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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 07:23:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356092827y57mf3xa37ganap.htm/, Retrieved Thu, 28 Mar 2024 11:45:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203545, Retrieved Thu, 28 Mar 2024 11:45:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Opdracht 2] [2012-10-25 11:32:11] [64c86865dff7d646747b84f713e71815]
- R  D  [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Opdracht 3] [2012-10-25 11:38:10] [64c86865dff7d646747b84f713e71815]
-    D    [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Opdracht 2 (...] [2012-10-25 11:51:54] [64c86865dff7d646747b84f713e71815]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Opdracht 3 (...] [2012-10-25 11:53:44] [64c86865dff7d646747b84f713e71815]
-   PD        [Paired and Unpaired Two Samples Tests about the Mean] [WS 5 Opdracht 5] [2012-10-25 12:00:47] [64c86865dff7d646747b84f713e71815]
- R             [Paired and Unpaired Two Samples Tests about the Mean] [WS5_Q5_E] [2012-10-25 13:16:37] [16b33a6b6ea04a122abfa008e94b9809]
- RM D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [WS5_Q6_korte termijn] [2012-10-25 13:42:15] [16b33a6b6ea04a122abfa008e94b9809]
- R PD              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Paper Deel 5 ANOV...] [2012-12-18 19:25:34] [16b33a6b6ea04a122abfa008e94b9809]
- R PD                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Paper Deel 5: One...] [2012-12-20 16:46:53] [fe52c9364b5a1ce87739c78bce22047a]
- RMPD                    [Multiple Regression] [Paper Deel 5: Mul...] [2012-12-21 12:23:30] [a185e86db0c606cb3c73b2699db0f6b0] [Current]
- R  D                      [Multiple Regression] [Paper Deel 5: Mul...] [2012-12-21 13:35:00] [fe52c9364b5a1ce87739c78bce22047a]
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Dataseries X:
1	1	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
1	0	0	0
0	0	0	0
0	1	0	0
0	0	0	0
1	0	0	0
1	1	0	0
0	0	0	0
0	0	1	0
1	1	0	0
0	0	1	0
0	1	1	0
1	1	1	1
1	1	0	0
0	0	0	0
0	1	1	1
1	0	0	0
1	0	1	0
0	0	0	0
1	0	0	0
0	1	1	0
0	0	1	0
1	0	0	0
0	0	1	0
0	0	0	0
0	0	0	0
0	0	0	0
1	0	0	0
1	0	0	0
0	1	0	0
0	0	0	0
0	0	0	0
1	1	1	0
0	0	1	0
0	0	0	0
0	1	0	0
0	0	1	1
0	0	1	0
1	0	0	0
1	1	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	0	0	0
0	1	1	0
1	1	1	1
0	0	0	0
0	0	1	1
0	0	0	0
0	1	1	0
0	0	1	0
0	0	0	0
0	0	0	0
1	1	1	1
1	1	0	0
0	0	1	0
0	0	0	0
1	1	0	0
0	0	0	0
0	0	0	0
0	1	1	1
1	0	0	0
0	0	0	0
0	0	1	0
0	0	0	0
0	0	0	0
0	0	1	0
1	0	1	0
0	0	0	0
0	1	0	0
0	0	0	0
0	0	1	0
0	1	1	1
0	1	0	0
0	0	0	0
1	0	1	0
0	0	0	0
0	0	1	1
0	0	0	0
1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=0

As an alternative you can also use a 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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0319181784019129 + 0.00188134144911162UseLimit[t] + 0.151605961889671T40[t] + 0.293316929440121Used[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0319181784019129 +  0.00188134144911162UseLimit[t] +  0.151605961889671T40[t] +  0.293316929440121Used[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0319181784019129 +  0.00188134144911162UseLimit[t] +  0.151605961889671T40[t] +  0.293316929440121Used[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0319181784019129 + 0.00188134144911162UseLimit[t] + 0.151605961889671T40[t] + 0.293316929440121Used[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03191817840191290.040351-0.7910.4312160.215608
UseLimit0.001881341449111620.0663390.02840.9774440.488722
T400.1516059618896710.0684982.21330.0296560.014828
Used0.2933169294401210.0623934.70111e-055e-06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.0319181784019129 & 0.040351 & -0.791 & 0.431216 & 0.215608 \tabularnewline
UseLimit & 0.00188134144911162 & 0.066339 & 0.0284 & 0.977444 & 0.488722 \tabularnewline
T40 & 0.151605961889671 & 0.068498 & 2.2133 & 0.029656 & 0.014828 \tabularnewline
Used & 0.293316929440121 & 0.062393 & 4.7011 & 1e-05 & 5e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.0319181784019129[/C][C]0.040351[/C][C]-0.791[/C][C]0.431216[/C][C]0.215608[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.00188134144911162[/C][C]0.066339[/C][C]0.0284[/C][C]0.977444[/C][C]0.488722[/C][/ROW]
[ROW][C]T40[/C][C]0.151605961889671[/C][C]0.068498[/C][C]2.2133[/C][C]0.029656[/C][C]0.014828[/C][/ROW]
[ROW][C]Used[/C][C]0.293316929440121[/C][C]0.062393[/C][C]4.7011[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03191817840191290.040351-0.7910.4312160.215608
UseLimit0.001881341449111620.0663390.02840.9774440.488722
T400.1516059618896710.0684982.21330.0296560.014828
Used0.2933169294401210.0623934.70111e-055e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.537264590863067
R-squared0.288653240595259
Adjusted R-squared0.262628359153622
F-TEST (value)11.0914334515832
F-TEST (DF numerator)3
F-TEST (DF denominator)82
p-value3.47168610426163e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.26439372536397
Sum Squared Residuals5.73213144497076

