Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 16:01:02 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t1258758165sc2rackf3s1s10w.htm/, Retrieved Sat, 27 Apr 2024 16:26:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58488, Retrieved Sat, 27 Apr 2024 16:26:53 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

194

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] [shwws7vr_1] [2009-11-20 23:01:02] [d447d4b3e35da686436a520338c962fc] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

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=58488&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=58488&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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] = + 86.3391357853997 + 0.135840111929575Vmtot[t] -0.0930657133231456M1[t] -0.292394135429237M2[t] -0.208940544943249M3[t] -0.324968665267545M4[t] -0.269297087373633M5[t] -0.153197830849198M6[t] -0.182251863028952M7[t] -0.0314922067843380M8[t] + 0.0321993571183724M9[t] -0.0516553718996217M10[t] -0.0506165310947364M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ktot[t] =  +  86.3391357853997 +  0.135840111929575Vmtot[t] -0.0930657133231456M1[t] -0.292394135429237M2[t] -0.208940544943249M3[t] -0.324968665267545M4[t] -0.269297087373633M5[t] -0.153197830849198M6[t] -0.182251863028952M7[t] -0.0314922067843380M8[t] +  0.0321993571183724M9[t] -0.0516553718996217M10[t] -0.0506165310947364M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ktot[t] =  +  86.3391357853997 +  0.135840111929575Vmtot[t] -0.0930657133231456M1[t] -0.292394135429237M2[t] -0.208940544943249M3[t] -0.324968665267545M4[t] -0.269297087373633M5[t] -0.153197830849198M6[t] -0.182251863028952M7[t] -0.0314922067843380M8[t] +  0.0321993571183724M9[t] -0.0516553718996217M10[t] -0.0506165310947364M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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] = + 86.3391357853997 + 0.135840111929575Vmtot[t] -0.0930657133231456M1[t] -0.292394135429237M2[t] -0.208940544943249M3[t] -0.324968665267545M4[t] -0.269297087373633M5[t] -0.153197830849198M6[t] -0.182251863028952M7[t] -0.0314922067843380M8[t] + 0.0321993571183724M9[t] -0.0516553718996217M10[t] -0.0506165310947364M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	86.3391357853997	1.223227	70.5831	0	0
Vmtot	0.135840111929575	0.0108	12.5776	0	0
M1	-0.0930657133231456	0.283748	-0.328	0.745058	0.372529
M2	-0.292394135429237	0.283645	-1.0308	0.310341	0.155171
M3	-0.208940544943249	0.283605	-0.7367	0.466654	0.233327
M4	-0.324968665267545	0.283362	-1.1468	0.259954	0.129977
M5	-0.269297087373633	0.283369	-0.9503	0.349062	0.174531
M6	-0.153197830849198	0.283383	-0.5406	0.592524	0.296262
M7	-0.182251863028952	0.283362	-0.6432	0.524692	0.262346
M8	-0.0314922067843380	0.283421	-0.1111	0.912219	0.45611
M9	0.0321993571183724	0.283371	0.1136	0.910241	0.455121
M10	-0.0516553718996217	0.303065	-0.1704	0.865734	0.432867
M11	-0.0506165310947364	0.302927	-0.1671	0.868349	0.434175

\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) & 86.3391357853997 & 1.223227 & 70.5831 & 0 & 0 \tabularnewline
Vmtot & 0.135840111929575 & 0.0108 & 12.5776 & 0 & 0 \tabularnewline
M1 & -0.0930657133231456 & 0.283748 & -0.328 & 0.745058 & 0.372529 \tabularnewline
M2 & -0.292394135429237 & 0.283645 & -1.0308 & 0.310341 & 0.155171 \tabularnewline
M3 & -0.208940544943249 & 0.283605 & -0.7367 & 0.466654 & 0.233327 \tabularnewline
