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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 10 Dec 2014 15:24:12 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/10/t1418225410kzbjnzvemv8bctl.htm/, Retrieved Sun, 19 May 2024 14:42:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265384, Retrieved Sun, 19 May 2024 14:42:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper] [2014-12-10 15:24:12] [8c0dfc7b9b8e9dc8ad6d66876f6d8b28] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11.3 62 72 11 0 16 12 15 91
9.6 56 61 6 1 13 8 21 137
16.1 57 68 7 1 14 7 30 148
13.4 51 61 10 0 16 12 20 92
12.7 56 64 9 1 17 13 14 131
12.3 30 65 7 1 16 11 18 59
7.9 61 69 4 1 15 12 19 90
12.3 47 63 4 1 13 10 25 83
11.6 56 75 4 1 14 11 23 116
6.7 50 63 8 1 13 7 17 42
12.1 67 73 4 1 19 13 21 155
5.7 41 75 7 0 15 6 21 128
8 45 63 4 0 10 10 8 49
13.3 48 63 4 1 16 12 29 96
9.1 44 62 9 1 12 12 20 66
12.2 37 64 4 0 15 12 19 104
8.8 56 60 10 0 11 8 22 76
14.6 66 56 4 1 9 10 23 99
12.6 38 59 5 1 12 12 24 108
9.9 34 68 4 0 14 9 12 74
10.5 49 66 4 0 14 11 22 96
13.4 55 73 4 0 13 10 12 116
10.9 49 72 4 0 16 12 22 87
4.3 59 71 6 1 13 10 20 97
10.3 40 59 10 0 16 9 10 127
11.8 58 64 7 1 16 11 23 106
11.2 60 66 4 1 16 12 17 80
11.4 63 78 4 0 10 7 22 74
8.6 56 68 7 0 12 11 24 91
13.2 54 73 4 0 12 12 18 133
12.6 52 62 8 1 12 6 21 74
5.6 34 65 11 1 12 9 20 114
9.9 69 68 6 1 19 15 20 140
8.8 32 65 14 0 14 10 22 95
7.7 48 60 5 1 13 11 19 98
9 67 71 4 0 16 12 20 121
7.3 58 65 8 1 15 12 26 126
11.4 57 68 9 1 12 12 23 98
13.6 42 64 4 1 8 11 24 95
7.9 64 74 4 1 10 9 21 110
10.7 58 69 5 1 16 11 21 70
10.3 66 76 4 0 16 12 19 102
8.3 26 68 5 1 10 12 8 86
9.6 61 72 4 1 18 14 17 130
14.2 52 67 4 1 12 8 20 96
8.5 51 63 7 0 16 10 11 102
13.5 55 59 10 0 10 9 8 100
4.9 50 73 4 0 14 10 15 94
6.4 60 66 5 0 12 9 18 52
9.6 56 62 4 0 11 10 18 98
11.6 63 69 4 0 15 12 19 118
11.1 61 66 4 1 7 11 19 99
16.6 52 57 4 0 12 7 30 109
12.6 55 56 17 1 15 12 17 68
18.9 72 71 4 1 16 12 24 131
11.6 33 56 23 1 6 6 20 71
14.6 66 62 4 1 16 11 25 68
13.85 66 59 5 1 16 10 20 89
14.85 64 57 5 0 16 13 27 115
11.75 40 66 4 0 16 12 18 78
18.45 46 63 6 0 16 12 28 118
15.9 58 69 4 1 17 10 21 87
19.9 51 48 9 0 9 8 27 162
10.95 50 66 18 1 15 12 22 49
18.45 52 73 6 0 14 9 28 122
15.1 54 67 5 1 15 12 25 96
15 66 61 4 0 13 9 21 100
11.35 61 68 11 0 16 11 22 82
15.95 80 75 4 1 20 15 28 100
18.1 51 62 10 0 14 8 20 115
14.6 56 69 6 1 12 8 29 141
17.6 53 74 6 1 15 11 20 110
15.35 47 63 4 1 16 12 20 146
13.4 50 58 9 0 11 8 23 90
13.9 39 58 5 0 9 4 18 121
15.25 58 72 4 0 16 10 18 104
12.9 35 62 15 1 14 7 19 147
16.1 58 62 10 0 15 12 25 110
17.35 60 65 9 0 13 11 25 108
13.15 62 69 7 0 13 9 25 113
12.15 63 66 9 0 12 10 24 115
12.6 53 72 6 1 16 8 19 61
10.35 46 62 4 1 14 8 26 60
15.4 67 75 7 1 16 11 10 109
9.6 59 58 4 1 14 12 17 68
18.2 64 66 7 0 15 10 13 111
13.6 38 55 4 0 10 10 17 77
14.85 50 47 15 1 16 12 30 73
14.1 48 62 9 0 16 11 4 89
14.9 47 64 4 0 12 8 16 78
16.25 66 64 4 0 16 10 21 110
13.6 63 50 4 1 15 9 22 65
15.65 44 70 4 0 16 10 20 117
14.6 43 69 4 0 15 12 22 63
12.65 38 48 12 1 13 8 23 52
19.2 45 73 4 0 7 3 0 131
16.6 50 74 6 1 7 8 18 101
11.2 54 66 6 1 17 12 25 42
13.2 55 78 4 0 8 12 18 77
15.85 37 60 7 1 15 10 18 96
11.15 46 69 7 1 16 9 24 57
15.65 51 65 4 0 14 12 29 112
7.65 64 78 12 0 19 14 15 49
15.2 47 63 17 0 11 8 22 56
15.6 62 71 5 1 15 12 23 86
13.1 67 80 4 1 17 13 24 88
11.85 56 73 8 0 9 7 22 48
12.4 65 69 5 1 19 14 15 85
11.4 50 84 4 0 17 13 17 63
14.9 57 64 4 1 16 12 20 102
19.9 47 58 16 0 9 8 27 162
11.2 47 59 7 1 11 13 26 86
14.6 57 78 4 1 14 9 23 114
14.75 50 67 7 1 16 12 23 94
15.15 22 60 19 0 17 10 15 81
16.85 59 66 4 0 15 10 26 110
7.85 56 74 4 1 17 13 22 64
12.6 53 72 9 0 10 9 18 104
7.85 42 55 5 1 16 11 15 105
10.95 52 49 14 1 15 12 22 49
12.35 54 74 4 0 11 8 27 88
9.95 44 53 16 1 16 12 10 95
14.9 62 64 10 1 16 12 20 102
16.65 53 65 5 0 16 12 17 99
13.4 50 57 6 1 14 9 23 63
13.95 36 51 4 0 14 12 19 76
15.7 76 80 4 0 16 12 13 109
16.85 66 67 4 1 16 11 27 117
10.95 62 70 5 1 18 12 23 57
15.35 59 74 4 0 14 6 16 120
12.2 47 75 4 1 20 7 25 73
15.1 55 70 5 0 15 10 2 91
17.75 58 69 4 0 16 12 26 108
15.2 60 65 4 1 16 10 20 105
16.65 57 71 8 0 12 9 22 119
8.1 45 65 15 1 8 3 24 31

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = + 11.018 + 0.0448807Tot_Intr._Motv[t] -0.0765344Tot_Extr._Motv[t] -0.0170048Demotivatie[t] -1.19985Geslacht_Bin[t] + 0.0975135Zelfvertrouwen_statis[t] -0.189899Zelfvertrouwen_software[t] + 0.0991938NUMERACYTOT[t] + 0.0387479LFM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  +  11.018 +  0.0448807Tot_Intr._Motv[t] -0.0765344Tot_Extr._Motv[t] -0.0170048Demotivatie[t] -1.19985Geslacht_Bin[t] +  0.0975135Zelfvertrouwen_statis[t] -0.189899Zelfvertrouwen_software[t] +  0.0991938NUMERACYTOT[t] +  0.0387479LFM[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  +  11.018 +  0.0448807Tot_Intr._Motv[t] -0.0765344Tot_Extr._Motv[t] -0.0170048Demotivatie[t] -1.19985Geslacht_Bin[t] +  0.0975135Zelfvertrouwen_statis[t] -0.189899Zelfvertrouwen_software[t] +  0.0991938NUMERACYTOT[t] +  0.0387479LFM[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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
Totaal[t] = + 11.018 + 0.0448807Tot_Intr._Motv[t] -0.0765344Tot_Extr._Motv[t] -0.0170048Demotivatie[t] -1.19985Geslacht_Bin[t] + 0.0975135Zelfvertrouwen_statis[t] -0.189899Zelfvertrouwen_software[t] + 0.0991938NUMERACYTOT[t] + 0.0387479LFM[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.0183.457253.1870.001810320.00090516
Tot_Intr._Motv0.04488070.03006911.4930.1380270.0690133
Tot_Extr._Motv-0.07653440.0422088-1.8130.07215760.0360788
Demotivatie-0.01700480.0759404-0.22390.8231770.411588
Geslacht_Bin-1.199850.544542-2.2030.02936950.0146847
Zelfvertrouwen_statis0.09751350.1126510.86560.388330.194165
Zelfvertrouwen_software-0.1898990.146168-1.2990.1962330.0981167
NUMERACYTOT0.09919380.04983871.990.04870560.0243528
LFM0.03874790.01006263.8510.0001858079.29037e-05

\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) & 11.018 & 3.45725 & 3.187 & 0.00181032 & 0.00090516 \tabularnewline
Tot_Intr._Motv & 0.0448807 & 0.0300691 & 1.493 & 0.138027 & 0.0690133 \tabularnewline
Tot_Extr._Motv & -0.0765344 & 0.0422088 & -1.813 & 0.0721576 & 0.0360788 \tabularnewline
Demotivatie & -0.0170048 & 0.0759404 & -0.2239 & 0.823177 & 0.411588 \tabularnewline
Geslacht_Bin & -1.19985 & 0.544542 & -2.203 & 0.0293695 & 0.0146847 \tabularnewline
Zelfvertrouwen_statis & 0.0975135 & 0.112651 & 0.8656 & 0.38833 & 0.194165 \tabularnewline
Zelfvertrouwen_software & -0.189899 & 0.146168 & -1.299 & 0.196233 & 0.0981167 \tabularnewline