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.537264590863067 \tabularnewline
R-squared & 0.288653240595259 \tabularnewline
Adjusted R-squared & 0.262628359153622 \tabularnewline
F-TEST (value) & 11.0914334515832 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 82 \tabularnewline
p-value & 3.47168610426163e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.26439372536397 \tabularnewline
Sum Squared Residuals & 5.73213144497076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.537264590863067[/C][/ROW]
[ROW][C]R-squared[/C][C]0.288653240595259[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.262628359153622[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.0914334515832[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]82[/C][/ROW]
[ROW][C]p-value[/C][C]3.47168610426163e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.26439372536397[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.73213144497076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.537264590863067
R-squared0.288653240595259
Adjusted R-squared0.262628359153622
F-TEST (value)11.0914334515832
F-TEST (DF numerator)3
F-TEST (DF denominator)82
p-value3.47168610426163e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.26439372536397
Sum Squared Residuals5.73213144497076







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.12156912493687-0.12156912493687
20-0.0319181784019130.031918178401913
30-0.0319181784019130.031918178401913
40-0.03191817840191310.0319181784019131
50-0.03191817840191290.0319181784019129
60-0.03003683695280130.0300368369528013
70-0.03191817840191290.0319181784019129
800.119687783487758-0.119687783487758
90-0.03191817840191290.0319181784019129
100-0.03003683695280130.0300368369528013
1100.12156912493687-0.12156912493687
120-0.03191817840191290.0319181784019129
1300.261398751038208-0.261398751038208
1400.12156912493687-0.12156912493687
1500.261398751038208-0.261398751038208
1600.413004712927879-0.413004712927879
1710.4148860543769910.585113945623009
1800.12156912493687-0.12156912493687
190-0.03191817840191290.0319181784019129
2010.4130047129278790.586995287072121
210-0.03003683695280130.0300368369528013
2200.26328009248732-0.26328009248732
230-0.03191817840191290.0319181784019129
240-0.03003683695280130.0300368369528013
2500.413004712927879-0.413004712927879
2600.261398751038208-0.261398751038208
270-0.03003683695280130.0300368369528013
2800.261398751038208-0.261398751038208
290-0.03191817840191290.0319181784019129
300-0.03191817840191290.0319181784019129
310-0.03191817840191290.0319181784019129
320-0.03003683695280130.0300368369528013
330-0.03003683695280130.0300368369528013
3400.119687783487758-0.119687783487758
350-0.03191817840191290.0319181784019129
360-0.03191817840191290.0319181784019129
3700.414886054376991-0.414886054376991
3800.261398751038208-0.261398751038208
390-0.03191817840191290.0319181784019129
4000.119687783487758-0.119687783487758
4110.2613987510382090.738601248961791
4200.261398751038208-0.261398751038208
430-0.03003683695280130.0300368369528013
4400.12156912493687-0.12156912493687
450-0.03191817840191290.0319181784019129
460-0.03191817840191290.0319181784019129
470-0.03191817840191290.0319181784019129
480-0.03191817840191290.0319181784019129
490-0.03191817840191290.0319181784019129
500-0.03191817840191290.0319181784019129
5100.413004712927879-0.413004712927879
5210.4148860543769910.585113945623009
530-0.03191817840191290.0319181784019129
5410.2613987510382090.738601248961791
550-0.03191817840191290.0319181784019129
5600.413004712927879-0.413004712927879
5700.261398751038208-0.261398751038208
580-0.03191817840191290.0319181784019129
590-0.03191817840191290.0319181784019129
6010.4148860543769910.585113945623009
6100.12156912493687-0.12156912493687
6200.261398751038208-0.261398751038208
630-0.03191817840191290.0319181784019129
6400.12156912493687-0.12156912493687
650-0.03191817840191290.0319181784019129
660-0.03191817840191290.0319181784019129
6710.4130047129278790.586995287072121
680-0.03003683695280130.0300368369528013
690-0.03191817840191290.0319181784019129
7000.261398751038208-0.261398751038208
710-0.03191817840191290.0319181784019129
720-0.03191817840191290.0319181784019129
7300.261398751038208-0.261398751038208
7400.26328009248732-0.26328009248732
750-0.03191817840191290.0319181784019129
7600.119687783487758-0.119687783487758
770-0.03191817840191290.0319181784019129
7800.261398751038208-0.261398751038208
7910.4130047129278790.586995287072121
8000.119687783487758-0.119687783487758
810-0.03191817840191290.0319181784019129
8200.26328009248732-0.26328009248732
830-0.03191817840191290.0319181784019129
8410.2613987510382090.738601248961791
850-0.03191817840191290.0319181784019129
860-0.03003683695280130.0300368369528013

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
2 & 0 & -0.031918178401913 & 0.031918178401913 \tabularnewline
3 & 0 & -0.031918178401913 & 0.031918178401913 \tabularnewline
4 & 0 & -0.0319181784019131 & 0.0319181784019131 \tabularnewline
5 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
