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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 15:10:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322165427cn1gm2y25e3euj6.htm/, Retrieved Thu, 28 Mar 2024 09:05:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147189, Retrieved Thu, 28 Mar 2024 09:05:45 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [deterministische ...] [2010-12-01 21:57:34] [f1aa04283d83c25edc8ae3bb0d0fb93e]
- RMP       [Multiple Regression] [deterministische ...] [2011-11-24 20:10:12] [cfea828c93f35e07cca4521b1fb38047] [Current]
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Dataseries X:
11	0	8	17	2	6
10	-2	3	23	3	7
9	-4	3	24	1	4
8	-4	7	27	1	3
7	-7	4	31	0	0
6	-9	-4	40	1	6
5	-13	-6	47	-1	3
4	-8	8	43	2	1
3	-13	2	60	2	6
2	-15	-1	64	0	5
1	-15	-2	65	1	7
12	-15	0	65	1	4
11	-10	10	55	3	3
10	-12	3	57	3	6
9	-11	6	57	1	6
8	-11	7	57	1	5
7	-17	-4	65	-2	2
6	-18	-5	69	1	3
5	-19	-7	70	1	-2
4	-22	-10	71	-1	-4
3	-24	-21	71	-4	0
2	-24	-22	73	-2	1
1	-20	-16	68	-1	4
12	-25	-25	65	-5	-3
11	-22	-22	57	-4	-3
10	-17	-22	41	-5	0
9	-9	-19	21	0	6
8	-11	-21	21	-2	-1
7	-13	-31	17	-4	0
6	-11	-28	9	-6	-1
5	-9	-23	11	-2	1
4	-7	-17	6	-2	-4
3	-3	-12	-2	-2	-1
2	-3	-14	0	1	-1
1	-6	-18	5	-2	0
12	-4	-16	3	0	3
11	-8	-22	7	-1	0
10	-1	-9	4	2	8
9	-2	-10	8	3	8
8	-2	-10	9	2	8
7	-1	0	14	3	8
6	1	3	12	4	11
5	2	2	12	5	13
4	2	4	7	5	5
3	-1	-3	15	4	12
2	1	0	14	5	13
1	-1	-1	19	6	9
12	-8	-7	39	4	11
11	1	2	12	6	7
10	2	3	11	6	12
9	-2	-3	17	3	11
8	-2	-5	16	5	10
7	-2	0	25	5	13
6	-2	-3	24	5	14
5	-6	-7	28	3	10
4	-4	-7	25	5	13
3	-5	-7	31	5	12
2	-2	-4	24	6	13
1	-1	-3	24	6	17
12	-5	-6	33	5	15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 1.85423823704048 -0.114537989510702maand[t] -3.92322970510058indicator[t] + 0.973210530445734economie[t] + 1.09728386413799`financiën`[t] + 0.908015384045379spaarvermogen[t] -0.0221617445973185t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  1.85423823704048 -0.114537989510702maand[t] -3.92322970510058indicator[t] +  0.973210530445734economie[t] +  1.09728386413799`financiën`[t] +  0.908015384045379spaarvermogen[t] -0.0221617445973185t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  1.85423823704048 -0.114537989510702maand[t] -3.92322970510058indicator[t] +  0.973210530445734economie[t] +  1.09728386413799`financiën`[t] +  0.908015384045379spaarvermogen[t] -0.0221617445973185t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&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
werkloosheid[t] = + 1.85423823704048 -0.114537989510702maand[t] -3.92322970510058indicator[t] + 0.973210530445734economie[t] + 1.09728386413799`financiën`[t] + 0.908015384045379spaarvermogen[t] -0.0221617445973185t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.854238237040480.6209382.98620.0042690.002135
maand-0.1145379895107020.044347-2.58280.0125980.006299
indicator-3.923229705100580.030814-127.320600
economie0.9732105304457340.0373526.056300
`financiën`1.097283864137990.1558367.041300
spaarvermogen0.9080153840453790.05790715.680500
t-0.02216174459731850.019223-1.15290.2541360.127068

\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) & 1.85423823704048 & 0.620938 & 2.9862 & 0.004269 & 0.002135 \tabularnewline
maand & -0.114537989510702 & 0.044347 & -2.5828 & 0.012598 & 0.006299 \tabularnewline
indicator & -3.92322970510058 & 0.030814 & -127.3206 & 0 & 0 \tabularnewline
economie & 0.973210530445734 & 0.03735 & 26.0563 & 0 & 0 \tabularnewline
`financiën` & 1.09728386413799 & 0.155836 & 7.0413 & 0 & 0 \tabularnewline
spaarvermogen & 0.908015384045379 & 0.057907 & 15.6805 & 0 & 0 \tabularnewline
t & -0.0221617445973185 & 0.019223 & -1.1529 & 0.254136 & 0.127068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&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]1.85423823704048[/C][C]0.620938[/C][C]2.9862[/C][C]0.004269[/C][C]0.002135[/C][/ROW]
[ROW][C]maand[/C][C]-0.114537989510702[/C][C]0.044347[/C][C]-2.5828[/C][C]0.012598[/C][C]0.006299[/C][/ROW]
