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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 19 Nov 2012 08:14:44 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/19/t1353330934z38n3u687jsnxvu.htm/, Retrieved Sun, 28 Apr 2024 10:04:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190498, Retrieved Sun, 28 Apr 2024 10:04:51 +0000

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7] [2012-11-19 13:14:44] [0d750c380655c9fc6c0776885d6cbda7] [Current]

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1.34  1.98  1.97  2.62  5.05  8.02 
1.34  1.97  1.98  2.62  5.04  7.98 
1.34  1.98  1.98  2.61  5.02  7.98 
1.34  1.98  1.98  2.61  5.03  7.97 
1.34  1.98  1.98  2.60  5.01  7.96 
1.33  1.97  1.98  2.59  5.00  7.95 
1.33  1.97  1.98  2.59  5.00  7.94 
1.33  1.97  1.97  2.59  5.00  7.91 
1.33  1.97  1.97  2.58  5.00  7.90 
1.33  1.96  1.97  2.58  4.97  7.90 
1.33  1.96  1.97  2.58  4.97  7.88 
1.33  1.96  1.97  2.57  4.96  7.88 
1.32  1.95  1.97  2.56  4.93  7.86 
1.32  1.95  1.96  2.57  4.93  7.86 
1.32  1.95  1.96  2.56  4.92  7.86 
1.32  1.95  1.96  2.56  4.92  7.84 
1.32  1.94  1.96  2.57  4.92  7.79 
1.31  1.93  1.96  2.55  4.91  7.62 
1.30  1.93  1.95  2.53  4.88  7.60 
1.27  1.90  1.92  2.50  4.83  7.55 
1.27  1.90  1.93  2.49  4.82  7.53 
1.27  1.90  1.92  2.48  4.81  7.50 
1.26  1.88  1.90  2.46  4.77  7.40 
1.26  1.88  1.90  2.44  4.74  7.35 
1.25  1.87  1.89  2.43  4.77  7.31 
1.25  1.88  1.89  2.43  4.75  7.35 
1.25  1.87  1.89  2.44  4.76  7.38 
1.25  1.88  1.89  2.43  4.76  7.37 
1.25  1.87  1.89  2.43  4.75  7.37 
1.25  1.87  1.89  2.44  4.73  7.32 
1.25  1.87  1.89  2.43  4.74  7.24 
1.25  1.87  1.89  2.43  4.74  7.21 
1.25  1.87  1.89  2.43  4.74  7.21 
1.25  1.87  1.89  2.43  4.72  7.19 
1.24  1.87  1.89  2.43  4.71  7.14 
1.25  1.87  1.89  2.42  4.70  7.13 
1.25  1.87  1.89  2.43  4.71  7.12 
1.24  1.87  1.89  2.44  4.72  7.08 
1.24  1.87  1.89  2.44  4.70  7.04 
1.24  1.87  1.89  2.44  4.70  7.04 
1.24  1.87  1.89  2.44  4.70  7.03 
1.24  1.87  1.89  2.44  4.68  7.03 
1.25  1.87  1.89  2.43  4.68  6.99 
1.26  1.88  1.89  2.44  4.67  7.00 
1.26  1.88  1.90  2.44  4.67  6.97 
1.26  1.87  1.89  2.43  4.67  6.91 
1.26  1.87  1.89  2.42  4.62  6.83 
1.26  1.87  1.89  2.42  4.62  6.80 
1.26  1.87  1.88  2.41  4.61  6.79 
1.26  1.87  1.88  2.41  4.61  6.77

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Broodje[t] = -7.42691561583133 + 1.04811765014689Speciaal400[t] -2.61442964511642Speciaal800[t] + 3.2573469180311Bruin800[t] -2.70183108968819Meergranen800[t] + 3.94236892588218Kramiek[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Broodje[t] =  -7.42691561583133 +  1.04811765014689Speciaal400[t] -2.61442964511642Speciaal800[t] +  3.2573469180311Bruin800[t] -2.70183108968819Meergranen800[t] +  3.94236892588218Kramiek[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Broodje[t] =  -7.42691561583133 +  1.04811765014689Speciaal400[t] -2.61442964511642Speciaal800[t] +  3.2573469180311Bruin800[t] -2.70183108968819Meergranen800[t] +  3.94236892588218Kramiek[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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
Broodje[t] = -7.42691561583133 + 1.04811765014689Speciaal400[t] -2.61442964511642Speciaal800[t] + 3.2573469180311Bruin800[t] -2.70183108968819Meergranen800[t] + 3.94236892588218Kramiek[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-7.42691561583133	1.460147	-5.0864	7e-06	4e-06
Speciaal400	1.04811765014689	1.811427	0.5786	0.5658	0.2829
Speciaal800	-2.61442964511642	2.218679	-1.1784	0.244982	0.122491
Bruin800	3.2573469180311	1.983677	1.6421	0.107704	0.053852
Meergranen800	-2.70183108968819	1.201291	-2.2491	0.029561	0.014781
Kramiek	3.94236892588218	0.334449	11.7876	0	0