6 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
7 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
8 & 0 & 0.119687783487758 & -0.119687783487758 \tabularnewline
9 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
10 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
11 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
12 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
13 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
14 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
15 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
16 & 0 & 0.413004712927879 & -0.413004712927879 \tabularnewline
17 & 1 & 0.414886054376991 & 0.585113945623009 \tabularnewline
18 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
19 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
20 & 1 & 0.413004712927879 & 0.586995287072121 \tabularnewline
21 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
22 & 0 & 0.26328009248732 & -0.26328009248732 \tabularnewline
23 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
24 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
25 & 0 & 0.413004712927879 & -0.413004712927879 \tabularnewline
26 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
27 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
28 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
29 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
30 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
31 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
32 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
33 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
34 & 0 & 0.119687783487758 & -0.119687783487758 \tabularnewline
35 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
36 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
37 & 0 & 0.414886054376991 & -0.414886054376991 \tabularnewline
38 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
39 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
40 & 0 & 0.119687783487758 & -0.119687783487758 \tabularnewline
41 & 1 & 0.261398751038209 & 0.738601248961791 \tabularnewline
42 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
43 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
44 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
45 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
46 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
47 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
48 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
49 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
50 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
51 & 0 & 0.413004712927879 & -0.413004712927879 \tabularnewline
52 & 1 & 0.414886054376991 & 0.585113945623009 \tabularnewline
53 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
54 & 1 & 0.261398751038209 & 0.738601248961791 \tabularnewline
55 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
56 & 0 & 0.413004712927879 & -0.413004712927879 \tabularnewline
57 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
58 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
59 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
60 & 1 & 0.414886054376991 & 0.585113945623009 \tabularnewline
61 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
62 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
63 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
64 & 0 & 0.12156912493687 & -0.12156912493687 \tabularnewline
65 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
66 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
67 & 1 & 0.413004712927879 & 0.586995287072121 \tabularnewline
68 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
69 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
70 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
71 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
72 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
73 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
74 & 0 & 0.26328009248732 & -0.26328009248732 \tabularnewline
75 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
76 & 0 & 0.119687783487758 & -0.119687783487758 \tabularnewline
77 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
78 & 0 & 0.261398751038208 & -0.261398751038208 \tabularnewline
79 & 1 & 0.413004712927879 & 0.586995287072121 \tabularnewline
80 & 0 & 0.119687783487758 & -0.119687783487758 \tabularnewline
81 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
82 & 0 & 0.26328009248732 & -0.26328009248732 \tabularnewline
83 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
84 & 1 & 0.261398751038209 & 0.738601248961791 \tabularnewline
85 & 0 & -0.0319181784019129 & 0.0319181784019129 \tabularnewline
