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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 27 Nov 2008 08:51:19 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/27/t12278011525fjn2fqn37zw2g7.htm/, Retrieved Sun, 19 May 2024 11:10:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25845, Retrieved Sun, 19 May 2024 11:10:10 +0000

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Estimated Impact

166

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Multiple Regression] [] [2007-11-19 19:55:31] [b731da8b544846036771bbf9bf2f34ce]
F R PD  [Multiple Regression] [Seatbelt Q3: eige...] [2008-11-27 14:47:40] [f77c9ab3b413812d7baee6b7ec69a15d]
F   PD      [Multiple Regression] [Seatbelt Q3: eige...] [2008-11-27 15:51:19] [3fc0b50a130253095e963177b0139835] [Current]

Feedback Forum

2008-11-29 15:14:44 [Tinneke De Bock] [reply] 
Een zeer goede en gedetailleerde uitwerking van de datareeks! Uit de grafiek van de autocorrelatie kunnen we toch duidelijke golven zien die op seizoenaliteit kunnen wijzen, ook al was je model rekening gehouden met seizoenaliteit inderdaad niet zinvol. Het model is dus inderdaad nog voor verbetering vatbaar.  
2008-11-29 16:33:43 [Natalie De Wilde] [reply] 
Je hebt deze vraag goed opgelost. Er zijn nog wel enkele verbeteringen nodig zodat er goede grafieken zijn, maar iedereen heeft hier wel last mee.

Post a new message

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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	8 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation
Suiker[t] = + 99.4132576769025 -2.44007113962896Cotonou[t] + 0.104017120867038t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Suiker[t] =  +  99.4132576769025 -2.44007113962896Cotonou[t] +  0.104017120867038t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Suiker[t] =  +  99.4132576769025 -2.44007113962896Cotonou[t] +  0.104017120867038t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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
Suiker[t] = + 99.4132576769025 -2.44007113962896Cotonou[t] + 0.104017120867038t + 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)	99.4132576769025	0.148892	667.6885	0	0
Cotonou	-2.44007113962896	0.226128	-10.7907	0	0
t	0.104017120867038	0.003747	27.7604	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) & 99.4132576769025 & 0.148892 & 667.6885 & 0 & 0 \tabularnewline
Cotonou & -2.44007113962896 & 0.226128 & -10.7907 & 0 & 0 \tabularnewline
t & 0.104017120867038 & 0.003747 & 27.7604 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&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]99.4132576769025[/C][C]0.148892[/C][C]667.6885[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Cotonou[/C][C]-2.44007113962896[/C][C]0.226128[/C][C]-10.7907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.104017120867038[/C][C]0.003747[/C][C]27.7604[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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)	99.4132576769025	0.148892	667.6885	0	0
Cotonou	-2.44007113962896	0.226128	-10.7907	0	0
t	0.104017120867038	0.003747	27.7604	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.95833448998191
R-squared	0.918404994688886
Adjusted R-squared	0.916390303199722
F-TEST (value)	455.853910948069
F-TEST (DF numerator)	2
F-TEST (DF denominator)	81
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.597942876975069
Sum Squared Residuals	28.9603904141430

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.95833448998191 \tabularnewline
R-squared & 0.918404994688886 \tabularnewline
Adjusted R-squared & 0.916390303199722 \tabularnewline
F-TEST (value) & 455.853910948069 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 81 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.597942876975069 \tabularnewline
Sum Squared Residuals & 28.9603904141430 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.95833448998191[/C][/ROW]
[ROW][C]R-squared[/C][C]0.918404994688886[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.916390303199722[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]455.853910948069[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]81[/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]0.597942876975069[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.9603904141430[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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	0.95833448998191
R-squared	0.918404994688886
Adjusted R-squared	0.916390303199722
F-TEST (value)	455.853910948069
F-TEST (DF numerator)	2
F-TEST (DF denominator)	81
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.597942876975069
Sum Squared Residuals	28.9603904141430

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.02	99.5172747977696	1.50272520223042
2	100.67	99.6212919186366	1.04870808136339
3	100.47	99.7253090395037	0.744690960496345
4	100.38	99.8293261603707	0.550673839629307
5	100.33	99.9333432812377	0.396656718762271
6	100.34	100.037360402105	0.302639597895238
7	100.37	100.141377522972	0.2286224770282
8	100.39	100.245394643839	0.144605356161158
9	100.21	100.349411764706	-0.139411764705888
10	100.21	100.453428885573	-0.243428885572926
11	100.22	100.55744600644	-0.337446006439959
12	100.28	100.661463127307	-0.381463127306995
13	100.25	100.765480248174	-0.515480248174035
14	100.25	100.869497369041	-0.619497369041073
15	100.21	100.973514489908	-0.763514489908118
16	100.16	101.077531610775	-0.917531610775154
17	100.18	101.181548731642	-1.00154873164218
18	100.1	98.8454947128803	1.25450528711973
19	99.96	98.9495118337473	1.01048816625269
20	99.88	99.0535289546144	0.826471045385651
21	99.88	99.1575460754814	0.722453924518612
22	99.86	99.2615631963484	0.598436803651578
23	99.84	99.3655803172155	0.474419682784543
24	99.8	99.4695974380825	0.330402561917499
25	99.82	99.5736145589495	0.246385441050456
26	99.81	99.6776316798166	0.132368320183427
27	99.92	99.7816488006836	0.138351199316388
28	100.03	99.8856659215507	0.144334078449349
29	99.99	99.9896830424177	0.00031695758230438
30	100.02	100.093700163285	-0.0737001632847329
31	100.01	100.197717284152	-0.187717284151762
32	100.13	100.301734405019	-0.171734405018810
33	100.33	100.405751525886	-0.0757515258858459
34	100.13	100.509768646753	-0.379768646752887
35	99.96	100.61378576762	-0.653785767619927
36	100.05	100.717802888487	-0.667802888486962
37	99.83	100.821820009354	-0.991820009354
38	99.8	100.925837130221	-1.12583713022104
39	100.01	101.029854251088	-1.01985425108807
40	100.1	101.133871371955	-1.03387137195512
41	100.13	101.237888492822	-1.10788849282216
42	100.16	101.341905613689	-1.18190561368919
43	100.41	101.445922734556	-1.03592273455623
44	101.34	101.549939855423	-0.209939855423264
45	101.65	101.653956976290	-0.00395697629029981
46	101.85	101.757974097157	0.0920259028426504
47	102.07	101.861991218024	0.208008781975611
48	102.12	101.966008338891	0.153991661108584
49	102.14	102.070025459758	0.0699745402415412
50	102.21	102.174042580625	0.0359574193744960
51	102.28	102.278059701493	0.00194029850746491
52	102.19	102.382076822360	-0.192076822359577
53	102.33	102.486093943227	-0.156093943226615
54	102.54	102.590111064094	-0.0501110640936453
55	102.44	102.694128184961	-0.254128184960692
56	102.78	102.798145305828	-0.0181453058277273
57	102.9	102.902162426695	-0.00216242669476121
58	103.08	103.006179547562	0.073820452438193
59	102.77	103.110196668429	-0.340196668428848
60	102.65	103.214213789296	-0.564213789295877
61	102.71	103.318230910163	-0.608230910162927
62	103.29	103.42224803103	-0.132248031029953
63	102.86	103.526265151897	-0.666265151896998
64	103.45	103.630282272764	-0.180282272764033
65	103.72	103.734299393631	-0.0142993936310756
66	103.65	103.838316514498	-0.188316514498107
67	103.83	103.942333635365	-0.112333635365153
68	104.45	104.046350756232	0.403649243767813
69	105.14	104.150367877099	0.989632122900772
70	105.07	104.254384997966	0.815615002033727
71	105.31	104.358402118833	0.951597881166698
72	105.19	104.462419239700	0.727580760299654
73	105.3	104.566436360567	0.733563639432615
74	105.02	104.670453481434	0.349546518565576
75	105.17	104.774470602301	0.395529397698543
76	105.28	104.878487723168	0.401512276831504
77	105.45	104.982504844036	0.467495155964467
78	105.38	105.086521964903	0.293478035097421
79	105.8	105.190539085770	0.609460914230385
80	105.96	105.294556206637	0.665443793363343
81	105.08	105.398573327504	-0.318573327503691
82	105.11	105.502590448371	-0.392590448370728
83	105.61	105.606607569238	0.00339243076223322
84	105.5	105.710624690105	-0.210624690104805

