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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 computationThu, 18 Dec 2014 15:14:38 +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/18/t1418915712z3ske8s080yg608.htm/, Retrieved Sun, 19 May 2024 18:21:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271058, Retrieved Sun, 19 May 2024 18:21:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 15:14:38] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 12 12 18 68 1.8
12.2 18 20 20 8 31 39 2.1
12.8 12 9 14 11 39 32 2.2
7.4 24 19 25 13 46 62 2.3
6.7 16 12 15 11 31 33 2.1
12.6 19 16 20 10 67 52 2.7
14.8 16 17 21 7 35 62 2.1
13.3 15 9 15 10 52 77 2.4
11.1 28 28 28 15 77 76 2.9
8.2 21 20 11 12 37 41 2.2
11.4 18 16 22 12 32 48 2.1
6.4 22 22 22 10 36 63 2.2
10.6 19 17 27 10 38 30 2.2
12 22 12 24 14 69 78 2.7
6.3 25 18 23 6 21 19 1.9
11.9 16 12 21 14 54 66 2.5
9.3 19 16 20 11 36 35 2.2
10 26 21 25 12 23 45 1.9
6.4 24 15 16 15 34 21 2.1
13.8 20 17 24 13 112 25 3.5
10.8 19 17 21 11 35 44 2.1
13.8 19 17 22 12 47 69 2.3
11.7 23 18 25 7 47 54 2.3
10.9 18 15 23 11 37 74 2.2
9.9 21 21 22 12 20 61 1.9
11.5 20 12 25 13 22 41 1.9
8.3 15 6 23 9 23 46 1.9
11.7 19 13 19 11 32 39 2.1
9 19 19 21 12 30 34 2
9.7 7 12 19 15 92 51 3.2
10.8 20 14 25 12 43 42 2.3
10.3 20 13 16 6 55 31 2.5
10.4 19 12 24 5 16 39 1.8
9.3 20 19 18 11 71 49 2.8
11.8 18 10 28 6 43 53 2.3
5.9 14 10 15 12 29 31 2
11.4 17 11 17 10 56 39 2.5
13 17 11 18 6 46 54 2.3
10.8 8 10 26 12 19 49 1.8
11.3 22 22 22 6 59 46 2.6
11.8 20 12 19 12 30 55 2
12.7 22 20 26 8 7 50 1.6
10.9 14 11 12 12 19 30 1.8
13.3 21 17 20 14 48 45 2.4
10.1 20 14 24 12 23 35 1.9
14.3 18 16 22 14 33 41 2.1
9.3 24 15 23 11 34 73 2.1
12.5 19 15 19 10 48 17 2.4
7.6 16 10 24 7 18 40 1.8
15.9 16 10 21 12 43 64 2.3
9.2 16 18 16 7 33 37 2.1
11.1 22 22 23 12 71 65 2.8
13 21 16 20 10 26 100 2
14.5 15 10 19 10 67 28 2.7
12.3 15 16 18 12 80 56 2.9
11.4 14 16 21 12 29 29 2
13 16 10 17 10 43 59 2.3
13.2 26 16 24 11 29 61 2
7.7 18 16 22 12 32 51 2.1
4.35 17 15 14 9 23 12 1
12.7 6 4 5 11 16 45 1
18.1 22 9 25 12 33 37 4
17.85 20 18 21 12 32 37 4
17.1 17 12 9 12 52 68 4
19.1 20 16 15 12 75 72 4
16.1 23 17 23 10 72 143 4
13.35 18 14 21 15 15 9 2
18.4 13 13 9 10 29 55 4
14.7 22 20 24 15 13 17 1
10.6 20 16 16 10 40 37 3
12.6 20 15 20 15 19 27 3
13.6 16 16 18 15 121 58 3
14.1 16 15 21 13 36 21 3
14.5 15 16 21 12 23 19 3
16.15 19 19 21 12 85 78 4
14.75 19 9 20 8 41 35 3
14.8 24 19 24 9 46 48 3
12.45 9 7 15 15 18 27 2
12.65 22 23 24 12 35 43 2
17.35 15 14 18 12 17 30 3
8.6 22 10 24 15 4 25 1
18.4 22 16 24 11 28 69 4
16.1 24 12 15 12 44 72 3
17.75 21 7 20 14 38 13 4
15.25 25 20 26 12 57 61 4
17.65 26 9 26 12 23 43 4
16.35 21 12 23 12 36 51 4
17.65 14 10 13 11 22 67 4
13.6 28 19 16 12 40 36 3
14.35 21 11 22 12 31 44 3
14.75 16 15 21 12 11 45 4
18.25 16 14 11 12 38 34 4
9.9 25 11 23 8 24 36 4
16 21 14 18 8 37 72 3
18.25 22 15 19 12 37 39 4
16.85 9 7 15 12 22 43 4
18.95 24 22 21 11 43 80 4
15.6 22 11 25 12 31 40 3
17.1 10 12 12 10 31 61 4
15.4 21 13 19 11 21 29 4
15.4 20 15 21 11 21 29 4
13.35 17 11 19 13 32 54 3
19.1 7 7 18 7 26 43 4
7.6 14 13 23 8 32 20 1
19.1 23 7 23 11 33 61 4
14.75 18 11 27 8 30 57 4
19.25 17 22 6 14 67 54 4
13.6 20 15 22 9 22 36 4
12.75 19 15 23 13 33 16 4
9.85 19 11 20 13 24 40 1
15.25 23 10 23 11 28 27 4
11.9 20 18 27 9 41 61 3
16.35 19 14 24 12 31 69 4
12.4 16 16 12 12 33 34 3
18.15 21 16 24 13 21 34 4
17.75 20 17 24 11 52 34 4
12.35 20 14 19 11 29 13 3
15.6 19 10 28 9 11 12 4
19.3 19 16 23 12 26 51 4
17.1 20 16 19 15 7 19 4
18.4 22 17 23 14 13 81 3
19.05 19 12 20 12 20 42 4
18.55 23 17 18 9 52 22 4
19.1 16 11 20 9 28 85 4
12.85 18 12 21 13 39 25 4
9.5 23 8 25 15 9 22 2
4.5 20 17 18 11 19 19 1
13.6 23 17 28 10 60 45 4
11.7 13 7 9 11 19 45 2
13.35 26 18 26 14 14 51 3
17.6 13 14 12 12 -2 73 4
14.05 10 13 12 13 51 24 3
16.1 21 19 20 11 2 61 4
13.35 24 15 25 11 24 23 4
11.85 21 15 24 13 40 14 4
11.95 23 8 23 12 20 54 2
13.2 16 11 22 9 20 36 4
7.7 26 17 28 13 25 26 2
14.6 16 12 15 12 38 30 3

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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.42276 -0.0519333AMS.I2[t] -0.0448851AMS.I3[t] -0.046362AMS.E2[t] + 0.136576CONFSOFTTOT[t] -0.0236427PRH[t] + 0.0354837CH[t] + 2.81169PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.42276 -0.0519333AMS.I2[t] -0.0448851AMS.I3[t] -0.046362AMS.E2[t] +  0.136576CONFSOFTTOT[t] -0.0236427PRH[t] +  0.0354837CH[t] +  2.81169PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.42276 -0.0519333AMS.I2[t] -0.0448851AMS.I3[t] -0.046362AMS.E2[t] +  0.136576CONFSOFTTOT[t] -0.0236427PRH[t] +  0.0354837CH[t] +  2.81169PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.42276 -0.0519333AMS.I2[t] -0.0448851AMS.I3[t] -0.046362AMS.E2[t] + 0.136576CONFSOFTTOT[t] -0.0236427PRH[t] + 0.0354837CH[t] + 2.81169PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.422761.496973.6220.0004160250.000208013
AMS.I2-0.05193330.0592021-0.87720.3819720.190986
AMS.I3-0.04488510.0550218-0.81580.4161130.208056
AMS.E2-0.0463620.0479485-0.96690.335370.167685
CONFSOFTTOT0.1365760.08502071.6060.1105960.055298
PRH-0.02364270.0102814-2.30.02305490.0115275
CH0.03548370.009736543.6440.0003851770.000192589
PR2.811690.20836813.491.49032e-267.45161e-27

\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) & 5.42276 & 1.49697 & 3.622 & 0.000416025 & 0.000208013 \tabularnewline
AMS.I2 & -0.0519333 & 0.0592021 & -0.8772 & 0.381972 & 0.190986 \tabularnewline
AMS.I3 & -0.0448851 & 0.0550218 & -0.8158 & 0.416113 & 0.208056 \tabularnewline
AMS.E2 & -0.046362 & 0.0479485 & -0.9669 & 0.33537 & 0.167685 \tabularnewline
CONFSOFTTOT & 0.136576 & 0.0850207 & 1.606 & 0.110596 & 0.055298 \tabularnewline
PRH & -0.0236427 & 0.0102814 & -2.3 & 0.0230549 & 0.0115275 \tabularnewline
CH & 0.0354837 & 0.00973654 & 3.644 & 0.000385177 & 0.000192589 \tabularnewline
PR & 2.81169 & 0.208368 & 13.49 & 1.49032e-26 & 7.45161e-27 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&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]5.42276[/C][C]1.49697[/C][C]3.622[/C][C]0.000416025[/C][C]0.000208013[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0519333[/C][C]0.0592021[/C][C]-0.8772[/C][C]0.381972[/C][C]0.190986[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0448851[/C][C]0.0550218[/C][C]-0.8158[/C][C]0.416113[/C][C]0.208056[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.046362[/C][C]0.0479485[/C][C]-0.9669[/C][C]0.33537[/C][C]0.167685[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.136576[/C][C]0.0850207[/C][C]1.606[/C][C]0.110596[/C][C]0.055298[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0236427[/C][C]0.0102814[/C][C]-2.3[/C][C]0.0230549[/C][C]0.0115275[/C][/ROW]
