544[/C][/ROW]
[ROW][C]133[/C][C]3[/C][C]2.87156[/C][C]0.128444[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.69081[/C][C]0.309192[/C][/ROW]
[ROW][C]135[/C][C]3[/C][C]2.73763[/C][C]0.262367[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]4.12475[/C][C]-0.124755[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.38721[/C][C]0.61279[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]2.25244[/C][C]-0.252435[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]2.95152[/C][C]0.04848[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]4.27566[/C][C]-0.275658[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]3.06643[/C][C]-0.0664275[/C][/ROW]
[ROW][C]142[/C][C]4[/C][C]3.31029[/C][C]0.689714[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]3.41454[/C][C]0.585463[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]3.70817[/C][C]0.291827[/C][/ROW]
[ROW][C]145[/C][C]2[/C][C]2.19357[/C][C]-0.193566[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]2.48442[/C][C]-0.484424[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]2.1606[/C][C]-0.1606[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]4.34754[/C][C]-0.347543[/C][/ROW]
[ROW][C]149[/C][C]3[/C][C]2.87696[/C][C]0.123036[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.77146[/C][C]0.228539[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]2.43539[/C][C]-0.435392[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]2.7262[/C][C]0.273797[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]2.80213[/C][C]0.197869[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]1.3281[/C][C]-0.328098[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]2.32186[/C][C]-0.321857[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]2.5037[/C][C]-0.503702[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.73763[/C][C]0.262367[/C][/ROW]
[ROW][C]158[/C][C]3[/C][C]2.58197[/C][C]0.418031[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.70369[/C][C]0.296308[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]2.0672[/C][C]-0.0671975[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]2.91228[/C][C]0.08772[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]3.04963[/C][C]-0.0496253[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]1.55829[/C][C]-0.558288[/C][/ROW]
[ROW][C]164[/C][C]3[/C][C]3.19015[/C][C]-0.190154[/C][/ROW]
[ROW][C]165[/C][C]2[/C][C]1.98774[/C][C]0.0122569[/C][/ROW]
[ROW][C]166[/C][C]2[/C][C]2.39244[/C][C]-0.392435[/C][/ROW]
[ROW][C]167[/C][C]3[/C][C]2.80172[/C][C]0.198281[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]2.78434[/C][C]0.215659[/C][/ROW]
[ROW][C]169[/C][C]3[/C][C]3.00915[/C][C]-0.00914642[/C][/ROW]
[ROW][C]170[/C][C]3[/C][C]3.11008[/C][C]-0.110077[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]0.887277[/C][C]0.112723[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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
111.35483-0.35483
211.34004-0.340041
343.867510.13249
443.941330.0586731
532.799730.200273
621.873090.126908
744.90887-0.908874
844.06156-0.061559
944.08345-0.0834548
1021.841480.158518
1143.766730.233268
1211.12225-0.122252
1332.552470.447532
1432.90620.0938035
1543.603080.396919
1632.436260.563737
1743.340130.659869
1832.648740.351258
1933.00793-0.0079274
2044.15813-0.158133
2132.915880.0841194
2232.875110.124889
2322.0183-0.0183005
2422.17182-0.17182
2533.13506-0.135058
2610.9001360.099864
2743.831830.168175
2833.00737-0.00737035
2921.953330.0466662
3044.33332-0.333323
3143.944630.0553668
3243.255270.74473
3343.595010.404989
3443.759050.240953
3543.772930.227072
3633.12602-0.12602
3743.644210.355793
3832.824480.175522
3943.483880.516124
4044.4381-0.438101
4144.34257-0.342566
4232.952860.0471382
4344.0333-0.0333042
4444.47718-0.477181
4521.804830.195172
4622.34643-0.346427
4744.28552-0.285525
4832.943020.0569772
4932.92950.0704972
5022.03981-0.0398059
5133.1346-0.134596
5222.39091-0.390912
5344.46458-0.464579
5410.2313430.768657
5544.12475-0.124755
5611.3281-0.328098
5743.179310.820689
5832.522210.477785
5932.615860.384138
6022.19997-0.199965
6132.612920.38708
6233.02716-0.0271555
6343.684350.315651
6444.22364-0.223641
6544.22364-0.223641
6632.899260.100735
6733.09785-0.0978514