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.02 & 99.5172747977696 & 1.50272520223042 \tabularnewline
2 & 100.67 & 99.6212919186366 & 1.04870808136339 \tabularnewline
3 & 100.47 & 99.7253090395037 & 0.744690960496345 \tabularnewline
4 & 100.38 & 99.8293261603707 & 0.550673839629307 \tabularnewline
5 & 100.33 & 99.9333432812377 & 0.396656718762271 \tabularnewline
6 & 100.34 & 100.037360402105 & 0.302639597895238 \tabularnewline
7 & 100.37 & 100.141377522972 & 0.2286224770282 \tabularnewline
8 & 100.39 & 100.245394643839 & 0.144605356161158 \tabularnewline
9 & 100.21 & 100.349411764706 & -0.139411764705888 \tabularnewline
10 & 100.21 & 100.453428885573 & -0.243428885572926 \tabularnewline
11 & 100.22 & 100.55744600644 & -0.337446006439959 \tabularnewline
12 & 100.28 & 100.661463127307 & -0.381463127306995 \tabularnewline
13 & 100.25 & 100.765480248174 & -0.515480248174035 \tabularnewline
14 & 100.25 & 100.869497369041 & -0.619497369041073 \tabularnewline
15 & 100.21 & 100.973514489908 & -0.763514489908118 \tabularnewline
16 & 100.16 & 101.077531610775 & -0.917531610775154 \tabularnewline
17 & 100.18 & 101.181548731642 & -1.00154873164218 \tabularnewline
18 & 100.1 & 98.8454947128803 & 1.25450528711973 \tabularnewline
19 & 99.96 & 98.9495118337473 & 1.01048816625269 \tabularnewline
20 & 99.88 & 99.0535289546144 & 0.826471045385651 \tabularnewline
21 & 99.88 & 99.1575460754814 & 0.722453924518612 \tabularnewline
22 & 99.86 & 99.2615631963484 & 0.598436803651578 \tabularnewline
23 & 99.84 & 99.3655803172155 & 0.474419682784543 \tabularnewline
24 & 99.8 & 99.4695974380825 & 0.330402561917499 \tabularnewline
25 & 99.82 & 99.5736145589495 & 0.246385441050456 \tabularnewline
26 & 99.81 & 99.6776316798166 & 0.132368320183427 \tabularnewline
27 & 99.92 & 99.7816488006836 & 0.138351199316388 \tabularnewline
28 & 100.03 & 99.8856659215507 & 0.144334078449349 \tabularnewline
29 & 99.99 & 99.9896830424177 & 0.00031695758230438 \tabularnewline
30 & 100.02 & 100.093700163285 & -0.0737001632847329 \tabularnewline
31 & 100.01 & 100.197717284152 & -0.187717284151762 \tabularnewline
32 & 100.13 & 100.301734405019 & -0.171734405018810 \tabularnewline
33 & 100.33 & 100.405751525886 & -0.0757515258858459 \tabularnewline
34 & 100.13 & 100.509768646753 & -0.379768646752887 \tabularnewline
35 & 99.96 & 100.61378576762 & -0.653785767619927 \tabularnewline
36 & 100.05 & 100.717802888487 & -0.667802888486962 \tabularnewline
37 & 99.83 & 100.821820009354 & -0.991820009354 \tabularnewline
38 & 99.8 & 100.925837130221 & -1.12583713022104 \tabularnewline
39 & 100.01 & 101.029854251088 & -1.01985425108807 \tabularnewline
40 & 100.1 & 101.133871371955 & -1.03387137195512 \tabularnewline
41 & 100.13 & 101.237888492822 & -1.10788849282216 \tabularnewline
42 & 100.16 & 101.341905613689 & -1.18190561368919 \tabularnewline
43 & 100.41 & 101.445922734556 & -1.03592273455623 \tabularnewline
44 & 101.34 & 101.549939855423 & -0.209939855423264 \tabularnewline
45 & 101.65 & 101.653956976290 & -0.00395697629029981 \tabularnewline
46 & 101.85 & 101.757974097157 & 0.0920259028426504 \tabularnewline
47 & 102.07 & 101.861991218024 & 0.208008781975611 \tabularnewline
48 & 102.12 & 101.966008338891 & 0.153991661108584 \tabularnewline
49 & 102.14 & 102.070025459758 & 0.0699745402415412 \tabularnewline
50 & 102.21 & 102.174042580625 & 0.0359574193744960 \tabularnewline
51 & 102.28 & 102.278059701493 & 0.00194029850746491 \tabularnewline
52 & 102.19 & 102.382076822360 & -0.192076822359577 \tabularnewline
53 & 102.33 & 102.486093943227 & -0.156093943226615 \tabularnewline
54 & 102.54 & 102.590111064094 & -0.0501110640936453 \tabularnewline
55 & 102.44 & 102.694128184961 & -0.254128184960692 \tabularnewline
56 & 102.78 & 102.798145305828 & -0.0181453058277273 \tabularnewline
57 & 102.9 & 102.902162426695 & -0.00216242669476121 \tabularnewline
58 & 103.08 & 103.006179547562 & 0.073820452438193 \tabularnewline
59 & 102.77 & 103.110196668429 & -0.340196668428848 \tabularnewline
60 & 102.65 & 103.214213789296 & -0.564213789295877 \tabularnewline
61 & 102.71 & 103.318230910163 & -0.608230910162927 \tabularnewline
62 & 103.29 & 103.42224803103 & -0.132248031029953 \tabularnewline
63 & 102.86 & 103.526265151897 & -0.666265151896998 \tabularnewline
64 & 103.45 & 103.630282272764 & -0.180282272764033 \tabularnewline
65 & 103.72 & 103.734299393631 & -0.0142993936310756 \tabularnewline
66 & 103.65 & 103.838316514498 & -0.188316514498107 \tabularnewline
67 & 103.83 & 103.942333635365 & -0.112333635365153 \tabularnewline
68 & 104.45 & 104.046350756232 & 0.403649243767813 \tabularnewline
69 & 105.14 & 104.150367877099 & 0.989632122900772 \tabularnewline
70 & 105.07 & 104.254384997966 & 0.815615002033727 \tabularnewline
71 & 105.31 & 104.358402118833 & 0.951597881166698 \tabularnewline
72 & 105.19 & 104.462419239700 & 0.727580760299654 \tabularnewline
73 & 105.3 & 104.566436360567 & 0.733563639432615 \tabularnewline
74 & 105.02 & 104.670453481434 & 0.349546518565576 \tabularnewline
75 & 105.17 & 104.774470602301 & 0.395529397698543 \tabularnewline
76 & 105.28 & 104.878487723168 & 0.401512276831504 \tabularnewline
77 & 105.45 & 104.982504844036 & 0.467495155964467 \tabularnewline
78 & 105.38 & 105.086521964903 & 0.293478035097421 \tabularnewline
79 & 105.8 & 105.190539085770 & 0.609460914230385 \tabularnewline
80 & 105.96 & 105.294556206637 & 0.665443793363343 \tabularnewline
81 & 105.08 & 105.398573327504 & -0.318573327503691 \tabularnewline