[ROW][C]CH[/C][C]0.0354837[/C][C]0.00973654[/C][C]3.644[/C][C]0.000385177[/C][C]0.000192589[/C][/ROW]
[ROW][C]PR[/C][C]2.81169[/C][C]0.208368[/C][C]13.49[/C][C]1.49032e-26[/C][C]7.45161e-27[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&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)5.422761.496973.6220.0004160250.000208013
AMS.I2-0.05193330.0592021-0.87720.3819720.190986
AMS.I3-0.04488510.0550218-0.81580.4161130.208056
AMS.E2-0.0463620.0479485-0.96690.335370.167685
CONFSOFTTOT0.1365760.08502071.6060.1105960.055298
PRH-0.02364270.0102814-2.30.02305490.0115275
CH0.03548370.009736543.6440.0003851770.000192589
PR2.811690.20836813.491.49032e-267.45161e-27






Multiple Linear Regression - Regression Statistics
Multiple R0.791446
R-squared0.626387
Adjusted R-squared0.606423
F-TEST (value)31.3758
F-TEST (DF numerator)7
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19821
Sum Squared Residuals633.011

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.791446 \tabularnewline
R-squared & 0.626387 \tabularnewline
Adjusted R-squared & 0.606423 \tabularnewline
F-TEST (value) & 31.3758 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19821 \tabularnewline
Sum Squared Residuals & 633.011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.791446[/C][/ROW]
[ROW][C]R-squared[/C][C]0.626387[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.606423[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.3758[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19821[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]633.011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&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 R0.791446
R-squared0.626387
Adjusted R-squared0.606423
F-TEST (value)31.3758
F-TEST (DF numerator)7
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19821
Sum Squared Residuals633.011






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.8240.0759754
212.210.31111.88889
312.811.6481.15202
47.411.5193-4.11928
56.711.2027-4.50269
612.612.0090.590972
714.811.08823.71176
813.313.1610.139005
911.112.4925-1.39251
108.211.3291-3.12915
1111.411.23990.160061
126.411.2086-4.8086
1310.610.13880.461237
141213.2689-1.26892
156.38.58952-2.28952
1611.913.0861-1.1861
179.310.8695-1.56946
181010.005-0.00495665
196.410.6558-4.25578
2013.812.36391.43614
2110.810.84-0.0400431
2213.812.0961.70403
2311.710.48911.21087
2410.912.1874-1.28742
259.911.0424-1.14238
2611.510.73880.761193
278.310.968-2.66798
2811.711.00580.694183
29910.3691-1.36906
309.714.3203-4.6203
3110.811.1761-0.376121
3210.310.7071-0.40711
3310.49.534210.865789
349.312.1319-2.83189
3511.810.89130.908695
365.911.2281-5.32805
3711.411.7129-0.312851
381311.32651.67347
3910.811.3425-0.542468
4011.310.640.660041
4111.811.46920.330799
4212.79.37713.3229
4310.910.9609-0.0608609
4413.311.76391.5361
4510.110.3223-0.222277
4614.311.24113.05894
479.311.6301-2.33009
4812.510.46412.03595
497.610.0411-2.44113
5015.912.52953.37052
519.210.4354-1.23536
5211.112.3659-1.26587
531312.60950.39045
5414.511.68082.81917
5512.312.9796-0.679555
5611.410.60960.790396
571312.26440.735639
5813.210.84622.35378
597.711.3464-3.64639
604.357.14044-2.79044
6112.710.23232.46768
6218.116.13561.96445
6317.8516.04451.80545
6417.117.6531-0.553141
6519.116.63782.46222
6616.118.3833-2.28332
6713.3510.52272.82731
6818.417.62530.77467
6914.77.426037.27397
7010.613.0921-2.49215
7112.613.7761-1.17612
7213.612.72010.879864
7314.113.04951.05048
7414.513.15641.34363
7516.1516.2534-0.103362
7614.7512.90511.84494
7714.812.49072.30925
7812.4512.15020.29976
7912.6510.09582.55423
8017.3513.91743.43259
818.68.371540.228462
8218.416.98481.41516
8316.114.53081.56917
8417.7515.81241.9376
8515.2515.7238-0.47384
8617.6516.33081.31921
8716.3516.5714-0.2214
8817.6518.2505-0.600484
8913.612.77970.820307
9014.3513.72080.629211
9114.7517.1673-2.4173
9218.2516.64711.60287
939.915.6137-5.7137
941614.0771.92304
9518.2516.12082.12919
9616.8517.837-0.98705
9718.9516.78642.16358
9815.613.38782.21217
9917.117.8525-0.752546
10015.416.1494-0.749385
10115.416.0188-0.618824
10213.3514.5354-1.18538
10319.117.02442.07562
1047.66.903260.696738
10519.116.98112.11885
10614.7516.3951-1.64509
10719.2516.76512.48488
10813.615.9241-2.32405
10912.7515.5062-2.75619
1109.858.454151.39585
11115.2515.7583-0.50826
11211.913.1838-1.28378
11316.3517.2961-0.946054
11412.413.8175-1.41753
11518.1516.23351.91651
11617.7515.23452.51554
11712.3512.5879-0.237867
11815.615.33070.269299
11919.316.73222.56785
12017.116.58910.51087
12118.415.36483.0352
12219.0516.87332.17672
12318.5514.65793.89212
12419.118.00091.0991
12512.8515.963-3.113
1269.510.95-1.45003
1274.57.32553-2.82553
12813.614.9578-1.35782
12911.712.2894-0.589437
13013.3513.8849-0.534943
13117.619.0861-1.48614
13214.0513.620.430046
13316.117.4184-1.3184
13413.3515.3418-1.99181
13511.8515.1195-3.26949
13611.9511.50840.441563
13713.216.3586-3.15861
1387.79.74168-2.04168
13914.613.59781.00217

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.824 & 0.0759754 \tabularnewline
2 & 12.2 & 10.3111 & 1.88889 \tabularnewline
3 & 12.8 & 11.648 & 1.15202 \tabularnewline
4 & 7.4 & 11.5193 & -4.11928 \tabularnewline
5 & 6.7 & 11.2027 & -4.50269 \tabularnewline
6 & 12.6 & 12.009 & 0.590972 \tabularnewline
7 & 14.8 & 11.0882 & 3.71176 \tabularnewline
8 & 13.3 & 13.161 & 0.139005 \tabularnewline
9 & 11.1 & 12.4925 & -1.39251 \tabularnewline
10 & 8.2 & 11.3291 & -3.12915 \tabularnewline
11 & 11.4 & 11.2399 & 0.160061 \tabularnewline
12 & 6.4 & 11.2086 & -4.8086 \tabularnewline
13 & 10.6 & 10.1388 & 0.461237 \tabularnewline
14 & 12 & 13.2689 & -1.26892 \tabularnewline
15 & 6.3 & 8.58952 & -2.28952 \tabularnewline
16 & 11.9 & 13.0861 & -1.1861 \tabularnewline
17 & 9.3 & 10.8695 & -1.56946 \tabularnewline
18 & 10 & 10.005 & -0.00495665 \tabularnewline
19 & 6.4 & 10.6558 & -4.25578 \tabularnewline
20 & 13.8 & 12.3639 & 1.43614 \tabularnewline
21 & 10.8 & 10.84 & -0.0400431 \tabularnewline
22 & 13.8 & 12.096 & 1.70403 \tabularnewline
23 & 11.7 & 10.4891 & 1.21087 \tabularnewline
24 & 10.9 & 12.1874 & -1.28742 \tabularnewline
25 & 9.9 & 11.0424 & -1.14238 \tabularnewline
26 & 11.5 & 10.7388 & 0.761193 \tabularnewline
27 & 8.3 & 10.968 & -2.66798 \tabularnewline
28 & 11.7 & 11.0058 & 0.694183 \tabularnewline
29 & 9 & 10.3691 & -1.36906 \tabularnewline
30 & 9.7 & 14.3203 & -4.6203 \tabularnewline
31 & 10.8 & 11.1761 & -0.376121 \tabularnewline
32 & 10.3 & 10.7071 & -0.40711 \tabularnewline
33 & 10.4 & 9.53421 & 0.865789 \tabularnewline