[ROW][C]indicator[/C][C]-3.92322970510058[/C][C]0.030814[/C][C]-127.3206[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]economie[/C][C]0.973210530445734[/C][C]0.03735[/C][C]26.0563[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`financiën`[/C][C]1.09728386413799[/C][C]0.155836[/C][C]7.0413[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]spaarvermogen[/C][C]0.908015384045379[/C][C]0.057907[/C][C]15.6805[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.0221617445973185[/C][C]0.019223[/C][C]-1.1529[/C][C]0.254136[/C][C]0.127068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.854238237040480.6209382.98620.0042690.002135
maand-0.1145379895107020.044347-2.58280.0125980.006299
indicator-3.923229705100580.030814-127.320600
economie0.9732105304457340.0373526.056300
`financiën`1.097283864137990.1558367.041300
spaarvermogen0.9080153840453790.05790715.680500
t-0.02216174459731850.019223-1.15290.2541360.127068







Multiple Linear Regression - Regression Statistics
Multiple R0.998851623219125
R-squared0.99770456520748
Adjusted R-squared0.997444704664931
F-TEST (value)3839.38459998338
F-TEST (DF numerator)6
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.16791092469633
Sum Squared Residuals72.2928441853266

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998851623219125 \tabularnewline
R-squared & 0.99770456520748 \tabularnewline
Adjusted R-squared & 0.997444704664931 \tabularnewline
F-TEST (value) & 3839.38459998338 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.16791092469633 \tabularnewline
Sum Squared Residuals & 72.2928441853266 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998851623219125[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99770456520748[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997444704664931[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3839.38459998338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/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]1.16791092469633[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]72.2928441853266[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.998851623219125
R-squared0.99770456520748
Adjusted R-squared0.997444704664931
F-TEST (value)3839.38459998338
F-TEST (DF numerator)6
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.16791092469633
Sum Squared Residuals72.2928441853266







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11716.00050288393960.99949711606045
22321.07858513500881.92141486499119
32424.0988069097112-0.0988069097111827
42727.1760098923622-0.176009892362176
53132.297113644966-1.29711364496595
64038.99564122492491.00435877507512
74747.915901348937-0.915901348936986
84343.492897318911-0.492897318910984
96061.9022358268797-1.90223582687975
106463.81885677833570.181143221664285
116565.8513371250321-0.851337125032119
126563.79163240457241.20836759542759
135555.2865177726709-0.286517772670858
145759.1369258668014-2.13692586680139
155756.03113626967540.96886373032459
165756.18870766098910.811292339010854
176563.09924855705281.90075144294719
186970.3415109530804-1.3415109530804
197067.8706189219762.129381078024
207172.8024541944872-1.80245419448717
217170.37618395846620.623816041533836
227372.59793278525520.402067214744808
236866.65798340871481.34201659128522
246565.4879143861214-0.487914386121412
255757.8275169712082-0.827516971208247
264139.93050697861691.06949302138312
272122.4911887990251-1.49118879902508
282119.93292797665451.06707202334549
291716.85310598308110.1468940169189
3098.916071296809170.0839287031908311
311112.2332070083928-1.2332070083928
3265.778310105552540.221689894447463
33-2-2.232133665571590.232133665571589
340-0.7943268891356930.794326889135693
3554.791060140927890.20893985907211
3632.527556042815280.472443957184718
3778.65225790918244-1.65225790918244
3844.48973777896334-0.489737778963336
3988.62941706266955-0.629417062669553
4097.624509443444941.37549055655506
411414.6230451518531-0.623045151853085
421213.6099235941766-1.60992359417665
