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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 15:13:53 +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/t1418915656pbtkhk7la1gmpwa.htm/, Retrieved Sun, 19 May 2024 19:17:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271057, Retrieved Sun, 19 May 2024 19:17:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64

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 15:13:53] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 7 12 21 12 18 68 1.8
12.2 18 20 20 22 8 31 39 2.1
12.8 12 9 14 21 11 39 32 2.2
7.4 24 19 25 21 13 46 62 2.3
6.7 16 12 15 21 11 31 33 2.1
12.6 19 16 20 21 10 67 52 2.7
14.8 16 17 21 21 7 35 62 2.1
13.3 15 9 15 23 10 52 77 2.4
11.1 28 28 28 22 15 77 76 2.9
8.2 21 20 11 25 12 37 41 2.2
11.4 18 16 22 21 12 32 48 2.1
6.4 22 22 22 23 10 36 63 2.2
10.6 19 17 27 22 10 38 30 2.2
12 22 12 24 21 14 69 78 2.7
6.3 25 18 23 21 6 21 19 1.9
11.9 16 12 21 21 14 54 66 2.5
9.3 19 16 20 21 11 36 35 2.2
10 26 21 25 24 12 23 45 1.9
6.4 24 15 16 23 15 34 21 2.1
13.8 20 17 24 21 13 112 25 3.5
10.8 19 17 21 24 11 35 44 2.1
13.8 19 17 22 23 12 47 69 2.3
11.7 23 18 25 21 7 47 54 2.3
10.9 18 15 23 22 11 37 74 2.2
9.9 21 21 22 21 12 20 61 1.9
11.5 20 12 25 22 13 22 41 1.9
8.3 15 6 23 22 9 23 46 1.9
11.7 19 13 19 21 11 32 39 2.1
9 19 19 21 21 12 30 34 2
9.7 7 12 19 25 15 92 51 3.2
10.8 20 14 25 22 12 43 42 2.3
10.3 20 13 16 22 6 55 31 2.5
10.4 19 12 24 20 5 16 39 1.8
9.3 20 19 18 21 11 71 49 2.8
11.8 18 10 28 21 6 43 53 2.3
5.9 14 10 15 22 12 29 31 2
11.4 17 11 17 21 10 56 39 2.5
13 17 11 18 24 6 46 54 2.3
10.8 8 10 26 22 12 19 49 1.8
11.3 22 22 22 21 6 59 46 2.6
11.8 20 12 19 22 12 30 55 2
12.7 22 20 26 22 8 7 50 1.6
10.9 14 11 12 23 12 19 30 1.8
13.3 21 17 20 23 14 48 45 2.4
10.1 20 14 24 21 12 23 35 1.9
14.3 18 16 22 21 14 33 41 2.1
9.3 24 15 23 22 11 34 73 2.1
12.5 19 15 19 21 10 48 17 2.4
7.6 16 10 24 21 7 18 40 1.8
15.9 16 10 21 21 12 43 64 2.3
9.2 16 18 16 21 7 33 37 2.1
11.1 22 22 23 21 12 71 65 2.8
13 21 16 20 22 10 26 100 2
14.5 15 10 19 22 10 67 28 2.7
12.3 15 16 18 21 12 80 56 2.9
11.4 14 16 21 23 12 29 29 2
13 16 10 17 21 10 43 59 2.3
13.2 26 16 24 20 11 29 61 2
7.7 18 16 22 21 12 32 51 2.1
4.35 17 15 14 22 9 23 12 1
12.7 6 4 5 22 11 16 45 1
18.1 22 9 25 22 12 33 37 4
17.85 20 18 21 20 12 32 37 4
17.1 17 12 9 22 12 52 68 4
19.1 20 16 15 21 12 75 72 4
16.1 23 17 23 21 10 72 143 4
13.35 18 14 21 21 15 15 9 2
18.4 13 13 9 21 10 29 55 4
14.7 22 20 24 21 15 13 17 1
10.6 20 16 16 21 10 40 37 3
12.6 20 15 20 21 15 19 27 3
13.6 16 16 18 24 15 121 58 3
14.1 16 15 21 22 13 36 21 3
14.5 15 16 21 20 12 23 19 3
16.15 19 19 21 21 12 85 78 4
14.75 19 9 20 24 8 41 35 3
14.8 24 19 24 25 9 46 48 3
12.45 9 7 15 22 15 18 27 2
12.65 22 23 24 21 12 35 43 2
17.35 15 14 18 21 12 17 30 3
8.6 22 10 24 22 15 4 25 1
18.4 22 16 24 23 11 28 69 4
16.1 24 12 15 24 12 44 72 3
17.75 21 7 20 22 14 38 13 4
15.25 25 20 26 25 12 57 61 4
17.65 26 9 26 22 12 23 43 4
16.35 21 12 23 21 12 36 51 4
17.65 14 10 13 21 11 22 67 4
13.6 28 19 16 21 12 40 36 3
14.35 21 11 22 22 12 31 44 3
14.75 16 15 21 22 12 11 45 4
18.25 16 14 11 21 12 38 34 4
9.9 25 11 23 22 8 24 36 4
16 21 14 18 23 8 37 72 3
18.25 22 15 19 21 12 37 39 4
16.85 9 7 15 21 12 22 43 4
18.95 24 22 21 21 11 43 80 4
15.6 22 11 25 21 12 31 40 3
17.1 10 12 12 22 10 31 61 4
15.4 21 13 19 21 11 21 29 4
15.4 20 15 21 21 11 21 29 4
13.35 17 11 19 25 13 32 54 3
19.1 7 7 18 21 7 26 43 4
7.6 14 13 23 25 8 32 20 1
19.1 23 7 23 22 11 33 61 4
14.75 18 11 27 21 8 30 57 4
19.25 17 22 6 23 14 67 54 4
13.6 20 15 22 20 9 22 36 4
12.75 19 15 23 22 13 33 16 4
9.85 19 11 20 25 13 24 40 1
15.25 23 10 23 20 11 28 27 4
11.9 20 18 27 21 9 41 61 3
16.35 19 14 24 21 12 31 69 4
12.4 16 16 12 23 12 33 34 3
18.15 21 16 24 22 13 21 34 4
17.75 20 17 24 21 11 52 34 4
12.35 20 14 19 21 11 29 13 3
15.6 19 10 28 21 9 11 12 4
19.3 19 16 23 21 12 26 51 4
17.1 20 16 19 21 15 7 19 4
18.4 22 17 23 21 14 13 81 3
19.05 19 12 20 21 12 20 42 4
18.55 23 17 18 22 9 52 22 4
19.1 16 11 20 21 9 28 85 4
12.85 18 12 21 22 13 39 25 4
9.5 23 8 25 22 15 9 22 2
4.5 20 17 18 22 11 19 19 1
13.6 23 17 28 22 10 60 45 4
11.7 13 7 9 23 11 19 45 2
13.35 26 18 26 22 14 14 51 3
17.6 13 14 12 21 12 -2 73 4
14.05 10 13 12 21 13 51 24 3
16.1 21 19 20 20 11 2 61 4
13.35 24 15 25 20 11 24 23 4
11.85 21 15 24 21 13 40 14 4
11.95 23 8 23 21 12 20 54 2
13.2 16 11 22 21 9 20 36 4
7.7 26 17 28 24 13 25 26 2
14.6 16 12 15 22 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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.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=271057&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.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=271057&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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] = + 8.09471 -0.0476449AMS.I2[t] -0.0483898AMS.I3[t] -0.0503942AMS.E2[t] -0.121209age[t] + 0.142932CONFSOFTTOT[t] -0.0221309PRH[t] + 0.035639CH[t] + 2.77015PR[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.09471 -0.0476449AMS.I2[t] -0.0483898AMS.I3[t] -0.0503942AMS.E2[t] -0.121209age[t] +  0.142932CONFSOFTTOT[t] -0.0221309PRH[t] +  0.035639CH[t] +  2.77015PR[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.09471 -0.0476449AMS.I2[t] -0.0483898AMS.I3[t] -0.0503942AMS.E2[t] -0.121209age[t] +  0.142932CONFSOFTTOT[t] -0.0221309PRH[t] +  0.035639CH[t] +  2.77015PR[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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] = + 8.09471 -0.0476449AMS.I2[t] -0.0483898AMS.I3[t] -0.0503942AMS.E2[t] -0.121209age[t] + 0.142932CONFSOFTTOT[t] -0.0221309PRH[t] + 0.035639CH[t] + 2.77015PR[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.094714.007422.020.04544550.0227227
AMS.I2-0.04764490.0596107-0.79930.4255930.212796
AMS.I3-0.04838980.0553386-0.87440.3834960.191748
AMS.E2-0.05039420.0483634-1.0420.299350.149675
age-0.1212090.16858-0.7190.473430.236715
CONFSOFTTOT0.1429320.08563521.6690.09750920.0487546
PRH-0.02213090.0105128-2.1050.03720410.0186021
CH0.0356390.009756933.6530.0003750440.000187522
PR2.770150.21659812.799.41663e-254.70832e-25

\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) & 8.09471 & 4.00742 & 2.02 & 0.0454455 & 0.0227227 \tabularnewline
AMS.I2 & -0.0476449 & 0.0596107 & -0.7993 & 0.425593 & 0.212796 \tabularnewline
AMS.I3 & -0.0483898 & 0.0553386 & -0.8744 & 0.383496 & 0.191748 \tabularnewline
AMS.E2 & -0.0503942 & 0.0483634 & -1.042 & 0.29935 & 0.149675 \tabularnewline
age & -0.121209 & 0.16858 & -0.719 & 0.47343 & 0.236715 \tabularnewline
CONFSOFTTOT & 0.142932 & 0.0856352 & 1.669 & 0.0975092 & 0.0487546 \tabularnewline
