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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 16 Dec 2014 11:38:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t1418729908rsd4qyilxpvwml3.htm/, Retrieved Thu, 16 May 2024 17:13:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269355, Retrieved Thu, 16 May 2024 17:13:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Multiple Regression] [Unemployment] [2010-11-30 13:40:15] [b98453cac15ba1066b407e146608df68]
- RMPD      [Multiple Regression] [MR] [2014-12-16 11:38:16] [eeaae55b7499419163eef5a1870a44a7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.3
4.9
5.6
5.7
5.9
6.3
6.4
6.4
6.4
6.7
6.7
7.3
7.4
7.6
7.7
7.7
7.9
7.9
8
8.2
8.3
8.3
8.5
8.6
8.8
8.8
9
9
9.1
9.2
9.3
9.3
9.3
9.6
9.6
9.6
9.7
9.9
9.9
9.9
10
10.1
10.3
10.3
10.3
10.4
10.5
10.6
10.7
10.8
10.8
10.8
10.9
10.9
10.9
11.1
11.1
11.1
11.2
11.3
11.3
11.4
11.4
11.4
11.4
11.4
11.5
11.6
11.6
11.7
11.7
11.8
11.8
11.8
11.9
12
12.1
12.2
12.2
12.3
12.3
12.3
12.5
12.6
12.6
12.6
12.6
12.7
12.7
12.8
12.9
13
13
13
13.2
13.2
13.3
13.3
13.3
13.4
13.4
13.5
13.6
13.8
13.8
14.2
14.3
14.5
14.6
14.8
15.9
16.1

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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&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






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 11.0556 -0.605556M1[t] -0.465556M2[t] -0.245556M3[t] -0.185556M4[t] -0.677778M5[t] -0.577778M6[t] -0.488889M7[t] -0.388889M8[t] -0.377778M9[t] -0.244444M10[t] -0.144444M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.0556 -0.605556M1[t] -0.465556M2[t] -0.245556M3[t] -0.185556M4[t] -0.677778M5[t] -0.577778M6[t] -0.488889M7[t] -0.388889M8[t] -0.377778M9[t] -0.244444M10[t] -0.144444M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.0556 -0.605556M1[t] -0.465556M2[t] -0.245556M3[t] -0.185556M4[t] -0.677778M5[t] -0.577778M6[t] -0.488889M7[t] -0.388889M8[t] -0.377778M9[t] -0.244444M10[t] -0.144444M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 11.0556 -0.605556M1[t] -0.465556M2[t] -0.245556M3[t] -0.185556M4[t] -0.677778M5[t] -0.577778M6[t] -0.488889M7[t] -0.388889M8[t] -0.377778M9[t] -0.244444M10[t] -0.144444M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.05560.86512112.789.5237e-234.76185e-23
M1-0.6055561.19249-0.50780.6127060.306353
M2-0.4655561.19249-0.39040.6970660.348533
M3-0.2455561.19249-0.20590.8372730.418636
M4-0.1855561.19249-0.15560.8766590.438329
M5-0.6777781.22347-0.5540.5808280.290414
M6-0.5777781.22347-0.47220.637780.31889
M7-0.4888891.22347-0.39960.6903080.345154
M8-0.3888891.22347-0.31790.7512550.375628
M9-0.3777781.22347-0.30880.7581340.379067
M10-0.2444441.22347-0.19980.8420450.421023
M11-0.1444441.22347-0.11810.9062560.453128

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 11.0556 & 0.865121 & 12.78 & 9.5237e-23 & 4.76185e-23 \tabularnewline
M1 & -0.605556 & 1.19249 & -0.5078 & 0.612706 & 0.306353 \tabularnewline
M2 & -0.465556 & 1.19249 & -0.3904 & 0.697066 & 0.348533 \tabularnewline
M3 & -0.245556 & 1.19249 & -0.2059 & 0.837273 & 0.418636 \tabularnewline
M4 & -0.185556 & 1.19249 & -0.1556 & 0.876659 & 0.438329 \tabularnewline
M5 & -0.677778 & 1.22347 & -0.554 & 0.580828 & 0.290414 \tabularnewline
M6 & -0.577778 & 1.22347 & -0.4722 & 0.63778 & 0.31889 \tabularnewline
M7 & -0.488889 & 1.22347 & -0.3996 & 0.690308 & 0.345154 \tabularnewline
M8 & -0.388889 & 1.22347 & -0.3179 & 0.751255 & 0.375628 \tabularnewline
M9 & -0.377778 & 1.22347 & -0.3088 & 0.758134 & 0.379067 \tabularnewline
M10 & -0.244444 & 1.22347 & -0.1998 & 0.842045 & 0.421023 \tabularnewline
M11 & -0.144444 & 1.22347 & -0.1181 & 0.906256 & 0.453128 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]11.0556[/C][C]0.865121[/C][C]12.78[/C][C]9.5237e-23[/C][C]4.76185e-23[/C][/ROW]
[ROW][C]M1[/C][C]-0.605556[/C][C]1.19249[/C][C]-0.5078[/C][C]0.612706[/C][C]0.306353[/C][/ROW]
[ROW][C]M2[/C][C]-0.465556[/C][C]1.19249[/C][C]-0.3904[/C][C]0.697066[/C][C]0.348533[/C][/ROW]
[ROW][C]M3[/C][C]-0.245556[/C][C]1.19249[/C][C]-0.2059[/C][C]0.837273[/C][C]0.418636[/C][/ROW]
[ROW][C]M4[/C][C]-0.185556[/C][C]1.19249[/C][C]-0.1556[/C][C]0.876659[/C][C]0.438329[/C][/ROW]
[ROW][C]M5[/C][C]-0.677778[/C][C]1.22347[/C][C]-0.554[/C][C]0.580828[/C][C]0.290414[/C][/ROW]
[ROW][C]M6[/C][C]-0.577778[/C][C]1.22347[/C][C]-0.4722[/C][C]0.63778[/C][C]0.31889[/C][/ROW]
[ROW][C]M7[/C][C]-0.488889[/C][C]1.22347[/C][C]-0.3996[/C][C]0.690308[/C][C]0.345154[/C][/ROW]
[ROW][C]M8[/C][C]-0.388889[/C][C]1.22347[/C][C]-0.3179[/C][C]0.751255[/C][C]0.375628[/C][/ROW]
[ROW][C]M9[/C][C]-0.377778[/C][C]1.22347[/C][C]-0.3088[/C][C]0.758134[/C][C]0.379067[/C][/ROW]
[ROW][C]M10[/C][C]-0.244444[/C][C]1.22347[/C][C]-0.1998[/C][C]0.842045[/C][C]0.421023[/C][/ROW]
[ROW][C]M11[/C][C]-0.144444[/C][C]1.22347[/C][C]-0.1181[/C][C]0.906256[/C][C]0.453128[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.05560.86512112.789.5237e-234.76185e-23
M1-0.6055561.19249-0.50780.6127060.306353