431211.71917423577250.28082576422753
4476.493848469214290.506151530785707
451516.8022639404889-1.80226394048892
461413.97311161472170.0268883852782716
471918.4039590673470.596040932652985
483938.36668723097640.633312769023613
491210.47139709609061.52860290390935
501112.1538310865761-1.15383108657608
511717.899995992758-0.899995992758008
521617.3325035210105-1.33250352101053
532525.0149785702887-0.014978570288724
542423.09573860791030.904261392089717
552829.1615622869855-1.16156228698554
562526.3260930021099-1.32609300210989
573129.43368356807851.56631643192153
582422.68130153721071.3186984627893
592423.45572014365080.544279856349248
603332.03361311127210.966386888727932

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17 & 16.0005028839396 & 0.99949711606045 \tabularnewline
2 & 23 & 21.0785851350088 & 1.92141486499119 \tabularnewline
3 & 24 & 24.0988069097112 & -0.0988069097111827 \tabularnewline
4 & 27 & 27.1760098923622 & -0.176009892362176 \tabularnewline
5 & 31 & 32.297113644966 & -1.29711364496595 \tabularnewline
6 & 40 & 38.9956412249249 & 1.00435877507512 \tabularnewline
7 & 47 & 47.915901348937 & -0.915901348936986 \tabularnewline
8 & 43 & 43.492897318911 & -0.492897318910984 \tabularnewline
9 & 60 & 61.9022358268797 & -1.90223582687975 \tabularnewline
10 & 64 & 63.8188567783357 & 0.181143221664285 \tabularnewline
11 & 65 & 65.8513371250321 & -0.851337125032119 \tabularnewline
12 & 65 & 63.7916324045724 & 1.20836759542759 \tabularnewline
13 & 55 & 55.2865177726709 & -0.286517772670858 \tabularnewline
14 & 57 & 59.1369258668014 & -2.13692586680139 \tabularnewline
15 & 57 & 56.0311362696754 & 0.96886373032459 \tabularnewline
16 & 57 & 56.1887076609891 & 0.811292339010854 \tabularnewline
17 & 65 & 63.0992485570528 & 1.90075144294719 \tabularnewline
18 & 69 & 70.3415109530804 & -1.3415109530804 \tabularnewline
19 & 70 & 67.870618921976 & 2.129381078024 \tabularnewline
20 & 71 & 72.8024541944872 & -1.80245419448717 \tabularnewline
21 & 71 & 70.3761839584662 & 0.623816041533836 \tabularnewline
22 & 73 & 72.5979327852552 & 0.402067214744808 \tabularnewline
23 & 68 & 66.6579834087148 & 1.34201659128522 \tabularnewline
24 & 65 & 65.4879143861214 & -0.487914386121412 \tabularnewline
25 & 57 & 57.8275169712082 & -0.827516971208247 \tabularnewline
26 & 41 & 39.9305069786169 & 1.06949302138312 \tabularnewline
27 & 21 & 22.4911887990251 & -1.49118879902508 \tabularnewline
28 & 21 & 19.9329279766545 & 1.06707202334549 \tabularnewline
29 & 17 & 16.8531059830811 & 0.1468940169189 \tabularnewline
30 & 9 & 8.91607129680917 & 0.0839287031908311 \tabularnewline
31 & 11 & 12.2332070083928 & -1.2332070083928 \tabularnewline
32 & 6 & 5.77831010555254 & 0.221689894447463 \tabularnewline
33 & -2 & -2.23213366557159 & 0.232133665571589 \tabularnewline
34 & 0 & -0.794326889135693 & 0.794326889135693 \tabularnewline
35 & 5 & 4.79106014092789 & 0.20893985907211 \tabularnewline
36 & 3 & 2.52755604281528 & 0.472443957184718 \tabularnewline
37 & 7 & 8.65225790918244 & -1.65225790918244 \tabularnewline
38 & 4 & 4.48973777896334 & -0.489737778963336 \tabularnewline
39 & 8 & 8.62941706266955 & -0.629417062669553 \tabularnewline
40 & 9 & 7.62450944344494 & 1.37549055655506 \tabularnewline
41 & 14 & 14.6230451518531 & -0.623045151853085 \tabularnewline
42 & 12 & 13.6099235941766 & -1.60992359417665 \tabularnewline
43 & 12 & 11.7191742357725 & 0.28082576422753 \tabularnewline
44 & 7 & 6.49384846921429 & 0.506151530785707 \tabularnewline
45 & 15 & 16.8022639404889 & -1.80226394048892 \tabularnewline
46 & 14 & 13.9731116147217 & 0.0268883852782716 \tabularnewline
47 & 19 & 18.403959067347 & 0.596040932652985 \tabularnewline
48 & 39 & 38.3666872309764 & 0.633312769023613 \tabularnewline
49 & 12 & 10.4713970960906 & 1.52860290390935 \tabularnewline
50 & 11 & 12.1538310865761 & -1.15383108657608 \tabularnewline
51 & 17 & 17.899995992758 & -0.899995992758008 \tabularnewline
52 & 16 & 17.3325035210105 & -1.33250352101053 \tabularnewline