NUMERACYTOT & 0.0991938 & 0.0498387 & 1.99 & 0.0487056 & 0.0243528 \tabularnewline
LFM & 0.0387479 & 0.0100626 & 3.851 & 0.000185807 & 9.29037e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&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]11.018[/C][C]3.45725[/C][C]3.187[/C][C]0.00181032[/C][C]0.00090516[/C][/ROW]
[ROW][C]Tot_Intr._Motv[/C][C]0.0448807[/C][C]0.0300691[/C][C]1.493[/C][C]0.138027[/C][C]0.0690133[/C][/ROW]
[ROW][C]Tot_Extr._Motv[/C][C]-0.0765344[/C][C]0.0422088[/C][C]-1.813[/C][C]0.0721576[/C][C]0.0360788[/C][/ROW]
[ROW][C]Demotivatie[/C][C]-0.0170048[/C][C]0.0759404[/C][C]-0.2239[/C][C]0.823177[/C][C]0.411588[/C][/ROW]
[ROW][C]Geslacht_Bin[/C][C]-1.19985[/C][C]0.544542[/C][C]-2.203[/C][C]0.0293695[/C][C]0.0146847[/C][/ROW]
[ROW][C]Zelfvertrouwen_statis[/C][C]0.0975135[/C][C]0.112651[/C][C]0.8656[/C][C]0.38833[/C][C]0.194165[/C][/ROW]
[ROW][C]Zelfvertrouwen_software[/C][C]-0.189899[/C][C]0.146168[/C][C]-1.299[/C][C]0.196233[/C][C]0.0981167[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.0991938[/C][C]0.0498387[/C][C]1.99[/C][C]0.0487056[/C][C]0.0243528[/C][/ROW]
[ROW][C]LFM[/C][C]0.0387479[/C][C]0.0100626[/C][C]3.851[/C][C]0.000185807[/C][C]9.29037e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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)11.0183.457253.1870.001810320.00090516
Tot_Intr._Motv0.04488070.03006911.4930.1380270.0690133
Tot_Extr._Motv-0.07653440.0422088-1.8130.07215760.0360788
Demotivatie-0.01700480.0759404-0.22390.8231770.411588
Geslacht_Bin-1.199850.544542-2.2030.02936950.0146847
Zelfvertrouwen_statis0.09751350.1126510.86560.388330.194165
Zelfvertrouwen_software-0.1898990.146168-1.2990.1962330.0981167
NUMERACYTOT0.09919380.04983871.990.04870560.0243528
LFM0.03874790.01006263.8510.0001858079.29037e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.464595
R-squared0.215848
Adjusted R-squared0.166453
F-TEST (value)4.36981
F-TEST (DF numerator)8
F-TEST (DF denominator)127
p-value0.000110257
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.00654
Sum Squared Residuals1147.99

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.464595 \tabularnewline
R-squared & 0.215848 \tabularnewline
Adjusted R-squared & 0.166453 \tabularnewline
F-TEST (value) & 4.36981 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 127 \tabularnewline
p-value & 0.000110257 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.00654 \tabularnewline
Sum Squared Residuals & 1147.99 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.464595[/C][/ROW]
[ROW][C]R-squared[/C][C]0.215848[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.166453[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.36981[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]127[/C][/ROW]
[ROW][C]p-value[/C][C]0.000110257[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.00654[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1147.99[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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 R0.464595
R-squared0.215848
Adjusted R-squared0.166453
F-TEST (value)4.36981
F-TEST (DF numerator)8
F-TEST (DF denominator)127
p-value0.000110257
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.00654
Sum Squared Residuals1147.99






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.312.3984-1.09842
29.614.7008-5.10081
316.115.79930.300669
413.413.29830.101669
512.712.9339-0.233909
612.39.61372.6863
77.911.7628-3.86284
812.312.10240.197581
911.612.5758-0.975839
106.710.3565-3.65652
1112.114.6431-2.54314
125.714.3651-8.66506
1389.91624-1.91624
1413.312.96050.339461
159.110.3273-1.22729
1612.212.8107-0.610695
178.813.4497-4.64972
1814.613.52241.07758
1912.612.37980.220183
209.910.9853-1.08531
2110.513.2762-2.77618
2213.412.88510.514874
2310.912.4734-1.57337
244.312.0412-7.7412
2510.313.8916-3.59165
2611.813.264-1.46401
2711.211.4592-0.259212
2811.412.5032-1.10318
298.613.1959-4.59588
3013.213.6168-0.416811
3112.612.25190.348086
325.612.0445-6.44447
339.914.0214-4.12136
348.812.5708-3.77084
357.712.1561-4.45605
36914.4768-5.4768
377.313.9556-6.6556
3811.411.989-0.589048
3913.611.48982.1102
407.912.5703-4.67029
4110.711.322-0.622039
4210.313.2138-2.91384
438.38.51786-0.217858
449.612.7975-3.19751
4514.212.31071.88928
468.513.0708-4.57081
4713.512.73520.764804
484.912.2034-7.30336
496.411.8359-5.43595
509.613.4746-3.87456
5111.614.1374-2.53739
5211.111.751-0.650968
5316.615.96150.638528
5412.611.21661.3834
5518.914.28564.61439
5611.610.80280.797204
5714.612.55312.04689
5813.8513.27330.576657
5914.8515.6686-0.8186
6011.7511.7831-0.0331425
6118.4514.78993.66013
6215.912.28523.61483
6319.917.7942.10601
6410.959.969610.980391
6518.4514.82353.62652
6615.112.41242.68761
671514.75990.240096
6811.3513.1952-1.8452
6915.9513.35452.59554
7018.114.67763.42243
7114.614.9396-0.339566
7217.612.05125.54883
7315.3513.96031.38969
7413.413.9922-0.592173
7513.914.8363-0.936289
7615.2513.5191.73097
7712.914.0052-1.10524
7816.114.63191.46812
7917.3514.42642.92358
8013.1514.8176-1.66759
8112.1514.749-2.59895
8212.610.87361.72639
8310.3511.8194-1.46938
8415.411.65283.74722
859.611.3666-1.76661
8618.213.87434.32574
8713.612.1921.40797
8814.8513.29581.55421
8914.111.59072.50929
9014.912.42152.47847
9116.2515.02041.22958
9213.613.20530.394661
9315.6513.74591.90412
9414.611.40623.19377
9512.6511.69070.959305
9619.212.57146.62858
9716.611.1595.441
9811.210.57460.625437
9913.210.71912.48092
10015.8511.83664.01341
10111.1510.92310.226882
10215.6514.56691.0831
1037.6510.2973-2.6473
10415.211.92223.2778
10515.611.87943.72057
10613.111.61391.48615
10711.8511.39870.451287
10812.411.34511.0549
10911.410.08151.31847
11014.912.62772.27233
11119.916.73013.16991
11211.211.8083-0.608255
11314.612.69341.90658
11414.7512.02052.72951
11515.1511.47543.6746
11616.8514.95161.89836
1177.8510.451-2.60103
11812.612.8144-0.214422
1197.8512.4364-4.58644
12010.9511.4285-0.478474
12112.3513.3514-1.00145
1229.9511.4189-1.46887
12314.912.752.14995
12416.6513.14063.50937
12513.411.97631.42368
12613.9512.57831.37169
12715.713.03262.66742
12816.8514.26752.58253
12910.9511.1248-0.17482
13015.3514.3970.953009
13112.212.04880.151181
13215.111.33213.76788
13317.7514.31743.43262
13415.213.18182.01818
13516.6514.26052.3895
1368.110.4002-2.30018

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.3 & 12.3984 & -1.09842 \tabularnewline
2 & 9.6 & 14.7008 & -5.10081 \tabularnewline
3 & 16.1 & 15.7993 & 0.300669 \tabularnewline
4 & 13.4 & 13.2983 & 0.101669 \tabularnewline