M2-0.4655561.19249-0.39040.6970660.348533
M3-0.2455561.19249-0.20590.8372730.418636
M4-0.1855561.19249-0.15560.8766590.438329
M5-0.6777781.22347-0.5540.5808280.290414
M6-0.5777781.22347-0.47220.637780.31889
M7-0.4888891.22347-0.39960.6903080.345154
M8-0.3888891.22347-0.31790.7512550.375628
M9-0.3777781.22347-0.30880.7581340.379067
M10-0.2444441.22347-0.19980.8420450.421023
M11-0.1444441.22347-0.11810.9062560.453128






Multiple Linear Regression - Regression Statistics
Multiple R0.0800503
R-squared0.00640805
Adjusted R-squared-0.102887
F-TEST (value)0.0586308
F-TEST (DF numerator)11
F-TEST (DF denominator)100
p-value0.999994
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.59536
Sum Squared Residuals673.591

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0800503 \tabularnewline
R-squared & 0.00640805 \tabularnewline
Adjusted R-squared & -0.102887 \tabularnewline
F-TEST (value) & 0.0586308 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 100 \tabularnewline
p-value & 0.999994 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.59536 \tabularnewline
Sum Squared Residuals & 673.591 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0800503[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00640805[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.102887[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.0586308[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]100[/C][/ROW]
[ROW][C]p-value[/C][C]0.999994[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.59536[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]673.591[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&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 R0.0800503
R-squared0.00640805
Adjusted R-squared-0.102887
F-TEST (value)0.0586308
F-TEST (DF numerator)11
F-TEST (DF denominator)100
p-value0.999994
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.59536
Sum Squared Residuals673.591






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.310.45-6.15
24.910.59-5.69
35.610.81-5.21
45.710.87-5.17
55.910.3778-4.47778
66.310.4778-4.17778
76.410.5667-4.16667
86.410.6667-4.26667
96.410.6778-4.27778
106.710.8111-4.11111
116.710.9111-4.21111
127.311.0556-3.75556
137.410.45-3.05
147.610.59-2.99
157.710.81-3.11
167.710.87-3.17
177.910.3778-2.47778
187.910.4778-2.57778
19810.5667-2.56667
208.210.6667-2.46667
218.310.6778-2.37778
228.310.8111-2.51111
238.510.9111-2.41111
248.611.0556-2.45556
258.810.45-1.65
268.810.59-1.79
27910.81-1.81
28910.87-1.87
299.110.3778-1.27778
309.210.4778-1.27778
319.310.5667-1.26667
329.310.6667-1.36667
339.310.6778-1.37778
349.610.8111-1.21111
359.610.9111-1.31111
369.611.0556-1.45556
379.710.45-0.75
389.910.59-0.69
399.910.81-0.91
409.910.87-0.97
411010.3778-0.377778
4210.110.4778-0.377778
4310.310.5667-0.266667
4410.310.6667-0.366667
4510.310.6778-0.377778
4610.410.8111-0.411111
4710.510.9111-0.411111
4810.611.0556-0.455556
4910.710.450.25
5010.810.590.21
5110.810.81-0.01
5210.810.87-0.07
5310.910.37780.522222
5410.910.47780.422222
5510.910.56670.333333
5611.110.66670.433333
5711.110.67780.422222
5811.110.81110.288889
5911.210.91110.288889
6011.311.05560.244444
6111.310.450.85
6211.410.590.81
6311.410.810.59
6411.410.870.53
6511.410.37781.02222
6611.410.47780.922222
6711.510.56670.933333
6811.610.66670.933333
6911.610.67780.922222
7011.710.81110.888889
7111.710.91110.788889
7211.811.05560.744444
7311.810.451.35
7411.810.591.21
7511.910.811.09
761210.871.13
7712.110.37781.72222
7812.210.47781.72222
7912.210.56671.63333
8012.310.66671.63333
8112.310.67781.62222
8212.310.81111.48889
8312.510.91111.58889
8412.611.05561.54444
8512.610.452.15
8612.610.592.01
8712.610.811.79
8812.710.871.83
8912.710.37782.32222
9012.810.47782.32222
9112.910.56672.33333
921310.66672.33333
931310.67782.32222
941310.81112.18889
9513.210.91112.28889
9613.211.05562.14444
9713.310.452.85
9813.310.592.71
9913.310.812.49
10013.410.872.53
10113.410.37783.02222
10213.510.47783.02222
10313.610.56673.03333
10413.810.66673.13333
10513.810.67783.12222
10614.210.81113.38889
10714.310.91113.38889
10814.511.05563.44444
10914.610.454.15
11014.810.594.21
11115.910.815.09
11216.110.875.23

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.3 & 10.45 & -6.15 \tabularnewline
2 & 4.9 & 10.59 & -5.69 \tabularnewline
3 & 5.6 & 10.81 & -5.21 \tabularnewline
4 & 5.7 & 10.87 & -5.17 \tabularnewline
5 & 5.9 & 10.3778 & -4.47778 \tabularnewline
6 & 6.3 & 10.4778 & -4.17778 \tabularnewline
7 & 6.4 & 10.5667 & -4.16667 \tabularnewline
8 & 6.4 & 10.6667 & -4.26667 \tabularnewline
9 & 6.4 & 10.6778 & -4.27778 \tabularnewline
10 & 6.7 & 10.8111 & -4.11111 \tabularnewline
11 & 6.7 & 10.9111 & -4.21111 \tabularnewline
12 & 7.3 & 11.0556 & -3.75556 \tabularnewline
13 & 7.4 & 10.45 & -3.05 \tabularnewline
14 & 7.6 & 10.59 & -2.99 \tabularnewline
15 & 7.7 & 10.81 & -3.11 \tabularnewline
16 & 7.7 & 10.87 & -3.17 \tabularnewline
17 & 7.9 & 10.3778 & -2.47778 \tabularnewline