6844.08505-0.0850502
6943.842170.157827
7011.3561-0.356099
7122.33554-0.335542
7233.16315-0.163148
7343.946860.0531397
7432.80160.198399
7543.904890.0951096
7632.932620.0673776
7732.818140.181859
7832.99080.00919554
7933.04492-0.0449212
8011.6534-0.653402
8111.60576-0.605762
8232.918710.0812874
8321.914990.0850111
8432.943870.0561301
8522.05164-0.0516378
8621.956370.043627
8743.87210.127901
8822.37302-0.373017
8922.08736-0.0873596
9032.879060.120944
9145.58657-1.58657
9221.732260.267737
9343.681730.318266
9433.06275-0.0627519
9543.195090.804906
9621.640210.359792
9711.16299-0.16299
9811.44322-0.443223
9911.70707-0.707065
10043.476590.523413
10132.662180.337818
10211.25-0.249999
10343.950720.0492796
10432.772550.227447
10522.06133-0.0613301
10643.950590.0494052
10732.735010.264991
10832.53940.460595
10944.00393-0.00393
11044.52152-0.52152
11111.59079-0.590794
11232.905940.0940577
11343.621150.378849
11411.36263-0.362631
11532.872350.127652
11643.475650.524353
11744.31545-0.31545
11811.54487-0.544872
11943.624620.375378
12022.22804-0.22804
12133.14183-0.14183
12243.845610.154389
12344.32435-0.324355
12443.583450.416549
12522.3508-0.350797
12644.28469-0.284691
12722.49475-0.494751
12811.40282-0.402825
12911.3605-0.360495
13043.757380.242621
13121.754360.24564
13222.31254-0.312544
13332.871560.128444
13421.690810.309192
13532.737630.262367
13644.12475-0.124755
13743.387210.61279
13822.25244-0.252435
13932.951520.04848
14044.27566-0.275658
14133.06643-0.0664275
14243.310290.689714
14343.414540.585463
14443.708170.291827
14522.19357-0.193566
14622.48442-0.484424
14722.1606-0.1606
14844.34754-0.347543
14932.876960.123036
15021.771460.228539
15122.43539-0.435392
15232.72620.273797
15332.802130.197869
15411.3281-0.328098
15522.32186-0.321857
15622.5037-0.503702
15732.737630.262367
15832.581970.418031
15921.703690.296308
16022.0672-0.0671975
16132.912280.08772
16233.04963-0.0496253
16311.55829-0.558288
16433.19015-0.190154
16521.987740.0122569
16622.39244-0.392435
16732.801720.198281
16832.784340.215659
16933.00915-0.00914642
17033.11008-0.110077
17110.8872770.112723






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8926250.2147490.107375
100.8071660.3856670.192834
110.7656440.4687110.234356
120.6765080.6469830.323492
130.7858580.4282830.214142
140.7317460.5365070.268254
150.7161670.5676650.283833
160.6723930.6552150.327607
170.7497340.5005320.250266
180.7379380.5241250.262062
190.666660.666680.33334
200.6740960.6518080.325904
210.6019770.7960450.398023
220.5292930.9414140.470707
230.4560740.9121480.543926
240.3885560.7771120.611444
250.3236840.6473680.676316
260.2934470.5868950.706553
270.272560.5451210.72744
280.2192020.4384030.780798
290.1730680.3461350.826932
300.1563640.3127280.843636
310.1208740.2417470.879126
320.3096550.619310.690345
330.3073260.6146530.692674
340.2630910.5261830.736909
350.2485570.4971140.751443
360.2123020.4246030.787698
370.2355270.4710530.764473
380.196460.392920.80354
390.2480730.4961460.751927
400.2973020.5946030.702698
410.3157290.6314570.684271
420.2699160.5398320.730084
430.227930.455860.77207
440.2518790.5037570.748121
450.2160120.4320240.783988
460.2254730.4509470.774527
470.2055750.411150.794425
480.171080.3421610.82892
490.1409740.2819480.859026
500.1209810.2419620.879019
510.09952920.1990580.900471
520.09838230.1967650.901618
530.1109750.2219490.889025
540.190770.381540.80923
550.1628270.3256540.837173
560.203320.406640.79668
570.3789780.7579550.621022
580.392760.7855210.60724
590.3923930.7847860.607607
600.3681270.7362530.631873
610.3660180.7320350.633982
620.3244640.6489270.675536
630.3117730.6235470.688227
640.2948150.5896290.705185
650.2741540.5483080.725846
660.2385910.4771820.761409
670.2069690.4139380.793031
680.1771850.3543710.822815
690.1550010.3100020.844999
700.1600110.3200220.839989
710.1679940.3359880.832006
720.15160.3031990.8484
730.1271120.2542240.872888
740.1096880.2193750.890312
750.09131730.1826350.908683
760.07403050.1480610.925969
770.06251380.1250280.937486
780.05050750.1010150.949492
790.041760.083520.95824
800.08216550.1643310.917834
810.1365280.2730550.863472
820.1138890.2277780.886111
830.0939890.1879780.906011
840.07665910.1533180.923341
850.0624890.1249780.937511
860.04994370.09988740.950056