5 & 12.7 & 12.9339 & -0.233909 \tabularnewline
6 & 12.3 & 9.6137 & 2.6863 \tabularnewline
7 & 7.9 & 11.7628 & -3.86284 \tabularnewline
8 & 12.3 & 12.1024 & 0.197581 \tabularnewline
9 & 11.6 & 12.5758 & -0.975839 \tabularnewline
10 & 6.7 & 10.3565 & -3.65652 \tabularnewline
11 & 12.1 & 14.6431 & -2.54314 \tabularnewline
12 & 5.7 & 14.3651 & -8.66506 \tabularnewline
13 & 8 & 9.91624 & -1.91624 \tabularnewline
14 & 13.3 & 12.9605 & 0.339461 \tabularnewline
15 & 9.1 & 10.3273 & -1.22729 \tabularnewline
16 & 12.2 & 12.8107 & -0.610695 \tabularnewline
17 & 8.8 & 13.4497 & -4.64972 \tabularnewline
18 & 14.6 & 13.5224 & 1.07758 \tabularnewline
19 & 12.6 & 12.3798 & 0.220183 \tabularnewline
20 & 9.9 & 10.9853 & -1.08531 \tabularnewline
21 & 10.5 & 13.2762 & -2.77618 \tabularnewline
22 & 13.4 & 12.8851 & 0.514874 \tabularnewline
23 & 10.9 & 12.4734 & -1.57337 \tabularnewline
24 & 4.3 & 12.0412 & -7.7412 \tabularnewline
25 & 10.3 & 13.8916 & -3.59165 \tabularnewline
26 & 11.8 & 13.264 & -1.46401 \tabularnewline
27 & 11.2 & 11.4592 & -0.259212 \tabularnewline
28 & 11.4 & 12.5032 & -1.10318 \tabularnewline
29 & 8.6 & 13.1959 & -4.59588 \tabularnewline
30 & 13.2 & 13.6168 & -0.416811 \tabularnewline
31 & 12.6 & 12.2519 & 0.348086 \tabularnewline
32 & 5.6 & 12.0445 & -6.44447 \tabularnewline
33 & 9.9 & 14.0214 & -4.12136 \tabularnewline
34 & 8.8 & 12.5708 & -3.77084 \tabularnewline
35 & 7.7 & 12.1561 & -4.45605 \tabularnewline
36 & 9 & 14.4768 & -5.4768 \tabularnewline
37 & 7.3 & 13.9556 & -6.6556 \tabularnewline
38 & 11.4 & 11.989 & -0.589048 \tabularnewline
39 & 13.6 & 11.4898 & 2.1102 \tabularnewline
40 & 7.9 & 12.5703 & -4.67029 \tabularnewline
41 & 10.7 & 11.322 & -0.622039 \tabularnewline
42 & 10.3 & 13.2138 & -2.91384 \tabularnewline
43 & 8.3 & 8.51786 & -0.217858 \tabularnewline
44 & 9.6 & 12.7975 & -3.19751 \tabularnewline
45 & 14.2 & 12.3107 & 1.88928 \tabularnewline
46 & 8.5 & 13.0708 & -4.57081 \tabularnewline
47 & 13.5 & 12.7352 & 0.764804 \tabularnewline
48 & 4.9 & 12.2034 & -7.30336 \tabularnewline
49 & 6.4 & 11.8359 & -5.43595 \tabularnewline
50 & 9.6 & 13.4746 & -3.87456 \tabularnewline
51 & 11.6 & 14.1374 & -2.53739 \tabularnewline
52 & 11.1 & 11.751 & -0.650968 \tabularnewline
53 & 16.6 & 15.9615 & 0.638528 \tabularnewline
54 & 12.6 & 11.2166 & 1.3834 \tabularnewline
55 & 18.9 & 14.2856 & 4.61439 \tabularnewline
56 & 11.6 & 10.8028 & 0.797204 \tabularnewline
57 & 14.6 & 12.5531 & 2.04689 \tabularnewline
58 & 13.85 & 13.2733 & 0.576657 \tabularnewline
59 & 14.85 & 15.6686 & -0.8186 \tabularnewline
60 & 11.75 & 11.7831 & -0.0331425 \tabularnewline
61 & 18.45 & 14.7899 & 3.66013 \tabularnewline
62 & 15.9 & 12.2852 & 3.61483 \tabularnewline
63 & 19.9 & 17.794 & 2.10601 \tabularnewline
64 & 10.95 & 9.96961 & 0.980391 \tabularnewline
65 & 18.45 & 14.8235 & 3.62652 \tabularnewline
66 & 15.1 & 12.4124 & 2.68761 \tabularnewline
67 & 15 & 14.7599 & 0.240096 \tabularnewline
68 & 11.35 & 13.1952 & -1.8452 \tabularnewline
69 & 15.95 & 13.3545 & 2.59554 \tabularnewline
70 & 18.1 & 14.6776 & 3.42243 \tabularnewline
71 & 14.6 & 14.9396 & -0.339566 \tabularnewline
72 & 17.6 & 12.0512 & 5.54883 \tabularnewline
73 & 15.35 & 13.9603 & 1.38969 \tabularnewline
74 & 13.4 & 13.9922 & -0.592173 \tabularnewline
75 & 13.9 & 14.8363 & -0.936289 \tabularnewline
76 & 15.25 & 13.519 & 1.73097 \tabularnewline
77 & 12.9 & 14.0052 & -1.10524 \tabularnewline
78 & 16.1 & 14.6319 & 1.46812 \tabularnewline
79 & 17.35 & 14.4264 & 2.92358 \tabularnewline
80 & 13.15 & 14.8176 & -1.66759 \tabularnewline
81 & 12.15 & 14.749 & -2.59895 \tabularnewline
82 & 12.6 & 10.8736 & 1.72639 \tabularnewline
83 & 10.35 & 11.8194 & -1.46938 \tabularnewline
84 & 15.4 & 11.6528 & 3.74722 \tabularnewline
85 & 9.6 & 11.3666 & -1.76661 \tabularnewline
86 & 18.2 & 13.8743 & 4.32574 \tabularnewline
87 & 13.6 & 12.192 & 1.40797 \tabularnewline
88 & 14.85 & 13.2958 & 1.55421 \tabularnewline
89 & 14.1 & 11.5907 & 2.50929 \tabularnewline
90 & 14.9 & 12.4215 & 2.47847 \tabularnewline
91 & 16.25 & 15.0204 & 1.22958 \tabularnewline
92 & 13.6 & 13.2053 & 0.394661 \tabularnewline
93 & 15.65 & 13.7459 & 1.90412 \tabularnewline
94 & 14.6 & 11.4062 & 3.19377 \tabularnewline
95 & 12.65 & 11.6907 & 0.959305 \tabularnewline
96 & 19.2 & 12.5714 & 6.62858 \tabularnewline
97 & 16.6 & 11.159 & 5.441 \tabularnewline
98 & 11.2 & 10.5746 & 0.625437 \tabularnewline
99 & 13.2 & 10.7191 & 2.48092 \tabularnewline
100 & 15.85 & 11.8366 & 4.01341 \tabularnewline
101 & 11.15 & 10.9231 & 0.226882 \tabularnewline
102 & 15.65 & 14.5669 & 1.0831 \tabularnewline
103 & 7.65 & 10.2973 & -2.6473 \tabularnewline
104 & 15.2 & 11.9222 & 3.2778 \tabularnewline
105 & 15.6 & 11.8794 & 3.72057 \tabularnewline
106 & 13.1 & 11.6139 & 1.48615 \tabularnewline
107 & 11.85 & 11.3987 & 0.451287 \tabularnewline
108 & 12.4 & 11.3451 & 1.0549 \tabularnewline
109 & 11.4 & 10.0815 & 1.31847 \tabularnewline
110 & 14.9 & 12.6277 & 2.27233 \tabularnewline
111 & 19.9 & 16.7301 & 3.16991 \tabularnewline
112 & 11.2 & 11.8083 & -0.608255 \tabularnewline
113 & 14.6 & 12.6934 & 1.90658 \tabularnewline
114 & 14.75 & 12.0205 & 2.72951 \tabularnewline
115 & 15.15 & 11.4754 & 3.6746 \tabularnewline
116 & 16.85 & 14.9516 & 1.89836 \tabularnewline
117 & 7.85 & 10.451 & -2.60103 \tabularnewline
118 & 12.6 & 12.8144 & -0.214422 \tabularnewline
119 & 7.85 & 12.4364 & -4.58644 \tabularnewline
120 & 10.95 & 11.4285 & -0.478474 \tabularnewline
121 & 12.35 & 13.3514 & -1.00145 \tabularnewline
122 & 9.95 & 11.4189 & -1.46887 \tabularnewline
123 & 14.9 & 12.75 & 2.14995 \tabularnewline
124 & 16.65 & 13.1406 & 3.50937 \tabularnewline
125 & 13.4 & 11.9763 & 1.42368 \tabularnewline
126 & 13.95 & 12.5783 & 1.37169 \tabularnewline
127 & 15.7 & 13.0326 & 2.66742 \tabularnewline
128 & 16.85 & 14.2675 & 2.58253 \tabularnewline
129 & 10.95 & 11.1248 & -0.17482 \tabularnewline
130 & 15.35 & 14.397 & 0.953009 \tabularnewline
131 & 12.2 & 12.0488 & 0.151181 \tabularnewline
132 & 15.1 & 11.3321 & 3.76788 \tabularnewline
133 & 17.75 & 14.3174 & 3.43262 \tabularnewline
134 & 15.2 & 13.1818 & 2.01818 \tabularnewline
135 & 16.65 & 14.2605 & 2.3895 \tabularnewline
136 & 8.1 & 10.4002 & -2.30018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&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]11.3[/C][C]12.3984[/C][C]-1.09842[/C][/ROW]
[ROW][C]2[/C][C]9.6[/C][C]14.7008[/C][C]-5.10081[/C][/ROW]
[ROW][C]3[/C][C]16.1[/C][C]15.7993[/C][C]0.300669[/C][/ROW]
[ROW][C]4[/C][C]13.4[/C][C]13.2983[/C][C]0.101669[/C][/ROW]
[ROW][C]5[/C][C]12.7[/C][C]12.9339[/C][C]-0.233909[/C][/ROW]
[ROW][C]6[/C][C]12.3[/C][C]9.6137[/C][C]2.6863[/C][/ROW]