\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) & -7.42691561583133 & 1.460147 & -5.0864 & 7e-06 & 4e-06 \tabularnewline
Speciaal400 & 1.04811765014689 & 1.811427 & 0.5786 & 0.5658 & 0.2829 \tabularnewline
Speciaal800 & -2.61442964511642 & 2.218679 & -1.1784 & 0.244982 & 0.122491 \tabularnewline
Bruin800 & 3.2573469180311 & 1.983677 & 1.6421 & 0.107704 & 0.053852 \tabularnewline
Meergranen800 & -2.70183108968819 & 1.201291 & -2.2491 & 0.029561 & 0.014781 \tabularnewline
Kramiek & 3.94236892588218 & 0.334449 & 11.7876 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&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]-7.42691561583133[/C][C]1.460147[/C][C]-5.0864[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Speciaal400[/C][C]1.04811765014689[/C][C]1.811427[/C][C]0.5786[/C][C]0.5658[/C][C]0.2829[/C][/ROW]
[ROW][C]Speciaal800[/C][C]-2.61442964511642[/C][C]2.218679[/C][C]-1.1784[/C][C]0.244982[/C][C]0.122491[/C][/ROW]
[ROW][C]Bruin800[/C][C]3.2573469180311[/C][C]1.983677[/C][C]1.6421[/C][C]0.107704[/C][C]0.053852[/C][/ROW]
[ROW][C]Meergranen800[/C][C]-2.70183108968819[/C][C]1.201291[/C][C]-2.2491[/C][C]0.029561[/C][C]0.014781[/C][/ROW]
[ROW][C]Kramiek[/C][C]3.94236892588218[/C][C]0.334449[/C][C]11.7876[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-7.42691561583133	1.460147	-5.0864	7e-06	4e-06
Speciaal400	1.04811765014689	1.811427	0.5786	0.5658	0.2829
Speciaal800	-2.61442964511642	2.218679	-1.1784	0.244982	0.122491
Bruin800	3.2573469180311	1.983677	1.6421	0.107704	0.053852
Meergranen800	-2.70183108968819	1.201291	-2.2491	0.029561	0.014781
Kramiek	3.94236892588218	0.334449	11.7876	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.987734791703141
R-squared	0.975620018740847
Adjusted R-squared	0.972849566325034
F-TEST (value)	352.151877134699
F-TEST (DF numerator)	5
F-TEST (DF denominator)	44
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0657388851358332
Sum Squared Residuals	0.1901504448317

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.987734791703141 \tabularnewline
R-squared & 0.975620018740847 \tabularnewline
Adjusted R-squared & 0.972849566325034 \tabularnewline
F-TEST (value) & 352.151877134699 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0657388851358332 \tabularnewline
Sum Squared Residuals & 0.1901504448317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.987734791703141[/C][/ROW]
[ROW][C]R-squared[/C][C]0.975620018740847[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.972849566325034[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]352.151877134699[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0657388851358332[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.1901504448317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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 R	0.987734791703141
R-squared	0.975620018740847
Adjusted R-squared	0.972849566325034
F-TEST (value)	352.151877134699
F-TEST (DF numerator)	5
F-TEST (DF denominator)	44
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0657388851358332
Sum Squared Residuals	0.1901504448317

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	8.02	8.04813038727822	-0.0281303872782162
2	7.98	8.06742446365087	-0.0874244636508691
3	7.98	7.98945109957894	-0.00945109957894154
4	7.97	8.02887478883777	-0.0588747888377666
5	7.96	7.977045721217	-0.0170457212170024
6	7.95	7.98030346280476	-0.0303034628047589
7	7.94	7.98030346280476	-0.0403034628047588
8	7.91	7.94772999362445	-0.0377299936244479
9	7.9	7.97474830452133	-0.074748304521329
10	7.9	7.88262153319603	0.0173784668039731
11	7.88	7.88262153319603	-0.00262153319602736
12	7.88	7.87021615483409	0.00978384516591103
13	7.86	7.7946265179042	0.0653734820958012