PRH & -0.0221309 & 0.0105128 & -2.105 & 0.0372041 & 0.0186021 \tabularnewline
CH & 0.035639 & 0.00975693 & 3.653 & 0.000375044 & 0.000187522 \tabularnewline
PR & 2.77015 & 0.216598 & 12.79 & 9.41663e-25 & 4.70832e-25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&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]8.09471[/C][C]4.00742[/C][C]2.02[/C][C]0.0454455[/C][C]0.0227227[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0476449[/C][C]0.0596107[/C][C]-0.7993[/C][C]0.425593[/C][C]0.212796[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.0483898[/C][C]0.0553386[/C][C]-0.8744[/C][C]0.383496[/C][C]0.191748[/C][/ROW]
[ROW][C]AMS.E2[/C][C]-0.0503942[/C][C]0.0483634[/C][C]-1.042[/C][C]0.29935[/C][C]0.149675[/C][/ROW]
[ROW][C]age[/C][C]-0.121209[/C][C]0.16858[/C][C]-0.719[/C][C]0.47343[/C][C]0.236715[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.142932[/C][C]0.0856352[/C][C]1.669[/C][C]0.0975092[/C][C]0.0487546[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0221309[/C][C]0.0105128[/C][C]-2.105[/C][C]0.0372041[/C][C]0.0186021[/C][/ROW]
[ROW][C]CH[/C][C]0.035639[/C][C]0.00975693[/C][C]3.653[/C][C]0.000375044[/C][C]0.000187522[/C][/ROW]
[ROW][C]PR[/C][C]2.77015[/C][C]0.216598[/C][C]12.79[/C][C]9.41663e-25[/C][C]4.70832e-25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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)8.094714.007422.020.04544550.0227227
AMS.I2-0.04764490.0596107-0.79930.4255930.212796
AMS.I3-0.04838980.0553386-0.87440.3834960.191748
AMS.E2-0.05039420.0483634-1.0420.299350.149675
age-0.1212090.16858-0.7190.473430.236715
CONFSOFTTOT0.1429320.08563521.6690.09750920.0487546
PRH-0.02213090.0105128-2.1050.03720410.0186021
CH0.0356390.009756933.6530.0003750440.000187522
PR2.770150.21659812.799.41663e-254.70832e-25






Multiple Linear Regression - Regression Statistics
Multiple R0.792381
R-squared0.627867
Adjusted R-squared0.604966
F-TEST (value)27.4172
F-TEST (DF numerator)8
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20228
Sum Squared Residuals630.504

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.792381 \tabularnewline
R-squared & 0.627867 \tabularnewline
Adjusted R-squared & 0.604966 \tabularnewline
F-TEST (value) & 27.4172 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20228 \tabularnewline
Sum Squared Residuals & 630.504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.792381[/C][/ROW]
[ROW][C]R-squared[/C][C]0.627867[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.604966[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.4172[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/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]2.20228[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]630.504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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 R0.792381
R-squared0.627867
Adjusted R-squared0.604966
F-TEST (value)27.4172
F-TEST (DF numerator)8
F-TEST (DF denominator)130
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20228
Sum Squared Residuals630.504






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.9512-0.0512438
212.210.25951.94055
312.811.78051.01953
47.411.6476-4.24763
56.711.33-4.63
612.612.14110.458878
714.811.1593.64103
813.313.07190.228124
911.112.51-1.40999
108.210.9937-2.79367
1111.411.34380.0562232
126.411.0577-4.65765
1310.610.09140.508574
141213.4442-1.44425
156.38.66138-2.36138
1611.913.2316-1.33157
179.310.9792-1.67917
18109.744090.255911
196.410.5885-4.18852
2013.812.53031.26972
2110.810.58260.217372
2213.811.97581.82419
2311.710.57881.12117
2410.912.1706-1.27061
259.911.1337-1.23374
2611.510.73040.769608
278.310.9441-2.64408
2811.711.12880.571199
29910.4697-1.46966
309.713.9828-4.2828
3110.811.1696-0.369631
3210.310.7104-0.410409
3310.49.711890.688111
349.312.2736-2.9736
3511.810.96290.837054
365.911.2398-5.33976
3711.411.8556-0.455646
381311.07181.92824
3910.811.2801-0.480074
4011.310.72150.578468
4111.811.48870.311261
4212.79.30463.3954
4310.910.8530.0470163
4413.311.66671.63328
4510.110.4263-0.326319
4614.311.3582.94196
479.311.6385-2.33847
4812.510.5821.91802
497.610.1076-2.50763
5015.912.66063.23939
519.210.5158-1.31583
5211.112.4943-1.39433
531312.60360.396444
5414.511.69592.80411
5512.313.1272-0.827238
5611.410.45460.945431
571312.39810.601868
5813.211.09282.10721
597.711.4507-3.75069
604.357.16197-2.81197
6112.710.28882.41123
6218.116.06872.03134
6317.8516.19461.65543
6417.117.6523-0.552343
6519.116.76822.33176
6616.118.4847-2.38466
6713.3510.6292.72097
6818.417.67560.724417
6914.77.556167.14384
7010.613.189-2.58905
7112.613.8589-1.25888
7213.612.58571.01431
7314.113.00191.09807
7414.513.31711.1829
7516.1516.3609-0.210874
7614.7512.63092.11906
7714.812.08162.71838
7812.4512.15280.297167
7912.6510.19212.45792
8017.3513.96873.38134
818.68.403140.196864
8218.416.76731.63271
8316.114.32351.7765
8417.7515.78491.96507
8515.2515.3036-0.0536084
8617.6516.26281.38717
8716.3516.6257-0.275685
8817.6518.297-0.647047
8913.612.91290.687057
9014.3513.69430.655709
9114.7517.0378-2.28776
9218.2516.72171.52826
939.915.5215-5.62155
941613.92292.07713
9518.2516.18462.06535
9616.8517.8672-1.01725
9718.9516.83532.11468
9815.613.47412.12588
9917.117.7641-0.664088
10015.416.1838-0.783846
10115.416.0339-0.633923
10213.3514.1496-0.799616
10319.117.00822.09183
1047.66.527511.07249
10519.116.9312.16901
10614.7516.3903-1.64033
10719.2516.65342.59663
10813.616.0462-2.44622
10912.7515.4166-2.66656
1109.858.141731.70827
11115.2515.9272-0.677164
11211.913.2282-1.32821
11316.3517.326-0.975958
11412.413.6726-1.27265
11518.1516.12962.02045
11617.7515.27812.4719
11712.3512.6657-0.315681
11815.615.30030.299659
11919.316.74872.55127
12017.116.61150.488509
12118.415.432.97001
12219.0516.90552.1445
12318.5514.60283.94721
12419.118.02351.07654
12512.8515.8981-3.04812
1269.510.9545-1.45445
1274.57.34454-2.84454
12813.614.8844-1.28443
12911.712.1911-0.491054
13013.3513.8273-0.477323
13117.619.0894-1.48943
13214.0513.73430.315708
13316.117.5253-1.42526
13413.3515.4827-2.13275
13511.8515.1659-3.31589
13611.9511.64470.305334
13713.216.3534-3.15341
1387.79.48501-1.78501
13914.613.5831.01698

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.9512 & -0.0512438 \tabularnewline
2 & 12.2 & 10.2595 & 1.94055 \tabularnewline
3 & 12.8 & 11.7805 & 1.01953 \tabularnewline
4 & 7.4 & 11.6476 & -4.24763 \tabularnewline
5 & 6.7 & 11.33 & -4.63 \tabularnewline
6 & 12.6 & 12.1411 & 0.458878 \tabularnewline
7 & 14.8 & 11.159 & 3.64103 \tabularnewline
8 & 13.3 & 13.0719 & 0.228124 \tabularnewline
9 & 11.1 & 12.51 & -1.40999 \tabularnewline