[ROW][C]7[/C][C]7.9[/C][C]11.7628[/C][C]-3.86284[/C][/ROW]
[ROW][C]8[/C][C]12.3[/C][C]12.1024[/C][C]0.197581[/C][/ROW]
[ROW][C]9[/C][C]11.6[/C][C]12.5758[/C][C]-0.975839[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]10.3565[/C][C]-3.65652[/C][/ROW]
[ROW][C]11[/C][C]12.1[/C][C]14.6431[/C][C]-2.54314[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]14.3651[/C][C]-8.66506[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]9.91624[/C][C]-1.91624[/C][/ROW]
[ROW][C]14[/C][C]13.3[/C][C]12.9605[/C][C]0.339461[/C][/ROW]
[ROW][C]15[/C][C]9.1[/C][C]10.3273[/C][C]-1.22729[/C][/ROW]
[ROW][C]16[/C][C]12.2[/C][C]12.8107[/C][C]-0.610695[/C][/ROW]
[ROW][C]17[/C][C]8.8[/C][C]13.4497[/C][C]-4.64972[/C][/ROW]
[ROW][C]18[/C][C]14.6[/C][C]13.5224[/C][C]1.07758[/C][/ROW]
[ROW][C]19[/C][C]12.6[/C][C]12.3798[/C][C]0.220183[/C][/ROW]
[ROW][C]20[/C][C]9.9[/C][C]10.9853[/C][C]-1.08531[/C][/ROW]
[ROW][C]21[/C][C]10.5[/C][C]13.2762[/C][C]-2.77618[/C][/ROW]
[ROW][C]22[/C][C]13.4[/C][C]12.8851[/C][C]0.514874[/C][/ROW]
[ROW][C]23[/C][C]10.9[/C][C]12.4734[/C][C]-1.57337[/C][/ROW]
[ROW][C]24[/C][C]4.3[/C][C]12.0412[/C][C]-7.7412[/C][/ROW]
[ROW][C]25[/C][C]10.3[/C][C]13.8916[/C][C]-3.59165[/C][/ROW]
[ROW][C]26[/C][C]11.8[/C][C]13.264[/C][C]-1.46401[/C][/ROW]
[ROW][C]27[/C][C]11.2[/C][C]11.4592[/C][C]-0.259212[/C][/ROW]
[ROW][C]28[/C][C]11.4[/C][C]12.5032[/C][C]-1.10318[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]13.1959[/C][C]-4.59588[/C][/ROW]
[ROW][C]30[/C][C]13.2[/C][C]13.6168[/C][C]-0.416811[/C][/ROW]
[ROW][C]31[/C][C]12.6[/C][C]12.2519[/C][C]0.348086[/C][/ROW]
[ROW][C]32[/C][C]5.6[/C][C]12.0445[/C][C]-6.44447[/C][/ROW]
[ROW][C]33[/C][C]9.9[/C][C]14.0214[/C][C]-4.12136[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]12.5708[/C][C]-3.77084[/C][/ROW]
[ROW][C]35[/C][C]7.7[/C][C]12.1561[/C][C]-4.45605[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]14.4768[/C][C]-5.4768[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]13.9556[/C][C]-6.6556[/C][/ROW]
[ROW][C]38[/C][C]11.4[/C][C]11.989[/C][C]-0.589048[/C][/ROW]
[ROW][C]39[/C][C]13.6[/C][C]11.4898[/C][C]2.1102[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]12.5703[/C][C]-4.67029[/C][/ROW]
[ROW][C]41[/C][C]10.7[/C][C]11.322[/C][C]-0.622039[/C][/ROW]
[ROW][C]42[/C][C]10.3[/C][C]13.2138[/C][C]-2.91384[/C][/ROW]
[ROW][C]43[/C][C]8.3[/C][C]8.51786[/C][C]-0.217858[/C][/ROW]
[ROW][C]44[/C][C]9.6[/C][C]12.7975[/C][C]-3.19751[/C][/ROW]
[ROW][C]45[/C][C]14.2[/C][C]12.3107[/C][C]1.88928[/C][/ROW]
[ROW][C]46[/C][C]8.5[/C][C]13.0708[/C][C]-4.57081[/C][/ROW]
[ROW][C]47[/C][C]13.5[/C][C]12.7352[/C][C]0.764804[/C][/ROW]
[ROW][C]48[/C][C]4.9[/C][C]12.2034[/C][C]-7.30336[/C][/ROW]
[ROW][C]49[/C][C]6.4[/C][C]11.8359[/C][C]-5.43595[/C][/ROW]
[ROW][C]50[/C][C]9.6[/C][C]13.4746[/C][C]-3.87456[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]14.1374[/C][C]-2.53739[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]11.751[/C][C]-0.650968[/C][/ROW]
[ROW][C]53[/C][C]16.6[/C][C]15.9615[/C][C]0.638528[/C][/ROW]
[ROW][C]54[/C][C]12.6[/C][C]11.2166[/C][C]1.3834[/C][/ROW]
[ROW][C]55[/C][C]18.9[/C][C]14.2856[/C][C]4.61439[/C][/ROW]
[ROW][C]56[/C][C]11.6[/C][C]10.8028[/C][C]0.797204[/C][/ROW]
[ROW][C]57[/C][C]14.6[/C][C]12.5531[/C][C]2.04689[/C][/ROW]
[ROW][C]58[/C][C]13.85[/C][C]13.2733[/C][C]0.576657[/C][/ROW]
[ROW][C]59[/C][C]14.85[/C][C]15.6686[/C][C]-0.8186[/C][/ROW]
[ROW][C]60[/C][C]11.75[/C][C]11.7831[/C][C]-0.0331425[/C][/ROW]
[ROW][C]61[/C][C]18.45[/C][C]14.7899[/C][C]3.66013[/C][/ROW]
[ROW][C]62[/C][C]15.9[/C][C]12.2852[/C][C]3.61483[/C][/ROW]
[ROW][C]63[/C][C]19.9[/C][C]17.794[/C][C]2.10601[/C][/ROW]
[ROW][C]64[/C][C]10.95[/C][C]9.96961[/C][C]0.980391[/C][/ROW]
[ROW][C]65[/C][C]18.45[/C][C]14.8235[/C][C]3.62652[/C][/ROW]
[ROW][C]66[/C][C]15.1[/C][C]12.4124[/C][C]2.68761[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]14.7599[/C][C]0.240096[/C][/ROW]
[ROW][C]68[/C][C]11.35[/C][C]13.1952[/C][C]-1.8452[/C][/ROW]
[ROW][C]69[/C][C]15.95[/C][C]13.3545[/C][C]2.59554[/C][/ROW]
[ROW][C]70[/C][C]18.1[/C][C]14.6776[/C][C]3.42243[/C][/ROW]
[ROW][C]71[/C][C]14.6[/C][C]14.9396[/C][C]-0.339566[/C][/ROW]
[ROW][C]72[/C][C]17.6[/C][C]12.0512[/C][C]5.54883[/C][/ROW]
[ROW][C]73[/C][C]15.35[/C][C]13.9603[/C][C]1.38969[/C][/ROW]
[ROW][C]74[/C][C]13.4[/C][C]13.9922[/C][C]-0.592173[/C][/ROW]
[ROW][C]75[/C][C]13.9[/C][C]14.8363[/C][C]-0.936289[/C][/ROW]
[ROW][C]76[/C][C]15.25[/C][C]13.519[/C][C]1.73097[/C][/ROW]
[ROW][C]77[/C][C]12.9[/C][C]14.0052[/C][C]-1.10524[/C][/ROW]
[ROW][C]78[/C][C]16.1[/C][C]14.6319[/C][C]1.46812[/C][/ROW]
[ROW][C]79[/C][C]17.35[/C][C]14.4264[/C][C]2.92358[/C][/ROW]
[ROW][C]80[/C][C]13.15[/C][C]14.8176[/C][C]-1.66759[/C][/ROW]
[ROW][C]81[/C][C]12.15[/C][C]14.749[/C][C]-2.59895[/C][/ROW]
[ROW][C]82[/C][C]12.6[/C][C]10.8736[/C][C]1.72639[/C][/ROW]
[ROW][C]83[/C][C]10.35[/C][C]11.8194[/C][C]-1.46938[/C][/ROW]
[ROW][C]84[/C][C]15.4[/C][C]11.6528[/C][C]3.74722[/C][/ROW]
[ROW][C]85[/C][C]9.6[/C][C]11.3666[/C][C]-1.76661[/C][/ROW]
[ROW][C]86[/C][C]18.2[/C][C]13.8743[/C][C]4.32574[/C][/ROW]
[ROW][C]87[/C][C]13.6[/C][C]12.192[/C][C]1.40797[/C][/ROW]
[ROW][C]88[/C][C]14.85[/C][C]13.2958[/C][C]1.55421[/C][/ROW]
[ROW][C]89[/C][C]14.1[/C][C]11.5907[/C][C]2.50929[/C][/ROW]
[ROW][C]90[/C][C]14.9[/C][C]12.4215[/C][C]2.47847[/C][/ROW]
[ROW][C]91[/C][C]16.25[/C][C]15.0204[/C][C]1.22958[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.2053[/C][C]0.394661[/C][/ROW]
[ROW][C]93[/C][C]15.65[/C][C]13.7459[/C][C]1.90412[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]11.4062[/C][C]3.19377[/C][/ROW]
[ROW][C]95[/C][C]12.65[/C][C]11.6907[/C][C]0.959305[/C][/ROW]
[ROW][C]96[/C][C]19.2[/C][C]12.5714[/C][C]6.62858[/C][/ROW]
[ROW][C]97[/C][C]16.6[/C][C]11.159[/C][C]5.441[/C][/ROW]
[ROW][C]98[/C][C]11.2[/C][C]10.5746[/C][C]0.625437[/C][/ROW]
[ROW][C]99[/C][C]13.2[/C][C]10.7191[/C][C]2.48092[/C][/ROW]
[ROW][C]100[/C][C]15.85[/C][C]11.8366[/C][C]4.01341[/C][/ROW]
[ROW][C]101[/C][C]11.15[/C][C]10.9231[/C][C]0.226882[/C][/ROW]
[ROW][C]102[/C][C]15.65[/C][C]14.5669[/C][C]1.0831[/C][/ROW]
[ROW][C]103[/C][C]7.65[/C][C]10.2973[/C][C]-2.6473[/C][/ROW]
[ROW][C]104[/C][C]15.2[/C][C]11.9222[/C][C]3.2778[/C][/ROW]
[ROW][C]105[/C][C]15.6[/C][C]11.8794[/C][C]3.72057[/C][/ROW]
[ROW][C]106[/C][C]13.1[/C][C]11.6139[/C][C]1.48615[/C][/ROW]
[ROW][C]107[/C][C]11.85[/C][C]11.3987[/C][C]0.451287[/C][/ROW]
[ROW][C]108[/C][C]12.4[/C][C]11.3451[/C][C]1.0549[/C][/ROW]
[ROW][C]109[/C][C]11.4[/C][C]10.0815[/C][C]1.31847[/C][/ROW]
[ROW][C]110[/C][C]14.9[/C][C]12.6277[/C][C]2.27233[/C][/ROW]