14	7.86	7.73503473782701	0.124965262172994
15	7.86	7.72262935946507	0.137370640534933
16	7.84	7.72262935946507	0.117370640534933
17	7.79	7.72175534501935	0.06824465498065
18	7.62	7.75203139750399	-0.132031397503988
19	7.6	7.64474230583951	-0.044742305839506
20	7.55	7.4779477445442	0.0720522554558047
21	7.53	7.49811583536257	0.0318841646374337
22	7.5	7.45313698782031	0.0468630121796865
23	7.4	7.32613933061903	0.073860669380973
24	7.35	7.26190488463633	0.0880951153636712
25	7.31	7.39028391407906	-0.0802839140790565
26	7.35	7.28529223911025	0.0647077608897491
27	7.38	7.32384191392335	0.056158086076646
28	7.37	7.32471592836907	0.0452840716309286
29	7.37	7.31143653556141	0.0585634644385858
30	7.32	7.20557084614689	0.114429153853109
31	7.24	7.27201284630259	-0.0320128463025931
32	7.21	7.27201284630259	-0.0620128463025933
33	7.21	7.27201284630259	-0.0620128463025933
34	7.19	7.19316546778495	-0.00316546778494753
35	7.14	7.14326060202466	-0.00326060202465832
36	7.13	7.14133640016419	-0.0113364001641886
37	7.12	7.15374177852613	-0.0337417785261268
38	7.08	7.1556659803866	-0.0756659803865976
39	7.04	7.07681860186896	-0.0368186018689557
40	7.04	7.07681860186896	-0.0368186018689557
41	7.03	7.07681860186896	-0.0468186018689555
42	7.03	6.99797122335131	0.0320287766486899
43	6.99	7.03547071074966	-0.0454707107496604
44	7	6.95336559064426	0.0466344093557363
45	6.97	6.98593905982457	-0.015939059824575
46	6.91	7.00652819799231	-0.0965281979923085
47	6.83	6.83642806259508	-0.00642806259508285
48	6.8	6.83642806259508	-0.0364280625950831
49	6.79	6.79144921505283	-0.00144921505283227
50	6.77	6.79144921505283	-0.0214492150528327

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.02 & 8.04813038727822 & -0.0281303872782162 \tabularnewline
2 & 7.98 & 8.06742446365087 & -0.0874244636508691 \tabularnewline
3 & 7.98 & 7.98945109957894 & -0.00945109957894154 \tabularnewline
4 & 7.97 & 8.02887478883777 & -0.0588747888377666 \tabularnewline
5 & 7.96 & 7.977045721217 & -0.0170457212170024 \tabularnewline
6 & 7.95 & 7.98030346280476 & -0.0303034628047589 \tabularnewline
7 & 7.94 & 7.98030346280476 & -0.0403034628047588 \tabularnewline
8 & 7.91 & 7.94772999362445 & -0.0377299936244479 \tabularnewline
9 & 7.9 & 7.97474830452133 & -0.074748304521329 \tabularnewline
10 & 7.9 & 7.88262153319603 & 0.0173784668039731 \tabularnewline
11 & 7.88 & 7.88262153319603 & -0.00262153319602736 \tabularnewline
12 & 7.88 & 7.87021615483409 & 0.00978384516591103 \tabularnewline
13 & 7.86 & 7.7946265179042 & 0.0653734820958012 \tabularnewline
14 & 7.86 & 7.73503473782701 & 0.124965262172994 \tabularnewline
15 & 7.86 & 7.72262935946507 & 0.137370640534933 \tabularnewline
16 & 7.84 & 7.72262935946507 & 0.117370640534933 \tabularnewline
17 & 7.79 & 7.72175534501935 & 0.06824465498065 \tabularnewline
18 & 7.62 & 7.75203139750399 & -0.132031397503988 \tabularnewline
19 & 7.6 & 7.64474230583951 & -0.044742305839506 \tabularnewline
20 & 7.55 & 7.4779477445442 & 0.0720522554558047 \tabularnewline
21 & 7.53 & 7.49811583536257 & 0.0318841646374337 \tabularnewline
22 & 7.5 & 7.45313698782031 & 0.0468630121796865 \tabularnewline
23 & 7.4 & 7.32613933061903 & 0.073860669380973 \tabularnewline
24 & 7.35 & 7.26190488463633 & 0.0880951153636712 \tabularnewline
25 & 7.31 & 7.39028391407906 & -0.0802839140790565 \tabularnewline
26 & 7.35 & 7.28529223911025 & 0.0647077608897491 \tabularnewline
27 & 7.38 & 7.32384191392335 & 0.056158086076646 \tabularnewline
28 & 7.37 & 7.32471592836907 & 0.0452840716309286 \tabularnewline
29 & 7.37 & 7.31143653556141 & 0.0585634644385858 \tabularnewline
30 & 7.32 & 7.20557084614689 & 0.114429153853109 \tabularnewline
31 & 7.24 & 7.27201284630259 & -0.0320128463025931 \tabularnewline