10 & 8.2 & 10.9937 & -2.79367 \tabularnewline
11 & 11.4 & 11.3438 & 0.0562232 \tabularnewline
12 & 6.4 & 11.0577 & -4.65765 \tabularnewline
13 & 10.6 & 10.0914 & 0.508574 \tabularnewline
14 & 12 & 13.4442 & -1.44425 \tabularnewline
15 & 6.3 & 8.66138 & -2.36138 \tabularnewline
16 & 11.9 & 13.2316 & -1.33157 \tabularnewline
17 & 9.3 & 10.9792 & -1.67917 \tabularnewline
18 & 10 & 9.74409 & 0.255911 \tabularnewline
19 & 6.4 & 10.5885 & -4.18852 \tabularnewline
20 & 13.8 & 12.5303 & 1.26972 \tabularnewline
21 & 10.8 & 10.5826 & 0.217372 \tabularnewline
22 & 13.8 & 11.9758 & 1.82419 \tabularnewline
23 & 11.7 & 10.5788 & 1.12117 \tabularnewline
24 & 10.9 & 12.1706 & -1.27061 \tabularnewline
25 & 9.9 & 11.1337 & -1.23374 \tabularnewline
26 & 11.5 & 10.7304 & 0.769608 \tabularnewline
27 & 8.3 & 10.9441 & -2.64408 \tabularnewline
28 & 11.7 & 11.1288 & 0.571199 \tabularnewline
29 & 9 & 10.4697 & -1.46966 \tabularnewline
30 & 9.7 & 13.9828 & -4.2828 \tabularnewline
31 & 10.8 & 11.1696 & -0.369631 \tabularnewline
32 & 10.3 & 10.7104 & -0.410409 \tabularnewline
33 & 10.4 & 9.71189 & 0.688111 \tabularnewline
34 & 9.3 & 12.2736 & -2.9736 \tabularnewline
35 & 11.8 & 10.9629 & 0.837054 \tabularnewline
36 & 5.9 & 11.2398 & -5.33976 \tabularnewline
37 & 11.4 & 11.8556 & -0.455646 \tabularnewline
38 & 13 & 11.0718 & 1.92824 \tabularnewline
39 & 10.8 & 11.2801 & -0.480074 \tabularnewline
40 & 11.3 & 10.7215 & 0.578468 \tabularnewline
41 & 11.8 & 11.4887 & 0.311261 \tabularnewline
42 & 12.7 & 9.3046 & 3.3954 \tabularnewline
43 & 10.9 & 10.853 & 0.0470163 \tabularnewline
44 & 13.3 & 11.6667 & 1.63328 \tabularnewline
45 & 10.1 & 10.4263 & -0.326319 \tabularnewline
46 & 14.3 & 11.358 & 2.94196 \tabularnewline
47 & 9.3 & 11.6385 & -2.33847 \tabularnewline
48 & 12.5 & 10.582 & 1.91802 \tabularnewline
49 & 7.6 & 10.1076 & -2.50763 \tabularnewline
50 & 15.9 & 12.6606 & 3.23939 \tabularnewline
51 & 9.2 & 10.5158 & -1.31583 \tabularnewline
52 & 11.1 & 12.4943 & -1.39433 \tabularnewline
53 & 13 & 12.6036 & 0.396444 \tabularnewline
54 & 14.5 & 11.6959 & 2.80411 \tabularnewline
55 & 12.3 & 13.1272 & -0.827238 \tabularnewline
56 & 11.4 & 10.4546 & 0.945431 \tabularnewline
57 & 13 & 12.3981 & 0.601868 \tabularnewline
58 & 13.2 & 11.0928 & 2.10721 \tabularnewline
59 & 7.7 & 11.4507 & -3.75069 \tabularnewline
60 & 4.35 & 7.16197 & -2.81197 \tabularnewline
61 & 12.7 & 10.2888 & 2.41123 \tabularnewline
62 & 18.1 & 16.0687 & 2.03134 \tabularnewline
63 & 17.85 & 16.1946 & 1.65543 \tabularnewline
64 & 17.1 & 17.6523 & -0.552343 \tabularnewline
65 & 19.1 & 16.7682 & 2.33176 \tabularnewline
66 & 16.1 & 18.4847 & -2.38466 \tabularnewline
67 & 13.35 & 10.629 & 2.72097 \tabularnewline
68 & 18.4 & 17.6756 & 0.724417 \tabularnewline
69 & 14.7 & 7.55616 & 7.14384 \tabularnewline
70 & 10.6 & 13.189 & -2.58905 \tabularnewline
71 & 12.6 & 13.8589 & -1.25888 \tabularnewline
72 & 13.6 & 12.5857 & 1.01431 \tabularnewline
73 & 14.1 & 13.0019 & 1.09807 \tabularnewline
74 & 14.5 & 13.3171 & 1.1829 \tabularnewline
75 & 16.15 & 16.3609 & -0.210874 \tabularnewline
76 & 14.75 & 12.6309 & 2.11906 \tabularnewline
77 & 14.8 & 12.0816 & 2.71838 \tabularnewline
78 & 12.45 & 12.1528 & 0.297167 \tabularnewline
79 & 12.65 & 10.1921 & 2.45792 \tabularnewline
80 & 17.35 & 13.9687 & 3.38134 \tabularnewline
81 & 8.6 & 8.40314 & 0.196864 \tabularnewline
82 & 18.4 & 16.7673 & 1.63271 \tabularnewline
83 & 16.1 & 14.3235 & 1.7765 \tabularnewline
84 & 17.75 & 15.7849 & 1.96507 \tabularnewline
85 & 15.25 & 15.3036 & -0.0536084 \tabularnewline
86 & 17.65 & 16.2628 & 1.38717 \tabularnewline
87 & 16.35 & 16.6257 & -0.275685 \tabularnewline
88 & 17.65 & 18.297 & -0.647047 \tabularnewline
89 & 13.6 & 12.9129 & 0.687057 \tabularnewline
90 & 14.35 & 13.6943 & 0.655709 \tabularnewline
91 & 14.75 & 17.0378 & -2.28776 \tabularnewline
92 & 18.25 & 16.7217 & 1.52826 \tabularnewline
93 & 9.9 & 15.5215 & -5.62155 \tabularnewline
94 & 16 & 13.9229 & 2.07713 \tabularnewline
95 & 18.25 & 16.1846 & 2.06535 \tabularnewline
96 & 16.85 & 17.8672 & -1.01725 \tabularnewline
97 & 18.95 & 16.8353 & 2.11468 \tabularnewline
98 & 15.6 & 13.4741 & 2.12588 \tabularnewline
99 & 17.1 & 17.7641 & -0.664088 \tabularnewline
100 & 15.4 & 16.1838 & -0.783846 \tabularnewline
101 & 15.4 & 16.0339 & -0.633923 \tabularnewline
102 & 13.35 & 14.1496 & -0.799616 \tabularnewline
103 & 19.1 & 17.0082 & 2.09183 \tabularnewline
104 & 7.6 & 6.52751 & 1.07249 \tabularnewline
105 & 19.1 & 16.931 & 2.16901 \tabularnewline
106 & 14.75 & 16.3903 & -1.64033 \tabularnewline
107 & 19.25 & 16.6534 & 2.59663 \tabularnewline
108 & 13.6 & 16.0462 & -2.44622 \tabularnewline
109 & 12.75 & 15.4166 & -2.66656 \tabularnewline
110 & 9.85 & 8.14173 & 1.70827 \tabularnewline
111 & 15.25 & 15.9272 & -0.677164 \tabularnewline
112 & 11.9 & 13.2282 & -1.32821 \tabularnewline
113 & 16.35 & 17.326 & -0.975958 \tabularnewline
114 & 12.4 & 13.6726 & -1.27265 \tabularnewline
115 & 18.15 & 16.1296 & 2.02045 \tabularnewline
116 & 17.75 & 15.2781 & 2.4719 \tabularnewline
117 & 12.35 & 12.6657 & -0.315681 \tabularnewline
118 & 15.6 & 15.3003 & 0.299659 \tabularnewline
119 & 19.3 & 16.7487 & 2.55127 \tabularnewline
120 & 17.1 & 16.6115 & 0.488509 \tabularnewline
121 & 18.4 & 15.43 & 2.97001 \tabularnewline
122 & 19.05 & 16.9055 & 2.1445 \tabularnewline
123 & 18.55 & 14.6028 & 3.94721 \tabularnewline
124 & 19.1 & 18.0235 & 1.07654 \tabularnewline
125 & 12.85 & 15.8981 & -3.04812 \tabularnewline
126 & 9.5 & 10.9545 & -1.45445 \tabularnewline
127 & 4.5 & 7.34454 & -2.84454 \tabularnewline
128 & 13.6 & 14.8844 & -1.28443 \tabularnewline
129 & 11.7 & 12.1911 & -0.491054 \tabularnewline
130 & 13.35 & 13.8273 & -0.477323 \tabularnewline
131 & 17.6 & 19.0894 & -1.48943 \tabularnewline
132 & 14.05 & 13.7343 & 0.315708 \tabularnewline
133 & 16.1 & 17.5253 & -1.42526 \tabularnewline
134 & 13.35 & 15.4827 & -2.13275 \tabularnewline
135 & 11.85 & 15.1659 & -3.31589 \tabularnewline
136 & 11.95 & 11.6447 & 0.305334 \tabularnewline
137 & 13.2 & 16.3534 & -3.15341 \tabularnewline
138 & 7.7 & 9.48501 & -1.78501 \tabularnewline