18 & 7.9 & 10.4778 & -2.57778 \tabularnewline
19 & 8 & 10.5667 & -2.56667 \tabularnewline
20 & 8.2 & 10.6667 & -2.46667 \tabularnewline
21 & 8.3 & 10.6778 & -2.37778 \tabularnewline
22 & 8.3 & 10.8111 & -2.51111 \tabularnewline
23 & 8.5 & 10.9111 & -2.41111 \tabularnewline
24 & 8.6 & 11.0556 & -2.45556 \tabularnewline
25 & 8.8 & 10.45 & -1.65 \tabularnewline
26 & 8.8 & 10.59 & -1.79 \tabularnewline
27 & 9 & 10.81 & -1.81 \tabularnewline
28 & 9 & 10.87 & -1.87 \tabularnewline
29 & 9.1 & 10.3778 & -1.27778 \tabularnewline
30 & 9.2 & 10.4778 & -1.27778 \tabularnewline
31 & 9.3 & 10.5667 & -1.26667 \tabularnewline
32 & 9.3 & 10.6667 & -1.36667 \tabularnewline
33 & 9.3 & 10.6778 & -1.37778 \tabularnewline
34 & 9.6 & 10.8111 & -1.21111 \tabularnewline
35 & 9.6 & 10.9111 & -1.31111 \tabularnewline
36 & 9.6 & 11.0556 & -1.45556 \tabularnewline
37 & 9.7 & 10.45 & -0.75 \tabularnewline
38 & 9.9 & 10.59 & -0.69 \tabularnewline
39 & 9.9 & 10.81 & -0.91 \tabularnewline
40 & 9.9 & 10.87 & -0.97 \tabularnewline
41 & 10 & 10.3778 & -0.377778 \tabularnewline
42 & 10.1 & 10.4778 & -0.377778 \tabularnewline
43 & 10.3 & 10.5667 & -0.266667 \tabularnewline
44 & 10.3 & 10.6667 & -0.366667 \tabularnewline
45 & 10.3 & 10.6778 & -0.377778 \tabularnewline
46 & 10.4 & 10.8111 & -0.411111 \tabularnewline
47 & 10.5 & 10.9111 & -0.411111 \tabularnewline
48 & 10.6 & 11.0556 & -0.455556 \tabularnewline
49 & 10.7 & 10.45 & 0.25 \tabularnewline
50 & 10.8 & 10.59 & 0.21 \tabularnewline
51 & 10.8 & 10.81 & -0.01 \tabularnewline
52 & 10.8 & 10.87 & -0.07 \tabularnewline
53 & 10.9 & 10.3778 & 0.522222 \tabularnewline
54 & 10.9 & 10.4778 & 0.422222 \tabularnewline
55 & 10.9 & 10.5667 & 0.333333 \tabularnewline
56 & 11.1 & 10.6667 & 0.433333 \tabularnewline
57 & 11.1 & 10.6778 & 0.422222 \tabularnewline
58 & 11.1 & 10.8111 & 0.288889 \tabularnewline
59 & 11.2 & 10.9111 & 0.288889 \tabularnewline
60 & 11.3 & 11.0556 & 0.244444 \tabularnewline
61 & 11.3 & 10.45 & 0.85 \tabularnewline
62 & 11.4 & 10.59 & 0.81 \tabularnewline
63 & 11.4 & 10.81 & 0.59 \tabularnewline
64 & 11.4 & 10.87 & 0.53 \tabularnewline
65 & 11.4 & 10.3778 & 1.02222 \tabularnewline
66 & 11.4 & 10.4778 & 0.922222 \tabularnewline
67 & 11.5 & 10.5667 & 0.933333 \tabularnewline
68 & 11.6 & 10.6667 & 0.933333 \tabularnewline
69 & 11.6 & 10.6778 & 0.922222 \tabularnewline
70 & 11.7 & 10.8111 & 0.888889 \tabularnewline
71 & 11.7 & 10.9111 & 0.788889 \tabularnewline
72 & 11.8 & 11.0556 & 0.744444 \tabularnewline
73 & 11.8 & 10.45 & 1.35 \tabularnewline
74 & 11.8 & 10.59 & 1.21 \tabularnewline
75 & 11.9 & 10.81 & 1.09 \tabularnewline
76 & 12 & 10.87 & 1.13 \tabularnewline
77 & 12.1 & 10.3778 & 1.72222 \tabularnewline
78 & 12.2 & 10.4778 & 1.72222 \tabularnewline
79 & 12.2 & 10.5667 & 1.63333 \tabularnewline
80 & 12.3 & 10.6667 & 1.63333 \tabularnewline
81 & 12.3 & 10.6778 & 1.62222 \tabularnewline
82 & 12.3 & 10.8111 & 1.48889 \tabularnewline
83 & 12.5 & 10.9111 & 1.58889 \tabularnewline
84 & 12.6 & 11.0556 & 1.54444 \tabularnewline
85 & 12.6 & 10.45 & 2.15 \tabularnewline
86 & 12.6 & 10.59 & 2.01 \tabularnewline
87 & 12.6 & 10.81 & 1.79 \tabularnewline
88 & 12.7 & 10.87 & 1.83 \tabularnewline
89 & 12.7 & 10.3778 & 2.32222 \tabularnewline
90 & 12.8 & 10.4778 & 2.32222 \tabularnewline
91 & 12.9 & 10.5667 & 2.33333 \tabularnewline
92 & 13 & 10.6667 & 2.33333 \tabularnewline
93 & 13 & 10.6778 & 2.32222 \tabularnewline
94 & 13 & 10.8111 & 2.18889 \tabularnewline
95 & 13.2 & 10.9111 & 2.28889 \tabularnewline
96 & 13.2 & 11.0556 & 2.14444 \tabularnewline
97 & 13.3 & 10.45 & 2.85 \tabularnewline
98 & 13.3 & 10.59 & 2.71 \tabularnewline
99 & 13.3 & 10.81 & 2.49 \tabularnewline
100 & 13.4 & 10.87 & 2.53 \tabularnewline
101 & 13.4 & 10.3778 & 3.02222 \tabularnewline
102 & 13.5 & 10.4778 & 3.02222 \tabularnewline
103 & 13.6 & 10.5667 & 3.03333 \tabularnewline
104 & 13.8 & 10.6667 & 3.13333 \tabularnewline
105 & 13.8 & 10.6778 & 3.12222 \tabularnewline
106 & 14.2 & 10.8111 & 3.38889 \tabularnewline
107 & 14.3 & 10.9111 & 3.38889 \tabularnewline
108 & 14.5 & 11.0556 & 3.44444 \tabularnewline
109 & 14.6 & 10.45 & 4.15 \tabularnewline
110 & 14.8 & 10.59 & 4.21 \tabularnewline
111 & 15.9 & 10.81 & 5.09 \tabularnewline
112 & 16.1 & 10.87 & 5.23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4.3[/C][C]10.45[/C][C]-6.15[/C][/ROW]
[ROW][C]2[/C][C]4.9[/C][C]10.59[/C][C]-5.69[/C][/ROW]
[ROW][C]3[/C][C]5.6[/C][C]10.81[/C][C]-5.21[/C][/ROW]
[ROW][C]4[/C][C]5.7[/C][C]10.87[/C][C]-5.17[/C][/ROW]
[ROW][C]5[/C][C]5.9[/C][C]10.3778[/C][C]-4.47778[/C][/ROW]
[ROW][C]6[/C][C]6.3[/C][C]10.4778[/C][C]-4.17778[/C][/ROW]
[ROW][C]7[/C][C]6.4[/C][C]10.5667[/C][C]-4.16667[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]10.6667[/C][C]-4.26667[/C][/ROW]
[ROW][C]9[/C][C]6.4[/C][C]10.6778[/C][C]-4.27778[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]10.8111[/C][C]-4.11111[/C][/ROW]
[ROW][C]11[/C][C]6.7[/C][C]10.9111[/C][C]-4.21111[/C][/ROW]