82 & 105.11 & 105.502590448371 & -0.392590448370728 \tabularnewline
83 & 105.61 & 105.606607569238 & 0.00339243076223322 \tabularnewline
84 & 105.5 & 105.710624690105 & -0.210624690104805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&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]101.02[/C][C]99.5172747977696[/C][C]1.50272520223042[/C][/ROW]
[ROW][C]2[/C][C]100.67[/C][C]99.6212919186366[/C][C]1.04870808136339[/C][/ROW]
[ROW][C]3[/C][C]100.47[/C][C]99.7253090395037[/C][C]0.744690960496345[/C][/ROW]
[ROW][C]4[/C][C]100.38[/C][C]99.8293261603707[/C][C]0.550673839629307[/C][/ROW]
[ROW][C]5[/C][C]100.33[/C][C]99.9333432812377[/C][C]0.396656718762271[/C][/ROW]
[ROW][C]6[/C][C]100.34[/C][C]100.037360402105[/C][C]0.302639597895238[/C][/ROW]
[ROW][C]7[/C][C]100.37[/C][C]100.141377522972[/C][C]0.2286224770282[/C][/ROW]
[ROW][C]8[/C][C]100.39[/C][C]100.245394643839[/C][C]0.144605356161158[/C][/ROW]
[ROW][C]9[/C][C]100.21[/C][C]100.349411764706[/C][C]-0.139411764705888[/C][/ROW]
[ROW][C]10[/C][C]100.21[/C][C]100.453428885573[/C][C]-0.243428885572926[/C][/ROW]
[ROW][C]11[/C][C]100.22[/C][C]100.55744600644[/C][C]-0.337446006439959[/C][/ROW]
[ROW][C]12[/C][C]100.28[/C][C]100.661463127307[/C][C]-0.381463127306995[/C][/ROW]
[ROW][C]13[/C][C]100.25[/C][C]100.765480248174[/C][C]-0.515480248174035[/C][/ROW]
[ROW][C]14[/C][C]100.25[/C][C]100.869497369041[/C][C]-0.619497369041073[/C][/ROW]
[ROW][C]15[/C][C]100.21[/C][C]100.973514489908[/C][C]-0.763514489908118[/C][/ROW]
[ROW][C]16[/C][C]100.16[/C][C]101.077531610775[/C][C]-0.917531610775154[/C][/ROW]
[ROW][C]17[/C][C]100.18[/C][C]101.181548731642[/C][C]-1.00154873164218[/C][/ROW]
[ROW][C]18[/C][C]100.1[/C][C]98.8454947128803[/C][C]1.25450528711973[/C][/ROW]
[ROW][C]19[/C][C]99.96[/C][C]98.9495118337473[/C][C]1.01048816625269[/C][/ROW]
[ROW][C]20[/C][C]99.88[/C][C]99.0535289546144[/C][C]0.826471045385651[/C][/ROW]
[ROW][C]21[/C][C]99.88[/C][C]99.1575460754814[/C][C]0.722453924518612[/C][/ROW]
[ROW][C]22[/C][C]99.86[/C][C]99.2615631963484[/C][C]0.598436803651578[/C][/ROW]
[ROW][C]23[/C][C]99.84[/C][C]99.3655803172155[/C][C]0.474419682784543[/C][/ROW]
[ROW][C]24[/C][C]99.8[/C][C]99.4695974380825[/C][C]0.330402561917499[/C][/ROW]
[ROW][C]25[/C][C]99.82[/C][C]99.5736145589495[/C][C]0.246385441050456[/C][/ROW]
[ROW][C]26[/C][C]99.81[/C][C]99.6776316798166[/C][C]0.132368320183427[/C][/ROW]
[ROW][C]27[/C][C]99.92[/C][C]99.7816488006836[/C][C]0.138351199316388[/C][/ROW]
[ROW][C]28[/C][C]100.03[/C][C]99.8856659215507[/C][C]0.144334078449349[/C][/ROW]
[ROW][C]29[/C][C]99.99[/C][C]99.9896830424177[/C][C]0.00031695758230438[/C][/ROW]
[ROW][C]30[/C][C]100.02[/C][C]100.093700163285[/C][C]-0.0737001632847329[/C][/ROW]
[ROW][C]31[/C][C]100.01[/C][C]100.197717284152[/C][C]-0.187717284151762[/C][/ROW]
[ROW][C]32[/C][C]100.13[/C][C]100.301734405019[/C][C]-0.171734405018810[/C][/ROW]
[ROW][C]33[/C][C]100.33[/C][C]100.405751525886[/C][C]-0.0757515258858459[/C][/ROW]
[ROW][C]34[/C][C]100.13[/C][C]100.509768646753[/C][C]-0.379768646752887[/C][/ROW]
[ROW][C]35[/C][C]99.96[/C][C]100.61378576762[/C][C]-0.653785767619927[/C][/ROW]
[ROW][C]36[/C][C]100.05[/C][C]100.717802888487[/C][C]-0.667802888486962[/C][/ROW]
[ROW][C]37[/C][C]99.83[/C][C]100.821820009354[/C][C]-0.991820009354[/C][/ROW]
[ROW][C]38[/C][C]99.8[/C][C]100.925837130221[/C][C]-1.12583713022104[/C][/ROW]
[ROW][C]39[/C][C]100.01[/C][C]101.029854251088[/C][C]-1.01985425108807[/C][/ROW]
[ROW][C]40[/C][C]100.1[/C][C]101.133871371955[/C][C]-1.03387137195512[/C][/ROW]
[ROW][C]41[/C][C]100.13[/C][C]101.237888492822[/C][C]-1.10788849282216[/C][/ROW]
[ROW][C]42[/C][C]100.16[/C][C]101.341905613689[/C][C]-1.18190561368919[/C][/ROW]
[ROW][C]43[/C][C]100.41[/C][C]101.445922734556[/C][C]-1.03592273455623[/C][/ROW]
[ROW][C]44[/C][C]101.34[/C][C]101.549939855423[/C][C]-0.209939855423264[/C][/ROW]
[ROW][C]45[/C][C]101.65[/C][C]101.653956976290[/C][C]-0.00395697629029981[/C][/ROW]
[ROW][C]46[/C][C]101.85[/C][C]101.757974097157[/C][C]0.0920259028426504[/C][/ROW]
[ROW][C]47[/C][C]102.07[/C][C]101.861991218024[/C][C]0.208008781975611[/C][/ROW]
[ROW][C]48[/C][C]102.12[/C][C]101.966008338891[/C][C]0.153991661108584[/C][/ROW]
[ROW][C]49[/C][C]102.14[/C][C]102.070025459758[/C][C]0.0699745402415412[/C][/ROW]
[ROW][C]50[/C][C]102.21[/C][C]102.174042580625[/C][C]0.0359574193744960[/C][/ROW]
[ROW][C]51[/C][C]102.28[/C][C]102.278059701493[/C][C]0.00194029850746491[/C][/ROW]
[ROW][C]52[/C][C]102.19[/C][C]102.382076822360[/C][C]-0.192076822359577[/C][/ROW]
[ROW][C]53[/C][C]102.33[/C][C]102.486093943227[/C][C]-0.156093943226615[/C][/ROW]
[ROW][C]54[/C][C]102.54[/C][C]102.590111064094[/C][C]-0.0501110640936453[/C][/ROW]
[ROW][C]55[/C][C]102.44[/C][C]102.694128184961[/C][C]-0.254128184960692[/C][/ROW]
[ROW][C]56[/C][C]102.78[/C][C]102.798145305828[/C][C]-0.0181453058277273[/C][/ROW]
[ROW][C]57[/C][C]102.9[/C][C]102.902162426695[/C][C]-0.00216242669476121[/C][/ROW]
[ROW][C]58[/C][C]103.08[/C][C]103.006179547562[/C][C]0.073820452438193[/C][/ROW]
[ROW][C]59[/C][C]102.77[/C][C]103.110196668429[/C][C]-0.340196668428848[/C][/ROW]
[ROW][C]60[/C][C]102.65[/C][C]103.214213789296[/C][C]-0.564213789295877[/C][/ROW]
[ROW][C]61[/C][C]102.71[/C][C]103.318230910163[/C][C]-0.608230910162927[/C][/ROW]
[ROW][C]62[/C][C]103.29[/C][C]103.42224803103[/C][C]-0.132248031029953[/C][/ROW]
[ROW][C]63[/C][C]102.86[/C][C]103.526265151897[/C][C]-0.666265151896998[/C][/ROW]
[ROW][C]64[/C][C]103.45[/C][C]103.630282272764[/C][C]-0.180282272764033[/C][/ROW]