32 & 7.21 & 7.27201284630259 & -0.0620128463025933 \tabularnewline
33 & 7.21 & 7.27201284630259 & -0.0620128463025933 \tabularnewline
34 & 7.19 & 7.19316546778495 & -0.00316546778494753 \tabularnewline
35 & 7.14 & 7.14326060202466 & -0.00326060202465832 \tabularnewline
36 & 7.13 & 7.14133640016419 & -0.0113364001641886 \tabularnewline
37 & 7.12 & 7.15374177852613 & -0.0337417785261268 \tabularnewline
38 & 7.08 & 7.1556659803866 & -0.0756659803865976 \tabularnewline
39 & 7.04 & 7.07681860186896 & -0.0368186018689557 \tabularnewline
40 & 7.04 & 7.07681860186896 & -0.0368186018689557 \tabularnewline
41 & 7.03 & 7.07681860186896 & -0.0468186018689555 \tabularnewline
42 & 7.03 & 6.99797122335131 & 0.0320287766486899 \tabularnewline
43 & 6.99 & 7.03547071074966 & -0.0454707107496604 \tabularnewline
44 & 7 & 6.95336559064426 & 0.0466344093557363 \tabularnewline
45 & 6.97 & 6.98593905982457 & -0.015939059824575 \tabularnewline
46 & 6.91 & 7.00652819799231 & -0.0965281979923085 \tabularnewline
47 & 6.83 & 6.83642806259508 & -0.00642806259508285 \tabularnewline
48 & 6.8 & 6.83642806259508 & -0.0364280625950831 \tabularnewline
49 & 6.79 & 6.79144921505283 & -0.00144921505283227 \tabularnewline
50 & 6.77 & 6.79144921505283 & -0.0214492150528327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&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]8.02[/C][C]8.04813038727822[/C][C]-0.0281303872782162[/C][/ROW]
[ROW][C]2[/C][C]7.98[/C][C]8.06742446365087[/C][C]-0.0874244636508691[/C][/ROW]
[ROW][C]3[/C][C]7.98[/C][C]7.98945109957894[/C][C]-0.00945109957894154[/C][/ROW]
[ROW][C]4[/C][C]7.97[/C][C]8.02887478883777[/C][C]-0.0588747888377666[/C][/ROW]
[ROW][C]5[/C][C]7.96[/C][C]7.977045721217[/C][C]-0.0170457212170024[/C][/ROW]
[ROW][C]6[/C][C]7.95[/C][C]7.98030346280476[/C][C]-0.0303034628047589[/C][/ROW]
[ROW][C]7[/C][C]7.94[/C][C]7.98030346280476[/C][C]-0.0403034628047588[/C][/ROW]
[ROW][C]8[/C][C]7.91[/C][C]7.94772999362445[/C][C]-0.0377299936244479[/C][/ROW]
[ROW][C]9[/C][C]7.9[/C][C]7.97474830452133[/C][C]-0.074748304521329[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]7.88262153319603[/C][C]0.0173784668039731[/C][/ROW]
[ROW][C]11[/C][C]7.88[/C][C]7.88262153319603[/C][C]-0.00262153319602736[/C][/ROW]
[ROW][C]12[/C][C]7.88[/C][C]7.87021615483409[/C][C]0.00978384516591103[/C][/ROW]
[ROW][C]13[/C][C]7.86[/C][C]7.7946265179042[/C][C]0.0653734820958012[/C][/ROW]
[ROW][C]14[/C][C]7.86[/C][C]7.73503473782701[/C][C]0.124965262172994[/C][/ROW]
[ROW][C]15[/C][C]7.86[/C][C]7.72262935946507[/C][C]0.137370640534933[/C][/ROW]
[ROW][C]16[/C][C]7.84[/C][C]7.72262935946507[/C][C]0.117370640534933[/C][/ROW]
[ROW][C]17[/C][C]7.79[/C][C]7.72175534501935[/C][C]0.06824465498065[/C][/ROW]
[ROW][C]18[/C][C]7.62[/C][C]7.75203139750399[/C][C]-0.132031397503988[/C][/ROW]
[ROW][C]19[/C][C]7.6[/C][C]7.64474230583951[/C][C]-0.044742305839506[/C][/ROW]
[ROW][C]20[/C][C]7.55[/C][C]7.4779477445442[/C][C]0.0720522554558047[/C][/ROW]
[ROW][C]21[/C][C]7.53[/C][C]7.49811583536257[/C][C]0.0318841646374337[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]7.45313698782031[/C][C]0.0468630121796865[/C][/ROW]
[ROW][C]23[/C][C]7.4[/C][C]7.32613933061903[/C][C]0.073860669380973[/C][/ROW]
[ROW][C]24[/C][C]7.35[/C][C]7.26190488463633[/C][C]0.0880951153636712[/C][/ROW]
[ROW][C]25[/C][C]7.31[/C][C]7.39028391407906[/C][C]-0.0802839140790565[/C][/ROW]
[ROW][C]26[/C][C]7.35[/C][C]7.28529223911025[/C][C]0.0647077608897491[/C][/ROW]
[ROW][C]27[/C][C]7.38[/C][C]7.32384191392335[/C][C]0.056158086076646[/C][/ROW]
[ROW][C]28[/C][C]7.37[/C][C]7.32471592836907[/C][C]0.0452840716309286[/C][/ROW]
[ROW][C]29[/C][C]7.37[/C][C]7.31143653556141[/C][C]0.0585634644385858[/C][/ROW]