870.04070440.08140890.959296
880.04317630.08635250.956824
890.03534860.07069720.964651
900.02832770.05665530.971672
910.6267050.746590.373295
920.6245310.7509380.375469
930.6153360.7693280.384664
940.5730330.8539330.426967
950.7336490.5327020.266351
960.7546290.4907410.245371
970.7265870.5468260.273413
980.7407260.5185470.259274
990.8353340.3293320.164666
1000.8566930.2866130.143307
1010.8498760.3002490.150124
1020.8334050.3331890.166595
1030.8031910.3936180.196809
1040.7827880.4344250.217212
1050.748280.5034390.25172
1060.7100690.5798610.289931
1070.6919880.6160240.308012
1080.7264420.5471160.273558
1090.686750.6264990.31325
1100.7664010.4671980.233599
1110.8146490.3707010.185351
1120.7844930.4310150.215507
1130.7966380.4067240.203362
1140.7893280.4213440.210672
1150.760320.4793590.23968
1160.7913860.4172290.208614
1170.8054360.3891280.194564
1180.8360740.3278530.163926
1190.830860.3382810.16914
1200.8049720.3900560.195028
1210.7826810.4346380.217319
1220.7508940.4982120.249106
1230.7446930.5106150.255307
1240.7492760.5014480.250724
1250.7456090.5087820.254391
1260.7359550.528090.264045
1270.8054780.3890430.194522
1280.8141740.3716530.185826
1290.8095180.3809630.190482
1300.7805820.4388350.219418
1310.7526370.4947250.247363
1320.7526610.4946780.247339
1330.7075020.5849970.292498
1340.7045660.5908690.295434
1350.6747940.6504120.325206
1360.622310.7553790.37769
1370.6874590.6250830.312541
1380.6481240.7037530.351876
1390.5912430.8175150.408757
1400.6254550.749090.374545
1410.5645710.8708570.435429
1420.6862230.6275550.313777
1430.8149560.3700880.185044
1440.84010.3198010.1599
1450.7952750.409450.204725
1460.8455580.3088830.154442
1470.8544260.2911480.145574
1480.8794560.2410880.120544
1490.8441170.3117660.155883
1500.8132160.3735680.186784
1510.7827860.4344290.217214
1520.7973890.4052230.202611
1530.7629910.4740190.237009
1540.7002890.5994210.299711
1550.6565070.6869860.343493
1560.7427990.5144030.257201
1570.6653880.6692240.334612
1580.7071190.5857620.292881
1590.6947270.6105470.305273
1600.5701360.8597280.429864
1610.4264960.8529910.573504
1620.295820.591640.70418

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.892625 & 0.214749 & 0.107375 \tabularnewline
10 & 0.807166 & 0.385667 & 0.192834 \tabularnewline
11 & 0.765644 & 0.468711 & 0.234356 \tabularnewline
12 & 0.676508 & 0.646983 & 0.323492 \tabularnewline
13 & 0.785858 & 0.428283 & 0.214142 \tabularnewline
14 & 0.731746 & 0.536507 & 0.268254 \tabularnewline
15 & 0.716167 & 0.567665 & 0.283833 \tabularnewline
16 & 0.672393 & 0.655215 & 0.327607 \tabularnewline
17 & 0.749734 & 0.500532 & 0.250266 \tabularnewline
18 & 0.737938 & 0.524125 & 0.262062 \tabularnewline
19 & 0.66666 & 0.66668 & 0.33334 \tabularnewline
20 & 0.674096 & 0.651808 & 0.325904 \tabularnewline
21 & 0.601977 & 0.796045 & 0.398023 \tabularnewline
22 & 0.529293 & 0.941414 & 0.470707 \tabularnewline
23 & 0.456074 & 0.912148 & 0.543926 \tabularnewline
24 & 0.388556 & 0.777112 & 0.611444 \tabularnewline
25 & 0.323684 & 0.647368 & 0.676316 \tabularnewline
26 & 0.293447 & 0.586895 & 0.706553 \tabularnewline
27 & 0.27256 & 0.545121 & 0.72744 \tabularnewline
28 & 0.219202 & 0.438403 & 0.780798 \tabularnewline
29 & 0.173068 & 0.346135 & 0.826932 \tabularnewline
30 & 0.156364 & 0.312728 & 0.843636 \tabularnewline
31 & 0.120874 & 0.241747 & 0.879126 \tabularnewline
32 & 0.309655 & 0.61931 & 0.690345 \tabularnewline
33 & 0.307326 & 0.614653 & 0.692674 \tabularnewline
34 & 0.263091 & 0.526183 & 0.736909 \tabularnewline
35 & 0.248557 & 0.497114 & 0.751443 \tabularnewline
36 & 0.212302 & 0.424603 & 0.787698 \tabularnewline
37 & 0.235527 & 0.471053 & 0.764473 \tabularnewline
38 & 0.19646 & 0.39292 & 0.80354 \tabularnewline
39 & 0.248073 & 0.496146 & 0.751927 \tabularnewline
40 & 0.297302 & 0.594603 & 0.702698 \tabularnewline
41 & 0.315729 & 0.631457 & 0.684271 \tabularnewline
42 & 0.269916 & 0.539832 & 0.730084 \tabularnewline
43 & 0.22793 & 0.45586 & 0.77207 \tabularnewline
44 & 0.251879 & 0.503757 & 0.748121 \tabularnewline
45 & 0.216012 & 0.432024 & 0.783988 \tabularnewline
46 & 0.225473 & 0.450947 & 0.774527 \tabularnewline
47 & 0.205575 & 0.41115 & 0.794425 \tabularnewline
48 & 0.17108 & 0.342161 & 0.82892 \tabularnewline