53 & 25 & 25.0149785702887 & -0.014978570288724 \tabularnewline
54 & 24 & 23.0957386079103 & 0.904261392089717 \tabularnewline
55 & 28 & 29.1615622869855 & -1.16156228698554 \tabularnewline
56 & 25 & 26.3260930021099 & -1.32609300210989 \tabularnewline
57 & 31 & 29.4336835680785 & 1.56631643192153 \tabularnewline
58 & 24 & 22.6813015372107 & 1.3186984627893 \tabularnewline
59 & 24 & 23.4557201436508 & 0.544279856349248 \tabularnewline
60 & 33 & 32.0336131112721 & 0.966386888727932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&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]17[/C][C]16.0005028839396[/C][C]0.99949711606045[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]21.0785851350088[/C][C]1.92141486499119[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]24.0988069097112[/C][C]-0.0988069097111827[/C][/ROW]
[ROW][C]4[/C][C]27[/C][C]27.1760098923622[/C][C]-0.176009892362176[/C][/ROW]
[ROW][C]5[/C][C]31[/C][C]32.297113644966[/C][C]-1.29711364496595[/C][/ROW]
[ROW][C]6[/C][C]40[/C][C]38.9956412249249[/C][C]1.00435877507512[/C][/ROW]
[ROW][C]7[/C][C]47[/C][C]47.915901348937[/C][C]-0.915901348936986[/C][/ROW]
[ROW][C]8[/C][C]43[/C][C]43.492897318911[/C][C]-0.492897318910984[/C][/ROW]
[ROW][C]9[/C][C]60[/C][C]61.9022358268797[/C][C]-1.90223582687975[/C][/ROW]
[ROW][C]10[/C][C]64[/C][C]63.8188567783357[/C][C]0.181143221664285[/C][/ROW]
[ROW][C]11[/C][C]65[/C][C]65.8513371250321[/C][C]-0.851337125032119[/C][/ROW]
[ROW][C]12[/C][C]65[/C][C]63.7916324045724[/C][C]1.20836759542759[/C][/ROW]
[ROW][C]13[/C][C]55[/C][C]55.2865177726709[/C][C]-0.286517772670858[/C][/ROW]
[ROW][C]14[/C][C]57[/C][C]59.1369258668014[/C][C]-2.13692586680139[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]56.0311362696754[/C][C]0.96886373032459[/C][/ROW]
[ROW][C]16[/C][C]57[/C][C]56.1887076609891[/C][C]0.811292339010854[/C][/ROW]
[ROW][C]17[/C][C]65[/C][C]63.0992485570528[/C][C]1.90075144294719[/C][/ROW]
[ROW][C]18[/C][C]69[/C][C]70.3415109530804[/C][C]-1.3415109530804[/C][/ROW]
[ROW][C]19[/C][C]70[/C][C]67.870618921976[/C][C]2.129381078024[/C][/ROW]
[ROW][C]20[/C][C]71[/C][C]72.8024541944872[/C][C]-1.80245419448717[/C][/ROW]
[ROW][C]21[/C][C]71[/C][C]70.3761839584662[/C][C]0.623816041533836[/C][/ROW]
[ROW][C]22[/C][C]73[/C][C]72.5979327852552[/C][C]0.402067214744808[/C][/ROW]
[ROW][C]23[/C][C]68[/C][C]66.6579834087148[/C][C]1.34201659128522[/C][/ROW]
[ROW][C]24[/C][C]65[/C][C]65.4879143861214[/C][C]-0.487914386121412[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]57.8275169712082[/C][C]-0.827516971208247[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]39.9305069786169[/C][C]1.06949302138312[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]22.4911887990251[/C][C]-1.49118879902508[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]19.9329279766545[/C][C]1.06707202334549[/C][/ROW]
[ROW][C]29[/C][C]17[/C][C]16.8531059830811[/C][C]0.1468940169189[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]8.91607129680917[/C][C]0.0839287031908311[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]12.2332070083928[/C][C]-1.2332070083928[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]5.77831010555254[/C][C]0.221689894447463[/C][/ROW]
[ROW][C]33[/C][C]-2[/C][C]-2.23213366557159[/C][C]0.232133665571589[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]-0.794326889135693[/C][C]0.794326889135693[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]4.79106014092789[/C][C]0.20893985907211[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.52755604281528[/C][C]0.472443957184718[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]8.65225790918244[/C][C]-1.65225790918244[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.48973777896334[/C][C]-0.489737778963336[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]8.62941706266955[/C][C]-0.629417062669553[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.62450944344494[/C][C]1.37549055655506[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]14.6230451518531[/C][C]-0.623045151853085[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]13.6099235941766[/C][C]-1.60992359417665[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.7191742357725[/C][C]0.28082576422753[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]6.49384846921429[/C][C]0.506151530785707[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]16.8022639404889[/C][C]-1.80226394048892[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]13.9731116147217[/C][C]0.0268883852782716[/C][/ROW]