34 & 9.3 & 12.1319 & -2.83189 \tabularnewline
35 & 11.8 & 10.8913 & 0.908695 \tabularnewline
36 & 5.9 & 11.2281 & -5.32805 \tabularnewline
37 & 11.4 & 11.7129 & -0.312851 \tabularnewline
38 & 13 & 11.3265 & 1.67347 \tabularnewline
39 & 10.8 & 11.3425 & -0.542468 \tabularnewline
40 & 11.3 & 10.64 & 0.660041 \tabularnewline
41 & 11.8 & 11.4692 & 0.330799 \tabularnewline
42 & 12.7 & 9.3771 & 3.3229 \tabularnewline
43 & 10.9 & 10.9609 & -0.0608609 \tabularnewline
44 & 13.3 & 11.7639 & 1.5361 \tabularnewline
45 & 10.1 & 10.3223 & -0.222277 \tabularnewline
46 & 14.3 & 11.2411 & 3.05894 \tabularnewline
47 & 9.3 & 11.6301 & -2.33009 \tabularnewline
48 & 12.5 & 10.4641 & 2.03595 \tabularnewline
49 & 7.6 & 10.0411 & -2.44113 \tabularnewline
50 & 15.9 & 12.5295 & 3.37052 \tabularnewline
51 & 9.2 & 10.4354 & -1.23536 \tabularnewline
52 & 11.1 & 12.3659 & -1.26587 \tabularnewline
53 & 13 & 12.6095 & 0.39045 \tabularnewline
54 & 14.5 & 11.6808 & 2.81917 \tabularnewline
55 & 12.3 & 12.9796 & -0.679555 \tabularnewline
56 & 11.4 & 10.6096 & 0.790396 \tabularnewline
57 & 13 & 12.2644 & 0.735639 \tabularnewline
58 & 13.2 & 10.8462 & 2.35378 \tabularnewline
59 & 7.7 & 11.3464 & -3.64639 \tabularnewline
60 & 4.35 & 7.14044 & -2.79044 \tabularnewline
61 & 12.7 & 10.2323 & 2.46768 \tabularnewline
62 & 18.1 & 16.1356 & 1.96445 \tabularnewline
63 & 17.85 & 16.0445 & 1.80545 \tabularnewline
64 & 17.1 & 17.6531 & -0.553141 \tabularnewline
65 & 19.1 & 16.6378 & 2.46222 \tabularnewline
66 & 16.1 & 18.3833 & -2.28332 \tabularnewline
67 & 13.35 & 10.5227 & 2.82731 \tabularnewline
68 & 18.4 & 17.6253 & 0.77467 \tabularnewline
69 & 14.7 & 7.42603 & 7.27397 \tabularnewline
70 & 10.6 & 13.0921 & -2.49215 \tabularnewline
71 & 12.6 & 13.7761 & -1.17612 \tabularnewline
72 & 13.6 & 12.7201 & 0.879864 \tabularnewline
73 & 14.1 & 13.0495 & 1.05048 \tabularnewline
74 & 14.5 & 13.1564 & 1.34363 \tabularnewline
75 & 16.15 & 16.2534 & -0.103362 \tabularnewline
76 & 14.75 & 12.9051 & 1.84494 \tabularnewline
77 & 14.8 & 12.4907 & 2.30925 \tabularnewline
78 & 12.45 & 12.1502 & 0.29976 \tabularnewline
79 & 12.65 & 10.0958 & 2.55423 \tabularnewline
80 & 17.35 & 13.9174 & 3.43259 \tabularnewline
81 & 8.6 & 8.37154 & 0.228462 \tabularnewline
82 & 18.4 & 16.9848 & 1.41516 \tabularnewline
83 & 16.1 & 14.5308 & 1.56917 \tabularnewline
84 & 17.75 & 15.8124 & 1.9376 \tabularnewline
85 & 15.25 & 15.7238 & -0.47384 \tabularnewline
86 & 17.65 & 16.3308 & 1.31921 \tabularnewline
87 & 16.35 & 16.5714 & -0.2214 \tabularnewline
88 & 17.65 & 18.2505 & -0.600484 \tabularnewline
89 & 13.6 & 12.7797 & 0.820307 \tabularnewline
90 & 14.35 & 13.7208 & 0.629211 \tabularnewline
91 & 14.75 & 17.1673 & -2.4173 \tabularnewline
92 & 18.25 & 16.6471 & 1.60287 \tabularnewline
93 & 9.9 & 15.6137 & -5.7137 \tabularnewline
94 & 16 & 14.077 & 1.92304 \tabularnewline
95 & 18.25 & 16.1208 & 2.12919 \tabularnewline
96 & 16.85 & 17.837 & -0.98705 \tabularnewline
97 & 18.95 & 16.7864 & 2.16358 \tabularnewline
98 & 15.6 & 13.3878 & 2.21217 \tabularnewline
99 & 17.1 & 17.8525 & -0.752546 \tabularnewline
100 & 15.4 & 16.1494 & -0.749385 \tabularnewline
101 & 15.4 & 16.0188 & -0.618824 \tabularnewline
102 & 13.35 & 14.5354 & -1.18538 \tabularnewline
103 & 19.1 & 17.0244 & 2.07562 \tabularnewline
104 & 7.6 & 6.90326 & 0.696738 \tabularnewline
105 & 19.1 & 16.9811 & 2.11885 \tabularnewline
106 & 14.75 & 16.3951 & -1.64509 \tabularnewline
107 & 19.25 & 16.7651 & 2.48488 \tabularnewline
108 & 13.6 & 15.9241 & -2.32405 \tabularnewline
109 & 12.75 & 15.5062 & -2.75619 \tabularnewline
110 & 9.85 & 8.45415 & 1.39585 \tabularnewline
111 & 15.25 & 15.7583 & -0.50826 \tabularnewline
112 & 11.9 & 13.1838 & -1.28378 \tabularnewline
113 & 16.35 & 17.2961 & -0.946054 \tabularnewline
114 & 12.4 & 13.8175 & -1.41753 \tabularnewline
115 & 18.15 & 16.2335 & 1.91651 \tabularnewline
116 & 17.75 & 15.2345 & 2.51554 \tabularnewline
117 & 12.35 & 12.5879 & -0.237867 \tabularnewline
118 & 15.6 & 15.3307 & 0.269299 \tabularnewline
119 & 19.3 & 16.7322 & 2.56785 \tabularnewline
120 & 17.1 & 16.5891 & 0.51087 \tabularnewline
121 & 18.4 & 15.3648 & 3.0352 \tabularnewline
122 & 19.05 & 16.8733 & 2.17672 \tabularnewline
123 & 18.55 & 14.6579 & 3.89212 \tabularnewline
124 & 19.1 & 18.0009 & 1.0991 \tabularnewline
125 & 12.85 & 15.963 & -3.113 \tabularnewline
126 & 9.5 & 10.95 & -1.45003 \tabularnewline
127 & 4.5 & 7.32553 & -2.82553 \tabularnewline
128 & 13.6 & 14.9578 & -1.35782 \tabularnewline
129 & 11.7 & 12.2894 & -0.589437 \tabularnewline
130 & 13.35 & 13.8849 & -0.534943 \tabularnewline
131 & 17.6 & 19.0861 & -1.48614 \tabularnewline
132 & 14.05 & 13.62 & 0.430046 \tabularnewline
133 & 16.1 & 17.4184 & -1.3184 \tabularnewline
134 & 13.35 & 15.3418 & -1.99181 \tabularnewline
135 & 11.85 & 15.1195 & -3.26949 \tabularnewline
136 & 11.95 & 11.5084 & 0.441563 \tabularnewline
137 & 13.2 & 16.3586 & -3.15861 \tabularnewline
138 & 7.7 & 9.74168 & -2.04168 \tabularnewline
139 & 14.6 & 13.5978 & 1.00217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&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]12.9[/C][C]12.824[/C][C]0.0759754[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.3111[/C][C]1.88889[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.648[/C][C]1.15202[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.5193[/C][C]-4.11928[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.2027[/C][C]-4.50269[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.009[/C][C]0.590972[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.0882[/C][C]3.71176[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.161[/C][C]0.139005[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.4925[/C][C]-1.39251[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]11.3291[/C][C]-3.12915[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.2399[/C][C]0.160061[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.2086[/C][C]-4.8086[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.1388[/C][C]0.461237[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.2689[/C][C]-1.26892[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.58952[/C][C]-2.28952[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.0861[/C][C]-1.1861[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.8695[/C][C]-1.56946[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.005[/C][C]-0.00495665[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.6558[/C][C]-4.25578[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.3639[/C][C]1.43614[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.84[/C][C]-0.0