86 & 0 & -0.0300368369528013 & 0.0300368369528013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&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]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.031918178401913[/C][C]0.031918178401913[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.031918178401913[/C][C]0.031918178401913[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0319181784019131[/C][C]0.0319181784019131[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.119687783487758[/C][C]-0.119687783487758[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.413004712927879[/C][C]-0.413004712927879[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.414886054376991[/C][C]0.585113945623009[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.413004712927879[/C][C]0.586995287072121[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.26328009248732[/C][C]-0.26328009248732[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.413004712927879[/C][C]-0.413004712927879[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.119687783487758[/C][C]-0.119687783487758[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.414886054376991[/C][C]-0.414886054376991[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.119687783487758[/C][C]-0.119687783487758[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.261398751038209[/C][C]0.738601248961791[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.413004712927879[/C][C]-0.413004712927879[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.414886054376991[/C][C]0.585113945623009[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.261398751038209[/C][C]0.738601248961791[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.413004712927879[/C][C]-0.413004712927879[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.414886054376991[/C][C]0.585113945623009[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.12156912493687[/C][C]-0.12156912493687[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.413004712927879[/C][C]0.586995287072121[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.26328009248732[/C][C]-0.26328009248732[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.119687783487758[/C][C]-0.119687783487758[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.261398751038208[/C][C]-0.261398751038208[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.413004712927879[/C][C]0.586995287072121[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.119687783487758[/C][C]-0.119687783487758[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.26328009248732[/C][C]-0.26328009248732[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.261398751038209[/C][C]0.738601248961791[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.0319181784019129[/C][C]0.0319181784019129[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0300368369528013[/C][C]0.0300368369528013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.12156912493687-0.12156912493687
20-0.0319181784019130.031918178401913
30-0.0319181784019130.031918178401913
40-0.03191817840191310.0319181784019131
50-0.03191817840191290.0319181784019129
60-0.03003683695280130.0300368369528013
70-0.03191817840191290.0319181784019129
800.119687783487758-0.119687783487758
90-0.03191817840191290.0319181784019129
100-0.03003683695280130.0300368369528013
1100.12156912493687-0.12156912493687
120-0.03191817840191290.0319181784019129
1300.261398751038208-0.261398751038208
1400.12156912493687-0.12156912493687
1500.261398751038208-0.261398751038208
1600.413004712927879-0.413004712927879
1710.4148860543769910.585113945623009
1800.12156912493687-0.12156912493687
190-0.03191817840191290.0319181784019129
2010.4130047129278790.586995287072121
210-0.03003683695280130.0300368369528013
2200.26328009248732-0.26328009248732
230-0.03191817840191290.0319181784019129
240-0.03003683695280130.0300368369528013
2500.413004712927879-0.413004712927879
2600.261398751038208-0.261398751038208
270-0.03003683695280130.0300368369528013
2800.261398751038208-0.261398751038208
290-0.03191817840191290.0319181784019129
300-0.03191817840191290.0319181784019129
310-0.03191817840191290.0319181784019129
320-0.03003683695280130.0300368369528013
330-0.03003683695280130.0300368369528013
3400.119687783487758-0.119687783487758
350-0.03191817840191290.0319181784019129
360-0.03191817840191290.0319181784019129
3700.414886054376991-0.414886054376991
3800.261398751038208-0.261398751038208
390-0.03191817840191290.0319181784019129
4000.119687783487758-0.119687783487758
4110.2613987510382090.738601248961791
4200.261398751038208-0.261398751038208
430-0.03003683695280130.0300368369528013
4400.12156912493687-0.12156912493687
450-0.03191817840191290.0319181784019129
460-0.03191817840191290.0319181784019129
470-0.03191817840191290.0319181784019129
480-0.03191817840191290.0319181784019129
490-0.03191817840191290.0319181784019129
500-0.03191817840191290.0319181784019129
5100.413004712927879-0.413004712927879
5210.4148860543769910.585113945623009
530-0.03191817840191290.0319181784019129
5410.2613987510382090.738601248961791
550-0.03191817840191290.0319181784019129
5600.413004712927879-0.413004712927879
5700.261398751038208-0.261398751038208
580-0.03191817840191290.0319181784019129
590-0.03191817840191290.0319181784019129
6010.4148860543769910.585113945623009
6100.12156912493687-0.12156912493687