[ROW][C]111[/C][C]19.9[/C][C]16.7301[/C][C]3.16991[/C][/ROW]
[ROW][C]112[/C][C]11.2[/C][C]11.8083[/C][C]-0.608255[/C][/ROW]
[ROW][C]113[/C][C]14.6[/C][C]12.6934[/C][C]1.90658[/C][/ROW]
[ROW][C]114[/C][C]14.75[/C][C]12.0205[/C][C]2.72951[/C][/ROW]
[ROW][C]115[/C][C]15.15[/C][C]11.4754[/C][C]3.6746[/C][/ROW]
[ROW][C]116[/C][C]16.85[/C][C]14.9516[/C][C]1.89836[/C][/ROW]
[ROW][C]117[/C][C]7.85[/C][C]10.451[/C][C]-2.60103[/C][/ROW]
[ROW][C]118[/C][C]12.6[/C][C]12.8144[/C][C]-0.214422[/C][/ROW]
[ROW][C]119[/C][C]7.85[/C][C]12.4364[/C][C]-4.58644[/C][/ROW]
[ROW][C]120[/C][C]10.95[/C][C]11.4285[/C][C]-0.478474[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]13.3514[/C][C]-1.00145[/C][/ROW]
[ROW][C]122[/C][C]9.95[/C][C]11.4189[/C][C]-1.46887[/C][/ROW]
[ROW][C]123[/C][C]14.9[/C][C]12.75[/C][C]2.14995[/C][/ROW]
[ROW][C]124[/C][C]16.65[/C][C]13.1406[/C][C]3.50937[/C][/ROW]
[ROW][C]125[/C][C]13.4[/C][C]11.9763[/C][C]1.42368[/C][/ROW]
[ROW][C]126[/C][C]13.95[/C][C]12.5783[/C][C]1.37169[/C][/ROW]
[ROW][C]127[/C][C]15.7[/C][C]13.0326[/C][C]2.66742[/C][/ROW]
[ROW][C]128[/C][C]16.85[/C][C]14.2675[/C][C]2.58253[/C][/ROW]
[ROW][C]129[/C][C]10.95[/C][C]11.1248[/C][C]-0.17482[/C][/ROW]
[ROW][C]130[/C][C]15.35[/C][C]14.397[/C][C]0.953009[/C][/ROW]
[ROW][C]131[/C][C]12.2[/C][C]12.0488[/C][C]0.151181[/C][/ROW]
[ROW][C]132[/C][C]15.1[/C][C]11.3321[/C][C]3.76788[/C][/ROW]
[ROW][C]133[/C][C]17.75[/C][C]14.3174[/C][C]3.43262[/C][/ROW]
[ROW][C]134[/C][C]15.2[/C][C]13.1818[/C][C]2.01818[/C][/ROW]
[ROW][C]135[/C][C]16.65[/C][C]14.2605[/C][C]2.3895[/C][/ROW]
[ROW][C]136[/C][C]8.1[/C][C]10.4002[/C][C]-2.30018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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
111.312.3984-1.09842
29.614.7008-5.10081
316.115.79930.300669
413.413.29830.101669
512.712.9339-0.233909
612.39.61372.6863
77.911.7628-3.86284
812.312.10240.197581
911.612.5758-0.975839
106.710.3565-3.65652
1112.114.6431-2.54314
125.714.3651-8.66506
1389.91624-1.91624
1413.312.96050.339461
159.110.3273-1.22729
1612.212.8107-0.610695
178.813.4497-4.64972
1814.613.52241.07758
1912.612.37980.220183
209.910.9853-1.08531
2110.513.2762-2.77618
2213.412.88510.514874
2310.912.4734-1.57337
244.312.0412-7.7412
2510.313.8916-3.59165
2611.813.264-1.46401
2711.211.4592-0.259212
2811.412.5032-1.10318
298.613.1959-4.59588
3013.213.6168-0.416811
3112.612.25190.348086
325.612.0445-6.44447
339.914.0214-4.12136
348.812.5708-3.77084
357.712.1561-4.45605
36914.4768-5.4768
377.313.9556-6.6556
3811.411.989-0.589048
3913.611.48982.1102
407.912.5703-4.67029
4110.711.322-0.622039
4210.313.2138-2.91384
438.38.51786-0.217858
449.612.7975-3.19751
4514.212.31071.88928
468.513.0708-4.57081
4713.512.73520.764804
484.912.2034-7.30336
496.411.8359-5.43595
509.613.4746-3.87456
5111.614.1374-2.53739
5211.111.751-0.650968
5316.615.96150.638528
5412.611.21661.3834
5518.914.28564.61439
5611.610.80280.797204
5714.612.55312.04689
5813.8513.27330.576657
5914.8515.6686-0.8186
6011.7511.7831-0.0331425
6118.4514.78993.66013
6215.912.28523.61483
6319.917.7942.10601
6410.959.969610.980391
6518.4514.82353.62652
6615.112.41242.68761
671514.75990.240096
6811.3513.1952-1.8452
6915.9513.35452.59554
7018.114.67763.42243
7114.614.9396-0.339566
7217.612.05125.54883
7315.3513.96031.38969
7413.413.9922-0.592173
7513.914.8363-0.936289
7615.2513.5191.73097
7712.914.0052-1.10524
7816.114.63191.46812
7917.3514.42642.92358
8013.1514.8176-1.66759
8112.1514.749-2.59895
8212.610.87361.72639
8310.3511.8194-1.46938
8415.411.65283.74722
859.611.3666-1.76661
8618.213.87434.32574
8713.612.1921.40797
8814.8513.29581.55421
8914.111.59072.50929
9014.912.42152.47847
9116.2515.02041.22958
9213.613.20530.394661
9315.6513.74591.90412
9414.611.40623.19377
9512.6511.69070.959305
9619.212.57146.62858
9716.611.1595.441
9811.210.57460.625437
9913.210.71912.48092
10015.8511.83664.01341
10111.1510.92310.226882
10215.6514.56691.0831
1037.6510.2973-2.6473
10415.211.92223.2778
10515.611.87943.72057
10613.111.61391.48615
10711.8511.39870.451287
10812.411.34511.0549
10911.410.08151.31847
11014.912.62772.27233
11119.916.73013.16991
11211.211.8083-0.608255
11314.612.69341.90658
11414.7512.02052.72951
11515.1511.47543.6746
11616.8514.95161.89836
1177.8510.451-2.60103
11812.612.8144-0.214422
1197.8512.4364-4.58644
12010.9511.4285-0.478474
12112.3513.3514-1.00145
1229.9511.4189-1.46887
12314.912.752.14995
12416.6513.14063.50937
12513.411.97631.42368
12613.9512.57831.37169
12715.713.03262.66742
12816.8514.26752.58253
12910.9511.1248-0.17482
13015.3514.3970.953009
13112.212.04880.151181
13215.111.33213.76788
13317.7514.31743.43262
13415.213.18182.01818
13516.6514.26052.3895
1368.110.4002-2.30018






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.02297490.04594970.977025
130.3990420.7980850.600958
140.2828410.5656810.717159
150.5165140.9669720.483486
160.3963830.7927670.603617
170.3361240.6722470.663876
180.3071790.6143580.692821
190.2462130.4924260.753787
200.2259080.4518150.774092
210.1758260.3516520.824174
220.2431420.4862840.756858
230.1823850.364770.817615
240.3922150.7844310.607785
250.3548590.7097190.645141
260.2887020.5774040.711298
270.2303440.4606870.769656
280.2588090.5176180.741191
290.2859750.571950.714025
300.2372750.474550.762725
310.2646110.5292230.735389
320.3425260.6850520.657474
330.3804550.7609110.619545
340.3584030.7168060.641597
350.4256880.8513760.574312
360.5120330.9759330.487967
370.6838390.6323230.316161
380.6585180.6829640.341482
390.6387940.7224120.361206
400.6849760.6300490.315024
410.6378140.7243720.362186
420.6219690.7560610.378031
430.573960.8520810.42604
440.6109940.7780120.389006
450.6426780.7146450.357322
460.694340.6113190.30566
470.7187320.5625360.281268
480.9263010.1473980.0736989
490.9613860.0772280.038614
500.9753090.04938120.0246906
510.9814170.03716680.0185834
520.9791030.04179320.0208966
530.9767220.04655520.0232776
540.9765790.04684260.0234213
550.9937560.01248820.00624411
560.992410.01518080.00759039
570.9919720.01605650.00802827
580.9890530.02189470.0109474
590.986110.02778040.0138902
600.9839390.0321230.0160615
610.9895580.02088360.0104418
620.9940140.01197150.00598577
630.9929450.01410910.00705457
640.9908040.0183920.00919599
650.9953010.00939770.00469885
660.9947340.0105310.00526551
670.9929390.01412150.00706076
680.9924820.01503660.00751831
690.991810.01638030.00819016
700.9946610.01067870.00533936
710.9933130.01337370.00668683
720.9980740.003851850.00192593
730.9973830.005234850.00261743
740.9964540.007091290.00354565
750.9968220.006356740.00317837
760.9964740.007051210.00352561
770.9974920.005016560.00250828