[ROW][C]30[/C][C]7.32[/C][C]7.20557084614689[/C][C]0.114429153853109[/C][/ROW]
[ROW][C]31[/C][C]7.24[/C][C]7.27201284630259[/C][C]-0.0320128463025931[/C][/ROW]
[ROW][C]32[/C][C]7.21[/C][C]7.27201284630259[/C][C]-0.0620128463025933[/C][/ROW]
[ROW][C]33[/C][C]7.21[/C][C]7.27201284630259[/C][C]-0.0620128463025933[/C][/ROW]
[ROW][C]34[/C][C]7.19[/C][C]7.19316546778495[/C][C]-0.00316546778494753[/C][/ROW]
[ROW][C]35[/C][C]7.14[/C][C]7.14326060202466[/C][C]-0.00326060202465832[/C][/ROW]
[ROW][C]36[/C][C]7.13[/C][C]7.14133640016419[/C][C]-0.0113364001641886[/C][/ROW]
[ROW][C]37[/C][C]7.12[/C][C]7.15374177852613[/C][C]-0.0337417785261268[/C][/ROW]
[ROW][C]38[/C][C]7.08[/C][C]7.1556659803866[/C][C]-0.0756659803865976[/C][/ROW]
[ROW][C]39[/C][C]7.04[/C][C]7.07681860186896[/C][C]-0.0368186018689557[/C][/ROW]
[ROW][C]40[/C][C]7.04[/C][C]7.07681860186896[/C][C]-0.0368186018689557[/C][/ROW]
[ROW][C]41[/C][C]7.03[/C][C]7.07681860186896[/C][C]-0.0468186018689555[/C][/ROW]
[ROW][C]42[/C][C]7.03[/C][C]6.99797122335131[/C][C]0.0320287766486899[/C][/ROW]
[ROW][C]43[/C][C]6.99[/C][C]7.03547071074966[/C][C]-0.0454707107496604[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]6.95336559064426[/C][C]0.0466344093557363[/C][/ROW]
[ROW][C]45[/C][C]6.97[/C][C]6.98593905982457[/C][C]-0.015939059824575[/C][/ROW]
[ROW][C]46[/C][C]6.91[/C][C]7.00652819799231[/C][C]-0.0965281979923085[/C][/ROW]
[ROW][C]47[/C][C]6.83[/C][C]6.83642806259508[/C][C]-0.00642806259508285[/C][/ROW]
[ROW][C]48[/C][C]6.8[/C][C]6.83642806259508[/C][C]-0.0364280625950831[/C][/ROW]
[ROW][C]49[/C][C]6.79[/C][C]6.79144921505283[/C][C]-0.00144921505283227[/C][/ROW]
[ROW][C]50[/C][C]6.77[/C][C]6.79144921505283[/C][C]-0.0214492150528327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	8.02	8.04813038727822	-0.0281303872782162
2	7.98	8.06742446365087	-0.0874244636508691
3	7.98	7.98945109957894	-0.00945109957894154
4	7.97	8.02887478883777	-0.0588747888377666
5	7.96	7.977045721217	-0.0170457212170024
6	7.95	7.98030346280476	-0.0303034628047589
7	7.94	7.98030346280476	-0.0403034628047588
8	7.91	7.94772999362445	-0.0377299936244479
9	7.9	7.97474830452133	-0.074748304521329
10	7.9	7.88262153319603	0.0173784668039731
11	7.88	7.88262153319603	-0.00262153319602736
12	7.88	7.87021615483409	0.00978384516591103
13	7.86	7.7946265179042	0.0653734820958012
14	7.86	7.73503473782701	0.124965262172994
15	7.86	7.72262935946507	0.137370640534933
16	7.84	7.72262935946507	0.117370640534933
17	7.79	7.72175534501935	0.06824465498065
18	7.62	7.75203139750399	-0.132031397503988
19	7.6	7.64474230583951	-0.044742305839506
20	7.55	7.4779477445442	0.0720522554558047
21	7.53	7.49811583536257	0.0318841646374337
22	7.5	7.45313698782031	0.0468630121796865
23	7.4	7.32613933061903	0.073860669380973
24	7.35	7.26190488463633	0.0880951153636712
25	7.31	7.39028391407906	-0.0802839140790565
26	7.35	7.28529223911025	0.0647077608897491
27	7.38	7.32384191392335	0.056158086076646
28	7.37	7.32471592836907	0.0452840716309286
29	7.37	7.31143653556141	0.0585634644385858
30	7.32	7.20557084614689	0.114429153853109
31	7.24	7.27201284630259	-0.0320128463025931
32	7.21	7.27201284630259	-0.0620128463025933
33	7.21	7.27201284630259	-0.0620128463025933
34	7.19	7.19316546778495	-0.00316546778494753
35	7.14	7.14326060202466	-0.00326060202465832
36	7.13	7.14133640016419	-0.0113364001641886
37	7.12	7.15374177852613	-0.0337417785261268
38	7.08	7.1556659803866	-0.0756659803865976
39	7.04	7.07681860186896	-0.0368186018689557
40	7.04	7.07681860186896	-0.0368186018689557
41	7.03	7.07681860186896	-0.0468186018689555
42	7.03	6.99797122335131	0.0320287766486899
43	6.99	7.03547071074966	-0.0454707107496604
44	7	6.95336559064426	0.0466344093557363
45	6.97	6.98593905982457	-0.015939059824575