49 & 0.140974 & 0.281948 & 0.859026 \tabularnewline
50 & 0.120981 & 0.241962 & 0.879019 \tabularnewline
51 & 0.0995292 & 0.199058 & 0.900471 \tabularnewline
52 & 0.0983823 & 0.196765 & 0.901618 \tabularnewline
53 & 0.110975 & 0.221949 & 0.889025 \tabularnewline
54 & 0.19077 & 0.38154 & 0.80923 \tabularnewline
55 & 0.162827 & 0.325654 & 0.837173 \tabularnewline
56 & 0.20332 & 0.40664 & 0.79668 \tabularnewline
57 & 0.378978 & 0.757955 & 0.621022 \tabularnewline
58 & 0.39276 & 0.785521 & 0.60724 \tabularnewline
59 & 0.392393 & 0.784786 & 0.607607 \tabularnewline
60 & 0.368127 & 0.736253 & 0.631873 \tabularnewline
61 & 0.366018 & 0.732035 & 0.633982 \tabularnewline
62 & 0.324464 & 0.648927 & 0.675536 \tabularnewline
63 & 0.311773 & 0.623547 & 0.688227 \tabularnewline
64 & 0.294815 & 0.589629 & 0.705185 \tabularnewline
65 & 0.274154 & 0.548308 & 0.725846 \tabularnewline
66 & 0.238591 & 0.477182 & 0.761409 \tabularnewline
67 & 0.206969 & 0.413938 & 0.793031 \tabularnewline
68 & 0.177185 & 0.354371 & 0.822815 \tabularnewline
69 & 0.155001 & 0.310002 & 0.844999 \tabularnewline
70 & 0.160011 & 0.320022 & 0.839989 \tabularnewline
71 & 0.167994 & 0.335988 & 0.832006 \tabularnewline
72 & 0.1516 & 0.303199 & 0.8484 \tabularnewline
73 & 0.127112 & 0.254224 & 0.872888 \tabularnewline
74 & 0.109688 & 0.219375 & 0.890312 \tabularnewline
75 & 0.0913173 & 0.182635 & 0.908683 \tabularnewline
76 & 0.0740305 & 0.148061 & 0.925969 \tabularnewline
77 & 0.0625138 & 0.125028 & 0.937486 \tabularnewline
78 & 0.0505075 & 0.101015 & 0.949492 \tabularnewline
79 & 0.04176 & 0.08352 & 0.95824 \tabularnewline
80 & 0.0821655 & 0.164331 & 0.917834 \tabularnewline
81 & 0.136528 & 0.273055 & 0.863472 \tabularnewline
82 & 0.113889 & 0.227778 & 0.886111 \tabularnewline
83 & 0.093989 & 0.187978 & 0.906011 \tabularnewline
84 & 0.0766591 & 0.153318 & 0.923341 \tabularnewline
85 & 0.062489 & 0.124978 & 0.937511 \tabularnewline
86 & 0.0499437 & 0.0998874 & 0.950056 \tabularnewline
87 & 0.0407044 & 0.0814089 & 0.959296 \tabularnewline
88 & 0.0431763 & 0.0863525 & 0.956824 \tabularnewline
89 & 0.0353486 & 0.0706972 & 0.964651 \tabularnewline
90 & 0.0283277 & 0.0566553 & 0.971672 \tabularnewline
91 & 0.626705 & 0.74659 & 0.373295 \tabularnewline
92 & 0.624531 & 0.750938 & 0.375469 \tabularnewline
93 & 0.615336 & 0.769328 & 0.384664 \tabularnewline
94 & 0.573033 & 0.853933 & 0.426967 \tabularnewline
95 & 0.733649 & 0.532702 & 0.266351 \tabularnewline
96 & 0.754629 & 0.490741 & 0.245371 \tabularnewline
97 & 0.726587 & 0.546826 & 0.273413 \tabularnewline
98 & 0.740726 & 0.518547 & 0.259274 \tabularnewline
99 & 0.835334 & 0.329332 & 0.164666 \tabularnewline
100 & 0.856693 & 0.286613 & 0.143307 \tabularnewline
101 & 0.849876 & 0.300249 & 0.150124 \tabularnewline
102 & 0.833405 & 0.333189 & 0.166595 \tabularnewline
103 & 0.803191 & 0.393618 & 0.196809 \tabularnewline
104 & 0.782788 & 0.434425 & 0.217212 \tabularnewline
105 & 0.74828 & 0.503439 & 0.25172 \tabularnewline
106 & 0.710069 & 0.579861 & 0.289931 \tabularnewline
107 & 0.691988 & 0.616024 & 0.308012 \tabularnewline
108 & 0.726442 & 0.547116 & 0.273558 \tabularnewline
109 & 0.68675 & 0.626499 & 0.31325 \tabularnewline
110 & 0.766401 & 0.467198 & 0.233599 \tabularnewline
111 & 0.814649 & 0.370701 & 0.185351 \tabularnewline
112 & 0.784493 & 0.431015 & 0.215507 \tabularnewline
113 & 0.796638 & 0.406724 & 0.203362 \tabularnewline
114 & 0.789328 & 0.421344 & 0.210672 \tabularnewline
115 & 0.76032 & 0.479359 & 0.23968 \tabularnewline
116 & 0.791386 & 0.417229 & 0.208614 \tabularnewline
117 & 0.805436 & 0.389128 & 0.194564 \tabularnewline
118 & 0.836074 & 0.327853 & 0.163926 \tabularnewline
119 & 0.83086 & 0.338281 & 0.16914 \tabularnewline
120 & 0.804972 & 0.390056 & 0.195028 \tabularnewline
121 & 0.782681 & 0.434638 & 0.217319 \tabularnewline
122 & 0.750894 & 0.498212 & 0.249106 \tabularnewline
123 & 0.744693 & 0.510615 & 0.255307 \tabularnewline
124 & 0.749276 & 0.501448 & 0.250724 \tabularnewline
125 & 0.745609 & 0.508782 & 0.254391 \tabularnewline
126 & 0.735955 & 0.52809 & 0.264045 \tabularnewline
127 & 0.805478 & 0.389043 & 0.194522 \tabularnewline
128 & 0.814174 & 0.371653 & 0.185826 \tabularnewline