780.9964150.007169970.00358498
790.9961810.007638450.00381923
800.9966940.006612560.00330628
810.9985970.002805590.0014028
820.9982760.003448240.00172412
830.9978710.004258950.00212947
840.9984620.003076950.00153847
850.9983850.003229880.00161494
860.9988710.002258390.0011292
870.9983810.003237570.00161878
880.9979820.004035340.00201767
890.997420.005159320.00257966
900.9966420.006716170.00335808
910.9949960.01000760.0050038
920.9925230.01495470.00747734
930.9902380.01952410.00976207
940.9897950.02040930.0102046
950.9859650.02807080.0140354
960.9920030.01599380.00799691
970.9961490.007701050.00385052
980.9942080.0115850.00579248
990.9928540.01429250.00714627
1000.9952410.009518450.00475923
1010.992390.01521920.00760959
1020.9894850.02103020.0105151
1030.9975660.004867570.00243379
1040.997050.005899620.00294981
1050.9981860.003628170.00181409
1060.9968270.006346510.00317325
1070.994530.01093980.00546988
1080.9906620.01867690.00933846
1090.9845460.03090720.0154536
1100.9804510.03909750.0195487
1110.9700610.05987820.0299391
1120.9540840.09183170.0459158
1130.9521150.09576950.0478848
1140.9729980.05400490.0270024
1150.9773890.04522120.0226106
1160.9708180.05836480.0291824
1170.9513450.09730980.0486549
1180.9161670.1676660.0838328
1190.9862860.02742860.0137143
1200.9718120.05637620.0281881
1210.9845020.03099690.0154985
1220.9865660.02686720.0134336
1230.9805010.03899870.0194993
1240.9888640.0222730.0111365

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0229749 & 0.0459497 & 0.977025 \tabularnewline
13 & 0.399042 & 0.798085 & 0.600958 \tabularnewline
14 & 0.282841 & 0.565681 & 0.717159 \tabularnewline
15 & 0.516514 & 0.966972 & 0.483486 \tabularnewline
16 & 0.396383 & 0.792767 & 0.603617 \tabularnewline
17 & 0.336124 & 0.672247 & 0.663876 \tabularnewline
18 & 0.307179 & 0.614358 & 0.692821 \tabularnewline
19 & 0.246213 & 0.492426 & 0.753787 \tabularnewline
20 & 0.225908 & 0.451815 & 0.774092 \tabularnewline
21 & 0.175826 & 0.351652 & 0.824174 \tabularnewline
22 & 0.243142 & 0.486284 & 0.756858 \tabularnewline
23 & 0.182385 & 0.36477 & 0.817615 \tabularnewline
24 & 0.392215 & 0.784431 & 0.607785 \tabularnewline
25 & 0.354859 & 0.709719 & 0.645141 \tabularnewline
26 & 0.288702 & 0.577404 & 0.711298 \tabularnewline
27 & 0.230344 & 0.460687 & 0.769656 \tabularnewline
28 & 0.258809 & 0.517618 & 0.741191 \tabularnewline
29 & 0.285975 & 0.57195 & 0.714025 \tabularnewline
30 & 0.237275 & 0.47455 & 0.762725 \tabularnewline
31 & 0.264611 & 0.529223 & 0.735389 \tabularnewline
32 & 0.342526 & 0.685052 & 0.657474 \tabularnewline
33 & 0.380455 & 0.760911 & 0.619545 \tabularnewline
34 & 0.358403 & 0.716806 & 0.641597 \tabularnewline
35 & 0.425688 & 0.851376 & 0.574312 \tabularnewline
36 & 0.512033 & 0.975933 & 0.487967 \tabularnewline
37 & 0.683839 & 0.632323 & 0.316161 \tabularnewline
38 & 0.658518 & 0.682964 & 0.341482 \tabularnewline
39 & 0.638794 & 0.722412 & 0.361206 \tabularnewline
40 & 0.684976 & 0.630049 & 0.315024 \tabularnewline
41 & 0.637814 & 0.724372 & 0.362186 \tabularnewline
42 & 0.621969 & 0.756061 & 0.378031 \tabularnewline
43 & 0.57396 & 0.852081 & 0.42604 \tabularnewline
44 & 0.610994 & 0.778012 & 0.389006 \tabularnewline
45 & 0.642678 & 0.714645 & 0.357322 \tabularnewline
46 & 0.69434 & 0.611319 & 0.30566 \tabularnewline
47 & 0.718732 & 0.562536 & 0.281268 \tabularnewline
48 & 0.926301 & 0.147398 & 0.0736989 \tabularnewline
49 & 0.961386 & 0.077228 & 0.038614 \tabularnewline
50 & 0.975309 & 0.0493812 & 0.0246906 \tabularnewline
51 & 0.981417 & 0.0371668 & 0.0185834 \tabularnewline
52 & 0.979103 & 0.0417932 & 0.0208966 \tabularnewline
53 & 0.976722 & 0.0465552 & 0.0232776 \tabularnewline
54 & 0.976579 & 0.0468426 & 0.0234213 \tabularnewline
55 & 0.993756 & 0.0124882 & 0.00624411 \tabularnewline
56 & 0.99241 & 0.0151808 & 0.00759039 \tabularnewline
57 & 0.991972 & 0.0160565 & 0.00802827 \tabularnewline
58 & 0.989053 & 0.0218947 & 0.0109474 \tabularnewline
59 & 0.98611 & 0.0277804 & 0.0138902 \tabularnewline
60 & 0.983939 & 0.032123 & 0.0160615 \tabularnewline
61 & 0.989558 & 0.0208836 & 0.0104418 \tabularnewline
62 & 0.994014 & 0.0119715 & 0.00598577 \tabularnewline
63 & 0.992945 & 0.0141091 & 0.00705457 \tabularnewline
64 & 0.990804 & 0.018392 & 0.00919599 \tabularnewline
65 & 0.995301 & 0.0093977 & 0.00469885 \tabularnewline
66 & 0.994734 & 0.010531 & 0.00526551 \tabularnewline
67 & 0.992939 & 0.0141215 & 0.00706076 \tabularnewline
68 & 0.992482 & 0.0150366 & 0.00751831 \tabularnewline
69 & 0.99181 & 0.0163803 & 0.00819016 \tabularnewline
70 & 0.994661 & 0.0106787 & 0.00533936 \tabularnewline
71 & 0.993313 & 0.0133737 & 0.00668683 \tabularnewline
72 & 0.998074 & 0.00385185 & 0.00192593 \tabularnewline
73 & 0.997383 & 0.00523485 & 0.00261743 \tabularnewline
74 & 0.996454 & 0.00709129 & 0.00354565 \tabularnewline
75 & 0.996822 & 0.00635674 & 0.00317837 \tabularnewline
76 & 0.996474 & 0.00705121 & 0.00352561 \tabularnewline
77 & 0.997492 & 0.00501656 & 0.00250828 \tabularnewline
78 & 0.996415 & 0.00716997 & 0.00358498 \tabularnewline
79 & 0.996181 & 0.00763845 & 0.00381923 \tabularnewline
80 & 0.996694 & 0.00661256 & 0.00330628 \tabularnewline
81 & 0.998597 & 0.00280559 & 0.0014028 \tabularnewline
82 & 0.998276 & 0.00344824 & 0.00172412 \tabularnewline
83 & 0.997871 & 0.00425895 & 0.00212947 \tabularnewline
84 & 0.998462 & 0.00307695 & 0.00153847 \tabularnewline
85 & 0.998385 & 0.00322988 & 0.00161494 \tabularnewline
86 & 0.998871 & 0.00225839 & 0.0011292 \tabularnewline
87 & 0.998381 & 0.00323757 & 0.00161878 \tabularnewline
88 & 0.997982 & 0.00403534 & 0.00201767 \tabularnewline
89 & 0.99742 & 0.00515932 & 0.00257966 \tabularnewline
90 & 0.996642 & 0.00671617 & 0.00335808 \tabularnewline
91 & 0.994996 & 0.0100076 & 0.0050038 \tabularnewline
92 & 0.992523 & 0.0149547 & 0.00747734 \tabularnewline
93 & 0.990238 & 0.0195241 & 0.00976207 \tabularnewline
94 & 0.989795 & 0.0204093 & 0.0102046 \tabularnewline
95 & 0.985965 & 0.0280708 & 0.0140354 \tabularnewline
96 & 0.992003 & 0.0159938 & 0.00799691 \tabularnewline
97 & 0.996149 & 0.00770105 & 0.00385052 \tabularnewline
98 & 0.994208 & 0.011585 & 0.00579248 \tabularnewline
99 & 0.992854 & 0.0142925 & 0.00714627 \tabularnewline
100 & 0.995241 & 0.00951845 & 0.00475923 \tabularnewline
101 & 0.99239 & 0.0152192 & 0.00760959 \tabularnewline
102 & 0.989485 & 0.0210302 & 0.0105151 \tabularnewline
103 & 0.997566 & 0.00486757 & 0.00243379 \tabularnewline
104 & 0.99705 & 0.00589962 & 0.00294981 \tabularnewline
105 & 0.998186 & 0.00362817 & 0.00181409 \tabularnewline
106 & 0.996827 & 0.00634651 & 0.00317325 \tabularnewline
