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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 16:56:11 -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/2009/Nov/21/t1258761449fj0riepwocjff52.htm/, Retrieved Sat, 27 Apr 2024 22:03:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58493, Retrieved Sat, 27 Apr 2024 22:03:21 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

174

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [shwws7vr_1] [2009-11-20 20:49:17] [2b2cfeea2f5ac2a1bcb842baaf1415ef]
-   PD        [Multiple Regression] [shwws7vr1] [2009-11-20 23:56:11] [d447d4b3e35da686436a520338c962fc] [Current]

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Dataseries X:

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Histogram

Boxplots

100.35	102.1
100.35	102.86
100.36	102.99
100.39	103.73
100.34	105.02
100.34	104.43
100.35	104.63
100.43	104.93
100.47	105.87
100.67	105.66
100.75	106.76
100.78	106
100.79	107.22
100.67	107.33
100.64	107.11
100.64	108.86
100.76	107.72
100.79	107.88
100.79	108.38
100.9	107.72
100.98	108.41
101.11	109.9
101.18	111.45
101.22	112.18
101.23	113.34
101.09	113.46
101.26	114.06
101.28	115.54
101.43	116.39
101.53	115.94
101.54	116.97
101.54	115.94
101.79	115.91
102.18	116.43
102.37	116.26
102.46	116.35
102.46	117.9
102.03	117.7
102.26	117.53
102.33	117.86
102.44	117.65
102.5	116.51
102.52	115.93
102.66	115.31
102.72	115