[ROW][C]65[/C][C]103.72[/C][C]103.734299393631[/C][C]-0.0142993936310756[/C][/ROW]
[ROW][C]66[/C][C]103.65[/C][C]103.838316514498[/C][C]-0.188316514498107[/C][/ROW]
[ROW][C]67[/C][C]103.83[/C][C]103.942333635365[/C][C]-0.112333635365153[/C][/ROW]
[ROW][C]68[/C][C]104.45[/C][C]104.046350756232[/C][C]0.403649243767813[/C][/ROW]
[ROW][C]69[/C][C]105.14[/C][C]104.150367877099[/C][C]0.989632122900772[/C][/ROW]
[ROW][C]70[/C][C]105.07[/C][C]104.254384997966[/C][C]0.815615002033727[/C][/ROW]
[ROW][C]71[/C][C]105.31[/C][C]104.358402118833[/C][C]0.951597881166698[/C][/ROW]
[ROW][C]72[/C][C]105.19[/C][C]104.462419239700[/C][C]0.727580760299654[/C][/ROW]
[ROW][C]73[/C][C]105.3[/C][C]104.566436360567[/C][C]0.733563639432615[/C][/ROW]
[ROW][C]74[/C][C]105.02[/C][C]104.670453481434[/C][C]0.349546518565576[/C][/ROW]
[ROW][C]75[/C][C]105.17[/C][C]104.774470602301[/C][C]0.395529397698543[/C][/ROW]
[ROW][C]76[/C][C]105.28[/C][C]104.878487723168[/C][C]0.401512276831504[/C][/ROW]
[ROW][C]77[/C][C]105.45[/C][C]104.982504844036[/C][C]0.467495155964467[/C][/ROW]
[ROW][C]78[/C][C]105.38[/C][C]105.086521964903[/C][C]0.293478035097421[/C][/ROW]
[ROW][C]79[/C][C]105.8[/C][C]105.190539085770[/C][C]0.609460914230385[/C][/ROW]
[ROW][C]80[/C][C]105.96[/C][C]105.294556206637[/C][C]0.665443793363343[/C][/ROW]
[ROW][C]81[/C][C]105.08[/C][C]105.398573327504[/C][C]-0.318573327503691[/C][/ROW]
[ROW][C]82[/C][C]105.11[/C][C]105.502590448371[/C][C]-0.392590448370728[/C][/ROW]
[ROW][C]83[/C][C]105.61[/C][C]105.606607569238[/C][C]0.00339243076223322[/C][/ROW]
[ROW][C]84[/C][C]105.5[/C][C]105.710624690105[/C][C]-0.210624690104805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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	101.02	99.5172747977696	1.50272520223042
2	100.67	99.6212919186366	1.04870808136339
3	100.47	99.7253090395037	0.744690960496345
4	100.38	99.8293261603707	0.550673839629307
5	100.33	99.9333432812377	0.396656718762271
6	100.34	100.037360402105	0.302639597895238
7	100.37	100.141377522972	0.2286224770282
8	100.39	100.245394643839	0.144605356161158
9	100.21	100.349411764706	-0.139411764705888
10	100.21	100.453428885573	-0.243428885572926
11	100.22	100.55744600644	-0.337446006439959
12	100.28	100.661463127307	-0.381463127306995
13	100.25	100.765480248174	-0.515480248174035
14	100.25	100.869497369041	-0.619497369041073
15	100.21	100.973514489908	-0.763514489908118
16	100.16	101.077531610775	-0.917531610775154
17	100.18	101.181548731642	-1.00154873164218
18	100.1	98.8454947128803	1.25450528711973
19	99.96	98.9495118337473	1.01048816625269
20	99.88	99.0535289546144	0.826471045385651
21	99.88	99.1575460754814	0.722453924518612
22	99.86	99.2615631963484	0.598436803651578
23	99.84	99.3655803172155	0.474419682784543
24	99.8	99.4695974380825	0.330402561917499
25	99.82	99.5736145589495	0.246385441050456
26	99.81	99.6776316798166	0.132368320183427
27	99.92	99.7816488006836	0.138351199316388
28	100.03	99.8856659215507	0.144334078449349
29	99.99	99.9896830424177	0.00031695758230438
30	100.02	100.093700163285	-0.0737001632847329
31	100.01	100.197717284152	-0.187717284151762
32	100.13	100.301734405019	-0.171734405018810
33	100.33	100.405751525886	-0.0757515258858459
34	100.13	100.509768646753	-0.379768646752887
35	99.96	100.61378576762	-0.653785767619927
36	100.05	100.717802888487	-0.667802888486962
37	99.83	100.821820009354	-0.991820009354
38	99.8	100.925837130221	-1.12583713022104
39	100.01	101.029854251088	-1.01985425108807
40	100.1	101.133871371955	-1.03387137195512
41	100.13	101.237888492822	-1.10788849282216
42	100.16	101.341905613689	-1.18190561368919
43	100.41	101.445922734556	-1.03592273455623
44	101.34	101.549939855423	-0.209939855423264
45	101.65	101.653956976290	-0.00395697629029981
46	101.85	101.757974097157	0.0920259028426504
47	102.07	101.861991218024	0.208008781975611
48	102.12	101.966008338891	0.153991661108584
49	102.14	102.070025459758	0.0699745402415412
50	102.21	102.174042580625	0.0359574193744960
51	102.28	102.278059701493	0.00194029850746491
52	102.19	102.382076822360	-0.192076822359577
53	102.33	102.486093943227	-0.156093943226615
54	102.54	102.590111064094	-0.0501110640936453
55	102.44	102.694128184961	-0.254128184960692
56	102.78	102.798145305828	-0.0181453058277273
57	102.9	102.902162426695	-0.00216242669476121
58	103.08	103.006179547562	0.073820452438193
59	102.77	103.110196668429	-0.340196668428848
60	102.65	103.214213789296	-0.564213789295877
61	102.71	103.318230910163	-0.608230910162927
62	103.29	103.42224803103	-0.132248031029953
63	102.86	103.526265151897	-0.666265151896998
64	103.45	103.630282272764	-0.180282272764033
65	103.72	103.734299393631	-0.0142993936310756
66	103.65	103.838316514498	-0.188316514498107
67	103.83	103.942333635365	-0.112333635365153
68	104.45	104.046350756232	0.403649243767813
69	105.14	104.150367877099	0.989632122900772
70	105.07	104.254384997966	0.815615002033727
71	105.31	104.358402118833	0.951597881166698
72	105.19	104.462419239700	0.727580760299654
73	105.3	104.566436360567	0.733563639432615
74	105.02	104.670453481434	0.349546518565576
75	105.17	104.774470602301	0.395529397698543
76	105.28	104.878487723168	0.401512276831504
77	105.45	104.982504844036	0.467495155964467
78	105.38	105.086521964903	0.293478035097421
79	105.8	105.190539085770	0.609460914230385
80	105.96	105.294556206637	0.665443793363343
81	105.08	105.398573327504	-0.318573327503691
82	105.11	105.502590448371	-0.392590448370728
83	105.61	105.606607569238	0.00339243076223322
84	105.5	105.710624690105	-0.210624690104805