[ROW][C]47[/C][C]19[/C][C]18.403959067347[/C][C]0.596040932652985[/C][/ROW]
[ROW][C]48[/C][C]39[/C][C]38.3666872309764[/C][C]0.633312769023613[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.4713970960906[/C][C]1.52860290390935[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.1538310865761[/C][C]-1.15383108657608[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]17.899995992758[/C][C]-0.899995992758008[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]17.3325035210105[/C][C]-1.33250352101053[/C][/ROW]
[ROW][C]53[/C][C]25[/C][C]25.0149785702887[/C][C]-0.014978570288724[/C][/ROW]
[ROW][C]54[/C][C]24[/C][C]23.0957386079103[/C][C]0.904261392089717[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.1615622869855[/C][C]-1.16156228698554[/C][/ROW]
[ROW][C]56[/C][C]25[/C][C]26.3260930021099[/C][C]-1.32609300210989[/C][/ROW]
[ROW][C]57[/C][C]31[/C][C]29.4336835680785[/C][C]1.56631643192153[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]22.6813015372107[/C][C]1.3186984627893[/C][/ROW]
[ROW][C]59[/C][C]24[/C][C]23.4557201436508[/C][C]0.544279856349248[/C][/ROW]
[ROW][C]60[/C][C]33[/C][C]32.0336131112721[/C][C]0.966386888727932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11716.00050288393960.99949711606045
22321.07858513500881.92141486499119
32424.0988069097112-0.0988069097111827
42727.1760098923622-0.176009892362176
53132.297113644966-1.29711364496595
64038.99564122492491.00435877507512
74747.915901348937-0.915901348936986
84343.492897318911-0.492897318910984
96061.9022358268797-1.90223582687975
106463.81885677833570.181143221664285
116565.8513371250321-0.851337125032119
126563.79163240457241.20836759542759
135555.2865177726709-0.286517772670858
145759.1369258668014-2.13692586680139
155756.03113626967540.96886373032459
165756.18870766098910.811292339010854
176563.09924855705281.90075144294719
186970.3415109530804-1.3415109530804
197067.8706189219762.129381078024
207172.8024541944872-1.80245419448717
217170.37618395846620.623816041533836
227372.59793278525520.402067214744808
236866.65798340871481.34201659128522
246565.4879143861214-0.487914386121412
255757.8275169712082-0.827516971208247
264139.93050697861691.06949302138312
272122.4911887990251-1.49118879902508
282119.93292797665451.06707202334549
291716.85310598308110.1468940169189
3098.916071296809170.0839287031908311
311112.2332070083928-1.2332070083928
3265.778310105552540.221689894447463
33-2-2.232133665571590.232133665571589
340-0.7943268891356930.794326889135693
3554.791060140927890.20893985907211
3632.527556042815280.472443957184718
3778.65225790918244-1.65225790918244
3844.48973777896334-0.489737778963336
3988.62941706266955-0.629417062669553
4097.624509443444941.37549055655506
411414.6230451518531-0.623045151853085
421213.6099235941766-1.60992359417665
431211.71917423577250.28082576422753
4476.493848469214290.506151530785707
451516.8022639404889-1.80226394048892
461413.97311161472170.0268883852782716
471918.4039590673470.596040932652985
483938.36668723097640.633312769023613
491210.47139709609061.52860290390935
501112.1538310865761-1.15383108657608
511717.899995992758-0.899995992758008
521617.3325035210105-1.33250352101053
532525.0149785702887-0.014978570288724
542423.09573860791030.904261392089717
552829.1615622869855-1.16156228698554
562526.3260930021099-1.32609300210989
573129.43368356807851.56631643192153
582422.68130153721071.3186984627893
592423.45572014365080.544279856349248