139 & 14.6 & 13.583 & 1.01698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&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]12.9512[/C][C]-0.0512438[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.2595[/C][C]1.94055[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.7805[/C][C]1.01953[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6476[/C][C]-4.24763[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]11.33[/C][C]-4.63[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.1411[/C][C]0.458878[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.159[/C][C]3.64103[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.0719[/C][C]0.228124[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.51[/C][C]-1.40999[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.9937[/C][C]-2.79367[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3438[/C][C]0.0562232[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.0577[/C][C]-4.65765[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.0914[/C][C]0.508574[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.4442[/C][C]-1.44425[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.66138[/C][C]-2.36138[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]13.2316[/C][C]-1.33157[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.9792[/C][C]-1.67917[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.74409[/C][C]0.255911[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.5885[/C][C]-4.18852[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.5303[/C][C]1.26972[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.5826[/C][C]0.217372[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.9758[/C][C]1.82419[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.5788[/C][C]1.12117[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]12.1706[/C][C]-1.27061[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.1337[/C][C]-1.23374[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.7304[/C][C]0.769608[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]10.9441[/C][C]-2.64408[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1288[/C][C]0.571199[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4697[/C][C]-1.46966[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]13.9828[/C][C]-4.2828[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]11.1696[/C][C]-0.369631[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.7104[/C][C]-0.410409[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.71189[/C][C]0.688111[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]12.2736[/C][C]-2.9736[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.9629[/C][C]0.837054[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.2398[/C][C]-5.33976[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.8556[/C][C]-0.455646[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.0718[/C][C]1.92824[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.2801[/C][C]-0.480074[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.7215[/C][C]0.578468[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.4887[/C][C]0.311261[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]9.3046[/C][C]3.3954[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]10.853[/C][C]0.0470163[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]11.6667[/C][C]1.63328[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.4263[/C][C]-0.326319[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]11.358[/C][C]2.94196[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]11.6385[/C][C]-2.33847[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]10.582[/C][C]1.91802[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.1076[/C][C]-2.50763[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.6606[/C][C]3.23939[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.5158[/C][C]-1.31583[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.4943[/C][C]-1.39433[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.6036[/C][C]0.396444[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.6959[/C][C]2.80411[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.1272[/C][C]-0.827238[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]10.4546[/C][C]0.945431[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.3981[/C][C]0.601868[/C][/ROW]
[ROW][C]58[/C][C]13.2[/C][C]11.0928[/C][C]2.10721[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]11.4507[/C][C]-3.75069[/C][/ROW]
[ROW][C]60[/C][C]4.35[/C][C]7.16197[/C][C]-2.81197[/C][/ROW]
[ROW][C]61[/C][C]12.7[/C][C]10.2888[/C][C]2.41123[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]16.0687[/C][C]2.03134[/C][/ROW]
[ROW][C]63[/C][C]17.85[/C][C]16.1946[/C][C]1.65543[/C][/ROW]
[ROW][C]64[/C][C]17.1[/C][C]17.6523[/C][C]-0.552343[/C][/ROW]
[ROW][C]65[/C][C]19.1[/C][C]16.7682[/C][C]2.33176[/C][/ROW]
[ROW][C]66[/C][C]16.1[/C][C]18.4847[/C][C]-2.38466[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]10.629[/C][C]2.72097[/C][/ROW]
[ROW][C]68[/C][C]18.4[/C][C]17.6756[/C][C]0.724417[/C][/ROW]
[ROW][C]69[/C][C]14.7[/C][C]7.55616[/C][C]7.14384[/C][/ROW]
[ROW][C]70[/C][C]10.6[/C][C]13.189[/C][C]-2.58905[/C][/ROW]
[ROW][C]71[/C][C]12.6[/C][C]13.8589[/C][C]-1.25888[/C][/ROW]
[ROW][C]72[/C][C]13.6[/C][C]12.5857[/C][C]1.01431[/C][/ROW]
[ROW][C]73[/C][C]14.1[/C][C]13.0019[/C][C]1.09807[/C][/ROW]
[ROW][C]74[/C][C]14.5[/C][C]13.3171[/C][C]1.1829[/C][/ROW]
[ROW][C]75[/C][C]16.15[/C][C]16.3609[/C][C]-0.210874[/C][/ROW]
[ROW][C]76[/C][C]14.75[/C][C]12.6309[/C][C]2.11906[/C][/ROW]
[ROW][C]77[/C][C]14.8[/C][C]12.0816[/C][C]2.71838[/C][/ROW]
[ROW][C]78[/C][C]12.45[/C][C]12.1528[/C][C]0.297167[/C][/ROW]
[ROW][C]79[/C][C]12.65[/C][C]10.1921[/C][C]2.45792[/C][/ROW]
[ROW][C]80[/C][C]17.35[/C][C]13.9687[/C][C]3.38134[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.40314[/C][C]0.196864[/C][/ROW]
[ROW][C]82[/C][C]18.4[/C][C]16.7673[/C][C]1.63271[/C][/ROW]
[ROW][C]83[/C][C]16.1[/C][C]14.3235[/C][C]1.7765[/C][/ROW]
[ROW][C]84[/C][C]17.75[/C][C]15.7849[/C][C]1.96507[/C][/ROW]
[ROW][C]85[/C][C]15.25[/C][C]15.3036[/C][C]-0.0536084[/C][/ROW]
[ROW][C]86[/C][C]17.65[/C][C]16.2628[/C][C]1.38717[/C][/ROW]
[ROW][C]87[/C][C]16.35[/C][C]16.6257[/C][C]-0.275685[/C][/ROW]
[ROW][C]88[/C][C]17.65[/C][C]18.297[/C][C]-0.647047[/C][/ROW]
[ROW][C]89[/C][C]13.6[/C][C]12.9129[/C][C]0.687057[/C][/ROW]
[ROW][C]90[/C][C]14.35[/C][C]13.6943[/C][C]0.655709[/C][/ROW]
[ROW][C]91[/C][C]14.75[/C][C]17.0378[/C][C]-2.28776[/C][/ROW]
[ROW][C]92[/C][C]18.25[/C][C]16.7217[/C][C]1.52826[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]15.5215[/C][C]-5.62155[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.9229[/C][C]2.07713[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]16.1846[/C][C]2.06535[/C][/ROW]
[ROW][C]96[/C][C]16.85[/C][C]17.8672[/C][C]-1.01725[/C][/ROW]
[ROW][C]97[/C][C]18.95[/C][C]16.8353[/C][C]2.11468[/C][/ROW]
[ROW][C]98[/C][C]15.6[/C][C]13.4741[/C][C]2.12588[/C][/ROW]
[ROW][C]99[/C][C]17.1[/C][C]17.7641[/C][C]-0.664088[/C][/ROW]
[ROW][C]100[/C][C]15.4[/C][C]16.1838[/C][C]-0.783846[/C][/ROW]
[ROW][C]101[/C][C]15.4[/C][C]16.0339[/C][C]-0.633923[/C][/ROW]