[ROW][C]12[/C][C]7.3[/C][C]11.0556[/C][C]-3.75556[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]10.45[/C][C]-3.05[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]10.59[/C][C]-2.99[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]10.81[/C][C]-3.11[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]10.87[/C][C]-3.17[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]10.3778[/C][C]-2.47778[/C][/ROW]
[ROW][C]18[/C][C]7.9[/C][C]10.4778[/C][C]-2.57778[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]10.5667[/C][C]-2.56667[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]10.6667[/C][C]-2.46667[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]10.6778[/C][C]-2.37778[/C][/ROW]
[ROW][C]22[/C][C]8.3[/C][C]10.8111[/C][C]-2.51111[/C][/ROW]
[ROW][C]23[/C][C]8.5[/C][C]10.9111[/C][C]-2.41111[/C][/ROW]
[ROW][C]24[/C][C]8.6[/C][C]11.0556[/C][C]-2.45556[/C][/ROW]
[ROW][C]25[/C][C]8.8[/C][C]10.45[/C][C]-1.65[/C][/ROW]
[ROW][C]26[/C][C]8.8[/C][C]10.59[/C][C]-1.79[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]10.81[/C][C]-1.81[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]10.87[/C][C]-1.87[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]10.3778[/C][C]-1.27778[/C][/ROW]
[ROW][C]30[/C][C]9.2[/C][C]10.4778[/C][C]-1.27778[/C][/ROW]
[ROW][C]31[/C][C]9.3[/C][C]10.5667[/C][C]-1.26667[/C][/ROW]
[ROW][C]32[/C][C]9.3[/C][C]10.6667[/C][C]-1.36667[/C][/ROW]
[ROW][C]33[/C][C]9.3[/C][C]10.6778[/C][C]-1.37778[/C][/ROW]
[ROW][C]34[/C][C]9.6[/C][C]10.8111[/C][C]-1.21111[/C][/ROW]
[ROW][C]35[/C][C]9.6[/C][C]10.9111[/C][C]-1.31111[/C][/ROW]
[ROW][C]36[/C][C]9.6[/C][C]11.0556[/C][C]-1.45556[/C][/ROW]
[ROW][C]37[/C][C]9.7[/C][C]10.45[/C][C]-0.75[/C][/ROW]
[ROW][C]38[/C][C]9.9[/C][C]10.59[/C][C]-0.69[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]10.81[/C][C]-0.91[/C][/ROW]
[ROW][C]40[/C][C]9.9[/C][C]10.87[/C][C]-0.97[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]10.3778[/C][C]-0.377778[/C][/ROW]
[ROW][C]42[/C][C]10.1[/C][C]10.4778[/C][C]-0.377778[/C][/ROW]
[ROW][C]43[/C][C]10.3[/C][C]10.5667[/C][C]-0.266667[/C][/ROW]
[ROW][C]44[/C][C]10.3[/C][C]10.6667[/C][C]-0.366667[/C][/ROW]
[ROW][C]45[/C][C]10.3[/C][C]10.6778[/C][C]-0.377778[/C][/ROW]
[ROW][C]46[/C][C]10.4[/C][C]10.8111[/C][C]-0.411111[/C][/ROW]
[ROW][C]47[/C][C]10.5[/C][C]10.9111[/C][C]-0.411111[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]11.0556[/C][C]-0.455556[/C][/ROW]
[ROW][C]49[/C][C]10.7[/C][C]10.45[/C][C]0.25[/C][/ROW]
[ROW][C]50[/C][C]10.8[/C][C]10.59[/C][C]0.21[/C][/ROW]
[ROW][C]51[/C][C]10.8[/C][C]10.81[/C][C]-0.01[/C][/ROW]
[ROW][C]52[/C][C]10.8[/C][C]10.87[/C][C]-0.07[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.3778[/C][C]0.522222[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.4778[/C][C]0.422222[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]10.5667[/C][C]0.333333[/C][/ROW]
[ROW][C]56[/C][C]11.1[/C][C]10.6667[/C][C]0.433333[/C][/ROW]
[ROW][C]57[/C][C]11.1[/C][C]10.6778[/C][C]0.422222[/C][/ROW]
[ROW][C]58[/C][C]11.1[/C][C]10.8111[/C][C]0.288889[/C][/ROW]
[ROW][C]59[/C][C]11.2[/C][C]10.9111[/C][C]0.288889[/C][/ROW]
[ROW][C]60[/C][C]11.3[/C][C]11.0556[/C][C]0.244444[/C][/ROW]
[ROW][C]61[/C][C]11.3[/C][C]10.45[/C][C]0.85[/C][/ROW]
[ROW][C]62[/C][C]11.4[/C][C]10.59[/C][C]0.81[/C][/ROW]
[ROW][C]63[/C][C]11.4[/C][C]10.81[/C][C]0.59[/C][/ROW]
[ROW][C]64[/C][C]11.4[/C][C]10.87[/C][C]0.53[/C][/ROW]
[ROW][C]65[/C][C]11.4[/C][C]10.3778[/C][C]1.02222[/C][/ROW]
[ROW][C]66[/C][C]11.4[/C][C]10.4778[/C][C]0.922222[/C][/ROW]
[ROW][C]67[/C][C]11.5[/C][C]10.5667[/C][C]0.933333[/C][/ROW]
[ROW][C]68[/C][C]11.6[/C][C]10.6667[/C][C]0.933333[/C][/ROW]
[ROW][C]69[/C][C]11.6[/C][C]10.6778[/C][C]0.922222[/C][/ROW]
[ROW][C]70[/C][C]11.7[/C][C]10.8111[/C][C]0.888889[/C][/ROW]
[ROW][C]71[/C][C]11.7[/C][C]10.9111[/C][C]0.788889[/C][/ROW]
[ROW][C]72[/C][C]11.8[/C][C]11.0556[/C][C]0.744444[/C][/ROW]
[ROW][C]73[/C][C]11.8[/C][C]10.45[/C][C]1.35[/C][/ROW]
[ROW][C]74[/C][C]11.8[/C][C]10.59[/C][C]1.21[/C][/ROW]
[ROW][C]75[/C][C]11.9[/C][C]10.81[/C][C]1.09[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]10.87[/C][C]1.13[/C][/ROW]
[ROW][C]77[/C][C]12.1[/C][C]10.3778[/C][C]1.72222[/C][/ROW]
[ROW][C]78[/C][C]12.2[/C][C]10.4778[/C][C]1.72222[/C][/ROW]
[ROW][C]79[/C][C]12.2[/C][C]10.5667[/C][C]1.63333[/C][/ROW]
[ROW][C]80[/C][C]12.3[/C][C]10.6667[/C][C]1.63333[/C][/ROW]
[ROW][C]81[/C][C]12.3[/C][C]10.6778[/C][C]1.62222[/C][/ROW]
[ROW][C]82[/C][C]12.3[/C][C]10.8111[/C][C]1.48889[/C][/ROW]
[ROW][C]83[/C][C]12.5[/C][C]10.9111[/C][C]1.58889[/C][/ROW]
[ROW][C]84[/C][C]12.6[/C][C]11.0556[/C][C]1.54444[/C][/ROW]
[ROW][C]85[/C][C]12.6[/C][C]10.45[/C][C]2.15[/C][/ROW]
[ROW][C]86[/C][C]12.6[/C][C]10.59[/C][C]2.01[/C][/ROW]
[ROW][C]87[/C][C]12.6[/C][C]10.81[/C][C]1.79[/C][/ROW]
[ROW][C]88[/C][C]12.7[/C][C]10.87[/C][C]1.83[/C][/ROW]
[ROW][C]89[/C][C]12.7[/C][C]10.3778[/C][C]2.32222[/C][/ROW]
[ROW][C]90[/C][C]12.8[/C][C]10.4778[/C][C]2.32222[/C][/ROW]