6200.261398751038208-0.261398751038208
630-0.03191817840191290.0319181784019129
6400.12156912493687-0.12156912493687
650-0.03191817840191290.0319181784019129
660-0.03191817840191290.0319181784019129
6710.4130047129278790.586995287072121
680-0.03003683695280130.0300368369528013
690-0.03191817840191290.0319181784019129
7000.261398751038208-0.261398751038208
710-0.03191817840191290.0319181784019129
720-0.03191817840191290.0319181784019129
7300.261398751038208-0.261398751038208
7400.26328009248732-0.26328009248732
750-0.03191817840191290.0319181784019129
7600.119687783487758-0.119687783487758
770-0.03191817840191290.0319181784019129
7800.261398751038208-0.261398751038208
7910.4130047129278790.586995287072121
8000.119687783487758-0.119687783487758
810-0.03191817840191290.0319181784019129
8200.26328009248732-0.26328009248732
830-0.03191817840191290.0319181784019129
8410.2613987510382090.738601248961791
850-0.03191817840191290.0319181784019129
860-0.03003683695280130.0300368369528013







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1540156770667910.3080313541335810.845984322933209
180.1168105739954050.2336211479908090.883189426004595
190.08140091314814680.1628018262962940.918599086851853
200.4003439820437940.8006879640875880.599656017956206
210.324735376995720.649470753991440.67526462300428
220.3332548921254960.6665097842509920.666745107874504
230.2681449728953770.5362899457907540.731855027104623
240.2111103508278050.422220701655610.788889649172195
250.2843115920402770.5686231840805530.715688407959723
260.2604030679530950.520806135906190.739596932046905
270.2053239216873260.4106478433746520.794676078312674
280.1843588974877060.3687177949754120.815641102512294
290.1423602347569340.2847204695138680.857639765243066
300.1074238974328740.2148477948657490.892576102567125
310.07920469946110310.1584093989222060.920795300538897
320.0567224958045510.1134449916091020.943277504195449
330.03972256138991390.07944512277982780.960277438610086
340.02937944296067260.05875888592134510.970620557039327
350.01993336178235310.03986672356470620.980066638217647
360.01320889538234440.02641779076468870.986791104617656
370.0206181223587040.04123624471740810.979381877641296
380.01806313709910110.03612627419820230.981936862900899
390.01194544148382850.02389088296765710.988054558516171
400.008472543309080430.01694508661816090.99152745669092
410.1559287869312630.3118575738625250.844071213068737
420.1511364732070950.3022729464141890.848863526792905
430.1167782100714720.2335564201429450.883221789928528
440.0940685666407930.1881371332815860.905931433359207
450.0699434559545830.1398869119091660.930056544045417
460.05089038817846850.1017807763569370.949109611821532
470.03621979904359560.07243959808719130.963780200956404
480.02520702010844760.05041404021689520.974792979891552
490.01714798999194870.03429597998389740.982852010008051
500.01139934054338460.02279868108676910.988600659456615
510.02293622618037090.04587245236074190.977063773819629
520.08773695999414110.1754739199882820.912263040005859
530.06470745180547360.1294149036109470.935292548194526
540.3201538040344240.6403076080688490.679846195965576
550.2638150853097350.527630170619470.736184914690265
560.4381940203652710.8763880407305430.561805979634729
570.4531010974581590.9062021949163170.546898902541841
580.3872940051864690.7745880103729370.612705994813531
590.3243208738262980.6486417476525960.675679126173702
600.5333615305217150.9332769389565710.466638469478286
610.474441924150270.9488838483005390.52555807584973
620.4976994930621960.9953989861243910.502300506937804
630.424362910184860.848725820369720.57563708981514
640.3687206864696390.7374413729392790.631279313530361
650.2998216993033280.5996433986066570.700178300696672
660.2366521318100020.4733042636200030.763347868189998
670.3620866123815070.7241732247630140.637913387618493
680.3132926325000580.6265852650001160.686707367499942
690.2434646888771260.4869293777542520.756535311122874
700.2676453340322320.5352906680644630.732354665967768
710.1998138009622520.3996276019245040.800186199037748
720.1422279715114040.2844559430228080.857772028488596
730.2038901464886880.4077802929773760.796109853511312
740.176118006838960.352236013677920.82388199316104
750.116242451792540.2324849035850790.88375754820746
760.08589356927774740.1717871385554950.914106430722253
770.04839476618541370.09678953237082740.951605233814586
780.2395161075781010.4790322151562020.760483892421899
790.2277647302516150.4555294605032310.772235269748385

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0 & 0 & 1 \tabularnewline
8 & 0 & 0 & 1 \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.154015677066791 & 0.308031354133581 & 0.845984322933209 \tabularnewline
18 & 0.116810573995405 & 0.233621147990809 & 0.883189426004595 \tabularnewline
19 & 0.0814009131481468 & 0.162801826296294 & 0.918599086851853 \tabularnewline
20 & 0.400343982043794 & 0.800687964087588 & 0.599656017956206 \tabularnewline
21 & 0.32473537699572 & 0.64947075399144 & 0.67526462300428 \tabularnewline
22 & 0.333254892125496 & 0.666509784250992 & 0.666745107874504 \tabularnewline