400431[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.096[/C][C]1.70403[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.4891[/C][C]1.21087[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.1874[/C][C]-1.28742[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.0424[/C][C]-1.14238[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.7388[/C][C]0.761193[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.968[/C][C]-2.66798[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.0058[/C][C]0.694183[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.3691[/C][C]-1.36906[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.3203[/C][C]-4.6203[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.1761[/C][C]-0.376121[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.7071[/C][C]-0.40711[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.53421[/C][C]0.865789[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.1319[/C][C]-2.83189[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.8913[/C][C]0.908695[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2281[/C][C]-5.32805[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.7129[/C][C]-0.312851[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.3265[/C][C]1.67347[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.3425[/C][C]-0.542468[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.64[/C][C]0.660041[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.4692[/C][C]0.330799[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.3771[/C][C]3.3229[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.9609[/C][C]-0.0608609[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.7639[/C][C]1.5361[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.3223[/C][C]-0.222277[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.2411[/C][C]3.05894[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6301[/C][C]-2.33009[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.4641[/C][C]2.03595[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.0411[/C][C]-2.44113[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.5295[/C][C]3.37052[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.4354[/C][C]-1.23536[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.3659[/C][C]-1.26587[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.6095[/C][C]0.39045[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.6808[/C][C]2.81917[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]12.9796[/C][C]-0.679555[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.6096[/C][C]0.790396[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2644[/C][C]0.735639[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]10.8462[/C][C]2.35378[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.3464[/C][C]-3.64639[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.14044[/C][C]-2.79044[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2323[/C][C]2.46768[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.1356[/C][C]1.96445[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.0445[/C][C]1.80545[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.6531[/C][C]-0.553141[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.6378[/C][C]2.46222[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.3833[/C][C]-2.28332[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.5227[/C][C]2.82731[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.6253[/C][C]0.77467[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.42603[/C][C]7.27397[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.0921[/C][C]-2.49215[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.7761[/C][C]-1.17612[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.7201[/C][C]0.879864[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.0495[/C][C]1.05048[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.1564[/C][C]1.34363[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.2534[/C][C]-0.103362[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.9051[/C][C]1.84494[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.4907[/C][C]2.30925[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.1502[/C][C]0.29976[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.0958[/C][C]2.55423[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9174[/C][C]3.43259[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.37154[/C][C]0.228462[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.9848[/C][C]1.41516[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.5308[/C][C]1.56917[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.8124[/C][C]1.9376[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.7238[/C][C]-0.47384[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.3308[/C][C]1.31921[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.5714[/C][C]-0.2214[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.2505[/C][C]-0.600484[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.7797[/C][C]0.820307[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.7208[/C][C]0.629211[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.1673[/C][C]-2.4173[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.6471[/C][C]1.60287[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.6137[/C][C]-5.7137[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.077[/C][C]1.92304[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1208[/C][C]2.12919[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.837[/C][C]-0.98705[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.7864[/C][C]2.16358[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.3878[/C][C]2.21217[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.8525[/C][C]-0.752546[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1494[/C][C]-0.749385[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0188[/C][C]-0.618824[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.5354[/C][C]-1.18538[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0244[/C][C]2.07562[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.90326[/C][C]0.696738[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.9811[/C][C]2.11885[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.3951[/C][C]-1.64509[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.7651[/C][C]2.48488[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]15.9241[/C][C]-2.32405[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.5062[/C][C]-2.75619[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.45415[/C][C]1.39585[