603332.03361311127210.966386888727932







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3232722022958520.6465444045917040.676727797704148
110.3800067925931840.7600135851863670.619993207406816
120.2544043272792320.5088086545584630.745595672720768
130.3066190201061650.613238040212330.693380979893835
140.6290597025667520.7418805948664960.370940297433248
150.6159221653504030.7681556692991930.384077834649597
160.5352090960651750.9295818078696490.464790903934825
170.5939627371031820.8120745257936350.406037262896818
180.5366592927983230.9266814144033530.463340707201677
190.827531502431920.3449369951361590.17246849756808
200.9135910315506050.172817936898790.0864089684493951
210.8820512705527750.2358974588944490.117948729447225
220.8349322171176160.3301355657647680.165067782882384
230.8074461636437470.3851076727125070.192553836356253
240.8309573662242750.338085267551450.169042633775725
250.8609576639333540.2780846721332910.139042336066646
260.8471779774738040.3056440450523910.152822022526196
270.9023119435917190.1953761128165620.097688056408281
280.8965242349788840.2069515300422330.103475765021116
290.8602463551684040.2795072896631910.139753644831596
300.8383278385410570.3233443229178850.161672161458943
310.8164013266059010.3671973467881980.183598673394099
320.7634256905224360.4731486189551280.236574309477564
330.7190525053852140.5618949892295720.280947494614786
340.6732832111767050.6534335776465890.326716788823295
350.6541590670499830.6916818659000340.345840932950017
360.6400241177984360.7199517644031280.359975882201564
370.644812050425350.71037589914930.35518794957465
380.5610338483693890.8779323032612210.438966151630611
390.5234914749172320.9530170501655370.476508525082768
400.781278860269610.437442279460780.21872113973039
410.7383628739400030.5232742521199940.261637126059997
420.7258628089027040.5482743821945920.274137191097296
430.7089159179219850.582168164156030.291084082078015
440.6271637317498730.7456725365002540.372836268250127
450.5530573665936680.8938852668126630.446942633406332
460.5303814412441630.9392371175116730.469618558755837
470.4215782068929960.8431564137859910.578421793107004
480.3732132246403020.7464264492806050.626786775359698
490.3861059850241350.772211970048270.613894014975865
500.2988353079528020.5976706159056040.701164692047198

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.323272202295852 & 0.646544404591704 & 0.676727797704148 \tabularnewline
11 & 0.380006792593184 & 0.760013585186367 & 0.619993207406816 \tabularnewline
12 & 0.254404327279232 & 0.508808654558463 & 0.745595672720768 \tabularnewline
13 & 0.306619020106165 & 0.61323804021233 & 0.693380979893835 \tabularnewline
14 & 0.629059702566752 & 0.741880594866496 & 0.370940297433248 \tabularnewline
15 & 0.615922165350403 & 0.768155669299193 & 0.384077834649597 \tabularnewline
16 & 0.535209096065175 & 0.929581807869649 & 0.464790903934825 \tabularnewline
17 & 0.593962737103182 & 0.812074525793635 & 0.406037262896818 \tabularnewline
18 & 0.536659292798323 & 0.926681414403353 & 0.463340707201677 \tabularnewline
19 & 0.82753150243192 & 0.344936995136159 & 0.17246849756808 \tabularnewline
20 & 0.913591031550605 & 0.17281793689879 & 0.0864089684493951 \tabularnewline
21 & 0.882051270552775 & 0.235897458894449 & 0.117948729447225 \tabularnewline
22 & 0.834932217117616 & 0.330135565764768 & 0.165067782882384 \tabularnewline
23 & 0.807446163643747 & 0.385107672712507 & 0.192553836356253 \tabularnewline
24 & 0.830957366224275 & 0.33808526755145 & 0.169042633775725 \tabularnewline
25 & 0.860957663933354 & 0.278084672133291 & 0.139042336066646 \tabularnewline
26 & 0.847177977473804 & 0.305644045052391 & 0.152822022526196 \tabularnewline
27 & 0.902311943591719 & 0.195376112816562 & 0.097688056408281 \tabularnewline
28 & 0.896524234978884 & 0.206951530042233 & 0.103475765021116 \tabularnewline
29 & 0.860246355168404 & 0.279507289663191 & 0.139753644831596 \tabularnewline
30 & 0.838327838541057 & 0.323344322917885 & 0.161672161458943 \tabularnewline