M4 & -0.324968665267545 & 0.283362 & -1.1468 & 0.259954 & 0.129977 \tabularnewline
M5 & -0.269297087373633 & 0.283369 & -0.9503 & 0.349062 & 0.174531 \tabularnewline
M6 & -0.153197830849198 & 0.283383 & -0.5406 & 0.592524 & 0.296262 \tabularnewline
M7 & -0.182251863028952 & 0.283362 & -0.6432 & 0.524692 & 0.262346 \tabularnewline
M8 & -0.0314922067843380 & 0.283421 & -0.1111 & 0.912219 & 0.45611 \tabularnewline
M9 & 0.0321993571183724 & 0.283371 & 0.1136 & 0.910241 & 0.455121 \tabularnewline
M10 & -0.0516553718996217 & 0.303065 & -0.1704 & 0.865734 & 0.432867 \tabularnewline
M11 & -0.0506165310947364 & 0.302927 & -0.1671 & 0.868349 & 0.434175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&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]86.3391357853997[/C][C]1.223227[/C][C]70.5831[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vmtot[/C][C]0.135840111929575[/C][C]0.0108[/C][C]12.5776[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0930657133231456[/C][C]0.283748[/C][C]-0.328[/C][C]0.745058[/C][C]0.372529[/C][/ROW]
[ROW][C]M2[/C][C]-0.292394135429237[/C][C]0.283645[/C][C]-1.0308[/C][C]0.310341[/C][C]0.155171[/C][/ROW]
[ROW][C]M3[/C][C]-0.208940544943249[/C][C]0.283605[/C][C]-0.7367[/C][C]0.466654[/C][C]0.233327[/C][/ROW]
[ROW][C]M4[/C][C]-0.324968665267545[/C][C]0.283362[/C][C]-1.1468[/C][C]0.259954[/C][C]0.129977[/C][/ROW]
[ROW][C]M5[/C][C]-0.269297087373633[/C][C]0.283369[/C][C]-0.9503[/C][C]0.349062[/C][C]0.174531[/C][/ROW]
[ROW][C]M6[/C][C]-0.153197830849198[/C][C]0.283383[/C][C]-0.5406[/C][C]0.592524[/C][C]0.296262[/C][/ROW]
[ROW][C]M7[/C][C]-0.182251863028952[/C][C]0.283362[/C][C]-0.6432[/C][C]0.524692[/C][C]0.262346[/C][/ROW]
[ROW][C]M8[/C][C]-0.0314922067843380[/C][C]0.283421[/C][C]-0.1111[/C][C]0.912219[/C][C]0.45611[/C][/ROW]
[ROW][C]M9[/C][C]0.0321993571183724[/C][C]0.283371[/C][C]0.1136[/C][C]0.910241[/C][C]0.455121[/C][/ROW]
[ROW][C]M10[/C][C]-0.0516553718996217[/C][C]0.303065[/C][C]-0.1704[/C][C]0.865734[/C][C]0.432867[/C][/ROW]
[ROW][C]M11[/C][C]-0.0506165310947364[/C][C]0.302927[/C][C]-0.1671[/C][C]0.868349[/C][C]0.434175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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)	86.3391357853997	1.223227	70.5831	0	0
Vmtot	0.135840111929575	0.0108	12.5776	0	0
M1	-0.0930657133231456	0.283748	-0.328	0.745058	0.372529
M2	-0.292394135429237	0.283645	-1.0308	0.310341	0.155171
M3	-0.208940544943249	0.283605	-0.7367	0.466654	0.233327
M4	-0.324968665267545	0.283362	-1.1468	0.259954	0.129977
M5	-0.269297087373633	0.283369	-0.9503	0.349062	0.174531
M6	-0.153197830849198	0.283383	-0.5406	0.592524	0.296262
M7	-0.182251863028952	0.283362	-0.6432	0.524692	0.262346
M8	-0.0314922067843380	0.283421	-0.1111	0.912219	0.45611
M9	0.0321993571183724	0.283371	0.1136	0.910241	0.455121
M10	-0.0516553718996217	0.303065	-0.1704	0.865734	0.432867
M11	-0.0506165310947364	0.302927	-0.1671	0.868349	0.434175

Multiple Linear Regression - Regression Statistics
Multiple R	0.914861614224816
R-squared	0.836971773182037
Adjusted R-squared	0.7758361881253
F-TEST (value)	13.6904189663891
F-TEST (DF numerator)	12
F-TEST (DF denominator)	32
p-value	2.20569318365449e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.371007714838415
Sum Squared Residuals	4.40469518302793

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.914861614224816 \tabularnewline
R-squared & 0.836971773182037 \tabularnewline
Adjusted R-squared & 0.7758361881253 \tabularnewline