46	6.91	7.00652819799231	-0.0965281979923085
47	6.83	6.83642806259508	-0.00642806259508285
48	6.8	6.83642806259508	-0.0364280625950831
49	6.79	6.79144921505283	-0.00144921505283227
50	6.77	6.79144921505283	-0.0214492150528327

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0286112398718729	0.0572224797437459	0.971388760128127
10	0.018027039300202	0.036054078600404	0.981972960699798
11	0.00659391287672156	0.0131878257534431	0.993406087123278
12	0.00333395292696867	0.00666790585393735	0.996666047073031
13	0.00102582151891101	0.00205164303782203	0.998974178481089
14	0.000314462564841599	0.000628925129683197	0.999685537435158
15	0.000120418122967972	0.000240836245935943	0.999879581877032
16	3.38774219488408e-05	6.77548438976817e-05	0.999966122578051
17	0.000297560313790832	0.000595120627581664	0.999702439686209
18	0.0545369156099051	0.10907383121981	0.945463084390095
19	0.141348303345457	0.282696606690915	0.858651696654543
20	0.250966088039983	0.501932176079967	0.749033911960017
21	0.206654037122055	0.41330807424411	0.793345962877945
22	0.186866136446901	0.373732272893803	0.813133863553099
23	0.130248792674276	0.260497585348552	0.869751207325724
24	0.119401339952221	0.238802679904443	0.880598660047779
25	0.146246349466071	0.292492698932142	0.853753650533929
26	0.103721535094281	0.207443070188562	0.896278464905719
27	0.120586921166997	0.241173842333994	0.879413078833003
28	0.0803419022594276	0.160683804518855	0.919658097740572
29	0.126731776065052	0.253463552130104	0.873268223934948
30	0.84788793474062	0.30422413051876	0.15211206525938
31	0.884436880705311	0.231126238589378	0.115563119294689
32	0.912046392152253	0.175907215695494	0.0879536078477469
33	0.913132331146872	0.173735337706257	0.0868676688531285
34	0.964477993112868	0.0710440137742642	0.0355220068871321
35	0.967867700318619	0.064264599362761	0.0321322996813805
36	0.970495648481237	0.0590087030375255	0.0295043515187628
37	0.99849643091403	0.00300713817194001	0.00150356908597001
38	0.996585488316232	0.00682902336753531	0.00341451168376765
39	0.990313691020988	0.019372617958024	0.00968630897901198
40	0.972208471149724	0.0555830577005526	0.0277915288502763
41	0.963230239867733	0.0735395202645347	0.0367697601322673

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0286112398718729 & 0.0572224797437459 & 0.971388760128127 \tabularnewline
10 & 0.018027039300202 & 0.036054078600404 & 0.981972960699798 \tabularnewline
11 & 0.00659391287672156 & 0.0131878257534431 & 0.993406087123278 \tabularnewline
12 & 0.00333395292696867 & 0.00666790585393735 & 0.996666047073031 \tabularnewline
13 & 0.00102582151891101 & 0.00205164303782203 & 0.998974178481089 \tabularnewline
14 & 0.000314462564841599 & 0.000628925129683197 & 0.999685537435158 \tabularnewline
15 & 0.000120418122967972 & 0.000240836245935943 & 0.999879581877032 \tabularnewline
16 & 3.38774219488408e-05 & 6.77548438976817e-05 & 0.999966122578051 \tabularnewline
17 & 0.000297560313790832 & 0.000595120627581664 & 0.999702439686209 \tabularnewline
18 & 0.0545369156099051 & 0.10907383121981 & 0.945463084390095 \tabularnewline
19 & 0.141348303345457 & 0.282696606690915 & 0.858651696654543 \tabularnewline
20 & 0.250966088039983 & 0.501932176079967 & 0.749033911960017 \tabularnewline
21 & 0.206654037122055 & 0.41330807424411 & 0.793345962877945 \tabularnewline
22 & 0.186866136446901 & 0.373732272893803 & 0.813133863553099 \tabularnewline
23 & 0.130248792674276 & 0.260497585348552 & 0.869751207325724 \tabularnewline
24 & 0.119401339952221 & 0.238802679904443 & 0.880598660047779 \tabularnewline
25 & 0.146246349466071 & 0.292492698932142 & 0.853753650533929 \tabularnewline
26 & 0.103721535094281 & 0.207443070188562 & 0.896278464905719 \tabularnewline