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0287637850952939	0.0575275701905879	0.971236214904706
7	0.0166795319629142	0.0333590639258284	0.983320468037086
8	0.00949006321659397	0.0189801264331879	0.990509936783406
9	0.00281317335631781	0.00562634671263562	0.997186826643682
10	0.000873297255353544	0.00174659451070709	0.999126702744646
11	0.00030546849890095	0.0006109369978019	0.9996945315011
12	0.000158405036329361	0.000316810072658721	0.99984159496367
13	6.5009554235575e-05	0.00013001910847115	0.999934990445764
14	2.64655722464962e-05	5.29311444929924e-05	0.999973534427753
15	8.80187542899812e-06	1.76037508579962e-05	0.999991198124571
16	2.47318068398312e-06	4.94636136796625e-06	0.999997526819316
17	7.74513637983709e-07	1.54902727596742e-06	0.999999225486362
18	3.52265030854979e-07	7.04530061709958e-07	0.99999964773497
19	1.73666481155974e-07	3.47332962311947e-07	0.999999826333519
20	8.54368005518868e-08	1.70873601103774e-07	0.9999999145632
21	3.71205994571837e-08	7.42411989143673e-08	0.9999999628794
22	1.56305650478917e-08	3.12611300957834e-08	0.999999984369435
23	6.37572388802256e-09	1.27514477760451e-08	0.999999993624276
24	2.41752392540165e-09	4.8350478508033e-09	0.999999997582476
25	9.6740216717329e-10	1.93480433434658e-09	0.999999999032598
26	3.83843495423199e-10	7.67686990846397e-10	0.999999999616156
27	4.57980842289523e-10	9.15961684579046e-10	0.99999999954202
28	2.33124801923991e-09	4.66249603847981e-09	0.999999997668752
29	4.09817397266218e-09	8.19634794532436e-09	0.999999995901826
30	7.75305006027848e-09	1.55061001205570e-08	0.99999999224695
31	9.61847390997544e-09	1.92369478199509e-08	0.999999990381526
32	3.19208078652940e-08	6.38416157305881e-08	0.999999968079192
33	5.65384121859244e-07	1.13076824371849e-06	0.999999434615878
34	5.22441373224721e-07	1.04488274644944e-06	0.999999477558627
35	2.12444803415897e-07	4.24889606831794e-07	0.999999787555197
36	1.03900684514384e-07	2.07801369028768e-07	0.999999896099315
37	4.92363374798797e-08	9.84726749597593e-08	0.999999950763663
38	3.08506150730011e-08	6.17012301460021e-08	0.999999969149385
39	2.08173434928437e-08	4.16346869856873e-08	0.999999979182656
40	2.29461019838373e-08	4.58922039676746e-08	0.999999977053898
41	3.91790071810222e-08	7.83580143620444e-08	0.999999960820993
42	1.29199100700718e-07	2.58398201401435e-07	0.9999998708009
43	1.71722933850648e-06	3.43445867701295e-06	0.999998282770661
44	0.00436852076728907	0.00873704153457813	0.99563147923271
45	0.106842554939712	0.213685109879423	0.893157445060288
46	0.367558358093752	0.735116716187503	0.632441641906248
47	0.639973046174149	0.720053907651701	0.360026953825851
48	0.776040730984917	0.447918538030167	0.223959269015083
49	0.831812829637333	0.336374340725333	0.168187170362667
50	0.85782061150272	0.284358776994561	0.142179388497281
51	0.867111718638236	0.265776562723528	0.132888281361764
52	0.85156778965184	0.296864420696319	0.148432210348160
53	0.833986567110115	0.332026865779771	0.166013432889885
54	0.820484119009824	0.359031761980352	0.179515880990176
55	0.788843995450127	0.422312009099747	0.211156004549873
56	0.767671152351556	0.464657695296888	0.232328847648444
57	0.74204157079781	0.515916858404381	0.257958429202190
58	0.720062153426438	0.559875693147124	0.279937846573562
59	0.672220065700784	0.655559868598432	0.327779934299216
60	0.660116551571289	0.679766896857423	0.339883448428711
61	0.688846192989882	0.622307614020236	0.311153807010118
62	0.658637539338441	0.682724921323117	0.341362460661559
63	0.789270352164096	0.421459295671808	0.210729647835904
64	0.823506037040032	0.352987925919936	0.176493962959968
65	0.849425466537084	0.301149066925832	0.150574533462916
66	0.936612368103842	0.126775263792316	0.0633876318961578
67	0.993128931481528	0.0137421370369439	0.00687106851847194
68	0.997932336569901	0.00413532686019758	0.00206766343009879
69	0.997229221130378	0.00554155773924322	0.00277077886962161
70	0.995568321539523	0.00886335692095436	0.00443167846047718
71	0.992933847284498	0.0141323054310044	0.0070661527155022
72	0.986340819085303	0.0273183618293947	0.0136591809146974
73	0.974215373862856	0.0515692522742878	0.0257846261371439
74	0.959169105120565	0.0816617897588695	0.0408308948794347
75	0.931623035984734	0.136753928030532	0.0683769640152659
76	0.885163829449362	0.229672341101275	0.114836170550638
77	0.794012614926138	0.411974770147725	0.205987385073862
78	0.68705900314681	0.62588199370638	0.31294099685319