107 & 0.99453 & 0.0109398 & 0.00546988 \tabularnewline
108 & 0.990662 & 0.0186769 & 0.00933846 \tabularnewline
109 & 0.984546 & 0.0309072 & 0.0154536 \tabularnewline
110 & 0.980451 & 0.0390975 & 0.0195487 \tabularnewline
111 & 0.970061 & 0.0598782 & 0.0299391 \tabularnewline
112 & 0.954084 & 0.0918317 & 0.0459158 \tabularnewline
113 & 0.952115 & 0.0957695 & 0.0478848 \tabularnewline
114 & 0.972998 & 0.0540049 & 0.0270024 \tabularnewline
115 & 0.977389 & 0.0452212 & 0.0226106 \tabularnewline
116 & 0.970818 & 0.0583648 & 0.0291824 \tabularnewline
117 & 0.951345 & 0.0973098 & 0.0486549 \tabularnewline
118 & 0.916167 & 0.167666 & 0.0838328 \tabularnewline
119 & 0.986286 & 0.0274286 & 0.0137143 \tabularnewline
120 & 0.971812 & 0.0563762 & 0.0281881 \tabularnewline
121 & 0.984502 & 0.0309969 & 0.0154985 \tabularnewline
122 & 0.986566 & 0.0268672 & 0.0134336 \tabularnewline
123 & 0.980501 & 0.0389987 & 0.0194993 \tabularnewline
124 & 0.988864 & 0.022273 & 0.0111365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&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]12[/C][C]0.0229749[/C][C]0.0459497[/C][C]0.977025[/C][/ROW]
[ROW][C]13[/C][C]0.399042[/C][C]0.798085[/C][C]0.600958[/C][/ROW]
[ROW][C]14[/C][C]0.282841[/C][C]0.565681[/C][C]0.717159[/C][/ROW]
[ROW][C]15[/C][C]0.516514[/C][C]0.966972[/C][C]0.483486[/C][/ROW]
[ROW][C]16[/C][C]0.396383[/C][C]0.792767[/C][C]0.603617[/C][/ROW]
[ROW][C]17[/C][C]0.336124[/C][C]0.672247[/C][C]0.663876[/C][/ROW]
[ROW][C]18[/C][C]0.307179[/C][C]0.614358[/C][C]0.692821[/C][/ROW]
[ROW][C]19[/C][C]0.246213[/C][C]0.492426[/C][C]0.753787[/C][/ROW]
[ROW][C]20[/C][C]0.225908[/C][C]0.451815[/C][C]0.774092[/C][/ROW]
[ROW][C]21[/C][C]0.175826[/C][C]0.351652[/C][C]0.824174[/C][/ROW]
[ROW][C]22[/C][C]0.243142[/C][C]0.486284[/C][C]0.756858[/C][/ROW]
[ROW][C]23[/C][C]0.182385[/C][C]0.36477[/C][C]0.817615[/C][/ROW]
[ROW][C]24[/C][C]0.392215[/C][C]0.784431[/C][C]0.607785[/C][/ROW]
[ROW][C]25[/C][C]0.354859[/C][C]0.709719[/C][C]0.645141[/C][/ROW]
[ROW][C]26[/C][C]0.288702[/C][C]0.577404[/C][C]0.711298[/C][/ROW]
[ROW][C]27[/C][C]0.230344[/C][C]0.460687[/C][C]0.769656[/C][/ROW]
[ROW][C]28[/C][C]0.258809[/C][C]0.517618[/C][C]0.741191[/C][/ROW]
[ROW][C]29[/C][C]0.285975[/C][C]0.57195[/C][C]0.714025[/C][/ROW]
[ROW][C]30[/C][C]0.237275[/C][C]0.47455[/C][C]0.762725[/C][/ROW]
[ROW][C]31[/C][C]0.264611[/C][C]0.529223[/C][C]0.735389[/C][/ROW]
[ROW][C]32[/C][C]0.342526[/C][C]0.685052[/C][C]0.657474[/C][/ROW]
[ROW][C]33[/C][C]0.380455[/C][C]0.760911[/C][C]0.619545[/C][/ROW]
[ROW][C]34[/C][C]0.358403[/C][C]0.716806[/C][C]0.641597[/C][/ROW]
[ROW][C]35[/C][C]0.425688[/C][C]0.851376[/C][C]0.574312[/C][/ROW]
[ROW][C]36[/C][C]0.512033[/C][C]0.975933[/C][C]0.487967[/C][/ROW]
[ROW][C]37[/C][C]0.683839[/C][C]0.632323[/C][C]0.316161[/C][/ROW]
[ROW][C]38[/C][C]0.658518[/C][C]0.682964[/C][C]0.341482[/C][/ROW]
[ROW][C]39[/C][C]0.638794[/C][C]0.722412[/C][C]0.361206[/C][/ROW]
[ROW][C]40[/C][C]0.684976[/C][C]0.630049[/C][C]0.315024[/C][/ROW]
[ROW][C]41[/C][C]0.637814[/C][C]0.724372[/C][C]0.362186[/C][/ROW]
[ROW][C]42[/C][C]0.621969[/C][C]0.756061[/C][C]0.378031[/C][/ROW]
[ROW][C]43[/C][C]0.57396[/C][C]0.852081[/C][C]0.42604[/C][/ROW]
[ROW][C]44[/C][C]0.610994[/C][C]0.778012[/C][C]0.389006[/C][/ROW]
[ROW][C]45[/C][C]0.642678[/C][C]0.714645[/C][C]0.357322[/C][/ROW]
[ROW][C]46[/C][C]0.69434[/C][C]0.611319[/C][C]0.30566[/C][/ROW]
[ROW][C]47[/C][C]0.718732[/C][C]0.562536[/C][C]0.281268[/C][/ROW]
[ROW][C]48[/C][C]0.926301[/C][C]0.147398[/C][C]0.0736989[/C][/ROW]
[ROW][C]49[/C][C]0.961386[/C][C]0.077228[/C][C]0.038614[/C][/ROW]
[ROW][C]50[/C][C]0.975309[/C][C]0.0493812[/C][C]0.0246906[/C][/ROW]
[ROW][C]51[/C][C]0.981417[/C][C]0.0371668[/C][C]0.0185834[/C][/ROW]
[ROW][C]52[/C][C]0.979103[/C][C]0.0417932[/C][C]0.0208966[/C][/ROW]
[ROW][C]53[/C][C]0.976722[/C][C]0.0465552[/C][C]0.0232776[/C][/ROW]
[ROW][C]54[/C][C]0.976579[/C][C]0.0468426[/C][C]0.0234213[/C][/ROW]
[ROW][C]55[/C][C]0.993756[/C][C]0.0124882[/C][C]0.00624411[/C][/ROW]
[ROW][C]56[/C][C]0.99241[/C][C]0.0151808[/C][C]0.00759039[/C][/ROW]
[ROW][C]57[/C][C]0.991972[/C][C]0.0160565[/C][C]0.00802827[/C][/ROW]
[ROW][C]58[/C][C]0.989053[/C][C]0.0218947[/C][C]0.0109474[/C][/ROW]
[ROW][C]59[/C][C]0.98611[/C][C]0.0277804[/C][C]0.0138902[/C][/ROW]
[ROW][C]60[/C][C]0.983939[/C][C]0.032123[/C][C]0.0160615[/C][/ROW]
[ROW][C]61[/C][C]0.989558[/C][C]0.0208836[/C][C]0.0104418[/C][/ROW]
[ROW][C]62[/C][C]0.994014[/C][C]0.0119715[/C][C]0.00598577[/C][/ROW]
[ROW][C]63[/C][C]0.992945[/C][C]0.0141091[/C][C]0.00705457[/C][/ROW]
[ROW][C]64[/C][C]0.990804[/C][C]0.018392[/C][C]0.00919599[/C][/ROW]
[ROW][C]65[/C][C]0.995301[/C][C]0.0093977[/C][C]0.00469885[/C][/ROW]
[ROW][C]66[/C][C]0.994734[/C][C]0.010531[/C][C]0.00526551[/C][/ROW]
[ROW][C]67[/C][C]0.992939[/C][C]0.0141215[/C][C]0.00706076[/C][/ROW]
[ROW][C]68[/C][C]0.992482[/C][C]0.0150366[/C][C]0.00751831[/C][/ROW]
[ROW][C]69[/C][C]0.99181[/C][C]0.0163803[/C][C]0.00819016[/C][/ROW]
[ROW][C]70[/C][C]0.994661[/C][C]0.0106787[/C][C]0.00533936[/C][/ROW]
[ROW][C]71[/C][C]0.993313[/C][C]0.0133737[/C][C]0.00668683[/C][/ROW]
[ROW][C]72[/C][C]0.998074[/C][C]0.00385185[/C][C]0.00192593[/C][/ROW]
[ROW][C]73[/C][C]0.997383[/C][C]0.00523485[/C][C]0.00261743[/C][/ROW]
[ROW][C]74[/C][C]0.996454[/C][C]0.00709129[/C][C]0.00354565[/C][/ROW]
[ROW][C]75[/C][C]0.996822[/C][C]0.00635674[/C][C]0.00317837[/C][/ROW]
[ROW][C]76[/C][C]0.996474[/C][C]0.00705121[/C][C]0.00352561[/C][/ROW]
[ROW][C]77[/C][C]0.997492[/C][C]0.00501656[/C][C]0.00250828[/C][/ROW]
[ROW][C]78[/C][C]0.996415[/C][C]0.00716997[/C][C]0.00358498[/C][/ROW]
[ROW][C]79[/C][C]0.996181[/C][C]0.00763845[/C][C]0.00381923[/C][/ROW]
[ROW][C]80[/C][C]0.996694[/C][C]0.00661256[/C][C]0.00330628[/C][/ROW]
[ROW][C]81[/C][C]0.998597[/C][C]0.00280559[/C][C]0.0014028[/C][/ROW]
[ROW][C]82[/C][C]0.998276[/C][C]0.00344824[/C][C]0.00172412[/C][/ROW]
[ROW][C]83[/C][C]0.997871[/C][C]0.00425895[/C][C]0.00212947[/C][/ROW]
[ROW][C]84[/C][C]0.998462[/C][C]0.00307695[/C][C]0.00153847[/C][/ROW]
[ROW][C]85[/C][C]0.998385[/C][C]0.00322988[/C][C]0.00161494[/C][/ROW]
[ROW][C]86[/C][C]0.998871[/C][C]0.00225839[/C][C]0.0011292[/C][/ROW]
[ROW][C]87[/C][C]0.998381[/C][C]0.00323757[/C][C]0.00161878[/C][/ROW]
[ROW][C]88[/C][C]0.997982[/C][C]0.00403534[/C][C]0.00201767[/C][/ROW]
[ROW][C]89[/C][C]0.99742[/C][C]0.00515932[/C][C]0.00257966[/C][/ROW]
[ROW][C]90[/C][C]0.996642[/C][C]0.00671617[/C][C]0.00335808[/C][/ROW]
[ROW][C]91[/C][C]0.994996[/C][C]0.0100076[/C][C]0.0050038[/C][/ROW]
[ROW][C]92[/C][C]0.992523[/C][C]0.0149547[/C][C]0.00747734[/C][/ROW]