[ROW][C]102[/C][C]13.35[/C][C]14.1496[/C][C]-0.799616[/C][/ROW]
[ROW][C]103[/C][C]19.1[/C][C]17.0082[/C][C]2.09183[/C][/ROW]
[ROW][C]104[/C][C]7.6[/C][C]6.52751[/C][C]1.07249[/C][/ROW]
[ROW][C]105[/C][C]19.1[/C][C]16.931[/C][C]2.16901[/C][/ROW]
[ROW][C]106[/C][C]14.75[/C][C]16.3903[/C][C]-1.64033[/C][/ROW]
[ROW][C]107[/C][C]19.25[/C][C]16.6534[/C][C]2.59663[/C][/ROW]
[ROW][C]108[/C][C]13.6[/C][C]16.0462[/C][C]-2.44622[/C][/ROW]
[ROW][C]109[/C][C]12.75[/C][C]15.4166[/C][C]-2.66656[/C][/ROW]
[ROW][C]110[/C][C]9.85[/C][C]8.14173[/C][C]1.70827[/C][/ROW]
[ROW][C]111[/C][C]15.25[/C][C]15.9272[/C][C]-0.677164[/C][/ROW]
[ROW][C]112[/C][C]11.9[/C][C]13.2282[/C][C]-1.32821[/C][/ROW]
[ROW][C]113[/C][C]16.35[/C][C]17.326[/C][C]-0.975958[/C][/ROW]
[ROW][C]114[/C][C]12.4[/C][C]13.6726[/C][C]-1.27265[/C][/ROW]
[ROW][C]115[/C][C]18.15[/C][C]16.1296[/C][C]2.02045[/C][/ROW]
[ROW][C]116[/C][C]17.75[/C][C]15.2781[/C][C]2.4719[/C][/ROW]
[ROW][C]117[/C][C]12.35[/C][C]12.6657[/C][C]-0.315681[/C][/ROW]
[ROW][C]118[/C][C]15.6[/C][C]15.3003[/C][C]0.299659[/C][/ROW]
[ROW][C]119[/C][C]19.3[/C][C]16.7487[/C][C]2.55127[/C][/ROW]
[ROW][C]120[/C][C]17.1[/C][C]16.6115[/C][C]0.488509[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.43[/C][C]2.97001[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.9055[/C][C]2.1445[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.6028[/C][C]3.94721[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.0235[/C][C]1.07654[/C][/ROW]
[ROW][C]125[/C][C]12.85[/C][C]15.8981[/C][C]-3.04812[/C][/ROW]
[ROW][C]126[/C][C]9.5[/C][C]10.9545[/C][C]-1.45445[/C][/ROW]
[ROW][C]127[/C][C]4.5[/C][C]7.34454[/C][C]-2.84454[/C][/ROW]
[ROW][C]128[/C][C]13.6[/C][C]14.8844[/C][C]-1.28443[/C][/ROW]
[ROW][C]129[/C][C]11.7[/C][C]12.1911[/C][C]-0.491054[/C][/ROW]
[ROW][C]130[/C][C]13.35[/C][C]13.8273[/C][C]-0.477323[/C][/ROW]
[ROW][C]131[/C][C]17.6[/C][C]19.0894[/C][C]-1.48943[/C][/ROW]
[ROW][C]132[/C][C]14.05[/C][C]13.7343[/C][C]0.315708[/C][/ROW]
[ROW][C]133[/C][C]16.1[/C][C]17.5253[/C][C]-1.42526[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.4827[/C][C]-2.13275[/C][/ROW]
[ROW][C]135[/C][C]11.85[/C][C]15.1659[/C][C]-3.31589[/C][/ROW]
[ROW][C]136[/C][C]11.95[/C][C]11.6447[/C][C]0.305334[/C][/ROW]
[ROW][C]137[/C][C]13.2[/C][C]16.3534[/C][C]-3.15341[/C][/ROW]
[ROW][C]138[/C][C]7.7[/C][C]9.48501[/C][C]-1.78501[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]13.583[/C][C]1.01698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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
112.912.9512-0.0512438
212.210.25951.94055
312.811.78051.01953
47.411.6476-4.24763
56.711.33-4.63
612.612.14110.458878
714.811.1593.64103
813.313.07190.228124
911.112.51-1.40999
108.210.9937-2.79367
1111.411.34380.0562232
126.411.0577-4.65765
1310.610.09140.508574
141213.4442-1.44425
156.38.66138-2.36138
1611.913.2316-1.33157
179.310.9792-1.67917
18109.744090.255911
196.410.5885-4.18852
2013.812.53031.26972
2110.810.58260.217372
2213.811.97581.82419
2311.710.57881.12117
2410.912.1706-1.27061
259.911.1337-1.23374
2611.510.73040.769608
278.310.9441-2.64408
2811.711.12880.571199
29910.4697-1.46966
309.713.9828-4.2828
3110.811.1696-0.369631
3210.310.7104-0.410409
3310.49.711890.688111
349.312.2736-2.9736
3511.810.96290.837054
365.911.2398-5.33976
3711.411.8556-0.455646
381311.07181.92824
3910.811.2801-0.480074
4011.310.72150.578468
4111.811.48870.311261
4212.79.30463.3954
4310.910.8530.0470163
4413.311.66671.63328
4510.110.4263-0.326319
4614.311.3582.94196
479.311.6385-2.33847
4812.510.5821.91802
497.610.1076-2.50763
5015.912.66063.23939
519.210.5158-1.31583
5211.112.4943-1.39433
531312.60360.396444
5414.511.69592.80411
5512.313.1272-0.827238
5611.410.45460.945431
571312.39810.601868
5813.211.09282.10721
597.711.4507-3.75069
604.357.16197-2.81197
6112.710.28882.41123
6218.116.06872.03134
6317.8516.19461.65543
6417.117.6523-0.552343
6519.116.76822.33176
6616.118.4847-2.38466
6713.3510.6292.72097
6818.417.67560.724417
6914.77.556167.14384
7010.613.189-2.58905
7112.613.8589-1.25888
7213.612.58571.01431
7314.113.00191.09807
7414.513.31711.1829
7516.1516.3609-0.210874
7614.7512.63092.11906
7714.812.08162.71838
7812.4512.15280.297167
7912.6510.19212.45792
8017.3513.96873.38134
818.68.403140.196864
8218.416.76731.63271
8316.114.32351.7765
8417.7515.78491.96507
8515.2515.3036-0.0536084
8617.6516.26281.38717
8716.3516.6257-0.275685
8817.6518.297-0.647047
8913.612.91290.687057
9014.3513.69430.655709
9114.7517.0378-2.28776
9218.2516.72171.52826
939.915.5215-5.62155
941613.92292.07713
9518.2516.18462.06535
9616.8517.8672-1.01725
9718.9516.83532.11468
9815.613.47412.12588
9917.117.7641-0.664088
10015.416.1838-0.783846
10115.416.0339-0.633923
10213.3514.1496-0.799616
10319.117.00822.09183
1047.66.527511.07249
10519.116.9312.16901
10614.7516.3903-1.64033
10719.2516.65342.59663
10813.616.0462-2.44622
10912.7515.4166-2.66656
1109.858.141731.70827
11115.2515.9272-0.677164
11211.913.2282-1.32821
11316.3517.326-0.975958
11412.413.6726-1.27265
11518.1516.12962.02045
11617.7515.27812.4719
11712.3512.6657-0.315681
11815.615.30030.299659
11919.316.74872.55127
12017.116.61150.488509
12118.415.432.97001
12219.0516.90552.1445
12318.5514.60283.94721
12419.118.02351.07654
12512.8515.8981-3.04812
1269.510.9545-1.45445
1274.57.34454-2.84454
12813.614.8844-1.28443
12911.712.1911-0.491054
13013.3513.8273-0.477323
13117.619.0894-1.48943
13214.0513.73430.315708
13316.117.5253-1.42526
13413.3515.4827-2.13275
13511.8515.1659-3.31589
13611.9511.64470.305334
13713.216.3534-3.15341
1387.79.48501-1.78501
13914.613.5831.01698






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2918630.5837260.708137
130.3548950.709790.645105
140.3902880.7805770.609712
150.3426680.6853360.657332
160.2613350.5226690.738665
170.1968350.393670.803165
180.2398380.4796750.760162
190.1915140.3830280.808486
200.1756410.3512810.824359
210.1587810.3175610.841219
220.112610.225220.88739
230.07574230.1514850.924258
240.05038690.1007740.949613
250.04090480.08180970.959095
260.04951380.09902760.950486
270.08613660.1722730.913863
280.131570.2631390.86843
290.171660.3433190.82834
300.3744810.7489620.625519
310.3388070.6776150.661193
320.2794330.5588660.720567
330.2292050.458410.770795
340.2362780.4725560.763722
350.193570.3871410.80643
360.4288540.8577080.571146
370.3737350.747470.626265
380.3423560.6847120.657644
390.3211280.6422560.678872
400.2703540.5407070.729646
410.2384190.4768380.761581
420.2677820.5355640.732218
430.2355360.4710710.764464
440.3697680.7395360.630232
450.3198560.6397110.680144
460.4426350.885270.557365
470.4387370.8774750.561263
480.4702270.9404550.529773