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Ktot[t] = + 105.065867086302 -0.0486175142110252Vmtot[t] + 0.0380062117884138M1[t] -0.201647603739741M2[t] -0.179270889616640M3[t] -0.173762836424627M4[t] -0.158416651952784M5[t] -0.21222427121419M6[t] -0.26500251046336M7[t] -0.283688585939234M8[t] -0.237265212191018M9[t] -0.0543179461956548M10[t] + 0.0224500909672896M11[t] + 0.0767557745848375t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ktot[t] =  +  105.065867086302 -0.0486175142110252Vmtot[t] +  0.0380062117884138M1[t] -0.201647603739741M2[t] -0.179270889616640M3[t] -0.173762836424627M4[t] -0.158416651952784M5[t] -0.21222427121419M6[t] -0.26500251046336M7[t] -0.283688585939234M8[t] -0.237265212191018M9[t] -0.0543179461956548M10[t] +  0.0224500909672896M11[t] +  0.0767557745848375t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ktot[t] =  +  105.065867086302 -0.0486175142110252Vmtot[t] +  0.0380062117884138M1[t] -0.201647603739741M2[t] -0.179270889616640M3[t] -0.173762836424627M4[t] -0.158416651952784M5[t] -0.21222427121419M6[t] -0.26500251046336M7[t] -0.283688585939234M8[t] -0.237265212191018M9[t] -0.0543179461956548M10[t] +  0.0224500909672896M11[t] +  0.0767557745848375t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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
Ktot[t] = + 105.065867086302 -0.0486175142110252Vmtot[t] + 0.0380062117884138M1[t] -0.201647603739741M2[t] -0.179270889616640M3[t] -0.173762836424627M4[t] -0.158416651952784M5[t] -0.21222427121419M6[t] -0.26500251046336M7[t] -0.283688585939234M8[t] -0.237265212191018M9[t] -0.0543179461956548M10[t] + 0.0224500909672896M11[t] + 0.0767557745848375t + 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)	105.065867086302	2.265547	46.3755	0	0
Vmtot	-0.0486175142110252	0.022122	-2.1977	0.035565	0.017782
M1	0.0380062117884138	0.156693	0.2426	0.80995	0.404975
M2	-0.201647603739741	0.156255	-1.2905	0.206418	0.103209
M3	-0.179270889616640	0.155919	-1.1498	0.259031	0.129516
M4	-0.173762836424627	0.156724	-1.1087	0.276079	0.13804
M5	-0.158416651952784	0.156277	-1.0137	0.318577	0.159289
M6	-0.21222427121419	0.155908	-1.3612	0.18326	0.09163
M7	-0.26500251046336	0.156041	-1.6983	0.09947	0.049735
M8	-0.283688585939234	0.158481	-1.79	0.083219	0.04161
M9	-0.237265212191018	0.158833	-1.4938	0.145337	0.072669
M10	-0.0543179461956548	0.166577	-0.3261	0.746553	0.373276
M11	0.0224500909672896	0.166715	0.1347	0.89375	0.446875
t	0.0767557745848375	0.008868	8.6558	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) & 105.065867086302 & 2.265547 & 46.3755 & 0 & 0 \tabularnewline
Vmtot & -0.0486175142110252 & 0.022122 & -2.1977 & 0.035565 & 0.017782 \tabularnewline
M1 & 0.0380062117884138 & 0.156693 & 0.2426 & 0.80995 & 0.404975 \tabularnewline
M2 & -0.201647603739741 & 0.156255 & -1.2905 & 0.206418 & 0.103209 \tabularnewline
M3 & -0.179270889616640 & 0.155919 & -1.1498 & 0.259031 & 0.129516 \tabularnewline
M4 & -0.173762836424627 & 0.156724 & -1.1087 & 0.276079 & 0.13804 \tabularnewline
M5 & -0.158416651952784 & 0.156277 & -1.0137 & 0.318577 & 0.159289 \tabularnewline
M6 & -0.21222427121419 & 0.155908 & -1.3612 & 0.18326 & 0.09163 \tabularnewline