F-TEST (value) & 13.6904189663891 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 32 \tabularnewline
p-value & 2.20569318365449e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.371007714838415 \tabularnewline
Sum Squared Residuals & 4.40469518302793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.914861614224816[/C][/ROW]
[ROW][C]R-squared[/C][C]0.836971773182037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.7758361881253[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.6904189663891[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]2.20569318365449e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.371007714838415[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.40469518302793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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	0.914861614224816
R-squared	0.836971773182037
Adjusted R-squared	0.7758361881253
F-TEST (value)	13.6904189663891
F-TEST (DF numerator)	12
F-TEST (DF denominator)	32
p-value	2.20569318365449e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.371007714838415
Sum Squared Residuals	4.40469518302793

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.35	100.115345500086	0.234654499913785
2	100.35	100.019255563047	0.330744436953394
3	100.36	100.120368368083	0.239631631916567
4	100.39	100.104861930587	0.285138069412977
5	100.34	100.33576725287	0.0042327471299166
6	100.34	100.371720843356	-0.0317208433560704
7	100.35	100.369834833562	-0.0198348335622384
8	100.43	100.561346523386	-0.131346523385714
9	100.47	100.752727792502	-0.282727792502233
10	100.67	100.640346639979	0.0296533600209756
11	100.75	100.790809603906	-0.0408096039064457
12	100.78	100.738187649935	0.0418123500652971
13	100.79	100.810846873166	-0.0208468731656339
14	100.67	100.626460863372	0.0435391366281996
15	100.64	100.680029629233	-0.0400296292332835
16	100.64	100.801721704786	-0.161721704785744
17	100.76	100.70253555508	0.0574644449200642
18	100.79	100.840369229513	-0.0503692295131011
19	100.79	100.879235253298	-0.0892352532981342
20	100.9	100.940340435669	-0.0403404356692299
21	100.98	101.097761676803	-0.117761676803349
22	101.11	101.216308714560	-0.106308714560428
23	101.18	101.427899728856	-0.247899728856147
24	101.22	101.577679541659	-0.357679541659482
25	101.23	101.642188358175	-0.412188358174638
26	101.09	101.459160749500	-0.369160749500095
27	101.26	101.624118407144	-0.364118407143828
28	101.28	101.709133652475	-0.429133652475308
29	101.43	101.880269325509	-0.450269325509353
30	101.53	101.935240531665	-0.405240531665484
31	101.54	102.046101814773	-0.506101814773187
32	101.54	102.056946155730	-0.516946155730339
33	101.79	102.116562516275	-0.326562516275162
34	102.18	102.103344645461	0.0766553545394522
35	102.37	102.081290667237	0.288709332762593
36	102.46	102.144132808406	0.315867191594185
37	102.46	102.261619268574	0.198380731426487
38	102.03	102.035122824081	-0.00512282408149841
39	102.26	102.095483595539	0.164516404460545
40	102.33	102.024282712152	0.305717287848075
41	102.44	102.051427866541	0.388572133459372
42	102.5	102.012669395465	0.487330604534655
43	102.52	101.904828098366	0.61517190163356
44	102.66	101.971366885215	0.688633114785283
45	102.72	101.992948014419	0.727051985580744

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.35 & 100.115345500086 & 0.234654499913785 \tabularnewline
2 & 100.35 & 100.019255563047 & 0.330744436953394 \tabularnewline
3 & 100.36 & 100.120368368083 & 0.239631631916567 \tabularnewline