490.5356780.9286430.464322
500.6624450.6751090.337555
510.6364180.7271640.363582
520.6107070.7785870.389293
530.5686090.8627810.431391
540.6097110.7805790.390289
550.5767110.8465790.423289
560.5330210.9339580.466979
570.493260.9865190.50674
580.4879260.9758520.512074
590.5863370.8273250.413663
600.6659250.6681510.334075
610.6914030.6171930.308597
620.6949150.610170.305085
630.660610.6787810.33939
640.6221350.755730.377865
650.6084130.7831740.391587
660.6782330.6435340.321767
670.6983150.6033710.301685
680.6540110.6919780.345989
690.944190.111620.05581
700.9549990.09000160.0450008
710.9479510.1040970.0520487
720.9482560.1034890.0517445
730.9359760.1280480.0640239
740.9260790.1478420.0739208
750.9281040.1437920.0718961
760.9270270.1459460.072973
770.9364130.1271740.063587
780.9183830.1632330.0816167
790.9175010.1649970.0824987
800.9499030.1001940.050097
810.9357430.1285130.0642565
820.9250930.1498130.0749066
830.9146540.1706930.0853463
840.9038720.1922560.0961279
850.8847220.2305560.115278
860.8702430.2595130.129757
870.8457790.3084420.154221
880.8221970.3556060.177803
890.7860980.4278050.213902
900.7443920.5112160.255608
910.7405440.5189110.259456
920.7078370.5843250.292163
930.918880.162240.0811199
940.9031290.1937420.096871
950.8926130.2147740.107387
960.8676230.2647550.132377
970.8458310.3083370.154169
980.8466680.3066650.153332
990.8236250.3527490.176375
1000.7865820.4268360.213418
1010.7420880.5158240.257912
1020.7378510.5242990.262149
1030.7604570.4790850.239543
1040.7775940.4448130.222406
1050.7335670.5328660.266433
1060.6964150.607170.303585
1070.6596510.6806980.340349
1080.6399410.7201180.360059
1090.6382580.7234850.361742
1100.6452590.7094810.354741
1110.5976080.8047850.402392
1120.5329840.9340320.467016
1130.49690.9937990.5031
1140.4496650.899330.550335
1150.4592080.9184160.540792
1160.4658310.9316620.534169
1170.3878020.7756040.612198
1180.6506280.6987440.349372
1190.7967680.4064640.203232
1200.7917490.4165010.208251
1210.805010.389980.19499
1220.9006720.1986560.0993278
1230.9693440.06131120.0306556
1240.9433420.1133150.0566576
1250.9398150.120370.0601851
1260.8697610.2604780.130239
1270.8546290.2907420.145371

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.291863 & 0.583726 & 0.708137 \tabularnewline
13 & 0.354895 & 0.70979 & 0.645105 \tabularnewline
14 & 0.390288 & 0.780577 & 0.609712 \tabularnewline
15 & 0.342668 & 0.685336 & 0.657332 \tabularnewline
16 & 0.261335 & 0.522669 & 0.738665 \tabularnewline
17 & 0.196835 & 0.39367 & 0.803165 \tabularnewline
18 & 0.239838 & 0.479675 & 0.760162 \tabularnewline
19 & 0.191514 & 0.383028 & 0.808486 \tabularnewline
20 & 0.175641 & 0.351281 & 0.824359 \tabularnewline
21 & 0.158781 & 0.317561 & 0.841219 \tabularnewline
22 & 0.11261 & 0.22522 & 0.88739 \tabularnewline
23 & 0.0757423 & 0.151485 & 0.924258 \tabularnewline
24 & 0.0503869 & 0.100774 & 0.949613 \tabularnewline
25 & 0.0409048 & 0.0818097 & 0.959095 \tabularnewline
26 & 0.0495138 & 0.0990276 & 0.950486 \tabularnewline
27 & 0.0861366 & 0.172273 & 0.913863 \tabularnewline
28 & 0.13157 & 0.263139 & 0.86843 \tabularnewline
29 & 0.17166 & 0.343319 & 0.82834 \tabularnewline
30 & 0.374481 & 0.748962 & 0.625519 \tabularnewline
31 & 0.338807 & 0.677615 & 0.661193 \tabularnewline
32 & 0.279433 & 0.558866 & 0.720567 \tabularnewline
33 & 0.229205 & 0.45841 & 0.770795 \tabularnewline
34 & 0.236278 & 0.472556 & 0.763722 \tabularnewline
35 & 0.19357 & 0.387141 & 0.80643 \tabularnewline
36 & 0.428854 & 0.857708 & 0.571146 \tabularnewline
37 & 0.373735 & 0.74747 & 0.626265 \tabularnewline
38 & 0.342356 & 0.684712 & 0.657644 \tabularnewline
39 & 0.321128 & 0.642256 & 0.678872 \tabularnewline
40 & 0.270354 & 0.540707 & 0.729646 \tabularnewline
41 & 0.238419 & 0.476838 & 0.761581 \tabularnewline
42 & 0.267782 & 0.535564 & 0.732218 \tabularnewline
43 & 0.235536 & 0.471071 & 0.764464 \tabularnewline
44 & 0.369768 & 0.739536 & 0.630232 \tabularnewline
45 & 0.319856 & 0.639711 & 0.680144 \tabularnewline
46 & 0.442635 & 0.88527 & 0.557365 \tabularnewline
47 & 0.438737 & 0.877475 & 0.561263 \tabularnewline
48 & 0.470227 & 0.940455 & 0.529773 \tabularnewline
49 & 0.535678 & 0.928643 & 0.464322 \tabularnewline
50 & 0.662445 & 0.675109 & 0.337555 \tabularnewline
51 & 0.636418 & 0.727164 & 0.363582 \tabularnewline
52 & 0.610707 & 0.778587 & 0.389293 \tabularnewline
53 & 0.568609 & 0.862781 & 0.431391 \tabularnewline
54 & 0.609711 & 0.780579 & 0.390289 \tabularnewline
55 & 0.576711 & 0.846579 & 0.423289 \tabularnewline
56 & 0.533021 & 0.933958 & 0.466979 \tabularnewline
57 & 0.49326 & 0.986519 & 0.50674 \tabularnewline
58 & 0.487926 & 0.975852 & 0.512074 \tabularnewline
59 & 0.586337 & 0.827325 & 0.413663 \tabularnewline
60 & 0.665925 & 0.668151 & 0.334075 \tabularnewline
61 & 0.691403 & 0.617193 & 0.308597 \tabularnewline
62 & 0.694915 & 0.61017 & 0.305085 \tabularnewline
63 & 0.66061 & 0.678781 & 0.33939 \tabularnewline
64 & 0.622135 & 0.75573 & 0.377865 \tabularnewline
65 & 0.608413 & 0.783174 & 0.391587 \tabularnewline
66 & 0.678233 & 0.643534 & 0.321767 \tabularnewline
67 & 0.698315 & 0.603371 & 0.301685 \tabularnewline
68 & 0.654011 & 0.691978 & 0.345989 \tabularnewline
69 & 0.94419 & 0.11162 & 0.05581 \tabularnewline
70 & 0.954999 & 0.0900016 & 0.0450008 \tabularnewline
71 & 0.947951 & 0.104097 & 0.0520487 \tabularnewline
72 & 0.948256 & 0.103489 & 0.0517445 \tabularnewline
73 & 0.935976 & 0.128048 & 0.0640239 \tabularnewline
74 & 0.926079 & 0.147842 & 0.0739208 \tabularnewline
75 & 0.928104 & 0.143792 & 0.0718961 \tabularnewline
76 & 0.927027 & 0.145946 & 0.072973 \tabularnewline
77 & 0.936413 & 0.127174 & 0.063587 \tabularnewline
78 & 0.918383 & 0.163233 & 0.0816167 \tabularnewline
79 & 0.917501 & 0.164997 & 0.0824987 \tabularnewline
80 & 0.949903 & 0.100194 & 0.050097 \tabularnewline
81 & 0.935743 & 0.128513 & 0.0642565 \tabularnewline
82 & 0.925093 & 0.149813 & 0.0749066 \tabularnewline
83 & 0.914654 & 0.170693 & 0.0853463 \tabularnewline
84 & 0.903872 & 0.192256 & 0.0961279 \tabularnewline
85 & 0.884722 & 0.230556 & 0.115278 \tabularnewline
86 & 0.870243 & 0.259513 & 0.129757 \tabularnewline
87 & 0.845779 & 0.308442 & 0.154221 \tabularnewline
88 & 0.822197 & 0.355606 & 0.177803 \tabularnewline
89 & 0.786098 & 0.427805 & 0.213902 \tabularnewline
90 & 0.744392 & 0.511216 & 0.255608 \tabularnewline
91 & 0.740544 & 0.518911 & 0.259456 \tabularnewline
92 & 0.707837 & 0.584325 & 0.292163 \tabularnewline
93 & 0.91888 & 0.16224 & 0.0811199 \tabularnewline
94 & 0.903129 & 0.193742 & 0.096871 \tabularnewline
95 & 0.892613 & 0.214774 & 0.107387 \tabularnewline
96 & 0.867623 & 0.264755 & 0.132377 \tabularnewline