M7 & -0.26500251046336 & 0.156041 & -1.6983 & 0.09947 & 0.049735 \tabularnewline
M8 & -0.283688585939234 & 0.158481 & -1.79 & 0.083219 & 0.04161 \tabularnewline
M9 & -0.237265212191018 & 0.158833 & -1.4938 & 0.145337 & 0.072669 \tabularnewline
M10 & -0.0543179461956548 & 0.166577 & -0.3261 & 0.746553 & 0.373276 \tabularnewline
M11 & 0.0224500909672896 & 0.166715 & 0.1347 & 0.89375 & 0.446875 \tabularnewline
t & 0.0767557745848375 & 0.008868 & 8.6558 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&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]105.065867086302[/C][C]2.265547[/C][C]46.3755[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vmtot[/C][C]-0.0486175142110252[/C][C]0.022122[/C][C]-2.1977[/C][C]0.035565[/C][C]0.017782[/C][/ROW]
[ROW][C]M1[/C][C]0.0380062117884138[/C][C]0.156693[/C][C]0.2426[/C][C]0.80995[/C][C]0.404975[/C][/ROW]
[ROW][C]M2[/C][C]-0.201647603739741[/C][C]0.156255[/C][C]-1.2905[/C][C]0.206418[/C][C]0.103209[/C][/ROW]
[ROW][C]M3[/C][C]-0.179270889616640[/C][C]0.155919[/C][C]-1.1498[/C][C]0.259031[/C][C]0.129516[/C][/ROW]
[ROW][C]M4[/C][C]-0.173762836424627[/C][C]0.156724[/C][C]-1.1087[/C][C]0.276079[/C][C]0.13804[/C][/ROW]
[ROW][C]M5[/C][C]-0.158416651952784[/C][C]0.156277[/C][C]-1.0137[/C][C]0.318577[/C][C]0.159289[/C][/ROW]
[ROW][C]M6[/C][C]-0.21222427121419[/C][C]0.155908[/C][C]-1.3612[/C][C]0.18326[/C][C]0.09163[/C][/ROW]
[ROW][C]M7[/C][C]-0.26500251046336[/C][C]0.156041[/C][C]-1.6983[/C][C]0.09947[/C][C]0.049735[/C][/ROW]
[ROW][C]M8[/C][C]-0.283688585939234[/C][C]0.158481[/C][C]-1.79[/C][C]0.083219[/C][C]0.04161[/C][/ROW]
[ROW][C]M9[/C][C]-0.237265212191018[/C][C]0.158833[/C][C]-1.4938[/C][C]0.145337[/C][C]0.072669[/C][/ROW]
[ROW][C]M10[/C][C]-0.0543179461956548[/C][C]0.166577[/C][C]-0.3261[/C][C]0.746553[/C][C]0.373276[/C][/ROW]
[ROW][C]M11[/C][C]0.0224500909672896[/C][C]0.166715[/C][C]0.1347[/C][C]0.89375[/C][C]0.446875[/C][/ROW]
[ROW][C]t[/C][C]0.0767557745848375[/C][C]0.008868[/C][C]8.6558[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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)	105.065867086302	2.265547	46.3755	0	0
Vmtot	-0.0486175142110252	0.022122	-2.1977	0.035565	0.017782
M1	0.0380062117884138	0.156693	0.2426	0.80995	0.404975
M2	-0.201647603739741	0.156255	-1.2905	0.206418	0.103209
M3	-0.179270889616640	0.155919	-1.1498	0.259031	0.129516
M4	-0.173762836424627	0.156724	-1.1087	0.276079	0.13804
M5	-0.158416651952784	0.156277	-1.0137	0.318577	0.159289
M6	-0.21222427121419	0.155908	-1.3612	0.18326	0.09163
M7	-0.26500251046336	0.156041	-1.6983	0.09947	0.049735
M8	-0.283688585939234	0.158481	-1.79	0.083219	0.04161
M9	-0.237265212191018	0.158833	-1.4938	0.145337	0.072669
M10	-0.0543179461956548	0.166577	-0.3261	0.746553	0.373276
M11	0.0224500909672896	0.166715	0.1347	0.89375	0.446875
t	0.0767557745848375	0.008868	8.6558	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.975852123483842
R-squared	0.952287366907923
Adjusted R-squared	0.932278843353181
F-TEST (value)	47.5940848060344
F-TEST (DF numerator)	13
F-TEST (DF denominator)	31
p-value	1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.20392119736432
Sum Squared Residuals	1.28909949676944