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0287637850952939 & 0.0575275701905879 & 0.971236214904706 \tabularnewline
7 & 0.0166795319629142 & 0.0333590639258284 & 0.983320468037086 \tabularnewline
8 & 0.00949006321659397 & 0.0189801264331879 & 0.990509936783406 \tabularnewline
9 & 0.00281317335631781 & 0.00562634671263562 & 0.997186826643682 \tabularnewline
10 & 0.000873297255353544 & 0.00174659451070709 & 0.999126702744646 \tabularnewline
11 & 0.00030546849890095 & 0.0006109369978019 & 0.9996945315011 \tabularnewline
12 & 0.000158405036329361 & 0.000316810072658721 & 0.99984159496367 \tabularnewline
13 & 6.5009554235575e-05 & 0.00013001910847115 & 0.999934990445764 \tabularnewline
14 & 2.64655722464962e-05 & 5.29311444929924e-05 & 0.999973534427753 \tabularnewline
15 & 8.80187542899812e-06 & 1.76037508579962e-05 & 0.999991198124571 \tabularnewline
16 & 2.47318068398312e-06 & 4.94636136796625e-06 & 0.999997526819316 \tabularnewline
17 & 7.74513637983709e-07 & 1.54902727596742e-06 & 0.999999225486362 \tabularnewline
18 & 3.52265030854979e-07 & 7.04530061709958e-07 & 0.99999964773497 \tabularnewline
19 & 1.73666481155974e-07 & 3.47332962311947e-07 & 0.999999826333519 \tabularnewline
20 & 8.54368005518868e-08 & 1.70873601103774e-07 & 0.9999999145632 \tabularnewline
21 & 3.71205994571837e-08 & 7.42411989143673e-08 & 0.9999999628794 \tabularnewline
22 & 1.56305650478917e-08 & 3.12611300957834e-08 & 0.999999984369435 \tabularnewline
23 & 6.37572388802256e-09 & 1.27514477760451e-08 & 0.999999993624276 \tabularnewline
24 & 2.41752392540165e-09 & 4.8350478508033e-09 & 0.999999997582476 \tabularnewline
25 & 9.6740216717329e-10 & 1.93480433434658e-09 & 0.999999999032598 \tabularnewline
26 & 3.83843495423199e-10 & 7.67686990846397e-10 & 0.999999999616156 \tabularnewline
27 & 4.57980842289523e-10 & 9.15961684579046e-10 & 0.99999999954202 \tabularnewline
28 & 2.33124801923991e-09 & 4.66249603847981e-09 & 0.999999997668752 \tabularnewline
29 & 4.09817397266218e-09 & 8.19634794532436e-09 & 0.999999995901826 \tabularnewline
30 & 7.75305006027848e-09 & 1.55061001205570e-08 & 0.99999999224695 \tabularnewline
31 & 9.61847390997544e-09 & 1.92369478199509e-08 & 0.999999990381526 \tabularnewline
32 & 3.19208078652940e-08 & 6.38416157305881e-08 & 0.999999968079192 \tabularnewline
33 & 5.65384121859244e-07 & 1.13076824371849e-06 & 0.999999434615878 \tabularnewline
34 & 5.22441373224721e-07 & 1.04488274644944e-06 & 0.999999477558627 \tabularnewline
35 & 2.12444803415897e-07 & 4.24889606831794e-07 & 0.999999787555197 \tabularnewline
36 & 1.03900684514384e-07 & 2.07801369028768e-07 & 0.999999896099315 \tabularnewline
37 & 4.92363374798797e-08 & 9.84726749597593e-08 & 0.999999950763663 \tabularnewline
38 & 3.08506150730011e-08 & 6.17012301460021e-08 & 0.999999969149385 \tabularnewline
39 & 2.08173434928437e-08 & 4.16346869856873e-08 & 0.999999979182656 \tabularnewline
40 & 2.29461019838373e-08 & 4.58922039676746e-08 & 0.999999977053898 \tabularnewline
41 & 3.91790071810222e-08 & 7.83580143620444e-08 & 0.999999960820993 \tabularnewline
42 & 1.29199100700718e-07 & 2.58398201401435e-07 & 0.9999998708009 \tabularnewline
43 & 1.71722933850648e-06 & 3.43445867701295e-06 & 0.999998282770661 \tabularnewline
44 & 0.00436852076728907 & 0.00873704153457813 & 0.99563147923271 \tabularnewline
45 & 0.106842554939712 & 0.213685109879423 & 0.893157445060288 \tabularnewline
46 & 0.367558358093752 & 0.735116716187503 & 0.632441641906248 \tabularnewline
47 & 0.639973046174149 & 0.720053907651701 & 0.360026953825851 \tabularnewline
48 & 0.776040730984917 & 0.447918538030167 & 0.223959269015083 \tabularnewline
49 & 0.831812829637333 & 0.336374340725333 & 0.168187170362667 \tabularnewline
50 & 0.85782061150272 & 0.284358776994561 & 0.142179388497281 \tabularnewline
51 & 0.867111718638236 & 0.265776562723528 & 0.132888281361764 \tabularnewline
52 & 0.85156778965184 & 0.296864420696319 & 0.148432210348160 \tabularnewline
53 & 0.833986567110115 & 0.332026865779771 & 0.166013432889885 \tabularnewline
54 & 0.820484119009824 & 0.359031761980352 & 0.179515880990176 \tabularnewline
55 & 0.788843995450127 & 0.422312009099747 & 0.211156004549873 \tabularnewline
56 & 0.767671152351556 & 0.464657695296888 & 0.232328847648444 \tabularnewline
57 & 0.74204157079781 & 0.515916858404381 & 0.257958429202190 \tabularnewline
58 & 0.720062153426438 & 0.559875693147124 & 0.279937846573562 \tabularnewline
59 & 0.672220065700784 & 0.655559868598432 & 0.327779934299216 \tabularnewline
60 & 0.660116551571289 & 0.679766896857423 & 0.339883448428711 \tabularnewline
61 & 0.688846192989882 & 0.622307614020236 & 0.311153807010118 \tabularnewline
62 & 0.658637539338441 & 0.682724921323117 & 0.341362460661559 \tabularnewline
63 & 0.789270352164096 & 0.421459295671808 & 0.210729647835904 \tabularnewline
64 & 0.823506037040032 & 0.352987925919936 & 0.176493962959968 \tabularnewline
65 & 0.849425466537084 & 0.301149066925832 & 0.150574533462916 \tabularnewline
66 & 0.936612368103842 & 0.126775263792316 & 0.0633876318961578 \tabularnewline
67 & 0.993128931481528 & 0.0137421370369439 & 0.00687106851847194 \tabularnewline
68 & 0.997932336569901 & 0.00413532686019758 & 0.00206766343009879 \tabularnewline
69 & 0.997229221130378 & 0.00554155773924322 & 0.00277077886962161 \tabularnewline
70 & 0.995568321539523 & 0.00886335692095436 & 0.00443167846047718 \tabularnewline
71 & 0.992933847284498 & 0.0141323054310044 & 0.0070661527155022 \tabularnewline
72 & 0.986340819085303 & 0.0273183618293947 & 0.0136591809146974 \tabularnewline
73 & 0.974215373862856 & 0.0515692522742878 & 0.0257846261371439 \tabularnewline
74 & 0.959169105120565 & 0.0816617897588695 & 0.0408308948794347 \tabularnewline
75 & 0.931623035984734 & 0.136753928030532 & 0.0683769640152659 \tabularnewline
76 & 0.885163829449362 & 0.229672341101275 & 0.114836170550638 \tabularnewline
77 & 0.794012614926138 & 0.411974770147725 & 0.205987385073862 \tabularnewline
78 & 0.68705900314681 & 0.62588199370638 & 0.31294099685319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&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]6[/C][C]0.0287637850952939[/C][C]0.0575275701905879[/C][C]0.971236214904706[/C][/ROW]