129 & 0.809518 & 0.380963 & 0.190482 \tabularnewline
130 & 0.780582 & 0.438835 & 0.219418 \tabularnewline
131 & 0.752637 & 0.494725 & 0.247363 \tabularnewline
132 & 0.752661 & 0.494678 & 0.247339 \tabularnewline
133 & 0.707502 & 0.584997 & 0.292498 \tabularnewline
134 & 0.704566 & 0.590869 & 0.295434 \tabularnewline
135 & 0.674794 & 0.650412 & 0.325206 \tabularnewline
136 & 0.62231 & 0.755379 & 0.37769 \tabularnewline
137 & 0.687459 & 0.625083 & 0.312541 \tabularnewline
138 & 0.648124 & 0.703753 & 0.351876 \tabularnewline
139 & 0.591243 & 0.817515 & 0.408757 \tabularnewline
140 & 0.625455 & 0.74909 & 0.374545 \tabularnewline
141 & 0.564571 & 0.870857 & 0.435429 \tabularnewline
142 & 0.686223 & 0.627555 & 0.313777 \tabularnewline
143 & 0.814956 & 0.370088 & 0.185044 \tabularnewline
144 & 0.8401 & 0.319801 & 0.1599 \tabularnewline
145 & 0.795275 & 0.40945 & 0.204725 \tabularnewline
146 & 0.845558 & 0.308883 & 0.154442 \tabularnewline
147 & 0.854426 & 0.291148 & 0.145574 \tabularnewline
148 & 0.879456 & 0.241088 & 0.120544 \tabularnewline
149 & 0.844117 & 0.311766 & 0.155883 \tabularnewline
150 & 0.813216 & 0.373568 & 0.186784 \tabularnewline
151 & 0.782786 & 0.434429 & 0.217214 \tabularnewline
152 & 0.797389 & 0.405223 & 0.202611 \tabularnewline
153 & 0.762991 & 0.474019 & 0.237009 \tabularnewline
154 & 0.700289 & 0.599421 & 0.299711 \tabularnewline
155 & 0.656507 & 0.686986 & 0.343493 \tabularnewline
156 & 0.742799 & 0.514403 & 0.257201 \tabularnewline
157 & 0.665388 & 0.669224 & 0.334612 \tabularnewline
158 & 0.707119 & 0.585762 & 0.292881 \tabularnewline
159 & 0.694727 & 0.610547 & 0.305273 \tabularnewline
160 & 0.570136 & 0.859728 & 0.429864 \tabularnewline
161 & 0.426496 & 0.852991 & 0.573504 \tabularnewline
162 & 0.29582 & 0.59164 & 0.70418 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&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]0.892625[/C][C]0.214749[/C][C]0.107375[/C][/ROW]
[ROW][C]10[/C][C]0.807166[/C][C]0.385667[/C][C]0.192834[/C][/ROW]
[ROW][C]11[/C][C]0.765644[/C][C]0.468711[/C][C]0.234356[/C][/ROW]
[ROW][C]12[/C][C]0.676508[/C][C]0.646983[/C][C]0.323492[/C][/ROW]
[ROW][C]13[/C][C]0.785858[/C][C]0.428283[/C][C]0.214142[/C][/ROW]
[ROW][C]14[/C][C]0.731746[/C][C]0.536507[/C][C]0.268254[/C][/ROW]
[ROW][C]15[/C][C]0.716167[/C][C]0.567665[/C][C]0.283833[/C][/ROW]
[ROW][C]16[/C][C]0.672393[/C][C]0.655215[/C][C]0.327607[/C][/ROW]
[ROW][C]17[/C][C]0.749734[/C][C]0.500532[/C][C]0.250266[/C][/ROW]
[ROW][C]18[/C][C]0.737938[/C][C]0.524125[/C][C]0.262062[/C][/ROW]
[ROW][C]19[/C][C]0.66666[/C][C]0.66668[/C][C]0.33334[/C][/ROW]
[ROW][C]20[/C][C]0.674096[/C][C]0.651808[/C][C]0.325904[/C][/ROW]
[ROW][C]21[/C][C]0.601977[/C][C]0.796045[/C][C]0.398023[/C][/ROW]
[ROW][C]22[/C][C]0.529293[/C][C]0.941414[/C][C]0.470707[/C][/ROW]
[ROW][C]23[/C][C]0.456074[/C][C]0.912148[/C][C]0.543926[/C][/ROW]
[ROW][C]24[/C][C]0.388556[/C][C]0.777112[/C][C]0.611444[/C][/ROW]
[ROW][C]25[/C][C]0.323684[/C][C]0.647368[/C][C]0.676316[/C][/ROW]
[ROW][C]26[/C][C]0.293447[/C][C]0.586895[/C][C]0.706553[/C][/ROW]
[ROW][C]27[/C][C]0.27256[/C][C]0.545121[/C][C]0.72744[/C][/ROW]
[ROW][C]28[/C][C]0.219202[/C][C]0.438403[/C][C]0.780798[/C][/ROW]
[ROW][C]29[/C][C]0.173068[/C][C]0.346135[/C][C]0.826932[/C][/ROW]
[ROW][C]30[/C][C]0.156364[/C][C]0.312728[/C][C]0.843636[/C][/ROW]
[ROW][C]31[/C][C]0.120874[/C][C]0.241747[/C][C]0.879126[/C][/ROW]
[ROW][C]32[/C][C]0.309655[/C][C]0.61931[/C][C]0.690345[/C][/ROW]
[ROW][C]33[/C][C]0.307326[/C][C]0.614653[/C][C]0.692674[/C][/ROW]
[ROW][C]34[/C][C]0.263091[/C][C]0.526183[/C][C]0.736909[/C][/ROW]
[ROW][C]35[/C][C]0.248557[/C][C]0.497114[/C][C]0.751443[/C][/ROW]
[ROW][C]36[/C][C]0.212302[/C][C]0.424603[/C][C]0.787698[/C][/ROW]
[ROW][C]37[/C][C]0.235527[/C][C]0.471053[/C][C]0.764473[/C][/ROW]
[ROW][C]38[/C][C]0.19646[/C][C]0.39292[/C][C]0.80354[/C][/ROW]
[ROW][C]39[/C][C]0.248073[/C][C]0.496146[/C][C]0.751927[/C][/ROW]
[ROW][C]40[/C][C]0.297302[/C][C]0.594603[/C][C]0.702698[/C][/ROW]
[ROW][C]41[/C][C]0.315729[/C][C]0.631457[/C][C]0.684271[/C][/ROW]
[ROW][C]42[/C][C]0.269916[/C][C]0.539832[/C][C]0.730084[/C][/ROW]
[ROW][C]43[/C][C]0.22793[/C][C]0.45586[/C][C]0.77207[/C][/ROW]
[ROW][C]44[/C][C]0.251879[/C][C]0.503757[/C][C]0.748121[/C][/ROW]