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.975852123483842 \tabularnewline
R-squared & 0.952287366907923 \tabularnewline
Adjusted R-squared & 0.932278843353181 \tabularnewline
F-TEST (value) & 47.5940848060344 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 31 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.20392119736432 \tabularnewline
Sum Squared Residuals & 1.28909949676944 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.975852123483842[/C][/ROW]
[ROW][C]R-squared[/C][C]0.952287366907923[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.932278843353181[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]47.5940848060344[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]31[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.20392119736432[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.28909949676944[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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	0.975852123483842
R-squared	0.952287366907923
Adjusted R-squared	0.932278843353181
F-TEST (value)	47.5940848060344
F-TEST (DF numerator)	13
F-TEST (DF denominator)	31
p-value	1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.20392119736432
Sum Squared Residuals	1.28909949676944

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.35	100.216780871730	0.133219128270414
2	100.35	100.016933519986	0.333066480014129
3	100.36	100.109745731846	0.250254268153627
4	100.39	100.156032599107	0.233967400892937
5	100.34	100.185417964832	0.154582035168482
6	100.34	100.237050453539	0.102949546460546
7	100.35	100.251304486033	0.0986955139670732
8	100.43	100.294788930879	0.135211069121431
9	100.47	100.372267615853	0.0977323841467326
10	100.67	100.642180334418	0.0278196655822193
11	100.75	100.742224880533	0.00777511946656353
12	100.78	100.833479874951	-0.0534798749513622
13	100.79	100.888928493987	-0.0989284939871578
14	100.67	100.720682526481	-0.0506825264806321
15	100.64	100.830510868315	-0.190510868314998
16	100.64	100.827694046223	-0.187694046222554
17	100.76	100.975219971480	-0.215219971479798
18	100.79	100.990389324529	-0.200389324529465
19	100.79	100.990058102760	-0.20005810275962
20	100.9	101.080215361248	-0.18021536124786
21	100.98	101.169848424775	-0.189848424775308
22	101.11	101.357111369181	-0.247111369181086
23	101.18	101.435278033902	-0.255278033901771
24	101.22	101.454092932145	-0.234092932145278
25	101.23	101.512458602034	-0.282458602033736
26	101.09	101.343726459385	-0.253726459385096
27	101.26	101.413688439566	-0.153688439566418
28	101.28	101.423998346311	-0.143998346310954
29	101.43	101.474775418288	-0.0447754182882581
30	101.53	101.519601455007	0.0103985449933431
31	101.54	101.493502950705	0.0464970492950365
32	101.54	101.601648689451	-0.0616486894512825
33	101.79	101.726286363211	0.0637136367893329
34	102.18	101.960708296401	0.219291703598866
35	102.37	102.122497085565	0.247502914435208
36	102.46	102.172427192903	0.287572807096641
37	102.46	102.211832032250	0.248167967750479
38	102.03	102.058657494148	-0.0286574941484009
39	102.26	102.166054960272	0.0939450397277893
40	102.33	102.232275008359	0.0977249916405709
41	102.44	102.334586645400	0.105413354599575
42	102.5	102.412958766924	0.0870412330755766
43	102.52	102.465134460502	0.0548655394975103
44	102.66	102.553347018422	0.106652981577712
45	102.72	102.691597596161	0.0284024038392427