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.7583[/C][C]-0.50826[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.1838[/C][C]-1.28378[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.2961[/C][C]-0.946054[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.8175[/C][C]-1.41753[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.2335[/C][C]1.91651[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.2345[/C][C]2.51554[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.5879[/C][C]-0.237867[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3307[/C][C]0.269299[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.7322[/C][C]2.56785[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.5891[/C][C]0.51087[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.3648[/C][C]3.0352[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.8733[/C][C]2.17672[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.6579[/C][C]3.89212[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.0009[/C][C]1.0991[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.963[/C][C]-3.113[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.95[/C][C]-1.45003[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.32553[/C][C]-2.82553[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]14.9578[/C][C]-1.35782[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.2894[/C][C]-0.589437[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.8849[/C][C]-0.534943[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]19.0861[/C][C]-1.48614[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.62[/C][C]0.430046[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.4184[/C][C]-1.3184[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.3418[/C][C]-1.99181[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1195[/C][C]-3.26949[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.5084[/C][C]0.441563[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.3586[/C][C]-3.15861[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.74168[/C][C]-2.04168[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.5978[/C][C]1.00217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&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
112.912.8240.0759754
212.210.31111.88889
312.811.6481.15202
47.411.5193-4.11928
56.711.2027-4.50269
612.612.0090.590972
714.811.08823.71176
813.313.1610.139005
911.112.4925-1.39251
108.211.3291-3.12915
1111.411.23990.160061
126.411.2086-4.8086
1310.610.13880.461237
141213.2689-1.26892
156.38.58952-2.28952
1611.913.0861-1.1861
179.310.8695-1.56946
181010.005-0.00495665
196.410.6558-4.25578
2013.812.36391.43614
2110.810.84-0.0400431
2213.812.0961.70403
2311.710.48911.21087
2410.912.1874-1.28742
259.911.0424-1.14238
2611.510.73880.761193
278.310.968-2.66798
2811.711.00580.694183
29910.3691-1.36906
309.714.3203-4.6203
3110.811.1761-0.376121
3210.310.7071-0.40711
3310.49.534210.865789
349.312.1319-2.83189
3511.810.89130.908695
365.911.2281-5.32805
3711.411.7129-0.312851
381311.32651.67347
3910.811.3425-0.542468
4011.310.640.660041
4111.811.46920.330799
4212.79.37713.3229
4310.910.9609-0.0608609
4413.311.76391.5361
4510.110.3223-0.222277
4614.311.24113.05894
479.311.6301-2.33009
4812.510.46412.03595
497.610.0411-2.44113
5015.912.52953.37052
519.210.4354-1.23536
5211.112.3659-1.26587
531312.60950.39045
5414.511.68082.81917
5512.312.9796-0.679555
5611.410.60960.790396
571312.26440.735639
5813.210.84622.35378
597.711.3464-3.64639
604.357.14044-2.79044
6112.710.23232.46768
6218.116.13561.96445
6317.8516.04451.80545
6417.117.6531-0.553141
6519.116.63782.46222
6616.118.3833-2.28332
6713.3510.52272.82731
6818.417.62530.77467
6914.77.426037.27397
7010.613.0921-2.49215
7112.613.7761-1.17612
7213.612.72010.879864
7314.113.04951.05048
7414.513.15641.34363
7516.1516.2534-0.103362
7614.7512.90511.84494
7714.812.49072.30925
7812.4512.15020.29976
7912.6510.09582.55423
8017.3513.91743.43259
818.68.371540.228462
8218.416.98481.41516
8316.114.53081.56917
8417.7515.81241.9376
8515.2515.7238-0.47384
8617.6516.33081.31921
8716.3516.5714-0.2214
8817.6518.2505-0.600484
8913.612.77970.820307
9014.3513.72080.629211
9114.7517.1673-2.4173
9218.2516.64711.60287
939.915.6137-5.7137
941614.0771.92304
9518.2516.12082.12919
9616.8517.837-0.98705
9718.9516.78642.16358
9815.613.38782.21217
9917.117.8525-0.752546
10015.416.1494-0.749385
10115.416.0188-0.618824
10213.3514.5354-1.18538
10319.117.02442.07562
1047.66.903260.696738
10519.116.98112.11885
10614.7516.3951-1.64509
10719.2516.76512.48488
10813.615.9241-2.32405
10912.7515.5062-2.75619
1109.858.454151.39585
11115.2515.7583-0.50826
11211.913.1838-1.28378
11316.3517.2961-0.946054
11412.413.8175-1.41753
11518.1516.23351.91651
11617.7515.23452.51554
11712.3512.5879-0.237867
11815.615.33070.269299
11919.316.73222.56785
12017.116.58910.51087
12118.415.36483.0352
12219.0516.87332.17672
12318.5514.65793.89212
12419.118.00091.0991
12512.8515.963-3.113
1269.510.95-1.45003
1274.57.32553-2.82553
12813.614.9578-1.35782
12911.712.2894-0.589437
13013.3513.8849-0.534943
13117.619.0861-1.48614
13214.0513.620.430046
13316.117.4184-1.3184
13413.3515.3418-1.99181
13511.8515.1195-3.26949
13611.9511.50840.441563
13713.216.3586-3.15861
1387.79.74168-2.04168
13914.613.59781.00217






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.371490.742980.62851
120.4054380.8108760.594562
130.2662420.5324840.733758
140.303320.606640.69668
150.2472280.4944560.752772
160.1831620.3663230.816838
170.1331350.2662710.866865
180.2213250.4426510.778675
190.1813450.362690.818655
200.1539190.3078390.846081
210.1217160.2434330.878284
220.08577030.1715410.91423
230.0572090.1144180.942791
240.03859230.07718460.961408
250.02880710.05761420.971193
260.03939130.07878260.960609
270.06446010.128920.93554
280.1047220.2094430.895278
290.1409890.2819780.859011
300.4044970.8089950.595503
310.3616920.7233850.638308
320.3014590.6029180.698541
330.2498640.4997280.750136
340.243270.486540.75673
350.1995170.3990330.800483
360.4322910.8645810.567709
370.380320.760640.61968
380.3364630.6729260.663537
390.3191380.6382770.680862
400.269270.5385390.73073
410.2376910.4753830.762309
420.2638450.527690.736155
430.2309770.4619540.769023
440.3521380.7042760.647862
450.3041360.6082720.695864
460.4428290.8856580.557171
470.4414740.8829480.558526
480.4826630.9653260.517337
490.5456560.9086880.454344
500.6838520.6322950.316148
510.6516640.6966710.348336
520.6225730.7548550.377427
530.5810390.8379220.418961
540.6184970.7630060.381503
550.5812840.8374320.418716
560.5351150.929770.464885
570.4990670.9981340.500933
580.5089590.9820810.491041