[ROW][C]91[/C][C]12.9[/C][C]10.5667[/C][C]2.33333[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]10.6667[/C][C]2.33333[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]10.6778[/C][C]2.32222[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]10.8111[/C][C]2.18889[/C][/ROW]
[ROW][C]95[/C][C]13.2[/C][C]10.9111[/C][C]2.28889[/C][/ROW]
[ROW][C]96[/C][C]13.2[/C][C]11.0556[/C][C]2.14444[/C][/ROW]
[ROW][C]97[/C][C]13.3[/C][C]10.45[/C][C]2.85[/C][/ROW]
[ROW][C]98[/C][C]13.3[/C][C]10.59[/C][C]2.71[/C][/ROW]
[ROW][C]99[/C][C]13.3[/C][C]10.81[/C][C]2.49[/C][/ROW]
[ROW][C]100[/C][C]13.4[/C][C]10.87[/C][C]2.53[/C][/ROW]
[ROW][C]101[/C][C]13.4[/C][C]10.3778[/C][C]3.02222[/C][/ROW]
[ROW][C]102[/C][C]13.5[/C][C]10.4778[/C][C]3.02222[/C][/ROW]
[ROW][C]103[/C][C]13.6[/C][C]10.5667[/C][C]3.03333[/C][/ROW]
[ROW][C]104[/C][C]13.8[/C][C]10.6667[/C][C]3.13333[/C][/ROW]
[ROW][C]105[/C][C]13.8[/C][C]10.6778[/C][C]3.12222[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]10.8111[/C][C]3.38889[/C][/ROW]
[ROW][C]107[/C][C]14.3[/C][C]10.9111[/C][C]3.38889[/C][/ROW]
[ROW][C]108[/C][C]14.5[/C][C]11.0556[/C][C]3.44444[/C][/ROW]
[ROW][C]109[/C][C]14.6[/C][C]10.45[/C][C]4.15[/C][/ROW]
[ROW][C]110[/C][C]14.8[/C][C]10.59[/C][C]4.21[/C][/ROW]
[ROW][C]111[/C][C]15.9[/C][C]10.81[/C][C]5.09[/C][/ROW]
[ROW][C]112[/C][C]16.1[/C][C]10.87[/C][C]5.23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.310.45-6.15
24.910.59-5.69
35.610.81-5.21
45.710.87-5.17
55.910.3778-4.47778
66.310.4778-4.17778
76.410.5667-4.16667
86.410.6667-4.26667
96.410.6778-4.27778
106.710.8111-4.11111
116.710.9111-4.21111
127.311.0556-3.75556
137.410.45-3.05
147.610.59-2.99
157.710.81-3.11
167.710.87-3.17
177.910.3778-2.47778
187.910.4778-2.57778
19810.5667-2.56667
208.210.6667-2.46667
218.310.6778-2.37778
228.310.8111-2.51111
238.510.9111-2.41111
248.611.0556-2.45556
258.810.45-1.65
268.810.59-1.79
27910.81-1.81
28910.87-1.87
299.110.3778-1.27778
309.210.4778-1.27778
319.310.5667-1.26667
329.310.6667-1.36667
339.310.6778-1.37778
349.610.8111-1.21111
359.610.9111-1.31111
369.611.0556-1.45556
379.710.45-0.75
389.910.59-0.69
399.910.81-0.91
409.910.87-0.97
411010.3778-0.377778
4210.110.4778-0.377778
4310.310.5667-0.266667
4410.310.6667-0.366667
4510.310.6778-0.377778
4610.410.8111-0.411111
4710.510.9111-0.411111
4810.611.0556-0.455556
4910.710.450.25
5010.810.590.21
5110.810.81-0.01
5210.810.87-0.07
5310.910.37780.522222
5410.910.47780.422222
5510.910.56670.333333
5611.110.66670.433333
5711.110.67780.422222
5811.110.81110.288889
5911.210.91110.288889
6011.311.05560.244444
6111.310.450.85
6211.410.590.81
6311.410.810.59
6411.410.870.53
6511.410.37781.02222
6611.410.47780.922222
6711.510.56670.933333
6811.610.66670.933333
6911.610.67780.922222
7011.710.81110.888889
7111.710.91110.788889
7211.811.05560.744444
7311.810.451.35
7411.810.591.21
7511.910.811.09
761210.871.13
7712.110.37781.72222
7812.210.47781.72222
7912.210.56671.63333
8012.310.66671.63333
8112.310.67781.62222
8212.310.81111.48889
8312.510.91111.58889
8412.611.05561.54444
8512.610.452.15
8612.610.592.01
8712.610.811.79
8812.710.871.83
8912.710.37782.32222
9012.810.47782.32222
9112.910.56672.33333
921310.66672.33333
931310.67782.32222
941310.81112.18889
9513.210.91112.28889
9613.211.05562.14444
9713.310.452.85
9813.310.592.71
9913.310.812.49
10013.410.872.53
10113.410.37783.02222
10213.510.47783.02222
10313.610.56673.03333
10413.810.66673.13333
10513.810.67783.12222
10614.210.81113.38889
10714.310.91113.38889
10814.511.05563.44444
10914.610.454.15
11014.810.594.21
11115.910.815.09
11216.110.875.23






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.5594080.8811840.440592
160.4959950.991990.504005
170.4436020.8872040.556398
180.3795540.7591090.620446
190.3289490.6578980.671051
200.298860.597720.70114
210.2804030.5608060.719597
220.2547330.5094670.745267
230.2428540.4857070.757146
240.216070.4321390.78393
250.3151710.6303410.684829
260.3817860.7635730.618214
270.433610.8672190.56639
280.4864410.9728820.513559
290.5152810.9694390.484719
300.5377140.9245720.462286
310.5609320.8781350.439068
320.5831160.8337670.416884
330.6046180.7907650.395382
340.6295650.7408690.370435
350.6540670.6918660.345933
360.6688820.6622370.331118
370.7508730.4982540.249127
380.8118910.3762170.188109
390.8548540.2902910.145146
400.8924310.2151380.107569
410.9086070.1827860.0913931
420.9210070.1579860.078993
430.9318380.1363230.0681616
440.9419770.1160450.0580225
450.9505050.09899010.049495
460.9571080.08578350.0428917
470.9635230.07295490.0364774
480.968360.06328080.0316404
490.9794120.04117610.020588
500.9858590.02828240.0141412
510.9904460.01910820.00955408
520.9938950.01221020.0061051
530.994670.01065940.00532969
540.9952250.009550540.00477527
550.9956710.0086570.0043285
560.9960970.007805150.00390258
570.9964370.007126270.00356314
580.9967530.006494320.00324716
590.9970810.005838270.00291914
600.9973230.005353760.00267688
610.9980380.003924160.00196208
620.9984720.003056470.00152824
630.9989290.002142530.00107126