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.35 & 100.216780871730 & 0.133219128270414 \tabularnewline
2 & 100.35 & 100.016933519986 & 0.333066480014129 \tabularnewline
3 & 100.36 & 100.109745731846 & 0.250254268153627 \tabularnewline
4 & 100.39 & 100.156032599107 & 0.233967400892937 \tabularnewline
5 & 100.34 & 100.185417964832 & 0.154582035168482 \tabularnewline
6 & 100.34 & 100.237050453539 & 0.102949546460546 \tabularnewline
7 & 100.35 & 100.251304486033 & 0.0986955139670732 \tabularnewline
8 & 100.43 & 100.294788930879 & 0.135211069121431 \tabularnewline
9 & 100.47 & 100.372267615853 & 0.0977323841467326 \tabularnewline
10 & 100.67 & 100.642180334418 & 0.0278196655822193 \tabularnewline
11 & 100.75 & 100.742224880533 & 0.00777511946656353 \tabularnewline
12 & 100.78 & 100.833479874951 & -0.0534798749513622 \tabularnewline
13 & 100.79 & 100.888928493987 & -0.0989284939871578 \tabularnewline
14 & 100.67 & 100.720682526481 & -0.0506825264806321 \tabularnewline
15 & 100.64 & 100.830510868315 & -0.190510868314998 \tabularnewline
16 & 100.64 & 100.827694046223 & -0.187694046222554 \tabularnewline
17 & 100.76 & 100.975219971480 & -0.215219971479798 \tabularnewline
18 & 100.79 & 100.990389324529 & -0.200389324529465 \tabularnewline
19 & 100.79 & 100.990058102760 & -0.20005810275962 \tabularnewline
20 & 100.9 & 101.080215361248 & -0.18021536124786 \tabularnewline
21 & 100.98 & 101.169848424775 & -0.189848424775308 \tabularnewline
22 & 101.11 & 101.357111369181 & -0.247111369181086 \tabularnewline
23 & 101.18 & 101.435278033902 & -0.255278033901771 \tabularnewline
24 & 101.22 & 101.454092932145 & -0.234092932145278 \tabularnewline
25 & 101.23 & 101.512458602034 & -0.282458602033736 \tabularnewline
26 & 101.09 & 101.343726459385 & -0.253726459385096 \tabularnewline
27 & 101.26 & 101.413688439566 & -0.153688439566418 \tabularnewline
28 & 101.28 & 101.423998346311 & -0.143998346310954 \tabularnewline
29 & 101.43 & 101.474775418288 & -0.0447754182882581 \tabularnewline
30 & 101.53 & 101.519601455007 & 0.0103985449933431 \tabularnewline
31 & 101.54 & 101.493502950705 & 0.0464970492950365 \tabularnewline
32 & 101.54 & 101.601648689451 & -0.0616486894512825 \tabularnewline
33 & 101.79 & 101.726286363211 & 0.0637136367893329 \tabularnewline
34 & 102.18 & 101.960708296401 & 0.219291703598866 \tabularnewline
35 & 102.37 & 102.122497085565 & 0.247502914435208 \tabularnewline
36 & 102.46 & 102.172427192903 & 0.287572807096641 \tabularnewline
37 & 102.46 & 102.211832032250 & 0.248167967750479 \tabularnewline
38 & 102.03 & 102.058657494148 & -0.0286574941484009 \tabularnewline
39 & 102.26 & 102.166054960272 & 0.0939450397277893 \tabularnewline
40 & 102.33 & 102.232275008359 & 0.0977249916405709 \tabularnewline
41 & 102.44 & 102.334586645400 & 0.105413354599575 \tabularnewline
42 & 102.5 & 102.412958766924 & 0.0870412330755766 \tabularnewline
43 & 102.52 & 102.465134460502 & 0.0548655394975103 \tabularnewline
44 & 102.66 & 102.553347018422 & 0.106652981577712 \tabularnewline
45 & 102.72 & 102.691597596161 & 0.0284024038392427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&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]100.35[/C][C]100.216780871730[/C][C]0.133219128270414[/C][/ROW]
[ROW][C]2[/C][C]100.35[/C][C]100.016933519986[/C][C]0.333066480014129[/C][/ROW]
[ROW][C]3[/C][C]100.36[/C][C]100.109745731846[/C][C]0.250254268153627[/C][/ROW]
[ROW][C]4[/C][C]100.39[/C][C]100.156032599107[/C][C]0.233967400892937[/C][/ROW]
[ROW][C]5[/C][C]100.34[/C][C]100.185417964832[/C][C]0.154582035168482[/C][/ROW]
[ROW][C]6[/C][C]100.34[/C][C]100.237050453539[/C][C]0.102949546460546[/C][/ROW]
[ROW][C]7[/C][C]100.35[/C][C]100.251304486033[/C][C]0.0986955139670732[/C][/ROW]
[ROW][C]8[/C][C]100.43[/C][C]100.294788930879[/C][C]0.135211069121431[/C][/ROW]
[ROW][C]9[/C][C]100.47[/C][C]100.372267615853[/C][C]0.0977323841467326[/C][/ROW]
[ROW][C]10[/C][C]100.67[/C][C]100.642180334418[/C][C]0.0278196655822193[/C][/ROW]
[ROW][C]11[/C][C]100.75[/C][C]100.742224880533[/C][C]0.00777511946656353[/C][/ROW]
[ROW][C]12[/C][C]100.78[/C][C]100.833479874951[/C][C]-0.0534798749513622[/C][/ROW]
[ROW][C]13[/C][C]100.79[/C][C]100.888928493987[/C][C]-0.0989284939871578[/C][/ROW]
[ROW][C]14[/C][C]100.67[/C][C]100.720682526481[/C][C]-0.0506825264806321[/C][/ROW]
[ROW][C]15[/C][C]100.64[/C][C]100.830510868315[/C][C]-0.190510868314998[/C][/ROW]
[ROW][C]16[/C][C]100.64[/C][C]100.827694046223[/C][C]-0.187694046222554[/C][/ROW]