590.5963330.8073340.403667
600.6671260.6657480.332874
610.6986160.6027680.301384
620.7006220.5987550.299378
630.6733560.6532870.326644
640.635890.7282190.36411
650.6293630.7412730.370637
660.6891370.6217260.310863
670.7192330.5615330.280767
680.6766170.6467670.323383
690.9596850.08062950.0403147
700.9664050.06719070.0335953
710.9601390.07972140.0398607
720.9580160.08396770.0419839
730.947310.1053790.0526896
740.9403470.1193060.059653
750.939140.1217210.0608603
760.9316770.1366450.0683227
770.9287670.1424670.0712333
780.909210.181580.09079
790.9157640.1684710.0842357
800.9512240.09755140.0487757
810.9383730.1232540.0616268
820.92490.15020.0751001
830.9136950.172610.0863052
840.9015530.1968930.0984466
850.8915450.216910.108455
860.8747930.2504140.125207
870.8501480.2997030.149852
880.8260610.3478780.173939
890.7915230.4169540.208477
900.7509710.4980580.249029
910.7549190.4901610.245081
920.7265570.5468860.273443
930.9325820.1348370.0674184
940.9179910.1640180.0820091
950.9095140.1809710.0904856
960.887450.22510.11255
970.8690550.2618890.130945
980.8695630.2608740.130437
990.8485580.3028830.151442
1000.8146170.3707670.185383
1010.7737260.4525480.226274
1020.7528750.494250.247125
1030.7746810.4506390.225319
1040.8109750.3780490.189025
1050.7716970.4566070.228303
1060.7365780.5268440.263422
1070.7001750.5996510.299825
1080.6834710.6330580.316529
1090.6805810.6388380.319419
1100.6910720.6178560.308928
1110.6412040.7175930.358796
1120.5780090.8439820.421991
1130.5428740.9142520.457126
1140.5070730.9858540.492927
1150.5055480.9889040.494452
1160.5236790.9526430.476321
1170.4485470.8970940.551453
1180.7009510.5980980.299049
1190.8363990.3272010.163601
1200.8306270.3387450.169373
1210.857460.2850810.14254
1220.9344990.1310020.0655011
1230.9738550.05228910.0261445
1240.9550580.08988490.0449424
1250.9661120.06777660.0338883
1260.9250670.1498650.0749327
1270.8723160.2553680.127684
1280.9513720.09725520.0486276

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.37149 & 0.74298 & 0.62851 \tabularnewline
12 & 0.405438 & 0.810876 & 0.594562 \tabularnewline
13 & 0.266242 & 0.532484 & 0.733758 \tabularnewline
14 & 0.30332 & 0.60664 & 0.69668 \tabularnewline
15 & 0.247228 & 0.494456 & 0.752772 \tabularnewline
16 & 0.183162 & 0.366323 & 0.816838 \tabularnewline
17 & 0.133135 & 0.266271 & 0.866865 \tabularnewline
18 & 0.221325 & 0.442651 & 0.778675 \tabularnewline
19 & 0.181345 & 0.36269 & 0.818655 \tabularnewline
20 & 0.153919 & 0.307839 & 0.846081 \tabularnewline
21 & 0.121716 & 0.243433 & 0.878284 \tabularnewline
22 & 0.0857703 & 0.171541 & 0.91423 \tabularnewline
23 & 0.057209 & 0.114418 & 0.942791 \tabularnewline
24 & 0.0385923 & 0.0771846 & 0.961408 \tabularnewline
25 & 0.0288071 & 0.0576142 & 0.971193 \tabularnewline
26 & 0.0393913 & 0.0787826 & 0.960609 \tabularnewline
27 & 0.0644601 & 0.12892 & 0.93554 \tabularnewline
28 & 0.104722 & 0.209443 & 0.895278 \tabularnewline
29 & 0.140989 & 0.281978 & 0.859011 \tabularnewline
30 & 0.404497 & 0.808995 & 0.595503 \tabularnewline
31 & 0.361692 & 0.723385 & 0.638308 \tabularnewline
32 & 0.301459 & 0.602918 & 0.698541 \tabularnewline
33 & 0.249864 & 0.499728 & 0.750136 \tabularnewline
34 & 0.24327 & 0.48654 & 0.75673 \tabularnewline
35 & 0.199517 & 0.399033 & 0.800483 \tabularnewline
36 & 0.432291 & 0.864581 & 0.567709 \tabularnewline
37 & 0.38032 & 0.76064 & 0.61968 \tabularnewline
38 & 0.336463 & 0.672926 & 0.663537 \tabularnewline
39 & 0.319138 & 0.638277 & 0.680862 \tabularnewline
40 & 0.26927 & 0.538539 & 0.73073 \tabularnewline
41 & 0.237691 & 0.475383 & 0.762309 \tabularnewline
42 & 0.263845 & 0.52769 & 0.736155 \tabularnewline
43 & 0.230977 & 0.461954 & 0.769023 \tabularnewline
44 & 0.352138 & 0.704276 & 0.647862 \tabularnewline
45 & 0.304136 & 0.608272 & 0.695864 \tabularnewline
46 & 0.442829 & 0.885658 & 0.557171 \tabularnewline
47 & 0.441474 & 0.882948 & 0.558526 \tabularnewline
48 & 0.482663 & 0.965326 & 0.517337 \tabularnewline
49 & 0.545656 & 0.908688 & 0.454344 \tabularnewline
50 & 0.683852 & 0.632295 & 0.316148 \tabularnewline
51 & 0.651664 & 0.696671 & 0.348336 \tabularnewline
52 & 0.622573 & 0.754855 & 0.377427 \tabularnewline
53 & 0.581039 & 0.837922 & 0.418961 \tabularnewline
54 & 0.618497 & 0.763006 & 0.381503 \tabularnewline
55 & 0.581284 & 0.837432 & 0.418716 \tabularnewline
56 & 0.535115 & 0.92977 & 0.464885 \tabularnewline
57 & 0.499067 & 0.998134 & 0.500933 \tabularnewline
58 & 0.508959 & 0.982081 & 0.491041 \tabularnewline
59 & 0.596333 & 0.807334 & 0.403667 \tabularnewline
60 & 0.667126 & 0.665748 & 0.332874 \tabularnewline
61 & 0.698616 & 0.602768 & 0.301384 \tabularnewline
62 & 0.700622 & 0.598755 & 0.299378 \tabularnewline
63 & 0.673356 & 0.653287 & 0.326644 \tabularnewline
64 & 0.63589 & 0.728219 & 0.36411 \tabularnewline
65 & 0.629363 & 0.741273 & 0.370637 \tabularnewline
66 & 0.689137 & 0.621726 & 0.310863 \tabularnewline
67 & 0.719233 & 0.561533 & 0.280767 \tabularnewline
68 & 0.676617 & 0.646767 & 0.323383 \tabularnewline
69 & 0.959685 & 0.0806295 & 0.0403147 \tabularnewline
70 & 0.966405 & 0.0671907 & 0.0335953 \tabularnewline
71 & 0.960139 & 0.0797214 & 0.0398607 \tabularnewline
72 & 0.958016 & 0.0839677 & 0.0419839 \tabularnewline
73 & 0.94731 & 0.105379 & 0.0526896 \tabularnewline
74 & 0.940347 & 0.119306 & 0.059653 \tabularnewline
75 & 0.93914 & 0.121721 & 0.0608603 \tabularnewline
76 & 0.931677 & 0.136645 & 0.0683227 \tabularnewline
77 & 0.928767 & 0.142467 & 0.0712333 \tabularnewline
78 & 0.90921 & 0.18158 & 0.09079 \tabularnewline
79 & 0.915764 & 0.168471 & 0.0842357 \tabularnewline
80 & 0.951224 & 0.0975514 & 0.0487757 \tabularnewline
81 & 0.938373 & 0.123254 & 0.0616268 \tabularnewline
82 & 0.9249 & 0.1502 & 0.0751001 \tabularnewline
83 & 0.913695 & 0.17261 & 0.0863052 \tabularnewline
84 & 0.901553 & 0.196893 & 0.0984466 \tabularnewline
85 & 0.891545 & 0.21691 & 0.108455 \tabularnewline
86 & 0.874793 & 0.250414 & 0.125207 \tabularnewline
87 & 0.850148 & 0.299703 & 0.149852 \tabularnewline
88 & 0.826061 & 0.347878 & 0.173939 \tabularnewline
89 & 0.791523 & 0.416954 & 0.208477 \tabularnewline
90 & 0.750971 & 0.498058 & 0.249029 \tabularnewline
91 & 0.754919 & 0.490161 & 0.245081 \tabularnewline
92 & 0.726557 & 0.546886 & 0.273443 \tabularnewline
93 & 0.932582 & 0.134837 & 0.0674184 \tabularnewline
94 & 0.917991 & 0.164018 & 0.0820091 \tabularnewline
95 & 0.909514 & 0.180971 & 0.0904856 \tabularnewline
96 & 0.88745 & 0.2251 & 0.11255 \tabularnewline
97 & 0.869055 & 0.261889 & 0.130945 \tabularnewline
98 & 0.869563 & 0.260874 & 0.130437 \tabularnewline
99 & 0.848558 & 0.302883 & 0.151442 \tabularnewline