640.9993150.001369710.000684853
650.9992910.001417830.000708913
660.9992720.001456780.000728391
670.9992340.001531170.000765584
680.9992030.001594080.000797039
690.9991610.001678650.000839327
700.9991180.001764920.000882462
710.9991320.001735710.000867854
720.9991360.001727330.000863667
730.9992710.001457790.000728893
740.9993880.001223430.000611716
750.999580.0008396320.000419816
760.9997360.0005283630.000264182
770.9996360.0007288140.000364407
780.9994910.001018110.000509053
790.99930.001400750.000700376
800.9990480.001904620.000952308
810.9986890.002622870.00131144
820.9983120.003376090.00168805
830.9977790.004442630.00222131
840.9970280.005943260.00297163
850.9964150.007170230.00358511
860.9957530.008494750.00424737
870.9965010.006997040.00349852
880.9974210.005157150.00257858
890.9951420.009716560.00485828
900.9909070.01818570.00909283
910.9831630.03367390.016837
920.9698740.06025110.0301255
930.946790.106420.0532101
940.9149160.1701670.0850835
950.8617220.2765550.138278
960.7859470.4281060.214053
970.6748740.6502510.325126

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.559408 & 0.881184 & 0.440592 \tabularnewline
16 & 0.495995 & 0.99199 & 0.504005 \tabularnewline
17 & 0.443602 & 0.887204 & 0.556398 \tabularnewline
18 & 0.379554 & 0.759109 & 0.620446 \tabularnewline
19 & 0.328949 & 0.657898 & 0.671051 \tabularnewline
20 & 0.29886 & 0.59772 & 0.70114 \tabularnewline
21 & 0.280403 & 0.560806 & 0.719597 \tabularnewline
22 & 0.254733 & 0.509467 & 0.745267 \tabularnewline
23 & 0.242854 & 0.485707 & 0.757146 \tabularnewline
24 & 0.21607 & 0.432139 & 0.78393 \tabularnewline
25 & 0.315171 & 0.630341 & 0.684829 \tabularnewline
26 & 0.381786 & 0.763573 & 0.618214 \tabularnewline
27 & 0.43361 & 0.867219 & 0.56639 \tabularnewline
28 & 0.486441 & 0.972882 & 0.513559 \tabularnewline
29 & 0.515281 & 0.969439 & 0.484719 \tabularnewline
30 & 0.537714 & 0.924572 & 0.462286 \tabularnewline
31 & 0.560932 & 0.878135 & 0.439068 \tabularnewline
32 & 0.583116 & 0.833767 & 0.416884 \tabularnewline
33 & 0.604618 & 0.790765 & 0.395382 \tabularnewline
34 & 0.629565 & 0.740869 & 0.370435 \tabularnewline
35 & 0.654067 & 0.691866 & 0.345933 \tabularnewline
36 & 0.668882 & 0.662237 & 0.331118 \tabularnewline
37 & 0.750873 & 0.498254 & 0.249127 \tabularnewline
38 & 0.811891 & 0.376217 & 0.188109 \tabularnewline
39 & 0.854854 & 0.290291 & 0.145146 \tabularnewline
40 & 0.892431 & 0.215138 & 0.107569 \tabularnewline
41 & 0.908607 & 0.182786 & 0.0913931 \tabularnewline
42 & 0.921007 & 0.157986 & 0.078993 \tabularnewline
43 & 0.931838 & 0.136323 & 0.0681616 \tabularnewline
44 & 0.941977 & 0.116045 & 0.0580225 \tabularnewline
45 & 0.950505 & 0.0989901 & 0.049495 \tabularnewline
46 & 0.957108 & 0.0857835 & 0.0428917 \tabularnewline
47 & 0.963523 & 0.0729549 & 0.0364774 \tabularnewline
48 & 0.96836 & 0.0632808 & 0.0316404 \tabularnewline
49 & 0.979412 & 0.0411761 & 0.020588 \tabularnewline
50 & 0.985859 & 0.0282824 & 0.0141412 \tabularnewline
51 & 0.990446 & 0.0191082 & 0.00955408 \tabularnewline
52 & 0.993895 & 0.0122102 & 0.0061051 \tabularnewline
53 & 0.99467 & 0.0106594 & 0.00532969 \tabularnewline
54 & 0.995225 & 0.00955054 & 0.00477527 \tabularnewline
55 & 0.995671 & 0.008657 & 0.0043285 \tabularnewline
56 & 0.996097 & 0.00780515 & 0.00390258 \tabularnewline
57 & 0.996437 & 0.00712627 & 0.00356314 \tabularnewline
58 & 0.996753 & 0.00649432 & 0.00324716 \tabularnewline
59 & 0.997081 & 0.00583827 & 0.00291914 \tabularnewline
60 & 0.997323 & 0.00535376 & 0.00267688 \tabularnewline
61 & 0.998038 & 0.00392416 & 0.00196208 \tabularnewline
62 & 0.998472 & 0.00305647 & 0.00152824 \tabularnewline
63 & 0.998929 & 0.00214253 & 0.00107126 \tabularnewline
64 & 0.999315 & 0.00136971 & 0.000684853 \tabularnewline
65 & 0.999291 & 0.00141783 & 0.000708913 \tabularnewline
66 & 0.999272 & 0.00145678 & 0.000728391 \tabularnewline
67 & 0.999234 & 0.00153117 & 0.000765584 \tabularnewline
68 & 0.999203 & 0.00159408 & 0.000797039 \tabularnewline
69 & 0.999161 & 0.00167865 & 0.000839327 \tabularnewline
70 & 0.999118 & 0.00176492 & 0.000882462 \tabularnewline
71 & 0.999132 & 0.00173571 & 0.000867854 \tabularnewline
72 & 0.999136 & 0.00172733 & 0.000863667 \tabularnewline
73 & 0.999271 & 0.00145779 & 0.000728893 \tabularnewline
74 & 0.999388 & 0.00122343 & 0.000611716 \tabularnewline
75 & 0.99958 & 0.000839632 & 0.000419816 \tabularnewline
76 & 0.999736 & 0.000528363 & 0.000264182 \tabularnewline
77 & 0.999636 & 0.000728814 & 0.000364407 \tabularnewline
78 & 0.999491 & 0.00101811 & 0.000509053 \tabularnewline
79 & 0.9993 & 0.00140075 & 0.000700376 \tabularnewline
80 & 0.999048 & 0.00190462 & 0.000952308 \tabularnewline
81 & 0.998689 & 0.00262287 & 0.00131144 \tabularnewline
82 & 0.998312 & 0.00337609 & 0.00168805 \tabularnewline
83 & 0.997779 & 0.00444263 & 0.00222131 \tabularnewline
84 & 0.997028 & 0.00594326 & 0.00297163 \tabularnewline
85 & 0.996415 & 0.00717023 & 0.00358511 \tabularnewline