23 & 0.268144972895377 & 0.536289945790754 & 0.731855027104623 \tabularnewline
24 & 0.211110350827805 & 0.42222070165561 & 0.788889649172195 \tabularnewline
25 & 0.284311592040277 & 0.568623184080553 & 0.715688407959723 \tabularnewline
26 & 0.260403067953095 & 0.52080613590619 & 0.739596932046905 \tabularnewline
27 & 0.205323921687326 & 0.410647843374652 & 0.794676078312674 \tabularnewline
28 & 0.184358897487706 & 0.368717794975412 & 0.815641102512294 \tabularnewline
29 & 0.142360234756934 & 0.284720469513868 & 0.857639765243066 \tabularnewline
30 & 0.107423897432874 & 0.214847794865749 & 0.892576102567125 \tabularnewline
31 & 0.0792046994611031 & 0.158409398922206 & 0.920795300538897 \tabularnewline
32 & 0.056722495804551 & 0.113444991609102 & 0.943277504195449 \tabularnewline
33 & 0.0397225613899139 & 0.0794451227798278 & 0.960277438610086 \tabularnewline
34 & 0.0293794429606726 & 0.0587588859213451 & 0.970620557039327 \tabularnewline
35 & 0.0199333617823531 & 0.0398667235647062 & 0.980066638217647 \tabularnewline
36 & 0.0132088953823444 & 0.0264177907646887 & 0.986791104617656 \tabularnewline
37 & 0.020618122358704 & 0.0412362447174081 & 0.979381877641296 \tabularnewline
38 & 0.0180631370991011 & 0.0361262741982023 & 0.981936862900899 \tabularnewline
39 & 0.0119454414838285 & 0.0238908829676571 & 0.988054558516171 \tabularnewline
40 & 0.00847254330908043 & 0.0169450866181609 & 0.99152745669092 \tabularnewline
41 & 0.155928786931263 & 0.311857573862525 & 0.844071213068737 \tabularnewline
42 & 0.151136473207095 & 0.302272946414189 & 0.848863526792905 \tabularnewline
43 & 0.116778210071472 & 0.233556420142945 & 0.883221789928528 \tabularnewline
44 & 0.094068566640793 & 0.188137133281586 & 0.905931433359207 \tabularnewline
45 & 0.069943455954583 & 0.139886911909166 & 0.930056544045417 \tabularnewline
46 & 0.0508903881784685 & 0.101780776356937 & 0.949109611821532 \tabularnewline
47 & 0.0362197990435956 & 0.0724395980871913 & 0.963780200956404 \tabularnewline
48 & 0.0252070201084476 & 0.0504140402168952 & 0.974792979891552 \tabularnewline
49 & 0.0171479899919487 & 0.0342959799838974 & 0.982852010008051 \tabularnewline
50 & 0.0113993405433846 & 0.0227986810867691 & 0.988600659456615 \tabularnewline
51 & 0.0229362261803709 & 0.0458724523607419 & 0.977063773819629 \tabularnewline
52 & 0.0877369599941411 & 0.175473919988282 & 0.912263040005859 \tabularnewline
53 & 0.0647074518054736 & 0.129414903610947 & 0.935292548194526 \tabularnewline
54 & 0.320153804034424 & 0.640307608068849 & 0.679846195965576 \tabularnewline
55 & 0.263815085309735 & 0.52763017061947 & 0.736184914690265 \tabularnewline
56 & 0.438194020365271 & 0.876388040730543 & 0.561805979634729 \tabularnewline
57 & 0.453101097458159 & 0.906202194916317 & 0.546898902541841 \tabularnewline
58 & 0.387294005186469 & 0.774588010372937 & 0.612705994813531 \tabularnewline
59 & 0.324320873826298 & 0.648641747652596 & 0.675679126173702 \tabularnewline
60 & 0.533361530521715 & 0.933276938956571 & 0.466638469478286 \tabularnewline
61 & 0.47444192415027 & 0.948883848300539 & 0.52555807584973 \tabularnewline
62 & 0.497699493062196 & 0.995398986124391 & 0.502300506937804 \tabularnewline
63 & 0.42436291018486 & 0.84872582036972 & 0.57563708981514 \tabularnewline
64 & 0.368720686469639 & 0.737441372939279 & 0.631279313530361 \tabularnewline
65 & 0.299821699303328 & 0.599643398606657 & 0.700178300696672 \tabularnewline
66 & 0.236652131810002 & 0.473304263620003 & 0.763347868189998 \tabularnewline
67 & 0.362086612381507 & 0.724173224763014 & 0.637913387618493 \tabularnewline
68 & 0.313292632500058 & 0.626585265000116 & 0.686707367499942 \tabularnewline
69 & 0.243464688877126 & 0.486929377754252 & 0.756535311122874 \tabularnewline
70 & 0.267645334032232 & 0.535290668064463 & 0.732354665967768 \tabularnewline
71 & 0.199813800962252 & 0.399627601924504 & 0.800186199037748 \tabularnewline
72 & 0.142227971511404 & 0.284455943022808 & 0.857772028488596 \tabularnewline
73 & 0.203890146488688 & 0.407780292977376 & 0.796109853511312 \tabularnewline
74 & 0.17611800683896 & 0.35223601367792 & 0.82388199316104 \tabularnewline
75 & 0.11624245179254 & 0.232484903585079 & 0.88375754820746 \tabularnewline
76 & 0.0858935692777474 & 0.171787138555495 & 0.914106430722253 \tabularnewline
77 & 0.0483947661854137 & 0.0967895323708274 & 0.951605233814586 \tabularnewline
78 & 0.239516107578101 & 0.479032215156202 & 0.760483892421899 \tabularnewline
79 & 0.227764730251615 & 0.455529460503231 & 0.772235269748385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&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]7[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.154015677066791[/C][C]0.308031354133581[/C][C]0.845984322933209[/C][/ROW]
[ROW][C]18[/C][C]0.116810573995405[/C][C]0.233621147990809[/C][C]0.883189426004595[/C][/ROW]
[ROW][C]19[/C][C]0.0814009131481468[/C][C]0.162801826296294[/C][C]0.918599086851853[/C][/ROW]
[ROW][C]20[/C][C]0.400343982043794[/C][C]0.800687964087588[/C][C]0.599656017956206[/C][/ROW]