100 & 0.814617 & 0.370767 & 0.185383 \tabularnewline
101 & 0.773726 & 0.452548 & 0.226274 \tabularnewline
102 & 0.752875 & 0.49425 & 0.247125 \tabularnewline
103 & 0.774681 & 0.450639 & 0.225319 \tabularnewline
104 & 0.810975 & 0.378049 & 0.189025 \tabularnewline
105 & 0.771697 & 0.456607 & 0.228303 \tabularnewline
106 & 0.736578 & 0.526844 & 0.263422 \tabularnewline
107 & 0.700175 & 0.599651 & 0.299825 \tabularnewline
108 & 0.683471 & 0.633058 & 0.316529 \tabularnewline
109 & 0.680581 & 0.638838 & 0.319419 \tabularnewline
110 & 0.691072 & 0.617856 & 0.308928 \tabularnewline
111 & 0.641204 & 0.717593 & 0.358796 \tabularnewline
112 & 0.578009 & 0.843982 & 0.421991 \tabularnewline
113 & 0.542874 & 0.914252 & 0.457126 \tabularnewline
114 & 0.507073 & 0.985854 & 0.492927 \tabularnewline
115 & 0.505548 & 0.988904 & 0.494452 \tabularnewline
116 & 0.523679 & 0.952643 & 0.476321 \tabularnewline
117 & 0.448547 & 0.897094 & 0.551453 \tabularnewline
118 & 0.700951 & 0.598098 & 0.299049 \tabularnewline
119 & 0.836399 & 0.327201 & 0.163601 \tabularnewline
120 & 0.830627 & 0.338745 & 0.169373 \tabularnewline
121 & 0.85746 & 0.285081 & 0.14254 \tabularnewline
122 & 0.934499 & 0.131002 & 0.0655011 \tabularnewline
123 & 0.973855 & 0.0522891 & 0.0261445 \tabularnewline
124 & 0.955058 & 0.0898849 & 0.0449424 \tabularnewline
125 & 0.966112 & 0.0677766 & 0.0338883 \tabularnewline
126 & 0.925067 & 0.149865 & 0.0749327 \tabularnewline
127 & 0.872316 & 0.255368 & 0.127684 \tabularnewline
128 & 0.951372 & 0.0972552 & 0.0486276 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.37149[/C][C]0.74298[/C][C]0.62851[/C][/ROW]
[ROW][C]12[/C][C]0.405438[/C][C]0.810876[/C][C]0.594562[/C][/ROW]
[ROW][C]13[/C][C]0.266242[/C][C]0.532484[/C][C]0.733758[/C][/ROW]
[ROW][C]14[/C][C]0.30332[/C][C]0.60664[/C][C]0.69668[/C][/ROW]
[ROW][C]15[/C][C]0.247228[/C][C]0.494456[/C][C]0.752772[/C][/ROW]
[ROW][C]16[/C][C]0.183162[/C][C]0.366323[/C][C]0.816838[/C][/ROW]
[ROW][C]17[/C][C]0.133135[/C][C]0.266271[/C][C]0.866865[/C][/ROW]
[ROW][C]18[/C][C]0.221325[/C][C]0.442651[/C][C]0.778675[/C][/ROW]
[ROW][C]19[/C][C]0.181345[/C][C]0.36269[/C][C]0.818655[/C][/ROW]
[ROW][C]20[/C][C]0.153919[/C][C]0.307839[/C][C]0.846081[/C][/ROW]
[ROW][C]21[/C][C]0.121716[/C][C]0.243433[/C][C]0.878284[/C][/ROW]
[ROW][C]22[/C][C]0.0857703[/C][C]0.171541[/C][C]0.91423[/C][/ROW]
[ROW][C]23[/C][C]0.057209[/C][C]0.114418[/C][C]0.942791[/C][/ROW]
[ROW][C]24[/C][C]0.0385923[/C][C]0.0771846[/C][C]0.961408[/C][/ROW]
[ROW][C]25[/C][C]0.0288071[/C][C]0.0576142[/C][C]0.971193[/C][/ROW]
[ROW][C]26[/C][C]0.0393913[/C][C]0.0787826[/C][C]0.960609[/C][/ROW]
[ROW][C]27[/C][C]0.0644601[/C][C]0.12892[/C][C]0.93554[/C][/ROW]
[ROW][C]28[/C][C]0.104722[/C][C]0.209443[/C][C]0.895278[/C][/ROW]
[ROW][C]29[/C][C]0.140989[/C][C]0.281978[/C][C]0.859011[/C][/ROW]
[ROW][C]30[/C][C]0.404497[/C][C]0.808995[/C][C]0.595503[/C][/ROW]
[ROW][C]31[/C][C]0.361692[/C][C]0.723385[/C][C]0.638308[/C][/ROW]
[ROW][C]32[/C][C]0.301459[/C][C]0.602918[/C][C]0.698541[/C][/ROW]
[ROW][C]33[/C][C]0.249864[/C][C]0.499728[/C][C]0.750136[/C][/ROW]
[ROW][C]34[/C][C]0.24327[/C][C]0.48654[/C][C]0.75673[/C][/ROW]
[ROW][C]35[/C][C]0.199517[/C][C]0.399033[/C][C]0.800483[/C][/ROW]
[ROW][C]36[/C][C]0.432291[/C][C]0.864581[/C][C]0.567709[/C][/ROW]
[ROW][C]37[/C][C]0.38032[/C][C]0.76064[/C][C]0.61968[/C][/ROW]
[ROW][C]38[/C][C]0.336463[/C][C]0.672926[/C][C]0.663537[/C][/ROW]
[ROW][C]39[/C][C]0.319138[/C][C]0.638277[/C][C]0.680862[/C][/ROW]
[ROW][C]40[/C][C]0.26927[/C][C]0.538539[/C][C]0.73073[/C][/ROW]
[ROW][C]41[/C][C]0.237691[/C][C]0.475383[/C][C]0.762309[/C][/ROW]
[ROW][C]42[/C][C]0.263845[/C][C]0.52769[/C][C]0.736155[/C][/ROW]
[ROW][C]43[/C][C]0.230977[/C][C]0.461954[/C][C]0.769023[/C][/ROW]
[ROW][C]44[/C][C]0.352138[/C][C]0.704276[/C][C]0.647862[/C][/ROW]
[ROW][C]45[/C][C]0.304136[/C][C]0.608272[/C][C]0.695864[/C][/ROW]
[ROW][C]46[/C][C]0.442829[/C][C]0.885658[/C][C]0.557171[/C][/ROW]
[ROW][C]47[/C][C]0.441474[/C][C]0.882948[/C][C]0.558526[/C][/ROW]
[ROW][C]48[/C][C]0.482663[/C][C]0.965326[/C][C]0.517337[/C][/ROW]
[ROW][C]49[/C][C]0.545656[/C][C]0.908688[/C][C]0.454344[/C][/ROW]
[ROW][C]50[/C][C]0.683852[/C][C]0.632295[/C][C]0.316148[/C][/ROW]
[ROW][C]51[/C][C]0.651664[/C][C]0.696671[/C][C]0.348336[/C][/ROW]
[ROW][C]52[/C][C]0.622573[/C][C]0.754855[/C][C]0.377427[/C][/ROW]
[ROW][C]53[/C][C]0.581039[/C][C]0.837922[/C][C]0.418961[/C][/ROW]
[ROW][C]54[/C][C]0.618497[/C][C]0.763006[/C][C]0.381503[/C][/ROW]
[ROW][C]55[/C][C]0.581284[/C][C]0.837432[/C][C]0.418716[/C][/ROW]
[ROW][C]56[/C][C]0.535115[/C][C]0.92977[/C][C]0.464885[/C][/ROW]
[ROW][C]57[/C][C]0.499067[/C][C]0.998134[/C][C]0.500933[/C][/ROW]
[ROW][C]58[/C][C]0.508959[/C][C]0.982081[/C][C]0.491041[/C][/ROW]
[ROW][C]59[/C][C]0.596333[/C][C]0.807334[/C][C]0.403667[/C][/ROW]
[ROW][C]60[/C][C]0.667126[/C][C]0.665748[/C][C]0.332874[/C][/ROW]
[ROW][C]61[/C][C]0.698616[/C][C]0.602768[/C][C]0.301384[/C][/ROW]
[ROW][C]62[/C][C]0.700622[/C][C]0.598755[/C][C]0.299378[/C][/ROW]
[ROW][C]63[/C][C]0.673356[/C][C]0.653287[/C][C]0.326644[/C][/ROW]
[ROW][C]64[/C][C]0.63589[/C][C]0.728219[/C][C]0.36411[/C][/ROW]
[ROW][C]65[/C][C]0.629363[/C][C]0.741273[/C][C]0.370637[/C][/ROW]
[ROW][C]66[/C][C]0.689137[/C][C]0.621726[/C][C]0.310863[/C][/ROW]
[ROW][C]67[/C][C]0.719233[/C][C]0.561533[/C][C]0.280767[/C][/ROW]
[ROW][C]68[/C][C]0.676617[/C][C]0.646767[/C][C]0.323383[/C][/ROW]
[ROW][C]69[/C][C]0.959685[/C][C]0.0806295[/C][C]0.0403147[/C][/ROW]
[ROW][C]70[/C][C]0.966405[/C][C]0.0671907[/C][C]0.0335953[/C][/ROW]
[ROW][C]71[/C][C]0.960139[/C][C]0.0797214[/C][C]0.0398607[/C][/ROW]
[ROW][C]72[/C][C]0.958016[/C][C]0.0839677[/C][C]0.0419839[/C][/ROW]
[ROW][C]73[/C][C]0.94731[/C][C]0.105379[/C][C]0.0526896[/C][/ROW]
[ROW][C]74[/C][C]0.940347[/C][C]0.119306[/C][C]0.059653[/C][/ROW]
[ROW][C]75[/C][C]0.93914[/C][C]0.121721[/C][C]0.0608603[/C][/ROW]
[ROW][C]76[/C][C]0.931677[/C][C]0.136645[/C][C]0.0683227[/C][/ROW]
[ROW][C]77[/C][C]0.928767[/C][C]0.142467[/C][C]0.0712333[/C][/ROW]
[ROW][C]78[/C][C]0.90921[/C][C]0.18158[/C][C]0.09079[/C][/ROW]
[ROW][C]79[/C][C]0.915764[/C][C]0.168471[/C][C]0.0842357[/C][/ROW]
[ROW][C]80[/C][C]0.951224[/C][C]0.0975514[/C][C]0.0487757[/C][/ROW]
[ROW][C]81[/C][C]0.938373[/C][C]0.123254[/C][C]0.0616268[/C][/ROW]
[ROW][C]82[/C][C]0.9249[/C][C]0.1502[/C][C]0.0751001[/C][/ROW]
[ROW][C]83[/C][C]0.913695[/C][C]0.17261[/C][C]0.0863052[/C][/ROW]
[ROW][C]84[/C][C]0.901553[/C][C]0.196893[/C][C]0.0984466[/C][/ROW]
[ROW][C]85[/C][C]0.891545[/C][C]0.21691[/C][C]0.108455[/C][/ROW]
[ROW][C]86[/C][C]0.874793[/C][C]0.250414[/C][C]0.125207[/C][/ROW]