97 & 0.845831 & 0.308337 & 0.154169 \tabularnewline
98 & 0.846668 & 0.306665 & 0.153332 \tabularnewline
99 & 0.823625 & 0.352749 & 0.176375 \tabularnewline
100 & 0.786582 & 0.426836 & 0.213418 \tabularnewline
101 & 0.742088 & 0.515824 & 0.257912 \tabularnewline
102 & 0.737851 & 0.524299 & 0.262149 \tabularnewline
103 & 0.760457 & 0.479085 & 0.239543 \tabularnewline
104 & 0.777594 & 0.444813 & 0.222406 \tabularnewline
105 & 0.733567 & 0.532866 & 0.266433 \tabularnewline
106 & 0.696415 & 0.60717 & 0.303585 \tabularnewline
107 & 0.659651 & 0.680698 & 0.340349 \tabularnewline
108 & 0.639941 & 0.720118 & 0.360059 \tabularnewline
109 & 0.638258 & 0.723485 & 0.361742 \tabularnewline
110 & 0.645259 & 0.709481 & 0.354741 \tabularnewline
111 & 0.597608 & 0.804785 & 0.402392 \tabularnewline
112 & 0.532984 & 0.934032 & 0.467016 \tabularnewline
113 & 0.4969 & 0.993799 & 0.5031 \tabularnewline
114 & 0.449665 & 0.89933 & 0.550335 \tabularnewline
115 & 0.459208 & 0.918416 & 0.540792 \tabularnewline
116 & 0.465831 & 0.931662 & 0.534169 \tabularnewline
117 & 0.387802 & 0.775604 & 0.612198 \tabularnewline
118 & 0.650628 & 0.698744 & 0.349372 \tabularnewline
119 & 0.796768 & 0.406464 & 0.203232 \tabularnewline
120 & 0.791749 & 0.416501 & 0.208251 \tabularnewline
121 & 0.80501 & 0.38998 & 0.19499 \tabularnewline
122 & 0.900672 & 0.198656 & 0.0993278 \tabularnewline
123 & 0.969344 & 0.0613112 & 0.0306556 \tabularnewline
124 & 0.943342 & 0.113315 & 0.0566576 \tabularnewline
125 & 0.939815 & 0.12037 & 0.0601851 \tabularnewline
126 & 0.869761 & 0.260478 & 0.130239 \tabularnewline
127 & 0.854629 & 0.290742 & 0.145371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&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]0.291863[/C][C]0.583726[/C][C]0.708137[/C][/ROW]
[ROW][C]13[/C][C]0.354895[/C][C]0.70979[/C][C]0.645105[/C][/ROW]
[ROW][C]14[/C][C]0.390288[/C][C]0.780577[/C][C]0.609712[/C][/ROW]
[ROW][C]15[/C][C]0.342668[/C][C]0.685336[/C][C]0.657332[/C][/ROW]
[ROW][C]16[/C][C]0.261335[/C][C]0.522669[/C][C]0.738665[/C][/ROW]
[ROW][C]17[/C][C]0.196835[/C][C]0.39367[/C][C]0.803165[/C][/ROW]
[ROW][C]18[/C][C]0.239838[/C][C]0.479675[/C][C]0.760162[/C][/ROW]
[ROW][C]19[/C][C]0.191514[/C][C]0.383028[/C][C]0.808486[/C][/ROW]
[ROW][C]20[/C][C]0.175641[/C][C]0.351281[/C][C]0.824359[/C][/ROW]
[ROW][C]21[/C][C]0.158781[/C][C]0.317561[/C][C]0.841219[/C][/ROW]
[ROW][C]22[/C][C]0.11261[/C][C]0.22522[/C][C]0.88739[/C][/ROW]
[ROW][C]23[/C][C]0.0757423[/C][C]0.151485[/C][C]0.924258[/C][/ROW]
[ROW][C]24[/C][C]0.0503869[/C][C]0.100774[/C][C]0.949613[/C][/ROW]
[ROW][C]25[/C][C]0.0409048[/C][C]0.0818097[/C][C]0.959095[/C][/ROW]
[ROW][C]26[/C][C]0.0495138[/C][C]0.0990276[/C][C]0.950486[/C][/ROW]
[ROW][C]27[/C][C]0.0861366[/C][C]0.172273[/C][C]0.913863[/C][/ROW]
[ROW][C]28[/C][C]0.13157[/C][C]0.263139[/C][C]0.86843[/C][/ROW]
[ROW][C]29[/C][C]0.17166[/C][C]0.343319[/C][C]0.82834[/C][/ROW]
[ROW][C]30[/C][C]0.374481[/C][C]0.748962[/C][C]0.625519[/C][/ROW]
[ROW][C]31[/C][C]0.338807[/C][C]0.677615[/C][C]0.661193[/C][/ROW]
[ROW][C]32[/C][C]0.279433[/C][C]0.558866[/C][C]0.720567[/C][/ROW]
[ROW][C]33[/C][C]0.229205[/C][C]0.45841[/C][C]0.770795[/C][/ROW]
[ROW][C]34[/C][C]0.236278[/C][C]0.472556[/C][C]0.763722[/C][/ROW]
[ROW][C]35[/C][C]0.19357[/C][C]0.387141[/C][C]0.80643[/C][/ROW]
[ROW][C]36[/C][C]0.428854[/C][C]0.857708[/C][C]0.571146[/C][/ROW]
[ROW][C]37[/C][C]0.373735[/C][C]0.74747[/C][C]0.626265[/C][/ROW]
[ROW][C]38[/C][C]0.342356[/C][C]0.684712[/C][C]0.657644[/C][/ROW]
[ROW][C]39[/C][C]0.321128[/C][C]0.642256[/C][C]0.678872[/C][/ROW]
[ROW][C]40[/C][C]0.270354[/C][C]0.540707[/C][C]0.729646[/C][/ROW]
[ROW][C]41[/C][C]0.238419[/C][C]0.476838[/C][C]0.761581[/C][/ROW]
[ROW][C]42[/C][C]0.267782[/C][C]0.535564[/C][C]0.732218[/C][/ROW]
[ROW][C]43[/C][C]0.235536[/C][C]0.471071[/C][C]0.764464[/C][/ROW]
[ROW][C]44[/C][C]0.369768[/C][C]0.739536[/C][C]0.630232[/C][/ROW]
[ROW][C]45[/C][C]0.319856[/C][C]0.639711[/C][C]0.680144[/C][/ROW]
[ROW][C]46[/C][C]0.442635[/C][C]0.88527[/C][C]0.557365[/C][/ROW]
[ROW][C]47[/C][C]0.438737[/C][C]0.877475[/C][C]0.561263[/C][/ROW]
[ROW][C]48[/C][C]0.470227[/C][C]0.940455[/C][C]0.529773[/C][/ROW]
[ROW][C]49[/C][C]0.535678[/C][C]0.928643[/C][C]0.464322[/C][/ROW]
[ROW][C]50[/C][C]0.662445[/C][C]0.675109[/C][C]0.337555[/C][/ROW]
[ROW][C]51[/C][C]0.636418[/C][C]0.727164[/C][C]0.363582[/C][/ROW]
[ROW][C]52[/C][C]0.610707[/C][C]0.778587[/C][C]0.389293[/C][/ROW]
[ROW][C]53[/C][C]0.568609[/C][C]0.862781[/C][C]0.431391[/C][/ROW]
[ROW][C]54[/C][C]0.609711[/C][C]0.780579[/C][C]0.390289[/C][/ROW]
[ROW][C]55[/C][C]0.576711[/C][C]0.846579[/C][C]0.423289[/C][/ROW]
[ROW][C]56[/C][C]0.533021[/C][C]0.933958[/C][C]0.466979[/C][/ROW]
[ROW][C]57[/C][C]0.49326[/C][C]0.986519[/C][C]0.50674[/C][/ROW]
[ROW][C]58[/C][C]0.487926[/C][C]0.975852[/C][C]0.512074[/C][/ROW]
[ROW][C]59[/C][C]0.586337[/C][C]0.827325[/C][C]0.413663[/C][/ROW]
[ROW][C]60[/C][C]0.665925[/C][C]0.668151[/C][C]0.334075[/C][/ROW]
[ROW][C]61[/C][C]0.691403[/C][C]0.617193[/C][C]0.308597[/C][/ROW]
[ROW][C]62[/C][C]0.694915[/C][C]0.61017[/C][C]0.305085[/C][/ROW]
[ROW][C]63[/C][C]0.66061[/C][C]0.678781[/C][C]0.33939[/C][/ROW]
[ROW][C]64[/C][C]0.622135[/C][C]0.75573[/C][C]0.377865[/C][/ROW]
[ROW][C]65[/C][C]0.608413[/C][C]0.783174[/C][C]0.391587[/C][/ROW]
[ROW][C]66[/C][C]0.678233[/C][C]0.643534[/C][C]0.321767[/C][/ROW]
[ROW][C]67[/C][C]0.698315[/C][C]0.603371[/C][C]0.301685[/C][/ROW]
[ROW][C]68[/C][C]0.654011[/C][C]0.691978[/C][C]0.345989[/C][/ROW]
[ROW][C]69[/C][C]0.94419[/C][C]0.11162[/C][C]0.05581[/C][/ROW]
[ROW][C]70[/C][C]0.954999[/C][C]0.0900016[/C][C]0.0450008[/C][/ROW]
[ROW][C]71[/C][C]0.947951[/C][C]0.104097[/C][C]0.0520487[/C][/ROW]
[ROW][C]72[/C][C]0.948256[/C][C]0.103489[/C][C]0.0517445[/C][/ROW]
[ROW][C]73[/C][C]0.935976[/C][C]0.128048[/C][C]0.0640239[/C][/ROW]
[ROW][C]74[/C][C]0.926079[/C][C]0.147842[/C][C]0.0739208[/C][/ROW]
[ROW][C]75[/C][C]0.928104[/C][C]0.143792[/C][C]0.0718961[/C][/ROW]
[ROW][C]76[/C][C]0.927027[/C][C]0.145946[/C][C]0.072973[/C][/ROW]
[ROW][C]77[/C][C]0.936413[/C][C]0.127174[/C][C]0.063587[/C][/ROW]
[ROW][C]78[/C][C]0.918383[/C][C]0.163233[/C][C]0.0816167[/C][/ROW]
[ROW][C]79[/C][C]0.917501[/C][C]0.164997[/C][C]0.0824987[/C][/ROW]
[ROW][C]80[/C][C]0.949903[/C][C]0.100194[/C][C]0.050097[/C][/ROW]
[ROW][C]81[/C][C]0.935743[/C][C]0.128513[/C][C]0.0642565[/C][/ROW]
[ROW][C]82[/C][C]0.925093[/C][C]0.149813[/C][C]0.0749066[/C][/ROW]
[ROW][C]83[/C][C]0.914654[/C][C]0.170693[/C][C]0.0853463[/C][/ROW]
[ROW][C]84[/C][C]0.903872[/C][C]0.192256[/C][C]0.0961279[/C][/ROW]
[ROW][C]85[/C][C]0.884722[/C][C]0.230556[/C][C]0.115278[/C][/ROW]