[ROW][C]21[/C][C]0.32473537699572[/C][C]0.64947075399144[/C][C]0.67526462300428[/C][/ROW]
[ROW][C]22[/C][C]0.333254892125496[/C][C]0.666509784250992[/C][C]0.666745107874504[/C][/ROW]
[ROW][C]23[/C][C]0.268144972895377[/C][C]0.536289945790754[/C][C]0.731855027104623[/C][/ROW]
[ROW][C]24[/C][C]0.211110350827805[/C][C]0.42222070165561[/C][C]0.788889649172195[/C][/ROW]
[ROW][C]25[/C][C]0.284311592040277[/C][C]0.568623184080553[/C][C]0.715688407959723[/C][/ROW]
[ROW][C]26[/C][C]0.260403067953095[/C][C]0.52080613590619[/C][C]0.739596932046905[/C][/ROW]
[ROW][C]27[/C][C]0.205323921687326[/C][C]0.410647843374652[/C][C]0.794676078312674[/C][/ROW]
[ROW][C]28[/C][C]0.184358897487706[/C][C]0.368717794975412[/C][C]0.815641102512294[/C][/ROW]
[ROW][C]29[/C][C]0.142360234756934[/C][C]0.284720469513868[/C][C]0.857639765243066[/C][/ROW]
[ROW][C]30[/C][C]0.107423897432874[/C][C]0.214847794865749[/C][C]0.892576102567125[/C][/ROW]
[ROW][C]31[/C][C]0.0792046994611031[/C][C]0.158409398922206[/C][C]0.920795300538897[/C][/ROW]
[ROW][C]32[/C][C]0.056722495804551[/C][C]0.113444991609102[/C][C]0.943277504195449[/C][/ROW]
[ROW][C]33[/C][C]0.0397225613899139[/C][C]0.0794451227798278[/C][C]0.960277438610086[/C][/ROW]
[ROW][C]34[/C][C]0.0293794429606726[/C][C]0.0587588859213451[/C][C]0.970620557039327[/C][/ROW]
[ROW][C]35[/C][C]0.0199333617823531[/C][C]0.0398667235647062[/C][C]0.980066638217647[/C][/ROW]
[ROW][C]36[/C][C]0.0132088953823444[/C][C]0.0264177907646887[/C][C]0.986791104617656[/C][/ROW]
[ROW][C]37[/C][C]0.020618122358704[/C][C]0.0412362447174081[/C][C]0.979381877641296[/C][/ROW]
[ROW][C]38[/C][C]0.0180631370991011[/C][C]0.0361262741982023[/C][C]0.981936862900899[/C][/ROW]
[ROW][C]39[/C][C]0.0119454414838285[/C][C]0.0238908829676571[/C][C]0.988054558516171[/C][/ROW]
[ROW][C]40[/C][C]0.00847254330908043[/C][C]0.0169450866181609[/C][C]0.99152745669092[/C][/ROW]
[ROW][C]41[/C][C]0.155928786931263[/C][C]0.311857573862525[/C][C]0.844071213068737[/C][/ROW]
[ROW][C]42[/C][C]0.151136473207095[/C][C]0.302272946414189[/C][C]0.848863526792905[/C][/ROW]
[ROW][C]43[/C][C]0.116778210071472[/C][C]0.233556420142945[/C][C]0.883221789928528[/C][/ROW]
[ROW][C]44[/C][C]0.094068566640793[/C][C]0.188137133281586[/C][C]0.905931433359207[/C][/ROW]
[ROW][C]45[/C][C]0.069943455954583[/C][C]0.139886911909166[/C][C]0.930056544045417[/C][/ROW]
[ROW][C]46[/C][C]0.0508903881784685[/C][C]0.101780776356937[/C][C]0.949109611821532[/C][/ROW]
[ROW][C]47[/C][C]0.0362197990435956[/C][C]0.0724395980871913[/C][C]0.963780200956404[/C][/ROW]
[ROW][C]48[/C][C]0.0252070201084476[/C][C]0.0504140402168952[/C][C]0.974792979891552[/C][/ROW]
[ROW][C]49[/C][C]0.0171479899919487[/C][C]0.0342959799838974[/C][C]0.982852010008051[/C][/ROW]
[ROW][C]50[/C][C]0.0113993405433846[/C][C]0.0227986810867691[/C][C]0.988600659456615[/C][/ROW]
[ROW][C]51[/C][C]0.0229362261803709[/C][C]0.0458724523607419[/C][C]0.977063773819629[/C][/ROW]
[ROW][C]52[/C][C]0.0877369599941411[/C][C]0.175473919988282[/C][C]0.912263040005859[/C][/ROW]
[ROW][C]53[/C][C]0.0647074518054736[/C][C]0.129414903610947[/C][C]0.935292548194526[/C][/ROW]
[ROW][C]54[/C][C]0.320153804034424[/C][C]0.640307608068849[/C][C]0.679846195965576[/C][/ROW]
[ROW][C]55[/C][C]0.263815085309735[/C][C]0.52763017061947[/C][C]0.736184914690265[/C][/ROW]
[ROW][C]56[/C][C]0.438194020365271[/C][C]0.876388040730543[/C][C]0.561805979634729[/C][/ROW]
[ROW][C]57[/C][C]0.453101097458159[/C][C]0.906202194916317[/C][C]0.546898902541841[/C][/ROW]
[ROW][C]58[/C][C]0.387294005186469[/C][C]0.774588010372937[/C][C]0.612705994813531[/C][/ROW]
[ROW][C]59[/C][C]0.324320873826298[/C][C]0.648641747652596[/C][C]0.675679126173702[/C][/ROW]
[ROW][C]60[/C][C]0.533361530521715[/C][C]0.933276938956571[/C][C]0.466638469478286[/C][/ROW]
[ROW][C]61[/C][C]0.47444192415027[/C][C]0.948883848300539[/C][C]0.52555807584973[/C][/ROW]
[ROW][C]62[/C][C]0.497699493062196[/C][C]0.995398986124391[/C][C]0.502300506937804[/C][/ROW]
[ROW][C]63[/C][C]0.42436291018486[/C][C]0.84872582036972[/C][C]0.57563708981514[/C][/ROW]
[ROW][C]64[/C][C]0.368720686469639[/C][C]0.737441372939279[/C][C]0.631279313530361[/C][/ROW]
[ROW][C]65[/C][C]0.299821699303328[/C][C]0.599643398606657[/C][C]0.700178300696672[/C][/ROW]
[ROW][C]66[/C][C]0.236652131810002[/C][C]0.473304263620003[/C][C]0.763347868189998[/C][/ROW]
[ROW][C]67[/C][C]0.362086612381507[/C][C]0.724173224763014[/C][C]0.637913387618493[/C][/ROW]
[ROW][C]68[/C][C]0.313292632500058[/C][C]0.626585265000116[/C][C]0.686707367499942[/C][/ROW]
[ROW][C]69[/C][C]0.243464688877126[/C][C]0.486929377754252[/C][C]0.756535311122874[/C][/ROW]
[ROW][C]70[/C][C]0.267645334032232[/C][C]0.535290668064463[/C][C]0.732354665967768[/C][/ROW]
[ROW][C]71[/C][C]0.199813800962252[/C][C]0.399627601924504[/C][C]0.800186199037748[/C][/ROW]
[ROW][C]72[/C][C]0.142227971511404[/C][C]0.284455943022808[/C][C]0.857772028488596[/C][/ROW]
[ROW][C]73[/C][C]0.203890146488688[/C][C]0.407780292977376[/C][C]0.796109853511312[/C][/ROW]
[ROW][C]74[/C][C]0.17611800683896[/C][C]0.35223601367792[/C][C]0.82388199316104[/C][/ROW]