[ROW][C]87[/C][C]0.850148[/C][C]0.299703[/C][C]0.149852[/C][/ROW]
[ROW][C]88[/C][C]0.826061[/C][C]0.347878[/C][C]0.173939[/C][/ROW]
[ROW][C]89[/C][C]0.791523[/C][C]0.416954[/C][C]0.208477[/C][/ROW]
[ROW][C]90[/C][C]0.750971[/C][C]0.498058[/C][C]0.249029[/C][/ROW]
[ROW][C]91[/C][C]0.754919[/C][C]0.490161[/C][C]0.245081[/C][/ROW]
[ROW][C]92[/C][C]0.726557[/C][C]0.546886[/C][C]0.273443[/C][/ROW]
[ROW][C]93[/C][C]0.932582[/C][C]0.134837[/C][C]0.0674184[/C][/ROW]
[ROW][C]94[/C][C]0.917991[/C][C]0.164018[/C][C]0.0820091[/C][/ROW]
[ROW][C]95[/C][C]0.909514[/C][C]0.180971[/C][C]0.0904856[/C][/ROW]
[ROW][C]96[/C][C]0.88745[/C][C]0.2251[/C][C]0.11255[/C][/ROW]
[ROW][C]97[/C][C]0.869055[/C][C]0.261889[/C][C]0.130945[/C][/ROW]
[ROW][C]98[/C][C]0.869563[/C][C]0.260874[/C][C]0.130437[/C][/ROW]
[ROW][C]99[/C][C]0.848558[/C][C]0.302883[/C][C]0.151442[/C][/ROW]
[ROW][C]100[/C][C]0.814617[/C][C]0.370767[/C][C]0.185383[/C][/ROW]
[ROW][C]101[/C][C]0.773726[/C][C]0.452548[/C][C]0.226274[/C][/ROW]
[ROW][C]102[/C][C]0.752875[/C][C]0.49425[/C][C]0.247125[/C][/ROW]
[ROW][C]103[/C][C]0.774681[/C][C]0.450639[/C][C]0.225319[/C][/ROW]
[ROW][C]104[/C][C]0.810975[/C][C]0.378049[/C][C]0.189025[/C][/ROW]
[ROW][C]105[/C][C]0.771697[/C][C]0.456607[/C][C]0.228303[/C][/ROW]
[ROW][C]106[/C][C]0.736578[/C][C]0.526844[/C][C]0.263422[/C][/ROW]
[ROW][C]107[/C][C]0.700175[/C][C]0.599651[/C][C]0.299825[/C][/ROW]
[ROW][C]108[/C][C]0.683471[/C][C]0.633058[/C][C]0.316529[/C][/ROW]
[ROW][C]109[/C][C]0.680581[/C][C]0.638838[/C][C]0.319419[/C][/ROW]
[ROW][C]110[/C][C]0.691072[/C][C]0.617856[/C][C]0.308928[/C][/ROW]
[ROW][C]111[/C][C]0.641204[/C][C]0.717593[/C][C]0.358796[/C][/ROW]
[ROW][C]112[/C][C]0.578009[/C][C]0.843982[/C][C]0.421991[/C][/ROW]
[ROW][C]113[/C][C]0.542874[/C][C]0.914252[/C][C]0.457126[/C][/ROW]
[ROW][C]114[/C][C]0.507073[/C][C]0.985854[/C][C]0.492927[/C][/ROW]
[ROW][C]115[/C][C]0.505548[/C][C]0.988904[/C][C]0.494452[/C][/ROW]
[ROW][C]116[/C][C]0.523679[/C][C]0.952643[/C][C]0.476321[/C][/ROW]
[ROW][C]117[/C][C]0.448547[/C][C]0.897094[/C][C]0.551453[/C][/ROW]
[ROW][C]118[/C][C]0.700951[/C][C]0.598098[/C][C]0.299049[/C][/ROW]
[ROW][C]119[/C][C]0.836399[/C][C]0.327201[/C][C]0.163601[/C][/ROW]
[ROW][C]120[/C][C]0.830627[/C][C]0.338745[/C][C]0.169373[/C][/ROW]
[ROW][C]121[/C][C]0.85746[/C][C]0.285081[/C][C]0.14254[/C][/ROW]
[ROW][C]122[/C][C]0.934499[/C][C]0.131002[/C][C]0.0655011[/C][/ROW]
[ROW][C]123[/C][C]0.973855[/C][C]0.0522891[/C][C]0.0261445[/C][/ROW]
[ROW][C]124[/C][C]0.955058[/C][C]0.0898849[/C][C]0.0449424[/C][/ROW]
[ROW][C]125[/C][C]0.966112[/C][C]0.0677766[/C][C]0.0338883[/C][/ROW]
[ROW][C]126[/C][C]0.925067[/C][C]0.149865[/C][C]0.0749327[/C][/ROW]
[ROW][C]127[/C][C]0.872316[/C][C]0.255368[/C][C]0.127684[/C][/ROW]
[ROW][C]128[/C][C]0.951372[/C][C]0.0972552[/C][C]0.0486276[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.371490.742980.62851
120.4054380.8108760.594562
130.2662420.5324840.733758
140.303320.606640.69668
150.2472280.4944560.752772
160.1831620.3663230.816838
170.1331350.2662710.866865
180.2213250.4426510.778675
190.1813450.362690.818655
200.1539190.3078390.846081
210.1217160.2434330.878284
220.08577030.1715410.91423
230.0572090.1144180.942791
240.03859230.07718460.961408
250.02880710.05761420.971193
260.03939130.07878260.960609
270.06446010.128920.93554
280.1047220.2094430.895278
290.1409890.2819780.859011
300.4044970.8089950.595503
310.3616920.7233850.638308
320.3014590.6029180.698541
330.2498640.4997280.750136
340.243270.486540.75673
350.1995170.3990330.800483
360.4322910.8645810.567709
370.380320.760640.61968
380.3364630.6729260.663537
390.3191380.6382770.680862
400.269270.5385390.73073
410.2376910.4753830.762309
420.2638450.527690.736155
430.2309770.4619540.769023
440.3521380.7042760.647862
450.3041360.6082720.695864
460.4428290.8856580.557171
470.4414740.8829480.558526
480.4826630.9653260.517337
490.5456560.9086880.454344
500.6838520.6322950.316148
510.6516640.6966710.348336
520.6225730.7548550.377427
530.5810390.8379220.418961
540.6184970.7630060.381503
550.5812840.8374320.418716
560.5351150.929770.464885
570.4990670.9981340.500933
580.5089590.9820810.491041
590.5963330.8073340.403667
600.6671260.6657480.332874
610.6986160.6027680.301384
620.7006220.5987550.299378
630.6733560.6532870.326644
640.635890.7282190.36411
650.6293630.7412730.370637
660.6891370.6217260.310863
670.7192330.5615330.280767
680.6766170.6467670.323383
690.9596850.08062950.0403147
700.9664050.06719070.0335953
710.9601390.07972140.0398607
720.9580160.08396770.0419839
730.947310.1053790.0526896
740.9403470.1193060.059653
750.939140.1217210.0608603
760.9316770.1366450.0683227
770.9287670.1424670.0712333
780.909210.181580.09079
790.9157640.1684710.0842357
800.9512240.09755140.0487757
810.9383730.1232540.0616268
820.92490.15020.0751001
830.9136950.172610.0863052
840.9015530.1968930.0984466
850.8915450.216910.108455
860.8747930.2504140.125207
870.8501480.2997030.149852
880.8260610.3478780.173939
890.7915230.4169540.208477
900.7509710.4980580.249029
910.7549190.4901610.245081
920.7265570.5468860.273443
930.9325820.1348370.0674184
940.9179910.1640180.0820091
950.9095140.1809710.0904856
960.887450.22510.11255
970.8690550.2618890.130945
980.8695630.2608740.130437
990.8485580.3028830.151442
1000.8146170.3707670.185383
1010.7737260.4525480.226274
1020.7528750.494250.247125
1030.7746810.4506390.225319
1040.8109750.3780490.189025
1050.7716970.4566070.228303
1060.7365780.5268440.263422
1070.7001750.5996510.299825
1080.6834710.6330580.316529
1090.6805810.6388380.319419
1100.6910720.6178560.308928
1110.6412040.7175930.358796
1120.5780090.8439820.421991
1130.5428740.9142520.457126
1140.5070730.9858540.492927
1150.5055480.9889040.494452
1160.5236790.9526430.476321
1170.4485470.8970940.551453
1180.7009510.5980980.299049
1190.8363990.3272010.163601
1200.8306270.3387450.169373
1210.857460.2850810.14254
1220.9344990.1310020.0655011
1230.9738550.05228910.0261445
1240.9550580.08988490.0449424
1250.9661120.06777660.0338883
1260.9250670.1498650.0749327
1270.8723160.2553680.127684
1280.9513720.09725520.0486276






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 level120.101695NOK

\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 & 12 & 0.101695 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271058&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]12[/C][C]0.101695[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271058&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271058&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 level120.101695NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}