[ROW][C]93[/C][C]0.990238[/C][C]0.0195241[/C][C]0.00976207[/C][/ROW]
[ROW][C]94[/C][C]0.989795[/C][C]0.0204093[/C][C]0.0102046[/C][/ROW]
[ROW][C]95[/C][C]0.985965[/C][C]0.0280708[/C][C]0.0140354[/C][/ROW]
[ROW][C]96[/C][C]0.992003[/C][C]0.0159938[/C][C]0.00799691[/C][/ROW]
[ROW][C]97[/C][C]0.996149[/C][C]0.00770105[/C][C]0.00385052[/C][/ROW]
[ROW][C]98[/C][C]0.994208[/C][C]0.011585[/C][C]0.00579248[/C][/ROW]
[ROW][C]99[/C][C]0.992854[/C][C]0.0142925[/C][C]0.00714627[/C][/ROW]
[ROW][C]100[/C][C]0.995241[/C][C]0.00951845[/C][C]0.00475923[/C][/ROW]
[ROW][C]101[/C][C]0.99239[/C][C]0.0152192[/C][C]0.00760959[/C][/ROW]
[ROW][C]102[/C][C]0.989485[/C][C]0.0210302[/C][C]0.0105151[/C][/ROW]
[ROW][C]103[/C][C]0.997566[/C][C]0.00486757[/C][C]0.00243379[/C][/ROW]
[ROW][C]104[/C][C]0.99705[/C][C]0.00589962[/C][C]0.00294981[/C][/ROW]
[ROW][C]105[/C][C]0.998186[/C][C]0.00362817[/C][C]0.00181409[/C][/ROW]
[ROW][C]106[/C][C]0.996827[/C][C]0.00634651[/C][C]0.00317325[/C][/ROW]
[ROW][C]107[/C][C]0.99453[/C][C]0.0109398[/C][C]0.00546988[/C][/ROW]
[ROW][C]108[/C][C]0.990662[/C][C]0.0186769[/C][C]0.00933846[/C][/ROW]
[ROW][C]109[/C][C]0.984546[/C][C]0.0309072[/C][C]0.0154536[/C][/ROW]
[ROW][C]110[/C][C]0.980451[/C][C]0.0390975[/C][C]0.0195487[/C][/ROW]
[ROW][C]111[/C][C]0.970061[/C][C]0.0598782[/C][C]0.0299391[/C][/ROW]
[ROW][C]112[/C][C]0.954084[/C][C]0.0918317[/C][C]0.0459158[/C][/ROW]
[ROW][C]113[/C][C]0.952115[/C][C]0.0957695[/C][C]0.0478848[/C][/ROW]
[ROW][C]114[/C][C]0.972998[/C][C]0.0540049[/C][C]0.0270024[/C][/ROW]
[ROW][C]115[/C][C]0.977389[/C][C]0.0452212[/C][C]0.0226106[/C][/ROW]
[ROW][C]116[/C][C]0.970818[/C][C]0.0583648[/C][C]0.0291824[/C][/ROW]
[ROW][C]117[/C][C]0.951345[/C][C]0.0973098[/C][C]0.0486549[/C][/ROW]
[ROW][C]118[/C][C]0.916167[/C][C]0.167666[/C][C]0.0838328[/C][/ROW]
[ROW][C]119[/C][C]0.986286[/C][C]0.0274286[/C][C]0.0137143[/C][/ROW]
[ROW][C]120[/C][C]0.971812[/C][C]0.0563762[/C][C]0.0281881[/C][/ROW]
[ROW][C]121[/C][C]0.984502[/C][C]0.0309969[/C][C]0.0154985[/C][/ROW]
[ROW][C]122[/C][C]0.986566[/C][C]0.0268672[/C][C]0.0134336[/C][/ROW]
[ROW][C]123[/C][C]0.980501[/C][C]0.0389987[/C][C]0.0194993[/C][/ROW]
[ROW][C]124[/C][C]0.988864[/C][C]0.022273[/C][C]0.0111365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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
120.02297490.04594970.977025
130.3990420.7980850.600958
140.2828410.5656810.717159
150.5165140.9669720.483486
160.3963830.7927670.603617
170.3361240.6722470.663876
180.3071790.6143580.692821
190.2462130.4924260.753787
200.2259080.4518150.774092
210.1758260.3516520.824174
220.2431420.4862840.756858
230.1823850.364770.817615
240.3922150.7844310.607785
250.3548590.7097190.645141
260.2887020.5774040.711298
270.2303440.4606870.769656
280.2588090.5176180.741191
290.2859750.571950.714025
300.2372750.474550.762725
310.2646110.5292230.735389
320.3425260.6850520.657474
330.3804550.7609110.619545
340.3584030.7168060.641597
350.4256880.8513760.574312
360.5120330.9759330.487967
370.6838390.6323230.316161
380.6585180.6829640.341482
390.6387940.7224120.361206
400.6849760.6300490.315024
410.6378140.7243720.362186
420.6219690.7560610.378031
430.573960.8520810.42604
440.6109940.7780120.389006
450.6426780.7146450.357322
460.694340.6113190.30566
470.7187320.5625360.281268
480.9263010.1473980.0736989
490.9613860.0772280.038614
500.9753090.04938120.0246906
510.9814170.03716680.0185834
520.9791030.04179320.0208966
530.9767220.04655520.0232776
540.9765790.04684260.0234213
550.9937560.01248820.00624411
560.992410.01518080.00759039
570.9919720.01605650.00802827
580.9890530.02189470.0109474
590.986110.02778040.0138902
600.9839390.0321230.0160615
610.9895580.02088360.0104418
620.9940140.01197150.00598577
630.9929450.01410910.00705457
640.9908040.0183920.00919599
650.9953010.00939770.00469885
660.9947340.0105310.00526551
670.9929390.01412150.00706076
680.9924820.01503660.00751831
690.991810.01638030.00819016
700.9946610.01067870.00533936
710.9933130.01337370.00668683
720.9980740.003851850.00192593
730.9973830.005234850.00261743
740.9964540.007091290.00354565
750.9968220.006356740.00317837
760.9964740.007051210.00352561
770.9974920.005016560.00250828
780.9964150.007169970.00358498
790.9961810.007638450.00381923
800.9966940.006612560.00330628
810.9985970.002805590.0014028
820.9982760.003448240.00172412
830.9978710.004258950.00212947
840.9984620.003076950.00153847
850.9983850.003229880.00161494
860.9988710.002258390.0011292
870.9983810.003237570.00161878
880.9979820.004035340.00201767
890.997420.005159320.00257966
900.9966420.006716170.00335808
910.9949960.01000760.0050038
920.9925230.01495470.00747734
930.9902380.01952410.00976207
940.9897950.02040930.0102046
950.9859650.02807080.0140354
960.9920030.01599380.00799691
970.9961490.007701050.00385052
980.9942080.0115850.00579248
990.9928540.01429250.00714627
1000.9952410.009518450.00475923
1010.992390.01521920.00760959
1020.9894850.02103020.0105151
1030.9975660.004867570.00243379
1040.997050.005899620.00294981
1050.9981860.003628170.00181409
1060.9968270.006346510.00317325
1070.994530.01093980.00546988
1080.9906620.01867690.00933846
1090.9845460.03090720.0154536
1100.9804510.03909750.0195487
1110.9700610.05987820.0299391
1120.9540840.09183170.0459158
1130.9521150.09576950.0478848
1140.9729980.05400490.0270024
1150.9773890.04522120.0226106
1160.9708180.05836480.0291824
1170.9513450.09730980.0486549
1180.9161670.1676660.0838328
1190.9862860.02742860.0137143
1200.9718120.05637620.0281881
1210.9845020.03099690.0154985
1220.9865660.02686720.0134336
1230.9805010.03899870.0194993
1240.9888640.0222730.0111365






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level260.230088NOK
5% type I error level680.60177NOK
10% type I error level760.672566NOK

\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 & 26 & 0.230088 & NOK \tabularnewline
5% type I error level & 68 & 0.60177 & NOK \tabularnewline
10% type I error level & 76 & 0.672566 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265384&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]26[/C][C]0.230088[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]68[/C][C]0.60177[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]76[/C][C]0.672566[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265384&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265384&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 level260.230088NOK
5% type I error level680.60177NOK
10% type I error level760.672566NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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