[ROW][C]45[/C][C]0.216012[/C][C]0.432024[/C][C]0.783988[/C][/ROW]
[ROW][C]46[/C][C]0.225473[/C][C]0.450947[/C][C]0.774527[/C][/ROW]
[ROW][C]47[/C][C]0.205575[/C][C]0.41115[/C][C]0.794425[/C][/ROW]
[ROW][C]48[/C][C]0.17108[/C][C]0.342161[/C][C]0.82892[/C][/ROW]
[ROW][C]49[/C][C]0.140974[/C][C]0.281948[/C][C]0.859026[/C][/ROW]
[ROW][C]50[/C][C]0.120981[/C][C]0.241962[/C][C]0.879019[/C][/ROW]
[ROW][C]51[/C][C]0.0995292[/C][C]0.199058[/C][C]0.900471[/C][/ROW]
[ROW][C]52[/C][C]0.0983823[/C][C]0.196765[/C][C]0.901618[/C][/ROW]
[ROW][C]53[/C][C]0.110975[/C][C]0.221949[/C][C]0.889025[/C][/ROW]
[ROW][C]54[/C][C]0.19077[/C][C]0.38154[/C][C]0.80923[/C][/ROW]
[ROW][C]55[/C][C]0.162827[/C][C]0.325654[/C][C]0.837173[/C][/ROW]
[ROW][C]56[/C][C]0.20332[/C][C]0.40664[/C][C]0.79668[/C][/ROW]
[ROW][C]57[/C][C]0.378978[/C][C]0.757955[/C][C]0.621022[/C][/ROW]
[ROW][C]58[/C][C]0.39276[/C][C]0.785521[/C][C]0.60724[/C][/ROW]
[ROW][C]59[/C][C]0.392393[/C][C]0.784786[/C][C]0.607607[/C][/ROW]
[ROW][C]60[/C][C]0.368127[/C][C]0.736253[/C][C]0.631873[/C][/ROW]
[ROW][C]61[/C][C]0.366018[/C][C]0.732035[/C][C]0.633982[/C][/ROW]
[ROW][C]62[/C][C]0.324464[/C][C]0.648927[/C][C]0.675536[/C][/ROW]
[ROW][C]63[/C][C]0.311773[/C][C]0.623547[/C][C]0.688227[/C][/ROW]
[ROW][C]64[/C][C]0.294815[/C][C]0.589629[/C][C]0.705185[/C][/ROW]
[ROW][C]65[/C][C]0.274154[/C][C]0.548308[/C][C]0.725846[/C][/ROW]
[ROW][C]66[/C][C]0.238591[/C][C]0.477182[/C][C]0.761409[/C][/ROW]
[ROW][C]67[/C][C]0.206969[/C][C]0.413938[/C][C]0.793031[/C][/ROW]
[ROW][C]68[/C][C]0.177185[/C][C]0.354371[/C][C]0.822815[/C][/ROW]
[ROW][C]69[/C][C]0.155001[/C][C]0.310002[/C][C]0.844999[/C][/ROW]
[ROW][C]70[/C][C]0.160011[/C][C]0.320022[/C][C]0.839989[/C][/ROW]
[ROW][C]71[/C][C]0.167994[/C][C]0.335988[/C][C]0.832006[/C][/ROW]
[ROW][C]72[/C][C]0.1516[/C][C]0.303199[/C][C]0.8484[/C][/ROW]
[ROW][C]73[/C][C]0.127112[/C][C]0.254224[/C][C]0.872888[/C][/ROW]
[ROW][C]74[/C][C]0.109688[/C][C]0.219375[/C][C]0.890312[/C][/ROW]
[ROW][C]75[/C][C]0.0913173[/C][C]0.182635[/C][C]0.908683[/C][/ROW]
[ROW][C]76[/C][C]0.0740305[/C][C]0.148061[/C][C]0.925969[/C][/ROW]
[ROW][C]77[/C][C]0.0625138[/C][C]0.125028[/C][C]0.937486[/C][/ROW]
[ROW][C]78[/C][C]0.0505075[/C][C]0.101015[/C][C]0.949492[/C][/ROW]
[ROW][C]79[/C][C]0.04176[/C][C]0.08352[/C][C]0.95824[/C][/ROW]
[ROW][C]80[/C][C]0.0821655[/C][C]0.164331[/C][C]0.917834[/C][/ROW]
[ROW][C]81[/C][C]0.136528[/C][C]0.273055[/C][C]0.863472[/C][/ROW]
[ROW][C]82[/C][C]0.113889[/C][C]0.227778[/C][C]0.886111[/C][/ROW]
[ROW][C]83[/C][C]0.093989[/C][C]0.187978[/C][C]0.906011[/C][/ROW]
[ROW][C]84[/C][C]0.0766591[/C][C]0.153318[/C][C]0.923341[/C][/ROW]
[ROW][C]85[/C][C]0.062489[/C][C]0.124978[/C][C]0.937511[/C][/ROW]
[ROW][C]86[/C][C]0.0499437[/C][C]0.0998874[/C][C]0.950056[/C][/ROW]
[ROW][C]87[/C][C]0.0407044[/C][C]0.0814089[/C][C]0.959296[/C][/ROW]
[ROW][C]88[/C][C]0.0431763[/C][C]0.0863525[/C][C]0.956824[/C][/ROW]
[ROW][C]89[/C][C]0.0353486[/C][C]0.0706972[/C][C]0.964651[/C][/ROW]
[ROW][C]90[/C][C]0.0283277[/C][C]0.0566553[/C][C]0.971672[/C][/ROW]
[ROW][C]91[/C][C]0.626705[/C][C]0.74659[/C][C]0.373295[/C][/ROW]
[ROW][C]92[/C][C]0.624531[/C][C]0.750938[/C][C]0.375469[/C][/ROW]
[ROW][C]93[/C][C]0.615336[/C][C]0.769328[/C][C]0.384664[/C][/ROW]
[ROW][C]94[/C][C]0.573033[/C][C]0.853933[/C][C]0.426967[/C][/ROW]
[ROW][C]95[/C][C]0.733649[/C][C]0.532702[/C][C]0.266351[/C][/ROW]
[ROW][C]96[/C][C]0.754629[/C][C]0.490741[/C][C]0.245371[/C][/ROW]
[ROW][C]97[/C][C]0.726587[/C][C]0.546826[/C][C]0.273413[/C][/ROW]
[ROW][C]98[/C][C]0.740726[/C][C]0.518547[/C][C]0.259274[/C][/ROW]
[ROW][C]99[/C][C]0.835334[/C][C]0.329332[/C][C]0.164666[/C][/ROW]
[ROW][C]100[/C][C]0.856693[/C][C]0.286613[/C][C]0.143307[/C][/ROW]
[ROW][C]101[/C][C]0.849876[/C][C]0.300249[/C][C]0.150124[/C][/ROW]
[ROW][C]102[/C][C]0.833405[/C][C]0.333189[/C][C]0.166595[/C][/ROW]
[ROW][C]103[/C][C]0.803191[/C][C]0.393618[/C][C]0.196809[/C][/ROW]
[ROW][C]104[/C][C]0.782788[/C][C]0.434425[/C][C]0.217212[/C][/ROW]
[ROW][C]105[/C][C]0.74828[/C][C]0.503439[/C][C]0.25172[/C][/ROW]
[ROW][C]106[/C][C]0.710069[/C][C]0.579861[/C][C]0.289931[/C][/ROW]
[ROW][C]107[/C][C]0.691988[/C][C]0.616024[/C][C]0.308012[/C][/ROW]
[ROW][C]108[/C][C]0.726442[/C][C]0.547116[/C][C]0.273558[/C][/ROW]