4 & 100.39 & 100.104861930587 & 0.285138069412977 \tabularnewline
5 & 100.34 & 100.33576725287 & 0.0042327471299166 \tabularnewline
6 & 100.34 & 100.371720843356 & -0.0317208433560704 \tabularnewline
7 & 100.35 & 100.369834833562 & -0.0198348335622384 \tabularnewline
8 & 100.43 & 100.561346523386 & -0.131346523385714 \tabularnewline
9 & 100.47 & 100.752727792502 & -0.282727792502233 \tabularnewline
10 & 100.67 & 100.640346639979 & 0.0296533600209756 \tabularnewline
11 & 100.75 & 100.790809603906 & -0.0408096039064457 \tabularnewline
12 & 100.78 & 100.738187649935 & 0.0418123500652971 \tabularnewline
13 & 100.79 & 100.810846873166 & -0.0208468731656339 \tabularnewline
14 & 100.67 & 100.626460863372 & 0.0435391366281996 \tabularnewline
15 & 100.64 & 100.680029629233 & -0.0400296292332835 \tabularnewline
16 & 100.64 & 100.801721704786 & -0.161721704785744 \tabularnewline
17 & 100.76 & 100.70253555508 & 0.0574644449200642 \tabularnewline
18 & 100.79 & 100.840369229513 & -0.0503692295131011 \tabularnewline
19 & 100.79 & 100.879235253298 & -0.0892352532981342 \tabularnewline
20 & 100.9 & 100.940340435669 & -0.0403404356692299 \tabularnewline
21 & 100.98 & 101.097761676803 & -0.117761676803349 \tabularnewline
22 & 101.11 & 101.216308714560 & -0.106308714560428 \tabularnewline
23 & 101.18 & 101.427899728856 & -0.247899728856147 \tabularnewline
24 & 101.22 & 101.577679541659 & -0.357679541659482 \tabularnewline
25 & 101.23 & 101.642188358175 & -0.412188358174638 \tabularnewline
26 & 101.09 & 101.459160749500 & -0.369160749500095 \tabularnewline
27 & 101.26 & 101.624118407144 & -0.364118407143828 \tabularnewline
28 & 101.28 & 101.709133652475 & -0.429133652475308 \tabularnewline
29 & 101.43 & 101.880269325509 & -0.450269325509353 \tabularnewline
30 & 101.53 & 101.935240531665 & -0.405240531665484 \tabularnewline
31 & 101.54 & 102.046101814773 & -0.506101814773187 \tabularnewline
32 & 101.54 & 102.056946155730 & -0.516946155730339 \tabularnewline
33 & 101.79 & 102.116562516275 & -0.326562516275162 \tabularnewline
34 & 102.18 & 102.103344645461 & 0.0766553545394522 \tabularnewline
35 & 102.37 & 102.081290667237 & 0.288709332762593 \tabularnewline
36 & 102.46 & 102.144132808406 & 0.315867191594185 \tabularnewline
37 & 102.46 & 102.261619268574 & 0.198380731426487 \tabularnewline
38 & 102.03 & 102.035122824081 & -0.00512282408149841 \tabularnewline
39 & 102.26 & 102.095483595539 & 0.164516404460545 \tabularnewline
40 & 102.33 & 102.024282712152 & 0.305717287848075 \tabularnewline
41 & 102.44 & 102.051427866541 & 0.388572133459372 \tabularnewline
42 & 102.5 & 102.012669395465 & 0.487330604534655 \tabularnewline
43 & 102.52 & 101.904828098366 & 0.61517190163356 \tabularnewline
44 & 102.66 & 101.971366885215 & 0.688633114785283 \tabularnewline
45 & 102.72 & 101.992948014419 & 0.727051985580744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&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]100.115345500086[/C][C]0.234654499913785[/C][/ROW]
[ROW][C]2[/C][C]100.35[/C][C]100.019255563047[/C][C]0.330744436953394[/C][/ROW]
[ROW][C]3[/C][C]100.36[/C][C]100.120368368083[/C][C]0.239631631916567[/C][/ROW]
[ROW][C]4[/C][C]100.39[/C][C]100.104861930587[/C][C]0.285138069412977[/C][/ROW]
[ROW][C]5[/C][C]100.34[/C][C]100.33576725287[/C][C]0.0042327471299166[/C][/ROW]
[ROW][C]6[/C][C]100.34[/C][C]100.371720843356[/C][C]-0.0317208433560704[/C][/ROW]
[ROW][C]7[/C][C]100.35[/C][C]100.369834833562[/C][C]-0.0198348335622384[/C][/ROW]