[ROW][C]17[/C][C]100.76[/C][C]100.975219971480[/C][C]-0.215219971479798[/C][/ROW]
[ROW][C]18[/C][C]100.79[/C][C]100.990389324529[/C][C]-0.200389324529465[/C][/ROW]
[ROW][C]19[/C][C]100.79[/C][C]100.990058102760[/C][C]-0.20005810275962[/C][/ROW]
[ROW][C]20[/C][C]100.9[/C][C]101.080215361248[/C][C]-0.18021536124786[/C][/ROW]
[ROW][C]21[/C][C]100.98[/C][C]101.169848424775[/C][C]-0.189848424775308[/C][/ROW]
[ROW][C]22[/C][C]101.11[/C][C]101.357111369181[/C][C]-0.247111369181086[/C][/ROW]
[ROW][C]23[/C][C]101.18[/C][C]101.435278033902[/C][C]-0.255278033901771[/C][/ROW]
[ROW][C]24[/C][C]101.22[/C][C]101.454092932145[/C][C]-0.234092932145278[/C][/ROW]
[ROW][C]25[/C][C]101.23[/C][C]101.512458602034[/C][C]-0.282458602033736[/C][/ROW]
[ROW][C]26[/C][C]101.09[/C][C]101.343726459385[/C][C]-0.253726459385096[/C][/ROW]
[ROW][C]27[/C][C]101.26[/C][C]101.413688439566[/C][C]-0.153688439566418[/C][/ROW]
[ROW][C]28[/C][C]101.28[/C][C]101.423998346311[/C][C]-0.143998346310954[/C][/ROW]
[ROW][C]29[/C][C]101.43[/C][C]101.474775418288[/C][C]-0.0447754182882581[/C][/ROW]
[ROW][C]30[/C][C]101.53[/C][C]101.519601455007[/C][C]0.0103985449933431[/C][/ROW]
[ROW][C]31[/C][C]101.54[/C][C]101.493502950705[/C][C]0.0464970492950365[/C][/ROW]
[ROW][C]32[/C][C]101.54[/C][C]101.601648689451[/C][C]-0.0616486894512825[/C][/ROW]
[ROW][C]33[/C][C]101.79[/C][C]101.726286363211[/C][C]0.0637136367893329[/C][/ROW]
[ROW][C]34[/C][C]102.18[/C][C]101.960708296401[/C][C]0.219291703598866[/C][/ROW]
[ROW][C]35[/C][C]102.37[/C][C]102.122497085565[/C][C]0.247502914435208[/C][/ROW]
[ROW][C]36[/C][C]102.46[/C][C]102.172427192903[/C][C]0.287572807096641[/C][/ROW]
[ROW][C]37[/C][C]102.46[/C][C]102.211832032250[/C][C]0.248167967750479[/C][/ROW]
[ROW][C]38[/C][C]102.03[/C][C]102.058657494148[/C][C]-0.0286574941484009[/C][/ROW]
[ROW][C]39[/C][C]102.26[/C][C]102.166054960272[/C][C]0.0939450397277893[/C][/ROW]
[ROW][C]40[/C][C]102.33[/C][C]102.232275008359[/C][C]0.0977249916405709[/C][/ROW]
[ROW][C]41[/C][C]102.44[/C][C]102.334586645400[/C][C]0.105413354599575[/C][/ROW]
[ROW][C]42[/C][C]102.5[/C][C]102.412958766924[/C][C]0.0870412330755766[/C][/ROW]
[ROW][C]43[/C][C]102.52[/C][C]102.465134460502[/C][C]0.0548655394975103[/C][/ROW]
[ROW][C]44[/C][C]102.66[/C][C]102.553347018422[/C][C]0.106652981577712[/C][/ROW]
[ROW][C]45[/C][C]102.72[/C][C]102.691597596161[/C][C]0.0284024038392427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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	100.35	100.216780871730	0.133219128270414
2	100.35	100.016933519986	0.333066480014129
3	100.36	100.109745731846	0.250254268153627
4	100.39	100.156032599107	0.233967400892937
5	100.34	100.185417964832	0.154582035168482
6	100.34	100.237050453539	0.102949546460546
7	100.35	100.251304486033	0.0986955139670732
8	100.43	100.294788930879	0.135211069121431
9	100.47	100.372267615853	0.0977323841467326
10	100.67	100.642180334418	0.0278196655822193
11	100.75	100.742224880533	0.00777511946656353
12	100.78	100.833479874951	-0.0534798749513622
13	100.79	100.888928493987	-0.0989284939871578
14	100.67	100.720682526481	-0.0506825264806321
15	100.64	100.830510868315	-0.190510868314998
16	100.64	100.827694046223	-0.187694046222554
17	100.76	100.975219971480	-0.215219971479798
18	100.79	100.990389324529	-0.200389324529465
19	100.79	100.990058102760	-0.20005810275962
20	100.9	101.080215361248	-0.18021536124786
21	100.98	101.169848424775	-0.189848424775308
22	101.11	101.357111369181	-0.247111369181086
23	101.18	101.435278033902	-0.255278033901771
24	101.22	101.454092932145	-0.234092932145278
25	101.23	101.512458602034	-0.282458602033736
26	101.09	101.343726459385	-0.253726459385096
27	101.26	101.413688439566	-0.153688439566418
28	101.28	101.423998346311	-0.143998346310954
29	101.43	101.474775418288	-0.0447754182882581
30	101.53	101.519601455007	0.0103985449933431
31	101.54	101.493502950705	0.0464970492950365
32	101.54	101.601648689451	-0.0616486894512825
33	101.79	101.726286363211	0.0637136367893329
34	102.18	101.960708296401	0.219291703598866
35	102.37	102.122497085565	0.247502914435208
36	102.46	102.172427192903	0.287572807096641
37	102.46	102.211832032250	0.248167967750479
38	102.03	102.058657494148	-0.0286574941484009
39	102.26	102.166054960272	0.0939450397277893
40	102.33	102.232275008359	0.0977249916405709
41	102.44	102.334586645400	0.105413354599575
42	102.5	102.412958766924	0.0870412330755766
43	102.52	102.465134460502	0.0548655394975103
44	102.66	102.553347018422	0.106652981577712
45	102.72	102.691597596161	0.0284024038392427