27 & 0.120586921166997 & 0.241173842333994 & 0.879413078833003 \tabularnewline
28 & 0.0803419022594276 & 0.160683804518855 & 0.919658097740572 \tabularnewline
29 & 0.126731776065052 & 0.253463552130104 & 0.873268223934948 \tabularnewline
30 & 0.84788793474062 & 0.30422413051876 & 0.15211206525938 \tabularnewline
31 & 0.884436880705311 & 0.231126238589378 & 0.115563119294689 \tabularnewline
32 & 0.912046392152253 & 0.175907215695494 & 0.0879536078477469 \tabularnewline
33 & 0.913132331146872 & 0.173735337706257 & 0.0868676688531285 \tabularnewline
34 & 0.964477993112868 & 0.0710440137742642 & 0.0355220068871321 \tabularnewline
35 & 0.967867700318619 & 0.064264599362761 & 0.0321322996813805 \tabularnewline
36 & 0.970495648481237 & 0.0590087030375255 & 0.0295043515187628 \tabularnewline
37 & 0.99849643091403 & 0.00300713817194001 & 0.00150356908597001 \tabularnewline
38 & 0.996585488316232 & 0.00682902336753531 & 0.00341451168376765 \tabularnewline
39 & 0.990313691020988 & 0.019372617958024 & 0.00968630897901198 \tabularnewline
40 & 0.972208471149724 & 0.0555830577005526 & 0.0277915288502763 \tabularnewline
41 & 0.963230239867733 & 0.0735395202645347 & 0.0367697601322673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&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]9[/C][C]0.0286112398718729[/C][C]0.0572224797437459[/C][C]0.971388760128127[/C][/ROW]
[ROW][C]10[/C][C]0.018027039300202[/C][C]0.036054078600404[/C][C]0.981972960699798[/C][/ROW]
[ROW][C]11[/C][C]0.00659391287672156[/C][C]0.0131878257534431[/C][C]0.993406087123278[/C][/ROW]
[ROW][C]12[/C][C]0.00333395292696867[/C][C]0.00666790585393735[/C][C]0.996666047073031[/C][/ROW]
[ROW][C]13[/C][C]0.00102582151891101[/C][C]0.00205164303782203[/C][C]0.998974178481089[/C][/ROW]
[ROW][C]14[/C][C]0.000314462564841599[/C][C]0.000628925129683197[/C][C]0.999685537435158[/C][/ROW]
[ROW][C]15[/C][C]0.000120418122967972[/C][C]0.000240836245935943[/C][C]0.999879581877032[/C][/ROW]
[ROW][C]16[/C][C]3.38774219488408e-05[/C][C]6.77548438976817e-05[/C][C]0.999966122578051[/C][/ROW]
[ROW][C]17[/C][C]0.000297560313790832[/C][C]0.000595120627581664[/C][C]0.999702439686209[/C][/ROW]
[ROW][C]18[/C][C]0.0545369156099051[/C][C]0.10907383121981[/C][C]0.945463084390095[/C][/ROW]
[ROW][C]19[/C][C]0.141348303345457[/C][C]0.282696606690915[/C][C]0.858651696654543[/C][/ROW]
[ROW][C]20[/C][C]0.250966088039983[/C][C]0.501932176079967[/C][C]0.749033911960017[/C][/ROW]
[ROW][C]21[/C][C]0.206654037122055[/C][C]0.41330807424411[/C][C]0.793345962877945[/C][/ROW]
[ROW][C]22[/C][C]0.186866136446901[/C][C]0.373732272893803[/C][C]0.813133863553099[/C][/ROW]
[ROW][C]23[/C][C]0.130248792674276[/C][C]0.260497585348552[/C][C]0.869751207325724[/C][/ROW]
[ROW][C]24[/C][C]0.119401339952221[/C][C]0.238802679904443[/C][C]0.880598660047779[/C][/ROW]
[ROW][C]25[/C][C]0.146246349466071[/C][C]0.292492698932142[/C][C]0.853753650533929[/C][/ROW]
[ROW][C]26[/C][C]0.103721535094281[/C][C]0.207443070188562[/C][C]0.896278464905719[/C][/ROW]
[ROW][C]27[/C][C]0.120586921166997[/C][C]0.241173842333994[/C][C]0.879413078833003[/C][/ROW]
[ROW][C]28[/C][C]0.0803419022594276[/C][C]0.160683804518855[/C][C]0.919658097740572[/C][/ROW]
[ROW][C]29[/C][C]0.126731776065052[/C][C]0.253463552130104[/C][C]0.873268223934948[/C][/ROW]
[ROW][C]30[/C][C]0.84788793474062[/C][C]0.30422413051876[/C][C]0.15211206525938[/C][/ROW]
[ROW][C]31[/C][C]0.884436880705311[/C][C]0.231126238589378[/C][C]0.115563119294689[/C][/ROW]
[ROW][C]32[/C][C]0.912046392152253[/C][C]0.175907215695494[/C][C]0.0879536078477469[/C][/ROW]
[ROW][C]33[/C][C]0.913132331146872[/C][C]0.173735337706257[/C][C]0.0868676688531285[/C][/ROW]
[ROW][C]34[/C][C]0.964477993112868[/C][C]0.0710440137742642[/C][C]0.0355220068871321[/C][/ROW]