[ROW][C]8[/C][C]100.43[/C][C]100.561346523386[/C][C]-0.131346523385714[/C][/ROW]
[ROW][C]9[/C][C]100.47[/C][C]100.752727792502[/C][C]-0.282727792502233[/C][/ROW]
[ROW][C]10[/C][C]100.67[/C][C]100.640346639979[/C][C]0.0296533600209756[/C][/ROW]
[ROW][C]11[/C][C]100.75[/C][C]100.790809603906[/C][C]-0.0408096039064457[/C][/ROW]
[ROW][C]12[/C][C]100.78[/C][C]100.738187649935[/C][C]0.0418123500652971[/C][/ROW]
[ROW][C]13[/C][C]100.79[/C][C]100.810846873166[/C][C]-0.0208468731656339[/C][/ROW]
[ROW][C]14[/C][C]100.67[/C][C]100.626460863372[/C][C]0.0435391366281996[/C][/ROW]
[ROW][C]15[/C][C]100.64[/C][C]100.680029629233[/C][C]-0.0400296292332835[/C][/ROW]
[ROW][C]16[/C][C]100.64[/C][C]100.801721704786[/C][C]-0.161721704785744[/C][/ROW]
[ROW][C]17[/C][C]100.76[/C][C]100.70253555508[/C][C]0.0574644449200642[/C][/ROW]
[ROW][C]18[/C][C]100.79[/C][C]100.840369229513[/C][C]-0.0503692295131011[/C][/ROW]
[ROW][C]19[/C][C]100.79[/C][C]100.879235253298[/C][C]-0.0892352532981342[/C][/ROW]
[ROW][C]20[/C][C]100.9[/C][C]100.940340435669[/C][C]-0.0403404356692299[/C][/ROW]
[ROW][C]21[/C][C]100.98[/C][C]101.097761676803[/C][C]-0.117761676803349[/C][/ROW]
[ROW][C]22[/C][C]101.11[/C][C]101.216308714560[/C][C]-0.106308714560428[/C][/ROW]
[ROW][C]23[/C][C]101.18[/C][C]101.427899728856[/C][C]-0.247899728856147[/C][/ROW]
[ROW][C]24[/C][C]101.22[/C][C]101.577679541659[/C][C]-0.357679541659482[/C][/ROW]
[ROW][C]25[/C][C]101.23[/C][C]101.642188358175[/C][C]-0.412188358174638[/C][/ROW]
[ROW][C]26[/C][C]101.09[/C][C]101.459160749500[/C][C]-0.369160749500095[/C][/ROW]
[ROW][C]27[/C][C]101.26[/C][C]101.624118407144[/C][C]-0.364118407143828[/C][/ROW]
[ROW][C]28[/C][C]101.28[/C][C]101.709133652475[/C][C]-0.429133652475308[/C][/ROW]
[ROW][C]29[/C][C]101.43[/C][C]101.880269325509[/C][C]-0.450269325509353[/C][/ROW]
[ROW][C]30[/C][C]101.53[/C][C]101.935240531665[/C][C]-0.405240531665484[/C][/ROW]
[ROW][C]31[/C][C]101.54[/C][C]102.046101814773[/C][C]-0.506101814773187[/C][/ROW]
[ROW][C]32[/C][C]101.54[/C][C]102.056946155730[/C][C]-0.516946155730339[/C][/ROW]
[ROW][C]33[/C][C]101.79[/C][C]102.116562516275[/C][C]-0.326562516275162[/C][/ROW]
[ROW][C]34[/C][C]102.18[/C][C]102.103344645461[/C][C]0.0766553545394522[/C][/ROW]
[ROW][C]35[/C][C]102.37[/C][C]102.081290667237[/C][C]0.288709332762593[/C][/ROW]
[ROW][C]36[/C][C]102.46[/C][C]102.144132808406[/C][C]0.315867191594185[/C][/ROW]
[ROW][C]37[/C][C]102.46[/C][C]102.261619268574[/C][C]0.198380731426487[/C][/ROW]
[ROW][C]38[/C][C]102.03[/C][C]102.035122824081[/C][C]-0.00512282408149841[/C][/ROW]
[ROW][C]39[/C][C]102.26[/C][C]102.095483595539[/C][C]0.164516404460545[/C][/ROW]
[ROW][C]40[/C][C]102.33[/C][C]102.024282712152[/C][C]0.305717287848075[/C][/ROW]
[ROW][C]41[/C][C]102.44[/C][C]102.051427866541[/C][C]0.388572133459372[/C][/ROW]
[ROW][C]42[/C][C]102.5[/C][C]102.012669395465[/C][C]0.487330604534655[/C][/ROW]
[ROW][C]43[/C][C]102.52[/C][C]101.904828098366[/C][C]0.61517190163356[/C][/ROW]
[ROW][C]44[/C][C]102.66[/C][C]101.971366885215[/C][C]0.688633114785283[/C][/ROW]
[ROW][C]45[/C][C]102.72[/C][C]101.992948014419[/C][C]0.727051985580744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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	100.115345500086	0.234654499913785
2	100.35	100.019255563047	0.330744436953394
3	100.36	100.120368368083	0.239631631916567
4	100.39	100.104861930587	0.285138069412977
5	100.34	100.33576725287	0.0042327471299166
6	100.34	100.371720843356	-0.0317208433560704