86 & 0.995753 & 0.00849475 & 0.00424737 \tabularnewline
87 & 0.996501 & 0.00699704 & 0.00349852 \tabularnewline
88 & 0.997421 & 0.00515715 & 0.00257858 \tabularnewline
89 & 0.995142 & 0.00971656 & 0.00485828 \tabularnewline
90 & 0.990907 & 0.0181857 & 0.00909283 \tabularnewline
91 & 0.983163 & 0.0336739 & 0.016837 \tabularnewline
92 & 0.969874 & 0.0602511 & 0.0301255 \tabularnewline
93 & 0.94679 & 0.10642 & 0.0532101 \tabularnewline
94 & 0.914916 & 0.170167 & 0.0850835 \tabularnewline
95 & 0.861722 & 0.276555 & 0.138278 \tabularnewline
96 & 0.785947 & 0.428106 & 0.214053 \tabularnewline
97 & 0.674874 & 0.650251 & 0.325126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&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]15[/C][C]0.559408[/C][C]0.881184[/C][C]0.440592[/C][/ROW]
[ROW][C]16[/C][C]0.495995[/C][C]0.99199[/C][C]0.504005[/C][/ROW]
[ROW][C]17[/C][C]0.443602[/C][C]0.887204[/C][C]0.556398[/C][/ROW]
[ROW][C]18[/C][C]0.379554[/C][C]0.759109[/C][C]0.620446[/C][/ROW]
[ROW][C]19[/C][C]0.328949[/C][C]0.657898[/C][C]0.671051[/C][/ROW]
[ROW][C]20[/C][C]0.29886[/C][C]0.59772[/C][C]0.70114[/C][/ROW]
[ROW][C]21[/C][C]0.280403[/C][C]0.560806[/C][C]0.719597[/C][/ROW]
[ROW][C]22[/C][C]0.254733[/C][C]0.509467[/C][C]0.745267[/C][/ROW]
[ROW][C]23[/C][C]0.242854[/C][C]0.485707[/C][C]0.757146[/C][/ROW]
[ROW][C]24[/C][C]0.21607[/C][C]0.432139[/C][C]0.78393[/C][/ROW]
[ROW][C]25[/C][C]0.315171[/C][C]0.630341[/C][C]0.684829[/C][/ROW]
[ROW][C]26[/C][C]0.381786[/C][C]0.763573[/C][C]0.618214[/C][/ROW]
[ROW][C]27[/C][C]0.43361[/C][C]0.867219[/C][C]0.56639[/C][/ROW]
[ROW][C]28[/C][C]0.486441[/C][C]0.972882[/C][C]0.513559[/C][/ROW]
[ROW][C]29[/C][C]0.515281[/C][C]0.969439[/C][C]0.484719[/C][/ROW]
[ROW][C]30[/C][C]0.537714[/C][C]0.924572[/C][C]0.462286[/C][/ROW]
[ROW][C]31[/C][C]0.560932[/C][C]0.878135[/C][C]0.439068[/C][/ROW]
[ROW][C]32[/C][C]0.583116[/C][C]0.833767[/C][C]0.416884[/C][/ROW]
[ROW][C]33[/C][C]0.604618[/C][C]0.790765[/C][C]0.395382[/C][/ROW]
[ROW][C]34[/C][C]0.629565[/C][C]0.740869[/C][C]0.370435[/C][/ROW]
[ROW][C]35[/C][C]0.654067[/C][C]0.691866[/C][C]0.345933[/C][/ROW]
[ROW][C]36[/C][C]0.668882[/C][C]0.662237[/C][C]0.331118[/C][/ROW]
[ROW][C]37[/C][C]0.750873[/C][C]0.498254[/C][C]0.249127[/C][/ROW]
[ROW][C]38[/C][C]0.811891[/C][C]0.376217[/C][C]0.188109[/C][/ROW]
[ROW][C]39[/C][C]0.854854[/C][C]0.290291[/C][C]0.145146[/C][/ROW]
[ROW][C]40[/C][C]0.892431[/C][C]0.215138[/C][C]0.107569[/C][/ROW]
[ROW][C]41[/C][C]0.908607[/C][C]0.182786[/C][C]0.0913931[/C][/ROW]
[ROW][C]42[/C][C]0.921007[/C][C]0.157986[/C][C]0.078993[/C][/ROW]
[ROW][C]43[/C][C]0.931838[/C][C]0.136323[/C][C]0.0681616[/C][/ROW]
[ROW][C]44[/C][C]0.941977[/C][C]0.116045[/C][C]0.0580225[/C][/ROW]
[ROW][C]45[/C][C]0.950505[/C][C]0.0989901[/C][C]0.049495[/C][/ROW]
[ROW][C]46[/C][C]0.957108[/C][C]0.0857835[/C][C]0.0428917[/C][/ROW]
[ROW][C]47[/C][C]0.963523[/C][C]0.0729549[/C][C]0.0364774[/C][/ROW]
[ROW][C]48[/C][C]0.96836[/C][C]0.0632808[/C][C]0.0316404[/C][/ROW]
[ROW][C]49[/C][C]0.979412[/C][C]0.0411761[/C][C]0.020588[/C][/ROW]
[ROW][C]50[/C][C]0.985859[/C][C]0.0282824[/C][C]0.0141412[/C][/ROW]
[ROW][C]51[/C][C]0.990446[/C][C]0.0191082[/C][C]0.00955408[/C][/ROW]
[ROW][C]52[/C][C]0.993895[/C][C]0.0122102[/C][C]0.0061051[/C][/ROW]
[ROW][C]53[/C][C]0.99467[/C][C]0.0106594[/C][C]0.00532969[/C][/ROW]
[ROW][C]54[/C][C]0.995225[/C][C]0.00955054[/C][C]0.00477527[/C][/ROW]
[ROW][C]55[/C][C]0.995671[/C][C]0.008657[/C][C]0.0043285[/C][/ROW]
[ROW][C]56[/C][C]0.996097[/C][C]0.00780515[/C][C]0.00390258[/C][/ROW]
[ROW][C]57[/C][C]0.996437[/C][C]0.00712627[/C][C]0.00356314[/C][/ROW]
[ROW][C]58[/C][C]0.996753[/C][C]0.00649432[/C][C]0.00324716[/C][/ROW]
[ROW][C]59[/C][C]0.997081[/C][C]0.00583827[/C][C]0.00291914[/C][/ROW]
[ROW][C]60[/C][C]0.997323[/C][C]0.00535376[/C][C]0.00267688[/C][/ROW]
[ROW][C]61[/C][C]0.998038[/C][C]0.00392416[/C][C]0.00196208[/C][/ROW]
[ROW][C]62[/C][C]0.998472[/C][C]0.00305647[/C][C]0.00152824[/C][/ROW]
[ROW][C]63[/C][C]0.998929[/C][C]0.00214253[/C][C]0.00107126[/C][/ROW]
[ROW][C]64[/C][C]0.999315[/C][C]0.00136971[/C][C]0.000684853[/C][/ROW]
[ROW][C]65[/C][C]0.999291[/C][C]0.00141783[/C][C]0.000708913[/C][/ROW]
[ROW][C]66[/C][C]0.999272[/C][C]0.00145678[/C][C]0.000728391[/C][/ROW]
[ROW][C]67[/C][C]0.999234[/C][C]0.00153117[/C][C]0.000765584[/C][/ROW]
[ROW][C]68[/C][C]0.999203[/C][C]0.00159408[/C][C]0.000797039[/C][/ROW]
[ROW][C]69[/C][C]0.999161[/C][C]0.00167865[/C][C]0.000839327[/C][/ROW]
[ROW][C]70[/C][C]0.999118[/C][C]0.00176492[/C][C]0.000882462[/C][/ROW]
[ROW][C]71[/C][C]0.999132[/C][C]0.00173571[/C][C]0.000867854[/C][/ROW]
[ROW][C]72[/C][C]0.999136[/C][C]0.00172733[/C][C]0.000863667[/C][/ROW]
[ROW][C]73[/C][C]0.999271[/C][C]0.00145779[/C][C]0.000728893[/C][/ROW]
[ROW][C]74[/C][C]0.999388[/C][C]0.00122343[/C][C]0.000611716[/C][/ROW]
[ROW][C]75[/C][C]0.99958[/C][C]0.000839632[/C][C]0.000419816[/C][/ROW]
[ROW][C]76[/C][C]0.999736[/C][C]0.000528363[/C][C]0.000264182[/C][/ROW]