31 & 0.816401326605901 & 0.367197346788198 & 0.183598673394099 \tabularnewline
32 & 0.763425690522436 & 0.473148618955128 & 0.236574309477564 \tabularnewline
33 & 0.719052505385214 & 0.561894989229572 & 0.280947494614786 \tabularnewline
34 & 0.673283211176705 & 0.653433577646589 & 0.326716788823295 \tabularnewline
35 & 0.654159067049983 & 0.691681865900034 & 0.345840932950017 \tabularnewline
36 & 0.640024117798436 & 0.719951764403128 & 0.359975882201564 \tabularnewline
37 & 0.64481205042535 & 0.7103758991493 & 0.35518794957465 \tabularnewline
38 & 0.561033848369389 & 0.877932303261221 & 0.438966151630611 \tabularnewline
39 & 0.523491474917232 & 0.953017050165537 & 0.476508525082768 \tabularnewline
40 & 0.78127886026961 & 0.43744227946078 & 0.21872113973039 \tabularnewline
41 & 0.738362873940003 & 0.523274252119994 & 0.261637126059997 \tabularnewline
42 & 0.725862808902704 & 0.548274382194592 & 0.274137191097296 \tabularnewline
43 & 0.708915917921985 & 0.58216816415603 & 0.291084082078015 \tabularnewline
44 & 0.627163731749873 & 0.745672536500254 & 0.372836268250127 \tabularnewline
45 & 0.553057366593668 & 0.893885266812663 & 0.446942633406332 \tabularnewline
46 & 0.530381441244163 & 0.939237117511673 & 0.469618558755837 \tabularnewline
47 & 0.421578206892996 & 0.843156413785991 & 0.578421793107004 \tabularnewline
48 & 0.373213224640302 & 0.746426449280605 & 0.626786775359698 \tabularnewline
49 & 0.386105985024135 & 0.77221197004827 & 0.613894014975865 \tabularnewline
50 & 0.298835307952802 & 0.597670615905604 & 0.701164692047198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&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]10[/C][C]0.323272202295852[/C][C]0.646544404591704[/C][C]0.676727797704148[/C][/ROW]
[ROW][C]11[/C][C]0.380006792593184[/C][C]0.760013585186367[/C][C]0.619993207406816[/C][/ROW]
[ROW][C]12[/C][C]0.254404327279232[/C][C]0.508808654558463[/C][C]0.745595672720768[/C][/ROW]
[ROW][C]13[/C][C]0.306619020106165[/C][C]0.61323804021233[/C][C]0.693380979893835[/C][/ROW]
[ROW][C]14[/C][C]0.629059702566752[/C][C]0.741880594866496[/C][C]0.370940297433248[/C][/ROW]
[ROW][C]15[/C][C]0.615922165350403[/C][C]0.768155669299193[/C][C]0.384077834649597[/C][/ROW]
[ROW][C]16[/C][C]0.535209096065175[/C][C]0.929581807869649[/C][C]0.464790903934825[/C][/ROW]
[ROW][C]17[/C][C]0.593962737103182[/C][C]0.812074525793635[/C][C]0.406037262896818[/C][/ROW]
[ROW][C]18[/C][C]0.536659292798323[/C][C]0.926681414403353[/C][C]0.463340707201677[/C][/ROW]
[ROW][C]19[/C][C]0.82753150243192[/C][C]0.344936995136159[/C][C]0.17246849756808[/C][/ROW]
[ROW][C]20[/C][C]0.913591031550605[/C][C]0.17281793689879[/C][C]0.0864089684493951[/C][/ROW]
[ROW][C]21[/C][C]0.882051270552775[/C][C]0.235897458894449[/C][C]0.117948729447225[/C][/ROW]
[ROW][C]22[/C][C]0.834932217117616[/C][C]0.330135565764768[/C][C]0.165067782882384[/C][/ROW]
[ROW][C]23[/C][C]0.807446163643747[/C][C]0.385107672712507[/C][C]0.192553836356253[/C][/ROW]
[ROW][C]24[/C][C]0.830957366224275[/C][C]0.33808526755145[/C][C]0.169042633775725[/C][/ROW]
[ROW][C]25[/C][C]0.860957663933354[/C][C]0.278084672133291[/C][C]0.139042336066646[/C][/ROW]
[ROW][C]26[/C][C]0.847177977473804[/C][C]0.305644045052391[/C][C]0.152822022526196[/C][/ROW]
[ROW][C]27[/C][C]0.902311943591719[/C][C]0.195376112816562[/C][C]0.097688056408281[/C][/ROW]
[ROW][C]28[/C][C]0.896524234978884[/C][C]0.206951530042233[/C][C]0.103475765021116[/C][/ROW]
[ROW][C]29[/C][C]0.860246355168404[/C][C]0.279507289663191[/C][C]0.139753644831596[/C][/ROW]
[ROW][C]30[/C][C]0.838327838541057[/C][C]0.323344322917885[/C][C]0.161672161458943[/C][/ROW]
[ROW][C]31[/C][C]0.816401326605901[/C][C]0.367197346788198[/C][C]0.183598673394099[/C][/ROW]
[ROW][C]32[/C][C]0.763425690522436[/C][C]0.473148618955128[/C][C]0.236574309477564[/C][/ROW]
[ROW][C]33[/C][C]0.719052505385214[/C][C]0.561894989229572[/C][C]0.280947494614786[/C][/ROW]
[ROW][C]34[/C][C]0.673283211176705[/C][C]0.653433577646589[/C][C]0.326716788823295[/C][/ROW]