[ROW][C]86[/C][C]0.870243[/C][C]0.259513[/C][C]0.129757[/C][/ROW]
[ROW][C]87[/C][C]0.845779[/C][C]0.308442[/C][C]0.154221[/C][/ROW]
[ROW][C]88[/C][C]0.822197[/C][C]0.355606[/C][C]0.177803[/C][/ROW]
[ROW][C]89[/C][C]0.786098[/C][C]0.427805[/C][C]0.213902[/C][/ROW]
[ROW][C]90[/C][C]0.744392[/C][C]0.511216[/C][C]0.255608[/C][/ROW]
[ROW][C]91[/C][C]0.740544[/C][C]0.518911[/C][C]0.259456[/C][/ROW]
[ROW][C]92[/C][C]0.707837[/C][C]0.584325[/C][C]0.292163[/C][/ROW]
[ROW][C]93[/C][C]0.91888[/C][C]0.16224[/C][C]0.0811199[/C][/ROW]
[ROW][C]94[/C][C]0.903129[/C][C]0.193742[/C][C]0.096871[/C][/ROW]
[ROW][C]95[/C][C]0.892613[/C][C]0.214774[/C][C]0.107387[/C][/ROW]
[ROW][C]96[/C][C]0.867623[/C][C]0.264755[/C][C]0.132377[/C][/ROW]
[ROW][C]97[/C][C]0.845831[/C][C]0.308337[/C][C]0.154169[/C][/ROW]
[ROW][C]98[/C][C]0.846668[/C][C]0.306665[/C][C]0.153332[/C][/ROW]
[ROW][C]99[/C][C]0.823625[/C][C]0.352749[/C][C]0.176375[/C][/ROW]
[ROW][C]100[/C][C]0.786582[/C][C]0.426836[/C][C]0.213418[/C][/ROW]
[ROW][C]101[/C][C]0.742088[/C][C]0.515824[/C][C]0.257912[/C][/ROW]
[ROW][C]102[/C][C]0.737851[/C][C]0.524299[/C][C]0.262149[/C][/ROW]
[ROW][C]103[/C][C]0.760457[/C][C]0.479085[/C][C]0.239543[/C][/ROW]
[ROW][C]104[/C][C]0.777594[/C][C]0.444813[/C][C]0.222406[/C][/ROW]
[ROW][C]105[/C][C]0.733567[/C][C]0.532866[/C][C]0.266433[/C][/ROW]
[ROW][C]106[/C][C]0.696415[/C][C]0.60717[/C][C]0.303585[/C][/ROW]
[ROW][C]107[/C][C]0.659651[/C][C]0.680698[/C][C]0.340349[/C][/ROW]
[ROW][C]108[/C][C]0.639941[/C][C]0.720118[/C][C]0.360059[/C][/ROW]
[ROW][C]109[/C][C]0.638258[/C][C]0.723485[/C][C]0.361742[/C][/ROW]
[ROW][C]110[/C][C]0.645259[/C][C]0.709481[/C][C]0.354741[/C][/ROW]
[ROW][C]111[/C][C]0.597608[/C][C]0.804785[/C][C]0.402392[/C][/ROW]
[ROW][C]112[/C][C]0.532984[/C][C]0.934032[/C][C]0.467016[/C][/ROW]
[ROW][C]113[/C][C]0.4969[/C][C]0.993799[/C][C]0.5031[/C][/ROW]
[ROW][C]114[/C][C]0.449665[/C][C]0.89933[/C][C]0.550335[/C][/ROW]
[ROW][C]115[/C][C]0.459208[/C][C]0.918416[/C][C]0.540792[/C][/ROW]
[ROW][C]116[/C][C]0.465831[/C][C]0.931662[/C][C]0.534169[/C][/ROW]
[ROW][C]117[/C][C]0.387802[/C][C]0.775604[/C][C]0.612198[/C][/ROW]
[ROW][C]118[/C][C]0.650628[/C][C]0.698744[/C][C]0.349372[/C][/ROW]
[ROW][C]119[/C][C]0.796768[/C][C]0.406464[/C][C]0.203232[/C][/ROW]
[ROW][C]120[/C][C]0.791749[/C][C]0.416501[/C][C]0.208251[/C][/ROW]
[ROW][C]121[/C][C]0.80501[/C][C]0.38998[/C][C]0.19499[/C][/ROW]
[ROW][C]122[/C][C]0.900672[/C][C]0.198656[/C][C]0.0993278[/C][/ROW]
[ROW][C]123[/C][C]0.969344[/C][C]0.0613112[/C][C]0.0306556[/C][/ROW]
[ROW][C]124[/C][C]0.943342[/C][C]0.113315[/C][C]0.0566576[/C][/ROW]
[ROW][C]125[/C][C]0.939815[/C][C]0.12037[/C][C]0.0601851[/C][/ROW]
[ROW][C]126[/C][C]0.869761[/C][C]0.260478[/C][C]0.130239[/C][/ROW]
[ROW][C]127[/C][C]0.854629[/C][C]0.290742[/C][C]0.145371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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
120.2918630.5837260.708137
130.3548950.709790.645105
140.3902880.7805770.609712
150.3426680.6853360.657332
160.2613350.5226690.738665
170.1968350.393670.803165
180.2398380.4796750.760162
190.1915140.3830280.808486
200.1756410.3512810.824359
210.1587810.3175610.841219
220.112610.225220.88739
230.07574230.1514850.924258
240.05038690.1007740.949613
250.04090480.08180970.959095
260.04951380.09902760.950486
270.08613660.1722730.913863
280.131570.2631390.86843
290.171660.3433190.82834
300.3744810.7489620.625519
310.3388070.6776150.661193
320.2794330.5588660.720567
330.2292050.458410.770795
340.2362780.4725560.763722
350.193570.3871410.80643
360.4288540.8577080.571146
370.3737350.747470.626265
380.3423560.6847120.657644
390.3211280.6422560.678872
400.2703540.5407070.729646
410.2384190.4768380.761581
420.2677820.5355640.732218
430.2355360.4710710.764464
440.3697680.7395360.630232
450.3198560.6397110.680144
460.4426350.885270.557365
470.4387370.8774750.561263
480.4702270.9404550.529773
490.5356780.9286430.464322
500.6624450.6751090.337555
510.6364180.7271640.363582
520.6107070.7785870.389293
530.5686090.8627810.431391
540.6097110.7805790.390289
550.5767110.8465790.423289
560.5330210.9339580.466979
570.493260.9865190.50674
580.4879260.9758520.512074
590.5863370.8273250.413663
600.6659250.6681510.334075
610.6914030.6171930.308597
620.6949150.610170.305085
630.660610.6787810.33939
640.6221350.755730.377865
650.6084130.7831740.391587
660.6782330.6435340.321767
670.6983150.6033710.301685
680.6540110.6919780.345989
690.944190.111620.05581
700.9549990.09000160.0450008
710.9479510.1040970.0520487
720.9482560.1034890.0517445
730.9359760.1280480.0640239
740.9260790.1478420.0739208
750.9281040.1437920.0718961
760.9270270.1459460.072973
770.9364130.1271740.063587
780.9183830.1632330.0816167
790.9175010.1649970.0824987
800.9499030.1001940.050097
810.9357430.1285130.0642565
820.9250930.1498130.0749066
830.9146540.1706930.0853463
840.9038720.1922560.0961279
850.8847220.2305560.115278
860.8702430.2595130.129757
870.8457790.3084420.154221
880.8221970.3556060.177803
890.7860980.4278050.213902
900.7443920.5112160.255608
910.7405440.5189110.259456
920.7078370.5843250.292163
930.918880.162240.0811199
940.9031290.1937420.096871
950.8926130.2147740.107387
960.8676230.2647550.132377
970.8458310.3083370.154169
980.8466680.3066650.153332
990.8236250.3527490.176375
1000.7865820.4268360.213418
1010.7420880.5158240.257912
1020.7378510.5242990.262149
1030.7604570.4790850.239543
1040.7775940.4448130.222406
1050.7335670.5328660.266433
1060.6964150.607170.303585
1070.6596510.6806980.340349
1080.6399410.7201180.360059
1090.6382580.7234850.361742
1100.6452590.7094810.354741
1110.5976080.8047850.402392
1120.5329840.9340320.467016
1130.49690.9937990.5031
1140.4496650.899330.550335
1150.4592080.9184160.540792
1160.4658310.9316620.534169
1170.3878020.7756040.612198
1180.6506280.6987440.349372
1190.7967680.4064640.203232
1200.7917490.4165010.208251
1210.805010.389980.19499
1220.9006720.1986560.0993278
1230.9693440.06131120.0306556
1240.9433420.1133150.0566576
1250.9398150.120370.0601851
1260.8697610.2604780.130239
1270.8546290.2907420.145371






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 level40.0344828OK

\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 & 4 & 0.0344828 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271057&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]4[/C][C]0.0344828[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271057&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271057&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 level40.0344828OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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')
}