7	100.35	100.369834833562	-0.0198348335622384
8	100.43	100.561346523386	-0.131346523385714
9	100.47	100.752727792502	-0.282727792502233
10	100.67	100.640346639979	0.0296533600209756
11	100.75	100.790809603906	-0.0408096039064457
12	100.78	100.738187649935	0.0418123500652971
13	100.79	100.810846873166	-0.0208468731656339
14	100.67	100.626460863372	0.0435391366281996
15	100.64	100.680029629233	-0.0400296292332835
16	100.64	100.801721704786	-0.161721704785744
17	100.76	100.70253555508	0.0574644449200642
18	100.79	100.840369229513	-0.0503692295131011
19	100.79	100.879235253298	-0.0892352532981342
20	100.9	100.940340435669	-0.0403404356692299
21	100.98	101.097761676803	-0.117761676803349
22	101.11	101.216308714560	-0.106308714560428
23	101.18	101.427899728856	-0.247899728856147
24	101.22	101.577679541659	-0.357679541659482
25	101.23	101.642188358175	-0.412188358174638
26	101.09	101.459160749500	-0.369160749500095
27	101.26	101.624118407144	-0.364118407143828
28	101.28	101.709133652475	-0.429133652475308
29	101.43	101.880269325509	-0.450269325509353
30	101.53	101.935240531665	-0.405240531665484
31	101.54	102.046101814773	-0.506101814773187
32	101.54	102.056946155730	-0.516946155730339
33	101.79	102.116562516275	-0.326562516275162
34	102.18	102.103344645461	0.0766553545394522
35	102.37	102.081290667237	0.288709332762593
36	102.46	102.144132808406	0.315867191594185
37	102.46	102.261619268574	0.198380731426487
38	102.03	102.035122824081	-0.00512282408149841
39	102.26	102.095483595539	0.164516404460545
40	102.33	102.024282712152	0.305717287848075
41	102.44	102.051427866541	0.388572133459372
42	102.5	102.012669395465	0.487330604534655
43	102.52	101.904828098366	0.61517190163356
44	102.66	101.971366885215	0.688633114785283
45	102.72	101.992948014419	0.727051985580744

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.00236432803753876	0.00472865607507751	0.997635671962461
17	0.00316598264273963	0.00633196528547925	0.99683401735726
18	0.00131760454579263	0.00263520909158525	0.998682395454207
19	0.000363828085264164	0.000727656170528328	0.999636171914736
20	0.000222413522485996	0.000444827044971993	0.999777586477514
21	0.000169890567029765	0.000339781134059529	0.99983010943297
22	4.0370384181367e-05	8.0740768362734e-05	0.999959629615819
23	7.39208768021937e-06	1.47841753604387e-05	0.99999260791232
24	1.82987597126666e-06	3.65975194253331e-06	0.999998170124029
25	3.95702080261132e-07	7.91404160522265e-07	0.99999960429792
26	8.59616489586631e-08	1.71923297917326e-07	0.999999914038351
27	1.17329899633525e-08	2.34659799267050e-08	0.99999998826701
28	4.35262142090784e-09	8.70524284181568e-09	0.999999995647379
29	6.784875984883e-08	1.3569751969766e-07	0.99999993215124

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00236432803753876 & 0.00472865607507751 & 0.997635671962461 \tabularnewline
17 & 0.00316598264273963 & 0.00633196528547925 & 0.99683401735726 \tabularnewline
18 & 0.00131760454579263 & 0.00263520909158525 & 0.998682395454207 \tabularnewline
19 & 0.000363828085264164 & 0.000727656170528328 & 0.999636171914736 \tabularnewline
20 & 0.000222413522485996 & 0.000444827044971993 & 0.999777586477514 \tabularnewline
21 & 0.000169890567029765 & 0.000339781134059529 & 0.99983010943297 \tabularnewline
22 & 4.0370384181367e-05 & 8.0740768362734e-05 & 0.999959629615819 \tabularnewline
23 & 7.39208768021937e-06 & 1.47841753604387e-05 & 0.99999260791232 \tabularnewline