[ROW][C]7[/C][C]0.0166795319629142[/C][C]0.0333590639258284[/C][C]0.983320468037086[/C][/ROW]
[ROW][C]8[/C][C]0.00949006321659397[/C][C]0.0189801264331879[/C][C]0.990509936783406[/C][/ROW]
[ROW][C]9[/C][C]0.00281317335631781[/C][C]0.00562634671263562[/C][C]0.997186826643682[/C][/ROW]
[ROW][C]10[/C][C]0.000873297255353544[/C][C]0.00174659451070709[/C][C]0.999126702744646[/C][/ROW]
[ROW][C]11[/C][C]0.00030546849890095[/C][C]0.0006109369978019[/C][C]0.9996945315011[/C][/ROW]
[ROW][C]12[/C][C]0.000158405036329361[/C][C]0.000316810072658721[/C][C]0.99984159496367[/C][/ROW]
[ROW][C]13[/C][C]6.5009554235575e-05[/C][C]0.00013001910847115[/C][C]0.999934990445764[/C][/ROW]
[ROW][C]14[/C][C]2.64655722464962e-05[/C][C]5.29311444929924e-05[/C][C]0.999973534427753[/C][/ROW]
[ROW][C]15[/C][C]8.80187542899812e-06[/C][C]1.76037508579962e-05[/C][C]0.999991198124571[/C][/ROW]
[ROW][C]16[/C][C]2.47318068398312e-06[/C][C]4.94636136796625e-06[/C][C]0.999997526819316[/C][/ROW]
[ROW][C]17[/C][C]7.74513637983709e-07[/C][C]1.54902727596742e-06[/C][C]0.999999225486362[/C][/ROW]
[ROW][C]18[/C][C]3.52265030854979e-07[/C][C]7.04530061709958e-07[/C][C]0.99999964773497[/C][/ROW]
[ROW][C]19[/C][C]1.73666481155974e-07[/C][C]3.47332962311947e-07[/C][C]0.999999826333519[/C][/ROW]
[ROW][C]20[/C][C]8.54368005518868e-08[/C][C]1.70873601103774e-07[/C][C]0.9999999145632[/C][/ROW]
[ROW][C]21[/C][C]3.71205994571837e-08[/C][C]7.42411989143673e-08[/C][C]0.9999999628794[/C][/ROW]
[ROW][C]22[/C][C]1.56305650478917e-08[/C][C]3.12611300957834e-08[/C][C]0.999999984369435[/C][/ROW]
[ROW][C]23[/C][C]6.37572388802256e-09[/C][C]1.27514477760451e-08[/C][C]0.999999993624276[/C][/ROW]
[ROW][C]24[/C][C]2.41752392540165e-09[/C][C]4.8350478508033e-09[/C][C]0.999999997582476[/C][/ROW]
[ROW][C]25[/C][C]9.6740216717329e-10[/C][C]1.93480433434658e-09[/C][C]0.999999999032598[/C][/ROW]
[ROW][C]26[/C][C]3.83843495423199e-10[/C][C]7.67686990846397e-10[/C][C]0.999999999616156[/C][/ROW]
[ROW][C]27[/C][C]4.57980842289523e-10[/C][C]9.15961684579046e-10[/C][C]0.99999999954202[/C][/ROW]
[ROW][C]28[/C][C]2.33124801923991e-09[/C][C]4.66249603847981e-09[/C][C]0.999999997668752[/C][/ROW]
[ROW][C]29[/C][C]4.09817397266218e-09[/C][C]8.19634794532436e-09[/C][C]0.999999995901826[/C][/ROW]
[ROW][C]30[/C][C]7.75305006027848e-09[/C][C]1.55061001205570e-08[/C][C]0.99999999224695[/C][/ROW]
[ROW][C]31[/C][C]9.61847390997544e-09[/C][C]1.92369478199509e-08[/C][C]0.999999990381526[/C][/ROW]
[ROW][C]32[/C][C]3.19208078652940e-08[/C][C]6.38416157305881e-08[/C][C]0.999999968079192[/C][/ROW]
[ROW][C]33[/C][C]5.65384121859244e-07[/C][C]1.13076824371849e-06[/C][C]0.999999434615878[/C][/ROW]
[ROW][C]34[/C][C]5.22441373224721e-07[/C][C]1.04488274644944e-06[/C][C]0.999999477558627[/C][/ROW]
[ROW][C]35[/C][C]2.12444803415897e-07[/C][C]4.24889606831794e-07[/C][C]0.999999787555197[/C][/ROW]
[ROW][C]36[/C][C]1.03900684514384e-07[/C][C]2.07801369028768e-07[/C][C]0.999999896099315[/C][/ROW]
[ROW][C]37[/C][C]4.92363374798797e-08[/C][C]9.84726749597593e-08[/C][C]0.999999950763663[/C][/ROW]
[ROW][C]38[/C][C]3.08506150730011e-08[/C][C]6.17012301460021e-08[/C][C]0.999999969149385[/C][/ROW]
[ROW][C]39[/C][C]2.08173434928437e-08[/C][C]4.16346869856873e-08[/C][C]0.999999979182656[/C][/ROW]
[ROW][C]40[/C][C]2.29461019838373e-08[/C][C]4.58922039676746e-08[/C][C]0.999999977053898[/C][/ROW]
[ROW][C]41[/C][C]3.91790071810222e-08[/C][C]7.83580143620444e-08[/C][C]0.999999960820993[/C][/ROW]
[ROW][C]42[/C][C]1.29199100700718e-07[/C][C]2.58398201401435e-07[/C][C]0.9999998708009[/C][/ROW]
[ROW][C]43[/C][C]1.71722933850648e-06[/C][C]3.43445867701295e-06[/C][C]0.999998282770661[/C][/ROW]
[ROW][C]44[/C][C]0.00436852076728907[/C][C]0.00873704153457813[/C][C]0.99563147923271[/C][/ROW]
[ROW][C]45[/C][C]0.106842554939712[/C][C]0.213685109879423[/C][C]0.893157445060288[/C][/ROW]
[ROW][C]46[/C][C]0.367558358093752[/C][C]0.735116716187503[/C][C]0.632441641906248[/C][/ROW]
[ROW][C]47[/C][C]0.639973046174149[/C][C]0.720053907651701[/C][C]0.360026953825851[/C][/ROW]
[ROW][C]48[/C][C]0.776040730984917[/C][C]0.447918538030167[/C][C]0.223959269015083[/C][/ROW]
[ROW][C]49[/C][C]0.831812829637333[/C][C]0.336374340725333[/C][C]0.168187170362667[/C][/ROW]
[ROW][C]50[/C][C]0.85782061150272[/C][C]0.284358776994561[/C][C]0.142179388497281[/C][/ROW]
[ROW][C]51[/C][C]0.867111718638236[/C][C]0.265776562723528[/C][C]0.132888281361764[/C][/ROW]
[ROW][C]52[/C][C]0.85156778965184[/C][C]0.296864420696319[/C][C]0.148432210348160[/C][/ROW]
[ROW][C]53[/C][C]0.833986567110115[/C][C]0.332026865779771[/C][C]0.166013432889885[/C][/ROW]
[ROW][C]54[/C][C]0.820484119009824[/C][C]0.359031761980352[/C][C]0.179515880990176[/C][/ROW]
[ROW][C]55[/C][C]0.788843995450127[/C][C]0.422312009099747[/C][C]0.211156004549873[/C][/ROW]
[ROW][C]56[/C][C]0.767671152351556[/C][C]0.464657695296888[/C][C]0.232328847648444[/C][/ROW]
[ROW][C]57[/C][C]0.74204157079781[/C][C]0.515916858404381[/C][C]0.257958429202190[/C][/ROW]
[ROW][C]58[/C][C]0.720062153426438[/C][C]0.559875693147124[/C][C]0.279937846573562[/C][/ROW]
[ROW][C]59[/C][C]0.672220065700784[/C][C]0.655559868598432[/C][C]0.327779934299216[/C][/ROW]
[ROW][C]60[/C][C]0.660116551571289[/C][C]0.679766896857423[/C][C]0.339883448428711[/C][/ROW]
[ROW][C]61[/C][C]0.688846192989882[/C][C]0.622307614020236[/C][C]0.311153807010118[/C][/ROW]
[ROW][C]62[/C][C]0.658637539338441[/C][C]0.682724921323117[/C][C]0.341362460661559[/C][/ROW]
[ROW][C]63[/C][C]0.789270352164096[/C][C]0.421459295671808[/C][C]0.210729647835904[/C][/ROW]
[ROW][C]64[/C][C]0.823506037040032[/C][C]0.352987925919936[/C][C]0.176493962959968[/C][/ROW]
[ROW][C]65[/C][C]0.849425466537084[/C][C]0.301149066925832[/C][C]0.150574533462916[/C][/ROW]
[ROW][C]66[/C][C]0.936612368103842[/C][C]0.126775263792316[/C][C]0.0633876318961578[/C][/ROW]
[ROW][C]67[/C][C]0.993128931481528[/C][C]0.0137421370369439[/C][C]0.00687106851847194[/C][/ROW]
[ROW][C]68[/C][C]0.997932336569901[/C][C]0.00413532686019758[/C][C]0.00206766343009879[/C][/ROW]
[ROW][C]69[/C][C]0.997229221130378[/C][C]0.00554155773924322[/C][C]0.00277077886962161[/C][/ROW]
[ROW][C]70[/C][C]0.995568321539523[/C][C]0.00886335692095436[/C][C]0.00443167846047718[/C][/ROW]
[ROW][C]71[/C][C]0.992933847284498[/C][C]0.0141323054310044[/C][C]0.0070661527155022[/C][/ROW]