[ROW][C]35[/C][C]0.967867700318619[/C][C]0.064264599362761[/C][C]0.0321322996813805[/C][/ROW]
[ROW][C]36[/C][C]0.970495648481237[/C][C]0.0590087030375255[/C][C]0.0295043515187628[/C][/ROW]
[ROW][C]37[/C][C]0.99849643091403[/C][C]0.00300713817194001[/C][C]0.00150356908597001[/C][/ROW]
[ROW][C]38[/C][C]0.996585488316232[/C][C]0.00682902336753531[/C][C]0.00341451168376765[/C][/ROW]
[ROW][C]39[/C][C]0.990313691020988[/C][C]0.019372617958024[/C][C]0.00968630897901198[/C][/ROW]
[ROW][C]40[/C][C]0.972208471149724[/C][C]0.0555830577005526[/C][C]0.0277915288502763[/C][/ROW]
[ROW][C]41[/C][C]0.963230239867733[/C][C]0.0735395202645347[/C][C]0.0367697601322673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0286112398718729	0.0572224797437459	0.971388760128127
10	0.018027039300202	0.036054078600404	0.981972960699798
11	0.00659391287672156	0.0131878257534431	0.993406087123278
12	0.00333395292696867	0.00666790585393735	0.996666047073031
13	0.00102582151891101	0.00205164303782203	0.998974178481089
14	0.000314462564841599	0.000628925129683197	0.999685537435158
15	0.000120418122967972	0.000240836245935943	0.999879581877032
16	3.38774219488408e-05	6.77548438976817e-05	0.999966122578051
17	0.000297560313790832	0.000595120627581664	0.999702439686209
18	0.0545369156099051	0.10907383121981	0.945463084390095
19	0.141348303345457	0.282696606690915	0.858651696654543
20	0.250966088039983	0.501932176079967	0.749033911960017
21	0.206654037122055	0.41330807424411	0.793345962877945
22	0.186866136446901	0.373732272893803	0.813133863553099
23	0.130248792674276	0.260497585348552	0.869751207325724
24	0.119401339952221	0.238802679904443	0.880598660047779
25	0.146246349466071	0.292492698932142	0.853753650533929
26	0.103721535094281	0.207443070188562	0.896278464905719
27	0.120586921166997	0.241173842333994	0.879413078833003
28	0.0803419022594276	0.160683804518855	0.919658097740572
29	0.126731776065052	0.253463552130104	0.873268223934948
30	0.84788793474062	0.30422413051876	0.15211206525938
31	0.884436880705311	0.231126238589378	0.115563119294689
32	0.912046392152253	0.175907215695494	0.0879536078477469
33	0.913132331146872	0.173735337706257	0.0868676688531285
34	0.964477993112868	0.0710440137742642	0.0355220068871321
35	0.967867700318619	0.064264599362761	0.0321322996813805
36	0.970495648481237	0.0590087030375255	0.0295043515187628
37	0.99849643091403	0.00300713817194001	0.00150356908597001
38	0.996585488316232	0.00682902336753531	0.00341451168376765
39	0.990313691020988	0.019372617958024	0.00968630897901198
40	0.972208471149724	0.0555830577005526	0.0277915288502763
41	0.963230239867733	0.0735395202645347	0.0367697601322673

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.242424242424242	NOK
5% type I error level	11	0.333333333333333	NOK
10% type I error level	17	0.515151515151515	NOK

\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 & 8 & 0.242424242424242 & NOK \tabularnewline
5% type I error level & 11 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 17 & 0.515151515151515 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190498&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]8[/C][C]0.242424242424242[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.515151515151515[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190498&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190498&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 tests	OK/NOK
1% type I error level	8	0.242424242424242	NOK
5% type I error level	11	0.333333333333333	NOK
10% type I error level	17	0.515151515151515	NOK

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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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code