[ROW][C]109[/C][C]0.68675[/C][C]0.626499[/C][C]0.31325[/C][/ROW]
[ROW][C]110[/C][C]0.766401[/C][C]0.467198[/C][C]0.233599[/C][/ROW]
[ROW][C]111[/C][C]0.814649[/C][C]0.370701[/C][C]0.185351[/C][/ROW]
[ROW][C]112[/C][C]0.784493[/C][C]0.431015[/C][C]0.215507[/C][/ROW]
[ROW][C]113[/C][C]0.796638[/C][C]0.406724[/C][C]0.203362[/C][/ROW]
[ROW][C]114[/C][C]0.789328[/C][C]0.421344[/C][C]0.210672[/C][/ROW]
[ROW][C]115[/C][C]0.76032[/C][C]0.479359[/C][C]0.23968[/C][/ROW]
[ROW][C]116[/C][C]0.791386[/C][C]0.417229[/C][C]0.208614[/C][/ROW]
[ROW][C]117[/C][C]0.805436[/C][C]0.389128[/C][C]0.194564[/C][/ROW]
[ROW][C]118[/C][C]0.836074[/C][C]0.327853[/C][C]0.163926[/C][/ROW]
[ROW][C]119[/C][C]0.83086[/C][C]0.338281[/C][C]0.16914[/C][/ROW]
[ROW][C]120[/C][C]0.804972[/C][C]0.390056[/C][C]0.195028[/C][/ROW]
[ROW][C]121[/C][C]0.782681[/C][C]0.434638[/C][C]0.217319[/C][/ROW]
[ROW][C]122[/C][C]0.750894[/C][C]0.498212[/C][C]0.249106[/C][/ROW]
[ROW][C]123[/C][C]0.744693[/C][C]0.510615[/C][C]0.255307[/C][/ROW]
[ROW][C]124[/C][C]0.749276[/C][C]0.501448[/C][C]0.250724[/C][/ROW]
[ROW][C]125[/C][C]0.745609[/C][C]0.508782[/C][C]0.254391[/C][/ROW]
[ROW][C]126[/C][C]0.735955[/C][C]0.52809[/C][C]0.264045[/C][/ROW]
[ROW][C]127[/C][C]0.805478[/C][C]0.389043[/C][C]0.194522[/C][/ROW]
[ROW][C]128[/C][C]0.814174[/C][C]0.371653[/C][C]0.185826[/C][/ROW]
[ROW][C]129[/C][C]0.809518[/C][C]0.380963[/C][C]0.190482[/C][/ROW]
[ROW][C]130[/C][C]0.780582[/C][C]0.438835[/C][C]0.219418[/C][/ROW]
[ROW][C]131[/C][C]0.752637[/C][C]0.494725[/C][C]0.247363[/C][/ROW]
[ROW][C]132[/C][C]0.752661[/C][C]0.494678[/C][C]0.247339[/C][/ROW]
[ROW][C]133[/C][C]0.707502[/C][C]0.584997[/C][C]0.292498[/C][/ROW]
[ROW][C]134[/C][C]0.704566[/C][C]0.590869[/C][C]0.295434[/C][/ROW]
[ROW][C]135[/C][C]0.674794[/C][C]0.650412[/C][C]0.325206[/C][/ROW]
[ROW][C]136[/C][C]0.62231[/C][C]0.755379[/C][C]0.37769[/C][/ROW]
[ROW][C]137[/C][C]0.687459[/C][C]0.625083[/C][C]0.312541[/C][/ROW]
[ROW][C]138[/C][C]0.648124[/C][C]0.703753[/C][C]0.351876[/C][/ROW]
[ROW][C]139[/C][C]0.591243[/C][C]0.817515[/C][C]0.408757[/C][/ROW]
[ROW][C]140[/C][C]0.625455[/C][C]0.74909[/C][C]0.374545[/C][/ROW]
[ROW][C]141[/C][C]0.564571[/C][C]0.870857[/C][C]0.435429[/C][/ROW]
[ROW][C]142[/C][C]0.686223[/C][C]0.627555[/C][C]0.313777[/C][/ROW]
[ROW][C]143[/C][C]0.814956[/C][C]0.370088[/C][C]0.185044[/C][/ROW]
[ROW][C]144[/C][C]0.8401[/C][C]0.319801[/C][C]0.1599[/C][/ROW]
[ROW][C]145[/C][C]0.795275[/C][C]0.40945[/C][C]0.204725[/C][/ROW]
[ROW][C]146[/C][C]0.845558[/C][C]0.308883[/C][C]0.154442[/C][/ROW]
[ROW][C]147[/C][C]0.854426[/C][C]0.291148[/C][C]0.145574[/C][/ROW]
[ROW][C]148[/C][C]0.879456[/C][C]0.241088[/C][C]0.120544[/C][/ROW]
[ROW][C]149[/C][C]0.844117[/C][C]0.311766[/C][C]0.155883[/C][/ROW]
[ROW][C]150[/C][C]0.813216[/C][C]0.373568[/C][C]0.186784[/C][/ROW]
[ROW][C]151[/C][C]0.782786[/C][C]0.434429[/C][C]0.217214[/C][/ROW]
[ROW][C]152[/C][C]0.797389[/C][C]0.405223[/C][C]0.202611[/C][/ROW]
[ROW][C]153[/C][C]0.762991[/C][C]0.474019[/C][C]0.237009[/C][/ROW]
[ROW][C]154[/C][C]0.700289[/C][C]0.599421[/C][C]0.299711[/C][/ROW]
[ROW][C]155[/C][C]0.656507[/C][C]0.686986[/C][C]0.343493[/C][/ROW]
[ROW][C]156[/C][C]0.742799[/C][C]0.514403[/C][C]0.257201[/C][/ROW]
[ROW][C]157[/C][C]0.665388[/C][C]0.669224[/C][C]0.334612[/C][/ROW]
[ROW][C]158[/C][C]0.707119[/C][C]0.585762[/C][C]0.292881[/C][/ROW]
[ROW][C]159[/C][C]0.694727[/C][C]0.610547[/C][C]0.305273[/C][/ROW]
[ROW][C]160[/C][C]0.570136[/C][C]0.859728[/C][C]0.429864[/C][/ROW]
[ROW][C]161[/C][C]0.426496[/C][C]0.852991[/C][C]0.573504[/C][/ROW]
[ROW][C]162[/C][C]0.29582[/C][C]0.59164[/C][C]0.70418[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&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
90.8926250.2147490.107375
100.8071660.3856670.192834
110.7656440.4687110.234356
120.6765080.6469830.323492
130.7858580.4282830.214142
140.7317460.5365070.268254
150.7161670.5676650.283833
160.6723930.6552150.327607
170.7497340.5005320.250266
180.7379380.5241250.262062
190.666660.666680.33334
200.6740960.6518080.325904
210.6019770.7960450.398023
220.5292930.9414140.470707
230.4560740.9121480.543926
240.3885560.7771120.611444
250.3236840.6473680.676316
260.2934470.5868950.706553
270.272560.5451210.72744