[ROW][C]72[/C][C]0.986340819085303[/C][C]0.0273183618293947[/C][C]0.0136591809146974[/C][/ROW]
[ROW][C]73[/C][C]0.974215373862856[/C][C]0.0515692522742878[/C][C]0.0257846261371439[/C][/ROW]
[ROW][C]74[/C][C]0.959169105120565[/C][C]0.0816617897588695[/C][C]0.0408308948794347[/C][/ROW]
[ROW][C]75[/C][C]0.931623035984734[/C][C]0.136753928030532[/C][C]0.0683769640152659[/C][/ROW]
[ROW][C]76[/C][C]0.885163829449362[/C][C]0.229672341101275[/C][C]0.114836170550638[/C][/ROW]
[ROW][C]77[/C][C]0.794012614926138[/C][C]0.411974770147725[/C][C]0.205987385073862[/C][/ROW]
[ROW][C]78[/C][C]0.68705900314681[/C][C]0.62588199370638[/C][C]0.31294099685319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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
6	0.0287637850952939	0.0575275701905879	0.971236214904706
7	0.0166795319629142	0.0333590639258284	0.983320468037086
8	0.00949006321659397	0.0189801264331879	0.990509936783406
9	0.00281317335631781	0.00562634671263562	0.997186826643682
10	0.000873297255353544	0.00174659451070709	0.999126702744646
11	0.00030546849890095	0.0006109369978019	0.9996945315011
12	0.000158405036329361	0.000316810072658721	0.99984159496367
13	6.5009554235575e-05	0.00013001910847115	0.999934990445764
14	2.64655722464962e-05	5.29311444929924e-05	0.999973534427753
15	8.80187542899812e-06	1.76037508579962e-05	0.999991198124571
16	2.47318068398312e-06	4.94636136796625e-06	0.999997526819316
17	7.74513637983709e-07	1.54902727596742e-06	0.999999225486362
18	3.52265030854979e-07	7.04530061709958e-07	0.99999964773497
19	1.73666481155974e-07	3.47332962311947e-07	0.999999826333519
20	8.54368005518868e-08	1.70873601103774e-07	0.9999999145632
21	3.71205994571837e-08	7.42411989143673e-08	0.9999999628794
22	1.56305650478917e-08	3.12611300957834e-08	0.999999984369435
23	6.37572388802256e-09	1.27514477760451e-08	0.999999993624276
24	2.41752392540165e-09	4.8350478508033e-09	0.999999997582476
25	9.6740216717329e-10	1.93480433434658e-09	0.999999999032598
26	3.83843495423199e-10	7.67686990846397e-10	0.999999999616156
27	4.57980842289523e-10	9.15961684579046e-10	0.99999999954202
28	2.33124801923991e-09	4.66249603847981e-09	0.999999997668752
29	4.09817397266218e-09	8.19634794532436e-09	0.999999995901826
30	7.75305006027848e-09	1.55061001205570e-08	0.99999999224695
31	9.61847390997544e-09	1.92369478199509e-08	0.999999990381526
32	3.19208078652940e-08	6.38416157305881e-08	0.999999968079192
33	5.65384121859244e-07	1.13076824371849e-06	0.999999434615878
34	5.22441373224721e-07	1.04488274644944e-06	0.999999477558627
35	2.12444803415897e-07	4.24889606831794e-07	0.999999787555197
36	1.03900684514384e-07	2.07801369028768e-07	0.999999896099315
37	4.92363374798797e-08	9.84726749597593e-08	0.999999950763663
38	3.08506150730011e-08	6.17012301460021e-08	0.999999969149385
39	2.08173434928437e-08	4.16346869856873e-08	0.999999979182656
40	2.29461019838373e-08	4.58922039676746e-08	0.999999977053898
41	3.91790071810222e-08	7.83580143620444e-08	0.999999960820993
42	1.29199100700718e-07	2.58398201401435e-07	0.9999998708009
43	1.71722933850648e-06	3.43445867701295e-06	0.999998282770661
44	0.00436852076728907	0.00873704153457813	0.99563147923271
45	0.106842554939712	0.213685109879423	0.893157445060288
46	0.367558358093752	0.735116716187503	0.632441641906248
47	0.639973046174149	0.720053907651701	0.360026953825851
48	0.776040730984917	0.447918538030167	0.223959269015083
49	0.831812829637333	0.336374340725333	0.168187170362667
50	0.85782061150272	0.284358776994561	0.142179388497281
51	0.867111718638236	0.265776562723528	0.132888281361764
52	0.85156778965184	0.296864420696319	0.148432210348160
53	0.833986567110115	0.332026865779771	0.166013432889885
54	0.820484119009824	0.359031761980352	0.179515880990176
55	0.788843995450127	0.422312009099747	0.211156004549873
56	0.767671152351556	0.464657695296888	0.232328847648444
57	0.74204157079781	0.515916858404381	0.257958429202190
58	0.720062153426438	0.559875693147124	0.279937846573562
59	0.672220065700784	0.655559868598432	0.327779934299216
60	0.660116551571289	0.679766896857423	0.339883448428711
61	0.688846192989882	0.622307614020236	0.311153807010118
62	0.658637539338441	0.682724921323117	0.341362460661559
63	0.789270352164096	0.421459295671808	0.210729647835904
64	0.823506037040032	0.352987925919936	0.176493962959968
65	0.849425466537084	0.301149066925832	0.150574533462916
66	0.936612368103842	0.126775263792316	0.0633876318961578
67	0.993128931481528	0.0137421370369439	0.00687106851847194
68	0.997932336569901	0.00413532686019758	0.00206766343009879
69	0.997229221130378	0.00554155773924322	0.00277077886962161
70	0.995568321539523	0.00886335692095436	0.00443167846047718
71	0.992933847284498	0.0141323054310044	0.0070661527155022
72	0.986340819085303	0.0273183618293947	0.0136591809146974
73	0.974215373862856	0.0515692522742878	0.0257846261371439
74	0.959169105120565	0.0816617897588695	0.0408308948794347
75	0.931623035984734	0.136753928030532	0.0683769640152659
76	0.885163829449362	0.229672341101275	0.114836170550638
77	0.794012614926138	0.411974770147725	0.205987385073862
78	0.68705900314681	0.62588199370638	0.31294099685319

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	39	0.534246575342466	NOK
5% type I error level	44	0.602739726027397	NOK
10% type I error level	47	0.643835616438356	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 & 39 & 0.534246575342466 & NOK \tabularnewline
5% type I error level & 44 & 0.602739726027397 & NOK \tabularnewline
10% type I error level & 47 & 0.643835616438356 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25845&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]39[/C][C]0.534246575342466[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.602739726027397[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.643835616438356[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25845&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25845&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	39	0.534246575342466	NOK
5% type I error level	44	0.602739726027397	NOK
10% type I error level	47	0.643835616438356	NOK

Figure 1

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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