[ROW][C]75[/C][C]0.11624245179254[/C][C]0.232484903585079[/C][C]0.88375754820746[/C][/ROW]
[ROW][C]76[/C][C]0.0858935692777474[/C][C]0.171787138555495[/C][C]0.914106430722253[/C][/ROW]
[ROW][C]77[/C][C]0.0483947661854137[/C][C]0.0967895323708274[/C][C]0.951605233814586[/C][/ROW]
[ROW][C]78[/C][C]0.239516107578101[/C][C]0.479032215156202[/C][C]0.760483892421899[/C][/ROW]
[ROW][C]79[/C][C]0.227764730251615[/C][C]0.455529460503231[/C][C]0.772235269748385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7001
8001
9001
10001
11001
12001
13001
14001
15001
16001
170.1540156770667910.3080313541335810.845984322933209
180.1168105739954050.2336211479908090.883189426004595
190.08140091314814680.1628018262962940.918599086851853
200.4003439820437940.8006879640875880.599656017956206
210.324735376995720.649470753991440.67526462300428
220.3332548921254960.6665097842509920.666745107874504
230.2681449728953770.5362899457907540.731855027104623
240.2111103508278050.422220701655610.788889649172195
250.2843115920402770.5686231840805530.715688407959723
260.2604030679530950.520806135906190.739596932046905
270.2053239216873260.4106478433746520.794676078312674
280.1843588974877060.3687177949754120.815641102512294
290.1423602347569340.2847204695138680.857639765243066
300.1074238974328740.2148477948657490.892576102567125
310.07920469946110310.1584093989222060.920795300538897
320.0567224958045510.1134449916091020.943277504195449
330.03972256138991390.07944512277982780.960277438610086
340.02937944296067260.05875888592134510.970620557039327
350.01993336178235310.03986672356470620.980066638217647
360.01320889538234440.02641779076468870.986791104617656
370.0206181223587040.04123624471740810.979381877641296
380.01806313709910110.03612627419820230.981936862900899
390.01194544148382850.02389088296765710.988054558516171
400.008472543309080430.01694508661816090.99152745669092
410.1559287869312630.3118575738625250.844071213068737
420.1511364732070950.3022729464141890.848863526792905
430.1167782100714720.2335564201429450.883221789928528
440.0940685666407930.1881371332815860.905931433359207
450.0699434559545830.1398869119091660.930056544045417
460.05089038817846850.1017807763569370.949109611821532
470.03621979904359560.07243959808719130.963780200956404
480.02520702010844760.05041404021689520.974792979891552
490.01714798999194870.03429597998389740.982852010008051
500.01139934054338460.02279868108676910.988600659456615
510.02293622618037090.04587245236074190.977063773819629
520.08773695999414110.1754739199882820.912263040005859
530.06470745180547360.1294149036109470.935292548194526
540.3201538040344240.6403076080688490.679846195965576
550.2638150853097350.527630170619470.736184914690265
560.4381940203652710.8763880407305430.561805979634729
570.4531010974581590.9062021949163170.546898902541841
580.3872940051864690.7745880103729370.612705994813531
590.3243208738262980.6486417476525960.675679126173702
600.5333615305217150.9332769389565710.466638469478286
610.474441924150270.9488838483005390.52555807584973
620.4976994930621960.9953989861243910.502300506937804
630.424362910184860.848725820369720.57563708981514
640.3687206864696390.7374413729392790.631279313530361
650.2998216993033280.5996433986066570.700178300696672
660.2366521318100020.4733042636200030.763347868189998
670.3620866123815070.7241732247630140.637913387618493
680.3132926325000580.6265852650001160.686707367499942
690.2434646888771260.4869293777542520.756535311122874
700.2676453340322320.5352906680644630.732354665967768
710.1998138009622520.3996276019245040.800186199037748
720.1422279715114040.2844559430228080.857772028488596
730.2038901464886880.4077802929773760.796109853511312
740.176118006838960.352236013677920.82388199316104
750.116242451792540.2324849035850790.88375754820746
760.08589356927774740.1717871385554950.914106430722253
770.04839476618541370.09678953237082740.951605233814586
780.2395161075781010.4790322151562020.760483892421899
790.2277647302516150.4555294605032310.772235269748385







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.136986301369863NOK
5% type I error level190.26027397260274NOK
10% type I error level240.328767123287671NOK

\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 & 10 & 0.136986301369863 & NOK \tabularnewline
5% type I error level & 19 & 0.26027397260274 & NOK \tabularnewline
10% type I error level & 24 & 0.328767123287671 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203545&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]10[/C][C]0.136986301369863[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.26027397260274[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.328767123287671[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203545&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203545&T=6

As an alternative you can also use a 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 level100.136986301369863NOK
5% type I error level190.26027397260274NOK
10% type I error level240.328767123287671NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}