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.159889689320657	0.319779378641315	0.840110310679343
18	0.0955462955625386	0.191092591125077	0.904453704437461
19	0.0575885512366176	0.115177102473235	0.942411448763382
20	0.0577449473878358	0.115489894775672	0.942255052612164
21	0.189869690817305	0.37973938163461	0.810130309182695
22	0.162059918214418	0.324119836428836	0.837940081785582
23	0.129044152837192	0.258088305674385	0.870955847162808
24	0.246059484657333	0.492118969314666	0.753940515342667
25	0.66183617001887	0.676327659962259	0.338163829981129
26	0.590064563777703	0.819870872444595	0.409935436222297
27	0.601779357410566	0.796441285178867	0.398220642589434
28	0.495008936103683	0.990017872207366	0.504991063896317

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.159889689320657 & 0.319779378641315 & 0.840110310679343 \tabularnewline
18 & 0.0955462955625386 & 0.191092591125077 & 0.904453704437461 \tabularnewline
19 & 0.0575885512366176 & 0.115177102473235 & 0.942411448763382 \tabularnewline
20 & 0.0577449473878358 & 0.115489894775672 & 0.942255052612164 \tabularnewline
21 & 0.189869690817305 & 0.37973938163461 & 0.810130309182695 \tabularnewline
22 & 0.162059918214418 & 0.324119836428836 & 0.837940081785582 \tabularnewline
23 & 0.129044152837192 & 0.258088305674385 & 0.870955847162808 \tabularnewline
24 & 0.246059484657333 & 0.492118969314666 & 0.753940515342667 \tabularnewline
25 & 0.66183617001887 & 0.676327659962259 & 0.338163829981129 \tabularnewline
26 & 0.590064563777703 & 0.819870872444595 & 0.409935436222297 \tabularnewline
27 & 0.601779357410566 & 0.796441285178867 & 0.398220642589434 \tabularnewline
28 & 0.495008936103683 & 0.990017872207366 & 0.504991063896317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58493&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]17[/C][C]0.159889689320657[/C][C]0.319779378641315[/C][C]0.840110310679343[/C][/ROW]
[ROW][C]18[/C][C]0.0955462955625386[/C][C]0.191092591125077[/C][C]0.904453704437461[/C][/ROW]
[ROW][C]19[/C][C]0.0575885512366176[/C][C]0.115177102473235[/C][C]0.942411448763382[/C][/ROW]
[ROW][C]20[/C][C]0.0577449473878358[/C][C]0.115489894775672[/C][C]0.942255052612164[/C][/ROW]
[ROW][C]21[/C][C]0.189869690817305[/C][C]0.37973938163461[/C][C]0.810130309182695[/C][/ROW]
[ROW][C]22[/C][C]0.162059918214418[/C][C]0.324119836428836[/C][C]0.837940081785582[/C][/ROW]
[ROW][C]23[/C][C]0.129044152837192[/C][C]0.258088305674385[/C][C]0.870955847162808[/C][/ROW]
[ROW][C]24[/C][C]0.246059484657333[/C][C]0.492118969314666[/C][C]0.753940515342667[/C][/ROW]
[ROW][C]25[/C][C]0.66183617001887[/C][C]0.676327659962259[/C][C]0.338163829981129[/C][/ROW]
[ROW][C]26[/C][C]0.590064563777703[/C][C]0.819870872444595[/C][C]0.409935436222297[/C][/ROW]
[ROW][C]27[/C][C]0.601779357410566[/C][C]0.796441285178867[/C][C]0.398220642589434[/C][/ROW]
[ROW][C]28[/C][C]0.495008936103683[/C][C]0.990017872207366[/C][C]0.504991063896317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58493&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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
17	0.159889689320657	0.319779378641315	0.840110310679343
18	0.0955462955625386	0.191092591125077	0.904453704437461
19	0.0575885512366176	0.115177102473235	0.942411448763382
20	0.0577449473878358	0.115489894775672	0.942255052612164
21	0.189869690817305	0.37973938163461	0.810130309182695
22	0.162059918214418	0.324119836428836	0.837940081785582
23	0.129044152837192	0.258088305674385	0.870955847162808
24	0.246059484657333	0.492118969314666	0.753940515342667
25	0.66183617001887	0.676327659962259	0.338163829981129
26	0.590064563777703	0.819870872444595	0.409935436222297
27	0.601779357410566	0.796441285178867	0.398220642589434
28	0.495008936103683	0.990017872207366	0.504991063896317

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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=58493&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=58493&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58493&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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