[ROW][C]35[/C][C]0.654159067049983[/C][C]0.691681865900034[/C][C]0.345840932950017[/C][/ROW]
[ROW][C]36[/C][C]0.640024117798436[/C][C]0.719951764403128[/C][C]0.359975882201564[/C][/ROW]
[ROW][C]37[/C][C]0.64481205042535[/C][C]0.7103758991493[/C][C]0.35518794957465[/C][/ROW]
[ROW][C]38[/C][C]0.561033848369389[/C][C]0.877932303261221[/C][C]0.438966151630611[/C][/ROW]
[ROW][C]39[/C][C]0.523491474917232[/C][C]0.953017050165537[/C][C]0.476508525082768[/C][/ROW]
[ROW][C]40[/C][C]0.78127886026961[/C][C]0.43744227946078[/C][C]0.21872113973039[/C][/ROW]
[ROW][C]41[/C][C]0.738362873940003[/C][C]0.523274252119994[/C][C]0.261637126059997[/C][/ROW]
[ROW][C]42[/C][C]0.725862808902704[/C][C]0.548274382194592[/C][C]0.274137191097296[/C][/ROW]
[ROW][C]43[/C][C]0.708915917921985[/C][C]0.58216816415603[/C][C]0.291084082078015[/C][/ROW]
[ROW][C]44[/C][C]0.627163731749873[/C][C]0.745672536500254[/C][C]0.372836268250127[/C][/ROW]
[ROW][C]45[/C][C]0.553057366593668[/C][C]0.893885266812663[/C][C]0.446942633406332[/C][/ROW]
[ROW][C]46[/C][C]0.530381441244163[/C][C]0.939237117511673[/C][C]0.469618558755837[/C][/ROW]
[ROW][C]47[/C][C]0.421578206892996[/C][C]0.843156413785991[/C][C]0.578421793107004[/C][/ROW]
[ROW][C]48[/C][C]0.373213224640302[/C][C]0.746426449280605[/C][C]0.626786775359698[/C][/ROW]
[ROW][C]49[/C][C]0.386105985024135[/C][C]0.77221197004827[/C][C]0.613894014975865[/C][/ROW]
[ROW][C]50[/C][C]0.298835307952802[/C][C]0.597670615905604[/C][C]0.701164692047198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3232722022958520.6465444045917040.676727797704148
110.3800067925931840.7600135851863670.619993207406816
120.2544043272792320.5088086545584630.745595672720768
130.3066190201061650.613238040212330.693380979893835
140.6290597025667520.7418805948664960.370940297433248
150.6159221653504030.7681556692991930.384077834649597
160.5352090960651750.9295818078696490.464790903934825
170.5939627371031820.8120745257936350.406037262896818
180.5366592927983230.9266814144033530.463340707201677
190.827531502431920.3449369951361590.17246849756808
200.9135910315506050.172817936898790.0864089684493951
210.8820512705527750.2358974588944490.117948729447225
220.8349322171176160.3301355657647680.165067782882384
230.8074461636437470.3851076727125070.192553836356253
240.8309573662242750.338085267551450.169042633775725
250.8609576639333540.2780846721332910.139042336066646
260.8471779774738040.3056440450523910.152822022526196
270.9023119435917190.1953761128165620.097688056408281
280.8965242349788840.2069515300422330.103475765021116
290.8602463551684040.2795072896631910.139753644831596
300.8383278385410570.3233443229178850.161672161458943
310.8164013266059010.3671973467881980.183598673394099
320.7634256905224360.4731486189551280.236574309477564
330.7190525053852140.5618949892295720.280947494614786
340.6732832111767050.6534335776465890.326716788823295
350.6541590670499830.6916818659000340.345840932950017
360.6400241177984360.7199517644031280.359975882201564
370.644812050425350.71037589914930.35518794957465
380.5610338483693890.8779323032612210.438966151630611
390.5234914749172320.9530170501655370.476508525082768
400.781278860269610.437442279460780.21872113973039
410.7383628739400030.5232742521199940.261637126059997
420.7258628089027040.5482743821945920.274137191097296
430.7089159179219850.582168164156030.291084082078015
440.6271637317498730.7456725365002540.372836268250127
450.5530573665936680.8938852668126630.446942633406332
460.5303814412441630.9392371175116730.469618558755837
470.4215782068929960.8431564137859910.578421793107004
480.3732132246403020.7464264492806050.626786775359698
490.3861059850241350.772211970048270.613894014975865
500.2988353079528020.5976706159056040.701164692047198







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147189&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147189&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147189&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}