[ROW][C]77[/C][C]0.999636[/C][C]0.000728814[/C][C]0.000364407[/C][/ROW]
[ROW][C]78[/C][C]0.999491[/C][C]0.00101811[/C][C]0.000509053[/C][/ROW]
[ROW][C]79[/C][C]0.9993[/C][C]0.00140075[/C][C]0.000700376[/C][/ROW]
[ROW][C]80[/C][C]0.999048[/C][C]0.00190462[/C][C]0.000952308[/C][/ROW]
[ROW][C]81[/C][C]0.998689[/C][C]0.00262287[/C][C]0.00131144[/C][/ROW]
[ROW][C]82[/C][C]0.998312[/C][C]0.00337609[/C][C]0.00168805[/C][/ROW]
[ROW][C]83[/C][C]0.997779[/C][C]0.00444263[/C][C]0.00222131[/C][/ROW]
[ROW][C]84[/C][C]0.997028[/C][C]0.00594326[/C][C]0.00297163[/C][/ROW]
[ROW][C]85[/C][C]0.996415[/C][C]0.00717023[/C][C]0.00358511[/C][/ROW]
[ROW][C]86[/C][C]0.995753[/C][C]0.00849475[/C][C]0.00424737[/C][/ROW]
[ROW][C]87[/C][C]0.996501[/C][C]0.00699704[/C][C]0.00349852[/C][/ROW]
[ROW][C]88[/C][C]0.997421[/C][C]0.00515715[/C][C]0.00257858[/C][/ROW]
[ROW][C]89[/C][C]0.995142[/C][C]0.00971656[/C][C]0.00485828[/C][/ROW]
[ROW][C]90[/C][C]0.990907[/C][C]0.0181857[/C][C]0.00909283[/C][/ROW]
[ROW][C]91[/C][C]0.983163[/C][C]0.0336739[/C][C]0.016837[/C][/ROW]
[ROW][C]92[/C][C]0.969874[/C][C]0.0602511[/C][C]0.0301255[/C][/ROW]
[ROW][C]93[/C][C]0.94679[/C][C]0.10642[/C][C]0.0532101[/C][/ROW]
[ROW][C]94[/C][C]0.914916[/C][C]0.170167[/C][C]0.0850835[/C][/ROW]
[ROW][C]95[/C][C]0.861722[/C][C]0.276555[/C][C]0.138278[/C][/ROW]
[ROW][C]96[/C][C]0.785947[/C][C]0.428106[/C][C]0.214053[/C][/ROW]
[ROW][C]97[/C][C]0.674874[/C][C]0.650251[/C][C]0.325126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&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
150.5594080.8811840.440592
160.4959950.991990.504005
170.4436020.8872040.556398
180.3795540.7591090.620446
190.3289490.6578980.671051
200.298860.597720.70114
210.2804030.5608060.719597
220.2547330.5094670.745267
230.2428540.4857070.757146
240.216070.4321390.78393
250.3151710.6303410.684829
260.3817860.7635730.618214
270.433610.8672190.56639
280.4864410.9728820.513559
290.5152810.9694390.484719
300.5377140.9245720.462286
310.5609320.8781350.439068
320.5831160.8337670.416884
330.6046180.7907650.395382
340.6295650.7408690.370435
350.6540670.6918660.345933
360.6688820.6622370.331118
370.7508730.4982540.249127
380.8118910.3762170.188109
390.8548540.2902910.145146
400.8924310.2151380.107569
410.9086070.1827860.0913931
420.9210070.1579860.078993
430.9318380.1363230.0681616
440.9419770.1160450.0580225
450.9505050.09899010.049495
460.9571080.08578350.0428917
470.9635230.07295490.0364774
480.968360.06328080.0316404
490.9794120.04117610.020588
500.9858590.02828240.0141412
510.9904460.01910820.00955408
520.9938950.01221020.0061051
530.994670.01065940.00532969
540.9952250.009550540.00477527
550.9956710.0086570.0043285
560.9960970.007805150.00390258
570.9964370.007126270.00356314
580.9967530.006494320.00324716
590.9970810.005838270.00291914
600.9973230.005353760.00267688
610.9980380.003924160.00196208
620.9984720.003056470.00152824
630.9989290.002142530.00107126
640.9993150.001369710.000684853
650.9992910.001417830.000708913
660.9992720.001456780.000728391
670.9992340.001531170.000765584
680.9992030.001594080.000797039
690.9991610.001678650.000839327
700.9991180.001764920.000882462
710.9991320.001735710.000867854
720.9991360.001727330.000863667
730.9992710.001457790.000728893
740.9993880.001223430.000611716
750.999580.0008396320.000419816
760.9997360.0005283630.000264182
770.9996360.0007288140.000364407
780.9994910.001018110.000509053
790.99930.001400750.000700376
800.9990480.001904620.000952308
810.9986890.002622870.00131144
820.9983120.003376090.00168805
830.9977790.004442630.00222131
840.9970280.005943260.00297163
850.9964150.007170230.00358511
860.9957530.008494750.00424737
870.9965010.006997040.00349852
880.9974210.005157150.00257858
890.9951420.009716560.00485828
900.9909070.01818570.00909283
910.9831630.03367390.016837
920.9698740.06025110.0301255
930.946790.106420.0532101
940.9149160.1701670.0850835
950.8617220.2765550.138278
960.7859470.4281060.214053
970.6748740.6502510.325126






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level360.433735NOK
5% type I error level430.518072NOK
10% type I error level480.578313NOK

\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 & 36 & 0.433735 & NOK \tabularnewline
5% type I error level & 43 & 0.518072 & NOK \tabularnewline
10% type I error level & 48 & 0.578313 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269355&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]36[/C][C]0.433735[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.518072[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.578313[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269355&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269355&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 level360.433735NOK
5% type I error level430.518072NOK
10% type I error level480.578313NOK


Figures (Output of Computation)

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

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