280.2192020.4384030.780798
290.1730680.3461350.826932
300.1563640.3127280.843636
310.1208740.2417470.879126
320.3096550.619310.690345
330.3073260.6146530.692674
340.2630910.5261830.736909
350.2485570.4971140.751443
360.2123020.4246030.787698
370.2355270.4710530.764473
380.196460.392920.80354
390.2480730.4961460.751927
400.2973020.5946030.702698
410.3157290.6314570.684271
420.2699160.5398320.730084
430.227930.455860.77207
440.2518790.5037570.748121
450.2160120.4320240.783988
460.2254730.4509470.774527
470.2055750.411150.794425
480.171080.3421610.82892
490.1409740.2819480.859026
500.1209810.2419620.879019
510.09952920.1990580.900471
520.09838230.1967650.901618
530.1109750.2219490.889025
540.190770.381540.80923
550.1628270.3256540.837173
560.203320.406640.79668
570.3789780.7579550.621022
580.392760.7855210.60724
590.3923930.7847860.607607
600.3681270.7362530.631873
610.3660180.7320350.633982
620.3244640.6489270.675536
630.3117730.6235470.688227
640.2948150.5896290.705185
650.2741540.5483080.725846
660.2385910.4771820.761409
670.2069690.4139380.793031
680.1771850.3543710.822815
690.1550010.3100020.844999
700.1600110.3200220.839989
710.1679940.3359880.832006
720.15160.3031990.8484
730.1271120.2542240.872888
740.1096880.2193750.890312
750.09131730.1826350.908683
760.07403050.1480610.925969
770.06251380.1250280.937486
780.05050750.1010150.949492
790.041760.083520.95824
800.08216550.1643310.917834
810.1365280.2730550.863472
820.1138890.2277780.886111
830.0939890.1879780.906011
840.07665910.1533180.923341
850.0624890.1249780.937511
860.04994370.09988740.950056
870.04070440.08140890.959296
880.04317630.08635250.956824
890.03534860.07069720.964651
900.02832770.05665530.971672
910.6267050.746590.373295
920.6245310.7509380.375469
930.6153360.7693280.384664
940.5730330.8539330.426967
950.7336490.5327020.266351
960.7546290.4907410.245371
970.7265870.5468260.273413
980.7407260.5185470.259274
990.8353340.3293320.164666
1000.8566930.2866130.143307
1010.8498760.3002490.150124
1020.8334050.3331890.166595
1030.8031910.3936180.196809
1040.7827880.4344250.217212
1050.748280.5034390.25172
1060.7100690.5798610.289931
1070.6919880.6160240.308012
1080.7264420.5471160.273558
1090.686750.6264990.31325
1100.7664010.4671980.233599
1110.8146490.3707010.185351
1120.7844930.4310150.215507
1130.7966380.4067240.203362
1140.7893280.4213440.210672
1150.760320.4793590.23968
1160.7913860.4172290.208614
1170.8054360.3891280.194564
1180.8360740.3278530.163926
1190.830860.3382810.16914
1200.8049720.3900560.195028
1210.7826810.4346380.217319
1220.7508940.4982120.249106
1230.7446930.5106150.255307
1240.7492760.5014480.250724
1250.7456090.5087820.254391
1260.7359550.528090.264045
1270.8054780.3890430.194522
1280.8141740.3716530.185826
1290.8095180.3809630.190482
1300.7805820.4388350.219418
1310.7526370.4947250.247363
1320.7526610.4946780.247339
1330.7075020.5849970.292498
1340.7045660.5908690.295434
1350.6747940.6504120.325206
1360.622310.7553790.37769
1370.6874590.6250830.312541
1380.6481240.7037530.351876
1390.5912430.8175150.408757
1400.6254550.749090.374545
1410.5645710.8708570.435429
1420.6862230.6275550.313777
1430.8149560.3700880.185044
1440.84010.3198010.1599
1450.7952750.409450.204725
1460.8455580.3088830.154442
1470.8544260.2911480.145574
1480.8794560.2410880.120544
1490.8441170.3117660.155883
1500.8132160.3735680.186784
1510.7827860.4344290.217214
1520.7973890.4052230.202611
1530.7629910.4740190.237009
1540.7002890.5994210.299711
1550.6565070.6869860.343493
1560.7427990.5144030.257201
1570.6653880.6692240.334612
1580.7071190.5857620.292881
1590.6947270.6105470.305273
1600.5701360.8597280.429864
1610.4264960.8529910.573504
1620.295820.591640.70418






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.038961OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.038961 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263958&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.038961[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263958&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263958&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.038961OK


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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}