24 & 1.82987597126666e-06 & 3.65975194253331e-06 & 0.999998170124029 \tabularnewline
25 & 3.95702080261132e-07 & 7.91404160522265e-07 & 0.99999960429792 \tabularnewline
26 & 8.59616489586631e-08 & 1.71923297917326e-07 & 0.999999914038351 \tabularnewline
27 & 1.17329899633525e-08 & 2.34659799267050e-08 & 0.99999998826701 \tabularnewline
28 & 4.35262142090784e-09 & 8.70524284181568e-09 & 0.999999995647379 \tabularnewline
29 & 6.784875984883e-08 & 1.3569751969766e-07 & 0.99999993215124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&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]16[/C][C]0.00236432803753876[/C][C]0.00472865607507751[/C][C]0.997635671962461[/C][/ROW]
[ROW][C]17[/C][C]0.00316598264273963[/C][C]0.00633196528547925[/C][C]0.99683401735726[/C][/ROW]
[ROW][C]18[/C][C]0.00131760454579263[/C][C]0.00263520909158525[/C][C]0.998682395454207[/C][/ROW]
[ROW][C]19[/C][C]0.000363828085264164[/C][C]0.000727656170528328[/C][C]0.999636171914736[/C][/ROW]
[ROW][C]20[/C][C]0.000222413522485996[/C][C]0.000444827044971993[/C][C]0.999777586477514[/C][/ROW]
[ROW][C]21[/C][C]0.000169890567029765[/C][C]0.000339781134059529[/C][C]0.99983010943297[/C][/ROW]
[ROW][C]22[/C][C]4.0370384181367e-05[/C][C]8.0740768362734e-05[/C][C]0.999959629615819[/C][/ROW]
[ROW][C]23[/C][C]7.39208768021937e-06[/C][C]1.47841753604387e-05[/C][C]0.99999260791232[/C][/ROW]
[ROW][C]24[/C][C]1.82987597126666e-06[/C][C]3.65975194253331e-06[/C][C]0.999998170124029[/C][/ROW]
[ROW][C]25[/C][C]3.95702080261132e-07[/C][C]7.91404160522265e-07[/C][C]0.99999960429792[/C][/ROW]
[ROW][C]26[/C][C]8.59616489586631e-08[/C][C]1.71923297917326e-07[/C][C]0.999999914038351[/C][/ROW]
[ROW][C]27[/C][C]1.17329899633525e-08[/C][C]2.34659799267050e-08[/C][C]0.99999998826701[/C][/ROW]
[ROW][C]28[/C][C]4.35262142090784e-09[/C][C]8.70524284181568e-09[/C][C]0.999999995647379[/C][/ROW]
[ROW][C]29[/C][C]6.784875984883e-08[/C][C]1.3569751969766e-07[/C][C]0.99999993215124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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
16	0.00236432803753876	0.00472865607507751	0.997635671962461
17	0.00316598264273963	0.00633196528547925	0.99683401735726
18	0.00131760454579263	0.00263520909158525	0.998682395454207
19	0.000363828085264164	0.000727656170528328	0.999636171914736
20	0.000222413522485996	0.000444827044971993	0.999777586477514
21	0.000169890567029765	0.000339781134059529	0.99983010943297
22	4.0370384181367e-05	8.0740768362734e-05	0.999959629615819
23	7.39208768021937e-06	1.47841753604387e-05	0.99999260791232
24	1.82987597126666e-06	3.65975194253331e-06	0.999998170124029
25	3.95702080261132e-07	7.91404160522265e-07	0.99999960429792
26	8.59616489586631e-08	1.71923297917326e-07	0.999999914038351
27	1.17329899633525e-08	2.34659799267050e-08	0.99999998826701
28	4.35262142090784e-09	8.70524284181568e-09	0.999999995647379
29	6.784875984883e-08	1.3569751969766e-07	0.99999993215124

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	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 & 14 & 1 & NOK \tabularnewline
5% type I error level & 14 & 1 & NOK \tabularnewline
10% type I error level & 14 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58488&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]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58488&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58488&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	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code